From 3fb3dbce6c199fb53ac77eda07f2b306516f2df7 Mon Sep 17 00:00:00 2001 From: aursiber Date: Mon, 2 Dec 2024 13:36:09 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20lbbe-sof?= =?UTF-8?q?tware/fitdistrplus@0d2e59dc7c55b6accc65388520d2d75616959085=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 4 +- articles/FAQ.html | 6 +- articles/Optimalgo.html | 48 +- articles/fitdistrplus_vignette.html | 6 +- articles/index.html | 2 +- articles/starting-values.html | 6 +- authors.html | 2 +- deps/data-deps.txt | 4 +- deps/font-awesome-6.5.2/css/all.css | 8028 +++++++++++++++++ deps/font-awesome-6.5.2/css/all.min.css | 9 + deps/font-awesome-6.5.2/css/v4-shims.css | 2194 +++++ deps/font-awesome-6.5.2/css/v4-shims.min.css | 6 + .../webfonts/fa-brands-400.ttf | Bin 0 -> 209128 bytes .../webfonts/fa-brands-400.woff2 | Bin 0 -> 117852 bytes .../webfonts/fa-regular-400.ttf | Bin 0 -> 67860 bytes .../webfonts/fa-regular-400.woff2 | Bin 0 -> 25392 bytes .../webfonts/fa-solid-900.ttf | Bin 0 -> 420332 bytes .../webfonts/fa-solid-900.woff2 | Bin 0 -> 156400 bytes .../webfonts/fa-v4compatibility.ttf | Bin 0 -> 10832 bytes .../webfonts/fa-v4compatibility.woff2 | Bin 0 -> 4792 bytes index.html | 6 +- news/index.html | 2 +- pkgdown.yml | 2 +- reference/CIcdfplot.html | 2 +- reference/Surv2fitdistcens.html | 2 +- reference/bootdist.html | 8 +- reference/bootdistcens.html | 8 +- reference/danish.html | 2 +- reference/dataFAQ.html | 2 +- reference/descdist.html | 2 +- reference/detectbound.html | 2 +- reference/endosulfan.html | 2 +- reference/figures/fitdistrplus_hex.png | Bin 0 -> 389854 bytes reference/fitdist.html | 6 +- reference/fitdistcens.html | 6 +- reference/fitdistrplus.html | 2 +- reference/fluazinam.html | 2 +- reference/fremale.html | 2 +- reference/gofstat.html | 2 +- reference/graphcomp.html | 2 +- reference/graphcompcens.html | 2 +- reference/groundbeef.html | 2 +- reference/index.html | 2 +- reference/logLik-plot.html | 2 +- reference/logLik-surface.html | 2 +- reference/mgedist.html | 2 +- reference/mledist.html | 2 +- reference/mmedist.html | 6 +- reference/msedist.html | 2 +- reference/plotdist.html | 2 +- reference/plotdistcens.html | 2 +- reference/prefit.html | 2 +- reference/qmedist.html | 2 +- reference/quantile.html | 2 +- reference/salinity.html | 2 +- reference/smokedfish.html | 2 +- reference/toxocara.html | 2 +- search.json | 2 +- 58 files changed, 10327 insertions(+), 90 deletions(-) create mode 100644 deps/font-awesome-6.5.2/css/all.css create mode 100644 deps/font-awesome-6.5.2/css/all.min.css create mode 100644 deps/font-awesome-6.5.2/css/v4-shims.css create mode 100644 deps/font-awesome-6.5.2/css/v4-shims.min.css create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2 create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2 create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-solid-900.ttf create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2 create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.ttf create mode 100644 deps/font-awesome-6.5.2/webfonts/fa-v4compatibility.woff2 create mode 100644 reference/figures/fitdistrplus_hex.png diff --git a/404.html b/404.html index e8a7f10..a880229 100644 --- a/404.html +++ b/404.html @@ -8,8 +8,8 @@ Page not found (404) • fitdistrplus - - + + diff --git a/articles/FAQ.html b/articles/FAQ.html index 0d5d05a..3b955e1 100644 --- a/articles/FAQ.html +++ b/articles/FAQ.html @@ -8,8 +8,8 @@ Frequently Asked Questions • fitdistrplus - - + + @@ -63,7 +63,7 @@

Frequently Asked Questions

Marie Laure Delignette Muller, Christophe Dutang

-

2024-10-26

+

2024-12-02

Source: vignettes/FAQ.Rmd
FAQ.Rmd
diff --git a/articles/Optimalgo.html b/articles/Optimalgo.html index 75848b6..e482e3b 100644 --- a/articles/Optimalgo.html +++ b/articles/Optimalgo.html @@ -8,8 +8,8 @@ Which optimization algorithm to choose? • fitdistrplus - - + + @@ -63,7 +63,7 @@

Which optimization algorithm to choose?

Marie Laure Delignette Muller, Christophe Dutang

-

2024-10-26

+

2024-12-02

Source: vignettes/Optimalgo.Rmd
Optimalgo.Rmd
@@ -563,10 +563,10 @@

2.4. Results of the numerical in 0.006 0.004 0.032 -0.038 -0.051 -0.003 -0.011 +0.039 +0.052 +0.004 +0.010 @@ -659,15 +659,15 @@

2.4. Results of the numerical in time (sec) -0.009 -0.076 -0.072 +0.010 +0.077 +0.073 0.046 -0.019 0.020 +0.019 0.131 0.135 -0.078 +0.077 @@ -728,7 +728,7 @@

2.4. Results of the numerical in 0.013 0.003 0.008 -0.012 +0.013 0.011 @@ -782,7 +782,7 @@

2.4. Results of the numerical in time (sec) 0.010 0.046 -0.051 +0.050 0.039 @@ -1033,10 +1033,10 @@

3.4. Results of the numerical time (sec) 0.001 -0.003 -0.213 -0.166 -0.167 +0.002 +0.214 +0.168 +0.165 0.001 0.003 @@ -1142,13 +1142,13 @@

3.4. Results of the numerical time (sec) 0.008 -0.001 -0.001 0.002 0.002 -0.003 0.002 0.002 +0.003 +0.002 +0.001 0.002 @@ -1221,9 +1221,9 @@

3.4. Results of the numerical time (sec) 0.004 0.002 -0.229 -0.221 -0.225 +0.231 +0.223 +0.223 diff --git a/articles/fitdistrplus_vignette.html b/articles/fitdistrplus_vignette.html index 196d7b3..982e677 100644 --- a/articles/fitdistrplus_vignette.html +++ b/articles/fitdistrplus_vignette.html @@ -8,8 +8,8 @@ Overview of the fitdistrplus package • fitdistrplus - - + + @@ -63,7 +63,7 @@

Overview of the fitdistrplus package

Marie Laure Delignette Muller, Christophe Dutang

-

2024-10-26

+

2024-12-02

Source: vignettes/fitdistrplus_vignette.Rmd
fitdistrplus_vignette.Rmd
diff --git a/articles/index.html b/articles/index.html index 7e2167b..d64587a 100644 --- a/articles/index.html +++ b/articles/index.html @@ -1,5 +1,5 @@ -Articles • fitdistrplus +Articles • fitdistrplus Skip to contents diff --git a/articles/starting-values.html b/articles/starting-values.html index be61489..e530ddc 100644 --- a/articles/starting-values.html +++ b/articles/starting-values.html @@ -8,8 +8,8 @@ Starting values used in fitdistrplus • fitdistrplus - - + + @@ -63,7 +63,7 @@

Starting values used in fitdistrplus

Marie Laure Delignette Muller, Christophe Dutang

-

2024-10-26

+

2024-12-02

Source: vignettes/starting-values.Rmd
starting-values.Rmd
diff --git a/authors.html b/authors.html index d7d8da1..6ce08a1 100644 --- a/authors.html +++ b/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • fitdistrplus +Authors and Citation • fitdistrplus Skip to contents diff --git a/deps/data-deps.txt b/deps/data-deps.txt index 8aa49e2..ca4dfd9 100644 --- a/deps/data-deps.txt +++ b/deps/data-deps.txt @@ -2,8 +2,8 @@ - - + + diff --git a/deps/font-awesome-6.5.2/css/all.css b/deps/font-awesome-6.5.2/css/all.css new file mode 100644 index 0000000..151dd57 --- /dev/null +++ b/deps/font-awesome-6.5.2/css/all.css @@ -0,0 +1,8028 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, 0)); + transform: rotate(var(--fa-rotate-angle, 0)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + +.fa-person-rays::before { + content: "\e54d"; } + +.fa-users::before { + content: "\f0c0"; } + +.fa-eye-slash::before { + content: "\f070"; } + +.fa-flask-vial::before { + content: "\e4f3"; } + +.fa-hand::before { + content: "\f256"; } + +.fa-hand-paper::before { + content: "\f256"; } + +.fa-om::before { + content: "\f679"; } + +.fa-worm::before { + content: "\e599"; } + +.fa-house-circle-xmark::before { + content: "\e50b"; } + +.fa-plug::before { + content: "\f1e6"; } + +.fa-chevron-up::before { + content: "\f077"; } + +.fa-hand-spock::before { + content: "\f259"; } + +.fa-stopwatch::before { + content: "\f2f2"; } + +.fa-face-kiss::before { + content: "\f596"; } + +.fa-kiss::before { + content: "\f596"; } + +.fa-bridge-circle-xmark::before { + content: "\e4cb"; } + +.fa-face-grin-tongue::before { + content: "\f589"; } + +.fa-grin-tongue::before { + content: "\f589"; } + +.fa-chess-bishop::before { + content: "\f43a"; } + +.fa-face-grin-wink::before { + content: "\f58c"; } + +.fa-grin-wink::before { + content: "\f58c"; } + +.fa-ear-deaf::before { + content: "\f2a4"; } + +.fa-deaf::before { + content: "\f2a4"; } + +.fa-deafness::before { + content: "\f2a4"; } + +.fa-hard-of-hearing::before { + content: "\f2a4"; } + +.fa-road-circle-check::before { + content: "\e564"; } + +.fa-dice-five::before { + content: "\f523"; } + +.fa-square-rss::before { + content: "\f143"; } + +.fa-rss-square::before { + content: "\f143"; } + +.fa-land-mine-on::before { + content: "\e51b"; } + +.fa-i-cursor::before { + content: "\f246"; } + +.fa-stamp::before { + content: "\f5bf"; } + +.fa-stairs::before { + content: "\e289"; } + +.fa-i::before { + content: "\49"; } + +.fa-hryvnia-sign::before { + content: "\f6f2"; } + +.fa-hryvnia::before { + content: "\f6f2"; } + +.fa-pills::before { + content: "\f484"; } + +.fa-face-grin-wide::before { + content: "\f581"; } + +.fa-grin-alt::before { + content: "\f581"; } + +.fa-tooth::before { + content: "\f5c9"; } + +.fa-v::before { + content: "\56"; } + +.fa-bangladeshi-taka-sign::before { + content: "\e2e6"; } + +.fa-bicycle::before { + content: "\f206"; } + +.fa-staff-snake::before { + content: "\e579"; } + +.fa-rod-asclepius::before { + content: "\e579"; } + +.fa-rod-snake::before { + content: "\e579"; } + +.fa-staff-aesculapius::before { + content: "\e579"; } + +.fa-head-side-cough-slash::before { + content: "\e062"; } + +.fa-truck-medical::before { + content: "\f0f9"; } + +.fa-ambulance::before { + content: "\f0f9"; } + +.fa-wheat-awn-circle-exclamation::before { + content: "\e598"; } + +.fa-snowman::before { + content: "\f7d0"; } + +.fa-mortar-pestle::before { + content: "\f5a7"; } + +.fa-road-barrier::before { + content: "\e562"; } + +.fa-school::before { + content: "\f549"; } + +.fa-igloo::before { + content: "\f7ae"; } + +.fa-joint::before { + content: "\f595"; } + +.fa-angle-right::before { + content: "\f105"; } + +.fa-horse::before { + content: "\f6f0"; } + +.fa-q::before { + content: "\51"; } + +.fa-g::before { + content: "\47"; } + +.fa-notes-medical::before { + content: "\f481"; } + +.fa-temperature-half::before { + content: "\f2c9"; } + +.fa-temperature-2::before { + content: "\f2c9"; } + +.fa-thermometer-2::before { + content: "\f2c9"; } + +.fa-thermometer-half::before { + content: "\f2c9"; } + +.fa-dong-sign::before { + content: "\e169"; } + +.fa-capsules::before { + content: "\f46b"; } + +.fa-poo-storm::before { + content: "\f75a"; } + +.fa-poo-bolt::before { + content: "\f75a"; } + +.fa-face-frown-open::before { + content: "\f57a"; } + +.fa-frown-open::before { + content: "\f57a"; } + +.fa-hand-point-up::before { + content: "\f0a6"; } + +.fa-money-bill::before { + content: "\f0d6"; } + +.fa-bookmark::before { + content: "\f02e"; } + +.fa-align-justify::before { + content: "\f039"; } + +.fa-umbrella-beach::before { + content: "\f5ca"; } + +.fa-helmet-un::before { + content: "\e503"; } + +.fa-bullseye::before { + content: "\f140"; } + +.fa-bacon::before { + content: "\f7e5"; } + +.fa-hand-point-down::before { + content: "\f0a7"; } + +.fa-arrow-up-from-bracket::before { + content: "\e09a"; } + +.fa-folder::before { + content: "\f07b"; } + +.fa-folder-blank::before { + content: "\f07b"; } + +.fa-file-waveform::before { + content: "\f478"; } + +.fa-file-medical-alt::before { + content: "\f478"; } + +.fa-radiation::before { + content: "\f7b9"; } + +.fa-chart-simple::before { + content: "\e473"; } + +.fa-mars-stroke::before { + content: "\f229"; } + +.fa-vial::before { + content: "\f492"; } + +.fa-gauge::before { + content: "\f624"; } + +.fa-dashboard::before { + content: "\f624"; } + +.fa-gauge-med::before { + content: "\f624"; } + +.fa-tachometer-alt-average::before { + content: "\f624"; } + +.fa-wand-magic-sparkles::before { + content: "\e2ca"; } + +.fa-magic-wand-sparkles::before { + content: "\e2ca"; } + +.fa-e::before { + content: "\45"; } + +.fa-pen-clip::before { + content: "\f305"; } + +.fa-pen-alt::before { + content: "\f305"; } + +.fa-bridge-circle-exclamation::before { + content: "\e4ca"; } + +.fa-user::before { + content: "\f007"; } + +.fa-school-circle-check::before { + content: "\e56b"; } + +.fa-dumpster::before { + content: "\f793"; } + +.fa-van-shuttle::before { + content: "\f5b6"; } + +.fa-shuttle-van::before { + content: "\f5b6"; } + +.fa-building-user::before { + content: "\e4da"; } + +.fa-square-caret-left::before { + content: "\f191"; } + +.fa-caret-square-left::before { + content: "\f191"; } + +.fa-highlighter::before { + content: "\f591"; } + +.fa-key::before { + content: "\f084"; } + +.fa-bullhorn::before { + content: "\f0a1"; } + +.fa-globe::before { + content: "\f0ac"; } + +.fa-synagogue::before { + content: "\f69b"; } + +.fa-person-half-dress::before { + content: "\e548"; } + +.fa-road-bridge::before { + content: "\e563"; } + +.fa-location-arrow::before { + content: "\f124"; } + +.fa-c::before { + content: "\43"; } + +.fa-tablet-button::before { + content: "\f10a"; } + +.fa-building-lock::before { + content: "\e4d6"; } + +.fa-pizza-slice::before { + content: "\f818"; } + +.fa-money-bill-wave::before { + content: "\f53a"; } + +.fa-chart-area::before { + content: "\f1fe"; } + +.fa-area-chart::before { + content: "\f1fe"; } + +.fa-house-flag::before { + content: "\e50d"; } + +.fa-person-circle-minus::before { + content: "\e540"; } + +.fa-ban::before { + content: "\f05e"; } + +.fa-cancel::before { + content: "\f05e"; } + +.fa-camera-rotate::before { + content: "\e0d8"; } + +.fa-spray-can-sparkles::before { + content: "\f5d0"; } + +.fa-air-freshener::before { + content: "\f5d0"; } + +.fa-star::before { + content: "\f005"; } + +.fa-repeat::before { + content: "\f363"; } + +.fa-cross::before { + content: "\f654"; } + +.fa-box::before { + content: "\f466"; } + +.fa-venus-mars::before { + content: "\f228"; } + +.fa-arrow-pointer::before { + content: "\f245"; } + +.fa-mouse-pointer::before { + content: "\f245"; } + +.fa-maximize::before { + content: "\f31e"; } + +.fa-expand-arrows-alt::before { + content: "\f31e"; } + +.fa-charging-station::before { + content: "\f5e7"; } + +.fa-shapes::before { + content: "\f61f"; } + +.fa-triangle-circle-square::before { + content: "\f61f"; } + +.fa-shuffle::before { + content: "\f074"; } + +.fa-random::before { + content: "\f074"; } + +.fa-person-running::before { + content: "\f70c"; } + +.fa-running::before { + content: "\f70c"; } + +.fa-mobile-retro::before { + content: "\e527"; } + +.fa-grip-lines-vertical::before { + content: "\f7a5"; } + +.fa-spider::before { + content: "\f717"; } + +.fa-hands-bound::before { + content: "\e4f9"; } + +.fa-file-invoice-dollar::before { + content: "\f571"; } + +.fa-plane-circle-exclamation::before { + content: "\e556"; } + +.fa-x-ray::before { + content: "\f497"; } + +.fa-spell-check::before { + content: "\f891"; } + +.fa-slash::before { + content: "\f715"; } + +.fa-computer-mouse::before { + content: "\f8cc"; } + +.fa-mouse::before { + content: "\f8cc"; } + +.fa-arrow-right-to-bracket::before { + content: "\f090"; } + +.fa-sign-in::before { + content: "\f090"; } + +.fa-shop-slash::before { + content: "\e070"; } + +.fa-store-alt-slash::before { + content: "\e070"; } + +.fa-server::before { + content: "\f233"; } + +.fa-virus-covid-slash::before { + content: "\e4a9"; } + +.fa-shop-lock::before { + content: "\e4a5"; } + +.fa-hourglass-start::before { + content: "\f251"; } + +.fa-hourglass-1::before { + content: "\f251"; } + +.fa-blender-phone::before { + content: "\f6b6"; } + +.fa-building-wheat::before { + content: "\e4db"; } + +.fa-person-breastfeeding::before { + content: "\e53a"; } + +.fa-right-to-bracket::before { + content: "\f2f6"; } + +.fa-sign-in-alt::before { + content: "\f2f6"; } + +.fa-venus::before { + content: "\f221"; } + +.fa-passport::before { + content: "\f5ab"; } + +.fa-heart-pulse::before { + content: "\f21e"; } + +.fa-heartbeat::before { + content: "\f21e"; } + +.fa-people-carry-box::before { + content: "\f4ce"; } + +.fa-people-carry::before { + content: "\f4ce"; } + +.fa-temperature-high::before { + content: "\f769"; } + +.fa-microchip::before { + content: "\f2db"; } + +.fa-crown::before { + content: "\f521"; } + +.fa-weight-hanging::before { + content: "\f5cd"; } + +.fa-xmarks-lines::before { + content: "\e59a"; } + +.fa-file-prescription::before { + content: "\f572"; } + +.fa-weight-scale::before { + content: "\f496"; } + +.fa-weight::before { + content: "\f496"; } + +.fa-user-group::before { + content: "\f500"; } + +.fa-user-friends::before { + content: "\f500"; } + +.fa-arrow-up-a-z::before { + content: "\f15e"; } + +.fa-sort-alpha-up::before { + content: "\f15e"; } + +.fa-chess-knight::before { + content: "\f441"; } + +.fa-face-laugh-squint::before { + content: "\f59b"; } + +.fa-laugh-squint::before { + content: "\f59b"; } + +.fa-wheelchair::before { + content: "\f193"; } + +.fa-circle-arrow-up::before { + content: "\f0aa"; } + +.fa-arrow-circle-up::before { + content: "\f0aa"; } + +.fa-toggle-on::before { + content: "\f205"; } + +.fa-person-walking::before { + content: "\f554"; } + +.fa-walking::before { + content: "\f554"; } + +.fa-l::before { + content: "\4c"; } + +.fa-fire::before { + content: "\f06d"; } + +.fa-bed-pulse::before { + content: "\f487"; } + +.fa-procedures::before { + content: "\f487"; } + +.fa-shuttle-space::before { + content: "\f197"; } + +.fa-space-shuttle::before { + content: "\f197"; } + +.fa-face-laugh::before { + content: "\f599"; } + +.fa-laugh::before { + content: "\f599"; } + +.fa-folder-open::before { + content: "\f07c"; } + +.fa-heart-circle-plus::before { + content: "\e500"; } + +.fa-code-fork::before { + content: "\e13b"; } + +.fa-city::before { + content: "\f64f"; } + +.fa-microphone-lines::before { + content: "\f3c9"; } + +.fa-microphone-alt::before { + content: "\f3c9"; } + +.fa-pepper-hot::before { + content: "\f816"; } + +.fa-unlock::before { + content: "\f09c"; } + +.fa-colon-sign::before { + content: "\e140"; } + +.fa-headset::before { + content: "\f590"; } + +.fa-store-slash::before { + content: "\e071"; } + +.fa-road-circle-xmark::before { + content: "\e566"; } + +.fa-user-minus::before { + content: "\f503"; } + +.fa-mars-stroke-up::before { + content: "\f22a"; } + +.fa-mars-stroke-v::before { + content: "\f22a"; } + +.fa-champagne-glasses::before { + content: "\f79f"; } + +.fa-glass-cheers::before { + content: "\f79f"; } + +.fa-clipboard::before { + content: "\f328"; } + +.fa-house-circle-exclamation::before { + content: "\e50a"; } + +.fa-file-arrow-up::before { + content: "\f574"; } + +.fa-file-upload::before { + content: "\f574"; } + +.fa-wifi::before { + content: "\f1eb"; } + +.fa-wifi-3::before { + content: "\f1eb"; } + +.fa-wifi-strong::before { + content: "\f1eb"; } + +.fa-bath::before { + content: "\f2cd"; } + +.fa-bathtub::before { + content: "\f2cd"; } + +.fa-underline::before { + content: "\f0cd"; } + +.fa-user-pen::before { + content: "\f4ff"; } + +.fa-user-edit::before { + content: "\f4ff"; } + +.fa-signature::before { + content: "\f5b7"; } + +.fa-stroopwafel::before { + content: "\f551"; } + +.fa-bold::before { + content: "\f032"; } + +.fa-anchor-lock::before { + content: "\e4ad"; } + +.fa-building-ngo::before { + content: "\e4d7"; } + +.fa-manat-sign::before { + content: "\e1d5"; } + +.fa-not-equal::before { + content: "\f53e"; } + +.fa-border-top-left::before { + content: "\f853"; } + +.fa-border-style::before { + content: "\f853"; } + +.fa-map-location-dot::before { + content: "\f5a0"; } + +.fa-map-marked-alt::before { + content: "\f5a0"; } + +.fa-jedi::before { + content: "\f669"; } + +.fa-square-poll-vertical::before { + content: "\f681"; } + +.fa-poll::before { + content: "\f681"; } + +.fa-mug-hot::before { + content: "\f7b6"; } + +.fa-car-battery::before { + content: "\f5df"; } + +.fa-battery-car::before { + content: "\f5df"; } + +.fa-gift::before { + content: "\f06b"; } + +.fa-dice-two::before { + content: "\f528"; } + +.fa-chess-queen::before { + content: "\f445"; } + +.fa-glasses::before { + content: "\f530"; } + +.fa-chess-board::before { + content: "\f43c"; } + +.fa-building-circle-check::before { + content: "\e4d2"; } + +.fa-person-chalkboard::before { + content: "\e53d"; } + +.fa-mars-stroke-right::before { + content: "\f22b"; } + +.fa-mars-stroke-h::before { + content: "\f22b"; } + +.fa-hand-back-fist::before { + content: "\f255"; } + +.fa-hand-rock::before { + content: "\f255"; } + +.fa-square-caret-up::before { + content: "\f151"; } + +.fa-caret-square-up::before { + content: "\f151"; } + +.fa-cloud-showers-water::before { + content: "\e4e4"; } + +.fa-chart-bar::before { + content: "\f080"; } + +.fa-bar-chart::before { + content: "\f080"; } + +.fa-hands-bubbles::before { + content: "\e05e"; } + +.fa-hands-wash::before { + content: "\e05e"; } + +.fa-less-than-equal::before { + content: "\f537"; } + +.fa-train::before { + content: "\f238"; } + +.fa-eye-low-vision::before { + content: "\f2a8"; } + +.fa-low-vision::before { + content: "\f2a8"; } + +.fa-crow::before { + content: "\f520"; } + +.fa-sailboat::before { + content: "\e445"; } + +.fa-window-restore::before { + content: "\f2d2"; } + +.fa-square-plus::before { + content: "\f0fe"; } + +.fa-plus-square::before { + content: "\f0fe"; } + +.fa-torii-gate::before { + content: "\f6a1"; } + +.fa-frog::before { + content: "\f52e"; } + +.fa-bucket::before { + content: "\e4cf"; } + +.fa-image::before { + content: "\f03e"; } + +.fa-microphone::before { + content: "\f130"; } + +.fa-cow::before { + content: "\f6c8"; } + +.fa-caret-up::before { + content: "\f0d8"; } + +.fa-screwdriver::before { + content: "\f54a"; } + +.fa-folder-closed::before { + content: "\e185"; } + +.fa-house-tsunami::before { + content: "\e515"; } + +.fa-square-nfi::before { + content: "\e576"; } + +.fa-arrow-up-from-ground-water::before { + content: "\e4b5"; } + +.fa-martini-glass::before { + content: "\f57b"; } + +.fa-glass-martini-alt::before { + content: "\f57b"; } + +.fa-rotate-left::before { + content: "\f2ea"; } + +.fa-rotate-back::before { + content: "\f2ea"; } + +.fa-rotate-backward::before { + content: "\f2ea"; } + +.fa-undo-alt::before { + content: "\f2ea"; } + +.fa-table-columns::before { + content: "\f0db"; } + +.fa-columns::before { + content: "\f0db"; } + +.fa-lemon::before { + content: "\f094"; } + +.fa-head-side-mask::before { + content: "\e063"; } + +.fa-handshake::before { + content: "\f2b5"; } + +.fa-gem::before { + content: "\f3a5"; } + +.fa-dolly::before { + content: "\f472"; } + +.fa-dolly-box::before { + content: "\f472"; } + +.fa-smoking::before { + content: "\f48d"; } + +.fa-minimize::before { + content: "\f78c"; } + +.fa-compress-arrows-alt::before { + content: "\f78c"; } + +.fa-monument::before { + content: "\f5a6"; } + +.fa-snowplow::before { + content: "\f7d2"; } + +.fa-angles-right::before { + content: "\f101"; } + +.fa-angle-double-right::before { + content: "\f101"; } + +.fa-cannabis::before { + content: "\f55f"; } + +.fa-circle-play::before { + content: "\f144"; } + +.fa-play-circle::before { + content: "\f144"; } + +.fa-tablets::before { + content: "\f490"; } + +.fa-ethernet::before { + content: "\f796"; } + +.fa-euro-sign::before { + content: "\f153"; } + +.fa-eur::before { + content: "\f153"; } + +.fa-euro::before { + content: "\f153"; } + +.fa-chair::before { + content: "\f6c0"; } + +.fa-circle-check::before { + content: "\f058"; } + +.fa-check-circle::before { + content: "\f058"; } + +.fa-circle-stop::before { + content: "\f28d"; } + +.fa-stop-circle::before { + content: "\f28d"; } + +.fa-compass-drafting::before { + content: "\f568"; } + +.fa-drafting-compass::before { + content: "\f568"; } + +.fa-plate-wheat::before { + content: "\e55a"; } + +.fa-icicles::before { + content: "\f7ad"; } + +.fa-person-shelter::before { + content: "\e54f"; } + +.fa-neuter::before { + content: "\f22c"; } + +.fa-id-badge::before { + content: "\f2c1"; } + +.fa-marker::before { + content: "\f5a1"; } + +.fa-face-laugh-beam::before { + content: "\f59a"; } + +.fa-laugh-beam::before { + content: "\f59a"; } + +.fa-helicopter-symbol::before { + content: "\e502"; } + +.fa-universal-access::before { + content: "\f29a"; } + +.fa-circle-chevron-up::before { + content: "\f139"; } + +.fa-chevron-circle-up::before { + content: "\f139"; } + +.fa-lari-sign::before { + content: "\e1c8"; } + +.fa-volcano::before { + content: "\f770"; } + +.fa-person-walking-dashed-line-arrow-right::before { + content: "\e553"; } + +.fa-sterling-sign::before { + content: "\f154"; } + +.fa-gbp::before { + content: "\f154"; } + +.fa-pound-sign::before { + content: "\f154"; } + +.fa-viruses::before { + content: "\e076"; } + +.fa-square-person-confined::before { + content: "\e577"; } + +.fa-user-tie::before { + content: "\f508"; } + +.fa-arrow-down-long::before { + content: "\f175"; } + +.fa-long-arrow-down::before { + content: "\f175"; } + +.fa-tent-arrow-down-to-line::before { + content: "\e57e"; } + +.fa-certificate::before { + content: "\f0a3"; } + +.fa-reply-all::before { + content: "\f122"; } + +.fa-mail-reply-all::before { + content: "\f122"; } + +.fa-suitcase::before { + content: "\f0f2"; } + +.fa-person-skating::before { + content: "\f7c5"; } + +.fa-skating::before { + content: "\f7c5"; } + +.fa-filter-circle-dollar::before { + content: "\f662"; } + +.fa-funnel-dollar::before { + content: "\f662"; } + +.fa-camera-retro::before { + content: "\f083"; } + +.fa-circle-arrow-down::before { + content: "\f0ab"; } + +.fa-arrow-circle-down::before { + content: "\f0ab"; } + +.fa-file-import::before { + content: "\f56f"; } + +.fa-arrow-right-to-file::before { + content: "\f56f"; } + +.fa-square-arrow-up-right::before { + content: "\f14c"; } + +.fa-external-link-square::before { + content: "\f14c"; } + +.fa-box-open::before { + content: "\f49e"; } + +.fa-scroll::before { + content: "\f70e"; } + +.fa-spa::before { + content: "\f5bb"; } + +.fa-location-pin-lock::before { + content: "\e51f"; } + +.fa-pause::before { + content: "\f04c"; } + +.fa-hill-avalanche::before { + content: "\e507"; } + +.fa-temperature-empty::before { + content: "\f2cb"; } + +.fa-temperature-0::before { + content: "\f2cb"; } + +.fa-thermometer-0::before { + content: "\f2cb"; } + +.fa-thermometer-empty::before { + content: "\f2cb"; } + +.fa-bomb::before { + content: "\f1e2"; } + +.fa-registered::before { + content: "\f25d"; } + +.fa-address-card::before { + content: "\f2bb"; } + +.fa-contact-card::before { + content: "\f2bb"; } + +.fa-vcard::before { + content: "\f2bb"; } + +.fa-scale-unbalanced-flip::before { + content: "\f516"; } + +.fa-balance-scale-right::before { + content: "\f516"; } + +.fa-subscript::before { + content: "\f12c"; } + +.fa-diamond-turn-right::before { + content: "\f5eb"; } + +.fa-directions::before { + content: "\f5eb"; } + +.fa-burst::before { + content: "\e4dc"; } + +.fa-house-laptop::before { + content: "\e066"; } + +.fa-laptop-house::before { + content: "\e066"; } + +.fa-face-tired::before { + content: "\f5c8"; } + +.fa-tired::before { + content: "\f5c8"; } + +.fa-money-bills::before { + content: "\e1f3"; } + +.fa-smog::before { + content: "\f75f"; } + +.fa-crutch::before { + content: "\f7f7"; } + +.fa-cloud-arrow-up::before { + content: "\f0ee"; } + +.fa-cloud-upload::before { + content: "\f0ee"; } + +.fa-cloud-upload-alt::before { + content: "\f0ee"; } + +.fa-palette::before { + content: "\f53f"; } + +.fa-arrows-turn-right::before { + content: "\e4c0"; } + +.fa-vest::before { + content: "\e085"; } + +.fa-ferry::before { + content: "\e4ea"; } + +.fa-arrows-down-to-people::before { + content: "\e4b9"; } + +.fa-seedling::before { + content: "\f4d8"; } + +.fa-sprout::before { + content: "\f4d8"; } + +.fa-left-right::before { + content: "\f337"; } + +.fa-arrows-alt-h::before { + content: "\f337"; } + +.fa-boxes-packing::before { + content: "\e4c7"; } + +.fa-circle-arrow-left::before { + content: "\f0a8"; } + +.fa-arrow-circle-left::before { + content: "\f0a8"; } + +.fa-group-arrows-rotate::before { + content: "\e4f6"; } + +.fa-bowl-food::before { + content: "\e4c6"; } + +.fa-candy-cane::before { + content: "\f786"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-cloud-bolt::before { + content: "\f76c"; } + +.fa-thunderstorm::before { + content: "\f76c"; } + +.fa-text-slash::before { + content: "\f87d"; } + +.fa-remove-format::before { + content: "\f87d"; } + +.fa-face-smile-wink::before { + content: "\f4da"; } + +.fa-smile-wink::before { + content: "\f4da"; } + +.fa-file-word::before { + content: "\f1c2"; } + +.fa-file-powerpoint::before { + content: "\f1c4"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-house-lock::before { + content: "\e510"; } + +.fa-cloud-arrow-down::before { + content: "\f0ed"; } + +.fa-cloud-download::before { + content: "\f0ed"; } + +.fa-cloud-download-alt::before { + content: "\f0ed"; } + +.fa-children::before { + content: "\e4e1"; } + +.fa-chalkboard::before { + content: "\f51b"; } + +.fa-blackboard::before { + content: "\f51b"; } + +.fa-user-large-slash::before { + content: "\f4fa"; } + +.fa-user-alt-slash::before { + content: "\f4fa"; } + +.fa-envelope-open::before { + content: "\f2b6"; } + +.fa-handshake-simple-slash::before { + content: "\e05f"; } + +.fa-handshake-alt-slash::before { + content: "\e05f"; } + +.fa-mattress-pillow::before { + content: "\e525"; } + +.fa-guarani-sign::before { + content: "\e19a"; } + +.fa-arrows-rotate::before { + content: "\f021"; } + +.fa-refresh::before { + content: "\f021"; } + +.fa-sync::before { + content: "\f021"; } + +.fa-fire-extinguisher::before { + content: "\f134"; } + +.fa-cruzeiro-sign::before { + content: "\e152"; } + +.fa-greater-than-equal::before { + content: "\f532"; } + +.fa-shield-halved::before { + content: "\f3ed"; } + +.fa-shield-alt::before { + content: "\f3ed"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-envelope-circle-check::before { + content: "\e4e8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-heart-circle-check::before { + content: "\e4fd"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + +.fa-file-zipper::before { + content: "\f1c6"; } + +.fa-file-archive::before { + content: "\f1c6"; } + +.fa-square::before { + content: "\f0c8"; } + +.fa-martini-glass-empty::before { + content: "\f000"; } + +.fa-glass-martini::before { + content: "\f000"; } + +.fa-couch::before { + content: "\f4b8"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-italic::before { + content: "\f033"; } + +.fa-table-cells-column-lock::before { + content: "\e678"; } + +.fa-church::before { + content: "\f51d"; } + +.fa-comments-dollar::before { + content: "\f653"; } + +.fa-democrat::before { + content: "\f747"; } + +.fa-z::before { + content: "\5a"; } + +.fa-person-skiing::before { + content: "\f7c9"; } + +.fa-skiing::before { + content: "\f7c9"; } + +.fa-road-lock::before { + content: "\e567"; } + +.fa-a::before { + content: "\41"; } + +.fa-temperature-arrow-down::before { + content: "\e03f"; } + +.fa-temperature-down::before { + content: "\e03f"; } + +.fa-feather-pointed::before { + content: "\f56b"; } + +.fa-feather-alt::before { + content: "\f56b"; } + +.fa-p::before { + content: "\50"; } + +.fa-snowflake::before { + content: "\f2dc"; } + +.fa-newspaper::before { + content: "\f1ea"; } + +.fa-rectangle-ad::before { + content: "\f641"; } + +.fa-ad::before { + content: "\f641"; } + +.fa-circle-arrow-right::before { + content: "\f0a9"; } + +.fa-arrow-circle-right::before { + content: "\f0a9"; } + +.fa-filter-circle-xmark::before { + content: "\e17b"; } + +.fa-locust::before { + content: "\e520"; } + +.fa-sort::before { + content: "\f0dc"; } + +.fa-unsorted::before { + content: "\f0dc"; } + +.fa-list-ol::before { + content: "\f0cb"; } + +.fa-list-1-2::before { + content: "\f0cb"; } + +.fa-list-numeric::before { + content: "\f0cb"; } + +.fa-person-dress-burst::before { + content: "\e544"; } + +.fa-money-check-dollar::before { + content: "\f53d"; } + +.fa-money-check-alt::before { + content: "\f53d"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-bread-slice::before { + content: "\f7ec"; } + +.fa-language::before { + content: "\f1ab"; } + +.fa-face-kiss-wink-heart::before { + content: "\f598"; } + +.fa-kiss-wink-heart::before { + content: "\f598"; } + +.fa-filter::before { + content: "\f0b0"; } + +.fa-question::before { + content: "\3f"; } + +.fa-file-signature::before { + content: "\f573"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + +.fa-house-chimney-user::before { + content: "\e065"; } + +.fa-hand-holding-heart::before { + content: "\f4be"; } + +.fa-puzzle-piece::before { + content: "\f12e"; } + +.fa-money-check::before { + content: "\f53c"; } + +.fa-star-half-stroke::before { + content: "\f5c0"; } + +.fa-star-half-alt::before { + content: "\f5c0"; } + +.fa-code::before { + content: "\f121"; } + +.fa-whiskey-glass::before { + content: "\f7a0"; } + +.fa-glass-whiskey::before { + content: "\f7a0"; } + +.fa-building-circle-exclamation::before { + content: "\e4d3"; } + +.fa-magnifying-glass-chart::before { + content: "\e522"; } + +.fa-arrow-up-right-from-square::before { + content: "\f08e"; } + +.fa-external-link::before { + content: "\f08e"; } + +.fa-cubes-stacked::before { + content: "\e4e6"; } + +.fa-won-sign::before { + content: "\f159"; } + +.fa-krw::before { + content: "\f159"; } + +.fa-won::before { + content: "\f159"; } + +.fa-virus-covid::before { + content: "\e4a8"; } + +.fa-austral-sign::before { + content: "\e0a9"; } + +.fa-f::before { + content: "\46"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-road::before { + content: "\f018"; } + +.fa-taxi::before { + content: "\f1ba"; } + +.fa-cab::before { + content: "\f1ba"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-chart-pie::before { + content: "\f200"; } + +.fa-pie-chart::before { + content: "\f200"; } + +.fa-bolt-lightning::before { + content: "\e0b7"; } + +.fa-sack-xmark::before { + content: "\e56a"; } + +.fa-file-excel::before { + content: "\f1c3"; } + +.fa-file-contract::before { + content: "\f56c"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-building-flag::before { + content: "\e4d5"; } + +.fa-face-grin-beam::before { + content: "\f582"; } + +.fa-grin-beam::before { + content: "\f582"; } + +.fa-object-ungroup::before { + content: "\f248"; } + +.fa-poop::before { + content: "\f619"; } + +.fa-location-pin::before { + content: "\f041"; } + +.fa-map-marker::before { + content: "\f041"; } + +.fa-kaaba::before { + content: "\f66b"; } + +.fa-toilet-paper::before { + content: "\f71e"; } + +.fa-helmet-safety::before { + content: "\f807"; } + +.fa-hard-hat::before { + content: "\f807"; } + +.fa-hat-hard::before { + content: "\f807"; } + +.fa-eject::before { + content: "\f052"; } + +.fa-circle-right::before { + content: "\f35a"; } + +.fa-arrow-alt-circle-right::before { + content: "\f35a"; } + +.fa-plane-circle-check::before { + content: "\e555"; } + +.fa-face-rolling-eyes::before { + content: "\f5a5"; } + +.fa-meh-rolling-eyes::before { + content: "\f5a5"; } + +.fa-object-group::before { + content: "\f247"; } + +.fa-chart-line::before { + content: "\f201"; } + +.fa-line-chart::before { + content: "\f201"; } + +.fa-mask-ventilator::before { + content: "\e524"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-signs-post::before { + content: "\f277"; } + +.fa-map-signs::before { + content: "\f277"; } + +.fa-cash-register::before { + content: "\f788"; } + +.fa-person-circle-question::before { + content: "\e542"; } + +.fa-h::before { + content: "\48"; } + +.fa-tarp::before { + content: "\e57b"; } + +.fa-screwdriver-wrench::before { + content: "\f7d9"; } + +.fa-tools::before { + content: "\f7d9"; } + +.fa-arrows-to-eye::before { + content: "\e4bf"; } + +.fa-plug-circle-bolt::before { + content: "\e55b"; } + +.fa-heart::before { + content: "\f004"; } + +.fa-mars-and-venus::before { + content: "\f224"; } + +.fa-house-user::before { + content: "\e1b0"; } + +.fa-home-user::before { + content: "\e1b0"; } + +.fa-dumpster-fire::before { + content: "\f794"; } + +.fa-house-crack::before { + content: "\e3b1"; } + +.fa-martini-glass-citrus::before { + content: "\f561"; } + +.fa-cocktail::before { + content: "\f561"; } + +.fa-face-surprise::before { + content: "\f5c2"; } + +.fa-surprise::before { + content: "\f5c2"; } + +.fa-bottle-water::before { + content: "\e4c5"; } + +.fa-circle-pause::before { + content: "\f28b"; } + +.fa-pause-circle::before { + content: "\f28b"; } + +.fa-toilet-paper-slash::before { + content: "\e072"; } + +.fa-apple-whole::before { + content: "\f5d1"; } + +.fa-apple-alt::before { + content: "\f5d1"; } + +.fa-kitchen-set::before { + content: "\e51a"; } + +.fa-r::before { + content: "\52"; } + +.fa-temperature-quarter::before { + content: "\f2ca"; } + +.fa-temperature-1::before { + content: "\f2ca"; } + +.fa-thermometer-1::before { + content: "\f2ca"; } + +.fa-thermometer-quarter::before { + content: "\f2ca"; } + +.fa-cube::before { + content: "\f1b2"; } + +.fa-bitcoin-sign::before { + content: "\e0b4"; } + +.fa-shield-dog::before { + content: "\e573"; } + +.fa-solar-panel::before { + content: "\f5ba"; } + +.fa-lock-open::before { + content: "\f3c1"; } + +.fa-elevator::before { + content: "\e16d"; } + +.fa-money-bill-transfer::before { + content: "\e528"; } + +.fa-money-bill-trend-up::before { + content: "\e529"; } + +.fa-house-flood-water-circle-arrow-right::before { + content: "\e50f"; } + +.fa-square-poll-horizontal::before { + content: "\f682"; } + +.fa-poll-h::before { + content: "\f682"; } + +.fa-circle::before { + content: "\f111"; } + +.fa-backward-fast::before { + content: "\f049"; } + +.fa-fast-backward::before { + content: "\f049"; } + +.fa-recycle::before { + content: "\f1b8"; } + +.fa-user-astronaut::before { + content: "\f4fb"; } + +.fa-plane-slash::before { + content: "\e069"; } + +.fa-trademark::before { + content: "\f25c"; } + +.fa-basketball::before { + content: "\f434"; } + +.fa-basketball-ball::before { + content: "\f434"; } + +.fa-satellite-dish::before { + content: "\f7c0"; } + +.fa-circle-up::before { + content: "\f35b"; } + +.fa-arrow-alt-circle-up::before { + content: "\f35b"; } + +.fa-mobile-screen-button::before { + content: "\f3cd"; } + +.fa-mobile-alt::before { + content: "\f3cd"; } + +.fa-volume-high::before { + content: "\f028"; } + +.fa-volume-up::before { + content: "\f028"; } + +.fa-users-rays::before { + content: "\e593"; } + +.fa-wallet::before { + content: "\f555"; } + +.fa-clipboard-check::before { + content: "\f46c"; } + +.fa-file-audio::before { + content: "\f1c7"; } + +.fa-burger::before { + content: "\f805"; } + +.fa-hamburger::before { + content: "\f805"; } + +.fa-wrench::before { + content: "\f0ad"; } + +.fa-bugs::before { + content: "\e4d0"; } + +.fa-rupee-sign::before { + content: "\f156"; } + +.fa-rupee::before { + content: "\f156"; } + +.fa-file-image::before { + content: "\f1c5"; } + +.fa-circle-question::before { + content: "\f059"; } + +.fa-question-circle::before { + content: "\f059"; } + +.fa-plane-departure::before { + content: "\f5b0"; } + +.fa-handshake-slash::before { + content: "\e060"; } + +.fa-book-bookmark::before { + content: "\e0bb"; } + +.fa-code-branch::before { + content: "\f126"; } + +.fa-hat-cowboy::before { + content: "\f8c0"; } + +.fa-bridge::before { + content: "\e4c8"; } + +.fa-phone-flip::before { + content: "\f879"; } + +.fa-phone-alt::before { + content: "\f879"; } + +.fa-truck-front::before { + content: "\e2b7"; } + +.fa-cat::before { + content: "\f6be"; } + +.fa-anchor-circle-exclamation::before { + content: "\e4ab"; } + +.fa-truck-field::before { + content: "\e58d"; } + +.fa-route::before { + content: "\f4d7"; } + +.fa-clipboard-question::before { + content: "\e4e3"; } + +.fa-panorama::before { + content: "\e209"; } + +.fa-comment-medical::before { + content: "\f7f5"; } + +.fa-teeth-open::before { + content: "\f62f"; } + +.fa-file-circle-minus::before { + content: "\e4ed"; } + +.fa-tags::before { + content: "\f02c"; } + +.fa-wine-glass::before { + content: "\f4e3"; } + +.fa-forward-fast::before { + content: "\f050"; } + +.fa-fast-forward::before { + content: "\f050"; } + +.fa-face-meh-blank::before { + content: "\f5a4"; } + +.fa-meh-blank::before { + content: "\f5a4"; } + +.fa-square-parking::before { + content: "\f540"; } + +.fa-parking::before { + content: "\f540"; } + +.fa-house-signal::before { + content: "\e012"; } + +.fa-bars-progress::before { + content: "\f828"; } + +.fa-tasks-alt::before { + content: "\f828"; } + +.fa-faucet-drip::before { + content: "\e006"; } + +.fa-cart-flatbed::before { + content: "\f474"; } + +.fa-dolly-flatbed::before { + content: "\f474"; } + +.fa-ban-smoking::before { + content: "\f54d"; } + +.fa-smoking-ban::before { + content: "\f54d"; } + +.fa-terminal::before { + content: "\f120"; } + +.fa-mobile-button::before { + content: "\f10b"; } + +.fa-house-medical-flag::before { + content: "\e514"; } + +.fa-basket-shopping::before { + content: "\f291"; } + +.fa-shopping-basket::before { + content: "\f291"; } + +.fa-tape::before { + content: "\f4db"; } + +.fa-bus-simple::before { + content: "\f55e"; } + +.fa-bus-alt::before { + content: "\f55e"; } + +.fa-eye::before { + content: "\f06e"; } + +.fa-face-sad-cry::before { + content: "\f5b3"; } + +.fa-sad-cry::before { + content: "\f5b3"; } + +.fa-audio-description::before { + content: "\f29e"; } + +.fa-person-military-to-person::before { + content: "\e54c"; } + +.fa-file-shield::before { + content: "\e4f0"; } + +.fa-user-slash::before { + content: "\f506"; } + +.fa-pen::before { + content: "\f304"; } + +.fa-tower-observation::before { + content: "\e586"; } + +.fa-file-code::before { + content: "\f1c9"; } + +.fa-signal::before { + content: "\f012"; } + +.fa-signal-5::before { + content: "\f012"; } + +.fa-signal-perfect::before { + content: "\f012"; } + +.fa-bus::before { + content: "\f207"; } + +.fa-heart-circle-xmark::before { + content: "\e501"; } + +.fa-house-chimney::before { + content: "\e3af"; } + +.fa-home-lg::before { + content: "\e3af"; } + +.fa-window-maximize::before { + content: "\f2d0"; } + +.fa-face-frown::before { + content: "\f119"; } + +.fa-frown::before { + content: "\f119"; } + +.fa-prescription::before { + content: "\f5b1"; } + +.fa-shop::before { + content: "\f54f"; } + +.fa-store-alt::before { + content: "\f54f"; } + +.fa-floppy-disk::before { + content: "\f0c7"; } + +.fa-save::before { + content: "\f0c7"; } + +.fa-vihara::before { + content: "\f6a7"; } + +.fa-scale-unbalanced::before { + content: "\f515"; } + +.fa-balance-scale-left::before { + content: "\f515"; } + +.fa-sort-up::before { + content: "\f0de"; } + +.fa-sort-asc::before { + content: "\f0de"; } + +.fa-comment-dots::before { + content: "\f4ad"; } + +.fa-commenting::before { + content: "\f4ad"; } + +.fa-plant-wilt::before { + content: "\e5aa"; } + +.fa-diamond::before { + content: "\f219"; } + +.fa-face-grin-squint::before { + content: "\f585"; } + +.fa-grin-squint::before { + content: "\f585"; } + +.fa-hand-holding-dollar::before { + content: "\f4c0"; } + +.fa-hand-holding-usd::before { + content: "\f4c0"; } + +.fa-bacterium::before { + content: "\e05a"; } + +.fa-hand-pointer::before { + content: "\f25a"; } + +.fa-drum-steelpan::before { + content: "\f56a"; } + +.fa-hand-scissors::before { + content: "\f257"; } + +.fa-hands-praying::before { + content: "\f684"; } + +.fa-praying-hands::before { + content: "\f684"; } + +.fa-arrow-rotate-right::before { + content: "\f01e"; } + +.fa-arrow-right-rotate::before { + content: "\f01e"; } + +.fa-arrow-rotate-forward::before { + content: "\f01e"; } + +.fa-redo::before { + content: "\f01e"; } + +.fa-biohazard::before { + content: "\f780"; } + +.fa-location-crosshairs::before { + content: "\f601"; } + +.fa-location::before { + content: "\f601"; } + +.fa-mars-double::before { + content: "\f227"; } + +.fa-child-dress::before { + content: "\e59c"; } + +.fa-users-between-lines::before { + content: "\e591"; } + +.fa-lungs-virus::before { + content: "\e067"; } + +.fa-face-grin-tears::before { + content: "\f588"; } + +.fa-grin-tears::before { + content: "\f588"; } + +.fa-phone::before { + content: "\f095"; } + +.fa-calendar-xmark::before { + content: "\f273"; } + +.fa-calendar-times::before { + content: "\f273"; } + +.fa-child-reaching::before { + content: "\e59d"; } + +.fa-head-side-virus::before { + content: "\e064"; } + +.fa-user-gear::before { + content: "\f4fe"; } + +.fa-user-cog::before { + content: "\f4fe"; } + +.fa-arrow-up-1-9::before { + content: "\f163"; } + +.fa-sort-numeric-up::before { + content: "\f163"; } + +.fa-door-closed::before { + content: "\f52a"; } + +.fa-shield-virus::before { + content: "\e06c"; } + +.fa-dice-six::before { + content: "\f526"; } + +.fa-mosquito-net::before { + content: "\e52c"; } + +.fa-bridge-water::before { + content: "\e4ce"; } + +.fa-person-booth::before { + content: "\f756"; } + +.fa-text-width::before { + content: "\f035"; } + +.fa-hat-wizard::before { + content: "\f6e8"; } + +.fa-pen-fancy::before { + content: "\f5ac"; } + +.fa-person-digging::before { + content: "\f85e"; } + +.fa-digging::before { + content: "\f85e"; } + +.fa-trash::before { + content: "\f1f8"; } + +.fa-gauge-simple::before { + content: "\f629"; } + +.fa-gauge-simple-med::before { + content: "\f629"; } + +.fa-tachometer-average::before { + content: "\f629"; } + +.fa-book-medical::before { + content: "\f7e6"; } + +.fa-poo::before { + content: "\f2fe"; } + +.fa-quote-right::before { + content: "\f10e"; } + +.fa-quote-right-alt::before { + content: "\f10e"; } + +.fa-shirt::before { + content: "\f553"; } + +.fa-t-shirt::before { + content: "\f553"; } + +.fa-tshirt::before { + content: "\f553"; } + +.fa-cubes::before { + content: "\f1b3"; } + +.fa-divide::before { + content: "\f529"; } + +.fa-tenge-sign::before { + content: "\f7d7"; } + +.fa-tenge::before { + content: "\f7d7"; } + +.fa-headphones::before { + content: "\f025"; } + +.fa-hands-holding::before { + content: "\f4c2"; } + +.fa-hands-clapping::before { + content: "\e1a8"; } + +.fa-republican::before { + content: "\f75e"; } + +.fa-arrow-left::before { + content: "\f060"; } + +.fa-person-circle-xmark::before { + content: "\e543"; } + +.fa-ruler::before { + content: "\f545"; } + +.fa-align-left::before { + content: "\f036"; } + +.fa-dice-d6::before { + content: "\f6d1"; } + +.fa-restroom::before { + content: "\f7bd"; } + +.fa-j::before { + content: "\4a"; } + +.fa-users-viewfinder::before { + content: "\e595"; } + +.fa-file-video::before { + content: "\f1c8"; } + +.fa-up-right-from-square::before { + content: "\f35d"; } + +.fa-external-link-alt::before { + content: "\f35d"; } + +.fa-table-cells::before { + content: "\f00a"; } + +.fa-th::before { + content: "\f00a"; } + +.fa-file-pdf::before { + content: "\f1c1"; } + +.fa-book-bible::before { + content: "\f647"; } + +.fa-bible::before { + content: "\f647"; } + +.fa-o::before { + content: "\4f"; } + +.fa-suitcase-medical::before { + content: "\f0fa"; } + +.fa-medkit::before { + content: "\f0fa"; } + +.fa-user-secret::before { + content: "\f21b"; } + +.fa-otter::before { + content: "\f700"; } + +.fa-person-dress::before { + content: "\f182"; } + +.fa-female::before { + content: "\f182"; } + +.fa-comment-dollar::before { + content: "\f651"; } + +.fa-business-time::before { + content: "\f64a"; } + +.fa-briefcase-clock::before { + content: "\f64a"; } + +.fa-table-cells-large::before { + content: "\f009"; } + +.fa-th-large::before { + content: "\f009"; } + +.fa-book-tanakh::before { + content: "\f827"; } + +.fa-tanakh::before { + content: "\f827"; } + +.fa-phone-volume::before { + content: "\f2a0"; } + +.fa-volume-control-phone::before { + content: "\f2a0"; } + +.fa-hat-cowboy-side::before { + content: "\f8c1"; } + +.fa-clipboard-user::before { + content: "\f7f3"; } + +.fa-child::before { + content: "\f1ae"; } + +.fa-lira-sign::before { + content: "\f195"; } + +.fa-satellite::before { + content: "\f7bf"; } + +.fa-plane-lock::before { + content: "\e558"; } + +.fa-tag::before { + content: "\f02b"; } + +.fa-comment::before { + content: "\f075"; } + +.fa-cake-candles::before { + content: "\f1fd"; } + +.fa-birthday-cake::before { + content: "\f1fd"; } + +.fa-cake::before { + content: "\f1fd"; } + +.fa-envelope::before { + content: "\f0e0"; } + +.fa-angles-up::before { + content: "\f102"; } + +.fa-angle-double-up::before { + content: "\f102"; } + +.fa-paperclip::before { + content: "\f0c6"; } + +.fa-arrow-right-to-city::before { + content: "\e4b3"; } + +.fa-ribbon::before { + content: "\f4d6"; } + +.fa-lungs::before { + content: "\f604"; } + +.fa-arrow-up-9-1::before { + content: "\f887"; } + +.fa-sort-numeric-up-alt::before { + content: "\f887"; } + +.fa-litecoin-sign::before { + content: "\e1d3"; } + +.fa-border-none::before { + content: "\f850"; } + +.fa-circle-nodes::before { + content: "\e4e2"; } + +.fa-parachute-box::before { + content: "\f4cd"; } + +.fa-indent::before { + content: "\f03c"; } + +.fa-truck-field-un::before { + content: "\e58e"; } + +.fa-hourglass::before { + content: "\f254"; } + +.fa-hourglass-empty::before { + content: "\f254"; } + +.fa-mountain::before { + content: "\f6fc"; } + +.fa-user-doctor::before { + content: "\f0f0"; } + +.fa-user-md::before { + content: "\f0f0"; } + +.fa-circle-info::before { + content: "\f05a"; } + +.fa-info-circle::before { + content: "\f05a"; } + +.fa-cloud-meatball::before { + content: "\f73b"; } + +.fa-camera::before { + content: "\f030"; } + +.fa-camera-alt::before { + content: "\f030"; } + +.fa-square-virus::before { + content: "\e578"; } + +.fa-meteor::before { + content: "\f753"; } + +.fa-car-on::before { + content: "\e4dd"; } + +.fa-sleigh::before { + content: "\f7cc"; } + +.fa-arrow-down-1-9::before { + content: "\f162"; } + +.fa-sort-numeric-asc::before { + content: "\f162"; } + +.fa-sort-numeric-down::before { + content: "\f162"; } + +.fa-hand-holding-droplet::before { + content: "\f4c1"; } + +.fa-hand-holding-water::before { + content: "\f4c1"; } + +.fa-water::before { + content: "\f773"; } + +.fa-calendar-check::before { + content: "\f274"; } + +.fa-braille::before { + content: "\f2a1"; } + +.fa-prescription-bottle-medical::before { + content: "\f486"; } + +.fa-prescription-bottle-alt::before { + content: "\f486"; } + +.fa-landmark::before { + content: "\f66f"; } + +.fa-truck::before { + content: "\f0d1"; } + +.fa-crosshairs::before { + content: "\f05b"; } + +.fa-person-cane::before { + content: "\e53c"; } + +.fa-tent::before { + content: "\e57d"; } + +.fa-vest-patches::before { + content: "\e086"; } + +.fa-check-double::before { + content: "\f560"; } + +.fa-arrow-down-a-z::before { + content: "\f15d"; } + +.fa-sort-alpha-asc::before { + content: "\f15d"; } + +.fa-sort-alpha-down::before { + content: "\f15d"; } + +.fa-money-bill-wheat::before { + content: "\e52a"; } + +.fa-cookie::before { + content: "\f563"; } + +.fa-arrow-rotate-left::before { + content: "\f0e2"; } + +.fa-arrow-left-rotate::before { + content: "\f0e2"; } + +.fa-arrow-rotate-back::before { + content: "\f0e2"; } + +.fa-arrow-rotate-backward::before { + content: "\f0e2"; } + +.fa-undo::before { + content: "\f0e2"; } + +.fa-hard-drive::before { + content: "\f0a0"; } + +.fa-hdd::before { + content: "\f0a0"; } + +.fa-face-grin-squint-tears::before { + content: "\f586"; } + +.fa-grin-squint-tears::before { + content: "\f586"; } + +.fa-dumbbell::before { + content: "\f44b"; } + +.fa-rectangle-list::before { + content: "\f022"; } + +.fa-list-alt::before { + content: "\f022"; } + +.fa-tarp-droplet::before { + content: "\e57c"; } + +.fa-house-medical-circle-check::before { + content: "\e511"; } + +.fa-person-skiing-nordic::before { + content: "\f7ca"; } + +.fa-skiing-nordic::before { + content: "\f7ca"; } + +.fa-calendar-plus::before { + content: "\f271"; } + +.fa-plane-arrival::before { + content: "\f5af"; } + +.fa-circle-left::before { + content: "\f359"; } + +.fa-arrow-alt-circle-left::before { + content: "\f359"; } + +.fa-train-subway::before { + content: "\f239"; } + +.fa-subway::before { + content: "\f239"; } + +.fa-chart-gantt::before { + content: "\e0e4"; } + +.fa-indian-rupee-sign::before { + content: "\e1bc"; } + +.fa-indian-rupee::before { + content: "\e1bc"; } + +.fa-inr::before { + content: "\e1bc"; } + +.fa-crop-simple::before { + content: "\f565"; } + +.fa-crop-alt::before { + content: "\f565"; } + +.fa-money-bill-1::before { + content: "\f3d1"; } + +.fa-money-bill-alt::before { + content: "\f3d1"; } + +.fa-left-long::before { + content: "\f30a"; } + +.fa-long-arrow-alt-left::before { + content: "\f30a"; } + +.fa-dna::before { + content: "\f471"; } + +.fa-virus-slash::before { + content: "\e075"; } + +.fa-minus::before { + content: "\f068"; } + +.fa-subtract::before { + content: "\f068"; } + +.fa-chess::before { + content: "\f439"; } + +.fa-arrow-left-long::before { + content: "\f177"; } + +.fa-long-arrow-left::before { + content: "\f177"; } + +.fa-plug-circle-check::before { + content: "\e55c"; } + +.fa-street-view::before { + content: "\f21d"; } + +.fa-franc-sign::before { + content: "\e18f"; } + +.fa-volume-off::before { + content: "\f026"; } + +.fa-hands-asl-interpreting::before { + content: "\f2a3"; } + +.fa-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-asl-interpreting::before { + content: "\f2a3"; } + +.fa-hands-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-gear::before { + content: "\f013"; } + +.fa-cog::before { + content: "\f013"; } + +.fa-droplet-slash::before { + content: "\f5c7"; } + +.fa-tint-slash::before { + content: "\f5c7"; } + +.fa-mosque::before { + content: "\f678"; } + +.fa-mosquito::before { + content: "\e52b"; } + +.fa-star-of-david::before { + content: "\f69a"; } + +.fa-person-military-rifle::before { + content: "\e54b"; } + +.fa-cart-shopping::before { + content: "\f07a"; } + +.fa-shopping-cart::before { + content: "\f07a"; } + +.fa-vials::before { + content: "\f493"; } + +.fa-plug-circle-plus::before { + content: "\e55f"; } + +.fa-place-of-worship::before { + content: "\f67f"; } + +.fa-grip-vertical::before { + content: "\f58e"; } + +.fa-arrow-turn-up::before { + content: "\f148"; } + +.fa-level-up::before { + content: "\f148"; } + +.fa-u::before { + content: "\55"; } + +.fa-square-root-variable::before { + content: "\f698"; } + +.fa-square-root-alt::before { + content: "\f698"; } + +.fa-clock::before { + content: "\f017"; } + +.fa-clock-four::before { + content: "\f017"; } + +.fa-backward-step::before { + content: "\f048"; } + +.fa-step-backward::before { + content: "\f048"; } + +.fa-pallet::before { + content: "\f482"; } + +.fa-faucet::before { + content: "\e005"; } + +.fa-baseball-bat-ball::before { + content: "\f432"; } + +.fa-s::before { + content: "\53"; } + +.fa-timeline::before { + content: "\e29c"; } + +.fa-keyboard::before { + content: "\f11c"; } + +.fa-caret-down::before { + content: "\f0d7"; } + +.fa-house-chimney-medical::before { + content: "\f7f2"; } + +.fa-clinic-medical::before { + content: "\f7f2"; } + +.fa-temperature-three-quarters::before { + content: "\f2c8"; } + +.fa-temperature-3::before { + content: "\f2c8"; } + +.fa-thermometer-3::before { + content: "\f2c8"; } + +.fa-thermometer-three-quarters::before { + content: "\f2c8"; } + +.fa-mobile-screen::before { + content: "\f3cf"; } + +.fa-mobile-android-alt::before { + content: "\f3cf"; } + +.fa-plane-up::before { + content: "\e22d"; } + +.fa-piggy-bank::before { + content: "\f4d3"; } + +.fa-battery-half::before { + content: "\f242"; } + +.fa-battery-3::before { + content: "\f242"; } + +.fa-mountain-city::before { + content: "\e52e"; } + +.fa-coins::before { + content: "\f51e"; } + +.fa-khanda::before { + content: "\f66d"; } + +.fa-sliders::before { + content: "\f1de"; } + +.fa-sliders-h::before { + content: "\f1de"; } + +.fa-folder-tree::before { + content: "\f802"; } + +.fa-network-wired::before { + content: "\f6ff"; } + +.fa-map-pin::before { + content: "\f276"; } + +.fa-hamsa::before { + content: "\f665"; } + +.fa-cent-sign::before { + content: "\e3f5"; } + +.fa-flask::before { + content: "\f0c3"; } + +.fa-person-pregnant::before { + content: "\e31e"; } + +.fa-wand-sparkles::before { + content: "\f72b"; } + +.fa-ellipsis-vertical::before { + content: "\f142"; } + +.fa-ellipsis-v::before { + content: "\f142"; } + +.fa-ticket::before { + content: "\f145"; } + +.fa-power-off::before { + content: "\f011"; } + +.fa-right-long::before { + content: "\f30b"; } + +.fa-long-arrow-alt-right::before { + content: "\f30b"; } + +.fa-flag-usa::before { + content: "\f74d"; } + +.fa-laptop-file::before { + content: "\e51d"; } + +.fa-tty::before { + content: "\f1e4"; } + +.fa-teletype::before { + content: "\f1e4"; } + +.fa-diagram-next::before { + content: "\e476"; } + +.fa-person-rifle::before { + content: "\e54e"; } + +.fa-house-medical-circle-exclamation::before { + content: "\e512"; } + +.fa-closed-captioning::before { + content: "\f20a"; } + +.fa-person-hiking::before { + content: "\f6ec"; } + +.fa-hiking::before { + content: "\f6ec"; } + +.fa-venus-double::before { + content: "\f226"; } + +.fa-images::before { + content: "\f302"; } + +.fa-calculator::before { + content: "\f1ec"; } + +.fa-people-pulling::before { + content: "\e535"; } + +.fa-n::before { + content: "\4e"; } + +.fa-cable-car::before { + content: "\f7da"; } + +.fa-tram::before { + content: "\f7da"; } + +.fa-cloud-rain::before { + content: "\f73d"; } + +.fa-building-circle-xmark::before { + content: "\e4d4"; } + +.fa-ship::before { + content: "\f21a"; } + +.fa-arrows-down-to-line::before { + content: "\e4b8"; } + +.fa-download::before { + content: "\f019"; } + +.fa-face-grin::before { + content: "\f580"; } + +.fa-grin::before { + content: "\f580"; } + +.fa-delete-left::before { + content: "\f55a"; } + +.fa-backspace::before { + content: "\f55a"; } + +.fa-eye-dropper::before { + content: "\f1fb"; } + +.fa-eye-dropper-empty::before { + content: "\f1fb"; } + +.fa-eyedropper::before { + content: "\f1fb"; } + +.fa-file-circle-check::before { + content: "\e5a0"; } + +.fa-forward::before { + content: "\f04e"; } + +.fa-mobile::before { + content: "\f3ce"; } + +.fa-mobile-android::before { + content: "\f3ce"; } + +.fa-mobile-phone::before { + content: "\f3ce"; } + +.fa-face-meh::before { + content: "\f11a"; } + +.fa-meh::before { + content: "\f11a"; } + +.fa-align-center::before { + content: "\f037"; } + +.fa-book-skull::before { + content: "\f6b7"; } + +.fa-book-dead::before { + content: "\f6b7"; } + +.fa-id-card::before { + content: "\f2c2"; } + +.fa-drivers-license::before { + content: "\f2c2"; } + +.fa-outdent::before { + content: "\f03b"; } + +.fa-dedent::before { + content: "\f03b"; } + +.fa-heart-circle-exclamation::before { + content: "\e4fe"; } + +.fa-house::before { + content: "\f015"; } + +.fa-home::before { + content: "\f015"; } + +.fa-home-alt::before { + content: "\f015"; } + +.fa-home-lg-alt::before { + content: "\f015"; } + +.fa-calendar-week::before { + content: "\f784"; } + +.fa-laptop-medical::before { + content: "\f812"; } + +.fa-b::before { + content: "\42"; } + +.fa-file-medical::before { + content: "\f477"; } + +.fa-dice-one::before { + content: "\f525"; } + +.fa-kiwi-bird::before { + content: "\f535"; } + +.fa-arrow-right-arrow-left::before { + content: "\f0ec"; } + +.fa-exchange::before { + content: "\f0ec"; } + +.fa-rotate-right::before { + content: "\f2f9"; } + +.fa-redo-alt::before { + content: "\f2f9"; } + +.fa-rotate-forward::before { + content: "\f2f9"; } + +.fa-utensils::before { + content: "\f2e7"; } + +.fa-cutlery::before { + content: "\f2e7"; } + +.fa-arrow-up-wide-short::before { + content: "\f161"; } + +.fa-sort-amount-up::before { + content: "\f161"; } + +.fa-mill-sign::before { + content: "\e1ed"; } + +.fa-bowl-rice::before { + content: "\e2eb"; } + +.fa-skull::before { + content: "\f54c"; } + +.fa-tower-broadcast::before { + content: "\f519"; } + +.fa-broadcast-tower::before { + content: "\f519"; } + +.fa-truck-pickup::before { + content: "\f63c"; } + +.fa-up-long::before { + content: "\f30c"; } + +.fa-long-arrow-alt-up::before { + content: "\f30c"; } + +.fa-stop::before { + content: "\f04d"; } + +.fa-code-merge::before { + content: "\f387"; } + +.fa-upload::before { + content: "\f093"; } + +.fa-hurricane::before { + content: "\f751"; } + +.fa-mound::before { + content: "\e52d"; } + +.fa-toilet-portable::before { + content: "\e583"; } + +.fa-compact-disc::before { + content: "\f51f"; } + +.fa-file-arrow-down::before { + content: "\f56d"; } + +.fa-file-download::before { + content: "\f56d"; } + +.fa-caravan::before { + content: "\f8ff"; } + +.fa-shield-cat::before { + content: "\e572"; } + +.fa-bolt::before { + content: "\f0e7"; } + +.fa-zap::before { + content: "\f0e7"; } + +.fa-glass-water::before { + content: "\e4f4"; } + +.fa-oil-well::before { + content: "\e532"; } + +.fa-vault::before { + content: "\e2c5"; } + +.fa-mars::before { + content: "\f222"; } + +.fa-toilet::before { + content: "\f7d8"; } + +.fa-plane-circle-xmark::before { + content: "\e557"; } + +.fa-yen-sign::before { + content: "\f157"; } + +.fa-cny::before { + content: "\f157"; } + +.fa-jpy::before { + content: "\f157"; } + +.fa-rmb::before { + content: "\f157"; } + +.fa-yen::before { + content: "\f157"; } + +.fa-ruble-sign::before { + content: "\f158"; } + +.fa-rouble::before { + content: "\f158"; } + +.fa-rub::before { + content: "\f158"; } + +.fa-ruble::before { + content: "\f158"; } + +.fa-sun::before { + content: "\f185"; } + +.fa-guitar::before { + content: "\f7a6"; } + +.fa-face-laugh-wink::before { + content: "\f59c"; } + +.fa-laugh-wink::before { + content: "\f59c"; } + +.fa-horse-head::before { + content: "\f7ab"; } + +.fa-bore-hole::before { + content: "\e4c3"; } + +.fa-industry::before { + content: "\f275"; } + +.fa-circle-down::before { + content: "\f358"; } + +.fa-arrow-alt-circle-down::before { + content: "\f358"; } + +.fa-arrows-turn-to-dots::before { + content: "\e4c1"; } + +.fa-florin-sign::before { + content: "\e184"; } + +.fa-arrow-down-short-wide::before { + content: "\f884"; } + +.fa-sort-amount-desc::before { + content: "\f884"; } + +.fa-sort-amount-down-alt::before { + content: "\f884"; } + +.fa-less-than::before { + content: "\3c"; } + +.fa-angle-down::before { + content: "\f107"; } + +.fa-car-tunnel::before { + content: "\e4de"; } + +.fa-head-side-cough::before { + content: "\e061"; } + +.fa-grip-lines::before { + content: "\f7a4"; } + +.fa-thumbs-down::before { + content: "\f165"; } + +.fa-user-lock::before { + content: "\f502"; } + +.fa-arrow-right-long::before { + content: "\f178"; } + +.fa-long-arrow-right::before { + content: "\f178"; } + +.fa-anchor-circle-xmark::before { + content: "\e4ac"; } + +.fa-ellipsis::before { + content: "\f141"; } + +.fa-ellipsis-h::before { + content: "\f141"; } + +.fa-chess-pawn::before { + content: "\f443"; } + +.fa-kit-medical::before { + content: "\f479"; } + +.fa-first-aid::before { + content: "\f479"; } + +.fa-person-through-window::before { + content: "\e5a9"; } + +.fa-toolbox::before { + content: "\f552"; } + +.fa-hands-holding-circle::before { + content: "\e4fb"; } + +.fa-bug::before { + content: "\f188"; } + +.fa-credit-card::before { + content: "\f09d"; } + +.fa-credit-card-alt::before { + content: "\f09d"; } + +.fa-car::before { + content: "\f1b9"; } + +.fa-automobile::before { + content: "\f1b9"; } + +.fa-hand-holding-hand::before { + content: "\e4f7"; } + +.fa-book-open-reader::before { + content: "\f5da"; } + +.fa-book-reader::before { + content: "\f5da"; } + +.fa-mountain-sun::before { + content: "\e52f"; } + +.fa-arrows-left-right-to-line::before { + content: "\e4ba"; } + +.fa-dice-d20::before { + content: "\f6cf"; } + +.fa-truck-droplet::before { + content: "\e58c"; } + +.fa-file-circle-xmark::before { + content: "\e5a1"; } + +.fa-temperature-arrow-up::before { + content: "\e040"; } + +.fa-temperature-up::before { + content: "\e040"; } + +.fa-medal::before { + content: "\f5a2"; } + +.fa-bed::before { + content: "\f236"; } + +.fa-square-h::before { + content: "\f0fd"; } + +.fa-h-square::before { + content: "\f0fd"; } + +.fa-podcast::before { + content: "\f2ce"; } + +.fa-temperature-full::before { + content: "\f2c7"; } + +.fa-temperature-4::before { + content: "\f2c7"; } + +.fa-thermometer-4::before { + content: "\f2c7"; } + +.fa-thermometer-full::before { + content: "\f2c7"; } + +.fa-bell::before { + content: "\f0f3"; } + +.fa-superscript::before { + content: "\f12b"; } + +.fa-plug-circle-xmark::before { + content: "\e560"; } + +.fa-star-of-life::before { + content: "\f621"; } + +.fa-phone-slash::before { + content: "\f3dd"; } + +.fa-paint-roller::before { + content: "\f5aa"; } + +.fa-handshake-angle::before { + content: "\f4c4"; } + +.fa-hands-helping::before { + content: "\f4c4"; } + +.fa-location-dot::before { + content: "\f3c5"; } + +.fa-map-marker-alt::before { + content: "\f3c5"; } + +.fa-file::before { + content: "\f15b"; } + +.fa-greater-than::before { + content: "\3e"; } + +.fa-person-swimming::before { + content: "\f5c4"; } + +.fa-swimmer::before { + content: "\f5c4"; } + +.fa-arrow-down::before { + content: "\f063"; } + +.fa-droplet::before { + content: "\f043"; } + +.fa-tint::before { + content: "\f043"; } + +.fa-eraser::before { + content: "\f12d"; } + +.fa-earth-americas::before { + content: "\f57d"; } + +.fa-earth::before { + content: "\f57d"; } + +.fa-earth-america::before { + content: "\f57d"; } + +.fa-globe-americas::before { + content: "\f57d"; } + +.fa-person-burst::before { + content: "\e53b"; } + +.fa-dove::before { + content: "\f4ba"; } + +.fa-battery-empty::before { + content: "\f244"; } + +.fa-battery-0::before { + content: "\f244"; } + +.fa-socks::before { + content: "\f696"; } + +.fa-inbox::before { + content: "\f01c"; } + +.fa-section::before { + content: "\e447"; } + +.fa-gauge-high::before { + content: "\f625"; } + +.fa-tachometer-alt::before { + content: "\f625"; } + +.fa-tachometer-alt-fast::before { + content: "\f625"; } + +.fa-envelope-open-text::before { + content: "\f658"; } + +.fa-hospital::before { + content: "\f0f8"; } + +.fa-hospital-alt::before { + content: "\f0f8"; } + +.fa-hospital-wide::before { + content: "\f0f8"; } + +.fa-wine-bottle::before { + content: "\f72f"; } + +.fa-chess-rook::before { + content: "\f447"; } + +.fa-bars-staggered::before { + content: "\f550"; } + +.fa-reorder::before { + content: "\f550"; } + +.fa-stream::before { + content: "\f550"; } + +.fa-dharmachakra::before { + content: "\f655"; } + +.fa-hotdog::before { + content: "\f80f"; } + +.fa-person-walking-with-cane::before { + content: "\f29d"; } + +.fa-blind::before { + content: "\f29d"; } + +.fa-drum::before { + content: "\f569"; } + +.fa-ice-cream::before { + content: "\f810"; } + +.fa-heart-circle-bolt::before { + content: "\e4fc"; } + +.fa-fax::before { + content: "\f1ac"; } + +.fa-paragraph::before { + content: "\f1dd"; } + +.fa-check-to-slot::before { + content: "\f772"; } + +.fa-vote-yea::before { + content: "\f772"; } + +.fa-star-half::before { + content: "\f089"; } + +.fa-boxes-stacked::before { + content: "\f468"; } + +.fa-boxes::before { + content: "\f468"; } + +.fa-boxes-alt::before { + content: "\f468"; } + +.fa-link::before { + content: "\f0c1"; } + +.fa-chain::before { + content: "\f0c1"; } + +.fa-ear-listen::before { + content: "\f2a2"; } + +.fa-assistive-listening-systems::before { + content: "\f2a2"; } + +.fa-tree-city::before { + content: "\e587"; } + +.fa-play::before { + content: "\f04b"; } + +.fa-font::before { + content: "\f031"; } + +.fa-table-cells-row-lock::before { + content: "\e67a"; } + +.fa-rupiah-sign::before { + content: "\e23d"; } + +.fa-magnifying-glass::before { + content: "\f002"; } + +.fa-search::before { + content: "\f002"; } + +.fa-table-tennis-paddle-ball::before { + content: "\f45d"; } + +.fa-ping-pong-paddle-ball::before { + content: "\f45d"; } + +.fa-table-tennis::before { + content: "\f45d"; } + +.fa-person-dots-from-line::before { + content: "\f470"; } + +.fa-diagnoses::before { + content: "\f470"; } + +.fa-trash-can-arrow-up::before { + content: "\f82a"; } + +.fa-trash-restore-alt::before { + content: "\f82a"; } + +.fa-naira-sign::before { + content: "\e1f6"; } + +.fa-cart-arrow-down::before { + content: "\f218"; } + +.fa-walkie-talkie::before { + content: "\f8ef"; } + +.fa-file-pen::before { + content: "\f31c"; } + +.fa-file-edit::before { + content: "\f31c"; } + +.fa-receipt::before { + content: "\f543"; } + +.fa-square-pen::before { + content: "\f14b"; } + +.fa-pen-square::before { + content: "\f14b"; } + +.fa-pencil-square::before { + content: "\f14b"; } + +.fa-suitcase-rolling::before { + content: "\f5c1"; } + +.fa-person-circle-exclamation::before { + content: "\e53f"; } + +.fa-chevron-down::before { + content: "\f078"; } + +.fa-battery-full::before { + content: "\f240"; } + +.fa-battery::before { + content: "\f240"; } + +.fa-battery-5::before { + content: "\f240"; } + +.fa-skull-crossbones::before { + content: "\f714"; } + +.fa-code-compare::before { + content: "\e13a"; } + +.fa-list-ul::before { + content: "\f0ca"; } + +.fa-list-dots::before { + content: "\f0ca"; } + +.fa-school-lock::before { + content: "\e56f"; } + +.fa-tower-cell::before { + content: "\e585"; } + +.fa-down-long::before { + content: "\f309"; } + +.fa-long-arrow-alt-down::before { + content: "\f309"; } + +.fa-ranking-star::before { + content: "\e561"; } + +.fa-chess-king::before { + content: "\f43f"; } + +.fa-person-harassing::before { + content: "\e549"; } + +.fa-brazilian-real-sign::before { + content: "\e46c"; } + +.fa-landmark-dome::before { + content: "\f752"; } + +.fa-landmark-alt::before { + content: "\f752"; } + +.fa-arrow-up::before { + content: "\f062"; } + +.fa-tv::before { + content: "\f26c"; } + +.fa-television::before { + content: "\f26c"; } + +.fa-tv-alt::before { + content: "\f26c"; } + +.fa-shrimp::before { + content: "\e448"; } + +.fa-list-check::before { + content: "\f0ae"; } + +.fa-tasks::before { + content: "\f0ae"; } + +.fa-jug-detergent::before { + content: "\e519"; } + +.fa-circle-user::before { + content: "\f2bd"; } + +.fa-user-circle::before { + content: "\f2bd"; } + +.fa-user-shield::before { + content: "\f505"; } + +.fa-wind::before { + content: "\f72e"; } + +.fa-car-burst::before { + content: "\f5e1"; } + +.fa-car-crash::before { + content: "\f5e1"; } + +.fa-y::before { + content: "\59"; } + +.fa-person-snowboarding::before { + content: "\f7ce"; } + +.fa-snowboarding::before { + content: "\f7ce"; } + +.fa-truck-fast::before { + content: "\f48b"; } + +.fa-shipping-fast::before { + content: "\f48b"; } + +.fa-fish::before { + content: "\f578"; } + +.fa-user-graduate::before { + content: "\f501"; } + +.fa-circle-half-stroke::before { + content: "\f042"; } + +.fa-adjust::before { + content: "\f042"; } + +.fa-clapperboard::before { + content: "\e131"; } + +.fa-circle-radiation::before { + content: "\f7ba"; } + +.fa-radiation-alt::before { + content: "\f7ba"; } + +.fa-baseball::before { + content: "\f433"; } + +.fa-baseball-ball::before { + content: "\f433"; } + +.fa-jet-fighter-up::before { + content: "\e518"; } + +.fa-diagram-project::before { + content: "\f542"; } + +.fa-project-diagram::before { + content: "\f542"; } + +.fa-copy::before { + content: "\f0c5"; } + +.fa-volume-xmark::before { + content: "\f6a9"; } + +.fa-volume-mute::before { + content: "\f6a9"; } + +.fa-volume-times::before { + content: "\f6a9"; } + +.fa-hand-sparkles::before { + content: "\e05d"; } + +.fa-grip::before { + content: "\f58d"; } + +.fa-grip-horizontal::before { + content: "\f58d"; } + +.fa-share-from-square::before { + content: "\f14d"; } + +.fa-share-square::before { + content: "\f14d"; } + +.fa-child-combatant::before { + content: "\e4e0"; } + +.fa-child-rifle::before { + content: "\e4e0"; } + +.fa-gun::before { + content: "\e19b"; } + +.fa-square-phone::before { + content: "\f098"; } + +.fa-phone-square::before { + content: "\f098"; } + +.fa-plus::before { + content: "\2b"; } + +.fa-add::before { + content: "\2b"; } + +.fa-expand::before { + content: "\f065"; } + +.fa-computer::before { + content: "\e4e5"; } + +.fa-xmark::before { + content: "\f00d"; } + +.fa-close::before { + content: "\f00d"; } + +.fa-multiply::before { + content: "\f00d"; } + +.fa-remove::before { + content: "\f00d"; } + +.fa-times::before { + content: "\f00d"; } + +.fa-arrows-up-down-left-right::before { + content: "\f047"; } + +.fa-arrows::before { + content: "\f047"; } + +.fa-chalkboard-user::before { + content: "\f51c"; } + +.fa-chalkboard-teacher::before { + content: "\f51c"; } + +.fa-peso-sign::before { + content: "\e222"; } + +.fa-building-shield::before { + content: "\e4d8"; } + +.fa-baby::before { + content: "\f77c"; } + +.fa-users-line::before { + content: "\e592"; } + +.fa-quote-left::before { + content: "\f10d"; } + +.fa-quote-left-alt::before { + content: "\f10d"; } + +.fa-tractor::before { + content: "\f722"; } + +.fa-trash-arrow-up::before { + content: "\f829"; } + +.fa-trash-restore::before { + content: "\f829"; } + +.fa-arrow-down-up-lock::before { + content: "\e4b0"; } + +.fa-lines-leaning::before { + content: "\e51e"; } + +.fa-ruler-combined::before { + content: "\f546"; } + +.fa-copyright::before { + content: "\f1f9"; } + +.fa-equals::before { + content: "\3d"; } + +.fa-blender::before { + content: "\f517"; } + +.fa-teeth::before { + content: "\f62e"; } + +.fa-shekel-sign::before { + content: "\f20b"; } + +.fa-ils::before { + content: "\f20b"; } + +.fa-shekel::before { + content: "\f20b"; } + +.fa-sheqel::before { + content: "\f20b"; } + +.fa-sheqel-sign::before { + content: "\f20b"; } + +.fa-map::before { + content: "\f279"; } + +.fa-rocket::before { + content: "\f135"; } + +.fa-photo-film::before { + content: "\f87c"; } + +.fa-photo-video::before { + content: "\f87c"; } + +.fa-folder-minus::before { + content: "\f65d"; } + +.fa-store::before { + content: "\f54e"; } + +.fa-arrow-trend-up::before { + content: "\e098"; } + +.fa-plug-circle-minus::before { + content: "\e55e"; } + +.fa-sign-hanging::before { + content: "\f4d9"; } + +.fa-sign::before { + content: "\f4d9"; } + +.fa-bezier-curve::before { + content: "\f55b"; } + +.fa-bell-slash::before { + content: "\f1f6"; } + +.fa-tablet::before { + content: "\f3fb"; } + +.fa-tablet-android::before { + content: "\f3fb"; } + +.fa-school-flag::before { + content: "\e56e"; } + +.fa-fill::before { + content: "\f575"; } + +.fa-angle-up::before { + content: "\f106"; } + +.fa-drumstick-bite::before { + content: "\f6d7"; } + +.fa-holly-berry::before { + content: "\f7aa"; } + +.fa-chevron-left::before { + content: "\f053"; } + +.fa-bacteria::before { + content: "\e059"; } + +.fa-hand-lizard::before { + content: "\f258"; } + +.fa-notdef::before { + content: "\e1fe"; } + +.fa-disease::before { + content: "\f7fa"; } + +.fa-briefcase-medical::before { + content: "\f469"; } + +.fa-genderless::before { + content: "\f22d"; } + +.fa-chevron-right::before { + content: "\f054"; } + +.fa-retweet::before { + content: "\f079"; } + +.fa-car-rear::before { + content: "\f5de"; } + +.fa-car-alt::before { + content: "\f5de"; } + +.fa-pump-soap::before { + content: "\e06b"; } + +.fa-video-slash::before { + content: "\f4e2"; } + +.fa-battery-quarter::before { + content: "\f243"; } + +.fa-battery-2::before { + content: "\f243"; } + +.fa-radio::before { + content: "\f8d7"; } + +.fa-baby-carriage::before { + content: "\f77d"; } + +.fa-carriage-baby::before { + content: "\f77d"; } + +.fa-traffic-light::before { + content: "\f637"; } + +.fa-thermometer::before { + content: "\f491"; } + +.fa-vr-cardboard::before { + content: "\f729"; } + +.fa-hand-middle-finger::before { + content: "\f806"; } + +.fa-percent::before { + content: "\25"; } + +.fa-percentage::before { + content: "\25"; } + +.fa-truck-moving::before { + content: "\f4df"; } + +.fa-glass-water-droplet::before { + content: "\e4f5"; } + +.fa-display::before { + content: "\e163"; } + +.fa-face-smile::before { + content: "\f118"; } + +.fa-smile::before { + content: "\f118"; } + +.fa-thumbtack::before { + content: "\f08d"; } + +.fa-thumb-tack::before { + content: "\f08d"; } + +.fa-trophy::before { + content: "\f091"; } + +.fa-person-praying::before { + content: "\f683"; } + +.fa-pray::before { + content: "\f683"; } + +.fa-hammer::before { + content: "\f6e3"; } + +.fa-hand-peace::before { + content: "\f25b"; } + +.fa-rotate::before { + content: "\f2f1"; } + +.fa-sync-alt::before { + content: "\f2f1"; } + +.fa-spinner::before { + content: "\f110"; } + +.fa-robot::before { + content: "\f544"; } + +.fa-peace::before { + content: "\f67c"; } + +.fa-gears::before { + content: "\f085"; } + +.fa-cogs::before { + content: "\f085"; } + +.fa-warehouse::before { + content: "\f494"; } + +.fa-arrow-up-right-dots::before { + content: "\e4b7"; } + +.fa-splotch::before { + content: "\f5bc"; } + +.fa-face-grin-hearts::before { + content: "\f584"; } + +.fa-grin-hearts::before { + content: "\f584"; } + +.fa-dice-four::before { + content: "\f524"; } + +.fa-sim-card::before { + content: "\f7c4"; } + +.fa-transgender::before { + content: "\f225"; } + +.fa-transgender-alt::before { + content: "\f225"; } + +.fa-mercury::before { + content: "\f223"; } + +.fa-arrow-turn-down::before { + content: "\f149"; } + +.fa-level-down::before { + content: "\f149"; } + +.fa-person-falling-burst::before { + content: "\e547"; } + +.fa-award::before { + content: "\f559"; } + +.fa-ticket-simple::before { + content: "\f3ff"; } + +.fa-ticket-alt::before { + content: "\f3ff"; } + +.fa-building::before { + content: "\f1ad"; } + +.fa-angles-left::before { + content: "\f100"; } + +.fa-angle-double-left::before { + content: "\f100"; } + +.fa-qrcode::before { + content: "\f029"; } + +.fa-clock-rotate-left::before { + content: "\f1da"; } + +.fa-history::before { + content: "\f1da"; } + +.fa-face-grin-beam-sweat::before { + content: "\f583"; } + +.fa-grin-beam-sweat::before { + content: "\f583"; } + +.fa-file-export::before { + content: "\f56e"; } + +.fa-arrow-right-from-file::before { + content: "\f56e"; } + +.fa-shield::before { + content: "\f132"; } + +.fa-shield-blank::before { + content: "\f132"; } + +.fa-arrow-up-short-wide::before { + content: "\f885"; } + +.fa-sort-amount-up-alt::before { + content: "\f885"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-circle-chevron-left::before { + content: "\f137"; } + +.fa-chevron-circle-left::before { + content: "\f137"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tents::before { + content: "\e582"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + +.fa-code-commit::before { + content: "\f386"; } + +.fa-temperature-low::before { + content: "\f76b"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-broom::before { + content: "\f51a"; } + +.fa-shield-heart::before { + content: "\e574"; } + +.fa-gopuram::before { + content: "\f664"; } + +.fa-earth-oceania::before { + content: "\e47b"; } + +.fa-globe-oceania::before { + content: "\e47b"; } + +.fa-square-xmark::before { + content: "\f2d3"; } + +.fa-times-square::before { + content: "\f2d3"; } + +.fa-xmark-square::before { + content: "\f2d3"; } + +.fa-hashtag::before { + content: "\23"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + +.fa-expand-alt::before { + content: "\f424"; } + +.fa-oil-can::before { + content: "\f613"; } + +.fa-t::before { + content: "\54"; } + +.fa-hippo::before { + content: "\f6ed"; } + +.fa-chart-column::before { + content: "\e0e3"; } + +.fa-infinity::before { + content: "\f534"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-fan::before { + content: "\f863"; } + +.fa-person-walking-luggage::before { + content: "\e554"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-cloud-moon-rain::before { + content: "\f73c"; } + +.fa-calendar::before { + content: "\f133"; } + +.fa-trailer::before { + content: "\e041"; } + +.fa-bahai::before { + content: "\f666"; } + +.fa-haykal::before { + content: "\f666"; } + +.fa-sd-card::before { + content: "\f7c2"; } + +.fa-dragon::before { + content: "\f6d5"; } + +.fa-shoe-prints::before { + content: "\f54b"; } + +.fa-circle-plus::before { + content: "\f055"; } + +.fa-plus-circle::before { + content: "\f055"; } + +.fa-face-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-hand-holding::before { + content: "\f4bd"; } + +.fa-plug-circle-exclamation::before { + content: "\e55d"; } + +.fa-link-slash::before { + content: "\f127"; } + +.fa-chain-broken::before { + content: "\f127"; } + +.fa-chain-slash::before { + content: "\f127"; } + +.fa-unlink::before { + content: "\f127"; } + +.fa-clone::before { + content: "\f24d"; } + +.fa-person-walking-arrow-loop-left::before { + content: "\e551"; } + +.fa-arrow-up-z-a::before { + content: "\f882"; } + +.fa-sort-alpha-up-alt::before { + content: "\f882"; } + +.fa-fire-flame-curved::before { + content: "\f7e4"; } + +.fa-fire-alt::before { + content: "\f7e4"; } + +.fa-tornado::before { + content: "\f76f"; } + +.fa-file-circle-plus::before { + content: "\e494"; } + +.fa-book-quran::before { + content: "\f687"; } + +.fa-quran::before { + content: "\f687"; } + +.fa-anchor::before { + content: "\f13d"; } + +.fa-border-all::before { + content: "\f84c"; } + +.fa-face-angry::before { + content: "\f556"; } + +.fa-angry::before { + content: "\f556"; } + +.fa-cookie-bite::before { + content: "\f564"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-rss::before { + content: "\f09e"; } + +.fa-feed::before { + content: "\f09e"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-gauge-simple-high::before { + content: "\f62a"; } + +.fa-tachometer::before { + content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-shower::before { + content: "\f2cc"; } + +.fa-desktop::before { + content: "\f390"; } + +.fa-desktop-alt::before { + content: "\f390"; } + +.fa-m::before { + content: "\4d"; } + +.fa-table-list::before { + content: "\f00b"; } + +.fa-th-list::before { + content: "\f00b"; } + +.fa-comment-sms::before { + content: "\f7cd"; } + +.fa-sms::before { + content: "\f7cd"; } + +.fa-book::before { + content: "\f02d"; } + +.fa-user-plus::before { + content: "\f234"; } + +.fa-check::before { + content: "\f00c"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } + +.fa-square-envelope::before { + content: "\f199"; } + +.fa-envelope-square::before { + content: "\f199"; } + +.fa-dice::before { + content: "\f522"; } + +.fa-bowling-ball::before { + content: "\f436"; } + +.fa-brain::before { + content: "\f5dc"; } + +.fa-bandage::before { + content: "\f462"; } + +.fa-band-aid::before { + content: "\f462"; } + +.fa-calendar-minus::before { + content: "\f272"; } + +.fa-circle-xmark::before { + content: "\f057"; } + +.fa-times-circle::before { + content: "\f057"; } + +.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-pixiv:before { + content: "\e640"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-jxl:before { + content: "\e67b"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-brave:before { + content: "\e63c"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-evernote:before { + content: "\f839"; } + +.fa-hacker-news:before { + content: "\f1d4"; } + +.fa-creative-commons-sampling:before { + content: "\f4f0"; } + +.fa-adversal:before { + content: "\f36a"; } + +.fa-creative-commons:before { + content: "\f25e"; } + +.fa-watchman-monitoring:before { + content: "\e087"; } + +.fa-fonticons:before { + content: "\f280"; } + +.fa-weixin:before { + content: "\f1d7"; } + +.fa-shirtsinbulk:before { + content: "\f214"; } + +.fa-codepen:before { + content: "\f1cb"; } + +.fa-git-alt:before { + content: "\f841"; } + +.fa-lyft:before { + content: "\f3c3"; } + +.fa-rev:before { + content: "\f5b2"; } + +.fa-windows:before { + content: "\f17a"; } + +.fa-wizards-of-the-coast:before { + content: "\f730"; } + +.fa-square-viadeo:before { + content: "\f2aa"; } + +.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa-meetup:before { + content: "\f2e0"; } + +.fa-centos:before { + content: "\f789"; } + +.fa-adn:before { + content: "\f170"; } + +.fa-cloudsmith:before { + content: "\f384"; } + +.fa-opensuse:before { + content: "\e62b"; } + +.fa-pied-piper-alt:before { + content: "\f1a8"; } + +.fa-square-dribbble:before { + content: "\f397"; } + +.fa-dribbble-square:before { + content: "\f397"; } + +.fa-codiepie:before { + content: "\f284"; } + +.fa-node:before { + content: "\f419"; } + +.fa-mix:before { + content: "\f3cb"; } + +.fa-steam:before { + content: "\f1b6"; } + +.fa-cc-apple-pay:before { + content: "\f416"; } + +.fa-scribd:before { + content: "\f28a"; } + +.fa-debian:before { + content: "\e60b"; } + +.fa-openid:before { + content: "\f19b"; } + +.fa-instalod:before { + content: "\e081"; } + +.fa-expeditedssl:before { + content: "\f23e"; } + +.fa-sellcast:before { + content: "\f2da"; } + +.fa-square-twitter:before { + content: "\f081"; } + +.fa-twitter-square:before { + content: "\f081"; } + +.fa-r-project:before { + content: "\f4f7"; } + +.fa-delicious:before { + content: "\f1a5"; } + +.fa-freebsd:before { + content: "\f3a4"; } + +.fa-vuejs:before { + content: "\f41f"; } + +.fa-accusoft:before { + content: "\f369"; } + +.fa-ioxhost:before { + content: "\f208"; } + +.fa-fonticons-fi:before { + content: "\f3a2"; } + +.fa-app-store:before { + content: "\f36f"; } + +.fa-cc-mastercard:before { + content: "\f1f1"; } + +.fa-itunes-note:before { + content: "\f3b5"; } + +.fa-golang:before { + content: "\e40f"; } + +.fa-kickstarter:before { + content: "\f3bb"; } + +.fa-square-kickstarter:before { + content: "\f3bb"; } + +.fa-grav:before { + content: "\f2d6"; } + +.fa-weibo:before { + content: "\f18a"; } + +.fa-uncharted:before { + content: "\e084"; } + +.fa-firstdraft:before { + content: "\f3a1"; } + +.fa-square-youtube:before { + content: "\f431"; } + +.fa-youtube-square:before { + content: "\f431"; } + +.fa-wikipedia-w:before { + content: "\f266"; } + +.fa-wpressr:before { + content: "\f3e4"; } + +.fa-rendact:before { + content: "\f3e4"; } + +.fa-angellist:before { + content: "\f209"; } + +.fa-galactic-republic:before { + content: "\f50c"; } + +.fa-nfc-directional:before { + content: "\e530"; } + +.fa-skype:before { + content: "\f17e"; } + +.fa-joget:before { + content: "\f3b7"; } + +.fa-fedora:before { + content: "\f798"; } + +.fa-stripe-s:before { + content: "\f42a"; } + +.fa-meta:before { + content: "\e49b"; } + +.fa-laravel:before { + content: "\f3bd"; } + +.fa-hotjar:before { + content: "\f3b1"; } + +.fa-bluetooth-b:before { + content: "\f294"; } + +.fa-square-letterboxd:before { + content: "\e62e"; } + +.fa-sticker-mule:before { + content: "\f3f7"; } + +.fa-creative-commons-zero:before { + content: "\f4f3"; } + +.fa-hips:before { + content: "\f452"; } + +.fa-behance:before { + content: "\f1b4"; } + +.fa-reddit:before { + content: "\f1a1"; } + +.fa-discord:before { + content: "\f392"; } + +.fa-chrome:before { + content: "\f268"; } + +.fa-app-store-ios:before { + content: "\f370"; } + +.fa-cc-discover:before { + content: "\f1f2"; } + +.fa-wpbeginner:before { + content: "\f297"; } + +.fa-confluence:before { + content: "\f78d"; } + +.fa-shoelace:before { + content: "\e60c"; } + +.fa-mdb:before { + content: "\f8ca"; } + +.fa-dochub:before { + content: "\f394"; } + +.fa-accessible-icon:before { + content: "\f368"; } + +.fa-ebay:before { + content: "\f4f4"; } + +.fa-amazon:before { + content: "\f270"; } + +.fa-unsplash:before { + content: "\e07c"; } + +.fa-yarn:before { + content: "\f7e3"; } + +.fa-square-steam:before { + content: "\f1b7"; } + +.fa-steam-square:before { + content: "\f1b7"; } + +.fa-500px:before { + content: "\f26e"; } + +.fa-square-vimeo:before { + content: "\f194"; } + +.fa-vimeo-square:before { + content: "\f194"; } + +.fa-asymmetrik:before { + content: "\f372"; } + +.fa-font-awesome:before { + content: "\f2b4"; } + +.fa-font-awesome-flag:before { + content: "\f2b4"; } + +.fa-font-awesome-logo-full:before { + content: "\f2b4"; } + +.fa-gratipay:before { + content: "\f184"; } + +.fa-apple:before { + content: "\f179"; } + +.fa-hive:before { + content: "\e07f"; } + +.fa-gitkraken:before { + content: "\f3a6"; } + +.fa-keybase:before { + content: "\f4f5"; } + +.fa-apple-pay:before { + content: "\f415"; } + +.fa-padlet:before { + content: "\e4a0"; } + +.fa-amazon-pay:before { + content: "\f42c"; } + +.fa-square-github:before { + content: "\f092"; } + +.fa-github-square:before { + content: "\f092"; } + +.fa-stumbleupon:before { + content: "\f1a4"; } + +.fa-fedex:before { + content: "\f797"; } + +.fa-phoenix-framework:before { + content: "\f3dc"; } + +.fa-shopify:before { + content: "\e057"; } + +.fa-neos:before { + content: "\f612"; } + +.fa-square-threads:before { + content: "\e619"; } + +.fa-hackerrank:before { + content: "\f5f7"; } + +.fa-researchgate:before { + content: "\f4f8"; } + +.fa-swift:before { + content: "\f8e1"; } + +.fa-angular:before { + content: "\f420"; } + +.fa-speakap:before { + content: "\f3f3"; } + +.fa-angrycreative:before { + content: "\f36e"; } + +.fa-y-combinator:before { + content: "\f23b"; } + +.fa-empire:before { + content: "\f1d1"; } + +.fa-envira:before { + content: "\f299"; } + +.fa-google-scholar:before { + content: "\e63b"; } + +.fa-square-gitlab:before { + content: "\e5ae"; } + +.fa-gitlab-square:before { + content: "\e5ae"; } + +.fa-studiovinari:before { + content: "\f3f8"; } + +.fa-pied-piper:before { + content: "\f2ae"; } + +.fa-wordpress:before { + content: "\f19a"; } + +.fa-product-hunt:before { + content: "\f288"; } + +.fa-firefox:before { + content: "\f269"; } + +.fa-linode:before { + content: "\f2b8"; } + +.fa-goodreads:before { + content: "\f3a8"; } + +.fa-square-odnoklassniki:before { + content: "\f264"; } + +.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa-jsfiddle:before { + content: "\f1cc"; } + +.fa-sith:before { + content: "\f512"; } + +.fa-themeisle:before { + content: "\f2b2"; } + +.fa-page4:before { + content: "\f3d7"; } + +.fa-hashnode:before { + content: "\e499"; } + +.fa-react:before { + content: "\f41b"; } + +.fa-cc-paypal:before { + content: "\f1f4"; } + +.fa-squarespace:before { + content: "\f5be"; } + +.fa-cc-stripe:before { + content: "\f1f5"; } + +.fa-creative-commons-share:before { + content: "\f4f2"; } + +.fa-bitcoin:before { + content: "\f379"; } + +.fa-keycdn:before { + content: "\f3ba"; } + +.fa-opera:before { + content: "\f26a"; } + +.fa-itch-io:before { + content: "\f83a"; } + +.fa-umbraco:before { + content: "\f8e8"; } + +.fa-galactic-senate:before { + content: "\f50d"; } + +.fa-ubuntu:before { + content: "\f7df"; } + +.fa-draft2digital:before { + content: "\f396"; } + +.fa-stripe:before { + content: "\f429"; } + +.fa-houzz:before { + content: "\f27c"; } + +.fa-gg:before { + content: "\f260"; } + +.fa-dhl:before { + content: "\f790"; } + +.fa-square-pinterest:before { + content: "\f0d3"; } + +.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa-xing:before { + content: "\f168"; } + +.fa-blackberry:before { + content: "\f37b"; } + +.fa-creative-commons-pd:before { + content: "\f4ec"; } + +.fa-playstation:before { + content: "\f3df"; } + +.fa-quinscape:before { + content: "\f459"; } + +.fa-less:before { + content: "\f41d"; } + +.fa-blogger-b:before { + content: "\f37d"; } + +.fa-opencart:before { + content: "\f23d"; } + +.fa-vine:before { + content: "\f1ca"; } + +.fa-signal-messenger:before { + content: "\e663"; } + +.fa-paypal:before { + content: "\f1ed"; } + +.fa-gitlab:before { + content: "\f296"; } + +.fa-typo3:before { + content: "\f42b"; } + +.fa-reddit-alien:before { + content: "\f281"; } + +.fa-yahoo:before { + content: "\f19e"; } + +.fa-dailymotion:before { + content: "\e052"; } + +.fa-affiliatetheme:before { + content: "\f36b"; } + +.fa-pied-piper-pp:before { + content: "\f1a7"; } + +.fa-bootstrap:before { + content: "\f836"; } + +.fa-odnoklassniki:before { + content: "\f263"; } + +.fa-nfc-symbol:before { + content: "\e531"; } + +.fa-mintbit:before { + content: "\e62f"; } + +.fa-ethereum:before { + content: "\f42e"; } + +.fa-speaker-deck:before { + content: "\f83c"; } + +.fa-creative-commons-nc-eu:before { + content: "\f4e9"; } + +.fa-patreon:before { + content: "\f3d9"; } + +.fa-avianex:before { + content: "\f374"; } + +.fa-ello:before { + content: "\f5f1"; } + +.fa-gofore:before { + content: "\f3a7"; } + +.fa-bimobject:before { + content: "\f378"; } + +.fa-brave-reverse:before { + content: "\e63d"; } + +.fa-facebook-f:before { + content: "\f39e"; } + +.fa-square-google-plus:before { + content: "\f0d4"; } + +.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa-web-awesome:before { + content: "\e682"; } + +.fa-mandalorian:before { + content: "\f50f"; } + +.fa-first-order-alt:before { + content: "\f50a"; } + +.fa-osi:before { + content: "\f41a"; } + +.fa-google-wallet:before { + content: "\f1ee"; } + +.fa-d-and-d-beyond:before { + content: "\f6ca"; } + +.fa-periscope:before { + content: "\f3da"; } + +.fa-fulcrum:before { + content: "\f50b"; } + +.fa-cloudscale:before { + content: "\f383"; } + +.fa-forumbee:before { + content: "\f211"; } + +.fa-mizuni:before { + content: "\f3cc"; } + +.fa-schlix:before { + content: "\f3ea"; } + +.fa-square-xing:before { + content: "\f169"; } + +.fa-xing-square:before { + content: "\f169"; } + +.fa-bandcamp:before { + content: "\f2d5"; } + +.fa-wpforms:before { + content: "\f298"; } + +.fa-cloudversify:before { + content: "\f385"; } + +.fa-usps:before { + content: "\f7e1"; } + +.fa-megaport:before { + content: "\f5a3"; } + +.fa-magento:before { + content: "\f3c4"; } + +.fa-spotify:before { + content: "\f1bc"; } + +.fa-optin-monster:before { + content: "\f23c"; } + +.fa-fly:before { + content: "\f417"; } + +.fa-aviato:before { + content: "\f421"; } + +.fa-itunes:before { + content: "\f3b4"; } + +.fa-cuttlefish:before { + content: "\f38c"; } + +.fa-blogger:before { + content: "\f37c"; } + +.fa-flickr:before { + content: "\f16e"; } + +.fa-viber:before { + content: "\f409"; } + +.fa-soundcloud:before { + content: "\f1be"; } + +.fa-digg:before { + content: "\f1a6"; } + +.fa-tencent-weibo:before { + content: "\f1d5"; } + +.fa-letterboxd:before { + content: "\e62d"; } + +.fa-symfony:before { + content: "\f83d"; } + +.fa-maxcdn:before { + content: "\f136"; } + +.fa-etsy:before { + content: "\f2d7"; } + +.fa-facebook-messenger:before { + content: "\f39f"; } + +.fa-audible:before { + content: "\f373"; } + +.fa-think-peaks:before { + content: "\f731"; } + +.fa-bilibili:before { + content: "\e3d9"; } + +.fa-erlang:before { + content: "\f39d"; } + +.fa-x-twitter:before { + content: "\e61b"; } + +.fa-cotton-bureau:before { + content: "\f89e"; } + +.fa-dashcube:before { + content: "\f210"; } + +.fa-42-group:before { + content: "\e080"; } + +.fa-innosoft:before { + content: "\e080"; } + +.fa-stack-exchange:before { + content: "\f18d"; } + +.fa-elementor:before { + content: "\f430"; } + +.fa-square-pied-piper:before { + content: "\e01e"; } + +.fa-pied-piper-square:before { + content: "\e01e"; } + +.fa-creative-commons-nd:before { + content: "\f4eb"; } + +.fa-palfed:before { + content: "\f3d8"; } + +.fa-superpowers:before { + content: "\f2dd"; } + +.fa-resolving:before { + content: "\f3e7"; } + +.fa-xbox:before { + content: "\f412"; } + +.fa-square-web-awesome-stroke:before { + content: "\e684"; } + +.fa-searchengin:before { + content: "\f3eb"; } + +.fa-tiktok:before { + content: "\e07b"; } + +.fa-square-facebook:before { + content: "\f082"; } + +.fa-facebook-square:before { + content: "\f082"; } + +.fa-renren:before { + content: "\f18b"; } + +.fa-linux:before { + content: "\f17c"; } + +.fa-glide:before { + content: "\f2a5"; } + +.fa-linkedin:before { + content: "\f08c"; } + +.fa-hubspot:before { + content: "\f3b2"; } + +.fa-deploydog:before { + content: "\f38e"; } + +.fa-twitch:before { + content: "\f1e8"; } + +.fa-ravelry:before { + content: "\f2d9"; } + +.fa-mixer:before { + content: "\e056"; } + +.fa-square-lastfm:before { + content: "\f203"; } + +.fa-lastfm-square:before { + content: "\f203"; } + +.fa-vimeo:before { + content: "\f40a"; } + +.fa-mendeley:before { + content: "\f7b3"; } + +.fa-uniregistry:before { + content: "\f404"; } + +.fa-figma:before { + content: "\f799"; } + +.fa-creative-commons-remix:before { + content: "\f4ee"; } + +.fa-cc-amazon-pay:before { + content: "\f42d"; } + +.fa-dropbox:before { + content: "\f16b"; } + +.fa-instagram:before { + content: "\f16d"; } + +.fa-cmplid:before { + content: "\e360"; } + +.fa-upwork:before { + content: "\e641"; } + +.fa-facebook:before { + content: "\f09a"; } + +.fa-gripfire:before { + content: "\f3ac"; } + +.fa-jedi-order:before { + content: "\f50e"; } + +.fa-uikit:before { + content: "\f403"; } + +.fa-fort-awesome-alt:before { + content: "\f3a3"; } + +.fa-phabricator:before { + content: "\f3db"; } + +.fa-ussunnah:before { + content: "\f407"; } + +.fa-earlybirds:before { + content: "\f39a"; } + +.fa-trade-federation:before { + content: "\f513"; } + +.fa-autoprefixer:before { + content: "\f41c"; } + +.fa-whatsapp:before { + content: "\f232"; } + +.fa-square-upwork:before { + content: "\e67c"; } + +.fa-slideshare:before { + content: "\f1e7"; } + +.fa-google-play:before { + content: "\f3ab"; } + +.fa-viadeo:before { + content: "\f2a9"; } + +.fa-line:before { + content: "\f3c0"; } + +.fa-google-drive:before { + content: "\f3aa"; } + +.fa-servicestack:before { + content: "\f3ec"; } + +.fa-simplybuilt:before { + content: "\f215"; } + +.fa-bitbucket:before { + content: "\f171"; } + +.fa-imdb:before { + content: "\f2d8"; } + +.fa-deezer:before { + content: "\e077"; } + +.fa-raspberry-pi:before { + content: "\f7bb"; } + +.fa-jira:before { + content: "\f7b1"; } + +.fa-docker:before { + content: "\f395"; } + +.fa-screenpal:before { + content: "\e570"; } + +.fa-bluetooth:before { + content: "\f293"; } + +.fa-gitter:before { + content: "\f426"; } + +.fa-d-and-d:before { + content: "\f38d"; } + +.fa-microblog:before { + content: "\e01a"; } + +.fa-cc-diners-club:before { + content: "\f24c"; } + +.fa-gg-circle:before { + content: "\f261"; } + +.fa-pied-piper-hat:before { + content: "\f4e5"; } + +.fa-kickstarter-k:before { + content: "\f3bc"; } + +.fa-yandex:before { + content: "\f413"; } + +.fa-readme:before { + content: "\f4d5"; } + +.fa-html5:before { + content: "\f13b"; } + +.fa-sellsy:before { + content: "\f213"; } + +.fa-square-web-awesome:before { + content: "\e683"; } + +.fa-sass:before { + content: "\f41e"; } + +.fa-wirsindhandwerk:before { + content: "\e2d0"; } + +.fa-wsh:before { + content: "\e2d0"; } + +.fa-buromobelexperte:before { + content: "\f37f"; } + +.fa-salesforce:before { + content: "\f83b"; } + +.fa-octopus-deploy:before { + content: "\e082"; } + +.fa-medapps:before { + content: "\f3c6"; } + +.fa-ns8:before { + content: "\f3d5"; } + +.fa-pinterest-p:before { + content: "\f231"; } + +.fa-apper:before { + content: "\f371"; } + +.fa-fort-awesome:before { + content: "\f286"; } + +.fa-waze:before { + content: "\f83f"; } + +.fa-bluesky:before { + content: "\e671"; } + +.fa-cc-jcb:before { + content: "\f24b"; } + +.fa-snapchat:before { + content: "\f2ab"; } + +.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa-fantasy-flight-games:before { + content: "\f6dc"; } + +.fa-rust:before { + content: "\e07a"; } + +.fa-wix:before { + content: "\f5cf"; } + +.fa-square-behance:before { + content: "\f1b5"; } + +.fa-behance-square:before { + content: "\f1b5"; } + +.fa-supple:before { + content: "\f3f9"; } + +.fa-webflow:before { + content: "\e65c"; } + +.fa-rebel:before { + content: "\f1d0"; } + +.fa-css3:before { + content: "\f13c"; } + +.fa-staylinked:before { + content: "\f3f5"; } + +.fa-kaggle:before { + content: "\f5fa"; } + +.fa-space-awesome:before { + content: "\e5ac"; } + +.fa-deviantart:before { + content: "\f1bd"; } + +.fa-cpanel:before { + content: "\f388"; } + +.fa-goodreads-g:before { + content: "\f3a9"; } + +.fa-square-git:before { + content: "\f1d2"; } + +.fa-git-square:before { + content: "\f1d2"; } + +.fa-square-tumblr:before { + content: "\f174"; } + +.fa-tumblr-square:before { + content: "\f174"; } + +.fa-trello:before { + content: "\f181"; } + +.fa-creative-commons-nc-jp:before { + content: "\f4ea"; } + +.fa-get-pocket:before { + content: "\f265"; } + +.fa-perbyte:before { + content: "\e083"; } + +.fa-grunt:before { + content: "\f3ad"; } + +.fa-weebly:before { + content: "\f5cc"; } + +.fa-connectdevelop:before { + content: "\f20e"; } + +.fa-leanpub:before { + content: "\f212"; } + +.fa-black-tie:before { + content: "\f27e"; } + +.fa-themeco:before { + content: "\f5c6"; } + +.fa-python:before { + content: "\f3e2"; } + +.fa-android:before { + content: "\f17b"; } + +.fa-bots:before { + content: "\e340"; } + +.fa-free-code-camp:before { + content: "\f2c5"; } + +.fa-hornbill:before { + content: "\f592"; } + +.fa-js:before { + content: "\f3b8"; } + +.fa-ideal:before { + content: "\e013"; } + +.fa-git:before { + content: "\f1d3"; } + +.fa-dev:before { + content: "\f6cc"; } + +.fa-sketch:before { + content: "\f7c6"; } + +.fa-yandex-international:before { + content: "\f414"; } + +.fa-cc-amex:before { + content: "\f1f3"; } + +.fa-uber:before { + content: "\f402"; } + +.fa-github:before { + content: "\f09b"; } + +.fa-php:before { + content: "\f457"; } + +.fa-alipay:before { + content: "\f642"; } + +.fa-youtube:before { + content: "\f167"; } + +.fa-skyatlas:before { + content: "\f216"; } + +.fa-firefox-browser:before { + content: "\e007"; } + +.fa-replyd:before { + content: "\f3e6"; } + +.fa-suse:before { + content: "\f7d6"; } + +.fa-jenkins:before { + content: "\f3b6"; } + +.fa-twitter:before { + content: "\f099"; } + +.fa-rockrms:before { + content: "\f3e9"; } + +.fa-pinterest:before { + content: "\f0d2"; } + +.fa-buffer:before { + content: "\f837"; } + +.fa-npm:before { + content: "\f3d4"; } + +.fa-yammer:before { + content: "\f840"; } + +.fa-btc:before { + content: "\f15a"; } + +.fa-dribbble:before { + content: "\f17d"; } + +.fa-stumbleupon-circle:before { + content: "\f1a3"; } + +.fa-internet-explorer:before { + content: "\f26b"; } + +.fa-stubber:before { + content: "\e5c7"; } + +.fa-telegram:before { + content: "\f2c6"; } + +.fa-telegram-plane:before { + content: "\f2c6"; } + +.fa-old-republic:before { + content: "\f510"; } + +.fa-odysee:before { + content: "\e5c6"; } + +.fa-square-whatsapp:before { + content: "\f40c"; } + +.fa-whatsapp-square:before { + content: "\f40c"; } + +.fa-node-js:before { + content: "\f3d3"; } + +.fa-edge-legacy:before { + content: "\e078"; } + +.fa-slack:before { + content: "\f198"; } + +.fa-slack-hash:before { + content: "\f198"; } + +.fa-medrt:before { + content: "\f3c8"; } + +.fa-usb:before { + content: "\f287"; } + +.fa-tumblr:before { + content: "\f173"; } + +.fa-vaadin:before { + content: "\f408"; } + +.fa-quora:before { + content: "\f2c4"; } + +.fa-square-x-twitter:before { + content: "\e61a"; } + +.fa-reacteurope:before { + content: "\f75d"; } + +.fa-medium:before { + content: "\f23a"; } + +.fa-medium-m:before { + content: "\f23a"; } + +.fa-amilia:before { + content: "\f36d"; } + +.fa-mixcloud:before { + content: "\f289"; } + +.fa-flipboard:before { + content: "\f44d"; } + +.fa-viacoin:before { + content: "\f237"; } + +.fa-critical-role:before { + content: "\f6c9"; } + +.fa-sitrox:before { + content: "\e44a"; } + +.fa-discourse:before { + content: "\f393"; } + +.fa-joomla:before { + content: "\f1aa"; } + +.fa-mastodon:before { + content: "\f4f6"; } + +.fa-airbnb:before { + content: "\f834"; } + +.fa-wolf-pack-battalion:before { + content: "\f514"; } + +.fa-buy-n-large:before { + content: "\f8a6"; } + +.fa-gulp:before { + content: "\f3ae"; } + +.fa-creative-commons-sampling-plus:before { + content: "\f4f1"; } + +.fa-strava:before { + content: "\f428"; } + +.fa-ember:before { + content: "\f423"; } + +.fa-canadian-maple-leaf:before { + content: "\f785"; } + +.fa-teamspeak:before { + content: "\f4f9"; } + +.fa-pushed:before { + content: "\f3e1"; } + +.fa-wordpress-simple:before { + content: "\f411"; } + +.fa-nutritionix:before { + content: "\f3d6"; } + +.fa-wodu:before { + content: "\e088"; } + +.fa-google-pay:before { + content: "\e079"; } + +.fa-intercom:before { + content: "\f7af"; } + +.fa-zhihu:before { + content: "\f63f"; } + +.fa-korvue:before { + content: "\f42f"; } + +.fa-pix:before { + content: "\e43a"; } + +.fa-steam-symbol:before { + content: "\f3f6"; } +:root, :host { + --fa-style-family-classic: 'Font Awesome 6 Free'; + --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } + +.far, +.fa-regular { + font-weight: 400; } +:root, :host { + --fa-style-family-classic: 'Font Awesome 6 Free'; + --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 900; + font-display: block; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +.fas, +.fa-solid { + font-weight: 900; } +@font-face { + font-family: 'Font Awesome 5 Brands'; + font-display: block; + font-weight: 400; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: 'Font Awesome 5 Free'; + font-display: block; + font-weight: 900; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: 'Font Awesome 5 Free'; + font-display: block; + font-weight: 400; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); } diff --git a/deps/font-awesome-6.5.2/css/all.min.css b/deps/font-awesome-6.5.2/css/all.min.css new file mode 100644 index 0000000..269bcee --- /dev/null +++ b/deps/font-awesome-6.5.2/css/all.min.css @@ -0,0 +1,9 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@-webkit-keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}.fa-rotate-90{-webkit-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-webkit-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-webkit-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-webkit-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-webkit-transform:scaleY(-1);transform:scaleY(-1)}.fa-flip-both,.fa-flip-horizontal.fa-flip-vertical{-webkit-transform:scale(-1);transform:scale(-1)}.fa-rotate-by{-webkit-transform:rotate(var(--fa-rotate-angle,0));transform:rotate(var(--fa-rotate-angle,0))}.fa-stack{display:inline-block;height:2em;line-height:2em;position:relative;vertical-align:middle;width:2.5em}.fa-stack-1x,.fa-stack-2x{left:0;position:absolute;text-align:center;width:100%;z-index:var(--fa-stack-z-index,auto)}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:var(--fa-inverse,#fff)} + +.fa-0:before{content:"\30"}.fa-1:before{content:"\31"}.fa-2:before{content:"\32"}.fa-3:before{content:"\33"}.fa-4:before{content:"\34"}.fa-5:before{content:"\35"}.fa-6:before{content:"\36"}.fa-7:before{content:"\37"}.fa-8:before{content:"\38"}.fa-9:before{content:"\39"}.fa-fill-drip:before{content:"\f576"}.fa-arrows-to-circle:before{content:"\e4bd"}.fa-chevron-circle-right:before,.fa-circle-chevron-right:before{content:"\f138"}.fa-at:before{content:"\40"}.fa-trash-alt:before,.fa-trash-can:before{content:"\f2ed"}.fa-text-height:before{content:"\f034"}.fa-user-times:before,.fa-user-xmark:before{content:"\f235"}.fa-stethoscope:before{content:"\f0f1"}.fa-comment-alt:before,.fa-message:before{content:"\f27a"}.fa-info:before{content:"\f129"}.fa-compress-alt:before,.fa-down-left-and-up-right-to-center:before{content:"\f422"}.fa-explosion:before{content:"\e4e9"}.fa-file-alt:before,.fa-file-lines:before,.fa-file-text:before{content:"\f15c"}.fa-wave-square:before{content:"\f83e"}.fa-ring:before{content:"\f70b"}.fa-building-un:before{content:"\e4d9"}.fa-dice-three:before{content:"\f527"}.fa-calendar-alt:before,.fa-calendar-days:before{content:"\f073"}.fa-anchor-circle-check:before{content:"\e4aa"}.fa-building-circle-arrow-right:before{content:"\e4d1"}.fa-volleyball-ball:before,.fa-volleyball:before{content:"\f45f"}.fa-arrows-up-to-line:before{content:"\e4c2"}.fa-sort-desc:before,.fa-sort-down:before{content:"\f0dd"}.fa-circle-minus:before,.fa-minus-circle:before{content:"\f056"}.fa-door-open:before{content:"\f52b"}.fa-right-from-bracket:before,.fa-sign-out-alt:before{content:"\f2f5"}.fa-atom:before{content:"\f5d2"}.fa-soap:before{content:"\e06e"}.fa-heart-music-camera-bolt:before,.fa-icons:before{content:"\f86d"}.fa-microphone-alt-slash:before,.fa-microphone-lines-slash:before{content:"\f539"}.fa-bridge-circle-check:before{content:"\e4c9"}.fa-pump-medical:before{content:"\e06a"}.fa-fingerprint:before{content:"\f577"}.fa-hand-point-right:before{content:"\f0a4"}.fa-magnifying-glass-location:before,.fa-search-location:before{content:"\f689"}.fa-forward-step:before,.fa-step-forward:before{content:"\f051"}.fa-face-smile-beam:before,.fa-smile-beam:before{content:"\f5b8"}.fa-flag-checkered:before{content:"\f11e"}.fa-football-ball:before,.fa-football:before{content:"\f44e"}.fa-school-circle-exclamation:before{content:"\e56c"}.fa-crop:before{content:"\f125"}.fa-angle-double-down:before,.fa-angles-down:before{content:"\f103"}.fa-users-rectangle:before{content:"\e594"}.fa-people-roof:before{content:"\e537"}.fa-people-line:before{content:"\e534"}.fa-beer-mug-empty:before,.fa-beer:before{content:"\f0fc"}.fa-diagram-predecessor:before{content:"\e477"}.fa-arrow-up-long:before,.fa-long-arrow-up:before{content:"\f176"}.fa-burn:before,.fa-fire-flame-simple:before{content:"\f46a"}.fa-male:before,.fa-person:before{content:"\f183"}.fa-laptop:before{content:"\f109"}.fa-file-csv:before{content:"\f6dd"}.fa-menorah:before{content:"\f676"}.fa-truck-plane:before{content:"\e58f"}.fa-record-vinyl:before{content:"\f8d9"}.fa-face-grin-stars:before,.fa-grin-stars:before{content:"\f587"}.fa-bong:before{content:"\f55c"}.fa-pastafarianism:before,.fa-spaghetti-monster-flying:before{content:"\f67b"}.fa-arrow-down-up-across-line:before{content:"\e4af"}.fa-spoon:before,.fa-utensil-spoon:before{content:"\f2e5"}.fa-jar-wheat:before{content:"\e517"}.fa-envelopes-bulk:before,.fa-mail-bulk:before{content:"\f674"}.fa-file-circle-exclamation:before{content:"\e4eb"}.fa-circle-h:before,.fa-hospital-symbol:before{content:"\f47e"}.fa-pager:before{content:"\f815"}.fa-address-book:before,.fa-contact-book:before{content:"\f2b9"}.fa-strikethrough:before{content:"\f0cc"}.fa-k:before{content:"\4b"}.fa-landmark-flag:before{content:"\e51c"}.fa-pencil-alt:before,.fa-pencil:before{content:"\f303"}.fa-backward:before{content:"\f04a"}.fa-caret-right:before{content:"\f0da"}.fa-comments:before{content:"\f086"}.fa-file-clipboard:before,.fa-paste:before{content:"\f0ea"}.fa-code-pull-request:before{content:"\e13c"}.fa-clipboard-list:before{content:"\f46d"}.fa-truck-loading:before,.fa-truck-ramp-box:before{content:"\f4de"}.fa-user-check:before{content:"\f4fc"}.fa-vial-virus:before{content:"\e597"}.fa-sheet-plastic:before{content:"\e571"}.fa-blog:before{content:"\f781"}.fa-user-ninja:before{content:"\f504"}.fa-person-arrow-up-from-line:before{content:"\e539"}.fa-scroll-torah:before,.fa-torah:before{content:"\f6a0"}.fa-broom-ball:before,.fa-quidditch-broom-ball:before,.fa-quidditch:before{content:"\f458"}.fa-toggle-off:before{content:"\f204"}.fa-archive:before,.fa-box-archive:before{content:"\f187"}.fa-person-drowning:before{content:"\e545"}.fa-arrow-down-9-1:before,.fa-sort-numeric-desc:before,.fa-sort-numeric-down-alt:before{content:"\f886"}.fa-face-grin-tongue-squint:before,.fa-grin-tongue-squint:before{content:"\f58a"}.fa-spray-can:before{content:"\f5bd"}.fa-truck-monster:before{content:"\f63b"}.fa-w:before{content:"\57"}.fa-earth-africa:before,.fa-globe-africa:before{content:"\f57c"}.fa-rainbow:before{content:"\f75b"}.fa-circle-notch:before{content:"\f1ce"}.fa-tablet-alt:before,.fa-tablet-screen-button:before{content:"\f3fa"}.fa-paw:before{content:"\f1b0"}.fa-cloud:before{content:"\f0c2"}.fa-trowel-bricks:before{content:"\e58a"}.fa-face-flushed:before,.fa-flushed:before{content:"\f579"}.fa-hospital-user:before{content:"\f80d"}.fa-tent-arrow-left-right:before{content:"\e57f"}.fa-gavel:before,.fa-legal:before{content:"\f0e3"}.fa-binoculars:before{content:"\f1e5"}.fa-microphone-slash:before{content:"\f131"}.fa-box-tissue:before{content:"\e05b"}.fa-motorcycle:before{content:"\f21c"}.fa-bell-concierge:before,.fa-concierge-bell:before{content:"\f562"}.fa-pen-ruler:before,.fa-pencil-ruler:before{content:"\f5ae"}.fa-people-arrows-left-right:before,.fa-people-arrows:before{content:"\e068"}.fa-mars-and-venus-burst:before{content:"\e523"}.fa-caret-square-right:before,.fa-square-caret-right:before{content:"\f152"}.fa-cut:before,.fa-scissors:before{content:"\f0c4"}.fa-sun-plant-wilt:before{content:"\e57a"}.fa-toilets-portable:before{content:"\e584"}.fa-hockey-puck:before{content:"\f453"}.fa-table:before{content:"\f0ce"}.fa-magnifying-glass-arrow-right:before{content:"\e521"}.fa-digital-tachograph:before,.fa-tachograph-digital:before{content:"\f566"}.fa-users-slash:before{content:"\e073"}.fa-clover:before{content:"\e139"}.fa-mail-reply:before,.fa-reply:before{content:"\f3e5"}.fa-star-and-crescent:before{content:"\f699"}.fa-house-fire:before{content:"\e50c"}.fa-minus-square:before,.fa-square-minus:before{content:"\f146"}.fa-helicopter:before{content:"\f533"}.fa-compass:before{content:"\f14e"}.fa-caret-square-down:before,.fa-square-caret-down:before{content:"\f150"}.fa-file-circle-question:before{content:"\e4ef"}.fa-laptop-code:before{content:"\f5fc"}.fa-swatchbook:before{content:"\f5c3"}.fa-prescription-bottle:before{content:"\f485"}.fa-bars:before,.fa-navicon:before{content:"\f0c9"}.fa-people-group:before{content:"\e533"}.fa-hourglass-3:before,.fa-hourglass-end:before{content:"\f253"}.fa-heart-broken:before,.fa-heart-crack:before{content:"\f7a9"}.fa-external-link-square-alt:before,.fa-square-up-right:before{content:"\f360"}.fa-face-kiss-beam:before,.fa-kiss-beam:before{content:"\f597"}.fa-film:before{content:"\f008"}.fa-ruler-horizontal:before{content:"\f547"}.fa-people-robbery:before{content:"\e536"}.fa-lightbulb:before{content:"\f0eb"}.fa-caret-left:before{content:"\f0d9"}.fa-circle-exclamation:before,.fa-exclamation-circle:before{content:"\f06a"}.fa-school-circle-xmark:before{content:"\e56d"}.fa-arrow-right-from-bracket:before,.fa-sign-out:before{content:"\f08b"}.fa-chevron-circle-down:before,.fa-circle-chevron-down:before{content:"\f13a"}.fa-unlock-alt:before,.fa-unlock-keyhole:before{content:"\f13e"}.fa-cloud-showers-heavy:before{content:"\f740"}.fa-headphones-alt:before,.fa-headphones-simple:before{content:"\f58f"}.fa-sitemap:before{content:"\f0e8"}.fa-circle-dollar-to-slot:before,.fa-donate:before{content:"\f4b9"}.fa-memory:before{content:"\f538"}.fa-road-spikes:before{content:"\e568"}.fa-fire-burner:before{content:"\e4f1"}.fa-flag:before{content:"\f024"}.fa-hanukiah:before{content:"\f6e6"}.fa-feather:before{content:"\f52d"}.fa-volume-down:before,.fa-volume-low:before{content:"\f027"}.fa-comment-slash:before{content:"\f4b3"}.fa-cloud-sun-rain:before{content:"\f743"}.fa-compress:before{content:"\f066"}.fa-wheat-alt:before,.fa-wheat-awn:before{content:"\e2cd"}.fa-ankh:before{content:"\f644"}.fa-hands-holding-child:before{content:"\e4fa"}.fa-asterisk:before{content:"\2a"}.fa-check-square:before,.fa-square-check:before{content:"\f14a"}.fa-peseta-sign:before{content:"\e221"}.fa-header:before,.fa-heading:before{content:"\f1dc"}.fa-ghost:before{content:"\f6e2"}.fa-list-squares:before,.fa-list:before{content:"\f03a"}.fa-phone-square-alt:before,.fa-square-phone-flip:before{content:"\f87b"}.fa-cart-plus:before{content:"\f217"}.fa-gamepad:before{content:"\f11b"}.fa-circle-dot:before,.fa-dot-circle:before{content:"\f192"}.fa-dizzy:before,.fa-face-dizzy:before{content:"\f567"}.fa-egg:before{content:"\f7fb"}.fa-house-medical-circle-xmark:before{content:"\e513"}.fa-campground:before{content:"\f6bb"}.fa-folder-plus:before{content:"\f65e"}.fa-futbol-ball:before,.fa-futbol:before,.fa-soccer-ball:before{content:"\f1e3"}.fa-paint-brush:before,.fa-paintbrush:before{content:"\f1fc"}.fa-lock:before{content:"\f023"}.fa-gas-pump:before{content:"\f52f"}.fa-hot-tub-person:before,.fa-hot-tub:before{content:"\f593"}.fa-map-location:before,.fa-map-marked:before{content:"\f59f"}.fa-house-flood-water:before{content:"\e50e"}.fa-tree:before{content:"\f1bb"}.fa-bridge-lock:before{content:"\e4cc"}.fa-sack-dollar:before{content:"\f81d"}.fa-edit:before,.fa-pen-to-square:before{content:"\f044"}.fa-car-side:before{content:"\f5e4"}.fa-share-alt:before,.fa-share-nodes:before{content:"\f1e0"}.fa-heart-circle-minus:before{content:"\e4ff"}.fa-hourglass-2:before,.fa-hourglass-half:before{content:"\f252"}.fa-microscope:before{content:"\f610"}.fa-sink:before{content:"\e06d"}.fa-bag-shopping:before,.fa-shopping-bag:before{content:"\f290"}.fa-arrow-down-z-a:before,.fa-sort-alpha-desc:before,.fa-sort-alpha-down-alt:before{content:"\f881"}.fa-mitten:before{content:"\f7b5"}.fa-person-rays:before{content:"\e54d"}.fa-users:before{content:"\f0c0"}.fa-eye-slash:before{content:"\f070"}.fa-flask-vial:before{content:"\e4f3"}.fa-hand-paper:before,.fa-hand:before{content:"\f256"}.fa-om:before{content:"\f679"}.fa-worm:before{content:"\e599"}.fa-house-circle-xmark:before{content:"\e50b"}.fa-plug:before{content:"\f1e6"}.fa-chevron-up:before{content:"\f077"}.fa-hand-spock:before{content:"\f259"}.fa-stopwatch:before{content:"\f2f2"}.fa-face-kiss:before,.fa-kiss:before{content:"\f596"}.fa-bridge-circle-xmark:before{content:"\e4cb"}.fa-face-grin-tongue:before,.fa-grin-tongue:before{content:"\f589"}.fa-chess-bishop:before{content:"\f43a"}.fa-face-grin-wink:before,.fa-grin-wink:before{content:"\f58c"}.fa-deaf:before,.fa-deafness:before,.fa-ear-deaf:before,.fa-hard-of-hearing:before{content:"\f2a4"}.fa-road-circle-check:before{content:"\e564"}.fa-dice-five:before{content:"\f523"}.fa-rss-square:before,.fa-square-rss:before{content:"\f143"}.fa-land-mine-on:before{content:"\e51b"}.fa-i-cursor:before{content:"\f246"}.fa-stamp:before{content:"\f5bf"}.fa-stairs:before{content:"\e289"}.fa-i:before{content:"\49"}.fa-hryvnia-sign:before,.fa-hryvnia:before{content:"\f6f2"}.fa-pills:before{content:"\f484"}.fa-face-grin-wide:before,.fa-grin-alt:before{content:"\f581"}.fa-tooth:before{content:"\f5c9"}.fa-v:before{content:"\56"}.fa-bangladeshi-taka-sign:before{content:"\e2e6"}.fa-bicycle:before{content:"\f206"}.fa-rod-asclepius:before,.fa-rod-snake:before,.fa-staff-aesculapius:before,.fa-staff-snake:before{content:"\e579"}.fa-head-side-cough-slash:before{content:"\e062"}.fa-ambulance:before,.fa-truck-medical:before{content:"\f0f9"}.fa-wheat-awn-circle-exclamation:before{content:"\e598"}.fa-snowman:before{content:"\f7d0"}.fa-mortar-pestle:before{content:"\f5a7"}.fa-road-barrier:before{content:"\e562"}.fa-school:before{content:"\f549"}.fa-igloo:before{content:"\f7ae"}.fa-joint:before{content:"\f595"}.fa-angle-right:before{content:"\f105"}.fa-horse:before{content:"\f6f0"}.fa-q:before{content:"\51"}.fa-g:before{content:"\47"}.fa-notes-medical:before{content:"\f481"}.fa-temperature-2:before,.fa-temperature-half:before,.fa-thermometer-2:before,.fa-thermometer-half:before{content:"\f2c9"}.fa-dong-sign:before{content:"\e169"}.fa-capsules:before{content:"\f46b"}.fa-poo-bolt:before,.fa-poo-storm:before{content:"\f75a"}.fa-face-frown-open:before,.fa-frown-open:before{content:"\f57a"}.fa-hand-point-up:before{content:"\f0a6"}.fa-money-bill:before{content:"\f0d6"}.fa-bookmark:before{content:"\f02e"}.fa-align-justify:before{content:"\f039"}.fa-umbrella-beach:before{content:"\f5ca"}.fa-helmet-un:before{content:"\e503"}.fa-bullseye:before{content:"\f140"}.fa-bacon:before{content:"\f7e5"}.fa-hand-point-down:before{content:"\f0a7"}.fa-arrow-up-from-bracket:before{content:"\e09a"}.fa-folder-blank:before,.fa-folder:before{content:"\f07b"}.fa-file-medical-alt:before,.fa-file-waveform:before{content:"\f478"}.fa-radiation:before{content:"\f7b9"}.fa-chart-simple:before{content:"\e473"}.fa-mars-stroke:before{content:"\f229"}.fa-vial:before{content:"\f492"}.fa-dashboard:before,.fa-gauge-med:before,.fa-gauge:before,.fa-tachometer-alt-average:before{content:"\f624"}.fa-magic-wand-sparkles:before,.fa-wand-magic-sparkles:before{content:"\e2ca"}.fa-e:before{content:"\45"}.fa-pen-alt:before,.fa-pen-clip:before{content:"\f305"}.fa-bridge-circle-exclamation:before{content:"\e4ca"}.fa-user:before{content:"\f007"}.fa-school-circle-check:before{content:"\e56b"}.fa-dumpster:before{content:"\f793"}.fa-shuttle-van:before,.fa-van-shuttle:before{content:"\f5b6"}.fa-building-user:before{content:"\e4da"}.fa-caret-square-left:before,.fa-square-caret-left:before{content:"\f191"}.fa-highlighter:before{content:"\f591"}.fa-key:before{content:"\f084"}.fa-bullhorn:before{content:"\f0a1"}.fa-globe:before{content:"\f0ac"}.fa-synagogue:before{content:"\f69b"}.fa-person-half-dress:before{content:"\e548"}.fa-road-bridge:before{content:"\e563"}.fa-location-arrow:before{content:"\f124"}.fa-c:before{content:"\43"}.fa-tablet-button:before{content:"\f10a"}.fa-building-lock:before{content:"\e4d6"}.fa-pizza-slice:before{content:"\f818"}.fa-money-bill-wave:before{content:"\f53a"}.fa-area-chart:before,.fa-chart-area:before{content:"\f1fe"}.fa-house-flag:before{content:"\e50d"}.fa-person-circle-minus:before{content:"\e540"}.fa-ban:before,.fa-cancel:before{content:"\f05e"}.fa-camera-rotate:before{content:"\e0d8"}.fa-air-freshener:before,.fa-spray-can-sparkles:before{content:"\f5d0"}.fa-star:before{content:"\f005"}.fa-repeat:before{content:"\f363"}.fa-cross:before{content:"\f654"}.fa-box:before{content:"\f466"}.fa-venus-mars:before{content:"\f228"}.fa-arrow-pointer:before,.fa-mouse-pointer:before{content:"\f245"}.fa-expand-arrows-alt:before,.fa-maximize:before{content:"\f31e"}.fa-charging-station:before{content:"\f5e7"}.fa-shapes:before,.fa-triangle-circle-square:before{content:"\f61f"}.fa-random:before,.fa-shuffle:before{content:"\f074"}.fa-person-running:before,.fa-running:before{content:"\f70c"}.fa-mobile-retro:before{content:"\e527"}.fa-grip-lines-vertical:before{content:"\f7a5"}.fa-spider:before{content:"\f717"}.fa-hands-bound:before{content:"\e4f9"}.fa-file-invoice-dollar:before{content:"\f571"}.fa-plane-circle-exclamation:before{content:"\e556"}.fa-x-ray:before{content:"\f497"}.fa-spell-check:before{content:"\f891"}.fa-slash:before{content:"\f715"}.fa-computer-mouse:before,.fa-mouse:before{content:"\f8cc"}.fa-arrow-right-to-bracket:before,.fa-sign-in:before{content:"\f090"}.fa-shop-slash:before,.fa-store-alt-slash:before{content:"\e070"}.fa-server:before{content:"\f233"}.fa-virus-covid-slash:before{content:"\e4a9"}.fa-shop-lock:before{content:"\e4a5"}.fa-hourglass-1:before,.fa-hourglass-start:before{content:"\f251"}.fa-blender-phone:before{content:"\f6b6"}.fa-building-wheat:before{content:"\e4db"}.fa-person-breastfeeding:before{content:"\e53a"}.fa-right-to-bracket:before,.fa-sign-in-alt:before{content:"\f2f6"}.fa-venus:before{content:"\f221"}.fa-passport:before{content:"\f5ab"}.fa-heart-pulse:before,.fa-heartbeat:before{content:"\f21e"}.fa-people-carry-box:before,.fa-people-carry:before{content:"\f4ce"}.fa-temperature-high:before{content:"\f769"}.fa-microchip:before{content:"\f2db"}.fa-crown:before{content:"\f521"}.fa-weight-hanging:before{content:"\f5cd"}.fa-xmarks-lines:before{content:"\e59a"}.fa-file-prescription:before{content:"\f572"}.fa-weight-scale:before,.fa-weight:before{content:"\f496"}.fa-user-friends:before,.fa-user-group:before{content:"\f500"}.fa-arrow-up-a-z:before,.fa-sort-alpha-up:before{content:"\f15e"}.fa-chess-knight:before{content:"\f441"}.fa-face-laugh-squint:before,.fa-laugh-squint:before{content:"\f59b"}.fa-wheelchair:before{content:"\f193"}.fa-arrow-circle-up:before,.fa-circle-arrow-up:before{content:"\f0aa"}.fa-toggle-on:before{content:"\f205"}.fa-person-walking:before,.fa-walking:before{content:"\f554"}.fa-l:before{content:"\4c"}.fa-fire:before{content:"\f06d"}.fa-bed-pulse:before,.fa-procedures:before{content:"\f487"}.fa-shuttle-space:before,.fa-space-shuttle:before{content:"\f197"}.fa-face-laugh:before,.fa-laugh:before{content:"\f599"}.fa-folder-open:before{content:"\f07c"}.fa-heart-circle-plus:before{content:"\e500"}.fa-code-fork:before{content:"\e13b"}.fa-city:before{content:"\f64f"}.fa-microphone-alt:before,.fa-microphone-lines:before{content:"\f3c9"}.fa-pepper-hot:before{content:"\f816"}.fa-unlock:before{content:"\f09c"}.fa-colon-sign:before{content:"\e140"}.fa-headset:before{content:"\f590"}.fa-store-slash:before{content:"\e071"}.fa-road-circle-xmark:before{content:"\e566"}.fa-user-minus:before{content:"\f503"}.fa-mars-stroke-up:before,.fa-mars-stroke-v:before{content:"\f22a"}.fa-champagne-glasses:before,.fa-glass-cheers:before{content:"\f79f"}.fa-clipboard:before{content:"\f328"}.fa-house-circle-exclamation:before{content:"\e50a"}.fa-file-arrow-up:before,.fa-file-upload:before{content:"\f574"}.fa-wifi-3:before,.fa-wifi-strong:before,.fa-wifi:before{content:"\f1eb"}.fa-bath:before,.fa-bathtub:before{content:"\f2cd"}.fa-underline:before{content:"\f0cd"}.fa-user-edit:before,.fa-user-pen:before{content:"\f4ff"}.fa-signature:before{content:"\f5b7"}.fa-stroopwafel:before{content:"\f551"}.fa-bold:before{content:"\f032"}.fa-anchor-lock:before{content:"\e4ad"}.fa-building-ngo:before{content:"\e4d7"}.fa-manat-sign:before{content:"\e1d5"}.fa-not-equal:before{content:"\f53e"}.fa-border-style:before,.fa-border-top-left:before{content:"\f853"}.fa-map-location-dot:before,.fa-map-marked-alt:before{content:"\f5a0"}.fa-jedi:before{content:"\f669"}.fa-poll:before,.fa-square-poll-vertical:before{content:"\f681"}.fa-mug-hot:before{content:"\f7b6"}.fa-battery-car:before,.fa-car-battery:before{content:"\f5df"}.fa-gift:before{content:"\f06b"}.fa-dice-two:before{content:"\f528"}.fa-chess-queen:before{content:"\f445"}.fa-glasses:before{content:"\f530"}.fa-chess-board:before{content:"\f43c"}.fa-building-circle-check:before{content:"\e4d2"}.fa-person-chalkboard:before{content:"\e53d"}.fa-mars-stroke-h:before,.fa-mars-stroke-right:before{content:"\f22b"}.fa-hand-back-fist:before,.fa-hand-rock:before{content:"\f255"}.fa-caret-square-up:before,.fa-square-caret-up:before{content:"\f151"}.fa-cloud-showers-water:before{content:"\e4e4"}.fa-bar-chart:before,.fa-chart-bar:before{content:"\f080"}.fa-hands-bubbles:before,.fa-hands-wash:before{content:"\e05e"}.fa-less-than-equal:before{content:"\f537"}.fa-train:before{content:"\f238"}.fa-eye-low-vision:before,.fa-low-vision:before{content:"\f2a8"}.fa-crow:before{content:"\f520"}.fa-sailboat:before{content:"\e445"}.fa-window-restore:before{content:"\f2d2"}.fa-plus-square:before,.fa-square-plus:before{content:"\f0fe"}.fa-torii-gate:before{content:"\f6a1"}.fa-frog:before{content:"\f52e"}.fa-bucket:before{content:"\e4cf"}.fa-image:before{content:"\f03e"}.fa-microphone:before{content:"\f130"}.fa-cow:before{content:"\f6c8"}.fa-caret-up:before{content:"\f0d8"}.fa-screwdriver:before{content:"\f54a"}.fa-folder-closed:before{content:"\e185"}.fa-house-tsunami:before{content:"\e515"}.fa-square-nfi:before{content:"\e576"}.fa-arrow-up-from-ground-water:before{content:"\e4b5"}.fa-glass-martini-alt:before,.fa-martini-glass:before{content:"\f57b"}.fa-rotate-back:before,.fa-rotate-backward:before,.fa-rotate-left:before,.fa-undo-alt:before{content:"\f2ea"}.fa-columns:before,.fa-table-columns:before{content:"\f0db"}.fa-lemon:before{content:"\f094"}.fa-head-side-mask:before{content:"\e063"}.fa-handshake:before{content:"\f2b5"}.fa-gem:before{content:"\f3a5"}.fa-dolly-box:before,.fa-dolly:before{content:"\f472"}.fa-smoking:before{content:"\f48d"}.fa-compress-arrows-alt:before,.fa-minimize:before{content:"\f78c"}.fa-monument:before{content:"\f5a6"}.fa-snowplow:before{content:"\f7d2"}.fa-angle-double-right:before,.fa-angles-right:before{content:"\f101"}.fa-cannabis:before{content:"\f55f"}.fa-circle-play:before,.fa-play-circle:before{content:"\f144"}.fa-tablets:before{content:"\f490"}.fa-ethernet:before{content:"\f796"}.fa-eur:before,.fa-euro-sign:before,.fa-euro:before{content:"\f153"}.fa-chair:before{content:"\f6c0"}.fa-check-circle:before,.fa-circle-check:before{content:"\f058"}.fa-circle-stop:before,.fa-stop-circle:before{content:"\f28d"}.fa-compass-drafting:before,.fa-drafting-compass:before{content:"\f568"}.fa-plate-wheat:before{content:"\e55a"}.fa-icicles:before{content:"\f7ad"}.fa-person-shelter:before{content:"\e54f"}.fa-neuter:before{content:"\f22c"}.fa-id-badge:before{content:"\f2c1"}.fa-marker:before{content:"\f5a1"}.fa-face-laugh-beam:before,.fa-laugh-beam:before{content:"\f59a"}.fa-helicopter-symbol:before{content:"\e502"}.fa-universal-access:before{content:"\f29a"}.fa-chevron-circle-up:before,.fa-circle-chevron-up:before{content:"\f139"}.fa-lari-sign:before{content:"\e1c8"}.fa-volcano:before{content:"\f770"}.fa-person-walking-dashed-line-arrow-right:before{content:"\e553"}.fa-gbp:before,.fa-pound-sign:before,.fa-sterling-sign:before{content:"\f154"}.fa-viruses:before{content:"\e076"}.fa-square-person-confined:before{content:"\e577"}.fa-user-tie:before{content:"\f508"}.fa-arrow-down-long:before,.fa-long-arrow-down:before{content:"\f175"}.fa-tent-arrow-down-to-line:before{content:"\e57e"}.fa-certificate:before{content:"\f0a3"}.fa-mail-reply-all:before,.fa-reply-all:before{content:"\f122"}.fa-suitcase:before{content:"\f0f2"}.fa-person-skating:before,.fa-skating:before{content:"\f7c5"}.fa-filter-circle-dollar:before,.fa-funnel-dollar:before{content:"\f662"}.fa-camera-retro:before{content:"\f083"}.fa-arrow-circle-down:before,.fa-circle-arrow-down:before{content:"\f0ab"}.fa-arrow-right-to-file:before,.fa-file-import:before{content:"\f56f"}.fa-external-link-square:before,.fa-square-arrow-up-right:before{content:"\f14c"}.fa-box-open:before{content:"\f49e"}.fa-scroll:before{content:"\f70e"}.fa-spa:before{content:"\f5bb"}.fa-location-pin-lock:before{content:"\e51f"}.fa-pause:before{content:"\f04c"}.fa-hill-avalanche:before{content:"\e507"}.fa-temperature-0:before,.fa-temperature-empty:before,.fa-thermometer-0:before,.fa-thermometer-empty:before{content:"\f2cb"}.fa-bomb:before{content:"\f1e2"}.fa-registered:before{content:"\f25d"}.fa-address-card:before,.fa-contact-card:before,.fa-vcard:before{content:"\f2bb"}.fa-balance-scale-right:before,.fa-scale-unbalanced-flip:before{content:"\f516"}.fa-subscript:before{content:"\f12c"}.fa-diamond-turn-right:before,.fa-directions:before{content:"\f5eb"}.fa-burst:before{content:"\e4dc"}.fa-house-laptop:before,.fa-laptop-house:before{content:"\e066"}.fa-face-tired:before,.fa-tired:before{content:"\f5c8"}.fa-money-bills:before{content:"\e1f3"}.fa-smog:before{content:"\f75f"}.fa-crutch:before{content:"\f7f7"}.fa-cloud-arrow-up:before,.fa-cloud-upload-alt:before,.fa-cloud-upload:before{content:"\f0ee"}.fa-palette:before{content:"\f53f"}.fa-arrows-turn-right:before{content:"\e4c0"}.fa-vest:before{content:"\e085"}.fa-ferry:before{content:"\e4ea"}.fa-arrows-down-to-people:before{content:"\e4b9"}.fa-seedling:before,.fa-sprout:before{content:"\f4d8"}.fa-arrows-alt-h:before,.fa-left-right:before{content:"\f337"}.fa-boxes-packing:before{content:"\e4c7"}.fa-arrow-circle-left:before,.fa-circle-arrow-left:before{content:"\f0a8"}.fa-group-arrows-rotate:before{content:"\e4f6"}.fa-bowl-food:before{content:"\e4c6"}.fa-candy-cane:before{content:"\f786"}.fa-arrow-down-wide-short:before,.fa-sort-amount-asc:before,.fa-sort-amount-down:before{content:"\f160"}.fa-cloud-bolt:before,.fa-thunderstorm:before{content:"\f76c"}.fa-remove-format:before,.fa-text-slash:before{content:"\f87d"}.fa-face-smile-wink:before,.fa-smile-wink:before{content:"\f4da"}.fa-file-word:before{content:"\f1c2"}.fa-file-powerpoint:before{content:"\f1c4"}.fa-arrows-h:before,.fa-arrows-left-right:before{content:"\f07e"}.fa-house-lock:before{content:"\e510"}.fa-cloud-arrow-down:before,.fa-cloud-download-alt:before,.fa-cloud-download:before{content:"\f0ed"}.fa-children:before{content:"\e4e1"}.fa-blackboard:before,.fa-chalkboard:before{content:"\f51b"}.fa-user-alt-slash:before,.fa-user-large-slash:before{content:"\f4fa"}.fa-envelope-open:before{content:"\f2b6"}.fa-handshake-alt-slash:before,.fa-handshake-simple-slash:before{content:"\e05f"}.fa-mattress-pillow:before{content:"\e525"}.fa-guarani-sign:before{content:"\e19a"}.fa-arrows-rotate:before,.fa-refresh:before,.fa-sync:before{content:"\f021"}.fa-fire-extinguisher:before{content:"\f134"}.fa-cruzeiro-sign:before{content:"\e152"}.fa-greater-than-equal:before{content:"\f532"}.fa-shield-alt:before,.fa-shield-halved:before{content:"\f3ed"}.fa-atlas:before,.fa-book-atlas:before{content:"\f558"}.fa-virus:before{content:"\e074"}.fa-envelope-circle-check:before{content:"\e4e8"}.fa-layer-group:before{content:"\f5fd"}.fa-arrows-to-dot:before{content:"\e4be"}.fa-archway:before{content:"\f557"}.fa-heart-circle-check:before{content:"\e4fd"}.fa-house-chimney-crack:before,.fa-house-damage:before{content:"\f6f1"}.fa-file-archive:before,.fa-file-zipper:before{content:"\f1c6"}.fa-square:before{content:"\f0c8"}.fa-glass-martini:before,.fa-martini-glass-empty:before{content:"\f000"}.fa-couch:before{content:"\f4b8"}.fa-cedi-sign:before{content:"\e0df"}.fa-italic:before{content:"\f033"}.fa-table-cells-column-lock:before{content:"\e678"}.fa-church:before{content:"\f51d"}.fa-comments-dollar:before{content:"\f653"}.fa-democrat:before{content:"\f747"}.fa-z:before{content:"\5a"}.fa-person-skiing:before,.fa-skiing:before{content:"\f7c9"}.fa-road-lock:before{content:"\e567"}.fa-a:before{content:"\41"}.fa-temperature-arrow-down:before,.fa-temperature-down:before{content:"\e03f"}.fa-feather-alt:before,.fa-feather-pointed:before{content:"\f56b"}.fa-p:before{content:"\50"}.fa-snowflake:before{content:"\f2dc"}.fa-newspaper:before{content:"\f1ea"}.fa-ad:before,.fa-rectangle-ad:before{content:"\f641"}.fa-arrow-circle-right:before,.fa-circle-arrow-right:before{content:"\f0a9"}.fa-filter-circle-xmark:before{content:"\e17b"}.fa-locust:before{content:"\e520"}.fa-sort:before,.fa-unsorted:before{content:"\f0dc"}.fa-list-1-2:before,.fa-list-numeric:before,.fa-list-ol:before{content:"\f0cb"}.fa-person-dress-burst:before{content:"\e544"}.fa-money-check-alt:before,.fa-money-check-dollar:before{content:"\f53d"}.fa-vector-square:before{content:"\f5cb"}.fa-bread-slice:before{content:"\f7ec"}.fa-language:before{content:"\f1ab"}.fa-face-kiss-wink-heart:before,.fa-kiss-wink-heart:before{content:"\f598"}.fa-filter:before{content:"\f0b0"}.fa-question:before{content:"\3f"}.fa-file-signature:before{content:"\f573"}.fa-arrows-alt:before,.fa-up-down-left-right:before{content:"\f0b2"}.fa-house-chimney-user:before{content:"\e065"}.fa-hand-holding-heart:before{content:"\f4be"}.fa-puzzle-piece:before{content:"\f12e"}.fa-money-check:before{content:"\f53c"}.fa-star-half-alt:before,.fa-star-half-stroke:before{content:"\f5c0"}.fa-code:before{content:"\f121"}.fa-glass-whiskey:before,.fa-whiskey-glass:before{content:"\f7a0"}.fa-building-circle-exclamation:before{content:"\e4d3"}.fa-magnifying-glass-chart:before{content:"\e522"}.fa-arrow-up-right-from-square:before,.fa-external-link:before{content:"\f08e"}.fa-cubes-stacked:before{content:"\e4e6"}.fa-krw:before,.fa-won-sign:before,.fa-won:before{content:"\f159"}.fa-virus-covid:before{content:"\e4a8"}.fa-austral-sign:before{content:"\e0a9"}.fa-f:before{content:"\46"}.fa-leaf:before{content:"\f06c"}.fa-road:before{content:"\f018"}.fa-cab:before,.fa-taxi:before{content:"\f1ba"}.fa-person-circle-plus:before{content:"\e541"}.fa-chart-pie:before,.fa-pie-chart:before{content:"\f200"}.fa-bolt-lightning:before{content:"\e0b7"}.fa-sack-xmark:before{content:"\e56a"}.fa-file-excel:before{content:"\f1c3"}.fa-file-contract:before{content:"\f56c"}.fa-fish-fins:before{content:"\e4f2"}.fa-building-flag:before{content:"\e4d5"}.fa-face-grin-beam:before,.fa-grin-beam:before{content:"\f582"}.fa-object-ungroup:before{content:"\f248"}.fa-poop:before{content:"\f619"}.fa-location-pin:before,.fa-map-marker:before{content:"\f041"}.fa-kaaba:before{content:"\f66b"}.fa-toilet-paper:before{content:"\f71e"}.fa-hard-hat:before,.fa-hat-hard:before,.fa-helmet-safety:before{content:"\f807"}.fa-eject:before{content:"\f052"}.fa-arrow-alt-circle-right:before,.fa-circle-right:before{content:"\f35a"}.fa-plane-circle-check:before{content:"\e555"}.fa-face-rolling-eyes:before,.fa-meh-rolling-eyes:before{content:"\f5a5"}.fa-object-group:before{content:"\f247"}.fa-chart-line:before,.fa-line-chart:before{content:"\f201"}.fa-mask-ventilator:before{content:"\e524"}.fa-arrow-right:before{content:"\f061"}.fa-map-signs:before,.fa-signs-post:before{content:"\f277"}.fa-cash-register:before{content:"\f788"}.fa-person-circle-question:before{content:"\e542"}.fa-h:before{content:"\48"}.fa-tarp:before{content:"\e57b"}.fa-screwdriver-wrench:before,.fa-tools:before{content:"\f7d9"}.fa-arrows-to-eye:before{content:"\e4bf"}.fa-plug-circle-bolt:before{content:"\e55b"}.fa-heart:before{content:"\f004"}.fa-mars-and-venus:before{content:"\f224"}.fa-home-user:before,.fa-house-user:before{content:"\e1b0"}.fa-dumpster-fire:before{content:"\f794"}.fa-house-crack:before{content:"\e3b1"}.fa-cocktail:before,.fa-martini-glass-citrus:before{content:"\f561"}.fa-face-surprise:before,.fa-surprise:before{content:"\f5c2"}.fa-bottle-water:before{content:"\e4c5"}.fa-circle-pause:before,.fa-pause-circle:before{content:"\f28b"}.fa-toilet-paper-slash:before{content:"\e072"}.fa-apple-alt:before,.fa-apple-whole:before{content:"\f5d1"}.fa-kitchen-set:before{content:"\e51a"}.fa-r:before{content:"\52"}.fa-temperature-1:before,.fa-temperature-quarter:before,.fa-thermometer-1:before,.fa-thermometer-quarter:before{content:"\f2ca"}.fa-cube:before{content:"\f1b2"}.fa-bitcoin-sign:before{content:"\e0b4"}.fa-shield-dog:before{content:"\e573"}.fa-solar-panel:before{content:"\f5ba"}.fa-lock-open:before{content:"\f3c1"}.fa-elevator:before{content:"\e16d"}.fa-money-bill-transfer:before{content:"\e528"}.fa-money-bill-trend-up:before{content:"\e529"}.fa-house-flood-water-circle-arrow-right:before{content:"\e50f"}.fa-poll-h:before,.fa-square-poll-horizontal:before{content:"\f682"}.fa-circle:before{content:"\f111"}.fa-backward-fast:before,.fa-fast-backward:before{content:"\f049"}.fa-recycle:before{content:"\f1b8"}.fa-user-astronaut:before{content:"\f4fb"}.fa-plane-slash:before{content:"\e069"}.fa-trademark:before{content:"\f25c"}.fa-basketball-ball:before,.fa-basketball:before{content:"\f434"}.fa-satellite-dish:before{content:"\f7c0"}.fa-arrow-alt-circle-up:before,.fa-circle-up:before{content:"\f35b"}.fa-mobile-alt:before,.fa-mobile-screen-button:before{content:"\f3cd"}.fa-volume-high:before,.fa-volume-up:before{content:"\f028"}.fa-users-rays:before{content:"\e593"}.fa-wallet:before{content:"\f555"}.fa-clipboard-check:before{content:"\f46c"}.fa-file-audio:before{content:"\f1c7"}.fa-burger:before,.fa-hamburger:before{content:"\f805"}.fa-wrench:before{content:"\f0ad"}.fa-bugs:before{content:"\e4d0"}.fa-rupee-sign:before,.fa-rupee:before{content:"\f156"}.fa-file-image:before{content:"\f1c5"}.fa-circle-question:before,.fa-question-circle:before{content:"\f059"}.fa-plane-departure:before{content:"\f5b0"}.fa-handshake-slash:before{content:"\e060"}.fa-book-bookmark:before{content:"\e0bb"}.fa-code-branch:before{content:"\f126"}.fa-hat-cowboy:before{content:"\f8c0"}.fa-bridge:before{content:"\e4c8"}.fa-phone-alt:before,.fa-phone-flip:before{content:"\f879"}.fa-truck-front:before{content:"\e2b7"}.fa-cat:before{content:"\f6be"}.fa-anchor-circle-exclamation:before{content:"\e4ab"}.fa-truck-field:before{content:"\e58d"}.fa-route:before{content:"\f4d7"}.fa-clipboard-question:before{content:"\e4e3"}.fa-panorama:before{content:"\e209"}.fa-comment-medical:before{content:"\f7f5"}.fa-teeth-open:before{content:"\f62f"}.fa-file-circle-minus:before{content:"\e4ed"}.fa-tags:before{content:"\f02c"}.fa-wine-glass:before{content:"\f4e3"}.fa-fast-forward:before,.fa-forward-fast:before{content:"\f050"}.fa-face-meh-blank:before,.fa-meh-blank:before{content:"\f5a4"}.fa-parking:before,.fa-square-parking:before{content:"\f540"}.fa-house-signal:before{content:"\e012"}.fa-bars-progress:before,.fa-tasks-alt:before{content:"\f828"}.fa-faucet-drip:before{content:"\e006"}.fa-cart-flatbed:before,.fa-dolly-flatbed:before{content:"\f474"}.fa-ban-smoking:before,.fa-smoking-ban:before{content:"\f54d"}.fa-terminal:before{content:"\f120"}.fa-mobile-button:before{content:"\f10b"}.fa-house-medical-flag:before{content:"\e514"}.fa-basket-shopping:before,.fa-shopping-basket:before{content:"\f291"}.fa-tape:before{content:"\f4db"}.fa-bus-alt:before,.fa-bus-simple:before{content:"\f55e"}.fa-eye:before{content:"\f06e"}.fa-face-sad-cry:before,.fa-sad-cry:before{content:"\f5b3"}.fa-audio-description:before{content:"\f29e"}.fa-person-military-to-person:before{content:"\e54c"}.fa-file-shield:before{content:"\e4f0"}.fa-user-slash:before{content:"\f506"}.fa-pen:before{content:"\f304"}.fa-tower-observation:before{content:"\e586"}.fa-file-code:before{content:"\f1c9"}.fa-signal-5:before,.fa-signal-perfect:before,.fa-signal:before{content:"\f012"}.fa-bus:before{content:"\f207"}.fa-heart-circle-xmark:before{content:"\e501"}.fa-home-lg:before,.fa-house-chimney:before{content:"\e3af"}.fa-window-maximize:before{content:"\f2d0"}.fa-face-frown:before,.fa-frown:before{content:"\f119"}.fa-prescription:before{content:"\f5b1"}.fa-shop:before,.fa-store-alt:before{content:"\f54f"}.fa-floppy-disk:before,.fa-save:before{content:"\f0c7"}.fa-vihara:before{content:"\f6a7"}.fa-balance-scale-left:before,.fa-scale-unbalanced:before{content:"\f515"}.fa-sort-asc:before,.fa-sort-up:before{content:"\f0de"}.fa-comment-dots:before,.fa-commenting:before{content:"\f4ad"}.fa-plant-wilt:before{content:"\e5aa"}.fa-diamond:before{content:"\f219"}.fa-face-grin-squint:before,.fa-grin-squint:before{content:"\f585"}.fa-hand-holding-dollar:before,.fa-hand-holding-usd:before{content:"\f4c0"}.fa-bacterium:before{content:"\e05a"}.fa-hand-pointer:before{content:"\f25a"}.fa-drum-steelpan:before{content:"\f56a"}.fa-hand-scissors:before{content:"\f257"}.fa-hands-praying:before,.fa-praying-hands:before{content:"\f684"}.fa-arrow-right-rotate:before,.fa-arrow-rotate-forward:before,.fa-arrow-rotate-right:before,.fa-redo:before{content:"\f01e"}.fa-biohazard:before{content:"\f780"}.fa-location-crosshairs:before,.fa-location:before{content:"\f601"}.fa-mars-double:before{content:"\f227"}.fa-child-dress:before{content:"\e59c"}.fa-users-between-lines:before{content:"\e591"}.fa-lungs-virus:before{content:"\e067"}.fa-face-grin-tears:before,.fa-grin-tears:before{content:"\f588"}.fa-phone:before{content:"\f095"}.fa-calendar-times:before,.fa-calendar-xmark:before{content:"\f273"}.fa-child-reaching:before{content:"\e59d"}.fa-head-side-virus:before{content:"\e064"}.fa-user-cog:before,.fa-user-gear:before{content:"\f4fe"}.fa-arrow-up-1-9:before,.fa-sort-numeric-up:before{content:"\f163"}.fa-door-closed:before{content:"\f52a"}.fa-shield-virus:before{content:"\e06c"}.fa-dice-six:before{content:"\f526"}.fa-mosquito-net:before{content:"\e52c"}.fa-bridge-water:before{content:"\e4ce"}.fa-person-booth:before{content:"\f756"}.fa-text-width:before{content:"\f035"}.fa-hat-wizard:before{content:"\f6e8"}.fa-pen-fancy:before{content:"\f5ac"}.fa-digging:before,.fa-person-digging:before{content:"\f85e"}.fa-trash:before{content:"\f1f8"}.fa-gauge-simple-med:before,.fa-gauge-simple:before,.fa-tachometer-average:before{content:"\f629"}.fa-book-medical:before{content:"\f7e6"}.fa-poo:before{content:"\f2fe"}.fa-quote-right-alt:before,.fa-quote-right:before{content:"\f10e"}.fa-shirt:before,.fa-t-shirt:before,.fa-tshirt:before{content:"\f553"}.fa-cubes:before{content:"\f1b3"}.fa-divide:before{content:"\f529"}.fa-tenge-sign:before,.fa-tenge:before{content:"\f7d7"}.fa-headphones:before{content:"\f025"}.fa-hands-holding:before{content:"\f4c2"}.fa-hands-clapping:before{content:"\e1a8"}.fa-republican:before{content:"\f75e"}.fa-arrow-left:before{content:"\f060"}.fa-person-circle-xmark:before{content:"\e543"}.fa-ruler:before{content:"\f545"}.fa-align-left:before{content:"\f036"}.fa-dice-d6:before{content:"\f6d1"}.fa-restroom:before{content:"\f7bd"}.fa-j:before{content:"\4a"}.fa-users-viewfinder:before{content:"\e595"}.fa-file-video:before{content:"\f1c8"}.fa-external-link-alt:before,.fa-up-right-from-square:before{content:"\f35d"}.fa-table-cells:before,.fa-th:before{content:"\f00a"}.fa-file-pdf:before{content:"\f1c1"}.fa-bible:before,.fa-book-bible:before{content:"\f647"}.fa-o:before{content:"\4f"}.fa-medkit:before,.fa-suitcase-medical:before{content:"\f0fa"}.fa-user-secret:before{content:"\f21b"}.fa-otter:before{content:"\f700"}.fa-female:before,.fa-person-dress:before{content:"\f182"}.fa-comment-dollar:before{content:"\f651"}.fa-briefcase-clock:before,.fa-business-time:before{content:"\f64a"}.fa-table-cells-large:before,.fa-th-large:before{content:"\f009"}.fa-book-tanakh:before,.fa-tanakh:before{content:"\f827"}.fa-phone-volume:before,.fa-volume-control-phone:before{content:"\f2a0"}.fa-hat-cowboy-side:before{content:"\f8c1"}.fa-clipboard-user:before{content:"\f7f3"}.fa-child:before{content:"\f1ae"}.fa-lira-sign:before{content:"\f195"}.fa-satellite:before{content:"\f7bf"}.fa-plane-lock:before{content:"\e558"}.fa-tag:before{content:"\f02b"}.fa-comment:before{content:"\f075"}.fa-birthday-cake:before,.fa-cake-candles:before,.fa-cake:before{content:"\f1fd"}.fa-envelope:before{content:"\f0e0"}.fa-angle-double-up:before,.fa-angles-up:before{content:"\f102"}.fa-paperclip:before{content:"\f0c6"}.fa-arrow-right-to-city:before{content:"\e4b3"}.fa-ribbon:before{content:"\f4d6"}.fa-lungs:before{content:"\f604"}.fa-arrow-up-9-1:before,.fa-sort-numeric-up-alt:before{content:"\f887"}.fa-litecoin-sign:before{content:"\e1d3"}.fa-border-none:before{content:"\f850"}.fa-circle-nodes:before{content:"\e4e2"}.fa-parachute-box:before{content:"\f4cd"}.fa-indent:before{content:"\f03c"}.fa-truck-field-un:before{content:"\e58e"}.fa-hourglass-empty:before,.fa-hourglass:before{content:"\f254"}.fa-mountain:before{content:"\f6fc"}.fa-user-doctor:before,.fa-user-md:before{content:"\f0f0"}.fa-circle-info:before,.fa-info-circle:before{content:"\f05a"}.fa-cloud-meatball:before{content:"\f73b"}.fa-camera-alt:before,.fa-camera:before{content:"\f030"}.fa-square-virus:before{content:"\e578"}.fa-meteor:before{content:"\f753"}.fa-car-on:before{content:"\e4dd"}.fa-sleigh:before{content:"\f7cc"}.fa-arrow-down-1-9:before,.fa-sort-numeric-asc:before,.fa-sort-numeric-down:before{content:"\f162"}.fa-hand-holding-droplet:before,.fa-hand-holding-water:before{content:"\f4c1"}.fa-water:before{content:"\f773"}.fa-calendar-check:before{content:"\f274"}.fa-braille:before{content:"\f2a1"}.fa-prescription-bottle-alt:before,.fa-prescription-bottle-medical:before{content:"\f486"}.fa-landmark:before{content:"\f66f"}.fa-truck:before{content:"\f0d1"}.fa-crosshairs:before{content:"\f05b"}.fa-person-cane:before{content:"\e53c"}.fa-tent:before{content:"\e57d"}.fa-vest-patches:before{content:"\e086"}.fa-check-double:before{content:"\f560"}.fa-arrow-down-a-z:before,.fa-sort-alpha-asc:before,.fa-sort-alpha-down:before{content:"\f15d"}.fa-money-bill-wheat:before{content:"\e52a"}.fa-cookie:before{content:"\f563"}.fa-arrow-left-rotate:before,.fa-arrow-rotate-back:before,.fa-arrow-rotate-backward:before,.fa-arrow-rotate-left:before,.fa-undo:before{content:"\f0e2"}.fa-hard-drive:before,.fa-hdd:before{content:"\f0a0"}.fa-face-grin-squint-tears:before,.fa-grin-squint-tears:before{content:"\f586"}.fa-dumbbell:before{content:"\f44b"}.fa-list-alt:before,.fa-rectangle-list:before{content:"\f022"}.fa-tarp-droplet:before{content:"\e57c"}.fa-house-medical-circle-check:before{content:"\e511"}.fa-person-skiing-nordic:before,.fa-skiing-nordic:before{content:"\f7ca"}.fa-calendar-plus:before{content:"\f271"}.fa-plane-arrival:before{content:"\f5af"}.fa-arrow-alt-circle-left:before,.fa-circle-left:before{content:"\f359"}.fa-subway:before,.fa-train-subway:before{content:"\f239"}.fa-chart-gantt:before{content:"\e0e4"}.fa-indian-rupee-sign:before,.fa-indian-rupee:before,.fa-inr:before{content:"\e1bc"}.fa-crop-alt:before,.fa-crop-simple:before{content:"\f565"}.fa-money-bill-1:before,.fa-money-bill-alt:before{content:"\f3d1"}.fa-left-long:before,.fa-long-arrow-alt-left:before{content:"\f30a"}.fa-dna:before{content:"\f471"}.fa-virus-slash:before{content:"\e075"}.fa-minus:before,.fa-subtract:before{content:"\f068"}.fa-chess:before{content:"\f439"}.fa-arrow-left-long:before,.fa-long-arrow-left:before{content:"\f177"}.fa-plug-circle-check:before{content:"\e55c"}.fa-street-view:before{content:"\f21d"}.fa-franc-sign:before{content:"\e18f"}.fa-volume-off:before{content:"\f026"}.fa-american-sign-language-interpreting:before,.fa-asl-interpreting:before,.fa-hands-american-sign-language-interpreting:before,.fa-hands-asl-interpreting:before{content:"\f2a3"}.fa-cog:before,.fa-gear:before{content:"\f013"}.fa-droplet-slash:before,.fa-tint-slash:before{content:"\f5c7"}.fa-mosque:before{content:"\f678"}.fa-mosquito:before{content:"\e52b"}.fa-star-of-david:before{content:"\f69a"}.fa-person-military-rifle:before{content:"\e54b"}.fa-cart-shopping:before,.fa-shopping-cart:before{content:"\f07a"}.fa-vials:before{content:"\f493"}.fa-plug-circle-plus:before{content:"\e55f"}.fa-place-of-worship:before{content:"\f67f"}.fa-grip-vertical:before{content:"\f58e"}.fa-arrow-turn-up:before,.fa-level-up:before{content:"\f148"}.fa-u:before{content:"\55"}.fa-square-root-alt:before,.fa-square-root-variable:before{content:"\f698"}.fa-clock-four:before,.fa-clock:before{content:"\f017"}.fa-backward-step:before,.fa-step-backward:before{content:"\f048"}.fa-pallet:before{content:"\f482"}.fa-faucet:before{content:"\e005"}.fa-baseball-bat-ball:before{content:"\f432"}.fa-s:before{content:"\53"}.fa-timeline:before{content:"\e29c"}.fa-keyboard:before{content:"\f11c"}.fa-caret-down:before{content:"\f0d7"}.fa-clinic-medical:before,.fa-house-chimney-medical:before{content:"\f7f2"}.fa-temperature-3:before,.fa-temperature-three-quarters:before,.fa-thermometer-3:before,.fa-thermometer-three-quarters:before{content:"\f2c8"}.fa-mobile-android-alt:before,.fa-mobile-screen:before{content:"\f3cf"}.fa-plane-up:before{content:"\e22d"}.fa-piggy-bank:before{content:"\f4d3"}.fa-battery-3:before,.fa-battery-half:before{content:"\f242"}.fa-mountain-city:before{content:"\e52e"}.fa-coins:before{content:"\f51e"}.fa-khanda:before{content:"\f66d"}.fa-sliders-h:before,.fa-sliders:before{content:"\f1de"}.fa-folder-tree:before{content:"\f802"}.fa-network-wired:before{content:"\f6ff"}.fa-map-pin:before{content:"\f276"}.fa-hamsa:before{content:"\f665"}.fa-cent-sign:before{content:"\e3f5"}.fa-flask:before{content:"\f0c3"}.fa-person-pregnant:before{content:"\e31e"}.fa-wand-sparkles:before{content:"\f72b"}.fa-ellipsis-v:before,.fa-ellipsis-vertical:before{content:"\f142"}.fa-ticket:before{content:"\f145"}.fa-power-off:before{content:"\f011"}.fa-long-arrow-alt-right:before,.fa-right-long:before{content:"\f30b"}.fa-flag-usa:before{content:"\f74d"}.fa-laptop-file:before{content:"\e51d"}.fa-teletype:before,.fa-tty:before{content:"\f1e4"}.fa-diagram-next:before{content:"\e476"}.fa-person-rifle:before{content:"\e54e"}.fa-house-medical-circle-exclamation:before{content:"\e512"}.fa-closed-captioning:before{content:"\f20a"}.fa-hiking:before,.fa-person-hiking:before{content:"\f6ec"}.fa-venus-double:before{content:"\f226"}.fa-images:before{content:"\f302"}.fa-calculator:before{content:"\f1ec"}.fa-people-pulling:before{content:"\e535"}.fa-n:before{content:"\4e"}.fa-cable-car:before,.fa-tram:before{content:"\f7da"}.fa-cloud-rain:before{content:"\f73d"}.fa-building-circle-xmark:before{content:"\e4d4"}.fa-ship:before{content:"\f21a"}.fa-arrows-down-to-line:before{content:"\e4b8"}.fa-download:before{content:"\f019"}.fa-face-grin:before,.fa-grin:before{content:"\f580"}.fa-backspace:before,.fa-delete-left:before{content:"\f55a"}.fa-eye-dropper-empty:before,.fa-eye-dropper:before,.fa-eyedropper:before{content:"\f1fb"}.fa-file-circle-check:before{content:"\e5a0"}.fa-forward:before{content:"\f04e"}.fa-mobile-android:before,.fa-mobile-phone:before,.fa-mobile:before{content:"\f3ce"}.fa-face-meh:before,.fa-meh:before{content:"\f11a"}.fa-align-center:before{content:"\f037"}.fa-book-dead:before,.fa-book-skull:before{content:"\f6b7"}.fa-drivers-license:before,.fa-id-card:before{content:"\f2c2"}.fa-dedent:before,.fa-outdent:before{content:"\f03b"}.fa-heart-circle-exclamation:before{content:"\e4fe"}.fa-home-alt:before,.fa-home-lg-alt:before,.fa-home:before,.fa-house:before{content:"\f015"}.fa-calendar-week:before{content:"\f784"}.fa-laptop-medical:before{content:"\f812"}.fa-b:before{content:"\42"}.fa-file-medical:before{content:"\f477"}.fa-dice-one:before{content:"\f525"}.fa-kiwi-bird:before{content:"\f535"}.fa-arrow-right-arrow-left:before,.fa-exchange:before{content:"\f0ec"}.fa-redo-alt:before,.fa-rotate-forward:before,.fa-rotate-right:before{content:"\f2f9"}.fa-cutlery:before,.fa-utensils:before{content:"\f2e7"}.fa-arrow-up-wide-short:before,.fa-sort-amount-up:before{content:"\f161"}.fa-mill-sign:before{content:"\e1ed"}.fa-bowl-rice:before{content:"\e2eb"}.fa-skull:before{content:"\f54c"}.fa-broadcast-tower:before,.fa-tower-broadcast:before{content:"\f519"}.fa-truck-pickup:before{content:"\f63c"}.fa-long-arrow-alt-up:before,.fa-up-long:before{content:"\f30c"}.fa-stop:before{content:"\f04d"}.fa-code-merge:before{content:"\f387"}.fa-upload:before{content:"\f093"}.fa-hurricane:before{content:"\f751"}.fa-mound:before{content:"\e52d"}.fa-toilet-portable:before{content:"\e583"}.fa-compact-disc:before{content:"\f51f"}.fa-file-arrow-down:before,.fa-file-download:before{content:"\f56d"}.fa-caravan:before{content:"\f8ff"}.fa-shield-cat:before{content:"\e572"}.fa-bolt:before,.fa-zap:before{content:"\f0e7"}.fa-glass-water:before{content:"\e4f4"}.fa-oil-well:before{content:"\e532"}.fa-vault:before{content:"\e2c5"}.fa-mars:before{content:"\f222"}.fa-toilet:before{content:"\f7d8"}.fa-plane-circle-xmark:before{content:"\e557"}.fa-cny:before,.fa-jpy:before,.fa-rmb:before,.fa-yen-sign:before,.fa-yen:before{content:"\f157"}.fa-rouble:before,.fa-rub:before,.fa-ruble-sign:before,.fa-ruble:before{content:"\f158"}.fa-sun:before{content:"\f185"}.fa-guitar:before{content:"\f7a6"}.fa-face-laugh-wink:before,.fa-laugh-wink:before{content:"\f59c"}.fa-horse-head:before{content:"\f7ab"}.fa-bore-hole:before{content:"\e4c3"}.fa-industry:before{content:"\f275"}.fa-arrow-alt-circle-down:before,.fa-circle-down:before{content:"\f358"}.fa-arrows-turn-to-dots:before{content:"\e4c1"}.fa-florin-sign:before{content:"\e184"}.fa-arrow-down-short-wide:before,.fa-sort-amount-desc:before,.fa-sort-amount-down-alt:before{content:"\f884"}.fa-less-than:before{content:"\3c"}.fa-angle-down:before{content:"\f107"}.fa-car-tunnel:before{content:"\e4de"}.fa-head-side-cough:before{content:"\e061"}.fa-grip-lines:before{content:"\f7a4"}.fa-thumbs-down:before{content:"\f165"}.fa-user-lock:before{content:"\f502"}.fa-arrow-right-long:before,.fa-long-arrow-right:before{content:"\f178"}.fa-anchor-circle-xmark:before{content:"\e4ac"}.fa-ellipsis-h:before,.fa-ellipsis:before{content:"\f141"}.fa-chess-pawn:before{content:"\f443"}.fa-first-aid:before,.fa-kit-medical:before{content:"\f479"}.fa-person-through-window:before{content:"\e5a9"}.fa-toolbox:before{content:"\f552"}.fa-hands-holding-circle:before{content:"\e4fb"}.fa-bug:before{content:"\f188"}.fa-credit-card-alt:before,.fa-credit-card:before{content:"\f09d"}.fa-automobile:before,.fa-car:before{content:"\f1b9"}.fa-hand-holding-hand:before{content:"\e4f7"}.fa-book-open-reader:before,.fa-book-reader:before{content:"\f5da"}.fa-mountain-sun:before{content:"\e52f"}.fa-arrows-left-right-to-line:before{content:"\e4ba"}.fa-dice-d20:before{content:"\f6cf"}.fa-truck-droplet:before{content:"\e58c"}.fa-file-circle-xmark:before{content:"\e5a1"}.fa-temperature-arrow-up:before,.fa-temperature-up:before{content:"\e040"}.fa-medal:before{content:"\f5a2"}.fa-bed:before{content:"\f236"}.fa-h-square:before,.fa-square-h:before{content:"\f0fd"}.fa-podcast:before{content:"\f2ce"}.fa-temperature-4:before,.fa-temperature-full:before,.fa-thermometer-4:before,.fa-thermometer-full:before{content:"\f2c7"}.fa-bell:before{content:"\f0f3"}.fa-superscript:before{content:"\f12b"}.fa-plug-circle-xmark:before{content:"\e560"}.fa-star-of-life:before{content:"\f621"}.fa-phone-slash:before{content:"\f3dd"}.fa-paint-roller:before{content:"\f5aa"}.fa-hands-helping:before,.fa-handshake-angle:before{content:"\f4c4"}.fa-location-dot:before,.fa-map-marker-alt:before{content:"\f3c5"}.fa-file:before{content:"\f15b"}.fa-greater-than:before{content:"\3e"}.fa-person-swimming:before,.fa-swimmer:before{content:"\f5c4"}.fa-arrow-down:before{content:"\f063"}.fa-droplet:before,.fa-tint:before{content:"\f043"}.fa-eraser:before{content:"\f12d"}.fa-earth-america:before,.fa-earth-americas:before,.fa-earth:before,.fa-globe-americas:before{content:"\f57d"}.fa-person-burst:before{content:"\e53b"}.fa-dove:before{content:"\f4ba"}.fa-battery-0:before,.fa-battery-empty:before{content:"\f244"}.fa-socks:before{content:"\f696"}.fa-inbox:before{content:"\f01c"}.fa-section:before{content:"\e447"}.fa-gauge-high:before,.fa-tachometer-alt-fast:before,.fa-tachometer-alt:before{content:"\f625"}.fa-envelope-open-text:before{content:"\f658"}.fa-hospital-alt:before,.fa-hospital-wide:before,.fa-hospital:before{content:"\f0f8"}.fa-wine-bottle:before{content:"\f72f"}.fa-chess-rook:before{content:"\f447"}.fa-bars-staggered:before,.fa-reorder:before,.fa-stream:before{content:"\f550"}.fa-dharmachakra:before{content:"\f655"}.fa-hotdog:before{content:"\f80f"}.fa-blind:before,.fa-person-walking-with-cane:before{content:"\f29d"}.fa-drum:before{content:"\f569"}.fa-ice-cream:before{content:"\f810"}.fa-heart-circle-bolt:before{content:"\e4fc"}.fa-fax:before{content:"\f1ac"}.fa-paragraph:before{content:"\f1dd"}.fa-check-to-slot:before,.fa-vote-yea:before{content:"\f772"}.fa-star-half:before{content:"\f089"}.fa-boxes-alt:before,.fa-boxes-stacked:before,.fa-boxes:before{content:"\f468"}.fa-chain:before,.fa-link:before{content:"\f0c1"}.fa-assistive-listening-systems:before,.fa-ear-listen:before{content:"\f2a2"}.fa-tree-city:before{content:"\e587"}.fa-play:before{content:"\f04b"}.fa-font:before{content:"\f031"}.fa-table-cells-row-lock:before{content:"\e67a"}.fa-rupiah-sign:before{content:"\e23d"}.fa-magnifying-glass:before,.fa-search:before{content:"\f002"}.fa-ping-pong-paddle-ball:before,.fa-table-tennis-paddle-ball:before,.fa-table-tennis:before{content:"\f45d"}.fa-diagnoses:before,.fa-person-dots-from-line:before{content:"\f470"}.fa-trash-can-arrow-up:before,.fa-trash-restore-alt:before{content:"\f82a"}.fa-naira-sign:before{content:"\e1f6"}.fa-cart-arrow-down:before{content:"\f218"}.fa-walkie-talkie:before{content:"\f8ef"}.fa-file-edit:before,.fa-file-pen:before{content:"\f31c"}.fa-receipt:before{content:"\f543"}.fa-pen-square:before,.fa-pencil-square:before,.fa-square-pen:before{content:"\f14b"}.fa-suitcase-rolling:before{content:"\f5c1"}.fa-person-circle-exclamation:before{content:"\e53f"}.fa-chevron-down:before{content:"\f078"}.fa-battery-5:before,.fa-battery-full:before,.fa-battery:before{content:"\f240"}.fa-skull-crossbones:before{content:"\f714"}.fa-code-compare:before{content:"\e13a"}.fa-list-dots:before,.fa-list-ul:before{content:"\f0ca"}.fa-school-lock:before{content:"\e56f"}.fa-tower-cell:before{content:"\e585"}.fa-down-long:before,.fa-long-arrow-alt-down:before{content:"\f309"}.fa-ranking-star:before{content:"\e561"}.fa-chess-king:before{content:"\f43f"}.fa-person-harassing:before{content:"\e549"}.fa-brazilian-real-sign:before{content:"\e46c"}.fa-landmark-alt:before,.fa-landmark-dome:before{content:"\f752"}.fa-arrow-up:before{content:"\f062"}.fa-television:before,.fa-tv-alt:before,.fa-tv:before{content:"\f26c"}.fa-shrimp:before{content:"\e448"}.fa-list-check:before,.fa-tasks:before{content:"\f0ae"}.fa-jug-detergent:before{content:"\e519"}.fa-circle-user:before,.fa-user-circle:before{content:"\f2bd"}.fa-user-shield:before{content:"\f505"}.fa-wind:before{content:"\f72e"}.fa-car-burst:before,.fa-car-crash:before{content:"\f5e1"}.fa-y:before{content:"\59"}.fa-person-snowboarding:before,.fa-snowboarding:before{content:"\f7ce"}.fa-shipping-fast:before,.fa-truck-fast:before{content:"\f48b"}.fa-fish:before{content:"\f578"}.fa-user-graduate:before{content:"\f501"}.fa-adjust:before,.fa-circle-half-stroke:before{content:"\f042"}.fa-clapperboard:before{content:"\e131"}.fa-circle-radiation:before,.fa-radiation-alt:before{content:"\f7ba"}.fa-baseball-ball:before,.fa-baseball:before{content:"\f433"}.fa-jet-fighter-up:before{content:"\e518"}.fa-diagram-project:before,.fa-project-diagram:before{content:"\f542"}.fa-copy:before{content:"\f0c5"}.fa-volume-mute:before,.fa-volume-times:before,.fa-volume-xmark:before{content:"\f6a9"}.fa-hand-sparkles:before{content:"\e05d"}.fa-grip-horizontal:before,.fa-grip:before{content:"\f58d"}.fa-share-from-square:before,.fa-share-square:before{content:"\f14d"}.fa-child-combatant:before,.fa-child-rifle:before{content:"\e4e0"}.fa-gun:before{content:"\e19b"}.fa-phone-square:before,.fa-square-phone:before{content:"\f098"}.fa-add:before,.fa-plus:before{content:"\2b"}.fa-expand:before{content:"\f065"}.fa-computer:before{content:"\e4e5"}.fa-close:before,.fa-multiply:before,.fa-remove:before,.fa-times:before,.fa-xmark:before{content:"\f00d"}.fa-arrows-up-down-left-right:before,.fa-arrows:before{content:"\f047"}.fa-chalkboard-teacher:before,.fa-chalkboard-user:before{content:"\f51c"}.fa-peso-sign:before{content:"\e222"}.fa-building-shield:before{content:"\e4d8"}.fa-baby:before{content:"\f77c"}.fa-users-line:before{content:"\e592"}.fa-quote-left-alt:before,.fa-quote-left:before{content:"\f10d"}.fa-tractor:before{content:"\f722"}.fa-trash-arrow-up:before,.fa-trash-restore:before{content:"\f829"}.fa-arrow-down-up-lock:before{content:"\e4b0"}.fa-lines-leaning:before{content:"\e51e"}.fa-ruler-combined:before{content:"\f546"}.fa-copyright:before{content:"\f1f9"}.fa-equals:before{content:"\3d"}.fa-blender:before{content:"\f517"}.fa-teeth:before{content:"\f62e"}.fa-ils:before,.fa-shekel-sign:before,.fa-shekel:before,.fa-sheqel-sign:before,.fa-sheqel:before{content:"\f20b"}.fa-map:before{content:"\f279"}.fa-rocket:before{content:"\f135"}.fa-photo-film:before,.fa-photo-video:before{content:"\f87c"}.fa-folder-minus:before{content:"\f65d"}.fa-store:before{content:"\f54e"}.fa-arrow-trend-up:before{content:"\e098"}.fa-plug-circle-minus:before{content:"\e55e"}.fa-sign-hanging:before,.fa-sign:before{content:"\f4d9"}.fa-bezier-curve:before{content:"\f55b"}.fa-bell-slash:before{content:"\f1f6"}.fa-tablet-android:before,.fa-tablet:before{content:"\f3fb"}.fa-school-flag:before{content:"\e56e"}.fa-fill:before{content:"\f575"}.fa-angle-up:before{content:"\f106"}.fa-drumstick-bite:before{content:"\f6d7"}.fa-holly-berry:before{content:"\f7aa"}.fa-chevron-left:before{content:"\f053"}.fa-bacteria:before{content:"\e059"}.fa-hand-lizard:before{content:"\f258"}.fa-notdef:before{content:"\e1fe"}.fa-disease:before{content:"\f7fa"}.fa-briefcase-medical:before{content:"\f469"}.fa-genderless:before{content:"\f22d"}.fa-chevron-right:before{content:"\f054"}.fa-retweet:before{content:"\f079"}.fa-car-alt:before,.fa-car-rear:before{content:"\f5de"}.fa-pump-soap:before{content:"\e06b"}.fa-video-slash:before{content:"\f4e2"}.fa-battery-2:before,.fa-battery-quarter:before{content:"\f243"}.fa-radio:before{content:"\f8d7"}.fa-baby-carriage:before,.fa-carriage-baby:before{content:"\f77d"}.fa-traffic-light:before{content:"\f637"}.fa-thermometer:before{content:"\f491"}.fa-vr-cardboard:before{content:"\f729"}.fa-hand-middle-finger:before{content:"\f806"}.fa-percent:before,.fa-percentage:before{content:"\25"}.fa-truck-moving:before{content:"\f4df"}.fa-glass-water-droplet:before{content:"\e4f5"}.fa-display:before{content:"\e163"}.fa-face-smile:before,.fa-smile:before{content:"\f118"}.fa-thumb-tack:before,.fa-thumbtack:before{content:"\f08d"}.fa-trophy:before{content:"\f091"}.fa-person-praying:before,.fa-pray:before{content:"\f683"}.fa-hammer:before{content:"\f6e3"}.fa-hand-peace:before{content:"\f25b"}.fa-rotate:before,.fa-sync-alt:before{content:"\f2f1"}.fa-spinner:before{content:"\f110"}.fa-robot:before{content:"\f544"}.fa-peace:before{content:"\f67c"}.fa-cogs:before,.fa-gears:before{content:"\f085"}.fa-warehouse:before{content:"\f494"}.fa-arrow-up-right-dots:before{content:"\e4b7"}.fa-splotch:before{content:"\f5bc"}.fa-face-grin-hearts:before,.fa-grin-hearts:before{content:"\f584"}.fa-dice-four:before{content:"\f524"}.fa-sim-card:before{content:"\f7c4"}.fa-transgender-alt:before,.fa-transgender:before{content:"\f225"}.fa-mercury:before{content:"\f223"}.fa-arrow-turn-down:before,.fa-level-down:before{content:"\f149"}.fa-person-falling-burst:before{content:"\e547"}.fa-award:before{content:"\f559"}.fa-ticket-alt:before,.fa-ticket-simple:before{content:"\f3ff"}.fa-building:before{content:"\f1ad"}.fa-angle-double-left:before,.fa-angles-left:before{content:"\f100"}.fa-qrcode:before{content:"\f029"}.fa-clock-rotate-left:before,.fa-history:before{content:"\f1da"}.fa-face-grin-beam-sweat:before,.fa-grin-beam-sweat:before{content:"\f583"}.fa-arrow-right-from-file:before,.fa-file-export:before{content:"\f56e"}.fa-shield-blank:before,.fa-shield:before{content:"\f132"}.fa-arrow-up-short-wide:before,.fa-sort-amount-up-alt:before{content:"\f885"}.fa-house-medical:before{content:"\e3b2"}.fa-golf-ball-tee:before,.fa-golf-ball:before{content:"\f450"}.fa-chevron-circle-left:before,.fa-circle-chevron-left:before{content:"\f137"}.fa-house-chimney-window:before{content:"\e00d"}.fa-pen-nib:before{content:"\f5ad"}.fa-tent-arrow-turn-left:before{content:"\e580"}.fa-tents:before{content:"\e582"}.fa-magic:before,.fa-wand-magic:before{content:"\f0d0"}.fa-dog:before{content:"\f6d3"}.fa-carrot:before{content:"\f787"}.fa-moon:before{content:"\f186"}.fa-wine-glass-alt:before,.fa-wine-glass-empty:before{content:"\f5ce"}.fa-cheese:before{content:"\f7ef"}.fa-yin-yang:before{content:"\f6ad"}.fa-music:before{content:"\f001"}.fa-code-commit:before{content:"\f386"}.fa-temperature-low:before{content:"\f76b"}.fa-biking:before,.fa-person-biking:before{content:"\f84a"}.fa-broom:before{content:"\f51a"}.fa-shield-heart:before{content:"\e574"}.fa-gopuram:before{content:"\f664"}.fa-earth-oceania:before,.fa-globe-oceania:before{content:"\e47b"}.fa-square-xmark:before,.fa-times-square:before,.fa-xmark-square:before{content:"\f2d3"}.fa-hashtag:before{content:"\23"}.fa-expand-alt:before,.fa-up-right-and-down-left-from-center:before{content:"\f424"}.fa-oil-can:before{content:"\f613"}.fa-t:before{content:"\54"}.fa-hippo:before{content:"\f6ed"}.fa-chart-column:before{content:"\e0e3"}.fa-infinity:before{content:"\f534"}.fa-vial-circle-check:before{content:"\e596"}.fa-person-arrow-down-to-line:before{content:"\e538"}.fa-voicemail:before{content:"\f897"}.fa-fan:before{content:"\f863"}.fa-person-walking-luggage:before{content:"\e554"}.fa-arrows-alt-v:before,.fa-up-down:before{content:"\f338"}.fa-cloud-moon-rain:before{content:"\f73c"}.fa-calendar:before{content:"\f133"}.fa-trailer:before{content:"\e041"}.fa-bahai:before,.fa-haykal:before{content:"\f666"}.fa-sd-card:before{content:"\f7c2"}.fa-dragon:before{content:"\f6d5"}.fa-shoe-prints:before{content:"\f54b"}.fa-circle-plus:before,.fa-plus-circle:before{content:"\f055"}.fa-face-grin-tongue-wink:before,.fa-grin-tongue-wink:before{content:"\f58b"}.fa-hand-holding:before{content:"\f4bd"}.fa-plug-circle-exclamation:before{content:"\e55d"}.fa-chain-broken:before,.fa-chain-slash:before,.fa-link-slash:before,.fa-unlink:before{content:"\f127"}.fa-clone:before{content:"\f24d"}.fa-person-walking-arrow-loop-left:before{content:"\e551"}.fa-arrow-up-z-a:before,.fa-sort-alpha-up-alt:before{content:"\f882"}.fa-fire-alt:before,.fa-fire-flame-curved:before{content:"\f7e4"}.fa-tornado:before{content:"\f76f"}.fa-file-circle-plus:before{content:"\e494"}.fa-book-quran:before,.fa-quran:before{content:"\f687"}.fa-anchor:before{content:"\f13d"}.fa-border-all:before{content:"\f84c"}.fa-angry:before,.fa-face-angry:before{content:"\f556"}.fa-cookie-bite:before{content:"\f564"}.fa-arrow-trend-down:before{content:"\e097"}.fa-feed:before,.fa-rss:before{content:"\f09e"}.fa-draw-polygon:before{content:"\f5ee"}.fa-balance-scale:before,.fa-scale-balanced:before{content:"\f24e"}.fa-gauge-simple-high:before,.fa-tachometer-fast:before,.fa-tachometer:before{content:"\f62a"}.fa-shower:before{content:"\f2cc"}.fa-desktop-alt:before,.fa-desktop:before{content:"\f390"}.fa-m:before{content:"\4d"}.fa-table-list:before,.fa-th-list:before{content:"\f00b"}.fa-comment-sms:before,.fa-sms:before{content:"\f7cd"}.fa-book:before{content:"\f02d"}.fa-user-plus:before{content:"\f234"}.fa-check:before{content:"\f00c"}.fa-battery-4:before,.fa-battery-three-quarters:before{content:"\f241"}.fa-house-circle-check:before{content:"\e509"}.fa-angle-left:before{content:"\f104"}.fa-diagram-successor:before{content:"\e47a"}.fa-truck-arrow-right:before{content:"\e58b"}.fa-arrows-split-up-and-left:before{content:"\e4bc"}.fa-fist-raised:before,.fa-hand-fist:before{content:"\f6de"}.fa-cloud-moon:before{content:"\f6c3"}.fa-briefcase:before{content:"\f0b1"}.fa-person-falling:before{content:"\e546"}.fa-image-portrait:before,.fa-portrait:before{content:"\f3e0"}.fa-user-tag:before{content:"\f507"}.fa-rug:before{content:"\e569"}.fa-earth-europe:before,.fa-globe-europe:before{content:"\f7a2"}.fa-cart-flatbed-suitcase:before,.fa-luggage-cart:before{content:"\f59d"}.fa-rectangle-times:before,.fa-rectangle-xmark:before,.fa-times-rectangle:before,.fa-window-close:before{content:"\f410"}.fa-baht-sign:before{content:"\e0ac"}.fa-book-open:before{content:"\f518"}.fa-book-journal-whills:before,.fa-journal-whills:before{content:"\f66a"}.fa-handcuffs:before{content:"\e4f8"}.fa-exclamation-triangle:before,.fa-triangle-exclamation:before,.fa-warning:before{content:"\f071"}.fa-database:before{content:"\f1c0"}.fa-mail-forward:before,.fa-share:before{content:"\f064"}.fa-bottle-droplet:before{content:"\e4c4"}.fa-mask-face:before{content:"\e1d7"}.fa-hill-rockslide:before{content:"\e508"}.fa-exchange-alt:before,.fa-right-left:before{content:"\f362"}.fa-paper-plane:before{content:"\f1d8"}.fa-road-circle-exclamation:before{content:"\e565"}.fa-dungeon:before{content:"\f6d9"}.fa-align-right:before{content:"\f038"}.fa-money-bill-1-wave:before,.fa-money-bill-wave-alt:before{content:"\f53b"}.fa-life-ring:before{content:"\f1cd"}.fa-hands:before,.fa-sign-language:before,.fa-signing:before{content:"\f2a7"}.fa-calendar-day:before{content:"\f783"}.fa-ladder-water:before,.fa-swimming-pool:before,.fa-water-ladder:before{content:"\f5c5"}.fa-arrows-up-down:before,.fa-arrows-v:before{content:"\f07d"}.fa-face-grimace:before,.fa-grimace:before{content:"\f57f"}.fa-wheelchair-alt:before,.fa-wheelchair-move:before{content:"\e2ce"}.fa-level-down-alt:before,.fa-turn-down:before{content:"\f3be"}.fa-person-walking-arrow-right:before{content:"\e552"}.fa-envelope-square:before,.fa-square-envelope:before{content:"\f199"}.fa-dice:before{content:"\f522"}.fa-bowling-ball:before{content:"\f436"}.fa-brain:before{content:"\f5dc"}.fa-band-aid:before,.fa-bandage:before{content:"\f462"}.fa-calendar-minus:before{content:"\f272"}.fa-circle-xmark:before,.fa-times-circle:before,.fa-xmark-circle:before{content:"\f057"}.fa-gifts:before{content:"\f79c"}.fa-hotel:before{content:"\f594"}.fa-earth-asia:before,.fa-globe-asia:before{content:"\f57e"}.fa-id-card-alt:before,.fa-id-card-clip:before{content:"\f47f"}.fa-magnifying-glass-plus:before,.fa-search-plus:before{content:"\f00e"}.fa-thumbs-up:before{content:"\f164"}.fa-user-clock:before{content:"\f4fd"}.fa-allergies:before,.fa-hand-dots:before{content:"\f461"}.fa-file-invoice:before{content:"\f570"}.fa-window-minimize:before{content:"\f2d1"}.fa-coffee:before,.fa-mug-saucer:before{content:"\f0f4"}.fa-brush:before{content:"\f55d"}.fa-mask:before{content:"\f6fa"}.fa-magnifying-glass-minus:before,.fa-search-minus:before{content:"\f010"}.fa-ruler-vertical:before{content:"\f548"}.fa-user-alt:before,.fa-user-large:before{content:"\f406"}.fa-train-tram:before{content:"\e5b4"}.fa-user-nurse:before{content:"\f82f"}.fa-syringe:before{content:"\f48e"}.fa-cloud-sun:before{content:"\f6c4"}.fa-stopwatch-20:before{content:"\e06f"}.fa-square-full:before{content:"\f45c"}.fa-magnet:before{content:"\f076"}.fa-jar:before{content:"\e516"}.fa-note-sticky:before,.fa-sticky-note:before{content:"\f249"}.fa-bug-slash:before{content:"\e490"}.fa-arrow-up-from-water-pump:before{content:"\e4b6"}.fa-bone:before{content:"\f5d7"}.fa-user-injured:before{content:"\f728"}.fa-face-sad-tear:before,.fa-sad-tear:before{content:"\f5b4"}.fa-plane:before{content:"\f072"}.fa-tent-arrows-down:before{content:"\e581"}.fa-exclamation:before{content:"\21"}.fa-arrows-spin:before{content:"\e4bb"}.fa-print:before{content:"\f02f"}.fa-try:before,.fa-turkish-lira-sign:before,.fa-turkish-lira:before{content:"\e2bb"}.fa-dollar-sign:before,.fa-dollar:before,.fa-usd:before{content:"\24"}.fa-x:before{content:"\58"}.fa-magnifying-glass-dollar:before,.fa-search-dollar:before{content:"\f688"}.fa-users-cog:before,.fa-users-gear:before{content:"\f509"}.fa-person-military-pointing:before{content:"\e54a"}.fa-bank:before,.fa-building-columns:before,.fa-institution:before,.fa-museum:before,.fa-university:before{content:"\f19c"}.fa-umbrella:before{content:"\f0e9"}.fa-trowel:before{content:"\e589"}.fa-d:before{content:"\44"}.fa-stapler:before{content:"\e5af"}.fa-masks-theater:before,.fa-theater-masks:before{content:"\f630"}.fa-kip-sign:before{content:"\e1c4"}.fa-hand-point-left:before{content:"\f0a5"}.fa-handshake-alt:before,.fa-handshake-simple:before{content:"\f4c6"}.fa-fighter-jet:before,.fa-jet-fighter:before{content:"\f0fb"}.fa-share-alt-square:before,.fa-square-share-nodes:before{content:"\f1e1"}.fa-barcode:before{content:"\f02a"}.fa-plus-minus:before{content:"\e43c"}.fa-video-camera:before,.fa-video:before{content:"\f03d"}.fa-graduation-cap:before,.fa-mortar-board:before{content:"\f19d"}.fa-hand-holding-medical:before{content:"\e05c"}.fa-person-circle-check:before{content:"\e53e"}.fa-level-up-alt:before,.fa-turn-up:before{content:"\f3bf"} +.fa-sr-only,.fa-sr-only-focusable:not(:focus),.sr-only,.sr-only-focusable:not(:focus){position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);white-space:nowrap;border-width:0}:host,:root{--fa-style-family-brands:"Font Awesome 6 Brands";--fa-font-brands:normal 400 1em/1 "Font Awesome 6 Brands"}@font-face{font-family:"Font Awesome 6 Brands";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }.fa-brands,.fab{font-weight:400}.fa-monero:before{content:"\f3d0"}.fa-hooli:before{content:"\f427"}.fa-yelp:before{content:"\f1e9"}.fa-cc-visa:before{content:"\f1f0"}.fa-lastfm:before{content:"\f202"}.fa-shopware:before{content:"\f5b5"}.fa-creative-commons-nc:before{content:"\f4e8"}.fa-aws:before{content:"\f375"}.fa-redhat:before{content:"\f7bc"}.fa-yoast:before{content:"\f2b1"}.fa-cloudflare:before{content:"\e07d"}.fa-ups:before{content:"\f7e0"}.fa-pixiv:before{content:"\e640"}.fa-wpexplorer:before{content:"\f2de"}.fa-dyalog:before{content:"\f399"}.fa-bity:before{content:"\f37a"}.fa-stackpath:before{content:"\f842"}.fa-buysellads:before{content:"\f20d"}.fa-first-order:before{content:"\f2b0"}.fa-modx:before{content:"\f285"}.fa-guilded:before{content:"\e07e"}.fa-vnv:before{content:"\f40b"}.fa-js-square:before,.fa-square-js:before{content:"\f3b9"}.fa-microsoft:before{content:"\f3ca"}.fa-qq:before{content:"\f1d6"}.fa-orcid:before{content:"\f8d2"}.fa-java:before{content:"\f4e4"}.fa-invision:before{content:"\f7b0"}.fa-creative-commons-pd-alt:before{content:"\f4ed"}.fa-centercode:before{content:"\f380"}.fa-glide-g:before{content:"\f2a6"}.fa-drupal:before{content:"\f1a9"}.fa-jxl:before{content:"\e67b"}.fa-hire-a-helper:before{content:"\f3b0"}.fa-creative-commons-by:before{content:"\f4e7"}.fa-unity:before{content:"\e049"}.fa-whmcs:before{content:"\f40d"}.fa-rocketchat:before{content:"\f3e8"}.fa-vk:before{content:"\f189"}.fa-untappd:before{content:"\f405"}.fa-mailchimp:before{content:"\f59e"}.fa-css3-alt:before{content:"\f38b"}.fa-reddit-square:before,.fa-square-reddit:before{content:"\f1a2"}.fa-vimeo-v:before{content:"\f27d"}.fa-contao:before{content:"\f26d"}.fa-square-font-awesome:before{content:"\e5ad"}.fa-deskpro:before{content:"\f38f"}.fa-brave:before{content:"\e63c"}.fa-sistrix:before{content:"\f3ee"}.fa-instagram-square:before,.fa-square-instagram:before{content:"\e055"}.fa-battle-net:before{content:"\f835"}.fa-the-red-yeti:before{content:"\f69d"}.fa-hacker-news-square:before,.fa-square-hacker-news:before{content:"\f3af"}.fa-edge:before{content:"\f282"}.fa-threads:before{content:"\e618"}.fa-napster:before{content:"\f3d2"}.fa-snapchat-square:before,.fa-square-snapchat:before{content:"\f2ad"}.fa-google-plus-g:before{content:"\f0d5"}.fa-artstation:before{content:"\f77a"}.fa-markdown:before{content:"\f60f"}.fa-sourcetree:before{content:"\f7d3"}.fa-google-plus:before{content:"\f2b3"}.fa-diaspora:before{content:"\f791"}.fa-foursquare:before{content:"\f180"}.fa-stack-overflow:before{content:"\f16c"}.fa-github-alt:before{content:"\f113"}.fa-phoenix-squadron:before{content:"\f511"}.fa-pagelines:before{content:"\f18c"}.fa-algolia:before{content:"\f36c"}.fa-red-river:before{content:"\f3e3"}.fa-creative-commons-sa:before{content:"\f4ef"}.fa-safari:before{content:"\f267"}.fa-google:before{content:"\f1a0"}.fa-font-awesome-alt:before,.fa-square-font-awesome-stroke:before{content:"\f35c"}.fa-atlassian:before{content:"\f77b"}.fa-linkedin-in:before{content:"\f0e1"}.fa-digital-ocean:before{content:"\f391"}.fa-nimblr:before{content:"\f5a8"}.fa-chromecast:before{content:"\f838"}.fa-evernote:before{content:"\f839"}.fa-hacker-news:before{content:"\f1d4"}.fa-creative-commons-sampling:before{content:"\f4f0"}.fa-adversal:before{content:"\f36a"}.fa-creative-commons:before{content:"\f25e"}.fa-watchman-monitoring:before{content:"\e087"}.fa-fonticons:before{content:"\f280"}.fa-weixin:before{content:"\f1d7"}.fa-shirtsinbulk:before{content:"\f214"}.fa-codepen:before{content:"\f1cb"}.fa-git-alt:before{content:"\f841"}.fa-lyft:before{content:"\f3c3"}.fa-rev:before{content:"\f5b2"}.fa-windows:before{content:"\f17a"}.fa-wizards-of-the-coast:before{content:"\f730"}.fa-square-viadeo:before,.fa-viadeo-square:before{content:"\f2aa"}.fa-meetup:before{content:"\f2e0"}.fa-centos:before{content:"\f789"}.fa-adn:before{content:"\f170"}.fa-cloudsmith:before{content:"\f384"}.fa-opensuse:before{content:"\e62b"}.fa-pied-piper-alt:before{content:"\f1a8"}.fa-dribbble-square:before,.fa-square-dribbble:before{content:"\f397"}.fa-codiepie:before{content:"\f284"}.fa-node:before{content:"\f419"}.fa-mix:before{content:"\f3cb"}.fa-steam:before{content:"\f1b6"}.fa-cc-apple-pay:before{content:"\f416"}.fa-scribd:before{content:"\f28a"}.fa-debian:before{content:"\e60b"}.fa-openid:before{content:"\f19b"}.fa-instalod:before{content:"\e081"}.fa-expeditedssl:before{content:"\f23e"}.fa-sellcast:before{content:"\f2da"}.fa-square-twitter:before,.fa-twitter-square:before{content:"\f081"}.fa-r-project:before{content:"\f4f7"}.fa-delicious:before{content:"\f1a5"}.fa-freebsd:before{content:"\f3a4"}.fa-vuejs:before{content:"\f41f"}.fa-accusoft:before{content:"\f369"}.fa-ioxhost:before{content:"\f208"}.fa-fonticons-fi:before{content:"\f3a2"}.fa-app-store:before{content:"\f36f"}.fa-cc-mastercard:before{content:"\f1f1"}.fa-itunes-note:before{content:"\f3b5"}.fa-golang:before{content:"\e40f"}.fa-kickstarter:before,.fa-square-kickstarter:before{content:"\f3bb"}.fa-grav:before{content:"\f2d6"}.fa-weibo:before{content:"\f18a"}.fa-uncharted:before{content:"\e084"}.fa-firstdraft:before{content:"\f3a1"}.fa-square-youtube:before,.fa-youtube-square:before{content:"\f431"}.fa-wikipedia-w:before{content:"\f266"}.fa-rendact:before,.fa-wpressr:before{content:"\f3e4"}.fa-angellist:before{content:"\f209"}.fa-galactic-republic:before{content:"\f50c"}.fa-nfc-directional:before{content:"\e530"}.fa-skype:before{content:"\f17e"}.fa-joget:before{content:"\f3b7"}.fa-fedora:before{content:"\f798"}.fa-stripe-s:before{content:"\f42a"}.fa-meta:before{content:"\e49b"}.fa-laravel:before{content:"\f3bd"}.fa-hotjar:before{content:"\f3b1"}.fa-bluetooth-b:before{content:"\f294"}.fa-square-letterboxd:before{content:"\e62e"}.fa-sticker-mule:before{content:"\f3f7"}.fa-creative-commons-zero:before{content:"\f4f3"}.fa-hips:before{content:"\f452"}.fa-behance:before{content:"\f1b4"}.fa-reddit:before{content:"\f1a1"}.fa-discord:before{content:"\f392"}.fa-chrome:before{content:"\f268"}.fa-app-store-ios:before{content:"\f370"}.fa-cc-discover:before{content:"\f1f2"}.fa-wpbeginner:before{content:"\f297"}.fa-confluence:before{content:"\f78d"}.fa-shoelace:before{content:"\e60c"}.fa-mdb:before{content:"\f8ca"}.fa-dochub:before{content:"\f394"}.fa-accessible-icon:before{content:"\f368"}.fa-ebay:before{content:"\f4f4"}.fa-amazon:before{content:"\f270"}.fa-unsplash:before{content:"\e07c"}.fa-yarn:before{content:"\f7e3"}.fa-square-steam:before,.fa-steam-square:before{content:"\f1b7"}.fa-500px:before{content:"\f26e"}.fa-square-vimeo:before,.fa-vimeo-square:before{content:"\f194"}.fa-asymmetrik:before{content:"\f372"}.fa-font-awesome-flag:before,.fa-font-awesome-logo-full:before,.fa-font-awesome:before{content:"\f2b4"}.fa-gratipay:before{content:"\f184"}.fa-apple:before{content:"\f179"}.fa-hive:before{content:"\e07f"}.fa-gitkraken:before{content:"\f3a6"}.fa-keybase:before{content:"\f4f5"}.fa-apple-pay:before{content:"\f415"}.fa-padlet:before{content:"\e4a0"}.fa-amazon-pay:before{content:"\f42c"}.fa-github-square:before,.fa-square-github:before{content:"\f092"}.fa-stumbleupon:before{content:"\f1a4"}.fa-fedex:before{content:"\f797"}.fa-phoenix-framework:before{content:"\f3dc"}.fa-shopify:before{content:"\e057"}.fa-neos:before{content:"\f612"}.fa-square-threads:before{content:"\e619"}.fa-hackerrank:before{content:"\f5f7"}.fa-researchgate:before{content:"\f4f8"}.fa-swift:before{content:"\f8e1"}.fa-angular:before{content:"\f420"}.fa-speakap:before{content:"\f3f3"}.fa-angrycreative:before{content:"\f36e"}.fa-y-combinator:before{content:"\f23b"}.fa-empire:before{content:"\f1d1"}.fa-envira:before{content:"\f299"}.fa-google-scholar:before{content:"\e63b"}.fa-gitlab-square:before,.fa-square-gitlab:before{content:"\e5ae"}.fa-studiovinari:before{content:"\f3f8"}.fa-pied-piper:before{content:"\f2ae"}.fa-wordpress:before{content:"\f19a"}.fa-product-hunt:before{content:"\f288"}.fa-firefox:before{content:"\f269"}.fa-linode:before{content:"\f2b8"}.fa-goodreads:before{content:"\f3a8"}.fa-odnoklassniki-square:before,.fa-square-odnoklassniki:before{content:"\f264"}.fa-jsfiddle:before{content:"\f1cc"}.fa-sith:before{content:"\f512"}.fa-themeisle:before{content:"\f2b2"}.fa-page4:before{content:"\f3d7"}.fa-hashnode:before{content:"\e499"}.fa-react:before{content:"\f41b"}.fa-cc-paypal:before{content:"\f1f4"}.fa-squarespace:before{content:"\f5be"}.fa-cc-stripe:before{content:"\f1f5"}.fa-creative-commons-share:before{content:"\f4f2"}.fa-bitcoin:before{content:"\f379"}.fa-keycdn:before{content:"\f3ba"}.fa-opera:before{content:"\f26a"}.fa-itch-io:before{content:"\f83a"}.fa-umbraco:before{content:"\f8e8"}.fa-galactic-senate:before{content:"\f50d"}.fa-ubuntu:before{content:"\f7df"}.fa-draft2digital:before{content:"\f396"}.fa-stripe:before{content:"\f429"}.fa-houzz:before{content:"\f27c"}.fa-gg:before{content:"\f260"}.fa-dhl:before{content:"\f790"}.fa-pinterest-square:before,.fa-square-pinterest:before{content:"\f0d3"}.fa-xing:before{content:"\f168"}.fa-blackberry:before{content:"\f37b"}.fa-creative-commons-pd:before{content:"\f4ec"}.fa-playstation:before{content:"\f3df"}.fa-quinscape:before{content:"\f459"}.fa-less:before{content:"\f41d"}.fa-blogger-b:before{content:"\f37d"}.fa-opencart:before{content:"\f23d"}.fa-vine:before{content:"\f1ca"}.fa-signal-messenger:before{content:"\e663"}.fa-paypal:before{content:"\f1ed"}.fa-gitlab:before{content:"\f296"}.fa-typo3:before{content:"\f42b"}.fa-reddit-alien:before{content:"\f281"}.fa-yahoo:before{content:"\f19e"}.fa-dailymotion:before{content:"\e052"}.fa-affiliatetheme:before{content:"\f36b"}.fa-pied-piper-pp:before{content:"\f1a7"}.fa-bootstrap:before{content:"\f836"}.fa-odnoklassniki:before{content:"\f263"}.fa-nfc-symbol:before{content:"\e531"}.fa-mintbit:before{content:"\e62f"}.fa-ethereum:before{content:"\f42e"}.fa-speaker-deck:before{content:"\f83c"}.fa-creative-commons-nc-eu:before{content:"\f4e9"}.fa-patreon:before{content:"\f3d9"}.fa-avianex:before{content:"\f374"}.fa-ello:before{content:"\f5f1"}.fa-gofore:before{content:"\f3a7"}.fa-bimobject:before{content:"\f378"}.fa-brave-reverse:before{content:"\e63d"}.fa-facebook-f:before{content:"\f39e"}.fa-google-plus-square:before,.fa-square-google-plus:before{content:"\f0d4"}.fa-web-awesome:before{content:"\e682"}.fa-mandalorian:before{content:"\f50f"}.fa-first-order-alt:before{content:"\f50a"}.fa-osi:before{content:"\f41a"}.fa-google-wallet:before{content:"\f1ee"}.fa-d-and-d-beyond:before{content:"\f6ca"}.fa-periscope:before{content:"\f3da"}.fa-fulcrum:before{content:"\f50b"}.fa-cloudscale:before{content:"\f383"}.fa-forumbee:before{content:"\f211"}.fa-mizuni:before{content:"\f3cc"}.fa-schlix:before{content:"\f3ea"}.fa-square-xing:before,.fa-xing-square:before{content:"\f169"}.fa-bandcamp:before{content:"\f2d5"}.fa-wpforms:before{content:"\f298"}.fa-cloudversify:before{content:"\f385"}.fa-usps:before{content:"\f7e1"}.fa-megaport:before{content:"\f5a3"}.fa-magento:before{content:"\f3c4"}.fa-spotify:before{content:"\f1bc"}.fa-optin-monster:before{content:"\f23c"}.fa-fly:before{content:"\f417"}.fa-aviato:before{content:"\f421"}.fa-itunes:before{content:"\f3b4"}.fa-cuttlefish:before{content:"\f38c"}.fa-blogger:before{content:"\f37c"}.fa-flickr:before{content:"\f16e"}.fa-viber:before{content:"\f409"}.fa-soundcloud:before{content:"\f1be"}.fa-digg:before{content:"\f1a6"}.fa-tencent-weibo:before{content:"\f1d5"}.fa-letterboxd:before{content:"\e62d"}.fa-symfony:before{content:"\f83d"}.fa-maxcdn:before{content:"\f136"}.fa-etsy:before{content:"\f2d7"}.fa-facebook-messenger:before{content:"\f39f"}.fa-audible:before{content:"\f373"}.fa-think-peaks:before{content:"\f731"}.fa-bilibili:before{content:"\e3d9"}.fa-erlang:before{content:"\f39d"}.fa-x-twitter:before{content:"\e61b"}.fa-cotton-bureau:before{content:"\f89e"}.fa-dashcube:before{content:"\f210"}.fa-42-group:before,.fa-innosoft:before{content:"\e080"}.fa-stack-exchange:before{content:"\f18d"}.fa-elementor:before{content:"\f430"}.fa-pied-piper-square:before,.fa-square-pied-piper:before{content:"\e01e"}.fa-creative-commons-nd:before{content:"\f4eb"}.fa-palfed:before{content:"\f3d8"}.fa-superpowers:before{content:"\f2dd"}.fa-resolving:before{content:"\f3e7"}.fa-xbox:before{content:"\f412"}.fa-square-web-awesome-stroke:before{content:"\e684"}.fa-searchengin:before{content:"\f3eb"}.fa-tiktok:before{content:"\e07b"}.fa-facebook-square:before,.fa-square-facebook:before{content:"\f082"}.fa-renren:before{content:"\f18b"}.fa-linux:before{content:"\f17c"}.fa-glide:before{content:"\f2a5"}.fa-linkedin:before{content:"\f08c"}.fa-hubspot:before{content:"\f3b2"}.fa-deploydog:before{content:"\f38e"}.fa-twitch:before{content:"\f1e8"}.fa-ravelry:before{content:"\f2d9"}.fa-mixer:before{content:"\e056"}.fa-lastfm-square:before,.fa-square-lastfm:before{content:"\f203"}.fa-vimeo:before{content:"\f40a"}.fa-mendeley:before{content:"\f7b3"}.fa-uniregistry:before{content:"\f404"}.fa-figma:before{content:"\f799"}.fa-creative-commons-remix:before{content:"\f4ee"}.fa-cc-amazon-pay:before{content:"\f42d"}.fa-dropbox:before{content:"\f16b"}.fa-instagram:before{content:"\f16d"}.fa-cmplid:before{content:"\e360"}.fa-upwork:before{content:"\e641"}.fa-facebook:before{content:"\f09a"}.fa-gripfire:before{content:"\f3ac"}.fa-jedi-order:before{content:"\f50e"}.fa-uikit:before{content:"\f403"}.fa-fort-awesome-alt:before{content:"\f3a3"}.fa-phabricator:before{content:"\f3db"}.fa-ussunnah:before{content:"\f407"}.fa-earlybirds:before{content:"\f39a"}.fa-trade-federation:before{content:"\f513"}.fa-autoprefixer:before{content:"\f41c"}.fa-whatsapp:before{content:"\f232"}.fa-square-upwork:before{content:"\e67c"}.fa-slideshare:before{content:"\f1e7"}.fa-google-play:before{content:"\f3ab"}.fa-viadeo:before{content:"\f2a9"}.fa-line:before{content:"\f3c0"}.fa-google-drive:before{content:"\f3aa"}.fa-servicestack:before{content:"\f3ec"}.fa-simplybuilt:before{content:"\f215"}.fa-bitbucket:before{content:"\f171"}.fa-imdb:before{content:"\f2d8"}.fa-deezer:before{content:"\e077"}.fa-raspberry-pi:before{content:"\f7bb"}.fa-jira:before{content:"\f7b1"}.fa-docker:before{content:"\f395"}.fa-screenpal:before{content:"\e570"}.fa-bluetooth:before{content:"\f293"}.fa-gitter:before{content:"\f426"}.fa-d-and-d:before{content:"\f38d"}.fa-microblog:before{content:"\e01a"}.fa-cc-diners-club:before{content:"\f24c"}.fa-gg-circle:before{content:"\f261"}.fa-pied-piper-hat:before{content:"\f4e5"}.fa-kickstarter-k:before{content:"\f3bc"}.fa-yandex:before{content:"\f413"}.fa-readme:before{content:"\f4d5"}.fa-html5:before{content:"\f13b"}.fa-sellsy:before{content:"\f213"}.fa-square-web-awesome:before{content:"\e683"}.fa-sass:before{content:"\f41e"}.fa-wirsindhandwerk:before,.fa-wsh:before{content:"\e2d0"}.fa-buromobelexperte:before{content:"\f37f"}.fa-salesforce:before{content:"\f83b"}.fa-octopus-deploy:before{content:"\e082"}.fa-medapps:before{content:"\f3c6"}.fa-ns8:before{content:"\f3d5"}.fa-pinterest-p:before{content:"\f231"}.fa-apper:before{content:"\f371"}.fa-fort-awesome:before{content:"\f286"}.fa-waze:before{content:"\f83f"}.fa-bluesky:before{content:"\e671"}.fa-cc-jcb:before{content:"\f24b"}.fa-snapchat-ghost:before,.fa-snapchat:before{content:"\f2ab"}.fa-fantasy-flight-games:before{content:"\f6dc"}.fa-rust:before{content:"\e07a"}.fa-wix:before{content:"\f5cf"}.fa-behance-square:before,.fa-square-behance:before{content:"\f1b5"}.fa-supple:before{content:"\f3f9"}.fa-webflow:before{content:"\e65c"}.fa-rebel:before{content:"\f1d0"}.fa-css3:before{content:"\f13c"}.fa-staylinked:before{content:"\f3f5"}.fa-kaggle:before{content:"\f5fa"}.fa-space-awesome:before{content:"\e5ac"}.fa-deviantart:before{content:"\f1bd"}.fa-cpanel:before{content:"\f388"}.fa-goodreads-g:before{content:"\f3a9"}.fa-git-square:before,.fa-square-git:before{content:"\f1d2"}.fa-square-tumblr:before,.fa-tumblr-square:before{content:"\f174"}.fa-trello:before{content:"\f181"}.fa-creative-commons-nc-jp:before{content:"\f4ea"}.fa-get-pocket:before{content:"\f265"}.fa-perbyte:before{content:"\e083"}.fa-grunt:before{content:"\f3ad"}.fa-weebly:before{content:"\f5cc"}.fa-connectdevelop:before{content:"\f20e"}.fa-leanpub:before{content:"\f212"}.fa-black-tie:before{content:"\f27e"}.fa-themeco:before{content:"\f5c6"}.fa-python:before{content:"\f3e2"}.fa-android:before{content:"\f17b"}.fa-bots:before{content:"\e340"}.fa-free-code-camp:before{content:"\f2c5"}.fa-hornbill:before{content:"\f592"}.fa-js:before{content:"\f3b8"}.fa-ideal:before{content:"\e013"}.fa-git:before{content:"\f1d3"}.fa-dev:before{content:"\f6cc"}.fa-sketch:before{content:"\f7c6"}.fa-yandex-international:before{content:"\f414"}.fa-cc-amex:before{content:"\f1f3"}.fa-uber:before{content:"\f402"}.fa-github:before{content:"\f09b"}.fa-php:before{content:"\f457"}.fa-alipay:before{content:"\f642"}.fa-youtube:before{content:"\f167"}.fa-skyatlas:before{content:"\f216"}.fa-firefox-browser:before{content:"\e007"}.fa-replyd:before{content:"\f3e6"}.fa-suse:before{content:"\f7d6"}.fa-jenkins:before{content:"\f3b6"}.fa-twitter:before{content:"\f099"}.fa-rockrms:before{content:"\f3e9"}.fa-pinterest:before{content:"\f0d2"}.fa-buffer:before{content:"\f837"}.fa-npm:before{content:"\f3d4"}.fa-yammer:before{content:"\f840"}.fa-btc:before{content:"\f15a"}.fa-dribbble:before{content:"\f17d"}.fa-stumbleupon-circle:before{content:"\f1a3"}.fa-internet-explorer:before{content:"\f26b"}.fa-stubber:before{content:"\e5c7"}.fa-telegram-plane:before,.fa-telegram:before{content:"\f2c6"}.fa-old-republic:before{content:"\f510"}.fa-odysee:before{content:"\e5c6"}.fa-square-whatsapp:before,.fa-whatsapp-square:before{content:"\f40c"}.fa-node-js:before{content:"\f3d3"}.fa-edge-legacy:before{content:"\e078"}.fa-slack-hash:before,.fa-slack:before{content:"\f198"}.fa-medrt:before{content:"\f3c8"}.fa-usb:before{content:"\f287"}.fa-tumblr:before{content:"\f173"}.fa-vaadin:before{content:"\f408"}.fa-quora:before{content:"\f2c4"}.fa-square-x-twitter:before{content:"\e61a"}.fa-reacteurope:before{content:"\f75d"}.fa-medium-m:before,.fa-medium:before{content:"\f23a"}.fa-amilia:before{content:"\f36d"}.fa-mixcloud:before{content:"\f289"}.fa-flipboard:before{content:"\f44d"}.fa-viacoin:before{content:"\f237"}.fa-critical-role:before{content:"\f6c9"}.fa-sitrox:before{content:"\e44a"}.fa-discourse:before{content:"\f393"}.fa-joomla:before{content:"\f1aa"}.fa-mastodon:before{content:"\f4f6"}.fa-airbnb:before{content:"\f834"}.fa-wolf-pack-battalion:before{content:"\f514"}.fa-buy-n-large:before{content:"\f8a6"}.fa-gulp:before{content:"\f3ae"}.fa-creative-commons-sampling-plus:before{content:"\f4f1"}.fa-strava:before{content:"\f428"}.fa-ember:before{content:"\f423"}.fa-canadian-maple-leaf:before{content:"\f785"}.fa-teamspeak:before{content:"\f4f9"}.fa-pushed:before{content:"\f3e1"}.fa-wordpress-simple:before{content:"\f411"}.fa-nutritionix:before{content:"\f3d6"}.fa-wodu:before{content:"\e088"}.fa-google-pay:before{content:"\e079"}.fa-intercom:before{content:"\f7af"}.fa-zhihu:before{content:"\f63f"}.fa-korvue:before{content:"\f42f"}.fa-pix:before{content:"\e43a"}.fa-steam-symbol:before{content:"\f3f6"}:host,:root{--fa-font-regular:normal 400 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }.fa-regular,.far{font-weight:400}:host,:root{--fa-style-family-classic:"Font Awesome 6 Free";--fa-font-solid:normal 900 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:900;font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }.fa-solid,.fas{font-weight:900}@font-face{font-family:"Font Awesome 5 Brands";font-display:block;font-weight:400;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:900;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:400;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); } \ No newline at end of file diff --git a/deps/font-awesome-6.5.2/css/v4-shims.css b/deps/font-awesome-6.5.2/css/v4-shims.css new file mode 100644 index 0000000..ea60ea4 --- /dev/null +++ b/deps/font-awesome-6.5.2/css/v4-shims.css @@ -0,0 +1,2194 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa.fa-glass:before { + content: "\f000"; } + +.fa.fa-envelope-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-o:before { + content: "\f0e0"; } + +.fa.fa-star-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-o:before { + content: "\f005"; } + +.fa.fa-remove:before { + content: "\f00d"; } + +.fa.fa-close:before { + content: "\f00d"; } + +.fa.fa-gear:before { + content: "\f013"; } + +.fa.fa-trash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-trash-o:before { + content: "\f2ed"; } + +.fa.fa-home:before { + content: "\f015"; } + +.fa.fa-file-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-o:before { + content: "\f15b"; } + +.fa.fa-clock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-clock-o:before { + content: "\f017"; } + +.fa.fa-arrow-circle-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-down:before { + content: "\f358"; } + +.fa.fa-arrow-circle-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-up:before { + content: "\f35b"; } + +.fa.fa-play-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-play-circle-o:before { + content: "\f144"; } + +.fa.fa-repeat:before { + content: "\f01e"; } + +.fa.fa-rotate-right:before { + content: "\f01e"; } + +.fa.fa-refresh:before { + content: "\f021"; } + +.fa.fa-list-alt { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-list-alt:before { + content: "\f022"; } + +.fa.fa-dedent:before { + content: "\f03b"; } + +.fa.fa-video-camera:before { + content: "\f03d"; } + +.fa.fa-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-picture-o:before { + content: "\f03e"; } + +.fa.fa-photo { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-photo:before { + content: "\f03e"; } + +.fa.fa-image { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-image:before { + content: "\f03e"; } + +.fa.fa-map-marker:before { + content: "\f3c5"; } + +.fa.fa-pencil-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pencil-square-o:before { + content: "\f044"; } + +.fa.fa-edit { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-edit:before { + content: "\f044"; } + +.fa.fa-share-square-o:before { + content: "\f14d"; } + +.fa.fa-check-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-check-square-o:before { + content: "\f14a"; } + +.fa.fa-arrows:before { + content: "\f0b2"; } + +.fa.fa-times-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-circle-o:before { + content: "\f057"; } + +.fa.fa-check-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-check-circle-o:before { + content: "\f058"; } + +.fa.fa-mail-forward:before { + content: "\f064"; } + +.fa.fa-expand:before { + content: "\f424"; } + +.fa.fa-compress:before { + content: "\f422"; } + +.fa.fa-eye { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eye-slash { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-warning:before { + content: "\f071"; } + +.fa.fa-calendar:before { + content: "\f073"; } + +.fa.fa-arrows-v:before { + content: "\f338"; } + +.fa.fa-arrows-h:before { + content: "\f337"; } + +.fa.fa-bar-chart:before { + content: "\e0e3"; } + +.fa.fa-bar-chart-o:before { + content: "\e0e3"; } + +.fa.fa-twitter-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitter-square:before { + content: "\f081"; } + +.fa.fa-facebook-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-square:before { + content: "\f082"; } + +.fa.fa-gears:before { + content: "\f085"; } + +.fa.fa-thumbs-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-up:before { + content: "\f164"; } + +.fa.fa-thumbs-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-down:before { + content: "\f165"; } + +.fa.fa-heart-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-heart-o:before { + content: "\f004"; } + +.fa.fa-sign-out:before { + content: "\f2f5"; } + +.fa.fa-linkedin-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin-square:before { + content: "\f08c"; } + +.fa.fa-thumb-tack:before { + content: "\f08d"; } + +.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-themeisle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official:before { + content: "\f2b3"; } + +.fa.fa-google-plus-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-circle:before { + content: "\f2b3"; } + +.fa.fa-font-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa:before { + content: "\f2b4"; } + +.fa.fa-handshake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-handshake-o:before { + content: "\f2b5"; } + +.fa.fa-envelope-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-open-o:before { + content: "\f2b6"; } + +.fa.fa-linode { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-address-book-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-book-o:before { + content: "\f2b9"; } + +.fa.fa-vcard:before { + content: "\f2bb"; } + +.fa.fa-address-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-card-o:before { + content: "\f2bb"; } + +.fa.fa-vcard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-vcard-o:before { + content: "\f2bb"; } + +.fa.fa-user-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-circle-o:before { + content: "\f2bd"; } + +.fa.fa-user-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-o:before { + content: "\f007"; } + +.fa.fa-id-badge { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license:before { + content: "\f2c2"; } + +.fa.fa-id-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-id-card-o:before { + content: "\f2c2"; } + +.fa.fa-drivers-license-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license-o:before { + content: "\f2c2"; } + +.fa.fa-quora { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-free-code-camp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-telegram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-thermometer-4:before { + content: "\f2c7"; } + +.fa.fa-thermometer:before { + content: "\f2c7"; } + +.fa.fa-thermometer-3:before { + content: "\f2c8"; } + +.fa.fa-thermometer-2:before { + content: "\f2c9"; } + +.fa.fa-thermometer-1:before { + content: "\f2ca"; } + +.fa.fa-thermometer-0:before { + content: "\f2cb"; } + +.fa.fa-bathtub:before { + content: "\f2cd"; } + +.fa.fa-s15:before { + content: "\f2cd"; } + +.fa.fa-window-maximize { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-window-restore { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-rectangle:before { + content: "\f410"; } + +.fa.fa-window-close-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-window-close-o:before { + content: "\f410"; } + +.fa.fa-times-rectangle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-rectangle-o:before { + content: "\f410"; } + +.fa.fa-bandcamp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-grav { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-etsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-imdb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ravelry { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-eercast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-eercast:before { + content: "\f2da"; } + +.fa.fa-snowflake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-snowflake-o:before { + content: "\f2dc"; } + +.fa.fa-superpowers { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpexplorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-meetup { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } diff --git a/deps/font-awesome-6.5.2/css/v4-shims.min.css b/deps/font-awesome-6.5.2/css/v4-shims.min.css new file mode 100644 index 0000000..09baf5f --- /dev/null +++ b/deps/font-awesome-6.5.2/css/v4-shims.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-play-circle-o:before{content:"\f144"}.fa.fa-repeat:before,.fa.fa-rotate-right:before{content:"\f01e"}.fa.fa-refresh:before{content:"\f021"}.fa.fa-list-alt{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-list-alt:before{content:"\f022"}.fa.fa-dedent:before{content:"\f03b"}.fa.fa-video-camera:before{content:"\f03d"}.fa.fa-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-picture-o:before{content:"\f03e"}.fa.fa-photo{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-photo:before{content:"\f03e"}.fa.fa-image{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-image:before{content:"\f03e"}.fa.fa-map-marker:before{content:"\f3c5"}.fa.fa-pencil-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pencil-square-o:before{content:"\f044"}.fa.fa-edit{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-edit:before{content:"\f044"}.fa.fa-share-square-o:before{content:"\f14d"}.fa.fa-check-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-square-o:before{content:"\f0c8"}.fa.fa-bookmark-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bookmark-o:before{content:"\f02e"}.fa.fa-facebook,.fa.fa-twitter{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook:before{content:"\f39e"}.fa.fa-facebook-f{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-f:before{content:"\f39e"}.fa.fa-github{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-feed:before{content:"\f09e"}.fa.fa-hdd-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hdd-o:before{content:"\f0a0"}.fa.fa-hand-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-right:before{content:"\f0a4"}.fa.fa-hand-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-left:before{content:"\f0a5"}.fa.fa-hand-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-up:before{content:"\f0a6"}.fa.fa-hand-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-down:before{content:"\f0a7"}.fa.fa-globe:before{content:"\f57d"}.fa.fa-tasks:before{content:"\f828"}.fa.fa-arrows-alt:before{content:"\f31e"}.fa.fa-group:before{content:"\f0c0"}.fa.fa-chain:before{content:"\f0c1"}.fa.fa-cut:before{content:"\f0c4"}.fa.fa-files-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-files-o:before{content:"\f0c5"}.fa.fa-floppy-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-floppy-o:before{content:"\f0c7"}.fa.fa-save{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-save:before{content:"\f0c7"}.fa.fa-navicon:before,.fa.fa-reorder:before{content:"\f0c9"}.fa.fa-magic:before{content:"\e2ca"}.fa.fa-pinterest,.fa.fa-pinterest-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pinterest-square:before{content:"\f0d3"}.fa.fa-google-plus-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-square:before{content:"\f0d4"}.fa.fa-google-plus{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus:before{content:"\f0d5"}.fa.fa-money:before{content:"\f3d1"}.fa.fa-unsorted:before{content:"\f0dc"}.fa.fa-sort-desc:before{content:"\f0dd"}.fa.fa-sort-asc:before{content:"\f0de"}.fa.fa-linkedin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin:before{content:"\f0e1"}.fa.fa-rotate-left:before{content:"\f0e2"}.fa.fa-legal:before{content:"\f0e3"}.fa.fa-dashboard:before,.fa.fa-tachometer:before{content:"\f625"}.fa.fa-comment-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comment-o:before{content:"\f075"}.fa.fa-comments-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comments-o:before{content:"\f086"}.fa.fa-flash:before{content:"\f0e7"}.fa.fa-clipboard:before{content:"\f0ea"}.fa.fa-lightbulb-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lightbulb-o:before{content:"\f0eb"}.fa.fa-exchange:before{content:"\f362"}.fa.fa-cloud-download:before{content:"\f0ed"}.fa.fa-cloud-upload:before{content:"\f0ee"}.fa.fa-bell-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-o:before{content:"\f0f3"}.fa.fa-cutlery:before{content:"\f2e7"}.fa.fa-file-text-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-text-o:before{content:"\f15c"}.fa.fa-building-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-building-o:before{content:"\f1ad"}.fa.fa-hospital-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hospital-o:before{content:"\f0f8"}.fa.fa-tablet:before{content:"\f3fa"}.fa.fa-mobile-phone:before,.fa.fa-mobile:before{content:"\f3cd"}.fa.fa-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-o:before{content:"\f111"}.fa.fa-mail-reply:before{content:"\f3e5"}.fa.fa-github-alt{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-folder-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-o:before{content:"\f07b"}.fa.fa-folder-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-open-o:before{content:"\f07c"}.fa.fa-smile-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-smile-o:before{content:"\f118"}.fa.fa-frown-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-frown-o:before{content:"\f119"}.fa.fa-meh-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-meh-o:before{content:"\f11a"}.fa.fa-keyboard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-keyboard-o:before{content:"\f11c"}.fa.fa-flag-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-flag-o:before{content:"\f024"}.fa.fa-mail-reply-all:before{content:"\f122"}.fa.fa-star-half-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-o:before{content:"\f5c0"}.fa.fa-star-half-empty{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-empty:before{content:"\f5c0"}.fa.fa-star-half-full{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-full:before{content:"\f5c0"}.fa.fa-code-fork:before{content:"\f126"}.fa.fa-chain-broken:before,.fa.fa-unlink:before{content:"\f127"}.fa.fa-calendar-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-o:before{content:"\f133"}.fa.fa-css3,.fa.fa-html5,.fa.fa-maxcdn{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-unlock-alt:before{content:"\f09c"}.fa.fa-minus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-minus-square-o:before{content:"\f146"}.fa.fa-level-up:before{content:"\f3bf"}.fa.fa-level-down:before{content:"\f3be"}.fa.fa-pencil-square:before{content:"\f14b"}.fa.fa-external-link-square:before{content:"\f360"}.fa.fa-compass{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down:before{content:"\f150"}.fa.fa-toggle-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-down:before{content:"\f150"}.fa.fa-caret-square-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-up:before{content:"\f151"}.fa.fa-toggle-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-up:before{content:"\f151"}.fa.fa-caret-square-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-right:before{content:"\f152"}.fa.fa-toggle-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-right:before{content:"\f152"}.fa.fa-eur:before,.fa.fa-euro:before{content:"\f153"}.fa.fa-gbp:before{content:"\f154"}.fa.fa-dollar:before,.fa.fa-usd:before{content:"\24"}.fa.fa-inr:before,.fa.fa-rupee:before{content:"\e1bc"}.fa.fa-cny:before,.fa.fa-jpy:before,.fa.fa-rmb:before,.fa.fa-yen:before{content:"\f157"}.fa.fa-rouble:before,.fa.fa-rub:before,.fa.fa-ruble:before{content:"\f158"}.fa.fa-krw:before,.fa.fa-won:before{content:"\f159"}.fa.fa-bitcoin,.fa.fa-btc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitcoin:before{content:"\f15a"}.fa.fa-file-text:before{content:"\f15c"}.fa.fa-sort-alpha-asc:before{content:"\f15d"}.fa.fa-sort-alpha-desc:before{content:"\f881"}.fa.fa-sort-amount-asc:before{content:"\f884"}.fa.fa-sort-amount-desc:before{content:"\f160"}.fa.fa-sort-numeric-asc:before{content:"\f162"}.fa.fa-sort-numeric-desc:before{content:"\f886"}.fa.fa-youtube-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-square:before{content:"\f431"}.fa.fa-xing,.fa.fa-xing-square,.fa.fa-youtube{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-xing-square:before{content:"\f169"}.fa.fa-youtube-play{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-play:before{content:"\f167"}.fa.fa-adn,.fa.fa-bitbucket,.fa.fa-bitbucket-square,.fa.fa-dropbox,.fa.fa-flickr,.fa.fa-instagram,.fa.fa-stack-overflow{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitbucket-square:before{content:"\f171"}.fa.fa-tumblr,.fa.fa-tumblr-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-tumblr-square:before{content:"\f174"}.fa.fa-long-arrow-down:before{content:"\f309"}.fa.fa-long-arrow-up:before{content:"\f30c"}.fa.fa-long-arrow-left:before{content:"\f30a"}.fa.fa-long-arrow-right:before{content:"\f30b"}.fa.fa-android,.fa.fa-apple,.fa.fa-dribbble,.fa.fa-foursquare,.fa.fa-gittip,.fa.fa-gratipay,.fa.fa-linux,.fa.fa-skype,.fa.fa-trello,.fa.fa-windows{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-gittip:before{content:"\f184"}.fa.fa-sun-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sun-o:before{content:"\f185"}.fa.fa-moon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-moon-o:before{content:"\f186"}.fa.fa-pagelines,.fa.fa-renren,.fa.fa-stack-exchange,.fa.fa-vk,.fa.fa-weibo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-arrow-circle-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-right:before{content:"\f35a"}.fa.fa-arrow-circle-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-left:before{content:"\f359"}.fa.fa-caret-square-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-left:before{content:"\f191"}.fa.fa-toggle-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-left:before{content:"\f191"}.fa.fa-dot-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-dot-circle-o:before{content:"\f192"}.fa.fa-vimeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo-square:before{content:"\f194"}.fa.fa-try:before,.fa.fa-turkish-lira:before{content:"\e2bb"}.fa.fa-plus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-plus-square-o:before{content:"\f0fe"}.fa.fa-openid,.fa.fa-slack,.fa.fa-wordpress{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bank:before,.fa.fa-institution:before{content:"\f19c"}.fa.fa-mortar-board:before{content:"\f19d"}.fa.fa-google,.fa.fa-reddit,.fa.fa-reddit-square,.fa.fa-yahoo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-reddit-square:before{content:"\f1a2"}.fa.fa-behance,.fa.fa-behance-square,.fa.fa-delicious,.fa.fa-digg,.fa.fa-drupal,.fa.fa-joomla,.fa.fa-pied-piper-alt,.fa.fa-pied-piper-pp,.fa.fa-stumbleupon,.fa.fa-stumbleupon-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-behance-square:before{content:"\f1b5"}.fa.fa-steam,.fa.fa-steam-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-steam-square:before{content:"\f1b7"}.fa.fa-automobile:before{content:"\f1b9"}.fa.fa-cab:before{content:"\f1ba"}.fa.fa-deviantart,.fa.fa-soundcloud,.fa.fa-spotify{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-file-pdf-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-pdf-o:before{content:"\f1c1"}.fa.fa-file-word-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-word-o:before{content:"\f1c2"}.fa.fa-file-excel-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-excel-o:before{content:"\f1c3"}.fa.fa-file-powerpoint-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-powerpoint-o:before{content:"\f1c4"}.fa.fa-file-image-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-image-o:before{content:"\f1c5"}.fa.fa-file-photo-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-photo-o:before{content:"\f1c5"}.fa.fa-file-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-picture-o:before{content:"\f1c5"}.fa.fa-file-archive-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-archive-o:before{content:"\f1c6"}.fa.fa-file-zip-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-zip-o:before{content:"\f1c6"}.fa.fa-file-audio-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-audio-o:before{content:"\f1c7"}.fa.fa-file-sound-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-sound-o:before{content:"\f1c7"}.fa.fa-file-video-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-video-o:before{content:"\f1c8"}.fa.fa-file-movie-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-movie-o:before{content:"\f1c8"}.fa.fa-file-code-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-code-o:before{content:"\f1c9"}.fa.fa-codepen,.fa.fa-jsfiddle,.fa.fa-vine{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-life-bouy:before,.fa.fa-life-buoy:before,.fa.fa-life-saver:before,.fa.fa-support:before{content:"\f1cd"}.fa.fa-circle-o-notch:before{content:"\f1ce"}.fa.fa-ra,.fa.fa-rebel{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ra:before{content:"\f1d0"}.fa.fa-resistance{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-resistance:before{content:"\f1d0"}.fa.fa-empire,.fa.fa-ge{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ge:before{content:"\f1d1"}.fa.fa-git-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-git-square:before{content:"\f1d2"}.fa.fa-git,.fa.fa-hacker-news,.fa.fa-y-combinator-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-y-combinator-square:before{content:"\f1d4"}.fa.fa-yc-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc-square:before{content:"\f1d4"}.fa.fa-qq,.fa.fa-tencent-weibo,.fa.fa-wechat,.fa.fa-weixin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wechat:before{content:"\f1d7"}.fa.fa-send:before{content:"\f1d8"}.fa.fa-paper-plane-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-paper-plane-o:before{content:"\f1d8"}.fa.fa-send-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-send-o:before{content:"\f1d8"}.fa.fa-circle-thin{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-thin:before{content:"\f111"}.fa.fa-header:before{content:"\f1dc"}.fa.fa-futbol-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-futbol-o:before{content:"\f1e3"}.fa.fa-soccer-ball-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-soccer-ball-o:before{content:"\f1e3"}.fa.fa-slideshare,.fa.fa-twitch,.fa.fa-yelp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-newspaper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-newspaper-o:before{content:"\f1ea"}.fa.fa-cc-amex,.fa.fa-cc-discover,.fa.fa-cc-mastercard,.fa.fa-cc-paypal,.fa.fa-cc-stripe,.fa.fa-cc-visa,.fa.fa-google-wallet,.fa.fa-paypal{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bell-slash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-slash-o:before{content:"\f1f6"}.fa.fa-trash:before{content:"\f2ed"}.fa.fa-copyright{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-eyedropper:before{content:"\f1fb"}.fa.fa-area-chart:before{content:"\f1fe"}.fa.fa-pie-chart:before{content:"\f200"}.fa.fa-line-chart:before{content:"\f201"}.fa.fa-lastfm,.fa.fa-lastfm-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-lastfm-square:before{content:"\f203"}.fa.fa-angellist,.fa.fa-ioxhost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-cc{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-cc:before{content:"\f20a"}.fa.fa-ils:before,.fa.fa-shekel:before,.fa.fa-sheqel:before{content:"\f20b"}.fa.fa-buysellads,.fa.fa-connectdevelop,.fa.fa-dashcube,.fa.fa-forumbee,.fa.fa-leanpub,.fa.fa-sellsy,.fa.fa-shirtsinbulk,.fa.fa-simplybuilt,.fa.fa-skyatlas{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-diamond{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-diamond:before{content:"\f3a5"}.fa.fa-intersex:before,.fa.fa-transgender:before{content:"\f224"}.fa.fa-transgender-alt:before{content:"\f225"}.fa.fa-facebook-official{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-official:before{content:"\f09a"}.fa.fa-pinterest-p,.fa.fa-whatsapp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-hotel:before{content:"\f236"}.fa.fa-medium,.fa.fa-viacoin,.fa.fa-y-combinator,.fa.fa-yc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc:before{content:"\f23b"}.fa.fa-expeditedssl,.fa.fa-opencart,.fa.fa-optin-monster{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-battery-4:before,.fa.fa-battery:before{content:"\f240"}.fa.fa-battery-3:before{content:"\f241"}.fa.fa-battery-2:before{content:"\f242"}.fa.fa-battery-1:before{content:"\f243"}.fa.fa-battery-0:before{content:"\f244"}.fa.fa-object-group,.fa.fa-object-ungroup,.fa.fa-sticky-note-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sticky-note-o:before{content:"\f249"}.fa.fa-cc-diners-club,.fa.fa-cc-jcb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-clone{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hourglass-o:before{content:"\f254"}.fa.fa-hourglass-1:before{content:"\f251"}.fa.fa-hourglass-2:before{content:"\f252"}.fa.fa-hourglass-3:before{content:"\f253"}.fa.fa-hand-rock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-rock-o:before{content:"\f255"}.fa.fa-hand-grab-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-grab-o:before{content:"\f255"}.fa.fa-hand-paper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-paper-o:before{content:"\f256"}.fa.fa-hand-stop-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-stop-o:before{content:"\f256"}.fa.fa-hand-scissors-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-scissors-o:before{content:"\f257"}.fa.fa-hand-lizard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-lizard-o:before{content:"\f258"}.fa.fa-hand-spock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-spock-o:before{content:"\f259"}.fa.fa-hand-pointer-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-pointer-o:before{content:"\f25a"}.fa.fa-hand-peace-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-peace-o:before{content:"\f25b"}.fa.fa-registered{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-creative-commons,.fa.fa-gg,.fa.fa-gg-circle,.fa.fa-odnoklassniki,.fa.fa-odnoklassniki-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-odnoklassniki-square:before{content:"\f264"}.fa.fa-chrome,.fa.fa-firefox,.fa.fa-get-pocket,.fa.fa-internet-explorer,.fa.fa-opera,.fa.fa-safari,.fa.fa-wikipedia-w{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-television:before{content:"\f26c"}.fa.fa-500px,.fa.fa-amazon,.fa.fa-contao{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-calendar-plus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-plus-o:before{content:"\f271"}.fa.fa-calendar-minus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-minus-o:before{content:"\f272"}.fa.fa-calendar-times-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-times-o:before{content:"\f273"}.fa.fa-calendar-check-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-check-o:before{content:"\f274"}.fa.fa-map-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-map-o:before{content:"\f279"}.fa.fa-commenting:before{content:"\f4ad"}.fa.fa-commenting-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-commenting-o:before{content:"\f4ad"}.fa.fa-houzz,.fa.fa-vimeo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo:before{content:"\f27d"}.fa.fa-black-tie,.fa.fa-edge,.fa.fa-fonticons,.fa.fa-reddit-alien{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card-alt:before{content:"\f09d"}.fa.fa-codiepie,.fa.fa-fort-awesome,.fa.fa-mixcloud,.fa.fa-modx,.fa.fa-product-hunt,.fa.fa-scribd,.fa.fa-usb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pause-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pause-circle-o:before{content:"\f28b"}.fa.fa-stop-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-stop-circle-o:before{content:"\f28d"}.fa.fa-bluetooth,.fa.fa-bluetooth-b,.fa.fa-envira,.fa.fa-gitlab,.fa.fa-wheelchair-alt,.fa.fa-wpbeginner,.fa.fa-wpforms{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wheelchair-alt:before{content:"\f368"}.fa.fa-question-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-question-circle-o:before{content:"\f059"}.fa.fa-volume-control-phone:before{content:"\f2a0"}.fa.fa-asl-interpreting:before{content:"\f2a3"}.fa.fa-deafness:before,.fa.fa-hard-of-hearing:before{content:"\f2a4"}.fa.fa-glide,.fa.fa-glide-g{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-signing:before{content:"\f2a7"}.fa.fa-viadeo,.fa.fa-viadeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-viadeo-square:before{content:"\f2aa"}.fa.fa-snapchat,.fa.fa-snapchat-ghost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-ghost:before{content:"\f2ab"}.fa.fa-snapchat-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-square:before{content:"\f2ad"}.fa.fa-first-order,.fa.fa-google-plus-official,.fa.fa-pied-piper,.fa.fa-themeisle,.fa.fa-yoast{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-official:before{content:"\f2b3"}.fa.fa-google-plus-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-circle:before{content:"\f2b3"}.fa.fa-fa,.fa.fa-font-awesome{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-fa:before{content:"\f2b4"}.fa.fa-handshake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-handshake-o:before{content:"\f2b5"}.fa.fa-envelope-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-open-o:before{content:"\f2b6"}.fa.fa-linode{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-address-book-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-book-o:before{content:"\f2b9"}.fa.fa-vcard:before{content:"\f2bb"}.fa.fa-address-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-card-o:before{content:"\f2bb"}.fa.fa-vcard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-vcard-o:before{content:"\f2bb"}.fa.fa-user-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-circle-o:before{content:"\f2bd"}.fa.fa-user-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-o:before{content:"\f007"}.fa.fa-id-badge{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license:before{content:"\f2c2"}.fa.fa-id-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-id-card-o:before{content:"\f2c2"}.fa.fa-drivers-license-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license-o:before{content:"\f2c2"}.fa.fa-free-code-camp,.fa.fa-quora,.fa.fa-telegram{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-thermometer-4:before,.fa.fa-thermometer:before{content:"\f2c7"}.fa.fa-thermometer-3:before{content:"\f2c8"}.fa.fa-thermometer-2:before{content:"\f2c9"}.fa.fa-thermometer-1:before{content:"\f2ca"}.fa.fa-thermometer-0:before{content:"\f2cb"}.fa.fa-bathtub:before,.fa.fa-s15:before{content:"\f2cd"}.fa.fa-window-maximize,.fa.fa-window-restore{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle:before{content:"\f410"}.fa.fa-window-close-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-window-close-o:before{content:"\f410"}.fa.fa-times-rectangle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle-o:before{content:"\f410"}.fa.fa-bandcamp,.fa.fa-eercast,.fa.fa-etsy,.fa.fa-grav,.fa.fa-imdb,.fa.fa-ravelry{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-eercast:before{content:"\f2da"}.fa.fa-snowflake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-snowflake-o:before{content:"\f2dc"}.fa.fa-meetup,.fa.fa-superpowers,.fa.fa-wpexplorer{font-family:"Font Awesome 6 Brands";font-weight:400} \ No newline at end of file diff --git a/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf b/deps/font-awesome-6.5.2/webfonts/fa-brands-400.ttf new file mode 100644 index 0000000000000000000000000000000000000000..1fbb1f7c32d46f5dcb89a50e10d00878ed43f1a1 GIT binary patch literal 209128 zcmd4437p(TwfI~0>wWK@-g~y5?wRRKW+s`Qt&@S03!P$K}1ous6h}xhfPsY zLD>YwxPV@bUM`}dB6sw1k*i$gDqJ_z0WqL*H8Ycth&uDWr>Z-dAnJYZ|K8{Gy61G& zul721>eQ)Ir+%ZfQYxq>luH$ldF9gKGvE5Ewy+}JfBr>hpXL0^r|(g!eTe6+7o2tJR%f026~y~VU%c?F3(lDijaHQ_^<$;n?OQK; z^R|0ioQ=wr+ox1^m!hpMk4JU9W&G`D##Zi8A%|>AJzf3wwM}<^cK%Dw4f;`@{mPLX zeE8Lm&VNZM=St%L;M~v%H<9w=v`UE6>OMwQOrYe`2c>fBXY>uqL+~d3T4IlAxO-Gy zT|-{wahaU*qvQL%N*#LAD>ti2)ipnF+d$ink~AjaS`9W-k;e(#`uwb(#9voIdDav2 zb3Dg+COQ84{GWh1!vAbs310}3N775ax{3>S8h&yAXv%D+pOmTO*n4fcOJLXOK6Z3zfuCQbCJ0|aBBQGv-orHOa7k|N-nBQaCHc6QhKVi~(BuxCc zJ|NsECyYOC(%85WlV)Q6MVo$pUR+a7Ge7-z)ciiDuDXefHRGFU8s(-4m;S~Z&r)wI zWjm&>Nz)Dqld`>lU(z{ODsAdl^M9dyizf-6IN)!V*L0-}Q_e2Z2$0^CHK8KLKh@|1 zWtemm%I(xu4BY<3q=THmyQ`T`${?MW@iA>8&TZ1wO}PQ$Z1~njU8YW3hl!&e(zi6r zYo^DI>E{}4m?X_EO0UUsi#i<30y$(I(6fQoBd{7=1sj5DuL@K zK0-PN$eI35nEEWONZyx%YP4sq4vk2E7=M9A9rA4HY_i#|`4>%jlPbqt zWUffKX@j&WMwrYgOYah2Cw{`D`Lm>#ye57Zb4;0jEbOgJmGA6yH zcRyvOOgpwW(oHqW;#p74Qx|SsMNM7e2aJD`u{)Ob-Oxxsq1sG+O65sY1RU6_;UA6g zB5vN)W%))EdWsq+7zISsID0(oSvS^OM?za}34j@#(7^eyVFm%dVV9iAWnFFW2_`DbI`MmgEqkb zq@B04-|&hCZftqVwzrWtN4e1ke%c!KOC3VjhWDE`m^|RUuCjnj;;BdGQr5;>`sF#_ zETd7k#nY|^PHwgHj&w4|(}E`{kF>G|w3>d(+Nr3(@?Mz_Y2r05V`A1iS$}1$r5(2X zNzz;V3y&3=v}?kg@{z<1xPWf!C#y?|MAD5LG)1|6zo4Wi3G}HaRhQ~f{c5>7Rh^|? ztx+OH>+*xV)YhviMmw1Rb5u8R$f^- zzH&n4q{_`xzNzF?VX9+l5b=^~QPe1mn`meV9>L<^{o>}qCkT>UcU31U9a2q`dwG;x_;LOcipk; ziCs@kC#M%pFPTOue&h6;rngPMefrAjYo^~febe-9(|1hY zIsMt`uS`EU{pj?Mrhha2`{_N?FV47T0yEK>%*@ct@JwxH^~|Q3SIwL?bKcB_Gh1ig zK6BN~duBd3bJNVtGasJ0W9IIe&&}+Z`NGV7GxyItJoD3;U(8I+o<4i=>=m;Uv)9gE zH~WFvTW4>d{n+fMX78Q-#_Z(mL$eRhetY(j*+*v|n|*Tjm$T2zK0o{W*+0zA&i-+B zZntN5WOsIV*Y5J}p54{mn|B|#`=s5c?0#bRuXpd<{l`72Jp+5Hd&c)1y61y??%eaq zJ)hZg@18I1d2r9edmi2M{XNrr_U?J{h1d%%FRXcC?F%1$;X5xp`ofcYt9#Gb`?kH; z?tS;(_wBuL?}zvP%ib^UePHjydw;n1zxO`1_gDK??OVI=&V6_9`_8@}?0ah8bMy0z zdY3Az#cD{c{C{Ro?hbcYz-9lw5fXJ}`R(H-1*$j*9Wblls?whCIIz7SYzT1rM?TqgB>HDT1nEuZ6W7E%0|IhR;M%S6~ z&qQY8jBb_DT`_YMqq}A1?3oJ~-OFbtX0Bm$Kg8(X!sy;L^G}TK=V$I=bRU{|V&>;& zbkCi=boL#yS2McroxO4P!?Pcq{rK$Nv-dH&|JE4Y?=ZSQV053F-Psu3J&dli+qXNh zJ8wp}cfsgB!{|P{C-{GBbpOMQ?hvDUSz~l>*n8{Ve`a*Q-Wc6qGPnb=2T=bMlt zV}}#o2eboiu~vQyu}mx-OU7ccK+GHS#9T2a`p4*==x?LXMt>ZABKo*e(MO^WMZX!H z#CLb}6VdI_o1#}ouZmt7oru08x-I(V=$nXn1Mqs_RngP=JuP}_^rYyf=<4Xo=pm8G z$d@8tjNBW!C-Q~Jj>zXCpN)Jb^6ALkk-H+FjC?$@9l_~ikvk)IL~f7V7P&QYOXOxk zZj9W(?{$%PMXrfl9k~ksMC6>vS&=g$r-z>k{}878gYdVcSMmw353dN1hlj&`;YzqO zoC_zziO{al??S%`JrVk0=+mLQLPv#)q0Ue|_>JHPgKr655PU=Mb-{CjTY^Ugj|?6b zToGIn90+y?%Yh#Teh|1Mur;tbaCl%@VA$|$TZz)o{Xh8Lt-qjm=-c!y`p!l%|A&q~ zQ6G135$utdNr)Z?-UFap;FnSPM<1PPreHPxTOU>^C9BjDNuW!AE7{)&M>=@P=SlM{ zr8Ym0H9ZY?(ztJ8jXR$;@c+w?X8CrpPJ5JBB~*(_s+3BrjLNDU8i#^vRc)$Wb*N5N zgnyUd;N7gzz349%sXn-QzZy`3YKdCP8hnkqUfrpFs(m`DTXehr7ZsMKhs@aEky+W* z0FeU9Ko5Y>KuL;~j;1zQ4n;G6~& za9`U1W<2tQg*IIF4hG-{$QTwnaGA{pI&sfyKoM8?h=BCtf(8ghT-X5V(?tyssb^~g zq@QnWKrgP)ra%RFa|0IPZfgKM3fabj;6^A~KyY+P0|sy}ZNMPzTN@yBdRYUOQbpB* z;I{Hg3yiX=9B;w+PrwhzY_FVT!T8}s6#(i`Q;dgzjL%fkf}-px@GGE(alxH{8o}LY zL5<=bXF-kOQqL51sBv8SGIb4p#(U~rz;*bKQ0l2U3yLzHW_AduBbEA9-Gb=0eznDd z+JyTP3u-e;!I%YgH0}xu>KI(|3#empKWst00{0FJiZ(v8-GTy7zowtRrhf`N{d%hf zbt3K#;0uJ)hhIMod>j9%O6?2*Vf^s_ozUV=p|w}xGG+oovpX55os5al?9M}gRrsOJ zops<){LtV|$qxwK?UZyv-{<4L(Sp$3&bI-V6As<&yb2H;(#Bn{vmoQOi?Re{+;?3G zkWOfA*Yy^JzIJ`kg3#73aIyD%-+!rzDcR)F@Z58zG!)UR&Fm2w4Fx8lAVAg#I$_a+PKqqy=6 zz{}M1?SSCqW4Q7RsE^}r2R@7c6S(&QU%~%*+y{UM@k?L613Zdf;vWNkgr9WN&jP=} z|F5|J1Nd+%=I{{pUe-;;f&%6i!3%Jmmfbjg84*}p^{RNk?5s)3@%q;-4sOE9O zy?};4&)fyvjUQah{1fmw{LuEy4h!0c`+49CgbSYT0l>Wu;@)pT$8f>#%)^Aoai0Ld zfrh4Meh&Nse;#+rf^MY?r(4i%xaR^FlZN)pg5TLI@b}}s1DL=MzGtE7*=zBS;9h4z zB$L_q0??yo9A<9>ZpFV6m%h&4j{h*+j{+aVzaAGloBb624Y>4s_Fnvq(=2_O{RaM1 za3?Kj#%K250Q#n1gG>Je^ane(Dt4WTF^hj1xEs+yWR6i3;HLxpRu4BuRZiZK>rMvwhCy*X3v8b z^v`i0wxEB3D{TTaW47n}7W5SEv<1x=?%8WWbb5PUw4i^*^o&{1;NS)NBcQ>>3u`QB zaPh)g3mSa90Idp$F8_u90H7xgUweW21@!N5X{&&KUa7r90Qk}HjlE}B(9p`>%Ye5L zz6LS`a1f-v72B2LyY;-CpqIxNyOXfa6ta-zcyOKXkeeoa_S^P6C%P6mZhG zcUy2WxZp#;$>RRNf|J9gYyl^a3+@D*g3`P1HDEPnw2#lHXf>4PEF?M!J2Oey0kg~b^_!Hx{iVBcA(;rbPL%Pfk)Q&^IpE*X^j*v}R1)d9^;Yf@4R?@Zc+(uk`T&WH? zMdvQ1ij>#2RjCqfDcy(q?{Nfw;wp=PA1k$J!fpVTZc%FKZUjuqsMdi;5GwCMn8ZJF zy;7qOC^ZJuu2X8+q*5!|0Ng`zO0C+a)Eb`aCo8p@M5iSjoQzWH*cj=n~zSA0aNyb$7Q?pMFTGe_Eu}XNmuu8=%}B*C6yz_B|5_JmmS} zGYC6BR_e>t`xV-9KW+Hxb4q>fc%>fLrPMbl>p}8N68BB~4|OZWWK`e2PpLp%Pe zWqcR^qn}agKZ^)A8wWt&oS z&m+sam1f1&aC+@*NAP$Efg=S>DeW6Vu&65?$|)T=okgNe>DWqyin!7(^SDajw!G$12_F2ev6)Tm7d;>VR9oJ1*cozg=C2p(ajN8L(~DS&k2)HD9P(#t4!`K18OQ5{ z+@^G$GS;39P{%sb9+3j@ZyZqi$mf*a%=6~$N+10xrH{cq?g6Ebr;jHrQ~IQ>N}s$5 z0fgsMDEIU!rO$Xs=`%Mdz2$nP&$?gfv(HBmA>TQa`C6V|_hST&21_;@#Cm%u0zn6M8F{YQtEr_l+tgbZI?fy^c9Pgo;VqJPU$P_N?(-&?o;|| z>V0<@cwXuE-lO#Oqj2VkcPssI>ih(0 z|B2_%(XSmFl>YqjO8+x;+@k>U-^=rTr2SF~ApKW}yZ;7&wtSU5U!%OQJ*M;nRp5e0!VHk5JZk&Q&^b)&r`<#+{c32 z223eEb3Q9ePU#m&x0mogQr;gw1w5(rpKfB2B!2EDPL?*X=v~8N_Z*Adxyr#F$B7aa zy$!&h*u^3TWHu=$H^~C#XW@DbgO;tzX)7wH^GW5DA5~6w9QcfKdX802FZrQaXVK}( zS=_Che)0^Qtehn~l(Y1HTj=iD~s zoJSnA=e+TJ<-BQ=a^6gxZ+=uc+diV4i+`+~OUQTWO66Qe-pi=}@*;4(a;_Kv_9y-23$CUHaN0l?xrktn3%6WQ9InSJ{oL>{Z^Lphxdz*58 zGpd~5-lUx0QReeJ|2{4@R?5S6Yf?|>Ni+u$cJ!^Hsj1a`wMsRS9Vw4gs=ivbl1<>0 zN6Oi1t!KPet&Z2as+nwtV6V^X>#FuvdMkx8j|A=62&u;?ZGo;`@_qY5Meb#mB*_YD#V9A&n+dB}XgOYHzvZ^QIHk zR5mj*TpJ%txW;Saw1W1~u6iI4jRyG0Z!sGV2E$qVt0!#uyt1xVb6a#Wl}c*+Yw80h zztEHV<7fikOnsSbwVLtysw34JJs+oh`!t-*q*Fd`xm=aFg-^7Cw)EC&Rf${pRI2oj zjn>9Tq^xpv;ggh{_0@bP=1WnZPY2Hoxq_hxUMH}^Hg|ox={XSf4^w&5>GYZDGxc^U z?U40SwDDQ=OjzeYFh0NYHxg{0;3GKFDhS<-LN{**L&Jihbh5J;A|Pb*MZ9J{VWQ8<=b0Ihzrm31iT!Lkr`H8LJ8|w}TtU zrH3NY?ocqgbf}|EyWCDFS9R%3HkZmo!(MNqC7;UKzB1PNLOSemd%WRLB#}I1+YyJU*}2L4)xsyG=jZ09^i|BgBNvP_^T5mkFSBCdXfGU5=)N(MwJLPb7}IRkj@cwV z-x`X9eLj~f{H8aDTrRIa90_f;19TC%voUW9yTE26MsFwjy2B(3S^ewQ6A_lIhrP>U z{#|PualZ8u7n00}T_;G^Dn7qU-=l93&9y$jLDDh{;Ut3Nkt*$P@RKo_(eM=+g>rRF zE8DOeY&|y|{G_+OY;YryY&yYV-S97@(4^&{GAGi8e}bFJSk3FJ@{~4n1e%AoC0sd! zgX`7}4mwSDdtYm-)~&65O;@XC^u&J87D+JCOkq4SXU$kDbSQnag3=ki#_WVsB^F}B z@KoFP#C9D|#J7YTm(L#whnLiCU$<AYf(NhRu1yYV))Nya82CUb*I_BOvw!94MSYI$d-oS@-Sa9P@J0VMt93S8 z(pz$!mi#*wYo~9~>XrT1HQJC&#vja;O4+P#;6TP=9c>wvwTyA64Wb5b0z(cB$l$oQZh5WbjEocitp7*(42h2p<0o9w+oCC*4aIks6_o0yDa575MPbz9(kfMA3x4X=@Gg-@)`8=W{WNRjFCQb{kTjAqduf?aqgE|JzkkXv)CY5X z=#l;87K_K{CM1j{EUaZT1s|^c{HesOIGaK3HMCWh(phi#O$#>c41gBNArE zR%V6ZY6QFu!8zr3Y{)FwW%F<#K{Jyc(%n7d#qpkQt;@M+RG#Yc6piM}dLkZ=x3;us z-O|!pZ+hazV$-|dqiqRf9_wylT{Y!N!L5as?y(8U-^?R!{iOj-dibT`&jqy%45vhz zvFm_c0nAD)Sjbw$NLQsFsk9fiK?L20eCr2>`GD`_L;Dx+rwdp2jRBUWpw|AnRtJXo zfN#5JaA0kdCfx4Y;vFOoNT#C<9n7&7UnO*)Gln8PRgd9b(uf}T!Fa95tj+ogGk0Iq z-mu&4@j(f5lL@U8x-Jt#Unsvs+E^f(PcD2gycvs7+rOmOjDqx!zJctF^bGz>FQH18G*@JF=gU-KAt(VMsUd74f&Ww6%4)o8)YR=Oz!@ zst6yOv;=!V;_&*KF>pEIWULJf`z^6hyP#T7kHD#p z8y|O^@$utqTQ)3TQ7UO&Dy>+)q2Wj77;NhvX|}n)=#SX6{7T09L1O5H1WB0|EeDlk zN-Y}srK?K|0!^V=KWUF`E>)6u@*rUq;&qU5eqrR*F13j9l^vl=_F#sB#*r#Uti(!} z##!NANw&4MYW5d_blQLEBDMr@#;~{Z;i3mO0+lVgbny*0+^}JTzFzi-b5mO+o;efQ zv`M0o^TS(oac=kLKQD9V059#tHbz#Lrd($wyM;Vda9qw(zX+Q$Y!JHdV8%LDZ*s2* z!9XI>)^5$r&$?(L5SX1UAeNlbgBFzqXmM&btj&^moge z;gR+ZtvlLBhSzM7zHb*4H94s^K{mFLl1#cjH`$b{1rPhzL3AL0HEs)b162Mup6bnE zn6i{V)nI_zo6ieJ#}fX;L5|?!KsyExYDbgryku{(ur;QBTj1ZQdD1p)`%7xIbxwl+ z75nQHk`yE_)KkDIAX3Xc4&p2l%3w@2F@8vYfA|Vp*9|F__rU7ar!E|$lUK>YCIm3B zMsI13g6=Lavn`88vnMZK;Vc|7rzX@S*%r~|-*0td2 zfWm03+I8?+G_P#M_E)P(J_irt#DsmGYq*p9+fb(s>?;?nlp_c2rw*)}Wx^LwPtlgx z+)Qb8@H2h0&5@n`0gYC(;Rn^f7=&0ojMd7?)UbPP%iWu!nJ_vNEj2f>-so~;Q^}L5 zc7FfHve8P*kV$2lY4QIHy_UYq;llx)g%5svU;q*att+rvVRa4eThldo1~hQTl7;;{ z^jgscu$%N>|8D&T!z6;+mwwgaW^Ui5)^pxrlOdOb0wxmr?)yLm^QH(Nld%vvxrvK~ zI}(wQZU5_(F=@-0vX1HVTm#>Ng9*YLb8%9CN_b;(oSmT6eLzSJ-4&`)v!6v_x*%-4 zmd!-(x+|K=G(>@GgNa1&+6JaG`eUA!efwHG{%n(&bmoz0^pUyWHX1XL^_#k%L>~GS zwwF@3rUub>$#|MEu1Ls%c8u53>6rF4>PV$ybAN35^_|}M+(UYiEq!hxh-pXI@m;IAMfYN;8i7ryfnX_%EtlogHbkP-bG+o9}Ke@_p>M5d>w|A22ww|9(= z9dgLnXb0=pLjM+0_AXxBYx6XG+gFW@bar%fj*P52;A^%|#L6*5eU2G#JAeDtA>i|Xq_S|OSvX(! zqw}Rw%Yt?1%-5O8%gj7}Q9h@gMSZKbnolX4(Wqlfb_tx^-oWn%o0Is`M0a_~s=h_e z)OJZG$(hsWJbujH_cLOxOADl;y zd}~>Bk~Wn|(-@B4N{!jv>pQ!{%?2aVzRc12)^u?m&2&FUOh|lYCheDBHDi!p2mKXI&k=9fo7-)$ySA(&5dq;OYmkGQ4 z=$5tipxTu3QE1a!#~=Nx#2!zb!wl_ln^L887s~F=PCs@$4SvPCAy(EeS&y2d`f@AK zgq;t<^YCrozwR_fNjK)pg7R5!8a#HP&a%m$7HBpN&ni-IQ;xS)EfD`rZBxyC!<22q zRCB)}&t`8ZdgxCW-ac+-U9y^Nj-k{H^?CxY$JXJjr4GYg7Sxf+zN9Yb$W;~XOQ}(38fCNR!V~=2tp9`y%^0Gx zx*?Fs`d;x0ejW`ohmW<{6{QNKV zm7GI~&ia79&u8`NLbI$#3->k)=W0#ARVKRdhIhS71g8*MhYx(9$vjM$(L{XU1EQ3& z9OM(9kW{1&eQrmSRebW3qOXt=c6@HZIP@_7Mjg^<$HLtsYoyFFVrc6MQ4I&-2e;dS zzg&=k0eG?Jn#T0pgz$ZUkg3=QlHh=THTixIf|7~IKI^w z>xDY7dMe-#&;9Ptw%EBzdDLlnw3^pMGCubu+aR4t#`Grq)aR)1{6l)Let@0a+o^G= zTJ@M-Rx;V4bd!6GvPS~@_M1&*QzMy?VKlIImn}Vqt5~&=BeEu&JQTZXwRNW-wl?PT^IRWHC0cxo zi@GzDNwt_QKKry>F7un zLg`e1Oo31&pYKI~>|0l>6}z;veA&rk<+7jS6p_`OMT~10yUA{~L7j{xnCPHaXj=jW70{IXZHKDawo+0C`+y(PBca-dz2 zgISc}1ZFE;U0n&b3GC?_XN`%*7E@Lb+EPxMbJ@z1_$nDn_DY(bd-+tcsw1RTwAeiVs5t%3tU#Oi+Xds zcHUk-=dD_$^&u;^9`E=2<38Q$w0C4Pp=f8Ct<&6}-giXQup4gJpPCIS=>aMPE z_?1ropuSrV4jy${HkgfM5`0cO?U}jyiscvfkB;>AICCfHTZXHzJ7;862MT;-J~?Pe zru2j0y-gj?uIYfRBI_U+Fu%|$xTw%;BdS!e0+7=@v?oT+8L5dBj2xOpng>Td{edOg zS+n}gGgq&1#O>?#xZ8(WDdDc^!&j_Wk>)(seaImYqE4@HX~(@G#{%Z$Hju`3;@HL$ zSFCWH6)R3W2{)?Uyq*f>u7CF z7noDQmX;K1tzlPn=+Js~2-Oh$Omud%cT%l|&LPAMEYb^t1-WCOISp#ZP~n1j_O22m zN34Q)NhcdR*?^=88zU^e``BZTb-T~>xVO07$3A+r<2jSLc=AUd@AZ^rQMU zOO`BgyO%l6GPirl>6iRr$r)Z}nd8~&(My+Ha>EnRGZ53a2xLcr+4T_9ZWgGLdLBD#700&DHg!f%oQGqnx6&wPmsq*2?hFqM-H~ zJ&m+4tWHBNyqen^4>9%)9iKGBXEZ~-zEWAfy$6OJCN2j&e631Q1VmWPFzkDHBqI#K zek+zJgbfR$_B8}{iOfpbHkHZUpfgdMRfv-0miQ7zsb{Gy;qsP25@?b=C^jHifrzZc zEMgwXIwmVw%J;3Db`{zR_V)w+P!y39`Ofip)45zUu@_KF-<=Nb1Q*EVacEo|l7 z&;Gh$ZIx_)f3|`Rl0Z)J--)~H8% z4kh}kjcT$Wf+UoquW`F$ahCd!@9o;}4?>9gY?mkACS(Fr2+23E5} z{bhHH8w%q;l>MqN^Lzdg0wpUB7j|om>ekiXl})$G;v9X@wL-lJrP*tRb>^Y@p~N z5MT~=DmGBG{ltw~NB-?>5?N5J@xa zxMp71?GRAkq9OpBktW0RN z>BD0bMW0E9xerpIh<+)gi5dv1ASud53)8$eEO#ej7>vas8ecq~&L%qA`#SS^6n)Wr zzO%oz1G%~sE0hxHa1v#8G@Xxf9Foq(bS9SM8a|UJ5KD!^uS#UsstWAWCZA>A4$Q@(vT6mm!Nkwm~B z!F(!|N74pZ<1Q#nM{05;80^AmJ~jAY5jgf*Y*sNsdj_D z1A+2>{qOpRPS8m^CFmL!**j*LWTiR^8jZhPsg__)){nx(`jBMAhwx~8q_;j&_ZnYG zKzbZtj6!y@IT12! zen;pmI1_E6Fmfh>cxBI1flh6~c8}9rtB7zRsU%UBXj>H6Drb$T#!^BKD<+w_U>a{a zD)kA9=nSnWF{5OmX;fv(qlEp%>%U}BawlQW?wpP}tk~Et=^%SXcfoPIhy!kHE7^Fu zv&aSTWF&w9qKGTuaz!1t+ljjHvh1@{b(}OlkK@XcoTof8fcD}dzBsJAI?ukqiRm0d zf-9*rF4xP~?4ibkYDTt@nq{^62X#~HODZq0r+t~q({A@I5x2jmZ&9xwKIbj^{U3I@ z2KA6T81!lfA;|9w9^()2R)i=LL{vZJ7>|xPC$rU{S1xa1Ea=7z)fd%HG~(Pr2iLgZ z0ccx-??@2tXK@T8e{e{GPRRZ3{?$2bR^6?AU0vxGcc_f+3hPM!$9VAqQV7NWdw^BX z&EbWPK8!TIAF~cv|9gm~kN*q2GLnqZL5OV(DJ%a!z^m(jkJj}6j+z+Emm{Yuok%&+ zQbsE(>Q!glrOR$NCoPE})&ocot;l9z%88g!!T&`;6moIw(sEC?XkJZB9yaWDhrs9Y z!7OrzMgi6WfAyl9S{lx~-Fbg5+p{R_#Zt+|8)Q6suNg%!f4kr3H>1w_Ci?5In*E-f zQI;xpE_o}=cDspSArnT}IAxJp%n8*9tjNe}yzFaTt-TFPT&WCKmkeez-cxBqDwWHolPG5V*fQ%gLZOlN6dTY3 z=R8p?e9XzIoRKz`$}zTd=t^V*_P)F2^V1jkTye$2SIBxzUR7tkz7QM4*V^^LG6Tce z;El3x5>^JEu=_3}g^KFA%UFULPD-eVVl+gKZkwRNwaO zv(L)p)$ZS9l1YCwv2yjH^;}Cjoow;>J&Rh|4QQACoLjrR!9em2=)-lu8w|D#lgDw7 zq?1Wec-`aD9^H*unCwB|Pc8@{U6NuCD|O}zhek9@Z7Somwl4Dd82mIh70%WUN|*>@ zp=8jpXgWX!S0XW9?w0cjM!vMNjQlhLW!+D}`|j-!xwYZ?=RL{q?yc z^_S<4j6@P$eSIAr&I!v>EiJ*hKl^)5iKf%}Tp^!M!4+TO)q3LSWy^~7ayc5)#Ul#6 zy#vcS(=EwRFrTX|?(B5>IhsU7MW3eF6Q{9zStswMFl%?YB!Pk&2(o-`3!|NQ6jM1l_eui}#* zp+8Zg6rsN@=zH1>&QU}_Ch`?yZTXfx?Cgxjq&XTR=D5_S>&5ntOcrH-IGgEcFV0PF zHy=IK2w1yxiPl@@CdDwM5!A?G6K-!jamc)6=~~+!ANsUEf_Hm=Mv0hTt9k_a2#pow z$80tE;otuDx7$#E48G+pZ+Z67=P$na;%%=hKDzvj#~yp^`Ja9N```ahU7;|I?)mF_ zhFgcPX7qaH4gkV}>5^~=OIA&v?Q%^Bi@wB@$7N>r(M>$O~N}WX2IO zTM$pfk(keMi|rIFHso`k-QL;RgYvGc6iu~v9NyjA*%}JAv`BH;2QQ^{ny(3MBDPQ>BJ(Uz8gKjsT2<8kfDWLx}l6Toq}%oFgtp?7vR(7e(8 z#W)dOjuqS%@MhVUNLTmSA3HvJK^o2w#D<%0WA5p$oLHmhx^zd=x27don0vY)S1by;1NVvpnLhdINHhy~ z`3No>h{#!z^`(W(-MAn#g~S_)*^`Fp|9#NjV%}SGI`2KYkaq!H!@EyzlXsQW%(MUr z6(mVPhKSS5WRd4Ywlg;VV@ABqicF4)qip+FiqfLyg*_igDBN94Ah~7LTM?5$2v2rj z$d=6z6Ksz;d^Sv&{sQm*EJ*e??+%ya3Tx-$qdcCB$MYI~UGP}fHmz@UTt{k8EDF~> z(m`DGoP)UJoC6OJ^dQBh1?o-DnwNOk_B7(~t+tUqZ}1zzY`s*(MP?rp9Q7hajnpLh09am5N*IVA}u>5Tj23cPa`DY z^Xf)ez~}3>vG|T_rrXf;K+A6IQXkXYP=Turt5f8iQ4M=e(dd|23#R33_(8?hD~d*T zvo$ov!m{~hvo!>LM%~PAPP>IS2zOw!B4>u=7Tb}mF9~xpBms-4FzyfoV}h1P9X38L zyZT~reEhJ5{&z0QWCO2@dICptJS&+6fi&`O{aeh6(t<-MOp>SqKf`r8We{?t0rAFX^-)pSDMcz?iRB1k|d#fbSBgx4LIyQ8q zVtkeuc_kmc@i;!0zxDY4{9bYE(nAkjs*ip0$tO2%d~)vAx4lijo>yu>SDT=#H*n*0 zkXr$#^7gGudArkZ!9d9;tWa#hg+W?bNsJCHM`dj-`+6C6ISlst8eC68Yp4fhZVGi- z`v|k93m zQX2EY%*%4iQY1Lrc59y}GM_hHHRqyDpS?+BvR6*zw9D7dxv|UD4zF&>whCRfMiaqc zqRr)M`w04MS)Dz;B>}lpinLH}g)Bp%w)Rq2ds`?J2(}i=C+b4JQYo-`%oi$^d_fPR zkcvxQbYB^c-{W3Sz~#-gb;M%2C7I;tIhEnq!|enKEqBHfI+;xBEiPI9Q_KWkiiLs( zI59x`V!FSDGw2snt0nGUTFPGlaiY7FN}jL7{VX!YF6R*Hq+XxLbL!yWphql8gF(-r zlTMXNokdh^>0)Q8luA42<+vyl%VpWx#j^>UE3P{oG}$h1%;%0qBgq0h$_WIu6Gq~7 zq|`WPSgqVh(yI`Sq9gvSPj|>Z!{zFrIazB zIPgM=C1oX1;K_lVBrJ(lAG@q(9bz4&Ofp`n2`q$BO zdW?#NB-Yh63Dc*WcLb9 zeZ}`KyW8!V`vf~f8uThX_ZW$76sO6g7}f3rvF@9xk#tgm3)W}%RJ%wxi8bVAb+)`` zky|%*=Gr^&GRb9}5SL_yFel=&yqGDeQOFvBq#V&@oAttrhcL2|S! z`Knm9Eey4cpsIh9cbG-PkT%a-((Jf6RW?SRPXV$v}6ZD9Ofnp`5xyl%n~>nx&|+7rX}5*ibP!Q99mM4&OXfRc2cSSK(LS> zT+RI&N4KWhL%~76ueH!Wx@aI6EX3P5bwMC=h-}Bzeuw+QJUM=@({a8Q3V8y9Es40F z!+8u<*j3AU|2MU~*I({?odzEom;C_<6I7PiHwfx#H7`ElPnN#3hQG)$qof^gpkGAE z6m&0KOi~+VKcj7D2!hv4Ftj<-89OV7b;qlN9obFkP;gVGGx%x=I4jnf*?dbnz9}4w zhBwDDJ%?m6hxBA(o5QhKXj430T#?PLDBckWY)R&7PInyeYO> zIQr(;3MpzuY;!2IDW>g%>Eoo)!$pu3#g-d#6A{hs07W{KRkZmEf)O@4LXKUeK#l(R z^K%of!NFR-HaOt=y!p|!!9myD#Pi&$8Sn0PCY|o?aRF|W@H-*=16^6;epcSpC@XDq z=LHV!j!ZD*@fzcSCJzw|mdoYqdWMF2u4}q1ouyKqPpMRZ(WMWHB+-qz+<^pkOn=83 za|>@|JxXqe^_Uy1as(#h(wIZcDCUs}z;?#T?m&Ek3iwt}uWT}8uJI*=7?FdkS-Zn1 z5hi1wsa2hc_O`LXQfZ**V^`1VF1C2(-dabe&gJ_0#(H>YEfy=oeSNfj?qgi(&2<(E zBy)za6|W>(I(83X+*TS}IuwodE>5M>SFNus(w&`0jdA4cZEbCJPUKjzQaNnnnl+UQ z{C=pCN1o{IS-pDG+m|FD?T(J2p|K;eQA@AJV0-C*Ivcjs*SL;O=U1=K<`@d(S?-2C z=v)Pz_o6LW$Gc&%cF|_!g&JfZ1;K+5kr*waWOpR?L{%3mB{9Q+Du@)@&q@zVqD&Dq zO*Lc4BN=O=dopIIB=_mXVed7reiwHf*019<5brhK@O!T2=B5AYs{@{EbnsQLdexC1 zE%xtV@!^cZyR>Dl59$7+sQCl8E{0%3bC2raonf!f6^pri-te8!_-IJaJy@w!-ga3f zFRH4-+2r1P!C8wpZ{7_0o$T88Ty)VzIvK#&0A(#SHS-FvyWu6!t z9pP||eYd2bzN%PIa3ChFvmB4(O2a7|dVJbbeH@<6Va%D!mjy!`jy%f<9&ljLFS-mq zFkg)Y0smoX0t3|iN^^SsSiijF6~`IwG}du&Ep=@*LmDkt*a)5?a-QHBf5{|^fP<( zv1gsNwr-k4_2(GfFIIr2G0sv6$9zPJcDV|tcvh}D>7*lgbwiNF@;papy1IIMi=~X? z;eZe$`CM2)hM zZanO;MJTcjQk}C4EYH{>xt6;4^LRj#Q#i@9a`lNP9dUR{3bh~7zgR6($a-nA+r7Hb z)zc=ppj20R=tQT?yKP+BbvUyd$F-)=)!mLCB-=Hjd;XVB5gYG{ysKIZbs0J}@{Mq6 zsLaTfR<$OxV;pw0U{;aC(qMxEeMX`(bE3*C(gJJH$duNI&CXXubi>-(fA=e4k;9zP z@@kdug@bFl_^b(-6cQh}i}=DY+d1O7uHhYAwrtg!Wy=QH*!aK9s}+z0Kehg(B?JB4 z+#rXoVXG%;)~tGIniGZw2Fj!f1rRd)DmMS9o`w(kx#hV`9g5x47WO_ju^QXWGU`OT zOv#dD#o;yDVBIrIiKoRjR+$RhXw zNqKIcPN%SNO*{HRhkYbFOXsbvWwylL3<~2E#*q1pxA42JF6^-EP| ztv=pS)TK}DN}vg9O~n%kqQPWKg7*q_>CdJdTjKxhZwSpft#iqe#jF>iKPndc7B5-S z*}+QqgETsN8j;e_zv;sgd-W#hX}(#@6D(z~3x#u8Bui6&qdlIM7*}sPJHp`(_QKg% zGT4$#(2_tl7ZKY_RBjEKCWZaek@CLu8e2|zd5owR_Jaq-OBt1mJyaAS3UKXDDW*3z zx|zcyY3ka?wy*|H0D8Op7ANIr0;u!HXL4R`=V=S4GFsu`{`CaEfQhG_B751hfjl?; zn|GW3TIt`#kP9t|%qp|UT8~R-rI1jEcjf5S?cz;Vcx`oVl*txgR;NIPGsA*ZMx7}NQ}sK+vyug@SwMGnA9l9 zb^7TPD=Zm$;=OZCnzB6MVtB#_c+>e4{P&G#)r&f)%Ls3FT7w`nfdx?@M64x2OLGH9 zH?gf9mpI4h*1mQa>|@bH$@n(c_88m4zVw)RtOd; z!5D&x4qeVtM2(oO$Y2PhNY1J+3UuTM%rLFQBSlL)ps!Mcdo`tOm{qStQg9D#C0}pN z#}$&X@p36WGAbL7tm%W4K|@8QiG7+V_eh<}ioh;{k`YCQ5v5QN(Onccw;p=p3H5rL z+g<1hMp`>{BoYr}s-`to3fzebX9 z1k)LGW&ThindC(qVL9wug`SUdJC9x!3bDJT1W#lcf~gyd4zsdoOfF){?w;<7%Y`+H zuZ6c8wPv#HL(S_Pj$S?Y&qxFi4=g9l**g-4<9d!NU9J-n30IMqtD$Y^=tMDi3Z;2m zthopbjweX_M8F^;)a21IwhBv^Y{I~%C+KSl>P#AbMU9+Th6-))QhzMg*2deOTp6q% z0tvD9$U)Z9(KB%=N^f4jWn^Hm+|w1$X6a}ofF*CbHJ1<4tk2qRYR@XKmlwp*&5-M6 zUpyhg1h3xmdL7-8Sc;^QZC%{o>vEAQ6iv2pN2l>3j78|cu&$=7S73M~b_1?OQmcb5 z0hK#kmpjY6f~g}0hexj9EYzb>V^O-uVYHAxgn|7$Y%N?C9sqfpIV@zAsWlZ*a?-sJ z?5i2~tB{0N%p|G4n}bImZ>C9bb{S8l+VdUoqos5AEN*=Cu}fj13sSVTmrL>6v)OE0 zJR#QRAxx2CtA{#^dRgNm1&o+jb74D`;w?}rq*9z4{EL3TS?_Fe&T-!8e8~BTbFcHD z^N{ls=UMim<6}NiUh`%F8Yf>LG7aws9A{Qx;%6oxzMA;PnWEyNJ@aK`Zpf_QP+flI zZB5L;G@D4spC|0r;N_%V_>pZr2xA{Fv#M0dm{d}xZ2Ei6EIp7uldZuyY9@!2hIJjk zRy{Bzb}8(TdYSj43qANl3v6%LON`3_8?7CK4IBNwX!l^!459gz9R=DhX;aP1u*!Z# zST}1?&FdRR;u$yf!uDx_eKwPPM0#gLn@UL-*I3D<=*5mBQUGFpN7B0k|5?5~EZ zqm&{c!mox{e23EX0=&bYs=cybv$!4sr-YA^dk8}nN*KfPLsI+Lp@DDocGO|aZ++wN zJI00f@+A-D(I=^cD)~qc<wz@4Us)yZOHh? zX;ZJ@Zj6>x$E)Uak6f-^BE0EdhE+Cla=amhdKmyg1r11hE3|Obv=$DHUHYME@7c?L7Crb#$ntM$m>(PY^Bkk1wcSyK<4@1-B~P+!jN zPA0X(Cbc!!){dE@_BdRH@$m1)0)7l2ygpwD8%S?Fn?V%xz^U1dz%k)vtir+z+&-6A zR*CSC1YETV{%<4eF~@uAMfJTDN^*g!>E zS~$}2y0~M>7KB3+tQB?0tC91AK8-D5*u%a@j>QA0Ep?u&tELDadsL6dFN{g{eRhvT zN_?_~qFG+M&4h))UVUWt1`sTX!feCqTXC4HJsKsAJLHYUlgU`zACo&TA#;4v-QBC> zTt|^z9jCxjocVJLLUiXjA-8Uca-%Wib0x~H~ji+T^jk$RT?e+Lu zk}j0`GHx8dA-n}qUCSUb>|$u6y&O`>!V-$f;NkaDh&O^-%^k<$9OF}Vg<=`!_j(2j z+J(_zA2^K#OWnffcSRTvR5!9OX8)qObt?6M7y>fY*%>E~jvCJ#!2v=R3lg z2iQ)fjf1l2~7$aMGT1OG}Ys+H|UaWoH``;UYOgh{ybtPO*qKS-C_e ztSuP$SVu>P3oAXTk87)iZQ)S1)RV)Q%Tq1ndwbW8@?zPT`(maX^htXi-pz|)N1HGP zLo^O|cPQDddpaw;CbQVH+Qa)mqF#R>-5vAvw6;e4p-}s9pjvECr+b5Kk_IDaYVTln zxs$Rr$84&VS1ly^x|cZo&p|l|MCuc%m5+lRCACWqe?VU<%4F#fpNx|`7LJk4$NWC0 z)Y-q3mjxy}a{Pl1c~9J9{0GB(u*JCm%ChDnjI0bC!y!W>OkZfm>LPq5Oa@lQzj>xF z&(Mll8OMdD%<+@gC)PNUuWXi^j9Bf|dZ7|hXRCS>_;3bVc(+}7Ph7#`Psd8|Y-kwy6~ zE^6^MmuwacW+E*fZ#>7@?d#XKz^|gwLDr>kBx=rAn)~ew)RljC$KAxDEYJI~$V?SU zVLuklO!}Kx#QLIlKMphKV~y>OU{L0?%)natWw-4+J5WWrvZYl=bfdT|_8fjhPtxD& zi?**=-qzaMw!GRJ6Lm<;9Z7WNlITz{qe-_ozEDahbZ zUcda-|M6HMSt#VodP(1g4Sh?OWQ)E+OIgl2RZyNziVt(9 zm3J&Z%R82Hs*CLl+g9OYf7J?61GxxJ>T8cU;)vV7^!|(9al~C;xbd8A`9+Hs{blhy z|F>b+jW<5<%(d6fy+xM0}%pW`c zP=Gn5GCUr7FpQJ5oFNDY7rJBa3Z*pXr}(qHO@NswzNUX1pII4T)5Bw<!xKM;bjEEEbVB_`b?T?g(x9sBD2=ASG2rU{i8r-k=(>NipyOcDcCBPoZE)i z%Xv#K#eDl}=3N@+0wFo;Op(Q+{7tYtkqkH1DTnvLUN!ZYE>a7Zzcv1*H*Na(6iv z^Ms6s^r$p;L|hifuqd2vL#u&3ei*d0Be2pl>=tEis8@2{$^SG*uly>g1Fisn6?%}- zmeG-YiHzh(*>ZDPP(`68X^hoPMb>d-{ex2(4sAEvmhVd$PC#A)Ag>E377ExW@%H9I zzSH>+-uxWr<%98jq0?FDCt^XeW?t;bDPfqBVzB~>;x?U+MaAES(%8va|I3DxZ&*sP zbK`)na|nB)1HDg}zBT?ogXvWldNkSf2z`$uy&&PRA3~q*#ju4tl@R4$MOe+Kjj&X% zCz?vXgDz$NrXwk@xlVReaDt(9<&sKDoCS|IyqWIh8Ot4*D0X!4FBu$^+3{35jxGA% z^)=Ij3}drs-ica7KP_Bx9VeX_-q{;2R@-c~&sGC%E`f>)TOP;udnAkKtjqgDHtL>@ z8-Mnp#!cIe8;_JvtCEbQ==_9 z^XB&Qn#H_rc=6&j<#w+p6dZBsc3wOc@_3WH7Y6yPFu3`<-`xzJT$~C|>aQU~AE~a^ zkFZ{Y0@;WQyA#7O8FZN#3%{bs6ro2Ne(+Za$t>37#?DN5D|>wO8`3kwhiwMYRvS%D zqgbg7tw#nynE5Jd1NQ6uG6iAk#y*EbVdGNZC`t)aposO&!(nb?^5zE9PAQz}&emIs zm&x4_#~ZP*?sQWbD;1lsX&%#nhKf>R9P>HqpefRL+e?%N2z|25(0J*H#R8>@2EZ_w z##Ta@4w_sx3d3pXN5;q2Ls|n)P0OjpC`!tsvgweoAc#I&TTM|QqpcJ1#>7eAh$pvw z;hCsnK$Y|^?X|r`p(F3Ipkkw%s($e3qdz);l~7zPwZbvP6Syol;nKPG4*543Jn}CS z0_`2SG@Cyz!K2mVKUrYXg-*~JJX&^-o^(niY%DZg4liP48_Yf(z8>dQ((ZH$b%B=+ zF4mbM!y`<}K3hZ~S!z%QW1_&46XES;D0JCHbMwx@1d~l5AD<6h0`Vw&IXdCrJp5m@ zy?2~sXLaXY&%IUmR?bzoa_p+=+|^xOUER|YdU}$CW)vn!NTZQ7@(9EbB#=lZctkW< z0uj~*BNiEvOwKDXK?JY8Yi#qeS*h8KZ zJmJLOIVT+Q$75m{5WRq1GjbIBKv_mH;X3HnqBV*u3r#y#)dI%Ju z5sn3em02d#{9Lr0h{uqlxtbt9xnVk;h{Y4-=-fP$ZnhE(#%Ltf8{P`m!WWOBkzquU z$H3C!MHc}l5A!CEf=}7Tdqjgqv-QQG@-TE$o=$6Yav*=av)21@E87tm41Ff;BLRx+ z#<=0LfpP(eCkIpr*DY2S%pCJ_6)WNA*^&~wASw{ej>2w<0+dLm)Bc1!%RD{-@T$1O^F*3`Iw*zidcAPOj{cx6lweqN^tI5@J%+6k{~cxkk_RPdRcqp?dNezdO?!QlW{K zE0yXu-u?i~uANoCuO_&VFHJJ^&BU&$wI8lY6raet@&F*Qbha7D8nk%ZgR@`XUuU3^ zbKD+r8)noLThFFnVVNsv0ygRk_u1U{(8~@sY&GpO+sxl&2!DsG^at_J$iT+1V;XT2 zBy}cJDv4zZTqKqsKyZ4lLR3m6tZPBrjFbNrxs%f(cx7ZpFpU9YGV|M}@6Eo~zV@{p z^2}gQVjfU?M67pAC4CRCH$u4*e@$l8#A0 za2s-V=Kxst5E1H3FUkNh!kHXQ8d=6d8L}`)khm^odgB2Snn3D^)DUuH_+ZOqaU4;k zXbj?JF-_Ux7m_5FP??NIh<4!Kc0f}ill=Y=pb-@kqN}j(D9T$P2Xie9lJfG1;Q=za zg`rtS*azk2hP(}V*LWDQF8Z-}^7@(-LkcN7WXGe>Uktt68|nxUmFr493T0+8BH(+; z(4`*of{>M#*ck?55RIjv{OlHF4)$mktyDS|1$q$P6$n&o z;Q(aVG~0PG0qQGBNf-{B^=9V8Jjs>eJx12+?XUvG&8k=9kzS}w4yafvCA10?DrZ8^ z_0>}`#QbvXaJ3$S#DFvMW(h(RZzTJDbHi`)fb=`&QqXZwRp^uuR571o7K85ojSxV7 z#T=Hvm+6mVk+v{?(@7jjlYuM8S_BF7hJrR!bxZ=~9Wjx|BLo5q)CpU%+*J1kUG1eV zR|J&>NU&TTF5+WDNE%xApL;Ct$Zj6Tj?mO%Uz12PZmr38FvR2Ya5r7xqab zTX90RB|1nh&$>?peRP~HOv8T9iT{g@Wu*ORF+(RyLB|ncLlAoi5D0AIks>va{1aLx z#wR)Of|h{~?(WcMQC>N26 zHn*bbme8iYtI&wd1h}3J!W{Yt^KNjCyd9xA94=sd?baiJV*JkPAKJN`9mwaJ_|9_oEBNG12SIy-l^CSaAZQgfdZiZmvbZ@1*4o0{7F79*XC94cYR6pSml z*1yLaIx{!8r$ZB|T)uP9JxJqfa&q%VqF<(d=bnHcQZY5PcQ0`UqPOwy(SJl|JxBZ| zMMhJmSZcJT*XD_J=LitRh0tKT;f-|cnsM%HU;ElaS9|+cUUfSy-fWiN@&WI`yS$fv zUY#30TfKhx>~|lSnGQVw|Bpt?AGqh9yYKcS9{u-6KhF^U3YiyE=*Vp}k##Z`9bp`u zB69RL&-1aSzt!_TZ0R~Lon>ni<;X7GhSI_~v1;4PgAU>*?5{6z| z+6V_@!=Qt^OrMD;Crr}@e!&%NM{HE?CXgBIu^p2wqDjEDb@yiBvK_AHo<}FzVkZ|n zmU~`?1h)(U-M6}Db(@XUjpkPvp^Zwo-hL6pTQ3a1mnI2SG958CXkzL-(*>Sz1~O=U_nM z$zwG7*7S7<0E%0;HyRCJp;WW#mFKT5c9|naC4FLgu{%FsuS<+Yr93;ks2+4k9Lt>p zvRUS&=h06Y z8|uqMApZh$Qir8=1at%y8EJ>oFI&zK#xnBMNRSZ29G`CA6@6I4=K=NUE@M0<{JPb)X(P-}45s56! zPm|4bYO>?^FE2MI(7t51FPT~lOTa5BRpSzzUTd$dPRZOBoZQ$R-XVTypU0y(j`5mF zh*nBA48*rSf#!&~rc1(fCazv@M9|#4T#s8*_YHqpybj`E8Ow z1WGI-{D=~Bwn)^hJ(d!S&MKe^^k4d2PM6+ZRaXu_s;04i|6{M^UjIiO3iJ?s z@1Sdjg8u1z<#NeG8OdILdDG#QC;9~DXK%a0=R;Rxbwb!gG9mCiO}`(3em^9cM+ssU z!sKGSGt$wMFD)3BR@!84AKbTBDJH!gYiEse7D;?p`m zCJ#KJ^x%?Q9#~Y9?Jpr)gw_ioclTaUTKE82K9{{lHVXuHHJE-%I=BiNmR0X^U%*%Z z6sR$AA++Qh$yFM({G6E0m@g5V^h?Z)tog0ryqyr_WWwfMwvW*sxr4{K&VPP#)e&&S ztmML?9y5fU|Hyl~iKGdrlQ=j6>^9`_9}lGbyZ@ILmt5-v=;`a|>(wq~O+EVm)DqkK z6f7>8lf?vfA&(IDAy>U?cd#lAL@Uk8jMH<{9iB2cMMM+}uwY!o%G6!FU=Kq)vQyJL z*9d=GXk_CFi!m09MH09w2t#Z(rqGpY<)oAH;{=GNF5KDgotvH!`KeT5LIb);Ch}kg zh*L}?W0hpm)G}Gx7jhzoKMlbj&pDm*W7p%}R=~%uxC8`9M~!53{F%gDN|=r|^lCDb zklEO&Z@t&eq@;amBoPPYP>n{8#}3?ezCX4<_!}>8+y#?24l%tdrOV%VegHg{-Z!B4 zPlh7YFGlYr&{oTJ*aqO2W28HC&1(AeK9ONqbF@)6!uL?yQ~>i!`@<5uC2bE6UY zH=CQA`xOFC z3*{1d{$oNFoL?J{H+`W+I5+#bG=Vmlh-E@vms~{IflhCMIFZ)c%!qDEa}nm*S?(>t zd;n!MAW0-WZ3?OFVPc0B4OVEd3omF?aJs`-x?H-Yg|K)HmYlm3frkn`aP z>Z3P_421gWV{b;A1Z+efi3QudbDI-LlkDovqLrH9OhLcq28C?>z69wQq=OCnHT##8nwbu1DS?(iS($FL0oD6(d=F-hC zsy^MIw_?#m&eqDmOb&D~*BvEGGSMuKbvv)!F~_&2( z(Wd$(WB>lMfa41);te@eQ1Dq-Oz;)P4CJSaXf-H&2&9cox zGHD@nrirJ(*cNKG$`Oir6gD0He!V9&0_Sa+!G)218Q>N>vUJeh4W-{d2E z5Lh;yt1MMbW8L*ZBG~==KTC<4{WoGlr>8VI0$LyxT#7ya{l>75#cyl&ePhku7 z`mk)(QV~L4q~TF>0U{{l&>=O~v;{1{I`Kn(vp(7*7}kw41U~G^VGn*u-O`n$5Ko zr7;+Z8uj|!@ddZb9Ev0)loiM|aB3x*v8ZJiGUa%jM3kyhU7Dj7IQf}p2)G8F;SX5` z4!u&bv}ImOSsgVIPqmNZH%=BU@Q4F^P{iR<^&b&4uQjr~YF9yK#1`>S!V zLRC3u_`x?8^W+_IoXVTonIZwD!LWK4@-DTB9#wxyTPCq>+=|D~gAq`u8Eq&6En_8{ zX$gPfg9{2*lgu!95O5QHfWZBP6Y;EC&;wv%bM_WT7C8`kkJCp~P+}wG#n{A+^hQ>- zo#?Xxe>#OHB#=tuhi)6GbaP^Qs?E&wwxtdo){ZF~M7U&7n4Oy9Vcs%k)@eRIfOt?bSE03e;lK_eE<`K{{MV}u_bRkt^`p{Q;FWvT#Nj#+6NGi zx8{~M7IS%oXVjo{Fp#$W#LUF=&eBG&A=zm(%feNP#ket(%?g~{Z)dYJ1G(1rYYVUN z_;A*|i2PF?$!Um7ljdlWcmUuWf9`jO{1r4byz=)?pFX{e!z{kI^Yr?$ou_xssXKSI zCc<6ckK;)9->2Rbe>bVw`JLa^b3x?zbLxIAU>kgAe*X61iFZg4H!w`qq2H|L8+Oxl`ReymNV3-FcqA!PGPx0efyt!WMgCF+h0l__22ob)bJB= zbtE+WctZUeH@H{bKD_tJD{q{dojrZDf0Ro@&%F4be+hfcC7zo+chYV$HAME5sViB# zx|e7 zS5hl;UT>|su$W3M2B|aiqi z1W|8Rrc@Tz;bi&td4E5>dpeo)UU5WSPSnFP>^JdEU~_86{(bxQV*^@>Q=X16((%(lA9-eY z>EoXe8Ys_bUxZEm=YY9b^#)=}wBQs+Vq2JV;b zeu--c0x=2@ck0xg(6d=!eNEN;>Q}$&|LXd$|N5`z-|GAHr#~J4lth9*zjpKQ^3xgq ztyI>VR8O6J&D7LXXX>Tr&Yi2C3-Cm(<0_1==KkFKCMRET|NT6Z4;kH+E0dlU_`#Dp zJApgeWM;^4l9@xi*@zbpWV1{l#-!l+gr&ht#IBEd0|N(onFF_iUx~&93K+pD3*Uls z!MohKCkMKA51E&;L$gRdcsYR&*VG?Oa)Ruz>_--Bb!6|_;`qCk(b4|3@%NidbSl5V zt88_+)mcfmCMH^`6)FNj5n7!=O4ifFAS>04RLFr>A~|WG)Tn1F5mECADXWl1W3;uA zmaQ_w@v--|-&gMc1wx{>ZY&S*6*BPWxicy3O9Q#Z=m93udQC3RBcw^P-b#Px3GXM5 zasJ^N>Bq{gvmPQ&6~jS#;OEVr=FfLi@8>tEdx!TE&3^ybpssEjFFjh4)I!NbiNuHU zU3|ymHz2HIY=8W@!|%w!k3V+%I%7@bZ&x3*sMfgz7seiq517#;g49TuVnFC&z{ru0 zJ0G+k7#h<1$=+CaLFOu8KS6pv~X^1 zf}jb6xT}>=zA&*~tA)c00Tr50B@Goj6i~;&w*-!pM*|gBfF`vtayH^ttzNu0xIg4e z;H`_Rz#S@ZG@xEt0M{tm>|VblX54N|N1(X4Hsp>fU*HRc^(2XNS zqf`0BpfV{IBCuOTJsOO^eVyr4rHs9giVcEtQbs0QET!_*iuJBMay@A}qm^mZq>sE? z^x3tQu`LB(%Fq@41k*=z1!ue<>0d>nvpq!ZI~YBU<( z__%uUCqMbghY`1#m()1eP_NYXxHV}l8bFAAeY_yKzeFByFiTMz`G&>wpWrQKr8K7t zx>xUZJy~XE_|N1J)SfJr#F36oiol1(^^J{nlKFrMGP`@)9oyKjJMBGh)Sff(o{j$A zK+0Mg%y*(#JR#r5n(I3*yKKjL6X`wh=oiQV{*XuVvE1wV0G06mMkUo9>etnmXqz-p zY1alTF*2AW;3Ya(ste|rq!P_GW_U3v>RPU2q@^o2lZ{LBB6m$*3I?W;>$0TR8Pj}= zDkeOMQKLVMAo;+vdb*CqXq{~&5O)K;V3iUbDQ(#1L>Xpe669IAi)@tfm?pRylcgY( z#P}eg;4AJ8%JbvL}n@pJ7Sv&gI710u)T*$K~21 zjHx!_aS|V%O7N=SA}mMhAZWQ-tVfvR(xBj<6U!hj5OVQVvVW2XicG8+ zQ8`thA~wOT{4G4;ZE`M}SzyLnVeuUolP~5-Jyq`AU^AI)IMJY&IMAffU3(%@twfz&oKn z1a$N0G4&{#s5h8QL<24a8v89~=|nsLpqdMK6$%4Qf!HKXFHOkz@Na1=fsYfY+;UPW z;&u`V;EDM%VX8#IqzlDt*sj+S`9ir^@Y#~jM44fWAL$9wUdrYCL=oa-qBF4*BQq7^ zfDVz_l4@5xPIWNe!vH=HV2(r~c;7Hhy_oQtk%qKs=U8CaDM)!5?NAF@HMlgyV|(dr8Xw`BM%c zfJDl+Vi5Dz%YX)#|`X?5kfnEdpvdBdKW8Tf-c$|W;r6pE`w$FX`#OP%lf9H)4AFbD*z)wy2` z?%8uR;NQ{h7W&4~#LUdzEWNV4w%Yv(2Hs4s)gtaMHvx$LA_CoWRu2PpveH@NO6{DL z%VoQ;9dgMu>sF)DC0UPJLOb~)bHVG&VyT*(oZE5yy-fmm*JozPI$*beF-`42EHGzV z6GS?1mwRed;FIhu(uU%hm3QXNwN+q=Hj?IqOKGpQR&~ZruTS74`EZa{S2$V7v8M22 z;X*fKQ~jowV!zkmMeRs38I|2yl>{^@`<10;gApG~g$dBePEl4mhPvAodGh|L^zBH)vmDTzdTHj^2*)92AwKhH?Kh7nlTS_gP8-3rASD@pK*rg;{u#7}}2g2~TF!_ES$WwH9 zc?{x>AK=nj9g8CrMR>)JH`J3TVGKz~MN;H+umjnQ4Fxg%W+r_QqIL|ERb1;Ru0C7^ z)c^u_^wzxPvgovWXl$>d0+!8%g+x{=XoUR&gxQY#^UZBIwSp1Sh@74%0a<+3ifUe<(wB|dcOevwW z!`nHSlV3MJ`E~A?cXh9lR5%_vQ-z|8hqQE`9=;ui?nAs2ZVnyD>R5f2h}HPX#mK=O z&*lf~ZjMw6IKz?=68U8uz%3V@*njy}e!vhU8Ic0}V)TXKTS1whnA*RszNCHw`^@q2 zvx-(EPs8(|Bk`x1%k7DS1G^(^~L@@ zGjmz;G79Pb+=jVcuZO)c*LCpac9|D>QZe}Z1VEImBUAXZ?z;H7Q!;hv3Dc!PV;jth zYzAroj!>55j}QH)&yx+VIM>0Ujnw6Kq-=I2L7P!a-%Hy`JIc|>;j+m+hNA}DUmvc1 z%@?N7FTrpfCl@5F+I(nbaS;cm@uc3|oC8(~aZ3RsPkO`qe7;?~pL(w~Kfk$mlKj34 z^MJo6jSyry)hbsa6jO?m2q%@Co?5;lTCLWrNI}tZxk@s$$kjoln=o(6J!=2{x}h@R%Z5Jwv8?JkCZ{&3LcX?k-PGRxOsyIY*Q&Gq z%`a|FO{(kG)~HXeoa1{p>-$dKuy0@dzTwAIxkPk)cd`TBzox#q_xOGBefw@WwNJ(g zdf@Njzxb5oaCRkb>1+KbUGKV>1Ysh*s(aL7O*16Dmf<-ypKHt0a)uByxA*0oMn?c} zG8WXiOqHKZT2;FX>*4U?HNyx-YOAZD9*i<H!02F-pedK@bmm^Y6eku0F0*^0DHtt~E#)3EP&&1OIfXd<30&&w^m-Msv08{VRRAPOaK>lUeXz>ZH6NvXue&OnMt>)V-&KX- zcl9ogx-(l&rLVg#ohoOY6DOW^;>7RZ%_zg)F>Gv?FY=y)o+q%Yj8r-@JM#>pQq%rD z$ptO_KrhKR(~@QS>%z8_yEf-a3~R=6#G4_23TYyhinIdEry^N<<1mcPCDYh*VS~!U z$9tKi>VeXa;Z39)@PT>Fc^H5iRyUv8>B^V-)Wm>ridCh#V&!5L2zLGc>TJH~!c zt(?k}LwVhtj(S#Tf0NDo_4`0D8L`^V%ez6(o0(Bw0^ZK0%ADWp0o6gH9g9@#={L7L zts`E_`Gj-nvjvAO*Jn=;Nd1^ViXF`rzM3}(NElrU9+O_kZCe3HyujaR3ybbC&)Zt~ z{I-MtV`skd|EKFd(G%fS80!uJYQvqxOZ`gEJE2(kI;DYD20r;3+3b5uP!FLT*xG4z zd|w*DElx?z`!xznEGduOCp~Nch^Zg?gGm+<-y@VRo6h2GM}!sOvLiHJ#= z849gvh=w?@(bWFt?vp$omOJ?`8~*x`*KFr1pZUyZ0%Umm#0!Jr>fGl_-R{oS`KMe= zXu80?^>d76?;kSd?{&FOizavba0T0+bejzC1f7)-zmdSxh3(+JQt|!!_g}jTcj@${`R;*gxQhT zc3<2bSYs@X`kt||_nF&U`If!=7Iy5IoiV8ux_7ZsA>i(wA9z3ejLl6kzixRBj6HiL z>HPG;eY3OZ1^A;=S0{7YC-xBNkcW8Z8>z2$m-xgO$YPX}KWTbRTX-5xxA1w=IepTx zv0LLa!?rHRz1&T< zXFD#_UH(=Od&**yq+vH;jzDsw(wSK#v~q1amdW#N)Xi@9&?T5;!J;A5W@0mI;cyBB zSNubgC9x=_b+C(kLH-pEAUU|`pukc#RFYCo5%)sjy=2JSh6e) zmKO3bkXT#(PA+r48#V+U_xR8V!JhpH<6I~hFZ%^tv@{V-N;FHE5w~a0^psf!e3VT_ zTT8)E++Y4Raygg|;3G7+_o#f%#PCOSwVIAicCd@(izTT7e!(D;S8)$91C~s3q#KkD zAiMx&tFU|(DWyY&Bh@G>qZGxY7>JS8-)Krw3Wd{Q zEcc2X>X1}EenR^bM)EC2FN_%FOUAmZ_{wCFS|d_hWQ$4AcpNC)W_&0$pfl zUT5O!MI&!=bs-d7ko{e32sq04r49Z?oufT2gF@kGk)ibX*N}*>9t0UmEHCSFSZNx0 zTk()dW5{^h%CU$=;9pd&w$Q~A)L1rhsP0#P2eeVo08@i`hyb9xarO8Jac|rHPaiyZ zP+N|wwS|N6_#r|&u^C*lBTf}Jsw|Qov09mbKw^guY#n#MYa6KY8obL$mRok^QcYYl93>M+ME9!b_WB?CFNiu%ZW|^prfGG``;F)~#fdl#biBojkXr4gJn|Av0c} zc;eAlg4#vxonq;gM`iH`daJwnMO1myKPvr^e?9!FQ>R|_k&k?&H1$s`P4V>+zMtFc z9sbBic>eSBD?X~@`!t^3m2t(ts>ywZ8np};ZDpflngd*q(jAXUZl#CQ2&*c+*MJ{M zEt@_kg2)ho3mhV6jW;s%`o9ldht`Ck>V2}H$hXjG2>yi}`#*K3OPZ1^bIalQf6ip8 z*kdEv%5o$obA`|uN|o#v_OXvI6g&yWdFRCd1|K1)u-+g{G4Kb$o-bc#DHU^!`&eO5 zgZ{N2{fx0jPupbGyNsUwTh9+Y|DuS)8Rd%vtf<*4{w*X>`GEEn9x9njgowN395Gc7 zUwL~OrDBMaWz<}Na3ZZ_7W<|aN*O5H*Txz8f;NSo0N;q0kU_8KxtuLi80#hzMQoU& zTD#vYJKPIqg0Rd-PZd2~^twn%uo~^mm)?XzxQ{IpZK*G2X7?lKx_fNK>{cXr$AO%< zSH{WLm#h7g_(ZYezAK*3M{Qy;C`}u%I{0Vsx#0H+VJvyd96&g)Hx`Z57T0WWc9>R^ zm88E^tNQ;Z({7y<0ISdv3@qfg4F>9@IZC9E2qfumDi!w>QdSxrBbJ#1h6^q(Fe;^T0wCWW0 zz;N@`uz5L9q-^bwX6qEc<~))?{&zvu`5q$m^IR~^#YIR65k({p9-rmM38b7GQW<q!K{x)pbk*x5H2V}s(Y5J+Q~?k zBW2ua>62qsx0EH?BbzNvBcZMIj~!iI^&+TEe>ecHg?ErfB9{%h4};+VD9E`hr&F;4 z?zC7njh`)*F1?h@OJ0(vCf9+s(>kGKJb^WbQs@+hrXohUR4f!H7Z>*(s% zi;I)r4gqEt2dN~)Fp3VY*jcZ+^hTpKN$%E2qJc9mktwEAXAytA1t+yDffqn>JB1RU zh?Oxh)0$7GpsVMx64S&ZzS0BU&#ky6o_S}5Nvb74rlR2c+^MGj%e3puG!qh{KU&~+ zlRU7lMS7X+ZQT*l8NLL0q0tW!<)NDWOOmBmy}}f5cFNv%Z5loAr^a ziN0`etpAIo?8Fn^rTJNjbO;8|%(gL*Gv;Gyi_sB@+%-EpYiF|HxQTOs07qx%5(s&m zm&4iy$Ri!e=NzO*+|4!v6B{i?ow+keqp?_QHd8DWeDQczQZyne9_MOg*aJLcMMFgS z1yyWzHY#QivOH$vF~TYHX?&JmI~H%LGjdimnvKlN#mIWd{pIoW*)g2J1sw9(=W<1h z$K(0*9p@I8z43%Ix*s$o9m(Z+zPE7Yl!*1dRC;dq%=l#(&VB;kwaVZm;%1-vwLGAO zObk_$SD0H^CIOm$fk-4nqe$^La`nb1JlH{~$zW`#=V4c`BO$Dk^8@*Sex$pY3=%Y; zXIDoU{R$+Wj>A{eABzSn(lbp#p=%Lh#7U+1+=;1kmO%d{xi#zt-@^C0s#afk?+2GA zTE^aACm~{%=#xn13UDmPPGC783{^c9?Crg)wc4(A457{DuIo#?t#~4_TYW}-)*^iT zjz7-ym$7LK-=5E{9LU^)126}nIrjaVjea2%{KsbVqdO)$%%}GHu8EeqCV^{v!?LS= zRDJO!jAXh1F%(rSbep{O3wUd|B=3yyBYCz(2GRkFO>vb7M;z3(G4ujhLcyNu_qR0< zlm?QsnM!G!cNT4xaB2N%f>o0i#oI!&xGeZ>R8TI@&-o@NCufL%Co1QzJ$v?;rLy@n zQI^if6P;I_8kh5PUlVI^@zlvYM5OS%dNOgQK`esVh%eP82!Sx`ml?&9TE1%zkEQpr zG8NvwccGBhx296-;SQ;m9ts9a#b5`80bv?4+#viduz>x!P7P$twVH0YzOhy0ja#JE zMwphdDNAE%#1@8oQ{GeFQHF!4HEq{!t0z2_ZkXya{h9h4(?*P7jU%m&YyK4JWNg}O zqUQ?bvuiNsmX$r+AU0~ zt5sxh^Tx)=T5?Y82!#F;+fK?B3<4H6h*_5PGC-dM3lcsEUKIGsc|)t*ji%j3lk<$n zn<+flIFfh&ZyTE>;sCDnh^FA0m1Jzn`)3_#pRwqe z(vm6?EE7@8NOAxYtULH*Zo0d~j^PeOcce| zpiSgB+jfod22HE*cFBqQRCh}kP0B! ztQqhRtOu+RFk<9UWdPyXre5%ZjQbHI4%iy|b(I&&9iT1<$V>+%BjKC3K^5gn@~d7D zk6K8|B?p(npEhtS;&5yX|3m{$xEe`BS9mB!D`Kg#7}orEmmk`@ckcnO_rPB8TIQVZ zX$M3kJ@7PN&iAtYAj4ngyUA=eP5EP>udSN+e#zlz?%y=c&HX&n!TnzE{)6Ui3}K#4 zeXLgV`!_!BGqBE49^`%ln-fPNY}-2?@EMMEw_NpZYqzon%?YxO+CN1z;bF;6q;@L% zHG2guviCJMKnv>)`vf4j?+rYueUH0*4+di9!C$DCskebMhC`()DP`K6siY{?uF|KjJc>wJ}{ zoCa07-AvX;1plF6scnXrj5!hAv&;?AqL)RiW=3IGY@#sB0@GZ!^!blZL`oNp7Hp@V zVPqoryIV5qpTGb8?}x+R4+h=;k9_chA3Sp7?2Si0_=N#KU(l;0MUtQFc&n=2ci(+} z|9w{YzK|^ocIdvab^4Aw?&$4#DnEOAcih+F=SkmnH{Ii!gznkCi+2^gwFmk1738El z<#`wVf>(6ZA#IvpcMV1bom)Z(xJ*m=2+h`Vg~+F3*+G|Rh(6(NcB#F4m_7~f48ljp z$he3&fzPPc&N=wEi3Sga975)p^dzJ- zYrR}Jn0ylB3)5Al7(g_ovoG&u`miu z0d2>|TS!SFe&!N^Ot!%M3xntUF@7y_AmD!$`G!B;lglK{)@@90NZ+D;jzoaOt5qQ*Bp`(ect|so^{V{* z+_krnHbdvxab<89UvLD!&PzRSgMPh`nL8)DGw^I@wZPmSgMu@O=3iFUWuZJZ|`ce9+! z*~k|1U?OEnC&?Xnj1|l>`o^rtqv@yCAro?tJTl)fU3pgNY5C@OMc&!y)W|0iyThZZzxBIci03@NHe>l-lO*+`b%E z)mmTN-+hM1_T>&v1iyt3DKNh4f62#t$-suNyp2Gz7td3V0<%~?wk%s^+cGdxy-mHA z2)j^AIS~>$+m6r7TSquaB;_u#Xu}^dt^RN5ts#F!znYT)MRg|5r?aM#^=45X&Txahj`m0_h*1&}U40wr8}>@a*qXw^Oe@eFW3W;i2CgjuUMU_aLp zZxWSS8q5;W!SSPU4XLf@x;MyDB-qKp3xzHrB#4`cbO??=kDJkX^kUlh-C8h0IJ!Z? zcY!)!UrOQ|wykL?dvRdXjE0HNAdy+Qlzkzlx=Xz#NojOWWwWn*XX*T;GfRy&t42Li zCOwI{O1C4VkO~??3ZUOm&m#woX{OS~XU}V6=XI{Toov<-XF-CT!PEidTst?%c#Vau zU^o>lwMhZ}4D3hv@T|+Ul{Nh|$xM~Sr7^t;iO(97F@HjEAQrj5unmDN{Sk7;>(C4K zpie#1^E}Brl)~209?flCF~N}-RvJw#_L?X&N$GN#+k$PT$Ayf;G&L=-IYg+sX!C5~ z0FgIvG@$8-pd$Qr+-FE7tm)BU(|KYIgV&fKmtig;(0% zFaoAYNQv*b@~8Je#3$tMk#cK-1e89XQw&E)XKt?TGR#P%q8doykeT^dg0%a|047GR zc}a&j6{%2wLKknBBIX~)5wX27sfEDh1FtghSD~uOf8z{%md%Fm`CJMQbNx5{X6>KU zL=&O*uVR4InrAVNs|hn6t2^Ng(GXLo=`XVV3mjPFrIBsRk40~e-r}Nl%y@Nl77XI@ zagYFIo=BMHcJeH_8`ML?jm)1`uWVJwVlEt=GT)Q(BGtB7f{5)na zV`1S?7r7*Q^OaW)2uBgfWv&s8CHt3Na;^Fy;x`FcsGb#Fn4gnuV4cp6`8rjS(9a{j zP&z#|xqOIJZOA2l-c4#397g`!0vF>6c5|A!qnJmR_v62QYlfP35UaMN89$sozBTjA^V^l5}zHZlU!`QX!WH^{(!*b4% z==w(=F-XZhilI4%-Rap#5lfCVKkuU%wwAB3TDsZUI?|b9JK?2zEHIlzA;JNOtzS>> z(TT#Q0p~E#XMwjbGh?5ese7>Ev5EC zeho!1saGmlwABMfx4Z9>`FVq*E|CsjiB%A#zU9Zpy*lMvMJN_7D>yD^Gl3> zYLGqp88u{#NN#|qg7zF`9o4uXF+&vnPjbdVOIiJJ~Gk?ZBurabgEX?Q&gpsO6o zaWE`C;$ekd@o~4VNc(dnedLl@)SHZ)in7BcAe3k$gkRX1&yDSx1>(07h1YMbx@OP9 z+bwkVNF+6UE2c;pKq0($#Vc_=)@ztkAp>4jEY|9zwz;AR=?YiDT^GD&BVqcfpP28o z7qGvpT_ogdCn)6=7cfX1_e$ciYu6FL_g^zooh2I0{8!N`c=k}MdCBxlG!_X@=mHOz z4N3b|1Ry;jzQ^gALlYBWj!Wgera@Xm@DUW5{8AoC*;x3Z&^)gvhyCh%w7ukts0h`Q zDD?AY{0?pGWFC@o_x3R{5=kf4H5%!Umtl#zNqdZmNo^tVh8!sZ*nbH!qm{gr61|N6=&3q z8u=zWSQlwz+>PxHRv^fpj0g8jdL6x;-o;jAX*GUDF zwNK^%bUJaC@Sy=9E?U5lunPAhn!79wENw@l)|0d=ElYJL`KG0BLj$I?gLzqa-5Z~& z?8@acvpCVoP2LNKveAh5pHaiDRHNBymu(W(B}BRlZHTix2tbE;$CY*)Cy6)nmQb-& zD$vAXbteOvmrJ(W^9$go#8!K+=}jb-Q4hT;@YH4l5sq}l(a4?{rZe0DyU2+Z*JIKL zAAS(J8iuk?>xzs*AL-(bL?Da9NX7v*1!b5t2U+;@c(ut$z;C7-gtx;L_x3><5iT6b z_>3<2(2#F;`POhMRrmS3{Z7YzPO#lxd*;7+L)ZFM+PaiDA)B(Rd#}B;*PU#&nypSU zd8rXP`n2Wl;;jATt4`f;=GIfEF5eGUFmUkj%kQ3FUhd!a6!8VZ3pRM>1XiU@5G8kd z?t$aEkLKnamxgzq0ADk(NsK1uB^lDPlx6t#dZW!ftp_aA*Meqqjichp(Y=mwM$-Gc zq><@C6SSEvQb|_?J&r<)@2Q8y8dztL3ZFC2Y9LN+oRdR6B1^+-@t)0eyis3tL*)pX zxB=JcWPbAQMpij#27fqLNv2S7klIOWl3^^w%QZO1JqTabn6(q6E7_DV(0$WLQVk{% z$Gqx`{MyBUI{*5%x4rFb<`ODWS)o_6{I;b6NeaVom^!tXqdBq%BTo0FS|#34Bp%EuSd=?T@YqXrs4er$L?J=kKlPrT#z@Z^0i3vP0k4akWX&1Q<756e}MZVxz-& zkr63o3f9EwDwlRI?>;ofcZh`G)%1CgRxAn~nn`qs@p}oY9Js+0;~R_*96yfSlx_NP zdirr5fBxvXw;wn_)RsqbAB%IH<$f2%w9(gjdnH(dX~Sf2;_U-?nqb57+Q_X=|X-=(^pz{%8gZi&iQ6huE4p4fD)L|6lrudS74j-WxwNWMb zBHk858sdcM7ycY(wA*qH@TdvY2@xcSAp=N7yjT?Qk4C44C=JRx80&uOzTw9(sf$GD zzCfA7p#V+`Ym-xu&v*w=SPdp4$?PE2#-!d(+5kfKxu}#4AHSdbjV(?>$K~VYm3B-w zCy0j*x0=&Cc&oPCSkh>tb_2G9m^oU(jP$r4NW4eRpf5n#w2j$0i<$4F~M7BCTGd*2(NEW&SE;i;3LC=I|r{@^5&K*d(@AEw9 z`J(5)d%g)JT85j;;3TlQeo>&r5_Dxn^E!bHNy&Rwiw#aOt^cTsr zC+BGZidX_fb>2(_s~I%~vNf!~x%rSjYXEGyaM zgumG#C==ltLwqEhS_Gs7{$-mRj;+itq{$-DO?htqaUFnfW(zlHOWcj-xd{}va#NtS*EMwv``oAvt9Xfm3O z9v$#={5W=s(X;Se77l_asHe0ACXWib`5YGRO+SM#o`B`8f7SLa`JDkOu-aDq8GQvcRaBnC3xUL+? z@bgH+xkAJ4TJ>w2M=vkkoJee^i4V@q%uM(8@9#}d_4e&McGXqKe(?jRQ4~5YemZzX zc5U=_&xAL_M_Y|QMX648;#=L&Y`*I=8ynrOG2Sw~KTG9?`AaTYSh)01JzI0?ufJq^ zdivPZ^$7K6UVrVi#Qc;>XU^=Bqdai;kv!%H-#dv!c8chY2e7XGh37|}f7Oz-o7Z08 zO5!s{O42G}ecQUQ+$Rv&mW7Wn+9A1AClxO>ng??A%0Lia7Mw}um+%=vWZ_Zn5z9-7rQ|N<<{D|1*f{tg)eioQP7?8B<<#B zRR(iVI;NfmDKmY6g!sh$sw8V(KPXH5@87r}Z`;7;O_Hg&N%20CF&k=5onRXH0#V#M zxPK^kY9_qox1S_R4%@m>2pL|AVoSj`D6I>>~QsT)?onK2y0O zPIDw;A5MpxufP6!fzDzl;&DxON2OUv(JkEm>7y%{qOd*o0yrq$E7Up9xRq0Vw=Am+ z3bc%(PCUTw3bs^VF z4knLgdlX;f%1}smOksbYS2fY=Qu}l7Dza=&=t9| zwmLTDn&LGaUy(D#kHA<{KZqL>M*O+BB&C{q=B*ba@x!y`f*{B6tR|!=t}&k^7fx6a zBotByM34z#Bu*met7h>@lBH;(N?d%y_eg@1M717|_Z-7t4#eX%LbZuI)5+ij5F|AWuJT+3 zeS!T7gA{Hn%{@}lWsW0lx*dpxLt+FN0Gdwd`A@U!&UKFv1dNP}6*P&zWTIu#%__LA z4Cv`pGD=R^M)OGs47_oTN^-clC#SvZ!IRSx(S9&sm&G)`bCz@$6y2jg{qpckC+6&cGvOZA|j@-t*6uVP-p{F zV5fcX)wlqn*eA4&;t^seXhI+qE+jGSgi~Z&!0#NZ zUT&zPi)0UBtk{ugsUBld)~uFM1f7>Pz1VprVym%`L0r6PjH25PDE*Ac zj5Tf*jW!CH*l{`6v=n^eT9Y)pQ(pWKf>2BoJOFhFwJHqxuGdj%Y84a8QYn#{kSBM2 z55Ehx>p^lqT&XL~Gx6lrgntMg*yRvo9E~*EvfzSgB6f>CNDh{EmjAA;*=>0t`XsZ? zv=G%~P76qXWnkR8Z{NPk&Rv(gu1;THlH&c`hMNEvomiWnFDTNO8`^*+{rW3>rlR|z$b~^dTuI0vw`LL(VVwZdqcF7H} zInVUG0x9{s(94y9_`c-teB-$9K3X5qLbu_>=(}YK67JsN)#YT@R7T5Ykdg53v&|73 zk|X5Q9@h}3&ZB#1@RC-ZxgC9=2P=&##Fks?8BE_uB`8w#$;`z>z#i!@`3N4t z(#N`U-#uTRrX%-3FSq4F>Lhq}(Wb~&nIeB$lxpA1!5A4$QaQ*{G)AWCU{L3cxB76T zxhGxQTN34~*)&5zsV0G)Kjc%5`ujA)2R^?Ol7OW+$@usYbN3;q3yBN zwYgc)Y^zVM7xKZUBT`f7BUr9fs>JzNH|(UHLZP`^2tQH@DiD<7UnOR~koV&vOp{$6 z)19q8yXZL6z_~?JNC1{aiH-%N2D>N^fJ|smgbreJ!Vw@o)YkQdBya)$NE%I$ccH#7 zRoq*x+DnN-!9t9PvE4Uld1^YdJ3=!#WiKur;%zo1oxBLo2+fZm3tv6+$c4Dl-WzYc z@!X9kZ_k}nQ}^?8=wfor@VC_T@L~BoNt^+8ujr#+HLpxkv&OknK#KtKbdu)SE$npKm=)?|- z`tn;_kb)d}d69Lvuntd<60sur===iAM%gvs3i8D($)gaBz(@AU8qAANchTqwFIACM zAmL0&7TAQmJKQ5Jgz);rG<-rzxCq8vkha1ar3d~2A=)wXRp1-`yxTPUiRkR zt#@j}L~n&eWIL~z5-AEz>1g8VHq!DhoK$zoai_0uU;nfhV6rgOb$291&CWIn=TDP% zcyl0*#7uKu{oys&Tw{78#!Wbe#WaOU)-+B_s>kawV|j0+o{)Jh*(#wAkOq?KUI#S8 zypbB(<{usA=Qv5#dV5w@R+PF4bCPkLv3&hg78Vw;WyLSK5=_ozI}yp$d-pv>eHn;M zICS;ZS08`!QHzMzL#2d*q@95?-}2-a8!vp}3wP~=QV`HZ(ltY^qz_$@&1UzUm^pT; z&ClT{V?4Mzx&BW0>~%NFfj8brts^XI*B{FgB)97T8EA}WoIZUT-u|vDxG9rfWS10t z=0otAtYm}`-{B;|zD4x-_`oOKj-+`(`UOztWMF3{q_=P`5jCs4xfiWp4+L6at zWN~?<7W{=TEe#GB@vem(uyO(;Eva%))9K7N4#Z>d?RnIA9?l^X=@o(+9 z`r_hFJD-lOuDqdAY+iokX(8{`o10B}ZqsAah->O)L`g3YA?;Cm{z{7b zmsfYr%(k?n7&fi))8=PtPU09)d6)_U88Pb;2I35(IHls3fw~LxG?-AuhSd>L!O7yaJud_+6QIvAm-Ob=r_ltvE#;jb-IEH%o0U|Z=3=fxW&b|Pwp5} z^~Q+-%>?vjY(M~e64BTT<$~&J`9FNW{O|w8UzUISw}1Of>Q%#E9R0mfw#(0R9dmGqH zB&e=rW&6+YPa8i2jsE-8)asmn)x5e5lL0&cAO{Q=kQ(|ll5+<17J^t(K#AlYhKEt2 z=0xK|jhVW-YIs)t>hSE%H}n4`@W89FR{u!NtgpYU9;`nQdJF%r32oLNXncsrr)5Lf zH=(^=k4OERI)}$|S&(Bgj>Tf&8hT{liPo54D2ig3V6cvKb5TVYpW+U%5q~f;j-xU( z5mVox&J9UDKa1n&EFySbeK~KKx#6cu5>rOV8sGf?hWj<%sLpZtvnIEbmmR4PNh9=|TJBOWih?~9=o;*%X~vm^SdHM&tu0#7Z^tP$z9>>s@e zGfy){mm;G$%l6Uv96wUPw9Oz*Vg@;y8`kSfyQilon$uG|2jr$JmDlR^dXX&8s9wlA zwW-PJ4Kg+v(=(fwE_OThiHYu_I1}b$Soaa}=t84bU*e@gevyf+bg@|8)oJspNy(e? z{2n>KjCO0W+iDq@Xr`uF52J3FC@ewPg|8$TW+pp(H{nOd)YS4UijmoATBp{ z6&dkZ2?3Xnv<7mVxKP~CG1)jWa|+0xNub+=R?_XJEl%zd+Z#v~Z8FnJ8_MYEYV}^0 zvij;`TWB}cOEypLURpwqDp#MkxF#Z1g=7u2xA$i3gDEd4yZ-X!hq1nx{r=^*_@Yt3 zv1FUT{+J^=qS)f?L_xt@8j41X=m1d$8C2Ns zqgn^!3yV}Lorohm2a3fM=_(8ZLqHZAmrT)-o$nlS(7ec}LT$8O1{*{c)^5l1-=aKvYdt8Tao*Uo{+(WhQ}?Ng6N_jkJwVFz9*s{0>$==N@{`}ME$@Al!> zIdnt!u_wrMl{ov3WZ)dZV5Rk?6KQ#ov>+Q|P}G}rXO0pDjyLN0@TntcYeuhk7}@(#@|WmaPHj;5_0@K zZYQya`oZ;k1e>f;#1c#*?lcP(vZEUfp1jz1vBBRU0IWW5Tj9Cpk@9)S@S~nLzVVGG zqmkmtdv72kD`>r+J^F{nLG@|;2Osc!&hvYoKk@tx8h!wX<_vMDN7O6STh&L^SJa=V zzf(UkLPpJ4H1-=OL}Im}gZv>m+8DV=2}A42xWxp(hw;lgp1={U)q6CRka0g+lQx$* zARn>E@{MexDUEEC`GCz$rj=Y%)PT`eQXt7%TP$guC4X`(X=w&xOpy7BSztvjV7EK$ z6A4P+g*%gEM{huRkSgolMW2g-m%}aFaml+roMneM!Cyh>E7!Gb=J@zh@~Ygb=OV22 zJ4sMcCksRb8=Q8T$b~88Vd)4A0pg&HKtm+|B85atXDs#Q9e4x&1WuRbX#NYw z;*F#g;8c!FW%bQFoXBGJWWsC$Rb(xLM<-l}y1aGhT9*8!;%m8HmLw{vqO`-2|ku&~Dcw zz6^v|lB@)CP6~bsjpmA8Jdxr=@EX&rt4o+%j=t?}{heT`;mx}S!T$Q%0_ID#>X79`#PG0}WWL{K z&`8}|lJf$6RqA78@WN6W3QFB4VlJZ8n*cl$v26?giN6>lVN)DtMzuj;_kln%7^Vmb z`JQ-3aGt>K0}Ks`=khZ?{8)_fHBaPb0Nc=<0beo<@>N7)<>!g9ILR?yQWd0e)zOex zl`xi30yq?_BLm>?^{KEIbOrT+8Gm`={~XJ5j8rP`vxdNE1JX4PVg@x=arOZ`O>kj5 zK;W9HSF^b80Coj@5vQaPC@{h>hnUd(a*o(-Q&sBk%w}qJsTi5fkisMTVWbb+cA{`` zE6#}GyqLAZn^DA61=1NW*1L!xQGDc8Iwos&W4 z1^?wUGU;O8ilI>j1Ibj3cP1aIdeMZK_)8P(!%tFWBpRT23GgrUuHZ+-5{>Q?u;Nq{ zi4_uY^<%vMxW`mY$JIgx%de5mkr}n+-)V{BGzjc?o_jcdgxA!fbbSI&+ zla&xc5)eW_1g6CVBoGKl5cv~mal`=^GXDr7L~Ib8=5VbPkh2(5(Z3&DZ(X!4|f3hz9Q zGk`t_v>CZUK<;nVdg8jXl>Y!5u39;9{IT!vpvE=Zg{k(@YPHdPi@m$?oO=NGVAyAY zGMe%SN)^CPlo5>;3XNvJQ{8B{5(#)KmMn5;rCxlJ+670zc6t>k`JZ?}d4o$G(`77y zQj|=rJVH*zb~v{3Kv*xc{&Wer66;q(60yXH?$AOU>w>t&-aDOUx&(Vnv>~>-UxZro zPV97_*4bQJnXAfK&F|c}cdu#g-FxHY`*u|<_4_C{1iMLkZiSc!S^R4w{++m?Kfa^9 z%&~x1J*MJ%TLRop(4d;TvmkdbZ!a%nDX15Jb$Fw5OItkjrk_risargqUxd^)qVl9AZ z<1j*q9;>(#7~wmL7oid>JF#@)X2AA`p>S27Rm44+1?s7?&>N_l*Bh~eQv{nu{ox2z zo`^evh}`{xu{RF^c?{|CmCB_YRB1DU(EfD2(~)j2mpa`_2er{?)Vsf6HY-Mx^!Gx3 z>jyx`{VI{!CKEWLno?Kl7hlZ1^y)-R3OYwi=y+Hb7o!PZ04G|DBE!mGN+FJP0n#ts zST>cFD;w(Ej(v2}?@2O9r00=TUqD88g%Si9u%(xHC^Gw{w*^vHz7ss4iS;xglc_5P zJ^7YhT-Ud&f&KN#!J+}C(8&{nfnf{=Cwes<5}2-?uDT1=@4Q$z%CC}-az^|c!{M2m z&TQ;4C?!9;qF%lGaB}$3pG8W=79={1gZt~@QmNG{mPkl^ArIc@HY$*xz8+;utt?-^ zGCd8xk89I+=}DE^fG?HYF!3O9Lj;_OrXv0@xpB+AMQ7lM1MBkbuBqcvi|+GjgDb7G z6Yqqp#!7chI`{w3xnyM;=+wzmM#yws!Ey+FHE^Ec$Tm#E~_LWEpLm>l-h*de0sKHM#B<0<%T$ z^hAiOc`cEAi>`H8djQcoDfZ++_~jb`47p@{Hf)0 z=Te2)r=R{^e%;O1JX@De6@KjQyYGH^;R@NBXDjD6sRj6~`Um)e*ch(#c*X+t3)5%O zbG7Gk_od18496SfTIHH|?tvU+f;!5tylXv3C8~-?U^38Al z7hsLvAv?Bmp;M;{EBPx=8nUDQkvnzrEpO5Hq%Jd$C@hC8ktE===cMiY3D zmlCmtl5?Wp3@-RJZ@$M0nqw7?@AEJY!!Swu%2~bMTgYw}u>xBbVf} zB-YijZ7EES+LBa9OS!Q}%sEk_m_08{w?x-+dx6e!R;3@t5)Rs$+rzqlrO2g)&LJ={(Otr1L{az1aWv|~={{fZBPYzuW-AqO1 z(K15m(t@OhV_-sgilDBrktQf#fF&USkf_5vz9_W@&fzIKz$~KsTx+!qriqZFQ}vqW zSpSw0Cw-%RE8`}RNzulo=hX*C?^UlFy_ZlztY1rmefzW%GaO2MEEo<4TG^gBV=*+M z!&I144z>iNc4sRZ!Yn6cNcf}w*3jYV_)4s#ZZpSIt(t8O%DFkKt1B0puN{;9!Wl zfDx3*G=s<@DJ%AFq%4bQp9oYr6j@f)MuxfA&g?-%h>@miywL{L;+%R6ue{kwx3{mk z=I9rjt*yOEU47=7YqI#WNr5yY7$@Uoqtw?{S8`E;ahlomEOi+ph&EVxQS>8aCtnKI zYlSizlNm88KM=Z~vUlS*E;i7w(!ZjRh^JQS;5eEJ z^5_W897=+j!iRqscXj7gMgom6+uGD%I(9j^4PNvFu*{FxDBA zQMy-|6kRgyJ8at)W3gpoXI5%Mj1%&IQd)^Ug4fH zGGka0Y!3cXeP}Gu1{wK=I^Z7IRtNQ=4bFS{f$x9nsiz)%s;T!KABS4VlXEHiXSuWc z)zy%aonjlimp=uC!>GW)xo|o94P4#$Qets$UGN3 zaZe52a|F2uRw5(-j!G0PW6dX;1!D1-_6+683mI}D1B6s8jxpP1S+>Yt9#wyU;JJ1t zeZ_4+8mRkIM*o8;x2VVc-}i@q_=mVj1E*Tc%Uj1bHX5T}hZnnemE-RVM>l@Cf+ggU z*pVZ59;meTv}c!&(z2nx@FnbEvyaS*v$xUQIHyrAckPYXfxpVQ_-oW1{RKtV3wUIb znkU15f#EIq8YDHs@u*ZK5n|0{CggIHb+TUaEJaC1E|Cag@*>$Tab&cW@2>o6h>1bB z?clMa?y}wo?>k|b{BBCSuA~rh1Z}mZN8~NPA=p|YY!C=1sTp|7>;)#L`;xOR@-MTS zWuAfQMVDl6@j-FyonK?~G4^q7Ax8X{mU7Q@h}4?hd5N*>vX_{7 z+*b%*hKv*Onnj9s>UF79$jL5?!6~1|HY95o=jYq8a5h<*pX=l)DU59v%&ka>(P-M0 zYA-uI)t^lmIsDyz64euwe{)H~*RauoR?8T655(-*{Sph*i@wH9-7l$hjM0zjM^+=2 zL!b$w*ubzPHKWN+zgO-D0*!nl9>epFR!iw661GyAeRJ~^O*Tu7^~S(XCes@S1gP-F z^$o3t9JaC1XgT8R$m17~%Jwm&ES{16m*>E9;kiKp<*nJMrqi8f%3{dqRoY#liLVyQ z)W+GRWuI@U)M?KSXAa$NSqD~ED8S$n5_S8*nc>|vVv445Q5C0Is;vM8kz7$9#pH43 z>ci-Al}*Fjs1Jt+-A*+3Rz|PI(Va~!%v-+= zyT}J>RfxS@m@Okmu3N^>#AwfbVOUtjd&D~K3wJ)B6qOW?AuBpVwhGOcnLe@Z%d+Up z(i7@i*yq=VcU*JeV8*G}8rQ7ErK(<`*zeEe3i;xRJsXAZFVD_(+J-t#PGoBI(nz7u zoNKiLf!2cUukj;1fci?bMMf-(l`ucH6$kYc5VtHP}nN zi8ZPGU~RY;c9Ezpm$Q_O4Cce7;_>4|BV#2-o2k5Vonkk-t$yY;=-$Wh+ua9Z{6{>$ z$$gEzK5`G-of}*z%la9MY{2z%Pg-*lFOt3^86)l*8%!@PD!A4}k&0bR9oH4h{_e5# zh)G$}fm$Y#)pU)F8oJ9Bqx95}pKO$o#wLuj?12KeoROg?duc}<A~5#4(f36pI<^Yy%fGGlS)-I~;ZdN)@k3*a-fu zn8*#^b82<}ejKdS6I)#!3<}$DQ+uV~D;24N?Ijg>VU?0%<{z&gJ+iT$0&Ymf_HSQ% z%~3B=>rYGe6+jkd6n_!t5e}Y6nzrprQaK&}lLWr97&h*qwmD%qhCfogrAy=iuqpjT z+#_CzosUF%aJXzVot}5HDKXjF$13`JOoQRKKe&+0!`1HIN0k2gO0O@ zOU2ZZ677}%4Q|bv0fUH2xu0J3dWpaVm?d!iU`ZX6WbOO7UmA@smiTs`ok&__Z*zf2 zS%}VDHQ}_-3&F$q9P?qpvkk($XdJG=QRtpLG57Jf8am^MN6X{LIM4%~cmy;6GJ?pJ zV+y#GVUWqrLK=AQj0>K$xDzdo$Wf3cNLOfDE(((j07eKzquZ0XmS&6Mr`%~^&4>~V zlgn=(Xf{qn@x&CUUjghYWfm9)#UHk2XRlnDnl4wUMla~O?WLt7hi{4odiZ?s$5Fwa z${)CXhzi5v4{i-k57=RudZ~Fb&oWRTag(h{8LJtX}rjRm*@#1Y|x3An+p7`=kS@I$L{ZO z1M`}oeu+Nj;~Sj#SG?jCA9_G7e)x4ys0*)n$!H||crJcdeOdhwJV-c@147)k!_@c`Yne+tJwdV*B>wE zk9CPerEF}8N+P~1-d-&2o5|IIq`A*Du6Jpqj(hP7{>2VbnLtp>1l0lTiw*)Lz+;=h zp4_~>{n_pPmjVk$pTB|EXV0Gftma2<42=apy4mynJIC|64q)+cNNTA>d$3HJsl?$+ zG9fYjh-e}%iT=vabL6INfRhB6j;$_!F72c@$9oD`rfax!uT(b5za#&0#s3t+lSy>d zq0pM)oB1J0)(4PFT~Q=@Y)K_fWNa&MpEi8)EI}G}wMLC?ZwWQHWL~;Dk@Rd}tdR z*&XEUPjcI2>R1vq;JC45u2p(xI!VGG-hY=P%d&2zqwSbLvNOb-KmnPkNQg2i<#?L% zuGlj1w3$f@GZ;W$M{bPIp&a*0svxD1;7#7WdvCa5?_RH@%Ti*jkXkv4o4QU39nh0` zIscWSb|5>%8(xaMC;pOs#Np{}Q1ouEvT!vwEhDJuaV7{*Qz(=)lZl+S9+$&W-##Fv z3duk?7)T}@IcdD^ZR8yMv|*WsQfKZ#W@{!5z3WPP&w{R_5shTJ2JYFTQWZzp4j$ha z!_)N@UVx{6k^HuO^ra`IX9qlsJGX@dEyJrBKsu_rp(P+>ZJaiXur7&mA4_wL9NA-0 zf@5U?fk5vpQ*FQ@WTFXF3p>7H-?3}gH%My)<8DX*B6-Wk^757IOHQSNStxEKGp(EU zY!IQ>U0<8?_J~VUS3(z(x%sV9p_|Q`vCQ23aDE!63vqA%Plp)k#(-D53BXavj}M2o zn?Rfur%TkH8GUhfs#i`OId)4=I7(f`XJ=PtO1K=uiB@~6T9Rn6jIc9X+dL$8?OEb< zK8wF^Be3gQkqS)Fw>`FBNwu?QsV)lW&K245mn z=q%ROH%{%={Ex2<8=t$-Avt2|^!C&gP?l5MC#LsQ=jPTn2aA<*xw1Ie+zU+Xx0MFV zo4Nf1hx^z7cWc|N_V)dEgmDKRHjSyNeflt4jfFx%@2n4ld#b#wWgtIvR&bsC#o|6Z?}DI$*s`UUxhB%>Mqa%9=Yw>Z+`Qe&WEnKVe~tyH~O8qe;h%+ zzWL^V{0%}rzIpA3eyVfZ+urlJ&wcK{mN%{d2;kXuJ?5l#^IxJTyuQ)*K1qYt zD&8bgR@A-mGV+YTn@W>|VwvO36+7_j@dlTrTzUBjzdWUTwz-jcTsZ)CdRj`tHA!Vtm*Z{ya% z$Vu)gE?tBTqvexb^auNS)h`&uLZN86*{?d$+YDL#K7y?=hTw+g|Us`w3ALUTC}(oN=(HiQi?0 zYHldue==4jk%o@bdYBm6T&K8vx^J!3=sg&)((ynjNZ0db$(3!G>gaUR%`)kAJ#Ds% zJ%kf`Z$L*R3;!>}AcuycIJ<9<4&kF%O(hWyLXnqQplFNRpA_awA&JyWU`9Aa`eKDh zg0|2z=$g}xRH6jAgd%E4$k7d6b-@S3ED}R2lP5$|XIe8e1T^BDDH5;V0Y#x7%3ii%#UcNu5SV+9|_Ot=hGWccxfza*XMj$nSv zn;sKx;Y#?za%H1BLNUA}M@Z`Ye=nPwyQb+m@IBja7#wNm^6J0S@C19bcawtM%b0s| z>N_W77m2U+ybL>ck=&R4o)c&-%pu|X638gFZi)RIlPb-?HK&&{!qP}`?1Zb6*GF?{ z$PSW)AeWx`Mjii2sI%D#S@i?0K8EU>;JQ{7nu7bgQq20n&%xxm0)xb+UuYN`6+Vpr9y4pw=Hh?7TUKq7Qh{XWHLZTi$xijx*Jo!5ge~M}X4i?bQ6C}E z@XaTRx7<=ZVf?#mR7NMU{yv}6XCJU=efoXN=xdzo4$_NqJpwu6LT?_8!kQ1W)`n zJh6vwd!I+5sI;F!O93L>G@9sOn)-3^L$M}~m2q1#`h~`3MAnvvt^-qs0tcm6tFJ-q zE0x7cu^5-ajqzf!vQVwS06NLUrU;OmiDYN=GvtO&{kOTr!BlT~dHMq%=k=CGt3@(n zB$5*h=0d*JY8*UxU@Q?HIC#y*##x6_9R5Hp-w60qXEzAS_GoNH;H`ksK%?X%m%Tcg~7bRn??1Gg*-F1I=+JzjrotpSM9> zh=h})4u z&?{uQq;b7Vu8i7=nnBpQ=!)k6Qco5r2x zc9lDtx-@Ygkp#87f);`O>PKxqwK`HLGhUn0-ldiGZYP~;w=lf>jbJ7#8C!${dWjDX z=c9xn^nmJ%N}`J4b+Xw)A|vS8*t+QFPob!iAX-91jmAKVp)9NN{V#6OACLfEVJoZP z%Gav1bJGW#P37AM`j3Ii8YQwBUEU}bogNL$nc0aMZZLrDOe443^8?tgR&ghM(JWqM>)BS20~O zx!gXZn|67Y*+jQWr|M?N{VZdGRq5gk7!zhagn@h0`+$;Y??515SQu0*V)Wb#bVaaI zyLw^4G*_GR3-b%jK3H)n=Y9-isT_%pzHqBv2OJgTjG|NP(UYow)v?_4Oz-a_WbH_Hq5gtheJLqfo(gIO$2he zMkr9sWT|+JnQmdByKBzo!HRe@edJEU7eMJzu3cKZ2^xkwj)N>!_b zC0Ht10Eff?FUp`akFrTLz{T2_UuZlDmQ>JG*fO%z&ka*14%|yBe~_;J!k9gyZ*^wp zYIOtfABz#$t~)siC2qCKq0nG9Rs{T~(YksLzh#Q7+)|@aZZ>~{ekHO95#JYzB%_=U zn{p~&sUY+ilw6bf8@TwJR9OF%=V66D!~jL`V4UeUH$HyzTk>L<&+c1POZT0DElN-C zCiRnTm#-B=$GDXoRd?*tK#cpl-soFhb{FQt2o~{Se6aC}#crbgVElG>>*C6sT&`Ba zv>GODb`HD4hcMct7t=UE^(m#bdvwy`#y4QAzp*F`jS+idUDw83KL*|@%?KDHybM1Mw}ehQ@)DwBgc%HYx~OES{rcXB8H3CrtVCo#;&Z$fsW%Lw zutB^F&ps4xF&%BvjU`_Mps0Wy#WzMhQ^~86^5|iU39Qa@o0r}0=!>8TGgr7DCIA%z z;oKw`@qu+lGDlFMgc8sbp%AwhCXY+*Rd<2q4@DB3yAvZ=EnHE=7{Fl@#^e7)>E>Hk&NZCL#lAaZp6l= z&E)&{9@cwpjgRY0jypbc*e7Q`ynlS=@p1K;cekis#3FjpbMBntKlnOcfBw)zfBrH` zVf?QAEeWEFfUr!yHC_EL>WkR0s`z)WMxR217kh<_9lU0Ag;>~x8j2ZpbvR`Em~hcT zuUm0(<|H1>SxiY^OU;2Ldb>k3de*)P+qnI7h2fu0&xAWV_9EZ??*|VaoG!JYM&Vd} zwna=PUZDQg;O=wZ3}QE4u(yG>>kEokXl&c`?&zN(d)p0c@kUvr#)W;pX`4ta4b?9 zeou%wf=Xw_=Nhu){>045H-eP-IoyAdTVzS5V3vR35H1N8aenuk-}<0(BpFV~ECLng z8V56NSFx5IIE2vrjI%s+xdo#L6a`^@a$y{9tdq$e zyZ?EF8rqk`$r<6BNKkUtufqi3+oVn`kVBbnxA7xU(VQYASma47lwe!*wHj!X(OMeR zzDzm@AiSSy<;2noQ5MM|02LteIgxh~%8=Yqk!nmeAGAX8FbS!nX99jQa>Yh2j`hI6 zp9ifY->(q`!e_r?;+a9w)|3y?R>f0h3{qpH9^Y4&NTT4cSVAy zu+|g_0n15Pd!gMfmkr{QoCWZ3rVwq}Yn*f3+I>|?4l7kxxA>Qzc zzM@jWh-8J4U@k#=Q z#D9#$tLeL@2p2ONE9hs$#(LXnDs3_DFWLG_*nDo&Tx4wR(F0OO#CZHMT1+>uPiFyY zIfwX~9hM_1mg^#L9j3#v`O96Wt?6P>SDxK={N2k(>*W$eA%WRpp;^zACwZ*}0N;+h z&X5e8Kb1OZ&3_@Ox5TeHKDZXD)}q-HhIZGxOb^XdVyTZ z3siK%*Q+P0I}tO`B-aflpft-}vC)gyw|+Cy3z(2uqRUijTRjAV%oZiLa-+AY+hS$C zS|B51=IA^X<8zkpo$vHnIUE2A8|nr1Y0gEom;^w$mPEoSU_)|vF?>(H#nLM#@X03= zHNyoA#nmip#cd%BPdL+1JwP!aRp}zM+#b8HcQW&XH@~?!Rm)T|i35mCS0X#$4V^0* zd3!_o#L}0}6A7gD-?LTdyk~Ky`}TV5Wu?fKM^;I{8!{7f` zPaC@L5$~jQkv2|fH~55dWg#Bp!O~@co)n;OocsRx|Kt_uJ0}M7eLLV?v+e@FoZYc85kMt*U6OQQy+d(sVDdGczwdbj7@cp{HQATa`mCMf}HRyHF#Z(sD>C8%ckt-Kd0X7AFR|7Y%Ls~mL7(F|*!Ea0^(Q@T@$mM$G z&PrC0`?z#BQJ_P<-*f&l2nvNbJqr$aQ6 zll4x}2e!71UrrBP>c=1}o^j zRJSMemuIbC$FSqY0Wr!;BLIsLG4=K7rWcNO@99k5L*Ykq^a#c8Y4S=$)j2 zNv4z(zX_ulDl+3@(Lyno4+7(Dhmj%D-oL=4%^iV{M50T2au+DG+!?PZ1|6V;4Y-w` zK&vE;W+W(s!$lw5Jw0|WPa|RbC~gZK_Qyd0i784q4FknTH&%ocn(O^1BU{W#>sojt$QpwQ|a!D#3wDP%Ts}xTr zBiS?tKnkm&*1~Vj!Ef$oZ0;Ia7zbU&f_zS0m_i=0C>z9K=*O%dd}aYxoE}g(>l&ye z3_49bh{R@qU_l{1+uc5Zyr8&9Y>V51vr0Wwb=}#s4^VWS`bO02LA=9_K&6O7hgP*+ zzyXIP6wJLNVqT{RqP3JsE;27Zy~JyvhYV(K=wl@F`LcJ}V6BxSm^6685amQ?@7}G= zJ_ZF;enQOI%}Q6w*pSJosqhv2_sGJ5vOk!QQN=wG@A+*iHPh^!-9&Oa81@jAB>nTt z>c?5?!jZ2b()MxB&wHMoL_Hv3iThDp#v*vRE@gL_YRF6=M?A6IG>bB8=mK|cHJc)^ z$%LYx=~?mVF1l@{8(j)3NsWlZL`mwsG==6AdZ#ffm93hQF1ilH43;;}umD4>GoL#d zQouPL%gpmdFtHLSD--lTd8qXp$pmg4z|}HejbMn7ScxZW0_K_6^sJGLXHw~8 z&>xRd2FDhYI0g6uu~01KNHmi$K1C=t;EYZIFB&cA^<+fk5b1~?7pGdIC=DU6)3ni$IB@qbX09tcj#(`9 z2U)~tuyOqHP!a(BaK_0-z=9$I7HcO}2h$MUP&*jPKxU<{gK@l;JFTG4hBRVNw-E>P zMdFD7VL6;;_X*|Hqc3x$bP-6sVjL5`Vau7aSw}nt-blh-m+f`iUk~^sQ3hN4u13VB zPnvXlZMmj^oLkF#ZaBI{!|yX_U+p`GCybG zVXIuXwQp6y(x9-ZoR%h&n&we#c8Hai`|H$_AF zo}R);ii==u=P7yCiEX=qINw)Z&S~@(G*=pRBAv<7Q6dq5VtG-@|wG5a-!BjCn0Fnmk!8 zeA(_7b#8aoOcs2T#^T=A`*kO4aDlsXiN5YqKT&3o`yQgC^DhHMW)Tm^wk03ZeK_My z(i8%wD`C33>4#=@C4Z8)`%6AKEf+j_bFeWhl*@77ORFMQx^G4T@0Fdqyc5DD5dr3V zg1FoWKnb5k;zw*SLvKt(f^qq${voc3cp#Mg<**&ZlNF4p*F_93Rwi$_i+G_ZscW2$ z_(L)Iw^FuUD^$X@I$_?bT5n{dfN2p9lS{`F{mkOR>Tf(fH57crp@cwhrz z*v5k8|FC2#$^ROtVsfv66YhOR1Mehy%SovwDM{RE+OjIyyq&{aoohFn{-s2s8mm-? z;Y!6hNp(a+3W21ciXkkBs8rlIUPS~W_&V$cc@vQ!(QZ3_GZR7BO=6wQP)q}#k_CQR zBwDZ&hLP~4v!n<3jR;0_3Q|Wc@(caQ6v>;T$pp8W2wlb_mZ;G>uJSEH%=6rbbrc%0 z3sAR)agor9$2g7%g+-8nU~13+QA{S0@++Fhik)?P4Bk2Z9$ld1bh4|IN?r}C^wkW2D592XYwc1n49}%=;?k`6i$|tZx z?bkJHJQ4=W0LO|f7A3h@qMh`#X~+`nPe>ZuA<&w4$s~|BH%IOa-fa$tTlNyEOeqquQZ7aJ}0hxv$Wx z`OoI&D7zAiM>he;2J1HkJc#-QMtEzfW)U5<&;fi=&Hw zWjw4NCj#;g&qv`HNYSK&yQIPWM6?0k%VSV)^TPiY$H#)gi6ON1zExvkfda=_nrHDBx z0R#;IrfC}u0;Pz7c>`2}WEBic#64JMtG%$$Zka?shs zBVmuep3N6RcAv!`Xj6YceFh>H}5w>!A!Xgb|Fy~s*35rG)Aw( z@yxJbU>LvppHPf>seljgM$uiTW~O6-ye~;ySWkTX;;N3R<#@Rgb8^vKJ{eQi=y%2F z0QE|76~vK{@>o^pxxUdG^>rrI!(8VxKtJXq)C7i8MIydx0aHSsaM)%)g1i+K6@{DPoRCL(}$K8LAx})nWH*7 zA*TMUsmg;TupgXUwH9q!q~~G&VZacinNMJUFo@5VvGNY`ET@reEEx$qkBH8;+v8Q; zF|ivfRLkA-z5W!DZ=vqwTiv>G^2A0vjOTk5L*>>Ln_I1xQolXS=VQr*`QfU3&(9Cn zU)t|gbA!cZBPO^M?bg=TsR+8h*h0R*wOl|-ok-Cl7>O3I9dca=!gwsofDljWr8P2c zq+Qm52WeTGvBdSu-h-iDM<5JA&B}9heZAR$=5A~pJFxEa=KR6#?DZQP%Db`Wy01TE zTb0!onTX`cPLqZV`>l#f=j*iB_RUOt`@PLU=R@Mz5BwjLt9oO7{RpTW&8AWHS{a*C zJ?7p$HxPbpjNWq(_Lms6V@feY;$`^`a~Ne#?w#CkC+~qazszc#DYHS z1FR1JdVXQC2f<-<;d7Yk&-4+?W(R|QFX3Q%^Mf_M+Mb@OS>PEU_QWXjj0`eAch{gc z-w{V18PY%mHq?2__4$bDu!ErQOAamkrqLn#mzulLW)r~R5V38k1cB*z2(w&QDt$2 z#allQnvkGvYpa^Wxh>ln>N5|!;|q|3x(A$m+TfaF1ln)a1_aa>((xeVwuexY8>o9|fbz!xH@V`E90_xrK81gwU+PgSXUV22UMRp8gj zHuT4TOBj37C>d#^;QhYW8V?z#ud&|72!Dg|hB)@Mjw*u?f%qRUK`7Beuu4&7h$!Zw zw`AxY&CKhJSDiIVzLIgX86(2+2)h#k7z~CZ2xVx66@S84BG~XKHM~;(`fR7u=@}>C zaGd###@+WA`wkd=6^_SD#=E6zgnxyN={e)78Dn<38S5JDF4*ss1SG<2sz+<{YFLf< zQB=8!E0WR-W)36-G3NMjbZOHW`2^z6oA^*_L_Zy)vfLY>kvv+45Q9$xaUyn$pHfo0 zAd75qY#zQ0d>LO7pdzjXNe@`1EJmIwu|VklwC^#dOAiItTV*;G#3Y$=ce0pMRS)iTT%1-|+nV{`N7 zo3=KM$+tR+Z8_!-C4Ll_U{pdMUfW89{IU4&HX4nXKbR=ksV#|yXke0y%{3~SY&zwy z?E|qqqF%&-@txf#kQ2H1FB4l?lU$|S>D~H^S+s|&rAq00&Zjqjihq8%GG>mH$$)vU{ zjj?`#Bt3dw0rk;Gqjo5s_oo`G^rL!BGeM8>q7$E>f@G9LbUWIqv~$^1(r@|eLqr%6 zC<7Nqp6j5?id?aIDxuz~Y4c}2U-EnliNS^P*H|f35@-qzjKiSR;Zc&3Xmzb&$H7WS znFGhtaZ)l~8A@*4=r|VD!hxYJu=uhN1BJvo5LoS?mZ8fSytL2Q8vg?O2rG2=;W~7v zksMTt0$Rf?fmoVvcJ=dHUXd#z?7=-emn31TnY7%4IN3yK)pxNn1#pf@WJ|~Lcjj2? zVqK_A8%p3*4H6Gc=SnC6#tFY2&jv+UK#&XM%0AUSgWHQiFZ4^`_co**CW&|kdjzuG z?pET%0z=BX5((FBy77Ws4j-;{w5sUiK}SRB3LIK zh+QEWjNLZny97%p5;JbiaLfR;OVimfW(tCMj(lPh!;jdG6JqVLlAHf}6K_jwCN)X* z5FmRHWjT1T4~rmNJMNtWqd4F%08Xc}2KJRhuh+7S=i;}GZS{WqHXyW8olX0&;G9B7 zWQ>S>*`*#Qv_;myJ4MBl;W0#plW$SUC0kX#ZOugKie zwdL2AO2^`vZibE<@#NlQ->Na%YrNY!}U^e_U_}Q%rgK?)SHdFuPT(@xj7uP zS`p9@m!=Xiujxoqm6=kxPYZo7273@=`;2+bjOy(Fc;*GyQ&_O4jG! z6!McJ=(+fh%!#*o!k!w^$`S4lcam?hjmd*g;kw+FI2N#txOc0I9Jz=9+_ttNIFd-_ zaJRJWa`*I%ac*>Bbb+!V+h4-^Nm-rM;mj=YPTr$uu3rCxLT#l!7pH2#X5;%0p1Shb zu_d+G-+$n$tM>1oil@G_y1M%8v-}-{Gyr&<&rjh93WkmX05ey1PJL%PM~`r z=e?EAR;hGl^wo-sAYD<*vNZZhFw!1~g|{P{3-M|^S-Gq1`#x{Q2qcmb!%Et(_fs8k zbnzngi??F+zS8qS&!3|*QTJt-l^%zDVB!(yx%9e(yo+EA)zY3o;K(69T|Otl79wx+ zWHbQ+IJ6_%p$*v~Wli9;?5x4(mPiz*4}|atF>xR4&M#cG%d#79T(z8Xfj$$sDCT0f z=^ICyNi^vgST#$ZOSu%+I)z%y-kb=Bob946e4&;(j)41k|2^Q;UiB7 z(GHuYVFY6ZvdP?X42Y6;5cD|;chp1hB0|Ega0yOFxB-Qhtduz6Ibb43+*5z`@Zp2k zUw!5P-cajDKKYi{l3GX^_+lZYLIKKy1yDvx$ccoj_`aPDV6ZWeR=wV2nS(XT;?uL^ zK(HDnrzc1q*D65>DW`I{hUAAW8N&cpDb5yMPKj_82Zu(Zb*J9wHgg$Jxd2 zZ?&!oB2lV$yhA?mR{6bzUWndKFgu0?DfWz$kK=j6p>me;4qlUz%vhIE0OgXRa%w=Q zd_l_ZSp_t)S9D2buT}vy^P1Nv>Xh@a>WHrN6u1fgV6$1Yf@EUlLB}!0?lboH?bA9m zvls&O0>)f3s|SG|h!e&Om6aGk8710XZfCksG)Ocs7&1i!(rmH`OrLY4R-7A~Wyjy! zMk;+UQHzrbvAwN)=}azh5aZ)E94|kY2@}_mnakr+Q|GDUblJ|Mi^N?a7bly4cG98J zo0DxfOWABT!Lr}Zl8H|!MgV(-cSjB`Q3>L$o+qHY)_6d>GO8=3x^gPu469u+D!EqO zOW%un?lF__S7HTTq5=VGm8IvHHexfE+<3#CZ)Tp%MA(p}56NMPRN??ohvCjD3GCLZ zhGR!YEM5p<;R*2(2()cDCoGjxs0@^(q6#>Xw@vK+W~hjU!$&ipBj?heELmIr zLLrIm$@Hcj#~ZMT%p}@{nUG4E7j_4=>|So>(IjTAY15doXYtsViO>x$k;}-qgDNr` z6mdc`hB<4^5~xzi6Y%0|UWXiEslDR!aIR;pG4ogPXaq83FMQ|X@2HV_mioNYWOrU6 zDEg^O{7h>X!ujCpsjQGqp;OX(LNs~Pm3pQ86ygIV?P6XQ4(LX5r_uuCB#)KnBC+J6 z%*v+hFw4$3?y(YIxwyHxwKdiCpSUf%tN=a~QgOBj& z7C`N+(uw0|kriUwo0}ZesGm7=8z{YrYmOW(lO1DTb?n$Nf-xh**6lbI=S2YPIwK#T=u26LxtU;3@( zDN!)S+x2e&19_45*t|CwKzBgFQEL)D*W zId!xvD2&x#x~x@6|LOzQGTxLrGDS(eP*t5wvhoJhwn*;=+-_3OTKy2^aHCQ+h7IF| z)t#kBca~T;-hqA+S<9tsIZ$`+^ZYn`d{t@)Vq~U=v@(D{K@T#==&O@d8U z2%TQ}m}yzr6t-64<9)t#0XLlC+v*a~Yk?a??=DC-ADHL=A2Tl)XthqCYPB#MGv;ih zXkMY7Z!p-ue=zXXbV|BXFCi0(Y%drXxrz?p=jkf-gQg^43Vxc*Sa=~S{&EJOJ*B=2 zTVnExWteyozjdp$vIN#N*n{iWA0&Z6t~^{WH-ZbqVuk0Md7Qjz^aXD)(5wMn096S1tJC-oO#cNm<5L6J z7ZHvDGalc!jeiw0eCUrN(!H>3XV9b@jg5|H?;ZQHWx1@Jo(r6=IALlo`?ze_GOi9uFbHfjc^7FUosdK+h9cJ! zmh62sOLExI|J)r5w0n7UZucSO&~%TEzq+9PMse|Bi5~8TflA&8xp%&StGbQN5A>7U zQ%HY?=TFxDhHCrL((hB<>08p^V~Iqq)@s#i3G&1*Epu*stz5vX zz#LuiyvFme=l!0KdpeliYvBJgQxR^KFnEbQK$A7G zg*ckLiRh$9s{C@7WO&L^wP8_ihb;o()e-d(1F%@%Ea6+be>R86SeLenvDl`oL>;}< z@CgC{_V!4{Cf6<$baHEndZXPg5g`>+v9fpu(n&QBHwZ0$i$t)(YO75hmmIb$Fq;#E z#y5H9dLD_g1ZxsKY9hgD1!}x*wQD1=4cI16 zUBqFrbqPSB4xP}lo5ebO?-&OFbdaaBB8Y@?JUOr_+i{;GfGaPsj38BU`A7fq*3B1hWBwaza{<*OjVkv2oEA`%x>sVAQ*s4;FJKFFo#u$cv^j^aTDONsp_wOIFQ z@nn%v;QH#iLWX#YI0{4I<(kVResamvY&M^KvWY^yj?D7fS4Hl)(^@oFR=pqhzS{l) z>u=GuA5`xeJ-EEA-gO$2;NX4b@`+9~zNzNsP0EdHzxlK3Q{>l7P57IZw4e~$Sfpc> zP@v@Pl7Er0I$d>rv{0yM*L0;kQz;ap>4OdY^SgD&;rx8#VEWxTBzf9M+gL1r75RDD z-Rf;N)9GFbEzA--Iicw@8DI3)yv|mD5uaCY7V9ksKZy><*eJf zOLJDpAg1r~1Ilk(9Qz2wbOWpz$(nWJ%d)zw zV(}ZF0c4w18eQ5|v>Fbr-TmTerrPoN1iB6UL0^jA&S5V_&x<6U;bhWrE*=M$s&9ie zf;`0Ay@(~0AAx0*q<3x=ZX?|bb?MMca&+l{4(*ZY*-svnyOpOaM-=XrrQtOccnLH_ z3=Y0K|!EGBEQ6afJ~FDM<9SP{hXn@Qs9%0;pR`$O=drvl__5q}NB4 zbWu|%hy|ODWr{WIk9b*1xtXepO1ONooNwEbHDP+gBqb~L=Q)X}1hl*qBU>Bzm{16! zPo4EviQ{_PDNg7k9U>{I5F)kX%`CMAMV}a;pGkxxl!G_IQcHr1!23aMZ@C=54MYYQ zIBA0PvHOYSZ{h`FXI$Up#Ss^V!bE07Jb`5rRAOG-DK;)ASIOhS1D=>aC$mx#ZX~MGRqsA=8 z34@Z#V!||R0<(M+ACcQ4Lt3stmRMmDDypjl0|Jjj3m_neWDo2{*_;LE4B)M9BEIo5 z0b8W5U?N2F(+^^IPpDf8*-)^b!-A#Aiu6S{nzf|h`6%eQ7(~-K3vHSn?FGHN$S=0f1v70LFk|)Q&+KCCUu@HrQzZ+|!mW zQH1D{#8D3ZOvJBz$xI?hAfX&gy2Xf-F_TP@iy#qwRB01PH4Mvi5`!g%6^oc%nvro6 z4@W3)4APY(p@gV%Ew*zx5-2h@*TWwpkUStc5+FsOB2ny^>*r>b3>^&FWLL3GQ6Y@B z_%2n5Fee|SMc{^JgyUENptceir(C(#L;i3Mnfeg1NZ;pqC#(kHMSE#Q5Mh$*X$@&v zfp8$YYR{_}nb?{U%*%gk+qU*1yQx!`!%?o(U(^f#mStXqCMEi~7BX=bw#kbj7{&ek z!6ja-jZd*T8&7AF`*&|vhj_Z$4()!6Ia~fG(Z%Z0(%N5Vn3HyTSwiwKnkMe~7*VWtBmo!+ zqwi|v%V-Ek6~A`1SPBFl0l(7NdW^5`K`lHq7KJkf9 z3^B_>$wbnYT8MbK^M&be;F&ROs~!v%69C(mN|`mRTvSQI-97dPKs&y|h>dvGJO}A7 z#vMwPbOr_zc%yK3xv{Y)*46r5Hf@V6mJ!YcKeEw}BnSc1;&mr@ZEdM1lKrW*vnNi> z&8gc!Oh-5ZUM_@Pv)f(1hWOHA{x5F5wQ&Np_RY;tuJxyq`C?(;)-}uBF6K)rKvh=f z=PPCQotryx;_RQ^%1dlxX?(4(MQ%Jse)t2-st#8uCdH1;l_IuiDFjX|k&6Ub1AM}I zv;eLWGb2_?wg4&09^`#stxU6paE9&}Hp-sUivEszdR+N52VVn_FrCEbTAzL*OAm zo#kP~qKG=MraeD6fjc`$!C z(h~4#f4nsq@MtJ9IT&mi<@}j!OtmO)({AAwZ?EEcE|&*`O|#LMnZYcB8DeQ^bBkJ* z6*W2^PM<6`CQap=%X94z>T%*%Ey2;35=a7`%s9-VR7vrY0V1a;Sb7IJb2=mn;QyMRp1kYK2Z|Cs^7pa61mOc59+be~ApTDQD>&z(c; z98|sZ!1#5ge1BLz^peVisW#(lMT7WOV$tU46d@?3wFh2*?X`XC#HJ8AA|tiaT<@bl z{>z>{U)|h%@18x!2(sII7iM#_Sqet8<#LX)7Be%=11rmSVpXsCpc8=-U?99`JF$~J zr+yNj)Iq#bVs$2DM0k+gkt>TcESLhkC9vILt+op3?a~A8HvL&&%nTulwkg_*%pO}? zqsK>*(WE``<2}@Kk3atSO}SbvmnVtYOr=|`<(Yh*8XoajIY)9-B#Uj?UnL>On@;C_ z-nFS|BqMbijcb1XFN}1?FaQWy%FIkp0XiOtgd4=s;q(;wpispAGPwHctM`|B{r*C~ z@1T?m2SSf-M8S(el#1sv@i-Ej-vn)wY8q9N{P)exVhP#4U#g@LHl)vBq<Pdb3$5h9>#e7Z_(zIM58}*C+5Kzt;0^^tiDBkq(f#MTdy7N_tNEH09D6 z=~}vG*wu}@GBGD@Xete*<4{**Uo9uF;>C;hGJ!SqmZSn`pBwt1*)1lbo6FUyL`*TnV##Ne3507~ z)@T|PKBg^aOfTNJ%kH*JB6bCda}5T#g;ctHSX~5%2}U({?R$k+nJi` zxK}Vo@H?tnjdZ7rc%?QNM_9vdI`&@%$F?Gagmv z)OrfYz=v0%Jh}1 z=us-`Y&YFpZbvs^l^h6a!8!EU(^ti65pIXv7Awh1L5&rQ`^0tS?!#Vc9pN2sTF^|9 zJ7WD>CIR>cX*^McQ190S*HbK?AC(XJp|<>F?qu&F`XjNhK*%ZyOXvqgDhnb9g9Tiz zNFZ`(fl(9&Q#dTyKcocNw3xw*hZ`c6DBlWLbQ+M>SXmf4kpxs(Lir%wzfi1|Rs_$I zD-x7dWPbmqgjS%?=}Zzb?1bI>ZNhl0(cd^utr3D11`bPnY~GX%?sAZr2R00L75!3JOk;MOFk#pk;h4T<}{^#2p6 zdck|3UIEf4CEp4Ef@nYJDkQXvMiWu=oc7o!d?e9%Ay|l|;-e?0$q6XTfL0L`!ImE3; zuLKCH*zoz12r1qK@FkKS>b+KO-tyJJY~ryPc!F&+Mi~N9Uiohw>-Hgj}WvEFsBe^W3JYRR$6_D${Z07UcPlDyu`qM>YBrq%| zmOAaiZH3wy_2}r8XC8j=iU496>7Q8@*uKLbe+f~8MzuP%FxONB#3)Y`9b$kE(ZVXd zLm$L^>}mj73_;M8=vPfGPCs~E@)e0hHy;k8FMY%~aNvn=@VcYXfu!Cbqvyw`Z%4lI zU;I^#*Q3{!y!)jddX zlFSya3t-NB4<0an@xcclM5_+WSk_*Gy8@FRdn|s8pWg-SxEFYOvz`q+Y+816`{ojw z+X~M!Bhs97nM-74)r#wa)q;}zl%fiT~Hr)HBr{+n65wI+8*=l@m%jAs@4rwmeapL%{OSjd*W4x zuEi&aF5VQwDy?m6$(2am3UD1Bi8v_qaU5qc^DkC_TeM$8@sHRk!`}0B8M(`@bkr-_ zl%qIIX?ZMfGI3P&vBw@O5x!IpJYZPa3Zg}D3J{lyuak*nI=;$tOCQ17U^SX%=)+#M zRKC+hd6@MhR|JDeT!Y5wpBvub`~7C1_jPncmY5j9Ns-TG$_6eE2T}nV*yrUX4DBdYay=5M;kLrk zEO|}nsS*Ucurh#W%bv2VQH-QLpr|^wV~H4hS`vAqx63F0ax9L)er%OxX)kLfB474c zSV{9Q*P)|!_(aQdAD}$v4v#XU32g`8=km~Xh00WXvE@~|OZF52MD~^Mtg!keN`Q%W zg!mzIh=+#N#GU2ZiONm(3kP!G!>o;~Zm z^zqxuT$X;i?BmToZwg!+E}%k9`w;O+mLL*g90rzq-x;~V$xAcjouBeccH8~h*(iJM z{^PW~+|S7l2yBbTIP^4NdA(C&nijJx7Pb{LO)UI@l&))*FsxIvuwVVW*Rck=PWCw1eq<;O6{r z_2~9ssm$eiYinyquG`+u+~YNWAa?B7%Lg-Zj)m1LxA%3s-uEf>K5w_XZ~MyCg^wWY zO08XE-o32sPF&A(*wfBq38!|X*tnp`yIk9nHY1%sQRZB?c1MT82z8dO=Rq|w^#dom zyw+(e)$XjV_O|xlg!NC*hq3W3?pv6iX<>Tqh3~!h-iMZRiaI0bnoX+6tgn-<)!n!M zGH{}FddevljPJWa^{b2df{e9E++LPQUSu@o#_*!A%J^btz+@O7|3dYp^oo>lk$;iw z8aSe5fa!Z<$E-{)VV2|Bt7FDfWEG0t9ic+X- zR@40E%STVC)6YMk9Qu!d|CwL7v8O;A!6Tb)Wt}~AvN??=6^VGAOpOcoH%J(ZRL~ZK z<#Jc%s^KYt(Q+s9_d)95XbD#fF5^1~LlYZaS0q@OzLPFx>hOkl^PEK(LqMR#m^`IA zTIbR;d+a=+Lz~kGnSy^BUh4Oj5)wDdt99hc**SF8Sg*Txt}GFqGr%RR4cCU1uzFv= z(XhOU_*Ax1DK?1#_m|77v*r@AZD_IHu+SRi7{-3DF*AEZtFc?b$yJ{D=84RV$bH`{_pGd}s;+&@>b<(ut$k}rfFy*}LTE!65D1J77y)9j zF>u+80UIR147LX&#*8nrfoTkwWd znUN7EPDGsLJKy?#3u!3dQlV;|jf&;O(%c--X1Ut@{K8fne%6j1eAgy=-9SH>53b`w z(({mi56@{qKF>13!K88M(0=JsE7i8 z*6nt8j`fsSW%PFsJn+DwZ>WFU=#zHvPuY?^i=ixw)#P6T9Z^PI$J=(QzXBDNA4*5< zd(+pvXe~PH_U4mVtC)e5jw=zP42wL(%4HX&yVL0cmSAkHnK0-CQ!S;v&ljea<>szn zS@JL=;ganm@+GDOM*9#8CstlyZBfQZ`yjtyf`Np_oJuRFO#`TN=Pfn#eC1lLHC?NM zKh-AYu07pfpd{!y&^b<s7WOrGB;CY7dZl@@v){SYx!SqId5&|h^JeD* z=p3vG7VQE_gYdSY5i~_;B32`K_3_l8g{6()(0IoXX+z3bskCTmnoQ(oYZGaMPc8ne ziT=c5ib2K3#Eott76zIu`H~Cyy}%^nO4h4hwfJ+-N?(!{tOvNS%T=%T;OIfh_I11R zj8cF5vYCG;oWYf~&CTi?exI!(u057O%2p;9ABE~z%^nI+bd3H$&`tR^vSQcn;kW;W zT&TA3joW>C%JG}~3SwrXeY#dqFz6cGW|5sA~NSBokn61Ac9Sc@q8Ls?ql{nMPsXzG6L_ zac82W9CG$WqFIua2SGI?o(rcG@XS-V5s`=-96JLN{OCJj@RBi&!{P~WHHZ_88(i>$ zr0l}(E*RoA;t*-NXal z{+6s>ieyxi5FMiELta!zA~4%(R)omoT|;D~i~bnEych6++=|K-FB0M-I3biSM@#KK-N`kaM4${{ycdSQu14dwC+Cp{iCR=l*>$1eq-BGjpfhQElT3UEl^ zUBr%H+?USQA$6VyI-iXHI8@Q4M2oTmH!4^MJOpA1g5~geiltJhibD|q@OcO@z~bH_ zK{Px?1VWx*IN)bMWkkN9g-)h$|8zKW5lIwfxf9IizQa&~sU;B_UOBe`&z#S@;HOCz zLxRnM5h_P39DRi90nh>tWOS3@7MvK0b6OMZKMU1GQ-I&at>MTX#|umvWq=qoNRw1T zSrbL4ALDE~RJgGz22%(PzXlI_jCdB2nI81KxJ%b%>HyI11H3&G$_eEUx7EbHX;25s zoJsY?8S)k7Z5vOBEk>Baf<|FvmX9)JPl6_Y|4juG-i5?A&yx= z?lCmKhjoCK)?G7eJl(NNs3PEjq-hH!0X1OFVSH-CX-uB~=HgNy~5G2{w@1kq(8;%X>s z5l(VY43RJ$5xrpSlMam(UlAZ4XGm=>zr#o@ge^ zU;0wOYuJIEf!J@*xKn8igG7o)_{PGPr~~yQGgl0F?I2dND-?VV9S`4VNU_HJ5|TF6 zL2X^8C5t82HT%G#PHxg8oG;4SA(}0j%Z;YwBFS@7RE8=r3!x{pA+5kmsyI%M#g_1* zrX5{ZZ=fl#wEfoT z_2&7b*Asl!sFPjT+!M^_r;tcO{UrT}E-hIJh0CE(8s0_S2-$wFFC_Duy&-&8DwCqF zrZT(<1A~DL=MciJLaA7i#Za)wJj4lEq=3opJzJPKVk%r*^@y8O4nkyU1|!8a@)8hq z&d?6N(B7VIA<6xaT=SNA})w(EeF@-dYM~~z*lNX z=FG6Mvg~Q|*T`foEQJ#rs3an4A+BJ&L#c88G?=VP)CR(d?qLd?RLw| zA&)*Cok6?PoY&?dKJD*z>+F^}eQa*FLiWUBwX$(+cCK8>6spzD(N~Hj!8-ffFJyHp z6S?2U3nJ_n6e6gximgctpmdQ#mO|YNf`|Z|MN)H8Uz2bi+9h;GSC_+k?#BZ^bJnkkXPLBx9%?)tttm{#CLWVF zmc}z0IF+%bH0{)!cKx*tx`m0)FDFj`o$0HO?ew)a%9%mXr-Xak@zIM`u3^Rz&*cJVE_4wW{2s|WW4Sya-@w*w!_kVu8*jGUhuIVV%AQn8 z(59hKz259ITUncM(2MT&mNnJes8i2g(O!D=46b={@3omU*dG4G#!V5)%E+|MXk$@m z3o$8Hio|Ji+~fr$Qf#SELE8mqoBa&^r0IPoYts0X=MDWeKb6=AmP#758C;Nw_K0ec zgO~Oe_U+pTa;08gIhD&NJJCpy(ME&-l8)Wa;(7VJC?F*iHg1+0b~GpQ)xw7C}_zW_1E3kJ&I zQ%gVh>AC{GCaehM70}%@yWa$if@V2hD@#_sqA=zkzIcfpg0FG3O=DTWGhj zWUe9TyWz7goY9y0EjRf@!!uTnX)W;IlVg)_yH`FOKG+k=@ClTcU1|xYQpJ`2Lq3!+oXi5x9_qj z>DoHiVs$-J4JUF@o`2vDm~)#Y>en4piWORyVzh9+erRE3K`!cyvEoJ-;f*yOj6NyA zHt_lM9qLJgKuwmgYk9XzI#Plj)IZ4%F4lutr+6B?}kYN6^q`{-~8WdU8Ri9lm`S2?aY;0f#lsidBeT{ik_$WN*A zxVGLr_xQcJ#SwMx`0Yy&IyyG*snaN+9lA2b5w-2p+kPDKkW@TWSGk;~yU;}^Z`~P; zhxtw@1DS%eCV&#|Jd_5JE|X1n0Gq{)mGG((GJMV|b;QI*!l**9H2^Dv$cwnhl*$(c z$qxtXuel$P%bGj-j^!ti0GWDlt2U^L697-H-C5+`HDOuSojmAwFywuh|1jW7aLxtk zeK1BQa43_|3Pr{kp3Y$xS}HFJ zSQe32;pwaKy2FSM&kMWnA*PdzGb+uALW%wfMk^&R1JbIXTys8#b0J^+0Y2zvbIvIj zwhfI2_d-+>c}>&kcQP?~1rVK|z94s(f?>@931|r>C}%zI4fxF!O|4Rn+QgpUNIG09_5Lc2- zZyY!{4?I}B5)4VMLD>=~#aOghsPpJj)ZhVE&g2ObCYU5fx55!Jobb3s(-cp0Olk?YZ8S>;vhT>1wmdQECUgY#g!r6!F`fhM6!!u4Yd&(5ticcfqZVJ%Djx2 z3~CXUekPH`MH=35I6%^Qnn4^k$!vhvNf{|Zt;B7GS~gjks%i?}cQkIu7aqoinM7w!B!&N-7nhVT zT^i7rY(>t8P2n;;mXby3x=sqP8cAqyfk5`r@96F5JYa*vLXHl4F`}waV!3F>2t72_ zXr$hd1~(BQ^eu>P7~&z^D$mU=OC5^7Ncu{NeOSk_9xIOObLQ`{ISDi9dgYZemiraq z^2y*CA~|+!XU+28{sF`(*JFoWM|^Usb9tXx+EC5=CqXprN->xb4vl}l=&$AcI0c|E zlqytae)7oiqubll)3PYbns#7+-{0P7aVAOuD}=*)VMiIxrkTcHn9q{iwT}GdnCYE9 zDJjFL`L(OBzI=bR7ELWKuCCTUmMfRb&`skM@+b_*8wTfrGCfiuGT>L17#tb%mVUFf zfA0nT?a?RJYE=ki0u82<$Ylw93?`FLhG8#u(YHhVoN%q$YLza#@X|tIX?p+uqYwsT zhByYm=0g({|9c;YujXS~FLIv|Ms$v{S35u`= z^B(Z7f+2sf}ioGB3;3w8;jF0SjNJ)ZptvCFC_Mi-z(~lb*@Xu!-T+Q(0 zZudt&{DIu#YePow+tUuT%07&sKe{P`vor>AqK|z6oyo`0?i_=MeK`my{|TL|eLxrn zrkYDXT1~1aR)q;;HJ;?*^sT+{8nUXPUWE6cs3s7SDaYj(ZQmLIOsawWhkbQvx$kp2 zI@}#_7;cIHbgrgoFKI!ER_Ee6pE}PlF$77X|Mht=ox4Mb(bCDKSrUJk)q}em_|u@N zBY#c-khpxIVs62GVNnk3={QS1NhM|aMIzn8Q@c$Tts_v!nS^#V3{xIq19JW(p5Z4*m5av4wePz$Ha!oopsvxvQ6NM);p4j6ND zD0GT1bEQ5`g+e#$JM<|rYsQ=oJuPHLFS_!|lfS&&S?Ye|BOmE5>-Y_kE3drrnj5Y> zc;15#KKRPTPN%cnUDCcQPhPa#HJ7T483uzxyJ5_oOJ5#u5g0MqIr@z-KuzX3PWaBv zVCdP#+_JR1ociJyzgR1LVQTc_x88ayaj;W=I6iTMrFC(2bE$j5ZMWTa!zDGekol>n z=BH9idcFOrPkriCp5)G=f2Xa^xQ~y6QkHrW0^@&tqIv;X&* z+SFk5o3l7Qk=Jox&ezuY&gSM7S8Q&8mA>yp8Z#~Z3>3%IRI6E>{WtuWLeAPsC^Y`k zM(AYF9N*!K;lS1Tp*`G>Vxy99l-l=<(QlgbfB0Iu>g$iq+b5Fhq}Xwr%av0LVy~xZ~VQ)qJ~BBg@LnJ`{CipE83%u#8m~ z1zN=!{RxbhxeV?4R6U&zfy|kV%*;;1%w1-l^Ob5k?GhE5t@IxLwNj~c*>njZi}QXx zoUin}%Wz1#OxnLogXLT9zWeTY{Ok+DPwu??J!a>d-~42o*U_79I%;+vgT#WQ zW&$_dV7~}(`I@|UOjL+~FG#y%|E-e4vVB84LZ!4= z7k^+|)Gj0#BWYPU1zKHP!?3(1*U14E7j)a}-Z{Ux#7$jAsW-!h2Fu+U{^q|DSMKPgB+Coc;@w4^pOy;%x zRWg}t`Rg+D@iM{1#7sc!;Ii>2f!W7_0wFg@5F9Bo0A=6WLP8*8P9iDk5mW0Up)gj| zAjZ`sdH(QaBj=R02O{jbE`fyEbY>R?0(ddpXfXVULge(nGue1Orke~I1scspe|O)4 z<1Fmk?Kd0Ed?`bE;56uM;Sf>AWSXtEpeF_l!;Mz8HdVmG4ZfZjIoofa2jA&qff`xi zE>zq(eI$j`#L39-Hy(>J^14 z#OwdVf4+%+bjNt`Pkm>+`B!xKhs8Fl)@|fXe=1~s=xWw1MSSC z{^sa=KD`X!%c^H8Fb4vCdPn3$51{2i!?|sU7IM{$E*^7Jm zU?9wloP(C0BFpp7a#y;cb-Gwn$x2pCF62!bmN+N$$>?v@x-)};7B3kDA+oYzF-G8e zH#tp{QlI%Xi?nR}X>(%}ddqIS7gym4QvfH9yKhN{{Mv*Hi}7%72Z5|)F(hn(QYg8Ewnf!~D7tQebKG@4t<=O5A^dh3Uxv9}e9`{tz3 zAv;JNWFHeIFx&}0qutMetBRmk;adu1_{F5rwWC&imjR>)tBk^Fsby)6999ky~w_LOg zn<8A9natBOJI?uWPATRbFVh}9dhh+t&gc{oxI69k{rcK@^z`Y|{NSgec~75iw@

O#Re#lfxr>sz;}@f8bqwz1up1^t)%zofq!AG zPRrEr$;I8Fh+}Teytjx3Bn8lDq$t;^Xwc2$b6Pg3xfyohyYIz{J|DkP0$ zubU#+jIGpkKXnoMdJx+(?lKW&Bt6P`{}*bOM(t>|_Urbn5+NNp0pm1MtLgTTXZo<1 z-QEhn(JSQp+#AqF?9OL%g=s=Ki$&#UEfy=aMI~-PpE4BX+>F|Q z8nf2H3T%bM-N(~FvPY{1AiQc7bi7CuF@O}2jg450Jaq@S4xVB}iqGBn8io~FAs=Ji zc@vl%YZd|e?ttDjbmDLZ*+C=U zrEO&1E|sqhw%Uy*p;V>PJcKFVgJ(=v4ZtxV7#I3vr6GvJsn+N?vu!%iQ64q zShG8)PVK-ijQ$FrD4d%9Z|Mv=2OgMJ<>Z=b6bbj~78+YM`g27U0+tH-U zAo`Iz9StE__OPVQi~XyFqmfE^0pm@zc4YLKiw|I?OC-w&F79`_^<~Gb)VrPj9eba1 z_wjxpdSb*%-1m6LY#U9^`v0upY%o}>NOxC8*ukzrbPbqV@J~K3SGJK+in7@Sk?omi zao)kY?rTdlIvCTu*9ewkIoX7Oqeqs?44RA@5k>VTtFKRpw+2IDHfKe_%R(Z6lAc+a zisKc^dJ8}?2jjCgsiX+O+=SXnkG6RRUyBQBc4}s#N2N+`WN;4i+iH&f`O9y6+o|*C zp0i(EdLe2uCi?vH%a6~^o7bK=aRNJFGIc@-P$&xcec}XZ$5G{^;+Xx?)`^IPxKRWA zl+lbNb;9Q~Bcsoo2JM@=%4{m_^`L-%)t7w)$%& zzc@|(0d?c;|28joTFrLQ$u?>==hI~8&IdzN8HjkjzJQDq4Q`WLT&{ZUmb`cqm@Yws zL{|C@{79$pYV*8~X%iF?EH@pXQ?$1gtW}TI8Ff#_rU|1|tJNE^hK`Q9#3vj&1dlW| zHFqKG4}5JRUTF7vl{{`rp*Y6q&Dm}zpZ5;UdWmQ#SgEY7!FaPQ0qrJx%eB-fsK>Q6l&G>5PD~3*80bMy=CaxFnB z$=Shv`a91xe+^wvli78Ry{@ikMUMaNnllrpxXGl`P1zgt#ztCE>Y1T>+P_=oCR^rt zhpxWvz(I#tx_q)hPORb-aFk)9UlhT5@Qszlg_*vCF7m3&&_z1^{=&ZEbKp6a)|;(x zsE+S8(J+|Qs|ROi9dqwd;~YNp)Iooy)ymFvKqf(7SM{>3*38VzQ;v?Va+Vf%b`D&< z4P(2#ef5Ezoy8?||BLOG<20Ic^D}w*oFo%@I9wvRr8nWZzlS`2Cl-MJ9Qbc=uFw!Q zG|W{G27_rYAPZSa>`>$(5dv^a@~nHY?}onu8PRc=Hf+-?40Jqt$T+8Z#K@J(5<8Sv z4uls#w)~`mnBF99YjM}ou619U`SBu7b@uIxfE<%f(-#HDyckrZ0(E*8;vgeeDnZ-!dVyH$Vgpowe7jC}! z=7X1BdXPvVh!J!on~mjg+KCplw}>+WK%&KLcJ!fzh2_JCmlqfH`J3pc{cA@-#^Qds zieO2R*Ao27WQ=75#M6&xs}7D1D!@9^@| z=P;-qlOdJPCHc2{+nzJD!-OkQ%J#d&etRbM_yLKG2O?+3%2-=v7Xy{O991Y>)1ENo ze2pvp^Tuc)^}+bfX<*Wz1JHY>H&{WS;n8}QZ}bP8ZpR+RZ_Gcf+?O}K@MZiv(sTIv znEdb+ms5P7H2Wwsc+&xhYHyx`>nlaCgy2*dKPflO;WIUr{{W{iJih=;#NQ@J=wUIR z&gIIDW>ek)fEgh4qJT}rk_p@<>fldED;sp(!Bo9mYqgep(|tnbc_Edi3B)sQc6P4$ zGen(1v*VxbbUF%d;v^?I)9<0ib&_~%fm=oCOjqet>WN=f0tJcVBe;gL9Zq%*iTp{B?e2m}KZn-HyVr9WQ> zJr$R$R5F`Su|yZ-1f)t6sX-SQ@#lk?mWhMkmPWP#G$W3S5*mWvcT7D`Y&x6N2d0XA zb?(r4=<*V2R*p!Tl+78Ti-R*p{9&$CN|OGJg~{ zBzeR&Zy!2_f)`1>&_Pw6v<`AKi`s_l5qRryN&IRZ2Y*FXi7z3phoLO|*!{X6H%j=P zl<2Mr=F*kFlgAQ}jYP~isYn=2102dvQ?8g*YzzL+QPmX!ALZX3`*Y_ww&FI~3^4Se zQ`18qI~#!`Q1c8Q6zc-&|A1lho3s zmtPvkweO;>t+=v#g?rmXR`F!t6Rd;HE>tsnPqJ<~>S4S!lJkw+n^hD`J4E z5wW+j)$`BGuBNM^Utw~me$#vuY?uak0gF8U`K(_o8muoocvw@uoYw=g#@KxWk~@1m zHR&4Gtvwl<6BH+ACp$jv@V7D)6NB>hr$7DaDVI30(cj~b7`zjyJl6Z4bW--mBEjrO zJ~8^+VDx|8{O-Hm;MFfCyXvSWlk(PAZjl z4w%SnrnMCun;^|?W-Ah+Ek-H0?TRucmki6Tb4IO{fyz37%z-f(}zD zE$gO|i_%Ub+*Ny*{={!+Hl!q`xXR{2*MkCXiZAvTW zNPS>Ampw<#&7+ZVMx(hJ!676J0@8oH^)M1>(J8$%l`3vCI3Gk4%-7kSxPMUohtK?F ziWO6-ca|KUU14(<^1o*fI@^h?R}Y|28FQ1G(f&O3S1x~*)Y1r7#@h~GyG;l3o-AUr7fVcDyYRI%Imr&M*7RVhnm2N$5y#mw2-I% zF#b!~EMuH-N8O;n73Ci>@flhti@?)CvA7QsgF03%>dy{8($D8^QD&>zl^MrZB-$dZ z8VM3Tovitsv!i)VM39n`oaumTildh5)_ z%c35Rs4UO*X~%P8UNm$MPO0QAj+m=)re)?}`y#7FI@8Sn!kj3D%{n74m2j^lKG}1H zX6W+Tv)w-D0`^M3D=+2z>#i3DK=%$->}WLWd0rTpE2ttt>HTiLfctv+f(V%d=}udt zTobAN8T^PcD#Ba@#kl6FugA!uzrf1or%fD4b2|fFMiY!_O=BHmZPzPNVhvs$6TR+> zHkbVY>+1)DkbC5+Y8S`ccqo;g`FQTudXm1?E_upKcCDYY3quO7v>Iy~ z2WpV)`Mo}Q`^FHlV5!x%pSdu*y)!@WIP>#6JNxGCIrPzT=S&s-wHDne>dV-IasTu zL$^LLr5fFEUzRoNmRmlQOg@COi}`W9djgOB4K}(T--DOG2`>Bv*p1H3r8(wom<9uw z6wAGcTk$t6({s+D$)2-iC@h9sC` zHw}lwi!ZXs^%O0?z?q#G)`)fW1`V9A-nU$3Y51YfO8kQmI~UT-)pQa=qL&xbIYQm5Zr6U58&xCcBBwA_H(H+CRuGEyq`P*g2P) ze;!0>CwBa2$(fki%6^9%yeaxDvPqOmi>1Yz@Sb~0{E8=2&2DXy)AC{SF#L4_l;WEL zcQCGKwoNr8Jn?YA4}bP)#ucqpr}<}G&E!4rfhB8}uvzw7hHgz!5wvsO9w%wF-x_}7 z`0?XgTgR_qex=EDf>2cM9~3s+VCJCNCeKnSMZ$5SkpLM;7o#%^P6i~iT5W-efvGc1 zE>{vsV+u;A;^eqbV;hD`)jzSqjeGuE^_7pD;_){VQ&YX!dLyzGTiAEx$ilwp=op!q zvhh?JcpI`+rBX;(L2p%%@$;=_c*9*-*j*pQ3h6Xdf-t8-K2-|G^2)etuK-=N6VmFE za$vJh{BNWW8^`Sph7+)JA8>kRaP7Wh$M$dMMlS-gC=_09uQ>bg8J$g?DxZJi$}6}2 zimbtF>&L<~eL!7*e*5xlx3{*o^J7Rxle0&p9pMDzH z=pyhXluoBWMkVJntOrOw;V@iR4uw=2X&Xsw`}`+8`RJigcvtnJ)M8P@g|**j zu6jx(R4fjni1x%>c?>-wzOcMx=L_n5WZK+p0yq|Q^>7=6g0U}{pjq(X`@v(MFu#RA z!Xh&%&=--hZp7RPYH%7!-__#-C(lxGoqv!p+8(H+iU*YC{2Oi~-M;?Jk^}g(d_x-9DAu`r&c>o z$Svi_?~=GHaZ~F&8c$^Z^_afB#gE|S(Z!)NIoOi=SdF2#CXwE@VP?krfYGK!h=bK9 z%`H$ipQ}0(b&8FKR-?_tPQk04b-Coa_DVXYH+9&zwk2h$UR{T2&NsD)+uJ(J_NzWP zEb8#zlY023^UrELRAcAi-8mRe5IXA3Q)p4PELa)YH$D4*ZF1LUPT)e^+1w2D{ zQFkz^Ol|lpBg(;+Ri~&ENDVP$`lo;;1*)6SKII?hT!jjvRumeBtsaXMKiHx8Iz~_u zjiwV4bTYghk&yz%Ag(r)`2OYO8pI%l@Id7R^GOxX9q9o95LSLXs>i3)%cP-gzyUf}L;>{_#1BOY>C-Lh-f0F&gjOXPf|8+j&BRE? zmb1}_sN4{?xDU!kqD36d`6igJ2+A>ciNgf>*Eudj8+aTvk*$|(540k_hshN9Sp+_Y zDt2yQ{~~7@PZv$mD73cRb;HCWgGJI3tu)pi^|#D{XYvC|Db{W8+aE82QyHGwFD)0V zoPCQw5eGM5&$<7TU;c5OA-+!ke40lYN43HBmDxy)R%R5*W^&%swgFo53fFC?Uz+~V zRUdf%Z9izeLe2ng0Sn~i$NSZUGAt{3?O=)&l^2JTOY0{p zxvkiXbI*F&%*D6NnL`mTMffyCo@tYak(f9VAuLfp3B@8;$xq;GVKOWXCf&_I zHfoMj8wd+j*7E&!58g+1Pb#tBL}wytC+|c-I6E2{L{i|v0*Vt%MK&S_HAPTuYjQAS zA6o6SamRzJTI`VSve6Th#iZ|a*3j~0I*YSL#HuXIX6X5;ujBx zM34-JO@K&;Pi~@Zb5a-X!>*4Xy4m+bzq{~8Ot&MElylhMEGC6jl8hvGE@i!jhCwn2 zD2FewL7|c*C;FgWW?-k&kpd@cV_iDd;SC>PWc8qHmnFIH`KU)ywfb)1K7x*2B3Ks8 zyGQq#myGW7WpDEm|NFrM^Yh+~#y!p*=;KCrM!DT)+z=L7b3X#~IcJRCq5Z=0_0K47 zb-M-_?ydb)qdBDeJ`L}Ant0+4_if`Od=5g68@Oy(s(^G?9%#}J&By=jPp*B{Yj1tZ zt*?2~lb&?lldqkGN8EewR~KJ!$t7<;uzBDLaazlps=sX89y}BEZ`lLN#-9H)XY&v(5)fv7G*`!uzy_2qi6m7 zrLxW!S+R8$I7<<1s=`0x^r7XiE+4w?_S*~3d*1Vc?$6{?**ovN({Y}SBJB46;PruL zKl|B^b0_iY=M)-!`7i$BFLJ5T*V4KFJ~{9YW_9$tW;GTWeTenI`vg`It#{8)=2a}o4dFG7!XKG?@f&f`;Bv7!@pRE)W0UBqVF ze}54o?}b2&X{oWH(-Z4XW)Wan1|?4K!f*N3SWch{@i7XC0ffx*YXwq!$Zm6nGt!ng zDRFU7b8-9jQUmj*Ru-E(A@pdO-QfvM8DoBo~H%5D!6WVF5wB8o>@-DacLej)5)3MFJ#K2d*gownUM1iq;H%O)*QZwPMUbYsfc2h)gCT+pBF9$+u;g5YJZuEdh(Qt( z0>#3k*W)t}f+t9IoJk&H$2pjW(1kLoJy$_96nN~@=Fi9%SVL~OGVtZVHv|8Z6%pOz zcs)d0WgE{nl9eKmvwcUSv8Hffg+~Mi@uz4EXXvdh5OMvGG78bmYk0}BGz0Mp%yoJO_!S=sxNec>xEhneR zO|YhNTjHE`6@lp3bU4%vAFVZ&+dA`u75?lG+NRYe;(up*KR9LM?fu(qkA}EJt#2GS zu(4iesoT5#@;t~SaClyrUZGR4W#_&|$Kq7sMot2m;ubs-FT+GfBcyN?|BL;Cl>(#* z7-fVWY!~pIuH$CFl|a8zj$z1S+@&JerlNSw{)KuJloOadu0n7?9jF(0Z|^DN?~m>d zhn;r2I~*Q6HXL^StwYM)3DEu8p|e{@T{nAktTpvkGC4DX;Fm*g3D^@x4jmeg)UpM# zTSwgD94;gp`HZMw4D}B{s(K(doGfBzVXzk3I0Y0I8*Zp@P&XUAk!s%;2iPNG7Gbnu zfWZJMUI-*6;Z~d&CgJj)?J2M;!$8ooQifpI9Q=L1Z$8C(a2T6OAn)rsWQUYJL>jRY zt0BX#Y-4>rO@1w#kl3>&7*QHft64jK$5R~=!BAxAFeH5u&90>7KgF}5eG3{JLHZtl zMu{H5xP;xIc8FMj`p=bQNnJS9(~rNom#bn0|8o+lqtOMg$1~bbg)}%B0oE6 z!)lm#?32V!pAKwbIeG;Y^n-!FfQrso?w|`L)KmD+u?nwXxBMry+8sL`^qYAMcR3-u z&`zB=;NO1Jc)YEKo zK39a&yBNCI>%Ys=j!P-yw|hxc-BYe9{l?=(D}c5=^YQ5Xz9%`o>80h~I!qN#xCdeM z9HhUG#l-9lq_K=As7Gkvd) zW5w&l2b#8P-1%Su|Bo5A7O?kd_p&|K>#n=*e!S-5kyI*^Z8i}})0sMgWCF_u0$mu} zd>nDa^YXb4(>xT5;yY=$P??(}CG!QeH^RXnm#)uy75#givJw*!~v2KOb< zhd}bN)Zw4+dMVdE+-Y;9KfU{Ovdm)L_6ojA+HG^z@;n@B97M2E%0*=7_~d**eITg>#4Y~r@uP{rO`jmNg( zK~fzOHxR)dhLsEV8PSP0;Mg3<6HuXW4v5~7m0|ud9IHB=o0t; z=puV#J8Rf3UxTzAV7ZktKufpUORXqMLHl?FVn`2X-!vA$4s5Fc=+O8Ih%P}AEN7tR zRd!w0opO(4Moro@yJ?-s*39 z&Mp!nDdjV|sJ%7o3NoVV+Pa?VnS4w7RPEZk_X=`)FSFM_SNBrp{#1|dQ@yQT*TT4t zj>|8t^Y!K|bGjZZ#2S!whmk_X?;S*xi&-D&dyBc(D-i8u6mj_;KRfgiU-5lifg9JtUS%; z;!M3BO~M5_*|1ACR?o=383jpAcTj}Phl+86jJdH$5tu$)bEgu;V51Q%ChGD*NqCDO z=x`t04Y)PLv!rH|<=#vty-}WZb@kF(Hr<;krRLIw!E`FKQ5d)zOK_TGgUz9|b(7J0 zeX!VQf-ZzA1nz+s*(=MbNu}P>+n>mUU*+vQCO%Xh z#6;`mIV>5?UqbNvrZ+gv?{SWy6Sv2Dss?QvKC_elvhx&w@IfBSmJ0e4*97)@4ej0j zR{kz!0{d2{dERlN`3J4CT%!r7#Xw#_>xWBcvAc3r8I7&o>qeTZD+jMT!ct2T0)#r! zGzCrCl&?IAy%dEq5pm7N`V(8>zrQ*^`g(nH^Sp0GfBUychhQ#XSqBNyQVWq>=cYIR z{bbh?;V6WG06fOPfjOM3%{jq~zc>2oXFvPd4F&BuvZxBwl< zE0N9YYQr$LZ=Yx)zo^M9W;PAV&NFt6Nx@0T0ZXARim#$AWEsKnq=2-J4^jkvnlMVq zWc>Sb71B1HOCj^T4sJPK11vxQ6Al7U;gu`%M1;BFQn6A^|JNYAGYkePJVFF%EYf^!Rdy3w0+rx4jJz8;)J1#7N+Fnn$Pj{&*r9j22M?m{urUPX*zNzy}G8tln$D z8irY3E*-Q2e6LV!vQQ^fIe8RGG>k|Kyus!>$Ys293jKuHH-Gf1;nEU@@gC|@rB-X) z_nFUp=8+8gwkzjmM}f~UQfjrMhbvX{EvA&!&o6Pk9J(#&aL`h-JfA_RSML?i8D4ec z#!sy8eBc8gF!SFwrP2G}uleac_V0m1=(x{Asy~b@|J1-w1ztydB%Xdfjg695wr)Y5 zCMN@jLBiW=r6%9%avR6)ue@Z{!Exw}de|Qjs34^l)7;~+%i@E46|rP5jU;Kcr4hG0 zJr{r&Yz?JHM*i;6Hd^p4KUE?W~*nWTk8bUV&-y9CzVg8O+J-_5l5gU7^d26LTNF$X!t500(niK z8?jg^4+bjsr2eNr{pnqdlnv69Kpt?Rxa?O=rF?TSL6{&o-j%o$n#sojs+3o099N!; ziM^=4?fK7tLlkq3j1$tq{He1;JXkDHMCZ9p)bzc9cY~1?=qrwHla43Ir9e~5H^ePI zF}hKhC2dn(i_A8e{pxy>&5t{0Z{KV5qMXqPpx5apP8k~obWJUf%=I%}irqk6DiSX- zlOZlZrXT7>BFt^zjafq5-dM?1Ou6u92x5g%dxBSP9@W-phqm(&Qxwa(F7>+X*iRdnd$u zP&$h#)&c44fS`Sxiy>1KjArDB{MQ4hTZ8C(ssa!0= zN4n`u;|=NFG_oZ*4C3+nZn>N&AfO?SG&T0qi4-sqGQi$BEry4j1*X{TZqDu7H!}bP zkfy8k`?GVs1_={Ke?Gghu~vFLh=&H*yYili2)R=JXX>as_z3!&PAJcltsz)1(gaKeZai0=K z8R`u?S^NpQ1#3eXQc>9{NlH03#tr&bheAt#9cz_09bAZ>wYxZkN892wAoWI6uW*ZZ?#g(4t zvERyD+j`nM*TcLR|?cnv)mb;$KPa~DG#lMV5eI0O3!Lz7Nnn9qiyL1Fow- zC$wB+?GNU#PKzIcWk>-6Uyv~4y7@XYRRJwZWr4a3Su~1mTrw6&PK;8`Mg1K8eej5s z?9O57Os7LZsC4dK z^VY}6FTetDgt9?%i2-tqSIB`0;nLh($Jyn>{;?4a#rWLK0jeGHcD+z180`2P&Mvpd ztwY?X+Z6Cgp^6|ne+P?TVa+};5A8oM5D1hGW6ZZ60H}z}8$-@C**TriOoj(@WwDm`JtDy;1P9aAFGP(0(jjaDiW}H!0r}wRp)ZxG35c2cGmx-0g3mle`_ewq;lKtuoWc z7rr&U*lSG#B5Hg0>)2ppb67xKGM4E5qWbhD`N=7hD#o-i5;}C~`tuJSm>nci^Yd3F z!&9|zf+P&N0x5xuXpQ6L6kgS#_-wCRl7CpyggfoSVcZ*?FdlfNa;w#>fUXjr4I$r{ z&{lUd7U`iRKXKLecJMcD-`!0{D%F+Mqffo~lCGOP|3o63nQc}vun1?j2$X5+{PXd1 zYc>};Q#As^;0(M7>YSDnK5#G;Z0v7Wsvz{FDwX{OZfLyqE&s_6lP?9|yE! zOAcUKWn7Ps8GaLcEb(Aco}*luCCa~os#9UrrCs)?c2JEd>)9jRf{K<%sp$DPHK~## z;uuxoX5Wic2`6^dmRHqa-dQT!Mv6K~)x0qFY_KxK(`dx2^1 zq>$UnVCy{zb+j9qni{}-1?zyS{}Ew^;Szw-KY_)DSGjls04mW=u&MC=#4rN3O>v!Q zh_XstszSkVxp=pLr}Npv#Dmn-8r8ZSy!u3vkG>B#Tu9`|>j$oSyVdSIy_BS}E7fU2 zJ)C-tSeuOM%{KF79FA7PZnYth`cCIAwDzqQA?cvaq2SNLvhQgKHHfc!d>EeMLAZ-@ zplxv-S*oOql4b}=O5W$%?8%>Zz{ap|{jJ$BM;>_Kfy=8es$Tv}v$&-?bJv(>;_m&y z!n;RnAN=44WADkE2S;u5=%`H+;zvin@(NzdxSVJ9$4ID~X+^gc^Ap(M`j^7Z-%h;y z*8<-UjNs>O-e0o1`24{Z21%JDQAzO$v%#Y3A1TQ-&}I~|R+AxfLj1f8Dv~_m$tRjn zrk;{&S!Z6$$!nqjJcvNLI-@#mViB$4zUyE?vMI-0R&&Ysa20%F?19nrEw)T*xQ~fI zu=kn}jPY9r_#e6^vlBayZi|Uc4Lz4AC8hQgtL4dyvFF^%+C5)z!Po~%h8di93-eme zJ#rYJUVSFnFr8cmbhGKHOqO1?-a>$O;zo>oo^F%EIGN0h-r<=`_MS)RT)|}>hoU=q zVooZ6#)q800tnv$&fV4qn@XNxw5WL4Q5(rF(rtT*%)6SU5_lHfW-Xn|b|N>S`^k2i zUb$}e|0On*>`JiWRLOdf?lNJzX>`57f>nbnqDykeL$L59ogC2m{AGl;=gAAkT z>E1e?x~w~#8N=DW)tX^{&)$u&K>g%XNvU0Z0akQo;m}5v1}Q z3G4h~H_P#pLs6sxgowxqW1hnLnnb3ECWuauWJ=IrtWwD){CzUjNL(c?3f5c}vtmrR z07i}wd5pY>B;?GY5WqATbT#8yLnZHIGdP>q2u!I}o4sJLQmQw~71Eh|EScOHei4L% z=I0TK6NxgjztNqlC)=R6CtT|b@Y3io14#n-qr^{ zXzso4y3xJn-uCA{H~N<8V1LTs6ZxRIpOrI<@1I2q#CjuTYhH`tp0wTGEnpeTo0!r$+uz-^a$P%d01)hs0`k`fCtFjl`Y<5}tL0biZY)U}Uyp7!fn z3N?`FO`MB=Q~U*blpE1bL<#?`!Et*=V&gE z`HUP$W@pEkf4mME8I(m6yOQ)!aU*^h@2VL_GHPC=LMGLW-PITuj9wX2K@lh*I z1SlRBTrMwmY$aoFYqHWJcAQTg! z5@7h)GQz>tn8jG4C06w^2mG}UQ=VPIt;uh^&+g&c82-EoGs1|sP&xJvIA1kj7F12T zisq9PcXr&`*~J!bYP4BL?eT)Yk0-U@43mX?NO>I*Q=oF(Vb}j=4p4Hu4b#Ap@K}bn z4<>jWpP%;yYrB)0xPohfyVYioQ({b>b$7iiz9O2K%!YCN;Vu@}o(XKdld}POb}I3SVGAsd2#@ z6&;6lh>$CpD?#zKoH7qMh|q}c8ljVw08uRAB?@Ky+q9apV&GW=lOZV#=Qng&h{KT- zp(NCRuLBoh=5jId2`(5>a&#weW?>(ZEwN~M3KmupCr?yB3$36HR@)zZ68A0T-Xit} zA0*YFZE35iup9@~0d!DiFUh>FffTU*=`JGXQ%LAo230BYy38>V!U<9&!KP@r1mYjd zIaM;1k|=|Og@eOnvH`u|ws6_Yq-#JSQWts=N^zR(7kdSp0Jy=M2A>cPn+jrOFVx@jNvXWPe1?PQxcxD}#$0u9r=>N{t}PFOxXA znoHc67Lh1!p|yz?aDw%#qlR-YnOHq~^yn-G({%PVdoIOzc5EJ@7crh?D-GnwIvp`d zaBgA`VcOpP_P4+Nx+6ybR9{#;e)n_8bM|#JJ^K1R_gu^C>tENr5Kkyu`Eg=dmhgPP zo;=9+5>xVlz=!-O{D~@L(+)H-Pn*8kW5N}YrI*2@K_(uRaQ*NGq@F#D?}>6o0w9BI zs1-)T520~mAUzIaTx=lv8rAP|K!7g3rW4kY<9ZY-ZudS&Wt+2cc@E&QDA=8M@)> zOs3r?dbZHvQ~AWrlhG5vapi;WsYRW^f{B!?gxF#Yt;Eu}siz6bgGj$jwHxvtN8eD8 zIX>JC=8w*3v3ketuyb>arR0Si?Z=M8q0EDDST2#RlN&RsS<0j65NQj?K+T7LE0?>o z=n~@{@{hS`FPcqzUgH>GKVOQjBYx!P2ZiE`O=LHcz`d3_I`6<&A`X3f0XEh(`b~?i zrnGuxq8qo0O9%wybFPp0*YO`b#us8yP8SGiX^|d2M`b!9QrQXs~ zuZKTG9XYiL!>oJ?sep0rHZQ}!>sf(Uk)i9K$$&(hsBe}LH#Fg#tV+XwlCBJ#KpRe; zK`JFdS&WZvZrE@(=_8d-OCi#Vv4os6smAqL!VcHh6BMX)O6p1vt&2Jey6?Y1fE4Fy z`NByWa)d1!N%y6iv8*dQn3{~MkMDMa#0i>i3X$HBP1W|k?wEl$W^a8>jeWp1PZjBn zf7G}p)y4U^QcSKz{VY;NS6yVgX=`(HJgQW0SGw1;ZDg-Cqe{iAPR>v{=!Fs=%hoEV z)|t&zkYJ&B{P1qOy$tU9HCOP9JC<69|v@{YWNa6!aX0fZ-Kx>0M#dL>B zj0#TA$;MF96A$9{OtuL66Er5o8o>yI?ZGn2l2XW*aB$%i{-O6S>9Xzx&5P0Ia`9%yYJP&T z4rWsD{94(B+{!4OY8QpAvMqRTnvMN=E5+_ zAPDgVRZ;gKI}`1Olkm%h%1C+STV6WCpOPVo+E9#t*Q>U~59VHs4-eOe*9~5H5i$WR zw(!cuY|HJU6pdYXJsrR~#ylegx{)lhz8}$}&W^dCHElC+Md0TG-(l|FV93wGs0Y4Q zBcD~v&Xn=QvXgo3%=|)6D?&M*I(?xtvE8wxDQiVFMLCwr6GuW7)cf&9H)DKbJihUE zfe8e6fAb85(2=qjK^0rK#t%(T?qu)u?BS0v(IP_=^&U@my9nwxQW%J=<9i+y$vKA? zhd5*3&_(Wz|8yvy_WWMqKRseHfie`faa z9HxF3=^!I_2VNnZ;WU2GG&}uOogD>cwqs9dGx5p5{u>kW#tbS{Q_bZgh>*+PDMfyu0Yyxs9zKKC2jet)9bdK!$b31F z{?(KE62{Rp+dsee^;=-rKh1opqgeopY+rseP|HwePp;u6@_Kb#M2* zy(Z~&x)VAbvXg}^1WXuB*ksW#Bb)LnFp4Oks0^aGL{V_?;}8*WW6(k8bs5EozUqwV zJHGS!iUPOa@9#NP-JK9(n5uiLPMzi1pZ~s?Fbk$Nmy(2u^)zQhD4V8+aA)5=eEQnm z!@Ztd+Yj$vd-|{}#2;H)T9kBe_IfNh9o&9;SGVYR_w-AY3}9*LX35w1xWaG3Mazsl zdCm+Au9shL7y%`+dNEXo0XG0bL!6q<03gMi25$h)6!=)RdwAV<@R)V{N?(qde@9w) z4>>|LOHPU=5!lH|k*!Mi^Au+v+uq)OVEgf>@=JMhrQLEIEM%$(A@ndZo(iUWj?-#i zd9Et?KkMc(aE)>iUCwG9Bpnj#(#I^Cd_*V-NSSuu?YG~4$L&}mT}nOuVg0<_=*gq@ zzavIn8k^;pVF&a6&__ili=vdZo(W|E?_=_yA(-qbPJ%f(Rxofz3g8Uc1Zi(9C51hp z+AxCCAc9^vA?(C0tP=(w$#=jF2)2u+!@oR`E6y3pThzC?B)dz;T<8H-o16AscQ4#LO ziFC%KpyLQY0xaELAf@l>Au{@Yh%CZ*LcxIK>qNk`zC^`HI;fat z082dnEAZ6!vuAg)ZF+7<`FVaS6tXV45ct7f4fd^BF&pL3R@+@ zJcDoWTb%EKAxUnxf5cv3 z9WTo3P$D1}44_j0n>5D!`-U@RX9Sa~Gm=&4PGWil(>fL6F`>Z*k&&piG?>F!yEHdg zIT0>HTju!cejlRWx&gR@J~Dy=wE?%CYc?0^772)kq?ni^;G?s- zc`dAiyRvoVvsamN@U^wApUA>3H=8Kf5u5|d7Me1N&|1CtMf*5*5UZhM(8~7^nc~|+ z-@{s^uc8j=g+LHTf-Gf09Q98f(J|k~FsE-_3c#b&rP5dy`$I!%KjrVprTekNDeHb! z^Y!nyv&89`Yt&;T%AI@b%wqkHv+YjIablhJ**k)BsnWq0oF8$X^amO7zHs<`Px!Rn zUMA9NxlA7Qe=Mcah5RrQ86p}FejfajMq$^9qYq4A7f{1<%GzXMui z5IPaM4?FI6AxZmW=nue%Mri{75$Jqe$!*Or!V56GtPp;gHDhGJy2RLHhoOrGWTR0D z^i^gHYa5$Q9zbogFnI{ssd-QT>$i?jPzHVA4KwPPjB zQwum^&pjNjkmxuXZ2}3>jS8 z8I9*t^+qz$f6;{-N2BWFq+?rK8)wp;vfuP^s``cfW5-uAnZ>p3?a>HfaC&oT8Jjc! z6UI@6$@Z~i>`jJSN5=^H>v@vNIvK(c!?kuY=}MzMTxhkkxy4!?>x5dX*?lw?&F5;h zK^YesJZ8wMK#tu^q1Zb3e{8ED9E2e0&5#f$Y^>{)0HO(B~J{PJ7DV@?=OHlv7b*p9QNEaE@>5%}cCLy)Qz#{`&v zrs|6K&>U}%rp7=-8ri_&4Q_MFl4<5)Fg4>W3$uxRjq0Pnv3WKoA6c6igZ`6(}=oG|3Ml67xxPN(xJ)26kn$m~4 z`MJU3Y9Sv*k28yJT}6_3jCe>Uzj{DuA(@5@2Q~*#3jc%64dcWxxlAwzL&jk64mysZ zmPS-STPJkEI?}kliNVf&$QQPV||`|jnyT^>owQyOo%FEb$RHTQxwp%R-7Y8F5G;0 z*Rdwscip51B%!cL6gZM^g#dP$`?f4q?wtKYfWW z2pl6T2m}~{J=)>s%{4>5eMz~LpQ^i$p^)3XWq&k6Jbjl}ES8%KJq&s67>-|T4;Tyr`5=>z zEYwqJYdCE6%2rjvI*gZ#OK-7rx&0+S5g!3_x`^M%L@ic365FlSZ#ldt_(9_b~P>7Yflk zA90FBf)K+y;&*{?^K`?Bl-iAa!XNDoqVA2ILBH9ESz0*mn;F&~OY_v!>9^gU>|7Vj z;laMMd-&#lyV-&iPPH}&uaZ6lz)8fLJC;Yd5a0fIp_Poe=pACKaQzGM#N44GK$~^F zduyY*_k=M$OH9?5f1_jSGoHbiYFxGSWOy?(jF9>jNV4uv#)?}u;a1s01Ujr>B%f{+ z*-)o7im`8R9CnW%-#>AqBGe3rySJfhbSCcb*vTb-{$%4qacS`?jaF3A!=HAvI*pxC zGC$hc84SpM>tk{Doq4yiN%-rR-5bAUbI@-ivSn~O^_3^DIe9If_*ZuAQvjSxEINcx zz^i}hnL?bqe}NZ0dZtk3ZDQHa%c@6hzY^Y@#ozK7?G`k5?3bx>&oCeX&AXRy|D>wc~+S0!i`2HMlmG1YF7xazirGRu1B3tsR7Tqm-NIXB|0cNfXGK%N2tj^h1t8?YkpRQpwLO9JeGB^ULx+fH{or}z_06IE;9Hg<0HUj>7L+#T zN#k@2w*TJ@E&TM@?5Xwz5_NZoE&@P6Wr@QSSJ$Bd6ZAevS1l-(q?5^Ye3~!FXlm8`y8ZgRJ%% z#t6Cy;xU=U{`hzW%a+aNvHju&^u}ZmTs@4tL^hWhvNa1!g(vOHRU`=C@W?^e$>5v+ zKy=2kp$!I&88MBaa}Wx^+DVX!h2=9Ip_d}w_x;RzgVbnHK_}b2Ui_%DRbbxHWGiFY zhFjQJFMb1q^udKv2{D~tZnx_j^Yb|6m39y1^Q-e@#|T@?>+4U3Rjb6S{f5WH-xeR_ zOD3$XSX|#?vnl@2MsN~5(*0|)K<{QoV6MoOm z&%WWABrFpM-k2aM1<482{Hx#a@KOxGhzMQ_a!CY&AcPd@I=wY11J5WFEbxpc{hO5k zm83w@p~54<$jTBHrYeSOfzB{oaGdF-P0um+;n6U?Z}yD2n~>SMFTZ)@2Bc1ywGdX$ z;={8_I`{_W{y!7s`>uFm^J;90SME+Eo_#8jIKLZ@-;rbnqjlds6HhGNkVsr}CXu-D z+C<{;@kHV#ADvHh*+&CPWlyM{%`H90jPF?`&g~tc=l^favIbXsPX=zmr=oJ0ArFj% z^i!8gB03F%ea5r!LX>f-HzloY`11H$`2H1K;;h8vfkz+U^$iT(cUY;9sH|-(`B7Xv zQri!|Flo=f`?mMI=WE8{BoaCD4L&Uo_!L+(IE4|P;b=!P6)XX8Ski$n6Ni!pN&fWA z@!}q2FpT>i-!c%|BfyupjfrKnwEzI&D}e}n{~H*GJ!g4&YioJgnSF0t(ew)nnM%9u z9IED}ZgIU7CO52&&~fA+$3r)O2_L{q_6{h?=O zngt=7vOs?^wI{;ZnSjU=HliRTf47nqj-YCdUl#P2QEN;L$uZvcw>PEo)6K>bT$U@w zB}yTT1@X0(R5`xYcTafDC5p>cB1rVY?RlisCornOiM3mWq3&4aIdbcH$T?agpC}TM z{l?(=2QTwcH>dnEI_GrxH?2*~sO*(e;ot@6;mW1{VhPf1iS#*c*Mk-=I8uTk3NB;9 zWN)*n=r;9;>uOv($`{#;T#YNM8IC>*tNW>&4hbJ!+z}NO~g4 z34gNc1KROF^du!TXwhRfjA;Y|<`*HzG=$TXkXVI7o{3TBZarsQKs22E#vp%U95?lg zS(R21s*dMDvs%7p))i{dRp7h;2TSJ_1V_{C=vYlMIx8v_0nK^|8f>*NB#`}I?wdV? zQn7d{O&TB^4(+HoR$NMqufLC|?|r0zPNbL40k{F=IYeM6xt*wcfzSJAZE4!F zZ{YuXlV!a3!-y{vVCQHwN7!2l1n@V8njDlca<p90p)ft3246*9X{e|vdxXN{=VYv(wUDPEstS&wVtjvVe-Z04tQpGf zPOauz`-fH$*Dkf&G88#WI6CD_$l_sW9$!Vsm@c(D8Dt_URO{uj;J(<7&3sqJk zS#PYxVuyEj7nd@u1TQ)#hn)eDvP56uC%!NmIj?)DiYI%#dF)sd$DC;Op>}0bt!By= z$++rPnW(H2zZ1|gkK0$XcZNpO0%)7NG=3wESs;Zpc!`BAV_w5DPyH<*Q^h@+#Voym z_6s{s11C~NM`CwS2Luoda3!o~p6ZXOcr-kD;>HUnP9os6Po2E~1%vMVH=kPYD+epi z(W7_Ybp+e7t1zR&iw63;gpFDW7-Rpsgq44uPPDUGvJ~TxP_NgCb{wwM|8%mvq(EM^ z+S2mm%+@${bUzY1`%t|>F1FLT`fLZ|8#Vz#z~bUkArGPXySa8}o?QtWiOF7B z%BeG+>pEqYmytEi^y$}Pi~Jp6Kdi61QleY#6uKxxmXO;9)(o;5&|68T>A8XFc<;)> zeM)AF@zFvoO?n2#2NGh=5NH=CGe&Ku-BMKgF@uiv5=#~Qh*dpi_Zsv-eUCLR&VhBP z93qlqQVtl%C`i1-kjSrXr&dYED%lLq3Au7Hnep)v^pJ(Z+Q_<>UIJ-KR=&k9tG+Dd z#ghZe8eTRAIQV(iUnY0RI)vL7uz>-}JeZLQ9aJ>ds66% zKo}e&>ZF>gvAc<%ykcD~OO2^aIeYPA_NMh~WD&TBOaYQsOr3v?^ZaZX@jcyNtVCex zbnj|`nZ3zlGgVVrfRqt*`4&iZJwavt*70ceq{2}eRA83sHf5?TlR})S($EiiPzzLi znR%-S3Z(^nYa&#zDH^KA?T>z7z0?9hwOfY{$r7Mi9aYwx@Xq1qY-}b{XV2b)p}_u; z?=E@6!T#Rq)2G7rd#u#L%1Zml$;k_W*aKonxVG1dX7C8blo(&wOxS)ywK4SYlp}{D zIZlQX_k1;!>otS@hz486w58GXwVHWrPh^JgIza;V5ZQy;#D^yKc{V_u(_C zTD@mlM=Q~@ddF9te5!QqwRhcpco*YIJ6asS^Zd0Z=Lo&ix1EEBb7|k-eDRA2i0-&+ zdzF0H&2MK}S5dNsgk55whaCd)&!yxYNPZy9V5PYt4`>Sz4%R8A<8C68*%{$<#5#$1 z#agwQ#vY+qXePq8&;XG)FqHJjkB|qGJdxhs4c!uYG2SZANQx!=t_2gwvI1!qR#L7v z(@F*N7~Eo3&L}rD`*!w>z#O#Ih*^EYC%8eVfB3bZkYx&DDv0=`Pk@_eh=;HFgoLAp zuPb3ZwY@EUPN%cIed@~N{f+e&x!s$s^^N^2kMBUggGB9)+4slX^mY5$(q!OQ8xchI z$#A{q54OwMqt~U~>o4F+aKq(0>S5G=sYs(%9!yHv{p(WR4L4cVh3gHm{M2&F%GWk+ z+RV>nzPnb~T1@4}@P+;M14fy-b^laOERHmJI!ii+2 z{ZUjG4X+yQ9X#5LR=q}kG1}7!>$^DS%)uXW!}9Lx;PZo3%G^N+^pwle`m-ms!?dVW z#V?$w#SqN?m&PC?hi->i#eG@BNZQ5&T+>Y#8Zt##^G$lCvS}+8D=`@K^o1zd#~()dJ?_V2YP~!*Q^%_NUc+LB2U1%QX8U`p(ZA^9mapq zA`LGnwiZKL2~z~T`n>h4)(3!5v;5oQkVG1>T$wK3eek4yPtVE&x(I_S>?x{A{{knx zA!$p19G10tUU*Z$NEi>Vo3Y`jSjFa><)S)xn=QauLr1xkW9)b+-3Us!Z+paDEFkN9 zR$wACb}^AC6zm~3O|-%8{yYkAdo>4&|2-{v164U#D#*_ zMIvo{M+meZ?!$cf*iH>$Qxl1jL*ejS7|gJv$}219!q=~?h{Co=B5~c(@wn4n8jT2p zbbNDzJi~e7a5jkvvbVI78A9+Taeld;0X+p-C*ZHrT)v zG?i{NmzMSl1$>&j4Ib+EH#U!BOfVcRbvxtnQM|=gtVVq>Xw*?3HUuovbxLt!NeqN2wA`3xfG3Ex<&p zgtMwmPwTB#1ZYmR@#Y+@R+|yP=w99d$QX{KNK+l$BPNmdt3{@j22H_S>KS4JaGiTh z`GTF8l}O4RvbGpj6!u*I6BB)2~dzI8l%%M(m@25k)7QD!Zt-;|F%QZQOl>nR?X z(YtWY3yx-e({4*5ilsGI-C^EuuKq2K{3E=3eKxw=GQPo?&xnsbL+)6L6Cx78fzB5 z!j)fzi1te#Qoo>?;Tm>RFvIX429C4Jb%r!qy4O5Gq&Pxvif!fuPAZWYR#Or%aT!UV z{@@@~5()#`&bhe0sABFgF@F;Hv%rpXdyLca+=yaa#p4khJ|he;myZ{&D-tBpXgFKE z{q<7I;wp`f1nEyHL@qY`cG>b+|4@wTmE((PEdWCPEP@nj7!d(rUtB!q)B$YmsDc;{ z+)h=fqo&=|-CUz^msArpRX-f~=9Ws*1!?Q2b=aqMV)i0FKyY{~hJ;=|aw6g9vRkQM zH(yG`_dmCs&eX8jZdlEwB3V~3o+TvqaU?pPPXsGeC=k1D22Ku#AP9y;)F!9_iEUD9 zJrPY&q!Hng{G;jtt#qOUT%cdDVc0DukqwcNG+U$%GD(W0z`uKTI9G*V11H53C}>Ks z@`O$fCmDqVCXR~JfMPDM2nftHE=uHH)%3|J2Gc}>Js@LnaRtIont6sFO(|*xNm!Xc z5LzTbh-W$0aSlZuiLyLBs3idgrI4^ApYnvHhq(A?X$3E}Uo(#kGMHc6MWg{9qjTKL z6*CKPnZYC6q>wTCPzWVLUj&bl^p^DV7%XDf=dqyhw?ZqJAnmG`LPo^!Xl+mr9+LF3 zz{p@B7)xu+AX(D*LrJAc1SmpJtk|h?GLkIeB7~D6Z{p+YD%`eLYR4l(EHyaE71j%` zln64}1&T4nkTx|2LH>{AIKNPD#86XI+nZbC4?DH`Jct3^a<}7GB354!$O*QLZRN#J zlRy0etGgz6oqUmEBF} zNK#AD6SHuBT35mEEBmS03Ep^{V+1+t2|5`No0(n^E8lpQY9b*(xUo+|%^Twdkp?Cp z5|&HswPD{77e?E&rR{@2Q9Kj=tPIEmi)1r`tRoho#!1otW`68zhJMnHEBkBYw&cpJ zDvns%@-iowqUH{kkNHB=Czf67{_>#cC@4fnW@BC+Sh{OSDMJ!XNjcND?TN`HQrrZC zo5xtk(=BG^fEi(S$RD9dp5{%5FV8X~tyMsq0@1=3@l{rEIRSD&X)>A5&$ruwE4g6# z6b!q`%2KT@YYx<@gvyfhGg4h-j)K5K1TN7m6fVfgX16LJhv}0%Aiv0DzO|IVokJks zpjqIxumO%tP~HV3(Jnrcg1}2@^DNI}hM&q+h>8ox;$;Dawo@z#!3%S#%K2_3FElO! z04XRBlcX({D%M8C`^PaGCD)6KBx-RSB6zBY1l=N}Ii!fZw)G;MOD0ALLf9vBLLTgx zjYs0SSmIH&j{tnxEWMU(2s8xUOk}c!DQb5-A}S`0=N$xx8_l53u`Pv6RVZ|cGEJLk zt;SKvIH*NGOsFP<4>6Rd-E>R7t(pzIv%I_^zcd~c6L-mC0nP*kn$BVsWAGu;`cL*#6z9C>6ru}KUPAOE4oqAS?nA$b;#z7uTiKv#JYMPb+Z{4kz za4F#t)d+fmk{da;UYyVn>PV%U(sYwXS|@c;1?gTC3B2eCF{1RXxieLnF6)Q`F}db$ z>tni^Kir&Sgc;D?(mX-{A``~B7}W%UXNgCGrxv)~JQRH3Y5#yJETNj+WU4?M6F7Xa z{uE9TpmHJkcp{_0#YiQAD|OJ8k8pxfaijTm8=aHFTNMa|i3<`DxMpjd*vqwzei@=o zoz@f67vg%Hq7k_#mrtY=c#%>y^%*?OG$y)T)J_#%lN2T8AsKh#0S#E?0{RwZu|~5* zp(}d`Gt(r5fC`C3`t3r_&zj*e^cEGeh^u*DQ)+VAvvsgDrLtz-FE&dpJf*p5T|qJF zMLI#A9M3D|vq{>)v=Dw&6gFL;S!yQ~8xLzsGHA3Rnm{to30`O)S&tRsOO}XnNh(bC z1_H)*eG`T8>?VdZgZn^&sx60>}{=RGbM7gh<5dNKX!RA5)@6s>M%! zsRZqr%iHww9kYit3Y0KZoh$kv5M8p>AoWsQpi2`EuM&-GVF{9I<)gtXD2IdCGBa4f z#TW;uS#v~fGpvsAlGk5#}6W=FIve)~nfD=pXZF{M1sj2b0GC_*2M7 ze<}0|{Pu|aBaRgGHw2VH=m}F9S){>XU|oZ;Kz<-}VYkur4Cl=qGmHlYG*=8!$gw%k zv(O$>N_R>(1R_db#X{_Q_x6_-SC$tCb0-gZ0R2X@)r#QNIqs1_g@9dtDW_}{wv#Kg z+r@mDK=q5OqopYEu}TFv3%i#`8%LtfjdmO4#++(Q+xQ8!@#9=Lw|Da7&R(bNIJN5H z=Mfw-ddT!%ulRZyQUGr-c?xMj4Q_6wHP^@j&ytGOsu_$&P~1h61lsHPMYbdnpg^ct z3e2^;1hIEhOG{&xX0%1%BYE@o4?$HOE)=&`&h#7MaBuGD@_Ktr+Ljwx24w1ytM3(l z?}xEkdk!;z@|i&0s&FbLT!dzppuf}@@x7+(*p&;Q?&f+ zDlAWqw`B6)?re=uUU~fPPrQ5X6>HCa_B+npRDW&pZ9nz)XCbb~-po$ot+)lMPDK&R zT3+fstPWC|=rHy&%sY|@daXn$^S#Hm$c3$(i=cMvRZWJFmun&#qL$sZu6qee~<8jSiZ$dptgIay*s~T)RCU zpFDxwm*X>7Yg{r5MZSDW9E5elgOYy$P{OlEZW| z62BcHS*arYVl4NwYWwE#W1E{e=Uc7z=H{{Eo9fdCh}-zdPiR4+$5dF@cRn0SYWg7ngj3k8B=e3 zu!8nZ*izdQcwcW)^u&s;jnOBdwkMd3Skv*STtXmPQXw$j>^AfZ@ZBgGbYgAn$Yx?6}A>nJE|!qKrBp&at=ot%?{GYa3-E;p?X07kgcK| zC|mI`x*1+IuNtcrVtKN}=UP!RA*M20=onNTYYWmfk$e`O7!V%~I|-Rz#U3(VN7qHh zBFQXC!Qtkbf6FaN0@PS3bVJohG|^~)tO@jmgeyg?xn>3Pzogf4aR(;IMYJ{;%=K3Y zWZa4N&=z`FaW*3Tv>VORz(~u7qAV&;b|!;GU)pUTpNtRYR&%}n(a~LdUU(Q;e-%|` zU$q_?hP}PJMo0Vo?CRVgj!{*^RVYJoh{P>~xb<5Rl^S)^{YaxkRirlPMLT%WuJq@C z=n!VfDwd=zFPSNo?qPc=1Z^}yGE{~(X}Lu#QLifw;Ii`dLu97H{KT?r_J{tJJHz4J z^v+QD;+HS}CkE@+uyeZ!9PwdAtdZ;kS|rd^urz03O_7*LOn@O5tD|6)W9$R1>#`w9 zHevKHvD_3O;Xt5Pi5Oakr-tSMzOm>=;BZ+|XXN*W1`_2mam)ia3Q4F#9l_g(@68^V z1fSZAD(sx&R|~m#sy4`E>ZA~`?T+l#O4%kf(66DRuvDo~| zJl<+VM!du0u=;%z@J6 zORO%yxn?p&yHW2|s!L0|1iVg>Zw}85tj=zzk-fZF@tvKeB~)iUR$wthnt<>kq4jeF zB{@yteAS5-F9ZEqKD2t4^}pQjP};v z(dcE;wOW6$WQ7mDT3KE`CAxHl<;OsT!{nX`lMFbWnS(7*K4=mHz^EA*YG3@X)`zVR z5udb4Jlsj>BsNBmhJH2lC)SM?mZNjqKErG#8nF++M58V_4j=`i9;L!4GTq3r0G!+k z&7hkn6Bk$_Ftar>&zWV#Y)Kvp-N@6tMX*XD#7!H0QqS=)f+8bR)6{CpxuoCx0_d*k zY(NoHiHJ8I);bD`GqhJ=A#N5i7pQGCHN~7&$up8=xxA6i5~xA>GK`1__->xiCEBaE z9#XNhW6}f|nbl-NI6>6|P7yiI8e*PDCaOm2oSwf_n)x-NHycEilopaP;|3++O?4nWnkjle0LC2EpKItVY8v87S6vfH+cQ~-TO7O{!t{x>qup&k;K zL-0OBzk`W+Ufv||WEMeH#8Em)1v(IzMOxJrCSF0fLJ4?gE-dB?6(XjFi-l_E#L43) zND2kl969(TnU4UQ`BIr0qUC~*5i;s1gkHmymAVn$6pNJ;t^XTK`kIp`;Ax{;qv?pN zA|#}YCl>mB^vkSt1Y8)!$+L-vDcW_jdB8W_7GFjJ4PrIO-fz?=wX{XoV#yYEHH5A1 z3CUs+s%|^&Z^hDNpFo;{9K^&CG5tD(GPz2frj>4$HaKJ?)g1 zo7XM+l|zSC4*A$v)N;8~Sp1hyNHGr@S|d<*p`9XwPTL3UJQT?z?{SKRc-d-q2!J2M zDXX}3;|r~5l9EZ`O+X3w>Sj5OOb;3nj;d59Sh!f;Fymc>)}j-38orfI!ATQoJ4`!4 zjku5Hi&(e|U1u;&Pmr#}Dl31Oa=ROGz^}G53<|w60r=>qa{6HlLP$pElKoaZG!lbOd)`WhN!H6uhE{o_MShf`8Fs)Biu{cDq;RzP|GK?KNKAUhscpuU6YT z^7jN|>vm7}KKONs*XLomRy@(|o|t{!dgQhv=L&^$eBCM@FpN$7W7b2^i;68iA39

xA$6E^?#w4)r)%Sw3|NzgKHve*eJ-FKmx(duR9N z&;4{UoAcNPU_V|;f>|ikBP;VGvR3zd3wpu^0UcXf_U4Zr8%B3`Z^G|C{5krdeC%IA z1~OD1A}%EAtbJx!OPnzXLM=3JQMLy92C6!t87|14if@TcM%MS9yl~~xx#u*9uhD$| z{vI~gwfXtwt-fVF-=f^1oK)HW-OiykLl~4I-ue?((=mw#NAu^ldtJ6Ssepk;pU3z# zlQ#acPP;&^FYvCt&C&^M;QwD_I&Tei$10hPNZDvCEaK|VDeJmN&}^SSqC ziVzr?AJo??o6XkzsQuo5rw7ksXv&> zys|wSwVTcN-XEOIkWRY!L%H0ol=+l5j-PlRW9I>D9>bF7G1l`EJ}NhaZiDW4wIpNU z^)ePEt~xam`y|Zz!i|C%aUg4z}{(bbJXi!z6K|6*dCLL`tlJ}>U6mM;2DrM z1E`Z;u!nv-pZ_?BBm41u{y)mE=bI9M-$Jv3kSSJqD<~-MV%Uv5n6vqoznITAPZ5ax z&wFGbO7{EQo#+!|$@6@DM$6VOMWYWSihop0JQ$5WXbzrpUkeT;m$P5k@Ao4abQbn~ zz237)*I;)+Svh-%OIWpLAPcaC(0RI<3t$e(5Q%jivL3-d0G|}gm@BI7hGyW~Y?2w9 zs10Iv6`#|$A!K~mK=oKod5o|*3dC4tMqXKa^wi}eK@96BT7Ef}A&Op>B%;x>-x?BX z0aJ)rAzjKh>czcUb-!4z7knc`Mba>;oj7qKKIq5htx>!;h@XHe$u+%YYsKqyycKKN zYYsqP?67ak=bDQO-k(e^HX(>hq!?0sl1-E0aPRA~QN5Ln25WA9bxlAoW0}?7cc!UMJzCQNFZ5wE& z5rj&fk(A7nOh_d>Fd!_b1$YqvH|7m(^~ib)IJ=bL13`5BVU|lHM*Q@KR!!j~jw=v? zXb{L%5|M8S#0@Douw@|-ly$N|wzP*W!qp;ahvStZ2z|u+Z@TFw*cS-@7|9Ev%)Cej zH>eDpw=NVi#KX$EN@$LjiJ{J5e{qww2YoI zyP{g2!ZvM`GpgkKO#**OC(i7O5PiX;LAQ z*WOE5uYC7t0(Ix+1%` zrAMQ!tAq!XkNeHy#}zt{-kTW%0ED>=_*fDxQm?l#t(9jXA^{s!2# zO>XcjeD6lW&RPxA_azI6OU(?{g4w6eP`8hztw-UHD!}l=e$l|XAQ|R1D-B+QhbRE# zDPuUP-^GiAHN~)--{MdeW6G9m12rwehaMmV1^La?lToy7kssWfu68~KjtW|Y77 zOC#>7h_(HZk9_1!;#}TnZA7C7Bz1VO^?yVhLmz(`c=84!$ZinREM#>}&|HqAbP>rF_^OGHM#3zVHIs>N+84h1C-&g0e^QWFalsxG;qX(1i{u{LH~;%@ zCh+n8(S7R;;vo(`cPV%7Asroj?p4=c|EkYjfBoki9NS;~V(epQ^3WQiu>*~D17ku< z)Cz@3$eA|yh8o{oXKHgfk>v#g_515q0gxriU3YJaQ{pD`nB&v#9A`_`598AOz%9?4 zfABT8KKJ0%wSRE))(r=r+&X{${IkwnJ8x|t{Fb$S)A>Jp>$SX&zxz4ItS_`4{gz_~ z8`g&oHm(1BusQzcgU^htPaOD1jvRU6Lw`E9{`TM+>tp8s*R1_tyzjpItn!#WuY9zt zjP;Gg*=-V&<`Fze{))X1M>5NEhu?t0@o12PAuf&N;7#f~>_tY3ry4N%0N(&E!))U- zh_J~xgadAsY^$u@!@n?_lV30=qpiN8W9z!s!2NWo?E`Jg33nU4K zMuARYP-o(-q0Gz7hG&n;IM+mW)_Re?!Ijb3$e6NhZeuKCD4X&6hy3W``m3MY;1x-p zdBxV&7H)yj+u+;8xZ{^*5qq6*9yTseTkv%!z2jLpOs*!jvAFXP7G_*~pFI_^-UE>V z2#+CY%vgnY0@q=(;Tmx_!_wj}6ZJ;D9<#mdejOGw>d1q7X>ofpfIBaGv0>qjk#Oz- z5&-joBa_NqYA38Ks^xaCa8t`DP+|&UWV$B7 zq*OXOxfYH5&p+p-FWr3b!Q0~T{BSfF=a19J56WNxyyNl164G~?$(Hi^wX;6tud0R<5)+coXy#(M#Oa9 z6GO>03<)uE=NZYnB%G68&;|Mm;^|qveSRsa0>|ta;iEuwr;v2c`Lz-?LK;TX9ImMO>cBReRL$2_Dug=)1VR~>Hx-OROqYQAHb4^|&MXq6 z7^b_Fga}+^s1{m6*Lw0LVf8>Lt}90Cl|!ng52r)gD?jY{53^g$ zh?S^?5!5kL98^6rZJ>Ok<#1I8-N62ELPIsP!X+-yNWB~KoPdkO>Sl-F=`K1pNT~`V z_BSXZvcPl1k!am8noAUvg0u|f;JV#@6tjv7+A*($cy_#!w9A;}%dE%9{1cgQw*@^H zjo8^lA%kg7lTa>cr%1kv_@R@}x%=2LS@}c>ee2qhJ4n`n7?!xpSZ-tBA`3&10|qo! zq?twEha!dCX`b45MWMNuH~VQkX~qD0i;IQEt$4ACPA4F6M@BYD0Zg+DH$(ZIlpA)&JP zN~2N4-pCWH5g{?TXQ8G`St&bVVH3untN@rX1&O8-*!2?tzItSN88+L>6|iR_$53+q zc+*OGja)WfgO3H2S4moh&=grM{8yBsF_Ut9OZ=4M5W>X?KP*|gMc%`1i$zDyVN4;* zq>Y9fOQnc_e~9gCE!WV(Fb|}eMy5dDupVN#vzdlQPMsxj4r381gU6A>R+?(AJVNUA zaW22AJe$}TC8B9J?!sRzAmhnath&Mjx;QhWh(Hid{~ZXtE#kn#$xSZgh}y%rzsu1J zpTY>in-MP4aLk(!7!$isjKjVaNVp!pl591$c2qvrdR|O zjP6Lspk8G32=Vc4A^d?R&4m}W?8OR}PsrF^tD!(;q;z@kj@@ zE-zk7Niwst{$mirO87#Sq6ABk^@OzE_DFh}sV`eOoYk;KgRe}&D$4~*Zp_pa#=YHl{N-ehXtO5&GlrwM{me$nWY_=L&*A;U2MITPFKn-M}9~0Z8?&FtBEH=K~6Y}{2ezGTN-gT3^ukb5(0kJBg46a zk0`=KegU}@I+j8O!4h#%Qt=$I^a*?7*w`hqg4={&&ZbMHdL1ea7a0tK32ot^pCbSW zvq>x1s@efad9&63lXw*6a;+qj+o+Re0iif;Qa6@(C523}q4-^rnrF)+PN`Sw6OEUJ z1urd8PSJX_W}^%4qYiR}HD^>OH^9V=^ntMxNH@O=B1?7wzu&ehJhId6A<%K51CY3DQW zCzsKKp+8{8XXYL*gb^r2Z^)|YJHQQmf(bwy5^M{V8a?OelG%#<2WChatc9C9tOC+2 zgr2U-^Po(O8j{+`^#mT{3bjZ8VEhTdHXc%^{t#<^VLG)5yLnK}?5)%xTOdeuk+d0N zfG~JaN9#T6(-XmaJSFH5IuNs4xHZs@fDflcfuV_mwvKvza2%d25^TxwhNcSt_5!g7 zRhU9`vpPXh8(6GR3k_p}PK-x^21%(Z`6>cPe7cdk`HO z87Z5~rk#o&iI4*!O@y)skV+P%Fb} z77`5`X)W-uF2RX#dkJF^ro${g`PNLk2H8rVT?vDInm+QF$>1@~t&!5W-WLdrw7Asi zAPS20y7Tj`M#Ndz+>lDoiM5(@i@mNJd+u}FF+m&;ke<(g4rE|#rK5-{> z^7L{eoM5#YxeZ?O)})n|THc98lZ9-K0xWxJX$w59`&5fd>?gg@7K2*dQaG8yMbI<( z(l{L<)K1_4!n2Vu5p(nyv>y1G#DIDdSo-Vbyy!u75ECY}5~)-JUjShRp}bP5Tvou# z@_Cek0;bZqQ%W9;E%5B5jb|po&cw_c{j;b_LNt-Okfz~gUWi~-WBNvsz*vnbN_V_Y zYO*jkQ~_6X(UZRmgstvF>>xXG2r)vlc`kk7idu!MKrjd(0No%e!=cj=2qQBxF64>| z(MZu;%JQ&uW|T}4Z&egQ9um()6X_Ib&cH@t{0KC8^1oNl^+%kbXr#mAVJHV~3h+Pl zk1&sZuwLA1r8DewNUaea4ewA>l+6yJ!Zg(RDEYNX*t!tElhQNTjpN-8?f4Q!Z7?BN zvngCqV@a%;J?}iLfl{Glejn^kJ07zNdE!PBRwDemZzF_Pq2T*iiB+0S1q86|UhmNA zXavfRaHW_Kw zK>s`jomik{>H^5v$*UWzSAGg?Th=wFIfnA{>!gePE6^&470eALCu)Pex#7CsPceRb zIF}%Xb3tZN7;2sUw-gXHR`;roBATrx4x^Ns!UIUtnopC6#0f*AV;dsUhv$OCehGwn>})gGC>Z(!O>JxHHtg#bS?abW6>ttgt>*IQ5|Wfis^{Ai@AK{R))gu&;zNa z*m^hyurf0G<+?<_r45k=_983-i?jx~L^-IgELN1El31(UC@B@!k)T7QLZeYy4)R{h z-gA}ST}6{KizBRGPG~`?DcGd&5JDhIhpsVou#fPk35J?yblN=a%weGRQU-*adtP{yeYC7z#E;qm6^V zqYzZY<$RvYUOtbHXXk0QFnB?**Zfn9P5`_QXI@$ z-)oZqHQUInXfGc8GkX_Ekf1PWXks@2BUs(q}yH#`NAFDE)eJIKsg*HD?il5e$wJ*L90`5@2l6WgDz4 zdyAedw}EbbNxOI_;K8wH!+#bC2+Btbi%bS9Li(=w691RStB?)1J#R!P!J=FC-ht4F z8!hE>W+)wnf&e8j5pvavx>W%u+!;O_CcUj@5g-WF#v~cvTB00q7Meu>l%|xhxWwNt z0S*F~$-q)0P1A&J^vOMn!2oe6!b`}?n6fajP>AFZ!O~vEStI6;8$D!Z=NShkS7Id-q^Ao4wrsx#^jSs!tER5?I0@wuZp6t{vRIc@#Tt`XJd4D55XtHr91#byHmE=3@pse|-AMDZ>a zVF?kufW=HajZWZIuA3vb7KCNv;9b`J2k#U$EY&lPMjp;DzM<=yb+M zp_mKD?oS~98+J%!HvFIA`&TgZa(UsX9del{V1Ke?WJAico^^q0&GxPc&W`l>@ zP@h#g;1ei8|J@7_~uq;E=*(XIawiCU&>P7QsXs zKEhCzi9ACr%b0{rLF1<`VjiG>eg4njW&Lna`=6m_J52WSBBli_BC?1$SQlX}V;mEy zJ(owrijXWC+p8lVi)GSyFGrxFs2vOXas{r2+F6WfR6QaCCF1$KsXdk`mI}qV^}?X~ zCpJK9w07}g$brsz9lGuH(DBgQ$szmU&_9s-{I$^XU$z_e4fd_}ciJDef6xB1Gw=MZ z^N-HO@F2VyJ|0G+KSiO8wPwJJtbxH?O@O9@A&1}yFw=A%-4fa`t$fi5dXLP6nspw` zwb9E2(+ZwbS(JpJ!#ECsSUEy;!d_s}YY9LYQ5&?6whV74*afTusx&uyli75$@Vqu9k5ZYu1awl~g$4n$@}wLh zurlPADGchU#J`@w5X4_t&us$8?CH}fV^ z2hAqqqU^v>SAoojNv30lFonGHP>-4RbDFw55pr%&jD`)##Y6;|8C)bInh$BZ7%isN zY;1l}@r2$7)^EeUZ>9~31|m`I74e1usx;ISET)plM{I0ZCu2sF1~LxRg=7uUnJ8fu zCv7a3L~T*gtQx9?ARA6-H)F^W(ode&_yp^hHbNu&J%G}X`gxd<(qK?1eH_wx)ve`# z4eVs(W20e+X_4j<^wAwD@)T>ntTClF4FEDK)@o4baAZ~yL%~v{SczM=O->>vgEP`j ztL?=}pt~Dss)Dji$$`F8T1(v<^bMga!>@4ZD$hJe3DOFxExI%K4xS2*wcvtl=sts| zctVf7)GzXJnwh0P)GxOZQGSE0_wUUU+6r_p`Z5;3!s)iXcGl9%d z{k^SpF*0Z*7yPaV;Ykb0 zm`XTKX`-shTw%Tz#;+!PPcofT3}+`7IpXKA`hx{wWf>Kp8|#%i?atixk-a*Z!S6?! z!Kl6sJ&jvVG?zQ=XfkfO&DP4DSDhLTm0|7l>2LWxCvK7?X>QSZ?@PL7qqw#`(W|11 z?_=>|#EKtHCheVr^iH=*W&HipL)*V=WM=O3L{BR_Tlo)scoH4~3j!IpC-_<~{k;z?ldpcf2=1$=eKSQEvCYF1!8qQ^N z&w@+V(uU8I3YosL{1AgF;5zWjusgDV6Hm2FhuIub@?nSUVFnN(&pbm8i-{(b5EB5V zT&q>BI`Kp{NK1{ILBUiCQ$r^WCYU%FiTuBDD zOjwpg6iDeJC^3`yBk^{s0`t?1EXD>@7lWr;l2pJ-U^;E@HzgS>o!Ez6vbx>t9Oog4 z5Mm>ZU~c$YMwa0rvlK(y?LyfBZk+701Wfi%CfFiG>|u{bs!MbkgAmdTkawJ!8`a1$curR;-~pBW&^39p3(W!66OB#Mpy6)_=L}w%a;8pZ#LzGvC+gbYAh>-7k*b z`ObIVV?FQS#g=vN!EgW8!H-$r7pjFEau!@={dy=Ln!}6ub)ol!emwMBq2CGp9g)a) zt*fjTTd%af54*Mx)8vtTmHl(}AK72DFFL!`^_R{G^)XLZVwC}>SkzpRtZZ)0(^ zHCp30DqRSo9ypqEpbEsUuJvWvdSxTG`WS-G1_J@0dZTBH^lz= z2-U{j&>tqB9br#mx*)APt+gL)}bj4Q++^xX(T zn9~xEiXJqLP$NeKZuV6Hz2z9LR*yj}iEz@LlJ5mf>oy(H5mnG3oekIv(^OT&)2^qP z7)~XqngCj;g<;OjRu<}e?Kvp0QVypyk1UU07fVebhgzit_ zrL3&UWnoNo_0t^O#R2gTG8u?H%+27ab%__KT;M(j708uaXq3)U1L3yf*!H3u{Y|<+ zuN?L1PjY!eFp=hcFi#A;T0NDr5tOADmnN%g9UsC-iQl$6U7{gWo6SUHqtx%Oxxt;F zO4rL2PHwN6TS@(7B_49UyAFN?+AP;tT3TOSEkY60{iQ_!d)!)|p5ItqEfB_};Sc?5FDgj#%racoBKxV^otXB>ww3+{o}R0n;`{5Y1LPxH4W(bzyA zWg-_c?Od%x@U@!>FVPKtrsHWp(MntconjjAh2QLjkIc`L;3qSRM21W_R7bbB9}A!D z^;k3sm+>EFFqp;=JD#v#*Gwgn@B^3cbiK3P9)s&v`vXEnZq2=k;ERO2IpKsG)kvh( zL(qsWrdBH+T=(QFAkm}EM)*4sQZv`(Vk{iTtsdi|Ih1|#&`kNcaI}Opjx@fo`5nx) z8Juz{5}m{G+qDbBNVtcHia-*>XqdE3?8ssv>=g?H^l)m~jQbEHw4pKCFhgZ+5qgEA zn=cQ5XZAAD*>L!7UpczR%@Z%UG*XItWnX-p&GkNHfwfQ@jEf#bzAB>$GdcvpS-^D@ zxMMUDtjBaoyXN!>T0z;^BuMe%a}>XV2EH7uBu)!Jk@vDBI(w zrN%6+{01-lo-Q6hTiDPR+0a;0SEL7$oEU5gCloGJ=K-B<^a^cb&?bN%{iPy0} zV?P@U6R%?9Ia}m9Xbdu`NbF~nyosP4{obHYs$^Lo{(ds^Wda9S(W7V2?)>G==gwVy z&51@`&X@M;>NVG#KE1vH$t9VaCA!D@@3ifwjLuM?UH>EOmW-qXv~U?|Oc99Zj*Nu~ zxc>4rgU3JwT;q+<0b~Lmns(w!)MF3_;v8uJ^)@WMo-~pmG5JVvpeSU~Wn@w42F;z4 zuc$oQw7qFP;*htaYAb3Ba7PxozNDVwZKX6JG$iUK3Iej?6NtGiWFzcmJg&U509;+e zppfuVDMAZK@rvBtFGC75vDkIy@kxioW$7Az2emv#CZyA2bK53y5%v|;Dw`g>0Kd$Q z_tJz?*%)b(d_qgKvmF_blV#tvl zqaaHpR(6_%{CQ+&=aIy-@B?c`**iPut^FHrxS@SV=FaxdU*B2z zt_L4{@Lbp0r<^-8ceHQdq3hfG=gys5d5n(-|A2%(iNx~c-`siUo!L9O=dHWB@%jha z)c8RBk$C)(oyVzfWoNRSD6N><%|DH0>bv}Hq{dQOfO6h#(^&Qplz`ol3 zA~kRF+&k`2Wjmc(UY>WcAC*t&zqcO={hf(Bv4U6VF`{d{koEXlGIM@+=!ZieqSuD? zVwy)tm;+V0WTJ`y5XktpAqC}iqE7W^eA}kn`^;JcdPyn>{E<{pU1v~&CpJ8#7Zi(C z>P`@0E{~|NcVc)}Iy@6uG#$ z@B$K*hOe^rNxpt`{^-HWaTL5W{BK~HiSW7t7fu1?SCqcli+uLXnKO|atW%~G<*@ub zx84zf{tRD#CUwg#x7-+czwi6C^5OZnjShFAm?CceZvTz{zSV-z!^9sPgG40~nLu-R z9xAl4=el*NKP8zXa2AHhkzJfx(F_77;lW**h27BNH6HIF33I)ca6*-s8h;I}c$rq6 z89$97|Kd@2&oIN4zYG)?G#|rkntu#IcuO2D=p8IO!(?!#H(jj;u5(ob?|MOb!_il4 zEVA&&miZj9or%gslpV`q-?lJNm#Vqd?&97qv`(xMuz*4EfXHmQU8_m72;qWnXtxEV zO=qxqlsl;1HU}6p+M+4=^n~H z`il_~0E5gKWXcQRr|wltva zje`-Zsmb1h>+6at(r#rJ(NkJCgZ!%Hr1kq{|M>R72@IXw)gL4vkc+gBk4Wx=EbM!B zc6N6U?QCr9d?!I|hKCN#^SN?pq4r~YdwUqWubpKu8OCOUxZ>qXZQ+-$kH+V+IoInE z9Vt(44De>MTHVVPt-|_r*h#61Gv|-DdyVDRJR0|9s))0?H@8|X!bAd7FX_R%WakV4 z4`vikuo8oH$V9Npr6XIPGs8I2d0D{n_ON?GUn8?$)-;1H)M#91VU2@zuBnnTrZ0_= z_5$~9v+5Dg2mp`QLxg#-|~nI4me`KOPyv3<01S@whM$;cuxSA^6_6-ucj zN>#ryAU&37@Xj_OLJ3GANASnN>?0Sp^SD>Im<`D1corXMF|-fO@Dk#Yyak*7VC;fL z7zi0$U?VBSfEB@Bme4@|T6)^?jL#-9z8IY`HfJo;fYjv{R`rd78Y#%u!l92H4F1-Xmm>dKYH@y$wokI)vc4A zLcVTKe>UuTzVJM&ZY~z;3j2q zIt4y*ybf*0q4~TQ8c&A=10lXu6uba$ zzhu4n;8E-R!J|qE%MPnp=VwO`2!-hX?AIJzH#=jU8txt0-LxLBja<6? zJ{9@o!Y^bpzmZP=hL_3wA%A@7S1XfwBY%HM(EoU%1GtSgAmn59C@}i&;2PtnI9-n$ z8n;RmE1cyuZdz11=|BT>mLLv6pTQJ1ladszw(nntl}5H@gm@C~O#%ZC4Gu6@f=vS; zOq4$n0Xh@tB?V!5b4992cVvD4!8@#%9=s#3@HcturNQ@K!-j{6<8Ou8$M3~a3Pk`Y zJ4t-BXl*50>vo00V_~loyB<~_K}E5YE_Py9$I8t{E>A>hDX{2uJ~y!5N5N01;2|PV zU^-s2>VM79mP-Xl6GG!hn=k4N9eWP1tZuG8Sgw)-JPaVNKWi{Q7aJIbL&QRQsoEnt!O(GL$OBl+Od1ZzZ2b7`| z1FhlB1OC;szopphkXt@msZI!Vl_CgFHj_-m)A>SY0-k~)>$J)`zhujCR+3UA3*3KUMF4RhgEtSvJts0v@o9Vm;4OX?fc4%P%=fq47 zdIishJd}yEuyAOtT7}Gs`-u7#^l~tgA7(B;*ALBLn`|hc zDMMgmGORbp>L8c~O_|`lp|09uKp*=A=HZT$lb8Z{P z77VnhD3fed)9nAhv-5y+>^ckmoTGBprI9q!)E!OL&dh3NcBk#Gz4orV7ZS(olEjXQ zucSLO+EKHvG{pg8Na7?8goHFm$OBS{%qr#lPN7v<&q@w@kwN5pneS!1V%%e^Yx43Ahiu4sp2g{X z63vvLEBO;}&Kk3h1hnqxSGulty%;3AFzDSGr^BJ~yG|_f8ax0u5d{lxB%R8OU?$<+ zR z_Yet<1BIW^YeH|(@$$_>;01~rIv`Gz0dY~l;TL1A#Ff&>7sJ;rbIaKigqX%BQ}9Wi zl_B&piFoD}*pD@#*W%ZT7cc$exPcWIuMxZ>P>5n1z^ecwBvy2ML?hTHMbQopCwB+O zrO<*DNMO*Y;&$TfEdxe26P4KGVDOwjohA!9Jm2(oDi=kf3Evw|lUFbZt%wuI8X6|u zZY-Y3q;V6CCGbDklsuZ?fpm&=Vafi?RVvXxG(<9+#LzH-9hVS)By)8v5hwezR?)0f z1dZfUpEC?&60bsnK*Vd8Sm{Ur7gJOTGHDWc%VUdyL?U2RFd79lMV(E=F2_*qL5n7B ziDAHKi;Wn!Sc0B#kj43L(uhq?PEilsJHhiMBT}9^A{pE!0E5KTkGDD|Y+3l?7DsTo^9MWkAQ$GI`+7hh^Lap?7tf`K^!MZis> zk1)Yw8_o{RB&VX;Ow0y%50XDt!p}%4icR0~D&P`vVHP_PqB&JqNxLPlFSkI$C#pPp z^YJw33}xRhE;mMuXPipT3}Lu%Z2f~CtPcGswUfSIN>C(14~uwOBusu#vV;?8oa9}D zdpt=dtM6+PvcuD`DxMIk@G&HzAcLw2@zNFiwZNlFyteJZ6+g0p2!%MN!==v z@30ym1uQfY-scK${+)RD-T@w$?23|LLNHD5Be+erg%kIK9T{dm8)DDP42U{~!-oRC zG;$k&1{|88?j9K)5pR(=>v=Gf8;>*1=uX3tY$S!oh`Qb@tJl34J*XNPoj<=O1ur?$5!iVPuUSY{>r)f6^- zm_ag3WKr*@P-{Bp5SV9>c4_P5EXeO<3fh^v*Z)G*eCVNvUTa+W0@&={zc|2Y`3q|F z_H#e*@WT&({vPuC1(pAvE3ZJw`nvB>V^^M3lUJTx*nG3XCt~PTm)q#U-tw9cGQn5= zO~qew-Fx}*%ik-}OD7+=@2cYIupGH(O$T0a4=B|6z7yq3i0!q!Xt zldv}72wXB-4r!RptDhjC|8A0q>U{oM6}>;IugT}HdKz+{gBnig&+q<9j2kT@eaT75YKqaZSMw-@#VMxBq` z)|E<}b`H!s`_8q<(}z}rr|%$FI0_Om2|JK4;JhJsbpuo~S$%sDuYdVlqLk%8+V6fh zAvIvWzz9>Tyr8O>n03*xIjb7as6>2tsNd1Yr$Mh#bVQP?m;#$bSOC5lUB>&6b2mLBH*V^23ZS9D)BU0`W2xd*p8%iWB&dTSQmsoGt&ksYqi`@ia4d;AVP0u1o(Sd2IQj^z$5yXXxmvGc{UPDqZ zp-b>Q^&vf?KG7G!&*nH%?sa{4_4O%%1P9>&N4}#w0(Cto%M&AU^c*~rASbTlTFT!! zKu8DD6;qy7pHVY2vooX%mhUT%_x5IIW|TR0{=y3=tPnarF_Fqmk=?i3R8AsZu6R9# z*fAXBhOc}el^D+F3rut3)(hvSaSRBn&H~&=c3}bU(#*n-^mfwe%tGasg#|?tPaPZ@ znu#0t^v+4cJF`>gZ@+ZIMC!!JlM8?B4bPyE3NXydxd%*tFX{_P2Y?9 z?ne)f2DU4bi`vdz6R1Q)9nKCP_pQa9Ti)B}#>ue*S0`)IprI&DGBPku6LH2_=R(#Q z>jMjdLjk&YMLb5Nsk=l)I2KnRzp+Im2^B=hWu6U9l+>w{cL}k)6KB|~ZoKirg=Mul zH~-?7%+I~>o_p^2{oDYGQACiT!DtSbLgB6mM!ikE{8AY5`x9eCYvbAqoM0v!BooG) zz1i)p=@4$C#v3b7Jn_WE*_jCe+=WeT!Xve^yI`fue>1<4^=<&na|va zdSGLjkV;Qr*Rm|IaRKE3N;Gob_^FPeqQ|H(mdj9rB>)wTjU|EsOue4Ik-nUsQTO-d zusDtYbbk9lvtxj8$(x;%UT%Ifrg zUUzEmqy)rxG2{{6G&hHC^y=fswffN;e&-lEZ4WMkCkS)%R(}!<7siE3Cn* zG8Qi6@HT?c0S{d9!R~TS_MA3|cg}FQzYb_3RmiV93eFaNI?59{+7&=V2%!T$?r3s~ z4~JyzWE2GyIps8&<+#8s@;j5^Pz6Vd5n+<{PWm*qH=sj2*O5 zvBTQQuz|-=CrKO_{3Hw?o{I#ngAe`qA11^X#~=<(nj^r2GJ_IZW;ZM)mIc?U@ii1%$c;*a6ug1;g;3}7&cvLo9H!Al@osa!`j#xQO)h9b(% z$OeXoVj-WA>`#+oDmb9F$Yz9NFtCC*N*IzOPy-#nZwVC7L0=%*p9;chV)MY~7QafG zdT^U@vbAzMv=Q-X+?lv6$Nb|G?n@$#u_#~q?5B3$=9|Ffe%{-wCkE2K#7sXVMmXab zT(CzS!#fyP+EkSAWz^KU(QL-wPu2nmgE*JsksXnW1^$eqm-9XKr1~ZG9%^Y^i@g~S z2!SC7F1pgIGrh99+-Y<`?8xM~#GZ3}01B=b;a}FQz=-p4byLhZO@L16&_Q8qpPw}X zgvAdB0yB@~jvX60<@XWyI}Z;YIQ&VtVOThJXA*;RKO@V`FnC zPOoY2=x9Ec%gxT_mJyQiWr@cJJ)Vslu0J_6G#Gi^!zjRD5Yp*fWtFs6YqK*rN=1$r zf^Zl!vuk)H;1%!iyM0gn8UXmHVO;O~{yo0$E{941@0s@)UQWjp$pP zUow(7eaiK;Bn+i@FSIH$<6WA=7D^OxKEz9e8KisX1}X7wT+gn{ZkD60Z+QmCn8mJ; zDnBkM>eo}$=q84La#)0WipS2JF%tdhp+wpk3MUib|EnfR@{qkix;KTN2a&0vXnr|H z$;!dWaVpIQu}5VlCto@?1~QJGc;STigwGR?ue>u(eNEJI(a%vpDSXW19r^x1AP3#( z?;l&t$HF?9pPCxX4UXyL04d8n@fdlvQ)$CEwsPZgn4AIR79;KC>8nU{sjiw|$m)^f zg;5HHgd68Sg#@TGPa>jvqv>>Vbt36u5$F5;@sW}Apb8yZ zAMoi+<~Y#|l4C>7kB(9(oSHV#q%uYak`x{pI4LUj6hrvWuy*(fsfT0 zWV8@+*^3=Y>)2}`pz8uhfTaUu6AqZVWWr%_00OJl^I+HO7b3Ek}<1agl#4aiUQ< zn#trtr%s((#kp+#s}t&GH60lo9?lGkas^W`?r($3zER)ua~}N27ZZt*v}6fEWEdJx zCTq8;{H)XviGc%uR{bzs?}}7;5V_Nz$5Uei2ac>bc65J@HAK@{1D&XxmEYdkyyPQw z+8v%zAAJ1r#|`6gzkexUyw>1pRq(_!*A;SHF zQl=DkJvjnW#;o0CUL8g%FV;N424Xps#jhT{@{oGy%0q;WQj_tax4lg^SgXk^cb&&v zwushyLC?>he-y2NTTt-Q`npyRMI*`c=dXNrA;R*QPe!6w9zBm~?XLOxc`4;{?ZUiz z8&&(pb6mZ?eo6R1_0*rMhtzwKy{jt)``6!tpEgTrT|l|(&-Vw55b5OHyt zoj?-vU4Uj&FFKOd^9@1bkBpVj2!lo?Bm^oK&Z= zdXd5*ko^=DkU)gmhi9)N7YeR;QhgmrlL|zI?s+ z>mUgkc4m)3g=UB?lhKtwceG_DoWb?eObmcB_LF&1SqKLY`EjP`aB5^0)}#YLH(cUg zz5ywfN813RMN|-_hPY42t#3(YGZHtQ6mvdS_yNNG@hjXlk@W{ z8>dhAzdD&4Plc(REWD*RzHss4#arhk2MrD>98JN4Abt%@Cg(6F#!j3d5nyC8(N6^@ z_OrP}Ke_qm`uoST94F^se`4}JPkJDLeGw)%?HLP5!lQRnJR&kcg+2)?9~cmCgXjGv z`a3c_VMF2q`b2V>#Zcx*BpiVwMIPsyj&a+Y1V1j5gc=gENsrR3Use>C!W<& z`EE!+jN`wB@$D3`I_r32&pMJ69vGP7Bv^yday~d>(*7<>OPPfe^9!kS0IBCx0$y-@>FU{u<=pUaeEGzQ(J}R(Uqc^A%&zD&1R3&= z)D(!@Vju~kKtq!aejUh3?FVpWl<-lGgcD*|ijuwo8LofHsVt^Wqee%(qAyG+Y2-DO zaipK64f-nIv?OhjUNN~ind#{+YNX^&+?@ZCj$U~vcmic4T?t?42c=e=BrDNycpT!0 z1UtwE3TqmmZ6LB^-RF~x5ya;4udl?3BU#4DU`kmnUE z$N=xYj^r7o?k5z0^u5lrld0Hwb`3os0chyAF`R(;1Ti2tiWu#a-$9AbV;JzByW#lp z$OuC2ay;JOe=;~Ty|OYfcRW8rM8xtWilJfL2d1Y_<=g^oVk1EEq$ebI9VaC&TLw<< zB2?p~M}s%_JNJX}bM7YxyEi;5Lm`gej5p&(;#d_6;p0Y*BZh^KTqtHpKrBrN2f1k&%ec{x zZh_x^HZb)lby}%Ww75(dkH2n6BYFmO9KoK^M_S`aH3*uKB`)r3ENF*? z6Zt|$OtvYYtv~tCkH3DB0stzLnfyL3g_9nn6NIUBim>OT{>EcE+3(pOTVc8EGpA^xsfnAN z{Gmx|9wBGA_0ztF{%W-E-SDC=aqiLv7cKEhf-LbwbDdY&6igzxM7RM*G-U+x&M`So zQ?MDho{@sj6O<&V4@B;=svHH`cUuZd>+*3u)=kI#+mo73*@c=I6VAC0~&T*|86vU=jPk{qTh3>X~ufAY`ZopyBfJ zOqD|!nNWtj%WAX5#AS{3L&bI-Y@64)LN&*x!h2crRr|L}(~v3!7X=ENFiJsm1W5qFsJyaJY+#E$*LBVhz8%Rp;V2v>fus>uT;Ul8C?1>MoaO+NRt;>@!0SqC347ZG79*%*FMD``dR&dZP zzJ{wDX8OQPr&v|6Bn|_`97-^V^gfl6%V;k?$)XF8oFMc~=trIg8{JRFP+tc5`-Zb> z@S0F)>tj%;?8mmCl_!S6Mp|6BA&r^L`|3C&;_>7q!O|*j$x+bl%nY@+48Om=F`#Un zO?^*x_EIXVZ6kQ+%B%N%dFDw?h_41(d(TU52n4ot=IdF#MINrJhlW|#fUjvj{CW;M;&b2g zUA!JRci5syhBPa9!*IX-fVhOZIAK*lr~te(^c+P?04^8y^OWcjK-20M9((MuU^o&y zz5=W7KSelbe|BIXxRgQ^jKxAM!hR$S@2S@Y0#3V+1_Gbvl0aZ?m6F^8rwj~M$yjW3 z@j;TR<8m3pMe!8|PRA<`W4I5}@T2nEg%Q{9GSQdCp7&xncniAYTB2MhcGly-2CgWm z22vp04eF~8tgJv)TyF$Hm9n~ot(I6H$6=FKaX3r}kE>UZxnJMNVz4LtLWBY0KE4bi zf|H%s_lYa#-{CnqhTL-uTr}*YG7X2uahoO++Nfu|zdy6=`Sw_JWOagad~t=x!^!dG z*AbqxeC&^ekC;a*W_;|*dDIX5J=HfJ!^axRpTHX<7)-|)1B2wlpws~crJFsCbM_-k zEYi$EFYI*)EwCK9N+rV2=yHR3UdNviY$qDN!Z!W9GK}nLb2U0U2>&Wpw z6q&@;jkpNQVXY;+(%Cc;`xl4US-PmKjSY3??9SaA8>yl6HRsIEg$qhuxX>}r zU6Xnvk*K#Wq~;PRJ3W`k3Kp4mzTykX`BZ8lwQ$QcbVW}ghbC?%P?f4iYpMCwOhgpx zk%8TtW@kMoFA^)Tv`7k&DHQI+vknh0@*L{oN$=S++dF5^B1PX(z~0I|oqHDwcf8eq z!>wv0I5dbkV&s!wVi>l2o$@ z0uuz#f|*B#?|l~mWSQx3U}iWMe;1lP91)z8a88+*t!-cVEgVr|L%69*<{G4aa$*ji9XN31UBbRjrBjnwZ-@*aRM>yk^PzjDrtX<}^Ep62lN(B<0+&8D zeMYL?8AfTD^%WroTVh>|zL%r#o#-2+V)|LU*Iy33IP}NC*#JgbXtl##}Ha0NvNVi?Gr4BhzGB`KGZd-Tyq zU-Y6Eyx`H@-ACd1)9K|!{BXT*+1%U&FHBHVQ(a6B5Ff1w@{PtOax8$HkOOM0)R+dc zNaLHxpbLKRIkkR;SB*19D_!krcOV#syu5M#zvE!t5_&5%bJwx}-5P_+yE#m}$}cW1 zE}eplhVxsO$F6zh9lw_) zt-GXl*MIwaIyau5r|hG=AIw|V2mhkPh^QmJJ}4$76^H0mpYcJr3ceQ{=zd_!SSvlX zso+YHKh{`$qF4~c9*hP`12HqnT9cLLY{T0-wUR)kGLp_QloyhuIGtCI;+Pp5iNbG! zfDzp7GfvYiI)V#}=cWW+rA!`CItBQ100?`a$^ePeo69olXEmV zb}kkqvS}ippN_Q0|q z29h+gEH6gU896tU+YM3@R*f7WsDWtVPKiv0EKrAJp^QR#BzO%~4esaybQ$BR=LWKk zo8Amdd2D>cyD@&u@d85Yz{`1a$nhlB4P{2hLL^0C3J`6*IVaHCY4HmJcpEo8-ED9C z%2(zF2eIqHPz?^w;U1YuL89^SlyqdlD3COlO2g6l(y2K@2k}xxekN-d9=1r02~jm? zR7|)n=ie~j?zX~%@b9|Yj3qe%+~c0UDPnKkZSRpbJ}O;)sC(Q`>t=U5#HxOzyB&rb zTJLTru$fGBxBH#`u|9Z(dsAOk&V0YVlq>ZnytRLte z_tVc`ceg`*FVREY?Qq|s>a`*BtGc;+ykGjizR^5rRkpUAlU}Y{=shpUrOn<}-R4_kVEq}-RrrSM@8n4>o?YI#NHChsc8zDXov~=L)jWLr z9OEW}VVyQM?PdJj_sy~0;8R6>uduz?cQ@x-w5oL5Z}e?I1Q$tce-dTY8rvs%;x&3W ze)!zEzGpspj?}$JdpS$%r{8w&zq#%kXX6`3jn(KVAE04>Dp|mu{ZEg8Fht+!#aA}U z8j_@eqH`q;A6bAR6f42p1N!doo9vq+XLL%X5f|{{R)g3)h|E%>YD^u&f}X>hZc!6oqHhS#=e4r_ZTtNC(veFMs*Wb>RZ&U>Z1B4_08%9Sio;1AKUHflDb3PsqRvD6Ic2Y^-^_@x>vnS-KV}q zyQirrJ{5WF)(+c2rf>s437;P4$3kDOfOX4{U9vp53BcJ-FcsSzxomNqw2@h2h@+_3H?F!lTQ|-el=^k`8|tI#G4;6mP4!#qW9sATx78=qC)KCa z@2KC!YV`Z+57Zy3PvZspS@k)hoc>t-iTYFZXX**{r22F9dG#0SFV$bEFQ~s(f200Z z{T*3Q{vNCL|55*_zNr2Qf4DEHe^&p3Ecaz%;l8TAhIR7a)W56$pmOLH^_1=tZ2}r~ za>h$ud#nOMGP#jwj!bzm9oGqbY?C@gT-5+o8M1m%59wh|(5W6HeaN`Z=?Oilr}Q+o ztywa_9oKVuUN7hqx}X>J5_Yna`jlSLt9nhZ>kWNcpV4RaRr+duPG6(1)#vpEeVx9Z z%yBpBoAk~47JaL}NI2#<>lctG`8NF`eY?J-@6dPZyY$`UO@4`fslG?wt6!$?)8C?B zuJ6~TF6xplYfEqHExoNP`m)~9RbA6{-Ox?_fNp79w{=JF>OH-$5A=ij75ZECx9M-! zuhb9e@6g|=U!`BIU!xz^uhp;9uO~P28}xVS@78bB-=p88->kn^zeT@QzfHegze9hY z{(k*V{Vx3j`rZ0}=pWQSq<>hyN55CUPrqOPi2hOiWBLR7$MsL>59*)PKc#aD+}Kc+vfe_MY- ze^P%+|Bn7${d@ZN^&gNa`qTO|`m_3T`j5!3_b2*K^`Ge{^ppC}_2>0p=)cr|rN5y6 zTK|pyTm5(Xf9t>3|3D7Df7D;p|D^w~{*wM@{V)1o^_TTm^jG!Q^w;&j>3`S%q5o4~ zAx~c)f&yG4T3BiZQH}?vBr32`49`QIUW_{lPd^FVDJtWkZd2IWFICN&*@kc#+a@&B z+zOW)Rft@{u58txYqbZfmTec>+h*NwJ!m9k}4gSASj)hJdQTm3foX<3D4#VYgFq(!G*X&;2kW~F*iYsk|lx@{_Tn|ElL zHD9f=Z?z12yV0y{9{9_a^`O-XTjedQP_?$q(m`me(b%fet9jsUb?mmkUD;_jb^@Kc z-K;Vcp;EQcDQ_|qma)~TRLfS`yIt9}0;{JATdhW?8F21ZZIok;QX9^|E|jfiwQ*qZ zfW?Eh73|bY+h(h6l~a3WyR=<1>xEi_2XC}0^)2sSqulY=8||{S8Q-hGJJidJX?f3T z?Rblgw(T#~n$=1UPJr zLOe2OijDnpZ@2cCpfqb*IWMZ8HWfU$JHGT2be_(6WT9vV06nW!K+n?loFF z20YtlwXv5F7&fbg8W3yMw*ZG?wPV>k2cFCOjDZVP?j>O^>Aichd*;ZYp=Tc|*v!F> zb@N{4sQt#c40p0EtjqZp9dhPt~@nDlTwpy0m?hW4If!DA zL(8^NZZ(=rM~rn;+9@;`T^Y*Y;llE7Rx72Qmd7mDgUn5_Q`)guJe^vx+UmWeyXP~T z&8lVW0okl;1K@5oDrFy!+1U@2Ta{w5ShajiN7D*zHaac$Dg5o0RjoDxEaG;B#i@69 ze0x@<*zmWkI{$-BbIYO+3vhED5gf+C*|H+tx7e-JtcK49Fn5ALL%GRt+I|5zZhl~H zHySJ@S^9KXE?3&Uhj#apz&NjBb()QOp;T#=s#f^OpS$=`d(zf@&sy0C?2ZArUfVO8=S)E3yRM@T9W|ZHW$s@N) zW~&@#uUxT90x{CUtXca(e%&5qivOo6_kmrRx5TpRP2Cq zphTb*#H~8`jN!1Vjb@+>+A9IOfz3vXxdJa$EwkS26#a66eGsv?E3GyNwb-fdgzXA= z^q>d^ZwG)i6MSZe4_8&88Q9x4+cuzTFxaI=rS7k>UOTnOK>_ixSgD&J>}aFet~lsh zUf021ymLEZ?Kf!*inr~mpI5wGD#rL>y@QkrrD~^`0Otb8yO#6@`nUDgR&Z;p3tG`e zx!%~}iR^l1r;_f@+cOS^wybuc*^pHh-lLJ+&@AlvZFAFXRs5yx7J{+SC80jRyk+(~ zNTkknJYTiZ0%J3TZL{H9TV8JN`=R&`Hoypt&Vvu~)-0F8Zm=i|rqHffL7@X8;@S~+ zNq|KvR^2PKGXUIGEWq4bYn1mROu!+<@O12Aq}hV1mD+{vPQ4v~0y_}vhwfC0W$w_i zke1uq;lr&$Q4k*#CbZWqT3Z$7xn=A%>9=P4t@>`IW%{-RG7aauu;pix%2uNXJ?>t> zuA5DeK)Z*1d;6iM7i5?bvfBml0qny;0~idpx2>90v030p@F?%}G!^uNXIN;y2Re6pRjq>_AO?YTItDDFhg*e8!xnUHwL&J$0{ld)vSXN?vfR@!1r1sI z9&^tI<<}a;%dAENX9ga`M~5| zLYCPAkyKh`K(i&hSg1$63!BNq%|m7*mG~8=xY6*$#Fg^|I~Xu@2xZ z!aHyT5M!pc6+N2SRRlnR$y+4l)kunM)RZdzrdX~IJ^;1sNu z%y-iYH@D4Vt5OnF-rt04<&wfCYu6HXF$}+dz~f6-e)#xptL$$cw72O21Xz$^7NjI> zL<^n)YEtHe-Kau|w?ZukH4FAJWK$3qd}_6_AGQIQ(l(4|r5-`7*{!gMg<~;n=v%9@ z51qHn9kUtY=m7%+pBQl%fG)qz4&w^pu63$#H*Hp9gIfyL2a~{Bc_D(xLJ);^nDJ_X zKopqqBXXF=LqLLRskv_WAgM~KyYHq>eT!@jTUzbf`@7Xf~_9!-sOdZaJI0%@G=N`P21DlZU!Id0Qn^o zwnf%q&GKI60gm!{%zLVbS$ibDP&|0{v3e=Dsil%J^ zgmG+?Wr-C)*FsR>Hry4n1sMwpSM010(GK(ow~H-1)e?MP|CR~I!U{nIg!h9hj~^aE z+M|!qWmqMb;)fBw%OLRqbGS_h4K*#qmG-V{;7J}3a?*j5T#U_U#(S9uV4w+oF; z)HlN22%pm41{oCuIoV-n-C7WEgftL*7fl8oHri%1Qzf$wauMBzDOdnjeA6%Vy*A=z1^1`!SwiovL*ZX>qB; zUZ4PCDWWQA`)yd_(zb|3tr852WqC!T;O`W{e;rS!X?r_1Uk7HZ?l&u~V!arW$>Skm zje|(%yiLn&`iq^-O&A}SlNLrTAUXwy^obw@Zxn*XvTR6l2|O(00#Ytpg2@kR@M{O& zJ@Y{e6ba+hGQd~@r$GnTLyESerAE7rGzlAInVm3#O`%?ZFm72M*hC+_BAD8H6^J9^ JOba38zW{E)cm@Cf literal 0 HcmV?d00001 diff --git a/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2 b/deps/font-awesome-6.5.2/webfonts/fa-brands-400.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..5d28021697ff1f32507b1bcbcbf9e6a41d0ac99f GIT binary patch literal 117852 zcmV)zK#{+9Pew8T0RR910nA(g3IG5A0~loh0n85r1qA>A00000000000000000000 z00001HUcCBAO>IqkZb^^I?9XQILnZ51&AF7ASE)4aqNK9;5+~T)YhK|WkgR}Egk?> zRaK7(!QHF&0}y`td$AQJqRvFji+m?Wk}G8j4Ih_EZKOU_U4%&)Igpb}Kc zmZee|y4$v7OWo|UY$@F#C+X}>hLQ`M+4KSq-Q}U4^$=%?y+G_G_#^DcIBfqszprNZ zoa@i@i@*QhtMdQ8y{fLR?rO0a?;dkzhM57MK`d!tFbyO@CNvTQ5{NDk0*xhPW3y}^ z8+2l_yTpl5<%c<6{(WXfBgrx*hU9XSHtxM8t4Zt!#4B3Sjy@u+0ANM51c3G@oLWC` zG&2IUz_P$7OL82tY|C=eq)n6ElwkL=O?zFSEMqT6#QncV&_km1{`pnvcU9e6b?E^& z&rJ7B_uy{7#wD%5a@LkfGQ`4I0V%+;6Lz8yv2zGF$^Q;9kN!ec`+d8MT>uMUQ940_ zAVorr5+PREmMuBej^vj5rCeN6ntiqEnlIOS*Pl!7`?uaCp67x6TK8cUXJsP#Pc=4H zW6!jKD`%Yybiyu-#>PeuYbRF!_59s6FcJiiv(g;#BqoUyM8Nak+~=KSw*dG`H2J`8 z@%XY0OyEN%Co{{+*SHE@qiZzz-PmrRBuw+vvlW!7EthV2{CK+Vf%^jSHwpxrtFY|B5<3Qb%CBb(Rdd8*{Yhz-XM?IF?S zF2|DIvTKxCqK&!-5v;EE`UB(a!tdO=Pnsi~4wLqW>`{wLaAPDW)X`9ZF;^?5E8Z zeMP=F`QJJmvz;|ACm0kMl=ii%)S%MFPYh=2wWI%T6S?NM!f&2ypMfim}g9tUHoA{h%* zDEhy;T6w&YH-^q{?R*8f5=An7(zcx+HN3y!;A`gIk4MO?t&jUL+m*Wwa!voP;JZBiV=S)CX=<&TUV`p`aFZ$n}}Ewo7O zeBEqa_r5=!t*3u=wHPSJt=QrsO8(pSQ>a{1RnfcRZy926dQp^Sb-Z!T;#yR`MBxJ(LZk7CI9Vy3*f-c5f*t_F^ z*t`qFF7XySKPXU z`55<8y9EM&)+Y7N0rA4jUv$N>*j`g{$2n}XTl`2ZHZS-U^u5MX2~sSTz#7NkPd23v zF}!R6NY_rr7x_dlc7^YWpSz@fopl{hn?B6?$oQ)Y#$pEq;g##1`{VbUt+t>ITsSVd zUhMQA!T=x`0IUFzZ9fUOfc2~-%?4J83?cvj@CApx2MBr_1P}&qAv0u$yigk2KxgOz zU7;IvhaS)qdO>gK1AU<%^oId35C*|u7y?6K7z~FIFcL<=Xc&{TaL%SVo8@etvt`a@ zT9}rgsagiDw$?;zsZG^pI{&*Kx+}RmxnFsNr;MkLXO(A-XJcC7v_Wb6({AX|x=qik z7u3t^mGyRdSADQPPM_-S>Fw>E?49df>|Nu1=5=BM=E6K!0?T1@Y=b?pH}=5+I2ecE zFr0vsa4OEjrMME;;CkGRC-6Mp#TWP)f1rWC$x0EVP%;&wB2zRM5!Eq`DSf8!tglbL_5~Ta2B?VdI!_-neeuH69pfn8t6P@WuNo`l|TW`EL8}`rev_&GzP4bDBBJ zoM$dFSDA;*Bj#E2y7|O>ZaPi3zr4SSf4zUZ|F-|J-|7Dp2mnB4$O?I&5R`+?&;`0e zH|P#MpeOXQ@ZXr`+DL82)f7q_l(skRx*nyc>Us72dRe^^{i3n@(Lp*%SLqqOBaPc}FCN7cc?!?uIlPEh@jBkb z+xaM8Pzk-Ge7L>{NuP zx-&X6Ue0(iL*4Xa(ns09*@xLH+LP>|wv)CKwj;J|s;=%)m#U4`8frGPi{nM{!gvAkoOm|zr?BK>!a&K>iz3|Yt7pCwQp-5)ZVMzRJ)c^9nH4*L3*ih z&*tMf-gecei~kdLU;EnK_V|4x=%TA`y6d5@e)=0=pg{&3VyIz;8)2kTMjK=!lg&}8g)XbSc}G!+LNnuY@pO~*lpX5ipM zGjYhFSvd4J&t~#Gb986&dUFh5^7`1f8}L_u^(&gZ5#wSn;5mBG=sCw68vW;(hsLlu z=A|)ej`?V;GROQh)}7D-G&Z2I5f-Fz5RKz)zC+_A8Yg2V=KjZXtU}w{bF51H(sQgv z`_^-;PWvZwtU*WbIo70O(K*(lW7RpItkz=Nvlc!TNOWO%vLX z&fDp{6C2U_BAqYcbQfifvX0Fuhfog3mXyl3m!@0>hf%rt zWscn_x0_>k%6&%IgYq!S!*SPF9!q&V)}rQ4+Z;V;?lMAmn#a&Q7H?7Wbe`h?n&*yi zAk9l?UWTu}=DRdMz){qkyUlSVss9{Dk*3TsfHdu^zj_*_1$hETlNKW_j$=q0&>US! zo6d0@X~zk|J*1sTyWn`zu7v%`cO&hIlSq4$_Q7eSeR&U^PTG&OKh7W>LOK#>k&Y%E zg9}K<(*!OeokBVT7n9DXIj$gGJb^1omy#~SRirEFR_+?o)udZ-E$M#JL%4(VIO$2; zLwc3;I_@XEP5KBAk-jEF9Gl77Ooq$cTCJWu+I^cMz_yO6u$RdO$K1+SAMxxky` zKIB1on>>d+2M3eqAhFc}aXqUX8puz9Mf)duT3sYw|W|BX39E4ISh? z$a~^P^4>JU&*a0&N3MFi$VZcpMT>kq`BeNyK9?uZg?t|QeEdtkfF{t5d?EP~{7=4& zb{R&JZy?`D=t{nYd>f%V`F7f6=t;hVd?%q7`EK(4gev)A8WDPvpC`ZgCG=h756GVq z`jfvPe@PfhZt$EioYv+e!U$SB(>g$=J!&0H>j=VhT1U}3nlKNo<7k~gn2*-U{9+9H zumSd5uor@Iuor{960|zltHE9aS`X~?U~dF%0QP3Ew}CbWdk5Hgpv}SF1NKo+6YLXU zp8)Lx_Gz%sfc6FZ95hG!gMEGuAAx;g0v!PMC9to64g~uKO`t=-z6JIZ(4k;Ir(NnO zuwR1x26Qyo@4)^7Iv(tAG)Jd_{e2EMfVC6obg(7Zzd>h!?a~Mhf(yXSI&<427lNyS zE&w+GE(Kix`Fn7GgC2yk3T5)Ow5VKeo5LWKW)4G8Hs&w@5={6*j|29@Bi1b;Qi zgP#q4F6bZdcTHmLClL7eCI|xXpM(Drf*$Z+(=LMwbf%!Q z1_U8=)`HHu5DY-)2AUHLL+6e;K^;0T%?VQIyfr77A39&o2^N6P4}Yv+LFDGWE`=Uu z!ORmnSx-=*M#4f&L!O6uW(Ly9k;%-2d77mICd@}BM^clIHF9Gb`4Xm{*3$t~2`k|e zW;`3u2G(x1TCMEvyYFr_Pi{7w&B?p(zPs7#ey3WTXz?VjNwqlXev!mAty&aC(RHg9 z#S=wQsJD2cDEgDQCe`AkyJr&Dq*|QlCV*hZ&V0cZx`|YaKo9g5^ilQiH|%^b{OFjP;CsfVGNG`VTyjkKN~c^N0t`fw6fMn(+1jFqsG zjc4Qba8+gKWlZvHI;qN{FqA8+QfprE(g}Xi=N#OJ9&tVCbB>)8kz2RbPNDdXt69ZT zN)$9NS!qdW~7UCV<(t z@Y|iHCi3IDSevcPt^Kpp0?LB9`Yh>dfLb4=kHYzuoC=grRGb)OQ?&2twy$_fsw{Tjz{rF2)z{E0C87<7BR zF5I1k;K$9c(c8${_J=gS2lobLt}j^n@Y z7AHh_QrM@rxsbvm#Jcrf?Kj_h^Yv%)+pnA_2;sNh=Q|DpRA2v3_!)SF3_=h^vdW|; z;g+5k3!`Gn&QnvEaaB$yd6uST3dj-cqp`MlbmZRQr6OU(l=uQ>F|50fP#8tiSjSr3 zm1lXDPb-TXV{zpt#P_AMJj)S2I5<5$IKcTo{bHxn0qAsgT7arF=Fi8RL8r4buMKV@ zWY1CrKZte^3bQ=p@d=)GzsdyK7%Jw!bFhGVP(Sl9;kFdwa5mrQ>}=kX%wQfmjytoZ z5FRGHyZXnN!N{>1&Jcq2r*yDpgix4pZalv;sVZG;wqh#Q6qK*Y@+|*o(%eGrsP5jC zYuh^*cHaN3aMtX)!bP@r0q<;IyR!E_IW7{0_1|hHiPnJWE6!kn zP7w+xl`&;en5a&9yv1X6JI@VnN5op2SQn1`8wkDns@jF(($tV?42y4zAH@JxKwuj<_ zy$OEmHSpz92#ZlFR{yhJtKF}%#eI-(|JO*TdL7;Cu8en!R&PtXeXF$`Y~cshmn-y- z_hh%(lI+(~ul3}JZ>_lw?!$YUcpl+PZ@;!)Ydb&BR?D8K{mdHw?k`{l--}Xojh~1c zi$^L_BQx^FY?fzvr6Ui;ip6#N$yH@B*0GM&e{=v`PYUPjhS*_$e|Gm~*PBwty4BLL zlzUf(*LYGm4uJa;d&dBP9eB;n{R8&$|I&_IY>V65SO1*YUDp2$r?5aTnFKi)%AjJCpKZe}6TD`uS3qmX|g@F6W!uL+aj`;+gqR&GJGOcJDpBfJiAkR2WyOoMu&-vv*OXOZMrLGkemYvTH9b(MWI*vu!UzV#vR4iV0BO_rVK6Lb zl!W*uRO3F_c!jKj8v6j*#BVvG5yL@D6x~hZvQM%&;>q9g3&J_1VL@ z|L*-K!4i}*E`(Su1AqW#LI@787eerNzLN_f1f^`@hM~J)lnR}c)zJ)o24+a1y{PTX znMk7_S?;~BcvxdcvBCjBoO87(+YI!4)AA4Tq`iXqHTuwMiA81@PU#R8bjT}DCX7-6 zeD#S-{FyNOi?UiiF-GpButlu03k)&0XB6GBKv>Gm6UA3b_>=MH=*1PE6bXF>>zQYK&V3dtz7gb-)YTm9dk z{Kkpruoz1t+ z!2%kM&SJ6ihkGzA)6LBw8I{8UEa#z+dd{yu!7c8(K>G+mmKs)U$4KWCvnP))mhL&+ zHCL{fqa$uXHNHPzF*rvedd`{*L9?H!5kf#*~}arakMMmxN&fNe0;or z^QJ4L>o{?k_0Ue-8R(ldj=`D>1w~q(56PXJ=<;2y4M! z*h02Gw$bRoY_$x}9ElP>bGm8U4RETBl>|*@FTCGf1i^4oHqb(J{>}5*&8RMSJ~)$z z;jLQ-9&{sTcgt1lotfAFTbHmvCq|77ru}Z*T^OX7s`W^|ub(?U!OeD4xN@X)T>xhrY;`kpSfDXN zLp4=K8lb(yeG)tz81n2s;L%xYRI&nSxm?=IG^Cs7kKAg0^U`lN8?@Y9XsQBA-Y+#l zn+f$cg6bTA%9?X%tIaF4G?gFp+wLwxkc$}#lqGT`jPOQVYEXUzdcNcMzZ_YFr4ieW zwYW3_!N^Zkm44IyXSmsCig}CsbHmL(;Jurx2>enis2tZ7(*2IZYaDaq-L{aPhl%~6 z_rsS4imtmj`qi4WS08_4l}5kj*3u)`wuSWm?8ANM_FiKvFSR+y)pe6Ro7!M`<~vRk z^9o_^`PHH>V9i0_us@*Qn_A~T8bP+>w&0!8B+pIxMaoLY{ty13AIH$&_ii@`+;=Zu z5@Prs*7*3x8_d=YbrQsX?E8Pb`p*^c+J54z|V6e zQnl45mQR51w(6_jhix;SIFD_s23J>qd;Z0FY{y|;a^Y~w{piJP!?0q+=WB~gB9aU~ z-ju%B8xCWo;^A;_>7`%5=GJ6-`SNtKwF&QQ&Qv_ic6YP=b2JoKVtw8VW-%R%8QM%7uJ$5&5x|eX5efYucq04A9&}Rda zuFG7C*yA65Q3oz3*nVoe)dak~bM>XknncF{u};7!Tn5(g>pSu!Zwvb9q1 zA-P4U=Seq;T*i)j@T}}aj_Wjh;Er3XZ*C1{%~%kda}2KK$j>%++v3Qn~o_K5|gs|T6 zP7=Ro3r8t?!6=jPU5qlh`Xe}&j4~#KSj|I#5avP%me1DRa8tQn(^oLKY6AGhP!c|;kL@ZFR#x%zt;!~pT?A0d;BQ6@aO znk%e8ul|$53g*%ijGJfI>wkf_!}sQ`2QpPIU%J1gwZ2&gU;Ufx?9>&c^WOJ5Qo0_1 z{n3vG8!z6 zH}uJS_Nny+yc-rM%YSVRS>sKR2U|0X99yjZ%cp(#uB&C_*aFn2(_t$?SYQ1jMD~KP z2Cw)n!$_=uA3g@Zfyn4qcVrAp{B&bIQUN{5s!5s==1UW3wvXCv z+pX6c``q5>Ts>%a2-Ir@cRlbt-**69N{MY}iUS5pKzgzsI217S`c-xM;MUC=w;c+z zgD7b5-5zw~IO?P^29+cUcz`j+0N??Rb=_tZw+;@nA&`xYE@^}qJePsRxNT!_DaKn{ zVcS!YgE2UsY_+m_t;SEQs@Dg6eCfGqn%aOI)LUQRZCD_ILNq{k&_nckG)Irnw;&Yj z1-{piA}yvSt^BQ>8l`zMm7c0;Q39B?>Do@Lt?gFIs+^_*J5pu~nJ>|~&QrrpYElB3 zyx;E!hNlZ!xIkvwF~s>a9Gm?LC4XDb_hP-ZiNW`y?R)p`-TPKx7WaK_G58amSesjW zdj}8wZGdh6;cE`|f9Ux5xaw_fB`;%)ZL#J?Z)3jtzaUos7sQ)qGk`{;tX}i6kA3W8 zueo{^;PS~!PuaFz=g`5MTRQgquJV0Yed@!H`HaFLWwpZ>UwrZQwSHfSC-L3^{16-* zy!=J&Y8eMX5WM)}zg^8vj$gkwDY7A6Jq_PHHed7w#RQyoyWK_zA&l_)0)7h?Xaju~ zLc_A+E65!oqQ>KD;<@ddS9B%H^ASCc=8dV!qB4~!i?P;5tHSqX_~;?v>(?GWymlSq z>(?IcWQ=Z(DTWY0eb&jzi2_3D<;#>3P$vX1z0(9naq|wvK%SZ2edgo@9u) zpTOpoD_7voI7vY79~@sEk1xYJl^SN__QqDHquMoYyNyN!Nirs8bYo^lMBkS#VF^og z6QNM&gHISNA6r#sYPeCRGE-A(4X!kQSDVT-gCr&23$!xYOigao|Gj_zKBW&CJ)`vg zpT7#ThYYOyuln;pf7N{p*h7Y2_2=;UhlhuhUc&eirH2o`=6@W%+QOGGdx^o(;n#f4 z;SsQx7`}w9S0Db5uX%tFA_%SRx~73zs6=QeppB9%o#a_Q41%C<0@BHBI;b}|I5PXG zsbJ5OkuRl;l=LmjTm2O*?@p&vsBd>-B|J~4xLx<9xb%yblu;~w-&ex(tpBo_!SeqJ zKG^K|GS(X#v6hlC@hWlGskHlL1bPvDF?uKZF7*2dRagl0fonfr#*;KjlQe7mt{$mq zO6Vj7%vR%KT#O64$p3Bb7*Ux6fa5F?V}gw;unVWhw3ix_DKiFb1I1ViqF=0DureiN(GN8ja3snQ2tWIes`^+{pcQyWRHl zjW$5LcLjm7xmk9YBwWzy?2P}{O4(^{)5ipQ5?4Bt{D@?EQ?YAj2UB; z5<&pe7qXxOHNBVBKUA*bMy~hvbiRQw>aITy{|erRnrOmQPRW-Zq0DuzbGEY0(t79C z@f9+PY7)SG@W2D`Yf&WLPU*jS@ZiA%7eLmcNX{vJyNIH?1n}T1>lpi@5!BvtUJDw+ z$GHBJ+j`T1LFK|}Z$*MEZZpQ>Z*i$+jG7Gmo^hF^zYMKQXHUWT4UA3@)O5KQtU z&C)E-^1xIk%d;s!E=|%=KB}&Ha?$)+W&)F>BhK@M$jfzjKK9ODS(O#ns?LlTWs^Lc zj!ZSu^$ulKPBXT$qGQF)sk*uE4{Ei*Wt1x6d6+cy zIC9;(=hkD&;33#Px3TNPa|8ff*IwQ0lCr#Wr>tn=*j)e#_$W;!#)P=m{@$faySrop zzeF39QnD3stEFd@IU6~*dfguRC*N~wway@pi4b@n90#~%DbE%%9&T(fAQ+Ab1?)QP z`=TcGgi9zwEjCup)0?NK+1~Z*V?*KOAL}=dtIbU^n>{}o3@I?G0WB9$&a9Q&>-wtY*L+_!YY^8$ zukPBm>(nAoe0HF(H}hs40BQjBy6fk;bRBR$J{SyO*q&@Q@BOEpogF1yAwv~KLO{^n zfLgsX8bPOC188lIJ&*b=8927IY~e+rka6Q)%YWVjZaE(3PR;vB{nuYn2Y`AU*RMM_ z99K#=eDL7GgE!881tAxq^*G|L;2}JM?|>hIUx7b>zrZe5I6-Kb>pYtV%3f|JW)d`4 zrMXE=wjA=egZ%c%9l1>UgTASMpy|V|T2_MdE9M%8d2M zXcHIXsxoDvjnrJBM%1nx~YUbwcV1D~}VNz6&Ivr4s@_cCdnCT?v8TTQA%w`&q z<%Jnfr)I((6v46Zm=wn1l(GJ@GA7pP?Wx1|+Ra|Ou zE7iBvLO#j?QhHE06dY_6>jukd6Z6z03R!i0cPMi)$+KPjJAL>}9LEq60NR8a225K3 z;9`tv8)FLyp)HJo5~>MiloCaV1C)}05Fb;DeL@04DL4QaD?%y5L{mZm;}#_ZER0(W zW9$Mzi({ceXPd7`1w~ z{cVICz=*o81(*G`R_9gxf0Ju(E z2f%jRuMHUGwg)xg0XVKJJwg9y?^=^Gi*|PUebt~|3J?;`?e75}B!KV3A2^g!>i7Wu zANsyeNI)nB2QX2D(#E~#?JwUjp)G*Z`etGB{w5+{-URB3dW#st*ZSU$Wg0Y6ixA+B zi%SA2r442WuI)JBx*Z>5-v=zu%p@55KkxfKVHT^sF&z#pMwiZ_H5d#i^&Co{a<%K) z;7HjBg|uu;jzo*nmfh6DZDG~xb>h@41|K_kRz__h?7!tjuuzZ#aO)#k>?j7DkSV$Ih6LD7ukn(x8n{U<;9$xoi2|KWM8egvA~IlLYnqc7|uXb?b^>8~m?nNFsI zYMc39p{C7fpA80U+Lvc}m89ctu)|XwD=RhV{hXUfM+KKG8du}f*I@Y8|L`CF1I7SA z|5F@>wjW%%c4O9TC>1ui&9*yj03h(=6o}#O5t$}VE(F0mwK}_^=r_3tes{1m%6GEdTy(k?{>R*ZyzCw z(E0)+SRjSA(J6WXp<%A`EKL+QkbB2Bv0C@SVpTjp5`bUvR0;Q8AD0KOI=2e@Sq0q`7v<1HHh=#T#B zKlAd*zVrC;=%_Ez7dfq|@&0U%vFvgr{a+ z^!Vlmz&-nyVQ>HPciGBb`Ydz$U=KDnkMk#2%Y&!jKj4yvCQs67HmyoNf*EZJqtz0= z=kNaR?;hL#I;_9&g)jW;-}{Fbyx;|oJ-zpP+jljG48xJ3veZT}0Ei8KR+;VR)Y7a-^p3J0``*vrHiXKnhQ4Hwfd%vR#SHwk!(_ z!<6i4?QVCpwzk$EI8nQEdYE^crkQuTVWKsl55T1ojKz&c>>E1pHRWLxhH;}`_oVPW zhjJ$fv~r~pdLHm34HTtPB3g!_6=Q@DPRIfNH_|0tvQ4fd_Yg8NVF4{>t76{6SN(n; zpaG0*!)QW*C!l@Xw3RI6=Ab-u=+L3MV>?dm{gYSz=Kh~SXaCRowG<$&^`Z0S6*wA% zjr~7sgh3ECpwkG0S8VuQ^TYSqP8vJ*`|q=zBysF}A+7Zb+P?pe0+xcX;cbv32C)vF zf?wr-T+;<|2YG-zo4kZPPTod-l6;Vmio+Te<$%){<<~fl%fYg+GRCH5GEE0kmh&=~ zqR6S@TKHOpm7}T@C0q+#RCys}l2$?{>7vSubnd~TH!Jc&et&3`Cez7avoQYiTz{M* zwE$mmCFcp}_j8_5WRyMz@DoU{0PZ>z@d~6G_ydR(A7Frb6eGqlV!ZeEhK?SVQty4e z{m6naesg2kg^+3z&l{|wE{RMbuF)5MbHgS4*?va zyrBTr$_tHxx4L2Ol9mF#AJ$S1r^54F-*FZ>tbRw{!ISWP_#|nO3AvR#kJD**oe~?8 zQM*!X5;6*n&Gtp2I7-v1lGVg+*zwrHN+-z5m^X+Tkqp5vK;OP7F9VIy@|Q+VEEc^U zK(Du0oS6OzO7!61JpZTx<`Y*|Z zkOI)`)$4cc9%0!#rg{i3LP|QMBqzz8Shs zrL=ws@sfB_mG=r_A~tP1TPm3VZg(bWIaKmlac;1lLB%(vaJa1^Pw&-YniTVLnohH6 zTxP#K8jafRd_8Y=vNp7an;qW=@cmBP_kF(|2q{GnaSkAb{}H?1a%|ga*6cyA<4Fmi zr0WYQh5y-JuLo}0Fbo(#+Ju45^E`j%3tsTC#~yp(3)cd_)A55>|4n`mS(fXW7Jy}X zu4TyxAdDJ~Fo595#~uXJ@?6WZEZ4J4crb_^C%x))2ZqOP9ua-$YD%UDPr)Db!3TTo zc`5O?OyS4#xS*uPS?Lqd_L)DuaN)uQICuX1`SUM-$qm2%2fgQR9y_)P*M0lj-#&Nl z+xtK9vX{ZbgeXGDRd^8|Ap^2bw#aSdS>(m!m&o4{QVrz1%!{lPGG@1tgE>DhuZJ{~ zc`>IBG=;Q$=zbRF@tibHE;gd~L(|wJxmCcuf*Y1bw5%+XbTlZ-GM|(BqG}vyM{q`B z8CDJ!Y(xtPrvvB{26LHbBFnj>tUqay)W5aLQVG{B%eYh%p=SRTQ*l$>1KE~ zbSXuUF02LyhyRR@NC5fE6e&fjqc9u}qVSnuty+M+KJI|$0eF)0+t%0DxnTep24BZG z8VtIInNvXsA@blqiG>&8F)}8{$vJW#`4RG7@+m^9S&?U1 zIVkd~Di_tXTvS!LD8|{i%t{fbh?8`}Hpo=vY#xq9it)G{uawAgDuX!D7`%JXXc|rJ1#N_Pb1<(5z-11}^x@fT)}6u{&}ELdvtIb_21H&)?=n6xVEpt4K07@AT))4qIMU3PAL#Slpir6 zyZ#Se`ZJ90e+Vc5gS%k=N#M(ib=jdjZwBmtmc4sg3Ih;*w<3om`~DD=0%?M?>F#aB`(;Ub4`Y@7RlE=M>$<` zaIs7H+d4v{zO?_TrFsLPQD17;wbr(+&)ssRCIQx`>g}a^0fu(=FFS_Th68(!p<(|A z8bE8;(T1TN$N2oLog7yipbf{-a7(lJpGEI+WA(R+W*r*Jv6a@RPHC-dM>U{++SOWX z_lu690kq+KaaBA0LQnPbNfUl>K(4~q!C7(}d4#-_ypepEkdcVQ+pKRbVjrW*vMd+X zc%GF5mk#%UiB#l;6xp~G*;q>$3LGU^$MXS7n@r0|j4*GK^ZKGJOGRn`b!PaEk)zqX zEYS)>qMCkI&gSrT&JE4EkieMGokq_xgMLgYz+nw$%?iW=((P5_<&jkDoi(L6TkWTc z>)zHzKH$7NSghrBlZyRI2omtQAON4YIsJL_tw`}*R|1{{fTIWu{*?zox@Iz^{6>9* zKLkLEfQUjeCy4XNwkfMIh5%X_hF};)tJ-MP*N?3B6hbppaB#%0Z<{T003#tG`U_6d{3Env;+WzP(lvA00(fH#AKD+PDr&-y&8NP-oI8< zh50Pwf1ojaF;U36#VN?9WfDhHvatfdA7K;@Sl_Fs|74g~Ctdg6TDM!f&*-FA#insp z(lzd@b-T5D-EMOAkH_9smg`toyUA!LNp?nwd$r}d=2c$YJCvq}dhgTv>KN`#lKYaY zu2Aagvn60+^bFVDi;pby4~6#LJ2wGUc@%(@VR|il7r|4 zMrjGTnKT5gDILyKiOXqO{?R||U!?2nRlQnYqrdQ?I#lcHbpPT%z~zgJ;Sew5aJazX zaIrrXUevW9zT!!pL4H3GmrfPEvR*cjk`oAw9l3N8M>5N@*N?{I(d%D-$WU(#2K9Qq zJ{UA=HHcTZK$gA!9ktp@)OC*ed%OrgPNw8aawj1p6{}{%-`qBwrlsbUGb-7Dv& zDMho{#GBe}S`;VF9X(nU7{}wHUI)lWM~+5OOIgZNP@XbHQ5NN#1e%mI zE%^UhxfK~&(y|yuRx+}z%Az9k{geSPGhzR8MNz!$r9~a~_AfV{w!FOj@6Wn-!Z%2bWR8WpaVMl6(c~umg7!k|t6}QLq$8P0~p@Nt0=kMp+)v_aFwjIygP%vIB1yxf*_Wh6^Zr_KYs@S~D(kan$PlV6>c%|wgZ3JSNZjw zE~?42cnGw#Dt653(JQbfW9rBN7^8HJGC{wN1z=dedGCl8 z)7g!P0KcPaDn(p|F=LFLW)Rw`c(eylFaSy!XJdFhXC*+nmZM+fn=S{TPJr~9>+Zex z_+DMx_&}qAXq6aolQ1*`*kp`TN*lNG!@D=%d}ffQ1~7OALelT&d2f&)rwkadImsDdcLecmqeLiy8P zRHK;qLXz@Q0(rBIV6)sjNrfz>STg|20j)#6*pot*eM1O7AI5|INh*r+?*Ob@1J`$~LK0nk3;>9!y2ZNQB&CQk7{veF#i^U+@*vR&O0C7YijuD=XQH&Tz_>yLpH5($S zJ9ZQ~c0Cb|zwh^{Qrgg3D#igR3ZcBHDTN>O;kLdX2-%E0pL4?q17Fd8A3ho(MloXi ze`~(y`BmDB+q)YZE6cr}@B4nQx4g2k(d__qz7|uAVuT1{I1alZziAm9ODW~XAbq`Q z)l!B~2dUFjV`%T`ab76t=X1e#Bt3|5m_czkPC!V(`@ROV$|R{ zCyeap3y}iA?y~_vW zQm@y$=hCH1Kzh9%^!6|Ida(ZD-rk<3`1s3kiPXtqCG9DI|F!*8$Z46RQ8ptRE6L)l zoIf6y&lgpa>aZIyHQD~<+ra!y3!vKtuzsUwV6BEK+jR+*-_>5~O0XzBb&668dc9AA zvTKO7dOrFG2nl`PuOhlw9!qvTi{0Mm)`91P=@?G-Rk>1|zc|NzJI`+1+ld8z)iz$(P?IMljpqN$MoC<@|XHk|C39&(7 zR#ukdGMPqlTo(U)k(0qvSr%n64s54oiUOyyETd9qH6j*L6ytJ`jYmDEW=pA%0}EzG z+Px&@xh&j|7GuZKypqv0nJ= z7L?imGJr5{CaFRg3=VCdz2?aNCn#k|DMPRrMMf#ozd;I=-stIO3UQ4YX40N)itSSB7~lL!6^Th%Q9 z;8c~cs2@E7`qP_xJ7!f4#d49=)hnL)tjz|v`xQbgOw6A^Y>0&UE`yn_vVf3ft3Jy^ zmS-2GNmBtC>kO`$Fcy0RVF*K)f9#8rWbTLlKE|-u&M>mx(gzGY6M_%GB~ZMc;?Am# zpYyC|J#3ZbZ#UJ~_I-a=)y{0C9Af-ukuYBW7CdpyVZNp z^fq>V6Cd(jzl~kr?W1}--VEKEZd|TA0KKqPU)RsE>G{!W)s5l6o-Xaj{TRo6*Y9jZ zM%USw9pgni4!e0bPs1FiImTmbFn%8o+3?)-N_(?BZrsN>%{ote8`Vx&ZN8h;KA!h+ z+V_E;^Khdl`zA&lF+UBPIL7g~S?k1It9`fa`aumVy4WYM+IPno$9Oo-lbSfjHjb@U zE!DVdo1ntD7^l5j?RUdrhya^FWWRAt;xu}9)V_&h+u7-O!0zIHh!ot|PQ$q&51~06 ze55qS7>|wOCf|~(xIVSoANJ=r8sqD9I(B{69&E$RG&ffJ*5TZR{b4_iOkKcEGSl%8 zrx@XjV~o+zk=pKtuHOw~JjRY9)?w})OQ!j8tAka0sPTv~ZjOgp?bx2TSZx#FPNMB* z!M5({@&`s!W_FHo?)pBJVQC-RS?x5>cgB6Sdv+5?TU?dtXw@E$G4AHZjxplnjRc}b z;;uhkot@V#FjS}-Q?hqXMXe1aqE+8HF~(Av)Hxv)*|0E+SXN}r2IN$oD^@Lcq}E8v zD2)-D7Y?wY>s;5y>zmbzC{bNoGnvoX8iw})UXU*q6|8dZoQR|blBNg>n<2HZ)@6nu z0ZbkWth>$^ld}vm#=*6TMEs;KLBx&f0;I0ycL+e3VWCjZqy#WqwaNkFpPiXJP4kA5{XPINdS@JH`3BKjrZHji#*__a7V!yos11J z2aq^)U4hJJ-n!AyY}&R>a{`1b7Kqf=*K7nNsZR+E@fD1!3b8sP&KjUC$7??9j>l(L zWv6^{2T(;$mDIte-oqIQ-dkoP9x*P41k8d9_t=8v@|cno#d>|=2*xdz?295Z1fo>f zq<=Py)MZp8-jq;T7UorJQD%H?>_lmHGr>z*BlOKW$DR%G{GP>+Gi<&DX_p08B)6e zix5%p3S+GUvw9x$-{dxS!)>_p{c^5@t9H|Fw)2oHi_sS8WVJT{^udRAnN&bF z@9K6lC?a*AAq-5b@R(&${O{fN(>yf$ah~Rb)wVxuH+Ew;ZL{CU={O&DJ-+SZ7$3)R zo{qhpZPyo=4#Rf8)%`VWHCo$kG7feqS3Bb?m_3*XMp-TaBSvlsgA-aXo$lJbm-gi0 zgX1e79I5Z_cYVKJUrzl(`ql0Js=6`5X1slV&M1qFXyZgzySapE7mKT5ebP4fo^yF7 z%MZcJ5YYc3E=5|xRe*qNfyG!hy&WE|N`SI@7#`Vb*~><J?BeCCl;0H+3HBX2GM7Lz;goGf6u-e`o0y;Scb2FYlYrVLnO2V8iyHJ}fl z3bY2tS?7S3?GQ?c&;8(^MPB*cwB(&iKP~kR!8>Au8+$)oWM%#>NzzjCcj79~?rVhf zPM^8%pXL2k$JCUjgWWU39zbt&`phU)P3g1`ZMT}u)^^!+DGK2-+wa!G7$NXGae&f{ zAoS8GhdqvX)-f$=dz>Xo1DK`*a0!B-x$h7EtLz`Sjj++EXRxt+_U!WJM%t5&AOpw- zM~?J|(DDM`c68HA5G=P|^IQb#Y7;Q>+JL74PHLk=QE6=p0qs0I$=Y%%WJ5XkR5RtU zW*)+tX_=N)IhEYhw^&a1TIhQUSbpfChhBR3l{alZ=LPqjxOOxD=tn==`gjA}_#}11 zi(d4i58ij*kG|)&+y47c|MXA)lptvyd>{TA-cD+yPY6u2sVr+!JDp}zT4rSkuQ_w( z%zJ!97E;!tAhcot$99x55d`OeDAAVHCylz&j@RpjVXeO1=ybdd zA;f6lx{9(gMrJ#)vG{erXYs8(}LP;NaoOr(~k6p&J-83z`} zQ9xzWo&B0 ziW9>yt+)lY?bu*Bw$lbF0i*;%C?VtmUW7f;B*(~Igg}y(Wgm7>u$ifd~fhR2%}CX3IRI+UKD#>oHE<+qfklQJ?@cZOZhk_2}$Si zypK-{0!nV4SNvWK76tOu0?+A}_Mq3{?rjnn{{QK*d1eMK~&XDdBYvtgIeA zy1D`mr9!WsnHILCB+^E@vO1ryuB3MZ0dn`d+u6~T6~vX5qgh*U)0k3do3>$c5w16y z0L{kw)xZ0@R}(@g5eJvyGjN$)L0%1CB_y0?)3jVvlo~%BXPyv~8a(QNl1z|co#%E{ z7AaJEX_8hUo97+MaW$R>T=2P>m1vF>3;ZP>mr2fTKnL-nny_`-i(<^tiHmWeD(b7o z<*XdTsLRu`Tm*=F;x)y_jdSbYuv?N9E#p zZJ7crTTEbH=!t$NhT=T>lMv>u z$|PWB2JQiX2Z|yuz+G>BO<-z)R;2c}?-)g16hMkCCh-g*GG*nmU~QHm^}F%vFoc{T zLLl#|cJ;=BcwsaL0Y1+?fyjGD2U8D&Q$pqOb9)eFWtv{xr<Hi)#t^7hc`EL{~}Gxj0pQD?;{ISm12ZEI#QE}hynypVx+7LEPW>g6aZ6}1pvn> zT0^gB7`}f65o7ZjVD`++YD5cH*{ZQ27*PdQAq|eL8pcDQ;fB?&Y3c=xKmi!Q5ENoi zFPg^H3eZDnMLtc#id4mfpsKd`Ycxif*^AV z>CfTE@N>z>C4Yw{{yi&cyN}aH4B_qAY`Z?CF{ZFs=W%gkm2CpX$GNr#ql1+4{(QK`q z)WK|Ehs$cmHkj3G+Rkn&5pQ>M#+eJw({UPhm5(;t(3fz1Tz^(bD&J~fKbWib;W(ka zaz0o)9t)-~+mCUN8^b#k#UfM;d5$5gRR)3!Z3OF>^b9M9n2O=`U zM9fM=C9+Bqvr=b3;4@Wg3mLI_MyGrW}35QJ+ZYL-g_sHy^Hxip3)s0$%wTC!B$ zwk;tq@*GG(CUe%3RjN{yHVbM)Ab4jPU~6H^hypq1nXI=)tJ!C{Beq24L`+~0l&P;Q zoHA!bfYGje-Ef%xuuat6nx`QP)@5bG^U3 z%DfOU@78LlH>Ft?2!#96J}{ohbeaXGPGkTlqDM>Wl@p8gkqHAg@L+WE?uPr z&7DiPc}*qY(llKGsv;q^V$}>PE0D^i%B_fiMTsw1WlB{^Dj_d&1M&gX67~d>7kQ8? zPFWTJEV$eVz(#CdiMX}{Rt^*Xi_;*PG))~~O~;7KGX-homdm<^a*u<83_ex|IRg*+ zCCOVc07SJYaY}Z>ZphNx65Fn=&iAmv4aa6XO}DOKEe^z@DolUUmj%-; zYMQ2@zrfkqz5C}EPz~QvBB2CN|C>GqpP4L@*Cz=p)eS>c*e83G$sx}#l2s7U#T*%V z>9LWJ!v$0GkN3{ZzWB%8YGwU;wcU0-(0AMCZMVM5KUgm|o8{t#cZTOY=gw_tRi15M z+=qN~7v_1_*F@LX&--d>mif!i&cJhhrdKQc1Fo*_J(#~>Sg(N9dia7bc>c`|?%sXT zyVk>S{i|Q#T}!yWe&GwRuaOTf{b%}Qd~*Ko3@>>bXDvrp?J>%F8(V(N(ER#+gFgAU ze(Se>?rm~>!y7+1%OlJYH}mFlRQA4LtaGQOx{C&j(mlXFfCG; zQn2aC^MVVRmW!&8v#KaXT9x^n7iqEN39Cq^<)V;f5~p8Il%kMjQWe$KmKOE|k}s;F z6f))IYmwY1BU36Xsoge=jI^rCq7ZcF@ zo~o$;;3wPdNx8i}zAkXfS{Toh>7aRt2lK&Vf^c>OR+@HO8-5aM@v;}k8|%}}G%+0p>bNe{ zTU(t_ipcpm%Mj*=N24S*DK&yH8y+d4-yhGOPFB}EiUips<``+ZIbGj~-va<}a(Qbz z?;%yv^K7%gp40vn0vOman583wS#tLY2d8trpHp_-lTw#3|*u6fVb$a4q@`~7}cC)&oEYUm7V z&b(8R7g;9nXfqiNHw}~=ER8QwmgDJoIxZ(=O>}KS3b_T>?SB%kt9dSf>(%x@IkfkG z%pE(7EZerC*tS{IlAXSx!_Y8?5XFOj6jQC;aAXS^CIFDpjz5}g5QYLSEsyK?XY8K} z*8|&0QriL3P%HJ$HHqywc5=-%S(+y4aPA~Y@|Y+zk z2#%X1t_yGOJ9oyG8y7n&vK{!(A>!~q0ceUTA1>F5?{xZ!>i{@zLU56P=1;NM1psoL z9XV@J?Lsq2+27Y~F--Zf`FuWOm?VhtmJvM|8Cx-8k|3MS9l%STf#X>C#PrUMjSWMn z1Tl%}D*}Mv6*Nu|6D5od>DLfzzHARIPRZR$Rj0VY?pt3*Cf|MgN-}8&&mVCA@?JCP zo7@Zn`1_sLi=@=WWp2@15Rb?`KYug^&g-A)3vFJO^g}tB7nA9v8aOZL{02w|Mcb#32K3RFECEb}nzwSzQNaTGYA z7isFBU0+#kwE$Wz+lr9#z^{V>?K*zoc7lMm%kmL7QyW#}@O~7uYPQ&Z=7o6)&^`ue7KZ(WgG<;|rKYknpuh*4?6MG;$uU>i{!X<6k`xk9P zYr}?%wxRbge&ZYeY?_{Lnx^S{ruokgMS+jtdC4Q+XxoMft@hdrh$iIVDY$&^tvp6v zL0-!^viwoI~Ij!?+oMskilPXRKVq{b6@;A80|Dc@Bl$r zo`+GKMCf^z07MUxq%Em(9ZPBjCS#~LH+5GuZ*J-rRj}Z8$|wa*0VoBa z4g|GLZ4CBoQ3ds}BGe{A7_g{FvhD`fx&KwF!+I*wvN!4uvn(IA2F^ii^eu}y^$p8L z;jzGkX{20hh|L01NJbGPnz0ikpK78V1q;@RdOdhz8`N2Y?zk ztclV@Y51(u?<0Y?*MUcqxGcxTj7tpYaWb8BFj>&ZH>nNH-x~(M3)$er@q2GOW7$`3 z4I2$Z8?HS{Q-~XD6r5tKI*!optZjCNUAy0HCtX;Q;*4rkeH#F45Jg>}s?{pc!tQpv zHg30>Wvz8O06iIuVrKVJJsKszp^IQLKAdoG)(t_)hm8 zJ&KnF?cT*5Cr+dv`;&aqIej|ql4%to2Y&?Lhffhje9|C4N?uRiLEcL~Oh_fN0qH{l zjDCWMlj&lfXX0RJV}r3;2hMCU(6N(j9N$qfdk%-Tjzi*L5`=eTIZ2cFFdP%bQ7kH} zz(QV741BYQ1{&#DGdcblVD@F(1x{Dvu*QLN;K!eF@+88>#?=9!?RlRmWucHh*tQwO ziN&n7ejf$55K#xUI(GWKu5Edqr4gwmC3t?L(e?m>t2WjF`h%_Q)9*q`5h+shjw++r zN6IKrhE9qeBBfItwW9sx#X}JoMM~jE@#M)zZrs~zfX&P_Mc^Vq$5tQ(0B1o{)C`Qm z(~)gkA_yAPv8mRel#sL$1j4fIXq+ZWp_z@^Hn*X%w|C>|s_J!N|7Ve|-lGbb>mp#* z+ZaXBK}G=?L!CAK>bY(_Hy$2NE3u@?_rO3g$P7r95X4d%tFdt)>B4+NZyK*>D-gc2 zY?`<0f$u}VzqNHL&y?1T3IW1%qwOpMSY17S-O93U*W^Ll9Z)HFr#;_jw*Zo)ADSj( z+yLl>wpEmk6hca5fM-@$VbT4kwzm3xxcsz!(++&!OcG`(1#7Fvj}}XSYpc8MX3L_2 znTj)NxSqG#?IE<9>(fx0-Pxh%wc{T3y{4}XfJUR;w!A2ETSs=UTL;%S*cPzQ>RlD8 zoq&=7_Io=Ea5l#5QyyX-wo`tT;^BNDr;Uemz{S{?n$A&VTnoi01VI=87@RTF3oJ=rbeoQPn)q{g|d{*mj@A-SzSBj{zYiDR+k%1v)u@M z06%E7Z5y}mLZ~;^8i+n?KWv~%)Jtv1zIww(@YQ}V>$=uJ>a}JApw(Ip+Mnr*|NXED z!AIdDDaalnqb=v10uW{oZO&|dh9imnGuF9e8)9y z)$8~0C3$D-gxJoccM~*%rxQ7+dwDj z;AEO0fz@Sj-L&gDj@M0H7xq8?&St%ikfxn?!Rm78xPZmdk&h6<4h{|u;LqS93CIcZ z5P2CPRV2$&$XlrukM6JbAR7dO_lmrlRreYQ2RhS9+P1x59LYq=TAA_Qgh*eGGNzVb z4}x%*Btj%BhT{# zOz(N4^~qjYt9z#DC>0qxlFBhnubxi!rh{5768Q&#QeXhJ+F&(FqykVfNmeTeLVx+l zsIZ@-hL@26uOZ1deO7|!!t%L9{QMIwCH(%q_uf0BN_pksdv`7zzW4AtJhnR;T4Vmt zo}u^u4wC)9gXA7q+c^Ba7|CiE;Sw>(CLv)+!?di*uz31oWwaj;92bbYT;W9Z(?jes6GBO9#3>6LJbFcs$lf~s^y3j<< z6@yHclj(%(QRkUVkU5?;Y@8Pb#57E06wGzqo5%BbzO2H8FS4*$XRO!VEW@xgT(@ca zjMZ(2qG80G8+(C|WfYXV`AE_*%s#+)P^$$YKp58QKBrv7t#+IMq)DqCbN2huk(D4| z*Io_RTnjJ*O8q~vD8iwWCr+HiB;MLcYx0=@h=y6O8wMiW^IwlV@(3WBR4s>(@LlsV1$>a>!TEKB-wku5};2QM)BVXXO0WgN5F zDS$QZ95dzmx8MHux8Kg~W0trR{r;WOIBv85M_1dWh1N^2ve3TzBR~A%4}aD|>*r~E z=OY$cgb+%|LEZURev@2rTwYb>xZFZ+_qnTbE9Lu{$g1*Ff}hf~>@s6R@g9!3+W7Es zsy7usPInDc8(eN``t(}zX*~t{=7k1b(HnLDMH=*r`6EH(`5N@E>Hp`@gGodU-f+W>H{5W;4f}s{QR;%KDIDS)%#PQ9cQ@~N`d2k7_8 z=e`$5{D-}dGyO4ItD};ZsU%!ADi+abI2?tGr4b;)sHnoxa5#z<#Ry<*ccQ_drKQxZ zK|k);*r_AZ?!^5mKXVX)@9usQrt5yEve$>teZ9I~9K)CDh;>s*smxYYi~u&m52G<3 zLdrlU`Sx9?wqd-g98P5v|( zanSMufWU7xpP#e&fsr0AQB$rr|D)XhQwaR#*MS8}?hWh{_5{hu#P{0C%1U8Z8dU3b z<#D0r*Sp@;8T{O!1GW3^yYFeYzKmVCaTmV# zz3+W*HX}&H!S~=l^A*uf{s(;`j^eQN5|oAIq8Ma-$fj9SpzcI)ev>%FC6p1!f^=rnZ zzHcW@D#nu+u7rhIZwz;OJwbJNLW+*iry`%P z!Vo$fWZSd=^qQCnF*D7WF$xZ%^t{9%xJQ?<({A1-RwXs#I5L2r1wP|&!IM%U09)cr zN`XVZB0G2rUJjSY202E^D6br>MgtJ-?4gtc4EqJcksp_;Bh5t-nhnzN_hvW>)Ap9{ z`?jY{zgAxxWavoGOOm?h1@)TmMYT9~U9qk;ZikFw5C(P=i_Wi9oh1&Oa{(X?gns`- zu@ZLLX$l}UfKq^Q(rktGZby8i9)%hV!|AL*MhIbq9Q0dhFUw|xgq5sp<*}a@R=if6 z4p?84X{nUtQx8Au_x@!XxUbP@{PIukf40$ReDcRzgTdgBei>f=tH1iI-xy$RGGq_; z;59Zm-A10!5ft_Z;!!2zIds6v(ap1gRJZAfw8Tymcpt$}`*7yac>mvGS_cH<@t7hN zf|fgbdpji+f+8Is%?pWpvdE9V(}`4k`_$iY;lc&ZIlr;lypeOxFJwE1uDtTlPDTu( z4?YLK%E>a}+sVhsm&sp40FT0Z;R}R>(X=e>uqER%mxDN&PNo4VSb0MW22y;XqtraJ+H-Q^JnWV`ypAH$fvZxjX4fg8FycjpI zeU^uv$9Yv`5|l_G7m(`yD2>X6%uz~OYU$9H6Ig8um8Pv~X;FY)D$7N|e{a^#`w(nm z4B7!R0&)}Ki?D-cmM}pl42+4>NqR7d5{1a?1qRtTTNJ8n8g(cNOqWB&cDM;d#q=1G zM8a4^ry37^ z!6m00fm&$N*ip({9hz(CMCM`37UC*m|zyQ!t8l*HBn5L->2dNYi z4MPA3I}C7_A_p)i7!-kW+Y-n$QqHJUitqnYouXxeAtS>?u#`ps6r3}r5uKrFTc%|+ zObv$NI$GPl9|W#vvbtSM(_TmFxY4K~MTY3uh!nX|Yqosl`w6ycH7&L0hC#r!vK>nz zx+?Ng%Y%Tiz_OGIQ^eR0rEu-kb44OiaEVgdmf=fiQKkVj7aS1qr5S~w8NKj-6cL0- zq7J?aKY&k>7Fi}+gut?FmblMHU0q@I-ZV3Qr2oi_*A2=w&!nMg`A5hX*BAxCTg{0oDHO9 zTUlQW2IG)1nB+zLc?aOfWfBDb($e~&8-6w$jnbXfRW}iGq|N2!LsAO5HX7Cm7ggLN z_cF8)63VuLOT4qKN!0^V>A6OC#~l9oyui8#?Bxc4x|nBq1A|k3$MEwE4;{KWN!rD@ z+XF~O{mjkYzp{R5k_|vgH?*{EC(8(i5g=%_+G+##2TQB{K7v%PZz#hIm(aGGO7&YU z#QtFQU2%Hzp+m?LfL?c8v_CwTz0IP1dlu^U>NGEn!o#^MarL1U=fw4tW5>jh0 zARILil6aWSv`i$N*#H0F@BjanX@U@(8-_aCY6%IH!iBn42xIS%XIU1tpAiCtx9`Su zi~+jm3@!xv-XR3{TQ0zFg6lc^*SOg5KP=U`USBF;0Jd$xKkva6hYue%5kfmeE~HLk zX;7q!#?fg2xZqIqaK~|%%vlPhzqLFrXR*;}u-9ecDal^V3@oC!qV_9JW%TxY7{~DX z{ZGPm(5!0**QlaKV0i-f7SWP+b}7dS}o4G)k<@b8B#2&Glga z7ka-}+&7g{%G`ghC6$uaTbF3xdfl`Cwb&wyUj9Z(l%ukeMd?^&?8Rf0CNh#gC=$NM zeDrzGd*1UDKrIde(+&dL2!l8Ub#qt#v-iB`J?}A+B(Max9k6XKtRP8@yk;(SaPU3& z0(_9HkQ=slK8=C0#x@=;3Wq>DFLKeurc4uxA&i841;*da%#ENJ*emj)n>cV^<|JKR zy)w9Rbu}INDd75aI>jWejngPnW8wStRurkZjN_KLdU*xmmGhf5O!e?yr^8=)1&!l& zyIymhdac_G0(Jt*@)kXhs{&N;vm_uDxq{qF9wV(@NF)5u+ zs!2H{_ZY(|O{^j8xe*nOQbq>S!QC!$iNd7wTwZ`kKgS}>&rQ{aTQO~G-5Xf|?} zac}>heXrYDJ$dEC_g#0aux?vZ3)AUzb&dYmO06zrzrVUtuL;@juY#7IkHYi1-C-zr zFkM|uYsUWXbZpx&8ueDwww12eIvvKSkywl}Be86hno*@0p+p^g6@CD}LTu6?%Xbe? z#rbiV8&@Qnl?eP7k&xejrRC#Wh}+w@Egze&_PUnU>#og@fByLL65O`EjkpEr4abi6 zPuzLei4)%Y_kRi6tzv0BUMgB`xVeA)*bVRZPMo;w&J%V5#*T3J4(5o;gASw;&uuK;)j!a^tawH+?T-EOxVN6;N_?pRj2 zwA_r_opvW#42MaqT}cOnVWxHlea8j>OcT@W9yTpQBY1A7v%v?0exFJkYPF(Rf8E6M z7&9~+8D;>cfzWPGH9!l{lG zz~vDQLMaD?k>KDm{32W?3F(q0a)^+s-|u5_U8aHpsR5naPZ#@TJm5x^S&%Nx&P4$4 z-@hN;9|o=P=1;bQa8&Va_V)e1g2w(|P5!|OgVtk7JB)6-EehL7dgG1HzVXK2hA?a= zNjnUojq#xC30>I$cd4|4m!!!IuY6TFvF0ksJC|vJ(HHz{Y3P(52<91j# zUdbl~*(TSLd&!H)8^{NIq4U`w?JXPgI&QAAep{T+D*&KOZDld*0717X-s0&rU4m&= zt&&+e&!YRz)Tj7nWhQR4KIHG@0k*u+3J7L%IzfZ!B=eMdPbC`w>;3Aq;{b2tk9Q@l^?c4R7At+iQ161At@y z1HeBvZnpsHyU_1%ZM8vOx(U7RVt)Mie9`T}uuUDvl^fH^-saL(d4!mzo@p)+>Wwvk zaJtN2zxj3mU~l8d9~OUkhS9v%`Shnhtu2IKd5K|l*FW1Dj}LEcJlFtYBkEM^$99>1 zO7Ht{8Qxy=L?g+NgMWg}WZLWXdU|PTd$ClQt46aqDq1ZCH}v-}zNOV_#erPao$y{l2%*G1cnba* zE)j<`$$*ehR+TPyYy62JNuD4jZXE$0|2EJcA ze&?NcK6EEc-}bh*z3o44gA1?6LDp*6US9M4fBoL~zV|)6*l+zC_!QY#=R$HasJU?E z7(C7+z~A8Bv1c_&e=r_6c4G6;p|w?tt82%~PRDiVTmFH4=9$Nj>K1BFhl7Y~kf~ z-v_W7G;(a}XnmJyd0mjo^Gq}Ml!UHtnx^4zho%W&njuF-=9sW!eVUO#){$F-%Xjf5McKrga3R%%6dJjY?@+O2Ue}RCvJZ%~p*8T!J8I z#un_W*P4ds878)<=hrYb09+^SJDc4OVyC<5^i#(LFjDj_i#G9k!*H7jO;>9eGyz+V zVHnyiGsKL6cFre++M>QTXUKy#sKr>w%H9?ou)|5Zs48rq#$NXMd0A2L#fxcKeSZw6 zWfVtqt5eHEzw0#|0?!AClWsSOVUVKta~|2pG97^L!J(dK-!Fsrj9%or?cdn&0K8|V zt9WAEO${UV;AM{8G2Knm*z^FrabGGdlSdE&{|*;Pi(IKAUD)Tst{`DCMsYHWx=6^w zV~{!K=oPcodPd;iq1W3S_j)jxVa<~2ra#THY>K_^=4P*lSgYerulgrmNz0V#*H525 zz1iylbbFhpJ?~77f>h?&L!Rfk!_7_?pxfE_a1F6`>b3TgePDB!i`~xJsD54=;CVhJ z@6d%T&%A+wL8}8 z_(T~Nc|^;%`!FG4JGVV3=I!6(;&dS662bc}vV(v4wWf>&if!evRd#K|;G{L>bo zCo`eqqFT& z^5E0B4VQ@|HF706PyUvCpZq&$LO3I%G|h3Qla@&dWNES=f`FEmXSAru&PaGRKQE=! zROVLECo373bI63x=9w)-i;9pjo6CVrWa=q&NxT4gO_1!7owEnDME3jyAw?=n5ffob z6CvPLVPgTb)7uu)^a^>iEl%c>c?w19VHwZkXz-;XO^c=9if8kDE~JqE!a3)h!AHD$ z-I4P-pwLpI!q8*3ZJIFGU^2%sIZ}gCL}W@x@n#LC==J&o0|1#5+NLGLI5N4So`nFl zn&To!kiy}y-w!}Xt|Ph9zUPnnh>`ERU$*EqwOTFdok2w8P7napTy6eOiU9nw5Nh3s zkU72&U{Z&txCSni0D$zaFs4$p{Gc|75=(F)>W=U%pufa{n(n`PJT;!{Ln(l@ zW5Kxvj9FeBqiwm4U>4OHD5YTXI+X%rMi;dKQYt{fwA5<(j$_-d0?Rdoc5J}~N?EV# z)G*9AqS^pQ!_7gb?Mmr{2!S8eHk!>Qt^N4!?yfWafMr>LlyX&xG73l-=^R{!54A6k zo+Y=F*C5epXl7J{i&#~K6wH-Zay%$74ABQQo8Mp1;#)Uov%qmy^yXsKu0wm+yL_2Xs~KEeQ4BMo=`5cr*5y6Fd@4>(*M zZA2kA;IS*@7V;YMYgtrL>&>T9=vzhV9CEw!5uu@Rnij-+VZ=dnhk5!v-xsw5av$`AoL zkHYo!`%TkuU3V>Rwi?{?l7{Dl>!dfVt*sTd#hDb2={lAH?%J9Y**5rIBk^2buh%2Z z1(U8fg8Lhu4~~=AtLrWprt6ybujRJInG|l+Xf~MVB`;W8TN6o=B*HWe*LBy+8T0&f zo!72}jOP_CmXxGw$xh zINOeekf9cw3(p&7>AoW|aSvWd0@5d&WQ*LW^l0ba0_4eu)3Z`cv+3e?S;)$H^8n>` zGaHR@acx?-Igu=CHp`1?xtNDce?iFpW(>Xl=Ffh7G92Nt_ntdM7_2+6+Zx4 z)l$Q_@OSs&ykQ#um}Q?lv{WGE`OfZe1ULIi&X=$~Fpp^B>qeqX@R+~PorSlW#;F@c$x#s;DL{an3+v|Z3jTiO<|Nep2nhv~4 zKMVlEu=gr#wqW+k_0;C<$~HjzZ#qVm-V=s9R?&9s%ZAZv8U@5jPu*wK065c|5QY67 z2SkI#sy*@+IZqxSFOFD)S1cfPSgguPCq5Q3&Y+I4W>(~fd65>@+od9xX%<3@qNog% zBA9!g$mXp#6QE@x*UE1`wwK<`!?4kbq8qaI+o{nkmYNONsYS0bPMv(9tF){Ak9WIW z9mG+nwbD@(_FTcabRG8>n=wXVGu9}u7Opt9V@JVj~Hx2D7FIv~A#PJ#r(t zk31)jK!nPiU6jX57>|fTOye>w7k#NBeAQ=iZj!}siqYqix+!JBJJVTpYyW^0`TS=b zE`)Gwr>%l82$fPQ2!jqZy3;=ZF9?z?sMSZKdL5uq&##3rY}NgM-+E1r{R3#VJ1u}# zr`-ZKP9Cm3ev@!)*Wp4~zEnzvVHjvBt*=`F`rk3O@p>4sB>n!3@_K!%6NUhen~v_i zW+%D7AGmJN@B5zT_v^N0+4W~bUi1cXC1{3&R%}OPc`RTziG{oX8`)$8 z$+8cM7Z|eW!ZrFJlT^5TKUesG;G6UL0zfbqtRuUJ7Y5o*$pFSj_l_bm7N8f^((e6N zo9K6?*uL)CV*p$vfbRTQwV1D3K%>5TcK^p%lQWBT@na7i#=FdR;5dbr);<6P9v6(R zOFMw8SWl_|F^;KcSvBD&kb79X2bc3aMP?dIiA{7*!8M?jrkKtdryy5TZ#gYv*t_q( z`{eB(wAUzyJMWY?z28}-9PWds{mQTWig8ijfCBFPRsEyJKovp=aR?!Eya-nkpA5*9 zuWaWB$&1L_$uE&Fl5dfJAU_00)hI8^C>zX+vLeMksT#zxOv{LbILt;uH1}N=WuD28 zuA(S5uNo?5yKR0iv+OfV7>$!F!|a?G6*Ay^l`LgBiy36YX;OPjMVS{{@Y52>@sVNT z14%8r(Wd?4bTR)~g$VeSAD~DFBjfBjf|sc$1Y=2;CgmJhy?cY0>gj(1IY$J}0c!|C z03t{USf@fzDkUArGb!0WAu?3Vg3^mNgQgQe3KU4IC5({_08#A&%M`lf%uX#S4&r772WrQ^c*ZS?8`MHxL3vnl4h1%m;*#;(0lv zOM`7O$>Mn&T&HrZpQnD2rL>Hzf!`$4c|7N*Q8E16-~HX+S=Qef#=o&oef;AezxwXA zyRZKE=c=lzKKIWj*F1#Dls$yr```b5rQR>B_nY$lO1)p2@3+Lgk3Rb7^yt%{{`99G zoj&^h(P%Wf;;(-Ax6D3mddpktoRh5lX{JEb+zi05=7}9jr5W(z$eLr4ULY7lfIy(0-ANg$w*Ic_bW2KoKr2 zmy_{w4T0iDQ3&2KP19_*M%lnL2ZKBASn9O3cD+H|T06X3uhC+uSfVFdj-lHQlY>Ey z$Qk4D(quLp4JnwbhS|P_he5p_da>uZR<~VKvR-RaWK43woj6Vt+XftDy}XttVHjGr zX{NOlkupsg?M-Wz$^JK$f=9u$-R7*3E&~9Bt@fC+wBH|QNrC`SON&vzA3c!)0Knm1 zuNO&gDNQ3ls+N+5)~ty+Ny7O?J-e=3Q~)RyXg|PqXjz5vK+?M4|~f_-cHR`WS<X!GA)?rSYr zBURD@Xlj}SY?P{ZT8tp0Bc3C7GcP13B(fD%N$-e-JeTRBDxl~4JL7T3(`K-Ag#Ue- zrW%Y?D6av~@RYc8WGOJU*BOs@e4oEK2!f>}%$v`{Vq}9a=#1`khK#VF+ndjiAD_>Y zEX7lC*UKX}JwCE~Ia>0&-B`qTMAv6xO$kxBQ0 zlYF&3QOI(9&&E&nWtl1f z$MplPK#G+x0yuS?Iu;}lA#Jykm)N#_j!_HIasmAWI0ycSlrl)y&|sp6HGp9bg14Rv zAe5$*GD85u3o!~Q4I})FbF=0*r%95g3247&7=Si_khOJyu4@Xz3XN8-q0E52$nWn-!#lX`}LY_!`Z?x5FloNu9vrL`iGw2oVk_(fRyUjqVM}YfbaW0 z-rwu>01)|$I3feu|9M4w{{q7-8^T|>=(*V=Kmmr-bNi_kGYZT1f-p?Z3_=K@QqKi!H9M19O}=<0{ zIT}(ee*Q~jl)*1c!_eBc8m=EO(d`X~VK^N2x`GA1+puh{4MTG9gj5FOw8(F|u_!2K zhLXYq-m)#*hFY_gaIGx?h=2%)AS|tU(rVVgwk+Z?}wE6h*UI zN(8YFp28_yA}z8^w#Wr?A9)@jFiCUUU6_z4x6?_*&=wWR{RkV#kfbVHN>U+{qBTe! z0CbpQ(mAT))muzwt^rjj?ZfqcKY@BVj02gj24B%Ue3&B+ikUnE@9Ms@&o&#=doVk9wGoXK;AdHW% zuE8AA6ofQQDaKDfHXu#m{fCPs#HHf!;iV-61?_R#YWHhGC@BQ`0se&6QWg(B4f}AJ zA7J6>NU?F({~$V?6=~y z_k8?Y^0DPYnxpLO%Bthq)==bO`W67RnoW%`v@O?JSv@lxT2jcc&M4L$ri3Qfe4%a{ zfSaG!xmu|mT!z2NN2#wv)(L^+yj=0LEi1`@q!nZO5nswmf}dyQmwk9$p6B@^MNt$V zx%b|CAG`PJUo5V^`s%As-VAfN_1VvU_SbK}{r3G|zy0=~2J-*TojbR;2SMH-JOA+D z^YBXe0trb@4w0+LJ$l@H2S`kD&|IXa(pBj$CL&GJ%s}K5+>{+DQll(h1TpCLq8O#I z$m}b1*6Y3XuHJwD{SJcn3{xrO4%3ES8?G8!0t4Hzu8@H09XmTa!Z6ezWQ-XPds@qP z2YqE2;)v49dxpU{+y8>5ls{y;E=pw_T0B{nrE(q7ph6gH%LZfY`!xkZ8qe_rH_t1> z6wkivuDcYXY!!w!=zy7)`^>qy%2a(cJYr@GAH) zSt2)bv_NC|Vp&$^^RQf0lZwd#y=kiVH>#XQqhcU|=__GNi)ZDy=2%{&lj&i6O@0nC z#u?HptsUmAc6Mv^cui^F9}cIMnK(B4Hv}reK{m?Uk{jT20buSq6sbXl)QF2bw>e|+ zPnykEtKlG6@R$dHpo2W$*i2$T#)7v~=KHfG;SBWCvjJKF4mkEvmr{U6W4BqadxQ{1 z_`y@~DY!%|(jqHlhmbHyh0Ke*SQN9l%%+*p5(J8f#(o*)v+7++FxB&G#XK*bsM=O& zSa!^DeCM5an*2!nAu9~~T#e^NA)jyL`S!E^o0+#Nh{UHH)gZ>a~>pukb|di4=xd(tdis8O7a+a z3n8Nq@vNHU%t8ZcK(&XAa@fC% z7F8<7n!ooYtyvWnHfmf>;Tu31^r@ZG(?@_%L8e#-;IqH|TaO++x>_)YJ9|(IhO2kg z>-DAj(whefq>;h6RqF(yX;}$?lsfjg@Y=mFfVcE|y{;<`uP_QTKpg!E2EJ0hhn#c! zC-yJFJ^Pm;$7TcBSO4|C_r33t_^bm|n6G=^``-7yNyFEM1p@j49H-_PhA^~ZRMAWU zI8JTyzW2QkoVSA3jB5piq=X!N5BJ~$T%YyTNT1w99wTohA0!_qUn74B4070lE8t#u z4BiM&z%RnL;NQ^nz>$R@8HqTGxIC#6a$4qDIu3~|M3Xofgo;r)D8+nSius@%mw7oW zXXR`vq2{KP>wWRK9LG|{TxW9cjR7i5%RI^MV-a2iyq0y+L1QB#n=a;MIV*bch`tRo z%B}I!ayHMW6B)(RGN0$u#gLy@C3h~*giO*&1y!?YkCN)mcyD*mO9Y5Ou9zi}4GIE2 zl3hkeMeb(vRlX&xE^QT1VOC;ZY&J<2fO&NKY29U<>cy@in&76D z#^p4f&Bw*K9L&|H`pR(@Wg=;+zBrmj)5(eO^U2B4N~ubVHEEfTn@$mwi_>zh2FPv{ z^-S9E=fLkg9`=97Gz|dbg&IOo&oCx!e*_*(%=+oCyoqK?V5!(y&MH`B%R9vqJT;%s z=Whi^3aSNHidrTrptgZ(T`9$s58x}Nme7`}NY$t^4M0l)NGYf`O&C(zm?*bxr4%U5 zoMy9$P>oGXhpz8-M@g+YM`{WwnPHj$mZ_PP;u!kfoPH9*d(Qk*edn)V?Yh^8r& z0^}&*!7`Oo>PS~vDh{n=L(8_TQMPiZSc2R4yAaEH~f&<8eDK?4~0IEPyigX!srPlv#)$Naf?L_;RF3TtVCZH?`S=R5LcLFDI&R11c-Eaflo)=(f+ae4@ zN6Ih~(g_XIs@E*bIIgsmp3k{$DnKc?@>9k_!?4d=X5d;joZtUX2f{(W+qLeI3!yDb z5kfe*PVQQf2g$R@kCP|f69NKe8guFCaGqp23P??Rc)VB@ilsk-m(wW`qN7p}kl!b?APZ&4J*XfzrXhkL!$r7(H27u|MPcFPrzdCG zM?Sr`Hy&dS`%m=#rDS7cW8>`E&CPRX`bjVCzwPYG%F2c1JMO>#{`=o{>#c8l+uK^L z`|sb~*x1-0=5<$1ks4Vd$H|@KW#q%;bL6|^pU8ieripFu3O1gw_=r zsy}Z`f3|W{&jesUKegJVA%aJJ2HtHEf)^sVpNr6q&>dqFJ%dufCPoSoUZoH|+t;~5 z0(+1FYf6N5jlu-5?XZJjQp$kuQMm7pJMI9WK#QCLP_S*sXTVJpyv99aE$61~-`ha( zQKe4-@FAeUADBVx07fpyX*m~F)D9|LMIIn8Bd;QFAeYEz2pQ#aT8biQ&2px>d_Unr z3d3(G*P;DLCusUEw=rUYYbBXZs@PW6&WkaNoo?$@Nyn-nuhh3D~U~9vdwO0f(>!OSz*1w;uq7R-veVV6{>((2d z2jDo3x^4eC)M|hr<^r(WrJn~&gMIxU1%MuypfzJk`2~o92Bj%eD%_g5*k*>|JzsVE z2XJ zk)+UWPc3J)-3AE5wbJ(mf@*YOD6ggv2IHLoP$(7k0HUaGntOQ;MSl8qh_-{Ai_ZA@ zi04G2*17eM%XsPm4Hq0Rgme@n>l*t?b(IN2oaGtnmz|bMngi*_BuwFOHsEkX|8KMe zH_~bZwqsdgV%cE2Zuc65^JNg2fFNzBH3Be=0D!SIfGBKiixD>K010I^tF3RuU^9*t zSYV}33r?{Y1mHM>hUbEzp;r>V-Z$P#A+0Slju_cCM3&$bz#Mff6UHos$m76Fv(qvy z2!bYVWezXGr^qt7&fENX*)&7TkD~N0!G;HFaJOg0v>mvhd!hZr#eSLl#nQecHSL#4 zJi;xZ+o@5D3LiRg;zZ~0?ln!dkDAXD#3b=5?!oP?8@D$$8jvQ4$^JLQk6+l_|C>@+ z5&KU*`Q(!cVluD-y_3r;ji-;Yo??<<+K1zw8@IL*TCGh?5=`sQum;k1)kKiBgUj$^ zaGC6p8_2WBuL= z(lXC-=||kkG8har*=7%90aT#bTp?Gj*zN?9r(Q9cPNrDuXU$4`A=pas3di+tlh;@Z z3858DQr&vRHOm{~?m z!#0Y2eQuf@2K|q6ZkXNxX=H#f4XprO*E3AeRuDL@l;Trs$JPgZ-_qKVmTj6qwU*LQ zh9gxNM3HGS0M4~e0XPG&?7(te3p`4LW+RRPD7E}ntI@CxirQ%2r%lF9O&P%7`rU4~ z<42AISa0+PbxTr~6h*Ix;J8U+eQh8G1Mt1jaRA({?e#T^+grEv8+GFuR=q~iG1{F@ z*F_L_9hO3x=K2FbIr!~%yXAcu)3i}n20|33sU@U@&y;Dp(hqz9*Yka;KuT9TUKF`5 zfNi^R7*JqnTb7-Kp`{r&Lj(k^B0(9mnh(v??apY_`w@zu4Q)``J{SwqLw6^x>;^@8 zSxQ5imDJ7X2QV$CPpz)5-n9DEo#~_N*xh&Eeev$wpIf^fmM>kpbm@#?Tj~C9z{>tN zU}gUsx8EK;?~QQ|p2P`!l3XCSkq5}j$s5Sq$@|EM$j8X9lP{CsCx1fzg8YB-ZvbFJ z6LMIG3a(_ayt0_*MPX^C8QP|1qN7}t%2LR2%=r;y1Y05=iYL`nD^#a8gW#<$zm3id zUZlmejOW=jcI{u4LD(%2*!TMxj(EP}wV4 zS%gVQB*3@V0zoRR%4wFcSy~TfEN;w-&SMwtWT#?0NjEZqtlIz4V*Na(*Tg|-S=73G!qjp zT)1%Eh3mw%*Is*4yxe~t;vu~6!VC2Cp7*@%pJ6X!La-~}HGSwY;Y6*|#o@z;4_~`H z91e#oD=X*yXGlunX}7~GnGo#WJMudoelcSV;kHMEP}!!Ilkm2FWEiJ0N7$75D^y9nQN0IIeO#jg*)>+&#$^=eSQ6=VCO>=;I_LN z0K5BcOG*iF$Ax5fcX#(x8~{G^-h1x_NIr5c03aYCNWz25@C$I6BxHjSpleD@Iiq{r z@kin5Qj9$5-VJ`8pHqEvJ`UEIt*@=h%VsW$VHW; zc~MQhwFv?3&SZL|Uc2+usSMr_H=lp~^@Ff6IDh_8w z9oxICyX6nQa_%u$+I7Wd60i!@2Y zf`&BDa+%A4q@`s5tpmRB!K+=@HMcf5xB8t!bc`>+tI8;jLM31S?0* zY?tNsnIkK!gTbJj?X0cs%*sL5?dDsXn_GFeo5A(}@gM&I?Us@@U;wKtu>T3K2FuG( z^We?_V7H4f*#Gc4)b{@dYB3ZA#QT5aYTa&Y?Gi!=C*t5Kd?{QaLoy>L$qnRwLPm>u zDTG6ynX4wFtQD52r zINDtY(dpWFD>z*T28M2%@+)sfyPa>__`iej%y`pH(oZrXfLbjxEw|Gd&aw<3%Vw+n zzWLKOqWv5P(Rq%IXg}@%IFH+i_JJcGuSMC2vnX2ji;cJK-g{+-mJv=!^sY?O4dqj zL6ggn~D0P=m-}lq9Dt&rKJH{{jrSF4@Sw7$9cn>$H(M@bFCfqq* zXQ85XUiJIC-7f29-ENn4evUELF`~CjrU@#aA z9QT%fzu&+6zuWG*U7R#Yuk6+mzbt$!cC@Wn)@F$P1Brz_s`m2`N~(m0I%JD>G$S4 z%|HIQFX;FC{XaGTxWC-*_xrzW*7oXtzuzAZ27|!`(=_|Za4;AQa>sEDr)Ar=VKc)p z9AiDTe8skH?Tih>u#N7t_axU^+Xh@`e4Vua&B1?~=HT}!81wCZaj3&{0x1DBfF~)X zR0{;)EDnVPumepgMMEJ1labkj{YfwTWlu)4GJ zs=m?xu=$gHqkpq`y#L|BFPNs84ouS|giyi=f&Ye!q)%q#3?YzBvw;+GTxMjHSq+c+ zrNw0f++Bqg15o;I81&LM4gBdNk33>3`57h6M`{%7@WndP+WxP#LgD+GRx1p{u=PiS zX2R~^qLSv)k|`C|_J6g8Sc9u-xc_YkgLccelW(=1@~o4FUamNv;YD~kdB}&~y0tnO z9AM9zk>uEl`Al8cRKBp3&88Kc&2e71#A>IJ9KP{*5&Hefk(HHUwz7P%ohr-5y{*D#xP{Vz;-v((Qp|hDZ$rLrB3}o)55896P?e47j{JTkG|Z4*G)u z0_Wgpt&K^q3!n`x0Zb(l)5LtVYnU9`olQc3kb|$lMffzifsk;ZCe@fadoLU%YKREW z4ccDtz6C4*_=F7?N`>@Mbfe~Zk!Qbm-Qit|aU4m;gx~_OD0XN49)MO+Gze`6O$#7R zlN1duO|5xry%({bX&RIpQ5ek!1K-2>>>PZ0ym`aC)dt6wTxr`0kBoAFR%_nMhK(jz zR@7>>+I=N`Z@E!R8p|uISsY6Rl&&rN184ej>&`sP(@YlIL&2|2LGPg`V8UhZ$t zyf5hAg;Op#cE<3$Q)9nM{rxo<7=bj{d9m`qENw z|D#Z6!AZG3zh*Xv#p0@K<}-XFl>)isQc}T9$7u?|9L_;2Lvf3O1Yo8aM!v@Ybh863_e>2^PP7kf@Y>9Mf(_>+n4O2teLun-xR%_>_JA>9^kc z^z+Wi@%VDH*<3W?(&fv~9rwmhJ~{4Midgp3w4FBUQ{X4ydIayFlwP1r!!)BK(LzL0631l~Mv51bVmwzTSzy_ER#5qMa7HyiDcJE!VMt7da=ngsgi0jO{zrE z)K0v|ZcC#b5P_}BAToK#LK%z=)Vibb^zE$53oeR?zY$d)L1c+{Z#mjMIW2`e)s1E` z$Z^p}6W-~zMH@>vBqEKrNGcTruLy0`0r5t2ISLE=(yEfDjG0xj24&NAO0vG)zoOAu<{Uj^#5B zAeBfcb+zlczG93q&ZL8uMf-}ouBihqgqAK7+|>YBtJPZWM?dPeYPA}`6^NFm6nLGx zQUX%#Q%VgbY9^yhaB2y$e;ZJ$YyhltO%bGWgfb|lz8(uHEDNOc*r$3)3=k*1-f*pt z32qqxTyWP}05HSzGzEZG`<&tX1_ulR(*$6T1R)Q*|B_mHoU@^20&?wpR0N>~5VJT1 zbOiv_uB(KQ5%p-vc3)DUhO6h5_(PrVxyqrePTx(D$80B1LXXDZn&T(`8C{k~5Q2 zZ5Ubyh7}2u8ybM31(Y*wx~}8cw&Ij3N-fUmu(ryW1Hd&8ENv)6V4y7E%topxWkJ6m zX~qD+LJDAj!A-XxXdNl!U@@+Ip_t1>Jq$4wBE>MQbFt60p$*T|sV%wVc#J9L`@RKS z>L3W9-%YgV8HTC3owgYB9EWqTeBY%x(A{{4=Kv zg~_D^R7##OOhYL}DdXC)9b5q^gej0wE|o$;C?N;m#J9uGkbsQHSwcpUEK8BG?j9<_ zB{W3-6$}`F!VxCtQ7-qI>^ed3)~17 z4%_nVzzKH|Q_kTp{#s1Z(jnZQ4?||A(ag#JJif5NaSIE_hpSu$K{zoH1|XOM;99L# z!+&P}h>d?5_`lMB0uc!j2|O8$S^pj?Ns_KhQe+i*6HCKkymhf8B;!Lzsg8;>t6 zaF4zm1mQ#=00_Z^s%h%KpDaFWU~D|a05G2N_(wnb(S$sj0Fa&|)kU!`Il3fCk`BSv zxmV}fYWd3mnk| zMddZZfeN5r4f;2qJGyIbZVoV`VKuBcWmr@d4$d@_Bn$wmVUpI8Rtw`+Gkqr))o_F< z@xAT$n~L(;Mx(*08g?M!G>v;VZsK!rOG%Gy2egm@2H(rH5*0x=+KH}%5L%R_DWg_+ zyvhNqR55IdGE$>&6`QDb^iTiL>t=`(Kpb%fxs!_=&dh>`a$G}rLG(_3Ta)~8Uq8b* z>+jp!?_=EW@9i|A&2Kjj*Tc@gPxXJQq`Yb$KXKy3k)pu3D2|*sGQSOn;mmXnx2*Dq z<-UG~w|=RHLAxD>$``-*#V;yh*lq`*3cGbnUX`s&@o04T*f0mkhsW~CNzVh!r~W$H zi7rOB#?pt-r=h+d^$WjtM*s4G`|e<#t$M$m<@+3VE0r1hsWVioW8G-jzK@~eR%Yxc?;ejEb&d$1zT@+j$KzH6&}fXuyVmCC zFpS5?FPfXf+_r@yzbchV8plnUssfHKX&PW%&n8PIwEz2c)G%;;Uw~F?JYHEHkDCo> z)Qj=n)p5~m!qy@;H~)~M+qQ9dZtkL!qar5f5lF=dZbucAvG%2hYc}!G%XK%+io&1m zDJIYK%*MvX#+ftIzO}hIJm9@xV`F2(J22ebwEXEaXTH6$v2pd8Gt-{6xj8uCKmY2h zuio(Y4>vb0Z~Dv`Sz9We`y~9!U)FC!jt(N6;*e{-9N4`ADhLxDKNH@^eFDgBuO>Tn z>eQ*Iq8vC7LKTHbk;z?ukY`r-OPN-Cxax}h^(6D7ay*q-Njq9jpWv%&MGrhL!VH6}`;_cz>*iz060 z;VqYA5Wn1QDr4B$uj>Y5N_}P{f0&E1U$-%1s;;W0La2^a1!G0UIwgv!s`_V(I0lI0 zqG-neak~gi(=>z7G)*()pORHjf(GagMl=!z$Qh?$?}&8)1xeO*a&FX4phI*Vr?(`p{pv2>JFXJ%Yg0rUp7-Imn$=mpJ2DCqR27Ebk)!mbyiUo-KzFJ|j1xuG(8EwSps*a&U9X3&bmbn|Tz13VulwCWn4sK*|+XkcA zz>J7WJUhViww?|*Z#})FC{V4%Aw9NM1w}5sLQ)j5UG9itoTa-FB&lXrL$e`?qAG}3 zlu|c>O+_g^b}_D^{IRC2Z2d(LA}5jLnXgIUYi1af8oYo-K^0{=cA}UgFY+h62~R=y zAp}U>Bt7ZUT)WwdK2k6V0hodaN!HCak|a%dxaeRm%+ZTR`7ke_FpP3$INh>qWlMc! z;bs}$oX|xhXt(RyQPXPFfM*c3<47vI&MS?P<;wC&G}rZ`vOQf5d{rR?G!+MVSsr)pb7B%I2Ov)F-Hkzm@P3TQ_849$QJ#*V%J(#7oWs+1*plBd})?WM!0m-o^%Yv~~ecxco#2IoVd0fZT&;HbsZ&?b{^a^2Pu`-G%7 z=of4r3=c;}Q$f*gvly9RI~y*WDI!9CsB1grcFo7{Wy2i)378t-Jpkt5rGdvTDyxFT zn4%Ics7@MfS+ZLm)&aot*a9~qo1KkyrEG??Gl>TbGe%|IP-Wmnj<3}_<;m{EbO~29 zO%o~tlvY+QzH0iufH8G!UeyfRVH3P;#q0zJADi&t`<tn^f5BOlpdAPS9BFsxFo!K#>4#DU`Xj8OycQc z>b%PsTOQ^0dJehAaM{XKado2=)hB}>2qqh?sF9aU)6C(e+`__?{9P3_lSW8yA;fu)N~9%|`2j+|X12KbV-9 zs`y^8ciX&wZ*_Vmi81ViaIQb~^2zVeo=0V-AvJb&65n8#Lp%_1?7Ja6G_KwC~2Q?*sUL z_k9B60W`g0n3moM0u5n=&ixQ0-?@$bmT=@8Ux+q692A&(4n%_pw{25Gkq%ebI~G}) zA21iYA=3b&rqvE>k|asmykpccrvv=o@#DuE&J!ftYQ_q0=&~Z)NmEha@!Z02{uw=$ zaM@(HB}p1Qph=SD^~c2mAy8@zt7T1NlBa~ho;|)Nm^|;;UhVohNz{Kp$M4%~ock2K z0^WgcK#xW*L0|gODEJbe!_6>=Nv^6`djf;au)sq>Drv9^VhOA=t%Iw>=2)(q#nJPV z)bi2E%7rxWu_zU$E#PvtMQNgxzPg;12ScZSOCeGfLSK`>Dg_4^@nmM0yFmfqk78WL zqQ}?CA>cU9d5(ykN(I1ng22%*2arxpHIpPQiUb6K05%mY{h49_On(TYojZFy3`sm1 z_51!35W?pZoiI>%Oaz|xMx##3{djKI>inFARTcD|OBY_Si-;nB1~{MubDTyAk2#=> z(vr#XJT)0%7-Pa1F&R#9hq0grCsRKF2!n~NTGfcE0H|uGW}0QqwrAa@OIafw zjnX6mL1Z9cPJFKjj1rDfc=QNha%%5Nrz1(OyA22tMQkx<=rRQm1U4l}ydZDAeMCsN zdsM_A9_tTag%?G71I0iAIBtiZ0tQC;lF4|Un2hj%fiY?_PQjQM`kubNh?mfbTF0iA z6hiL!tV0Imb*w`@_dr z1gLcGhj1ERhD?NF%{j)=F}ah`tdvDyUwiGfFSrS^7v1@6ID73wwzhO{vj2sT!Xr^~ z7m@~Km~930Q%p`I{xx54aF$y?u_Fz6-qbad)*M}{`++r7Y|~6ajLS~BdGdS$$HfzC;f|?FlMv|!4j)4pp>ui$!!BBx1I>6SvHQ%$CA%3|JbCiu zLzkA`wX_qD>?XI~yS~1@{;svPckygHo}r!Sq3B-pGo_~*S~(&%uqIEO%yt0g1l@YM zpXONy9=JTniVmNRz;F?EDT3u28Z7NNjeiQkID=5{;9f`nQ90TFVQ`*2#&qhDjsQT* zA?y!K3kFcy>+sCu4`XVSMN#DI4PGphP#2Am!&`f1(7Cebw8)pcEWzfqE9EXziVfFzZE4~lFU zl0;gDEH40y(llaNvYjAs90_ueWs@^Y71A`oaT;aQ6j!FEFrJ!P5zUg@a2zn2q*ZaL zsVJc9WmnhX4p3Fc7Db%eng%^bRRJ{BNwFx}yLcc3cvsgn-9-r5(3G9U2tI@+&<=EY z-wUwpEcy|?7zS$%y)&P-S_9YivQgB_GK)pKAPNq2xzx;Pg$IJ7W-%-#p(6=A-wVj$ z3te{4j@=i|%~z`swFiY|0tEvPhX>~dZmlK)7}#{$C$`N4PSUyY4C#bbfP(;FR%W+{ zep)Vbx-&Cd%uZrPDT5a#08y*o@Au;fgCYx*0+^M>P6r2*VZdPPL(|<()mq(svf~E; zO)#dX^Qo}h6_ipt?$*OWtp*Md!t;O-SCxx=6W)a^G>tAmh(XAlU38Ic9%RUKI+q~{z`)WNlasBMEdPzRyU-Yvo^Mp=_doTiPyOVaj;u3d z8t6S9Js-V3j!9z*jo>_uwrp|SCp^R{9McMXSQNE9>HvQy8s(Vf>L>*k*Kw-x+Y{uD zxFzswC5d7?Y70hqW%qLiENw#9fnNndK%fmIpfL$aj0He|*|k7q83>`gzzd?NC?aSY z1_408Z5j|5s2xZ^;GH<|{Q$oc$kq=@NJvPY2_&F2An-EUI73LIK}e&)a6-^tJX?s6 zVH8KHXc!{B1+$qZCgep z7J#rWz`(emZkdWC$?8?iGX0uSZza3tn-=4lEi0DqI5o=Dn9SF8(RDXgYnm)evI+&E zWSziS%H;rn}(*=Y80>54{3~+Vbv1>Kk(RG&-rKtr)l{ER#y#4{(oWEcaKf* zD)Bx2)wtrp&kRZ4D@#T^D;N7?#<5p{f9?g2eD*552?1)ymIU5pF2@!z`k#_r;U*5) zU>oBKz#S(i&@%);MKoQ@bc4+KTC>??a-L#$X*P&@<_# zNlK%rCG{Ovl1;muD_{920>9R!-3s((cdbrMIUMdM!FZ(;wKyC^js;-4tyTrLdr8f< z!E&Mi1^296EI9 z!M(xBlfhp6unWMMoSa;n{O;uBwD) z+t}E6@#ohXH{8%z`}ngrHv0YkmHij>`~CiveeRKd3iI&J=)t4ZrHE=H0UXcRFsZcy z9YvX)pE^X$)TPU=qZlBy&|n@lDn=>%6985*yi=aTc=iLc7;~D9(^0%?C`t>i%Nva6 zStGv=TC%GD1!MdU%C$Mm5%RRB5yEk~E<)ddJKYH%4s|L7fK)n%{fTY|5hS1cA^Z(q zhB_!iJ2a^p zZnsuemZwUE{|J^>E|{NRb6r;##D>=r1#4}79wCIpHq#0o%F%YT3mrrkp-a&Qx&_6Q zyMbU}`?yl6l%!Zi#493n#7Z+86*1&;Yv7D%0f~5S*PZx=(iaSzRTXpVIi{j2iUrr1 ziW&nIZ(dxyNumH0uA{K^pFMl_?2$*K(OpYl{^&tQVHZ* zF0RYl7}SfJ$;|wHng_SnDBJ!}Xl_m?v+#9x?A^gh=C5dno)QM9$&yMMF9X;@UVai zL1QpftH({M9|BdHDnMTf%?j^c@k_8TC3|2PjPrI%!`#5zs>f&ug}hAuWp4 z3kY;a8=(?(1tB>2zFT))Y_!ZAg zgwkemf82=@IQR^F7A_M_j*xrf52S*r(D3Z9%0)3Nk{%}rs!gp)+E%koqzdvzEQ(B+ zt~>`s@-(jcA`DS;tsnTbZ$h5$vS&kx09w}@<4LyzI2s+U*Qn9yoga@WU1Xc%&2hFY zWf-Q6jI#aeJ{*zbGD{Z^})_Lh>Q0n68(t*x#ty`5DLKt?to+|_djmR|7gj1N;Qjo4?GS(Pujt5Wpeqc~A zqBv1tSuR+XsXg1NM-IWSefQ`2`bMvh(sl$i>Z5*I6Lz;V9JWn!vhFsUbr=lKZ`A9f z(5JFB7__tO!`g8?A!66%rX4s21uA7&?{tzBu)lZ8k?4c}1^*78AddsE+nUd_tlC_Y zC|DjI$boEqY-DjeMDZZQ8_4cCbt^fgl_r?aE|rvApju2PWwIqyU5&kom!dl_13MBXY&O05DIRVqB5$e3^u0RdphZOy1S`_e-vhvgDDOI z!9q5el1t_YEzPzNfIzjjoN%zRG6)?@Yby0FYnxIFhjIi)yM8PcL7$?hgv+JlF^rUH z4R-@S(uQS!nzIZYc@VfPbX)lV&Gd^O2!?(X<3%zritsCmfiFn&ZN|quMt`0 z`v@qNFPX=zRmY0*58&YD(2fGm3q}1TdW71a=0e!z>i28aPK(!Y% zmEy6q?*%o?AgBX?A9RAy^2{(aJv#_Gfe!%nATW)$Q~?mVQjGvXc%CPiV#KA^azQl+ zDivLyHA+$eipz-2X2}#2;Cg|g$heY}g5pvM0U(5uTmhw0afVb0O0m=F3Mr+*(R8#s z>QWGh>9FQHrgUO=|9F&!>(qVUGeH7ShM*l2nJJ~@-EIdM6(k>d@b3eMeYY!fP$zp- zf$?52iVPws(KLRzXo{CJjh9L>#)At^C+TE5yRBU?Nt1XoJ@*QiiJNv&iSd@=#(uCM zSf$W#r4-e8e!9z@7VYw#EHBI4=9*iOuuMv?l#!<9Ofzl*CN?iIWd=VLH4{vQi~#`mwVk$F zsjYol`z0(?OfXG3ZL&`&wbMrV3@XaBZ+J9@36rBmsYFzQ(c~-zW>eQ~HeHw6NuTCG znNWz5QVY2$7=mdHDWEi!;EYmj1zI}owxd*FX-XLv$`S|^E`?#^Eyw#`;Kr$0M5K(0 zU>_4M1b?cCPOb_9pg?AtCIzqpE+`PBoeEJX)Aqr%a@Ul$EloE!E%0qq30I3*04jK3 zDHn~`rV&JO#5m)UqSS&1Yy}Q(y=!r802rEE-I^QuKoyEYOQe!B&Z0O1q_i&qa-1V_ z%B{c;Msh?ZC5i=Xj#Re(GcEpeP92lS`g1*F-2(#TfLy!?1s$}u_i zYrXi(6DLlrwnkxSS#E!Q=sJuB{_>H@gBQPH;QxTJJOc1o2vOMigJv8<2=&J35Au2) zww`d{42qDJ7fj--lamWkE z>&X-3=gF^;FOc6S{{m+3JCW~vC!cczT~THr%p=K^_`DE{*g15+&AFDFIt_~S1FwQ5 z6vmhCvXD{MXxC2SQRRAQp`m^xPG^b*h3b9&LB@5hm$ zK&de7`9cT;ii~ny&$1=0{d(#HY5*oU0EJCMn4aZvzW-URTvsaqrCnF)iK-*k>dXBm zi=`O1JIfdbh(U-Y1|eb?U2r}*O=g(65M);~91k|Y2 z);4=SMRl3HUl zYPZo<5`m)RhSdWRNj*fNZkJg`DXi@&+WGD111{!$x$BswX*v&gTFWad5T{pTnhBb= zH8}_o!(K=+>W#fc8urr1e|Wvs20ERcsn=(RH&#}jGp zfaBCx4Rg`&!y{RiWy35R`o_*$2bbadaJf%on3q$htrM4i3x@U~1Q|(xniu^$Qda$7 z*MAM+9=x!Ic;pCTZU41!kJ~2QKepl0{Y!g$SQGqBZ{niH0Xt(JT!vo*KVuGp%IuS6 z5+_A7KtbJr?x_cclDx=cb!?xF({#?5m$K>i$qJsmv$F%80s-!IpSg5p{K`9C(Ol`p zo!CFF5U;Ti0s0$Ftjj~dN2AdwsxKJ^9DC?UBY#`DR=lq7KG3qSIlZtQtTaHaef1z1 z`d6$GLI@Ih@DzLoE|C%0BV?3KGa&(MHqCLT05ZT6rd1R=15sLLXBm;@RbGj6_UorR zoeoIlx1)X>;m)CJuQ{|saWvZ6KI|sRXeo@ifVVqwoNXqSHXEL2y1}z4efD#yziSu< z;Gj_y7DKtO}EII*;d3sRtL8WtM0swjH=W3Y7vB^`SHCCS`I86d2iL zBnr|dAWSKuFp22yOQ%_p7x#LMay#vMF4%Un?Q+WO6L9RUa3|%Qf$OdU*mj;Nl_rX7 zh1lf634sgF48wIKH8|G>6}7t7OGudz`V*`w7Feu# zxr%osr_;B8Q17~rQB2Hk8suCECQQ#|%(pDprD#wJh8IRW=)b3A54e^zO-*SG{WsKmq-t?99!Izz&C2*( zaTy7nJ13ag*_pj5T|T=)QAa{_Yl@V6)KDsP!s+%DDp;4&P|6g-P}=A)#sy=7H~7ns zOlMsoJkN0*1TBR$!^j&;DY(>z`KGhszt1`DC~DYNq@@J3C;-$sI!Ot{W+H-VY75LpqsD}p)~K%45>6?% z3@z*cks=4rqiLXBFSa?S%x^YZprrJ|$Po2fHf(u8U?))|g-7%j+0R6cXW4v0)gW*S z8!zSy=M)qUcIV|}s>O)B792cyCLWWOgETIqWjbkEide`f=0BL^#iZz!4ff&yVl43& zA!LK$fg@p2vA|zic+!ZJPqCBQPQS{P5u;yUPI=46tq;(H2pM0PWt$PMU)85kRfm%BZ_F z%k;e^B~#M^fHxI>uxeQR0J2ZP=i!4yu~|R)0(mKUJNZ@eRn3R?w2X0qsh>q92%`Ye zi_#=bLtGQQf9d2A(vxn-^Eg-&w`kY3<^1z;DcRCkSvM-9z`T)5FUc~i-1|z<1nl-H zTQ(y4*x$;Cu^gBEa$M#yaDnzC8au1x5d4UWWvZfo+i*A>9@koLcAePnZXf_V*{Cbc zgDO3laUreMOrZ(L2zF?|xR8=_vaBP8R8nw;y>)R63LXzp2)+OEm%s6i=Z+ma_QpH!y#4k!DlKGo?u~DJBSpcKh{k~wlmR~f)?05y z>dZBz`207%@r`Q?DN;rmawc*_Ed&*Sl8a&E5qvnhL}0t}9|oXPW>S8<|h~72FxyNs-@9`ii{B({a~_aLQR};xxY}iabrw zsn{K7h(H@*X`VV~~PGb?$~zF8^#BmXzMd5(N#b$7VEy0VMGLc2w6al?5Rx03tFGwwE97D{a! z=RMFIrO@!20`AiTRuSW2Q!x)Tz8FPDbm)vx;=s7?L5E+BJTjehIsl!{WP0TB$zlHs zH{X2o=;qP0H#axkN$E@zaZ-5JsZ*yqC)?LmRh3rh<#lrXr}&G%P5FMr%ZOrsO*E3iAKRzY|iO z=8M%L4H2V3%aI&hToj{{rTI7H3xw5tLCn=nnbZL;B%~;!OsJfQ%W+X0gMY*YnP(r6 z)W^_t;|J0kCKu)TXBeF^h3r5F=q5qkz)X{8bGitT14|rDlQfyXQ+*ol<%?Ns`Zhyo z(-6wK$pC5^K+Tu>w&D4<;eqFCEAX@#eBP3VV@kt@7ud)w3z$`)p@AA2ud|iu+RAiA zC}pqFv3mVB0Cf9<#DTsa0M;VUUyjT1`g~~#yHeCnb5A#?l}fFI0BRVf&*J8tUK*YW zKso1BN@+We)Rrj`glR>tfl_+5{_>U?3ON@hg6kS+n3jb?NZ?#3A&^SxSZR%WrX6~Q z6Alat+MuA%D$9_{G9>&VH4R{fiN9-jfo-^<{V^->lofjVkYQWWv@P|1W?R5)3$`?% zp@HfvOxISX>*zNPhQl<3%rH^_h=k}Qby=%-I<--jsj2UG-A=c%I-T@N8z^ck2*S|y zOkW5FTmUGF767Fbfl-xX7GQ+z>hY;5WG*a<7>Ufv7Arr!-w1l@;2ZuIG8 zl++GB13wR!$uYk*VAKd@9i2C(p!anQ=%yB(MA0Z!@vMbSL; zh@%ZSmdG>1WcWMZuPiqkTqH|9z~$xbL$jjZz5K)zPeg(%6}=R@`O(#)U*xS8G?sem zrrL%jr0Y4mwgFKXg{VB&IWDx}ycZlNNgQVjRra62T75Vi6id~KlMz6&-{Cmf$DY4Co3b&?mz88>OO*=kadcVfLBdvM8+QnF^N z)#{aiFLP+8yR_8pP$88s8}&v*f+L0I{lVJiXvp3}T~lML)oL+QYq_oJ4Ji~KWZ94d zDhh0E{qWBE2B*xlZG(0?oi>yD%K&hkqQU#U)iTei1O`fL4Z>0=ae!$WIq+f&7+9ud z0+UkE;b=S_4XH*1plI{e)$N_lO^!ejY|FBNbFQyIMuDPb+cp9+3SbzvZ5jZ82pq#8 z3{W5;ga|_Hbw8Nv_3^*8zwiwRW@a&sgE2reC($t<;wFyq%4yg8=DGEBW{oQ z0mKe;HHq8%FF>v0nKO#N_1i!hV+=GGt|1uU%G#={>pbvVttDVA1fPY0atC z0~f^pHvyDs^||CY(=-smpbM9T@b_Q3ueAm6cp$`QT`B7}M=EW{wt2TvE$O2E}LO>>-vp`RC?BTHBFj$mfeASM44d4gvmmRj<3jc(Ypkn0#}y z+P-UfsaugJom#GE!UpDm| zD&-#o+@=YG#n;}v$bg*L-p&R%=pQ`u~0kmEpuC+S=^;#}@lPN#k8jUzEEgjumSz)c$bBYrFK?5O#AaM_#f?tA5qu#&$I#r5CqX=Gl;=4oDn>14Vnd}IO*ZC8dWvhbPjZ;s0)&DU`z+H9;W zuPqxI*C&(ZB|qg1lx{W`qt%t3;7Mu#bK{r^fX$8jC%w(wG_9mx%wfMjfJ?U1;x3>x zOy$VHbs!1{xzJi^>10_#sadPX34mq!bwL5BfaTrM2=+gcAo!kvXt|CuwZVvaa6n4D z2-lDyStpN^pCFgW&yrsR7wRyB9YRL3lCqR##pcS%VLn+2$yQ}0D_KocuG7$#Wp%jZ z#3nkYtGc&tt7?7xwk;O^Usr3ORBeB>l)2m zw3%?6cGI#DXpuOqXQ$m&-sT<-8l#Z4C;+$Z=90_mx!8u-6%v5)fIdsvQfM)tN1-dY zkd|R0=>J)()s*+OR;xt=Y+-~A_Pj^&g?QZs@mfTDF}~Ov4u?6k9m7Cyy;d4U)X)l` zi@>?tWGxJ^g%OQkfX}%JTfBvLP!~OQ8W95o!?0{^0Q%5TK5tQKoH=~>aH#t0d7dxf zmA>!O0AGp^K7>b)<1$#DOTn@P^eDA0T;0HH*KmC`a|;|5*h4T(lhTzXJbIj-2g;P9 z6xx{vpxmG}W}!d|%5WX603a_2A)Kg#ueM$HdSs29Wh-X^Rm*hgCD3i-MKv|_LM7Eo z6f6{G84?}G*9*;`<|KIdn>DJuvO~3x@zaAh4 z(_@EN)uI0p=cXf`km9~}yB$f_GZQ~{EY1wZ!rm-E41)ET`3D?ndUX{J78r!^{LD9$ z>$Sk}yz0^(zPiB!uXLeR4Bi1mIm7Uq_ynKd=52`6BsqLg4%n zb99pBab^cf(^?7hvb+#go~B8$ka;R}rL45(5&}e&t~W(tVom;{kfsQ?}T45w3N7w?X3#X{T% z02f`u0kDl_lY)=nv5hDOtr}EPNYFsz1tQY>o=O#j| z_BPx7`*&AP4jxDbS^D~ZtGWK*xmNtydcEGy1`l4_XuWB-nhb}wlcv)Z-(^%q$Btj; zxqrU@XX*0t#-J8jz1`KNMSD5`HkvEr%(H{!`sL;2xqwSAHx*%o9Q-F*|Sq9myaF4_S$2|R=n^B zi^bxrUtN6lt7qHo_G-Oeudh(LWteBe5I5SXZR6&vW?FZ9r=W9WGz1t9j|MIwgy6w{ zA3TK&E|EI9qO|~BE-DWVL9XZ+&56PZ3og;38q-RH2S6)cjw29$l4&X7EBjxCt^Kcd z(wf-jzSn`R(LFrW>-8Q@!z5Uqd54@ZJ{9Vx^DcEf$3$IYcMG@#Vbt0Gy-vr)KGz+X zb)pdVf7)@JXS>#(y*u{0UeLL}&7a3QsQti35y%%QA`bQs4)Ae!71<`&ke?)f4+vfC zDvi6*%Vuaz9g&Gnrtph2$SjqTiEYJL;bJLGWGn$g#2qwYP12-Fd-^M$=M?~5GEL%? z;a{|v!aWV9bmDQ`9|8=*+|S3-CeSWw7a6bX0-s#KRpHTNXevI@feNE3#YS-%B%m;c z2-6UY#Ym-H3`$)ED)j^Vh93^K8IF@>IVkfBF&5S04es_=K~iQT1uk?gcjE+-xMhsc zvJw!5HoBN94AX8s#G7iCi4@$L?Kl8RJ0CM~qWf3Dvxa?2rH`Cpe?(D*eQr_FG#g+A ziekTyR0ZI3-w$MTbN>|rqTx0in}PA_y=D`eqm?3V>c#iR&LQ(K7C zuhkeVFHW60am)GZk8?`JkA3t<9yJ(OZQX2yVCw#GsD;)o6G1A)|25H^GSmo+1#Psf zLEG~Hq!-y1Qkw!8nsFfj!?bg%2Zo_76Op5ok*zhP#!oZG0Q6hlLKy`F^|rSmV*n@s zl^PB&7{EEa;SIn!2t>t{DF6UC<%}W-(@>luM`oGr#P@(vO_?D9B>-cABLpA_&W*tH zM1wPUxQHU?b`LYg=mmf=lS1|t00kWZC?m_7!hoPrHwdAG96VKy(WCX~jY)khZFWG9 zSDG8CC@ppt)gp8UI7rZhSG^T%wSviS2`=r2<~ZdZhS3SX=lLA=_JBum%|8)^xCc_! zH{y=4>uuDfgf562SixTHALK~_h8bjkb7iVDq{%32B}w8d4N4|qEEOp2VU`#j&Tq%R@mG{S=B#_^Ri05 zqc}5&RjBe?sp~b&rfHgH)6{w-K`U_429^AbDgYC-20IgiQL||orrETzdd*T&1ubzz zH5vh>E@e@gGA)zDrRX5_!;oGsS5KSOT}Q8rRf=nJ9Z|Otg)s#VFywpjyX-AN=6p|G++cl_X@993fYc=k;VKjiVze zG!2s?ufnd(VCh$MprR!8cElvfO&?OsbhJE~o8;;pp>!-P#c{eUEN9fqx^S!mzWAD0iV$RWcpto=LDwngiTb2qt+x_i638ygY! zufP5tms0=2=`(F<+w|JAXU{r-_E{Uz1{>+IZnx7pEQAN{nOPJ8MA7Vw3m)MbmmxL+ zJc=h9kI7!IUM)YDknrksRV9MNX>e^eiv>MbDMX`8wbe9jqsstcb=`G*I{plJ-tw9F zWY@CbM^2tR8JsRW55TgzzkBlJ$@uiL=UHa=S5KZi8J;OT*R;C7Ij6RkTUvHqnEX#v zczVTgEwc+(2CfUYf^FMNw}R^iwr#_!Zw1GRY}}HrVKUIgA}!jy0)WN zEC8S?2x8IcbULaXdjbLe+nCH7ikOW=D{;5Q))=K20GJL1fLE<0E1scJDZ_dm(_DYu zaHtI|OaR#dq=qpZp1)~%iG4;f?h0eMYIrX$axv%9}mqOaY%~nnw=j z+(B4skC^TBCQ0m_2;JiD2X3|717kccOKUT$8x&ywbIj0LPigAds8x4W*_5iwuIpYs zi9)m}XzF=K_gt51K=V&+Z-qIO>14TM!r*kRb3BT6&Nb>*Uaw046vO(+$Oiqs*b_4( zuA@-4)iJ(`PJ9z#+lEHZT|j8llEgoUpF;mYZ`Iu>WTM(6AsV9suJL&-tE_ zix*$~+~+>`xl8xIruNT1NCJ)@?#0i2?u&oC$(A02%|{jwz5@RNUqO%;?&(>YU5A~! zg&y2+@c=ETwXm<}$=#^fUINb1C~7u65AQ#dH(gO}wOUG#Mq3xIyKcVVkY$GtpZ!&H zb@lM>@~T#i`ROxP|4P&I=L^jBkyeA+o4P9C@u?j*15($XmDG#Xi3Q#~dAmg)sDq>?M8dWfUEGK|$` z6QCA>)~2PEGVNOgW9+#~nTAq^`FD%t;?LT@xLA9V&erV&eY4wDjP_IowR1O?5uaSX zNmxwuMY7xEm6;;>i><%-i|s!@Afs+C41%!N9Su$J?d|RDKmUuZzxay}`F^iA9QJxd zcA0f`7jGj$iY!I7{C+CF}oIt3O7 z(hQ!tEid*~Y2V5&dSp>8iqsz37Ib=?l^1^9wkSqv#>-aWyDb5yuM-sQ%fNTbV(IYG zd{NA@foY_*Ex$h)jasW|YJl7J0D~xiO+~>h|6B$^V=)}rc2MJtA$5J9f$KFJ%PV!W z*&LUeD}T#IAIwB2m3(ir_X08d&=Ca6Iio37_|d)=Vb4iIz@^g8vr zV<|lx1_4cNi}N(;_Xl>G2GJmI$B_`Wwe%3UOD=%BB%jC&}-71?tXvy>0*) zbrZlO;^4o@MSLaLBqztoW8`u2bL1O@*bWFfAVU0MD{fefPZx!pW-x!@(H;)3v~pMo z^$CRVO+`8_6MDOB6w0nSpFsw4nU8I*p}Q#Z!i6Gxpt@DLC~}$PavGCoQ5Z)Tr$x@F z3QtfbkrxrH>MPc-F~TmYFU$!VnsYdLGRj z50OF_7yxBp*x-7ui{Q^(!-Ro)jW*n;NyzS#p!PsYy(B^P8(|1n0PhHn6bw7C9Mgo> zl4F_xModUtVZE4J<{PNcL~-RmT6q68nPq|q5D}Syva>>tnXl3|=ls#<*!Mk6lytse zKf|6YUnc(Px2w2rb8~a@&UX8X-0^l{W0P)TUOx7#@Co=7SvkiAQQF9KR3p!ULRwm| zX+#H;U-db~A=(N|?+8uD+`n5$KC)b+S zC5iLm8X`z?@L61eFOtLL1LW`EUaVo8kT9>JbWx?9CJi;V1KW8vyEqiL<>RARq?34D z4&rf{7b4A5SHY7fSz7&*Dl5m?SPfign!4m0oOea&K5Ps%3TO7|T z=kA;iS}5mb)bW{)#OimlpO z7aWsI2-@|Lju-g;2dyy z(0cD*!vRrH&Hz3cQHm&NJ2ab}e(JdjK*q7}wL}mgr^8Z9uT~$nYK$V7CLqO{bRas5ddsMgH+1%C4i<3!9u_=uqaY0w3MEf%C-&5bT}Ki;JTg! zpxAdE&OAgw=>yQ%_Jg2peB4&RgV;BuP@3bR5uc)+ur>g^NIKC8W^JI{30}yAy$nq()ZBNpdrJ9(fbjDOB$r62W!SG)d>u zeM2}kDF%|GyK8##4ny;)bzD-O;k+k)!XKHC}s3Z-ts>;ze6SuedO@i>>d+0iuj%X&4$3(0}Mdjy4R# zwn;*Jv+NH|r2ms=U|HD{5!=u`2!iGTSs{izA^8U9FT+#tMbakAd?j>AT1mq`@yrxX zt=N!GsjV(>z|FZ9UI;?z%Gp=BEUc5H*+s9L_fOezFO#26FiYXI0GvNO)Mx*o+wF!y z6O`nP456=O3{|b3E5~)d+V*|(6TypCujq@0Witk-H3(tf?lc;441%-sz+*R#GXd|0 zMYE;#Yxef`gk=W4wU_6)^pb=jWu`DkzR$xXmhf|0Io+pz{^x(b;=AK|QUk!SJkJKx zs5e%>ZXrb}`i9Z;A&y(Ec&pRt)EoyO>Jvzi!&i8*Ti7Beev7g~lp)yidG*zpYF^2y z8iWQJj=Y>qfcgD$T9!9oam5u^9618-4whH8Zo1-%jSaX5+U?EFs*|K<;JQUIyKv-a zqgnrlyYC)cvAeswyZh0t<&~h`Y#cpuVOA8b8<=U*S!`~0+HenSY+P~0O@Dp&-FM$j z2qn?MQ}`%cB4^3%7z9e{5`EdeqBCqH-PRcC>7 zNKyc>eZ2Oh_u&5jN?bSGK-P8BFDI@GX+KU~C-t1&!b_oHg6o;4=YrXQ8*jL99-yp4 z+5phD4=vyClRbeG*2Ikz>LtK$zGMGi!1wEQ-vjWydi_#+aNH(><3R^)?xL0`^QN~k z|8oWLMnnfLk2cLW!J0oiGM#oi@Mpj<2-xB{Y}c<(Tmq&iAC4Aq3EJ(+WS0&GYis>J zg-&NW-9?ab?EWPyy0ST#d~q=v;<1P?!`pe}v)T8P=4C45d7SW}Y}4Qz7_M!NGuhaP1A` zPVyjm4tcRBeF*1-)kx+vfq^@UJEp|7YbEv$U|x=7A49^>G$W0NpH;`0?Y%-#_$E5X4p-MSSQO4x;O1`&SFoY;8UJMK|2A95kCD(p9)-s%=-L7dY*4DD)^V!o-$sRWVbZh6Qg|mkb%gXXk!B3MT z3$PNaUNw_#P@Ns;N`(+&3IHagVjT!(Nv+x2_oa}?o^1E G?NiJ&y^c zsAZXHr_*h4esp6!K-ha$v)OEdTFV%WzMYQCNx$7nTZ%ekJh|TJ_p_|gG;OZQnWeU~ z&b`nToUX+HNNvY%;3krcyh61XmHFxLr&zK@MwuYV&*=`6QeW_LL1m-FQ%i37je$+oQ@)5%isg`bzVBGy^uBu9i<~h_jtlWq zaOqeW^aT+9bTQmmSb42^Rx`5qjGHz$VSVk6kA3X=Mb&J?^oC`c)k zVIYE*$QU;)TiYkc0AN`hEE@qW8>C_2nnS_!(DmDQM^PAQ;ka$70Bpb-0HCBG8qHp7 zZc>V97@SE72x>QLB?(8A2mm!KM0J9aHQN#U|Cpw|fyI$Zr9=>fff9_v43KIrG)3xg z0stzRujiKSK{ri+Q7MBk2n05Xtbn&02n@&MBoqum1qCn-L@qdg1j2d*U#I)9&zSL0 z1fWgZLZGM&q=2SrA{WW$q&8R%?X}PP+;I#>xjWgfj)MS@-fc1#_>L2Bp^Z4!2Gnds zNVyo=u4Msm?g)yYmC`F9WCVzI4YYP$KNQS$Z1=-GE-1QIl0=b+uUUuGN#Y*jJYWFK zR|*7D$!^dTso*^~Nwg#~;TRr8Nn*vN8vU<$5#C5lQX_3L#g!iW?#dOIvS-3=)L=tcjKZ$>~>ei8yUQt5FpOMWthMh$&ipLp2yQN*A17El8>gy zN|9%I3RAV7>mIu~c-Z%gx@E<&_fvTM_`ARU^{;>ZzCrH$cq^rdA6&iHm(PLU`ej3M z+VtNRMO1R%565yGqPGcO6+7eWOpM47F_t&IH$p7^TV1&Ml9eIF6rw?6Jpe zO7)r$hq(~?+scj*LdO+jZ3vF7k#%y2T#325pd1x=byInnrSZc-g8V^y3$3CB-}fJd zS2ZB)+2f0sf9B$Cjf+2X`C_AS+ikZs_VzDcym)c{B9Mz08y7D&@{1QQUTj=^@8#R} z8t^k$qw&Ry7xlVEeFrYWTgVQ%g}jJ`wvEModxKJ3eo4eQwn`ZV?oRoirF802KL5@; z?{u$5x$`S+13-3icz9HAz$q@QfbqbzFCOPDZ+T101l`xqMv-(Z%Q9A04XeZR*p74F zd20Go6uB5=)OAkm0eGa5Av9OWzXYx( zKsb-4L!ePOj>_boO{wX@wAo2Kj-xmMZXb{1h-@1SX$pb387H^>lQ=5VGFi?aYZ{l6 zIQd!Qv{@!hG?pZ3RF2be8l~$gO`|d?$8l07aWaMB>pq>7lj-DMZake#C+T=PR;T~m zmf|rx&23(8wVV^b+Qsx3ql=*<YF3S_aXG8T)vTP%XVqjro{y)q zYBr@PNXOiUMT_~ARcGa*oRy3DY+9AGYCNCJt8!e;hT+L5Q;o~^=_^qt1#Y(Ack=h1vTpUlU(qsQ~vJQY_JeplJP*Z5#-!myec zUmF@eW|2;&^Aec9izAI4JE<1aNgR8}MwGIJ$#gMaSf#+d-Z1*r#ypuM31bNbpf604 z`}u?ceVj5k@Q!(vurjMP8nxLnMJX9)OexCNqBU>e23O7U{hhGAyj%vMKK0xVZ;K(L zPdU#+C(FyNQE+m-(c8?r;Y8gf)nNShceK=v9F|aMk3|6>0ZkJ@8ithVzPvJ&f!0(C zAx#57U>j({zW+`7-#}*+D!qcU(BlFCIwt342T%wW!Y-^eXGgEc*Ft77%(DlzH|zaV zoPM1ze9h~;@~y=ZdRR0Xdh9o#8_i|7S#Q1}y8MSAAYJo%KRqlWi09wHcdiBmy#DC^ zf2U#B*ZiuEHVmVE71w?@tev3%wDIdG8BNmy@8*y z^x!`a{v5snA0jm}I-{d?Pb_uP7N`CJ9k-+F>-HFw@u16q+`eM7=wJpo^TVq(}%1q)UF&)k#Wv(yo*fq#{FWBA1rr>ao^nKGZ?QXZ# zmP*JV2m;;m4byab-FDe|#Mg#4!YHZR4&|Z-bocNbcO2fObocNbhsLHNgb+&9!B_As ze2%=Hyo0=#5XhxS`FrTj%S9#k6OlleiidQL!HL%uZhj~+%n=w^6!e)I`2H(gBP^uz z@=-hcRr=OC^D}i&H{cMUb7;GDYX}Tz$uSI?& zMewole47((a@+08<;9`+(_@ya4ESrI(_rrUi=WFFLTeH zefnh0V9XdVuGu?U6zrVCi#8-@hzbf)Vy0BQ9b&A!4WNEQXgx-LMw+44;gPP5rrKkh%6 z?CJZNEZ>Mf1<&6c-?jvv>j1>jFiCgXZJ?_F%(ht?_oR?&fZ*93rIgWb7k+n3TL+(k zr@>`%g4{-0xoEtY>v3C<`}$Uq6tNcxd?f8O70@J#qq#K=r)d_UDljhT!-)WJ$7~f5 z!wA8eE-y`e>o7d);puDEHz*kHQgL{r-7x{yAgygxTUEz`cP&q^wBK+aG;OLzh^`tS$eaQ*ey z!{Yk=|2cl|d*6Hg`!Bo(n)`nV&HcZuz2z-$c?)>^-v@91`)>jFt%NXAKe!Bk&24JW zBONk|#MkAe9Le0ePy%NlLD-Ys8yN9XQA#-V?Qee@YKQm#E(VAJ_7C9acXoD;9Xsr} z`|ki}xi^Qu{f+N@=Q|%f{C8$;|8&g+0_4vIgTdah%nUOA6NLodX5?)Jg>Wt(SDSL~e9S`tLA)CpzYRuL( zE1u`NQS5twp2r~yo84LsAdH%uo(F&!fMt3#;zytQeHlg(!mT6>EH02Kp=djfjT#J( z%r8R6WeCs?1Hd0^)M{zkXmp2-I_13AZnt~G(fY7akFzMUlwho$L{S)1N~!C*VbO25 z!hlL?w7We|qEw!3yKWTuz85E{>w+0Yh@HWpuxvsCf(Mu3M{?PE`W1PQJW5^%SHRuy zYMfxxB457(;sxJ=UO00)Icl`3fSB7G;cNL34(^*HD8?ICVtJLV;xkK$Tv zc{U$s;m?q?R^al7&xUxqMI%z>{5lukN0~T7i<*y>{ER~gs$=h`^7*HIfQ> z>-)mSUN!?EMK6h!ZJHP!p#aJ<4Q6*=+itfNQdELV!A%6D;x~CnfpaFWQrEHpkm8q8 zpnyVBYAQ=qI^HnBQkIoiR%qLtaZ2r74ceuEEY=D*fHX{sz->FUtk|;d!@&gb&Kr*p zCO)I$qPddVqmU>?N&^)dgIDfI(sWdwomImu<(XAJ;F+NSM zAXtC-7h0Rln%G&5q=@p6GMx))Q3}x80Q;n%$QHP=Oc0p!_y-G_IRMtKj0!RCv=GHT zEcZ`O_e<=>5hyl!QADNF3_$8l@d$ZHKoEygOkm5w0|)6uSrza<~xh!~jx}{{<4qN~xGh;j_auFA5H9AXibG%L20kLK8&< zA;dvzx&o^xrxZFc0*a$x5TdY4?GS0_g7MCi5k?mbmB56E6&L}qsFYKpROb3V#NH7# z!e}`QV)2=Cf&?O-iy}{ld3Jafk|b3*K(5jx31^2{4h?}qGO^#VD#kjU0~+T9P>T!V zSq4+>=p>J5V++TkU(r4@fD+udAp(oIga9Cr_qB~95dk1$gpoi?h*&WtX{3CTcon4y zvEnF*20|8N2*BxLYHRPw4ym0u$-w>=(+_oB3XAG?{kYo=0HNC*ho-B9rL61FH=mIe zx$Zf~3PL$LLkQlbOKh4nH4MNeO`8V0J^-ff#iP;#D0Z%=i#+=+Fb0ePBlzc}NXHr! zC7KXAsuNzg+xa*6(M|FgA8O-S?hyqyP3+SbgKQtvmN&1nQCP!YzW_Pjfy z+POhEkDMeck#I)flDIja_~w`8@T;rZtUT$(yKSYrx@Nv^CmWtmPRbxuZeiOjTQ#_j z36A!%JP%5qP3C& zF_7eo#bS~FIZh`R7t;yi{y`f>(LE%B*WI|t;cXCrvD^9}L~y;=>pd(=YbCGhN74Vi z`OR-m(^QmZw!q$)5GRfa{t+f|&KM3u?CQ!pOGFOwRro@uW7cm6h7m*@5@{uhEi)3L zI7fkGX(7*%7zGyfX<*7E24EWlI}M}}#R?)DEl3K`s~w2Q$^xNtSM)2t9Ex|El^9Y8 zEbNgcT7x1aEJ2HSPW7NT5loo@KAzq!Z!8SR2@Gom(B6ld32vRBBERL>-sc05E#PC^ z>l}0*=x{H?jGhDsznlMgc+u7scMyP;eI`|~(wlqTZ}08xJ$y8Fo&d9bI(5*|l8^bK zcm9HFP3-}qBfMi>@4o06TcQWw>-?g}>}WFW2)2_J*U`TS~& zg2=ShY_)Cx*VyyAss(=F@>AfAm!G1_Vl*rZ4R4~N7z~SC9Yw4aX^y%|H%cW+bJ*5~ z@IDvHm(rkBqEw?*ck(@A&QL4*NQAIu+7KMW6bXg29-(5~xnG|Y51UDG;Bj*sE zi~56sBNH-2DK;pg9~sPObkDP?Frmg!R0>KVh-sWO-Y+l?Sb5`oBd#$*FaoD7)krMq zGq*=0BMQeIr6DeK`d4tbv&RMz^QB+&3g;RuIvq1Sf%X63EkOfwz48#ML){vMok zyYLQB*ZT+K({2~u;nr!lJ8s+l-p~&y+`a$gxZ6#j>UG!mAD=YcwCwdZm%mi?dfz!A zP|mui`3?^E5Pon$W1at|}Cuz|L}}lzSAPjL|eMkE3AW z^q>FvpU(xHjq`yKAN1z<(^2c6nh5C<@mM zf+N(AYdLO?jG>4XAgDO@4ne8maD2pyNGpSAADCXX7GQs^7~0Hf1*<0!@^mf zrb!N&&$BL(HL%a+a(;Gprjx_M1k%KL5gk}lg)HkTvV6R#i;aHe)d#971Atp6q5yqb zRjJon0SFp1Ie2BNAlH?NAbIuGWmU82K@cJ|!(lV=)T~FNljGw_x2ufi*hZxC4|(v; zc+5hbrqlhbKI1>$dAjo?bQ@9SVf=A&U;M3si;Vtkh@aOP9SmGUg8s8H? z6hEuO5E^godixbZ_i|gAudWHj71%GCtNG$jc&t@cDtIf~4Qv}pf^2x^Ed?R~U?s%4 zz-ttpixT%Ogg9bE#TBCK14>_LX1eXuNM^##@cC6)Jte*fH=oKFi|HJ_&C+r;lf`jS zSFCE8&A0RQZqD;WUu+`e|?%mWmy9?2-(aRK79NC&6bwJ)=xh+c5g=`^O;ECY? z&=DEZTJX^nD7uiI$uTl`w}dN_8h6VT{Dn$2p%x=x2t_4O+B8cJ7VR-oo*AjLOme|a z41_drP96pzK$bvQ6dF@hnhk59^KR~IMF9EoFWM9yRJ12A#;|IPu=swz|E|?Um=$3& zwL`juX;nu-81Q@9IQCgpLSi|~@oDDDYN#=D zs%k=-h}Nka0)X`)cxH_l{M|;$wkSn(QBZ`6MU;t@%98bso2OZp`!1;%fSpo4 zj=Q!hhe`{RLa3W2EjUR?c}GeSqE zRjclx+Ykve)XoKfEYG8#vZ9o+S*|RK)H}e3Q`Er+aE@h7p8H5RO$VycTeBC5>OHN` zPEPho#Wpe4__FK)4$ImTfB~iBkY(wc2~;a{64Ig-sSs>pfR)lzrny)@g3?MIXwqUt ztf4*_F$_v6eWS={JAdX|u{u zwCJnktRheh;iHw9qpS=t@p^zmRVOiPrPx>zMB_~$gqKFnBPcD(`4pM;eMF=}u5}g% zQW=^!axC5g=$J_<6#;1THcB}HrL;oJtPv68Jg-O*AtYHI5h^sIl{W^pwZ@V-u+9^5 zh%9P^o;AZ!5+VqOyrN(L!B_+k&aaA60BiygAStSq!j3If`V;U^@V$P6j(fcGQJoIx ze(^vU94JM(J#Y&ojgf`rilf%c)La4&i#U{$rD->d5ZD!-&tS-!#oOsj5l?*)O*osw z2j711-fPzm4*|yGn>X*j|Hk#B;oN#}(QchH7vlTOAk3z_-Em^s{M2cdX8S{}5`@G1 zj*jw7$EVBJyylhHdwm;UT(s@*d-JAgD$Rgp;gGD>Uh8_FO}8xJ8?R9L^-iC4G~TTzbzrlhHpEWY=HZ6BZpuy{8iiH+w>q!&dTzS zCLPC{DElew!1s0zI`?;;=FGu<9ed`|2?>`byk@d>>k#Vyt)u#?l2#qCyC?EPq%xvM zsra*{>Kk#-!xYw-ypJSuN0}_4=IOloj6%Vj^>SB_x9&%1>OH`-xtKDJ;9b$4U)*!^$(8LU6a2tw zwq9q`1Q+P85t`+nnoM8T&BC?~9pnzz=?oiiE=HyXR(@>|@uUvt;o37~13CeMpr zuQ2&R8si@f+29~IMXy)nIiGU7{;8&Enxfk)^4#K#ZYc<2sYglhCDP6tzey$1ZrWWjZ|0dZ-?FM* zREw%@7u7;TAX~R>yQr3=;o6qCCPqsDU4aPHts9g_3!Ao?#5aOFZkk;7?cYAR3IOn=dHCUnU;E_4 zH(&IYx4h+}E)EV3u1>C)haZ0U;YVJ2bpkIxEp>@@aPzC*^~%q~!Vd9)y}JBvjqeTc z-l*SoL+ZT%-to%StMO0&^iTK8pBh|#z%$Q01J}yIZ_K+#u#PV-F51nN$(?7PefG(h z^@l}K44xP&{!Aq}=#oN1a{gk#;#}VdI~4^ z7`wj9rh-l3B)gshvnkj>ZDx;um-p{~22QW98Z;ZlTaK-)BCf6++g@Has8V5xJ@TCU zewwCh<@vjuNF?wA;*mY_41(=}fm6_ii|2D52G-yqlS9MyYLSm&XEB|W<)S+;^CG!! z`tlO4VyFkt%m#G zM8kxu!O+c?>$0w8*(zTeMiE>M&-q_<*A*J6U(25S#Ux3t9wrGuyS?@+JgjB%>a@|g zdad1tt7!VP=RNOvrun?b!yr^bDu@|SN>5u-Hk(Vo41^GZ1P?C5=ioAl$TA_|`@Ssw zvM6O4mIaseCKr-emcAdODg3f1WBAq z#I$g4;2C`jXEiSgq1|bZj<2pEuB{#)b=sXU@#>7ZNL|jFX&N;f^=|qxW4iT5GfLA2 zXC6gYem?4}xB{Od4N{V`EEBLP2E|-y$qL94G5nN`4D}xXlVUi%IER2=i_{FL#uPz?eUbSJFOcQbA;u?$4_i3yS)0UlgDj)-SJyN zu=6c@hkpd)ala{qvm)hS>B!OTha`X;O8~iar#R{nF5h|lxF=mIKhx4Xe*Dh+S2){@ z!xOf>QC@xZsmMLDcWm`v0KoR#)#36=v9?uhmVz%mcVQb?m9beL@cAi6q~)xfPY1S^ zV;=?i<3Il6KmOZ0^Kj{J+6e1E<9fDh*Im5eZOk$6ZD`ys`UK-Rj*-W46al7bnj(wi zD2B)Oc7G2p?td8W-2ZUPFbv)P{|%)Kqy34tp|#O|tf`e@v}zq~7-sADKI;Yx!O<0R zh@3^Ce)=;0|3j#dK0O|w{bFc74tt52;>>)C0xomixM!KZZ(1)7<2W3QdgJjM1yix( zC9embZ(5e=KV?w*jUT`H8;5${Q?32)>!p*+ER8t)667cJ%_78x=!iKoCbvo85IpW6 zjlfqI?s?`jpV_arPM+MV`pNwxZKMo^QNj&A^=LHla;4Eo05_&~s7|~Z7#s|+YFE1~j z|De(6bPjE-3z4RRr>9S!?u^Us9qa2G>$gF7Ty{@^+;Bb5-H@>VLqKlYw(^%e)p7-( zY}@1r%SzLiDKHF>3T+$ZexQ(NQU4dn4dg9^1cJcPuV~$h=Gc0bXuw7`hH11A(1OKv z7Q4o!j~$hz$SN!eNIDvtpA6qWJhwb=%EYMAIF$TKY*9UB=pzz zg}`bYAc}Tw!^3>U0D&TMfEMC2fPJ0fBZ!Y6{RV}OmdgGb2jE}ZLPXUv)2}DyGYsPy zX7Y{LdItYm+p;+4+|WvaR3Ss4#{dp>EQ>NJbqBN#8L!7Nz!Aa;A$zz7J)%gRtdNRa zN609fW^=Mf4)obd1G#U;NOyDW#$??QgZDl3s-yFK?d?Bx@e=Os-*fTe#l2qd(#4Ax z_x7H;eEITTuh;8c+S|K$`SRsn?{e?rJ@;I^)Vp}`o_p@O=OQr(AxH2c94CUb$u@Zg z`8o0l^84ifkUuB?Mo0w@Z+SY8=jMu?vYe(9;$rs=EaDS7a^x-V%*!nA^f0Ttlr3JV zIi$NOn>`}q%*1gy9ZiB!>Ab}sMu|3yi$V796YwAK(gdT08d7kUb4SId zR??s%`1MAk(SUonsTqKs4IHja2H*@w&N<WK9^XrXi)>Hzij%rG3oIAofh zVbI%dyKVWd*OlJ02nv|tmClX@<-%QEr1 z$hNHX5^K&6w#zltO=^oRNciapu=}8=(8URL7WGEntoeL2Fw*}=WIA)Z%ICp%DBrJd zQ6=duwA*WUn9@nIB2^e>LO5<6YPG&naWujp2m_jG`;Q&~ThQ;Vt&PWPYrQ^LroVU3 zJ$t@s?f;EZmZj9advJCV4*!Q_!skN`xUqZT!mhypj^B0HarhKanj~B3d#34u?>mkJ z+p!%)&Q>r@(;C;RmaYL~^~RC?FJEy8`TmD;=!#K)(Aq>8w+8*uL#w^FEHjjanC=o7Ne#5pBO(L3>V9>!^5*RQwRGeuTV?yoZpG)JET_;-Z?QP)^Hu zK9A?JoTl+SUR3$%#c4U`B@p)EeMJ??tn3L03+RtHGZpXCnSOIo%&Bu)S~SgLUgr?3 zif1X4xjN%Pa8uQ>RWjj$=Eg zn$0F837k50>eMOM<-_67F$L$YBGJ3EIBz$UG`Af4?hCd5`scp=>tFx+*L!!~d1vS7 z8=%K88?tiwkj7q^HlEUzwM`X1y&^4+@@r_e=^2uf%SrN6~LL8u%q(GNi2O878z^ zYlhaEZsi54=lzs`qEePk5zvn!_;nDX*-25#KJ;i5(zJuVk03;|OB?lLEynDdKpW7) zMx)g>+UWbZKVZx{e3%0?o6BbrXc(boM!hVng<4W(`av*X8uY_Z9$u1`X0%qDOv*B{ zk~pStBl;fw6O2L%Qg;6Y1aG15VN$Qt5R?L-)D9W*ybJ|z5ki2(2bbaR;7epc2;@cS z;fM~=Tq1ihxBCPB4!gaL?c>L)qm4R9QLoRJlf<>Xs8R1#y*@xb84cZ#(t2-a=VWo@ z$fRV@X^(no!!o6_RBRKP&tLWz;WFuviq#3qU;^#b!Sx1;aqB7>1|SgGEH;Fq!D9QT z7HN{g^ABIR_0SHwZWP+>ENjNiMjUfH3|$vs=WVt1wS0s)pP#>aK0_Rh)>h-^K(Dtk z?Q}V7fn|rGZ2{JImX;7M9tGSze0wpNB7U;#`@mgp$l7E zh+A714(;u&ZyQuG}<;Q&>AOuG%+d-HvSwNuwHJh=Ys~VY)5POoqO-Scjv0Bb~p=^G_8*B z`$iy@($UUUSM9_~OJSL@@9+P@+S=ys?&kXX=I-w1XKdSEzt_0JC!;l@)}YX~P(w(S zhKUl#p^A*E6TmP*q2s)Uj0Wv!UH&bwVR^mn(Y^QHTWxV3NM)K`KTQMA^p(^qhH5KT zS_#vPgEaNuwYj^y`J5tH>+_k09Q-9-geS=TNQ1Xz9DoW@8^x<3qv_9x?XMR6C zpoS@azUH{B`c_8@N!Pomqs8%{B&rJ9dF^qQtIrKrNQgjb%5b^gz&#>Ze23kqD+p;H z&UU+`$ctR%Ure;)YLT*0&>7Fi&x+RD@M9BZz&NOvCYxYkP|jZZ#j4449Lppt%jng< z!=N0Ov!%@<{W{Z{0W-qTRX}~;jYBsG(srj40yOiy*RpJ&re)ZcP_>q6na;Jx2vIa! zX`|cS%!Z?O_s}$2hps0m2!Cn0^F|0lv`q6W{eHj3Y`$toJ0E4fWueVDYR?N@&vOL> z)U7D?;!l7T_+hF!upnqHO(rY>KF_e(UmHF>bhPia9oy!BDl`oMCk#7H0ce^wVB~rZ zrKW9%k>|P)Se7k#ZM4$b{}%w1j_rqz(tbo~6`BoLPBCaF@F=w`&(vJF7C=(BP0Abz z@J9%((6ucI5JsNX9Du8bUgfNew&jM5DYp^%9;3FCfTlDElEiiyrOcGv6UaFN$XeRz z*BhK_9qH#!=le^s7q_W%}xX=>LnRskWB%6TD%mSro+)dt|027qTGe(7>(ze@ZE12Im^kQQV@;1b71g#J&&!j2l`ZN=E3xe;xcep2WPraG2a4 zTR~+vDFWT@8fIlZN#SQIU849bCoFt zoLekR#Q=D=WpPTGX*#xf$D7{tCfm@AHupc$fe#4NP*JzLwG~D2s|?Sx4JpTqY~Iu_ z-8$idVBG$}ci~szL!?bcWSt!L_=W^fyNe>1g(}og*VO1X`C^ecbyD$s8XWdWC<LXJmhG3E@9WQU8~IbN~NR{U7)K)bj+r z@S{vADFOW7{_Wqs?Gee~I<0j-G)%gNhT-mi#6z^N|7*u_@ersbue;FcU;WGeM}&m% zD>?{058%+3zVxN9YbppKgb{M^f8eX|5wbxl@^tcCqDk$t(<`%#2;(CqWOj6`2GhKP z{}wF*xde`4ptvJxo?|g`QngbrQ45Qz@b@wjJ|!$WU4k%dJ_F?RcrO|6M4kuFxRhF^ z^1SzEWSBz?Uige>JR_NG3OCuF^i30<@ndNi2&K5=q$3E!mSocMnLrHwz2>?wZiNwe zZu(&C*uwLKZ70i)>(n=o{2pw)r~iJ>O&?5LmnkJ2J6*ONr`fL6x;57wx55zK3N&wB zT_-Jb3EdcmAO;f(1rZr$bGq0FT)b%&rZAX*3=*Ki_gCmiu$>r__c~6zWk~tSt*tFX z$_E}LT!Lm}dG1&C=eM@DV#j%Jg0W+Rl*U;pjem=>`>#!lysj?-!2)`F#-ht-hB0!-RZ3IFw4z*4kU}Y>DhT6P1ISKWE6osvq{7fTs_kgj zEmHFbrZXjLHPhnEw4`LbUgylv*+77?u@Qw5k)vspOe-CQ@qtS`&-aA{A#^fkR_jO@ zy`gwYZbogt^f)0rktlvbNZkcnxFX498iR6KjgB8%y)|%x4=-|jTLy$f-Kr=O8?4TB zl1hRKgw<~ukL-iJ%a<=NG7V}h2B!N_Els63u+|WzFr;RWajmUDDY$^;{rdPY45JtL zt~c-;M_UPpDM;y9;yPg%QV3}nLWuu1_f~?{*8hi8a5H?H{UY$s9NTZ0omrw;ekkf4AWW!OSqNp<~Ub z`+u5lo!Nh}2YrJ1-+7+H=%d;&oWf(=THLn{4$fvb>?s5^Hb%>RTT0Pv4(!o&gPyom zuYUaT$ItELu$6uekH8mmS?k{-&mwOmB>%nbaq{-sZ1h)+?mQ()VPy1MB)2Bn@*YU`vOs>%gd8iG8|UZ${ z$Dj7JQ<$tsy&$OND^|scQw)VI+e;0dut1H;=Io#6y+dx0AhLlOtXNW8C zL9wnINa*k}%S1LP(<9%HtWVr_+ilOawMwtN;l|tEWqO|ZQ?}MAZ8Ti=KnL!RW_LX9 z2O&P~X}@6GpALf%UPL@X4*mfy!;9i+O6$g^3HPG~(~twFc-Z8@yh+ZG#HB3bk^;y_ zSPc}$2EeGGBR5>J%^9XEzN7le2kx$_>WXXb{lLb?#-OSn>-Bp6$V#==ZD)|wYBk-E zDPt@Z^==%;G{I&RMKK(1x7$$XdAHl;OlzF;Ha3d2%q?x3{;Qh5y%#vAgv%p_U}cda{0Z7=|~S z*WT1_w~sgCTE@5r-7o-1($P*n0x0qw*a{7vIAJU$LzJ!?>wFEp zVUji)YdIO@Tds5+k24d`<`EycYh(%ZJcc9fcKfl<*I)g$dl+MLtJ%z!T5Yv3)>e-k zSzFcn$6SuS>A0q0G445zavM~ZMyr*tbUS<|*4B6X=F$F{eE{TEM1IpdqXF|L+nvEjOU~2to+HXYy3erne095!VQy&()&FjA~ z_jVXRaq6aX)$sa8*FH1uM#BrfL*ENNZ z(scy~V9Z~Mqy(VM50)&~vn|f0bbQ7EDD^>pHF)sDgWrVTfG?1BayKEPv`974am|(Q5EThD<7c(19^$JMZ%f|xQuxG^XH;~A4SBE3VBHIlTs>igAjK~ z`IADeOa8w&m%k;r@wKmg4H=VvAo=RYq?$-6wxv|!ZZ7Un;)6o1OYuHL{8`S{ggC}I z=jVmEg7bHAJ`nQDi2Nf$yy6f3;13Xhe@KXxpXR&}LR6e{euofex%dF*Lm>zugb_jr zT!xDzAaim9c{(8=q{P@{z8Iy`(-zqQVXe4==I8Hv9#9jrl~k|Z`wYCz23O&_26H@4;qXz+6a}BItU^m zwGQ$|;KP%U#Qk0p1H?(MA144#J@5hipuYTx#=Rb1+fQPEAZ(}4C>;9WSWoB&sQh%( z55QDG5QR!fN-66h1b*|={WwXMjGBR_2M3pNuhR2JTtY?zrfrRgkbN!zLze^ug-?4p zXgkplW3X{tK3>PdM}iU3$eS|97A;Q!ND)rrb#A;p=-_Yg`idWBP9IGZkV9;@c^O4_ z2TcLR-d@Cflnc1QD>doSE`BXMpL9Ad7x8#;r;*|WK+t_|nInls(^IuzVZ1DO=8xuAJS`7y zc#9CM*^4x1CyqkW;k;(}>Z@-|;iakCHc%C&OWCTWg)x0x3nBaPlBId2dxq%iB-Lk!w@J}z-*3(a&LYQnc8t}tU;;B8DOoXf)R+a9ByF1)!vM5G(^6 ztyFW=?$Fd+=$C-+gNMByxC77g+y-K!P@dJ8ZvXeQ0qSwAZP2-LF+}ZhdQ=t4p&@Ox zLh7iQ+Ch|5d@OEJ6cImN_It=-I9#mvy;{AUErvq`4*mY5U8{Nh^(Ei}FtvoBTq%%D zX~r-ygu;gVrEbFJQg4;u`& zqZzs&;sspAcUWw%tjPFo`q{iW!oIBp(+!I$}4~3(EQM~Wm#66`Q~f7ZK_;^OGJ=}bjT9fBnz@jPI4hv zek*wzd4xQVyqNqbc|+|WFaKfk3Gy4{i-e?$Dl3z;h+mjS@wA);fFmC-n~R~hj?3x% z_dCbyJkU+2*)+v5TWK%aSc%ci@AAW@>Ih+6b zBkSeEo8Q9&`E_|Vmwl922*|u$I zFS888I^}kI~AtLTSVEeuekMj4pzhgU;n#Rb`lzRF+ zql{XCPbg6bpTif!7s+Yz9P(~LKu9fkS2jrtU?yx8+g+M1nb~|%ji-iP4BCn2d&xMC zvb7Wyjrm z-O<*&O|MPNXKABe0tHTG;6s&7QV#Gjh6ty`G64M=qU98_1Sk4wLLUUcYv29ucPqp2 zgpiC&$`QJnd%UaFe76;M8Vy@p@YW-}zTw>mOd3~!oi(-PsjXmti zq!d{(vQXg=&14n8N@!r8sh}p#PI+>LrxunoEz0MCRlrsVRK(+GI*nMO)j8@k#Smqf zumvE!UY(xXE7_Zr>iyF4wO7f!>NNVI7wN`(}rYWR_F z8cM{=VDJ5TN*OgAj~h93RuzKVv7kUXi+Y}*(Ry33XvfRt$HQGlxA|d{ajIb5;3z0E z?!;#mql~#ia8B*v(M`!HXO7^UGwXUye^7r_-ObL5xyiA#SAJ0$@-X`%L@5Fz!<30{ zE5%gcmbaV4rQfjS$Gw>sB$uq0-Nf_%PeJS6)4G6Bh;Z`$Lj$o^d-@P!dLj!OrPPB%@_1tN%b@HzMoxJ+i`QOrM!KS%yTgUti1X%QAfn9AUyq(p>*vi!_n&S-rA)BWA;m z&1?$DS-r6Xh=!q*V$AW5tgHxRZoOVhmFc)WM+RXWSq#N+I0xJDJY~Q*ju-`4YcCa@ zE}YFrvSph3O~3^}Pk>mV5rk1LK&&Mx02iw)XFkeQb2 z7$5{!QkoV6DZ`)-j59+8_1Ki~Rx=1Fm}Y0|fx*Vc(o!%3!(dbhgK^JiQc$3#)$VNF zvwNiZe>ZA1tr1WN3Z54RT01*7!8l#=b9D+?s;L9hRtp899v5hq!P9nXw5hW-;W{#xMeCkG#t`4 z{WHBcB+m?*)8Q-oSm9L6+~WFwZ2!IR;Qo8FosQtatf&7GG;r_uMl0gnHVvD_o@pde z=!~@Eu=e?GPfRFWvh8s8UB7$t{7thsn73MPq;>4N$hPi{qK%$cqf}mlrPeQ}2M6RH zd?gSvB~NGQ)8(*4J9&|Jw6@c7tn1k-$MydJvvSe6WLPfB`t#Y6&F53hyD9tC%I02CN#JFWKJ_6uY&qaVO!>kYQLEFaXD#&(i z#4G*nCRZZb3DJHNpNqDQQ{BT$ztGN^`&8N>&mym5k4B@)Y_c-EWI@10G1!(0IUGFE znTCfS&%?E8iEt;7rXiSWK~5%;EuGY2gVO{I3Y@0zYnKW+l=m4O+ZqPN9TBxBygS^= zbAX~)Y>h_vKeS@1&s2oD-luIVJHF6vRqp!3LF#w z=Xlmjs6iPI4MWlTRVjD({{aR-=1Rfg@0{JdqTfi8Vjz>uVVL^vT^r z9wq-x2;f~c2=xp1$-WH(B@U-Qm^lRz>&$NU4??(N>E_Mn|eBv_vY+I8w50#@K#QjTEV{cK@d=XE!%DD^^4c$1jT5Ua^zm z$aShH=Zl3X;<@XNhN-n;rGlB1`!xZ>GzI8%*7H_N+Y&kYTtosTbmKVIW)vyaGGo?g zu-I&B6~?BDl@$e2HLaMo+cdUXN=Kn3*FCRZ^E}V1)jiL59B0W+v)prM>5^D1M3GE< zH_H=e*^P>!UZkQLn&HE0Z@FbE^i0Gh^w@%fZKQiD*Pa=dfv5diQF z1JFnCD7cVbcm2teCj)>+b$vIeGgR>h<;woITl4ve@98i827(P|5Ksm2K)%UG0PlY1 zKaSp`jb;;Y8^E-@S`9H?ZY(3c;`{sG_{vwlve~!H@y?Oyq=WIG?6=_u@L4h-XUHQM z!D->V*{JVKBPr9`N~2yEJc(;{_tz+mqdNH3uTsww41gIruo*=v%}!?mmD^}Y zVH&#ArgShI`X;4}E5$^JiPxz8_jEfQS0WA8YIPH_JhFSFpT26j+jA}7HzY+AeOoIt z8I3lYNs3`quLr+KY3O;9AsA`D->+MinYY@F#L~VWcc#-LM-OKMU`#|Uq{~}dWk0l} zv>n6pJSS|m!jG7iraGn+pv!p6p#%vYTqgT)nbb&8cUYT276e(PEdTPtyv&F+BTu`P z=kK}s=9~BK8N?B+t{gpIZEUpL?X1yAJNN0dAOG@~zx*|CFxb9sd1;?~8Ej}Yj_%g# z@I4uQZv!rqpq?#Ci51#iSWZh?#%1=Z=TztKn!RMY_cK59GqCbkknDftBeljW`)_g> zFTx3;$Sw=6B|k#mL_SJLC^F_@y-XE|oOA-h{lQIpt=;sEClxk4`|6a+Dv<~`raA_Y zQA1em%X!7V3KDRG_fRJXbI8MW8dA_#Wei@Qr5&Lk!6RZn%+{J zE5mHNwh)d~%G5$ymK265m6Vh*YwZ`0VqjRNxWBW~@dH0-pX+29$q_h4dMdfv@o*qu@la>fPpozQk6bOJ#ECwl!={qmoyDoc2sq<^o0|egIgRv$_WMV>Ma~pgok-Pp! zHmNGuXxD1(aZrOa`ZLHKztWzqm8~@#rc!|v2vTXf@L%wZOBI=#F;hE&bEO%Vjxt^k zmK9Coc5GR28|J=gLP-6nF#_zxM)Y7D$91SDLu3x6jJ1u?Up1kE#!(wEQV90{+`k0( z>|ctWN`N-B(hCj40mHDb1Swrd3NR>AEL1`Pf*_6^AGt9YM_r#SBFlRHre(#ouC>gh z;48#t3&8VEcCWwwX~G2`2M_uA8PzFjJvYNhu9` z(HL{QyGTVww$@S<7c&qF?MX2z{2f_Ul3WQ4hl4{^vEpo8{;Q-~%;)~JoK#jB@Qo2x zrQ%&9^Rg%hne>rnryHa_p3g<)>uwZR>9Pzg?$|gkld6)-aFdwmxmi|}<4j6R4p~$v zpB{*{@$&-Jlh0CI8JQ^@rZl7zEd>YQ&==iCb})@ZpJ|T+@Iyiz65R=hz&KR zRHye{2XvRQFKb;2z(#Z(0yySlfcR^S?b1mqQ8uFV2x5pBBJknW02ccNZLs$LDF>iO zDBGoQCBW4IF%(m#%7Oyu<8jnNyXXTP2hhwo4sbjGrkOl0S)bDWKpT&H8G}_tumB)} zz(+)?EkG${G8QQm4$Hf|HhKh%u!ZVuE1S`?w@v5L0kK$~*0~ zGiQE;**v)nDZtBL1=aa{VkGL|_YvXO$&5VRf0otw*uLhi^N*b7-lsoRyXHHg7h#H4-d{I-Mlu%ra9)G@Hy^$_9zg zIK;sFyNxR)n&9W$8Vvwf57K6E=vO!gyI={6=fBoslkK3wNAD2gOzv9Y) zuuq?|xVO})*C_gas~$%tcTS%+nA_>B^_vZkCGa;xRgKFo@^ zaH!b|qX#VLwhA_eC>ctteK_4+FvmWgEhvXvLgIz0fuR7c1>B$)K{@PtHJ z9}-6LLIS|h{Mu`Ek)*j?R8U6DJXAlj^ldgz33PCny@E@(RkwU7EAqf&*^kr_c6QP=pY41qd^}*QfAep7_?mmahxe_xUTV7y&L#8 zWdgCEMJeZX&vjkbwY^uPYo%#RiZsmn2$BVi8?Iw0IgAs4G|hiJv~2*}4nx}puO^sxCb5gC^=hYMnTFX$id2fGVVG90wsb_g-CqB`#8y()>O-X!wi=Ar zy$B%mJE1LtF)}($Ok|8!E5R-u3z)hOUuj4xO@XVVi?BH zML)Ga`MIC_IpYH%e3HtC%GX~HPwjv7Rj+#0t2VpcuD5M$dY;$STRfajG{cK<71<(B zCm$!@B0nJegp8^n7%o7n2!zUJTmq>K3uG0Yq?q9ogRdl4fi%-usk3eYFFH z-f)U}CE_BR*=8E?QUKS!P#=t<%8#cP6|MwZxI9~$&GYpCDgc{$nl={&1MB^1^3Ncq zXG5`0(Sp8VouXu5nMkLYCNjeZ0AVE(5dzd|01%331^n1oaa|$d5z1l=JU%A#_ukh@5=fFkr{M;EU)>Gh`~8kgTd@6?u4(yWTEn_&+3tWM9k`Zd z*AVJ->|3U*q=Xg-se6FJwCz@aj-7<2$pIMS(gwe42my|hB!y86J2LL;O+_LRffR3GnrpXsy`4l8^QN&5m%!fKS z1P%vP1rO}usP4km|L=vL!HkHY)H$HHfHI!COxK83JkH5Tg9LYAw3 zjt_2qTk;6EjoSz*%*!V~lS#$0-K_QF1*cprvlhTpK}=;D1pE!;PGZk0geo^{lfB&W z(hA+1OtYV7nZZ+F;0f-;?XGGUyKUP}DN74mqG>~h@`le%wB8I2Ely|>i*oyV{+!KR zLvv$%j?~>oeCTSis|l4HNGE{Z9NqP9(Jne#R8NJ38bYghHCzGsue|{>8O4%jtaX)A zKvCp_#$*&A**Z>P4vn=|vntyjIv_j|F^T&Xu!_^vLp&rU43O2`JQo=ZZ@g^1_AX5h zr>jw#Dn=r7Hcj^SP8Y+GjjvsQ@yYyn@6dT=2E$Pq0U#v%`*AQP>`OTi{IERoOu*V1 zDGUcZ+y{z+2onGok+pz|7?Bkd65fbN6DFzs_&5aGNLfy@0*PuItfcFpTzly(01ez*W^B7X_Ip z3NZQK5P4Hh5yoliXRw+}t^(Bb1i8~d;qHAw;-oEpZ@Dg|(Ct_S~->z-i>Ad>i%gwx* z)7WN3{q_N>cADYZLe);4B%2O#ImWrAYz;?~W;IpxH|EsF*G#w)?9fHLid8SM@k0y? zw>WIDH0Ff2p&-hMOsa*dby_xMGw1BaU{11|F|hS@>+ft;6r4Tk)KTk$shj$7VyWD~ zceh*Lf#ty+kWJBgPzpdc%Xcq|VqIjnZ)X=pQOwKty?y(3c5#v2ek4hLDL;9qlW)`7 z+7RM61Z%a{+8PHK;y9?ujMq9*O2j(ntrbyf%&)ZGx&1}(hR6N|;QsrS^8nokwgjp-CG%)jwSu>9;O#BP&!|r1V>`#?tS>%BE zSD&7qo__R4zvJ22+1WdP^hba6(@%f2dj9$6pMTSv-t?w7g|K?^=B0R1fn!e+8$#@@ zd*=PldT$+%nS!15Z!aP)R*J|VM6CNKAN5fu{VG+hHKa(zPLTG!7}-)6Gr2=e-B=`U zltq%Dbr3?Un(>#?WF2@*+VafVN6*gA&W?|LS&Zv^7(Negf@|M8f`Ul8!ii+@sI5+?5_?aEI913eT3e;rsWS{Ts7e(4 z$)owzwgY*yM>`4f#S{0dtYeHJ`Y#i8~-XT9-z{6&yvE}&N zc0=Xq-|aXokkrFg>fLrTS&G|bP({x~D~sKBqZOCqt#v|G550w)F%+`7ti(c<%<68+|ftn9(6NzIg)b%8*G-yU*#dBV}dkR3fE4U_86p*?12iLQyEs@*JFP25}6C*0P2Y zNhN8L0O9}rFghn1kyUjKnbw*SopT;g1R9?vC^6zRO_>E2aK>02jk6X+6nc+0D9gm$XKFIK>zrn5Mc_lh zdto2dOrt@BH~->&NHnt3yetkS7>3}#tUmG9Jz=*dHCUN8tdY5Oy_q?{^EqnUL&N8z^N z)6Gq_jhoH3zgw2T$`CS?6cKmxR7Ur{Vg0|Qgx}TiZs-mXRd@nxumfvf_t)g8+EhZW zb2iPh*qhIo&1PkOy|^~tyMQkU*5IH|U`!a@xN+mgLpN^RxUrX@=k-YhQQ-m@C0UB& zaunuBELaJx@ZG21JEH3s^+~TMfEb0-8_w2QIZ=obU@&&B?8W=TK@@1A>#XFWm;XK? z()H#PZOtO!AIq1)1<%PTOfpB-cwAm^8b&$joL(utC-hJukectCi zcKh~Y=jZ1a55Dhv!`;i@g~Q9=ebbvBee}^sfA@E7)M30lez4PJj%n26oe$_d-}$`G z*LJ=|6W?@)Y~Qp*G&UOZ&V;G28-Haa)H>PebptGHXxc#&@zeuKfnGcE^Jge8wyW2e z=Q*FsK=s{i<}Bu4bVt>v;JVSN)#`9-hS14YG_$WcJ3EU)Ra=fCoJ<}gqS?81PV3<9 zljrB>=a;{d)OB4C(hTA_sR+_ux7X!)s?H=%OT^ct>HdD6m*v5Jp7$f?;BDbi5U~Y) zS>NLP98A+oY{s~}>k&`8~VrBuGtcp zc0Rj@fL6=fw~wxXk$QUN%5;h_9G;$>jeV$>muhhb@O|OKJB!YHbUvo@o1K4yBY3ww z(T4RUca72%0wf4Ootif^pQAUMujfUeQC(q~1Ol5*r|qnvbQQ2}5!GL--MH&Lut6F{ z;zcWQ5or?~H_xr)((fkOp+rZOaYatA*X)=iE6;o{X5(f}aqe1U?IkfrCE6I@AOD z5s^4P1%;U6NKY8rYeo@>vH7+iqX0A)Nm4tjC{dTWYOMj@O}O+L;dz8WK*~B75Q{0o zZ%I+Bj}V|0UWLrMa#3XwO$c>0>9hdI(%U12kblT_!%V0((Mw*+tTi)G_8qj=1`ulN z9($Z9JNUhKQe-4YN5C!#UJtdKzlHM8?feBC!ISVQoz89zt|`!@BR$aZe74!38_R7> z*BPlZo6ngY$r>5dd$N_EdJX@TyX|hBnNDO;F?DzI>&;#QTS+LJC{X0Ti`8{h_lVRkK(?h)=nX#o};UkW_cV75x@&6^>sN~4t>WHEe^ zNERzzeIVI8Pl#mTSZFHy3t(-U;)Pe%i8vfam(^ZVud6kHI;X3QFd}7*F-V}CBlvJi zlB`I~lU5W_0I_w5gbpAInh_PBUvZKeq%^rWP7ULyDTcqQK|Shui`Mmt00jW@7`L&C zYz@FCDfPgex;j1{^slX~RVs@bCEEx0r&XmDfN`M;`+MheO>CFQ#%5`r;Irh5BxYu0 zv(eR{RjL90Jp|T=7y(pq9ECoD1B?mKqe?iNCDX2d&k#9d41N41)#{~Pq`H~SwN{E_ z=8r+E1Y_n4G56k%0^t@^;kDKRG~KE)qSUjw6$0|2N~)#rF|9MJ0g@oIc#abHq!glu z7ln9dt>Pq#-tpVM?c35aG42)=%uQa@Wsd={OCH&b%QBe11RR3k&nk}kTI2VXL0t4a zXLBRx;y#g4!~FdxL=IjM9RgkJibA`zU+b!`wA66Jih1EKTsyx*WN-O-TeOADn|b(7 z_k(MnfARbE15Z8m)Kj1@|L23BeDcZP`@JW9`Tjlq!|F2Apzr2uKxhdaGnPosDFt4}}u^zHY4jQtJDl;~?429eClk1Pc6R#wg}wU_XKCHJe!KMgop<}S zU;DLRJ3D*b+1Zt%FXSWK+3&m#aqAiGco-r^fkx$C?>w1IS@~(sHaKT+iC`SiDtWA+ z|Asfb;SJA{2d*(-yN?c*UV1PYju1z~$wAtaNB(Zz-EOzYjf z9=?|ZsHEE)?-7mn?jP>iF*x6JdR&jt@en;dWU}_4tvZ~MJEtBg_ zS(r(nSYS)fJW|2yw5|f3wpC=yo<6K>&WxX!=3mH{WF`J=`fQTPdY% z7*P5t))KCOq0IM1^W6ddUk5or4|4VvUN0EaGZMEO_lASk8I#^ z#SEhng%?5VBwhX#0=+eaZfA3&+kuY-VZ9mn%!=>m2k-->EK4h;Y)dJBY#CC+i||ma zl}~sTxtBaneu8|Q5Ktqq>1xkoELQcmp3VnSi9~u{FqzAu#q5>kAgipryrrVjkO-<);uW%yV46e-D+Tw$%&jB&y98npC>^PMpF zR-|?TF}#Om|2xkbp`Tp~90GvV;V6#dcr;u+x4td_8H=FTud0)$s;b|E+aG_YvP>|9 zYr3_%WeT1o?N+{6XIx;FR$dIC(dLO(UpR$KC+wOmK zcD(;*^n>)bM+QnSF?Q*nR}1e*lHo8(96SEk*mlAo=R6O>yK?*gPH9SMDg--tBY7#M z=>uEsP6ZCTule9V4?YKv!(}og$H;@^CFCc`FOV;jKjz1>H7`Xk47z+bWr`Gi3I&ZN z*&3VU@&qGJlQc5?!ma{=C=W>}X_Pj#bSO$1cFhcY2dFBF5Gy+E5C0 z9CbRO7e=E;S*_k^DAo21O~G-rwh?W{f^a4PjyQH0^f(jTw{4|2HW4uH?(A$IYFdtC z1a`A!nc36NUtbjUr$%8g9?xB;m!$2KQT)~2qkHSFyE0xZih`o;Ojp;s-EPV_E=OUt zIBMzFaYg|sqZFBxLI6_NmJ-40xlXOs%$GY|&tGXYHC{q$#%LeqBCWOGj!NH&4Mfh2A2O%J2SqfQ}LJBEvC z8ZeDL@=-W-Gb4bK`^4o%wU7<1eY2>>C0?9{Aw%Gd8_M$?$+T&jXXf*5%U**dEN8P@ z00fUuE4TmPgAcy=#SaQ|f#Z&5*8!<%~`}BH_)vWuNq;Fq3+09o-cB|-IOb4Kj z7L#fg;*NmRKww^Wia`R*3^{pxLc5UvwLhT#>l7dPg>%`&L)$m)7DX7roid7&_VQrR zY_+LGq>jD5cF*O`=6L;X6$W8z(CQ76La{vwZrTpyd1sJ7J?S^kh=`)qH=ytDuBN6@6p-{|r96t7G0ydNqj}Sz z!#>50eSdoa@5jb{4NK4~ty#sApGJYd5jAF%Oql_vY|F~DOv^}WZgr7OvuRlx#3B*U z1RVjPzecegQZ)6L;s@ups@`YrJHEA*4PaSJf7A9MG>@?~Z5mm&wvrmsww)vl)$JE) z6czc7$(y~nnlW~!MS0w91!h_t9x8EtZ%I=xi{`OryO8vR+G5{CwE4v`7K;rUkHUn4^s-VhnJ5`{q{tu>WSgGLq1{ zbGa4-aEvg`^1Mt-;RwT8i%%=u^{z6rNBUYQ8?oUf`Llale;}oQv~@`A?d=}B9x#gX zeEXI|hYrz7sD4)R`xl1B@TjnD7KhxS>_e@pyphKd!KGz-#^nmGnqzFaxH@>YPD*997jW@wbQcgE7xXGOlfmt`RNxY>fj=eMO zE1PR;^P9G}s05w_svN1{SAu8{Wk5v|k-;YE_YtG- zVMgxnZx@M-8=rL@3fa!_S#MawAW)kpmN5))S*16Tr<3P$SH$h& zZrV0LKg7V~w6>V7%5fRZXLQadahb}94Nx2wNP0h*XaS2cnmXzHx{1qboBD%OAL5*I z*L5ArYqfUa?;YJa@ss6gc;53sE-fvMis5{HeVZ%!LrUps6jBJVil2}?*=+6A&@`v* zqr1`hwY4?eSl>M4d7kIH`ReLwxEgMoU&ou|-lZoX^$n5+6J8@o>1s^Je^=z797ujn z@1DKRAGCV5jN&wP%T~*@pxZm%9Sn>kbQXpIf@Rq@hlvO$Wwf9^0sq``97_Rd((N`U zOG}7xyuA?wn@hgW7_5~gSuTGzf7PZwk%4eEsgg9x5Ob?4d7F+FQ^!pz!R7j*v}%+T zvocxrU^D(_&7&xia8|k^j^?)HTeEq%$Z=5)B41`o0qA8#tK6_|Tcm*jy2)eg>b!e) z_~@1I2@ZyiOMPcWO-|LJX-Jgo={IOmM0+m;idV?l38lDlmX? zE;$SI;hT#KK>4-Ja1IpZGY@M34QN}3_9WH!+A>*_pnG=;3I?EMRc94|0o1XN-fe<) zbI0#6&J3%W0Jm-%0O~3b5Vs_Nita0&hD4vbL*W5^l-J#djKMMXukH_$FG&S~xXk9= zIppBJb+3a$D>h)<1O^Q3HN8>Sq1X)hVwHRGNXTqJ#(A;u0j4v9d^$y5aQ7VLYkh{K z%%oZ)=co;291R0GLqnrrs0~zrN-<}3D(SR8mkBc;=9q>Gib}Cyx^qn&#~@`Q0c0X2 z#90Krn>N|m{ys>3r(sS{nP9BnWtumfFwN_>4dXUT1E^*y&Kc(FHKuv`oM~Qv)-(_8 zndXI5A*i)fyx`zl5I;YKecm_smE<<^v~7G{4vS1QQ_Z+^X73h?UoIwK^lPS^e-l5( zdcX#-UuVI>1~lGa!QwZUmzTk;p83gVKJ%H++~55WejMyyw*l-0SpUO9uosU$VBymH zZh8OvmqmP>T+1B8-T9M?$ECRP5iOpNCuU=*&qSdTmGx(H_pFKbFe2yp@Zp3L`tNHU z=FE@_1gSd93_z-msg>H@@1J~_uC2{xYism+#RVwm{?M&;yL6)-)H_`&r2WYHaYeF+ z7h#WFL+&NdByS||Vpu57%5;OK!4+LFzvKr}Ks(HM(1tM9G+ zR^lj%R%=Nj`ZA8Akluaw-FNdONn#54tYei_{*d%x@*46<3oRzn2ho<4WO^ZyU5KK*E%QvQ339D|E(O$uUqPRa)0HA3 zRX~CT>-*@SjAzw2y*%iyT$<;_$gFldN{jNzfg>{}Lm*6(m`_e0hKumc(cTH?EX*xL zQoiXFIv!$q)8TDpKA|IOSq675Z86lhLlqV2!cbK(RVAUGr>K|2aYcoBI78#pe3z9ZK4aZ(*9j2+000xmTEj4Q9Rw#Hm*r}|9R{`l#*KRG@Rfbj z@}&@=fK&C+y2ZdM#yhjO0zeFgfsFEzLdc@!ALr6+G9C)n^lf{(*T8KsG*ZTpi+dE#yWw3`N+S=Oc|FO5gS(?svSMoRlNRw`_(Gak^u^CZZ z?RJF(xN>uI6I{>rVB;zN`+`lN)QP*j(6XX#H+GQ%*yC2yG9AtscTB6<8f$%MYkOtY zbt#o<#JSc(DQy5IlDM{96!aC(uPbdB?W0HAhM`sc`Q2Joue%8}qo@gqTd!AXj}S)4 z!Bcn&E|HvEOYSC*5HiY(vdjyL1*maUh>$jFTk6-`*Mw;7=X1>v=)YvMgven@R{)YksF%TWvG|8jaPp>fCH@AKB&l%I7z; z;j*)pkFeJ}b!CqNqYMO(qLtM!;0&JhyS=5nswx1+;)HR8b5aPJ5JCtWQ-7zk+eiB)3>MJQ# z7)SvI=f-*9E8xcvbWrg6W41J`AwVt=F!BB`QvlcBfDM;1Pzo4xpXE}9?6`_$rk-Q- zQS5+xouTcXVc-sqK>{VISEHLTLc>7jI?g(r0vHVXmCL<@&)^ikL>A<3@*AG}L;-kH z+)xg<9EAMWU|v43O%8>qr6EE9>OX`sL#F;h0{INGA0l+LajXmX0ofT+TTC-*aW3Vc zqOqUh68Qv6k_7@~N4ZRdlt~8EW%!zUE0OpooU8 zIr9Jj49;yu^@=nyKtMw#ra@8LTxs910tvvB#hKJ51VRkpeTUB3jpksZ(*@{uHkR9M zxwlCJJ3f2%&btn6b6}{V`Hx?B_V{Q-X@+$FQQt|^>BCRIo>MBuhid^7!NRi`RgP9n z_=++bC^@n@*NRcixJfB8MMVbz5WL7Sgz36lg`_qBa2Yk~ZAU3P3fm^b5RzH`a!|%4 z6;>bs+nRqL*(NuUhspmU{eRN<9pCBmHIXL~-YdV|;h z*_=*T{9pg)+Zso*@G!aciQ&D)y4oxSm27{&VM?W%A0)vw_A zq1$P_o!0$<>2!K{_}bIc)6;6Ts$h72etv%TL2rHQTW{QW>#tl~T)nUC@r#R#i(h%`jT>+6 z@I_u&Uq5m$>b#=!sh!X7e0k^VJ8$WHf9L-p^cY+{1KAoX9rGu5K)q>GZK@RACR3Ki zOaesaA{NWLCW@X$9R+UT0DWL*AcC>c31Y>xNg!@-)z0Pzh9H}Bc^!K&1m~wZ#kdGj z1hHm3$lbi{5D)Js@^k3^TR+&tD@XbH^=sG9^TVrA8gAA%je7N~mAP5pjFP?N&fndT z?0rw3=R@h4$>m!nrYFPv)J%SSj|HD3!+c_Tm%o|k`T6y$hrfS#_4;{!ZPh#%{K0#{ z`%9``ulHPcu?P83daGBz_*VPxrjfqAG2=YXkFH!lKfiwUF#nRzlD~SGpI`srJkQ@I z-m>O)A=5v>4t$yJh(2uhKmLKv&vTOlBXORE2eg7?4q#&=gdID@8W_&685VNHI&8dM z&6v(Ge}Iw;T1IAy{pqQSdpm?&0w_z3#jupfzX+v)y0JDnRGq7v^YjO-CgPizQwlIX z0V=pMkwsf_pu<-uqdb1u*dv7^GPt$(?Sevb!erKoFnQ*`Jqr{@;Q+_r#Y_bV`_NCK zI3!sXHQM$qC4LyWL!kQz4qY_#3E95YO_XIxh@)g*0}e-1qT|!keq6ZW@O0XAZ3rRQ zZZka{4qXxVPfw4DrlTQ(&VmQb2cEU$i}Hg;Kiw*!DkZ>o$f_>7W+`v z40KY=CX-olLcnzye4OP`PrD(Lg(5o`j}ervuwSPk0ECbdr6B->kk);yv;rKD53&ND z1|PC4cmV(3hWQ-F#MUv$-^;xbcoF(sVO8x}7%$U%RfQ_|2$oXDcHe?;xsy9D{673B zd~N5;C4X?1M4>#9)^StK0eNJFr;E1aZi;5TC3=5&l4)1%#&oQeqb7}muiCX-@n(ph z34956dR0ws9zk0Q&>PvtLNJ#&wu?pny@yY`K!_78y)O!!5jAyF<5Z~-;uywS*G*L+ zfR1D6-g|GkS3n3~G9e=DmG|D;od7^x)s5DB7{?(fHHD^b8lo8%h4&T33Bnafl7gkG zFD_oLUU6|z)xwY`=|i_SoBe~c`MleO`&P^8G|vYI2cvOO+&ViO4dV#%e5vCE7uN2s zTz$!%Yu6P45M951=g#8F&f3M{GS4B3hoiHzTSYM*?;i{y&!^Mn>OSc9X7jUy{mte! zWZ4A_2Zx8l0nuQ1csLlsMV59tosRC*FWiN%fxGZl_zMg;#oL`uGuyQ7db5QdLuA1) z64Y47G_bVYZRaA)B68VYw0!c#R_?(a^H_r>Q&N>pefkoXmZ)si7YCt2zFxH3^g^Qr z&2rVSbLbs-7yQoMx}O8zbU^1pxOHY8Tw(z6xcPcB+sp*4x!WMi+GwToWQlY@*-EmP za8fWIZ2E*t4KrP#$c9}2`Y16c17XelCU(rqZ1S#miBBG~!}X$$%+S_Frz|m&$xZvo zw2}aat;TMMtCqc1=L#SU4K7FfD{jiBY)~=+VP~|SU&^AwxV0em#V#02!%RenOwqQn z9|CTDkCAe^Zs{ZirzYV}l#X_s5lfJ?JDOD~YuEfsxKJ#q1*B%Rj|h>&BZVtoD?^`5 zNo=IF(Ov#iG2W^M&Xpi~ALt_zADyz&)_GwN)1XQQdV!ZcN6I5+Z2q1QAlUs$ytq zQS54>+FKwF{wyo4Ej#6vSP9x*xEKdbpfrl$N44;5835u;Wdac4Dt8#hQS8LPdimh* z+wZ`IQi#hl0f5&>N=dHo_f3|rqsc*Dd3Vn*pT|ibvU~`mv$E70sc zQKUHuS`j*y+8Z=Us*OZ&Vx6VP9(g5%0?edj5+)=u#7gCXNSI7oS|+Pq>qyw&LY4K% zDkkf^QE`=!N;0K_Bl1y^TdP2^&K-W+sUrY`X#DE6Mnomn8q$sfYfY>GQ%JtxD6*E2 zDAhhr*?$=TXwgx`f~J|qBN!r$s3;;t?>Rt2^W|Yl!!2tYmHv)PkwGg2ON6|ChmKK5 z-#3%+eGyHp6(eDMj2??he4`X8Lz0UK;D|LS-Z4r7f_ zmUym7ky1rLq}2sRbtLom!@j8b7l0N@NM z)M1mK42HG_=M^L^jWHPr{E>^Q!=O^>i z2{We*`DvZY;yVxCvz7onK4mUkwkfuQescpW*^240Gf^AiTYC%%i*s^~Y0T#rtoQhRez&9x zfZr1cvjby1crL78yl>!B4$T(gdDF%G)4+1RZolGO)s}5pEv8>)Z?m3HrY!C1L`gke zx7*dyGJ9^Z+OF1{+4P(QBR;w&Z@0Z|mdM%D-FCNII_-vrtnLu*`_;lS5>{wGUBqm{ z*zEkGc~o93p0pFd?Im2XET-$tVw3F_-9Ws=f(pJEx4dS+1L0_2Sh%JOa%jHc8JfW0 zPo!I}%68Vxi*L)vB5zlDtO z`7{lr$~*9;zQR69i^F?{#2E?NcjZKiO;Q3NJOhmt1^@x=aa9ogN%gjc3q^BHjJD9T zJcsHyB=XEU3P$p<=iy}dag zqH{%|6?2HhtmhXI9`u*fI#R0TVJ^lZX*K}p!ukDCY;Eq4V}#=t2z|tNfY9|hq;^dR(E^b9p6gwY+U{+7}imXW&5h6&yC+_)P-Y_$qRU|Lcq^w9G zjs{IRs>Y#Bz-liS^ZfaSt z7Oj=?E(VTchLL?HrU56<6 zr=@0joxw9)zOc>{FGXsbu zYF$zKL{i6{kHlP7Y{TJH>e@-+m(Gdr_B>MVF(>^lb5v*0o!)wfXC zR0|hoM!b_8!VCcM;*@M;@L@c(#yi$<9+VIpmzzkSEWaolrINx}&`b*~w#E`};*Ev< zQUQQvxQ7UF%(^a%z~3FC9*&KXbu>mTF~wwH~QBjr3$1Xgb?b-K0ZXQVZFT->TpEZqPEWXmSBG1U_)2DY%bAEc~^oy+98;#qo7u~mmZQJ(k){CB+ z|2p7^bInj1yY`R$)ds>Rvc4%;$mp`()hHgtRQ`LW}3?q>VC-TD0ZvD6*yk8_)wo98#a z^5xBJy;iGzVbrz7>p16@!>D6%<5t5kqLlMA(*7r6 z@_m2YJ35_qI{=;Tbb54eI_-4eXQtEXm%lPOKkdE;e7|%4*wQlW--w3~-`;C)edWtX z!|C>Qw;w)?Uy9?n#zZYPL!S_0lGedx_zSp9o+5uo{+av$034`6A9C1)!-Vj(NUJg} zgSq3$IsFT8CP~-EHUO?&RQ>AJY z*A{mF8Zl2QN%54WzbYds(n^dXjNe0}dS5Ep3BneL72TZJySd-cc3M`-JDDhsWEFWL z@#A^c9^xuVPfzWfmUA0#ALKZs7sXLpWa=e1p37O4qy-J>QI$Kni9|dmwiYbIFa)<) zuU|#SnYmUR__7o5rU+HkRCU=XRDpfgpavum1T{Ylz_h}SVLF^LtJi9?L+qI{So2ym z?)PHW^45I$40?#S+C7Ug<{D-vG))XcuO0-DfWaDP92Fa~t{b5aM2qSi&K$eF z`+t(hxy89@TGx7>XDZr4OIv0gxH6h81PFYI0HG~}-~M~g^DM<$U@OzCb7i(#h~WDY zF|@h#+cnp9E!h!CQnw7D8pGizTL;tVsX;CWvM=Kn&m5t=F!XpQMKq!ypy0Z(>o}qn zMcf#UHvK_%q`332WH~Qi?0KH|;*#e~9=@|Ul4aiJXlU>#Y6-`2W7h>51d)N5cDNUY zo)S*RTe0lRfy@W0XPB^_jfTU95{6Yz648;C8^?E>rYSk1GAF)EnPq{99LK&Q8iJ%j zbAz(LR}`>;A2Ur;a-8~?Fy_C4gdP0X!RHX*OC%>3$m8T!xN_oXg#n&`OTZ=5*kAg2 z;i9m-rHP5a!_#s$KkXLfWe%-SFwU5rLY+Y%ILkuU68i9sVT)!`1!2?5%7{I1iBLdk zFgY-r4Ecc9?N`yX9{Pse7`kq=MHym`u{epnY`6{xaK*+(`b^}~vK>cCN=?%Zd>;}> zYfDUP*G-_+8q{hlD_c<7TySn%uA>yz*4IxXP`WnA{5Xyo?ILmvf*?fNHH=4n#|fcU zU*=l7ZiKDopjKa9-KNsA9a~AHhT&e*u+1Qmf4QYB}ymN=tCYEYo$0o2%R;c<^=j75Er2NIP!1 zosW};$tTGd$hYBocrhWPMVR5M(B@|vjN=aqy9|dG|qWFef+IhMvllc=i;_gQF zl|pDgoDroY!)0uW}Rsm(Y? zW!rAPzTV_o14L2nSm*yk95-lS20^Rd;K&#U9y`8aE@zpGAUJm@B2!W#wQZLRE`%2V zS5iod0Gcv{P%@xJ)@o_+gwR%}+wFRQz2UIN1c!bY0N_tH+dvVt?byI6#1YJgLj!&f zK$Iu_(mYnewaB?NY+EX+K*Zygq0$iCF42Kf zA3!3N)V5sJ1@a=4ZXA34UPr2c3(iH9);2cQH&V;@8@_)cN#f)fvn&BFXw+^evu;nIq0zRY+4T>AvKWGtp=AR_r=2*OGt08NoX1Jxo%W_o*>*&Z zd<^IW*plOhp@r70gbn(+WFm;1Lv{t%Lo|&TArVpoV@jKO%zmNk0#{J65g4S54S+g} z83ujEoUW}V!OySfkNf?;P#tggzoXG; zH2Q;IABmmAjmF`f|9AZO@qYjK;OFL$2JFfAMNt$T>-Udc8+r52Ejw3*;Z-}g?A(lF ze#JSs49~Yg7cY?=eqkiIPq(-!wtx&GNONX!D*4FLf3G9f8;Fh9VjULpGlJK)i>3NH zP9|4fIhi6(rdMu{Mu?-)_SPr|$VXf6p4>L)dMeQPS}qLM5N_s94GLfaHGg|-S@gtQ@(%m6Hh!b z@fP#Hh)pYUHL3|;1=(xJE#zVHQasc}T;*Zm6StWTh=%70Jw@I%Jp}?plA-MJI4!5A zK5$1}xJ6G_E;Yy)7E*vtIvKCAT2#Lq1fTZ-z0U{1r#(dP8Kz}fVY{Z4(zQ2tyWK!= ztx`HuXApGKl^|%J@Vwri4lK*GvdpwBGfR||GEJ0}DuGWa^-MGRlgNCQQqQ_k*BuXb zAn<)3AIP$dyPo6VgM+~h=(yMi^nJ`jY}6Zjmu|g*K~BCD^YaBiMhYAsEH20+{3W}b z?`X53m{UczjDq%2(3sJbP_HO2+!GO5IJ>ftDkssqF5CjYAocxxCrz~5Z1`TY;i)9; z8atS%{Xk()I(0lfG$M^~NmUS+i|``5eHu*N!bs)0)z1lqT@#CqMbg z459ym7xV$MPsEO`6tv#_hAu$&4R3A%479;GVT6zqcoB|}gj_=&AwN$DL~;4OBTc+m zIyylIxQ=G=P-?Z0mu9(AJlRgOz|=@vj3`VQW1R7>^Rg`GX(cN^N-(W!`eSL~@w~K@ zU~0wFig}IwmsAD30T_#2U`#7*Jep-$OE2X{E90&g9tYFZh7^)(!8s@ioC$*qDNJpe za6I%p_9!?)P--B8*8DvDx44g7xNrf3z|mT}LNdlBcMYxFAb<-OFbG|tlqVEtObS&d7VhBQfN5T->^*NU?rpjHt_ zgHk{yghkm;GNyI%6Z*!Nzx?H3O3O}^fk>sH65Em{y!>SVrZCK!HYG(gRAO4n1b7)C zNc14?QH5tQ6dScU3*aR8F-&AeveNBY%(hVWKl<}YXeoUY zo1wK-R14{nO_`0E%H)IVehcy@{kx$;hitQuf+#eq8jF(-x=xShi>a_lRLstaeH!iW z=n(VxMKLc2#q0pu7ndRyF{G&MtjMR6>Eu3*(#ZfEr{ljXJDno$_bsd6D~e7>IMyo{ zqSGmgUf;6%{rr(i6p``|anA=I;5rmiXl<~3_a6E_=e~z7z;yuJ_c($}eUHPQ5l2ay zE8!2WG>Kxv91hplhePwRvi(0p&tn)j>h4fxg5gB6O3`22+_;4z)3B_{kh*ak`4)hC zg7Y(Z;Ri~alLWD|w=a`bw1dDrD?ZGvUr{?nE37mKaAT(Bq=NA~-}%lrBLF`o_~UA- z)Kqn!&BX~0i(misUw<7k#$O8)rS@GZpVfH_kBw0O%lG?3>SRK4`%Q+%2-k#YxIntT z&ie;l+@DPUY&Le*CVtAM!Sg@-HV%LI?a+1{JH%mR+m0O`9654i_xF+@aEo1n=-FM&p z%$x38de+NszIXq`*+0Hb=F2Q2|f5PIzb>DmQ*|TTQ?!E7hqwr^) zPd@MH{sca@KZW1lpU(ed|Mv>`;(ofbv-807zcYuw-#-nX`F+%vVE5M^eDFa?<}c&3 z;>w$N5w0f=X_M!Yi{vNCU$H2|(%G^w(k&a7IkBwYYWeg|pcVr=V`( z3M@+kstAN>Xh3WO%rs@k<(_9d3M|W9Af^(n%oVr`2prz;l_SQNN?HlWh49CfRsh;C zG=^YX>MzV@tJ%~jz1=2Is;HrXuC6SqA+_yD>O6ccXZ|xWwQZ@;x9#qQ0qFf`Xgreu0BB3%+Bq-{B!nO# z2bh5L#w@s=$~w3~LTOH&aD)iwBR=LjN4AVTG)b`=WAJYkfTzwb3~}Byg_rLC9?bTC zuWmeMJZnc0Wb5mjwLBlsk_31hAF0mTQvUKC&wlpwSS#u_=GQ_FzST0G#R8RV7*rF)@z7q8U)~X!|62aVzZgqj&1*aCyZeKJH8(v zCTXLC==&}q#3gue8GZSJY(#=LiaL7e9L?E?+wkm=QaOIdxXEN2vBBmNS*nzNpU5JLQ;%3*D zjp8|lv%mq0Ye<#Sxwcna@0RKn!eXz)33a(;RX4kK*>0tjnM{}>i)V9A5C%28;7Hy# zb1vhSntEDJhNR|%yWw2I28^UlgoZA|k|qzQyre99w`sRcB#@By&&p&vn@Wor1N>vf z(sFXJP{b3_YI)`gi8M&&`({Z=k`)?taiU^SPc1Ck<%$>#G_`gBbwG;0?P855)!Kws zwB3PEdxvS;ayxOSY3Gea4)CMMUXtCst=IKh@N1VH=qD&O;g zL{Lt!CPd{KHQLl!r<|*7Oxj}xggAew2u;joFIY- zJyhcm2rr5;fEW|`OeqtE-p%v#eh(olv`#_$aD3kgA{l0>>kkG+3D6r3_m#0)k+IGM zVGTeHI7A?-07$)&hzKal4#suE24YcIy8;As6!i|SUI7+3?_W%(0qe-xRLcHD)-|9{ zlcC6xVv5A4(Ym&z;@EaaArP`b8VojBe%1QWh->jCX2+zWlQ_@PE)3!bfr%talv45E zp`?2i3*tShk!KPFGXUY)OOGdA4r_vKWI{_wypM z?}P04hlrH522v;P$F!{n3o5gDO$IUS zWk6T;LDR7}#mVW_TX_A(+i68{0$C>fQFUEMNKoe>Q!o>nkg^eHQx8Om!<3a0D=azA zMozh4y^b7EGf@NRjnEqehrnF{i)=5&#e*S(^5K-GQm=eq}9y zp;W1OFr*Qn37#v70Fau7o8~;N=fN`o&?BI_B0XY&pn`JC1O$rN5Rh_|mt}yVMbJs= zpsebo3zQDJ`-1^$jcHLvq>!2<1AzV4Mp5KB;h+#yL;(m%tbupd-EJEDaA@^aooY}B z2w*FvjOmq-G7vc;B_ae%Vx={$L@^O)pLH_>fQrQc`4B9EfG|RzFDJ+YTci-8*y_&Y zi4h}v&0#(p&|v;Fw+^xltYKwqiP->hr;~Qzg}d-0aJO>}QurYFRz!S9{9gPE>eGwq z!{{^Uo9TP#t@N|j&D)n@ziJ>K#K4 zNVHgQHea?An}~7=YG7cMthJsyiDJ{RkdzB~>Qz18Rx)$8(_XH&KK*q5{brh~<=q0$P`YTh&EioigY?asvf^D?2PE63l=rPG zR);ZipPk8~o!3%#YIKb)G8TTl8Qez~W+3qhJ^PH*GHGY)ORucTRjr@_gGTA1owhSS zHPBvZ%WV3d(Yi{OZ7r)}+}0aS6rE0vPpXTq(cZc$EBe;xpV}_{vVo}yXmOGAA8t*@ z?bf@kOxNXTURKH){ITfFGh%O7HfC$?)-h8}Oo7sHP&gq9gvRTiCXT_&tp?K8y z$GE?LdbYohO4-T5@$q=5>14G$Izl8n9vz)d4%oi^+wa*pIJ1a(o;bkSwp)lzWK0J5 z14tBk=RG)72o|)WIMySjhCm5|V&MblLBwfK2#FG{9boVX`2EKx0D?fyMQI2?-CnP+ zby~S;dOiKU1U&ey9#)-JEfe`FZ)H$0j9SM}@S%y(Gj1+@EV+pXT>RO|7^}|I`F!zlj ztVuBsLg}^FSUWReF6gj*^{llqMuG)o0#L$G=so8^ht#1T;H(g7-B*(Op%fTfiej~@ z@Ek~%k4;$+fHI(W5zd)QNe=1)lt}L)YWaw36j=i8=yg1ITgcF(EGJNaC?&Jf&XG43 zPX%(;CC0~*8fpzn1(fxUaYi21z@_tL&=|cB3Y2m|>o!2DKnttYR)8Z!?sfNP`}+p1 zna>^3gu-92|^K+!KVDh$0Ng0`G~ChhFxPfM{qg1dfT_0*=O`5doq^ zE<^~5^4M7-)gKIo)?=lG)c#Zj%#=bv)HaZk*dYN?&xKJ2fV4F%Q{;nI){KfYi~SzD zNg7vt&N%1x$}HEf2L#?{7@mp&q;w*@N-P9=9#$z`qK6_%6PgkQ+)n`!9A$nlC9M(i zR0h3{M61j)6O^c|wp!h7;SnE1WuVeb5oqT7l?R|QqPmkE&JtT%!!d4K!o>|NB~Gh3 zWRHx&_X4FPa;%^ZKKMw<;aI*5l#OHK0N~v``Ym%5tSXjL81gAnlqR$ddAOmCw1OaV zfF@Cty~nLJTxN&+a9 zrb@ER=pZ4Ow*m`*FuV%_Ga3L?niZ#MYD}k7bvm8L@p(9^{}FwTW#wtVyYmZh2q*9& zcrSP}d>VW!{0RIa{0-JP$Cu;B*zRD<0~KrMTK<=k&34{$!=#HQ2xJfaW(`CdE;h1@ zwwC3tvG?(7=G%D*zFK%4V!P0SB>ZUdwUq97UhlGc0A`HSYSC^RO6p)dI%VPM`nun) zQp!Uhf9z-k7)igWj>0*fAI)}kvnHJv13-2IE1_MlB>tBjw@q1bwcX98+h!v5D#l-3 zmiG@1oMT(wO{Uw1tF3G|ENkQz5XsbLnFQB!%W2&-lX-n$qr>kEw^duu!E3HGo0g|i znqnO?oYE1`Paq7B`OR96Gc#YV+ICw+1^`eNZK@)308Zm9&nMGeGnZ0kzpBpZt>Tkm zpke)yc!`Zza_!I&77o&zt*|atc#3E~rvsLzWc%vfY&VhbJ-6L$SF`!7Y5Qj8iBF0weiJB zEpVgMfP~5!M2n1~wEfls7$Bw0nqT{jgTM&k9a3{fHgAHljg^5NLWu%vl~xhVQCww2 z*eGkN0hQ=5jX~=(`Im#-n0&Gq$7woDE+CGI%l|q9k3rmyJJwmOxs@m)vFI=YS+pWl zsFlY&GVvWbMMy@0fP}Pic9+$NN`O#rs|HuHT0x&c%@3v?AbNXfu*H{u6s&UIdV{}a zyf=v-g&+=y;uFN45le{Vj0NbCdZuZb_-Gx!ROFHP)^E(BC<+9?7b2*hEA`rne4uF8*QIHgvC?=wuSz)LEv=IO?8sN`FM2b}HiOfaiCD-zq zh$XWEuK*Py1tKA1wgP|=Ww^%zz`FbMDz06Y6u}s$E(rZvRlOy#F~(TN&P0_4Ce0qT z24(}~TXxXW8Q_V(cbI%dL@PP)1t4bdJt5$FRMXXzR)sZyd8Gk>D5~oye))F0<%67= zzECHCD6&vh8YhUvXbgz~2z-I~z5qI0mB9$07DVYlwV5RTWEC(rSysfNL@y7zc~0oe zMnkSz3I-4$hxb&%lxS^7$v*SV`2bo(4>9l{RW@&$1`tFA3?XKRDyhsXP4#l@cDr4? z)_o=QABov%s9|XN;{{WTb;)`@80R`(rMV+wCnj1 zjA;%J@h1uFiKZkJ#8DwsGzhzZ3qwNNs@7_`tAK!-F6!K>l@DajG9=Y&hL@iY?{fM1 zg>9>a0^a4;4ft>HesFcs_a2_vY&QG*o7)9KaqqkMIJUF>@#tXokLq2NvTtMbTcS{0 zety0TuMfOS5u(fgOVR|s2GS(C^XP0m8)N?F0>^Kx0gKOg?zzuc4vdRGHl6D_hxX%b zal;;7pDZ_RyMRC@QW8`h+%}tg(>AHXB-qhH5I=zsq^mpBY2^q#6;eZ)17ebvnUOo( zLJOrD5kuz>F3P_iisHuJ{$9T;KyF)|IdA=)9LhIc%ei1JiNIRciPT}-e{{%P-e2Z#7dd_8_5 zeh2;v{tq3~-_XC&3+hm<)P3reoPU~c!Hbr#=Xf(lBSJYkah#|tT+Ou;8G4uT4>%+) zFB#=L*9OMMBG!v-b#MjFBwy855Q%rpmpdnRGJGZ2zul~Z0G#8R=qLbkv^Q_2tEs(l zl6gYD{+N10tzLoeLstf+lEri;M`pjPAmoUDPF9dWw4Q?jZcbbND*SFMVu8$8>huZI z-J-2_i^fZv+jBo}e**6nJbmi44j1qN;@Q022cW*h;-;=RduQEz>HMJI*yZmjSuNU~ zK||LLSn7IMYgEcE9okMxo#SWv=RD2G{oU9 z5hfHEW4{mb3dzPP2#XG9UtMO`wxzHb0k%OwtI*wTstt}|T|W}lZH&rn2TO)O5Eu}h zH!sYlP1x#*F02&O!f%n5REKmj?7>~e(D=uoxm`BD0UIy0FQ<=1K2-6qVW zE*A`4f-2)cVZ}93A&3dLAz63)cG^DHTDC0DQvD7RiVA=xu@O8{<6o7B!>cE4+k%F6 zarNYUH~>KSYLtp%n1Nt9=0i51*&r9!_OaK~y9)Z^2wR6b8N~R$Vw^vihR;R<&`qRVbSW)XmYQP_TiiINNg;J$SckN`ny_@tWw z=thzU89&M)0C-V=2YW|kF;;2kBhpYr(ZmTVa3#@H2Ea$!2!b*B2P?OL#&`y21vpZL zj<>okxW+(Wj=&bV$rvA#gjlKgloSAx0rX^Hm=uIRjI-X80S#!i(pF=@ybLocBu2a`K@-uG9?IZC3Tn1t^^6?T4Cqcm$mz4R<-_L@kUB$k&Way+hTt)qB` zoSbDDsiNoy2K;U>*uE-|BJDnp4km|379h0CC*lwKj@IjrhXp{kmpLENI89TO5L}jk z?DfDXwup}P?&7@Phs)n*M9xxC#9*EO_&m?=@MxgT`;q0C05%Y=YD&Xi(ow3B5uKQD z-%3ZUbb;wW*#Zp>@#Jf*05U*L#WX-%up1{u#lkvI9<+U<2%*!rA4!SRN(n61T2?Gr zA;Fm<&rKX#zR~l<19)pSrm9$_Jf%n&F)J&ijMxt(67y12l|giYP=JxjR7Xas6UyA9 z%#29+)>p2SSBT&w(+n6KDrnTyZM1r$PGiMUj5aT9s!h+}{3J%}&z2`sDo1!Mc72P= zD5aZZU@iF-ql7R^vsYP#DyV$~?SNG~@|$ii>MC+0eQFOQL@P0#1x5gGw8DEvXaK7F?gM){No&y79 z=ZKVNvkwK$2 z0(U!cr`PEm?PND?rKOhIrY&eSUG3VUU9|+i^ys6H?qB=)zuo`Y&)MJKfB&C3_}lsC ze(vYK0p8*AT>+lH{F$G={3`gIPQODhd_O)2KheoMN1fMn-rV`(&ewH*y7LR2zk@kk zhX>(Z;RE1v;LG7V(BT{(#Bayn!oS5A=sLZ;(`hO)$DRy7=w6!UZm`ypY09dc^H8PR zrfD--&exlAr}9$8&2qHftv6dQj6?}a`95=dFnVi0og0VVLLNd@LE;6#S1F!hq1mnL zmLaaB4QT?jeArdfgL5qGWXE0gdar0+ps{;*d{4nUgw#>Io-d%u^m9|SlV*wZ6yYzP zdohhXd)u^GAT8N`LY-Q)X+W>JyJZEe=V zaLS6b^4c(t;90OA!rKVaa0c535ZvQwYs6FFn0<>W&v}Xfkg1&I)I-~+#2aQX9l9_s zGSoJ(z{-eYuflHDi<;rhrd@1S4%}7E%r~Pub9s%ngfsnW{rmu{ z^B%O^R_&r$tQA0ZT>NU{cKSc?w5S#w-NSfAa)_dzA|f1VDT5|mFK(d5x~hI8j$^Ns zkCm#4YNbvEAVs8$^aqq^^uSs(yo#)aHh}N)Yhti)Td7e>>4yzbUnxJosAB`F@* zF8+EwFaq$;u{?G86=+O0JUeTzUM*{_2hG_jYUQAPYzE7#SBpmLVRP8jD$!b57FI-- z4-chB1kU!ZU5mPa;BEKtkWKyyZk+J@YkoH^F@oiAt7{c1cD-xY8`eg#?cEA&6cLNg zvt^PJ5@ZLH>WxbWbP>k@iTsX@mcs>6GxF^A!(v&M3tpSPXOK06dA$5Bz_+7HM9D3G z-L@~i9J;4D3dRDmd^nEK<|PpoL`1jfkE^gD0d z*}(+7$%klE zUj$6R?I_fl1t4yRszC@Ce81hVnyw2JAPf|>;}V5oudC3O&~yd6U6qC*CJOi-<-X@z z7D;W%o(=#j27s{w001zwtRVp0&N*KQ%D3l7qFhlp&6lvQ-)`JDG5M5?{|IAWO`R0w z`5`WkimIFtk95m+1?)}16VP|qGAcP#ouL#7g0*JFKRTi3P6W%z*nHu3A!~Me#4jzaC1c#9@k}e7vNHoxYMr9NzI=fMq zY&7UwyMP7LX<(NmNs?ekC7>vj0tq8sCzvsS{5GDb3d5W;4pM45r#hi8WtyhzlnG5r zu$Z~R6ct+_9W$8ddK6OpP@-TaT%06H0`rV`8!}OxBwR{QXu~{V#%4(pMX^)>XLRNQ z38Nqs)QlBsXOwmis6s$fOap@@rNj!MD$nzJoq(n)nx%Li{Fkbpr&yYzYC!7sJm)GU z3YOAJD`%`|0B){|v}{|^RgS|T3JDNOb&}dEC=9U0YiW{VoFugxx42i$DTXj~N(mpw zsg2OVAIF6-o*@%usE3B=26P*`7hR4=g3-9NKY_j;O;EzC5g)Yl?FkYaMh0UCaifu+ zbY_SaJm}gH*hcyvrqM7z>9k_qAf6cCF2v|5q%Ldz^>b)K+IjsGR0C+`dA|AVt*xz|zPPw3L~mLbLhMebyW;fa%a`+u z)9G|qh~4S_ON*k|ws)FmVC~eYQ~9>s$-lDI?7jBNl`ESCtmS#WBe(NYmoH!5%GWkG zH+y@Vo10gDv)Ahhu{e3k&d!d#U7Ue?@;u+VoNsMyUDkJXeRujtLWtgUvM7RHesMaT z_JmlRoXPWiTkhofj@-`w;qv9no6WCo=5KB`n>+S)^EF$|wav}V%WGR(TfGN&h1i{3 z+}_@{cbcaW!Uz9%@MBEjJE)s~7KXvq=yzjB;Uv?Yvb&%xWW*ujts!O#0S?nb-eD^n z_Vkz;kC<8`i5KS2I-aGjD5g`d=g++P#N2#9Dg56w6G1&m=I75izNHSR;nwSoXP)@$ z>}O2vI5UQhV_CDR#!Ps+#Z}9)#-_R0D;a~SscmopL&veqSyfler(3FOnR0BIi;J_2 z+2qThx8hy&_gvtmJv2tgD&lX{GtdjsE72R!2hnF)bV5d8$b(-xsoIkk218f%^F`x! zEzhEw9$e?#tvOIesN4j|srH$p6jozgS^cwuLuRR61;q_&DcC^-I7F@qC8&_!%O)mM z6ax)Gx>eA@?ec#(eE4v9IQ)lm%AKEQU2Ib*>R|4^#Ior1ur@n8dvx~b{_*};! zKP;>a{7_&pmz+9m4$$hGE^?kN)mV1R|HF>sTbc$q2>bvWLb_4{DT<=ORIaLk8+j2} z23{mj;JnC4JIvG6_-IZA)AWi)9W-K%maYR1{2&0(G|P8g{3M9tnroV1NoirQ?I;Zc zjD?lmToNZS)^$;(9kqhv#6>N`fE_n&)$14(g$qpu5+`wj70QLKV+eyZkQ_sJDGTE! z$-h~`(#|(Mg>(G1A*H9mWQPVWRHHY0r0R^jp$7!&UmZ9Ljcc?6=ws5d( z?Nx)S>=gN;ZL`o_6e0;oFO}yE25()-6oRDj*G1U)ivzgSb-Lx@lcUk-Xc#_c`cANd z(2`7#lO!=GN3K*AhBVC^_PlA^a67f_BsAdH!f1WGzrh`cEx*TA7-Npj7*m{2oj-s6 z{B*jyx-?x`nZ71X)9%vJY?^j^OLOT*j~qD?$9De~8^AEVy3jcf<8u_4|g8>c> zP8yFY1dYZ@d5DrdcKtk8;`uuv3>vBLQ>=&|FTQ7*U|N2yHWSAf;;3Cj5iLBg08KY7 zA;57c>^+q{ZPv2F&mYTO(l<2ql~EH*YqK5SG)&VAOanR)#WU?F1_*=Z7&|sr6sma8 zZ%b6}Q0!80SxxOVn2n(PJX%Ayp-0g3^XScG#}(0JEF*4L-EIdCopWfk*)78-CCnyN zDW4t1-z6}70-!z`*hSmaTA)j#QPp~Z8LqkBC4f&w@}57lTmld-j8NOKvnS#c z=sb*q{y_!s7)4&q%Q(iAt#7$;$H^xP*L9B^UOO_YtD4asjYgxw&{SQSf;(K-3#T@- znDlg=+Kkms&&+ld76uEp?lOg%Q0HTe5I*>ygFW~U*h4d@htOWojQP80^VG_WANnhU z2s3MezZtFFaOj0c4jV?t_YBjL^=$f3ISZzWvE%d&08$JzjQN(q#Y2$FNLS0yU>#n>U3=r70x;X-y@DW@paw)RvAslV7}`o zB_SS9#_k7Y-8a+v$(Sd`MmHb~lL@+gjGyM$n_SiHIGN<0YYJi5(h$1kdd*23+q%k| zc_;u3v(fQ9OU7}xlcuKUdb4w`=b35J?Z&aqF>AFOCI)-|mjIw?%Tko7n7S_5viSlJ zLz48D=H>{tq!awv9+L&@{M2{jrP)JsrDLo&+aXW3>$79vtL6i4@})!3ChNZSWnK`6?q$+L^3IHf8k)nT#iX`yRR9k0a}2*+D;G zJe<4%%T2YS2faP$5AID?D#j&@fpeAF&|x<@OI(9l`9%}JmY(Lqm5dUjZeLi{6RhnC zKI(^Ug{=N+9F5B`azj00T=vT_>JR#p`(dr#Mol*2vsd;CQLC(@4Fgp>j(GPhNXLCF zCgvW|Ln>zSzOZ;ikOhMZVWjLP5VslYml(`hjH>9Rg6AMB$T|x3saoK3cqXOPv^+4V zOBJePJ+C0+6@zh30b4={6=Q>Dp>iT_sT>ooS`2_$1Qda>;10x3@Fy3C2< zw62S1n`~b&>$1pl$g}z;q*_esL*Pv+Y$Yz~uDZ2G#{pcPPb8j9 zrfE>>Aj=>RBY;uJAdTo8#F6F@5saYJKv$|2DCN(hA~(IGd>S=%{6_EME?VoI_fbL8mP%Jp zOc4*lnMh&*{37sLc@(km%2_RdtPL+=W0?>nuA69@AN3m6%Im&bW1L@vg ziT!@BhwmV)yH%cv7|GMJv>y;PbzQTB(CpFr{I`WjoV4{As}D8PfEPtlg0w@Z1EFeC zMhl5TRS|}ct7@7ShTaK#cs&U6+_v+Q?qduut1HL|b;ut&Yglz98-Z4we>VokdKzKS zQ*;%@)A~khsFJQUrtFq*M=h;kphOa`#$hv;f^*9>gb^7u&EJT6V=z2^Cjo$3Hora^ zH=96biUQ;5^xV0#+sy(8;p|5=tqvfq4J>m-O$|FTl zlvNoKk9bvk@^}(egHE>l{(Awg6h#qLQRQWx=k2gl@YLPi-BeZ`%v`sWk1Gc^Y0!2|CdnwfS{{UM1f3Wbto1u{;$;%&kre#@gedXuEFx>wi zkar#b=>Goz+y5V6FELHiecF?5zy0>NTJN~^ry)Za1qXZZS=d8adkZ!TO@T7NnX(@h zCtY$3s*{!75LFZ~$H2T`;-h7`|IfH6ih_`$C<^@VcN|*9`;;CZje-Xf zKsh!TjgI$hDeYcQN-2Bsc9ms#7w$tU3efM^x2DbW25%@y;t|Q*p`6FLm3m<^jbx;K zo`#@d;yG%yiKv#=mL2p7Y3PwQ>|U~lQ(XCzZ93C}leSr~-r_!(J}v4b53yI_xC4}i z?O^SPprvRA<5_pcXEjE-#wcH=(g}Qa%|BJ?*(p_!o!VE0nH~=W1;Q=LwL|luDMBL{R5zv`J2F%=1!y+vwyJ5Ws$fkwENO7S zINL8sg4z{UFd##RWrK3f!CvkkAE;2BplZ9qxS zYD9q3{ZD&#T9!QkW}nztpP^I;c-BJX`_aNe7zE+MC+>Jvd_P=x_~wNLsLx=mB}qr} z?TKe`Thv}Zd+z$eTdb;u-=?Ay1fXe&&syzc7%@Z-{s>+GA437^p`+*wx`19;Pfk27 zm!iA^`yQ@s6DsIjH6TPJ#WnQ34bp)m5Ksfx%CfpHV1E}RkBh+<$BehYE{pqTqyd~z zjA!_uVwzBZ@5hl|+zTt!?SL`9t*YSg4L59T^x=WI`8)5LpF6d^ed{kbgE+wu_(^Ow z)ufEv>Qqf5r$N(&N5FCHdXf@d7#@Ia%eYpG^h56SeB-t&}l*qjQGtWGuwZ7Q*4&e2r zb8NB;bP-oED$0K;Uv){S5^NyX)(Ke&4?PtMyXy7G{|I zY?V9G%qi{CY*(K1O_Z&T`9d73#xo0yOjc((Xbbq+LhEt>A;cbnPm?GX^|(C;dY>29 z7Uke%y&8@GpSpABeP*ZY#q8p5=<%Cw(@j!<`|w&FeYW#a zonN)2O)DMT;|5YpEFI78Zqb$&9^9^~3gGX6=M9BXDq}+`*sIe^HwB2^h=OGhTA-H} z6$`PcSSfLG$#eK{O6!$T+A!f@O34oKEXMr4^Bg|0pOq&r>h-uF#q8LTPAe-LAq9#Q z1C+#iAB@qAR`?%`MeHnIFsZJY7!j01Yx+Tg)1n^$?6Y7J^?CIw)y<;FiS~hGs}Kxm z?Su#^M6r#cpxCn};@JB}Yk{b)r7`_p?6l-r8HuqKyt70}q*a)J@!HE;hO{>fRESOk zpiQ0?w%_kT6s0K=rwBkvN)}ry6Jb>3$ziM%kls&2WHjhJPJ+c5J!SzmdRZN~!d@#p zs7jq4WmTOJ5yn~p!C32vP!&-KCX~hgu&FtdU@gBiF^Leo?YZ_dR<2+8efR;uLmxpp zI)*OC^Cz5UnTYWq^44g7mF5)GT!w=ICLZ<+N(?Q|{s^nS9O}B5hHIVO(I0iWn$~Km zs&-z}Y&2>sOo^h_yzguJV!u;{p`l3WxpwRaGB(0+TdBQX6uz$OwdG@Z({-c5^Q>W# zsNAV%D|S6A7wdI>C969MrSz8NnZ@!6{x`Fk+3a?k zeN~~pWl0$mGP(Y`DoJAFv8zW8TP6nIZ$3O6Iqs<2!61y;o~C2m>5ho5Ya9?lDnbW8 z#0>V(-ROSwWb_>LWAv-&Pte~Z)EQ5zN@g%nTZ_tW#nZZAY1wFiQ%vJF%9a-Fvx`Wd zU^|vl{y-%&vfQ z`W26^X~-G(RF-YD^XTy~>bA)UKozSHhWRazjfNBWNl#OWW?8LNCj^8McUHOW_=)ZF zM7Xxl;c!cmINDHz08L6&RduuMTMCQgR-RC*amNW;t)HmZ!7$9lQw3dAh?Il(NJ|$@ zTbjRV+d?!5t-V`Sn^J<~)R#(2V7F|y<7QmPZeU|fq?H4BKIe_W4SiwSQWh0jwdrBs z2RJ;x5aHTvFWYEfVVEb*v&1h49QuGkQ4Eb`&7vKK42ORJlv308-C^MpElo>@$y&{i zL2rx$1&84**6eIlh50O_Mm@_~rVBg^T#0c|rRT!WgD9Gdd)P9$Ow!sT)0wm>G@VX$ z;k&k?M4wZ+>-o0*4aWmbCBPZ(E=k+y)$69Scx(hDv#I_aKR$gDwAEg(=3ynVolMn0 z7_l!X0UQe3wq_PrGsqz@qZR@ z6U-QKnualF+8^LJOw+ipWJ>?v(|_k5Y`f-Lk^r$~lDqDe(@=H+5o8_w0el5MT>D=3 z2BgR9$0QCy1Dd0hXutxVpI_fwSXgi>ctg&O#~5}^!(CtB|BVI1bYb2#%>BLf z%yn;{pPz?D-aEE14{zTXjq)Ze;PLoeo>DLMhp-DDMHzZIdQ<(Xz*sRXSizjC_VZ73 zKR@scGRS~0R7mN_i#STyOrlSBX+s-ix;i2kjd4VT5zUK*XG9?)LLiXmzBoUg=6R|{ z!80>WQ{W1Q)4FXpEKCHZ+?#AoLh~#Wbgcnk#<4+%OU9Vel%n4O;(`-_j)H4des9|*MkPDqM=4DH1d~0nyZna<GtxH+%@Kqeszu>fVG+M6MS&+7z#op+z%HY^I_)V%IH81slPDMWPHu zt5+nTa5|~o(lj6wf~5b}M`PLjz~=z_7l4uv?ctzlBBXI39zIf)7fnX_K6ZIKs=Du40-&%bha}VJ$9N!Z2F4qf=rSB5K-BK0iO_IC&%tgHYYETTdc>pw>BO z7<^J!g8;+(jU+XKKw1En6$F+AU|GTSJ{bf?n#eNObhTc0IU~`b)#=pJ386&uR;Pzn zL&CUQuj`ry^)Z;}nyp0%;C6f2G8Y2T0)fnPoxUQ&&L47%}*g*4F^lP_^O9LZwm zyPT6+g@OSY#kgPfgQyAzeRfJ>-VeO8QR>4_`UFQ|Ani)!q&{41ChfFcOv|=vOE~Jf z$(xn|?oP*+BbK}EX5Ox+lS*Y+*J?Yuz*1tH4mh2VQF&y&TQ&$n&x+quN%9m2OBLBDd^hhFWan+mj&AlZVLSB zjCPAB+RhEYG-?ELhs zxMw;=TBgqL?_Iq$J6P=Z`^n<`d^m#t`%el11u(xru`yBhlk63Cv6F^KxNk; zD*a|+L)$N500SB!J2NE)-Da<~R&?7MKF=?mXk(xPcoo31rsXmc3$f&yhqGBu6frzrXt@O%ziI&q?cIiZ(jPArr#7nn(^%PuLA}v7H*3K|2H?7z zUw`ho=LSs^M^W4~gO6#v_rCeKvV3mPpaN_+gKw_k?+L!OEMI$S?>DS9oA*zWPnbY4 zET4RS6gS;|zuUwSl*shMQkLWWl`+rg14k=n^MQsruj?G@X7c5eazh@>%hR8gFR_it zYT!LhD=`tbF#sFRcYcxpJs@o(ladA~hg~#RfgWvpEkh7&$(iPxkxdXSIx!e~#!`+- z{$q2*Dv8!OOuEPOATq3U6hTqU0mS=3Q_J$NCk~wE$1xBK}u|A5rq?=}c zV1!JPLI$W@RZf+pN~My+(I^lWb(7^(hG%mhl?U>bembN^Q8v&=d;4cd1V~O31LSdt z6!YoP?4V~1z$l17h)g)Avx0auy!wz~PK=JSIPUhlA+k0}e9l}5B1WXDG$Ylm>(C@g zLa?rzNlP}Ft1xOM+#8xw%{r}JIebR$y~dCtDuEx&=9 zny7B*{fKtm&kHqu2>75V&-#NjQ`$SAy1F>%_W0lLzqZ>SR29TY33VJ}S@!cy9L135 z!@7It>=fYYm6v~iJm^nvuUC6}07pmnWX0iV1Z|or)j+Lv@>BhOzu)io`*$M3D2pP9 zqBIbNqN)(^kmF`32Q%ZohXp*2rf3b_i=KjBUh}?0KgZIxB<4XT6^)~o&8TXZ`AJp8 ztL>;->61;%vZ^ZAV&u|_ zvSn%x|Be+!Nh64l>iUD%B8;PJ59%5$CW8AJK0MAg3_ted(P#wNr#^Lr|Bmp3s@gx2q~+WA zY^oP8Uj6va&JH0+zCZNWio~U%(3{1Q*w)-Ohs#jZ$19_&(@Dr_17MM{Bc#C8#2oZR(KGEu4QFQ&(|GC z=(^=N>I=AOiNRIP5ZdFa`XkK{+Iqs6v!BRD5ju|UME9WkYfFrR>_}D%!4P_HtU>k; zVK5jBM|XKLoR&oim=v-|7sBvqaGK?4jk zHD{U#J&&!SJ%bs^9VhIQ7h7g+Sa^|?5L8u__aDpiyx;gO&Kb-DXZ#wR!Kw!+w=~@Z zaBR~6P4g|G>)gPwR3U&!i0;%g&DSi0Fv2Ij=7N~yv&O+S_&4|#{!zW)zj5)E=ndXU z?Z^X%ai7f7eVO<}G#V1L zi*O@b?Z~C;|H&l$jyo8h#V3~2G)=0k~}##6AIVjGN}j zn;(XT5r&2lJp0COmsBSqO_!I_6xzBlL;-2Kw4A1JvZ9ApAA0oAp>BEet-T(=#>TUr zwXp%v>)rY`cKr#cX-S+W#w8vELBLoL1R;A{rncK^4F)U-f`GFy2m5NNzmXX8W57A zMoIr-W^w2{sBcj5YS39*so{8j*==V+LV_FULG#qH^>i`cZ~fS*+xgpH``XuTU0rtF zLU?Q!K^VC~rJeUSo6T@WsGYPa-}}T9PuL2Z;1joLMd96}hxUIeNfIkDgsu>-8bJ^Q zMqmj+G3O>=_z)DDG;dG2|6YtSw0*BdC=P>W-S^oc@{R3wd%N@IwLF!7j3u6@p8NTk zRYh@Z&hPDmrWz(8fEeQFJYIzY#b^;7LTAx=^c0yKI?^OCOz6KD`m%m(nG(H#h$`{eHiXqbLe--H}d$ zf%pNzK|uaN5EeybxTI;6Vrh{za!6sh)@hWtkpluJuX%PWqHAqW=AA->GnMx)V?nhrk#VeG!{?Afzt zS8Hj#J_Pz6TuZ}d7{b?Dhu!^m{!&EP%}aOjU;Ywq=H+}N2gHlR1y7<^BLwn3wjE*6 zK=qR0l0c0LejjyUs-xg(l;Onq5l^77bXJJj3;3w##wtdB?a28(TYiKN{fWX4!pJXS zk6>=qOerPf*Epk82%!_ItAyyBV=_s(l+vtO9FrUEFmmWKH8i@Nx#Oi9EX>X}7TWDh z*V=P)!CaQDFE3$A;vc)&gp=S0G%vT^8FTHga14ax`$W%fgWX?m2J8f z?9i_P2Ha5Qg~I`0H8^$i zf6~654zJO%K*w9?v@M;kq;rAJ^>itx>ms^+NB3rW)Y7wpo{jW=i$06!*PnhZ^j}pT z`u{c7!=^E;l|q5xl?;E55qC4Pl96XJs)^A}j9t&TW+rrILNgQFGkG3UJ5yQB&YQAx zkzI~vmlk#{vD=sIxr)7N*r&igE$q9D{pvXIN)9@N>PC)g&(T#J)4;L&aO^EidyeT( zG2=C6&SK`T9KR1IH}KDTPFcn&MgBF4Q>&R*!Td?gFR|bu7B;i!ZWdRuq`o6^BncFxON=ZeaH>BaN}TZ zyp)@IbMsT&+MnCTar>s+-b_OschqzDD(?A^`@W>Hg$F8lu$G7J=HVib975A~Ja#FM z*U?L~c7Wk}^FJ9xzS$uN~#RA15|9OdTzvO?j`2S$OJD%^4=ZClWaUXvClh$f}|B^o% z`D-43m&B%$eIzeQS-ouXlx$WZn|&%<6lCj4*`~d0ceHHZS$3Et<>m5^ePqW5+3{1U zm?jks(&i~?S1#>)ONR>SP$wNr(z(5Keond^FI^9iZd*#XdD6YVbZ?cO<0OXM zHPZK7>Gz!sd`pHj%h2^w7%aoLl~J3@XfnD+#`KmkB^h_Tj9(<$$#GRO?M#{ekj$)+S@klzRpvY-b6e#2L*&FNsX0JuTIA$ua_V52cZ1A-OBSq` zh0U^Pnk;IP#h=R3-m>gDS>9h(Rm-ZPtge(b?PX1)tbIy0Y%3c|a>m_qX0@DkshoYG zoZTwtERu8YmP@{rE9S|S-R0_C*c{pdFWhus3;HD$|JkVBdwn6d^W?1-dAmX09VhRWj(tm`pbL(EW0AaAv@RA4$KoZi zL>nx*982xSGMlm7bSys~D{RI}C9z6LtU3Uz&ByAWu@+b-FV@+N_10s<7T7o%n*?LK z^Vp##b}Wir(qi`(*s~4x?ScL3V*haLe;EfX#)0>7a5N4ni^GC(*lrw=5l2kMk$G|C ze;kt+$Cky36L8jNTv!kn6~)E3aa}`Pw;Z=s#BG~#drjO~5O=-C-KTMH3*5IJ50u1% zJ@C+NJdzZTw!ovG@pwTz8;|F}bLa7VUA(v-FWtr~W$|iVyuKN4e8=0X@y>RY;_T%UI_%$BCl?0LpVhI2MscB09 zfB@-`hu^5%hXPLX|4_&?P8QdAINhOjdPI8a&shOJcIX`M`FZF(xA}4C3OBi@S6|~| zA!_X`Fi^j4|60W?I23RxREI)#)t}+D=?<-vsr1k(25SA#Ilc$>(0O#Qap($f)%3bt zjI%2<+HB)Gb00pMVs9C@ySDc7a9c+5_indBzc-+^W?$n6j+46dF zyFb~hG?S4Y>fW0z+Z?r3QF?iuzL6(B^@})p2m0X|Mwi|U70t%2&xAU`TlN{OJmdd)L~067x#2P<3&qHG(TwGAHlqU$_L1#j<2Ug-3s$ Nb?&+EAr}Jx001#^dC~v? literal 0 HcmV?d00001 diff --git a/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf b/deps/font-awesome-6.5.2/webfonts/fa-regular-400.ttf new file mode 100644 index 0000000000000000000000000000000000000000..549d68dc023ff6e31b8774d784c2cfcc231e7976 GIT binary patch literal 67860 zcmeFa34C1Dc{hB{o#oEHFWNQINHek}+cVlmwy_zEZ43q?%;vC)1cKR>vcyTCQ5Kd2 z!Ye2tG$oA^2z3+ExFl~%7FuO%NK0BbO(`L1s%(^|U;2)O<&cDq-~a!dduK+rY;4-( z>-YUS(sS>*+qvgF=Q+=Io^!5{LI|Ij5)P4h-PvmfFTMVfON5YpI6Lvi>n@u-?XeH< z6T*114eh@g!43q3+bN#dUjoR z)9!b6h@T_ByM%CzUwgwFFZ+{!z43V=+IHgHC$76}_oVo-xrnp}_h+uZ?7Azi*zAo9 z@#_HI>6*OZ#+%lD>(9R_#KD9R-l{-Z9WIyn%+4FzE)AcsDEtN<6XHk3Z`^M0559cx zN#joWG>$z&Uq<%$fBN8)LKsuH{<3i=&n)?UN1geO`H~2#^SmyX+~t(}%5=!Fe(%A9wfCdGDqU9Z%`}#?mdC8f)b1DRzIIh7bRM2x zQqEa*y`h|{d;#Wb<&iPUI?ep;w4FcmRdvEW>bj^sXFbpMww~ubvkDYl0w<(VtE^W0 zi9?@1+A-Re_fH+|o@2ZQTCwYdEWx9M1JI+>o?#rZU-w||1A7=2P7gY&dva zy`o>N6&Hxh#2dsL#pU7(@g{Ml*d?wKSBq=JwcqhduH0Igs@zezvvODEy_NS> z9;iH6`C#S4m6^)NDt}b@dgbZLT;->g7b^c&d8u-s@{7vLmFj$8K02S6PtLc@x6QZD zXXpFoSI!U3Z=T;ef6Dx6^JmWQm_K*^qWLT4@0h=LetQ1?`3L7eHUG`|Z_Ph9|K0iT z&Hs4*XY&Wg|{udec{~;cQ4$t@cxB+ z7w%v9z{0}|pIi8|h0ia1Vd0AlUs`x-;m;Rl7rwIa)rD^_d}rbN3qOC!c&YED3t#&9 zOMkaNvcGr#y8S2aKWYET`?u^rW&cI{Z`=Q={m<`zVR2xwxH!6a(&BlGS1!J7@t(zp z7eBuE*~KRpXBWS+`1HYpXyq=E6)QzSobcbMHJz{0n&%(Znr}a1Yd(tB{D#$$9nitMrK7YGv&HGeqe)$Nkc}lG{&p~Ux8Lj!&Wm@yIhqdOH(VF`jT65=d zwB}nE-?8}M;-ib7ski3Axr6_F@OuaU@!&t8UH@PIb$qg8NQlU4z$z9d(xHnKNkrli zU-;MI13V5>15&^6zVP3MzZHHa{EhI}!hgY2;imwfQK?Uc{}}1V!tV{=6@Ifivnza= z+Fu-ojuhUi(&g}p-18S+5pE7QVV?}g!!hiAVMj>4D+IkNG#mO<=o6tC-Whr@G#$Df z`*#Cw43)4Q1{6YTLaRfqNTtfH<_k{>d2CfZU6Sz8XRbW@(92`6S&+!TU^Iz;g!+(N*%)iFp=Wq2l`J?`b->>+u zb%%WJ`2UP&IP*5PlpQ7{Vf3FQ2&T#(e?If1j# zc&+0;32DWj?8atBrP^@R!W4fj7vAk@7e6)_PP36T^jkrqwRid#ghXcO(CLv$jKF3}B+ z+yfb#6M5vi0^GS@42V^D#~LvxhQzQai7~Mr9D9Q}No*1)i!EX+bmh~Xn?o1T(2o68 z7Lfm3Z9ymY*I1Cj{#pxIkLxTT6vV$!J=jlLzS zLDK}{pm#4?@b^aBk0Zg-M1mZG>HY0$tL=N~Rfc8WVZA$>AFLJyOz;fo8FF>WALK;9k z{)HGtklKA-`VFY-kVq|f|j4WNCI->N~S&qf-6J}dI)Xi({Mkp_@g z=TRntN`qGOq*s(V{{ao?_oBa51A36?Z_}XCpr?KS>(UQg6VS`W4^dVE`^^v0ZUlky zRva2wuZk0Zvh!UPyo11JE9hec@Cs2uxd~zhd-O2^`%opVL5yIJzC=Ku7ZsF~fSxZZ zXj=j}iKw7I5X30oMh&8j{mmM{Cq?D$fLrnGChVs) z2$t~<4d9}pa;FCJlFD5g$V)2k)c`&#Dxe#J!ubP81H>uVKd6EIrSd@y3MU^%8bEJG zWk!QI75k5A0LK=UM*)9?drrgt>l(zF*nb1?G|s;c`#BA&-G7QSK-KF7qyeh@KSTQ8 zklum)OBz)E2apDkS5|(Zfqbs=vIfWuQ2~7sAU(u9`UrtEIUm)a(g~yi#Nm8W1LTjG z2ald7{p`fPO#}JQe7gqH$$VA=aX8a_)u7Vf zLmHrP`(va5d4|F6Muw0a}Gv@M#dYVGlelfY!wC zVh=nK#1!^tXb|tj9=IY~{h#MEVizL4O4CN7(f{^vD_C$N7(1Iw^DpaD9rSS)IQ zwksA#HBjGMM0p5o$HntBh|geur3Ud?>_JP5Z^J$R8~b+vNGE99MYQW8a4f!#J^JJ# z@$(G!d>+qJ_+0s1Z0rhe})3`6S@CP1=K<1{>v4Re&qgp6|ha^{@+(XIU@Ie zNP$lO0n(r;b^c+bSzq4sQ3XPrjs4>a*cNjC6AH-RKcRP{zr{FDjruE*JkofvWo=@BmPCUqrnCs_u(_gEVMIJ^K$x{{zw=#{O9a z?ALPfUlma1%Eg~4pv;wvzfwTHCl5da1PJjY_ELcm-^AWfAjB;8XeWRWDDMHM0wKPD zy-R@*DEk4o0wJEl-m5^MugL>G1p>4!4+IqmSg`UyL;-Coc_6KTeMBAr{sBUK1AEXp zKnS$$fmQ`V03Qe16bSKE?AsLx*xd3!Mu9-zln1&M2=TYr=M)I>Iqdrt2==1`0}9wr z<$+ZS*dOJA)e6|}w1d0>+Q>JRe3W(7igOUPf$DNuCsiytEWLzEx% z^NSxT(CHr|{bTF=PmunJmHsKxKSg>k_Ae;V>3>1`U##;lBK@M3M*IHaU#&FS_ZL62 z(*K6^zv=WZfpdVO!(WDw4q53Iq+5`_9s8^TA<+MRxk7;upuu0F`~dyz&yoH)(w70o zA#Hyqe@8wkUxC-=dgEc^2aZ9<)s8PX4QI~znDgIV3D-u~<*vVUpXPp>`+2j?e1kb- ze%sUSImh!Z&y${?d41kZ-rw0;`@Yu*uT?%kN*k(zXvjbt$|wte-rEoJ`{W< zbbaW#@crR`kF1Q`8|{w%VayYIH2#J}TjI&&K=N(Lze%l5{V3g&{;Q_ZrcX3q+p@Z4 zf9n_8`r009d%k_JeYWGwj?ZP^mtE-D)bq36>0EdIwERq;r|+V^Cs+LLO83f%m0##j z^k2|FJMcTJdRFaUeeLS6tr=VM{lfi4U-8!B!rvpc2Ssz$`?fU0Vc;AN28(!IX(}{g2zFdCxq=}QhziI2HzdCv2$q%0V^5*L| ze}7Bg*0!y0JEiTE$G5H9_JvbVICbjOhfedI_WkXu4W4_^PS3*L0$kQ@^2KO+C_9udnx%9;9mOs?luZ{)U8AL9akSJ`Or=ve z?Ka(JS235*<)eig@oRtKg%@tTahl2VRI>Ul84M+4CJ_osrmAyHwLFGj^|LDK#9jv+`q*F=yc&Dbp+t6-&j@v`(ecZZlitIXmUf z=Srm_&)KP5eze4AvcQE-jiybrWU6ydC5`Y65i^x^gic{0&T2oCyYr(w6~005wl!SY ziJRT+?d>A;p1o95=+q+#+Tw+$y9ADqSy{95?Yro(8{i!F>Li``ka zlpAfvKh^|)JcE|QpLL~J983=mB^7>0(8~DJ*Ym6w{&=P|%4gM;ktg|jm$Rp{%j*sV zQ;FuLCS$lG=yW-pon51`rlx4pZ^n~ttqF(WK$TNQd-G86kXv2hXnT7{XH(L2COcNj zJw!yoh$U8bHZ=tUrpFzKcs-tIDi|~!ZnwuwBz<1fbjU&8Sd_l#$kH2=UcX^DU2aD* zWqMpiY7rPwe}{YrXsC&PJc(MFvIuOHq)XM_R5dEeB?oT~g`&~wEVr^;Jt)&nO=)Ru z#ge1fMWdlmb(ULM4!4E_fpDwd9u$p7c=f?w9h{SQfSwDav~;pYXXzr^BcCrO(Wi3x zks58~i@lrN?r{NPYM$Uo;Us=bT_7>O1Sh#L=N= zK=V>W4p!t|ISmi3?o~<9t{Zq5LJ8QvMnLbQr5sv*i57A23G{2Wd8rt+>pG-s7=0)Z4D5<}L-Ebytktev+>Xn; zLMU#Th+I+q4C_abyH(!5L_4yM{m9$RoE1-C-)D_@M)M2B9q~@S3D|sKWqS|lq^EzQ zc|u>_kOPnXJ@c(rB5s%e@7ttoY0aB*QnvMw@Us2V$oHMFVdcZ>)uFKeFWdU^tu0cO z;n!9fU?%~~iYHs|WeLc&y75k%g4njiv?>R&t#}x5o&q_z-Tn{sjrHcF%=M04*wNG+ zlAc)7Z$!H;>`6CC*_7_NT;23Fpa2|IqAdT5_wt!YxW$?Ddt%NGb)$NK*Nv1a=YOD_ zQ}Qu*|4~k~gM;lbv<#)8RHTV)vGkNTo(N2%y9X8Z^$|yX#KFGX2$zl0x7Og`=vHg z9`&21%*cD`4a9n5U(N5wFJ&Lr%*Z(#{`2-lBi>=JG{VZ_rTE?hX0T zAx%cQyNr^6D2`-=q&}E&HQRmScTLmo2i(gQcSmB8z^S@7WZj*4yIXuj+I@=q^1vq} zfk^BE;tMkNd3iEuIbqS!AYO$w0v_T@qLD%(=nL*jFcI@T|AMA~Kp;?^jCq3zd97+^ zSO6A1cEbz#Mr5pOM!rJMf~~5brb6^bNJTYX|_N=>=&)d&%UpVxH-LBtNJVCW9xO-7?QZ(HN3r;Mg-C4U1x&{{GC>&$6 zN}-zbnA`Q&@^*PU+f}AaSJ9AALKj_&W2IzY3MHIy^MiW`rPXo+J}=V&Q@xI$T$sdRUyJDrlUa~Kqc@`py6A?15} zMl++mJ?hNF5@l20jmKQMRCn`AgS9rIzV28{ONWb($e!L&g`n9(6jAEBvj$_VPNVl! zY6(d)Vc#LyXyP9&>2tO;wOItu6OxeN)P@I3b{*4rRCU1=I+qkX*RMB>_3L-C6x-IW z>+Y7ayL;W*ZC1LaC0}gGjt=SbLjxHzm@R_c7J6QfU?6g6vIL~A3B{E0ZvrtvPSUv=?uDBBttYl?)W3`d&oA?Jw4JJHTy zCf8nj(@i%?^IDsC;OGH6eUB;S3?JFEM?H+&d)qt0*-_^3^fazoOsCFl%xRWi9{lCO z=j5G`AMmltT((%W?H{ywqgIc`#viDMnx))1x@F7g;2^gVC?Er4_zet%@Eg!+=o=Av z!tn6u)~%z%!#IldhwxodlI&@cvz+jor-3A;`l9BPO5(9u3Lc;YUFkV$D)_nMOT@LrEPsjgf1 z;xRc66x8I7F3F*7Ngb9NHA|=TS=E#(U2C=q`$EN(PjdgHH1%;~`>^jz^C9l;LZPrz zZBcu7_7FN+qCp?>Mn1jVn`ZW@*Dw2yS>SrG_6~9Ywk~T%!lcBed?9`txP-Fr=4?!F zE);wp;2S;?^#`PPj1 zr#7~uIF^)gwvob2EZYRK4jETpwv{m@3j1v3OM2?`@?Cif`feBMK?Fr>_7>Qg;%#}_ zgvweOsY%W82f_$3`_Knz6a0(i(5OtYZnunoC+ZK!pZ_4c=D$3vaFwz2Fo;HIA;b%N zAZdj)7)u$?@yQA|s?c}oW{}gWkh(w3!fxup^P}l_F;3-Ed3%@xWNEe=Rt3F3I>%_# z_u0?-z&)7H!zK)MZLUP*N1UPR|B=y<(;d}~Da$|i`+r`AC5v}YN8MK2{u6k~BZ}KY zD?!f0JM#zMYIsj1?3X#!a^bMQ`n*oz9f9g2Qn^&BlM&PDmzPwZ(D_G9QmXeT@)JAhzAc@R_)vI-*A#^SL%@z9kn(*uEE7VhSvb|$?Wc~V);f{8> zBt6-bUa@lZ>Xj?#G_X^9Hw+DRc64+O4Q;5W?DCaSzNoU7m#D-fK?lyTv$ITEU7?6q zy=-i9a&kbwd|p=c6C8{l0}oARLGHQqz6JBJoGQZQ(V zqtDB4C>)K!18D26w(Y22P7dQbjL85yP;9mPqCnm;+J7N9N%+E*HQp@?W7`^ejm1RP zjRq0RI66xPCEKovg8^Mr*le?_C-as#XngX5jVFQegv(aDvM1svEC{nwqYV z2MR6Z9WKeUN(2y1*S{qckNU4z-HxWr=hjW_o|>AH=2UNUvy{!vy&f$LX7>_f4?V^{ z_x?anPjOu?FX#3^qTwFi3l?(lm4n}vo6*NHwvSRlr^Vl3x}f+7(!ioy*?hTtH~J*~ zJG?{vp`_a9)}1f#8vjb^qN;~KhuhkEHl&k&hsOh_n>UqkNeM{^51cnzztQJaH#(iX)o?nIU&4WR8%<4K43TP{ zLK%@%IH9g!`Ei$U?P<1zBFa4$x4ujD4C&F)9Xm!x$$7x4UsR`Y`9;3rMRfr;;E3;0K7`xl$F)st^FZpM$lU>-TsY~w z|9+)$nqitD3HgU+qWW=pA(hSQB-Kwjo%E@^UO6~tY(}(c56W!u(^BfNJeha5$~%(u zz2%MWXw?12x8Mz~X)_veUv`;05_R9_O307664n1LPm4$yseVpwPq_F#XrB>za`mYQ zcKWqSPyDXZ6CpE|&(j1QV@^Iw|t4SRBHg?4a9}LGL!9Vx|c3(R!---?wdE$vk zFdX~*=h^u_W2IHy=m(=NN~=)kZLy}B6wCLcV+9^z3p^5t#zPi!9c`x*E=heTP?A=y3YP+PMYRZx8{OFU(>&|#@r0Y~svk=CHfB)hu0 zXn3(0BjwguOv+fSRZiKdKkQF-yeA^V@9~+d0@06#WpzT%5M;PIg+`j;?bTWPb}5+Z zPnN?H_sei*VtOwPiM_z>|0l*~fX@%Z2l@ug63nNe6zGlzCI*y}?b4CMG|}gV8LpLw zcme#c04(z|dMi1ivd=NXgnp|nbDrgOvsl2MLitAyhvU<0r0mL!Y#a>v-5!^FaN|g( zOUgB$b_Ao*U=Y}CYHG=*V6bOC{b_iPQ>koAQxkA(`d|tghVjU%lN_UcdA~pF4VrG3 zGwbvB^^H1CTJ;F@TzJ6}36IBRwzl*-209;kq;tU0+tO;fJf1`XJP&d*dGJd{KWy|q z`Wc`trm3}or<>e^H)F)i@&yAKKtM^=D2cHkR-gBnJcH+Wy~J%kFEe3t^Y4dUPTvhJ zC`RzT?+pe5M#~L8rz`yXo6YdZ$cQU=%XuD|yxQ--Dq(ofyCvuv!I)L>{V7K%8Oit2h;_n&JL?#zEQ3Hz^+CEX{~6)fgV5xt2L1&b{=lacG2fN0#{7FuMYxT zZTCf^9Ua}>9UW2p?9>{lX&;l9c(Of%7Y$~6JVj%0X!}LmhXxI=Cs@5T=<&+(s-{$` zX_ek)+uLK&Xso?GTTfN@7CSrPo9QgZ_#kt@b9kP0q@6zt`kYmI->I-o5{>HJ$S~?@ zSFlSfJj!W;)2v^P!;1JT!JZCW)ho0bjBW*vckSvL}r zM?A=PQ*R;WYEPSoX9i{_!*Z6}YPqcVV~yce%Vc;Hjjk{F@8?*(BW$)cy4Ny|4`P%g zTve~ZsGE$~Jhg$lmdlL~^Cg6d!xH7jY?nL(oI@Y5`pPQhEz)K!?Y0I;P2PVG)WR|rPEJ-J-AwEo=7F1u_+e&woF{Y@i7+sC)) z@zB2N4~sil?8_73w(gdoHz}K99xHhw`tX-%-x4Wo)dnsWIUZGPN7kxA*9b%F^rVb*p}P zxE$_Eqlcm^q8mbhkfL07nSp?Lc83SsaTlm;syb!2dtC@ur^XwHT{WGUd%fOk`+dGi zH>ss?h#XdAIomj@Yw^fey081#;zPcSI{~dSSvHA{|dtFqz9{Kv$zaDh8 zJAJ;;<+0$`lgU7;y**X^v2Ccx2vel=fInKTz}18OBxz?--i|DzQDhkkeO+fMqwCzR ze?gX^=;a}w*U|0@ex3ORlF2+xnrg+)S7)KKlM(Fs3N-g2eD*wGX)s-+Ok5&1G`Edv zGPDeCHmqqG;%SE)Ox8x8&2E80x=Plpp;x4ymeX}^ zT~8gEZ_Bd|&qL>TSiMhqt@TzTqIJzYRf31UUcGv%UcGwi&8*}Ns*>esbn3XPr=Gk) zp{g7TO<8(DO=e!o*+0F*; z>IL|kC8F)i+J(yp>>G5z4Y(k1sg#oLVj-Kth#M`%M6sa8Q%8>6-DIZN*Voyei2MEV zM0;mnU-8i6FZUD*8#WXQJy{NJy8`KSw&9H5VZDvg06gM8ED0ZZ?$t6}vN zSZzGk(c0dgPPezWcEsZH(BrP|G^1$J-CfyiTOiPu%`S81pP4^j>hX}f8yKL%!Ln*3 z>?Dkbd>$)*d_&2bQ!(QKvDV5Km6p<`unv6dKVmNroZ4tt$F(t9STX55`O}~pm`<2H zuD8f2BIEFY)u6O-^0=M@mrtn17%b)E;XuR~=k}`hL;@}q8>vJ6E4wB?IC{F>2EJIV zc{IfFz+!gww|4BcCJPbOmWi-AZy zTrF_BqchXl+Axt+{QLuEPA!wmnFtg`;F#!pAlXwTBo3&?KM-}tW5F!V1l1V7#^l0NCi)2HBTxO_tpYj3p)ieYp zxb>Twz;D_<8kgbnI8IGNPZ*!(B})n8laT^qxDiAoqc2xeSLez z#&!Xx_!Y*+X8QX2!jgk*{K;_N%orT2hT;>{jhZ+EimGAFwM#k#cf}TQ5kyqOKn{?{ z%k1{t7P5t`J6kArYef_HYD@dpL{nekQoIf?zLfi(pmca!LRUJBs0)U3<1nvEj}_wC zG=h%YCY;B9rvv_7!?8Bx2|LylHf}7ead2Pk3nyDsLEj3ev!!=<%epmd+FI#vT(f4K zPRBw~pWBeIu-zVqll+i6^PHsv>L?s?FzrpzV}en3BRh7qq`fe=@xQOJ%bC1hY#nqG)yJ+*c>!yJjOr8;h}_ZujTH}aH>o8_n&%d zf4}5D5o07%QwvAwODkeo29$AcTInHONRq1U>Sz# zcOXNjj7D2Mj#Z2VR6Z#=Ccgqd2ci*`wdJP91?@`tmw;X6g;Ej8ykdKyq6qzwGVnm7 zv&jn&OFOIp-nev<}-@#b_f&fn1MUwkEeKhI=HXj0V+|iYQQgt07@J=o|HeL%2*^iuF^u zAa^`#L%T zo>ama$>oPq9%ll|4fFMf!@2zG)dR_RBII;I6I}jmfjUgU*W83RtXh?bhg=><{nWbE z)~P!K9UXlMrzbU(&qbVxlt=k}=ZrG=6XTT!F%#=F)v9_ZeYDh-R>Dttpo>Q_o~>bW zWlRp~A#;^BzzM71LUNYOk*svCTCmxZoV8QcG6sqfiLEwM+)krL?2xn*`E2KFY`6K;nIYg(iYPN@I1fzlUm~-6B=APLiEdd*VD8=>o9K=CfH8rCC z@kA+M>X&AVNfV_|Csir3a`uTQo-i`gtmPc~OOuMxN~I9Kl=g%|M2HNBWu`T`&#v+g z!{~P6D%-l;bV^~3VXP^f(geKAa6!qna>Ot)Jj`IY97>RVzDbrn9yAGjb?h~W2rKk; zbOxNR&Ui4Ijz-g3kKzM;edB04yn%1^c=}?#0B_rBiFKh;z(@} zhir7C4T!4Il4W_a4K!9YX)#)pJV{5-v1PRFI)cFrwgE*eTa4{6q+)g%*J{PKDzQb< zg}UJ2ONO*C&L`6PrBGgL5?YmFsQmxWCSV)DMxg&OjDBzfW^dok*~D@5a5@I;2%)r! z$4IgjbRVGqYqUfePG^EUYsWY#ipEF~+kj(4yo+r>{sE=I0v_vjbKMu;1-WWT1E<9}D$vTjV;jOGaZDEa0?LXG&(-wzAYd@Cxp?bjxWC$N4 z#cT5gXXKP6pznc6f}|4cq0OPhJvJwwvJ>i30e6vHGnq+{wg}73&sZ|^nFJmqGqwyX zWtG*9EGh%~iGue1qi!?k@mn#kTf{NqXUp?p1g%g4*5x+~Yg=2&1zM{HtE9)?$SJbu zqfi=YG@whPq;%oCx*Mz|7Vkhy{Iu@VVAIT|3<#p}>FEsoff)`Es_jH|78AKtB^OOR z-MVNaKB}k1cOf{6-hc|+f3bLv-fFQ9yH?eY<8QXEO=W~tIf^o7sUhm+EX2|` zv-Bt&5MLk9T6TnjWx)7~rC&r5QL@p}p`gM}%fqP*CUHXCaL9#1^%brhZ9c+zxT zrRL^RtiiZpHEB1^@ZcMn7540g6&a7OHN)ZhmS(@df2|$Rx3=H!lg%w0!_Tz(Ja6Ub zp`8cOU!7t`kqGky-6;Qo{3>uWfE5bP0M%$sK{F7=CK<(~y^3_q!1G7^V3Yxb1G!G* zaViAeN?M$e?(C@%b7-`&EEL`Tuon&hISoVK6NZzZCp!iTVdQRKbss_)5a0`!-c&r^ z(h`r$SzL<;`uo>6Z!K{W17Za)8>NeyKJbWWd&%i&Q=`KO{uo^bUoF0$7?(LYEK>e_4=LvlUlxA{s%Ar zgO{bf`sftVh0EFwVl0n6Qi8p<9W}e4BHoyy0tn^03(8~E;vSeBq<)9)i{Ll*&ra$6 z(j(qupRrT+vAws?*YCBj*!v^j@teAGJa0by-rwe#ItC7Z>wo@QJ%@VgXz!`t_1`P? zYNc8J`fEE&4T617_quv&w4`hmPE291)5?Z2YjG5%D}g2pZDDpxZmD+Yt=)rp$BP_z z*~yI>N5RO#Z~oWn*=4_QC*QjF<()ebPo_M)V|r_(c2;-c>~E?thraDDBegdLH#d|( zDwXA(P1FUOV0ndQLlVXo%Io2#I%n4bDi+Y^;bzKO7eK|-VL)nXUv;k<)k!oo29C7-TMJu7&}+V=0%z

9M#I5qTYFz$ds{RZjz+sN`V@pU0Ba!#U-xW1$qUDfosB_+ zAz@%W7{(}LOuG|f7{Lr*uQSx!B~h_zoTc90XJSPzjJ>|LD6X_ykFn`6aOHf_w&`yM zo1bR9RxsN=FwmV1xe`gQCz}T(>;&L0tHm?LO zj~qvCIp^${Mn#Am-ozigop8XYu}-=(V8IVznFE;g%DcfsPS{shESa-BG488}|J76D zhtQWrb{5*6BuAN6n`J2v>mxM;t2d@A$f$;$Of7}8yUA+^`nySBJcn+Us#+)jfnYAr9M*_$Xyb7>HGh#oI#4 z>QM&IIMsIDm{nK+&~?@3%UEk9gLX(IdqOd_ZUPQ3y);~a)bZTrk3{^pdVRj|DlWdT zs-pwTr8%*rpfn-@EXRR>DgGQTZ>TjM!w36lF6_0<1^oiPMUK~L$jR~PnqIt}v$bEF zmZT{v$L1k`p3sfWd`-C9aZe-1+o$EwefQl56g$UUL3l*q^2)3+c76*ES{_XS<{ zeoLb?N0&OJPn`g4STo5`8D;;JH1+7v8$EPq4JTnq0e9?nvhPGfJT z-7@F%?DE0&zK@f4cX@n~I>%^`N_uQgTRUaXq(4lTd)PmTsc_4b`8fF1AbBqOj~ZQJ z!wtPU8#uJc$G`+$z08<-$H zalNDja^kq#1$fqT;4pT9`TIqfY@`gex*(VrC9^8K)I*F`@N9BP`8i-qC3Qw|T7kh% z$k@kP;uokfT&;Pq{b|aKqefG>Z<<7cJ6fyXtC_h|_S{vwEE_Q2LZt2SK$}-8;U7Jm z+hK+kDK(n~ooEjYSTCcf(iYc4F=cp07ot%gvWn93m~tj)U7D;!r&w~7m=&zfA%}g6 zTx;X&vbPo0z|tF*1e>rE3a@kEK_AE1-+Kh+;HqIevJZeYr?Adaa0VP+kNhExa^p|$ z)z*re#yO4#Q)18nVYI9rG0Rax+*SZm{=r&N1MGLM&og8&)R9){T^^UqjBtVt8q|Jf#zAn z6#+q}Jja+)O$#bb3CeTat1V|PDJ@NL4AMHKZvqrVV2dCwkNF|htHWui&W*A;&`2Oq zM793*aNJ%)F`Nc#(+w!bY|ht=1+L0nJ9uC%5Lc0b`_T z(xswn?1}XhaHuekJ*ZN(fTqrTF|TJ=5|w;JZIKO{NN>?tyj5+jZ1jrIXeGUxkBUIH z=BuF#KrXX+(42C&BM|VNy#q5N9kDkAT`qI&T99b)4Ka28Y~Nw$uRiIdlfsV3nXA0s zRcA&V;gdq4LytopEiGIw4a@x1Q{^S`CyyWUD*ujUUe#E%3s$W<((CNkSF4A-QoVd~ z`M8yLqYp$>RE~~TtS?4cax2C|QnuV@=mu<%oC!2$yAi0$j-V!SmI^91iA)5FC}*Jn z+}ZoO+ChdwvACg^SGT(52lzQ2+U&`+lxg5ivi%g+wF4s z3GLlp6fElvj`#jHXzk#gIbHuF^1jHqfBr49F8k3dg6IWGF)@@FE>oEEyOIWRAU@sJH9q@ z3SwTa;+KCct&M>|Bs&rz0;Q4W=;zUF&R6yOn6G+=YNv5~7-F2Qc2MY66Smr+HG!Q7 zWaedTrjkFA0J zb2hMeGrumx(P55Fv9D^rafu4*doDm@QaO**5hAj1^XV=9h9xi|;P(M~SL#-+jnZOkrZK^-1O(ZgI z;YJ4?X825`Axw?h;s>U+`bHgc5)P{-zCk@+7>ABV-K(J5TenLXw@qQjUsz5|vH-Z3 z8`T2kGMEW{4LXiFjdg>tGHVGjjjZw#WfM3D6&J}{DA{yW66n{`H`6(-l>x6x=BYi| zs}gB>H}{1G&9gz3*J}i!W-X7EsEHNaSQ)ChncD&=g|5hhVwsBUnT7=@oWieUFTP?>xEeHFtBPBr?vL>Hl~lc zp8pyNgyU=1awcw}(3n2dhB%d9csza^0_sF9u3T{|9lK|(7pwI>YI`89hL*LvLPN7| z(@u#2duiCsn=#vVAmG6-5IGjpVjo64m0^;x7meLDhG&rj=vVnUR2)8Sx6Sl(9DiN0 ze!ewSQS)Y`>4s9|tMO!EXpFP2TO2VacXg}T?{-noJYBa*M-?C@IswcU3}SB@U7AJ;5J^mN1+ zT4o~-O!^gZM(aTzL(8W4X597eOLWvxtohq|r4!Fqj7YW_V*$Y?Ut z;U*L7k|QhY9t&kWKDsGtF*?QdG~3;Vk$*keo*cvD*~v-z5O|s+Hqa1x;2DaB;A3CT z4d58%I2YKX@~e$mKe?uzjMb zDUk?zv5ab62Cxs|dv?d~0kLKq*=8VgOlz2nzB9`#RBLCA9E-1H9K?nV>nG7W9bx}MA#3bp>SMC2WyRM`q zpe+%Og-u*WpLrn^D$s-8**T$i_DPG{k51{;f96BT0NF&Not@o!XP;z!6_0>_U>5T& z*F&2+AK#ASx+m0MN-C;84TFbiEw4EqMKhFjqvy!35j(zB+q|`g1Rh7vcYrgHS8@?g z+|DD|qj(ss6uQ$WSOzSQJPgx!j&9#RI-Y86O^t6}YZz;>{!cU-*nH`wo3U(9C=^)k zTsP(^1cT!PQVuYw9%eS*!2wU=VzzNzqju}$_|dbiziLUYvZww!zM)p&V&jTSh}}L2 z-?|~cjx{;@preYCTlq8@udf5JZOcEduqxBNu3k<|&bxrRQqd8KN+WiU&l?K+Hr^Tz z1|4N15{XsI!}7f9C**nGmv5J^!>6O3Equuzk9*f$8S?r9rwzMI#NN*&{C@92(el-= zesxg}-MwYYmj6?I2JH-4X!-j`Fb|=OuRWcPugYG>FNXHwrF0jdDLGey`g7LA7EP{* zS2e7fUST!2YI+Aad$u?P6T?hnl@P4vnjgZPl@c;br7bpSIU_(a_|wjaG}a~|&45;v z_#j>78|u=U<4aj zuGesE$5N{_iC@MikG5lZM4YXU@6}US<-HJe#@&$0-{m^rgHI*p z))br`({pxre4Wd+F5Z1M4xEKGIl1-$2;?6lU%ta|{1{#}m$Uk{#t?xe>8)OG)*l(8 zDmfPMXNAdeI^}x{BerJ?w6zoId>mv;_pts1m11h?E>0J=CrmUf zY_rqZFjE4eV^h!XeLmdW+{t0P_>VugVZ(+$&6sO2vjR(LhGj>pK7FELv6?Lbf8)Ff z$gg;~bkluJw7@WfVsO_ca9^$M)a54->g zKe3gFM9jZ3F%ZSZIKpZAq}X%9vfqbQJO4^;*6TB%_?|^SFg72DZKg{ zwuC*eH?Srs!XaS(x@C2)@knBMX(JSLu-x*t+A9JMmig-C%e>5KyPQ}vZUM@Z5)!P>u4}uC20jxry{Q4Y+o>Or@Nq)V&!EdaG*eL0` z;t*Qwpp6Oy9W`q3JgeO?bD-;SG8n|;&9Su<11mE{Pn+8w*r#$iu$?UzzUb?B$B)H9cz@2i?;POQ#V?P4NTmLe*#TYW};`J9Lk+o-U(^)>Q0T zQOk<^&ZsL^G@-JOYd5Gw1Qx!klPWVS2f9UGGu9(WMv#SF|5~EK-W|paW2`>|%}cL3 zrF)fHLkNY`-d;w8u;A>m|<=S!9rxJ6CC-w4cDI^N%`kE@TmOcS@i z)Y=`7cegf_e@JXnUv<@E{6xJTO4LoEIlMhPLk}jh@o4XL#i7)b_i(NUDmDTIiR?-@ zt=Y7+z{^)^4_5VSmvge?Ec`lpZYMExxdk=Q|m#n2%BfZ5XU$Z!q2bu3$pl@R$vCPkx-HZ3NPayD5|;T_#sE*pav zLo(r|c$J--YE~(@G!ELu@D}Lh z=Z6%_n!B##$YomQctAmv8H*9L`v4&cC8No0LheV&s{y zi0*e=V{xMl6o)?=F%hs*qt*#ERVieIn$m6lXaH+U$h9HE6+ZW=kjn^tDFWf=`{Lt> zTr>v1Ay?kzOtisb%sP5j+@@A#LNl=ANa;jzVn#S5-4Z?_CoHU5r$gA|ZEej) zb1)Qy76Pr(6%HARl}0G+T(QO!;_S zhPR~7%nW{8kJyn26}{oKv(F4F?(1)-Nv$5FN@bEJIQf=v*0T#Lu+K^MVu<1{ALfmONGBvGOl%nXyrW!H@ zDxAm|rWLaTNj4lI+fU#d*cet%dpd#$2yS5&CRLk z5c*|vBzlGm-*p&u#*^M5GZb{5>WRgYCkFf;|3zz0!4uw|);8}LTe`)a2qMIy9iQlR zcDfQV*E)Fv-^F)cv$ZvZRqODX41S^~)%s7v{D8v9DST1V;fw~~-I8wdnT}-0;R^j( z!t07p1)XN-3r!ftA2fYI-zoiw(9QlJ>~w^7#ih?1crfggp+CYxztO*fTbye?s@VPY zViK0Yd&LJVIYq{oI)bFKR3%v33gf`B&FW}jaZEJ{Y65z#Zb{Bdy5mqaY0#Et-KIWP zA6BzCEi$lqZjL^>+97lx(p|)lRUfdA`dl7{ng#q$x2C1B4Q)-$k&{h-(3|JS&D5}c zB;AxqL(ZLy<-GdjO?JmL-SLdi7wd*I4ulFNi&58D&^snPb(-KlBweBxt4reIb+>*Yc3u{n-=j_r!1cZHwp_)@ZTwXltP zP$^<$%gJB2CK3)a>Px4&#)xO6slG0pE$t9l1fOxMAK$HVM;+mi_nuOzlybX=9Kn$L zPwfWD#rhy1xfUJUl2!o`)<^E4)(yrms_!)WX89M)aYL~M9%e6I{a`vmgRfCy7c_)t zoo)4-GZFYHZ2vXqNuCSa`!#yK92kYO`-J^LRgO)tE2$AnSR&d>p&S^;?g3MAts8p% z$++3wZE!Iccr*)^TcLcsE-<+ZAGcH9*KIrEra!qoJ;0S=Fi#mXUC~fLn5E}~ zT}Db)J)G5GPe1}mwjQS3FTxHZy z?_sQs^E%X%KRX2#W*HsCiQ=%x%|_nLIFW`gHeUM?Atbs#$vq)eK)ku*X>0maNko|4ZtGu{K+) zgB(^;*w&@Nyr3N(PdzVPgC9ra4*jeIH0I;0_3N1v{kezdcPJh(`ubncwv0`nGNVN6 z@z?wm^hA5|)1@r;an|=LJa12gx|A4%C{q0FpNulT`nofk6-QH zEyt#(3-~hTZuzh$P~97GmmS824NlV&kmKrL;|7;`MfE4Lt@@KyvVC>+N3w%ZsQ##c z*+%NrPqdBl=gQa9j#bR;Z)7GZ1=YN(D42sik7Ed~LKI5T91KH@G63KKIm|DmyJxm& z&d^nTY9MkqdA^$d5S}QwqWGFsJ$kObcWtA6^~hXU-s*%V;q)am-B}d0Q&1uf&0h4y(n8QrBHL{+@H0Vpk zY^e64yabPYjWva*?l6zN8ZpqiLq?T)#c6A*U|kCGMx(Bl~Yxk7#WR10r% zdYkK$_fsBl1cT--s1$P;x*X^?U8GBNsIH3;51$jr4VQk!J7){kX0`l^L z$5W_2jOjw{>N@Ug2|Mud11yGSG`U@O-|ccY8E{vtFrDk-iEHgX{R+khrtk%n$Hb=v zz6T}AduRngJES~i=zo#6HncnZQPo#7-*v|F7)r{5oPJxUXEEa|eL?u*9Ha3y1e1#^xsH!cf@lz2Q!9sqxMcHL`pe!{j zw$4E*pK&}M<#LpcXa4ryd-Xz;_!9i2DfdMJ&wyh2+4pG`HJo||F<#U68?GSNqYS!? z`+sut&0LD|v-lpb`B|Cp-XCp0Xa^V{Y>(dWMcGaIP!(U`_~|I(K+Zu_!f}nF#zCwg zJP;tXEYq!wC%Q~59okxQSm}m%0A>X@lH z$ZkCbt#LR(`Hk2O-N=;NM_CUQ9qnisgw<~2bIJ6)V;Oor8q%JovZ)-C4I6u`2iPjx z*qXH)K~F!OJ0h}_mQ1-dVGA#im~ho>wI+-zZ-Vr>2=XVF=V+z2O|<8XS~?gKB`FXB zUd0pUsP;}-3x_C1XN3+$VWZ(b+=XZI*x>-Da3DVz&UCIQujtH##tdWq`km@q7Nr&Z zj0+6(j&esbY2Hwe2&$*j0iO?_C{o*<#>#<#m4@CHdN92d0mVJ*&a*#^ao#$$oNgOG zwH1xEjmsmf8-lM8#}f&D{fXPYL41R?Wl(Rm4x-|bGw`sVP7kDn{?N$1iK3ceLX?3i z(_y#F>a|?bU1O1=fk@;=p_Q^h3h=lxSRGmx?JE=!gZA0cgx$tE1a}5z<&rM8boJnA z3?pck2~MaLGkcPk9368CsL$Ho5KSPg z4nER|Fhsg$hz*-pQqhDchck-}uZiG@9R`J+Wnom+GAPm!KK8J$k;0XywxNfpUL!N8 z7NmVxeT<8F5}{LyQytn}8f&bq-$Qzc8pj0>=J(RpKzbw~HOMCID%zesd`%Vtn}6x9 zh7T|6_NyVhI^}SL0uhfJD=Rd9Z23(N$5gFkv+8!k^hW%_J7(E7vp_rg=^P3ZLF^}D z6oE|YOF*vjFUx(bc(o9zb?v&`TcyW)xfrnysnVl_b|1FATO2NgFBmoeVjZ$T8uO=C zmW-o9tng9;odhhoNDN3#eL-YSktC39Qy=DdC_1jrMYQf^lU+j~IcLD=b#&QGDho!N zGgI}caDKEy`4tGN1IjeF_jN^l`XQoVNva^>#-Mt?x$C1qCk`7GH@uUKXM^AR9JdVVfxzjPz89`jDNmkagr{~UO8IR{#w2_|I zKwn7^F-zO<72wMmvmA%6tp?51$W{rOm-YDx%MC=HLaw4&<xxhITit$xT%KFB5 z!V&E;n_xMUS7O;6b2!M4*=~vOjOva0BW8-B&agMC`pSH6oh&OxG|O+X?n8}ry|Jb0 zG8dK~_WQ^2!OBb2!Lkt@il5D`gFqxs`DNERdYo%{0XK@bDY@K(QP@+#2rSWhIET{1 zFXUeQ7}F9-jjj$%!p{M3hf+?+R3$$Ib2y*s^HDQ_V&e8aM8iMN*hItKRyRV7VKSx_nVI7Gh z=vUS6n!bcFp<8gmNchY*y4`PVw9BmeS|jNy!nIaYjFq@-edNEERQprW@|nkwC_>Y=7Y2?QpKRRK~ZMC?!9yi zutjytYF5Zzty~CPbLn_}Ju<&s1O2Bfd>B2ywzy{pgCaP*QD^#WZS5&itnb(deU@*V z;~wl;7GD@o{x$fu3*VJ;QFiJ!R1ZdfLR)Y-;tn^Q4-WXc zA>Guf8&p01`26$F-}&i(+_r7oJJ$|A@kI4uz=Q96=Q~dwlTW<(;)|x=Z@%dFO`3s9 zz?}5?JbhQF&BB|8)Iuo+E3 zg}p8l*ponNN1ey6<6V~Pr6qd4$ZF7F=#-ibX(Lj_Br2{tL7Bl+bw&p&*0f$7oxOLt zMIs=2)yQ{~TKCqYm-ZWHE;Dv^Dp9j=8kNPV`XU$gQ~?HbO|X+CEB*`p8uC`hs@oBp z)PiZo9CdC#cHp>s$x}k{ZSqWM_qc=B~hlwVm_(Cs)D}1=ILNLUYV(~@cW-PA8@Gscj%^uGt zDyZua353tZ2MaNJdebRGQe)S^r2Smv*?r&q<~I}G7FWO@`}deX;A-*3sNBX} zfpBKk>d~%LG7xMHnPxDMT-n>((H>#`d_|<)=i5Y$c3plI?lH|!EAB~kjjmpm2?t#G za(1aQmuy7Udeq1K};LI-EkMlj{8QfLw3O;wg5 zylToQPW+<1(h7nzf7$lLj@soPYc|juF>I*m^%6BqnJRs zad^}vqmh<-^!496aNt0s`m=~^^Tw;cuy7++E9B_$+YB@7laXjkz<=AcVQ>XX=UHI$}r$RMWo;-u!eA$b-GlKql@fOq`%?vK37k~swD7Aw8OPO70e(0to z3^qKSs?gu@kw9`c5*3k>Wmi#1;cUY$Xs}(qbi69J3an_Q@!%HaLUEAzNNPX6{`%{o zH@YK1BN82qhXT>TQNMf8(`MiceiBP_1%vTh#z#jCN4nT-82P>v?gh!nQQ$_N(9)tv zruu|Ji#H)x#JxX71Y3+Crw|r7?e?+Z49!5i#1_(Y=ufBQphoD2aigH}p-7d=636F*Wp}%=YwTrQR|C%3ttjv)BYlMCf-2 z+fd;fX&(Rev(G+@-n1|7ZFSaqk~i^9*|#E@NF-P2?QYDO94B>juCOCNR&?@scDZhX z`CY$)ui6b-38=4ZzX@77_z7qq{024QPEAun_5W{k*ZSSYb=)z#z%HH(fDeG;Lxf22 zAz9%2At{n=Igw2(uA^ACW4pE`5Ll8hK>)^rl&G|c6Wd9g)Tx@*ZInl=G_B(_O{2JN z;-+cpG<~FPn)>9V=cFg6{gOYRuhX74H@s{ku`m`rTKtdCUg*Wvr2dm9 z`$GphdN0gA?r#YOL*ZB?s(HI({`R9s+x@ZbooMJ%?T%Eh+R+vaX__8t|Liq5*mSZ1 zvNIA!!|`8SerT=+L6gd zrSm8oEriy`v<4fEbZ!^Ai_l|4i4{cP(zrfQ#gjUU0yvo{#q15@1+ku2Cj;ak_aAAe zU?56k(Lj*ekNC#|6qv+LRt&fN@=y$v6pQ>1yvtf82j2LeLr%UI#8MJ#dEo?M1-P3-)~Zd&ZYrLK5?>)7$RlPBO3 z1}Z;!Vs7rlNiL6iqH7X{@|z89mHPQg31@Hf8valWJA#JCojmG%3=bhr6*fley3X@8 zY5u6uQkzR%md{QNbbh?{+N>>B>WNATXKU6PSm)#T`OU|Bj|mlt2Ve$M4-bwQ0MsyB z0Hk6ioGtatO=NU$n=LIY=H z1}g%?cI3o12dVd9Vweku0P+f)s71;+>EXD8pN z!(MTkT(xT-*I{B7=H)?i`KZg{t? zHv`Olbuj0A+!tJzunvBe_ZID7M^SCgZR5CL7WPslI6Y%K5MLf(IpvxyqYu@NOgnxD z9s(94=!E$VWHJoQ&?Klx{EP{vG58ziK)A$G82|r39%9NXToY!*rxLZ&OtK>u>qyG) zYqgTdQ#3R@48IBS-H^#_pDID97kpt%8t=a z4uLdiO=H6>&!Dyufq_|(`oO7S>H|3KMw?$Z;OYC26Xh#@T~|W~c-Q-ZkcxS(Z+N(G z&`w2R^O#p9>i~vs96tISIydm(bp8RLsYLX?kmK@8Do> zZ!6-Ag^x{49Q*E$P;JOZs(id!no>XtSk{1J1qee41Txzr_x|#S?O-Sq-2j2v+As^0 zZjTQ#C#h6<<)kr--abI zEqiQ(i<}w}8YHqZ2yS3YgxDTBhMaNo4>MmyLjqI*Wf;1e_|f?I{*1>nya7c++#raI zu-K>`cq)crDu&%mQ*Uf))I3h3=24p;uXkWOsaxat{SZ|{auhU!kO zm2v!*s~ZvOGOka$z6U-pCPiTwW1&I=%r$IS0xC0)a0Y^zHQP-1Gd0B8jbz>q>yE|c z;HWc<{c>6rf(u^-Ql>qIF(?826TS}=08GdHHz~$lUR>UY$6X}&5t85A7BZ9v5U{!Cd1QHJ)ZY+zeQrlY~mgb5q>IHgVgi%i|&% z0rmF%f%*?PSey4@9jx&1VUUGRc0qf@>wN@SaGD46XCP0x5ea1sLD#UFdCM)TM@P}` zc00u}kJoO7FZ1g^e*G22OW(%Y=qM!Ii->OeG1upq?}5SawgsM{VQ&+2i}a!tVkwvy ze`FaLi6$XKVdh~@1*XIwly&<;vOmBq)QDLRR;U0^#%We2rr84@HX;0I7D56*FRVfW zw%3MXD;0Tc17_hx6RGXPV4%S zXp2`r;t$+PA3z01+3r?vo4f0{Y|r|F*re?Db#?Xi^%)cz>^~ge`0rFI<<~T|M@6VR zRXgrhJutg{(=I7mwQ=J=`TpIl;{N{^^*we$$;!hz!k4JSM*D$AQ z>dUJ8gh!MPDw^h-=f;hNy1N_UO|k~)@ zYWSvpJfk(}7w2IdteFkjdbNA^fx8utM~M!4c)P1kU|W6IMD5nQ!1g_Z(HnLvX8$gI zbECd_`r;|Au@19?H2Q9jr_V!BCc6eah@XhnD^>Ljc0$o4H0;p!r9hxMP4Tn^SyC4^ zm8Y;?(+$78`&^dm!>(_-ehj`B1Iw=$Duh5CXF9VjDgdI+RqlwHM92S-sCqF! zo`6%Q?z!jGsYAMkd}2y$dg|=isp;5X-aC6djHm*U$k{vR=kGiliJ%fbK6~$fgj-rz z_B4!0o8eQCAPPe0k67s>$02nc>4rJaEpHh&1uflcy`x|iV?XaN)QpSo-A6f&~8TL#`>ILeTDY9t`i zIrE%|Z|aBd@0-7-6R?%Sf9AqmBRCW{4zj zc7Zn!BdYcN$Wr?Rd@m56WPq1`F&aFJRpv5w!e9o>sLPbY6a+F67S;wEb5t;2z5=f| za%mRB%ZgwuK8;`Fx40J0)>*xAmJN-37rwu6<`DWMm)hyk8X93G&8CrBDtfevvenY1 z2-aFc!FX?PhgdI*-eh|q|93$dG_ax{3Wd9BZQv1yVCsiW1Ia*t6s9QVP~o6WGPPuN zCvD@RN{9VW6vXaQ?3W}N+6#{{x2-W;Y|!9Gsim;3_L+-_EVIvgL<3%?@p!$7wavy> zu1%K07n@$#+}T!Gzo5Cr#`Ox~b^dG9Ftqc|iTT7SLjts|9cBgPK`JC%@EQ{Rl3`t2MMvO{f3xkweJ%{MoY#&26MeAz!>z}EgnVMbb=;U3v*wBPO_=o%b@DUFnQx?bJ zR`G0vL|;--_}Q?YtEK?vv&1vD1#q^X1-94h({T^LL{ zb1ff{r=&G%Y_+FOk9t+@mPQ=Q;Z8eoEDiQxhwW)}rC!j!v00dy_pnO|EKqTTFT~N~ z4jV0PaGrJK5KZh6>HIy~V>i>SA6mL<+X25efEh>IA4VuOLSoH4dx!rSa6wGT7X(~!*r{{+of zg;8l_q^}S8Q6Z02N2=Lb<|IbKs|SuAKoGgk14pm!G+k_(FU}!v2BLd37gMD%>;%dE zta9+JWAIbp#+%0Hfj7lf>wT=wVTJ4$_jmk$^49pRgP`u4C4LjEzbfqTb?W90cKFS( zeT=gR>?dt((dtWz_#;djo>HnAl{(MCEDK+JHI#ltwgM9(h5}S}`X4~44 zy+E3Qr#VrBG-}kDBp^6Beczzx?k)TeIX-wX#Vhl1$cY;px3F+sYlnDs=jO(s-ri>T zA;<2qB(rP4F;zHG<0#UQBx=3=61(VAD?bdlZpTi8dDj)txiGY`A&&)AHn4IT;y|rK z#~zJGY2~)zTKz(2Krky@0pI zU=YJ#ACIoBtuY+0$0>H53u`@tRjD+IRjns>X#w&h%uK}~^awoFoZD0iwYk`0$UVpT z8W%WUXL$e|^iPMAacI~>{gMjX$ z$`o+33n@}2ssVK%+}<*9bGtWwsscUXo)JVSFX4VVdWiGVjNHLNI2;o;aPg^{GFq^+ z^uY0V)(ZTQSF2-JEjs7vemckrc8*e=@6^sZ|9N=&exvDmN&=rT zazx_K zLEEhHVr{pw#>>$Fp>;y@1k&FYA0N??Yojo2 zfz`c$)ccx+6#xS`z-poa1DL@{ubG)yj?}$%U1?vvXuf=Ke7yG{+&jE2eS?F2aOlvL zgGcvGbichvReO5+y;yhkhQnSgvwHh`zPKkx8dr=AnH2$0?{xjhE@KxY>LM%>XiP`c zbXitihtML+?wURxMD+TEeO_~QATE#W<42Ulyj>2s;;!%5<%p{lYf-XKR9s(mksF@t zt|{eP*d@(nLX5)ywabdjpjEr9y6&J!yX>y%<8ft_ar?aH8l%tKWxwko`e(ZwaE&N0 z*yV_8ke;^7QNI42g?u@2=CWxOR?Ngq;?9z3ChjvAS97V-4o%6Kk?E0%?fdah?TgOv zC6jj7`ByPQmj$2qc8E*GtnqoWJBdrH32NV>4{)>v zTK}Xom8VQ@y6XL4ApGq7Uh|kLER6}ah$+0fu&dHnYY4st?nu((Yp$rBY5&M zK1JN+BwXD_AsEVWO4W_hhpAORi4bl(|(-AsKx6m<4(g=+ry5~4e&?HUKG|kW~9j6m?lHNk6 z=rr9*x6$o%hVG!Z(%a}P-AQ-R9NkUl=pdV-cIM=O-40wQEQNhPwVOslj;m+1+@298f8G4pJKp&(J(R1`+`Uw3JJx?E{Uq%GdU!jlDuhOs43-s&s z8}xCyPM@Gp(x>Rt^qcfs*sJ{+`fd6w{SN&uy-1& z(;w0w(I3-S=&SS!{Rw@I{*=B>e@5S+KSz#{Z_;1Tw-Bf9ujsGoZ|K|fxAb@P_w*h5 z2l_7kW5B$U&ZSmTWv~lw-D_CYm%NNQSbHTG@rb=bERZf-MtCm^vpf$a0l+1KFm0!%6 z$z0YdyBBh)Melr}u)LBgEqk&nsYNqXH1o-FA!*^^N@g&fEv3=>3U_cS=_ND09JX7Y zTs19zf7M(!efUpWIRGM*PUXye25`uv)-4^(aK+4*HTk3g`+*C%l`p-7=SQA=E~Rn{o}9T-$OqCTGm|YR)2UL%kKUPNrj%VXBLHD0SuAAp<)q-U;hGaUbDSWpjPLfXDYbunE{8Eap<{ zLHl|u+5D>IR0N=-7T(!R;$kcIpfEV`a8}g*4FF@7ll)TQx-+ZAmb$utl6yfsAHIMB zdDHThmsVHit>kJkV3+tps7x$6GNR} z3=~u757?*jrnazJo-gEldGoR*PWySBBo_L3jKy0=C2;_Uij1tdMzApd1c)KISSqX* z4ZE_M7u5i02qRg}rkB^5aPp?HR9G!70<$fFn7&HY5J)zkN-rlDn4-8U5zk6zEvrzn z0-{GQdsU(@aMmh<6oXal#H-7S2?X+&%tc(t1kP89h_TFY&?YHxq*@iUS*;4BQCCt$ zeFdBb6z!00F$G58;G9)16oV|UByEAk$BzJ1TQ)-u1tgh3gQ-lW#4L;1M5V&C=v<1~ zi#MAA4QCcj9R+R-U(V)%NXeDdmF!CPs#&RL^P+Cp?YMNgP%?d1zHk}bXxa3YOW+^4 zMqU#bV)@d!Y*7%7b3;Z@r_31Xyr39qpN*cYMRn0!34(u^>&f{nP+~k><$g9lTM{pp zwU!D+Kj4P9TxQg;i+Lt}`+PCQ_Y_^g@yxt^x&)v&tYBd^mkZiV4AfY*!c~Tl#Op3g zt^oF1T9?I4_UhI3py;)L5neJg@+X3ENa%~k#S%b~HhoSdZ&XUSaK@~u&YP)~wrVR^ zlh$QSFp=s>F*R56h;52xSOoY``7lU-Wi-Z zIS)p%EPqhK>#uE!OA>)N5^r;AkPEC8ug01?UVtEFNoYuWQb KISZn8x&9A2X%@x+ literal 0 HcmV?d00001 diff --git a/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2 b/deps/font-awesome-6.5.2/webfonts/fa-regular-400.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..18400d7fad27fc52cfbabbda495b871bd912d045 GIT binary patch literal 25392 zcmV)yK$5?APew8T0RR910Anx!3IG5A0RDjh0Al0=1qA>A00000000000000000000 z00001HUcCBAO>IqhEM>n0Lp{92+M;i1&9R)AO%2wWkK|$7WWWQQR`t5sYyKms%n`K zZ?751 zKCeLv{I)4^{$Y-|<=o3Z;3BxhK5}zh1Rrq`QsP6G?f;*aX@Bo4c!lp3zXml{tE)AV zMpY_xm!uiVJ>x--WA}_nhS+joGiEI~83#M;u$(1Q*k`|UGE3Y6|72N9tOcUd|KZgB z=MG9E%?K<@ro@gNOAbpWrNn8wkR|AD3JcVeM_r(HA5lJcKjsnl_ftRT5%&?17amOK z`@H~4_9#_aSFJK~s|}^F%&gqn?2Bu>}Hx^_7ld#ZWnqU$FV65Iu z`}{AJtImRTLiD5y-!WOc+0>RSDxZXI4WrhNVBkNEPz@{`#!;u~m2a_}I&QGSxFRz6rh zm^~w}@)!L{=uJOqy2o`I&oas**T)ayg&K%;aU3h%`?}8%PbJe(m*ry7>Lq4z&(`&N zwU=c*I^r8vmGWMSMe$y=5j9kaUZScJH4(M);po*GRYLi2RLh5=T0RoB@-eqo=|k^Y z%cHI}YVn!Y|CJ#AK-9{IXxwz^-ZW#$C)*Y`c`q%e^U6mzey=MZ$-=73hhQHqPceOj ztZG2?`ZMz7__4khR?Ek;TKP~KzkEF0p{uWzkLFifKDasQQ}vmw_dOiHH?7fIKAP3y zQtrvBNTr`jH7panD6jkC1J!m_?~IwE7v<)rIYZ+-ty6W#-p!l%SZCaR?BAD9F(Q%>-6Eod-uu* z!xp55c0$9iuNn}GdeouI-_|@`-N*~N)VVh7e_31K+)8{WX!0HHx{Wi(ZigIp#8Jl_ zcfv`hoOZ@p=bU%JMVDN5#Z}k4!HsTmvs=8&`#VQ>-qZPb_Zz#v-~CAUqq)3KJ|dr! z@6Pw+d-Hwy{`^3GEPr?Yp8Rt~Lvc+}DO!pt#j;{Wv9;J%Y%h9>qs7a{4I4LnT-Ufw ze|*{>A5V3uAvLC^)ISYOgVNA6ER9GVsVhxP3)8Z+JgrE((%!T$ok*wB`E)T|N!O>_ zQY}3_JtI9cJu5vsy)?Z%y&}CXy)nHx^{~#KvFGh``^LVtpKQQ>w?FJp`(L6-42dmq zB%vghB$7(fOGe2hrKPNtlZsMRYDyhxENx`0OqJ;}Q|8JRxgb~MzC4ge@=89+cljZ| z&nHTH&hTfB$fTR*d(!_VuN@ay{R z{DJ;tf3d&D-|g@BkNYS6^T8mHf>dNAJB4XMOIp#IHngQ3J^En6-FLkQHpr~DXY9HA z`*{EHqpC=CsUwY~wTv<6%y=LtE-3fpYHJhiq5XBheeZts2|cCPAFBF~HNYR{&-GXP zyZ);Op9h}>9|i9P@9>f!>87N)AbM7ao&_L!QuvC3a85b9oMp~Hr;-yI?OwD65UqPOJv`fTrsZ_Ysg{#1 z+i0n;{DN}{EBh+zEAuO}E0ZdtE5j;-D}5@hmCDLTUKVovVt>YE9og7S)|R!#f8yTw zNBk|TkNe|y@vm%VHaDA_P0S`|ljFnji}+dG9lwch#Qkx99L$=t#yA>>V?(Tqm&TT? zZ`K+gjpr`dPT{S@lZn5@2Z=w$--3r?zizxbUQV2j(=j{-Gtu$Xcq*}s*dM)B9lN6g zu`rgnb^nTSOtFF(ALHZm2ghhX^CjYTG2M&(xU***je|sgXphlxEm061s9*OOP7Jjb z#b9EIpDjL5IiBFOvp!2S6OBYYk)^he$BfNR?RSCta@S|*8z!1)v^Lw*AyfBz`F}}$ z^wm#)0}M3CU_%Tw%y1)&G)kLx9XfUCmMe@l##rNwH^D@cOf}7Pv&=TndggC6qtu-|?T;AV;jaDd_(u$iI)o<`9Eo=q_YG*B!9Z=hJguv-E8 zQ*2|{?HK%d+b?>+)*c0zO7Sw#$hcwPgNz%_uw4K>jN1gJ@yDlu$^7wgFojeXV>79q z)KH$E8Udz}n!t2YfAB`qKyZvS2+SZ21#ck@18*md0Q*QCU?!;x%py$$vq=lV9MUo{ zm$V!lB&|r`0I7$x3-d^O4O_bpU_R*tXe6Bi4W#p6A?YGmM7jd1r0Ww{OuChH8;#}J*on+{I~zbbuycXxVCOMk>=FRw!7c|XfL)n@+F)0M zT?bSId&VT7D%f*i&jZ!KJ~!O=_6@jg_AR&#@gKo{0&0Q{fXlGo0kj4C1Ly+wCr}6M zf1oZ%G@u?x4EEdD07`^`XGr7&oBq%}yJd80tafQ+3h ze3@#2H6YVtSPe1*Waj3}Trj`v7R1m6Xhj1I2dx5(0Id#;0fC+J(Ql{ z5~UxwOc?;KPzHgklp)|6Wf-_lnGJ4G)`KaOtKcT(8*q#AGuT7626f2}pdQ&3)FB6e zTI5u4o1Fh4aEDw?uEAY$Be@gz$$jJzyiT4W&*DAuHhBjhkPi&_hS0Kn9)Var3Cbi`a*^vWq|`fzn0f7Rm&X`zTvQo?*Utc?sM$ zFYkadLgYirJduwnt3*EM_Ivpf3@_gW!^;mK>Gg9^j*9$(azx~BlmQ}tr}q$m-2ngp zHU($`c7X!i2-rmm@Mpj-qW~SiZh`{b1lS#*05=16lN8_MgfLj5(76teyVArDnzX$B*DZn2AyMq+q4}jfa3h-;d?g$0=Ens()0{jNBTc7~f z0CtNM;GclqfC5|(*q!0AKfLMJIV)Z2moxTkA zdv6)w@#(!KAXbM!0geNF%clUh1HLsy0qy{N>q$wYui}gc_zWZg`W-wHJD`C94h%yB zAdYz)D(ThyTBvyJg`QMA_G`YU;)y6$DvBo*dE`l@ag9%?APhoTSzljYAARn*=hoMo z>+9?5AM(D1y}zR&94t7(Ks1EY+jNA1L+y6E-MeAy372>(slKN)0L1U%_aF;-C<9;= z1{{^j)twF8BKAvzJ8mb3gXD~R6|8^huATWk&!2y zATx$@45P4L1-2Mnx-}ym;{zWs9GTgADX}Ae_xy!c|6h0|Hep;&N_?ep@@h&3ev+Ds z;vn=@<4s1yTErN&d~?IE2Qu6-6}ySc*a8=30c@=ikVl?-hz`OaY{VK@O5?6H22mWv z4Jj)HHaEBHu5)wDeeYIfynt93uhbxrWRJWKw}yrB%8}HJ4DdS|@P{x8VAsQd^L}rP zm29uAwero87wCq5piB#U*A-%n4m^+;qZK?+aE&h;j?7H;uF)?Zem(#6TkRZf2Ef*M zkNHtNzOtJ3U=7K!JeE%Boaxb$ltPqdcHmnkLx!{MdsNhb|7B^(>^&ohL&G7*H5@4k zA*DZqES%i3{q!Zp7Yj4{hmpx}c&Lr(cj}>woMyf3lwFZLjceRj#V`n^sE)kaxK52} zM|-`7zzD(8wCuT)^W^|BD9?XvEI%kPm2GOoS-x0$T>vJk+U{Ic) zF9&FuLj!I$l^Yl%=0thI%$~uhu6KT~zlld;8^Tu>20WoOk80AEQPLO{-z*M^D?mf9YAhuRi{PjEG%8jL`+J0&mWV$D+k2@II(msgShqB*y4g z?*zsiKnw!o34pz0A%=%B1EC9#&^n)WZl$z>jXP70!Q zUGJ^s(uRYF_H{bZwwM+aVORJZ03Ov}DX>&>8-?FmZ(0+t`Hhu%CMJrbxTY$qqFiG% zDoUw}@`FP}E{EPstf22VCL6wwY--rD(DxgAHhdo~Yj}!%+p??zxri8>U8A=%@;ZL`T!+Vp6#iW)%} zgc{elr}^!WgQMzaMx|>PBEtU0cknnoE)R((RdSwr?aL|%>v35{9)6eSdXHc^m&&@m zD|Iw*#no}$)z3OR>*i7$nko23D)$|gC<|8bN~}N@Jgrwp-ozX+`-JP~q3Yq9Qk$Z= z)nG-1X0Cj&4y%6$kF4X>w&tCMnn!V2MR7eWtAHm`IV|@c$)&O`uIOm=u5`1zJ8)t0 z4SqCWVD&e#f-R`S9x(5SsCceob%tvkt!rdIYRo#Wgf1hB?M3AwmXW+-q399fc-?kHLipg`8s~nGN)pkzlEp+-0t4m9a zEiJ7QW3=bOVzG!=EG{nW=`R`@3e}Jw)#CbGn=NpIn4#OYZm^}L)jiQKm3(vsO}4bO zn(2~6EGxc8kAy=*$jW7g#bRxjd?t}{|Fwp zhDeXBP>M#XNi{eh#bpw6idnGo?r7D)B9XCuDVX8F`u_gMAOQj z+VFj}?eUUADLM4eZQHgXKU7_GZrir)9MutZDo1RgY2`OIGWgbfzfm3?GySOH`}n6O zS2#Xz&T&V5tN$RLfLFjI00zENBD+L2j#l<8FT7$uU9N?=8tt}QRINt4?G{;5h}rr1 z`T5ydA%vLqpEW{A3%Atk(eArP^|}x;H)EQnIWxDgu&^*YYYJh`N-4xVmVYF+K*F3( zn2MZ9r{JSd)U~VSUkf?U5&41ps48PswagDBACXZ&zV*pS9G29>9ptiQ&v1>D~;+XUf-s=?y&zN#^_%hZzRU(M&w~- zeZ(^E@e3w;_F+K*hf!a&)YlD}+1xvnKIPmIwT6U~O!x_2TvmfBux$NK4R4z=G|0KjB}oeF!G z(2o(Niej83W|mhkT1*UBP47)I7S-w!^=z0L&k-NOJ@5*cNHp9tCLQBj5UN;3jwRb* z&fZtfF>Nbz(fLA1`FyBxYX3Tt7^APQ+$*!D{qXrx3i15?+@CIBoaU5BR^}KHK`2!e z&l7v_6z5LLwHWnI?3HS4`a?PAdjBDm0PM)Km93)0r(zIF#g*n4!%*E95yetwuDv!R zti3cd_eSGM=1t0IiN+j{AxD)YOk};%cK@OhDYKL5kH7LRI=|mVVXU} zeFc`8#h~?^X=Zy5ujS+A|4?MB`~=1-G3A~^WfLDRzi%J44#lge86H3lh5&xoeKN@l zQ)-bU<&u6Z)Q4w_yM%j5zWPy)(!roosZ<7obnW9GVWYV=JauZg)@?(zs-1^?$-qH&=_R(k59yXdwOU(v;h2k-g#S>@FoLD5toyw6=vo|ok?nm*<{nWV* zQ0Q0JB?%jDQ>7|ZbFJbICCcj)WWZC^DM$ z>4QC0QdJ(GLafi#MTJ=^+ZbI8s?`uRB~feo@^2II=UP7fYrL14@ z(D>*_Rp-Dc=k!Gy;CIoW{1iSIP$Mk&7$U>{&}eT@|9gBy&fzC9b?!yfZXjhU?Y92` zJE8H0SF5h8zWNPD7%2PHb<{^6RUKD-K?(4?m7x4Vyv;#$${)fjfntQ^9!GTW*77GE zbau@-mHxI?Ot*5+3N%ZyK2?Rt(y$ck92?(gbDw|HH*wFgzx~^7$6N0UKId`U`M&RS zZ0`N&k9yqZ-+y!VbIwt_0Px!nahkK=#EZ;#txu3BBA>8apMB#u;y1Y#^Zz(I?Ec3- zv)o_!g}!RXci{mSNS$GIidiR1Yrw3NNsKw+R5i+9!8tzcd6QGsz?Je(0;)EfS*2dv z4cg4a$1)0!fcC#3hq1bVpq(G;UYSL4mSp`nrs`@8kwtl)MMBOLgq=<&%%Z)D!*Sj7 z5IwJs)3vwiyB5RdyBy^2GU&49evD(eiwET{9w0BL9Pt1>uU^icJP493hGUL&*+Sml z-M`5Z`OTSOe{=Pj!xF&nghhB2fJj7=WEYpwZKCyg88VwTDjCGr;iX0o}U@(%-EPsMn7T$_M zplJ%N*XE_A)+avET9W2#b*gAu5Fn*MAUysFvWPPnz(oLzgvdk|iHOon(BN3od#@;t z>Zy3?}MYqbztt<1ZUuM)DFU$He^*KDsZnaWGm zU(*PhPIo)_`KS6zbJu64cMpjoGTT^LY0S+vR#rBak8ojgv)AqRHa9Qa4)y%=&p&U| zvFZ72<4qCC(^b3f`ZdM_;IIIv;WFF{uZGvd`@o0$oeat5uR{?*#Q^*H>fjc{-K0Ov zb&(_*(-+qx7P%Gu3NM95G$UhQ^oUYES$TeTCEy>h{XM8-LLk1@9#2T`I8)y zUxZY&7-lX4N!Oh78PXJ>SO3HXv{~>D#@{T8?BTmD%W~bu$p&LDH+urm-Bgvoc=_d*nfn@M=u$(mUyd4$$O^** ztJV4PA59RNL6bYsaZM|u(qQfIy#Tlhs8Q+w;+ifnnIqFpWf5VkJhVsCr0#oLcCAGKY@K<0Eow4tmyQxDM~E;h@5VstqbQLSht?enQhI=~z*W zt3D4XblG0eW&vCy)@n0 zO4D8}grz8!5b@`;nTlcx5r4j}b?+h)^t`&JVI;bu>?w$Q=zGn2qaj42QEz%azW)a5 zbcH|^qT7k%Im?=h5%Fw7tvRDT}yq_duI)MxZ zWJ9Y(;&aO(6Q5hoV~rp%Ww~R|mM@;@?0xg-_HkK<;d#{&!`}QTi%-{bpjX#~eI8C=Mjds@8*XP}J?9Gn?4}aWpd|sxOy|wM>n7;qHxwi|BdvK^h!8Hen2Kw3cZ- zsh*s}0~iA+`8vf_ZqiY6j{9OUS+X2v`6(-28ohi(M|UR2X_}^(9QdP^FE0=9C^!$+M_%RBCNR-fq`D5B-`iO-X~IA4f+JLk720 zbh;yvicxJOBTSnV^=TNmF{2{vbUIH!DC$O-y@kr%RuJF}Dw^yRI*dqVe?NYu47P$! z`HvN}rm|Q>&t51Z7z8ccjmUKmWnVHi_O83xH~>h;|9d03 zhV+MKJ>hOk&5ksehKsAuV{GluFXO$0sGysF;kQ zRF@=~tY%lPR6I^%c_>R#T|%3il{tb)PL3~UnIfvF&d;wbc56*ZQWQd{DM^|nqwCf+ zX}MlQ$feGK>-Ytz!D1D&4NanGR&r!{9WKs$h}Jg*PNQ_M(P%V2zJpidPb*&eHb(DE zmp(TUA9AF(ojP^OUqxfQ7yZxU-@>opi;%*D@H7AhC}NCFb-pprQ*evXBwPB>zT*g- z7Z1ZucJK$NfDWd;wle-Wms9OZK{@>9L2*x!S>T6DCr>7Q&P|071$CV*Ih9=zp`njGLBDugH|=l$g5$)(T_n5-~ezdt1@ zMxtvO)yk>OQ_IVN&lF9%fAQwY#ouc+n}a~sqCDw4au8|0U-)_IO{l;sY{PAKG{Wh@ zsOSg*NM7hpGXq+LTF9Vm&Zjp=fMeN4jxz^&Ip&B*WjboA-Ws`%#-;*=n_&g^!ad~e zHpl(h%*}Itvl*Jx`SQ@u!DZ1l3I3~a2e#oNO#JZmVK&M;*{T?Nt_VY5bsWLrPNvd$ z7!Dk8SI7_~&Cc!0P#F~fDN$T@RE#-l8csD0c{z0?MXsEV$Pq^YJ=m3fwQszy0{4)A zoRE)m{KwthU5>~LrK6)G(UL#f6*BqVw2?+#61X~!fB;UxhAnsso`ZM8HvnLq$59w& zz+*`*D>qKzjqCxxOOfagM@4_QR`iGX0|Hc-2^5m?`v?#ZMWIUn_f;B?e8BFzx$j4i zakQ;e1QhLD32B@!0Q|WQXW#dI-$&Z}jSO_1RghoxQPe{e8e@_Cv7BSO9v(J(wbuyg z$|yASy!yCuF6-9`b9eD-En&u$q=rCQ!ZZ}T{&6XuA zE#G#9>k8b&;c!Gg7)+3(=hf?;hnhC<46Ehau0gQ6mGokIFC>p~>BN`l{jNqXws!UT1JTD#b7yg7I;pn}v^`u-6z%NYNeFk5$@_yHx#!YFt)n zJT3um2fwBl0lQn25R};p!5?w7e9t^X*80+~Eek}Bp;qiEHf+zS6$$swV z!ZO?gFdyf!GVkY^IDsvS`q}p|TeCVclF&!)fX5Veu_M7Q)svMET-JKpSNP>|9rsCG2^NQ7+U#u+s2ZqUle$&g|^@V z+y&sft}D}P){-KN!V6Rwf8s=wtFVM}lJ%P+hiN)N5e~x!nIftTc&nKPefFVreeGlY z|6cQN>)wVWnnlFnIN9HCW2@b6VQF}`oZ;9a9Q$un>H775YX(99RD*m?TFDZ~oylY} z`Ny8`d48{C*tXQhGKU3y+v@avQiV*YA}~i=jx{E-d55lGgOY@Zn~!*ZlCtC&;4@Th@moj<0#?rI$D& zf9Y8vp1r$W%IQI5Vv1k6M-Hdq4tN~E*F=;Pxoeo6zA{J;@<&DPcy?mLqZPjtbg2Z_gaV{l8!nwlq(s6PB=ve>wwH|ahGF@+BXtU5} z%nr5ME(9;HUrmbvNZ=gLa1Up20q*BVQVd^YWmX~)32EkK8E!0MMOT6>%RJ38KI3TC zwro@+W|B>l0}3j2Gjhl^MvHx1h!PUWCHPPxMGHMHXOhl+O>3g}7Hevn&vhx2xgS#H zvHV<@^DKvDIU?_h2oEq4-7mKnG8~p&Xlq8j-Dr4jt!kQ!8))whZ7iAwR%@=;Xte8w z_8i(QsApN^9Lp(2qU$(a5up!Z0sJD)UV!FN__YfDku2PQsoGKb$MC`@uy3W#88wcfKl4tWvVLTpB zeZN-oeVpk!^R30jQ`HNFZ`n4YZCieEp?Yd@(ejzD?_#@^R&U$dx~-bF+E^aceBbwL zn4T5L6ld~^e2ur8^L^mTcMwD^4I|1SLEh_878wvyhjwF5MGBR7Y;hn$v7DW-Z4u#t3osqQC^(r#;jnq7!8*S36!7<+a9*gob0yID{2q< zOxN>`5#EnK`*sWA9MAk?k2vkV*{Zq2xHuT-6MyeID737IAEWz#Xnla)K2j&qjs_7md3M&WLG zfV)tk6tG_0e_9;yG+jc>xuV#rjE>tz~K4PtsvO>6rA%v4_Maw4VxQ=ladszCJ2PCsfsE|GI0@2B$5qq zP9n+Z`E%?qjUICXJ|iF+WmH3J?frSqd7C3fVUKwPM@N;n`6G|;lfzC+Cp{@BVdi1s=v(W%u(w zh&r;Y(nP>BKTEpU_60G2;e{8tVaV%>UC#JYIi0J0q4VqO>#||+7i{IhIbV~&v1_g? zc{(0J*S&~1S{>d#5jtwhfH?&>>0VTb8B-3FCg|02d_|v{y654Mp zj2fzBir7f^{#e^Hxs7OZb5Gmvw8kugc`{buIu=DX$_Fm(Za18U*4Sq=_VaKyD%>P| z5e?L#$1N%)fb=1`u?KN~FCcn(zZ*X`%uhvljvgUo0LrLvGafy27_22ZVdA(e0JPb# z4fQzYIAOYeux%Gg;MlxR*Zc?kfbw_;Ikt73O)y(7?gwVidcd!jzuO?rQ!(~u%obb* z5Th55m!tr+ar3ThH>e%~$bH_BWS4ocBM~J)>`uA%>2;41&F-S25Da%;t`cJ{*@Gb!CTNgHsZ|Q~=Fee&d+2*iJa|Cu8xkMy2z-ER2 zp(!!o*;WE-EMUCRFnIPqlo-!7t5sc9?KPWwP9Ey276r1Zd74VudpM$QNs=a`Wh=5K zq3+9w{L#SK_L=EzHM)=1Jr4z91tkyB^Xd;#MbQ{*l)uMxUDw%|>AJ47F@C4USR)Ad zhU?mVz0wK-S&M>Z)v|5mrmR?&uBf&oS+*gormP2qa5NVT*Jlg9=hnS8RlSa(d-b~K zq2mP2YE}PEU1vv3*L9s8F7!2jN?D$_dzE(d2)JX8BnnbDmi!M5erv^;b!fEXlC6BEkww7dFv zse{k)OBi3fj(@}%tiWZs3m$`K;p+iJ`kd2O(pe!x)Jm(i!HWfphNBdKXi8xR4rO7M zuLtOj>JKB+mDW-?BeWrpA3My3<1|fE3`py9Qe|*xuib9bFcRML9U^Z>ghYlR`S-1cWj@<~TNe`IO@n z`IzJG9(aptrlXJ>kH-_}ocFYV9PR@sT-SZOqFF!j?=6wxZ3%$|ZeW&@!~or$q}pf1 z2(myIUTf{sg%$;Js=BUPt*Wl8rg9KzlQ&(h0#3`RD!MO@vr~<}e}Dc{AZyWqgXr9K zmxJgWM4BAjeYb1mHs$F4TvIy)YM(X?hxpPZPOG}ofX_{qT1cxNL z9?!Mg#IdjM^>t0NEKP&$}AyK!F8G-bh3 zbX^(9vZAN^x*{u=>?)(Gr-H7t6-rP0nl`4orfb@DkU4S#l|1(ec&-m%2c~x14a@OT znM%VMlwfCvkss}ce}MCH=X&Q8KTlKg9S1ftkkv76&HQx%ZIE6$_y?Q}Y>fKH{SiU@}x-3&`$++0c! zi6UBP_US?T6eBT4I0l3(odia^vD@H}_L*VOp34!r+oOh#_j;Zkc)DfjUSRta>JO*W zsi8>2?rQDnVfDo*cN|L&)GkFNk1XVpC8SnJY5n{mUhpX^1)- z_Lr<H4xHn~0_?EzfMc;lh6F=QIhL*G(i%=VA6;7&E^1Z-slnV(X~Z^(;jp z6&fBYmVA^k*G(BqU6-*|Yq!V?8XqKr@(*3Nl5r_xuG?yLANkRSae`vz^(LHx%WxmO zmivT~VhC9;MZz4*Y+{NKj4eYdjfA&mTO>wBi>-u5LKev~zC?=@8kwL%d`b&s_DzdS z4Fgdar+;AIKDu&cG>Pi9$xs-}cI5b12SxkodiW*@XM3E3tL{zHXFO=IU zk)&?x#CcWI)bl4=T}dMJc13(YA`<*E66uO8%ZP~oNr-$!k|cs(zOcC&132l7vDB7* z)j92`zHF!Wsj8~T+I@<0pC&76eJ~Hx*^m@7|?RO-@((xbY3LqW_5`#dGds zMEVn5*A=<^lHvH!FX(z~I_nOiv+kHN(7fO95YEG0@Mtz5Vv8>gXp>}C(-^Ll%V@ zB)VS|_efM8+9ruGjYzpioj|E;Zuwj5IO)dZ;bw{DjiOY}BsICi$pmo^G5^iYO`w3= zVdq7k8-eH{Sa9-LmNgj)MB*pHMU0!F!kOOPp-B@exy_s%S<9CA)4+87Z13xTlvk^* zwADucPkv}?YwLU4bOjf=rcjQ}D1|~Ltl8_lW#&%w(skzZ;_F_haGpice|7o$%70JO zboJD{bw1s~Zl_bU0RF8nm7S5KKg=>l!(1`yA#wW|L_x5Jx(V~xA5S-gsJ7cx*I_Kl z^TGW5AkP!VT&LP@SLwO>f4SOjS1J|8Jg48Uvc`286T*H0N5Jik!vfp}PvxQ+x8KHD zMsW0jc1k6jO1!Eh5C~!q*v@fQy22uOmyJIkF~{Tf+|ok5j##fREX~>6b69i(f7gB= zZ5FH;jj>ZYnl@NfcN00c9k2EGw6U=*&#^g2V5v+iOS&Rr!QuAt5ozPWb@Ym>@HA1J z!xDm%M7nveo=Rh)@SSS8Yg%>mJXNoAM4DRM3TC=P+aiWL?YH^dmy|EA;T2jKYIs2l zL#_NVN!Qs_k*k?9U7xl|WSYjaXUE&CYx$QSVXOq9R{nS8MO6#q2G(wo=!%=#GYf41 z?x({dQ=EYVSKw}V4Lk*JhM$6;gWrO`0`S-Jhy78pn&ckzlOzadk~@{$&ep2K2r0t$ zS~!zJEp|6?oALwg4!uJ&9rTB@QIU)4dyWc4rQJBo`pH`I@GSE=XbXO*R5pEyFpC6q zw374-r3f@>p!AawG5%j>(kuv7+Kp4>NGwEdOQ~sko@t`z)dR<&KT91asCynR@^H`D zRmXABs8)@iEtsd~t_FE|h!zq?B%33?_vFcwt6WHbgzMzlxk6(g$GAb6el{Dg=Gd_mK@?`kPojqH?;szKkIEE&XdQqMFL}h#IS0m$ zYV(?F95jNHoxQuHS!n>=K`;f_J8@rA4Q{w+(_t}fr=lc@ZpLaB1? z>c7;xzTa(wpTk*YSH%47FsLXP>fq~Q1R(;7oB2UIm<<){DZ4kueKI_f3bP(+^*NUs<%_$KAzzL&fvY* zg@_2vLNHLXO_PjCZ!f`^Aym)0y7d1!o7NQ>127-~B~fJ8*ou7pL;ZwiSC?XBcJX*H z%!d1e+BI0rRFv(4%|a=Xg&MAho>!M;umAt6sCyoI%=2{Nx-G_Z!@9aX153AUeGv^q zq2ndNi4MrAs!N-7*DIcs?WJ`9D@~z7Xq)POK`*p1JAJPo`;fOs|LQm(_1;Y zrfG;sF07=gL$3}!uWlLMfKr1AMtFNua<4Z;RJ41y?=uY3T(TI$=L9p-mC-t@l6*g? z*8H7b;M0|r3j`5wx*|U(se2yEU9wB^M4{(7B46+41R!4XXAy_Jo{Xn=Ow%wHO@%st z$PxJqI7Ti1k=GzJh7SW2Eajyl&rliAp^<68AGixg&`E&%sD7>JCu`YSM%6t-g-Rn) zh)5vZjcumrfroabejI0Anq(rQSr7*#{stYd=t#$bg6q{Kt;U!_!>ImQN%rb}bDB~j#H4uc?;dJAvS;}ad|6e7y5SN8~Ks3K#QOgrFl34+#J=BlJ> zGuv?jL|fsE1SbgN@(I~R0mC9Z-IHpl>UIep3FrRt(TReAz(+Z&=Xs=`46`J(2xb^~ z!Ifi?s{(72T;LSkuwQhzlCthX-$K=~Nur3XykKgNxy}*!i-vdTSK?MHx|7OxIJ%>~ z`YTa8!}T4GA|el{JpzZ!1_nXPVmJ8-SY0a!kURC)oLGA@A|!gUH}{$Zo4Zuwct(2o zB{&UJ0Kab*+Cp`Ll=93_FOSeF721uvozJ)j2{v3TM#7ni`z3Ba5UcqrNiqldi0n{3 z^8YpVljM6;%dXWTp*3wa4C@ufxKp=nwP1t6P}g*A=lnyVEID4a8o9Y)m{rGpg~FIL zlKoK6DYNCvn&*YLSf*w^v~f|>HN97#)7J^(a!n0vs*wgVF1M7xQ#SFLS|l{pvTILV zs52%jo>!4&8b$TrUu(4xhZntSH3}u!wclA26@yA4lN9#Dfv)&dTc+&CDznrmZK2>jY(&b3`Q&io$OdaXi4U>zwse_1lKwb>$J$Oy6Dvl7TT0 zM*)o;_q?mh7Dc{UZ`q1WWx||1&82L+@>*M`<|dO>H$XVT8veg0$&6QP;#z|JVdjcq00AdL4UdYo zY|Z;B5Cvl5JcZ%^)Pq2xt|3INYMR=x!uKR*yq_YXN*t$dY8q8ti8K9!MNvemnoBlQ zzxOg#``$p)=)S^vw~M;YP#_?f0>M|=GH$zqoA_zsIQ6ZpW}3P#MD3eIOvhEcz71bss>~jb>`|7?nDogmA%@iI_6pPWsrR*bULBFh zb1(4PPTbna?YnsHf!DC@m%?GVyo(5>A(f^^rD7Nk-59_trb9_j*w*E@=FQNj-7djM zbax_>!Vyz-gH`eR-rk;G$2u-u^QjqL5ldYWA&GXzW6=(=*5`bo>3;P0CSe|Q<=L51 z4285(iu{`q;fN&758g{NYlE$D64wvVjA8MspqEt@AEV|0ziAXyBp&2yb*BpM8wY0sKy2y(W9c75r;I!_CA zLVZyM$ks@$t(X7E%F2qg#GYiV<>zz%%F6rTJ+s4p&LcHx{m{j|y}bmeseYu* zBaiLv?XhI2XFeiwaF+aiGR99p8x~*{P61%te2``r%^SL6D&ogaG>YrGMdoG{UDr)A zHxorc3pb;gOGXB=7j^B24=mF_{-UP+$bo78-+$k@f$RJG>5UsVZs2!lR(aD=#}e7v zlBs6l#8Jl*+1^st%uE0lT*a5}oJArRS(edeREOCx%Q!Dv$G$1t&IgY(dJr%hFI&zBIA!f?L%y3T zuBPhSd9b#f{gUnF2*K;?q|sX~{|qk|V_>JwnT&#<3#B-eF2SqS>5bsiFk zj7Je?h<=LisSvWgO-QADkA<&EH7zZ_19y4jIcsJ-s&OejhIBk86;=7b2NbnJ%0sA5 z`6)lpmOJGCk||E%o$zV+9Q+6X%HMfv>}VemnaDB#fJkH_NEw-A6u1-;T4v&G+7Stn zTg_{Gy0unEDYgrlyr zwy)o#XmhcuhjaBxP^oxz+S4puXKkkI$Ooq-QeWIOsESUi)A_l6(zu{$u5-zC7{@DI zzEZZ4bM(g8j_3X4;M?*%SDgx*V$`E`U6&+Fl5|toHIq?=8kXzc-tj%rY*x-Dh`l=U z!^LH9L$4~P9!SJ>5WkR#_u)_bkRz$gBOdSX@2j0E*3N2%NiFMEo4f869(=y-teQ2t zPfJu`2`&Qot67?-nP-Nf6yz`TsYrsVgGHvDDSgZ!S!ujHg9~;w{rDL1=cSiiKX6`> ze*Tv}{NWFiQT9Vx?om8+te+Rs?{Z%y{scuoZ#%gEQKHx%wyY1^3i;@tKKtymLOlBe z6p{V_2Kq4Qx zPDkeu9he{Q?FcwJ9sveji9_Q&PT4Le>LkQJO~sLDieAIv=fo$DWae8&SVMw2uM{9&-7?#-jlsn|a($WdyrM)UQO1;f{ z#WCD_H*cFa)|}@ey58JmS|5iF`a$61)~6h-)+&|Ss)|YO(}qpB9qtD}Y-sB^tyHA) ze$`?{kxU2lhbTi-Se*q4aky>B^6>MM-JXp51N|plfmOH#u2g(ca$VOWV#Vvi3*1j!jfZGu7s)zJ;2@uk8Gctp ztHmUO51GRuq5bu)QrA8Pgk#`3RU`>^`5a-|Wg=r&yR?^Z?rWa|=)wwsAB2jU+)VcF z*`4ZzW>6u%+yYj_cek8M60;mlC8^-@_OFtpX=j$Rp)X9R?wIy_QlRab9?)wAaKHp8 zDjR`X&NB}OQ|HSobDy~!^DSYV6EYYk7)&%=N6H1Ti~L#>ZPU;&>tvy?&J&yJO++!Q zB4Qt%aJ~b*a!S!%D32VNG+$BG?5ynTI^)tc-o{GPHc2l0KzugC@#}2hn3m|r9r+T@ z;6*ze=JHuI3$tRMk1Hm2Yw;s*my;~~k#mFCB)5mA(qK{itPpg>nC|>|S!BcK{=+`Z zM#WJ?gzGZ4aW>S#P}b%HTlePMfuu!uoIQI-MCGtG@9B0hugPJkg=aSyb6r8A*M3V{ zp-fK_&Pa4pFy^`&uSi}I$~0<)x))fHrbU4i_WPj}M4A>^fv1P9NRxs)`nPWYxymOa z^7C6U_$|y;9htQd<}q43mQ11=+?GEBtn8hxJ{lN110I@`5L(< z64Z6wWROv-#c1eeGBXKGJk61%|DW&sFZh!Duq=H;uh(Pip>e+TC>DNpZF}BrJlt^q z?ml#meBv=XJ@JEcb8~aDTrY~EPz>jA$RX|AzC{JG^1AX6)ADc`Tg7yIc(`!EMv{H@ zJ7D}BAHx>h1J?oiB4DLjnObIl0;f8U!8|;X1~xDE;C1r)$<(y(p;%)8flq~e^S#5@ zQQM|#Hnl8z-XTmq(m`z*w>+Tbr#Nho^f!^t@21Mxe6@;)Bp@#Cp*h}o-)Wi?*DxMN zR0oY|m7~pm65G9WwK{)PC|5MsZKocL;W7YDuQ8((LNFvk(2CC11dwp!7zW}L=2W>d z7`ROp_t;$;7-|?cn_;LLzcodVcSy9e6A@<$^f-SK;XGr_twS;?RFH=5T~)L(yRHU9 zxDP-{%Qx_$U8_Q?R-`oWa*Ohu#WKwFv;PH2f}IWeMU@67Y+AalHP2BJZ0g3~y}20> z`h=?8Lk)h9>o)l+!NoD7&j}e|wB8$n11XMkejK$~MAJ2`1wwYlM4V;}p;9=1ia85` zOv>~HuesGUpwIIt{m#?4ao*aYHcx=gd!Ze-P(ir5$cx)v%)*FwDcuz6_q?Rh`d=nQ zZ<*59I0F#X0$Dw85eYhJ8`up?eM}K|h%$b9W zaQ?gB{jRVq@m))<3j3$FSd|t$aOB95Bh^bUz4X#Y-}SEa(n~w*--QeVkmH<{@~ktb z2>!#@Qbs*=8M+Ix`bSo@G-)KwWlz{sn{&o*QL-2#2kli6qCBkZ?Q^ugY7!GzI+iHH zUj|35LY5eJpJm8pYlz>Rb_^>g64z*T`VEQlqMSM)twW(y@)dCoh@8R_YK*(&`TcF8 zRb^yFXzw(`ZV@=6(>p<~r0v_nHRpa|fxoToaaN@Rdu@vF76ulMrn!~#@#NgR>EsOxP z zFe}Wq{2%+4%_`D|4IKK+b{cc@!-=qHtEv`4*~RI0dwk5r0002k9g}jm=V`q;nJd~# z2whcnF`O9A&ovyI`5_*qjYgxf3~t|$zGd5%FO9wnh#<6v-G3K4gpM&iD%AFvV+jei z^I(|wdSMiZ`uywx@1wAF`(^{uYxSaRBFo$BGd^+8nV@3Qkil~VFR6Gguwj2Yfc5HE zTL9bpLk6B(dC7AQ@fW}T^{?I0_g$!&h0$-$j|=m^q9_BxC>=%zT(4qVkFH^C0azHX zJ!;t%&xOv%-JH>#jYi#t6W}96bzl5*uz{j4oJ99);us{5#LGlt@WRZJBIU^&=8&Y& zAnWI?YoQoa@`*Vp5xJPil5Cf8*rIlb)2^RoL~q z1#NrXp~bsK4P)GZ0{BW5PAv}ym_%6_ejrwpJ>q=7pp4i<<#V70hq> z82diPKCCTp|IRQwxXG1ZS(CO`a3&MOZiX13+_|Y9>8PL-9t@W~@63yAwY8Jr(dkB` z(Wv+PXRXEsaKU?P&&RPtxl!YgZ=?Q7eg-PILu7P9Yg$>7wOS_!CQozhb)zcnsK!OV zm5DGzbLfOZ@L@*pyVY)OrlgdzQmfUnQcHyxhY{4ZwQ>|<9DeZApZ>J_@9xEdS*5*c z*bLqeo$9u*6+E!Kyj%lQzCKlCmpfGtd$ntHmD7XhQ@?*|19R9wHrj_SL$@OY-cv+E zsNe{TcNeVAu|gNcEQ)j{b|})F(om1dLiK|GD8993aL)}LW!t{ICBLd(D(Tkd%`H_f z)vwBLk-lv!Cv-h-)IwRDo-WF;){inPS*d^F8Ke~0igTfguUf;dTbagqy5hQc?Nxym zoGUvh5$v@vZh3h0`bNjHC^lNI*YOBm8Lr(!^bqbp5F7=7)DzjwLhA*AQipk~?gfR3 ziHUpV^R%PA%1+*#e>=j~GU-_$GdWcR+wvgwEE|ebGPS_!5q@^s=+7V=eIG*i*Dkn) zvy>lwyi2*}Uxkw>gtK%5R##V7DNYQ1_CRS5eIKZK+CLQLO${4w+_^a6zPSmRl*{>(zYps^#=q|B0s%=9kK^vWgR3HE%r$Sc2W zlbhRZqWUlwczr2QRg-|4)JIig~M zw<~AgKgz$InDhPXY8f3*k3(NZIa0D$e;8epRD=JiFz7$=yjE-5D%IjL zHmSsrm{iZwNZ0t!p!i4dq~}F0W*K>WmHo~(9!%vzMTAedrBY7Z;ECdO5RB4p?9aLY zsm?lbCC)hASkW}kl@^p)Ta9E+S1uGk&?J;guz&l>X6XwI;tnwEWfd{bJfK zsmMe|HHRtJQHFBxWY>fiyjVv&4WVi3tV%&`gGhWP>CW9upgG`H!R*JF*FA9oT zub0(H#CbE4UKwxf3rIsZWeU-)Cr32`^*4-8qtR%rG#U-Vh`S8E{@l59@E2pJ^U|eA z;JAzLaI}WC(*L1-x%?UYS*Dvlo8byO=m5adudj1ICmq z0L3ZBKmqewv50Z8SmREKeHeM7*E>-SU5ZYize68KOsotswBm4gc~Js#x!27j1Mn;i z^pSnj1CU1;#)WYXqlY;eCRqlk!0r&@mPXhYvn-S?YX7M;as||3m}a^X?#tm5lxoI+~jb9(Yf5g z`!-{v&*okwbxh&CDM8~yUrwG;ccJG_q_9B@TX5n5qZn8!quwfEhAI0K1n(9oI*-Eh zwcy0#dY9&cP}|ZjrM9IXrj)wcvMCYwza@Eu3$1LM7N{reP1LsZE=@dYTUv8Ln5zXR zCD(N+6-g*q zH{4(tHwebb3TRZY$-Yw>?qYQ6y zg1~XKs*U*)3t^4ccU^V$VT>8xv9y#AgqZStLrh6)J3LSP4?mxRk{wG27LV&)rmd)u zX0_UEHZ`cYH5rY5b@b>_$vGJ#oHN1~DPb7I+rPRn|E?ST+|jC5D;&eIc7nix^V)HO zz_EnD(jTj-ZQCt5B@Ex25`361E$zSz=c2#u{|9?h zR3`&VWHbkg6-Wm|%bwpaLeMg*5y`^xbXW-P>_N;J_F@K@)&x*Wn@vios8YZz_SStI z5Cg4>$CjcByK04LYUYDKoF8Va$$&9ZZ8Ji!?_)w(Tp^4BYu=xETU8Y()5PNoSVq>$ zHI$<((KFDC(A&{(5o(78o6>3}UR4Yz*#xXz~@1$HQMz^^f^KB5~`GrFaFu8T>HjKAz zy_H~~2N&k?=nFQ0atZ}47$X#$ezj_=Lq0urwd$JylrScAq2MS0n-=`HCv2MPUckhS z)`zA|_}Zt@K6E|02ffh3a1gp?Z27dBB4C9c4N6SdMvTKMgb2w9qOmO+6bBsaL?*Fx zEAZ0|!bO5_Ouas!N)n;>MQxfwDmtjwq2Va>v~s(;#pzCh6L+lMQ z^R;cyvWl(ywiD!Us8)S#SF&t70hC#8kVd;-mb=()x62rO)ARwC+pIP*aq!(5|XnOi)4J00d`~-|`WP zs?(BlB}TU<;apBvqc3@!`gtWa4R%j5GBZP|B!md!Xm>`xh$cs1CX08_9&|N2g6>B5 zqrXC^JsdP>bd^)|LV0-S9((`cgZM!2o}N|#bO z$Idp9tku!}WHJXPW!GBC1RD_lqAeL?IwDaRi*IykTW>l1m`w44kZjNh^&48So;aYQ>Gus z`nl2dgghw_s{bO$U>h>glu>Ns(`fgmbXCu@4AZq)$o4+T8=^ z&^ys5(N7U-UqHdAK!njS($d(Rq`^?s&Eq6V2PEM)7iGU{(q3BZX00(RL`UXxk+y;~ zMbP@5EC%b%!Xu_7lz-WTVF_yg^|S&Ji7eg>aEXVO>I~&j=*JvG{X5(`3wX{2MebF~{}Y zS{BQ_i|G(LLQj;VIG6Wri?;oHUS2yr9q??zh4m?m3!+}nvzu;hiA|U^ysw35&4*ic+Vhc zye_=ue|$!poJr@HvgJMcw|FKsCoNL-cAI>D5#EiCqLb*wyh`cYQ=CaO)sgvNqvK?l2gOtY)5vhp zAEZhxG|YI4|3;ouLiz3M`S1!MHndXU^r49&f<;VX!%M2;3hfs#7V&sZU`wD!r#qojMg_96lLA_~b=LbdAN7 z>9v}RxTrc&$=xNd{ies2CqHMjpI}cZj-VV-lwEhDNhuo4!nBX+o=Td_V8!O)f<| z&(pL2MozN01w-Ljq;G|<{e!z0hw}ejQy7w9kJNqKRp1x0( z&`s#hxGFxbZz>~D_*7~C20EWIDZ_s{q zruUD&;lH|YET{z+inGOsiXRE*!>>n^(FaPy(i_Y7R^}_eto~Q6Q#)Jx{8)EvWBju5 ze{a30_4CQylizQz#m(eUvfeQ|H+L>fJvPm!SEm1KCYZT#W@Gm0bCYwwoWFPeT?>l~ zUrhfzli4HL&%3L=p!b)3*?(d%J2*8=hhNQa&Og8S#HP~b>o>1&vA5j2<+EE~v~BOU zKW=~Kj?RwX?)>zwm0e%ly?^%y_H5tt*L!}u_vGF$?(6KY?SJS%?ZBItHZT3x!L0{R zA3S?V9{S?p2QJ~4?4i~ErDM9|WoLKi#V&8xh3@k1TRo#a4|@~6|N9R2z3V?aP(Sd0 z@cxiFTt572asboaFkz zO19EJu#Sy9mh1O%up*2_%YmS4^KHN&%)bE_YmC4^5w*sX=0&%G#UzZHfhBld_Xbul z>}nZUiQBj~u#Us7OLuxbYm7jnGPS6gla4A#5f+WMl%iLrX7W+{-_txOZ6~8l5s3uc zUSCNl;PZ#X(RIyPZ)+di)=X#N(m?2LTdy<0+e|?9{_Wp1cM2HZ;jTav! zgb3inPYA)$eP(C3BukIt&bSHT#?LX*XfzfMrFig=Joaq#=DUe6O~9EopW3Uey9Dy^ z0yY^^M-UbqCC;&sNw5dMpY)a&n?n-g2o8b*>LSX0fwff*H(!uB-cN2KaFD;jJ fpiwCDTdTI#*49}++4{9ga-tMM6hbi+Arzqqr$l)Pi&O}c z5JCu{7_X3*B!m#Q+7iM!XXnTIp?z-m$K#x}yxy<(`}_U=@%{YsUYF~+-Jg%&zn=H! z4c`|RxcVMF7Bgj6Uh0*5vjCPY92WV9xX0K?8t;hsRPYJ$&{u{PW}+yW|L^xIb%=7 z_!c%b**2hUr*ImRaF}=rT7bg<)Zf$~s$u)76(5U0(;*ytag5}!{Y?kJA%q_^%Hb{H zJe;2M`N_+pldxI$nKF~gRBd_qR|#>f~@k(;;cw(PYXGG#%i&49Fub z1<(Q+Fr$7*Qqy0}@f>F2n)YEvfJw8@gi#_tmubQ&i_@Szg$&BbNY;g8ug#w<2g>;q zWtu)zC(83D`hw%;F$>r*)Z<6L3X%5BFwY_^C|tX8A|4BYO-;ZZ^Ahaa6e2vep0+GCsiN+j%#6Y`#{ByalFAPD7?VVaKm2ZqgzQ<5R#ir}LY1ygm-YYpOa? zPr$_6wk2)kN%YNXGMvo%>tM?EV>|-QV?(CRNN39B^!`NoMw7>hx-g&IAFOF!*F3%{ z$^40S+V=cCZ}J$$B-%id!vcQ??c_21w{T8pC*lMrnGe<08zUCT9N?ZbU;nU2fCu^C6}H`sS%@LFgl znV!=cFzUKbq~Mh=e0&2=|>n7FmXmP z4=wVVHE#2hNt?xeFy--a0@E)Zlfz-~k>)sE`V;ArYSZv}oR3m!{#-NdBNP`T(y0*X%fDP=Z*2(coWBd-hafHoA#Tz#M(hQS*H9z z;u=U?Poj_2h{tiVO&o5<2w|pdvhg!oC|@V*OVrg0 zd4#E>I$4&@(Of!%LZ!_OcOFuo9Dj(Ja%6+nKr6Jv#WOkU%U zSobKaAlW{)fgi^lR>0#7nvauxOV(q`C-`wfJuL~CddbB56YcR+TO1qTWWq7;RNW$< zpGvq4F59jN>pRTaNbYA4-&~L1>{HA!%1gzr&(_Cj{MP0*XsAvcL#5xA!EI;;VHo3s z<``>|^Ppa%#^$zJQlN}IrbJl*j1x$<4Q28Ek@sPVvJThTB8|PqRGYeBBYmK`JZ_K4 zZ_@j1cynIl5hfoLdHy?+d6LTcfN5*OHe;BGx9bvRn&U*-#&;s0O@npM>mqp!+l2cO z&jgGXux-*n*|upg9@U97WW&&Qvu0V5royB%YoIwDuR$)C#~AzVgdYhc;-Ss`;B^lv-j_q}q%kd-3Z_4JmuyrJ_%ZN|5qh;MD3~3LK1N<8wbAG>7*k)dn%Hd|s z1)9rj-ba`?N`|u!Hnv%}N7V1Pb#lGHj9H>xe2wO|rkOTyet)98K#OZ!z~)cJ{jI%( zYf@8lpE#{eUm*R7vXUCG<(qi6TS91mfPHs!KWscD$_VgXH~R&XE-}71_S-U>HDJn3 z-XCCpYSW;7nJve}*qk>&F53=sZO<@cU2VdTY#++S`nP*DV<*$t_L%-7JW-apN0NRV zw7Z9#d& zc}>`1;3$IWvPASL~W%Q9)O*K1j(*-!ct=L?y#3OJ9g z%j_G?^Nd8@cJG9A&GoSzK$`+kzYRBO?Qwxg$2NhavQ2i+c|Zm4`XM>GLa7LPVS@ao;v05G4h}s%pX4+Nz9#XgJ~u3gDPdCtwG?Ql0P4px^MbFan^dh}ZZ__svFKAnkSJ0_oP{H7Wl?AH` zo-KH;V0FRTf^`KO3cD0m7A`8hzwm*=#f1+RE-iek@a@8lUD|ZXFH%LiD7UCvQTL)A zMLmliD*CMGm!e;belPl?C{)x?)L68y=s?k-;tgG^y1w4^v#wja9_V_|M?T?GKJCl& zW&1k$3VdCBMZT`S9==|_qkKpE2Kom1j`R6_$NSFno$s6Ao9Mg1ccE{x?^55jzMFhE z`)>6Gd~M;xRSF<&MBEx@?go5lBM1Ib?@KP-7~jmUeDs5V`^MA?wU3= z!)k7*xwB?j&BHa%)~v2sTk}TE+clrnY_IvLrmnWEc1W$i_RQK#YA>(7s`lpEJ8JK% zeWZ3(?V8&4wHs?U)qYv~RqZ#mzt=X@#%d4kOxxLIXYZZI?!0T~@||z&e0%2yJO8zF z%g)_9f7_M6>!n?`m`j_h0?w+~(rl1U}pc8ZjJ;AoY ztYCJqU9dy2V=y4aGw7(7w?A(4nvhr-WOF^TWNv1HyyCCxuTApB5eyo*14Q zzA-#Ad{g-5@a^Hd!wbXrgzpV63f~`oF#K?MdHAvL%J8c2>)|)TZ-(Crza3s5-VlB# zTpfNt{Lk=~@R#9j;T_@n@Xz62!@q~a;aGTI_)vq>klv8jkl)a)p-;oWhVvUHHdHiR z(lDoCZo~YB%7&*J-feiV;e&>c8$N6Jyy1t2nuhv@-y04z{MFDDaYek5E|KC$*GRv} zfXLv;kjQb7VUZIeBO)Usr$kPRjERhmjE_u=To}1Ha%tqM$hDCfky(*jBDY26M&?KE zj@%nr99bH9Eb>I;g~+Rsw<7OE-j8gId>Gjh`7-i-mw5Mh&-l^tW8?n#3GtEfQ{!XfXUETvUl_kU zJ~ciqeqH>Q_?-B>_=5Pt_~Q7I`0{vFd`0}}`1A1>Ui!G2b8GIXS^8gi>0R*BgO2dh^Wdc){%^c=;lFw5Pn*5;%Ly+%(s=0`>TXJS z=~ZPzbT)Spp*Uj6j?8|v?>UsS)mzN-G&W-m?f(i&bm zCFo6f>6{i`x)5G^nDNphgJTk2x+3AFuLw?qm%hD)m%b-h8GIn&rI!b*;H94ot_fBL zw+6q4m)@E1(mw}-2`}9kB6#UEcxiXYoAA=@jh8Nfmo83t>7Jp|Pym z^x2{FLlvRxLvurS7%#o3*-I}EJ#W19E1`9v>d>dnUV2BUCiJWE(v9%a2f`HA;a1_C zaACM_cwpEMFMTS!^cnEd)8VCO9pR-bo4xd-;i~X6;kEzfrQd^>{wTZ^Ui$lHFC7X; zjhB`UY0X}`ykWfY(ia;qeW&r#?LK{uEyN>&TChosr#<{n2cA>E7_tec+|b;iZp{ zJ`jC8x-$A)^wsF=(YK=SMn8&v8r_=k(m%HF(g*+MrHf8ym8?glS? zRQ#A`FMS5Q^m*|M;H9sOUlYGJK06+W-yXjcUity!r5}fvUKxKj{zCk<_?z(3@4`!O zjDH;8Y`pY0@jBzB!^TVRhnIGmy>te=bhh!*CI6+DzPWKu<2>V~tB>^3O*KtFHGSXo zP19HK*Z=$Xe`y2?dKPpoDC`{V{A1^>ovS;)+xg|rFLhqk`RUG25_O*6c~0kBJNN0_ zvvar3Ih|8F%lxMNaQ?6PwfW!Vf0Dm3|IPeY@?Xk-Dt~4Ellk}N-<3ZOx6_7BPj_0;X*v-MRI-b-BB8cjngQew+JM?&rCl zTz4kkP|BPS?##RcpYy=&JcyZdKp^v0e9kt%cDWPVZ0NuVLXl@ow{e>HXaMnRlc2eeVYEdhhE9dlkIwebW0l z^f7LW_g-)hxZ50IRJ?P%0q?Ee>tIa>=(+bA6E_vs6z^sDtniNap5r~sJJvhKdzyE& zca(R8_jqq#Zy#@W#O7uk%s7y-F9UCZ;c?{G8Cx>m%2<){M8;zok7O*%Semgg;~yD! zX55y68ZxG3T#<26#+Z!LVIQ9{EaSM0p&3Imj>#CDF(_kTM*oby8GSNJGkRro&*+v> zl2M#dlu?*bkdcqn9Wy#)w9m-S$jWG&(Ix{U<2mHn=ZSh6JYi4B6ZHJ*+3l(G?DW(i z=3o4ln&%y`0jvjad*1TA=~?G_-Se7ft>;zGE1s7<`g^R#EBXNBi+ z&!e77&pn>IJps?Ho>`t5p6fi*Jy(0K^i1|#?76@*!E>Hxyl0$etmjP67|&?WNuE)j z6Fnn5!##e_ah{=`V?D=s273m2dU<+!N<3XWg`Rv*J5O6rrpN2?cv^cLkM<~!^au~R z|8nnlH@f5Qh&$~5&Hbx;kNZdW5AN;mZ`_}|x41ucZ*qU=e%t+~`wjP6_e<{8?&sXk zxSw(_cR%8O$i2*6>Au%}k9(o}F8A&3x$axsH@k0g&vM`BzQH}seYJb4`%3o}?#tbm zx+l9Qxi4{F=sw$hhWm8)sqWG4Q`{%HPjC<8xgv58aUbIz>>lVYclUMoaQoct-EMcf zJI$Sv{#W|G^k8~@dR=;LdQJK_>0hKjmtK{=B>h1=UJj(2H|%T~c>b~G|NTFg-*vpJ z!qwijz%|a5<-F*sacW%c@UIpA4R#f~y19nCI=Uh$+Lh^q>?JPmro1M+Bqg;LP&xe+Db6n0kXO;7`v(h!h zgdc_QE~w#ZXT6TON*$l$cUG8x%bY5d@EHD$c1}jUGo78Tp(x`hr@brB+3PBGb#!7* zDoWJO0LSI>qO}2MrSq$^TR*K=;@=8qj}z3Vp^x+Q66aQZnjWnO>H&JL9-`Ny9ShOR znJ9Ug{#6@4m7Iqo)!BUH@&BJkYTN&l+BDu1RWEW%T?Gb$75Zs>b~Is?u-fT1Y^&E5 zycrbUQ}ttu|8y&jr*O*nW3j)tSi<*e&|4Zo=`XWE{6l-kCf)tR|JY79n*2 zY?8b2ITPG~9LLMy_=E?uf9g_%qkZaPP$6gFa{|YxLCWpiJXDW$n0B#&CEZG*t-6}ZL<)G16qTBiA5n;xTo(8K><#z7>{5rIeU z3eT|}a#1F=p|+HTccR--4zP<({(R3d@Chiss#XaJF@qkz&mWhYNV`7C^EnW~Wig(06#YXXg_)vT#Hi?hLCt|bs zRBRQWi!a32;v2DDd@Ftsbz+bBL;NXXA}*y&lR2`3>?FI&qh+b=Bg^GLIY=HWkCP+h ziE^YoS)L+C%TwhU@=Q5So-NOn)8&nFmYgkblk?;q@=iHlE|7Q0f5^M#{qh0%uzW;5 zAy>&4|%Kg&Jx7x}CFP41Py%Rgk35=tvarKoh}Rvwk1 za#RP^QI)9fs)y>Sda0w-U^PS?tA;ASI$n)ZXR5JkoH|RLtTb16J+F4Coobh=Q}t@M`dRH!zpIeW))(tb^c=lJFVj`<_s{58 z^qYF4{y=ZipXe?63;n&W(|dKJZgM=hs&sRXcKYEuF^I6^Au7$Yt3eBBVFI>`Kv%$D zg!BMCfdfhbcAJy}hJf=xCb%d;J1BgKAqRR%g7#4O8AAu?>-$3fZd^i+chOY zC+Ot~uyeyVAC}J0D-#qzvA;BMKdws91qzQ~;6CwAlqm*$tz)>K*r^(PQ1*0xfFCy;4Smug?|?pKk@KO?TI3e! z^A>p`^hJyEKwr1WYUtY*ITHGfMLYnFThtz+g0>d%0~F)S$TOfAUq<4(Q-HZ;F4u-YQ$LDyPz9dw<=835g2kqfZ&Fi(tfLn|%f zYv>}2*bcqlBGA7=^o3x><$ebN#fZnC8!fV&s7o6Q zk53n@Ge(7QDkzH_4b>KTDirILQ5=T#!AR6q)ZHRBL$N*>@hKGRgVEX0hb-a+=w}xG zRZ7t>7O@HXt3{xVMZa6b$Iw44;-64%A5h0b8!XC!Hd@4o(0vy15%hpXEP)=f$VYHG zU@jQ745vetMRtN>jWOZ_DB8)$anP+6@eUMaGA8{&*nn+5l^FRjR9IvV6ywP_{h$~- zM&miUFViAtLor5-g93b97LaMs0*l@Y#d={JjJvPM;^4gSb+u^p!-uhFG{(@^%c5U_ z9%a#QLUA548sqO9XwlC=2U+CV(Bmw!9_qKqT~LfWBkQ2&S=7zY^DXLD=md+p4LZ@H zW>uqf^?&ND_%hhp6@`0)t&9?=i%Nl}TNG=Ch3^$ga85D$5@;KX zz8KopBJPJ~S&WTyt_1M4qoke1*qvYlzMCp3v}lY`NtuQB1|`ESY6$d1i?K(;2Bw_T zU<1DIDj93R4w6bRXNd|s41Xu&R&O0XUo_S+>(EquM}-p`^g zg7&wl3T$QE7QROG%(bXHpm`Q!7sCd;etM3vh=oKo=w}V;ZrLBHu_eBcGlp$A@r~2VGD6utY?PzRW++EbUt*gg(g7Xu+T*4 z+ZKW^srk%;y&ctTw-EeF4c09~70_Lv4mQT57IVk&9<3I6YB6tw@vZe+c>h&`nq$uh0y1nm?MTT-#al!49$mrV4(%je_4pj z_!PiT;Qowu?zRxt+s@xCbPv(4d<)$ReaV8mDBAThcm;VYp-~Ink004$J{fudI>JJW zp(DX4gg*$yoH4W%iaB5i>#`1GU3VkwhoBh8I*cDZ0!1ARJqqQr0PZTO?kNjB4#k|- zy@2p4DB4+vd8MbJuUd%XUjv&F{wx&zuKN-8d(awy{?ixGJplcuZ=t_i=m+Q@7E|w^ zMD+@3kiQ;&u)Zto251R58a|;7v=2BPb}sY`FdjD6dp*vv`fFh0T&>4itjD?&=Rnc- zdd!tLABwrDzYq3R&_w`iS0G(I=DHqZAZ~?LfhS-Gpy*HibFi^a>R+-5td07$7I6pB zZmd^EU~TNi*fIk31<~}NguMWYJ~8a`gBmyphu;gjEbNPdDZqnptc{=-w1tg14Q5%C zADV3un8#o{i^LiW=70`JkNyNZTGTiw#)wfEhhUyXpr64`7Kt?x%(w9VIammWq8!Y7 zkn00{-3ek$gToQN0Xo7WaZUwKwD5H;I1-$OH19#j0E{>9Z-WyoVk>l_MSKpuz#_ha zR)9;9{%h!E7QPk)r&#z}5WL*N*QVeV;A*7*5jqXr3VRncU=d;H?ch#?WBmkqK7hO* z%JTu_15m67M#P~!j=;KjJUw5)1E>gG()9HFTK; z%YcGdW5LHzuYgv8Ct&khSYeUJLZ1XLBAola2D}2h4YV3;fz9*JMddqbhkyspg)5@kiHKz2qLftL!%Zo6w2uUeos$99xotQ zL3xZ0!9I>CgnounV4nz0wWtfAX`nU2PlKjg_*xut0}sN_fOXU@SxV7I_}D0CYhbti@1~h37hiu?v+ToZHsTLR?RGi@X@x z!@_%&P)~3a(q96_n1=epz6@FpFisNwB!u}34TU`&>IWDDc^!15Mb3hb0vH2%6Lbu~ z7|1zL?gPNHI0~H&&PDj0P;NKc$me*d0!)T|FZ6ma6ZT^0TyQ&V%x~xpumJYMP)-LF zmse>~T+bqawJRTkK4{_R0HGxoIRd)WB6&O?vdAZ)Si_-5k#{BZdGI3ar=e>s@;T_s z7RBT73V0Q1IPE&{HtZLm)!<{;oc2?&1vcVB+bwbf^jnLuzk?0rJJ9bfX8r7d{UgHP zh1P)Gu-}LN3iiTA9if;-Zie#Q17kPB-UplO-*3UQPYN9XhYw4zz#wOf^ur-IYW5}@!H z*nsc-!)JhruqAY=MP)#zgBuaFQAWt$B-9e6s`hTJE{Qs z3|IxbD|9V*9d-%y4U59L7=F{DdO+W@82fG5K=p*Kw-_7iCJaKJIfh1YrbXAAquFxDQU;19#U zTa+If0$5OL7&Hnn|LO$jK8wP67d~WB@Er{p-v-RT8VOAU>99|M=2_I~P>d;~uvQx2 z#~5`6w2wu+2t^+nu>SaYTElp7KJ0POi5A6S6&8NB)^IVn1ZmEL&ap`3Z#~5`DQN(3Y)1Y1p&u^rQMO_Om zwy5i%T`jymBN*FAKa?{AI>4fCgkoGGgAqOxI>f@?-bIeH@N=XH=APkaL=ntA!_SB! zBP{%UDT4WC`1w)<^UbIL^fZf_107>gn3Kp@i@F_(xn$HlDCQ=DIp+J{$b}X)A9@kM zJg5cGOD*ajP>gj1W2Wwg^0;6O)FLSN6R5>d?&mDnIOikWZot?52)7&U<>!qNZXepE z9)@x|P^VfB<#K_lg5Cq}h5ZDy5}<7LBoy-$Sqgg<^f8Ni4$A3(Y1<310sFMbs}{8y zx(>XBaE{*qu>RBG)>}^o^BZk^QdxKJh{{}6y;CnZU_5uA7{wEac zilIzsIq)OA0XodW{x*6%!2GDcpbvmY;f7CzJ`PsE#yW_u1kb?6T8LsCqc6j*fW8XW z!p59O(f=sMPUD=4qMuQWoxTeCF8BcUbm&Lm6WBAM7^^78O5XzA3ci4ixsQHh(Q~2S zSv1yUbcaRHgJKM$7(hYq*sD(Fayegb;3MPr^~r&~1Ef9wp4UI`rw&O$j) zK{1z%#(5E&2rhvAEc9ZFejYjr;C$9N7h;!#D`CF`#auG{oIG}oMZXH2VbNFzu^TM< zH7MpdhI3qFUBuwCVzXhdgJO;ujWrUR1Lnbg8;bdhVXpMM&?OfAK6Dv)1mXXLa{tk9 z{SovTi~bnOeE|Aj&=)NH+&;#2zk%@0P_6^;bJrNoX-0nrt+wc`P_APm!as*@vgj|N zoDS%1P>uuoD=5bS{WX-2f&K=XKyQcrtwpo`0I+s7)^%(r*adqB6z4vp5f|HI(LX`= zg5MEd0}X;OY|gjeqU)gtEt<<{vS01#pP{WmI@13F^;q<8&@7ORaFiX-vFJab`4;^r zw2MWf&UiP_9qB{Ro)+Bz#Tt$ujqnKc7;r4?7}Rgk`=BRSG};>!=UjK2uG2>Kd$9d=jfo8T?jCD08P2jd=p7gQr0z9as=#pwmb zI*osT@ZQjm0oJ~Q`G{`@TVR(#w_5o7i})87r#}?yGyWaY41m@FtQ}`Cbg#uZ7Ru`Y zI76Xf&;a{5Xw>2igT^h+@zDJMbL(Kv`3KpIGXjcvYD|HBA{6t%@N=ie))u-Cnr<=S zZrH#%37P>gKhDWe%njp=hGK3SF(=NcP|OG8oDRiUGtL+&#<8&nY^<-wqb<%^Q1pj! z&W3Wiz&QuX4yka4gs8!xdq6QEaFoQcp`7KhW`3~oVstldVe zyT-Y&FNV$o^I=bdF0wdNpi3>zjmNe=5^Abeje z8VU`h%Ah_2;oKJuB?iLxWul?Gft(a*4+EdGqM^5e=UOyyeym@$9@@u1at(BVft=Hz zrx-{ag7Wcc@F4-{=>}_0fsJu>IPDb%()rLC2Eu)oXt>cp>Yvb=2GU&590Q4;pmPl* zKZibGAo&z@se$)(qTwL};T}XZJZ2zqFZ6K(NiO>t1BoZ0s|=(LKwmJB<}rN5K&lq{ znt{Y!&}sv}uP1O1#1NiMiH7$L^(%o_Xfh=Ks0b!HN@w;XxM4s zXMFk8Z-F@)#3B65a-6#5Z4%RrjT#vB8F4j>|v z4WuxZ5sU@kYr2R845YEnBbYxx3Vp%-3PZTJ6}Vqv__`n>7$d;XjYZ^PgS8)l{YWBw zIc!{eOqwTPKY?}*fMVPMze_42SYv?1yHFmlXHnOQ(A5UM-w_dK;K6W6@Z_pnMgy$Xt_hJl*Cg@KFzNUysje#VOWvzjno={#ZJJC)DigSSP z*>KM*B0&Q`Clry8fz+kYn1KZRLf7}$pkTuvDhKSPCq)cH`XbHLAeL=@{B zka!B(%0S|2XlnyM0~FD81IbNLw}AxO7R@xELTDQUi6@|#OTgbFh-j99@ZEvH{U*cD zHbu0Zfdtl0G{-=a!`d52J`U|_AW;h~F_82^yBSDhym8OU5bmKww4Z^$Ll?MDWvo3C z_DH1Rew}0>{RZ@81F4swrx-}T3>|IY`wJ1poB`4wLa|-}@1;d_tbxDJ645ISX6~X_ z!@ktJhlu`SAbgi3qQ4pl z-zACYegi*$=C76*(#JrtUin^KWBtUWf%FMb_%A?u2o!xb_wQKGF`TP_^iZhFK-v#Y zGmv@*ig5>|F>W!OJAm|YP^@FV|Ci@Lxg9wO9|Ohu2c*x0b}*1W1Db0fJsb-E2uPm@ z?PMT50$N}ooeAw?AbmO1XCRGpBG%JDdKR>of%IT#Zv#JX5qO5cke&hUYrwA#paTs2 ztVYC+H}H2`0?!Z_(i5R`45Wh4I}QAuf{0-*0pa^-5xd7g67v?TH1M+>5nE&+$z!<0 zKoaL>Y^j0p>_y<7ogw)m6z3iwydNcE%MFD4N)da)KzJ`j#8wzc{s?{Az|TWO>^TD| ztdZFB1`=3bvDF5C?k8}+&yY@qt}zgmL2+FJB)@^KHSj)P;F$nJIs>}FKu$Rn>m2a! zK1A$211X-1%?6SUP^@!6n&)$?fi#!#g@MHH&{_jYp3_|h5|2QCGmyR%y4OH@GL*+6 zh;z0F^nii%WB4k$#$bG79AyG)?}W|Qpm9)?53F4WyDnkZ!^Zv!&uj(m?HFtS4EyJV zjq@$OCt+iJAUt#~6u+irDZ`R)DhHJxl!F!K=9)q>p!ET2%P0)@8!n%ngN98He%mF1JP*cApZzX!1g%SoL#XRIF09Z+UH|wBxE~pDV zNLh{loW37K@SiP0o>rQuHG}YULYquKzuYpo<5dW>DDxwZ3t0Rs(uELK85TA>5c{t8P zTqiI7)0GJ8Je;Tibrp8Q8{ISU#`7k;q3p-ofC%f+h&TK85cR6Vi}@SjOc2%w?d`jO zs6X-!7>^hCrVtH`;71QgGq{@QSd?)b(jA9#{5T%AlIVm~qTvNZBc>6ZScCugKjKD# zlQt5ayaSVj@Kce04BB=^fM_gqEaJwY{IdoUoejO#*2Fs@Z$=^UxGGG?u8#tvxgEftXpOf-P)e$ zHrRnlM03F0c|^DGAi4v2?nK+?Bkr!zc!{kW(cP%)?u|qXD~aww-S=v+5-+KZB3guW z_oE#TY$aM;gBQ^@;bcW!%g~O8mJvOSbdOZyC9}mukE6~igsmvXk0mzaM-nLOX|!b( z+WqW$qUTZfYP8|SJwz`x61{?YUo9nCi}t)Wj_3{4w+{8ZH4{IQSU|LXFVTjBMDL)i zcTsLN>Ugh$=>2&_{|pdq+)ni25TcJz_Q&ms{xzQH6ED$bq~EfQXe;y!unqNnjlA1Y z=6BPGcAya@U{}=RYfqwN4~cG@NOVUXJrIUBXGO1K61}IAI0|(g zRY#(942d#$ygtZ_^GWoF4nX>Wdr1r)MPkSf636x;F|?Y*aY*Y&*zqX)1mqooHjNxe z;v|%LGTL#=ipAI7ZHS0)B+e~6Q%DWC_ zU!O|i29$dv($CyO;-)MTHzUt%)N?EH-MW`Vpce_84`S{r67vvt$2jwHpP1hbFY^`P zq0Tm3I2Pj}0`lEkL!uI8EgDba{!!Q%Ll>vw#XZ#ZU?XlIQP;9{xRF_l&BPcI%ZG#Y zBpzKr;<2gNR3lFnc!B`RS%I`GQQuRuNjx(URFhb>hs1Np_xv*4(4j9ct|aji+WGPn z60e{wYZ3nX5EARq?l;lCx3-d4zX~twEyTkFwEtbCt=^0m^HA3NNWU?a#0RMF1El#V zox~FLWaw=al$y0v^txyq~6#z^Nf>^YAbS3hlBK-Fn+~91+jm>%z!993NdJs42 z$j?vdB2&O7+>M~Ud>6wzzeWva;0g=fHyc#rDme}y{ec3o1`pey2de2Q?Z@00_%kMlyRU*oK>Tl$(RN9Ms)@HGWipayu5|<`LQn zX*wZ)eicbvA7w#5l7(3$yP*D}UU*Ru`HGRQE9|bD@v?{(^5Rmkn&c(WOHlqK+RKse3bX-!M_z?`u0|QxOd&aKG|A~%U;@c&yWwTP3X(IbNM1jX^-n5bA%_#5YZTO)G>{}L+#5G#px{~B=sQcTK~MekyM83&0+d3vs-#k>ouAlK0Lec^}$P86ml7 zD;}b*!o$YQBPomr>BP3TYCizqlF9;&;nJhd&9!K)o@g$!cP4f9vfVkCraI=Zymo}4pc?ii@ z5dP`}u$Sa($n*LFl5bRzT(^zno3ly22_5Ta@zak_*LE29dw*~1xL)xv=@I}EYl3y$)xea6f z)f8}$5Ys z9Z+`1DPRw&+%cr`P*2`!Qk?>%@@JFk3@t!8g`mq?QeBa^1o7Qxkm`Z_z2@OXJO=GL zdMZFUWrY9KLIFUV`XWz1r0WlRz;;sQ+ei)EN@@`LaLi&-Ll)o$GeT-8${4x{FWs#n zNa+;}WuJ~RPv3)= z>JUF>HK{YappMj;$Uhc&$EAV*sk2G}+IBX=&he8PUjR0dIu~*0Rq(@Lydaka(2j{X zo`^nNfVN$5kko}}Zw2yQ)D2XTy0{mqOQw;Ug!WECy2)tYWVG$l#iTCtf@P$pAnlZ` zq%NO9>WVyo_Dn?`SEU1_y$0dakY@ToQrBv*jns80bH)Tx*N-7}1KM!oXuP1dfYhv1 zP($h_=*?NAW~01YR+73E_1uQ?um@6e5H@EMsk!S&-M$dFjH~fw62j)AoCSVTcTFYr z52X7?9jUvkNiD?jJ+n#O3%w6*z7K7xTuo}xJW}_sCAGMK)PtyF$zoF27pbKONiC}+ z^$^lOjPf2S#fxI7|IzWJ9-B$(@qVPL(7q=SwgP#c97t*<%6MuOsizl^dIoJ<6(RLp z76_7By@u2a&==Q{S~CP}BlR-!y;4Q$)c~orDDyRh!KbJaE*|4;O5qXvlkU@NIjoAJd_7O79rkIjhtbT}SXtS9x^6jEE$ zNqvsApVyH3av`a0%SnATiPYDnq`sL!YWrF|0N66A%$Xot8~D@nH=LppsMX*be%mgC{vM$+Exq%&udZZi<9CEa!% zY4{JFjr{EzNw+U1-C;cGj^jw@P9>c;igc&hr1N_Ll-n8Q7HlTn1@T2_OYwTrU8j)t z6_75;Bi-#F>F%hf2h#N1M7kH+(0e&)+^6VL42p$xtk{&yO^jXVD zpS_UucpRUL{O6&r^JkNuFqQN~lzTxQ=?hE2UeXl@Nnf;>^ucVzsR+Be7wKz|2KO#{`ZUtlqP*)+-i%eGuOCPHhVi6t zM42;-NzbYvebaW*H!mlR`xt!-+HmVa(zk6Q9hgdb4%#?3Li%=;f5%MHca9=GKb`af z3^WSyOHl6B7N@|fVB5DlCG>Hy=Wcj`w_PoZGSKepv)zeq?e8-y=)-qhZd85 zxRLZD%SbQB@ndLb73y3uiS(1Oajxp8x08Nm59w9t&$BpwZZGK93$)k0On8PjB~={@j}8Ra{7-UXTTIZU`Kg_ zh@8PlJ9rN{IDecWb>tkom7L=ikmKJ@&aiFdoPe~$hm(WrEWQ{6Q^`4L0zll!qscj? zJy?Y=X=jpi+IWDnPTxq*7}RyfLUPVT{;|k=)&g?Qo=wgk`My`P*L0_4m@x|!?AnT4>M(7u}yJ{x7-g7)1ugPg!X za^|27bM}yPdmgAJ=Z?`}FFALith;BEvv3|c_aN_msQ`9mDLIP{l5>9zIk*lw4eBotZYDeVOhJJb_jN*xKawsd?mh;O#gZK zD|z+RSI;{)a4!D(!aVo9L%W6BlO+nWJZ{0(p&GUZ--*9N5lA%*k1vr z;{Uo@L#N^(o4VlXZ@-eVGG9@b6jyd;SyoQFzJ1CE^v_fS$_JnoXb;-uPfPQ7((uQh z1v%-hTBYaMPZ6-;O=MNjp$gG9E32)rpQb*H$&aEMk5I1Q-yOGYms3{OE+wU`Z&^8d zJ^>kb{W)A1j~bEUU-XJ%)6%W^wN{2zK- zbtqHq+9QM8o#M1@+aW6l8ALTcibX0H?Z#ZBrnu9+8ICI@BV9PDsp+1Mxdoltwr!QF z3hg8nsg#s7S9-cTT{}*S%cWecTetCYhStJ`+?*lLWGF%g!o3zX#c>YaW!Cr67~BE< z%gXwd@ER9ou57!;MGD3RqlWQA&&3f3H>Rbva=Tj{LQ7eNKQ1~K6m%AePdei0Bue<{ZJA$J*v_lD&HmAKtsSOlnp4+*~1YbGs*1P%=8uY*cW9Kr)3f_?$I!NqBdt zJ6eI#F?)%#6P_xw}00+IP0gKfi~N-MbAP(sMzg4Q<XoG2iZHp_#cxLuq?0wKaUgLe)_m^{vip*+G`fpwtL9`=dUc14*5+O=_ zgHJE$n42LTt<&+BBHf)%@0^t-L{?VkDJJ1#Xui1+iMH7pGdTz6%=C8R3@MyXcQDCK zHco>&cpML690Pa{5*w(t1NbUvJc25NhyCwXZ^D=0I)FJY-@p}Mwp@Xfv{solc8m(_ zh!)tR7q1l8atcm)DCo_|7-OTy*y!Y0urs-)cs!5eHkBWF9W*10+5MkK6jy=r@{485cr7GnygVn%oSkOf+ABD&J6NyS0qjW=0@u~_ zv_)N;Pg2>Vr~f$or)}Hibm)+Si^*Y2T**l}%j%wkgb*@$&K}>Zw{yyG9oyN|?K&Q| z;JbJ~G8|)^-@Inc{woDDpV(=%<+B$p(@np9qWaE=={H7~aeulG=Z!*B^p z7d)Fh9(?_={;3Pjic2u5KC`xpxO7}^@R`k{Vb1_~g^~f-rWEz%Fgv$=R`6K$#kiHi zbMj~ZQggvBG6#nfBr}OpQQUPv!GNyCLKL<4c=*WAM;=f6A`$Rp~STO2cKTl@O_ZL5HXKuvO*s z?sa@}O=w--V>J?|ai&wuIyi)D@myX9qMcchPMKr87dN6S{K0_ouIAb-ZZYTXe&I^j z+DXAmI8>b}M5gfbi6O4$&)aY3aE`BPh3!Zc=dk19B|ol}e(XnjVm!KVP38(&*nCYa z%>FM!OYP|^5*LR~`EU_+RuAt5R`e zMarRl$?ge1ckfX3|Ij^&eLxM?UKI_Ylla`{D*)ClPbXiEc{*F}0_-(v0CuRngzXuT z+#?|K;aiR|yt3@F%n?5cI+lvWc)3$^+vMdH z>f~~^!H24kxK&|4RxJs8@G&$IE5hy|xF^XgDYx04nRdY*-f_56k}>dd?8$I#XxVR$ z#cJoF=Ap-ilg=D4K*|9F&a`bAHE3{`E<$waGI-FagpEB%@4Vu^$u{>aNOjw^_~eZK zBetOd4&pNHupCj6DYd}tUkVEa51=VDClzVOBrjE*xB7_9Di*KA9_L51UoE6=m|wml zRD2I+R?q_6_%pR4M zm7_+9g?x{AsHTGBan86$kLGCX^V2It!J)_tFYvi@*q3&uqs`SNxv#T3yOutb$G9j5 z_48(vrwt1|U?o{}28#E7vM<^baURvg!-Z>M}A@;mkIb6f@Yy^4n@=}CnNvayMj$RzxS zs+0R_9*5@ZAfAD={rkMNxEny_|KZ4=oQ4{^@@o`-pZV z{m$R+OecYhE@HoR$FH^#(!{goxCJ@kXt^dfTzGBwcg_h&%&XYjGE+# zAJemE`YpDu$}C*o(}oT`ujL$#Kb9|Sya0L~Cn}P&Ac~6y+Ln1dIpYTnmMy1Dmh)=j zY~9e@a`ulNbot z<3kCpZf=7gZNPnHiz{W{BkrdTUpI>o-eUC>*bM&E36_|E?D6Rl*!kEp)~ z2E;yxu}|gr)Ufy3*1IQXGt)dX5nOX})DvA!+*IPoRNK^iW0`2Bb;w9%nrX-X$N$6J zn}A7jROg}s>}JlC=>HrLN?Y+vkaf4)zC@A)D7nwt0jaWX2i zs%sVrdC{n|G9x1+Gftdy;%sq3U537cUOYAn8~oNCfK1=K0#-{_=WySqYJz6~2YzPR z+rPu_pe+E}Wcb~Ws(Un(h;HxwoWQ30%mZ?A@8zAC-+}=q5&Io@8u&dGM~&fKTyMN<+OuH1;@BS0WLm%_a z=yxl;f%Yzrryk=5OWg0+Xch`7WjpW47gF7SH0SE^P`dk@YED}3wu|9VN?p?ZBlEUm z81NGwOdc@G5fyBP)C;~dAPr-tqOrnJhqUPLs6(Zl#o1X16UEutMfYq+O-!z>A3C(Y zHi`Mmd%lI5^@WAHJmZ{gU$VM7H90x8x_XIwX15_~14pfao&(Y&8^t5R@U77B`5TM)1{(fisJU+6K8Za2fyBfCOO1?;mdeRQD0#4XBv&5E2LqN$Xv0V>VMCA4=kke*D!c1f zDtsbVD8>_Z$RCKs6487<8Vp`kQ7=l28Ek7ZNytO^=7a=g6CBmV2R?HIjbN4{al=o`G&x*_e*Kd)^d z8=C5J`*LOswI!t9@M|i}Q$m0H!$yGLrf3i4HkeR9%VYOz>LN()LEi7SDb%YrPJ{9e z>oUAUa~ID$ilu>f0gwFqXnmP>-dTXLIr!Qe!=+OA#@FgGgvsh;q*$_l=!e3^V&o*= zmr#%M*!{MeAtiMGMAd27V(o6bU=>uU`#<>8W?LLfY#;h(>PO*+qRra1_t|1kC(dF% z^6EBTT5YN~lhDZ<-uX@nPBB;=-uqs|Jan11M7;OCG)f5v`T3t`E!2*E>eGf*eDtHV zSFnXof66lsvlxGE4spK2t36_lB$eR~zV#qY!%^^q56QrPh~6dWR$(~)9%DK0f9oe@ z)PM3@yu%v5jsSPUfVSgz0H5o4pV(`~_N6FCVJ0NU7mKm-+Q3@*NI2g8+kcj3yB%Jt z0x!k#n#rZRpOFtznOsU;gLAwOb080@4K3tVcq63BMo%4TV)9>IEkT4a;=fnWvBe@H zaG*lhvxaKxEFU57sd4n^Q^2o{8H@f0dta@yC11KluZ=mE@7tC;cT^7@s?NxH3qPDE zDy2vwSE=L@krJjaU@>fqloMeUF=ugkf`Sd7TDGiZse>bDi&A7M5Lk+oM0f3Id%_Yt zpKtfjce4j+Y>K_FR-=SRS&GzdJUX^9repA7j9v$l_ zs{7fZWfeE|p_4$5w~ZFM%?6UuortnuHY=TZ^KvY4si|Gm=&oY`tRt`^OtCUR48vctX~Hi)KAJ9!8?kM?kRJW`2{!dH zxGiQ^**Jx})mck@oNq-S1=_8~R|)IYc$KZRG|J*N-5mulTq_c3rE@t5DTDBQ@Lv|5 zN_2q+Sufcw#Yn2?J(1`K2ImUE>4BpiDLBymLBaVH!TFSc6aGsN6uV?sfUT{yih=6!3v!@g^=L%`k0j{gYYD*Py;S+JPRh+?ZAAFo)qt0*M`t^=z})B1-6^;&7Y zuueqBc|-pY&vh+KkLJ8sQb6y93Bg$x{o62e&J-F4i6a?%a%ypLYSR8~N0o1}wA-qr zR3@cTTj4PAG#uXKPbzbQ5a8bl`AMnn={@CQvAjo)UTy&80!^Lq5&0HI=_wyw5G;RM z@Guq^wCdNf#n-9a!w)~qvk%52kEv9Lr#Pfx{BcfIs(Vs3=H}?y<J{o<^&iMTfo65kJy1@Q?(j3W6kGzZgiC23Jdl_;3sh?M zO=DWzhdDQ2&`1m7he%2E#o2z0AXtL7V`IdIoOw#2**U+&xg> zU$nV`hs%rbilNQBuXOLPfah;C*VZ@ot*x(Nl0zg5{@V|S|2|A|YwP`=!4H}SMP4Y7 zLcPud3}C<1=xQZfduN@P01n$6%l^kxKbP>2$0R@Tc&ovdTGaZM2h?oel?6;qbT>N~io*D3nduv_nQ>SUBmo z)9H8~&wP1hWhER-q|;t(Y#D@oSU387d`~!q@3BLvNTl~YnPl&KOdr0_Pg!XTlFCs% z=?4)(S4^Y;lUOt!PK2YWNG2PO!~v5)Fp~}hl7NX)$*7-D(f>g|VYdT?SUMd-3sD&C z5FM2p^@mdFus@v*@||Hz1*10Pl0>918gL)z{yiIU!Up6!R!CR;>wZEv*!!etZ19r; zS-kkdKWW4t9Q>p(Ki2)EcM!h2Iz95q}?M8&6!EFaMgVx zkS~S$mQE)xxp<&)VoV%PlsWF%UMhH|hXz~o?s?D2+5?@iaas7$_G5p8BH2 zrv{n*#h4`_A#hsMQ=_BqP|c}g_`sdCs$(M(mXa7I&nm@S1k*U0jd&R_8qHO^TCIxp z!;$=NeZw2xFp-2(n@mjH7)Ola%;*_kdN1a;L=s^Rnif`+CX2*~7U__v0h8F&^_H(o zU6ga9gv53b9g)kyn-aF;!Qe|7~NWY_6UxLBv5`+FJW*LnDg z??Qhub+a#`akzR%Px}DpW)C5xZ&1z!f{`L9?W2>f?4ZRd>adJ1qBzBS%~?Pb=#SWs zodd*z16FaRXn9W6W&%7o3U+z_o5>8)Eg{O*%l6dvII7C_N0w$D>b_62p(hR;*wuz^ zu6vS=u(6xTR)oil&)}CWj=l_nQ9$>dG0rbTwCd0KI-qZv^w@1uo@=!lybhtym~S!U zqUjh^pazEt&haJ~4~uEbFH$BRgz=pA0+HJ>_dy>5K4J1L(Q={N#=K=vj0Og+2&?l} zVDH=xyJxvjWNN~)Cib>+w_g6bIA|=LxY(Hi1U|L}L!&)n+EPG_<~3_ zpSWVtL5-zyxm-YmzM5e1-Z2E3f?>nFgsV##Ep~AO?0hVfQC4I16iNCPSPEkg^6WvL zeVAt-?v*`q>oNj!ZPh1&^j&nH_A+O+a{T!5Ms0e;?+-VZmnUb#*7f;BB>T=tFciJF z5RYWvboD;z;FSvt3wB;vYJWZ+&9?R;tROtcx+T7Gc4V%x9EOBaC`?2m(1U3;h{cBf zPOxLJ-yr_sbJjc6*Pu7u2F^%%T{&}c9kgj<0sz2uj3`Z6Wb>ji)oPk@H4kJRA$w>F zipG14vCJlVV#h<~w~Ch=C<4wOJ7v6~$+PZTpLTF#XPVzVkEvfc&SW zr6rXjmqV++@}Eq5ot>Q>DFge&|5C2?_YU;;4k9pLO#6sP5)_J#acuMNnrCzyjxuCf+Tly>ao0 zTd{n+*-ZN53(-V8mw!!GS-E?OY@1ebZkrHHFDnxSD7`&lq^}Q>QSh&9O6E+-+^&+H z7p(4|L|D(BJxB8C^>`{5PgIirY#urg(K=kD#rOC~{*r5jkI8Y{G}wE^1E%B&^NO-5 zdBBwTf*SAN0Y0z+8R<^nkN6&dHJ39n0gk`@dI?L7-dnid;buhHSq25mq=LPHp&@wq zumlaD8|I`3an%6=C4!5YAoxi(7>Gtwx$mD!0|u)XUE2r#ZcB}-L$GXHxfd+Pr@dof z-EF$rGABJCzh!!Q+8>*?Qd26EOr_(%##}l+x6}vi(47bOJ);Qm!1NRpfF8Vi(zMu9 zF`_+)f`vHty$bL-kI0FeefI*7*O~wXn4%Xi0bLM#9}u;OP3JvlPD-3w;O;vEo@)=v zMib@+STJ@?^OeZ|8EQLt_{?AY#b1oCZ(Lzu7E!0Gr2}96>Q|%r50m~Li$2Pqhm@Vp z6yB#&iQI~ytQ>c$Qy)oYwhf+L`3t}B3kPd81Egp|6(d^s)*Qp1k0-Vf!riCw3OtOy zvEjR4_$hjQcDi#%8oYN$cLlD6vC81baXW^CB1;xmW$qMp;c7*E~Ih^qcFoJrc zN6IBb+{0*^L3J&3y5m4`C#9@HAqUSxBom2%d#cokK8?q6g@Q#k#{2Qu+3xqyW6rR` zA;hiY2r2@2P1f7yHL>`Gy=L$;w+wl2_IR!6LiE=(HMjLSYZ^B&*NZ%7^chI|wYrT6 zWGxeNeGq|N)e=Bs-|8%`Fc<(T#uCiaWznK6SQUC>RD2z9WT)`4^`%t zJ>YCNV$)MXSsQ36SyL=(qj)ofBzq(86LFdjZpcQ_dl7i%?ECkd~ATf1|j zCnvMUfwN6EEl!;u?{rqs^Bwe~f-l2tv7&9VDpk=g8yK<+b$)V-=2_ep*G>125q^8p z(HwvdMn(c;(6>-ex65KL+il-p`~KedPage_5}D|CC6T^qYT1CzQ&SC&c`zjQf=XDu zQA8jQcsf-ED0eVfgT1gmdpM1MQ2Cm?D+DNGFN8$`(_lb#;sXf~|MY8kMFPb?zKVov zQ#;Ur!`H!JYW}fKLEDWr&S5P|$cAMYy$~GS58ol|!*T^ypudd_65ZnutUoOPIFW-( zIFfu@B9l(R0X9)9V4BVs(wPukTlHA%SS4S~6pOGV;6KZNRO@KTWU-&5pFPR{U&oFe zi^b~j%7rrN0v6ZC3$+O>v4#C6PIHHTC6u|meqkz=DdsD$GxO6i!ydGw_Q81kLGB;?h(&bDwj|G`Pq2VAhR&crXK+D z*pX~D>nG%F%fhScjj74ZI9Ci$lq2+};T?Q8UWZP3-VpdzK4VmiA4}ufNT8^K#QRP z!geT{PqG?X*X@an5+Wr~%|#=9A^UZIu~b=FECm9sbb5tguCT8Nx3L217rYz@mgMrn zQl(TxU{XAu?^Rt%r(4@w08+e_Ot#`czrG9A_J;F+a;g9=uj2Q&vc+O{?{R~rZ?v=7 z;{H#1t3HDCL=MQR*8jhaPh8v{_I8B$YpJn$&PbsI0b`!<~<*FwYv|z z;QQbIzCUVNuYIj$B~<9(r!#()dOc$|s#uS`4S8!8EAgnsI%CWL1B|g_u;YYQ)3MPq z#Q{SY$jPr3GX&>AB3G@XpC`i-tS}RT=JVj8#ZtR{u^pJE+c*-LE>~$}BI}8bWV2Sz z%4H`$Kas_+mCcU)nzM1~3+3`Bce7)d*^PYuQrJ7NLY%@J z$DWd(CLU4+VMqB$ByppeaK;vLGWKt=IkFzfMT~pBXLInTSNlXsRRP`$_Ty;6jiFVf z%dncmbZ)CnxovC*o60`GI|s^`-&-AMfI2K+hca*VT0*NPxVckiO?F(_P8I}tXW5og zysO;lcE~EtGo&wx4e|l^;=U1Ra<}S?7{oLU4d7YHd(m`P)*HA%fB`b%C^E5-)hd__ z!6*I)BT@@k5lcj#L>v}do90KSz1G4LUgp2m2fT@~OqRgtm|olrXOuZtEpv9pXJVu} z=A?qj$m7A<_D*ONT6?eAUL1>+%TuN){*Ko9&fa_U$(CUQ=9lmcVEthSYb!g@J+AZJ z;(Hf3mb;RY8U}&Fz5}cvLYc9J+R1{&9LB()8tQPJsysm$iD=8oolg^(A-gdxvpeVW zCYxn^9oRk6j0kV%V|tOJwy~X(ex|S4x6iT;9lH7ELx(I?0m6{Y7Zxva#PF>XMVbqf zsiOIf)5glbH%9!*&SnBCG0S_&M#NgmT2{7|wF-@ZifgG3Z<$UWIIzj)`I^?gvdkmJ zS`^a(Y2p81>szLIeX@pNeZE8=;t`bz*ja1ZLdBAmPcOxim}>B*bfvTu3|euTGU3}b zw%xtHOQ8F_9PqBY(>T={z?^!Y{F;?IoCT`@6Kua+kw+u27Qk17dc7Vi8|y9yiou)( z3*x!p3iBF! zOH|+=WBT-rj|f^KN<(RzEGnZSL+VX02^QYdlMUFRr;jfRg~E|+=9gpPk-7Qu$b2ZY zkWLj2hSS{-r^BTK>Q6rKfe(B$lM9DKArgXWHgT?{oUUcO4qEfS0=~dD28)0J9_xbhF_Uq503AH{CcDB6$8DO8Hfyb+9o6B z_{(hOIfI}lTvt}_*BglQ?Um8ufoqK|hVOfz@ugqNVWHmS@8UU9bvbK%@uF>2uOAoz6L??88>OXbqBp1iDExsx#aW$5t#xV5HPJ`8q8v|c z$uo=7(nCh+%>SYeiHDn!+?v<+3)X;+hdYCK>SVf(_Yp0TLqo9I0FnXKaHUT;ADa2! zUzwT=%bM8mNFq^pLbv1fL}CO>pJ8i{MkXp#CX~5SnFzBi8fEQ9On7&!;nogMPF4IT zyt7){sM*GM*xxv+3!0wgQ+73emf9WfYz@b~ytR)`k0Z`_msjaRVCY%eNjN%VXg5k5 zI)EdOGp(FG%fa;Wv=3S7)Y#eJInZUl+WPZog8bMz@@V&rQr$DD;*gQkV#t< z{mmsv(gV##BEwL?@?ZUnA*$1MsQFjF^Ehr&A)u2OsxvYn z+#V2JegFjpAHTcR4zLVtS_dvgv(dH%Q3%5@9wbmK(;(^b*@J{R)!x^>k-fK3Wot1vKDtE%$n#2(yH(m!TL>}<{ zIJ`QL3MfpFRrX6nFf<`hjU1HcfV}gKQ_y=UdKPh4IH$%ex?^fW1xcySt#&FN z3&h@y5SJ(>8hz+~8&|PdG^!6zec}_JNLyC=DLedxk+{B`iXV%oGFOFI;7{9jh|YdH ztzR6vDx2i(FYC8ti%%rt(+_xC#JT%2@IxJ&q~Uol;Q7T3R@9Id7|7fK}@hM*JWxMq9pDe*xD&`YKjSZ7~PetXFdrslY7Rpp;G70y4E%#$aq5K#K%NLRH&6;`$^N zpjSOAYS6a6c4jIwW_E$!WP}xPm~(uTwx?*ko<}?bv=OtKs3%W(Who$=F;MzlzAo%Uylo(W zUP1>TGW8Yq=z9cITi|hAc;0aVVH>M}$hTd?NRK7z+@EwgwWaD+Psu1eb(J*K?tW4V zde(xLbM5>9=JX*lN9Z=>?OS}WWDgtQ0$w{1pzRew0DBTDNSAhE^jM#B-t~byZv6njEkyw?Jav_LdEiC@?=B+7~<>qR_`TCb$X(3LuQh6y9sl>^I zPavuJw?CPh{R=O9U(+gRo;bYLl2|%MNwYs6gBnS_STCnw1HZEuf`= zoCZV>zJbu>G*NSe6=yhW$UdR+Wtd?EAn1>CCxlL;`_VL3<{PNs^u&pi=Bi>81T19N z;zR{&awjGlv*VabCk=QAWpf`RabI!aEf|wZ#{g%m<$)7;Cv0DoL$PeK$?2O4697Sr zlsWT*MM>AmoaD?mF)|Hn5A+Ji#Y3P)OwU zX0e3H96C}V$lf#b)HVwWMUJxJbp-e!Zu}vX%BzimLAA}<)zSF$Tc+cGt5VlSmA_n= zoGj$I6d+&Qzi+1O4@R++^6dB2LsM{d!3N|SUzIk)PEA?X)RPdnqd_!UK)RqzCO?hj zVr9RIUW;&m;WuP#E5NDSfK!J6qg@)lwxkLN>S^uTc3lS#S?}>+En<<=@Px!<^1U8i z+Ji`+C5OZ8or%GG&SZS~k&!$TIl_1zo7vl17#me;bZo)UOf9jxcKrC-YJv%VF}k(z zSOh`PG0O_4>}Nu0KE}t#!nHe|dFGkebai>TIvqoYXm8)Kd>HczEX3C$`tmIpcZpW# zpu-U7;H9iCkc==03V=Q(GM>Xe$S|7dk;_mF6M9yaN!*BxB%KjmPtg8?leS#Tu`T#3 z$JSZzNP?YHCPts8A??=a;6djp{{6YZ zZn{Z5bc=cje)#0w_kH`8TX2s5ze}DFQV$(f9WGMAk`)?vg;Q*+htxxD^s|(?yUlan zYpI8}cMK!hA?8h_#1!Mb=Pczs2bl4(tbS?=YpeKN4z7&M=tz!F zsh=3$rrhalT|E zmydjcnP}$J2dQ%M@4$sW)a}LF0>82_Lg0CZgFuv!nAVTUA-|bk}^CmD2+AT#7&@?{) z76OQJbU*&u1|GCv?1rTmhD7Ne_#i0BlWIS3k!axo3vm(XlXemC8>SyvNa;n{{wr<8 zU`=(*09Jj8xCq4Fjf>=%eORmWR`9{&-~8q`2VuLbp!+|}J91O1>bKP#;9jzyRy)Nv*CPU7D?rUTI3!g8p%HzF<$VWr}if`;Qg350{`fzk5K_`JL)5l&EQ`I45&o_nG7^Q*ZhYP{PV&{ zOm_P@137g$uT9(hqtTTAh_^Zm=A}z8N!e2x4I%3ppmps;gxM z%6?K2M~RuPuzst%gPgng^Wi@o&kenD8_~^|b1knDyBueB@!wI`6ZwK;vmc`-+Q2&G z2=UO-xyvy5FrxqI)o`Vvc2yP(YXh)W|6W2K&RneD!l^& zAo6ra+tBLlRxEbJ4796HcH3KCN8u*T2HIqUeiCfjj^Hh*czv1cv6$xt>I-~dGy=r8 zThZuSi9~~n=9c&IQ^wShmT^lDUbns9_L*q3*E(_T%s&DCz*gTl^fX#HHxN%icp45` zC-7l&#jzRG`WfbM&Mw-(gISyM4|EIHygt7bM;9r-Q*rl?A?2Ke#TNO(e(8@xzcava?{iG`t)=T z|CzIUcRw#}q1%qkPFg!+=nOsfQX2d3y#|E=FGR#4mC52&&$T)?5S~O{?}d)n zHsOH@t)M;Lq-~<^)g}OCP*(T>OK`S%QhraKlHb#s(QG@~VoM5J@`&@}+;e$o2l=0m zBA>kby=G4HekpmpjHR}rIJSEV`YiPtQmA&WYMG*g_xGDqc!jTNPnRXd?*c=JE5?@6 zx+PK`nbwxtUFH;;SI``4FdIocIv=B<<;OgxlxLu)PLGr$#97V{I~K=*d6BS*Dpq_` zZxK75^h-;Wq+Ok>uZOV?<{JE!{$V_9ZI{>K-Zju)m9qv7G8;`RP0_CFKs%a7%DFkj zRHV1c_UPS6-4Gg^N<@p7%V4B`51*NSRly4W-^4Z5rqj8rkLnQ$ZcKQ80(BME@o%RR zaaFoI8qHjdBy|{Wl!u>b9SWW@j$ibZYZ$)-eC=R2w=v%q){r6tl!DYN4txqMcAp#7 z-f-j+^bPEY8ntHedJ-h>a_(!L`E2KKfC44J?5NT8H{;hFQ7n)pwP`-}cEp+5u|z~4 z?#yH!L{A+;E)f0BavHqb}P@`#IrX!Wolt|eik-)E=dF^@K`uzS0V|0 zP^T~(5Ssd6!|R%e*fD+BN+ut)1A&;MwR~uCcBV98+l@PD$;48vSnO7w-O95!@$5}b zS@-=#HMy{`@(Pu55-ICv)$d0VD=}LiL>AGx0Oue%T?rqd%)aN&28ca8Y)qVW%nq1B zKwLO6(O8aUANYIrWPr*SS37+&ht_9xIx8r(t+QqY{7y7k>5HYM4otA|dD0H^@BS%t z@@>r%9HW`XM5YA|nb;gl4Opm_Dnf!WQ0_Scfal4S2lk4N)@s^J>BG>q$QbZ@AZa_+ z(wS(ry8J$q8FVKTvP+v(Gk;nt{xp>yoiD_AEKyz@6)aeqoV4#U2}ie+#R5$Ratm4* zPS_{#gFl13_!08uzU(GK-y)TtjZ1r_crTA`svGrIEwJ-`N?urz@2$wyrttGj6x=2P!~i`3ngy4!N~miMzhpL;~gAMwn;_kc$mvoGn^OL!OoXAA)AhNfr7 zEKHrldoB73`eDXU*CPpYoXF>;T~00Vg_9rQY+-yn%I(0AU&|k>Ntt_h*`iSE^uZW& zGkc+mOzlTqJu6j}a(2txm#@t%vmr!GCihG@IX7Rf)XN13SIk~Y`B+{e$MvfZEiadu z4#LStOL+wTc+bv|vMOFYu1(Cj{c=2KRHM1p!S&bM((rsD#9Yns1o^8`QFb0RrS}_z z`$@(-AWd_~Z$BYliQ|VFrf~XU&p2H+Fqg`=Q11}2!;^$i8(zKlIeLPVBob%7#2I}J z^2MAj<;-zYXz1z*(+mJT3*Enz&s5{|HJCfbLjjtge(SR^10lx$1$sXj#-mV^!kh?d zr4%4GR?;`;N5Kt#A!!*iMU6S-aIO0#-Zkb#AqH`F6>$r;L0(WqL;xi0>gIf-iB{cP zsp@ttV(V3f`@i#1efXZjSH1ZbPQ{{r^jNI%EFC&XL_X6{jQDw zunpvmwpux|&WrUUE6TocB$u;w(m*z}Vy3iBUh$8hB#-$Lc+77w7P~KZGbdu`{`eE! z-^9cTHjr{P?vjE*eN6yMa0L)2-=%8itVq!Y!$wmrb014E60D@MD=U-bNJa*mh~jvy zuQOGO;XI0GLOOcML7ef4#hm&88(^)Mhr(3_(<^Rno|cAA8_j6(+osmVX;%2q9TuE_ zrq-UR$>0Y1PTh0|xU`KGBQ@V1=tqaK*5OXjfmq*+Z-Enm4zdS>FVca9mH#GhDsU!< z+BJ=44d98GNGN8YF0c`g;(vpb^j5Gfwz$4U>Dyf#-QA*8u0``!OAGR9OMW3D@Yaqu zaQJC$nVX(oRVOKcbx-Ks=IC_NvOQYvoowsIwsq^v_!uk}S0Q3w$LoW_&Nl0q^eKaS0p)EoUp1J+41q`V@F1JqTe zgYLL(ciBbn{?!!zyZ<2mQ}5@N6dl2fwmjO_We$VvNLlCiyZ^ITY~6H|*l4v+|KIQa z!6N??AD4lNFYEE+9_Iy}k?xArW5jkvnBpJN3Hc@6a1OeqD9BW`HBV!q#zpc&P-ls= z%Ib2SJO07Do;Y&!)vrE!1e?a@A-b!0e;MaO0Vj?sovQ^;(gDBz!uvzp-#Pln-qB`*-su?&#OL~2-mREtFwMv=E1NCUMc=FejTUq&KKB>TeoWddhQ6$Um6Z^<1s5I%H*p@h>IqQ20{^ z5o7MIG5%CIWG8<~y~3xcIUSeaUe~Q(tuYD@IGLlLtwC(55zigS+;px6M4fMYvEl*s)rG3vFlyB0UM*fr9Cy z`|D~@B9oiQGW_a1YtGk@p3L81kr80=P5eIdCkSV*_>rWu)0o3gAv2X^qvDDIazrht z0n+P)G=~r*2efl1F2}M)KO4qG!u)7&qZnh1MkpV69)VSm4doSZ2Fsgzt5qufF0_^q zi}Ljs8;QNuUkr=;F;2bwdqRGYm$c{1ylv2l))a6wFSxvU+_c*LBl98$hC-wJ^qRC+ z_9#_bic1;T#RTI;I~JoPDZpGK?u=;!nS+#O^^k45(Xob42X|*bqLXx5ZE5_*p2#}) zjLvai0o1A$TU{jxpRi5U*ra>9ApjsPbwKYOSfDo`>)iz?ggibO%65#uaeqlRjt>of z1^QIbe?Uu;s*9>V;76deOroJuz{g2_AzRD00^yZ&%%JYuhWNx_foGXUH}W zeMYtmAS<*c9oL)|d11h-uw~gb3pD1*k^c0sUA^9eJh)Vk-r(f2hiXzq_C@38uK+$3 z%oCTx)Bi9aL_Zd$^C8(&weu8EMuue_76h?PNn#UyM=3nYY>cJgIUCq#tMlCd+j_ko z4AzqtmJ=u4vo9u-HKdzKskb>N-G|k$z3Nr3D!;1St%bt}3fyp{aFK0aBxeT{GTIL* z{Q&cs_BYL!bhnY!F;zoyl(#W?4NlPh0cn4FuxU1Y#y6}~d>jyfzR9?ByKStVh|HD;MNSY^!KySFL|As5XGbI$|44kIQJ;kEH#WUAm6)hECXj(FKD|_lPdew}@G{#v zRGEr~5Atj|Xvfq~(QW+F+eHHl;t|4V}69i3qlxb`YR-6d4+MuZ&ERRun@++bb z!_JUZuoucg`rTh@?rZopZ6lrI!?gfpq;9CFDh( z@M8<8q;31lM-ygeIsRr+&4oSgzSHiRI_Y(L9`^^Q!WN=c5hO4*mR*dMin&FVNtfQk zHre)DCa1acj%nM!k?(#EHrHJ{pK~5KqhRre2yDcSC_0iD~ggoGf<9m!_N5wgK1AZ8pd42>dF#jV~ zBYzy3<|(%E=dvO5q`05*o*jE!1D=IYl*MP{%#Dv;$AKr3L``UpBWg6`( zhrl+pe9QY@ahvg*R*WRpll|_+=isw8=I3+CQOg=l=H}-&ZZM*o`Ne0+>DC35^xEb; zX89(;3$`$i#kIGDB(7};7#)O^X~9LSI;3}?HdCcjyb-QI!yZLHEa-5RpuVfVMb(HM z!?y0&DGqk?gXKL49ffURET0dCrs4DeBBtjA8;#8#bGL_Kbbr(?H^WSr%tmf&*hn;$ z9jn@-BQvCeE&So}E$8<(N8e=5M6C0HpiWhbvw&Eqg*s);TYtVp*$sj72WS-C-LLb* z)Ant281Xow)2t&kw5q`Sh$lK%4#jvZgSC8AxGUuh)70rWa+H>IUzZ`F22^VQ!O$o9Avk6d{JmcH1x#uJxoTjMtH&U|A7`I^0> zGwdrRup8gwnBt)-YyL|XL9R+hv5x78M{>`G4F?ukv1po8!@{D^wZ#)!KG{fKAjhG0xzaDt2l>{A`=*?0^+aP_sp8P4c$ z0~TF>o|bTL%$c^%?Z`)-kawQwzv4~hAh2FPWY(b!)N`IP!3ok9Wf(tj{1W&oQoage zZAFI3(J})~6gnv2EtmoQi>wRr#SC`RbCAS6P^|O8RcY@8v9f6=rIrw|nQBLH)wW~b z^k=PN)*pyZSC_iqSgKCP1L_pivq-lC?u0cItsnBo@esK4PSoH1-HacZ%@>f|5h)iJ zn4hnE=JVL6=d18Y{iL2V^~h`g2z4X<4_Gm+bD`x|S1{eJV5Ob*{sBk0RSGf2U{GD6 zU6I*UT-z+4(DdW_F;dvFc(f?FCItp zjHWZ;jitTVVTdUZ)pR%zPvkQBLe@W@3oE~6Bl25gW&RrclUQ^Z5ui=DAj>`9cKy)mYi$Tqc4IMbK6)uC_AaSV*CTK*)w? z7VyP+BwN@}ljSmu!TAMb_eZ2vENll=jC;%`v5Qa;COd8d!?*J*u#6!X3R#hCZf#suGPRhYIArCTHHi)}{p8|%0YgTB3E&1WqAg`ie%mJ{CwWz1#s=(g8M-G&~ zE_kg@QrYaD*tUwtTG(ACAIMtm=KM{;;G63z@HS<=^ic>7*;M=;`NhQpPncOBVjmb3 zc`&8gj*txX;4b_)$4bF`A&SN9rQmeUua36%wzAn5ak0dVn(9PuUBUL&Eh9EKu^>M1 zf-T>>eLn-ba`~Ak0(g*`ltfOzU?zAfvaMGPsB3+TO2h{1tm0S8#aj5&;s3(zg2tFg zP^0l@VP4fsys$|S@zBOHVC<@5mEbL{u1zEBswx&8H-9Nt#>VXM{m0`aATzX7tAZ+d zRV;ARl`dJ%g_>On-E1JN-^cd`x$j%4mMS3a;Vg_U6LCh>qEarDNb!w)b2^6np@o1` zYi?pPQA8zjzYDU@Vos;dwgYj9{>fEDgchH?iihe*tc%X(S6nR zRIiP_?pkA7PKFez6R@8c19R_0oiIh+ZF8o!I_9)jwtb2g-zW?0Xq)>! z`bH3#MJ}lUkkdDwl zTBxgqstE-pH95W4WA8UW(*rF%*3-jqAaa5zm~c@OGRkjqQlkJAXEz*v4MI}@VWnYzA{okqr!ab3A6lw9eeoriUw+BO(^cg?{~36v z0~9xc3D8I?oB>y5Hb?Gy7)=H2(O9H}?O>4P#)f+JtrE6bgN?+WL(am09Y}TmjIZY3 zJFsa`GUQL-5J+-AS;mR$gC{55DpqE{oUufG83{KK73?~MC~W~`+C8KrjtZO;u`MKX z4i+#}h@$jbfRGr6Bige|!ZMD{@Pup~P14aJiN&5wbszE@JyQ34tv*OC@JJkX4VaL;IlLaAFhxD0kVV5{P;4Pt zrT_8n3$MEBs{2Cq)27BOmWw(yF0F>%`Yy${ElMnHlqE>rn(j3Z-$qy32WDz2pPvo}omCWA z$lPwPG={7@KoFIy|Mkw=r1Lf)(nv4wdBL99S%hwBuGng~x7_6h?iu)p6Hc|_p7mfs zooWK>Q?K>C4!8v7O#%k%2GWpJC0VDS1fjq#LevDXg!c*i(#bS>3*cFU4kHGvOAw6! zRsyc*s}wd!b~3AewtKt!*{QvIr&5uqN#}Wu&f|%V_*$J7XJkDgpQJnj2yWsRKil$S zJjBX}bl+3h`!3}q_I$lg>RG|E_nyOfv(irE$(0p6iYz8891dxPKRw5_Bg+Us-1hzN zzF*a{jA2gfxYrsH0-zvFYZ;utBmRi4PeD;w7Ucu7FD-s+nL*f`$P19VsY{8}s%25i z;smm^B&`zEI>%6t5%MWw8C54BU>)!z}Cqpd~+69uG|$ z$$UCAUe+5=9-#d_R-Reb=DDf?oRt_yOT;u~`14XRU z1U>0yq{${;=Q0e3O>l_aWcELo37&PBhC-qGa&^x?|MNdv>a36TjqcQ_m(@NvSvwD4 zMFr~(WA*CptG@cxuVNJjZ=4rl0tgak-rx@6A3E4m@DATYzV}0x-o4APMqd#(7`$i# zkT^tQC2%)&)glORp25hJ#bD|bhKmvKOoc&hFdFdnFf46jy-{?fcZ@OVRp*xx#(-mC zUOP6lk7C}*URlwLxouJm$VL;DO;^q4W;~8nUln;crWRc`?4Dg4jba~WnP|EvP8G+o{eQxU%`Y5a5#ASKU+aUbRFwhulf|0qNmqF;BatGNx>v=v- zLL1@xaKKRPqJ@wA+m8eml#6XcPibb#f?Dek24ru}z&(8Z0Z3!$s2rNXf8G_A?)ksq z0DWc_{|#TAIXF&tVwW*moSuK41=kJD!hKxpIZ0GM*a$&bUNPM33mE5f2wcydZ6z{Q zbz1OvudP1qW-u5Ib2lafN03G2u~)OfM55!njl_?s*|{$H z;n{`!Dsx4lAVM$eY4KHOlH3vnXWroC*1RYtTl_IMJKI7gy_*q;v(jm-Xf>w@iqlVF z@y{FN<{J=7>9ljUJF3~1wxKyVC*ePs z1}gX3=s{@TD${@TAcTD8#vc1@6oH>Y0zd;+U+n5(5H+1`d2dn`AfC3J-=}$j^Wj=T z6K|2RT6jZxw0HrqhKk;F!HhEl@lCwsyqIhE#$*wm&TFBu|A_BLeV+mc+32%?(rRd= ze2Oug1|dcwu9&fk058=G5l*RMV!ouf(<`E;<+(%+Xzk~zp?BcynPs?c-sA<8WojS^ zo$hphNmZzWZP64uM`^lj%%j`74CW;`C-oui{Ne)2t0_G-w6yyPLzM4dD*ISH3Md<{J{kSA&zRuR)YCRF!bH2?Nq=lO=d@^9hIdPXae;7(g`%SG;E~c??~TE2z7IlI3u!=!xJcBha*cToN z>U7-g<$6`)J(!b!414W8PTtnH^G^S)>&_B7WE}`jxMR#PngGA&0;7r%1W8~#`)nJl z-f9T8I7>C#duP`B3=|Fb8E2AS{f4lD-xgMYV;kNjPhd~md$Hm*F!b3x`l@yNHdpGa zcgouW_AT~lmatO|C8E1E=%+$HX=S(DNFoVBz~+p0+B5SchE zpZsEpF<~_?1352n6Tz(8Ioq9H0$+LRQF&^C>!$`2gV&@Vkl)vQrrsiKdipw$oz{UVnCnN^!c6}rO=ZlM{LAp$E&8=n3!fUZ_=t)V^iVeG? zp8@)d;h)S-pM~&x_Y6cf_$<@qv(Wug@?DG<`f##nm1z3l#*eg0l&s<(dGo%X@?NL| z-fZ;UKN=ewi|2EZ8HCjVwlu|#S=LM>myh?ZdA}ad7s~mO{Ivcp7xHmzSd-^(-COrt zkfhzOaK1~y?0H{_U&j3~`K6`%iq_Y?-?8PrnDc(`J%~ohMdJA@tiV35G{8r0>SB&=whukFnB0aE?3%$$gjHCS_bjDRLVI4wK?6GKHC_M`@5@ffeb59KQvG{;mhX z9n>EzQjZF0$WgD0(jif})*({IHEH#cn?r~LW%F3l(CT{>O~5ZxLF~W|_{cccD%|X3 zB*feS?k`d<<_n@M)^|fhTD657VEg7Q4~7E+1(!@Ay8_}isI$qAOq@_e4u*7~i3fo% zbtk1BC28&S0h)6;I5lwPJG4+?daV;FX1tod2=FG?8n;p zmmEIK-@$u-I;;(~wY%bB{~^e$yIyx_;Ycd8yN%AU27FE|r4Kv@Ie2m*?g#+RYzxRb*@roN3&=x_Xcefe$VEs=o_ z)lHk#gZy&OyQJhE#8pQ2s>?waIvMzb!y?U`X!u9}3>e0jlKg}M3 z{onxfq`AYXvA)URd+OX94uyc%03p`lU@!;~PE8_h1mgWBr$*!P{;O-e^sM$#Zs~8e zJ2_Z_H(c8Pef(mG6tp)%HS8c-l>6-(?kG%37@8P*I22y0z-Y+GVdn6k{V~U%-{Xzm z$Aoonx$OO^e(p{=T62C+mB)G*O()l5s6oDCY7sf-8QtKBEP6%IJWILmwyzdxLE6%b zh;$|TSiT2&+=unn>yWY@@V%oqV;Htg)bzqZ>!rJAL&L^_cV;ditFW1D#?ZfJCp{o) z_5<*seKL!BgGwlXER^t%>_j9o`WT&kJ!12!>d8Ly*Z`aymHy3M%;|ReaQG9#Zbt}L z0~r9Q&j@jbEA;s#9!r?1%MWuZ-{oz%<9(+i%R1}s9UYKSBw~24b3-A(NDN~XWwo8g zC@|WhzS|HTG`4+!R_r>MghAFrtoaqD$TzBA*Cz;2lAeQul80$bFvubyM8oa?@qm^t z(|c3~Xqd9Md~O|3(;#q;I^b2HLCH|bfL7uf2?jP@tpkGuDW>dBA!{(p@JmsKA)KN= zSU3!VAZ1yT_+V|xp%qAN)272Sn!Y*e4r*=ciK6?x>?|MR!?TdNP)FOI27_JEWXCw5 zJE%mS!nGq6^rxw_n@B&zLcM!)IGe)sK(L@trf zCFAjAE|1d$s!)(W(_c>kf^kz@v|r<(jp=g14$sXY&B9a$ zTP{X3Q={qZ_+&Oa$v@M3b3L9-j~-(i{5v5(^fb5qI7)e>NvN+E&b>}jN4H_WzCvNLl#S+cr4kakW=oTW0=8b@_eHu{jF<6vF2>9>M22iKnSJT7#f4s7poVe04t^Er(2h^V%>u#kqAhqs>xmqqb*$E= z4MS^|7-{N;yo<5s{tA4Qh|RFSbuRrUYj^fU+gAm-_C^=Wa!7L(Lvv60WkLT5g>1pd z%8XRW$QexF!?XPT-@;-XJ(5_2t~8&Ky^C6>ow3G%yB?3OHrs~7|4+QC+u!W|p>7b( zA_MW8`N*5$CI1vpD*CXAJX)Yt(C!Wph(#E1#yaj;E$GzOL!-V;=ZO(sM6N2qH0-~g z|8$A)V^ZkwQcA~O2X$3)iJ_@!G?b%;hmByC18A`z_NX`Lp$3Qv;yz zYVhrqS%d(Xc#WS?#9&~pNAco+nB#R)w(=nf6c-4X?*Xr1f}3>t>J)U_^@cApdO zo*lyHj|qDIpnb0RpXU+q=MMOB;eQ$C$sb0h$=5PY(aEtTsRB4Jkb7{_ryfNm)YYiO zh9I6r@=J0NS{2AH^_7kI2|m8sFeO15T@;}W;dd^~ zOk1hQ_|gEw)r>?z8d=%gJiWQ89w2AMgPSIDTKMa>{CF~1wU}~iB%DV6t^qcz*>1(< zxI8`7pNhvGv z+zB1mhBf3thElW}2MHVE2>U z1Xm)g(mUSgNzdJ>P2n03z5?lz@LiW+v3m8Hc5`@}Xu@)B&uN+=j zv{ux_X4{-H>7e~3n8 z+&k9g*jM95?IEUlP8;+ojD;X-Q&cnO6tW7m@btvArXISI7Sprs zu#3n*6v$QTDSsFyonR=Nt=F@;U=Svrus>C=0D|gTbZ6prb4+wv>1$y$Hy{>F+^0PlWhOS{s^g-J8l~ zVzZYP1I0}%7RIZC`9?OD&c_1rJ=t_DJr$fOWU>eoiH36Psn}RP9ZpPT(+O-Boy(AB zp#nNF!;7%}T?>BrBZ%C7pYPMYuYrO!1$x){6{s;8BoxZk7FWbEkyDBUO%$!|j%#WT z)F6BGJOGRstg*Wd;nR*TLn{xlow!~{5?tT_>4I$L;j_+(2TuYILLO5F=P*}` z5cQaT4q;4HaA7hl&YHxG^`@hNJC}HLu!dyH={+!{#_l$rojAT?0PGHXDYy6fT}uuAwFZA@7_sfrF#c5re<(K^ zu8^Fa|4U?xYr_0@LHFkv{qw6b|E8F5F74;v4k^y5EQNc#p#kggaYX0ei@kB)!^rdV z%m}?qoP)f6Hzyyg@mywurm@1WV&go{NLm8Xn%0mpH$3Ns!Q_P#)o4;GIQW4@31zJ8w3Vh{u&_OCpH zKd0p{yGQfI5oB1MdVZ(z4u=yU9=^(8`0ysP?b+aOeX?8EkI9t=T5 z7aNO~D@(h_Sc^&*Jj6ExVp=AGIFy2~r~+qF>JgUkZ-wm`qU7&akUdb}ipCKj#yU6| zKE`}h9C7Cr&`z?kZR-j%%B!VqVWbdH2*>$hPT*s4PfrZ30H^o^ z$jK3pO-yb?A{&zvOl6pS;BI87*k3GIuIBY~88lT6BW>880|%A@gG?HOCohMa&MWq} zy%!!~yRXvg1A&!Lx;j5!ua^RWQoTMuUrmQrZmQSozdp7wKZo&T-mgv{-QLfYy8nW6 z>NyF{)_S;10Yb|TooW*OLCG;UjK1C`#E4Ne)JVb`E(TpV(S7o4G{nVu#27gk;?q|c zjGl-&WuGPrxxUJ}h_CJ655PwY`E-u&e| z$jlpQoYNa2mqyX z?0rwWca}cko?IA6S{bb6eR}#z?pZ&251+pM1o^D(8^OHy zSCC_3$Rc=y?-pcedcCiW&hR^&XboH23u(#a=Qfxd%m_4|Fa~C$lRyUvYY-O94w-c? zlzf28+T19oiUwkVMBT2kVK@lH8K3}M!JwDD``z!31#+izf!Nj98?VBU>ooKMMaL3P ztjc&apC5hqXuc567_9qi^LY2kWO9YknmDMFkVF~psSlDTN9R%5{JM&+uSZqv`s=Sx z&CazdXcnzk%x`OMHg$a&Sx%GXhfTv;isb9mai>AFiuRp%F+9{0PtE;^>D$EqaZWZv zvJZ)e;3^7IIKeBiP{bJ$d)}t*Z1li-BgcxA^KpX%p<{m;b#Fti=a3ak&d)|u`Ho$5 z5Zkpev-r+8xw%y2Zx--hDD-yILJwG6!-o9g z7USFEfXPp2PVYu0GjSu`QGJn!zzRSCbRupx?Ml#P-pEF!m-GsYl)bYILONa$Jnvc} zWamt=N^l%xbr`Qtd804GX`lg+A%T3jaW3Rtn^=qEk86DqJcSKZ^lgO{?^;0B%0KZ^ zr7q4`r6o?0D?lur~6bD+g0=*3ri+c@j zd!c?T%YC_~#<{O-4BH)1BYk7n?#x9M1dkR$H!Ylnr@vL}VzD>lRcfp|FfjQte(W{sGK z&9unNV0!>2t`FI;u!DW~qIDC2DWQzyKC|41ParSHn?lKC?%)`r95Fy-Lh+@rN*)YZ z(Lz21Rn!eU1`;^&kKJBit8BBWH|z6O{dfklbT%^vR3nSTek7v-7#D+9J`^cfOyAP0 z4c;H>t*M{!?T1WoEpkb}4>TxCo+ReaK*6LR=lJrUpL3y5BZzCZNH95v5s zps_PiD#c6Fj2^WNXg#n%Fv<;>))M(_bS06?M%0zrh`IWf_r_gr`M&26%h5@vMz9N) zGZ^MW_{)Hf?w^1R(T7M#<8>cG9Lqi-_zQ1i5rvRl2AKJnNiHuuqv+Cq3M0$V{>J8X{AO%Jy# zy<)AoZkwjXZ4di5C!v(ulpkzR?0s0^3yL#2tM7tE94pMYqpnwWltm$2$pGF0IY~=H zMUjs%BZ0Gk3QPpot_MqJy+eg;S9Lvt2w%Z_xVXe4((2FXpaU)GK7U?=ok~I#>^2f_ z${FN>=I^@qS=&>cmjf-d`zvE>(18cuJJ7m*57V-G+9g;$mzBXbD*gAtjx-M^Znu47 z*wx`0_#=#ct4R0?boCMNZB(6=t+WJ_Q!_++t!d9btHgd{^T>WXmphnk)K`-T#;3Vs z>;FUIj?A}-NouZva5wc5*EjE;4SMO@e7KzaSUkSCJTV!JC3Rw)?%yR6A3aSx-FrA3 z$@?2aGP~}It7RUAj@3eZ)FDv$D>?gu&-&3*%tAOLVFL=xl>ikV7>78v5+tME8FqLv zl2T4#(M1)1?t)SYVJFG1LgaEy>FWQD=d$*re*bORL@e>5D>-X%M%%n5Wk++j1%sD{ zld1Hb+&m!>$mauz|J=Psbt-}|X1Pse$H?dEGwEt4CcFl8(>XbZL6m!07UQJ z2+p;t7kt`PtU1D6%5YlB1DITZ5rJ0p0$^DlN;8J$pFpGy##!PQ{{Y+&YHHaLNB&C5 zUlDM1n58HVPihXO1b08+*j#>3~0DsbiV^gSgp(r0($}GJ z{02%x#`Z1Gs)r&3fX*NWh_2KS1NX*iZ)_VNE|L{32qacbkRVJcBbUNFrlAokoCgv^ z(_)*Um$^MMmr!;EyWONzxdi&i&t>EBb3$k+czf9J6Cv!q0yu||?I95kWP%YbK?LK; zv3wls>tgY&Gk1x}{VS$r3_HB2MN@&0SAPIv<5M$la{C z;@$Nj8}R~x@RnWzN80y>)KW-q6_3PYk*RW7@1uBR&ywYzuGS;5Ja%1GR<3SgN6<(F z`-1k?RCdXSDqthzwjKH9rJP|AxZH7S$Q2)vGaS1+y;C-E63jYcPb@4fo825Q+PhNu z|A~7OD9MiUOf=)>zVA!znU!6cRlU~Iwe%)scdMn=UIV(IwjZZwykXBAyL@`AN1_4jg?!soeSB!7Si}mxIQ09W@%q$l zul6?3{4y4G7H3k~Tz)Z@&J~xJi@9`cF`vt(W{S9V#Gg(VkQpnV$>b}Qav`1eANjj3 zfKZdlkn4i0W7rAug~)s9TMQLLnm5tKFcBQ6xxlXG2YF(kTAf?XxEP5E3D1V3_fq@! zU?}AUHwzEm{k?QLOD)hImA3xQ+}>&#U%S1gr;o;vS7@a3rz64ADaob zjtq}@T%@K%UaOWWo!!tG*;dqtPuNpk7_j(&p|Da6lMa@@mJEgb zL1b>M4THl$tO(_z0dCKYJYV*{O%cVSF4jXVd05c@s4McMxUP>iKs-260 z1X4hHFAmqXzG;3}K7j4quq})I0jCw!k!~wGf1XYwsUm`Hj)wgfmPKHD+~GnZlB-$d ziC8Xyo6%!o3(X&f11URHOhoeL@ylw%v8{b{nxKHl2eYxxU-Vwv1~^ciu*r-EIEhf7 zHk__x(?M*}bS!Gn@c7u?+TdV1f~;q`=8;^}do!3A-RImS4W}-&fi+WW$R~FV>_2Y; zjcdr_FvwEe+tXHi7{!ALj$n!|X9cDbFJ#&`k@^HGAy{=Cnhx>SeOR?3ZkFy;NMgxA zA+D6bRUlMQmh$8ExkwCKO1)**zBQZ9P3)`hyUjL0Ye3T*9s`AlnMaNJ>}5XNd6zL4 z_|i+51+E2Rxy#OePs#2KYnfi<{hvVUi*4=$RubKxWEUVrRqu?E8}MZ!oWQ z3A;n_38QcyYp`CT+zL%crZnbknA3%`Uj1UKa%Q&=GlziE`z*iOx6b*w?F|_Itrglr~slj>scJI3fCK`p*M!)^1 z&nsWkqm2Z5Vr=x#Gutns&0NM0b{FwmdE`)X1fyB(C}jCFB?~Wf*86>Hr8z!Vb{^SW z1=3kI$D3|n+>$J0(^lX(txeD)=a=NSuvWlBU$5&tNq$AR)F?TLl!Me{>u=Z~hjR*( z0NiX9wjTvM%H(-=93s-xV* zwVsKG6EB`=by~uj#jnA-ymDCrdjUS~`#fY%@Fbl{m`7zuB!%!e`fgJvz^e;4P&6Ad z6FLKQ916UOcNg#$U?{o+pTxpW`UJ(SMe7)AWk`xZ8yW=cLRk7KLqMa2P(A7>62t;J zilHRkQ^FuJlCV#AfW_3^@+OSj7mvq_*bF`r;F7;Mw1{M8XtdE79ZqB2UZL|j>~@@= zOJxbDG=y^IlBuQ#u#y%Q!;^#g2Zj)Wn6;+4lk(65`N5N7wXP1NtD1GDn&EI}Q*(Jd z2J>{jpi&@?zj4n!_mqOgdY!BN?#U)IxeWJ&$4@f*0G^&)o*c@<(iD!z@oA^Jf!hAt z`HlPo!_5aGu7#flRld(Re%z3s!yzmElp$5w%ZJCme*G_BSw^ z0#s17t%iW07vBHQgH|6I$z`*-k&(Jv>U`_!jtb--}~&d&%&3!XX)mdSt}olg%%pk7`EhPjWHCOjfK$hy?^Oi-`&zbhN27DIrlU+ zh>h3<((zu{AQ$Ep!e)C$+$3yBsMYKa%5a$wv5?Vke7w;Z$DTlLX_Ft}@*r_tKiF>Z zBLr;wrQ*j6d_Qr;P}p9mnWrv6EJML{A^b3?;aKt<^W^10qAyN#gZfrI*4)iFan}_? zV|$!{Y)lV>nm8{H2>r1EW=zMhdwKouiwO=0x6FfATydYb$ACo> zle{`lD@oX6wpA~Z24*@wB|UQ`X~@gw^GL9qICk`&V|#YTQ9K^sz313HM~@{sfqqxN zt^%wPZSqVKvb0dGFlxFgF4Ip~0I2|MK?hY8X;(&Wt!5(toT zu?Zcp01-v1DM+dc8p37EoS85zVc6AqrsxOj)>*^36ass3<`h6yW@v!pMNxXITByEZ zHlim_y!_hJ!uXf+-4Ed&I>4%FCB*sV;*wLeaIl;nAxrhtGgc&112UKV&L#Zfx)Z)X=Gik3=S5D zhX?YJL~)=9_W)#?JL(ql!}}R?wx=AgizIFqC%veo@YG}H0St@9r_HgUSTxu9mHpW0 zw>(s@4;2G}WTKFZAdemDO(b4W**l0#_VKtzyuoM%Njntk{HeLaTVvb%sYi0r*ih$B zK*V$Wv5ZmK2Y>7t=DL%7q(CuBepHAW8gvU|KJ1n>?)3J(#K#~QiuOri2I>$* zbIp;^<48epABLp|n%_st{$O+=8W;)&hXQutGn>I+K8dZvy8m~+>g|<1pS0J8%kR1m zMk3kGUGDrz0H5-HFgRrH^nz@|Te&Oywap(Re&TMSQ44}yvt@#&5e}lqw2BI?DNbXi z*>N?9o0z2y4S4A2i*r}FS>$oR`}$<^FA;zo?_5Mu*HjhKKel*A*o3K8plsk$%d}hp z&@F8e1y|^zvOX6!(T!LNp}gjlWzMRyRv|6QT#gW^bIpOpOSxY?QX6uvJuhe|LOAo3 zxB@$eSeWM^2=uXj4{R zNs)jC7ujtUSgDpF2nc#xpTlD&3CsVPFs0qKpBxe=| zhKGhe!nY3}m>vv8_s63qx)-}`^D}2R`)x^sELm?u8_6(M*Zb_ezeUM6usP=dg@6X> zutSs3S#iz~fPjrC;zFZ&ojG)~W&?e0ou!O?mMwqxyWd?bZe+rd66`i!|1i#uV`UwF z))<~C>IGi?@MDiXmdUKA;LcmeDQ>U0Kjvlh<>|g>U-mVjH)6FZyv1${IUG7_GAt<5 zScxmmrk_N>_HK!PtAt^r?4mKnazxqoCJT3hbQlIQaHOyFRWp~*pUZ|KG#|TrZH*0+ z+-6FVQ1;x!m2ZLeI_`sZWv`jfCKBxwSoDp?Le4nuyM-3qh{ejOcIW)%M_#3qx+&Vb zxHH=2zs9}PR5p0ddcb@ccDZ@X(5HQWigDXsYi%xI(y29rmK{fVqmlx9Eae7_6HTF< zdI;>GByW#0CNXw7#}s}ZV+LK}`MqYUE?IX=Pdk~iyxFrpfo+#_Y46T!0XP$g5BdBu ztS?tMyjPA8yX&TJywXW0hnAaF>*St^U6mjx5BF+I`K>zzYZ8gW6MKH^N{~Qt=j@fd zqI>Z5%1k!{bJ{?r0yo=+UrZz+*axqi(1(B=)MAR=XlOb!sM7(lD1s2ByrDkM2PM=8 z+X@+5Tmos@p5%XO9CK~dTE#;x(6RoREUVaV5KN<`M6N%f|8Qbtcw!(o(Y}CcE|tB~ zv4Q_F;=Nwt!*az5Sy(NR$c`n1UWqQ#6In?!E6{gT#B}3PrBEJWJQAm*PB~S6x>0oq zF3@y^Ax>El^G&NzsCWLAKKUxTWi!CPN+y{WY*H4fI+l|vvPrlT#x4CtJbp~lx?xvv z>?f@4iN%ge+B$4yjt$L;Q?Beu7;&}2)qoO21R53k zIU9=9vFRk6SJ09zA==`K0CicC=w(A{)djX&>V_At_n0SbTNGtd!t2dHYG{=i!zY3V zD84u#g~0>4h+<&nR{?P4ZMX2=^~1q%b=zIKCm_3Ut+UErOW2*p_D|V9ciJnnl%YtA zFxkSeyig)zn7quS7!Xl8VJVq+?XAsB?7WX0~uM`PqHj2gh z&65b`NX$zS%|t4rc(#TYCLx-2K8!ln5oncK&t#qv(aH_Kj#_47;$#6!1R?92oj0zn@5yJl*l=KAqKE)^ zY~vFQBu4AUc7Gr7o!0P}vjHoT@tVX+KEE<|*wclq5fOk zI?~*L2}H}K(TRZp6Je{c^dXcollfTY4UiI@En2Zh?rJ7>I*~Zt`PB0f|CD|}#$U%i zg45h>#Ex=N{Xv3w1jB9a%Mg?jP@m`_D37G9+b%SsTCuL&lRs6miSCL-DXi$-w*Au^ z@F6kCe5bP^F?uMODkqBl7Jt;X)ouT2_?Dl19({n785qy5%~3}IN8k*Wy){|z+<-_E z3Uk*t`zj1UYGf55cab6wU%YhJ&tf@w5DG_TptLXmHPZxlbN`L$-00TJ)9$nMN0dCz zUk1obP@cz^@I7oD@+fu>>0Sz%;!S!+!ji7CLAX}La%&&{Koe=JhA-66+pBgzU0F_c z$^V@0OWX=3Ipq?^DovnJdJ_wF_gPAZ{ctO6o$}dJ% z4E7^zJ6jss!S_C(uOd5+ny#Un7NYVkc^GfGlLVo>`ndgL$EQ^>CWF2fR^UE>{l(vd zd~6>j)>DVxr_HTnsUQ|?}dKBS$pHn#=&D#la@6(b?jiH89$vWJ?yN;e7KZ4 z{qx=x@y1uO#9wiN&<6C+ZqPd3<_j0F3yAY#1`ux%*y<$BWIk~2+&R7?2Q%$PdAX(P z%-EP=E7%${HZ}vaLfuj&(9*L&ehmZdAT7pz<1GF;UtV?I(Ggr`q6A=cb=i4=yg29^ z#2kAFqb(;;bK>sEo zpZbs!A4UOAC(Z2#IY6>2c-9vR@eq6={!wn)`dh+&mp#45UPH6<^Cp9(()Ota zJ#8Q~XU=0q>11mff5uGbf{}8kRgMI6)8MAK4az~9t*_yDHf=W6<=>Z}D(TL9SbFy* zj+vv!ts_G81hVBht4>uv)5%2>CYD#*ZRfHmX%}N?BIwZp(EZ{o4{ zNXBFSmI;Sb-NJoc;G#k1QO0%i@s7lXvt-)4`2sJ*{45zM;pt9W-<36M9v|wcZ;$V& z4;=%c;bh?-NsfB^I1=~|gxZfEbWkZ9gik1g;8YwTk>&w=jlHyeI-r>3OU_;q9JFs> zkLpnbzfL6&9=!36g$2tR9cwm+xS3YPOO_bTN*TotWL>k{4R0Ska&yJ8mm-Ddp3%|p z4Rq8jEZnj0$dSdR8djYBw3n{XK&$2i2=XY~{pZg#E28_*7rgkri+^ji%o^X%IPy^2|C~J<|=BBZqb5uCYI6{%qJoz;LtCykhAY?U+_yk6- zmH`nmltLLMKyV6z7KV0VwV7My7gDKoYj-G_icFP&7|{#dqu};ElZk9Tb@1jN@I_biat8VWKE}2snZv(pMSFkSH7iboG1Bv51$xgSI79?X*`aJ zt9B8}V#8Q%vV=TCJGYbR-BdE;TwvUddsgl_+rHhMZn~r{%jy=kZGHq~mOYij3RW8N zL=G*GAfCuUXc4%B>fFbHzfTG761ZWJ4pqQ*XezmYBWbEo;mmd0^dM%Nx(4JcQ|TJQ zFvp>@i(up$#AM{;V+0F|n zrA&x-Kl`vNJh-L*8i>aOBe%rEq2T0+sO68}7Q`~C$dMzV=#(ksYez>wzOf9dS(*?p z)3LpKVuKv`D#27`X)v~D@3HBL0Yo;2GH0HL=)DWj{jlPwflGVpd`q%5ZpL zA!N;++TTQO8EAD~yV{yB4XN>^jGsU*&;q^N(ANpexy5cA4v7b*K$+8DfVapiT;5j1 zgD`RcN=h132PAt*ud_ATMGxW@oIJVUHtCu!677R z7~DN=!gmei^KgMx4qdlOcCYkWcsvM0+q9A4v4)rcpC;pSxp$VEloAkGp^>$j>?S$Q zBMBQHYIbzk^{T0MHjHp;)owS?o0fiCS3hqywPh>*MPvSM@o&o$H!cRP^5g zNRLNlwf&305g)-?1jnwU+@M!!$JlIUc519Ib9Qm=mGuYXeHmB$N)GvC-Ny#I-DSf$ zUTtkTe=@{x{&jU9;T)H^BKehV4X^D$v(qh^_6kUke#HJ+*RPbUZuLu-am75W{D!On zYy5l{KLb4Y3ZmBzA#UdN*trII$FQmN0;h;9>I_lL220R5#-;(2bQC8MQYn(dtWV=0 zQgUFP=<>Zeidzs)nDUJ?h+9nxv!$pd%C?d?#>a8qK_^qLi%AvUKE~22lYz9t;TG9P z`QwD@$IG2N%4MhQy&wJdx4#|2TDa2dLxoJSn33gqTdx4JEdjZJyS3pGIk#lq&kwdh zg2F9+qD)wqJLm9WDfQkphvLanI%S4R>2&_DS+{#$_!~i*mLL)jt!h8IhvD&syS)2a z>>Y4F@>V|v3lMVff%})RU+@fo%bBQFS`d9%GlE~rH2^6Gg;4}5hEp1(RTmOs&t9NC z`ajtx5*(reQq0XG(=3Ur0fIebA_xnZNhN&8{7t~mcmd$&Be6sxcHezmYr|DBXN$#q z?sB%SPenUZR2$vBcr~FY0#R`lvY;lQ5gpUM)}xP_KMaO~;T4?)`By|ta_v%o|V-ap#}oL3s4iO5)1XtCR!3j|+1KgZDYS%!) zdoQ>k9z)&E^@ktAb0T=B_4qh|#hE3CDzr16$N}~M=gtLVKh1X}U8{3JJdWUhrSf>% zw4DM^C1tB~;iB({LWXs%Uym7w^qqDJB8aiw&p6-Xlaf8wxv=W(bH-^sFJ`=VTqNqE z)n#K7rfQeQ{yYpK5T&V?;V7%JFv{dA4V+`H+Q$Zs8<@9hiFB-qYEjguF!SWTebjj< zg5C^;c4&XBp~U$Q>Br8xS;n_;Rwm8B+oeTwKAqehJo)y~U3Dg#E|tbVIGKo)8}Upo zJ&mQ2v&fYi4x5rSixrX6X(%6!N+dD$!EqW428ZgqM&EuSv^$Z?Wu`+FoqCnfbS8H= zlbczLhF!hq!qKI%YlWa72)Zb}K7-Vz zW(fK+ITp2F9&KvaB%~LqG1|uk!T^vi;~5Hk3yABW6kY_p^eHkl-nWmKiWD077YZ{P z85`t5@6a*@LyRNf8y1?(uTHQsueq2Ht7T|wiLcXM!kN6eq^y^Bx0XmIoUbrFozziu z3Qb!|t;5|W;%fcbWN0*aXoYBKbQ)H_RKmMG+Uht`0>KmyDh!UHg4|Y+39Jtw#^3PcY2yU z8^WxwYE76GRak?Z?_2{TW%N}h*(~8-^Yd9i3@yrH zStw+HuJ2ZN7ko8jw0FTK?&KvV9+IyU4TxSMl=b8x))))Y4=vH+Ve2!>kvIXu%A&Lm zARwrPTPM%AyfGnG6Y8AE^ngjB=Cm4Rxu##YKkYMLz%`NsMLF}AAdi3bA>4BR@u#bA|xfqhwm^bF*(b#j)Hc{b=AM?wbPcV}* zFKqCmr^yFSud)a$weyX$-49n++~TtPz^~tb|NX70C-yP{cjwDlW+&z0ux(R$H|!M& z*cq7%meG5l3m9ipQ_YDqUIktJDtJIG5F9`4&A8u*m{Xn4S@A*mX`^ooClcZ5H^&Qw z_%~qE&s0p+yFWl@4Tk8!Z2BAV`0ZblHvM)gLxs<7&k6GIbqha5a!T+zp< znkV4-|4o^Fpy438A&d?PG<5z&9#v?zZPS!>#idVlFT(*uhOEfX_C>D#&gC}+)-42>FJ~Wsb(r3)+o;vQl^G;Ut*Is*X z=K|`@4(zt??dIEi^zA*}FQ8WOcXK>{6XQ96HAF{!zbn|>kijc03Yl`JT38MCy;xjB z+`9Wn5JU>^VJR&dB2e;6f1>+!npia ziiN~D&iLE#K5HKL*8Y^YS#+9cV{kn0z4HplrTJ*0aGe#HS$BYBR09`u%|I;3#z=DZbm=69lpdtEhD8H4*Mjl$0&vxQmpe(7}s0|Q6Z(ta!)NKFR(W(CPR zkiAcjnEbh+Hbx@8%2u#7Z+xd_o{uB0XCVX{84Q`oU?N~zwZYrYKrr&NDKAb$7mk-R zkyJ1cKuZ$g*u+>ap9*Bhk0N@-^Mwo7TyxD4wccM7j_%qQfO(!a)(Gs`v*wG)jeQIF z%+r{eAzLFQkTF9rq#O{2&~TpVER!`w_E}bjjyS+)!;m>#e4x2;D{{y(ioSJGlX2!U zy;u(_@SF7sFQC0; z8P*lU06Dzmy!_1q6A9zmkzKUc1>xUZx84eUX9ynWdEW`&Cou}a2{@_9fuM5CU?~^D z3*2X*Ets9lntD!PK3H&hBi37BBEpj7FhxuVvHRgE(#tZZg_bzA+!AWX*!O?8(eIAE z0@y68ofu#VF@JLBLgxa4z{ud^rUnJVOS{f_Pt1~+Uj0wdOh8Cz1WY*hwndZqMy#r8(6?P z$53juc-J`TI31Wml2frHR8T3P2z{h=7Lu9ISl0>b5Gm0i#XMrQ-MJEKPcWywW zICJKVBJ1Pkt6JzhGs3atwit(#rP9FYAbcK&W$z6Dam1V%P^z-bpJk2mAln*JK_(L~D*{iF#g}nhP<7(K7fky*-}w&mUfH!u?a}DqceGJTyJ*vE z3orT+!jh|&>^L%|e~!e_l{bVY9&BC(SpEZoLAHX?2JjCB%^Og2G`b0j4t(J|y7+^O ze*|TVczHK$(!INsX7DfgQQ44Ed+z;og}@K6^ZC!BG-g+~=r zH@rI2UtN3cwW)kQlLSdMNA0D^59UIlXbgEZ*NhbjhOJ=CI$0R2?z*KuI#!(XBS4z~?4k8}u2J6P*E6FzuXK>+;9Tn5129YKJLe!rF=*tE8b& z1yNLmPf-G*2@BI05Tw6%qlc~8%xuT0s*|qse{JXEjbL>}aCTs65>A`=EhC%ot<8T5 z!hhI)-u{C=VZPqq!5a<)q1eZuDT;oRuh`~--(eJDTt+VZZcjT9XW6Sxx)~@gWO>^jqKaiOWqV_HgwO>a!Q4m zYCLsxDfb9^pF4EuP#iU(#@IdMCXpW0gm9>nH}o=%E+jY6F@?a= zvJPx!eZAGSz;p|@p6Ca)?GeZcTx+dY)jQAXG2drF(pfL^*mR21 zE>2$Na>#v^1lDTW=<}4jD#k5sT9(wSFm%qlvu&EZmtS~UiDapC(@@>B8<2#Nb{f=xM$>Pd%(iy13*)0_DQovs}lccUg6*K1!3DA z6t{Fi>)w*BARX?C9b)0M|poen`x7#$xMy1=qYf^K}P| zE0fUEBu*KlPXN~Q0bhUIgIODn1NL^>Q+l3|?2CZQn=9uBK?k?aWvpBOkwdoVgw)Xc zq-<5~sXJ|TaP`3LOm$fL0haz7AY4u0cW3`)?v1dCF8o+<-IfHsawC{sS6;k&z&{d0 zo|lP(y1+V#>;V;4oUO$_E{5gocA;_oFF zH`jVR23q6hqY|jY-cEv`@)M;kk+>*(QNrY}cphDSDNGT+A?_@SqmJc5Dp{n+D#0`m zTKNjA(GZz+g=jW2f}Ij-gG_sn$0iJ!M;t@xBi9~lFl@!^EWvJhSC-=fDGh8jr#gig ziud@jX$B0f!IxQqiennCqzZ|#>SVR;b%=cVd88z;e1|VSXZ{g-g*R=bTr)l;yiIthg`1#FsmaH)J=?xnJbFtmC_>^i7I^H_c2= z=Hr#}Xbk5)7HCXk%wylh=d3rOzc<3pz_Mns@q}vqz2`5q0`@EDMPowvlE{+sMQvSQASZ%}N*sC02{2F_H%Jqzf2uZyJ_^B?oxwihj~d0QC? zQ&BuZ&5p##){kD3sSMafmrja4H4mGV$BEHd+t}FXryjOHI)JG5i#~@8lKdTV)TVn8 z6N~;n2i=kOtsSq2zQ5u8iqko&CQe{Ke5TZKC+E+nG0xd&l`<;Q#Kpau7LH=a*3*KUD$FbB!XRu+ zj6qNyq&=hPaTs&RkQEwKQUaPc8_PK`v=BMI;`;wCT2>jWb*%IU5E5r^)B7Ro8BLJ& zfnP6_9{K%`ee7e;E=1FrR5}od+?pNwB1VC*SU0~PC?YFjdMs^{FPG3l)DXeHv&g}M z8t(Z>xQLhc--{Qq;Zi2`GHGiM6!dQ=x5U$Ut zYwYLpJTz7Zqc_>_de#xw7Gwed@v)8=f3kvsVrY$y9JohiVl>ZtDW%7Xx5>}7nNwx{ zC7WI5N&y_qc`grFmP2RSDIw(RJbW$oGdSxN)IDasOB&JJ1`F%7etyLHa;ICu-DUT_ z%2!Fy&#L(|MA|f%1rrh_4aTT>y^U()box(-q|G$Lw z&$F-{!_UA;h=t9L#~_QYmys>UJ#RFdVJ{7E?C#_}!db|wNiQUtD8%|8nbJGRC$MHS zvyrw%$8}qEG3kvSA9zF}+{JIL@jq_7kGK~8C=zMMa-DS>Hv3-ln$BO?(sgNcQ~fpKX| z08X$ZUr?&4t_g?a+TL!mFrp=J4YODL-kQb()bzYp$hh-TF*ymW*3p=pxjd9>UGU9d z9w^?M!*T9YFZN*G*_wycm6VuU-f`EuyvG^cD>m_4y%JUIt1vvtF{O{Zn zk@J}CJ$H64!spQlew>Zy@w4|C%Mbk6$8w@kpRq$u8DQFtSD^Us4m|nek8a$)W{=y` zI$Tfx=tt=fdtw}VM+L6Cp853wM6CX2-*e1RNqz^}0*DRt4*$sK+2xN7nq)ghLCnf( zKo=T^DcJwUzsqQT*f9ZjS73JK%kGsQ(Y)MY#6RdRo%#S9n32OXPN8Hy&I`J_O6Kf1 z@S`qzDCzGnfD`?A@dA1(qx%~eT&39H>JW8lCc$6RoVyf-1#@wu9R6(oZfIt3Ifxs=vD274oP#Qb5fcWwUFba-< zPtpaf7d~#6%iL$40jpG&WDhRvL9d#^PHAVUhlMC1h|me(mUT(=rz8IC?;u-L(z0Fy z-6!TB$!3ei`mI=oFl_o-#y4e5H|zefv5`9;2}V!k{4+P~2}X7&O|@zw1K4hMe`8`` z@0}xKWB$%+KcDHA?Q=XaX@teSzK5PSH1gXo@tr42r;vyOm0Jc8K}hmZo!AK=7y9+V zp#+Wv;c@}ubG^H6X~psLu6oi`(oX5(2O_l*eXe;q=Z#FR!&z*aJD%s{ zs^nyLC3i=Hdv2KV=T1a}kKFklRC0;rxiR3-2KJFz!zz}lZ$Io3H)AEsS*-Pb8mo<+ zmBu<5bUO5|FYY~qNhUE*t#f0Yt4MVsSmi9X#ePaB-|j7 zxq3(fl z&fX>Kl856~-^0GQW3)Hpvz?cU`n85r+89s$lTsH4%h5$iDs?%NszWuiJ)vDB+o3Z< z*$*usGDpbW%PTheQ=#NE5IEx3?5Z!}2UqhMzp2-ksJ9|hYhMns&E|IEBkKAe`K2|j zvU+6Cp2SI<s(FG6t+5Z&+u|GO)6$Y*tiv97h zuVB!3elu^)Rh{2E;3D$zIV;9XS2~~jVT^&82;w(ajQ z4<0$c<7Se_#kgi(Ua3^D;w(@(7`CinBWi-d@PPy2U@+PU`mON6mn)ZT+dmtkJie|~@P`6}=k1l&rqopcmCW}I z*q7IkWoHVR2~To2R+~1uwsmGNp-+N6V~SC(OP|9vB%!K9)#goHYKjF}t|Mb(Pl%W1 z@T9#FNl~iWL>%wB3r#eyzv-r%LighT-6#a_Lt!lxk_$JP>Rs}T`-Z^8$<_I(kFu^- z{14)Pu&#%={H*EoPF&E!M{Rz}_E02|J1p*>`-}SVy#LJN&V0B-hbuohq?avOdk)@rwhK_G$^q%UZ1! zR%E}`V*M+`AG{5;q5Y*Qb7~D9!3V_e-7~)IBj5;M9b@rAVMP@kZxxDZUj_^W14z=v zISw&-VNSEbqreJ!qtg3C^Z(V&H{Y!7y1AIig!g2#+#N)(;OGxvjRqS1wF)S(|Iu(J zTX?rCzqzH&vHWXJ&lB;&<9_U5Vy}$nWn>VHATtQI$3t#FoZeE1Cpzc)3UZsG_(_Yr zaCs+h-crmAH}LRM(SaHGyh?nqvrS2|=<#Z>1Ue-z1N(v`329Iqlp z^6<#sv2Z3+vxfA8U~ z$nILWuG6YotsktrNi6Jc-kd;cmiNAU^YM!ax;6jY`Bw#z9>u0^=N~Y9K-%>&!vPegsg)E{RQLSIZnQcpTZq(rXH;26Km1=dVQYC?j=-Rq1 z$1`TFv%)_M&iL&fsv`XWmi@IWXO@@G2q&GSl}STgSdvpDJ8grz_Pgc0DD;Q&B;%Db z$coI^OVA&c7iD=~xh#Q72soOVnHC{HqFS-%7AFZ=UBg~wE9)Z>)j>OIhygEj>~y^i zFaWJ1F6#iI?_ytyA#wQuoV7#Y(cM$kszvr}gfUyy>eTL03xeM$mO?Vl#7*DFS%H&g?G+<1L)=?=#=e#g>ceH@WXe(OAH*z&H{*_E08i|;scFaD(Oao9nB z6ZC;JQ95p?nXjSWln&Y384UKqVhD&s)KRNv0j6N`b|+S=sfJR@OT;0*$W&=+?R2gz zs+i>s!`eZ~#+HFy&Y^{A8U%4AwJHJi|`e1+THa5ozbjTG*nJOaKW3Yy>>6vHk}*hnI*Zq{hkM%h|_ZUgZ+pt1Z7uFr1FF2iY9$LWKXkTxNz4@&yx|QX zzj#Rw83<^05qk~0*WHqb7S1DA!9B=H_k{1=zTX4H7a)mIC?TL-0FxbY;Sxc@49zvU zmleJSfYbZtfgB!WI?vuK7y~zEXc4y^(MJcaRPOF%CQ>z|=gV=En*Ci~hWu>I1UG86 z2((t`g2Uk+ed_OBsJo>LvxAk&;H(}sA!wCgOUwSsA}Bkuaj(GaOqsSMp2KIc11U#l zg=2&r7|FH%%sU2c@5yOv_ujp`Ej=P19tN&E_A6aU+KDTl#+9~yogi2sb5Eg z_#R|MZ~1(S>N9oJkvYVEa_nj{Vp&OfA|e$<0WK#9bW$%>2!Gc{ZSJ@BC~~ni9YH`j*q^>d6TYK8PuNsp29qn+?PA` z*Y7N>;q1_vsKzs@4Vpb&lG!&w871{tcNx>dV)=3afLxfI9($4RjYI;j=4pfM4)@2c zw3(bFx^YO;5Vh=1>;Q$%|J0y&O2#JS@-nAHIyS zXP5*>pW{y4kOkU|Lu&3c$ZujA(t&(9oF8D?(%cK<@fRXB>JB~mJ9#b~&gq+X#A0{k z2kg1FfpuGJu#GNYl^@2>jt0@R=2@9xHZ}CvMKutOoP`tJ8mm{$$gzA(>m@?tZ^_N zd`-*>bc_Bp2-FI%FZ1oWQ9|xHfPfuOhAwszyd_3 zi4!-{h3sQo6+F2~C65oo1kmlPx7RCPE{c|Eo*J&a8pw|<$Gecy zuCn`Qy&UpkXSvpcTsoFtT;kiTWhL#m;=8fQh|*T4pZ8)n17;T4zNa>|qNpd7iycIX zont9X`h*gQEjgcReJBw=i|F{GZX;nwg=>^?cMkW3$Qtzad>;EWVF`33T&93t4o9#c z8kh2UN#SGVocqXmi~BGG*!Ocdt12teBaGZC;`ayMHJe}deq1Zx`Qsl^VxmW}Tkvz5 z3t0Hq+ZZ#-R5vPc5M-3=>`4JvO5f}BrwB2m;DBYsUGD)laKo7cXqDx-%vg141wYn` zP&%BMW`0{Ss~2VpGexi&C7%Cq+#%LWI2(HGwR6m; zC-e$alM}L`zm9#kPD`gEGf=kdu5orEdV;z^B8{13QMjz=jonm800~!8wg$$5znh{+LFP$Y1^WM=nJ9ZvH$ENtOS?}9~{tb)uY!TA(&cKYG%qMC|NwR=L zvX+>4guk*ZK(2pUj#ngE1wp88voAWsPg?u*+>{ zrM(V}&s@BSJ|9PpuO@oCfLT{6=ed#<2{9X(W1J9n;3wC#F6J@!AiL)C8*!|9i^fvR z!-ePr{!oz43f4L-!Pv|B-SG7&PEEToBe9YL_dYz#AD#wsmK~oz>Qz!9@|+Ms#`te>2aoa2tC1`|lUt5N&{m{@H}C15|ABVUibd0_oUtYv@?RG% z9OaT;!+x62Ceyjh=qS5)n#)N~Z$AGu?P@w6M>ijhn$S~O6OX6Y5|Os2k4@k07_ELi zuN^BCG}qtw_*~}~YoEJh=YDBZw_utbo8%Zy&S4LO(KqeT zHQ=Jb9Jm(zXb{)dn~RkWo|=`d*DhYN&p>b&Py6An zb8T^_-UELnm(r7<0R!+9__#M&NeBT|yDkcXIbUuM%`g1gul-so`Ai`YevgBG`TQ?m zvO}MF;)y5x(ROBV%0|0na&3pE3k`t@YTcK`{t?jBD5WuDFT-4vOY6R9Q~+o~V~ht& zSv*tMU~IB~QW~G|0*T>3$d5RjHYj;kCLOuOtiiodRl?D#bXJL6>b){&E301P2}pv+ zR+zNSI1l7ora!-r?_l|Tq7Tbld!HL->@6hhV@ryLB1y3hQ2rszlr#Z2eF@x!5DAh~Z} z0`1`U&Hn#ht$yN#`1iBLcsO5gHtYCqabRR*px_jo8(qBhVGooWph4dQ>HHzh`zZv0 za)B)k0Jah`BM5OiFEv=zkuX=DQ-etGpYY3OMd zH=Iu^mF-px-BUlCHfO!wT~8}|uAh&F%TxT|nvN@0tCZ?f<#5zdm`Q&O_M6xICHt^L zvY;Y$N6Gmw0`3Bk9a(L3KTe^$V$y>EY|h+&|NSQtfgpB>$zxlD%<*I{i@V8G&iJyyK;p#xyt7g$6o-dOFO4%zhplh;un$2u&NWZgCRTk`r=T+>C4z^6o60IU_BtDFeX6FxlKQx_#)!()zohR8SI#(N zaHbNzC!Nl$Qt0Rd_c=CP0%O@&NXjI<{rVM zmYE_qalj)0HYP<+Vkujc=gXXOUD3VS^s!^=MwuJT-)is8DTWZDTH7&~WCW;dP`qxP zgZ}B+7z185Z=x;aIZ36Ozr`8xTTMIn$g(?Ertyuze6^(%%fV*nR~vIaf9pz5nw9CE z?;U5YP4fOGzlA&|%T7$Y7q)KzAO3N`7Q4sL>dL`rVD=Hh6vM7ESl309C=|1aM7CJ4 zs1{sJpsv!;TdLKtDK(F@+1mD5@P;+;1?KiY+l44+HUF^Zq5M)`+8I|lr8z@|gE|Rd zh`Y2R(jvGG=2pd|Fp)brm76yKiU>Ndwqj^iZ|Kpi+_D4y*%9|^wy4qgZ0X0Dj>093rnBDF>gIJ~4A?N7F0XGr*!12_<jFuSyCDa z5ZKgGOVpvM0H|3Hrq=msEDs$C7fkt zx8%;s)rq)7VLcbvc;UG%xH&ulGoPC}n48!z)jjxkcjtnaBJ}zNz6pDb2p_RTs9SiG zBaS#!R?fxA&;|UCqa&iDF^}!Sc!H|;5~yi!Yrb~y;K4m)%PUAl7WEVvUe4=!a9D_( zk;iczq2|9i3jh7VXsG!&wOYk%-EbyEm5FOawfNjtTE@X+2R-wt2fmEqe ziAkj<$C_AH#Nc=@%2-qU!Ia^!H=19qq%-Cz(aLhs?+Na>NREz^-jY!gX;m-?62btBJ(omcXp#`)5nFZu z^#T3iYb@w33 z&r)UA=TQ3#FJwM}9^M@;6oylIfv5TU9e3P8AQR@f)JUNic_gU1lr{$4^H@SrVjq1miKatClI4g+q+DNsnO$ph+6 z>fNP|A@Lj{F&)?=!La~kaKVjp#u4e0GMEq%y+bIu+`6MPMRDr19IXAPPoJioC5IoJ zvkN%0Aa6NOb^ezZj;{(>B}|(DvJ*C-orXfjOr~}XrIJbH(n=?j*gUTUU!FCv6oS1! zp1R(g#m3I*bm{t395(;Z(tHoM{!I7tXo|LqU+y52qwDhOUydg^jgoQ8s&o6j z=tK|ar^mfCU=2co4=q~*Y*Ps0{9w;Rr1>tb8t^fL^$Q>aJ&rZ`@99$hJ|6k7Dm0P_ zp?5h~xo28<2Bka0dI!fdzud9jer~OFh3Ivsgy?*rr9@caSC(S+WE*a~V?ijGS1&Cs z^$f{9GLBPedt}p7KkwzG>)z2vyaJr3o$j|aM4U1cKfSKMrnT%H7Ie5EN>UzretH_@vc1l8(G)vO+l)+l@cO`R#HV*O- z%qDl%fz-KmUONCr+D&Y0DnyXU-Eu-$aRB2&*O_Drs- z7jah4@cYiEIxCy{-}7PD*0AOqhnMPlSp{L65NrbnEqk<}syv&OfCb80v|!b*Y>l4} z&}p?5b;nG4c#abbrz@>i-%#q!i}~zV@y5sY9%|H@x^{wdvI;46rL$cq{n23t@*#vD zf&8}YxsG)oYuMBHFfyR7`aTHE*1Q4KACwA#7hpqFQg@){;`F@eTafP^J)#~KH!e^N zj2IkVcn;+tbpWG8j3!Zu{cV@!?Dp|HI5=mr!=YIsl-UL zF$#xke0=X%a@T%?Xy0lg8sBq$IJBR29I1}QB8OSp9}LHzYhKQ-Oz$VdKO+YKm|qx) zkM6GVE2E8OZ}~b+=XfeHJj^!M0@cJgmK~Yd$)q0*3D3tv!Tqf2XgFG{9cF2NB$Vh3 zxho;u(u$xY5Ng%C0@UIfTC)NB-A&>jcN7s$A+^_WqHw~f>rPpYdyP{|pXIDos;?nb z7&gu-<~8HAX*340G^p7)d>EmZ8k*GAjC-zDiRMC^1^ZV7>jwrKcV|A$S6RifMovBB zXtd94y76g#Fyi7av{tlo3?-g9oT_YsGp=uGdly)*uMTfd;y z^!qmNZ1+59yv6txbKHClx*1|GdNeXWxR;ADv=eyGfJdMQu7{AcA*7T>^tnYHjk&oE zujKTUc7&@*0Y2G0VQeIV-CDU>#F2@^hbIn4jDNIoMNa@iY4Q+|d| zV;rm=^JlDDg_A~(4^5Qv=u-mw34{y7d$g})zi9a0ksS6P;bsz%9O9S9@?aIFw~vn` ztD}6CRg6p(J3r9=I$i_#0`?5?eePp|J?v8<+h)3MwLl~4@Ry&0H0oHPopIwY8Z4oE z%#cQUk!3Oqq=r`I0n8M})bb@$5csGQuc15brCoJcgvWzH4Gm+ARlB|7(lfWTMMR;p zOF5;P*}w0aeG?NE31c?xR=a(f;z>8&%<1z_UQwTQP4=eVs|Ca;9%=;R9^e&fF)oXm7HXKTKjoshhllziGSM&R_-5 zdf>k7wMO=r>8!WJ|FPoK+F4_1)|pIip1vuadI-^c9ybzb1EvpbgFeii`zSAonNRtp z9mYzY3Am8Ia@HsiH-)--o*~%ueJ{m?9o9f+(fIcLRxUe+-6KL{*_^fCAWDm2Rs&-^ zZn|f`q13~icH-NUnT68W=twX)GCEdT$RznD>nWvp+1ZsnYhc%| z-BT0hdT_{fWa(=$Ji5)H(Oz*u%IkyJt16v}AE@PXx!*{X%R`75$sD>7;}d{vux`GB z@wwgi2qIm65g0vAP6M?8Qm0sNfs*2Dpx#Rlurq#gU5vGK_~0GfM>p`EN}Dw3LO<&+ zOCyJKG-J`C`@}+*MWhuc2}Lcw@}hhKafO*ga%i{7?H)=dGWm2OnSZT>m0R@_NOlyN zOhy;mi_zp{#PSDE)I}+qeC>@oAhycZwz-7DWkv7l(X3)W`UE~wHKxky5ve{+A!_Zw z@QDztUeU!x#2kh~Cx#EKQ6QT>i&|`iU z`QZ48iTes+qKZ?pxyhWP?t-vriXXar1i(e7FtIuBT-ZC|`a9n3wK75|M@pr{$)!=N zQqdJN=g*wE0M%U&lG37uZf}z=RV};VUdID41-7EOiB^lu%uqMPzjpUP*j*xB>eYc+ z*F@mL%mH2(oL3uWtutq2?}b$YZt2XK=MOdkm<_j9>oS05UHmuTSrBu%>Dv$N0zN@* zhjBm*WitCjLs1KsgeHUH))?J{(BEn?8czL9OA*Ya~)8)uY0ET7BM&e0UzSldRrrYnlTki-ZtxoU(8l}mG2!vj`2lWRYTEHM7c!^vPc zzuGb%XL!!rO8#6v_0W6riFow7>!R^Q{yh(+@;QGAluyS5=)|~YtjS&6-jab{)8Rq% zRYjF(Q{SkMjKI2-8yTs)rTsV0&01X2^ENE)v1aFPe(`!XJ32Z(J~|3RRJSzETe|p% zFZh5W(2r@08vunqg2;%MARhKDu*iL46OC3}wka|rdew-}@u5k0^AdS-{U7#;?GpfQ z&sRl;HbT-#fjd%~cEi=oS$f4ac&!6-h?a2eGOkOwZd+$Rf2wW~RH3aQ81mJ1@!$&w z=v=s*0J-e~=RDV|U?TcK`-iHM=Ze_rbSxu?ydH$chD=EY;~?we$SI}J1Rs!?Yo?1K zg`CeSn)UJin02heUfbY7{&lT{d0AKQ3>Z=X?;UXNN0&@gI#cZKg!z zUk7d6?yNrwYoG8M|Fz}FPGLxWRnV~&?Ak|uH-o5F14)?6bTv9^7Cab49O}Qp)*%;t zmeEwXfKGZl<=o<2>oJJiHSrfOh7cw03ev+{oxDU_(GjK0pxoQHtMe_kX;lu}Hb)j@unlMe+K{<;-dq4* zE5g#cX-8XO=&%8(SPnKumad8LY=@2d{&|(gBB+2Ug~{+}JTXFUD5$&(8U)he|4NsKC1%$gXuo;5pj^Ubp}Fp`cqamO1wW>%o@474}j0Qvft z8SK~z|JDboo!AdUvlrtZqpawKA`$Do!iOE(a1Z~{lNB$V*7RDRJ6n^P@dYcxVUC7y6(bC4(ICBO z;><3I7LDjiT*b&!2wVUiu}fk~L``K}2bQn|l%jB^h+{4UVtXxXZ!8dw24jg>YN=33 z1k#04shA2RDuty~EEx?(!=9(2$-Ym;0@3gV?_PUmAQhWTB$J8BSSpu|WYY0KE)zYB z+^9(6e>j@S1>)&UB%4jgn#oO1#SZCHhholCxo5rMHlTNu(~p}!21b$6kQ3Hn5R*7O zjrppNBi4}E#mGC=W7H#~ID>ogA2c3$<{+LEn*`U;09LbQ7*@hoi*Z~~_2GueSjMty z6*yEds2oOItKgQw4@Q{I|V6-z6|Mu{^?q|q*4ROAUq^On zMl<(PTQ85a6)D@Sg!Pkt#V)E)G`g~@s${7hi`=^&bX=$o-Mvw{rEuxCa>yGuwn|Yy zxxX+lnBVuHIZLSX&!N2Exk=x`%QmaO={qplK~StZ)ktAqKEF?5HI zSBR#fg~#PMH;rZ|OW%w{UhFjP#gWK2OUYDg-DHQU;o-0jVrP5<7ylMK@3&#ua1puWxE0C)|F#(kOCB=``S%v)z?XO*0`^#{k?-b47ecQ5jBr1xq$hJ`%I z*i5T6bKn4vSZ3ZO{`QM=S?>#4=1bA2zJer`%D1%-N1^%o*eo`~YdaCsJ~zY!2e&0L zj=-ia;Z5mZAzZ*sYOVSL<~;|C6$_tx+``$Hjk3;ou%Pk1Ua zwro$@GJy52*COBVPe7l+3iKWuXOC>($J7gYgOFcav{lIUN%6Aik=-C5b>%p3Syg~_ z2Xi$SgNsS%zl$dTZnXxk{4Nly?hyMD(dKaXqs^>!+-aFq_icqq;;RxM=@(Qtu zq>(Gh&g-INiOhoD0AdfPyw?mL3adeaCN!AbSJ#|u94%{d>e#WVNh{L@Ll+x4@!Y^9K2cDHjkFM!$kX@7tKW?agA1_Qk>TKoo{*J&}tnyb>nye zA?3+zbXTKMUp#WkA(Pe4c^m24ne_1ZU@}_#ZY~lH2Xnc>R5l}RQF?d+4O2?4*{#&Y zLU~yu^JTILzz8z_=WtyG)@+&28s7^J9g3%PITZXX0+jLb!-r0vK6IF_pHg|;@p@%a z@k57RV9r`|v$tc{zEnY6EwZF=dfI+mUY(xSEux%9LbuP(fshl9{~Zpy-}DXP1YZYa zG#p!LxvUGuFlO;gE}wqqbP7X_J1j-gVSF44IsA zhRikcG4ce7{-HmH;{*Hw7$hzXw;))WeN1$<`DSwjTh@hbIpmzV5oi zho;!O+Vu3HL(|hW_VDJR1{XXxhERQa_x0EB#t$xTg2Q|Ig3G7&MvN6{5P*_&hWXOLqXPhRU zmQy(hd(zv_xLeD%x~*TsniCN{%_^cN(;exiSwob*=eoCC>6|sI?kh+gp*hg`ZN6vH zN?-oT`~l!FL_LPKZ&6m%Lm>ty4U2|zWosO%FMlpSI8b;;E_W_J^gfP-E75=}n6tn1 zOTRRTXY%jhW9RZ5yB_}Rqpbh8kb!?2b7mfSFMWpo5l{?KnaOap4Im~#n@>WfqB^C| zfC)iRo9`oFn0sKfZ8py`M(pgsK%0SHo!@2)(4=SVCOwm1^>Q!s!6%=5a`(c*?$`3^ z*ACU|Lr?PkFQ6an9p2OK;+Lz5y6*~IR6bgl^#{@`Z7P>X)gh z`IBlbC1f&sJg<|qER$W!3yT^s2%njegcsdUS34VO`*b#gLBPa12N#D7@@&{e47n%j zU*9I@7C$D;rh4T9pXT4RgLarJDkHR^6M(ldiCzgJo`kf{DQY{5-+Vx^&@caT2qB7u zb7wiZei?}Kwh;#uozn078cv&udQ#6wDKeQ!EY+)T5w}vIK{Lt z_W_M6&oqmbOiIpCfDji~Rz^;q9K`FLHN4kqWirI+6^Em*peAAB!p)y@1Vf!`D&I`_ zT7V?h5~+w_qlh$AV}}@6q`s#eLayEyi^XnszUsP#ZjL3!pBPWPaii+J06iu4-xGbR%BTlEmo;GVQDlmk&zfyNU`)#lZ8h3d-Ny?iA3hy zfWd1GnVFbB2y~Z;llu>97LD;L3IeP;Wf%9Vy-htxI>v#I3)+*2E1MyFF%AOlX0z3* zYUA7b<0K#;NjZC##KUgvAJT5qVjx3Rf*e|#&kg7G<{sLWPdij-TE7CPN{ayx5(&}j zIU${Lm@M`*yyrxKVZWv?x3>X>c(1m?H4<^)40w=kZwO7Gui!d~0xXc< z^@wDkp@g!YME za(Jbl?Qn}@->gsoz-B_ zbtz*GxzfI%1cK~yv$8}-!xSG+W_v7T=zWfvSHv%8#(~z}xpz|ys9)@dV9|nCV zsaCy()W(hL^w5_?tyUVjf@JjF23V)BLI}a&zC+(wqBONCKTqoDo2nj>zEcR@!Pi&Z zH<2mNtA$@{$W4OCrn=VPYM$xX`@9+ZCvD( z_O}j|&I1p4U_qw3Ay^DuTmvvKdXWz>sTA=A@&SHYFxibWLh@kiIP$)@7Cd?b`PUTd zU4ZhR>KmSbZXL^FiGD8IPN&;h*bZI6(l5SG#R^Ca1`kv#p6le|tr*tAt1OJUPFvKP zXd5ctj>yg*reWTVJ#l0->Qs#ev?b5^tnKT#EzsaIb0L~c*>_Du6NxC2kl2~_(`iU9 zZ~i0ya)Hr?b*#vEM=ciHg45ZgQi&@_H$661G5RYq@UKWN^eHHysu5C~7OOLG!i(%Q z9cyN`(U^><)A7khV|K=}Ha70Ob7R9Yft537nwzy`vbNbgb7qD2_)Laspm!#jg%}yQ z;GjSXOj?#}3Mx8^RB`Y=iwEJ#$g*a~N*Z$oOgYPCkA6w6qdSgC;^Rq0k+RCesTLj9e#?iQPH?A*C# zli{Jw=DBmn*t9F6-(y1k)l)BUV}wnB zX9M!&vi7z^x6DTo`T43D)|3A)vDD>sB93ZU!+&68@iX!^&)gUEJrfwvIQCm(69O2E z@AviDi@c(Uul?6qqOn=(!lFfjf<9FBLDZI`(RdzdlmOI37?hx2$E2nLA~j_!B@;bJ z3|*!oI*huLV-LfZ>0FC$gR6(-1_b--Eyv)rfAq^EKO!*uCJeFfl9@yOMT{1}4tRsa zuEpLVG|1;9tr(cp>2N4ydJM@J3_`MJC+X{G!2y!&#vj)QZW4;e<6Rx2Z-SP+3sIGb z9|6=ICP^hrDS{J~M14c^caxDSLnXB}|8F7laen*b{!f3LEs`IT3r3UgdlQ@RzBf5| z($eFb-pA(YFE-{wOtI*K+8*ZF4J`N}Kwng)0gai2T~s$`u{aB@o8NJP@bQoTj>00@ z<1HTy-go%$`|gH)y;D_)WHNDN!;TeO0{1@#-S|1oQOQc9GmjB4O(Yb%1jmTMTJAAZ zEyK43v)~8Psq|F8JB7oQ^yEYaOWTD{VeH7K)=-O5R-M6W?QjfGFm`)$d+5nPbFR87 zbhS4)8=Rn>o(T|VW>DHO3l8qFUl`<$wCNh0b0mWt%@X&GQIm3nPp&g*|?vdk= zFJwQ)?|#gWsvlzuhN40f&%xKrGhOw`d}WdV-DK`5cIHo-HA8;v#T zSa;S9u0VPR=;0=HbNvLQh4giM5{7p+)}nsXq6yk}W)2Wso`NH7Pf@-u^v-Q3 z;%~3f&ft*8Vsx7>?(FQaTKQ=m)D~ovd6xh!N~1Q%W2h@pb- zS%wKY8$956XLpR;4HrAw9^AY;_H3Pt!dl6mafMIq8t~E$=pa7`5z>Z_ZsHqZAkO7l z@|8ExC3SGqxC1brv(T|O@CA}rFQ#q+*1Ef{*(#l(5JXk_1^j?uBo=5O7WC5S1*8K^ z*B#B5B*bOJqnrQINIG3OJ;{wiW~&_8nH~HKoQX3F| zQfYf41SL6a6(g~d^|^sWf!c=qyUD#it}zPSTZLW#onQXB#XDHqc3fi^oC&|NHoA{X zj7(t5U`K!l%qnm3XUX5L13Py`CZ1Kk>|*^@KTL+k?|k3~5mDgAWeazKP2C*=H|E40 z4iQ2)WDRvNDkoRUA1t$H6^{*+NPe*QjaL{FOv4kh)`1C#81Oze5;o}Y#pA~joh){o zc=Gs(+q5&ocaLAh-6!JJ*=2pYJX?)BTlkbl-+jUlO0lM0yK-Mc`n%6wz4y`9Fz~&( zlR)%8R<$i4jM&x605AdxGUkFqzelN!Z|MKMN%a4|Zm5!-TRl4GoS0<))d!KlIv)O= zNv@s~U3TuMb5igB{Tn>e<(&Z~IMHE~?~wD3;C+7kGp91`deHDNOkHJluQ6*1;{NZx zMwwt&IAT{X9#XJ8q=mb~-&@#_bu4~e$ZtjU=|I#-+zY}vp4gx| z;Kh_KTpiWZuRN_I=hv&Q(GBwx*J_S{)c;NBG>jGX!4Ix6g(P(zU;nSslZyAr{UFyq zLN54O;u;Xm@t27i03;bscVe7c45)wNp3@$D-8O63-j7Q=OsP0FhArXq2qU#^t9)~4oHBbp0%rkx!TreEmy<$W^uct zh1_>gP;v#iZ|GB3=XCC(P|Mw{%lD#N%2zvaWE70l4WM%VzMcfH2?fW6pzrR3p!2TZ z#pr`@cVA~a4rDw~xgEnD7YJ1?#BS?Z+cl2*x+<$geMNBUg4?Nw52ch_uYOeLQ8v&u zR|30OUlhQO5zsFkubzR4eeP_AGJv|@*jr&4J! zWyS}Iz(tWO0)ZB@Th5>|IW9TuE<=@oo#OgQ%4<{*B;`0+Ii;G?FV7*?^`b6hvBpyG zh#%H=y17OV$IbZog0(g|Ha59tEsP_b*%sEkTr~CMn1$D{k{BA+PR|4&xl_^VnYUu! z6BNNiQ!JFR$%22aZobSqtiy*79kEz5N}VZkP0nvzQCIb@sCHL&6(`_R0IlmHw%oPz z4q~7rLk{MEy()qSDINPn{YTwP?4v__84F>uH>|AW*KU;Py6nYq8Op=7g9+!?E(r(PO8w9L?ePD?Stv`p@nQIot`L_ zGjLa#>6x_?=gzI2n3*=#kt4U=yilEu+WB~DJe9-_i0NcT&bAL~`+Hr3i!W z>`Zlbdb-kDU&rb|JX>FH-Ei^J&HejJmBoembaFb8%vLJ7WTINBluu4g2ZGphYuCCN zuwF!D$IA!smUg?nV2|`qn~YzbI%+CVN#8ZYuQ%07gxMrHvdY?FyaPLN$y9G#U7fEZ z19mu!6wUQ{#G*TQu+1k zsbq4hj-)%#x%c1M9hJC2oF_pm7EiD~);n7$SlGOdc{_uH9MH4KC2N&Z;ee z$N(OMR|9b3s2A;qWKYl@ku+Tmzxs+E@FY_m5BUo_YCPT0l5o2M8~2Tr!_yvYh@ZfA5^F&cePw07f=9dHL}L z!-)UIStLntJDE-1`gELkc>Ir(jt0MnmSPAC{hea5U`zp9?rj&YMMEj?^{f_b zBwX*W3Jc*c>$&ht#oo(NL=_cO63Zz_6f2NPaE0?S&&^*<&&~#ev$NBW+#f4!=7Yz- z>qt0y7#qbRB+jG`N5ev%A~S%}^L2rNiIKyGXaZ%Hus@zYonU|Fw!y=A3LB*l>5}K)oQd;yT-5)yEJjd z{IE|QbC1J8jZXab$^izKsBUiwMw&RoPEfz9e7eonHTG*NA7bTZr58D)yacy~=j{ zWqkLu{`(>?{E5iYbAeOHgcZ>JMm=*cut&Y0%1X&yY_Mn=J@5olg2vWC;t!LEtDOK? zH5BJW{Gyc0w>Nms`pu6QvJjmls;rugA6}ZBIaW1=r0_6@W2!pcvd|&^MQEQ zy)73gtyP$RWv5$IyIN^RVWl?L4=)(Ry#3Ghb)M+}rZzt>v2bSLcd?q4W?P>5BIAVL z3J)N^#Y!Y+EiUc+a}8o2`U@MR{#)m|IN&J%RM$|7SFQk@vJ_@>wNPJNj)#ND8i~w{ zx$rS&QyVO=xq2GwCMHcD7APwohyG!O?DWLK$y@ZknVdRyjHE}#Xcwr~1z+Ww5{W`r zrmCM8F$RrP=8CIzvDm1DBr-D7^0^|D+z6PNX>!yPYtu8pSExSJYhW28wiC#v_TC@zZ(u?vQkLw?@ktq;gcqE6sh0+(F33Tnhv$U=f@Rj(ZNx$B+T|oE7T6 zYne{JijHdfhnwHlv6X;tGho>Lqf*P^@1v`i*p zl$_#NX|kg;y}!*c6ii%naVU5Vq*qHg2VI4sx7;`nvC#A(e(A*LQcSajB5f_EzpVjP z;1mCaLlbrd1|`U6$l6lCSkdIfDNpvUMk`xZw7BU42J;IDuB44~jCKUUeUD81DHgK- z{KK{7<=VrYO!`k{dJSgV&u3C=RayK-Tz|?syu5ta(qD7i`1p96T1(5e;iSj<9XF;* zo!R!d&Is0c<-`P%v!BpkbSdEZ!bs-=j?I~4$OaZEx4^6H3VA9FVj4p(F0U0Zi%|%L zK}XD%oBoBz=!Zay;IBYj=2G0QO-wCOf`@)0zir7j<7A7On)r|%M^U&o@Sk0aLRN+x zuhM*hb0Z`&`V;wGgB;;!Q!1! zrM~EA0p`(oCQ8U8!?9F!IukS|m_h3ASU73pb~F<&B11BMP%0c9RSGeAgHnh5tj3%= zxN84P2bc0UV5sW;=Y|1tP*(*Y0NZl_h_i3_4}do;VYL|ZInZbB;=jhsVJ2Ye@*i=C zOtV~RMkJgCEMlL z?1VzHy;9lj^S-2{`b*DKrrPO$#G&0H*Fi9BIoHnNBYdx;g)b>dzob=lz@$a5!Alz7 zH=y`pg(l3N`3J}j?*z<_k~{B<*k<0LGN-l3gVc9OPiRA+uE*uoK6hso)w|~H>BK4I zB8|nf{T?8(r+#JUrt7T`79lQB>yC z17XbxWyft_YNr=}{p(*>;El@eG?3V+!nhZ5@}L{RL0Z&*=FiNpnxBC^^%nGY5e#Rs z0o>$Q^9ch z#KJ-*m`;a9FYQ^D`BldMVl^;&B_b1h!7&;LSh!21<#pp{BOr&DCoOZs4fo&w_UZUo zFcQhMCkspKhYyV(FU6M3&t)Q!;8=Y6?f2h*!wtroybKZ=S&Eg8j~_a`zEqfOgOmvE zR9n$F<=>(BRi%hZKx&l@q4LPiSHbt4bI&`|Y`LT6MUeJ;_h#*4I6gkHQNm*1bP&1q z9IH@z8CGimcyHp@R|ZioUfh@%kB5tPb{TuAirpy_Lbeph!r(>tV|Hqo7$~WL@bQns z3CO6_?pRgCn(L||ZrslL4vFj-_di5hC~N{B$~|I?_Lz@+L@}TZ)nD*HFCy4m+4E^Cw4av2Zn-l{PKL@?{WZy4CO`N!cvG>y3_w{F zIs#8F*{3rfR0>jwMih2*TJ=A%w$tv@Sm$DWf`;Zi)#{<)?$U~QWZ~$swV3piJx1$A zI9NM&bm0iCi`t^rO0#)GEOtY)xnjCZn7=#^jTYtG>`ce$da*FSOcVQ$+u3ZXl+D^d ze)~J|Z>dynmrF|l1A6~ULKl%0iT*?MMpR3*7tGC*+w~e$K$2*lRRW`pTed}Re?LK$C~qeu6b zF`wCg@0#yrLT=rAzEAp$mbm6p1%mJ^w6eX^{6hGI80j~H}n30PTu0qSGw+YNSFDS|??5m10-+rWW6Z8Gkj2LObez;Z? z6N!hw*5jq7|9YUyI@uvr?Jd&;PER0{8jBEcG;$Hi2w=1hg7N51 zjDVf9@>xtF7*~#|8U+>`LFj~l6$u9kFc_bVb)<2?TA&f_gB<;wkN^#gVNU-t#`agR z_vJ)j9{6__Quw{_FCiN`VmdLDa0CJ2O59tU{6ozcC;fni=D&{E)bbr} zjyhoPP$NgA0mwJ-F7T$YR#OcHbJ(9@Z1tMWZ1sZdl5`1BHV@l>b~l<|}f8zW0cE0L9KUvXAs?v$Hk+-_k>E zw)(1#Tw`goxl~v8Ons@jxzxyQysGM!cxI{TJ$;21*CPHo%bh`7zYS&AMNClAdm?qLzugAZ#+H?C|aI9|R-3Jw5S>;?y)`;o>J^g9RuU>j+}vm@nk#0!2jI zw_v+_ec-K-Y)Mk6XOWN)1&Mm4Q8$}T{w*I@qfS;DecOr9_4_7Snn(k(20#J%pUk8# z+Ax+ck@A$A+5FyXE-KkoIb{E#g@;3VMYe}LB0rhUZ$<4T(wFj$nN)Z9@_`{K?!b#- z7d~`?|MkqDSP|%}6Qaqv@d(2G2|;i{!ZDN>H&@0J@yw?TFt(NYR3>g7vZ&%S8404tgkFC+9d1s!n<^@@2+#1v2i>OvY9P&&R=bJ?MLq+-P#OjRyX@mm-% zNwllue;Sd#HnehkQ*^i<7C%BLZE+{Y9oZJKgY+W+W(!~tozF3*?HGG$$u?1hIEnHk z+S8J|k>b}JzUT!dbR1h!0J7IB1JD4SoCEaRsH0YzO?-ot?`nPeMD;0h6`eDx_zAz~ z9q5ICD>|!SaiUzD_4N`b)%FLOwD(4=jCv|->8UhcS83WEHC{{WlANPGM2hr4JI7&f zxfOkWg!9RV;MC~{*zWABNvV)FLhctzIuTx_*9s$jva#XBX2AY}kW(6%C<37lT27%} zVFFsL5eO zup8Xi8!MIa^wc=FMuw>#4Ou?9);N54d+a)1!Dh(*R{&euD|dmPUD)RX&9X>sYV-pH z8(jKH>otE#M2aPi$`3H6*lbH?#Hs1!2t6k=mGb=j3c1mdi3zhUG0aGUcxt5E;g91{MicJ;1&=1Q)}A}n0YUCHF%rB8nC zUHCUYySX_#@5DBkKH5op>GQ;&g?$zkI0Oq@i3kD6i@Mn@uPN@FY#6e3B2LBG0+4nd z8`{VQir`hi6(_#gA80fhwn0Ld)maAk&#smW2qG`LS3KHgB9SOhPnUnR@}@Vvi6@D~ zzifTkA49M@#N$}!o+=tk(V>sz&15Q7D*fnBpvF=umHL;hzw%e{w7+uSxJ`=v7@SBw zH=cUyrq!W%M`Fy3k+jk6FLCv4Z+lxYeIZTf)F7oq7n_&2dJ>l`t}Z^r(v=J8;(r+g z3|D#9!MdZW#1l&qxd)sMJ@ZBLdGjgR8D$af6h`}sJguCAGaSSc_lBHL*SMz?2K>dm zt}s4c_;j^eJ94Dvg_(rBvdJJNj$~lRkchpTZoa{d*Vlg!!7+VILml0io6m z-dC>=30gx8e+bcp$CF>HC!uhAVdCpR{hm%A9)``=U6%DcAzz=s8*MGzat>X;!5qz2 z+9K@+86UD}=b&IIPXeL@yk_`5B4UK^TAC`bK*g}!$bMB9P_GJ>dnHH*40~_GVFTyd zz7=QzbltW14rLm|eYqBjG4#YZz1Pln^)TWBaEJurP7b~#d@kItM?A)W2<3i20wdG( z0s_!xRrsD;0zSHkz3M=*l`w$}wfs}Lqiq}#iP`(A-rt{!uj7AvNYXV!S6irM0EO;> z7h8T(a@AiqY)1yXC;&X4!dj)fpk2OG@P;xB#9GIop|%`~uZZDYZowI+fdq82(GF>&oGKh#)qVLS-z!$fqWiNxTr-zLgGy_Ru(R zPLk(-1u%XJbmV&ky5vOo1D=Q$3KSydtR63xh?idAf`^uM2%;*LqHI&R7sdd8PD~YE z=c|-S8EeJrDp$zSd0UPaAVu4WVw{MrFl9XO55?5ISAU*;~$zPeA!|1 zc?8wX)Had!rC@?s%a=-szcXdQr(GByEAaJj^ab&dS)E9wqgE&wWXv1Lqs z8om=Bnr8Cxc(m{ykFD8F)fFuFgf@-mA=?B#KzcVe9J^_YQ46!r-0tB{>=PA@6*v zEB|)&(DgYRJFfSz-{~RXx!vLq{&m~!-MuV;MIAiui9XDIHv*MKMhrC}ZaJdJVCPPQ z_+Kv1sHD?r^Y%=kkg2EB{dZ;x#dO`g@S%qu8hdCglP(rA=I!ZpohSWw;=~m65$8+@ zuuvWDoJfE7VeBL1BZGK2qBuugwJkk!iMZx_bhtBwAdY>F4t3qmdmRej242UU!Goc7 z2m`rhfB=In(-T-M3VbGHMt(J%q9sW9Zc0I+?JA>Z`CH(m`Sy#U& zulJnFr2bY<>Z*=XfWfW48EBbXfR2#TnuE1F9AC;emCBS+Bbd`f0Z)+ZriVO{D<0v z?vp3+2XnB5RZuQnDvR9=cz`(tY@~INvrZ?Y_%&$CRr-?uYN7B|Y?;JGpKXa5Qp4U? zbEQ)5tC%WyFqZz;$>hIIk4dE8)vH(C5xe@~RO-VRhb;CC{43ovkakJWp-E#dP26Ik zfQE4q&$<{Q8wZb>0xvUU79mo_i!C;9lmeuCr8|U z5|(ZERj&>j#hudILr<6Zq&ifwyj8W$(*Cn-;~aww#jjsXFS2@A=?llW&_;Y8M?)Y6tuwpwk3 zF-DV7+vM_73~=Exd;htOhgdVyZnctmG@*Yr`7qNwB$K5utHUyyWGHU34Q{iA%DspP>I4^<*1#? zFARoRq$gUUUeI;@Y)BA2!(vDk3zL+Qy#lj5oq=JM_n($4p2B=}A?^3K1wlI@yP+?$ zurA)@cnMEJUt$B%|xTs zTz88x`&~wsZb^)#KWX&QhDjujoN`x(ojL*wedAb%g|pHfv&BIZD)l~p?Kte`0 z6tD||;Eou>Cnk+ zpAChmt0CBbUNiv&ZrA6I(fIyzcrBgAo9T4phlq|p)L<3P*tmT_mYwVD1G#`^uO)_w z?~N10wy&cH!)<48p1u8lytn1-jm~eus|onZ-VWJXqFYrs5b*$r4(^I^Si|Jtyk_W| zA`+*JObkdHZoo2)qm(KbQp8z8f*=GDoy6>id05}z{NejlHP$Qc+JfEs^mHCM%dwY} zch)|I)I&&^W_}Qv1h) z(vdTdy2c}*0`R#C95e|f(*f?&&8WPY zz~Y|*Q}vGU{!rw{Xi}MQtr!!j2+4UOHGzyqras>c&(1EBv|tM|QWnxF=vyRTw_$9# zVlJ61PSD_E?$rM4ulXqN#oprTuy2X6R@NT15nuV1Ge-`mj!a^Tfz;g=D;CDxRZHWA zV(c~{b}L73O&&RN<}Iac&IbR9q<%|BPk#ZskYfkw1yabMvjJ z6WtpAUx$*j#tpOxALU1Ma8d6e{SfWUz5;hUobIbc4RNwR6KEloN!69FO=sxpJ@B!^ z)lP@|(!j&MRPA?7yNWabG?52HlkNC5T+xww=GQ={mt|FgnwoV4`76?&t8>cKaK;pq zrm(SIuj`VuT|j-4%NLJDb*EY;47684!RniU*Gi$)DikKL#J`8D0>N6i*o?_ew@5%3 z+qhIXbL2>}fLi)et^ln`eH&nt%tNpoI1lJL=CCzFi4#&eyCcidZbE3Yfnb2)+9=a5 zNIteWY>9Ug)KwBq^aM!=P8YUSE?4MdawszOouk;zNR;`yi&?u}0+(-g*g1Uzzkb`$ zEY|zHHt=J6q+eoy+I-j-SUHa8=o51hgC;ZfMgtk#Gxo4Xqba3i_TP<+aL*Jjz$IAQ zv;+wlO>-JQs@1AAEx$bqA6?2MTRfAKoo)P+Gt!nV;qda*s*bw5z1{yB2oTGH{D6)P zPR$O>i=?x<<(_VttEu%^yoHUB_I^OV<=K8o^BSOemxOBoW{Qqf)@V@}P&x;} z70fn>905NN#kus82<SM)A3OPTx3 z^IPc?HI49>nwjcn`ZitVgoB`>Lm#?M%E|15k0cWhV98nffkg6=2St$BrX&%qHe)HG zo?;)4g;cCrg{NwZJ(1;CElEoQ@p31$XlF9^Vh9^(sTQ--V&^-AEK=v;!@S3%)iUQi{hsmxr3<2g!~epPF+~YdN#+};6{M^+m6P|KSJ4_# zM%QYH8cC)~cXD)gWw6_uo10OST-$Y9*V=dDdOwi&v0QtgQpm>_=3GQE?f-#n9HHm~ zN%Q1Wgw5`OKRVXCkVR?VsoIp=QZ~bOZzWL?{@sFjp#0z=VI~{K4nQOI^J9~0aWQt)XyAzQ^rL7*c&0A#H z@4d&jmjJ)pVtYq7v;P~dmOS3`3RrCwT4E2j%4z1(9FeVBZD?WuT;N7v0~4C6ir!~Yx(6+Siq-1c zaaCULvH9}8)^z*)^uF648POOPJfM*Z$F9gs9BaSDQQGCthE$;STTGOLX8Rt0<2iiU zmo$3-^wIqh8VPy<{BXwFvyKb3BaOshx^XzNB?2wd5=hS5{>RUyjg@_{JpeB_)<*C# zAZ>gWY#f1aBQXng5`U^%=d&szy9g4y(a*<Kg{1BiwDT`R|@B=b3Z7c|DTNsasbAu5av z(d&Y@KGEgDBMDzu`KkVbc#$qkJ9(y_l0MAzex1M?*!1?df$V%B3yo;Qmdhe`2PfOl zy#)e?Ko!kxBU&6QQ}In&4gIkSK5d1M1guBXBWYN4*OV$cJQB{r45@=)^>^VB3=uMFeO~PCDm*QJk2x?Lu*4 zB5D`Rjz83(-}Ya1{ofi=V?LoTYeiR6W3qdoNuphy3WZ zUM+LLx~V{jG{>oTJ#dl!!)vTDYp7yR1l(}Epd^M|Rs*37v(Dj)LeCq+yRFd9f51*O z- za!b-I5@N;@&R%(1BgOOqc_NJcuvo)thr6#0i-;N>A%)^k-B}HiqHhHfuMB5W!PMN` z(W7&7ILU#s z#Yp69;6!kU8ObPD_TvH#N3QuA_KA@z61TjOW^X&gUEaYG9x6NXbKuaME&r%i{k`nC znwB%xfdFiGj6IkGpFutcM?dm&b?|j8V)H`WZNYn*_JH;1>V5@8skA64J??|U{c}vf zJp*Z7^(QKP(4UELmud-OH4(7sogvpaYH;7@ap;}*4Ar*hU8x0~O|!!mWE1FtN1(mj zjtIav1l|#NJn*5wF9g02_)mfVEAWj$|3}Ch+!>l(Rh3w5D9V8bxAvECc+@pY0GMh^ zajKOCnAiNux1+@VmJXP;W_=MUsWp_9J1f>1`5C&ub-;a;clGjZ7=4Jf^5d)Lx!xc8 zuHQl~qFg|Q&p|r#?ZGFjzBXPNdDmC=s@EUi-RRJbq5OHbtUp=d0c z?|-cnOHIs8Ww0eS3{$c6)_IfWM?Z&{&hzP;k`}hM!Q6~QZcYZxZNcQNb_8lfFnJbP z9IcC?@bj~mp0Hx!i&n&X;rnm9?Y8(WRyd65EP4vGALu@7fa{{fkgEE*cP&U`bQC#S zLv5>vC6PRGTgh)QyX(Gi`iJzfltO7J;wqZT+k^p2m@ze!% z^%|%_K_YapFz`6i1iIl!Wkmr7ABnP7^V`?#r*hcqCpPOIiTm2T_Jtu#V%{`XMJeJF*(~mutPQTCF?<#Mq zVidQrc+*in^ z?VZHWFDbaQOJF-f;WtjU^MWSE1KvB3MW^ua`Z@IM!A&Liy%NOakM$c;*VEyG;l(T z;h1J)#82xd#3)Mky#?>_biGY_KI~GaTW`c|U!kv&-}C?eZ|Kn~n?bnF82!eVbA*|_ z*PU#Ow{hf?U-y@FYwf?s{Sx3d*oTG@)b|BmC-cJFi3W6A_Mv{Bg>V!dv%K^cN6)J+|rhk)~$L<}yzO`M=BZ%nG`ug&Z{YGVc zyf_nm_@AFTb!zm&)*DYe7+jfITwMIDf6tmX+;i{()%N(IBg?f$Fj#x|r$Jf@wZr#J zz{r2_3ul+^Ji9uLY?S{3bBJ`j7wDOP16}x(;M@i}2IK+EoCE`E_~3l<+=H&!M`sYP zhVsf{WVZm`k()E{^EcC9cO$1hYmc6 zqzT`?UOp>C5A2k7eMDz_2LaA>_V;c7oo50E0gG^GfcI6+2e4Y=DDc7O;aD3Wfi7is zd3gceR1Pwb!PNiyum3uq-zrQ#w5zRu+dK35_ZB9`u@`RapKZPEy%)^8QG_?Puy+>6 z;hnsAZ$97uz}DN}do$6|<2T!+GMFJG#$+VQT3wqMDXVv%?x8gvB*%1s%as!h|Nk*479f#(WS^Ean22KwH;X z6=%&Cg^&(;g*kHg{3_YU#f$u1J%9L!>?-2Ms(|sLXx9w_kdv>lIB2Hqvz{EYDw8eccMpT3>Y zK3I{4r?Kc<*R_4dyynP}Bl#j5bjk#M}9Gfb(Q)2v7;}Y zn0V>YV-O`0@!T(BfnYDV1=o3T?Uq|^I)5aYJaYb~TW(n^!kNB>r7#Yy6B%d)5!(_g z=1Md*2Xq{F%4Q_(#0c*yT;rBt#1zL>(bk9=iQXYUB5BowX>+G8-MY)PHLwI~GAGTd zyyQ?mcjv9Hf2%FDlY77QK$Cwp(874rZp`=@XpK}NfSM|RIn5I!6*%FH2+N2CkEIIK zDnU5F2~G9*Lu5ys2N4^|ng0%~Bi${AZ<@~Kck{XF&52~O5Y5-Bh}IQF?xt|Dn@;`_ zDTByoyP`T%)i$4rXWB@mAFPH7_4LH#+|JJ27Xe3;D1e3{6bj5RRC+yV3JfCZGCCzm4e}-c0Ayh<;4o zJ!c}}8zv?~K|7pZz@}0s4hKVb9)fBevD2@$!{O8)`^{S(FQp9|>wOThgiOqeOn^O6 zNAx|kw1J*L+yJ8Mpk6Eg+kaym?p=2=g!jR8`oWQj-`>KfciqL352Bch>yF(UZE2QO z_9QFApUf#DIsPgUOfT7{5AbD+?0`AIn*NV#3T3T<4IXW|hOM=!@Q`uk$YYhkNK5LT z{t71C1y z5zt56z=L%$Py)3W2s81c~bCdOA**49>5#wWZpaJBwGe^-=5CVQd z>;~EaUIZK;EWPX?x{l__xiPqC0$>0JumEME*ai`sB4%(sPpkuP`zu;6yJJ1@mqvfyEe?Ihq%l@wowe~^(J7@>(Kf8bjW960+ zEw?q8D?>t!5EbO=CrkuDGCP1hC0F4X+|XEXKen{oiQ+{V^n392+_Je@tBF`?o!a~k zinli%K7wD!&C@f>Wy_EH8b;#HG_C4E6s^@ZH?gIKRsN36Q}}J;^vL0xcw?EHG?(8z zm_Dd)i07;fVk|~Y4}`iwxKc}u+G*{;LHPZ7r0`GfBoaHx)JQ)3-FEs4XqSB%$t|Ln zBUps5f+u}bD~OVY$JOz|DUrtTLy#e)-Q~XoFK|OmaE+So5X*-Y?YVRv1~>Ylv7iQ% zSbYh)-P^i=-%8I|)=b)>xTgLd|DApdQkM~5O@9|BfRwu*O}_{DspF-U4kza365;e< zK!@*aBl?Zs!#m3 z=+LGOp1O`v_ggav)uFuz#UKx+=|&9FObe1&cj$PVoaU06T01;Iy652%y4eokhwuX9 zGHcJ%2Y4>!VzK9m0d}}8g*i%t5@DyjDJMQ2oj+*Bo)9}VEMHe!T;m5D)BoN5jS(a9 z-f_X4xJJAztshi2d=k*e(J?s$%;$0~vhHJVAmbiAL_d#W>2!+)j=h0}IWGqE=m=)U zwOYf~dy*1NToB-JXkJr1EFzatdSxOd=z)UugEPeMM&N~llz&?Zd*!5l4{1YgdngjS zAr^^P872W``MtE}b$lh8P2r_v@(F!~rRBXL+=gEYEdDs=udWzUIU^^AgPKrs*$-(T zl<6d{JGnXce0rBy{_=MdIPX@3esXmCw)ynk1m(LQ;5*$iZJ8=#uLAEkxZ}}+6X~x+ zTr?>V79}kdfv6;kqV1$wlM?Ie2+iU&ReGakNMuN+{GC(vQYMROA3b^$TgsbY78au{ zQu`z-Vn~jYl7hN9@eij2($T7>$d+$xnJYp*qp+ zM272!GKuZzh;|%ILy%%h)LXSVaEki!!}zA?-e!zO`e$p0vzL?Pc5#-sIA$72I~D=z zi$RZVDnSg|Gx_#)b##7{y=?_R$ZvLG%jgEa3;d=C=kF@)GexXU^1K6_gg#YEWGw&n zNv7dI@+QxBKwjm)<^=TBxau(7sj-cXi|(@1Wvm7KK1G-9z?k+hSMNHCm8 zq{6XSxSW8Ph|iG1uhi%A9e$f__z~Vk6S})SQg$|>n}usX7>$K&JDw~R$ByJPb|@U= z=A9>AH}8m^OrWpi%@pYC|rVGfeNBu!8n0PHM-no?klXINIDR=XmlJ zb`Xhwk@@%j`^~@Ldwl;f9h0gvG+Kd!kj~A=_zvHF@<~^AroMO^bJ;rnINEwWdb0QG z;GPYTvI2R4Q;fI-P}yxXq^gZ^t>OKKj>3kG7`puXPkYMU~N@ zV)YQyG(Vu4H0E&x88pJ7aRe>qgopJmKtk9>^&Or#Y0)4}cRDIYcF+OehSi-`r=zx` z!FNu;i{M$thP5zY6|6k#$B_tly@dTffkRT5oU?rtCo#oez;%kH0#N}nJO+6ayCs?#aVs!@qdR@L3=*8#t^=P~OWF$>-F zF0Ki`BK|Fz_xl0Z|3j_89CZO0Z}xQa=Cs05}9^;jN&H ze`ZC^hXaR!kq-oZ03y;NXSG=T%b3+e5`zzXbZ;95FL`2*7txrL`MT(cR^>HpUPW8S zz-xj@4Z`YtgS2Bf>Av|wIDYU&PMnsBrxI~gU-y7x&sl7msxbnzqCjG4No)OW725yQlI3AkJW8AfBH-&#&-n1hv}R zV88h=_O5&>@VZPKly1AitmN9W%ZP8_|Dq?00;Rcq1?I#}NCHeP;#tjI*@yCiIaNfq zYwXyhDNn|8NCXA%=By-6dF9O5;$h^cwhtvN7^I5*ze2>kqOo%@ALRYyQk@|l)9G{t zq4U8+dJgIpqd&3}(`$>X<430>wY3C4^g)2nM;+XL{wey|)qbjvT>7EG4>a8&Fhw@D z(0N9V%i-6|DrE>R`ag0-I>eB(n_zVhE z9>?TX`N$sQJ3#N6XEjPsEu&OVJvB9;zFpPU0fX%(UbC7Ou; zs_S29dNI#U??cw&oDJfT3?D+Uha%OxyyKV=AkeHM9+#-lk6k(n;gk<#qb;F;) zr(;K=51&fleb#f&csi3wkKY4oZ?lq94@Xry#=8R!k4|&%c=nlVdM}CYwgCdH%i}W>rE=;_#OWadtEF#y*5qD=_N4gu=c{htURee& zOO;>g_HFggwL}N~38Fu`H*yPWV&5c`Ra76*k93qolm|m%W?W@fVwl3%s)e~p57xj% zq~QbNR{%l`#TTp{9JgRzV_U`7MMJ^x*|XS1Ao{wZWk;j;+!F<>-*X?ga6kH6qw;<4 zdKvn>GxN*7`l8qKooBo}yGpmbY_QHyo6H7~# z2=D8k$`W5}$4#*v}WP8qSQ5~bKvtZ;+R^`2)T zho@!W?d<~js#=@}=<5R=KCq>VyU7U8?xB6Xz*yji1+yqOYU5n43z#(R4L2cpWfdF7 zQhXY0@}+($fYT>bflLUCb0%8@J#d3x7aKrPN9ZC_E+UVPCxWrRKv_6nO@Llq><;!G z?6yURsnAyJK2e(3o+z6ZX+A8dP_(U<_Oq1<23yJu&MmCdG_k%QNup6K7|gh(EGXwF z23mtkL#Q0z2=QAFNp;AQ$c1piQ&4-}*%f4=YFW-V&($LTl@R=|k=i+@oMlxPbhek^ z+LUqv+g*G`<2c;7KF8M~W@E<(0*eO#f#DYSopfJpN!Rr7i20mD?=Q8_>tXqPYVfjO zBc3mJaJ12AADTDoyt9@$DB7q!iWIm$KWusjTD^ijGU=b791Us>c-S~43zDfyG<2?I zvhrHuPa!z5IE5;~LBUSBQejfWa#$M)ae zVufNdGcjJ1guRH4`eDnmLaAiFl1>FH;e4(VHs0W3#p7|tOe0t6BW3vLvTo$B{*0y}w&I6gAy=9R9paB3`Yw+NKTtqzd~)TO!Vr ziClIE8x$oa^t^3m3x(x$dbv=THIE?I8a>vR;eF0Je8r(Hhp^hsV<0@@k`SZK z4q-Zh?jnUZjPAqS2Xh662G2}9IP3R*692+XhZ_D#Yy=dXe)Q2t0e;za=}{8k(JtVp z@4N553Co(e&q8$G-FM&Z_&UGuZiZPwp85aa=MR}LAinJd%&H@KtAKzU0*z-N=rLby zWR~|MnALnZ zS~0y!G@OV3t+muzZ7p>aH}%*2A_gZhHfBW`z!9~^#uDvFgl z0{^;-nEH!;QV#uds{_1ji9S5XeFB6n5(9~coOJ-cgK4^qML2f$YSihD_VEl1XM1HJQ`nCRm84;IoUi>`2&(C&P$(D$M+J+76|Dx{&%Ht=F1v zwEwkavgc-Vz>?p5KKs6m>4xsNvst^;I6k$!P{>2L3ua9)WS>7=E{-3auO!cgu#ES{ zX!>+WOWq&qzh?>y&9UfV>hZNmWbJWun?4P6zJoYA?q#xxh@sAaHX#im`s|1~HR5Nk zhLp`rXH~OF_GP3odBqw{EUeTeK2d+)W?BBDqe`h>%1nK3_+uzP$j^v4s@6N z7dRJ=+Vhy@GXX~;Y8jk87KVef70Koh5f+Of9ds*?h}Fe{sphTaEY>aKKcrr^@|kEN zTd*V9d?*)-WZ{h^%IPeZD_J}1v&-zuOea5xa@_S#!?w;U{dL~c=R>p3 zbVy65Z@zf~AEI#7?qkR$UeZ@{b+9{M0Cltutoq=OD*VN7*8u!4LAQP__SEH`9TCwj zln(nN*f|)Sx=tKW@CrNsfd$6oqQw~drLI;l@LBc)*}3c3`*BH;ZA=y>r18q zlQ~I<{P+@QTsh$WP|6UnA=W1~Z^;rWTuKIH)8(avl4vJp^V6oLIOo#I8h*c%*qnD&! zzEqf*nV+ATDJ+S}BAJXjszB9r=CN}}VP1dxJ>Z-oW3R>+qo4`rG!^|hX!T;OsP(i& z8}t!;f&TTM#afaDM0dZMIz4uVr%tLZ2cDqS_J~^>oP7Mh!V>s%;)=zmMdIn$W==MB zNmEm+QbP`)g7}M}TYnjk#mC0S#*pkvkMBrZ!9t;2&ZL8u9A_uz>vOeoxi(jypA4r{ z#a!-krE(c`B$W=ER~CX+I#Vte3RX~#+hf@A1pIOw$9NJ)^Xl5<6xKcGYI4kFYp)p} z<1((;xO@DqTN{fRX2!}aHg3fUY7Uhnk#Y!M!_7A(D`Vs1(68O&%VJZLwHjhh>k-^QS&9^x<<>3v1RB z@IBrdxD%F}` z)l?wY(r;voeMj!HcTx^|xT(MJg)hA0ju!@lKV@0%Im^`A<;olXS-H}#8f&f%m1eG8 zEb>9%PW<~@w{+poJHHVO^34Zszy0<*!{&X{)6>-|7}PQno}2m1;`i3GhvV_%$K&zC znfdQs%*>l@{6wR8^d_v$ySsYN(#*`v?Cgv&zXOS|+MfR%SX6NRQ7kgOuX=CTl5zVY z#*KTQ+!J_IW_B4PH;{sK)BqBW9vIkQj)3}+>u{7EU&26pCIB%vNO4us!@C&s1qi~f zX9sVI?11_trrdA30G1$fzxnT{PoA7^Or}$bU?p0BV?17te8i)2Nw$28K*~ zJWV@7+ki(SxVi;iEvu-)W1t$z{*M7@DHF`xRgPAIVB(W&2+SIAK3W|zC(Ms61E^=Q zO;kK{xfmP^Wizqt4NOiw1#%L>{^-f6-zN=Ps!Z~YYBmx{AVRwp58Ih4s*52d5Lz+| z?jFr%!j(|zt_*fm&fFPw(pZD`u#r<0tR;a@4E8Xzg<>)Kw^?H1KZ1$70W8?8U zw)b$LBde^)=&{YmWj}{@QvxYDHP7e2B61~d-GBf6SQm?Bu@qv9JC66#^Ao-qs zufCB}9tqj_rbfptHeu)&f*6;rbys+{ea^A!L`^{dk_G6qJsi)xYxXd(hnUKJPIs| zK_>y6y~jm3k-qfA6Hi2}()cCYD%GAJM#z?a>LVp9{QO$UvM-I7tjH7LXf$j-`{56N z7(pl2CH~wK!H?6ls#S_b?!k|Ji9a8X=-M9m1lZ0J?66o-&TR=O5p%Mu;zf39Z!}di z)7!kY(PY{^9EKiYud3F$SG?j}3r0abHmeo)v{JqG)vvy_|Gj$j?wf6YxThPY_wGdg z0r&iYd}7R)v3Kerk$i{XHH|3p&vY^n*_p$?z0GP-mOt`|R!ewMZxD;e9cm&zS>CnVx&&_! zhX+o~>0I`6Et~QfX@#(E@a2I{I2?obID!>tAsa=oGjh2YT8%)csnvL6HB>A|fw1VK zFHbT_mcX+A49qI`uoL&(T?|FbU2L8GidWz|+bu^!#XWtw^*Z}Pslw&FJ`O3ewD8b_ z`f=Yp{$X3~__1S&Tp{B>Ke!M7>m>ehH}3Q23z&xS2R&9&uP@hd<`^nw^vu1U$r|? zBod59x1!O~tsqA(P3nLqF;4b4DC_eBuM~;mdgczutxG>-+3EooPBB-~bL2A+AhH^9 zkR33XT~Dzbbr!M5(qs0REWjjHu__gzHu7`uU<3#)U1%ZNz4K1=}raSSX}5J7*P#0MZOh@=rg4D^Pyp0s>Tq-Ti7j)JVE zefHgeOF|h2Y=PNB97x3?i87-1vBEtPOMNH25cYhH{j1H}@wH@vr%Z_q1c)CDn!iBC zWbW>kizHLARH-~MQ7+*m8JP{oJMnPPt_wx4+rfG)o*y62Ls?8B_CA(Os9fs7=j?&& zp7&8;clpP@fFY1GK!meI`Pp3K07T&|X)BWR@V6`Yht&+Q#f5?g_trHdxo^H6+D>c6 zGKmKh1~xj%*Lx+1Yu&rXZK3#!*F*CwvQQ^d80lscSK zeQ=E){W)LeOL!A6<#Ka)**Vn*fdIrQXPsUG>(fsoXVXhz!w6tO;$ovIu7nEK1ia2Otz;(ZCk+hO=2-Gh=gLR(+dk>^ESG*)$!=KY( znY?(acPV}n41XR(zR(*M7maJYH;W55rXsrJ=Z+KDrF3rY^r?#%Po18dLw?Q^T$L3v zUH%r1pE&O(Z$5wGctL|OBcbv0H{Gq}_1(K~IzJx5SERk4vn=y#jGHiJJ4l&QmfB+smqa#ISbRr)A z;NC;J9K6H?uYz%6NO?Xi57f}D^6PWqNMhE@XMJ`Ru{Y7E6-=Cau^$}k$Bx!NQMT-h zoE100h4ekBA`uDaUXMfv(D`RKB(XLjYifKQHi760Fv%N-VzIt%l^=}T7dfMDdO21B z6Bl+d54+;?b_m$OsIG!FU0bwMGU^eD?KDtFVz9)r;a)qT6jayM8RoNP3_Ww}3xL?U zYH@aVZy@U4>yZWAeW5@rx3~Qg#?Fu0CAv!8dx zrgKy|4SKECt|O0V?G%b}qw1!iarM=HSBufFpl59+!C^6>VOKE>SD+0t!^T?!j|D!B zC_s1Z1kj1G*Wjg8S~WHk4Pcxj)E@ed*Y-cq}UBjYv8=1CWC{j#%f?#XJxeA z8i32V<@@M?HE>LuOpwals^8Gwe#5638jHHpEos32{^YY0hIpTV0^5t-3JNMmdJm2--j6{x2c%>$e zMI!F&a^wvuBJ@q?{`i414%=1>M@0XeYcTzo?v%`)@Y%`rhG>^MCCbp+ygijY8E0@t zFBnzHvD|ljM{dlOPIfMMPqV|z^wDFPK`|5$1y>4QF3JML zCif-Ma@yt9rdAEKBtIUhIWSrR7A0Xd$a7@_tOU#i_}+jZa9qNrby2cASQHgFPz?Zp zjuO{+L5kq04d%Cz^asmVgSn%&eKZF@OfY*iVoc=d4>DB2np?Ph=8R>XIdhrEWH1)n zdI$5-h=AT$JRPNs8#O-y+0V9DazQNg%&l;nSe&D-+E3PmUbjs|qKq5aUhBUGrN zxa{UY)3o1YaKaf;4>F&92$|WzVXb^+Vxp3_B6jq)$6}BJQ`onvL^}K&bUzEzm{SC4G_HEP zIs87>A5K>0t2N82Rp%>{OQi^GNX^N~W;r?;DbW_>RNnSZx7~XOd0MkxSzqIPcQJO% zceV@O*$w2tJwZ*UIM44Xt=RfVXT4ALm+N9z>q;4#8}sLsX`96Gjq}c}2ct9Hrp@siDIG-$qqv47B{?Cbs9WG5eH}AU-CsMN7YKa`6?~(qwQ2=Mw zSL#bQFg)porTWVH8PtWBu+g!eAS{c{KJ-&)m+0%+Kn*b#o;N_lDi-T&u6w|};mbU& z@nn9(HxYV$AWziV_#QjcbB?a9#n}fMcezWnHVW-0&vxC+a_r&;o>sXV^vzoAX6_bU z)%&Wa={Oz2PAeBN$6kfKR@@8_y6bNa^JlP*S%!_W2cs7dsZas!uswrFCOAED5!NhZYT6AZhI`puiGIAW|Xa zK|du1$)=A0#Ws+EhE$zY9)F`bj{Otp3< z25uCOty>$2aFz#}cTaAknhmS#k6U10~DUr|y0uoCqWDA=Vix37(urvr` zn*wYP*cPN)#zrvaE8EzBZA^mOKzkWiyWP{qy*S<8fCfG>9vgSh5H-KwIq$}j6x39bgc2C5tr+%Da@^_MZ10S*u^rx!FBhb!y^$<`w_=kN;>6h2o)b z&J<>|^}1|q-iJs$Fui1CFuB6?-xLHYe3tLq@mJ6E!>~D=d zuJcYjk^RSPBJSkP$H6akj&K6mu+l%&{N*gApscxrIXmGb=08P&cG_wOFhl#G8E)3=EVK`ux-s-!B&V z4%#fR;KMjp=ySz9`xr6eHsRYXDimK95s}AT!{|kse&8++CjB;W4g{h4BAl7K&YwRo z(G>t~^3MpNI6pEnHZwEY{LA7{ER`+H%$55eTYO!gI}yN-c@i)1DLe}xmsie@&diLB zj5PnezdScn$fjaL#n&x97MO7R@FU}0g4}V+d=5Mca=$?;By&udog#R;MHf6KaWr58 z$2NS3hN-r?wz_8Sm>>9g#`<6oli!#$ugbpq)vsQnGYf)J;sISy7Z@7wJa^)MCevv+ z*P7_Bj2wDs5Si%wW2Gf1(V|gH>fqlGR~zVkgnt5bVqA%HZ}$CnlUFFUp`U6er;VJW zy@GM?eFqyDb@CVX1=nqRVQ@K8ShC8o)s^a66`?%*kDdvnYWD0fmjM2ePeFz#T-LkQ z<%90nH*+ghk*fKw_&QKK=#fE>A#C9ZKte#Ht=KZoNayePwDw4yP}lD2@9z)7D;_TK z!G0(u`x^T%Jk)i&|95mDk~4^>^65|j9_Xeo6hxo~@)f0W`DCaMmjbTa`$`~`&ZlG9 z3>iw2VGniIO+B8fA zP*{@u!#9SoTV$|LvMvSD<{)>E45H1!?lUwW9phbycj)bYcbDpa3iwlfet%!ezyCLI zr+)8F4=*W?I3|9TZOE}S-|D{@1(BBMHHe~|USd!2UW4LFAr%YD14wV!m{rJJ(62SO zVRmor=@q&@l88t0p-9x7Mn;V2ZOjyCac%QrCyQt{n|ruUl@)( zAN7J)<2pZywUmG_!UhGwr|GuEUR!k7Gu z=F`s6BQHogPefwJZ#%udj*S4EL@ItF8u`#SFa5<|{6*5ym_IlUyy*HhZNV_F(n0khA|DSbH6lMAEHSY~C48;cdUNpXLxDVxmVEh7bgR0rv>em%;vtZ_g zaH>{KQKtHNSv7@nz;vwxRW%K}MaFKcAkhcA)*c~+W}_WQ)-FMDh21FZd;D$Kx9sl$ zv1OpSt^QOo4?$5J5yNSViF+#QyAGZbqN)%%b7@l9pl4-)>o=Zpo=wl`Qfv4D+k<}4a!M_<3BYNib-*yFqBN$VLx1fqADJ` zGr#C`&PFc#ec^27eGtyE5kWG5QMO^yg~}Gb++_3pTXK zbSg8b+h6lCa#84627kjp4l%VBs;H;gg6xtAOCH+f9OvD(Gm5fP2)nrQl3!Pu!mBBr zKYqef2LK)7{}K+n-Y?Hpk8$Go`5l}{gxy6r>gwg~9sTtl;;VQ_kHCOCl!kfOCCW~f zaIcP=e93#Xl~M<{SU+!iy1(4g78KJs_gC#L-zcb$*F|DrfHj-!MM*sOXRs zS@+OP5RCx5J2m;VJSzXdF$d#8Jb$&ve4>ZgZ7B^z7W`5nk;)|F!wXhdzc3t6W>SfQ zbw|6v*oLbwp48$k7Q>*-#>RE+C4kWPyoV zeNJI%JY{|sJ8<{Sy4mbNb1x9cs@E1;n#~1PVJ;{@`O^`yYc@MA`G4wU78r)Lz;G{H zGRQ3GjAG8SALvgcN8m~XYzEoOBweS_JlFYt*XDS}x340p5@d_IM;)WnupLmps&wIS zQ3X}2UYi0Bqp_+g-w;osH+{d8LmAv4RLAcp?QRIVb<&IVSZ&#Eks0oSGI|(x*OfH6sJNIH70k3Ni%)H@+ z12~3Ma1eeKB!e>4T##IRazt6}8tcMX5sr7UqPUaj77ZiH}Q`^Qk$Mboi>v<9M2hQE@J_p+;f<9tyI_IHKNm46V2WT* z+zSVdJ5WreBNO?&|D*{YhqN?ZhJid3H*ce|DO-lME_OU@PWtotiAXwC9DoWVyn*nJ zp$PUib|Qmu$a3>*Yx597?}2%-RINAQ5midn%|#OkVe6g2ii^mkLLwMMQi_=0c`0(8 zEueCIWC2;vUJ3<*DGUaKi2`Dt+{)mPap1gu$qWPn0|vF?#RR^w1tzMoTcR$qQ7Q?J z+!G!2^AK719^Wqj@)ZLxnZ9z0iBr%v}eW93YDiEy?v26-AnZO zNGFr&nR2->vX7rU&j;|5J2RLw&9A~AnVjVsNt*p$fBJevrPwQoMp+zz$=He{BO{BH zT=ly+5?_hk2>6Pziic&#QjA;+)uI4(Ht3PEh(t7`RH`ncWX)l&xXRve*$6)IhMdFB z`%)pUd6>qPE4YVke7md!!keIdPBbtyH57&?zkT%l6cxC$_dujw1haUtj-Xcj%)Kf2tld+#g>OfB&xq3wbA; z3Pt0&R5a=)!tfBth7&F#n{)AKC>3_{g_-Jk>E5VfVDFczB=3yYqVvhxRgW#+5G znnquBd^l26DC&p>J><;Y6V#T@D_j_QhhR?RYTIVUy#P{92Jsf5YB?QL%hIQ?!S>CUdW$yBi91Rn15hfTS>S}B)+>;_8Z%4)f6 zLiwkjdMe;LrNJMHoA{mnQR4>%-yy1P?s#RwB%T{E{e!{vsp&`{65*>66sD)v zg9H756~|}yz8vIE@ZXtrl2weLNt;g@68L#Oy6^BtW!RE<@--_o;+ zw-)wi|GFDpRCZ?cZNu&&LBB6Gx+a>d9-lE$pD`JJoX#dpI5;rv+WJM`5eZs2^ za%z(G%KjQN!o983>PR!=O<>oBxOb}%x(a6+>5OctgA z7Z=+apLU7k(6;S7j2sNUEY>biksute)P`#d{m>st3kXTUwFP=RGtA(g-MRkik z$7!4H_G3BO?9WZkuCLFY9drGG2#nw0L&na+j)9?T&$^a4bY?WEZ{@~ZCbmv)J>4sL zli6iGIoS))O`^I{d_bRv*F3&3yUtO~a9+IRDJW$)s_U~!eGOmGCSCTm$FyA{i~&D7 z+u$h&;lJ=Ru*DyY5o(sQ0{bcQq^d>KHma^srjhCz`fIdNPJwZMx{i5~*ho|ff|)os z*H+zbuZ42)VBFe?f%^-Ik!eXY1v}C7NTRTK?COYlrnJ*u-0jl#!PvMHXa7WT6j@vF zDfwh!q`xc(^x&66Mdk06C!G}ia6@>~AV{l=!L&q)tC^fU$TUhE=8A{RHM+GYar4yl z``!}?fnUv?a$ZUHt%tuGF#dF^2wvO+ zW2ZD6G^2Tt@D^q-=R69J)OQ?f_??R$;fRl!G$~Oj^nz{lN(aC(My3};8}R|}3DM>; z0nEYxl^la3`#Fsfxjs;^t-Ystp}mOPC2&rJzrp}*^aCUN*_>g$Ua%uuz3l5XYv{Ys zD98rC2ahJ~QmxrBu!@Kvsq@fMPOP|GT^2d6!sA1=N6Nbh3a!D8m$wl&nP9e zv{(o!2##KzA#tid+>@;QRwPm<9Z`Y~M<$O93q=P(L4353dQhKw^TTao(Jo1UOTLuz zTC)whf!OJ1rW&@x;KU%0k+@P$(9$~44@r6u#DRlBL?oq4y+-@vWS=Z^+}bm~#EINY zBsK}2!nuw2J5Ytm(6rA6kmzo^828 zMo_-~8fAjFszC8pw$Ya|+cFneh~STe;q75@OCLhJjs z&(^J0p<`pghU!c>%~b+6|C*n=`hy$xLCwF{s#Xf;>`mc@S>cYz{I<#i9`f`SN{g1o z;&3o}!>07Kx2U?5hB3`#OY3`E(6MXCq8EA?KVlQkLnIA{4iUAt%h%RLbH|l$YAqhFS3Yf3vT|s4HaJ)VttsEtBdi6j7+C^ii(D0H1f_Vpz35rZkGc9a<+4ClaCK-&s_QgxB z+|>~c@WJ-%O98(Vnu&*NjaoQ9gQ&d#HpT=;j26Qm6kRkvZMayhx^kPNq4Lz^;9w9Q zUO^MC)xz-3!O7s@q$Vxi(wFVeZl`&p)BlV8eGq#iA@g#Vr|q-pvq~r0XBBYP82iv3 z2=AgytN+TNHSBQ#bljD^@7jlHQ#;C^OK`PrpJ50uDWvGpngjKTYZ=3aK5EC1lVZ!b zW1Z#oAx~+gpCUZ@N>1Hu`I5dh$@M~LS1)%G_A(-sRR!CL-L2Ez6t?mrGf<{iXdcG3 zHFJsf>SlvS`hL>rEuKwM@_JOj{-_PJtyv7smO*9_+S7Tc^Wh!)Rtl}G<0Y}3Z#5bM z|NpDno3d#s{};5E<{aU$u6=wj!8)@0Y?}JLf`xxZTesmB@vOjrJbDKl5F?PMAV)!n zggdp6T=kCV_E#atu6xlz*(*@05WCqMOa&A>08~Ix96vf?Q=k@268r0a1r+Z?@poNko#Pp0oM^4WDaf*otRW=klyIV2}cqkDEe1JiAdPF z=I5T2r{N_qu%$R@Yao;Hv$A*?{+)rTDY$RqqkPQY{SwA8^f6Ause?vN z!)M|SL}}2r%04NCaFldIXqDcWS)QW+3OQLWm)mM~v{K zBequ4mB*(iOUAUWl`4c(p+K1}im8EdziZS_< zD~IWo_B}gpuf!m|l2zi+Y}l)_v)-j*y5C-Fy-K`;Sj=6l?-A(rx;YU~00yJ)QAGo+ z&EcV&3I4Stk3D)8$X39JRckCR!rQl0FR6H<@mbq+G1%kG8Md?>ev7#f;gyF9pC4 z^R(_dZwCAvLzLsazAfKtfDAp}N}U0@u5=m6c`HbX8-%iX)H^H7O%P$O11R!~Q{q5N zk%4MB5hKvoN%$>bUr%@cb#~M+>6e1~E5nmJTC!0~JCos8dTXgTJ6qhiaN)wabLXm+ z@vuMg667lly(Hofk5{VAH{Ep8P47APBOO;ONbiE@Y{rC{bo0xKu=HrA(=%FPj@;(n z8{V*mjOXL&#e=?aKFBHswegppd@vcjJ2IEJ#0xV$sPIN?15{GkIsnO*Gx9~a~ zASK3G_%8`6!Iax;6~7k>x{gsJQ7CDT&C;1OOP*I`$>Q&>izV>uXU-s=e^+}FJ$2~J znM0?TXHu)cvHM!{<|zJ~89sAnWE$P@ zHTP(5H?@;W1>7@d+`!JYcT0LT(%(PQ{DTnRZS4{NJ)0xjvmnSTNbB^o=+U#Cqt+A+ z&veYsNx<2Cm>-pL;8F_`#)V0affhM(682i9Y!@ea1JQ*8m-Cv2y^5Sqq?5*~CGry! zoGTl8_l7i^4ypxyTiPpOS``Mpm3sxqT>+Y<76e9{4G$KX9LLa)8LmgWzIJ{>B4ewP z)HTJ`1&I(VO?e>Yesq^Tq7zyG=ttE)UJ!p*c;|&7+bkYNd)QQNC{MhejWgV>^9XW* z>(!AJGMYo7?C8|7>A5XkP~V>{jm>VtWe4~^mPPVf?~iNA2u|2T`{B`Q+K~~q{cVrc z9JV14UoUWdoH=XOn@Xl?zQ#u*zs2-2Qrz0ABlP};8S4*XvzmN85P}m`G7&f>b0BSPVE;lJ4ekZyLwCY|O5D_Fio9|y zKQL`SAxN+Jgy_HNNac|nhLzKQ>$rFl3UF*nOVIpHA^k?r5B~Q-ORoc*J}p z?go)DE*k$r3Ub1`ZAz{`#$IrJYb`MH4-L(mEF32TtgI1s;vZ!ltm{`1Ig$AfTqE3n zeHROC7qCQq*aIz$rb-<$4Gd|*5hda@UH`D*z&BQy4)hXZgb`R5h)QDwm6-w?<#D&l z+kzKq-Dp@if>A2p4NED)&)mes;5h_D#uKSzbb}u50DdohDvO);ig~{R@K45^5ikl^ z{{jywb=m97=f<~K1*^K*oNoRTUxr%(4k=sk@>A$lj7h*Z>WxVX+&uY-cVJXhbw%k= zHJcjD<0$|Yd_|er)_N&y%dJT`mr2l#9VXIc(VJP`u8nl)or9sqk!Xg-G8teH41(Dn zfVu8107Wl2@`4BC*!KOu+;PVpH{2M&rXJW_ATV^pppMW6d%eN%m_#O*3L>3+CP6#b z$y0}-lem@Y7y5pQsAq?jYK{EqDDuWHf95lvd6ISW8~+tjv`(f>34&s<_jxuJ3x~xF zw-lXP8yd)@As~TAgU+F09)Rs*6R3e}qa9&@P3^seG)hg#jfuvU1U_JMWwq~h%iQGb z%nOq6Skj@mE0Rh@?$R>6m6DG>+WbW}U9Wik*+tjP9qC}GU{!oOufk?MJKAyMD};jS z4}Op#;Cp&$PhZE8Ye!{1AJ)zwcAmKrfU_=IlU~_#V8c-RMX;4_v2EL1voSmBM3?U! zbG_vh?x8lt86S7E`RL>c+AFHCKWN+Pb#qWk9HfY)f@Pt2b3W>3Z$C$C$JVx&>Ac<{ zyM|;B9nrg#xl2w(JJcgc#%?W0@Kx|nb`^wYg4G}?6beogCf}@sF&WbIW9q1nfA4yFm?Y1>g z>@=UYR0z3>V0i6(RVuOa0P*vZVCXg;ViP&$il-I0bQp*x>|Iwn%^^~qT^caTL8ABI z3a7#AkhBkdLVv30X~Wy?5vR=^X{2=NA|VG1A)c_E*w?+?&OUvL9pc~qesJzAKD8}< z*8LF8n(#8C%`+cnOn^zkKPLPS6fl!BBG_8jP47z^Fny@31yIR$H-7F`Z-c>7nd zTph4SXwz+bun$%22|1uKbx{#KUEQ>+qw)R(Ao%&$$?<;^WFWqJh^Hp*ooAMdt9T<| zIapU4E)9fkf%+<5N@Pn#0B`T_C}fSUqwd(CU;BltGgUn_itRmvdR{ z(^#Hvm+C?SVY1_!#A(g#C!c)M?|%|+;61@%4tR?u+FgPDpYlC@%0ax8urWLZni?`x z;IFLs5m4EM$c#jT`f0*D>7~ZAtWg@~Es9|zoO{SBrQbBKa|2k>)qwi}ujM}c?$keE zFPqol!QHxda|geinm{!gt$KMU_HbS*=@s7IqqRY67uxZcPThRS7xERsE%^fQqUW~R z=^B*x=n5c#5rM8jsEq7n4;-BaCu15ERZDM*BgL~xyr=n!_q^vl-pO#SRx@v|HQ!{+ zTbpk*Z#|3*3i0NzB;BLt4M=+1eB%ld@5Rk+NtZCFd+~5Q_iGbnI53(GO20FVv=Yd$0KR!Og_5cPi+KAI07v z_%VrW#n;huADSvJ#1INmCkpf7EAB>D|Z2Gq~a2ytHm8oA}6^)PDv07mZw_I ziGJ$Vqp#(^@_tLS5Q^z(a}k8aXD`{f-Ak-T-C5}m*z%-l)zP-0d;wWku7{rwHAM`w zab@+YbqVVi%T-n|ptPlZRQVjM8#@tF6RCU&b-MW9Y9i3Zu3O*X_OUOzOJ{x+%ze=e zBN{EZs#orM5rdtc_m$_JuyT_~J490rd&NA#?O}gSq3u#0d6nb&OM3Qej8uB!iRRy9 z;qx6ibjnYi*Sq-QTj~1V$htR|IQ-%lvpQD$vPtuw z-uT8hqJDVg2jlmweK6{V;~xw51)YyUu7>T$0d+@g?J^{$=ZL-~DE<*$jJYuK1orWc$2~zWq!}O7lx!k%l6w69OP#m`Rps&kPJq7Mvf&$`8(j z^UDLXb89o%p{)eeP5qIPH$M2_@#DrBz6ZE^Y;1M)dRBGoDl*mM)m;TIJwn1XNOH^j zlLH9vExgtr3?{dRvNLOQvjfZd@C+a>dC!nzjvs&U!8eXX7@p?KUH+4L#xJUHMzs}@}7c2_j z?0od;o>#XzU+hZJ*m&lb>%G1XI-r-kzAhwUe?xmeYP#BVv1fHX``xQGd!4=5={%`- z_Pp1t&*IA$uDy}!_qz+e>+`Vfz8bp3pYi>Q?+>x|rv&p6moL*&Tn#hzT_O^3UfPMRb~^D45>nMQ!Ib>`lHL#~HTK92gka8kZ*~iUp|0(K6&%8~Mxm;bB95aB(b=%?$*Jw6#WF3?_5c8^zUr ze&Gz-jmSp$5C3&%b9Oo#Dyg#Snj{N9l(O(FQKI$EW0`ml>JjWG+5mh!&-bM8iI@*- zIe=hD$bfN;9291AO6HqFE44oYHJDf40RixyaOpSPE*tf4D&RR>w_)D^CqRY=7oy`A zsfDvD-@qLb5`Cg37W7v62|J>9!a6azelAoSa{I95Bp8HPQPs^w6!9On^v+E^F9r%i zgT;flW4=So5qrL9Zm^Zy6~C5BHj%yH%1YkLZ?d9~4UL6qxpEg{*q#!m5G0)N^wL>u zpc&*&yW{bKHNp&^VJoofPUz01=6&ByB~f74V98Uu6op3=hxqEBIRc%)-2^^c$h4-WttkRJf&@h!&{}`g(sNY18&=5-tyiTY^%(b zHy2h-%o%v5GCv4s8?Nb^Q$i%d;Na5ncmi%$x!A3UJ_?*fDSl>N2cY?<0|NtvxMN0m zb&1=T_66dlBG0ZP5FeigPMe>*UX-!-ao?yn5R<%trJrNz=UUeuf6)>Rex?LxoaxH= zfZrdgEiH{shMe1S@o?rH;Xp9*a6T5!yyoU*o$4FG!n!$3OfDA5)DImx6b{Ys(IV@a z9GID23SB>ki6dG?rixy_BWxt`T3;)_?-=|DuNTed6!3}hL^}ejU^K$}G^b#?s3Q%y zcj0rd*NgP4n8sZLoQ9kSm>m6g7`E5Rj%>f*Ka$HufeDJm%0#t#Vr71=y#1-E<>mP$ zWI~vRn`8gL7gwUq(eW|tLhWEju~h7aSU%tUHqyL|-W7@1=f8sf8($2%zNdi? zCWTYAy`-?4hPX$~H?^cBuf)snyS)!z;?~wlY{;9fn@9T{=j2Jp>2JQ>Y$ZqJGHwqh z{mmW3o%9C-?|N4t*bf2{1P!?i8q)BU5pD7ScExLOhhxDsEfl_vVWn#d)rivsNYL~m zF%$`(K@^(WJr z7zQNy_U3PhqeTg@x>>R;oOv1xhnHX#E?K_JJbc@2w?&ZhEDo0A)=)GSO*KB_97^Wj z2sD*Ja*_U_q3r2Us=9XvYYs;_;@BoC;$Vw$eDw9V!`DKW#NMv0bQd0edJWDS_+PJg z=+#od`@R}9os{D}ZN)P87%2$21#=U@a+xX8LTqR;6>`*L#n1e7~i#9+y>%_KGut~zdP*;aI9+TYuTeoZ)={qFi`9{VrPfAGH zaZA}Np60dDs#Qub4Wbf0%v!rdgl%4}%VFCVlIBADs;-AUM|BMxmm84r1oC!Q>kWK!F!7WEXj_oXA~Yk^CIub$RNO%VA$|tb0Cvo~u@~b+eFB>?4oytX&rePa zH6J-hF$d*qK9|eS>ani-Xph2LkRENcwQlWF%2XyMm`sF6fV}EaynO_8ZvXl7%J8N3 zVrb8u&(7Ut-_}*YzGho}`;b&ETlBUtX3%udKFs|hJx_C*#z=F)QbMB#xdGZ5 zm%V|+B=i_upVc9zk;D#qqb_w{@k({y+V}7&&3@7K#d;KsI1*ku{yY*>q|-Y*nBh`+ zG&MR)otp~2yUJSY5gN0$N8_`1A2>jNMkBt6m;d z=paOxHeu2wJINFxJI~0km$$f)Lo#SGp;$Wo5H^iT7Dka1x>zdb3tk9|@dmuvsGBiu zDR#w`UXC%b@~dK|4|%0WA$2Aa4`IuZP{st4MN`O^JtMI`{A^RNFoXrNO!w z!nn%u&$BBFQD0d@)ruBSt+{TpP_X&;3WZ50aAFdEucIR?hp*FJ8O-G53FxVvKsb&V zB+rUx#^T|Cvn*6usPpoR29e|z`FvcMR+GtC4BjZ%`7xUr6#t--9-rGkHGRltW;rxH z)ju?9hSqC0cwdsQ-cVZ~GNVINVK*5XYG(x>3PBK^J7ZaaGjjzK2@k@i*q?x+Gmz+q zb#X8pzF>xihY@QeN8%3Zk@o;!w6D@5OL1Ti8L$X`lJ0ikdUvz#stzCiZEauA36EF zMguTeKF>nZ!*x}YAbLs)t|^gu^5!C>doO@~&vI!9V*vSWZi+nTv%_*(O6;$jSVQ$_P?>XTH9vc9f*< zqbltD?rm+K#%^E7-c$MR12ca3F{g^d-h}-#|2105Rjm4H&#M&3=*p6=aaAUCrcJjB z2AxXPrs^b8ui7qg`Ey$NEnaVxTnKRLI=bIgsTD_mky=X5-3+y(Go43{4)3b#?X^Ca zg3?B3&i+u1BYD{iGyl9Qdw%+%ZU`&w?jmlT@fx28KK(q#-ulp5c+fyug$=nT=&ut_ zx?o4{5`LiM6F8d2+}Scnif6i5Yq?*r1ik~xLa_%CHDd{Sx!nXVyI@i75?;ZXi0YlH zM_X0}{2s;;+gT+N%O^6Se7-Nx4^vEt)|-Js&L1kAIez?1!R^cE2WZ0y;bwmTXQ9jq zaoSBP3EE$S+JCf$(Hq zsP>Nq5P*gGWpQ1BE_-5jeCX?H-}%O04VPzO;)G5av}}2P0o0=K4K>Al9XLnUGH}l4 zd{e#`b7ut4BCVV=S8R8pU&iziUk1pepb6kYy7>?`Q*FhH5~cuJT&Q@~#qtH2#(!(e zd9xE49UB^!2>&+1M>>;uq7T9{q->p5Vqs^r4?;2$7&tK}k-!1j3gGr@l8LG58@IWt z8#?lhZR%@Naew^raHP^d&_~chJ$rov{gp`gaol)25*>sdhoU#}I5H!I-~N6h`XqzA zpI`=)3!(@`FCZ(e4IJAF%@m@Iq^f@CaQSf)RjoK_Zr?;657c5 zc^dOm6&)^jz=0c>NI@%5+f-Xbe+r<6DbmT}w8_fh9y!avY&@^Yk$@`JC4CY+Bd{eV zmmU_RUGHwq&|{&l8J$e^1%e2xiUwkXZ|a!o$y6Yy*P}z-@7lTRp6JOX*1@{ULr;Z5 zAxYrq0YUrT_@FoO)=z->yDwtyYw#Vn9`|Z+Q3^~@W#6CxY)O3_5z^6?e`&Yl=tW&6 zspl?(#r3u;H>G~X68ZTf9yed@CxzmlAWOV7p4iyfw40pQm-`jx6e` z^iB51x`w{d;=oDKt#h>hv`H&RtLwFW_0JFL+S|X2t~GxjeX!}ryuMxbacFGcjBmhO zd(4=D7Atps#X{4iX2pIAVIH3JmZ`Y}DDPTI&xjl!d$!22uf?050*pQ6d%aLJ$kw78 z;<~0D$o}e?RihJ}RLI1RAR=sXXAoX>uk^(%wqrakkAzJzSRr2^!R8#AcsLmF_mP~X zL~_U{WN)&e!8A6fXPo4=oTt**r-SDS1i%9&VkaEX@mmyJ^|+@UmJGrK z{}I^wKWkhU>DonR!TX~bRyL6jWOGAyGqj;xHjtm-S!R@NNv4@n?s8|frOpq&yw4v= zr5$W56oJbP91DPk#qP4_)I~;FgO~nROHL8o+KTw$AXx9+iUe;1?W#|={!U*xet1R6 zkWL6HkQKhRBO^ybv1Lomov*{@g27H)!0s}^a6J@_hSD3@BgXy1&JJA=Yy~$|Qzss3 z?$Q-i<()lSmlPg`Cog=4OgtWgyGh~sNQcll764nPu-EA#yGP%K?_B;Ja~ZgpmYUmW zbDf0UmsWMMz``aIyQzu}{ja$+wBr(n0H++W748iz&C#2PE}aA@#+={{+twbZ;yA`-&!6`U7mbP~cP1VltDN$qo+Ws_0xa z8i~l^%jlZi{bDNrI{2{pCxWRj7C%xf76VT4R*YpS;g~mq6)Eati5zN}&SpP?O2Xk- z6o+UGd-ZTZ} z0Qd^LK-&6;v~V6yF#0(5_;DYP2HoU2fHTR^xup9@B6h)bFOUngnP-}NCP7)jHv*l{ z{aQaKfKq|PT`?N8EgDlv7<7L%8NCn+U5F;ZQ+q2O^~A>m5&di=(Dm8w`X|XhQJajf z0G3&QXXqMX+T7M|Xe&nv@(~~S0kq!$o_5V?9V4@Dfym?UTBKh{CDl9TXP>rAV6 z9UqV58>9`N1@7WL-810xZw2mJ7av+HC1YBYWkD=HQnxp5?(OvS(X<~P6j#y1DU$HT#(N&17TA8KHl zf9%Ea^l?luv0bCyXuO=SrBZRc7>oV7zUCyIpuEd*WBjGr13qTn3W<=iOfG@lP7=j9 zvb$_NuVYop6)I7&^UAD74Mxx@+Re=yuo1$3u}w=%ENO#l{9GVdSCx=UiLFKYbWG zd^an|*#iL8((T7cs&NCbymx$e2(un|L&dd zd}q=)7!N#;hbMx3-htVOaQ<6ZPQM4beHs|7TrOMPoCnkZby`5J#A(`SUpbzjV3rP^ zsH7$>4j<&IEe?Cju(hkB3zJvJT~jw(bKhiu)YZ7IyFArj(v}!TtmsE{lW61FP}APL0G48;n-f*_|C`-f^yacTYP?QNP5l zN&HNmU6VZw>sP-m{gYfiTZq+L7n?3w1+J1pv8KqLNT{v9(7;S8sM`9Pim7*|bAKW> zKY#4l{Cv#r=CJ>KyEB=4b)$mab0#VqItATl!tXc447#yu0XCo>^RbWHe0(J`+yGEwK;&@ zLGPX#8Vr=|+a}b~vOR7~FWvvj|FQqV-D9TvJEp&X>au|!Qnp7)#_!x6hTt2*9U4di* zy^V0_9$P!*zQ&C`9doxM(XU`TNcStz$o5pgfAl9|tq<-ZNhg2bcf=nE243)jK+t?J z5_x|Lsy+m$_!6o2&-4YiVo94sTV~N0@N!nsP9tOZ7 zJPhcsrCDvI9=Qd`NH6Y`UzmnzQwMO9@k{q~340zwD&YafNbN%8>;Co?@{;95l=7%{ zm>{g2-7|i>Q--?Y9JAASzo{X_VsmavOP9EJw)|!f2X_aN_nGp?AW3_fhQM_9jk4WV=?op zPWM%T$$4y&ryDfC_ z=a|dE!*?B@=L_&7u@YhDcua}6jzX!BoPFb=;<_?nR~re)o3;fzR~rYmQusk{3|s-$ zI%0jlZ$$+{ig&dd!MUb$Yc3wCJm=w0c(?cQ@uvM=K}nf`tr`- z?s{L?<+b{*exTQ`Th&xw6$$rIYsNXlAJVM>z+7BN+ckX$}sNnZ9E=(h)y7?f|Uc{pvi5CV37CZDR1MmPq4lSee#(e(!Vuvud z=n=S{Ae!iQf@<6AruWz)K4q*_VoGlwt6)<{?;Zh<8VW@}8U>|oEsAp`tT#%$9s&*XE^|MUSw z40f}3#YbYxWU|SfXmlr;HBW?db=g{RauUW9OmZ}YB&Lap=I(GBfHsULXoW?4kFOr)7Kl-3SA~A$o5TN z6}xodT3Pk`+r%ADh{NRYjM!FS1`|abw}>w*0PDE4E$3SD3{N8c64$NicnDH(65tL- zVI#o^X^c$kN~@LiL?Ajo9t|XVTp_Hq^)`Us$%8kc11K+JA#!@uVUmqO0MvAkymK!( zv$RN?gZVrS6^lz}erKmwLOY0dOK0!|p5zOH_Pw>VgE{$L`5iVa`ffH}@xAgpRQ&1! zzYcl$RWT`#DwbKbK#jX55GfE@O;cmR#gC=OBeWHtOsA2|=x%J0-qKu8-`#vqOUXTb zxAy>5+vd}W1R}QYo@RWgr^m+A-)*8<^}cT>2-CA*SM=XVQa{=M;t+ zNDU)6Ny!TZ4y$WEl1)63iro}VPOeNP(yz{@o=7BS6WMz@MfE29pz5@$;Q&|PCsNs0 zr}6w`GI~=C&m;PEHqqSdR0WjN={l`yIC%PhY40ZNtk|!oMp=tElqw)@5X5#KUO9|u z=f7NDbf0;M?r;qyO`yJ(PHW^S$1CF)c>Y_JB#=}D)M%+hh0@CayJ|07=Z1=f#L=U7 zLRL&99Rr)YW6m8pIzDcS1L088p9s1A`Kvx~G}aeNoGl0933vFILGC46js^bG1n)e0 zG*KwJp@csf3J(;``1sKy=ipvw9PnsYyPH6`av~lmpG}1NV#kJWZWmpQ8RJNHWz52` z(vmJQm(oq*Ee3@!{q!*5m{JH4(+E(eu9Y(n>OMYA&pxh~pc~-m5GW<_))c}LSj2ld zR53j*nYcIsWN_}j^UgafCU|POxRA&dj?LY<8;u6d!ilGDxc^dr(KsicIJW`cw zuOOA0r{f{>%{9+o)fPy}PXi4gf*L%Ueks%*&CkyVoWiAo6PVvIB|W>yvqmX2nP6oIQ`E|OJ9i3AHtpzryVPa6#$pHBu*WZP788_7lC?2FHaf+)7jmynTr*Lp@Tn@Sb4>-o6LP&tlxn47N7*KX zsBo&Cfr+UpzsaO?ySX%W7V5|Hh(vy1YGMG?K2hJCot=f2++?6=pWPG3yuo1k=&|Qi za{2JkP&l8fY*g=$Ao%GAj+TRisIV!oV0$#`+Myefyb*X)>;T_~eXCyWd!z5|SL4m7 zm7v~&pk#l^WGiUd-!;s;&b|FrT?gOeJ^V7~xjcpnC3o!#F6btG%l1}_h3aHSDF{Bp zf4!p`dcM@?xZ0Y-UCdz}YpCsE-=VaY&Z^6y3x1h)OV#kPHx^~Eo4iVER+&T{_?4XH zswD9DkV1-k7x-^!?%m=?rXuJb$(kin`1>?w)|5E8&5vVpWomgHPgL<}$o=%E-4I@` z>NUREoW?t|G_r7R(G}2n>n~ab&vX-dBMXM2iFdKL263Uztz?zE$-RKXqq6mT{u_ek&lqlo0w#LTGb$)?DC= zLvKq*+(guI{FAQh!hz}TOzA`EbW&7mnOHC)LNx{J=Fi#=&CX(du|HM7D)(6PvWj-8 z@l}Hjatbixo~>e=>qcJ7?w?UvJ~FznFggMieKCx&bB5wOu|ck>(C5(H|BJ2N#eTBI z;xM1$)5xZY2NOeq?d`x2O8=vyVHoQ+jftUcC*cuBn_647=&A$1FfBj@VyNXWn664$ zIaADpn3!RS<9}|sT<(M$7(F}V%YHc6p9|#{7I5eXqna+J5=fTjcLEQmnomC*V4^kb zol#7ufh=k?6&XI0Qp3n2h}9r50cT zX;`@00aV>0dHAt{srDv{Q#wI3*ZVw&9v87}gddlU52%_}Kl-2~I|GpJTQb1URBX8= zNa@|B_)4m|ms*KmER{;uxM`BIdg{{OpWy28fomq(p27R>On2!*Dl&E9!c-)+Ws+6Q zE9SVGG_0Z3WnD|uIWz!!HQy_+i>{5vRlK0PqO?^Z78B?mPs9V(t6{Ay_*0!56p3)l zD1Fub5{c*rN{U$c9G%z&Z*HN5C2c5w_@`&QZg#uRyPs|B?%HUHo%)WxMJB>yB z8ml`f;<*|G?Xq@ryjVd`WhDcuU3Qx%kWi*@J6Nf8JtLPr{<3>R7zG9e1@d7mp%KO6 z^q)=_!A^Tdil2yt6Oq1f9Fb5a9CTK(4_`bHj)Yt!r%F76zxIVc=EQQ6@e9@kN0h-A z@#tHbof{mSn_aPHjq6c~F&d2ksvEwDMvR+fg3q>;3DML#4{opxBLFdl>p#9yQ8nmZ_Q@1_x$Zf!3YU!}Q zPNMJ@YnymWAwjflWyd<`c?nqgW@2TgwiG0!{v}MYOzB4CUdW39`pW=@IkVz5fO#Xg zro>Wkw%B9<-cBtX-W>6=X*q4=tbm zR;Pq(KFNpiHVF31M}Vcqcf;ZThF4E7&l&n8Jp$6|4D`j8E1^Sy*K~^098!q!o=gKt zudTY{{ww^$_8mI(eqSBWorB(_A32a-iW$(v)3oZ60RYjFE+O+1`mORYdk&HWVUHRC z>zT!;$^LVaR%-!~e}I<&(g11zC%_Zf3aHNPO^uGGzhaJS;L}PpHdJ0{zUN!t`j(Rp z^!e8&BiQ&Vaum=PDTPhsbi_m=6DDn5F-UVcjpl>x(oQa0`s#6w6<9fa`nm!0I4X+9 zDkEkBxkLGOxCBjX_$Y=m8lGD7kB@!?brc5%idvgj!k95f+OEh)F=)HyfXSIH%ZG$! z#8MoGT%NtbiBM~Xp3{!8u9^iGqoT|_rQP)!o+teB;q@BCEPaQg;rM1|gG0mX&s#a{ z^o0j*zWt7K6Gucrc}iZ>lPlyS}E)X5u0{70vzQWO2b zrKQd4Ehoa+>u5=8ZS34xBo5Z!l1)&Q1h^hien1pqr_kg&i`vbn|oW z_Lu&As8Tt0?52^@&Bu?GUvTW~#M;`)vH3gBo|$UC)=bY%F2P;$S!F?ta6o0UIB2oRC0Ff7WXl zEiFPQ(&2OM6&YWS8BSo0f^*b38xAgUBHYCN-~~lDkIj{-6c#sM%m94Gi+1e1 zL4|haiYsOh+H$Lu-749A*xMGDt*zEpJ&#fT<=qFAvL34bMg4CtnEJMT zuN5fPdarc_oo37_AP;<06j{(g5h#4WJaba&|a$D5xb?WtXz8KNa8b> zE?rtljJ)!a=UURXRs5?5AAGQ31|NKI&@@VWw!3?sG++YplH2$u1WhhoJB(bwcB~1u zM1X7l?xF7a%yNaw<^yiQo2CGU78=Txe?riJq;f)Q8`3glikSg?Ce5d8Lw9UJug{ng zEb#lT71Nk|9Q8QEsdn}otm%xvomws6HAy^u&{b}}hw3cWcIych>gdEsga=bd-8$Gi)h^N9R?*kT<>DMC%vNfELjfqdhJLXg1$6d(i?nd2qux&`H zgCps`(4R^gaO7a>tLz!pO}lsjOgvcm5C)aq-EYmE;k&Hl{a3sxm5?w3|22UBMrccz zq(iR|JXM3E$q;tcPh)jjG^0dQ9ydUd8C(J}V9~q=DXmvufw*);tiNJ4=G4}o_dKu# z(H90SQxbIgXT!K32+#F9pG9twcfZUZ4EkU8Zf9(KlRhTv>zm_a%Zb#Crt+9cCHmI! zMld|P4w+Kq$}c-NV7(;4Vc+b#3$wFM_cbhcZ;C3A3~YPJKe1^AK=is0>nY5O&JkxQ zFeUG<<6Uv_F9s3vS_LOC?$&MkqpiBlYvxZkMiv&Pr`^!tn}-(`X66F+j+sSb=X5HY ziJ#uQ{_{wmje(FY!~`<8_xAQAzc;cZ{p!fTZ%>CpLnDieGg2rY?zi{qnOrW8#Iot{ z+jz;BpjY=`QRjxf{@~X<9|ZM{>;-_FnI69WMd~^@0xkPW!M$dW|3?tC;aMOK!Gwj7 zhimux|95!TGj|+%1#KA0cL=<(Pj$qe+%5h7J;o5*)#gd)U)J_2 zMYjp|)G!jtEsS&e?eb))ny*5iMYfekl{rypg%x69DBFP=l>3khxZGhBU=482C>}5WF-)ZE9J}#_X9@9{crFIxPzRhQapa= z=`Y#wd&rK&S1eCpw(Ss@lry)mFz4v;_rT4@e((ojQH;c31mZVSS@?_KGr?dM+ixIN z6yJ`)(9aUgQ4&lA%+Oe<> zb)oS)LANyYXd>nM9Y3siC3=3SnA(JNB;u+(yuQ%BBQZC({O#ZVZ8Bo|BS{ocYJMe> z+!!81WuiJ=oR}D$op9i!haCU{xjgp7#dZXV#L(#I#KdAte#IyB$%)yn>&*sS-BL&s zf&Z%9EXLFI3(PKYFH^el`XBm&f%sDHL~e<@{4VqC{Rx(2 zTw}3Z^XpFKG~Z+mXJ!~@457I?Y`BH;%lK&WMD|3IFMgn11wce;B=lXozO%kZ&_7Kv zuim8e*CPzT?W;w2R^jc{edg&rkg<$``dp?J+79@l$I|IVum}JqU~1_(^j?Y(smDSi zNy-bw@vPrcC;u1P#nbvslPm%rO6~9=c;_%-2ufR_aA+E|C%h$BECeN+lAI|Nvd_`2 zs9nZKQ7xh+N_0BiL}DL%PYM^pNZT+thyxOE;UgF;^0bzpnD9NfMYFo8mGuU$a3eUV zr-9F|iCJp1NIo)44K!|Nx6wF=Ioc&l)aL9(kOD?;G@d3Z=;EW48{%A-(Ec6F_35si z-qq{c@;jEMP#=J)l87zxckKyf1S@d@rqJu-7F)a>>B}5Oz;fU44QvAT*7Hu!v#A<) zXL|*#gll`@%KhPRjhIqPjTW0&!t_XYJUq7N)WI92v7_ArGCW&u!NMn6v;udlG6bcv z=d5h`R=AVm%Cnn+QmHO-Ppdg4%2FMiX-9EK=3!SFzH8m!@yjmPc@<^CZSN`Z)}Ie^ zBJo{Uu~%~wTu;`<_}dbxoRUhGdYh)VNhQPz+A7q|L1yTO_#f%Z<|dCq*|jJ<>MrJi zeqE;W!^Fdi?2rOI>pvkqreKQg3^U8_+HCCcNV(1@zNhJh4=K*rrOpkp+Yqf;r0vnx1w ztG$S_N3(-pAZT6eB{d~W{hYX+&O`<~TgFFifL&f+7(^Yi0(-$_(ljNN8n^b^V_kd9 zs9@#mhOdE42uB&E1%DD~Da3nQ&BE4~d!=ltf|xSeIcWj{6}U(vFvnx9q;p&He(R2J z--RYU)OYrHXeiFE>rdASjTLpwNgwqNf-6$+bv-8W-^zdMD&bUk943dA%Seg#T7V)F z>65x}@@~-eZUP`z)+um7voB)!76EX(B_%=rp{(P~2N9_ITOkvvEXbzlx#PCL8^7(E z2!vg}DV4M`Lbrsdy~`2f{x%|TgE_-3+ZQUwZ6RV%F>aubZMk+pY%%J!(wzf9@H3Sw zY3ZTDpaPqM8o_Q~$%hf`Ngr~TO`9^39bTBVwu#jW-VDiN*M+lNm2Nv$lg_%*jY zO_t&sRu4M+jJ;^|M#op_3bbC_8ngrJw~Ck!=_jkX*S%=1_3^TA2RHWHG`L20h39;!@8!PNb@-tuh6HAW zS_)VY%DbLrj~l@N3Sn0j4u-WR);9z!Xa+m#u@$Q#l7aDTij0bZ&SC5Fc-Lhq)cO*S zmr^{YF7kL$!R7+XNXe=;sfI0h3m|(-c5sC&9N6|^W91ewXhm_a%P2YeFJ+^Zr$?M&PuzoRRcEF7DE1h5?U780RENl=RmhH z8U-RWU_c9Dw9Zp2tFT3?UX+;N?rV5{-+La<(v5$JXV;WF1RPJQ*xB_hKr>i}Wq8c& zzl?#I&D(vV68YmEq&9k9{f=;wW=jFExkC~MVque>=w^^ttS8v(qcccJGG@k86Ed6M~{nbSsOKw+?LiF9XO@Pd};9f5Gi zyiICzIIp~fNKe2PoFy+f9rd(I=F@Bv)ltc*8h_56K-UQPm&B0>XIgg!1K@69tVv#S! zBZuT@FEzj2{2MdW{F}5Bo1Z&&Y;HczFwVG^V@}!({oAQ!p~VA{_?IHFLvpm2p5vry z6>d3FsimBflibjgjU-4cCQ4#`$eY7=x`wsTbHv+YFF2h&0^cBfpEp-qso}06_C|VX zbbeuk*6TgY#l0Nedr#eaFUE7YYdpPSUE-4?3-hCUse3u_NChK&fW!9DrsOkIU|_7> z4lzj`W)DNpG|xMM36!P@T~rKL%-u9kBQ-(utEw~zP7j-+>}d#}ldaN^zVChSTiH5q zKB?IY!p*M^&jb`BnIhlA+j#8T@)*WU`7fW@@s&UgpFc(!@T2Adjs*Ohj5JRIKh9^s)) zw+Pc`;1lI5g5w8Wu1!J>6d?VE0ae*|P5GpWlk7FZIq|C|ESLmDu~c5O1z$9pF-ZSq zVi|1QT&Nc4sU|@-a$>&Hyl6I?7vaYRX|DNx&Cc0u%$O6+-!&(4sbuKkK;Yp}GL>uo zE-An9_Wy1E6VG6t+t?^L+`p;5wTb`OC(&sL3{n0>1_PZfWi`+p%$I5aA@8eGhol=h zP?_znYKl!&hqVGbp=HNjYUA)1z<;)6LIah`>};hnuwez0wmi476&>$`lUiAKnZ(hg zDkCGAbUHIKQfZgAwMt^+Ht(sD_f9H$Tf*&&--;dV(ppUie9nmn?7MA`8GNJt1$^34 zJKcmrD$;+nSQN}0M?r--U^9k+yA+17)WH3bqf>T-*InT29vnBQEdrBOJq_HaMZ&gC zl&%ootm`k1#+USLfZJplwpT(SClkZQeW`)R22z=DJeF}np%uNk$wxLJ;#uwjWv|$1 z)o3-Y_$uosH3RD|+`I55H|G4PG#M>2IIkE@;%olgjjTv+my(9M5v?|5KLp$qg&g)8 z^C9G~J%ce`2b)_H#@Ex83h@h)3`|eeYT}FoYnJSe4v-_}lv_|TkzjB%SB?du|L_md zz+5mGNIqFi+;K;u_+%3KSm#WzR;$Ibxx$+wk<=e0Q-7F3TF`th8>>0yr^91o;pZpf z@xW3b`>uCo@e_|Ho`2U1U--f-Gm!jJDjx4!>BIl=cbnHQcW74#-C`Y zh*v%ofKxCwvUUA|$ih@5Tis~SBxbJbWJ{-1k3P799y)aN=%GVufJFK1>f|8S2X@6p zCOV{mOlF?$e512mYJm%`5^uEQV2i%%Fuzlv)P@sf);p(cxOh?v9V1 zfEN{mV;;Kk2h5wBA2WA0KbAFD_Isn+84Y{ikxlKIvwlbOZWC|*y@^lGUD-o~@Cx1B z1Anpu?gV~8o}JYyZP-*GWh_|E@fzg=NF$a}R?VkcL#!5*^9+muRvJ16q^()2GxFZ} z7&OmhVas&zVt{xX|8_89`T64R8w9 z$Y8<`UWso_sl|Hj=kvp}la)Rv9Sau*KU7*+M5fDNCLRnU>e}S+XHn9|(kNqTfaOb1`QGAN@zpKf6wqSs8Bu{)Pg$gmC2>^JNzt+Z z_aF3lvYH{i#R%Y|j98uw!-VQe__+SjqYsk*L%`imDUi<4U3`T2#3H#OgD%m$z1 z^RQ%F-`C*@EIGb3_+ku+fhyDEdrp~80|(qrsVqCSw(O}`L^^-MMws>zk0$*S$88N=2GKiZ<8!N5}ALCWETRM*CkDiM;HKy!m465F>q?7)Ajv zv)lKuW?+SOKX;*@zJML+J;@(L*mL+B&*vnyI))xx3xSkCXw3@%%E}b~K{hR5m1_{@ zI);iOnowv#E?nO*KYVwigt-gl``ki09t-)6KMW0ZwjaK+Sme+@C7_CjS=Y%082aKk zNSxsMu~0)}zVXBN2P>NqpD-H-`=asEcrxfj$uwi1VeBAK z$OZ6i*N|5Fu}yQp^hIK&N<83~hnin{#+gt)o4yZ4SPtRcV~9k7dc~&Wsh}y+f45_p z>w^>0DagI5ph};H|Ln5>vroJ3*1)J^bxzLcz*h5<&)7S=<{R+;KJ9x4R#T^kDe0n+ z8RU0TJVwybWWG}^Pg9Hq053?O(x3=NQ1Df;q7Z!m5n7&_NCPX921>vx;gae<-nGg< zso350c7`8%AUk+kkaTGvSyGX=lmaq{q!TMyvzJ{-qyb-{RLK)*lLIBOEuS9DJ}@km zAUpOwDnxA5&rc6OkR{^3ZK*uaekY9NQ{mW3c4%f3K5IA(WmjThq@)VBzd2A|x{VD% zwbSS88=hDKo+NVl_nk-0r=V+i6L2Q_u6@OD2#Tb9!IepZK{QEa=bEfsNEX;2o0}tW z9P-n6l@BVxW{E@P2`0p?%h*p_cSVAqw`fiAi+RDK<~yva;*@SkrEUeT$;|ZiZ4c*i zeL9e-$3r9=mza5+S;mL*xlrmk-lN!L%j~dkem{@#EaI==p7+?7{C@vg)^%$tHH*~e z>EXw;+vid==Z7}%pG{!?+CU~VeB>tY>8S)cBOG$^zGN~_hdOFK1?0NXL9I*Fw z8`MBBD^z-L9MxNlu(XbW(2><~wNRwe2E4vS>Ht>5lY;BaX*kz*Z*2W5a{^L;gXabg z+TwcCKJxMd_PXw|gY}aHtp;E0wUc%0<^ele!_Ei&n`r{qK%O_N;22a^$zhRDXdQPM z6`*eXgpVMubALKJ1iD5k$YT)kuK3L7b8u#GMa%`jK>;x}b@=eqlw)2|iiRH>!9)+9 zsQ}fagFhBv3%A((z>fvfPGq8TW-yf;ek>dNe@APPk4AsuuaY2@R_!r`#_CX&B!_-Mk({XFzti{o}2 zrAok{s!q__@N8TIsbUoqdwfw{2wwZjsZ*zxQY4<Iidhm4?yM zI$F#MutJCl!q}wnWw*^K8f*bO9X+CQIb*(+!P#y zeZ??nYYvBt=$}-BVx9gk3TUqageA2DMLr_Dmb&3*RzNC%+Y9!g=L}ZTla=Y~Co2=# z@}XPKWlF2A0mgdcmRoMQGX^23Rm@!fhR2RrQ4rEd)H?oCLxbbPL*--8sMl92BZaYn z_WFI<_(&w>Cf08U`n)&crXnNp%*~!ImAdOQz~QeAy$g^);->#?%%E1LK^_pYh9!o$ zYId?`=L^oVKi>o%G(_G&maqo~Jk2I>1;h@7&SN_?9o5-+7^2rb4bcrQnR%4+`YAm% z>pn}=`G1m`Xzc&madtAD0E6JcQRg*w7y?T&50z~9q)H^A<5zJ^vwjxaek+w)uMQ6n zRMQUCzP#!+hf)t-{WJeexl$>1-{&EyaGa=HsSFKO{gb6?rCdy_w|Bn@Ep7+DnYgrP zKgcpupEv&gx}i*?)4F+6K=h~=o?dnwa^x`!!L|L?#OmKCl(!y1k}KN)?(F9sw|L%fR99q(<`dhb~_u(83;mQUc|K|NS{Ta8Cz$>Cu%8p~4ECis5aXhYXFs^c6AS5U6_ss~8Ue6{t6iz}g!u>?-@d56j0s+>0;l?V$*6FHc=< zWNK>O1JFP_dTv!{uK>gKV9q-9&-QGc%)J9`A}SAOIK6n!rloNZ-gF!QE6B?XzW;d7 zJ-A4O46XuOjD_Z1fVR;{6Pil+7tN2sr`>{^U7Soaz|XF zFbA*S@D$H{5PRk{B1O-u3!U>4lQA+f_d;G`bGNAmtuLMPLH=Ni&m?bfji}|`kd#*7 zT5L|s9_N*@(pral1$?aTg4fH>f)CT!n`lDwlH|A8ZO9qRyS;{eQH{J*&rbtR>C8tqnYZW*o-!f=zaIyw-`2- z4hI3*#|;`1)`ORzrN!7kxu$Et0-cEv1K(n{uJ`d&I~_~KPv_NwI3%>qQ~015$Iq-If%sVSZ2en{5tz`wDUsM?EIx_nx>A( zG7x?ZT9Sgyu?Tuj-#|~NPARhs%}@^uzb%}b=+JzOjU~})>6pOLMTd?b$t@x$*Blu> z=;dly^Ymg12Zu*Y_YeZhte>&e#}ZI-Mial*nwn0$FgU(1OiWL;e(eSAHeM|-p7cBG zA5TIjmnxTRlQoB%5RiC48M_`ZOZ*#(N-HKWUTIFm-=%(-&1V1UXg3{SaNNF{Z8Q!r zaj~k^mR`}Zw!Cb7+80OSF($yh-4%KVGB6SQs@5$_tzY%!e4D)mCqudV8HGW7Wag z@rg3}%n@ie_ehipLw8$GC>kRU(Bp8w%;_>g(Zz&J_)8rAca#v(vQ!T5|+Gp6X0lN-yubs z#ZnUHa61-91*w!9>Ab}oUC5m%?1!>`sOcAvA(uxy8nKWo2=(M*rWL_q%thWGC_uY+ z#o-6E`!28uadnQ#75Km3l$gUQ2zlvzHj4^I$i(9UG0W_n1-}9lQ8wY&cEpM#QmHhP z4J8uT#M7x1YCuO|!cS%pXBsvW8TKY(Ip|C}hWtc{1d|luzNCZGyzgfQVX#jR4zlF# zpq|&?1&747x&%)HXx3Yom}^zW9^((d9oMc?zU++oz?@-@^vvR(=AyIrBzP-Vjl*DQ zB_R(dJ%l)q1t>cASV0(h|g13T`sbiVs(1!0M7P*sz<8%r%>X9=%i{WJX(^s-s$_=U?m2pdbU^;eel4J6;o-ry(BUq~w}9H}|5_oRVMvfp@V zCUH7}zswS8Sv1*sBWcsf@Dgfss>yIJXQdAN2K>XZQ#S(WZB%VMZD(@f$*65&(&)mo&Z*~vX0}1HJ^pJ`lTfm`WOpwL(r$LcG^fO)Xn|~84GKw*_saHbFoRHxFMS@RjxD*gDt3JB8B(>%ZdU&>&7FI5hx+y#=<85 z=H%Rb+KAkeOx_YP((`jCJ&xloJ)1x2>l4PC1rIO4?-S7-mx_m%mI;p#`^U*N`4kuH z5x=-V0*VDr1{S_x7YEr#P4jTEWR$Dt)XKvTKa3ihiTKd)YBah!JOl?q`iigW~P| zW!CiMVi75@5XS>zMd$C4jtk|DFwkRT#xMNOGxPK01l6Oq)%jsis`$%9y)mojcXZ|9 z&Y#;$i`S6W|A|i*UhC07W*xZXv4?yp=2ncp7tBR`;foI*XENGLKJz1nKOKEx34{M9 zSnRo%rgFK|HLm;8T=kZ2W|)f(JA6{0v~t#f9B!%RUh2Bnu!)!E*1Fnn%9)J8#V_bR zBZ#ElmNtiK?U6`AfTBVPZuSl+!31H-lYJU6lrAsJRuG~99%zSGO$dfU!rz{#<4y>C z^)V~CT5k~ZKMK!-2bFp&@qeSfnzY91a9Fay;WBP`Z#?o1IMqDzo}}SU&m7k1|M1MT zYb4+E$OxQj;9n4lc=?=(2bsHS4%YP7gg&O?3bj?>r^fZbH-xqagD#8a3?ymQi)tT$AkJdZ7cCvQJ8-*4)j>TsNlgC2+P5hW()iR6WlNXcL`uE z)TJv6ko)zCDF?IUtynOxx?LeQ)T(P1*kgxr4ZQ%Ep;=+GxZh=?HOT_O(ynK`ChvpJ z@Oir718uHqE(KrOAFp&i7`=q-Xt{4qAyaC3dTw|anM=p3^x{hUigbRSN{8<~G%=x! ziaKB-S1A#!gZ$sP&$bh$`QGX2>2kq{fsr+gXeb($1s31;Jgw`yKY2V z@VNN@xai8MTGSUP?IAZv78c!SdGNRxB|M9@1a~s=L~E z0+)A(5lh-u7)}q#SC!^OulIWY#M^8PfxD&opv`*xjW?s_%9#si7DgLL!FXpRosQgz z^ofnph0dF=x#pU8pLt_+w6P#pqwC&P^%geG7RSd$F&Va|{4=wb+4;O~9b45*HKzaO z5&W#HVzH4)eauzKkEJrBg`zsg(?8dMUaIdBY~;+dve7?|NL&ZL1nX!O>q+zYd9joG zUN=npB)U0?$7%ILv||*zWBlWfBFApOQS|7l`ZwTWX7jlt^m4($qVB0{QJ=V!xR)M`yZ4wg z+SAr?48=p%sn_`|)CL^9pmQu7J<)WZ5i&%zY+Id8$D(2T7s<7~FZ(Dz9?8ChA9rU_ znDRC%Wgi1hy832ACjj<6PCDEE-i0na0d&0>3xp*igL+?x?mE8@S|t2PY+}uSUUoc- z7g|7TT(6|Xe{0aT)1a0N_?sP&z!$7n;(UQAoCnki$Gfc8B9+28iCYC;NOVE4Wf`}A zfK0?zs#ZHNfq}}Xh!d^U>ciFHp~CfrA$-+zX1~Y0YxTqAHejZ?(Njx`tl2IM>a~ZfY$JR|I-)B&Y2E7R|Bk<|P>=76~i+nG9I-ao8X{b=p3Yh%y-hS`8dT(=fw#hp!%T6pE zIkM!tag>7x!JA$-p6z^F!;_lIj`Iuo#ml9^!4h8>WHT`~H8qyN4qv?*JP6+8*`M23 zUn5eLYLy9)wQ2EawaFZ<^y}-(w5s#0Mj8s< zGhxFt--HU%zkYmX-coPZK7hEpm({iNtp}OWH8Q%G2v-cy;#aYHvzlJKLc{vDzm0T=s=ssSI#_OcK>g$g8<^4!oYJPI z&bD6He5SLB@J>D9dRx;^ay@DvQ}T|B(2Y>jtCyROY{&@4AwJq|13=moXiT6Q&eLJoYZ10L`SMD-!oBZhM`?p;V|0{go*YV z9uV4Y$q_RS=B!361ec(;Cu8Y2WdNzn@EUl9kON2v#fWQ2)@rg%938MP;)fQv{j)ta0h!j~t zlgOvWf4OUgEP08y@b@6nb>Mdyv5MOE1TG!7;;CLEY+FBNgw*)l4VHcUwnV}h?!3%Gk+@!T&mjIzU_D==cmW!9qRl*p<=^f9464hfNzbx3Ca(*FlgKPltSqp zHHeCE0|WUy7~&i2dB=^VMu!XFUd{CZXd2ZZZch~kMpH2~Q$fjCgqtYSu436$k;Ef4 znod=chIw99u>m-R=pO(? zPhbR3kL-^`J_K6z4JM@Nd>Hw*BM~?;T>I1WF-Ve4I2S9~n?5noR5v9JJCiOgRi}OZ z0rUoy%J@*{yL>2yhY!Ch@5Bms%J#+CrKKy{k-X&=lkNz@gRn07j~B-p+&0MPAmSIv zq%@7<6cDf2zz+Rv8`SFT6%EMLGN6|W&^!3vEkyq!!JuDFwSs_O2~Jhn!*Fy?J~yHd zu~oBHCX1MgQZ{4FD(x|iZREL0$Zm(#*+fyFE5%=(gmig2=63J-VcAWk-0}~*ZMnE? zhLf*`ejzEc!EZvl(2%%s<|1ZA7+_6_A4i|?et3VeAd3nJOI#0q)1Lvs9X#+ZK##0!PTmGAv6kKe za053(W6w$vK2JcjseKhmVZalVc87pEqTcSVd^)lqh7h7C;8-w(AOH=<6Qu(H&*o_!o#VF`ucD=+ z3{0Uwl++>7E)A$$U2%5HN?4Vba92oJmhOP*i1EJns|DYDJNV{(S9)wuFzURo+kFpj z@7t`$TXx~;^5Cf3mEhcO2ET0YdE5&240Gb_F((g)-nl0ad0F$a$LwjtK%b<$$6b5O z@nz0lE!fAtG3mQf>l>|ge=+SluN%Q;+rfu7_r22h3~>nUCH-sQ^5%Vyc=2b18QQ^D z-UxPPZ@5|C_x`??L>3A5?;`7aj~cbj-RpbLx%a(r^-FE{z5bpP>AUpmJ2!)!p&XHZ z`tQ}_>V2YnpC?6xz)8IhIFwcQ>lz{h}uKb8`+-sFz`npF%)1e zaJ13?S^+tE993y{j%2{Au1+%IY3{7uLnu^JCjs=p_I&5O9*Oo2PxOuMD(HI|wXwb9 z-L%ZvhOTh{)(Y}R;EqH{r$VPE2s1Q1gCKK=B3<75*j`q^uYPO-E+KFW9Gf3x9 z^WJm2?I2l!gCA}YQm~E}@_JJva$i@QP)nP$Nav90d|1D2O>J!lVPnIe2b|P+v&}WB zQ0wUPkX*JIU!kYaZaLr5xbXZLy83h6> z9HVR)B`BR`zhPLl@%F5JK&b=wj?K?6w|)nX1aZDUSXGZteLnKgg}^ydwJn@zfiFj0 ziqA^WF9LikIzq|{o)0h%aTh=65cn7T0`gg3&3{5;eKK|Ww?Juxi$k5S6^r5L$6;Z4U*iUQM!mlWCtz{Q0m0Z>|+^{u%hrUaT{E+5Pox0gB7KVL34Y((t zhrE?e6&l%F&UrBRN)$6IjtpN-pZ3#M*fq>-4uKH1HLh>q^xt~bRR<14h9H)AQ<{70 zhx7Ve^0}T?L_5Av55V41?)TVL$hVkI(UUXgNdJP*cL)N$5~mOt7k|}Bl0vPIBHv4u zuz=(y7gK~H3%N*CUaP@_DSboE526H@?kDQ;z_jff=^;vWm!b$SqGq;gdCp02jRB60M-xNMnsqB9o2A%LZkc&CnM@-xRLZ!JNyCmx@pm6fq+H z&jNbA2yoV&0yv>t6aGcqx6p!QU~GH#oD*L%~MO3U{S56u;eb3-s);YOerVsn{EK>-x28ArCki3l?FS}q zL<@!Jh98W0_uZW*f-p+lWg=q+ZC2D3+<>f*p8roEE0dT{ytOKJDk!rEao&H6n2H!~ zg;WQ0knTXf_*np@S%kidFsFSur8j^mz#E{AM9xnQwpqIp88UEU6R8%@Sdq7qOp(#%LzO!nvt_5xWsT%= z8*O*C#bTuodb8eeIQ1jj`5NidT|{Wz9} zuf)V+S%%$7=ONM~fz=r@kO$=0%$YMY$4*SbGTwKjaxi6<;n_vL1s-1IGk5Gf@uz%B zO`kb~>ScI#SPrMIxn@cqrf|Q0cv!cB<1=M8CdXye4itR+*MftN(Qc$8f;fLItYN^T zKWprj?obdzQv&p{HM;0)B-AK^;sy>m&T|~t!|70SXrNLlA84`AovKt15cifUNSB3A zR4?~Q%`v#aD)qW3oK&9@_Zqm;BJn<|?yb%os630e&Dm;-pUY0{OF=z=(A(a6(uv3U z&Rj_#j7SK_)rB*#UZD0mcps5qdq#n}dY8YTI$K;&s@+>qo%ec>T z_dft`LX%G1cB@s_j-Nj5?>T&d&NkOBE)kxs8eSw58`-$mozS;UM;xiuL(r`D6P;Zzd}b(y-GYw^s;Buz_cNBBZVYN zYux@KJUy@g-0a~|%UN4v@4T(=67zX=GMnd0m{)3M`dJHv{4-~`T^K08#4F{!h&G z$3u1Gusi{+xz5IeC^~e9%ZKbJwiyzBfGmFH(-P{1vC;UD7myB_3@e#VkN3(ci9N~~hITuG#4g$Qh__y+Nn=sLC6JmUe9j0G{ z>W!U0R4bi798MG?k!%K*gK!KUH!I$8B$^6GW4Z77L)-miQ%_2>n_f##O8Z(56VECpdL!_}yi~4(1Vw zp3=#Pjg)W5^OV6I<(S;D_vmfx8odv=E(AqTaTQd5)r+P|iEzIiGFUy9(Rm$W|-yre$F8%}6)x2`|#=KcGe z<`Mu;03Mri@B(bfXOSlmelFvA%rCM%!0JHCA)c}&5Pt@%l8q5U&OhAO+dS zvQXJg<%?xNR$n$v$un6CKIILiH?I3$>C7UIxLJ|A%=lfB-lwfq2*>f3q+Xuy#}ZtB}I8}(`n z=us;>K>d1qSQxFq1r%ut#fXix8WHDmRGdcux18i|Ddk6I4c zs+1Ev7qgv|dNSqwypPqM^l8F*{|wi4MAkKB=Sk`P_2~VJ#Z$zu_vUN4GP1%w`we=l zIZNUrGtj-|efEh8Fx8qbE8%IJz}y2@kTQ~iAg&-SSZ&ocFXjUdI5Ek}@UX9_>OAT1 zAtb{UT32J|YU{Gyx%;-DKlBHJj>DY`2n6l|iy@wh8d`L%A1c{8Px)V_(%d7{4r$6G zv^zgHg3V6`zxd2vfDDp=Y{9>${#|{EdSj`yN!_aWsdv2_x(WIr!HblOJA!-ELf||k zCG}}WJ)CphWt6**Mi-{>5sU5kSoZGe{N9}FGO<`{8FlSZ+Z*EC?A z9-CyCmzNn#(3QG5Wt>H>^4)tor4ioepX2On8j=wVv9K62++=ROSE+4*W8Lm7uet9_{Wz@Ctmv zKY}}ENIKRXUTBRS57&GR9!@8bf$A<)4SZwh-B<(@;EDX%=R5lpM8Ch*%Zd;O7-?!} zLJlv%hbRM*3j5kU#Y$0u!#(bR-e?uygB}C!fSxG;^J@r%WL(wF+3edM^gH$>=_8r+ zC;c-IzU?33)YU~d>H@Hjchot*@0ITdYk1z?eL4+3|BJEM^EjlPEq=wiFA=QsoL&RZ z`=S?@-u5T~K5^#!o@Y0M#Yr(Q@^bPV;G4RqEyD)AtH9e+k3Mw(%$8Ox(XVQ$1zjOL zj2Kk6!=Z;JAN6Oc%)QaGNI7SpT}?P^@#JLZSI?h653MD2lIM%X^2kV8eLfjqa}uj( zkxe*$HhOO+HQD(t?&2-&X!JbqDHicQv}bGmZ4K{pGkmChe=o|KS~r0=qxS}Wi76&V z5w=8mT`M;TxH;hv1@847Jc-{?|M~HqPpHlM_&5USMl0ofzEU0?o|+mNG1S=T^1(&8 z&0@-HHO|B4;=$$7F=dP(-_I~Kh53BBGKvHfp`YsAN!I8zIYdEiR!>GeX8 z5AJEq%sj|_;EsWyeGaW*G}Y#TDx4LCne}*Q=sO8@fEo|q${Ox$1#=&%P%x9x;>hgm zYA_Sh%G@lx;0VbJ1X5)`t#t&pp0hnMR{$= z){Kdegp^GEqUn$5y;>X%+i5x)0}A5|c#$!q{uI9w7Rw>yfd(hdd^i$k;o*W8-gx7U z>*p3uiUxS}@35qAS*{`DG&}OvN4)cmg_mD_n;N@(JJy#@|3+k3`a)|D28- zDWuXUs4!J2Pfb=TllY%PK?T@d7b4MYrn8k{kp!D?uO$lX-;UA=$>j6K$H!rv9z>a9 z|389L?^o7O|1>0O_13nJ|JrX=O5`PtA1bJ*a224TUiXRw(tuOPTcKy79%lkie@STY z*USj9`e+?$Fy9Ld{srU4>VN!!v6_y>?d#oY=Ygtwy&aFG2RLEd99O7ng%1SB*H$51 zibwCyLl(S08plH@`m(tu!;kydaNt?&8Jq8tjo^C|*+P$A49!X<3+=cE9lj_1YkQLC zkF)c%r9B?{Y)*JC#O61&KqvKGfJ^&Ns12NvOa2wrC*5x9hxNAaAh`aj@ZfrH=)<9p zhd$Z0B9YT1KxoVgOBsI0CouY$2Qc$eE`E&0+VhBP!t$~y zDr0|TEsObwN(&-rLW-6dAKz5Y1&4)S(&Y=x#*Fgl2VH_H0MY-znf%~}f8<>g%DEvZ z;oeF7Fx;7+Y=o^%@~Lw*4bOf$)EYt}4#HLn#cvSYyO;$7qa@amc?Xy&jc`qrsE0y^ z0CW-{E4~D}C%%-5vw(a6J?@qv_-4Y2R?78yxe^N}l8u2k!7dm*6ip;!2l@6}eEDT0 zxWzdH_`Y$Vkp#Pt7{*y#9!{v@eNh)&5uS?7{J=+kKbT>Xaj;Zw^gi-Qz$Z^Xp`IoA zi_dxyg{yis;%&g{poCX}FRWjh1RgIZFUVy}`iU-LLTtV|{^{1k^ZFdmuuBsB%F|v` zp3fCRb^=amb)e};6_!sg7S+ftM@B{)0|RcVz(VB6(vuiSJJI}7#4MSPorI}0Ts&Qj zqz_jr6*D~PhMnW&1f$Vo{pp3@kC2H(HIlS!iJ^_Gl%Yz^jf}_GB%5|4>C&myXyTkC zG(^+4Kf79QfBnRX6XEde7&2GrV6LOW=WYZ4ZlaplG0dh0vTE)2 zrtZQ`&!?tyf#9b+FF5D~)`NhyQ>nHsvkIE6f%+-^p|_Y@%>$F?tHQrxjlM>JE%B(@ z1Ls;)pLlGIwsh&6TE6q+3MN$$t47PBcv*&C)yJe759ftuk^|TuU)~anCQwZVwPX@t zSdooBRVbuVK$0IrZ{w~VMmA#GOpXAqk=kG@I_!pRcO7XTk;Vxy4gZBbk>I=H38aZ+ zf+-h`#*i*B!tEn?20`Ru*R^YA(zRoP9k8@xC_-x1xIrwQ9_PUP7GwqQX(i9}&eD&N ztDEZ_oU(X>Q?bejwv2~9{Nh=D*{pWkS9$t)1U0!ggOlg$m%BALJ?zD}yHE(hu1{}a zt&m4?f31L5-(@Ddl5NJ;LY#b;NCS zSDX=}(a%{1G8Nb{I$4_)DZXWJE9R)ME z&c@MpXA57)FTsbrMZ0_EQLuxSoh*)y7L#^taAKzP%cYr#!JB*8;d*=NiNVrLZKgCh zaeeS9tgWYi`1J3ppHW{6-HEY(SLnY17pxNPEqhhPxDUiBkt;-=&`)cBAMB4dHRDW_ zIqHb#BM~J+E_9M?qF3mxwf7lmL5b3H33KQ8>P`z7bqQ_q`xf}wjr~FUK++UuR?*Hu zpkqqj2hoQ1VB_~sG=Br>L!$9QF&0CPkgQ=R;)uC5a=EA#ixrCTC@y!e=W>RPtZQ-q zda;1(cL(!6bpBxmE?&{XR52TgjKk(^)el8&%ltl>@<_Hg1^0;=)3T$7>Q)rxjlQt6 zwZOMs(%XC?m{j8j_uO-j%8j9hkYzrXUa5-_3c$xuywbGb>o%5C_fU$CEJ8pAUWBd! z8~UOQ5QoF7nu3WZ%mBVQs-6wj;+$dzglY#yP{<$&8=3(v?)-5z_x@<~{kf`ICQk1B zX(ls2Pp{eedAi&7_O;ZKdmqWv`=sQ#3gMQWx9Laln#AGT&>e0^gJ2+%XaSopDUJqLXg9n}! zZ0JF`{teoi)F%E9aqnK93^%%Q=0;<9e(rEzOVZ?Wvk4_T6z|QZ)I#olkL(mnzdn1x@n+{>7T;|r9K{lq5^x$^}(@5-FDo=Wz zx#`C>I{n^kF*7ibDb8JZH-92_4387=%4KdH1XUiJFBImFEl*EZ?Z~@>REB*c|F;GG z(iS*`n^BVqUYLAcVl41z#7{mTQE-=Pay}6B?lQIU9{LY0C$p!pum}wRiNV-V$Fkb` zsA1<2-)`ErSupGv2Rf!?YVY$;f0WBf))b*gIHbZIejLcF_&6Jp zbDh71!lV08x0%`n{V39I3O^)t^H4WNt|%(M0%ET+YNcUcG!YaFJ1x!I>nBgrsS|(X61Kcg>>4Leu5aSdkC~Mak+hy|z1ApP9L|38@t~Cy+3m&{{vv+9I&I7&WnzKFTh(E zd#nH;5Bkvo(0#l}d4_93Q=pdi-evrU)~^C*;%WJ%jDz20R9|8BOJpX>vid5}{ zQ5@CH&XgmO%IruZVmOi55jtrdi6QbJ(ioYA&t!Qfi{MgJ!9x0fSoSv?a zW{aJh?X)_dw(a<*+8oT=Z|AoCskr^5&)*`@n~9S7h$NuSk45i-=L783pL)=DynFCd z2~_aZG?{v>0r*5wt|EyF%K93iK)oQQVbW*N?7!ewrq3QaWT-9U(4n(w9#4LUTlFNj zodwn=E*katiQ~_G?(q}xI*&TtFk9lla47NgAE|#*pTRdDfp&A9SdbWy|KtYzfACSO z{3l-2|9hl_zqCl&e{Bq3JDeU%*{)qVR%zU_qiC{1-_j@_DO-s*1)tmsKH1l1-x&D5I38bg(QzQ()cbwLgMnmU(H#Te9~zP7 z;9$&5Ob-n?i~DwaMiIO-9B{M-ODMc!_vxx@R>~t?g~ZeeHF2)4NGu)P8zx zweCxmHGSTTr|WEroQQ?Bfi+c;%9l65$Lh||y|9qJPF5H#VL{D>ezQ8w9~2lRp+=V& z(kTNppyz!EBfW<;&>`4=G>I<}%?OIw|4di!VT62Eu@kDnFHd*S`+)HRgb0NHX>Qw| z|Kes!b)NLu0Wl|$yszzlW^cbdf`40o9Y3D(pZ7o4e5N7opGoh32FpkD7SIb(*V2Ul z)qQYpTh+Q2&O3gIc|Ga51j(9Y6sLifo2$St7n3oTf7A_o*IWtO#6A>{kB?JBJvK%y zac_Ssco5ugt9@)m+d-S%hjzCZY-IO7$BI1X6EarU>Pprs@6ZefF$_OonzeR8a|;|b zB!Ka-F*q>#r%BWmhHe$>tysd5D4V&TgB?%w%wSD4iZ*vMUkKA(89R9J%G%iK?Cc0M zs*-$T^2m{^Ru&FL5{DvA#5tBnIYt8o#-MVFUfr#IR9KumfK=g;Ly5@bYcv4qty(W; zpVv%1_rX}H*kmpbHoG)5sIqq_97JPT(N;2kZzj(MrQztqkc*+LAg%0tD__4po@^mk zXL!I#T&-Ant=l@sj5uf;_VtfrB_p4z(#8uK&b5$7u9O-m*7VhTWWjrTp!p6h@6hal z_+y}oj%MTpeYb=m!>U*RoQb))`wpIX`I$2)l1N&)bb{LxOWa;%n%7_X6{x>?*DH(J zLFBJN%9D}fFM82&M>iKn?ja$5$TcOC#=WNd5RdN} zS=c;YoxOT`dYboMW1%c%Jfe`%hC`By#Z&|*O5_VH>3bV8Fl}DPpvk-oBT)1>cfbd= z$>Owu$zK&=eu4la3>g`p)h3o2XqAi>p{ps2v4qjUFP0vbC_FX_JmURC)bL2Cz3%3l zO!KCjU*~^51=V6>aAGi?jm_3zx>3K0gf7KmI%5yjYD1ry800zLQ=5%tTH(8JLt!-gHRh`>PfHa&Cv#EFyhN3d4x#IFQHhOcwN zF)Lhm1WFuZ$Uve`tQ`p^a8Q#K_tR|M+SxgmX8riETD08ape1pHrx zT5VuiZf`>jqtGzL6??dBvjTeT^ zr#?1OCa@|iQgU#*lv;BYe%~iYs9QLb$(*5zVuYAs|Gs?Z#V>yG2=Bwqc*J!_Ud;9H zzprr`G_#5M1q=guYacT|+>5HM-I72?Yryc@H+Z> zzW7xI!bs@MA_Xor7O6u;c_w#@?wA0@77apcK)Q^J}5e@d?OC6&iZ(Il-lr)hB z?bQaA0%?%&Jq&fnK7qzpq>yNdh$W1U&YXv!7|id&Ec54U)F zyA7;0(t_thg1H<>)sZ6?<*-p>y60U?%vYjvK}58D2J{1nBCa5q13^PG4Hs1nYqdn& zO*I-R?AK30*{p-U&M>6s;KBWe>-SKf57`4#&g2LPg*!9^h9F+6aew%jp$_o*_FC3h zV|$anoYBTu-0Az&?`ea!i4+kpLu?Ox19k?-Q19sY?EQ(LZ{DX2OMNFkJ-TZ7>DXi_ z$uR*^yGRX)Z8xn9hmToEc+l=AdwbHysMM>u)2A?MV!xs_JBJcc%7CIC9o$VPrIO9_ z5BRk8UHcZa>FL4=XD2QZ%Ak%S^NEoV8bspB)r}OL`@qg5QE@*7-Px%gC(sdGzmtGc z9xzaM_j`bYrg(wr$%a`W+G^dV^^rH98yb4e z@WjOM6NSS2nXhi;rkj9f^u?tW+kU@gO?SR**yq&evh#jJu) zS~Sr!c>ee_R9sD{gT}p{5s!F~&G?vfif?_>iuYPic&~Zx=4kYBbm<(j>YtpJ*IV)4 zRy8&8&!m_ZBbCv~gxS%XL9Assp74g}+)Zr!uhG$SP6R%S7?u@plsf;5#Jf_Ff@K%^j#NP9I^hhY^g-O1y6Su`pU|JarEfz3zJhrX%yEQ zU0OP-eSC!r{6kb`fHwsI-1^~AC1IG6L^hpa^mbS|b^_T=jK!tv?t@>3Gp3;1cM=&3 zylgyvA4Or;K!Jl4sv)?kVlm|+{?2w{nXp8NNvgz}ZRd(ndu;3WqeqQ=u8~Tnvs2}= zb~T`d8wMsD3!@pD+5(H*A%J)74J-?P+EzlvW2xZKEG1R z-`~|V07s%9y&S*PWgSEm(Y2vlF6ujk4*HP}P~#BJMBjzeP@m|KE^i(0AFHGqzf?`L<#zv8$m)p)ZL5=@g-JPD#bZ-x=j%Ms` z4?cY+*syNKZ$;!2u@`zey;*Si(7Rkjr>Bbqd+3N9J=``{DbI*9Rbwg4x{amjYg;fX zI2K()sQ0qHy)`{Cba7<4OqOfFzh%p?gucKC4jzLN3+1t{zS|=sR4e#-aqC3Vb^HxaMxmz-GUIp+(R*#l z9NZ;Y@JYX@nbx8d$pv1+qhsKmNrk)Yj-X6JreHRqd$`k6AnGLUTG$0_q9ra$ut_p! zeME%xg%>}K>L~CZjJYTqh!Teg&9;Y-%qTq;kJniLs+p!)x+d;;=~xA&19K`;w{29h z%E#^GKyG#Qn(47m_svQW{ND^O7$-VqBicKI@Jq&in=lY%EGS@r3WpPUxVIt*HMM}@ zOg1uwgkvZhnxi^u4qy(mal^V|dRnEv+ykNBo@eWqv1F}2<=W2nbBnB$bhIc;HO*x zB`8Nq$_=~unM?{f@U0X|NS0hEi;y@t%@UE;>6Mj&dLJ_9Fhfn+&I}o_?FEM~u>Ukd zFiTD*Iy4D?q-+z#9*t-vK9IEBK{%;|-AF#{q@$?3A*zcAtRQ_#%v=zhBSG$ZKvFnnReCTOlM`k z!GE{uq6~(CP57MYf{!xXR5-3bzCj!oKe)15{drj#E zsmRoO*!J1R7BSWrr=!5dh{EeJN;oDP{NoJMqX=9N=O!l)E+b41@>=xx=`+Xv>6s11 z*P+0T&1$>NYz%-*3E{qOXVLNg&I4Zr`G{xUe~H|2K!?Pm2CPE?%sF_8?jfCV_6CzZ z`~R8X9Bl+UurC$)N8fP%^eqk zzPyX@kiERIz7?JlVVij<@I3}(D#IQu6i`kM^(5r*Vpp|36L{~duu&g{742t3PlON^ zD~RY|>)^@)*mC@0xoMqQQ)?r6xYQ~Zfb#H5Mo73@+zPG`2cT5EMV`eGM;r^d)hqNz zW=&3FCOCC+XN%7;-gE(aVf@JE@*hQ6((((PL*$A4fh|6XCSW2PY?UVI;cvgxNPW{nNI2v5V?iNGy<#Ak+qo2arOl zR0d{Dgx{zpQFAEUKv^$qeszyLGG8b5*ojeYy}WtthlySI=Gmc_JfpQ%6g2GdC1Cvs z8KR}(Cm2UCf1E*pgm;hJf%Ln3QmQ>s(C>gGn;F|Z<=PITe%v&}bKDPs14u)U8iu)% z*N~UVagkjpe0huIx7pSy7kQiT$#1aI{Jt`#D2(Z*);63mafkO!JMi>%p%*@*aT5te z6efbIef1TRn(`$0G>V911x2@oF)Uohh;`u89*H~`Dsppd3=rukZ^ynP61fA#VJ>fo zJ`TeWa(sptOk{Yq%o+ITBcrPcDaYSkKy7w?8GP^4-+%hIjfnb2Xb=&zw})OCdRyqD zz#v+K=Xrxd=M0Pj6nt<6dAX#3E)fKQqot|3O@@Xl0*z1P3KCDPBA!7|4Dpc=H7LHc z8VjeP(9~_LHW8*z7i)cjBFF?G5MP3j2>B|UWAlizKn#U^jry;td>$1@64fdk!4Uw5 zlJItX)-W8Te@+(i!%k!>7C&+7mRqmGIRpdY+=%1&=Q_VNoR3k$_FGXe3!{Af@X|6u zHfI;+Cq|>uZ0B=k_^<`kZ=1u=V@2U%f zAV9z*WG%{sT1+ol;e!-I@kGG$BgAiD!_HRl_#Sr+FD*7f)8sr z8;y>Q&o9i~Vk+xU7{xu6a~A?*48Df-<-6{>i#&@Qn6`HvC}Rig-w}H2MfE+16$9s? zsX+k!Q+%c(9H)YTxtKUav&c*V?kS!x_ur_cc@bfD+!)$V$WZSm#@>m=|J5=w6BcOk zpre398VzjVe1ox{$Qag^Tm_vRK)&-`7kgZFZ|{1a!3(13Z(0otb}DmV+(HFbm}O8x zb$kF4s=|efUE?@w*^#!aIb>3NN$Aa?-wu5b-WxVRT@zQpuKpFsyLh?O-WabRJ9ho`$BxyZ z6~8q~3`|hi+0uj{V~FG|{PXirQiWQ1>n??R!Yp&Vb#Fz-AD2IKg#J5in%$Ralc zxfDFvYryz|J7b1xMY~f2gI0AaLFD6Qlie(A(Y` zx*zLUD0zvtJb;SucK{Wk+|=w7Y;YdGfDZibA$*orse0%WQA;b#buO_X!as7a7yJ=Y z3k%Oqjf1msV#gqBQU1z~r%=MYFD-}{!{UpdJM^1rqhe$&6U7s(EOvdVV(aXwQv;>J z`GM)+_)QPY50(Z_ojMz2vi4VbK85ghHVM3qI?J=Zv=YRFT%Rw%`n>G%JIKKT0`v@7 zvN|;vFfO7k*5NKbfZ^^x_HaG`ejvadz~JJu3Bny5{jtaM)npF9Fhr~_!w^P&Xz&g@ z8HUbR_ZeXEsSw@E9P%|?4-EKf>pZ*LZCrmc_&2f54%X+AVJS39XiU)VaQXdhM7o(z75BNjuK{1N=B)-(7k{_?oh8?)JXM2}G< z4jpnp#YDOqs==PHF23$Jpx(%vVO{lo^|jSP%m&cD(fqwq(8jhsuCxF^;7I%%165LW zBs6_0z>xN}_Dr7HK)%ccT_SJy2i!@57kAuy@vG`kf2&(N4#D*Iow>~G!ozICBcJu= zmZ3TI#+s#b05lvJC`U0!zI2GOZI`n>Bz-;gY9 zUGDYu{t4?py7+uu{HnIwYy)R*h9+QzMDDI`1=mg(@SzL`=Xs{e^uf=Uy?G*HMtPJHLZBBli;)qKR{pH=6nxz`?wL0YWOu<_1Q zd7LB2J%XnvlwyG15G{MFEh(6nJ?BzeZ2P7p}-&^T&j+qK}4TET+;Ef1% zl?)sk2te)LW5JT0uW$!zVv3`^Z_s@OV28av57mN3aC&gix9wfybAfki_k`g1TwpyO zRH{IW(sBt9bb-S~Ki2c!NGjEy(|jS;!fONc}(AH6s?#;S`~Tv zUp@QF0KBZ29rhl0cfULI5zI2VQ1X#rpTsD+3XhiwzGrhq0WWJ7)6X5o$>xkvq$J5N zl!W%{HBl^KN%+fQwRMq8;8OSbN_;J})L=I7R^%;IT2@9gt*aI&?sT3a4EIvF{MSO} zIaOUv#djv{`b3`Jbq0VStpwWb{6&z((DjEbf+8ZvMrt)goy(T<*0q}*f=9?5u$Da%`gzRSr%7i; zLg=L%%LvlZgg`qb$pq1V0AztgAtQU77VSL3ySRp1dNyb?l~cR#;7kKNE^^1tHgxSq zOrs~{kh+JkeEiHTdeJ`aFTTh1F}CZy9Gn+8iZJeDgK5+|DfQHzX9M?6BH9?n=Srhd zsUZB&iCqX3wvK7^57NQ&Yx|rJ+`k3IRV~K^2)Pb2ct+M@;mYedA9;(x<8Xvq^4B0Tu>)C(=ZCj0N#Zp#E{jvvEo|DHZTg>T zyv)g3Ks`uJqsUr`X}n2iaJ@G=+8Awk(YEj*!JSxlUfY0*lAknB9V=jbWQ=NsDH$h@ zSO3}0$DPQL#i>dHS-ntii-U*{!%0-8Cg&GsuW-y4CsNMw)!WF$ zg2jO924TP?cMAhBVbR_X8^^a~)CeiyV^T-A0AXs9kT#n~5fAAxa3VfJopzlK} z{y871*y>@8+iPb-{qvw7=flLvwvz24791jor*n=6QF|ksq>~meCY^Bnu0fpm2P_J2qjPR_`b1sUH zgo>cNBAGZ&hC|8$XnMxj{ug1&jVpLTI%aBS=E#wmnbiC<#N8M{48g&AO{rS_;Oq2M z>Fo~Bn;;IJN3;y0w=RN%_wKU*{ADA!pD0$`W9(l0zX8YfkL!#2y$UYRDgj*8Klayz z?tTVi|C6$$?=gVm&)AOs`~Bneb?AwHv<2Oor;;4QvSVDHMZfyXhRNOQuw(~Rwi5{U z+MDOvV<2{{!Vmk(TIixh-QC{L0J~!x`qp99SJP6JjShC@jjPe$d*Lzr&aT8L+WArj z;#^O59EIt`M>+>vJCBHpG9oKdlGHVKDK{1t;hEW=b(rqzE3{PUZ}HS!yNkn66qGDk z2qk+`qmnC3Ul=rhzqgxfl!_20sN_NYEoaS9>lc_Uc#Ua;x3*!Hq;D2V19hH)blyD_ zL4sMjI@^A()`%p_qON=Zj_~COKd?>K1Ve4x~8~ni_ zY93s{Ty^6DE5zGkL*@jmk{~TgvV$tLSFpqa$srTPxS&{JT)vM~IObLJW zt+y2#qvPX)@+{gI93LM=hGFM-kpdq1q*6)8vF1Rb;lnj|K z&jjlMVN8stJILqE+CO8V7DmGl+N6q1yv${0SHWG;28n@ySZI2Ybr=GDwbfGp6Yn;d zN-}WnEW_TxjjzaWr^ zOsNx?JDdM_R3}<3l3nBxbf11~!bGzf4jZMHXQNkLyYyo9edHooy7sDQ_T?ob+_|+} znwp-Qo1S70YJcyyBaxC}EhU^WGa3%tv&jFPR8bWka}rCIQBohYof13|?vEY(Abt+U z?oTPB-zKhW^=z3^;)v?FW(DfW%#JfxSbFc|em|!=t>iSO;ckNBq+DL;y9gl2k zU6bq>kP;O|_Lw2+Q6q3d%nthv6i_;wOCg6iw5q&yDiew6L5M{%Q(~^@KgApMTq57p z*N`W4fK_mGM&MEo;k7v;7ATsv+7g;hr;d6P+9`*1G(gIz6#Z;GzJ@@Q&Qp^ryf#F2 zuaxFodsrcVvD67epCr{IK3U*rEo^m=zugM2b^ckbG_c$}uKTUG#0j3>_RFC(^4n3z zj`79PS?2l&P+7fn*06hGihu^R^|L9c6Xg_j^0K=6egst@|Lol_LF8m-(gTZ;;~B?{ zM%*no5;dL7@sXOofTY%+g>R1Y!TVx~MC`NrbxzF96$=CUhJiwHZq6$iiP!AD=N9aW zpk29Dp<9xqP!##yY#N@nSzEq*E03US?>@~BaCrNSks;C4*Zf)42{Sf1lAyX5@KS?} znS=@*`dj^qZG7}p`)&Wpc8B^uVHxmr?T%O(QNEJQDV4?vZ)t6yPslfnJT~}6pK6E; zZ6T}p4e-l(W$5)gdPs8Q_b`Jsps^z54^ptc#N43eh&aJ4;)m2rr+38D+|fpAt&(+Q z2?-Sg5nB<&LA5#HPme{nN4iqu=MKc02)+qh@$f=8ZiT1f;b!bWDjdi8Lp(cgcF+D= zFDG|zPt@UHa#U)&H*A3nHyhtBF3wh?R=hAXQ;1v9>g;0ip4n<~am`BPXJ+y!dB9V* z^)hhx_K?IvFG~$8h<_J9k_;#bd{A2)2oeN(7{c4of%HH(QB5UUp3>S^Rp((EBk;G> zgQc+s+_P2nPvh;_Xa5K}r`oZcGw|v4a9D!?A>huX$iOY?SCERgSZs`yKND-m|MYJW z4cLy~G7wq1liZrVIUHX9^njBCEDIg(s3$@h$uSMe=962G6yprv%>)GtCNLuqFx^?P z1*07(8a#*8`Btm-hC}J}T((%uu0acZVI*<8o&2EX^Qioc$9}=hs>n#}fbnMa8;1@Z zdP9r1&!y8hK+ky~Hq!Zi*8PQ8+~+m%u|In3vBz$6-Dk77Aj+39ja^&K zZo)UlKgfGmZ+lGrI+|iDXe^uUd|H}{g@?^(%td=YU{h@8*)A)gF)v=XDQfE5D(_vr z?JQ;FH;AMFf&a0kKi9j^h+_{G=_{)8 zf)~6Xqaxw-!o*`4Q>DIAbJ7FpLNQv-++ZbcjV3$)EBNr5eoSwP*t*9au70c93>^UF z4=J#aBq<2Sz=&`W$`MCMC<$SiOf<}Y(DrL1l1`n>6{!;^t2cc0t6!~F=0IEC^MWI8 zvhGH&iKX6-#O;~WL&y$SI6N|NZ|ALejlLeM8?L$Lnpm>P1ip7JRc$97OFbuUzJ2_< zJmPQMmCk#*Z;eMWo%-DcC!c04T27NcsdleXCG1Yi8g+;x5Ce?a5lseVA<&p2{B)BY z13pF9SV)%-YD8o^weI&WZ+N+S@AK06zQjL1J5h$szc#XP;R34Io%h$KSi3gYbp!2< zolp74XJbRdldLqpDK-80H2c@%vzx$;*D+h3MOldQ9OY!bE#KPr86nE?u9-zn6Io0w zT`uKi&$&NvnhtjCBOA~nA{$Wav&+k~MA1h^Ar%ITzcogW@H})2{jPMc(|Xn3$(IN5 z8GAFE%l$EN{u7NON6z%=R*xKMJi#k}tjwUYA*c&|G3=!NfOdB{K3X54dG02#^No~5 zTE&tOQ_eDeIYye*2EQ)@1!p*(1aj8gDel4`2)i_@xmk)PvTCuYJ$*NssqX2J3WO*J z_n%U!tL9k=rMZG!rt;2+RnWmq9Fiw@s7igFUBkzoockKYl4VvwnZ_WkAvh^rE~Ru} zQaebl#M{oh2SY#x#t>je_$VcvCeTida)-X%X0RW7n)0s09C93!${ z{vMA#My*~XJ^)L`tE0Gp4~!s28)8FG#BQ(O4z}P2L#onb%E(`B_&Zv+ zf?B!{tITljl}Kg9I)H&YhJ5tlns1G%JLQ; z&6HopNeDKu5Nz99V|t8k3B3T^+-osDJ&aDSlgN;(O)nK8lVnDG)><&gD47sJoV0aw zsPr>*KNPz=2rFOD%HAQW3SvYK_1;qj4?h(Dwr;;Vfsl}atommvV3DMe0FIthm-B@tb@%yrnq zQ&nWqds*mX7ndLP0_Ebw!oxsl2%|$gvQU{18i$ycYFFiFhltDeW z`07tySO0}HiV>?s62$}K3DhBH*kK-vJ$~1ZrL}iLCj7GA09j=Pep#mxA^Kck##j0j zQdm{WuYFlS0-3Ez{J4-!Lh`8Ok!F&^mIKtl3a}2H@K}@=u}Y(;M-$kQ3PMfX)1qGL zb)`%;e9+2eNB*+y;W&>J{v{yXTDG#8Qs;qpKKS5+@ps0KzUW0SDm*X#ikH0PCGiIe zAAIk7-y8o#{A>5!cVFRw*gCGopEoj6fTK^U_;hFl>f5#l`ohh$A^DA{5R&|~@8!>e zZ1G{+_X?k|ZM1<08yFE%2rtT2m^}g@&=~O;e!Je~BqD@Nfm0CB&n=H4nQv@#`O4?S zvaAGq{MdE*{B_5Uv;1o|_M9spN>;16Ba@Rya@DGfq*L~v*A`RhbZW6SjT`ih`Y}8{ z{k6o@^i}nhmHJiFQ=kyY0qf^7IE8r;QwtW+}U zEhzI-R>?%+=xQP5jt!)jhZl;d?xoyWE1a-W;dFYSkWLPa4wa9%>1rws?I>z@5C@2S z?8$L6!mT_XnJ6C(y+8B`^ao&~g?J`Xpnjz5(#pB;-B&sOD_ zW{?Gkj}j)3n#L~G-XouXzb#ux{zhABd2Il=ghp)$G6YT8CM9_)SS+cJk@nED2i`%@ z^suPD(nq7cHKDHvt0)O}o(}m4_(`Abd2%gSCrPFCBYQO2_ho%6r0)gUy>1%;ea$Lj zIcRIpd=u9N=SS9uP(jWW&-jj;9>**ckN$m9iA)^=oQe8H`jG>;r&6T``CDU{sI;Vt#VvOf;Xi8`BMd*1?ASfdP(fBK8Y0pPkS zEC-=+&<|=0M0G%!+xuNy%#hY#m0%r-v`blU6#@YXdJ(IJE3JrCR#LTr=VYwH)$2pa znNOPObarDSn@$;(dy%jtcL7R;e6)Qb=UB;m%V*rVy9WzbxmNY|&i{Bp4w(DOD}l*# zkKNY8V$h}-6aU1&m+-IMUTWJ5?%>hJXN3e=O|a8X|25_55J=7fvDi0rLe_9S4R;ocFU zwh(mGZ3j8}zI|wHdaI^yrLFaO*y`9&$|WUKmplTw`g;0lc*sn<@oOz1Cp(inh-yT> z!bLPG=CtM(07;zU=E_cs!Yme)B9E@LS(%w>S9w_kHCwaK8C;Cp2t4 zOWg-8(-JEsP|E~}W#G6cbTx#2>b`i!j&;bZVg9bDCd}g4QPFp8VKEJI2u{~9PUvN&|GdevzTF4a#VU{#a zB!-&E&n_*FL>(tOvbc1hFoAlZuWt5Yttix8>dMs5@CNdKs_kpP@$ajQR*}4OF`m%xe@C4~4^@@LuvD<`( z{B42LaYEMbQh8D56>b zjPou|hic!v+WFR?!D8*7cZ_%kyzMp7vz%8TGI^IMnXx#Q=)P@x*?e{Evjq52nU1B7=jGA;>z`nCX<_C|*A^ zJ{U$)zgV(7uHKdB`mxIe)QJ2OFhrTxRSl#1x(ppIIs zYoDs~toQrb)m2^HRozmzRNbw9NS3XVY{_yg+a15S#6UUH5Jd)UP2pwXu9TE@| z2n->Ub|x7xkRTW)2@pb)7XgN#n_=C_B$qpVXAu^Q0LwFL;N}iP>3-k8 `GTC$U2 zx}@{iXPcFi4cTh!V&RjO$(3mO(S{ zZWu}`V5poa*f*%9i2qeqXP{-j2sMo8}m|D*Q= zyq<1K#A3?K=l6%hbyFo1CCo%*YtBSgtKqMPYvI9%FffUm!xKjk^84t@q3H{SLg7mq zW9sb%D7Qgx-mmMsX(WV64R(qMQJ)2; zo$@@sqvR=E#5*o>)tlDi6I_9?*yjPjBhe;?N)NKoL!pou@}lrdGBs^r=CvOAyhM1i zeyEGL#hY*V{9pG_eKMS&7Ir%WmClbv4MzMcGVKNO{5-!lw=eea_=i3m+c&rNJQDr< z@sH!LRR++P$}PNd$R5h;=@8}Jv(Oy=OZajRwX0*WXc0VpHeNI>cc&&!VQ}kYOhbhxZr*9$~Fz-z}UMC zn|*k%!lk`#=a?ECQ?Vyy>0go6s@gz8BZ6#cB?1h57&+ovc*1BOM=-pix1fTz-J#_i z2;RdK(LG-Ys#<<~zhw#2`sEYUfCgx2H{s=dLlkA7yh_!srhJFFmLhDfY zmT<)?5Zi-r;UmbKcP!zk@v5Nk{0x53ssJOtQYH{w5?PEVNhG zFfl=kBYbmp^V+fGj1nbIMUQ+G}!ZM(cz z*$E?3gli?@#CS3I|Hr&Ij^XEZ*Ql*b`oo5aoTIE1jTUr;l8L(iWgLWje4ycN(J{*w z7+%XNTlP;*HJh~+Z?;e@7G}MbTC<5XPnVb6Sjb^rQ8*eaxbCkZlDS)mL41gqsyk}P zG0%?G`a!=&4wVjnp)xx9xxu&L{`BoqD(son zKSpJ>rc0EwMpuwOu9|KnRnmAtbemn#*~qAf+o>J_-Rp-4^kkXW#gw931N}-Nl?*!$^(zq&S14w&9IC)TMd4(saGqBg#i)SNd<+T3 ziV><_9EVqfCVf+owC?WqF|-cMc2WCM_lo; z*-|B!bCKo+ufXT9Ep?u4VQguC-9f)>h2pSIzX~y69>@5CD=UC=(nHce^qi1BoLq=TCeDE3nzJWdBOJZmgcrOs?M4&xbLk2qlioHF ziEcmr^wUvg{!zn@o`UGvLgGBQOIpbKheX!S|LedxG@bB8L=q^g^?U8nGFAQySaT?qttipF^o%-K!M z*(kCw-i}Q1kLo!~U}8rXMXaIkSBuU8geH=CV%<@!9L*N!xk$(p{0yq&tE2r6mHwq$ zKJEu@M6F+0Sy}nMNhklv%ah)XA6KD=AAWe^z$fdaVr(u;hpj}@u2iO{D;4`kf&+LD zxoUE9@l|fMn#C$O*$)18+@`Pk{WDo?`u*75>W6;lhgt|hUVYR_CG+QSbBkVqg+eRX z<0;*xR?GgiiWSL$9LTnDnOlI5^LyZ}-hjC9qdZlCEHV)*9QnMp(9klqZo_OrqVFD$ zVN&GW)R>KXjn@cYLN3?~oz7pxQmNS2V)*Cp@3RqizNd*4zBUQ3LL193Hq>v9Rg%ij zB7Cg3&X4W#wecbA9>NxGz+YR&d`I*QbD>A@j^ZDMfCu3AhH813vBY+Hy&`#nRWgfb1K={?CB-#tT+{Zf54^_;*Dq*8)6ZBahr8!TwL+ z0y|z%I+aSD`qHUWr>-a^jUDmZCxjylDQyYYMnsLNv>}GDh&iPNLLYMj*LSq*TcD>P zFA^3%PfysIoc}?3FOpX+*N8us%6nM!HPOn)vdD{+LWGfW zxgCw3Zo)I;6>-;!e(yX;Vs!r1@tEz#UxB*K)6r-fcox};kxD5W%eN*_*UM8_k3BT>k3Kk66uuIJ*fU+W8yolDdvE@@5APws`w3!l4cT_!+#Kxnm;_i{ zj}^DeJ_Xa*06K-Wf#psYi&z&*%AxWJ;SZ{nGpb=auOP#1M5g=KXe?75JP951t-@f^2bWxO^WLy)_z5h@uSsS5AiPunbVESei856l$t5 zwZ_kWl1ibmtAHzFBcZkwW)@d2`g7&&u~-~wdcxt@+ut5T(3_Nv*!yq4=EY!|yzgd2 z?upjez^PNbtp=9Sy7_$?K!;aj&aSKcKCpUzOgQ?2o`!t!CW*bf%DDneNJXROgR~+N zf!Sm=q_DvxNTe#PLh$R1W>_%4?BUu;8EYB@e4`DAJE#R!H&m6wQ`QQpt2k|Q-AIN^ zG86^@zjR?Z|IuBkaeNT4-MYfAOMCin_o)~76SA6rLfwJDG+_V2c$>SFk-PMP=f~e; zSKlrma@(sy=Uybf`^=U7;CtRdN9*IGDTH|; z6H&Va5De^Q#PC|TVD3>7xPpVk3KYIH$z3ZHsdOb88D7}C;!ABYL`#?&x=U%tsSA)H zA#s8Rll)%bW>_+~I6P!|ch|*@;2x>7`^JrH+^4k93|?jAC#phStP>=XS?4p@a+jM? z(FTMFUM~gLB}U??NZyUN=pwm$boIiE+>LnyJ1%s=dfX3S-0#D<|7hsvL!UtOyZ?xh zx9ngnN8Y342jOWi<20`1g%V1SC?R%up|e*-c<*rNcU4qP&2DOd;lhdlCScQ|Lj?6R z>);uDD}2XzX)JVaIEC?z(G!J`pua?!-^VkoU$_ zVYpYMTzKV$oo6D*Un*tsSUTmoMl{)Iv?1*wx}iC}v*C5okVEcMYNoo|>9R-hogpn;Vy3ei5ohVwUA65vKk<%!Ob zk0oR2QVCk_Xu?Z=^vKc&9Rq1;kna(XMsh+(0FIe*OebuxRPl|%cr&G5JC(_pR<+Vb zq{c|Yv2#W^0zOf6P1}GU`pHhi7IMQ7Pozak z;CkPKbq`+)eKQndtm9=c=&d!Vk+jU47i}@+WW2ekQAEEhM}Z`)<>tWaleo$h*%#$E+T3RVZ;Zc8g2OQZt!nP{QcL@p?@C%Hi}s-oB6~6gKw%cwaC#xB%LbEwL+iVtk!GSKuAk-{g)It(4uDU4 zPia>+EgX-;t3!-NF%-6VMnQ>k6yOirmPnRlsSryF8$geqbItJ+^?hf~ocYkpe~Ns# z9^|ItvJLTbJuy0@AxCN?7QZ)Fe~t9x*z8h`@xBpjPCtdE)VReT5XX@w(KJJCjgCiy z^NfzWj(8wrOGd|!1N)ceQx@U3bsSPuz@2EUqt~`*ywfaF`-?Bf45mR5s0PFXLaYt# zFXlB=#bhzTX$uoUmV(oAs-u;FoOVmaA^G)r1R?DI2CFFSOg?HoU`6v;_q3fz!d{op z@5_IhbXa&bz4PouI#7?qqoC-#e>>pMwbkQ&&_5uHG$h!J6V0EG$B*yBpGdWAWFD)* z*kT{7DwpkRYY|lazC0l~xWKg-eykQuYlLm$xD%5gqJd`LSgZj`gOax36KKQ;<^zw8 zV!|8yfoy|idA*#&K!{w&^(n45Xto3B(RH+iYk zXqNPl6XI~RrO!&uMu(02I&fh0?NAGOEnac?_zt}Ywd-eE!@6oVn|T;E0u(%4nG(a`Usx71LISwg%$@rvifD`03j3ruNvPux7%CM%^sgy1eJ zL0Cp~iEnk22F_9ES8rf`P9eZGc96RX{kXgUOW_E69>a9#l{+5l5~T>yXkm&&cy z92~MRmQbdqO5#1T4P!0(Nu+f(3&u#PUN$>5)oxEsW$_mro%cNvq}vqMGBK2%gMfN$ zt`Fp_nw$xUcbrtG32izQ=kUuP9&Nw}Uu$)HqSl4?9A$p?;W7AQ zycU^^-W~dx(8ogmF7yYX|Ag4Q{|KC_1pLMZN z=kEj=W^e&6G=x>zXn@rx7lx$``j-TiFx0`x`QQMdyT)6glS)uM zHL9K(m8$c>yL3+zd|)d*aw@kCk@EYhN6_ z-?{N}!M~7wc3>CZ3ax@`ygbgMR)mnq5VjDMrPa;D{N(?Ek>hk;D=ceMH3^lh7%Gy* zTSUMfPGSv}#N>oV-U`%A)_EB}#GONSPu$gkk((%+?Gb5UX-jBVppL^w7$owbnRbxl z4`KFAZ@%5icGnTF2zgyIX+#mg>Ojkd<~~yh8%0FIM@q#M6wm31ouMCaHWha)WxH7; zlSYk_CpDl|sU19ckN^`QLyJD`n##%^)k_jSz)Px&c=E*%<~_?Jl_ZH`*WDpnyl$w zX0Pau9R=GD?DvJDtrZQ7YsW6-7C)dq{-NW^%wqy`8`hYC{mx31OCe7BRocRS%T1Ww zWFCAz9XDz{KE{Sxa$(k2)E2YM961%;0USin@r zVj^T@W?HztJw(0A?B5ga3Pa6vKS@HiKnuWNXACm+%Q%s@p`n#>sgiY z;0OzIVfD*{sFjjLM$=HG$0MDrq;>#`yc?ZuZcm*F4)z`?^IEMb&6&miS#93NOK zd<@?LT)?2A&jLNW;Bav0oaeM3k{*!p(hQeKaLa@f`t|cH;NDx1?j#N;fL8W+KJT`J z{Thf3?kEQP4!Bvr%9ZQp)tzvh$lJ%m&U@XdD8jj7X{cJb&Y_UxT(_WHEH`zdQ&@1} zdLB5Jt5))qr$*HVU#w4?$@u=`cE0O`kJsc0-A8BY`{T*T+a_krR1DX0i%z6>eE%XBCO{8Mk@8l&A*LCy`SH9|N^=rI-W&*EaKEBs0`qw^$Ucr0Y`G+)* zrne%GX_#1-72aG1>VVC%5%{a+XBY5@8-xMjNc2$&{$s>d>{SVNs9_UsL>pbW)tNpGD z)cpV^a+uek9T38>UAtDWU3B<}w)kJd7Ix98F-ipvLMR-8b2qKhT*M-Ll_Sxzx=@aK z$?v(o(@~kQ(Tjg7=_$lC4w(4S(Gv=h`;hM1G%%Bm1jA$JRRqa^jU-qD&=!PeO@+fr zGYm6|5sed+SS1W5ASE@Pr!!|U`|kL`gU9El4b}BhckE<~gI^r+_oJgGGKfTsM8Y%- zm+YdaOt)491H+`!3cLaq#^JGH6hEB3`uw{VZ#Q{aCPq<9|N%E1};(-?J~Y zX6|Z7oaovkW_+tXV^&0BpwuAIwop^Y;nB2;W1(dVK7s#PYxG}>XY|R^8p2>pgt59d z41mWQNnoe|3XVxs9xoWPgsLt#a)e>f|FG|YH*GY8E()cT)1F4Tx|&gV2B#EC8-ZlH zZ4?}Fa^JO;%dFDpf84XR+3#;|bpuk`)#v_2R^W4b4YV|P0j1(tPma5RVw{irwcQ4f z!Tz6d3w90l3&)2Cr^NIH`n4?j$OJ{O$@$S5`Xod54pGbP~;sXezUr4U3B;myEJWz~#spEI&!)ED~9XFlL zoxv?&CnKq30{Fvm(hx%;KpQlvC?g8fiWfB)Un>)j8sTI*n{pl7NhVTB6Dwj>@$5=0 zkt*B`H~Uhwb3BvJq2ZEiXV09;+Aa>wTt0KW6D`3-|L#I65nDN1RKI{V=P3(3a2mE3 ztUj~TsaV{kt~$eP;D`i49km&crP4N5p(T>ZbksGiR0f?MN&$~|F$T15(YYRC7`kmO zD_xYOSE=7=7R%uxjLzYabZ{FE!vstUDn59M1w;>?CFX_hVh(rd`#@1SG~n!b2MCxD z3BdabmzT&H+hH5Re6k%)3J}MZPsUw%AGwPW0aiQz00kuwqcsr$D+fiQry>&;7A5A5 zNZYd75ksz4kYwb*#Dp?BUbMo6`i||+)v7TSt|2NgOk&0X(@i=?tJiz^%X__+;Ur!2 zfPu&(2q;_&t5~%*=i0a}nXE*;j-e(d4j3u|NByxUJq6srf-F#Y1yYGU_{)gO@#Pv} z!=_KPZG@#p+KAlXPPMv+XJWBjeWFz^MjgjQT(_lgxQ5jlrddG-aikwo&P{W3O3lsP zgmH7Rp0r$n17J2N{hek$O6>Tj8HZRq`E={BnE@PVdZJcl)(q8>Ii$p;ljX(nvO$qh`&xSR2XKO zK4lnXK2lP^oftacGTDPD(2bp5h=(AHh&LkckkvAfKe&fKtM6L1hBJZ=*R~q?ti7UBF?K~RCDQ}OxPQ6mJt%+$%!SxZjsYJI&tD(kr)LT)fL=QSfdq=7=lG6 z21+mnZnA@N&Rad3J4gRQ{=NYjD3n)&Zz8$JH&sD}iYhbsJC#|9ht1@EmA@47yi2IC zpKREQ0(bUX2`e1$@wBJR?5l7V*yDf&lo6?ys+qM zmHL8OR!7xI&M!fb)Tji1;D`aOIU9}uMIuph$b}+MBXnZ=kJOI-mgpRz^O`oi7~eOL%u~c=8XP*L9h+5mO9t{Zh$_f|k#8_f5|=5FRjWG2YP4pw(eB zMg;9-scywmtz6OpLoR=&9fd-PD`Fb^ruJR_TBu^WT{;+7p^}Q84i4039B0;v;TM|e1@z|ehO0->xuL`v;U zO%-dU?DREWZuV^%tXM_vyNSnw1IVg=?2a!W3&n;H>GUWUJgj9_V22gLF(bffv4rA; z79K`>0`l&gmKK**R$fS@el^m~nRaQ=E7@kQ8~N1>yturCeuswRyHwoVu;)k z3L&Qy!MF904kTvE(3miw3&RIN6pRm|I+hhRo_XdO1onzPBeV?{Wue8kNb!A~ZlPQh z51Tk(n^1iOmV^e1;y*x;`hhsE+`|RLU{xOZah%{1vS*Fio{Grl$G&hCJ~{#|F8 z@A&#Z{kb0aZT&zW0#QbmYI{>(+}hIn&A=qw$9WHT1pDpF&atY(-jCo-`Zi@-`!7F- zru2%kf2KOOr%pi~TQ@8())QsPwgtLWC6hY2Ha}6{zcxvZwOX!B zS%VOKNJv&sk99hpqRWodv-y0senkBv;?L)56If!}n5gAqAAB}5vzdrX$`J%$LKhF$ z)-d1EYgG+B+!i(x-w*5x#?o%LkPKEl)I>S}_ra;SME**CKF0zwvx45&9Er08fCvNNJ|pVOQawRoE{`B{71e#+E{?E_#IjkNBu4!0fRgWk0o{1)RS;o z!+`1SLo_^EU>k&+K)zz6P^%rR#x+w!NQboq(yNoIG%IljDn+IKO)MQK!loeiKy&5} zLo&d9Gm*5|7(|l@{3UTnrX*~p(v8Ln9bD4IPOa)DObt-&I3!p68Ax#*$B}IninE-0 zoNVSSYQ`6%^v7yXKo?@WG58Q+jj8|o_=p=|QiUTYtZ~A&m|g-dp3t~D@)lZ$BrgzE zMqKh{zuu!AK!1W^l=K=e(Q3mvHI2wT@;(sT$crH;j{f7r^VJ%c=faDpHG%gNM1ao- z7d?mOUNCN8-Owet%}EuCpwjpVk?vT#P!XrEJime8GJ;+DZm@45A;42qbGOS}U`>Jt zZHAzR0JNYBz$DjzD^q+8>;{INgHPat0FD_>z?7y$hyXiJ00D@wu4eR6qt!(%5yt_l zd@O#_h6XrZVLO`nFYr$A+wd%A872Zzn6BwBJk_nEmS*DFCa()fqCVe`yb#}mtpUuX z&Cb}$bk;x&Cfx_{JRoNSUn4~Bo|&nV*M^NEut0>3)!fS!TLgNyKdj@;~nkjw4A2X~-1Z$R?A< zt_3Q?&s3ZGb5*jOZg^fJT@F5#`+6Q`3V>t`5jw+WoQgr=<%h>*IqJd|BZ06#~rLSh-$B^xYox zi6C`^>JGTS6@f4~4MUBTCpy$P3>-5o!;}#@EeI$Y5VhpF^Wf|%LYq8&Y92JKq-Gv` z@WIvDxw2ZAKlSwBth&nTx$qNDJaJpgeeg|hdebN3%ZF}R*|!8g;3wSH(!P~j4lTz& z@upXwI&x%sYHIq(kyE21pXO{Luh#z1Veq{YZ9$XXKtRy2&a*biGDtACZ6akl-^(O` z$`i7+zXn8CgY>X7S5U(6bIkjyFz23$M$fo^0F@8W*2e3NH9=ja*Ker1-uT8hmJ-!( z_5(2Kf3un>y)o{daq%zy=gI0TS@-YOKgnBPSxv709gq%rk{#&y7$c#JwW!zjAPv}7 zax+9=?gg(ma*G^n_cZ?3Tk?m)bDzc1=X*Phk^>F+FVyqf+Sa#h zZ{li0A9F`MyT>>CK5=@1%`V?r=w4t|1?}Ybig`t7vHx|#=8rC2r84@LcGd0OpZ*=a z_b5cfcr*UD>d(7+zu}t=%CYJ>^@otsZX{O(MVcUaAYGe|!J?hRYyyfVU;;cDubCi< zYs`y5U9OgcSPL3hz~f}2F{$+SZN*~ILqgX3u>i!2rc&|sN_ii`a>crcfLB?)Pg{L1 z);v4^g9~bNJnZzBq5sB0jp%9K#@iRBn+giVt|Zz<>gt-JsfjUs#ay8LB$bG#LOS0Lx>yD2n> zvg(R}GdzjW8Oy4vJ&CErpkkr}y#zsm%Jm%j10BAimGK>d3<6O}o2X9sB7ZFG3OZFF zi`}X<++>CZJO0GANJ;ZJnI?ck}3u9-0r;Z1f$*Rp;?{H76hPgiH-O_}FB@3&&z%uK>diZNK}-lIEy|hjOSoI}6SeB-{!k zzYy3wfxT^mw5rBPQ}FB{+-Y3c0htQEmdG9-9&lbfC1Cz9IL@ig4@3P1=cIS8oT8^( z;4$|ZvDkjM{%|jxT6rfMb=@BxIpMMCM|~f*@7l|4B!}s|-#5K@Z;^W>c(AtsES#Vt zvJ%i65P+-n#3rB_s!4={M3Tj5G+QbeTyCKO3EQiFYlAoO=5}YcfGC@@o%Zl1G!W1W zJ?>S+*0~XDQ$j^1r~x_vdZ5n7;-rvQhi_XL`b%PqHi!nlD9;7KyJ`qJX_3nz$OMkq z%fFUXOTGA(D)(kY;%QX!2O&X6W63@#_x9L@A6Qb`;@hXMOw^jkI&dF3f4-6J)(=u- z%BIwZQUw^AKBw%NYIvt3{FGx`eqr8+s); zPWfJbGwd253oRW@oxr%nZ}M?jyE7AfEI*xd8Xu<(n1A`UypSP5rd26>6@ouD+fBP9VUP6^$m}v>q>&;_GipMx&NrayTX> zaD^~eS8-Q<-@{e>t^Pb#2=SC_j-HT`_e|k$l^OQ#zyH4b?sK8$h`th;e6p`Z%Tbpl zX!jMs?73IylKWfuTSw5K`Sn)oCwThTp>Kt+HL9ZT-Lc z{+ZGvgNTX_{$52Jrg?KB^OUfuQh-7<{!}J$vuQS-aGaXq)oPwm8y%s05PdONbCM6$ zv2y2ih}`^k_z*u6`pM7-LLUOQ#AgH3iPBA&Ytapmw~+8_h`mh5#nF{CnYR|4|1@aP zz7ljLT`L3pLVdf&EyPN&076`BEdr>xJe9ZNa#4fYng%?wF92;@10HJvDElJ~kgo~a z(r%h+bLdVTpvu7yhHDc@d2o2rip8wS!zrW;*?s;F0m9?;di@msP5u<{Y%`t4r>4?< zxztBzATNvIY>^U|53D8hpu&EOt#ZWdIgdjg)D27bC;7RYcaD zK6gm|N?0OSJ5#G2W!X`lJj%@&|8f{B)BT9d=&F%%jWX!yoJ5$C=oHbKB!mZ2gZ5Jh z8btweg>>_v4mmi}h7mDhjYNi!P@!HQKTUOFVPkP(axj>jSTum@e9=Wh+u~DwwlMg7 zgdwsa#j7`bQ^lQ(wbMHuZgED!gZ6zDDCQ`^}$f$M%Tbb+xum@gV1N5n2z zyhxFbNS;5CI-28%-37w~@9D_K(5A^X5(RDQ3D^?N1YG@0Gr_E-qb^bv%+6ia^Z~F+ zq#=YA{D62wEd$`!n7tQ+v%F%0gTM~h1wk5_s-jH23lx|}dHMPxSYO1Wf&dY*v1-C< zw2{FE8Zea5qsIt$8VihgJga+TYu9cT%9_kVql4A-@IZn}3bz>HiFU)Z9mf_ms)d#D z;o|knPxt%Vrysu*dgjy5z~$tZ0mc+^l#!lJ*?1iru_y6cHzO6OiP;WX2wXvNG- z!SIJQaD*K0jY7tZwNCIB{_#cNJrl1$hu1}mi1-SPs$r+5AM5qD2m=^X%7Nc7ZdM-y zFOIeLVBo~vTF4T*5RxCeSMUY|Q_`SSA=VgD;bHq2jC-U z@$LEU=~ZDz0@qEcBCZmJVs?+vMtd;Jp+I#W>?ODy!orPoSED218$FM(_2;858YQ@; zV8yl=T8?c2cU?u5wmZPGKS6c#=!Z++h8^rMVpSfHs9N-Z4QIoIdxqd7Peq=XF6nz8 zHSK1z$-dHLReQYa5yv7hFmWQQ1`3NX8?*o!OlrT%NiFC8L@O3|z%anEx}cf}^ANE< zOo{pfnWwmWN9Hl^9?Dz_rF5LWAtBfaj&{Yh`u7Mu?xdUfd^2Ms{P=u2zL*B0UyP^c z`)YphXKFqVO#qY|$r4opva~>N8boZ3piw#Kj}M9Z{aF4;T7{1v53BT%e5~fgS5M+^ zHSSgcTN1Oum+uc@_4B=PH9PNM(nCk4g~oW{wVi@E3joxMdP^3j74}61*Gh zDt-p`azj}ex3+NE1ZW@g0m1K&krcVu0utRd@jtFq*R0jEXl+r z(n%2zK*LZ3ewQkx;qz=XO&}78(TA!B!hu}G)9IuJ?`PIEUN|q9rGm6+{A4fT%)mRR zvTtsAE190J!L?F>z{IPNZ>n(HgYFDbaDx%-1|z6K2wNYCG<=NnzBl?P42bJLS78i6 zyKEosxs~=k9XtSqla^hq^>g$9PS04@3>IE`NX%9#)Z?!R+8x3QrkeI;g4Z5pN9Y2$ zV3mv$zguhg$7FixE1$s>BlIKRqwR0_e(No6X9d^}eXea-|(D-fGP z_LyvQYVO$F9KBbg&LKm!(hG?FoJ_xb4YX)u3W{0YJ5z=eH5+4&2_hDxfXEl>-~(mN z?56r0sLnl%Er5#zUrlmok>@lRsiBtE*ayNXU{E+LZQwZJ7_cXUwu<{nQ*0iJ2@1EH zFR^Rdj*+;>!Sc{{BrGOmp{@LaShR}VYh`d?evPFiEE9I_Nf_YCZob)ecIAVF6#t_A z_j|$JYia44fRcw38CvC%tm(|ep)YS57b}|FU0VG?n8nf+uD%%@U!{_05U>JGVE;f~F7P)RW(Bq^L zA!G#^($7M+dnbI0J`(yga1&VHg7lsUao`_qj0XVT@6k4Ta3-iDj`a}mir_(k?g{yT z?*h7ig&G^(&r6g{R@yCqye1k`;_tGwOtITXUzka}7onL@NU@)1GG8cw1I78OQE9yc zG8NoME0f#_Ji4-yaBdI7v-Kxzr^$8HNA1`Fz`s)M+i$KRNF> z=TV~N+Rp+t{ysE#b2<|NdlZZr`ViBFoSHW7aR!RmA1-m0z%vY+(uY}cL47hB?ZYM5d^8eyl>7cxzmI~ivFq+j z)V|0)`|^vm#C_c3LZ7ro<6!7KpeeeGnpZ80Q%gpjA-ANDoY^fd?V#?Qg057qT%uXN5B{J)fbwLgqGv>L+d>k-6KR@jsE+ish zWFbjlU4O(b)i0bIau=$Jgn5WfUO@1LLWx9aviF*uogF=ue;}Lv0e$qBnMFyK!e&xP zr+Oja9`N0fOaCOkf7`Nl;vvk?kmBSRk(6aRjNt=dDxh{@3ZF$yg#IYMl;OoOT=b|} zU7i5}5}7Rn;Pu{Sr*APeWZ z2kVE$87g1R#v7{nSQ$C8CMOS04Z2{yEO^i-;Q#c~31!Y7fF0)G!GlI2Z4I7^CsT=c zm;Q=*I8Oe{C=!{zQ{9L}RtQ*O=8;z>s%CD3lLFHAH% zZGf9!ICx^=@QQ=PsKA8CdNBj1DEGeF(tc*$I9h^9*@4;>okm@w8UTJ^#a^Q(&}spK zg%kj-`9Bf^sG#7O0IWmrId&L2j^$ucc#NNrk1e+f$Dg?6lo5aj`q?rZ@v-F@{I_`V zEV7G0lM<)n8PcL-tyt_}_XJ}`d@Al$5ylYuKz)OX294N|A><+`wo{s*uNO5o?y=c9 z#Yhup`7QA90!&aJP?Bho4*(wnP6OQTIZs3nm+1#qZE>4dB2x7dyTH z0_2L%Alztu*4o-I8){+g5JeC{1Z~tpW)CgO@!Q+d%1m3hI8GMIcv726Cyna0*av2G z_h}0Z4&gGm+`9a_bS<^HP-57cVhfEIS1wJfSJca)o=RqJa$wddH(-K{Oqxj|Tdy`T zQS*R3>MrIz93iE6>2EAA~=uIJRUFsyiI zvhm09;xyafjm`ji06Ay6j^|njTo54@&z|vMc*)kDHBCNmZrX#@`am>$21+?^dX8nL znT;O!35l3`*=F;yb2;51N*TPstxAb&z8sHj09QJW7U8N13FSfP;h!IB0;t{9UL1K* zvT!JU=U)>8zp_~5@(aunp$=Lwq%vkS;AE_JV_@+V5Mh&uEAW5SSerD6fT9tV)YS41 zHOE0yoW#U;Bt)w)m%-++1+2VzkwUwc+&B66X)4W;B8bAeo#OHXZg2)45njOVN5K}d zcUm#!%v4l7p54#W{lnAOqT@uGs{>Ub&2Jq&xyk2j0*RmJqh*6Aikq7|Xb|aZRFYhW zy}^w>)z8`p-X-Ql(y@swEOLCav9m^NaF4+6vt_4TTlbLoMXf> z*H=gAU>kkX<4E8?54ZJRZ~MDW5}90zxjZt^lrmKvS>Q?@VK&&M_v;Q@XJ4*M06?~M z?i}01e;fx{ZTtGP?-vKV`!Su|Xf_seT}I;cYuz5pN82mn0_uASvHuYgQ1eFH5@$z8 z?hnxrL_J1YK9{JF3S)sfbj%O}HM6Rr5lC_mP3s;S##zLXg)69JP8S_37F9<9j_xEu ztO_q=ugarI7a;oh?rxK>*B_n5al}ZeM4+b~{I`@5p--#VvMhuXs#{je181iEC0zQp z#(0B-E$YU%p&Q?Zt_oCg#`32YC)lb_5ep<4@LvI4kt1{fYvF3g1dA@05x<236Q{8H zR|~C9al=vW!CF0Yc;CXbmvYkSQX+A`Y@PJrhyMDn|Jt5={s*BCscB;19YAkIDyN zQG6$)$&ZA79hC7;L;rW^Z$tl6LHFWEy&`$xi&EC`#qaSV4EE?csXhdIYA?o%|F_i< zYN<6q6qpd5f$A|TivCwm$1+0@i3={^@&fV%K;N3&%N3jp(x;#$x^W^HqGbwkqdWBU zUr|Spj!ztY(Y+Cd9U1IgUUGxD`TCc6@y^R@9Jsv3Z@g%YE84i?DFZCTY>0|@xZS#< z#PR%YHli9}nx)*uqLu+CaCA+N2JLW!MhK`?F)zt=i0D|Zc zR}wD}L*Qywi}yioAQixv+#%Y8PPo}@29#wKkhJ%PH@u;^`2GlRraBr9zv@q8v%qWK zYyu&aBME&R`^m2{^nr!7?0M{hqi^qJGH-nATi;rld@yXJ5e?^OAT>J|TjOo%TAU3Tk5{ShRw|qoWpu z*nqjwB3TzRo~U$1cr8(aup0Z34Yt>^q)tS@A`fC->Low+_IM!SW#|ueX^rg3)(I#hR{NP1lP4%jQga0SW+rIyC{IM;`Gz-1-sXJMN_3|zc@GALvvK1naYKhWkDnNBsaK*QL1UlYT zn2c6%52prrjBPAMLx9@`meW{#u?@+J*1xq|hCx-+qm&Vyh{nswj8m9G0#y@>USs*Y zEsFzzudf@!uKX&Ld%3KQZ0xNw$j-$tg-OKdn5EJb6hVONd^DSe*Vz2hAuuagKXc4O zTN9CR$xuJ4d-9|81I8yty(78@<2dwfw~Ie3SqS(WaS~%6xIwq&^8tzI3N2abSx>bM zR5n;Rz(@xo2?@_FYrk!y2lsKPUC1pz*?xI6_7E1o3oZKzbfRV<0R#LV^x>-Xj7$K4zOQjYKiR1F zBJk0chTg+;QjIqsJ65ScA++=|r;{xrsg_;LcAS?jAxdeba_rcfx5rmlsNT3S2IE&l zu-U`+EF4f%2cd3=ADmJL7VbHW;IlQgJzljp-iklRcc6FLLe}INd`Y30_0^8roOfs~ zi?4w?zaxIU{4T5n2<4(*a~bqn=U;cf7R?>U-yNBFyzHeGA?=liEM^45uk0H{sa}XR6gSbg!*+wK~(^bs~5fwCuCMJ>=Ezg_hv5m2IobOUoq1 z1%RgBlaB#H!>5s83%~+-2N;6nj9fVWgTuicA~$2k&~tr64cP>%*m? z8qMzJ(Kk~M7IL}x%2_tarXQh{I(=tM*lrH9$|hH{NmDh8deubHmx5KbVx?ZF~)(p{eMKCO;buid#g}gXo0}3=t$N8!+H?i7|D;mqobopZxyaz9+zN~dm{yk#Tq2Pi#hj_zy<&7aG%~uDa>*X9V%SgZ~tGD1@2$KtD zVA-TQ2W)7_t))H(`q4lxj8ll95mK@`6Zm7dUQ=6o2%&qVe1t9<*6rbFa_EP4GC&4y zB8e8#Cr1{b0gwQQA<@@a>5ex@mT-9fBW&#KSvJfE+;}4!-m46N zpf3G?h@bhi`U6^^5OYk4PppEBWG1u5MkLi<-Y%U=`)B? z_=V8F=6${msVFl6v%zQkwOA{YjS~(3d5npsO;I@KxDdKf#wkW_P~1B>8He>`dh%}n zIjE^?`Om?*raU+w+A4svFmyb}`>d_~D=S-Yo6*wYjfl#+g`Amnt4EJkysVikxLFn1 zI9!T0-9pxCAeWb+SNtLWywSMwzRLA-AExeQ_jTj3oP)pu5HKv$#;T+)?pGf!xK{R_ zsmkH1iX6_FZehLR<#JwSz2KU;!x2?IT$#EjYq^EH%}8UGs~9WQS;X_Z`#ZR=o51o~ z7?>iCMEi|8@SBGGR7!Nw4u&gahhoqJ_c324VP8B6spY;)2qz^Ljne+L%#GgH6{DhaPUh}3__ti*9R zsoTT@s&zUjL5bRKA3C)3)>9{|@Png}T&p)$R?diX z@@C+s9PBrVUIJ3g{jj}&9*SXzZI4mNz@A3Fl^TtI9s1SK?~6u=xDccRU7>? zU*H;0C|3nKZ6PQvA5U5S%27bSm>S3d?Z#li6-tc^`Y}}RifU+;8V$L!S|nGs0`^r_ z3UoN2cp(fO@Dp}9z#xFr~6nMtdhnTVQ-ygt-}6%`TEud@C$UI z5U-0~Ebaj-qJVhE2FntUc}3TSPffrM>59qBFF*F!V~y|YJg`tI6iN$vtNMWgVg@PP zjaFeI5|NdcKX^G{Y<{gl`j{!R0Ccq+p06m zv^=s!jA#8KJuIV`IZ#OL$AeHqkBQ@v7(bx0gDPC z({b<*k*^j-7K%CAP?3`J4g_k=SCF!b@T()ElA3Y~Vft{AQGvjT_rmL6|N2!$sgNBcIsoahEqMJlTJt;-NKe;v*JV!1UvmmxuZ zHS!;3s2>Jj2#F=sfmCrEIHTEwo!?)jg)dwsYF;Dg2NGGDXoN7g3IIe^oa_>Jjotv9 z<{%Wzz{fk;Msjj8*~qq^2+9VHaanbDm;gw;09YLbUrqIDO4WK(!d5G3^TM@cUM+jt zpn3aYYkd<|rul35Ku0wF;1H>yAzJ3LFCZ(pg!%Ts*2E942Cmj){kQ~>pXEIAVFb#}U7dRTcsbTDCyhzpJzJ0I4;wau%PqmE2Jv4gTNSK+Y?c!hI4fk@lwt&TvV&#Y=X z;?o4qKuk~24bTXtP~_Pvg&IP8Q9Vl$0iW%~VxGGgH*MD`eI#2c=kCvDpDRyJPUez} zpwxq}pEz+M8bwkNy;Yw=XNh!y#R!YZJSvu+%VzKAogXQ2xmbKr+8lfWH@faa(Wu_y zV`9qG`Pc)9fOqxUU&Gw1S|z2#o8?eu!X&#{(0KJru2U zm%$F{Gd!@ncxVM^pJWx!FCwAb_=&7@S%3mB7Y=79C$r%&=&HRCKn)z1h;rM-M5WRc zXE5<<_ccHaJ%tpu&;b}h;6wTnp#(G+eh(tXzeuGQ#w|Kf36CX>+~Hdwog!nMg2)Ci z=Qu)ttJPGRVpi1iO64GqA){nn`S>{etH$0DmEd>q3;uaNJ~_JWKZzp^d>hi8YFReM9)vP!mGXr3ifn`IAY4?=o2KgK+2n& zW?KNgAYi}z(oWR>gXNVDdc$bJOt?A?-=LW7v`b~I*2=>H5FQ@iCl2t?rO0Sja)<|3 zVdu6OF_s){YVgf$qTwO3RwJ6}zt|yIje7lHyj*UGg2UlrAXP54OSxPO3*Zf`LWX01 z9Y!+8YE9k_* zw#}GgEi0i{V_U7Q%g%oYK3=eN22P5;-14Fm6gCV3fk~ z=BBFnBT@s{~UVd zWav&<%D)#nnJrK%vW39dKE5VINe_Smfd)xWU=Jd%T?2i<37a2$@r664@EY#| zez*yC-}i?;6Z#90arHDNhf6t^*Z}-T;Hr)@fG_!q4%5y0{DsyhAlWMA)^>->5JkT) zng;EpL`(m;ybEU}A>rg`z_D+Yrv?Q`vXD(aXd(U@V(RrYt2x@g4_pZX!h}=ESZUxp;gTLMq)-q6*K+kM zSHA{YE^s3<^N&HM`Z;6_{AIvR;K#~YT@Zs4_OJ^P3utH|hA03+hGiHkW-%ZxxOTav zv8mK23~7iGEeJIs{*U(cqE>!DNfYQnK%%rIV_3q$J9*t&tKFV<(^&AZaN>AsvRNso z&fGD@;~Q=~c4~d)$}^Rly5;0`JuuN!zGFL7Nr|@x6-w|>(|V<0iG*8Er4yA*CgQkG zW;#`>*@>f>&!_T;i7-`5rZ)V6J=Hvh-(h}2KhR(W z&m5|@f&z>cWco{=hZOx;ti3r5DL9CFw2F}-j@I*g3bdW`7NWc;$~AATB`nQ3b3*D7 z4*H+H9WuI$XOiGxZ(Ce6#*bA47`9k07Yj7S1xJ1T5qkT=9f#NBDs?OojvZd#*jQgr zFfB(M$K6Un-&UxMj(omZ_dV1Uer^HsHds*y5&vVUoQ{(gCQRqO3Fo z88pC#qDU0BL-X+F=2PR@2cE)^`8IR%ZJp2wtXc)?{rW+~ML2xq-n9bd2Kbf#G_-bonB9l&MCMH^=qw%jAYERYg-FoY-bu5C5ysmcez}me>4nxa+@Yk6U z>GO>HqJ9s~;`E$?25WS*3pDy80H)lqZS>F=N%0Hr!`=sMqedSigV!9LBS(=2*_E`? zfg$LN1KNX9ByrG$hQb_(4EhgykpPD`iEnHRSfdR%(6!QV830`DvKbPkku++fcyKq? zQuvReR7qn+CkSxU3nJd=UK1SwQAt1Hxe&&}Tt0+2!W68SY+=~9n-|ALDKn9~MPI#f zt~gnR5>Qw4r-#=L^b$cB{ve@F?I6MTg? z77l|vpQulvW(B0K5f26aLwa8nxW`T6q~xnMs7EFI|E-I_&JmT*&!osAxecs42h%oT z7q=3rG*+dB?TJh?h77>e`NF5M;gY9=xLYhvFL>S-m%^PoWJ1y%9^wnIaw8sZVjVB) zW|CH^)SN69oqBE)j{iUiM~phwlt7={ES0Pzs$`%bnW@arc<&|xJ5v3-TtaY_!IV`HD zL(J(xR{Qtea{BDq)3@xyQar7Rn3_6r__TDdK8K2#>Sg~bx@gNk6juuEs-R1Zg@v1L zT39etUx%ynXL?^ZuOAhZ>6h4Mmj^|G88kTOJM_SW`QYa}MaWZwGCqw@B`zheqWe7^ zIs*U39}j(1cwBAA(H#dDp&89KqAM=vL&3WUlEofV9(cj$Fkhr9aIvWBUbI2vzHN@V z)rbro#E#>jYviRzH!uk8!Aght!8`o&f7(%75hFH%C5p&H1xBx21_Ow7a|kyZsl|-Q zd+6PN)$1R)#x*0dy$!z$usHr;L)@k$PsGy0fNcBwO%)2p0HiToMzS$vx~n3flfku~ z$p4Tc%u3zUQm6-;=qmsN z1z!_M!`TS$1ga&C+|4EpAW$v=FSR@L2jLlO1NrQ_hLIwI(-#}`40y7&wA8AK7X;KE z$p2ce&n>EA$vJP7f}qjll()rPKzuJxJW#%VzuXtIrsPM|l++)nx;Wz)TGun~zK) z?>Sc2@}WC{sjD9x1pCvxpZ|USU3426RpE%q3;u< z=MT2T@HWAfyvB+zkt4qae!RkZ0cK~J<~Gx-zFv=~T9?O)0R_|JIN|_DM}uwZbEwxb z;%Vx9IQr>?lJ0Na93WkIoFP)y%jR<-5J+~qyMEig6{UZ*e7br8YqWFK=7<(~xD4P1W z_&i0Rx!hAvAF=JtD;W#WxCnVEEA!S6cG zM^cEMZJC~9-JXlSAJaF1Y;vC4X513Yv;OM7W68{WPM$nzd&y*L(lp=NoSvpT!R^^H zX*6ZjZ5gk&=&6|lCD2kY0dD|z3>_GzI(SR%KhdI0#1SQH4tG7F&ti06T*KIsqr?^Q zEDd~|*Tiq0>_sV^ou1C7OJpZ_UPw1wtY&GX3n{2lQ|ZYs%#Xo@jX1I|JBqtCO6pKUjS-cy^zl)_($w4E@NiTQde(Ec z>(5*f9HbhY889atPN&~|kSkjdgljXA7`&bn4c>OCVCD>@rjEDCYlDlbdoYoBGybR3 zy+mR&o&G;qcXN|h#aUSnB(BH6IH8_~F8GazU_k3S@jPA+d;IW97I2iN3>qEj5{cab zizRJ=t5EuW7dOEP=DWL1B{JF8zI`pEZ>b@uemD$uUpu$3v^WWnbSD>==JRdj=b>a2 zuOUlJYv2CX%y)Xjcm4>_@VCceaFZz^1+&eU;>&r-vI?gdRwWfy<)p`#;>+1c){HeP z<-yKNzatUj9kJN?mwtKc`yL)AR)9_MDeWC8p<=m*=JvDXbd` z8}XT3al4qCPQdf|funHqE5V^Dm5Ox{(8F1J*Y($5zl4?4(Jsqku~J1nI+xGo@^gB7 zn738x^$J2XAP-Q=t0ukaX)jsjbAAiKh}tE6V;3G1vC}A6IUR$8Mpr+iJmcfu-v;jb zH2Q@;Ggu|0noZh?#U-YQy`HUsD}>Lx<3mM_iRsZ^L6}!?G5mP;#p+s7->iF<35oeX zT&c`^v8eqPafxYYO4>-L8ef` zBT25;o^DmENb{YjR$J5U9v(J!Pj|dE;%f8KCD?I~gO@o2i{hiO{X7FmAZw^4dyq?f zI6jsiNF0FA(OX*4@o~!ljWs#eGCo32NnL(AuXnQJA8$*|R@>(w@gOt?azL^G^4%iz zv4AJ`l>Kq&^o{h?h!L=|4lTH~R;xAJhu8O8h_9Yo zsZag?w7my>oJW~IzVp5_@Ah8Q<&|U=+ge$&Ey-~lN2yLFCXFO*TBJ@D6qA!uMHdHTiZxGEjAl${4a>5+6QnB`HrnyTYLdA6%F{~e&{vg{y_AP z1MR6;z}K>M>kc|ctVUBw@0-IMX`dV#K0XYLm0mpN5LMv~ReS8Pqx6s-VP=RLW2CW^ zdH89x@sL1SlOK9UIin;2)(ig#D|*&hw(h#7FCgbkz%SD&qAZRhHT~9(C}8@uM!W}0 z_Zr|2opn}!ABmsDP?DpY&L-ziWL9a~hQpcDZ}mGwwl-{jgZZT)*O{)%FgA|ug4DpI z*QUkl^i7jlf-vW)8w)(ja*ZaqQ9vP$49SJWkN2#iQMQCJOuc|pJHAYg+z>)3_&42d zuF)eFVp)?v$$WS;4oxB$Pnq1XQi^MX4-XU;?%h|3|Z&$!4FN-Mc=lDLgoC>RzgSv8;%snHl` zydNkOckYpXA}cA-8*JbP-Lc#_fSRMD;nBz`65{c2=Qe_yZ);K(4zt9jl`DJV31jrLG~+pLKl+l{8ElNWugSDt}W0}qVCRdnWGjMx#v~ct7s*J8w{>T!E*#EgnauPceqh@WUNxeDjatfZsZE z58dn^!jX~v<9)pV3}017I14NG24J*h>||Yq=HdmeS1<*Fe2g_q$cuOPkxiM{59ATf z(td}mkSBP7Hkw#xM@f(SCfJ3DnIXq`Sc%3sEVX8-f$WHgaR?EebpKy+CNzq|#)DW? zf*31l$s ziVn$u$41X?4k$|?vn^%yOqCq{707N0_SiBMeoiF943;N^8m2j9ewDog! z)qvyVl@YNDhg$+flw8xL^>t0>rHM9Fb@^dL&+COgKkIsm>!s*F`bSaYN&Sdv<}(zi z1IgWXCcV zQ6gm9)YQjJQXihNx|7(e@m<$ne|@VxLO;Cq)>~Vq)6)ng(cZN0KGTTA0^z`QG^!A@ zLm$OiHOBCLy0f+M61&{i`yg(35rl9H&bJ~h|9P1~!|d+r^ZFA(3}l}Xk0Y#h7;#1& z4TM;$jw+Ax1voRJtr?M0B0ggR!w-=taB)j}TiYcz`{WF=*VhAcz1Q8befyB3!9Q*1 zaN2`V{bpjly}22oL~tGte39bu7{#C`nhk}USjAj$++U4=X_?YuBbMpmQgsNJST7qf z>tAL|X)eHnk_<_|E({RZpN1&vy)#Z+h^8>R znjkUhcl0nh?g#{0wNsnupn&HFDXG|cnpsmRMIzbRliJeR)YF+v zK+y%h^Z5D(uqKy+abcv>9RN~XOZrrzvy*nawIl-YflV@NyH3SIv5lr^#2Ri-iX@!p z0LZ+JQs9}-SIw-^)Hq7q zF-#8;ZUSt2gTR{XsDz)%tEb8kKw3o_wQ4Sv0#ebd_SJrg?CN$gl6a|7+ib-G9)^OJ zl|{-X{PBrgnhhT77rw5pF7Un5+zvL^2Schi(CU*2GvEovu;~D)TC*zT}cAV6Q_ykx#V>@=V zY+fWD;zX4X`~5ftnlIvbD|`$5UO4czBYv+!i=@}|S!h#8VlNlsRU+a&4ofiMFDip@ zJ_M3PdvIt1nuA@UZsKTSk!)Q%k)9 zM^O_)jtk8t(tiLb`~k}BKa9gT`3?|3_xDpIUX;^#Mtm(A9hM?;DfF7n2*vO71^e5( zEwz0IShY52fxN*~v^jO@HYJBcp@Gc@>(y}LLm}GkyY13cb2LT4fQ+`D-Ma_3DXY7^ zKM1v`iZ(-CdTB(+;b?T=FZGHnqSwF+F7ZlO!f78k@7&hQ3A#7!>i{8{`$D`jda75I zX4uD%Z0*{0iD4FT5S6+AIw_h4_w4EF5@P3jNmcsZokDbV?GZ1s{ExYqjQa=`F0H9c zq&lu<1Ih(?)ZIvQ76-`s9S8+eEQSR+6!XPiKi1kq_Y+xNMvMpT+I&ZLK_T2*Wpbo!^OZF`@ZW z((_`PLlKR&fwux`!J;K@uDzWOyL9qRiY|}*Py+d)K}9Pj>nFJ3W}V`qHlf;v*PUw9 z_5&P+5bFs9o8!?JQzM}puC|gG%WCUS`QZBcalm{Ut_j%kM#cNVF&)AwfMCFBG-;|d z1t6DlnhaVr#0m8Sz@UNv$KCC4&?v!WY_~C*QXp=Ehg8#q7#e!aUGVA>;jZ3}j^GHk z?8I7n`+fN|9d@D>4SWJB(W(EI3h3lN9$*u^;09jd~|{pqMVVWVDDB; zLYLz1!|^|WEBtD9qxO3Vd?OK9KOjB>oPDVbhvgX^xB~N1x}#ko{X7xe{GABGS;jyp z8&Dj>++^FfICLCzCUo=NL~ZUSRyI^85Bd?!rISyAatZi;X@v)9CT4)fvqynrBsO}C z#iLP(HFQV3r`|+G8W9?5nizVbR}$FGE*7FtgP7R-ZU2D-mqa4z@l0mR-J2gf`|PtP zMaS#l;Yq8Vnb~9Bf?-SPn8IqFDY#NrX<9piCz6)^%CQ0Hp@HSDjL#-!AqajZA^}|4+l~hzjvtjy5Lv1Ud zG*g1P;F)-+x)4qfC*9!SC~VOMKV3`Hxk}_juujeh5y$yxxZ&jAwZ>O!ufuGSf}+kR z5ZVaLeZvh65m!pJlm83VJwfwD^E`R~3B~K)CH_!g9v>7hf}Oh$BXy9+30#`yxxsZ~ zxx<~=MhluD1IkG4rXxJTnh`^YGWY|#*3Um!Tg0$`yU#ct0{4U6aRb;I;l5xX6}tdO z7zT$%M~4vK$$ddAMPiH5cYzy6X$`dEm>`cgv{8?>gi7s1??wj)w;=98;IT}yU}Jnh=lh)pUGq2{jj5BY!R$e z%tEdurERJST1SQztUwT@-MdK!K>|tACJ=chVmyw8jC%kG(IbS_JXOFO;o37e2$r#R z5CpoKsByM@!}RpffQ}qEZ~y~Mie&s8K!<2DdOYwI zKM?6k<3@i4SKtH%xn>>mAH{wmYwPuhHVQt5;RBj)OB?fE>45$I00<6vbw5pyfFc$ZP_L8r7D3|9gWhVdYG|~ zJX<2~;I!WD)e2&%={X1U&YIDq^q=92r=+zE?YioCRYpcJL z-o!Z*2F}j34+@LuY8vB7ZQcvSs>kvbVaTTb;0)xsc&u{<9tAj+;}!bZs(^UE$tq`Y zZ^~$}0B=HKpM)gQCP@5zLOPyE#M4g*;dRmGvD}lo2FE!U(*=3`uQu%CXDa;>$A6M|ySRw3!+aHJQN{`+BPU7d$=wv8#U8Hw#cp&6o zj5_*AG)qJlL%w7zaxE9Rb{q?tcD0XR`x=1ufeQ}2N&gUUJsFK&8;K=-p~VQ8H5>Y* zj4t|u1H*&8;cIn~ae5y%z42=|U)xZJw#hM0=)@j=($8wbL`$3q6)j?-2lPiNa>dg7 z_O<0vmtYMAVFF-4?N>S0G@fD|P<<6t0r4M%psU7F>ab%py#wJG+v_{hG4D5wP|r3x zlRV^x4*d1D^_1SKT2$E!v)+7LHS2y96(W+0o@;uieI9ypcZg0V-_`^DrvEnEz>6l; zjJ~#>0DF!dHYFvVr^oA*8W6#bz))?^OBx8^$-vk)s;?zwS!9t zXHs%P*G+i%Z_Jw!9zG2|#8Wu^o8`7D;6J26TCgeFBK5$l0pr-3Hi4!!_PW*uGLK!ab?jqnE-?MJ6+2|p%* z{ricFk=lu);bFYJoP%0;#W^9Iaug27uO+7X+8FTQ=I<=ag1s_yI_X~a2O?I`V+9*= zDcS6d_V$+Z_t*C0T$*ZYiz7gB=toyyeRVjHxs9!zmt{Sa}yfXmh*o^wD!~3}MAXjidL2xOeuvi<>(3 z^amr+@I~kNd>(fU>a%3(vw_4H5xFQz??mAS(}fvm+udphscjRM(=;(CmBBhUv$Suv4C{5w>QBg0r? z`btZeBqIw_k*#N^bEZJLwxf?jY>*bRk|X#0N-;pz+puZM@5?E`($gb`|Z0u6Ho05Es&pG-))hfe9Tw z^73mE9^dVB5><1!Uu^!FswB05hdzbNd%VPSG)9uV7KgxHQVTXobev={y^rQR_9m)n z_q2Vz(T1VN%+Uq*kd_RKpAaI*6^|NdT2D{IE8A+AR2^`nF2&5liKU48YZd~lorjrd zCIAwcw4a50O+yj%(WN(7>5W)+dZ&td+}JDxC6RBiuO~jdcg^pM#0ee76*jryrj2ge zaD(3@OajicFg!!*AL8z1h||$SC%|D;yLpI!vvc zq$BDGeoe%`T*rq#iF0KZTt`V}U{*>a$9e?Gh?P;OrQ;qrh6qBj1iKIpsC*Mt5t@V% zgQAfNKOHxLh6$|_&q`45ZgB3r4^oc1WqW7y?G))8DAIqy!LBG&e=!+&oaJe!b%{ek zH`k(}6nS8$EKd_ydnLQyKt5Wa{h(Frwr|!FpZiLuPZPEs2Wz5DzM3)l>3J+o9xvp<1vC#cjwG6JPevf0^8 z(wxlbZ{x%==cCoI0}SGn3ym;0Ff5wET__l*%&1f*Jp$~zS!YRcK780qUUA*#(s3JH z6ef%gF&G`$jTGj^u}5Q;9E=Sumg7COr=N6%&H!s9MX?tM(t-bg^N0#2K}E@wzo#Fw z240oXp>~uc1lZ+vCbG$yHoQzTTsO612X>1TCl+1+UM;rmiKJp8T7a0lqcgL={>Y#I2`@vXhbPLXxHJ2QXnDfh`1bv`-21nJC zs+vRiiRmk3iZyukoL-d}Ya#HoA~R{z4t7x06r-M}*1WU}trioS1Uk|0*4Fg>s-|kI zxN{6&_5=Nc>xNEkpd{MGycO$UcOnMTHB}v_qctYzz^pUPCZ!ZPSkk-_$swK{9;AX< zPh#uAY!W#ntZAEIne!T%__Mg48h|Bg&a05Y)!Sn!@w62t_W28@z&H<#LNO0 zP2;**6!Sv4Q#%hqQRhZ1m%8-cZ-d;^BH#;;{rDNNw6ZVUjDixC(pdlaW|Y_u%G+v&b-Z7VGc~;3GQg?JDq- zM{qL4sdk{+G9~%v5hG%>+gy|~}Cdnys#AwDhx z!=9Gy+egfuBO^Pb?XYZn28VatNG;jU{4UDw}3$*nC)V;4X*_7LCIR!(k_~gsJ%afw)AN^i1gS*k2UnxU|4HH zh*mQ^qXch7g=kYn;Q&?`yNHDVHVDz?Av`+H+`=#;!@8uHq;e?6q=L$$EU`YY_N^ny zH9#+R(u-UN!yCy(uf<%tL?0X2$;htPAzf`*u1XjB+``qfe?QPYYwJK}^;Sg!6mw)` z|Nb7hh=xKvJ!eu5n-PhavSIPeo*sDQVu)>dl;p9nD-Br;gGC?G5Dl2dJpqM{1V*|j z2(f7Wb1*_$Px|mCTOAG8y@2#|?2TU1UJgS+?!fD+{>D1?I(@>m*ENZ6Slm13^e3lk zUQSTKKJ+4rkYV%(E_^=b^q;@hC;|=mOIQcB7GntJ| zpI8Hi)TZ^M*W3#2?77fsl0B1VQ}cxu&~!%u>I;Hk@NL3_%KqvVc^H@H+6{I=m*@c`_OKQc6rvrR}im+Muif zy5Vf3yPe(qghJ78?JGyHG)AlSpOouiyd(0XiI7$m=_}z{P1LEx=%WVpJ?Ugvq@o3ee-uuDBwoVP z{M!Wife^5pk@dLP1}|>Sob%6QcXxNfYBt={ns=VIyo+s#o{KVj&e@Z>xapj8&I!1^ zmr_7_@d0{mO&7z@ai|j#ZHr#_l&)^tZ8Tf)o{Ra-i_YaYjp(IbH}6r^zD;Y;$I}iG zlJdD5APVHH3*EWav{07-i;OL7zd}~d5ZguFBVYMGddInvg70J?Z*ussQ!IdA0fwV3 ztsVQ@;XfOL;DSIEIEoRLPT%&fEs-Q0coGh^^!6AywXD4j#%8w_i#B&B?F0QZ^shQ% zUHy^}cR)4Qj6*YhIODIQ1(i3qM1#@hj!4uBnyoDes)nQK+mcD_J?~FOBG4VgW9?g- zuxY*h4!s=oHA3)D{|?pXYP@$ZnBKcx?XC=bc>4Omv-3Y19_lpy7`<9<6QUnRmc|pK zhKK8LX>4slAbM=|L!?R-N&ar(5&fGC*7=t^` zJg{$U=T02HByPfKcb=YIBZJhizRVz;%r(rCezyyeSZOa~&Bre}qOp=Th>*-di)|GC zo)A4rhdZvra6#J$%>$;Ce$m`y^bt$g2!yZ-T*%iqFi0DE9;SodkT-aazBwt6EoUe^ zsVy=S8xs?2_H@z@|65}>6WpxUvfytX!5MuI01veR180DZE@iKJc8nq;AOG>_+YE7+ zt@wa(=_FxV*-Qts#F!zQ7NQ`<7ii#-&j`SULc|lpJFmHB=Wqf(zFtpY^Oh?d=zG8c zl%kV^UBX1TR~=D%lT!d`v4*llUKt~ApdQl<_V#vn8%B3`Z!b11;9HYUeRx498M#sN zUZ+Oqm&OeB#$Uuak-m=jCSq~5my?e3 z)9ogfx3A}Z9Dfmv^C!_DxJ`>VXiUZ$+;^DcPob~zAXaKT4(IHsSjXiJ0U81Kh1BT1 zcq0<;>^r?*K{D;#IyRA}x&m>yXulU#6OCd0`2^-RgI$d-;k24sOK?dOq<5MU(gA{k z)tB1JmIjkh7tK3E04&E{!UebyIEZ6-FS%8@jIQpz!#J~p7`chxM1&PS_uM<~IQQJJ zB_jX$u>c|s88cp?u52G2J!j(VbN1|MzfuYBjDc!4u_MQ=5V8ZYm|8;04Kds11;CK3 zBc90hkZ%#y-{u80fg>_rpQ6^$t_OlrXN+x^rbncRm>7j#V+2qO-mls?Y4Or0`jV;+ng^k4M687QWfgnppzFpv$OFZ|sw>?f^l{j>eF)q0&? zzn379C46>Ci^R9KcSI&P=#$88tWei|Z~ajxUq+3nnCnBu&iH9Md(8Dr*UK^IDWbIY zc@s2Jf*s%8)N%L7FldbaBNYkJQp$DmbZUgTNaXP_febU%BB0PBlD^jNHOP6Xp4rY8ColTA(M!*dgjRyf&; zPUKC60*GFQ;OzDt92l^VmJFzmrqt%`&bRU5uSDtFb^_n#wMqD7jp+;IH=%)!hThqs z!_dux&EQBI=x8xwTbw9VcdujXimiAY`-PJdo4>`e*L28d9w;;I@i_a^5v3eZ=M5awiefR=-8|BBct*tfPiDe&~J60s)OFaygn)}bb^^>mUbpKS0^T5 zOSlf329l|+&DO3Ykl#b_pJLKs+I3V4;oAIi)G(r5AoTS1?`ZuEpTxV8Y(Yp6`@~vc z-#!x_11I*1##_r&QPR<9GhmH;YoQq8gT~}Q3efCS)2>=~nEL3B0qZp=LL;Pv+RK+` z&{g|+XpwEtR|5Uw69KA8)^mX*JV(d zfq*9rFLF;ZuzA-#_uRwiZus({orsAZLzEQh57GW1^0WIm%L(1!Lx`U)V8;4b8gx@w zTy8lwKPHEhjCO#g=`X&=act@tw)d6yo+5B?lU8ImDE3aydMl33REQX>Lq!fEmAmA4sDHRHF%}SfYv|ChtKYW zCD8Y2AL97#gl1#aa(8aumc~)r(C%!mliJ5fr?+kIG_XJDN+T9C0={NM2aiRKIAV-j zo?s*{WsJ5unSlsD_czgVoIq#6WDD&=G-jDmSZpT`dOdDrdAtYHc%{2%eDCFs1?%#? z<2~ImiaUNy%rJ12x;wC?)9=IHDePl|(ODoukBr1{g~7@$5lnYNzCj6WO2S4sGa7Tl zWsOh>#{pn_L=W)DKDOmP3FH18>>POo(OBa95j%^=nQRWkk>IFHN<~S$gEvN37b0%f zwkpB$1uNKiKZ%NvbNT3VmUdZsz-k42gGVGo8B4~%yO1E7ns!6GN`=Xjq<<7|V`uR= z@^Az=?aqX3Oj0pwcI#(a)5e3pk*)`#v`9P1RA+w!l(ilIx3^>ajBmVM;nefCy_iRN zsvP)%?4|$lQmAb^UE9yYtCXN>#`yqzetP2M2k1jhciV*>QqWG5M<1<5(i1qr z`S;$0BB5zX?Y_@N3}FDAc`Pt60r!9P8_O!_lKh5j|v5&$P1vZqn^d zJXNeBT-z$*kA&iXjE90oa^2@XL@ogRL~{DcV1LN%Bdr%+uAw-DWnA5SN1Ot!$Hf&B z?n6|t|7k=lp4LyVA9DZG{jwJlYeJ&`Z~dwf*#ASfSc+uYK5^=f0p{2baUBpx^M&(a z<1i)?Z#Kp_lzea!w(}9e8$>Du>DFk1Fes@Dl5Q@`?-1@Ktg8*KRN~Pq;wMF7((j){ z_-P7{ytNk_r=hSUZR1)x8aNoh|IuBYTSzgxc{^>x+S0jeGD3(mg8$tR!>UO!v2WkL zq<5>7TfNDBQVuwAkO!o!2D^X$bCp{G#vEM*K&}BQC-G}jOG=)Y<=C7z z9eXrhp)x2cChY`eIr}PB!IKAEXW+!B9HOS4=+_7A3cPGDHcU2|xkyi`H9ofgovj06 zIT|XFF-$L(DgZ%m6y*w#LHwqE8Ltzk|7glJdNsCTBSR38?aCV=oMZd45KKO_wM1!a61i;(po4|I#XkS5pW;N<-AL*h~J8{2JAMmag zxxbcrr00Ma_*gO}9x`~0MlI$SwT4b3 zeeK%agsgwc%pbNw3=0|TGg2Xr!|6ks9Kt78AMN!b@Gdd zcX!f@Czm>fme@SG)Ia~llN+H*f72Mdntk)DJzi}n z*zj7zqsF%yeG(d4&bq;Yqr_O$|>PZdM;%F`{3z|3c#s~>^&`kTr6n}#Rp>eSa4IU((`;rCE|-2TnGAI+=vWg6O-uK&hr z{SA0c=ng(+oQpl+0r*B~|A!hqWnPQ;X3$#@ZM}z=QsgGaTo>_SkgUc>Xio#lP$|f~ z#FG)WU|CY{R&t={IXaqco#}|9hg{^V!h%cUQ z+PpjAz5?O)aa0JC$TNB+qO-s&Qx5yn*a%d&vHztBoCdiA#|ox;=emcm=X{NH%;9h( z9TlC=_9btCIOIuA>QTD5wLq0nWyiZnSS6i5>4(-`6C9rpP(l)B69<@bSPn3lbP5*3 z8Sx$H77H5$KBP*)JLFMIpmYioq=2W@+1=fK=`WFCMTADj&S^zZ2r|2z)!pq=IzL>? zuAfoR^hbW7zW$AdiKbYvAA#xPlh9I9OukzngOiq9D=~=KCia>9v`)+!V^*6zdWb*6 zsH+!}G;^vXVDa=b7pfU|cdrFuot+CAvIvR@?4W}YzYh?@ z;BXm}^*2WIS}3sjIOw^((F0L$pv{pL*PTS$N;D$R0Yz8)l> za@}>;!JEG5aeMsL1a>OTG<9~QpovZRd*19gz6OCv_V3T-ZtK9=c9s>MY>N+!Zr|22lJpLUH-{|?r`vVh zmdowmj|e2!z?;$<@FquEwrw9Bh__AJ@UQW73-*YepqtSmB~1~z1hc?QT2Or7PTSV5 zakSA6mv{p6cYVa2P`;Mdv4jswIWr)`_F*ugK`8T2C`?nIHBru&7;9}o&?IQJ$=S1)+*_z&qs90+g;Cfz1np*+TGRFMYu`>vE%kb4Y!XQM4PKp$ZnE903at* z5Q@JLwO2Y|80(vvWu{BE-kV1QQ$t#Vs+$)K?GJmez<<{jM}6RvC%`^WIN+{ru{0vz zCx_O(QIFs6nebZf@S*h(*4E^+>zDQB1K8t>j8$L?J&aZE+(XKJAi#&Xh(mP`*6Dj2 zP7^q>;P+pd@_G*;gcTwd1djXbaS>EtHZe8K`}5FVj}IA7 zN4%a4=+8B*x5f$Btb*wU!LW~WV2lFD2cDt`B&^i~?DaPd!xx$STc{i<2m49G=a6_k zbu3OoXBZDdGkS=wo4+2Mv!IO*hM?Q@t5C4{MQ@|Sr>vXb;#=vu@#xstSm$ot;WhoU zJt{sNz($)*geQ-OzxC>7Y{Ldtv4VpL6iSNf)@z?C2Fe$GXUC2m`T7|;fVCxE{SW@o zh(RXnhwb5J@^xt<0;$(Cp}i(piAaPYwN(_osUpLw#LuV^y>t{?jCX-cwPep#XsQfU zi;5|xBnTMNn(l&d0i;2VKj?!Y@EUj!z{MlzvpIt_Oox8TyXF}LOi66kyU6Z;?PfCW zn}`s}q+KWOYY=?~|3O_HnefFE0|NsHvF-CsQms_eRxzYUZu35b#uhksJm{YkFF_vs zoFfr)v;H&H(x*lgPR-0%vYLeope?#}6Y&dI2}~1Wqsvo&gRgPhJ9# z8VUhBHNbN;syGen|1tMv4-brQf#z3_f&Bwyr|<0_Yyr{t_Rq$~hQ7Cd9vL9tuLn|J z8(8CqzB%<+K3a$zuZbOX&{!vo{FAwlG?onmnQj=y$%a8(YZ$_fM+Qp&KQo4e6E_SG z;Bf%ZIE&b@E<}~l7@l}YH(^9uT8r`mq+D2X;3?MIM@ygC^Auho^;H$)vr188d}74= z$JoZCH&hcc1R}&rt}XPM{{WUg4k3IP+~^C5o=|w$ksB=Rq@Wc(Y#SLP!7dO=7AmU0 zZfvSRj0M_yGy>KJ?KM|d*g`y##qcct3LZbn0R ze-l1-cE?u!3cl8vYN9wSrx@!v@$t^6^b_{9MYd=I;TYS^B0?S6sha7?c@ML|6owG+ zSQ8`&aIBQUGexM>-AAs7(C}*sGNI><-x+_zu@0U%z*Z2OA8B{xa5$tm6~HJ^4t}@M z`I`0!Cr7*>H*h?Gp>wTnq)$YtJkpzg^+kw>(zA){9^S!8--;;j^Qms^I>6pQ{jG6t zQ>xP$;H@2J(^u$g7-+p$W&(U^H)3WS1YFFAQacP1&7Q*p3*h4K|YHuJ2 zWwuWh!8a8mHk1ScZKx(Ugwdukut0jLfCXaqmJ}ScP_UqiNzXY!*ku7N0GI15ira&G z89LjWjQ1f}ef&1`{SbRuH+$J<8$&QjMmORe@iWX`B7f3ezIR=yU3(|#XB{$We2oaV zg-O5{sA7y$AiWkBYnjX{@HOEUsBnxv25V-s*!vHiEwFV)q(^&(w8=3$-fU#zSpOtw+FKTCIX!kU{mSur$FHMJRsSE}@-*o<_|UIt zD&AjTc(vXUttV4olwNl3Gd*kd1>v9m4=5Av0AA~ZCx({dhKZkpVUqsPY+6DrZ6oM3 zawsG;#dPcmgpf(%W~8j_%l=#yPZ!cuNH85`W@r zTlefa@4P*uJ$>m=BqW=5^d2~H!wm-xY#nTp$Vm5zRRb)RWeg1L-qQ8DEXV=@{o-VH;Y&>x=E%(ceKwWp(rqo`zVefsQ!oNYJ-c^sSey3goX16Fpc` zMO)q=Bmmga8C}sWWG9n%;utjw*|AHcw#EVhEZr?zQd?9s+H86} zpXl@4NivDp*!=mn-M*o|zN8frdrv#xG8o}k3~ zItBBNAV3Qz(rMT^oC2$~R=5xi57}SV4HNsNdR)V74a3e10j%ievoIhY>H|a_z|}UE zo{?SxW^NNrku8}lcRq5}iBJrepXofpq^&Ap7 z@~+aF#s$eqO48-|o>W5KF1_(Cxp>@>1+`T7*iY2MILmMJ)&yM|67{~66l3j>YZ|MU z>4KJS^a`-o076BwRRTf}$d8lBZmBo|`=JlCJe8VOJVaPt;i?@)SxePSYPD1q{{uy> z*FvtVCR0TFTLUsYV2I{YeNp|{#-T|l` zVCnEe^(jOja$-&pLV38mB6%f5NRbtiI1djlxOO^(^W`-0OY5IuFx51t`tlHI@fPw} zxPU5pWdb!OZg6yDNZ=Nk_!6^6`g*}LG^E&gze)fo0se&64B?e+@Djff7J^29!(_eO zi!)v|Vzf1nq^hBHO(Q@&iRS%?UM6uO5;jCtDXLcE@bfsZ6^0d60U$}))h!Ou61Mr` zG?t4UP$0sT5jM@$G>r-k(geD+p}Vze|4i0v{RC4}LKtg$NFDDg`p(7!liYdzB5zcn z`s`{Eyt^(k65$hMo|o5JKw^7 zqIEm%1BKmXr~QaOe%ww+Tye~V{%(}(H(f#!H@U}nEj({1EnK^dbL_O?@)_T-)28cy z_?DfPb!8OJ2Ks}YZ@C7I6+7*7T_diw(|*^Gk+jnhSD$#dosLrdSLaIQd|^2~KE8Ws zc;vKE(4Nus!1;ycO8TrLxpHACmp-hE>`RXgjSlVFcIA97eIz&4UrsL-%9V7nP+VEe zmeToY)Kbn>(%I#ibfr*OT*z038tNHKpI^%5PLY+qqOh2s$#C`al}fRUASttGK~^_l zXu7cUkZ;igyP;!LeTM5gr%*gr%FoSL(gV}m()BO%V+2|zhl(;p_5xI$X>9i!huU)p zh`{DE{rjvbz%72B;Lh zGX*tO8R;dwTjsioNEKZxu0ENP-MiA*a?5A5wT0`7JI~~7!%`SuQ*NY6Q_&);tX*>oQbU# zXTe9~9C5BVPn<6fiVMVr;v#XexI|nk9wRP;g88xHa&d*Y5`G+4i^mD9adDlvUfdua zk03QqfNM-vOo?eRBXVL^%)!?qFK!YGVo@xKWw`1W#m%B5%Az7x#9?tn92LjJE$|1q z6|v857f%8`eX@9pxI;Wu{EK*+c)ECo_*d~v@htId@f`77@jP){JYT#(yimMIyjZ+M zyi~kQ+$mlzULjs7UWJ%luYuX&wKya5_2LcUjp9w>&EhTMF7a0JHt}}x4)IR$F6>!) zxA-^l9`RoBKJk9>0r5fcA#soRu=t4hsQ4JXlh?&3#3#k4#HYo-i_eJ9iqDD9i!X>T ziZ6*Ti?4{UihIS^#C_sF#Mi|)#5cva#J9zF#COH_#P`Jy#1F-PiVg82@ni85@n7Pn z;%DN&#r@*v;uqqV;#cC=;(x?%u(#~D;(x{O#P7u)#2>|<#Ges){jcJ0;_u=gVpBYb zeMIonHB1BXaSY3F8y>{5@fm(2U<47lE^I`wO(15(jf9aj5KYHuHd>5UqYXzAbQqn+ z79(wR8Qn&YvDN4``iy>Kz}RMNH+C3 z>^IIZ4j5+|6UJG_*~U4>xyE_M`Nl!x0^>sCBI9D?65~?iF~()aA>*;e<;E4pmBv-Z z)yCtDYm94+>x}D-8;r*rHyTecCXK8yWlS40M$VWu=8SnGZ`@=o7>mY|v1}BKqH(iP zGRj89STPP8M~tJ!G2<5FiN>wQZN}}!lZ;j4$;MNRJB+6q|6)ANc)IZn<6n(u8qYGG zZ9K<#uJJtMxbb}B1;z`F7a>~6ON^HqFEj2mUT(a?c%|_w%8O@iXJUjr)zC8^17qY5dCgwedg3 zZ;S_w-x~jG{Lc8j@dx9N#-EHo8-FqWYW&UkyYUZW(|FKynZiW$A_THgCX5-T$HW;E zrr!+UT#1kwHX~-#j3MZG!c3Yev&n2WTg+Co&1^RjTfy97rp+$1+w3v7n!RS9*>4V* z+i-%$4s+1Vm_z1HbJ!d)cbU7*J?5x6W{#VC&C|?%=IQ2s^9=KVd8RpGo@JhGo@1VC zo@btK9yBj7FElSQFE%eRFEt-yUS=LLA8TH2USVEoUS(cwKF++xyw<$VyxzRQe7t$1 z`2=&)%$if?v^itu%vp2JoHz64P3D5RXfBz{X2C3)H=8B1Y*x$_^RRiuJZc^@Z!w=} z-fG@v-flk0Ts5C;KE=Gle5&~`=F`llo6j)+)qJM;Ec4msbIj+O&ohsk&o^ISzR-M; z`C{`W=1a|&nRl8mH(z1C(tMTqYV$Sbn)zDub>{2MH<)iU-(-W!`PR+x$24J?4AO_nGfEKVW{){E&H%`C;=T=10wsnIAXT%}}<9O}5Jp*(tZkwCs}IvPW)}y|PdC%K^DfZkIdcpv=f2xl<0y5xGn5mV4x= z9Fya6uRKlelc&r5@(g)Eo+&5fS@LXojyzYMC(oA$hol&_Mnmama(^0o4H^7Zl!@{RIM z^3C!s@-F#S`8N4>`40I``7U|4e7F2J`5yUR`9Ar6`2qPs`5}3a{IL9p{HXkx{J30~ zpOBxFpOT-J|1LixKPx{cKQF%^zbL;XzbwBZzbfyQUz7LA|Bzpo-;m#w-;&>!-;v*y z-;>{$Kaf9^|0y@*kK~W#Pvn2epUR)f|Caa5pUYp!U&>#}U(5fIzmX5f-^%}$zmvb0 ze~^Eaf0BQef02Kcf0KWg|B##VLFH0H88|dXVsD0}+{&Z8%BTD)pn@u-!YZPoDyHHp zp^_@4npCrDQLU;?wW|)*skW%J>QddRM{QNTs!#Q+0kutSS3A_8%BUf=Qw^&TwM*?* zd(@~JQ{!r{I!*0Ur>p(y40S-AsV3A}>TGq6I#->i&Q}N31?oa|k-Au2qApdBQJ1Mh z>apr_b%nZ8U8Sy8k5kvEYt?n?dUb<(yt+|6K~1Wxno`qhM&;D3np5*CuWnKcYEdnz zWmQl`b+am|vZ|;RbyyuyN7XTPi+ZBERo$j;S5H!_>dERU>JIf(^)Kpa>gnnk>R;6} z)w9&I)pOKy)$`PG^?daL^+NR`^8PPqm?bq<*Y^qW(+$RQ*i-x4K{bT>V1*QvFK(TK$juje0=+R{gK~ zo%+4{gZiWTllrszi~6hjoBF%@huTyRS}sdi1|rf*OIa2qS&!wle3st|SV1dfg{_Dc zwPIG>N?1uNWi?sNR*ThYwOQ>}ht+9qvC>wT)ou0Q_{3hT&+4}ZtZmkIYlk&xWvn4< zr!{PiSi7v<)*frr8nec&z1C^gKI?RAzjcOnz&g{Ku+Fm1w$8E6wa&B7w+>ntSQlCs zSr=QESeIIlu`aU?S&y|Yx2~|Rw63zQwjO6)V_j=qXI*dIU_IWt(RzY4X=SY`YucKz za@MRhXU$uA>n3Z#TJ-0RPA_JcvK5GW?)hwazLK5u&lDCRxMj-ux#d%FrryO|xtyuY zXP2$q%`4f(a$v5M%T{tF&iiLLO=~v0GM%gV^-pG|lrJXc3oGSZW_muqw46JZIg($V zDI5vtOlnwmF;&Se6?3I*Wu=tMWJ{&Okqn9~Cr-{@DY`4AY<@9U@=Rr?(a?O>YbREg zd{c#^nM%G~UdbiUrJ2lpVR0tEJeOI@&E%)Ei$TsT7qg`W^qcR>nW>ejDcq&#S$RIY zki%dt6&LZdh%pS;W$XJ~b_OM8(4WG}+k!)ppKC^4sSDr5vbyNN2N&$n;osCrr7}-jun8jq+kEwBGomSi+KVNl@ zo71_nJe(_60(8N<_(ZN8(X&`7M5dI$z;bU@BjkF65W_CNx!8tYj8R@L$H<2B!*zg$!MmFs;Gq>=NK8Qz}#d#olSm zyRKn+K3l3_$`)6ami_u>F1uW*1h8a^=~f~GQiQUEKfjR2e4`y+U;|x6a4}!WRY%pglwHp1(e@I^W@fX~Iqy;) zou$|OO9fiGQ@Ahtma};bF=boJh008B)>F(b7fL`q{$dW=A^psYv>pwQ2HKUP9}UW9 z=k=@Q3aTl4D*2_{VtzU2uas7%@!1k|JyGC+g?xD)vuuAz9nP*SR^mr!Q7>iZ@)#0( z6?>0haaA(eBg_t0WIGmq}>IKYc{bC$2Heaa1B-ZcrLQ7*_ z9c?+Gw1!~2G<~Z@FKt@%8LaQ@bg2LuLvtK!d_sleI*avqnMwhzuN-S_#0_($!V0Dx zxGPs`ZhXXf8A5$_J@}rkYNk*r>%20fAF7+0pz%&EPh(fzWHd52Td^y!%lr9*K!8|c z`s0+w(&4V5S@eoVcA%rwep=Q+`z>UkA$51TW4WmFY^8)1N@&b!C!l_r>Y%nerO+X} zCEh8*)1YWMV8BWRGd)ux%1{Z|w>%gq1@N9N6lQ=JkLJq2$I}bI4%SpDKQosDIMR;; zBzmSB^Xg~1@rj;kJC7zWV5j-Z))dxnt|9@B<+iDnyr#T1H0o*N-X>3c#*H}Xjk;!> z^aeL1SgmJvF$*%_d{H;dxm%tqgsYDt6rdr-Q7-P>i5jB;#xF<)e|l3`*2oDc_6Ujg?pUpPWU zm50rPYKkiW?cnsv6y^(P5yLo>%q<_zEf$K6Q{~MaErKqfDm9xcm5w#lulTwVi`M5e zeoEBmIPLLsradaNd4Lysyqqg}X9@Yv=9kMpj4g=4;e2+{4_u0M%#DuMr5!Mg(>ld+ zC+@9m6W}N_Tn-mTH(t6-uguPt{hEAD(c+Hlv{R7BlR3C@m4(wo8wIi_I<9dXaRRZ1 z9EPRlhOD|_X)Md*(pVuO&Bj6uYACb_(?^l$cKO&6KI=u!5;!HI!bBsphqGYJ@O6D# z!r~~y+>vuQJ>6crb($O2Sq-hLf50EMX*x}flLV|sHE9CVP_WVB){}CaA*x$=b`7*X zT6KAwpgCo^dFfL=s%z${-{t1oy>+VPb*ZbXlvkFsOHfp0OJ4lT@cIhfgb|!242W@E zDF$x>A)CqJW)1|_57QDF1nvqL4r2?t5A=~b<#s7)(L#DoS{}6&Vky~Iw@De%qStF zl3yg&4)+x>yjUbfzyw5`r2-hH9Js|wsSvMmP#Lhe%VjL!#ysMbSBjCkay&{NSm176 z$yW-fPA9-BRMZl!n;>1mM=fItE5}g4zQbsHz{+Qi;n57Z&K!6kTAB5W=!9a6D|2(%xm--k0ru=^WY~~ZH=&JXH8Rv?X(f{w zT*+xH62ZjIIVg-MLbN`EztU8nQ}>aEk_-&Y3yV(2xt}eK zd37H-p)`NSZLh0hbB>aMDXkA^36gSVc{ZQ2 zlQsarT>xA}ZYE%7YXt%ZZgw_PUWTqE1WZL=fkq}!W1|~ zi1J{8)or88RDkxE2SwRp!Aq&du9e zK}ggNEVuzC5N}F8()U3wf{fh6bHtNTwskp7*c0{nq&y@rj)P~F0711L1p_?mZZK?>p6^Tfsb(7@JgXfDg76=(QW`6}bjm&(GSUex6kK|`6^FBPA10aLe1ay)# zD!`xmhAB-@r*8tk23Ac{N8E)KD4Xy#0_Eijw2?Epg34oTa{=_R%In2!_-2v|bz)&5 z=eAcIl&LIXYjosD{a{-dtJF|P5Gv(`BhUy!9Lp7Jce6l=q<7|L60mwq6```s0dxvQ zHYJ@#yT8qWzr4LO~N=%2^OM!Sk|+O00|0 ze3kvhdh9Tlp9P@hva?t^rJM;hhZp~}b}3PTlAh2}rtS^Js154cMX4kYtUhPEAuN$QzXUL z^jLMJH0Y)3hyp2iZ2dQpk>dO@YXwq1*85@(!hqrx>rha>u~vW&zy(c1O9)LA`affq z%4Hwsj1Y4EaLzLYrXDIi=(=;jg0rxGRB{oLvNd<9XP%84b_R`xwvRCTiNUIW%Hw9w zdku=5l~&go2OJ%kiZ;B&pf$Hxn95m4pf8!8_tC7sq!j|MME&yUB$km*@G{d$jnQoC z!6t|@Y%-;amoEIWn^P-AkE8a{Kx(T|7*nXZEHpxdUZG=#>V7F(^Z?LG;7qc>LclLx zHi}^8ry9VkHeV5~f6^J$=1_eG^z$^lXwNi+S=fazAcZnUH0LQp{-i09Q}~*1i4@Lc z4Iig@lytU(*cj^&V&eoAwY0t<$dgflj8>^YrCA=7*A&T4OvoK~v^H=4Q;E%h^< z)54TJs9ZNJqI33;qgJ}JpAxL_y=@!j5#S6*S;4Y|rDaDGlA&BG6qfyD)CUtf53**$ zxX?JzRH@GfK-f)ukYQ{aAh4lrJcK}tWlm!UV@S@K76;Qts!D%t9WIApj+=8+Nd&SCoa3s^mVE!=y1Ynkr<|PS;o*7sPrm!A8 zJW<7&S?Jf%8c0;I?@|hU5v&UODTxG3f%PM1Z?&HOmOL4%b#``TCSTA=W`lK&j7k>s zvrsu=;W}(0Y_!w!er?ldt}Dv2MLlhClVpHbKsrlgjV#W)S$$n9`!&F8i-rxrby*-s zOHl8O0m<2!EEQJ@bHs8& z!0|fCW%pEG&(9PTI~W8Q*im1RM0s=`UCk}zY~KUSAgMrB%3)6Fg+%ZT(|#88e?|gj z+5{68UoZjS3eYXCAG8zmUV+&pcf`-KC2W+7<(y*Wv0Ne+1On6aO6gdLZfc5U$o)_a zL;np91pXAHYMv?3y7zNAo%ZPml%un?kfW>8X{^sEzhU3dEuRB2@-> zE~fj`TUI7$1(P|Io1vM>F9-OV0d+Z&Jr;n?3zZzp%!0#AIX7UPpa~&05T2E*Wr$G@ zJ7w&rkfwA6O#Xv>D`6kcr6;fW}%TOn;2HKxned zM7W@a!%~IIM;H=xNo)3rU9W^{_tYS==G%`k&fe-B`%|D2@0jeazZ&VILV6mkh8uC!}j;U4c zmSw%#EfXjYWM6WTIpzlYM}`d_(F0Ofl`AH)09NHP3B)!^Wa-o)kR+sHYCvLaSqW29 z2DPl-M^=`BWJ_h}h_dW!;Hah@P4SUD{0>-O&j<+C8qIHD0R@<|$Y>3M8_Wt!AUPj| zCssn_mbHIXCR<+2U_k@NVaZ_8d8ot;1V=^VRu0k9hb96`hpe2Co=KIbw+ON65V&U{ z7j$rUhQzx-cE(oHLo-pm!LprkI!@s2!RDi>ENtIyBQeaL~yQi60OC!2t>FsgizgmCXBeg~S24 zZBD?ZfOygAzPD;6_wkGT4i%>go%yRzQz-|!xvb}=%LUI2#AD(zJaC7p(07A#1z6#@d6ira9$t_YU~Yp- z-ZzCe;D(4ojvPuTp;^o9SX;|^a;2F8>%oOcX}So>4&@mwVKgdb=t`l>0;33y)%Lyt z{lPb8qE)4aBN1x}3o95(SUockzKZ1)P~~uSNoa8qLb~HfLV62FaZX>XnOn`7<*XuS z-(z5tz`CI33J7?BnMzwM4{Ej(Y6K`Hm&m~Gw8mqvq4|J~9C=X(iO|BoO*_w8MRXR1 zXR>XV<3Qh4)DQ1VVz1jyk>|YZMO!ov1a&_9`2iKW%S#2CS9clvIG3^FG{BZ2SwJ|( zu2jIBB>MzZy>LzTLb;j)wJzrZM_8o{a0ek(M>DW}KqGxrFGpKd$1@UiZg?RncnK1> z0JM_JbGQlD6cc1rS%4r?%-QB8*ied8BRD#m1b9i}1^5O+k*-;BAKu7miwPT#GK?wo zvk-HMm^bLIvnv&iNwD0odiV)T6`GYKrKyKAK_K)~X5E6EXUkVPU6v53F2ekePJ$o5 zDXY~{8qw)&od_PrN&d^sQniX7G#?oYFFnMAImPWql21`vmHr(alJxp8# zEP-U`10KjSomPwyNTRSE65#daBs26q3lj#Z6hH+(DYy(n3jl@{s>W$j+8j}IS(flS z&w1$rp7OAh0{`jZJYamCDCdv*IEVQTomV+hux9c!Iq>2q?@Sh0KxvVt7Q1Ibys%Ov z@b8>wE(-(&M@Tm_zHmRQcjV8ob3g1qjvEvBNLU16xb!aMkK}=NOEX~-2-xjkm$sIm zeJ&kK)#-|uqG@0V*HNn@z$nqW`w=*a9frih^o#){R+q-Yq=n}Na?u}h1F_^F5`=8= z5{rSbLfG+pEtU!-3y^S~gO&@h4}7CaK$WEsU)d86a61#xx6o_kZvo?&U9_J&N+U~K zxncM&<&LnP9BR(_0u*Fq#VV`v0&u<`>kN85#!QNNFo=4EVu%#SkUFw+bHv7zhzEy> zBiY&9qMLTYV^zUul83aR^%oce_O7t-i|%P1Ayz0@cT#3RlEQ8Uz+Scj4YtaX;mAJ& zCvls13ryv1flC32Lx-Vu1rG&)gb?h5=vpHf%gb3%OrncqRc1kvJeBIqo-j`1HRxPH zl3|UXMxApxYZ_n9=fL>T4`#oDG~{i&;uwh5e1X*ffaf{V@9O5yU>Mi7NlG1mpL zcx}hYL#Qgrk5uJx1kwr_kcbA@kf+agx@SOZ|G^r;>N1y})90MF(;H|_@KJsYRN>e+ zucp0rtRMO2D^dq*#5v@u@^IT(*{>81t(L^^>vy`G#FibzlpG|IpbqfxN<z+?OBDYj+tuyKOey=#=T>&eY0b8CB;vzl?3d*0+$(Y?=itc}F-nUpCRo zgb04F4&!ffLV3SY4-oujI?5j%Wlh~1p_z{NnR+Pj6!@##q5b}&y@9v=orb^ACB6)? z6NHDW9!urg3FTuRcr*kuP$~O;)sKNm1RT2s{C~b-``P;)djcL)Eho1PxcORycd88t zuF?{zk^nm`jpl-qBUbd3Qv|X)^daUQDssEMO?A{Z8#`lBE;A}GXKpumPoKS2B02PA zC|qxV&wLgJj7r=7me~`kq2XU#73`cIo!^&@cZ+Eo)^Ur_5wXF)n4dpD=OKjvnYO!m zI03zsU=k>S7HiEgQ%9L=(%m0IQ0#+v_ls6tsGfo)ql$yiF=~dlhCB>NV?JZxN69Wt za51NQ#3=R#XA}zfDUkhcm1HnI>~@!3*)7;38DaqADq_Wmj@Arw1URlgz1-hH^sp6% zEhwlV*1SoJ7{OH@6@RPs9V%exgxBc_$dF@_{dA&V=pb@ib32wt)V@}^*jj|8@R^pO z7{`}LB)TwRdFzsp`bNw=+-i95=xkL+%JGfOz%bOOG!q$33M)+AA6vngD?FyyP38V% zTt=krCuj&DU|8DdbgvLHuN7+IrLLarGuJv?`l06{ABPS58oLy7ASMnzQ#=fN;RS*d zBSc_-MP=sNcs-ID*tO!kBG^2^*P-A)lFp(Wu|(L>v<+?=5Lw3N#_$M+P!%+iv1*kN zyrYKa*c2@)6d{yF@>tThgjl$tzIb^@4l&FBl?lUYstjNzXMngjx#D@k`A|Y-q7us9!iA9`vPu%vKQblBo&x7d6k|)PT|8IR!;R% zJMw-eW%YA+{FS*@??62bAl!NhSSm;I7)D}SVMF8-@>(K+i002enKd^K_sDuT+v(&0 z^u(p4waFEY!UP+7%X|AD-mm@ogDc73|F<8`rx`a6VD*rm1^ffWO*xrdQ6Ft`i5CD{ z1(5++nr>wQhMk97Thtx<%M$dZ2WE*DOenA#7A@w`0jqH7%Fg@7uXN>%1PgNVluS|^MhOrr$ z-*(ps$c`DmQ^$_hhw78%ujKV!3cFQx*>cz4t`u^yJJv{E@-rkOcZU6i8L;QNigu*g zzJq0N=0k-PXwRMTd1%)gSE#PuDLAjQmki3qPV^l?}D7mzZjBJZdqg-BD z3Ov6F)|uHJQcOg?Y0HHLPtW$Fb>PVI7YhPuk-D689A4VnI{dEcg@AEoS*KyKU(cY) zJ38Y$GnM=jiaE)Z&{T`7KQ1bk{IMb{IafS(e1BHtd54dV^p%%6PIW)XCHVFes)_!7 z1eCAe^S6Yk=OI@M-y9#;%oSXyEy`}nMC9+g%+Yn^3C6ZB!RT&onLgBCtbn?jZ<=mb zX!J*$0|wbUtB3}4%M-^Ar zry2mct`M@Ah(wdLtyLi-G{NiNJdcl1RZ8=TfG=ofc^vgR>F^#=CaC>v;Zd`)*$GPq zsl?6-89@aJb67a5oHpAAr8A5h>^$8B>J*Y}Dpv={ZI15I*e17*WXt`;OuV+Zws~O9 zgNu~+r6Xm}D38w1&)AA=Hm12A_FR%s5QjeO8(PV9@2#9L%gUaFg9sw>8&)U+LO4QL z`GOH|Zqu8;ovK0I>rDTGXx`Y~So5|HC%#x-2B~81mL>Lff{~tV$l+O@RUAoSM0%IVTDvK$oz+zX|X(yA|NcA0w zUGJpTu`#03^P4P|Dg{#2^9}LK9XrlpFjz#uxq=~ayEv{Q=!nmRuZW92ohnJO8rj8( z6p%=zw^^7O-S-ScdR`gVak1aAF36P0+F>BT>@Gs29PGuLECE4RgdW^E7J^#PQne6q z5P@47=ZytXLViySlSPJm*cL?LgFqcBmK%{WE2j JQkvHO^gqOStd#%& literal 0 HcmV?d00001 diff --git a/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2 b/deps/font-awesome-6.5.2/webfonts/fa-solid-900.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..758dd4f6070c7cb399334ae997ae9ff6523d3b55 GIT binary patch literal 156400 zcmV)&K#ae4Pew8T0RR910%Gt03IG5A1{^s60%D~D1qA>A00000000000000000000 z00001HUcCBAO>Iqt3&{Skt)iT#vIG5NCk&&2OuRJ4wHwn`v3r{z$k+yfTBl2y8URduF8ULtPe@HSJ#Z$+N0aEt zDOdp!abQCwmh$Y|>_L5?gB&hfU#4vaJsUegc04EwIBTw>uN? zg(|LIQpJjP7D7_JiW6sy|ZJcPDQHa#AlbO zcFOrdT31@DtZ$db2mk+n^fJ@oGz-{>F;a#))@Q*T$ zpyG%fd!KZ6Lo5|L*^q84*s){nV<&yGVK;LUTOliUjGQdij-6OBM>_;T7rL`mh;Kt( zWV!JF!O!O5-vC7kDdlvxsAu=9v(F9A=<}8C&qume{WipK2rA-f5v|QAH@cg&z+wt@Nt|Ub_6!9Kb z;Wr4)@iE$cuU?7n=_d5wPYR)`H&xHBn7_oJQcz$w1;r493l4`F zE`TsDfG~hS?7h#y#X09*fcFAHCZZuT5M(NYjdDZ;sVtJx%Rk1u0Ah?nL~x8kM9>J5 zGJ>Lv?2OE6`XHrLk(Ba%lG6KMlvLx3so&+Rz6(}Mz3Q^O_rCR>_sl=aD!=J1vpl0r zvujo}6>0uMDN8(V(fbq%hSR?=b1VX$bX6Iy?p0yNYDvqBE>Xx|t1C`6F<>G2sv^tk zy`6pD5-5cLi7m@Y&qSR0Luws|%%YPzld+_>``+_bYpo(efMhbkBIc_5m;}+4NlfTa zUHl^Iucr^V`vY#Y8Exz&9t% zkE`^X+fsf`w5o4F0R`Z>p$c+l6`;tmDvcaOj^#O!=U9=$b>a9CydZ$4i1VuXTM7Dn z1Bz_<8r`7MwD76RSzd94?wlO{c->0VrAT=@M-Bwe_FgH%^S6wR9N|pHg^}%#;1%tD z_%t~<`#9{i$X1n;s+=@fGI=jirQze(1NfO8gUU1WJ#Vl1?5P2}n0{9(GS7G5xPFQvf_v%{d`_d3n(cS(aO(`R;B8UJ1criXs!8_y{RW}iQ|{ETc6{F@FN zIn+<%I`Di~pH{}VlI-YQ_bzdc)K#LN>RU|LsqtB3&5;GB%5lpvi+vlsAh72P;i7yy zmsl6abk%y8v0(bB9^F%WruWJ?Y{4H^K6Qo7g=t@hYVpml!X9@YM&dcHJU zD(zLyd9y?FLi>l_IdMD#jm#f`I%Vv2Gjd%|72Z79BUeW0|q+L3s}tHS4P(?oqF;k3U}=xx=$EUuy2x z^ZU~O6sJ;M75<)*LXRKW*S44RKD{5pI_b+c*Oka_)6?n-t~+Oi%}l;T&j1w&N9CQ zoFs>aJKq-C1QxtMJ>85MGv~RO>jO61h`mcPQ%COz1G@`v!2{HNr17@3(v;D3a=+Ip zHNJv!IHm5-Y37sZa?DySbTY?36Lbg-C)gG0v6ECsZRYkVDXW&fN(I62H2Y+vGlw*2s zxs&9e#M$5b@-Ah3T4&cKe}2~gKeR4pS7NKgqOE5w5j6dkuJaEkSMt1%N{tu2tNAnK zo#f9emBZ8im)Sh=rM*9CFAX)Rd}VeVNzuJ)BUt`)pdsYogQbU(!|7)!7e%di}Wa0DlC3TJT@x9}X_O@=8nMW(H3XAYa==7c$EPMgc-y18Q`cD>zT zH`+~hv)y91*_-y3eH=-T6g$uPuE5oCbzMDI-|cX-nw`0qx=_>OnoC$MvSZ*0=gezv$NxJ>(39L*vjU3=Sj0?65ej z3me1MurC}AC*F>EI}Xth9WfCLiI5yAkP@ko7U_^48ITc~PzhB~12s_#jnEz)&=H-` z3%$_?eK81=Fa@(P8*?xh^DrNauoNq?3ahaOo3Itza0th68~5-U4iP8<-r_5M;3t0J zH~wdA#$kLWU?L`EGNxckW?)8UVm4-HZsuWL=3^n2WI0x1E!JiuwqQ%PWheIJ7*6LL zF5(g{=Sr^PYOdu*9^w%m<#C?pHQwe^KI3z~;A_6+Xa3@E{>8ufzeTm!7T+>iK`Uit zt*+IxM%KhS+5j7EV{Dv_x9K+D7T7{tWJ_(Ct+aKv!8X|r+heEfw4Jqc#)lWoMRUnq zHdoDcbHh9~FU(8x(fH<@`Dy+bW5k4-C>zTrv>9z?o7Wb%Wo-xB$#%6p>@+*w&aq4F zE_=mZv$yO$`@}x8pKO5j?H?O%Bkg~RO-U&`<)mCxl1fu~sz_C+8r7g$REHW+V`@q* zs4aD(F4UcR(;ym7BWWB>qA4_;=FkFKOe<*(ZKiE>h)&RXx=h#UCf%mT^nyOn7Ye4Y z9FGfdK`zS0xD=P>s$7ki@h0BRd-xcitk(n}2 zR?7z2CVOO`9Fj9~K`zTxxhZ$$i9C}R@=D&wJNYcZ@>RaeA2AXp5%7=@iIECvkQv#L z4+T&Jl~5HmPzR0B0i32m2{{pRD&%oUK*)D}i;#bQ96yg=+;8T$ z_lNnT{dxW}f1|(E-=}qXDu#-wVyieRspd}8&!(!XYP;1@O((k^e}6SpO;t18BDGYl z_&6KYR<&L2P)F5O^;Er90V+tleN$@PPJ248j;9mqL^`Q@X^ys;O@pnWmO$S9H}$2VG?K>9B$`aqXeKS7MYNPwtACa*(bZq) z5xt<-^qGR_D}`|?F35$r7#HW#T!yQ0bzZ?+cqi}Y6MUM_^A*0skNBzP{ye{kGtMdb zq_C8c3L9!6ZKacRm!8s3+ZrnqWQt6aIkH;T%2rLDp5KjKK1tBdzeOEJ{v!brBjucV zQ2>Qe;mb5bYqUpa^uquQ!Ej8(RLsC^EW`?Z^exzqo!Ey%IE8b#ge$m?o4ALE+652y z|G(+|Iv_nYF*T)MIJHP>@da1u&rMyNx@x~$1+tjfx) z#ELA-GAzwfEWzR|%ACx>EKJXIOw9zwGMb)b6hj!yAO4DrjhDEO zd$@@kxP`jHf#Dd2AsCFl z=#8G}f$r#r&gg`W=zun8jC!bpTBwN{sD=tCXJ%%Gk|=@VD2gH|fb7VOBmfQ(2y_43 zFZaVGqn8#II#%bKaZT{ZJ0RVvCe@%g%xb|4!*&b*a};} zj$OBh{T6^7dtehAVIu&mwY?7-4g~js;M__y{!@b-{J6sY+i(BrULZO4L)i?#I=p|I z00c{bgq)c54Qw7@mx9-TO#^HS5VQ)~0D(O8MzqEc+Q&8ee&GA)1n@odSkJ(WXdk~f zt2=ZDjQBzJ7fKsol^SQBi#CDo$3HRc7_g(j4g%W)PStmD8MjecJ^Aa}5Kbba3iW9~ zLmJVTCN!lP&1pePT2Vo3+EAcKB{^*=5tM01dpgjOPIRUVUAdaO>~g!puC(jydb`PP zv0LpfyU(7mC+#VF%igyS>_hv=KDJNnQ~TV$urKW!`_{g*pX_J*#eTIvZJsT%6}Hkg z*hWd!)l|(@p_00)n|i3P`l-JLYp6zRjK*q$CTfxo(o4d-aH()SG%oALRV^;%xr>v&yn>dn1{m%Y7r@Q&WeyZ8VfP5AzW|&S&^cpXIZCj?eX7eJ|g~ z_w~d5SU=HE_0#!_qGH|?W2b|#A11Gu;z)7wn zaI)(JoZ>nIr@Ah{Y5R2r?uRY}9)vCi9)-RF9*4#NPr>T|FG24BuR~*jpJCSn8{vzf z&=B?~6k0*kp-=(MfI@$00u%;76QS?`bT1Shfi8l=qtK;Lcntat3Qs^6K;Z?Xc~E!- zSsy6830(rkN_cH3UIY6Hir2z^f#P+rpP_g?>{lq>0G|QH8{spdcr$!gD82@DgW~J3 zyP)_1)E6on!2X2F*6`JkJ2VOM1Wks#B|Jgi4%!v+_OLG??*RJ}@{UN2A-@&&B;;Si zK8JiU>=VeBz&?O{IqXx&SHRwfd^LOo$LFpUVTTt36amsBps;EwD2|%|&7*JCPN7f4>A?<{y51j$g7+FA6 zK-D1%NXsB9k+p-!k=21HA>9Mf0qFyXPDsx|bU}I*qASwp5dDxIhUgE?ffx$ShZqL^ z4>1Co2eBLcbco&IXF==%KO16C_^A+k!Owu$8-6Clfw0RV4ubxKI1YLe;xw_Brz3vl zS-@9AoCBW+aV~rz#Ch-q5a+}HhqwU#AH;?5`4HDb$3ol$H;7wdw?N#EbS}g_uz3*o zLOmevgBC&D4=sjx09pd^AhZDDA!sSY!_Y#AN1$a8k3!2K9)o5k6?2=_`osjW6$7?pOxYoEzm% zm1QrJ5x+76Is?kpp{`J_f%H3+Yr?BSxfZ+@l%ik!mfjIBQa%T zVAn&riBML6^bnMbNb=AN^yYz~^p;1pIK35xa&=_A<(i;cl-_zU0loFblpC<{Kl+;9 zMu;i52kA6=7r-!j7b1N@?;=#Am}P!c+c3)lsJ3RdF;T6@Y?Grpf!U@I${9djpV`(P zBb`n?lzJHErXEu;FZFo8kol;WqFx5`Q*T0jAQqxNf%-&jKz$MQ#n_1YQo$zFx4@>< zx5H-C_rvDY55X4HkHMDIZ(-;n)E~oE)L*T|*3<*k?Rc$e=3ybJ$w!L32FK31hIPIg7Q}o90rQ%f?(yb2G3X&E2p+&Es$Y z>>|&C75Q&3kY#%?EG@%|~!3%@1%G%`b2`&F^po&Hr#D{gGohivG0pr^C_o=MWrA ze+f8_{wZ)g{mbD5`VYa0^dEzh=)V9b(|>ggr_g_c{+l?J{-EG=`rpAB3`_-QGH}-z z&SKym2JXe#4E!cImv(VDk9LJIoKL$F?aH`-c6Y%=wEKNU7tGHiSJU1_dpB;Ry-#a#GwlPkua9$oqV`SNFYyd*O*@EpX-Cn1 zhfipK0KTC8QMSFE^AlZ#FX@KU&4^#B5g)@3f)8!NXOIN zPj^4*1O^Xg@L-6HnBNKZxdS}y+drdmh=I!HL>G7 zp^2SpB48(`Tm*km=OfXq$b)0LoOyd zq*27+TGH>t*Tj!wyRAeJzmgXvegoB##P7(b5r2%CZzldE{wFU*K^Z>3ZORD7sJ5hx zq>N5plQM?ZlGmqsUc|XcZOUWz`?MGggyfSHj@~Y%DNr#cwQh^|^O|O8%PkIQe_>kEG|wzai;G z^6$4-!*OAILI zOH4$$aF&L=PPvG3F)=aaQp%OYq?BtY*Ai1v?xfsJOhdUx64O!clbD`zzaxDBi5Vyl zI?{)bn33|ZBYl*JAtzBDqdZQ`M0r9bV`j=Tl$VKFD6dl9Am*XGsfgH(@&V;TVgbrm zl30lHjl{x~?1P)>c5h~+0_3fcA*U|u`6v+ENk+)0UAqgtmgj zp|q7H4x_CqaX4)ai6dz17Q~UX4QU(AfwQ&=5pfJ{Gur0Fv9zsKGLENhOWTP!fwn7c zcj7eKo{ESwY5UOjBhI27q=K{v?O@s==NwD4!)S*S=hKd)9Yb75JAppLrL>c1Cli;^ zPNkhmTtPdVcFwietX+)6^|Z^}{-rk|aRcp+KE{o-dujI*H_;xVJxtt6dzAJ#aXalv zN!&$yIwJ0-y+nJNxQF(J3gSN6+q4g^8JB1u(>@^{rF~BOf_R+vHLiU0{);{w@g#jL z`V7~7vp(YzVRZUT^jV1?=(8!p81&ica}mGL=T?N#==0DQB`SR}{aYE(7pI>_{7XNB zei4I}=$FthXRt2)O8PYnHl$xqzm37B^gHNxGT4@WH~n4)+tcr-Kg3`s`XltG80!J zoPoH2oQ0f~xQLvSoSV3WTu2ddIk_0QIB^BJj3llimzTJjTuI^@a#e|I$+aY|BiD(D z>&f-V4Tu}ajTI3$lUtBm61R}ss$|?oZcpw=+)nO7?nc~2?jebL$-N}*BlnlMpFFT+ zJU|{y9zr}w9!4HcJWL*?lJO{c40$~97SPBqvRududCpD$SU(~b_@i#RyH4E_%HJeJt zf7G1RT*Uv>V$>2uQcFu>6t$ehXlf;iG1M9nF_v0~T8|hJFN$`7qvI77qu_7AFU5{ICTWAA9XZ!%r&7BbsTj(Z4h-Lbuw)TbvhAk zICUO%K5Ybbp`=YrT^`XUp{}B?rcFv+t3#koPF+vkNSlJXg}RM4HFXDdCv7_F0qQ~8 z4AdjkqqLc*Clt|Up`N0irp-z{tH?Gx^*r?gZ4T-c>eXuuJ?eGpP1-!v+en+Adbgl0 zKz&4gdd;;*eNX*JTa@}8X-iOlMYJWUf2sdyOHmz3TZXELwk$Q88be!-k=+#0R$ydf zWKY_PjO-)n;=^+xy9y^mc5{AdcOqsTEU`5q7ZO_wayhZJAy*Jv2XYm$bs<+5TMu$0vGpN07ux`GN3jhd_wA!? z1i3%tfnXa$9twFF*k+JNKpqXY1>~s`+XnLN2-_C&5y(fuwu5{@Vmm;-Ew&@%yJ9;* zekrvl7-nV{A^P1Lw+u)TOhxP z=p^J9ld=!;tBEd0ehsOABfpWf^O4_1>Zi!>Bsv!PBc$$y{83WYL;e`C*^xg^+IPsG zByD}~j}iTh;t5iYL-8aj z+o5>M@Xtf)swiF{`V+;Ar1nSg5-CGbyi6J>-XOXj#apDljp75+zDMyPsVk%Si0FS5 z9}`;<#V4d(jp9>cQ&D_IilO+5)Z9NVgc~u*9n{hbLZ*IRa^gIU;G#Vva;S9&~9NIZ?XGKr5d*C3vcxr58UkUQhbN66hE@gwG5q;;eF;67zRm`)VG_NjE&^m9EZ*yb987jCn2b zCd}(ddja!$S0_W>1{v&#c_(RsdAG&)_u$HR$a`^hIOKhhb^_)Dq}`4Ah^x;bAHxyy z1sow?#^U?8AlSh740G5zg6RVL} z0;?mjAXboA2x~xMVXPq}7R4HlbPr<6nux?wSd)?%4Qp}|+hI*jVlJ#1NbH9-BZ>L3W+JgI*32aK#F~S|oLF;{ z*br-R($>OSlEg+>hmr1AtfNW$6YF^5QCKID_Ab_`B(}#o&E3ywsIwvM7OZnf9E)`> zX*Xk?Puf&i7n1lD>oPK!59@N$KE}G5#D!SblkPjL8;EaV-AKC2ux=tTEY_`r*|2UW z?H#N;$zUCs1mDVZBcL3F|Y`w!`{@ zcmdWAB(}!-k;GP5zmmc9Sbvka7>9o9+Q%^TGtBNk$M9cZe*R0$_^%+{Q8@H#!+(R} zzs3CgcUU<8Jr>9RfQujck#J*?(2tDS{n(K1f9%I6-7wfsM7q1NpMu0<*iT1daqJf( zF(vkklfn4dFF^(qV80~k7Q%jI((Q%)8f35l_G^*B7}&2*21{bU59waU{#3%**k3^6 zGwiP>@h$duk~kdudr0h${Y#{shyAA{j>P^mOBeq&Bp%29JJPns{(I70g}pu8{~;JZ z0eJ2Iy8FHk*Zw~Q@=CyKUm} zut7isEf@k+6|HjJP(@ngnpP@T4Na@GNR_U7Wu0ytS>71kamO8Z zNa+lwcjc;fau>idgIwz@?NU2#=tw2SepL<|Mp>14 zmfO@;i5f8`C1Z?9$yi5m*X2ra*X0Vwk}<~Q93*3mNrd_c!zc<7!zc>zAD_wt%>Nmu z_*QrY+z${YO6!DDUFb@eRjCr?as>BP5=YcdQ>`OtQ#;S{tg5t*U^mZ(gQ_eFt^LZX zREk#AH{#=xQgrY24A&EqQo;^)dH7sxnc#ifLVguHj^lgddvRj8o;Z0d!P(mIJ_yRap)7pel=r zWQ-Wt7t|q!=gPhQ>M|N8@fNg!iCTyf$!)vQT#Ocu>VHARaB(T-mTB4C6OA|ybZe0i z!*Cb1kt{4ki4fEZ!^KE3CUMG`Tt~<7Tq!6e4i!A`Et~orEyv+`WmBzf8n>-h zzGu$zEC;f?dw96JOa8w6WV6*mY_&Fr5rWjm@4FmftF<})b_@aJXIH>?!X`kNj!BL;2P|M+e1JL3@m z&cFRUq7M*uDb+Q9(V(i*qTOy&U7zCoM`U@HKh|AXEv-fE?NirnY!cjj^oecsoK2mCjq6TrKe8XwgkkW7g>F|VEYqJl!U-G#go8@!vM6+`Zu=8# z+Bz@Hb6su=5~Xya3SCxv)sgBJ{U8Hl@=GzJ{tqPwS&o=z2hZ&+wR~ckCg+?}B37Q; zjv~Y;YQHS-cE2JS!|~pI_1M3Nd3JD+<=6;TOwKvAOp}|7QC!ro9>)Gf#v}mFzcZiV z7~cjb0KzhFG2aGiQ$yP&IMwZmmH3|3=PlFZf^&OJncJA9Q`$+@w3BKPA9^L;Cpfn) z)8zG+0^dKM=y!6ykl+8`AWi-CrMqd*c)O(PQQgLxy*SO?#qW&o^B~a6Jg>d5D6BI~ zZ}E8rKj*uQtl^~vqU+j)_cH>2iJk7mt32+11moX-h9i6{q;TEUfvC1=6i0Di_kD5u zEYI>vMIP!9%W(VKRb{cS5|zaNgCV+};Oy}paoC@;wLQ9iL5M^xE~-R`)~DkuJ;516 zbU(0t0uhM=k2t%tYd!OSFC|;tFIl>t^Z$;<&Hot>aRRG$d-Ph};1{i-MSeCy({-Gg z!#U@MVgJ_encT_ITO1Y5Y zI0%APvx(SjwnmM`L8sGy{Z)#48ModmhwIIyR?keG)>3nQIL2nPwfYOTnoS&iBsSsy z#g2=X;5a}SDXkN4GCwj8YEU~?!pX`iglAD~HU@T6RwJdazU|ERmTmlo#`@!o3(rM# zMhFVuG_iJf&f3Mtm}a&00T+=mF1%;j!Z|fQhz|x#ta6E^Eym@0!}C3S4)`Yj?f;W# zbJMga=e(Xa5F0q+oKt)a=bS$D5apb6)3hdT7`hY7G`UQ=1u(*o;0R*aj@nhtMCxmi zW$LwfN!kz2RRoAh>q%cb((|_IyNf3aYaE|r2yIO)v9yI#zK06F$1BGgmT7XN^|ezx z2}jqZo*tM$FrQ%`CvXa$j>i_OvRJD$7fTJNvT}&X<`qfaasRNqZ8>k6jpgke&vCQ2 zwd@DBZvE}&Q_c4JXEt>*IbnrYw_k#9CW*g-R{AO zr?q!&wKjK`*Nbtl-tIU;!OtgUdEdNfcuZj-a0lK%PS)BN1u{FxccbmXuR%z zIKgJKHJPmNYp?gpbY-Q{?U%iEoQ^}G<#Smb~`u$>M6>)W?*uP#ln9>U2@FBis33IK>GZBrK>ktz1<1Gx+Aw#pV2qvwSA?4JWs^`J0CE zepezXkzIG)9eVnd+fKWP%;mz}xgL)+(ZKce_~>3wN_E?b1ERKpNX3M3T|~BlvKnNJ z6W9mn#lu)gIojL0MuJ-#g1nP8JnEQ3&qjplbSkezk=1eis9JyDKDC>6jh5|2PIcuq zoy#sO)H_M2CPC;M8l$e-YqU%TpC3bG3jk{|Esf05)krMDCmQvQ{!Q4CjM;x>&y~b7 zNw(|r5%_Q_teER^;r`;WbNrYy4?A3V z9xri$@6&QwSbyuHfQnyv!_+f~<8a~q;g8uwwc|VW)w)!BqgL1G=GjmN zt45MB72CH?+&9;gvA87ddf!Ojk-ZNoRsX0r>Q`Aq=d>NU?w_WtR3hK`PMIXGzQe^=sS&qlu z7{@-+<7|pzzrNNUg}-74^>5*Z7)Os2$M*4b{kO+oGESU0?5A9~HspSW2KG>^81W;O zO;aKZ1$H%%nr-)bNgOA=-gfKoXSlLD7@j;i9IUS3!-%6e>1A76+1i!YK_;h*qP@D> zE{fBa3Ax(~O`r|O_?tmyuB!N)p(@fMZBJvUNX$At@}gCvqobpb^av;t$4K08cs6y8 z>J+OMd=Y;=A6qd3sOrOyQe{;yFlc%)o7&V0mYbAKl@6KTCLfXA-NT*qX%3?23c;S* z9>lY7ad`M)?Zsr+Uq2k1t~zscizftg5WSnXD!1+Ye0NK0H*Pt7!d-pHJ&wEP!%u7* zPNGfDXRjA&Q|2-ed&0dhb>Qv>acmD&p0NVc+5Ai+lGF+x?U|RKcQV0~n%<1Z}Ntp_muwO;4^z|B) zu#G95*YjNjyJcBkX{s~&!d26-OtEsD;9_e@El6R@gX&zkuozh;K~Ws}ns|aT#3+o! ziOx3v*>k5O52|xsoNz&H!}L7iMv-e-CpvhmXhqCrO^KAbez3B-K3Yh);ZRBtE$#_n zTP88krZ#cp<6_GiDaFw;_k2k~ZNo53(r!CGCOrNaCy-*2MB-EwVlu!1DV|^tYs#R{ zxW~wE)&8BjrGq&E9GdD##<)Co@1_8BL_5oOgg!~mQJcy9@Er;{1YLUF$D6#Bezm&ZSHEldeG=M{xc( zKNAvm{F%SuTsRIf+|PY3KY4E7p3m@MoIqCpD#xmE0qbB(MiFy({lC8xX^yR@5py`k z-zkU9G{T_%I7GG@p^wihv(mqgd0rrZW7vk51BCG~&Z*q7m9BJAD6J^CM|L9WabP^U zKn5Nq`QXHURSw~(nrrSpk!z~wASS6!ryagm3M5xVuXf+cC`@rfPwxDLi%uUI7oz`G~ZS z&2_HRlxmt6$gGZ~1$AAO)j{`ktSF<1H z3aDD>`$Z`kA!DMGuHK6Ji>+o8JCZRM!SgtKC1bK985?q}Ka~)Yod0IQR4|5? ze6AB&KD07MWR1uVr^Ee9Yfq6>D9YE8hnu!3zQpC+y(;aqd;Rb;krdd88TGF|1^tna z-@LH;TL~e_a|Q!Kl7wKpJs6yEbTfo8*&#`9aS_*e33+yq5d0)#QyGQ*{#hxb3m8WJ z^mq68Ex6L(kLp& z&s9n%#eNlF_Z*{;Q6vQt(V$Gj8iMru?G7TYaIc}KH#Z2bAY!N8U%h=5eIHlJPi2gr z?{sE-uHK0ml_OUOcO)Gr#`8anG5gM?AYP_!zgf$1u=LIg*lG9Qfk=AA>CFv7Ha1Te zJ%aD-w>wxLp^(uUFMLG5;A@c-I6{u`O{ixWhQWxjQ4oeG(+}|*FA?Sgl{}yNg<<=& zXmv$;Lci0IzCC5Z~*2rGR6sP!T`>|v*8YS8GIi+ zQqAgX=o%z>b*`vOpg)>5R=RDd%Sv?@cS%&Nvs4Y^VO!^`n>NkzJ;&l<{ON>5VNE<5 z<_|&2vxBNE`o`F$L08+`+wE2Q`*8jh8=;UjOGwYfm=&0#MaW*yNJHjCvV<=i1DBEag49?5&hR; z_|QWS`H23{arTqwPkme%3#QZN4(QicFyGk7@ppXTdN(f_13ItgUmy?SZ@{m>d*HM1 zTlfNeCB7K|GfKxtq=PvPrF6^L=cfDGj%^ynwvOUR$2!(pYuzpOmDahA;%GcuRXS0h z+V_k7O6jDU(!KoXZqa$@Xi}=PPhSfoSGm^vMN(8Ri|TqaHqG+zvOQFnaMnBR{rkEc zmepWb>cMcJhkE!Zufu-Y&9zo~Fuawgo>8j1Y1cf?dbgcdrWjIbt+O;s6=i5uU;7=i zuaX#bU|U>xuH^*2Wi7^*>kG$@0#|yBdj@fBCvZ&Zp@E2&8L#-hVF!U1n}#3Q7LtXq z;`siflF-1he8*WPcHkStwwr{xcrJH@RY9We*Nx~s@wWmUGD&JK##v)US`ub{@E@X*1xdhy5d6I|0W_n_L>d^ z0OtI=^Uo6le-nNc-UUy<=MYy%ec0H!K8fPK|LXYr-N~o2^N9=T;eS`)T)r32N3FHe zxl|7IB=+0?a#B|KZZk?8)AySV+l?b?t+l8O8?MueWHaD_Zy1ufv1^AMErLjFx}2jQ znNA`a9n*|s$Ly`RjfRw-G?0kcrI9C7;v|u2x;*l|Mng(3JZ`7@R+?0?Uv8E-&%?uxhaf$ z9lUc?3QvRE0YXs&4|s`0-GOSV*L1FP4R=~pS|iZ+x^kGyapk^-km*Yuxx=~XDZqA%%^0GQ@97-48ICfwnWEB-K$&$*J-;WKnYVxwJwbF%u`o- zFdPgU$4ACUQ&W*dUFosAt}xLNxz2NycQWt?_=|S?{EB_0@S7fKdgP&<);k;8ti3|W z%HAlv`-azXj|YJ`wc~A4LMfq(lrDa}y+7;s`;OC>#BqrHa6CBMw!@S$dDTbsKN3py zN5*IftKZGvbfPwM+if>Py#AFu2n6>I2qI!|z(rh@jMbmM6Wr5D|5VCrJ`H`x>DRMu z-w!~cGye?f;IBgydaw_#f;YmC!@mF^4DF&;M_8?b-Z(O-#0fPR_ECCLRrYt41-(4!SiR9_|Ph^2~F)TTwDwd2h^qf|iz;Uye~>LL)sL0b5L%BnrBwbrR* zyq04f9odXY3#7KedJ>UeM5D2~v~=v0kjGdf=Fg!nv#w*!NE~h_!~ZgfI|~a7u|UKX z){MkntJOl&5*@{wk+_};&QIk@LRyUmlDzmxLP&C#hv>aAAtZUDhv@xMLP+vU9-{Zg zgplNsyN`K>L2=BOl#DUPI3xd7>o0|7EWI+=fBMs(o~PYK!*Hr&$5wj|d2S?F^D?t+ z_mwec&F_1r&f2f+Q@6IZXoLj!BkndwdwYA#*@y&dD$g)zZ*e0(<_JOKc!jZ*+Q84DkUFyiRLkaQLxYSFH&| z#P^Ky2_<_;=P#=&t3331K+cCg4=xjNSuH)AdyW@>bSq~6Kd(N^GM~1vywBeTb zvXH(Rf1v&m{J#1}njS|XqgUPKhoNLf!w*A=1V77z(69SE2>n+y#2cFNDe4M0csmLi zjUy?L?D_H2a}0)!d0ZSyFGI@K0=f}YijwIz~Xu5yeNO^I}}bRZ*~K5v`VS)M9rlEsFb{0Lg^ zBK{3m>wjA|SzG-ZRaGs9&q8NVTEj1`okIV7C#Pnz@8J0Jc0J3#{_DT~qA@3)+}YWQ zbhC`fU;C*XJvyDup!?KSAFp1XFg)_qH5|gNa0D-h````mL-1n&RjtY4J4utwI@?jL z;~Z60ns?FZvQ9$(8=67GhVz-!?mKu_f& z@I>kTX)H;q6Z&0eQyQ|om_xm?vR=+{DgDh_#!j*NdyCVZogHCqH>@17X_>C;hu{53 z!2Pf|@$6UdX6g|x;Yp*3B)o2$ci%&uyzEi!=$VzF-|^RXyIsTU62FZN=?5II`eDE~ zb}aq|?Ux+rZ=unQh}mtE?`lMC`weAPr)GK`9h^c0J8&Dk5}+DXO3%7beRHeIl{gs2 zZl&6$Y7jYDXsQR@eoNc^|BD&wvg+%ys8an*hy9a1w!^Y-{rkWF`|_|nwA=fE;ng*D z{C9rmciiZgd_?~z-A8>y|1C&^M*KMXBJ6kGg|oHxTV-R?*A_nYUGI9=L7H~UmMbt} zP@kg4vllSByAiyE?Ej%Eoz05LVYZ#(|%5GSyjt^{$Dc}8tH77^W()K&?l9hJzi zT9uMLD!0#MRhefrw{m9{1XBH))Yp@Y$*Cm|H1m6k4#IGYum}GKIZWiT?)72+^Rk5tMU1G3;fqQgTD^dUsdS6iP198 zwM&9&=H@y}GiveZ43&~Ztzq+tTo*kJ0%kfD=<5VEay01(#t`{1+YEh$>v>*1cNvo% z4Wd3H)Ew;0=Uug3z(jRNrT0mH>LZU+-Fu)s6xqy`Tu)&<%gcVLY){!`2Edqq z`}?q6DB(7Muq-q+rxO?`U{By6H2ut4?O9b}Jd6uH)M-9t2Jom>Xr(GijoZqq(8Ir{ zbf(-Zt4+owHz9T=FHNU?$yK%ELsP2D4oAO(pJZ&xB*xi+6(1Q6&^Pnyzq@kf$`$l5 zHbEcPHhi4?pCS19Znx{tf*P#{2s=<2r$Up9Q9Y5Sn*9Uyl zL6)Q;b9GE-sncz@(yorEy`I#QrOp!gg%gSvvPx7Z6IEn2X?`}D?5CP_os{QH%vZyJ z5Rz%M|K6Q+spIG=BcE5_)Az!__k5MKQWdZ)2^Yh~(DS`Ll1wE!Sjse5taLMU_$nK& zYfvg4F>K2O*DJ?9cRkOyY!&kPCS$9S7uZ1{1ONao6%bB4Eg=Gb%HVG){`eCA-|Ffr zW6gXXRNL}B&*gqx_FNFQ%}w*NH6gw&G)+73qPs)Hy{m*XZ}r2RKP)KyEdwykl|2{A zcBPs$XJ=})fsR_qdh>y>j9gCSh+`eZ@><3iW8a9;alzKo+W-!KJvTO)iY{0`!rO>!%G2dn6m z^xT+2Gfk+PAgKYJhg`JSI1td0^GQTVFy|mHLKx!Jn3Nd>;Fg@8QV&gjgVfQN?H`~i z1z|Kh6UuVWvRYsWZ)W#1dYoe!ehp@wKA$Dax#`&`3?vzI?(O~#wk)eR?1K=zCq$16 z0V@VzF#q(b&sTj9a3o1RCkUK{!#(vGUrih*SbTm${eQJ5Bq_q+`PIe1amcIt^gDKV z!3hGVHyK4yWT`6a*>+D?RBKH?F)`o;C(r?e8rs1qsf0DcFy$l|9L^;TrJ)i}gA@52 zxz5HcTAkbWc4hG8!-o%-t5r#=R?DxxnYzo4H2H1t@yBUz5z&|pB)rFlH|;z~53j>>p!6lApHwEm4$c`2b`ZHfk$AG6 ztL-Se-4x3^4tBBYl)qATT%LZUsh0U_- z0eEhCvtU{9KolKLEzDo@p8co#v{m^eDBM}v+OY?=PtCCShJziKE1ITo*TEkT{+*&Q zO;IeA%R*H}xm-XpGPk}4|HhZMu0yw?b%lnh7nAT8GR{%mWGQJT30WJ?JRQkgT}Wp6 zk4lD0i25~KXW%#s^QQ}i)AI|C15CHSX7rHx7qrW=nTJmM{{!i3lC(xl(`z3%uxIZK z=QDfv95~SSOp~m=qNQFzOYWG3T{fEJ9LlGfa}9b0LXaIS3@-Ts5qZpZJ`%*g?WJXqo4@7@E`%C!QFgBF1#T)__-Ye2B|B{|E4-e}yT=2O;` z?|KWtd3uz~StcMhLUKz?3rceYCcA`b<#MC(D2gJgzrzn2hG2v^%A;9ytty6>{8k7v z;c{7az0{rd=^8n|D}u}CREtv=fGZ+}J>xJIsJ;uA5W~WH2TbXZ>}NZgI3a>QZdw?2YK6^uT{xU6vRtnEAWvbcScF0|>%O#~fC^vmt+KX8 za1ll*Lzkg*=qaWyd_RZZ5@$53?Idj`$^=o!KZmynqkyj8hTric8|P^zDoN_lH5-`P zYdoZVN1{kIOK~Qve7vxcwF6z!(5y z@Mhyw62-vAn!?X8ZPsYV^{aAjL!w>-BSU!S8}F zgZ>*$EL!UK`wW2Hcm+5GTP6;!V-RMDJ!_T;>^K-ZRus-E3g&gL`uB@lKs(VHbUl^{ z!^gQT**dKxZ6>u|qM8sX96n51VZUz>r%8Qnois`PFfNc!5z1SU^#;8eIB4liJthj{ zR~Lj9<14Sc@@7+Ix&_72XheU}n|!IlCf@jE&V2Wo^GmqSbc@kfc>o?Sy>KF+%+lFt zIOG*2d)gCue6h5zkKaL;qies6c2bomp?&td4gdhNn2VD%;~L+uk^nBNJ8`F}G;Jn( zdUP#a%peI9H66tLc`L6{RgF6KXPK6toXX{=RCU@i{T0m`f56hhW%$!~zx&twj{gv5$A2ghLWw~&-T7DBG&L@M zv#E05W>0IK&T+!~`{8J3zB#+N)J||~Zv6Ux`Imq3!(*LJR4G}O36wg8JzsBD&IHFP zp6CDOrRV^<65WVU647nvk-%3;Qsz(Du;oP_|KVnmq;dJP)Y5L2q;Zz??4AbuzW5mz zL!~l5o%1mi^Iq*VTcs%viTEzhgvtK-N(Hd{$5ndReE)K@2{;c&Z1Sbv|ITr_yo&il z@owzSS1O=0HedJh@l>%0cp?g-dKdt%S6_fiW!}Z5nQcR8Hkb1l#5+bztw5arA|69; zL+?e}FRW3-p+I;{Zck1c3v%HT@e&QF;<>W}x4KhIJsE`;;`8eX%`z{ zT8gR_EiZOP%Ns_MzI}DxG>p7PscvwcmZPe4kALE*tmy{Vb^T)r`)MqB>RP25N$bGP z=sJ9W)2~L-=7e9fYaaVOdOy*1z+i}SNMzN*V!{{aS17$Pn#Bg>iZwQ6~6OM6}kf8>q zXGt>>?uM=T2u5MJllY!IgvbO@pL*Vt|J;H$GE(dIUfo6!4s6qJfJ zRBJ61^>UUetgjL6>e~b(7@^zI1L%ziWl5L>VK*ab%$w!eh%lo&JW#kyNh@vff^sT+ z8YQLmR_YPd{M1TS5%k((mJc!+hDmr{&d<7`l9BA}y>!ydet&s+xv;QMI4H%v-)^m? zfgwBv$|f<*eBLyPSq5eHJ_D08nZ2deYUw&-H~vY-KW(*IjOqHGKm7Q|KW<@c{g7zS zSzQPIy2)3$X&+HNVSr>2q5`OdSP#BfJ>m$y%FVBD#D$#TIfK_9SZks6fD#*`I^FpW z^ZGMJGTU>s=3(Ko_}KK}8K*SYar;AqF>je;S(=QQ#| z3u9~iT2OxM@|_392DRlhO^Zw~nYMzSmf>rNa1Z)u8$yj7OXSQt{gcCnkd{hPIHtlh zhGa{1fz5qhHw^u@Cw2Msu%PehQ|>MI*#AhUK0)d2Z=P@HLrQOdS~J4gE)B+ep_5q>ME-JA~@t87S+SAr$Is5&&B+FnmMrcAyUD!$6VaM+XjQKOr zv{7_?*9}4G5sNX-or9aXs8x+Xm^m-!X(6Q|YPEX4@@%iw>P3#Tj>1cOMT}VwO$YZm zoGaiBhp5`B9UTl!MS1Fj(s^C6B{g?espvk1OQ&n6qVu}%NGA2s&QP))q^rzTf3Ha(%Knb zL7*G4T+?W`OEnGZf@`KS-o$Z9*aeLaiA@ZBNtb1HW=54|{UueeQ9-E`4M0j&x*tLk z>TPY}9t=?f9m+_qlFUHogf29X#s;K0T4e~dS6|9lc(DorF3HkvI|zLxgY%F_Nb28G zL?LM=B)@$@=Z|z<_AH731nlCV;L;Ip6n$ux+mnP|r!AAMee?@%>svTO+suMrb=a zA zdeg-MK%qE&-Ru$@?WMzqm)b^L_7&2K)ZmO^teNB&szm@ZW^h{6;`6`w#V=UBHa}mh zGtB+@HZg)Bicp4*qx0yYTjNk}BJ6JMmQCqY;bZRAJ2>pVeeZ#Zmyq!DuX&4>Gf z(msijQy|HX_UdiTH+}K=EDp`;EMYrtmr|O0`kXA)Q`X;PJCKeN6RW@1+hb9{NUIy4 zLg~v@`B4w=yqPvp4mDc31mbq8etSF3(k!j#ab934A!$3A8O11{@fSUBtkh~XzkNAa z2S%q)pN2=z!K0GY6Bq9L>A7>~ir)C&6&(juc=RBQ#t5S^YAyC3!lUr$px5h#_JsjF z3Xc-Xrr^=x=Cz;O)H-X8V2IvpC32rg=);6Z)EM7Es0ecqZ--;3vt0VEfgd=Gm~&>B z25mQVI$O%RZM=?RO8krQIYLI4kW@|TNwYHtE-POD5G6lx^99=m>SDkcuvE1irC{6O zfTDl{wp~yhE5aB92JGly+l7JystWi02@H|*p`DLPRhs>+fL#i^s7+IwXF(<)siXW$*o6mH=RNnRI$GeRQm;|!&IxXKZ9&>Je|K4bv;yQAH zF%B_^UJM z(sej9H7A#MJ(@H~rAfV9WHMDO!lBxF>>(%?r}?s(S2-~ayi>o;6jAOFMSC_el?NJ7!p zi*O@+16_%(SGozPV3W#iA@o@TC7vCHWTc5^(u#A-WleLNNk&LUYWZb_8Oef>q|IbY zBB~*nF0;K;)8IH&r|^&=1X02V+C~nCm zYKm>!wbV{)O16!Q?11qiYL{aN*hPO}$hZfiaSui?61};5(=&a*nVYw_!Vl$sJD>A4hI2N@bPupb3-}kDl+r}>qHI1 zH}rZvA}RVnS0vJ7?11B^e99BXgFdxKuc0*+PeisJw=XCi6&9k*#;qO39^{(}FZ6mn zqEpay($n?VIGaAn$6n-7Fu%OiDk}`OZ~XJR{s_c5F#mInBqfxgoIEa)YDJMR+}=r( zw3#U`2|xunUQ8|6(rt$jdpZRFLm;y}T+B1wyklpQfFt3a@kLXIUr%Z^&vm_8Etz_4 z+jX6>MuRb3pP2Zp!1GA~-xc?9V_{*>)C7i>Zk^b4!{2e;dcE1K*IoB#mpj&hUyj{A zJAffNaIJ-r&p`N9FZLoHvN@Qg(efp*y^;%gc1NQyxi&|AxvHKT^JZXZSw%h7+MTYJ za#}ItKk$zh*=PzOrB;Jo zNy*U+0}0}IatUfsq#ac|dj1hwK?l*=HfKUoWb1ge1oEdGELQ@zIz(WSV5B9z6Z~gwcbLXtve60LC48{W(Xrs`r z3Oo_@dUYrj>w;2X+!~q&hwy@~+ec|gsaO+~YQN}hS1tyBD+uK=>KC;wUbBIUsE=NQ zo&YbzTVD?l4B_wL;kTwTw}EuMoC-I!{4p1^l)(-?`U@jd|; zd%Y~Ap(cPyTCJ8r341-NFT}${mYL`Bm3aS5VqExx!(p0mH_|k zf`E?!#9=cDqu#HlkWoTeHf6wVf2oVe)Uf^3F(~ljQ~$XheQs^cq+A;~8wf%et)dcv zV^)OEC7d2#M?Pw5hw%7Grl(3jx-1A{x?n=D)#l%!4!YB(t&(sp?|Sro72kRv_1wH> zNDiRLYEWp@W@jrEa;xa*^iTvoIKTLS&ZmYkUoJd=kD$W{DV%uxqD(EdXUTi z>U#q9!TH-YI=6M(i{Uj~55nWHt(i2FIMa#ExIXqCP;&P;7UE+SciV;~8-h|z)3mPR zGSj7!E)mTzm@cunJ8_nnH;-YW(M50t&|PRRV5n3VG0)K&z=(NQO1*eu-e9^U5hCWw zx|+01E`?qI$!7L_WAi-stueBU^p(8$MK8F*fA1sF4lTg@m}fJGgy6_ zQ--QaGUG-l$Y0-la*n$B$Ln-u^V$mwMXF!M-+b z_N%#3pE*xZ3WcJw-Pm-oP>xUq_50Lm%0ZYjU#qk6-xMr>606td=Sq|Uxs>}Lga* zMbY$?Im-kqACCWy5JFrxeO~`>{-3C4v==rd9`(!U+&{QpZ^K4qygm+Jt-opJ zk2_9rLJwC&k5%gp*QbYT?_Zyjs9bs`z&LohU9YF$On6@PWqz{$)3GI{_n{-`a?X0s z#6JLn6!Z)`OuVHT zhm;Npo7QTJ99iPefvL<~s_%tvd)_k)!|(_(4jjDm&VvUGLOg>5@bYN!7~s|)Mz9R> zutunP@TQv%T9nWO8e>}FDq_IUFud^xJj1}MX<}@eDmDxc&X>l6wcRMCf_=dd92)=z zHiU5%rL{*{T~NPtZU^l_$NW68cDngY9na1wZH`(sLE3R!tYVdFR)xL>N}~Xe;+>Qa{Y0DuDBdez@P`VQ|gr*8+vgS z^sP_*1Uie(qdWJcBm4AdyPc(EA89{^>#0{G==X@hP*6JLL@6ANxIOWKY*W_?SGq63+m06niii6~T5ECU9TzM ze+!lJ@tc1M58xuw6sh`=s~yvqrb)3IcwrJOqifa|=>|9yFu*tLX6u-$rJaT*jg^vP zl#*yk6h>o&Wg|c#IkwtMdb6g+BUp`D#?OV_l=f5U4S}>MyE@k=n^(^cZ)<)0<2C!b zKRV4zi*MGAcX~kY_yU;p%8qF!c1SW(<9Br<`4lNahZ^x~fCHGS(i!R8Uq>V;t-|XZ z$AQQpxfgQ;KtX#{1R7f%)}xD)YL3H`ZI4 zn2Q(~I1>NIACtY;80&7u?sp*iBdxr4&EURa2 zTD_5V&m8xLbNP3vBX{)y(zs{MdxYoi9#*4B$cTobERHkqy1qQJ*U@ji=bn2^0P`L_ zds4V}V*T`g@^utiLHp26=0#!)Gl8WA{L%&!XqEoK7Jg&c-w8a!l zJ@qp5=bZH2LahEpG!>;e9-6Le#zMd&rt67Vi1FjF{1~P66+J~wTlTlmyToyiB=Bv- z(3^CvN!A@xRK=R;TxKql_9o71i-L=5Ox{IF+taUJl)!%rVcTbJyr^$;#>!>As#F$4HBOy88I%?n}M+oP(ktq5V; z^9o_`UfUMp%-g#`P|C|mv&<^fHAX3`G?Uk8e=flO2Y9K(IAb+A;-wN}{O6x2m;cO~ zI&a%T>>#vOTX~t9*HoToIs{&VZy}D3p<6LUCoHN-RjUSzVDmT}ZXO~YHW^UfVfjF% zs+pyUBw~wDkT@nJ1+^_`Lr>&&>K>|1aZ;}|)ttRLP-LrlV<-%%`~)GUq})Mc%*QVl z&&}<(&1`yZ6zT!k2z|afhb5(|Vaymev})0rVPW9TfNh0u6@*BV&WwjD^|2ypnCnOE zdgCt-hK(_;^d|O-Onj4~bQ^@J5g}S@@Ii#v;~=u-i@kz}#R6K`JDCu=jzQB5t$=?t zp;(+6I(Q0-#i=k_BnBt*g8S_dh7ApdD&2E+9fMM=_Go&Ti@d$tJsBmH$Oz9oaM~W# zW}%=0LL9{(56V#iO`=2S3UmW{C3+aW8NC;M41IPSnM{ya2`K?*6@*r!f=N+)`EE;^ zNw%&+yyD|a+BjQhW(O8-yNClTz&FNnmNj_-4mLioe_7KQe3xk&l*=tp3qLEU&?=W< z^A3lt0Qd1ve_g%&c;2V;K%u`NLT4nLA~v3~|8DROrfHhSx>HlGC}3O=?$lKGl=&wA zD$&5T_Dj|J!AEaZwnos`OVMjtwhqH0k(!CyrLOr(Hw&iNHmqs`c10AM{xAal5ZynzU*s~PR?P1%+bt-je>3J0zg zH{F9$#3o~{q~&eY^T5>VhkW`5t`nc2z`2il;P;iSMx`?1yDrY9=-r?akC<`Lj|p ztEF@&B%}NXQvwKdMm7~$);CRiH=8_ zM!|&MW6F-FX*y3zb~X0Pv;(%DIp;jVQnjRgr~Bse)xi!2q(N8V2JsU{F*QQ^#E@=L5rU zmJ4}dXqw6mqAJE805eoY=7Lg1k`+}|%}|pNQW4tP#0Z9{fx2iHI)bi7HzDNuy0GYA z*Y$PMI5UTCB6prM=o1a9oT$W46xAe~pcYE`j+2XFmWG3%p`!fUB_J;>99YhM=iIKq z#y~?~t@R-eR(rm#c(Q{B+0}Y(y}bagyQs?k9!Cx(qf1rLL{VRB>h9vT$fxP+O{d zEVkRWm!=)FwHPFeNiqq zY<85@DhmKYXxzs&zNHNyg zTIn>_#i3n@Xo-jDs0t=EE%|J)9z`Dy8NvgL+>!C{SG_-as^0YtFUrGWkdd*yE9j2# z6x|sY1~J722uJDWFv>H{^}?5&<4YaC6N`Te$*t#W*-hJrA`UH#fT*o1`Ml4~+ltB# z+V(-FD)#(yJ(Scbm-SA8Az^Z8OFY&GzS1FrB?C@9tvEs8C{OEp?*u~dgF+nSLIiN4 zdi?9q+d{ow&$MlG)6PZ50n@h4o@!aD+-rA?VTd$TN2d{LATW;9e05N_1ZYyTQo*@* zP4%6jAkl?bWmMEgi(os8@EauMLL0++7(lbAz6MP&*P>pp*X!L-86<5B!n##7XIduo z?)4M$aZ|?bE&q0bj^Amw{WNY{)knUG7U}ev4BLk(Z&&vNYgwqn2JiDZj(Z!{aC6PP zu|q*v-;l+FbI5Xczup>vdVg&I=G_q!+RM$D-|YxSsDMuEz0Z3SdN)GK>Kx1xSYy!o zd_fQ~HMNQLWAyiE=gz806#F(x>cx^qX)pzXNw{Pc;dJUfc*8hrO z7_uxYs$!^?WokB-hyW0(0$4X?QeZS>rr@ScER25?hM`6&NUq9snaJk&FVtTS_J&+8 zCsfJE2~`$rQ55MqWA9yDx_|T9Xgj(dJuuMi=+BY65hci&AgStu6cZ-l+5EJdL3+g= zEH-$Mhl8+_CSG@$;#x6lIT`;#!8u1w zN&2|&?ndXy_e~XxplSRGu4&jJE{<2@0bwqyr+o*Y$5gyp4}eJ@8lOCJWX4X@e~Jx!C=s`J=kJ_Ny{m!j>d`M13_tG z&Cl(cYcz5_|0%+tHyHGd4@%dLqD``p%R>+R1@E*29Y=Q~=1nleSxcZuAc^B#*xsf-zKcL^Ic!Q1RMdG9E^4;YA;xZJM2SDcNFuN z8uA)pnoWUxG#pvC5hLY2aJuSYYPjl0mZN~;%*cuPjaUnDjtODg%R<;T%aznRn{7y4 zE#;VP3$g5haxynDZ#gpW@UDdx16{etIbL{fw+^4}fy4_JS@;f9&tAA_7n44^(mLK7 zJ9AmTmEO$K7;t@PE;j@(SkR}@+xt}l=|zaHmf(0fLqT5<8YDr*KhBm3V9Nvj%g0Q7 z7(Z+)CGn@Dc0JtwfB*M?7^+GNaG+WkZcp`aB|dD{I{CFwJ37AYTi^N?rMp&TfgOx- z=WF1g^XO6ZPK3bIuySU97L^2{`kt&HoyuYHn~X`d_9Bj&Z|+l5mKYSCyoaEA`#Z_v zlVedS0yM;!KZWCXso^OUNF5n;8&W6KZg(YkZf^hD8s~M_RZ~ADgPB43tvIghtBg(R zI%DOXjG>Ece5QWm|SN7{iD&%Dx&MJ6b}ZnIu2BhSO3GNy>nh&o>(9#I*$L zD!B#S&vm-P-c>pslrG;$vGc2{Rjj2cP19PR*6_ljYn6tsX~V9;rHt#=TayVM+}!&S z3WIivSYtaBPEa8ZniRa4Esc>92jaMzTtvw;rFPr~M8?8b*(%L>Qa22}PvqDsg~V!q ziu5S-Ge|Tx>fI>q^{QtCdQVNZ=LDtG_k5_{GkuUA`@idu6_o-)QrI5vh5?MwmFPUW z2fZFWhftW2teAeeI_XkYM5LagUF0J!r4!S37%0?(9%oH=?7jVd={+JKcC=Il^{LRy zz*3i5Z}7PHmnntOtl!`B^PRrODg@s9j(2O^3@*Y8%G7n@kd-{FO1MMx&hsX-iA=a z)$58nPSXp6wuu9WaKpxOOjVQqV6h8t0)?IV&7?_^xXl$Zng&x**vBXZt{+cN$F6hP z#zu+npy{4%`@UPQI19YuMy2ffzHR$d>r*OzSUqmV{d)aSPS5~* z=$XjT#KBFU7Zw$Jl$vZWX5ILdgiE#3p-#B6WGmF+%A=qF$ZzhXqN;ugE~^!6&$G*w zPl>83t{GEnjl42Gyn}DLRi^>SLtpBhqg==3xgWTkdmabVP%zecAMNsV-RY;z&pxN) zt2wX0T@XSHp{=jrgSxkRpJn5vZb$c{*Q0l!51~)mz+k*j=_Sq#Fc2$@iy}$z{9^b~ z^A+0r_14JhALKrbLb8>m`qlP#ho0znW&m;{7I63OB@|jrhTd9Pi(>~%K7XWekxelG z7tiLOz(6>M(eG4V?Yr#@+$Jbp%j@H+_=ohfdb{;Nqg5HDct+o|CMMsyISPM6nobS{#LQNHkTM{!#+rxu$Ma@V@wU z&y#i<#M)hz^)SDwr5lEhe5bbbtr5OYmZB3cl*wn%_aooS`z#>Q;w|m)?)+|WA7w%b zlaO~5cRQ0Trt)D39O13tl$eD1ubvU0n0lACzjjaRU9EE;@5n}gL)#XjXWKm?Y&*ok zkFnni7ZV0xIXcHLYy9J4vB*7_PGDT5RI~)8MT{q?>+wIsx#!X*_xzlf_v(-TQFFe> z>t4Rl%)j+Vz224ZwJm}X4(Ux2@JErtGt6((t~H za;I)8?9O} zf*GSsP}~#*5#M!+77C@0PK`&1%&vwN`8MBe6EXn}Pe(*j z^mP+-oz!=pwk*?rxK^tXodTHax+0N!2je{w#BlYVJ$odGw|D*X<7@)`%uldK+8#xx ze1h%M2Tc1~x{eN#M|O+Lu0hCE<1}p=PJ_WOv5j%iMQA-kz~>nn5dcTOsCK#-c2e^5 z8YZDL+7yMF`iS5-MJo33(=_fgx6n>>7aF2J z^1B^(nmB3sKoh=8Ow<%ZM7k|;JI+K=^{|xBQ-5@Oo9o1KLq9DhDOmV<)6Fn_FqW!X z@D8n0cO!r-JE4`PhoQ2HZtHLJu_P<1Lez)1ySGysiSp=MUDrx>rZ1>08J|VB734{7 zhq*LA`{TXh_S_#u|lJ`s^cANBhw&=n0)K+jc^QmdnZ`6fH_UBg17Wv27(W0x95c+DTz`rBO^M z%}(kghRJlN>yVhZktE5*NVQTBkS9fgMt?OwqEuYf>m@p2Oi|WtPexG`#}yCI8^BZ= z@Ut-*tQb!u}--O0$t(b|%3xIS6ch9X|>$p#s{DR?&^<5%j(Y z8sm_A;Yc+{leB5kYBM8hR?TB;98&%Cmf{h(9dd-D5-C+RgRqcs@HmhWv`DHZHBAN) zlgj?!VNaO0y<1#KHV1D@bGH!p^L(MvNYh56kT(>?NZH0!^()Ab$AD?uLhNoH;&B7K z`!d0m1=?Wl)|w-eO5??xc*JXl{?yS_wMry z2;rAOcYW?UQ3(VW4bVbzMkL@8VEpe<+Lbxb>F+RnyF+}*4?p((aC#{qlSBM;OPd45 z;?&dAUStaW34k2QqS=#G5KR&rZK0eTc}SJgm^vwA3n6*y=QjPxMbtsxiQh1507_dp zZqCRI4sv)#?7`)jWi1g}t$7np)G%9tl4|tdGu>d}X{#rXTpti?EV58pao)m%v%%na z)Q+Q%A5P~jv@mLfSx1{}A#Ruq9FtSUBJ5DOb-XW(W}tLBaLTRKYSdp`lZHgZxU@Xr z285Z>R^jhw&q=(TD2nF@m%YX@83;9^4DWK_8fx`^IDW6k3+M=PuC}#|Q?^aKw}@}Bjez*oqb9o==q&~ixHUWe_v-easM$sP)(2@S@6`P*PZ)i%dN6UG|& zFY51Is%Itj0N7GX)Bc0k)Y+4@@gE&j_hi!)+tuL*Cv^RjvW~uJ&4EkYtMphNn_DN) z8FT}>p~s!A`KcR(l8|vEF?OVE*`SjOsp~bxlURBDgr}0)V3Ni;A?QgwB5)NLOl zcy4WN@Q;B=Ne6lZM{zPRd1-b4z3cr2UYw5T#nuq*K}US4JzV#rH&s(t zbBuKI=)qbeR1j4N7!3}K!6s3-gV_MYkZLnqcOt6fv2KV;ZZA#aW!DAIE?4xV-l|I3 z_P}+^T+^T(PfOE(nm6~e&H7U_vEw3?-PQ!xG;rPWBg}(PDgLZF0Pxr&W!D8w;}heb z%3rIT(-%bJ>zXYz50?NH#L`@ewMkswg|yrp;f|6CPJ<~I|1?9?Y zYqnBet4N!8@k$^WhWn4MVMu`@D1}K))3iO)GZ@cI@6oi`D5R^itA#KjDE*@qo&Fh|O+Hqu)rjk}LV@`%sawRBN#IMG>_$yf z(jW*F)p43l$5oUd08KKRk*jFf4^+h_7988Vcdx~SyQKPothuI16fdBLVwHHq(qnG9 zX5NYFRbi^GuLlY%@uF=M4b!oNDQR%!;lqc0M%rO#t(3N!J-$*7aq^^q-#LOZhmSI<{yByh{FD#$b5y zBoEmHOY6ziz4G_jbPG+z!}c*&2GfKxV;A+PmvtSrh4PoY8DS+2UIC~k$CsF?!Q!Y$ zcTGlxJKW!3uFotr!KpRGOg2{=o@b_DSGISW7MR%ar zptqvuT8FT^KL|5|%{cM8{b~_-6++ZjTD?mt-yz+L;@k9ot~mLt{Cnp< zrD7aG56poHn5iG7zklY?PILlYkM2f~p{LQWqVLoggnbS#^@A{rcisdhr5FG0&{>Kr zP#w+JFtSHZAIAR|fp7*5C%;gA=i8b$teIoY2buKIzde5ty%Bu?edSFcQUWRhRQp)R zgA(w09ZKePMiM6B&cI!l89RN!*129gb*9y773m%(;2;Vxm`{jJY-=GPkDK3&ULvtF{XQo6gZ_m6YrwREVmg)Pg+w;OG`8Ip);8*(O(f7ix@>~RaT?8p5MR)>XN8bvH_Jkejx)PiHxsYC z9@EXp1n`55>h+z0Cd>7oW+24ATOX(N0Rg~SxzeSyTPZURARf4I{q@%`20o^~{%rDL z8_X!D0l?hy@~ljQ6$S%{w?6Z>z35lTa!a*pQ&Y`mD9d59IW<+Y)Yj={v-#EH^wgy9 zpitlai}!x??tYuS)4_rW@Zp?`Wz)Z`-7E;ZWLLM3*++zza$MTDsvWPV=lfAV?fJH| zy_P^JnfV|OR`L0AU|Y%36E1w?lacQ9oj{j2C|-LcD5&j7R>0e#DC z>bm(;Q`ezTOm0bvg~{?yyX8)l%2t1KC|%iVsQ=;|CVTtunYym)=J}t7LNP%WTEo6| zA3_(=H_#uVzePU>862pNr6OVeU~;;EO%G-pxs-|g;X$QslKNi|_z5~MYz?<{;&z<2 zvERwYot}q|f*+lHA=w-fim$alWOs~(R)qmdSEdJgfNX_#dF#3JPKa$KdUG6Cgv9T` z;pL9txE0o}?h^*N^`~2UmUWBw>ATyKmvIn;?A!|h?nP1L%>(}EC4u}*>i_j;qksE# zq=CII;nO;7aCn{*<~m*>oFAeP#bER2(WGe$PSr5P~tG2C1&%#$J7xuGJPVWf+MD;_0>2l$AebayP&TxYATO;^;7$Ju8^3UXE z^s4{sHy2U`Fy1DN*;yw+LGz-Fi+%!X^PzAP5PrbbNHaMEg@R1 z^Ysf6{{egw>nmdN6+}ZstpOSk>YWU!T}u~z6i)f6N3^}}Zsyz|Ybx#Jl{T8hv`EFf z_|eRu7r1pm$0^P*DEg`}omV@iQ2ipX8RR;8?ORvBgN@i9|GE(3Ljo(WR&dx??RSLc zRT$-_Km!jz&Gnkqb9BaZr)SmHO&WtBUQO3qJ4V!;oHXe_PESuytLp1;fz2Q!p-No6 z7s1g%^fnZWp_aTYgX%=%%7?EAgtbH2>7sZV-cWCP`$eyu_EvOGJ_w=Ug+gG$ChM!;vvXyo)R_ci1?bC331A z9q8_Y7MNf{K%#x)p_%(NeAZ)QI6d6}b5yvb7ZKbiY(%6n$y#qJ2auv&p z=9E6y(EvPg40bxUl8PI&K)-g#I9gjG_KWD0*E%U zwpK0PQR;&}-295_)AnQf(VH@!I72BAiF%c!l6Ay{(if)AsOB}3OzCzanrJ5pe6(oW zSZWJvVSv9B)EN>}j`dC%K;&9*tug)|`Adr0pyZuUC%#|iTQvOI5%$HhdKiEd)tJfkR=)0?210>3%gV7c6QjZu5(X;lb!rRdlI)ksY*&1W?bD=OA~Pwi%f zQvue292t)ePPm>YDLFR4%dn5xx(<$AoN%46+}?~J1(^y^u%}WVzi(NVO5sNvbGLq6?^K^-!N@Z6AykOeE}5wfEf!(?r)z6# z4i)Z$v6|r}^itcl-p{Ih5HA3JUw6{bE_4Rng6=_Y^gd5vGqEdlnYz93xIFK05<~{G za7${e1h=Dv{H1`{L{gWq`&40I&?zmyaj$-~eTv=J*irr7UNJr%fx5awV`o}Y`~8Pp z#g1(DJ{O{X5J#JcdR1`F;6tb$(ulQdJ$!}X4v@w!ZuRu7=rQywOmBnT%t&3ZoUMl; z%B`4K*9aM92ne2JQPNxZYH#GTdQ09H;&!ytE4U=75#a{Umd-Ia&wg*4lTj+j5ta4! zGB^-hYzPB3MP+aQ1p3qxb4Mk^{gy_5ltgb4UzU2)KX$8#QgqCN5i+L@aFc-!On`u< zX5DHM-A7&HWA$K@#z_Az&QD!;#YviNk#XAl|7H8W{nHZWsZJ&UMYwu2EQsAljH-v` z#IJHbGhePKOn~Ej^Tz*T7|&iRIu0!C(WoaSe~Y^OnZE@$XA= zrU~1;s%$%sU0&j@C07*nf^Beq2Hx&r?7a>0~pI10B1z5jRB4!08>oMe5OZ8^BvtR_;j^$ARF>8NBFXuT0W zvNYkOXzwsIvVFNZ*f#=b#xnW**YD)d9*O!{)Nb3MZ*V0iosQ(JH%D{vB=h;EYxYP= zrCr|-*F#_O$$)`yVgvKQYB%GEV3$->?>GPQ!`h{dP8vX>xIO+OR4xR=@FpG3bHv~K zZK|pcQ7M3-$+kiV26t7sD03OZ8flGS2`E*ll9hH7PY+*RGPHRpd(UCt#%9u$y154algzSSb-i;n`lWVHgG_ z<>~49$REEu+ZI}pCb@tyc%xD-#|h!NT&d`|u2T$?AlGZ{mGs4}Vn4)J|7d*a7&`aE z{%7l_ct89a+J=rr&=icZM--y_f-sJrS~vSI%=I5BX(oxo{=b!iYrBq2%VFqT>)Vz- zKd)Q1f2|XSWhy(4bJ6}IDMy6O09YrbRZ znd{gh^*Q6)CDZi6T+X#DH{EV9 zBtwjRgMu8qx;t%O_;XkL(4je?Ao1+dN#oxzO?#?X>|is!PP3!Z6TKdw!N676W$x3# z^~&S#mOU5r`wo6+6e+Dwrb=&nzO48=@Gsj@|Fs4O`~7~ur9b`Nmu#=hsB^mP*)P5K z{u^u3D|ts?{8fbX|<^jC>Ek^CNhd(De^qd+oK?av>~B z2w$!KG4#0e*C%z$veJSKmogmMqPA_@|7RP~e=cQw3fqp!x#`%((5XkeX#B_j_>cei zkE;3gRa9?rOC^VMr&MzDwhdt0d88t;^=B>G+SFSfDFx(f`a`n(Vo zFzIHCKAz55>U6Wr;c7JqX-LBRq@X;~nClU7oir1`7n}%#P~F^zgVamIv*)eH%|z*z zqfz}JiaszQ9y3WdYb*u8g1%!13yC&1NgN^s+*RU3Aw|uT+$a1ULscb(F{4)Ef`8tG zLuJ)(**+?lq@v@18w?D_U4QQqxZpTNNn(Vp$$m0*RB)W4;N@t%B}dxmzOYHD_U;ON z>O=k%XEI@OB!7pf{GYIof7dh>RRz#@5D6=+1h!T3xaQRGwKWXFR8jWe z=yT152Yzn6Q$Ei?Vtm0}#n#6^OrU=i$6!p^jH}kDiS6;DX6N-s2rjz{|q2^lOXV1ZDzC`dbf76VB*ZGFltLvh?c;+Z2#a>hn3M zf!5F+=soB+(Qlz2MJ6sM_yqN&*&*Fpyc#+dxUD^b2V-MscJ^v38*i|bY%h)1@P3c8 z)9%FRA{}CvaCPD3O2iMd1!cB1Vo0=$7n8)rk`>_KY*Pcjp6w^~cDPw^ zH%}SDTvAWmZ~NdLKKW$DcXJNz!wB7L>#0~UV_+GB%gT62g{dXffdirO#=dsB)Fa^1 z2;)5F&j=wI15jknmg~e=0D7zJ6-3wUm6)?`B2LRA{2EY*YVv8qYx55{r=nZ-U-GJ(NxDysqmi4y7L=f0FwT2hoWCy_nY&WpIrRh0Nm=!Z_ zXcRzSDmQmU(XM8BNe7^s!C7oE!q26>aWDFhw5mPXtevAadH{V5)6enU+zP`a^a2El z<1|xgC)iz$Bxz)M=Q;pBxj#u^b}DHYxEZ>(lwUEET$^oKw_V#2mwY7XVVGN|Y%Zm6 zhzU^~=OR;gccBLw`3lc5#{58$HCYJ)V6JN#RAJ5TR_*NU&Ubyb#yRo1~1J&$QSN5 z;&R!sZKqt0m;MNie|Cj#!?r7xdc9Jy?Z=0)cQxSubXX7Ghvv{Kx)~vNdIYNkc%66= z0GmTaRnp{>_0u3!>|<;&0SjsnPX^?Fgm%N$=RWs2Zdn|c0WYRUXC?q@wWYWgtCw0t7+=0q7v;dC;m7|pU>_!orCb`K&ottt^THv51;6(+J!%AC@b>+IXP!7q6WLIU?8V++D5Qpn7?O z=lx;1Jr_Kn%nx8?)pEpUR?Au8Y84fabg9*9c~QJ$(`aRIDXaF#3Xrl|1?md$P!6OZ zy!8zj!;8p3MKpynbQ_B1h7@Y3tEU)xuZU{VTcfD0#T|#(y3_6GcHN?%9HZd@E^v-C z{e2pz9r)UwJ$v}h9cOpWOtU?E_UvKPGds`j*vZH8D~0ci_dcRuTYvNHj-7lLy7$gU z;yZVoUCrVzclDO`R&g1783ZG=fR@o|^lF3}-QKZUa7+bZyUmOj;qFvBDMN?9wpvP> zaY~Xf$Zfru|G#>MmtA&QG#?vS9ALA* zdpCwC>UQ@nH*WmzmcSW4fy)u!-tn2gB2qRq@CUq*ZQ-qqHEAA5o!eYk|Du% zar4$BBSkP;5QLqrUI;BT(Y5U24-!NTCM2#V*o#j9zEr4Ia)p9lD&<{``+1*pZdv?f zjD-lLuqrAn))F`YvdJ8z78X+~#^?PY#B6|Gxt&FwI^$oNx3Lr!Nn@7~@JLr>lMqrgEt6ycVEmmkD~wW+Oz_<8OYPG>%lMnJ{Gr>FB3yAH3Y#C`$banv_&VB-PM1^AV0^F_ zL;f5clm?#}_K-9^Xc&LUG8{VJ-QS9 z>M`u<%Hq7S1BtTci&Ei=9$kkrc6NDC(&oeYz|WOBQe3T6$_kU#`2<@59vb8&{TNr1 z<~ejXniB$OsTm*8K8fS{e{GCTCNS%6VAhbT{mGtC+}=jAMb06?aUl}7!2-U;nVpLw zDob&>tnYVZCiaTJ5 z2YtzNo~EBWE)H#iR+`4C9WasZ8A!cV>y(P#Q%^nR`t3`~EwCi)Z|fU#wS)n{3N+Qe*l>gPu)b#Mg*6?ftD7({d696zae1#r~K43w6(f3f@4oJXPcf5<-E( z{`csIdAZccJB`812$oKoE!vB&Me7JPjH^i-S;cnfReaQ&2uMcQb-}tz%G4*>PPc*ZhvGLQ*oT11nNKOw@>qjjO~PW)01Fd7qK?iXy$;x z?71&s+pg@3cAJ{Hn=`t;1G-L6c4|8O1SOq*@oz!*{DM>2;|>GOLtm@3gNcO=k76e|z0H*>@o_!m9vCW+TuD5Ktw6Y1F725|4z29o5j@nv+Q zpCAV-Va875)Ry2iq0gz?s`4n$C}+ZMX?^@4o+ma1r83#OwMVJ=-cwIK1>=_lrBR>} zYt#2Tr-qb+Hup`M0~5w0=nVy>llR|$|Mv(gZ4{hvE6H#wXR*#Br0j8t@QRnN2s^Q`I+kb>7uWnpj@rmq*LL3 zaOJu7%bj{{CLi)-{d1G58qvg=?K?0u8vDEeFaU-+`bD}{(Y7ILnTvZjBSK^)1 z|7QTkrK%|K4cqoL4S@Gux8D}^7GyQg1x@wm>zLu?<v+Tv8g2jOxv-|18gRz|9!ANJw2@eSXMsh<}C|AnXWiarR7v! z%C)vX7fcg^_pCJ8Dxf@?mX%lBhitjeht(4*_(#^jdXCqh%m?HfP@-G3zID0aKuHpi z?g}dkO20EoKfR=-;v`qx*$23)9h-ou=(;ki1cCTQHda(H`(T>TH$mC{+E8T{zM$*3 za^v_FZs6iNiWzu`R%(I|c!Yb!af9EQ=k-A6bSg!M5ZgQW zP8Zw74ccjjflUL~tHqkbdKq%ieeebPTq3EvQ0v1)qdRbG$c71l`79|u7Y}hg_p_OW zadL|?!RCC=I%+!7rP#ZNj-&JFKJ-rX0rUyoqx6;=2Ms5|up;-$-5@^);>7m7o02Y* zX*VN?2F?uxz&6>HhhR@&L(KfNNNhv9zO0}%JIj*h(hMdnTWTh3RRkBt0v>ZA5UX#o zimw4;gJ)ibWf@!FgvhFsm)F*w6Onvw5Wx6lm}yzhT)cQOz&LoaDYSIH&WO@#b+Q&t zs8-XljzZr^zHf*_hd5UY@aB5#K6V4p3KzY)W*U^%qqQDJ#8c5KY>n`(+|XN9)J8o` zdmkvLAeCrj{SXl(A;1riv^SAuQg+_m5Z?N~tr3=CgeF-x=i2yxJ@__;RDe*P5oNZN=i7sJD@}nN(#3SK z(*bL3O+e;ODl@JwZh8c!b*b0uP4-&7$sRzx0-ttt$nxdTG`pd&TeOlrzlr+_8`3WaYN-5wp+iE-A^ zb2f&+ul>h}8}pgt5e)JcG0&pF3#$D-`OhJn*scXW^fx1dRurhO!$V77A`O`I`~Bmm zxwuIN@X>is>auUnib%dQu5!A&mST&G+AeTBdfaoDl4ks(tmrkzgM~{RGMFy)fDrK| zz9xZZDHU)Zy|(&zu=Hn!!88ye#sh=t(wDm-M`!nitRuOdxI2Oos-PLP1N9NI7z3uA z7+{hzJqeu!3Du+X$e_c2Y=^i4g~F7vv~)Ny#Me-%CP`5jLeGJ9^nQtF(0kMNtJgQ2 z{7j`n4$aMC-0hy6+rB+pu5k|clr3vs|GTAZ{rA>SIO`(bt>kX>5W6tp^`@J~`?lF+ zt0v8BN`FbyE#F=e^dg}2IP*8rY$kCy%K{WW$V{AOX&9^PVK2pbRt=#|1*&1tm$~JdCuSzO?TV$N72WbgYb`N6hvs=b2mxxf<>dob+qUh6${CDt zt7d;CXIWrh)`a((0Hz6G!Vm`m#&h$l$_)kvmMt0>^zkLvKHhV^YfWT%jn>-#)=)~W1ERevNiV&Gah?={GHmWd~G4y zDIC&zybnT_VE}M^I7t)ug{7)231)WA=F6dGb9*jybu4{Tw>O&rxqQNf z0357eP#wPoe;_j^mya=`DwBIDme^$q=v3RjU8PgtxwReT$wO%JBxsB@JD!g&&=*@HS&qBsMdOb2g1`2!Ckt3B$PBt56lr=$9#<`GU;|z+&(a$G>gs=q*FaQ zL?)AvHsq?r3su%&mk?*VIKqEZcS6>ZY*h_>h8ZyNgDxb*z!{aOM5*1iryV)o?KC4= z7Nwllhby2~nZeKAT6pZS$4JEA(4lyH6$~Tri$y;$45+S59HJC|-H#RPaWYwt3ov=> zt+x^|&0M5HNti+M`;{Pv?(&3pOJXFSC## z#Np4Q1-WheH)fZI>Us9DKW(8J+8(=;SA$13-7P@~I$15XWi?kCritrOa(xs<$>Owd zOE%Fk`eD#$s0_wy5R=#FcmV1-e5}z3Ta;pc)A>k|Mh~FNV%5*zYgHWP(AQgtQDQ?Y z`zU-$@#e2=Z6wt^ba!?^4c?Mof4D+ZRI%W%b$M;>2@Rnf^be?|lfs~nJyJf};aZp1 z_Q8eff$W*D>C1Qk-$q5W4c&q6MUSHQBLu$qR5(rEaF_-tJ19CTvD|cgflm{u9k)&R zbbE@h;LsMtX$4``bjvFga|kGIvYU0YD10AfJM9n+f(Ub0_!uF3eLBE6_;ffv8HUT= zHv7_l|M!1aL72h4s%QDWWulj|R%qEH@i*;lwwyH3`#rZube&LYIAeS<49PV(Tf7ah zjTwO{pct5@`^QDcfpvev`w;|SVz<0$FDm;@#*hx(U4k-5*(Mf*_#3P)xn% zYce}DtX~PUc_IKDm<2R)FQzM6X59ppT+o zM_)iA^m}NQ)@&$61^NL3YZ#Uo8?YP$E4PwtN2)sm(vD5c;d0#wJ*1=KL!?|reNp(1 z%x7Z{v|5NWLvRqSudfFf2Y=)=EE@arI}~KA?82h{9-p z)++uKKVI7}X6@8I zsspZU&?TGTT=K}VO}BIeJ!UVf#R5PO)ZaJBwXn%7$81q8Si}Jk0s&qOG}!r3VXT@^TjH*{XR0fKV(m;1?>$Hy@j%`q>fqfq4oDI6Rkl?{|UwaUlkLW!twwp3C zDj}%s3}&cc^5YcHP$B1+hU>!b(v7((p((i_p&4CjP$`cjpz!37te}1`ln%Q#z(YkS zO_IdZGYN+9`Vq=uZn)^}=(aly%qDU(_k{;59Cz`il+_l5_V<`#Ahi zy$%rQHh5B%ZBVjuG1lY$hj<|D*Zu5GH{H~mdT7=Hu*ToA9UH)QY*^vHte?W`)w6i8 zPIT%9T2BrSDj!1x8sBKLbIUQakDt=BToV?U-~S~-XU1sOgb zf^8RU^T&*x_?_cg@Nr?~EOTDQT6^J}=V~nk{H^SqQWofd^m~l<`{goufu&RxJAR`rCT^fX|P%M>-@Jwqu z9+i0zzb1y`eWm(a)-9BXJ^Y#^s(|eZ^7QC&ZC=}}x(lf;UQ{VTKrM=Vc-2P10iv>}< z+=H;XpJ%z^iYx4V-u`3R{iyNFdHV`GKmGcg>3XpsW-?1W^C?MGZDFwOw}}VvF~m_G zEubCfQ#smUe&lnP$`CP|PkicwMGk2IE|{bje$1uFtluBphTrzf5nQ5LbVu<&8h{6g z9Np)M!lhfRaLI=L&Y*ZiV;RYdcps+uMLdDtjXs6Y<1MMe01EFx$LHHX@(n2sCwY(n zhLWYU1(P=?OIz*5Ob8Bu&YNVyztDAxpqX$n#q05j%{a~`+X%@B6)%}SK%e-Pv8K;# z)TB9j`%~as6=jrzZhJL=VJNbKC0ft`z}A98fvlwZ2Y@PtlD6nkVih^(M3rU3H$|~n zkE>eKd))Oq{P(9{mCxsQ^tNBjP9lp*SqdVK7@RK{2uj(;1RB;~^CpFzg97fVsw68a zXaz1quU5G1jG>ZwyIOvZ)9#+o(!|W>y$*V#>bOF->pP;Hn|^uMu3g*F7Uu3EbYf1H zV=H++ZEJ)V;Tvc-I)=`or=<|IVc0k!3Bz=*!k`WnvSHMA0+X84cA^q;LwB!bki5T# z!2ak?<@;G`usO{nhM#Fjt-ESeX1Xv`B2jl<5gCAfY`aTiKpdcFE_87mM0!q}F*Sbk zsG-{^GMBA1b2Bw~L)CQv9Jd;$^IToT`>W&cZzlW$XJN6rKaL7LekbRx4w$A;PE&*jc$rMfr2QZ9&)vr z4Hg}(^)b&6O}ow{Nm@_1l{`tdl(jGjJ++W@{R*4orYro>?%lhE>sp2^5!exQ!dLJX zM^&lrIL;8TrVy;?c%N5|CXU6Gs^6Bd4Env`2Z0ZdilgDx-jAI)al*owbJH?)I)Glu z7C>8f9M@4*x(14>>8^v}=8A1wR_TzU=(=sR^9JWe{(EQbSHI4ei#~E6hEi*;e#$IMQMxvqOczae1DO5Um`2)gf`VYK&d8$N02X~3Jl{OIq4yE)7XGWlW`in%qbb_ zOOV{RV{)6?SW%c50PEzYw1tHQ4$B_#NO^7|-8h9CXMjNMA(|_ymbvYB_9kE0wrv~t zWk=~}>%fiR26`%Mtd%~Jl5PL_RsujK;r06Ja$;IJfy_ihCKee?g|#w5Q=k%O^@$Qxr~KKTv}}JjiT0@kv~LE zxSy`6Y$HDz;a+(eJAnEOVyMx3udeGOr+-7dtUcHU!tojl_}}> z7sYMpzF!{9#-g%P$Nzc2kkeUOmA|?UvM7VS{7_ncC|>b%)^g~Z((iZzrk6m^zW!q9a4mbKK`5X^QKA*?&3{AB>Ee#aET(zP$ZIM!3@^N-EDzje&r!5voJ zGdn@JV=_Q_%jkrEgb!SK=iZ>#cl7#YCpuS9 zsW-8#Xy5Lj|3sDq+HG@DhDPb%%o0_*XL(HcPjxnL3Ss8Y{%TmhM^g#h)lG*CKH1}b zyf*5g`z;0|#_KphQ^q`3QJ<40wZIyk_K<6%SecQ5b(`>wm z+jj44h5`LI>us1d;DUSu(bPm-e5VB1Wz0}+z;aGc@mX`i9Og35t!=w1CuT#> zX>a!79FZ^wuGOJZR9_1eit{DQqEuXLP)UV%4}B_*16TuRCFY&WwBODQt`Q0IUkLL^ zDSW+-q?3~_%Y_wP#Mvx%s}{eAAAu1%j85BJ@yiobX$z!2mnWqCrcCKJf*Vo0~(#XBm7VF9d9%CeUuX4B4xFf$E+w-i52^O7sYNa|}RE%XFF<$*v^9vKN@s z@c|b{WC}Jk>Ei`lLtl1`NP!IUM5+>UC8zlwf7N2_4drrg6c01bSPa0&>fAzWid&Y} zS{zzd%G_efbzuIYc@fNIhsCdt31Dcd(Go;8S`&>Y|ASU*<|5M+M`2G7Y>x}uw{M?j zSTg$rx-MJJTyWgdEwdeUm>jtcUsMf2a;jlyIiz3_J35A!kbx>_kyAAp01@2{Xh(u=WY#ied(QgE<#;Ke>1Ob06dIy(VDu8RCR@0)GHMXL0R%pLx~`Zu`t9 zKl#b$yVSIVDa+b%yZ8-5DOVc7XJkLbU{*~qZmerLefgTEY3rQXHsfbMO8ifL@{{vd zo3c!)X^F?m_V*%}<_ACuDsPS8ui+&mPz9YtcU@l*tE-6RkbhDY&|9*Uxe!2SJinDD zvil~3rshbsofr^u!xa)^z5H^i2Y|w4CZ@7?0nz@AHXT{3jIyD+x>LD zw3rL7=c*=z;Oa7FvP32%e2mJnX__WI-T9NGI+7j}!RHm#=XeX==gY=l=iqRgrv3KA zG=A6BS6_V>#{bQSA(O3A}HYy3+rD-TRI`l~id zXAD$Eb9hblYyFzKrKb`T%dL^$#~@(5PLZgz6QmxtGgG#`PQt9$BFmSaxB&@X8403g zDH(fC1zDC^gLg-vtl)M7s((lTSg$kzTd%zTHLrP%WT|Z^{t%^NAmI%UUW7jRn4YRU z`B51V6R|!@%w!FBoMP_@P3v)Cz!_X67Y&Bsz16)})8MmMSOBK5j6IYpJTxf%Ou3x% zN~b)dsCpg)w?8HAQHT(UQk*tBj0t08Syzcks8O3qRDIdm{nJ%T6MS(L!%K_6w-LYc zkKTx0$$Mc(N3Gq1>ro??y3dm?@qKD>Q|J@uSw%?n1U0$TTDk+I&{|jUYSnK2n{BTN z@!%g7F4?vaYxQw!Yk_UUrM0!=j?=a$SXMTXzGVPs-&wus$l7@83?bImj=@@mHuUY= zzlZXuiSF!GYFkP$JjYT#VOMP+hW$Bpwdbh9?lBpNjY z7jP@p#^P6Ed9@hUfld(o7PD`*Z|ko>RD(~*(vZr-O4{V7 zF=g%@lN|~-LtuqRVXT}5%d$3kl`|t3b|-?lxel&1DR)-}$iaI}un&&-s-_Ke2R-!6 z++BB#f4Kdao}&+bch$Nz{@Ix`XIg>Wr^R_>b=rJdi4ih9@Kf@*r_o*L*ZVEnbYcK+20Iy6)h)QA>n3MuH%k*2A{N>?Y4`kUyPMANeAxbs%TTwV(gztB;}I zL@%Q6=1(D6py5e$+SL9er&aaeLp@9gCc!FB8YhP@*}BQEnjh)LiajS zdT2(`f@l8bYWaFRHQ#LHXRjz%tI~L5?J5QEUQtdDeRcGytMJ$Qx~9`t_D{NdZ)pwG z+HWU*=!#Z*(ygZl%E*WAo>W<>+4NONp{y3uS1++LqeykqEZL3Lkd8u`73Ocj*PZCt z!OK}MYAo5jz3z%VT{{Xt`*ChjOLFhW*00-=_9gziGMdnJV$*&DSFG4>-mbfcW)v;w z{P=tDP%1flsdC#D?=+xGx-2gk#xO(!Ya+oH5R)P6dX5(G|gmCx{Cj``dzJVsW z)yU@+;P^JO6p*JZ6QT;8qBR_U=|DQZ#CowALRG4R`Ox`|^oN7TL>~yWZK_!)67QVb zNwLtxlkm(Is-ffP0(wJq>0)A}w7ru{J>qAF*=Y3ZfYBIMN+HMFa}B<3X1{i(Z|+Ip z+?g$I3Y=Kkm$;S$al8x+bEIEIvN*V-b)rIlEs~*!%!#(o%%rnLTFAeqGjH`bjN@j*G z!0H!CspC$EptC8tZezD9Y31?9<=$_;AHLNuiSU?#2X{g#6rsGdy8Gku=90HU|9z|9 zD5g8|yEBSnCbxbc==5Eg*PqqD;AHR6-U+F^0(jK!80ncN(Oz^8-G`83NK90+bE?Gt zT^*WPv!rx;YN}*yI^(mwDIj@X0k|=j%Sl9)b4vtcrAa|i$if0q6xviIOqOoEPDod@ zWOEl``F6Xh-+g#D3CAa<#3w2#ZL(~jhnghN=gOXd*sLw z_39())ze3g98s=0qFy~z)t${F5M6nk^D3}O#HqC77d{aQN_*_{?DL}sn*Aa}1U_`6 z`w2TGdbHNE%Ocv1?m-VD1gLECcGnqXOZOB`wB1M23U5NNC<3F!fsqad3$Pa=pa+D! zr-dEp1KL7Rl%&S>IG8%Cj(acKbaE0x_aaKgb8BmBYw|x5>oK`fGT$+mc&HcPFWFAq z(t|Tkoprg_>%$h$^|8xq#2pSWpC^aW`L*xTwiq9l{m}5_J9&-YkI(ihB&u&M3%OW$jJ=bXRyWd8skYqx`!q@b7a;6E=eZ;c8r}_&) zd44-Q`=dGY9*{gQp4mrO)pVYbg|B@NOfyF*9P=0-yOv0CI8v0;PQQZHr90!VbycMK zJN(b4P&`*Drb?E-qe<9x)Q3w4yiAmtxvZNnmyo#`iG?ft3y)=v*b(0TFwI=#Tf0WM z0;Te7l~|VF7{t9oJSix>c=5|%Ao(_6$eB0(9HlwF)b{QT8cu$$T9r?GjPEZUVa!?w zD=t$61m7CKbtplbLK9(8XyGdLF-4g>TI)#HA_rU!krn!>i4JmaKy}Oc_i@Lu-K+5a=US`P z?6j8Jie;^=TT$$;0Zto+wA(?Li=yRRb>PlXE*AwD2Dvk9 zYiFz-k>y+UfC9yP65|03a!<>PU3rAr5wg%SXSnDy5|}#^x|r5!#6%7oL~(7w+%z?9 zV-hrQhFLU4sp$%qWVy1iupoqO zFD%%$5ZgBf{Xl zOh36N+{D-~EqFoKuQ9I)Yx)a|jsq)G>`UJOQ& zR`rD0bB%_RoWQ|KzuI-SR3*w$Wwk$=5UsDf@EtpG_7)S{_9ALxd(r52<(8{$Gq~a# zeOoB&>af0brU~PX=kkNU;1k8cOz6=%x8-?XXQx0)pO^cTYt{ka@)h<9drnt zMYo`b&{OC)uDy?LM9*L&1j<9jRc`L zGnwIA@lxps%(se6GD%Xwy;Q1?Uv4Y6-1o$vr@WH~b6;L8z|C@Q4Wr!3E4F zI<+!=A{Q|`Fb&tpI}HO#O+$EqpJ3pc@8v^bkSTLl@$~HMG=4PkfMr=a-}L3z+IM9N zJO<=P>)bM$LEAW&mmx{Lom$4H-bm3!d07niXm{4d#jNId%xZqJ-|xF7rw9M2!{LxI zU0-nTU@#aUoNBH;{UHOmx8LvUI%D7elSISeP}dpTKK`e{fVWS*8|8>a-p#UnPi(u` z(3;j%mUi1K#Jt|zB?K#*m6*?~zXB*I(xjrbG-Xm!bdXB4%U3$`eE^U^Z@;z{Al!4$ z_fn9%s@j!--X~aDzweE&w$@eCKWBTu{$=LxtfX86fU9x)d@=K{V%jpvn8Z_?TXUEQ zJKjmOqT|4z%nTPiNQky_SLF zmWpmxV#r`lN-T>{-O7U?>ej-#xURjRvbF4%lIwt*E#D`J=hQbP%%-=aJWsAhudptX z@oX6`+UijD*oEM=Q1v)HRc>+^U~paAwuLY?5(XFtA<;}BY}|0+#N21MdUi3#{O~!bc~dY&1E_anLjdFyivqxZMGfl~+}yB)(Z5 z>Nzt1E6bqyWCcOD_s9?%{W+aLrgxsHwA&``$I@0u(fhEMdZ~|ix&9Y>k+0J0fW0*oi`cTyMfg#bDD_6b4{ulxhY5J5XoyNCvXKhmNB@`qrwI zZfZ1m*o0sarZzB)wK>N_?Pgv;26c>@8L0~{Z zMk2CA;f*_pu!U2k1OTsqB!>Xbz_!gdzVVHE4jyZry`k$mG9F$pQcmu1BCzc|?!ZG) z$*~aytL`#Hp^XOBZEdHdQh+tbTIGDza!h05p#bCHp$Wqbg%}XSWCK!eZ)IqZ!T8$`sX*^T@%Sz4 z!MD*2I)yM$#>m-bKIonu||=7Hjqt@SnBgKyNi_G{32bO(C4rwy;d)WKq9 zENbW*Ni#u|4bxn500iZ#gajP{M7fSf;*!h=J{Z|-w$m_ix-y+5-K6=|_$!S!n&fNH z`@dbg4bBzcS2#Dgq9~T7C<=#fY&*x)KgD6+{08tR6<2+xT2`X zjXi7mBg#qN8vg*JLc^GwyGueZlXIB=lPLP^sZ*y;J?=B&#z_*pg!%b$Iqz-AldA0Q zjNqH-5V{PVMSXOu?N4eHbbzfEbDY#bz*gFvI8N(n>yba)5h5@N>1vX7Gm{z>k|O!n zp_izu#)M=fOOr56!Xzb$^^xmnCh@-uG32tW=q{x~i3sSMx*lL01>0BeK%@vtU0so7 zK0LgA`}UQ4?^P6K_ip9Yf-yE9-!(Tk$LjZ)69um)d`(r?2{>T3l-|8M2k_M~=Vc-ebXS%rt zcVO7{$tI3)TRo4ZQ$tW{#X$hLI*)nh23~*UB}&4nIAF zrsfc8L?5V$-G9gykqV&`;xaSh`0spiJ%KYf-E`AlE-Tg+a)qI;DYj$mxrwpsgJQ|| zygoBc_D?t6bkiQgvDG!j5Dp+)k@?=6m}#=U=lP{#a6MxPAtcwgk>5hHu93?;sFWe( zB$&DYGg!>H$MJ=sGuc?VTm)6N@@)v)(v8MwYr(&nXG!i%-n^kRIdaxCt&t62k1TTm z@{As$0|Rwm1?5$)r~RFDO;IZZo#}X1FiQ%P8KJMQ&4#ly<#a$UIu0bKKjV5~=$}4)dS=Gl zeW0*n+P)1}@WlNPC~gl?vBVsMZ~%I`(V(A_+-F9*=@a@|oae}}i}^71g_wIzh9gDDE;q;-+|>vx`eRN zF}efY!i~L?v(R!M@Isw;?5T#S4s4u?7@Aa|0v#tu5CT?$ur2-vu&&HnyyTA0hB|SI zex959PR_L82~8#1YZg_NXxChg%MzGY?hLLl)TD=xK%JcuN3wKhe)NpnLy!S~nWEMM! z6fh{yadLg4w%sJXk2r3D*g|+At2aIvlqSLf4l73g5;}>qT{z{$PPcFP&-J*XqqrA+ZJUBp#c6S)&-B^1+57IfobTu6=5xNEo2UI`3J2rX z)&Q&;Z@YUql!rluQV~_cklApXFDTt5S|;6g7qZO-A>r3e$QOE^O{l*=Y!eUQdBjl- zb@1xz3;ht^5G4peVk=90iKGshii%0sMN5?%p`5CDL}r}jzILn5`UY<~3Z-Jxlj}CcY5)2g75*)WtK&a! z$6RlFh5o9=@}Z!#5`>(#IdpG?o{5EFWfzvjLJ|+Q#LxHX2Z0vGIw#UG)Al*~47wTJ zkKTmd#YGLv@DJ8V_&l=FBB>hI4uY-jfTud64wz{AfOCUbXbv1XLQ*FHl%YAra2CNk z@Lyu4a9A8A#44U&U0sE6F=^Ysytr}1st|b1K5}6E534_wJ7usjf0$$dtEsAMstPbE zCW^u^M6qZZhFL5IrU^K|tlD;-iG#47$6%VlLBaC2tuA+3?h*L%M`*0Bu70uwwk^b1 zd`_6R!%>C*E&sQ?GNUN~4$Hu)# z{Yuoa0$e>f`SI+5X#%`rd3iZo^x3?uO_{lSf;sh;#0-Q{u18;0g~Sc2!+DV9&{*KR zQZ#SKiF3g;gJN#XE&$MGMxM~5cfYQ%mfK#;mGeDRLv6IL=aoExJ{Z#*r>rb(QMYFu z!+Nq=+{}$ami&dpkgv`ZgV4W%7G_H&K?rmRiIr-e9g1r86SM4?j<3ZndwjpPE(l6- z5a2;2I>}jcD${zKNWJ8NGiNwdO(+!+QVook_ndGBzVkUwJOdj?todS<8fem-4Aere z@Dw*rd82VZ4g4?e%r~?TQ60^ogI1_{8i6M{s%pISjnN0N?E?qF!`MkH=p&HmA z%{12zLR*qv^+LKiQ>9;e<4j*T(+4Y@x$rBL32Lq<`8HxGI`p$qLCAIe&SAgpaJG~# zbkg@e{3F~cHh3K=WK&%!`Zr`MddC27o5vOeCAW`XYTlXr8vdbotP z%L|cZLfCee+4lAZfcH7i_x9q;3d4F{91}|1<-flQ-ZiktIWp60X#M*2+S(ey9{3%^ z(Wy~cQ37HX@EO^??WZPS*pb(|{fM{t*2H;^W~$XP-(*XHY;UZrG*+~zy*@@u0S_r> z8(&;o3#n^mA$Wl=<(AkQ!K==7qZ7z;<7zmMj?xSHS?Xy$xz%`6w>AQowynBX0x5yh zPG-vv`LwT_!8OM&x1;v68Dy)Y% zal>qS2F1IQ>Cbf}U6kG-CYZJNyD1{2)sYBkQo1PN^S(p4f^sT;ddsqsL`Vl2n$ay) z=Hy6E&&F*z9DR0i>Kuw-ShWCqKfjwd1yZ_d?p@G)b3Y?=;$_tg_G^ zVir=BSI-9U+;$qqaXUt{wQVU2rE$q*Z*z0gEzOPgAllsA+%Fvy;h`z0yWZjX`T3bo zEGOiCN$FQzuGOE{aK(|y4<9*lL^*JzC*KDH*E5TuHy+8?*t@~UOf+T{-;HjQ@Uqy& z3U%SvwvL$MR#@{0vwPTpop1c4($2@j%^c{?7PI^bA&YuxvW12j4 z8UpJBU*&Scax88Xr@@&n8r*VCZn$?Ft2rQ!_bL+BCJmYmGHOg}SW@=J1GC2D-G`G* zRKWZIapL|KcwM`B%i@+}MF5fIn%uI?78Hw9!-djbRaJpzS?D6k-qHeJf3?=Sl^iyf z6OZ0IEf5`%$w-E1hIQoDL39H3*Bj8|jBBYX#th(Q0b6`UvF#zRZJYTKj9L!*mj}C1 zR|2F)7uSs)%0a;QEmv?ZrwcOg(wQ?apE>h@gs-zI_9>N_rF&%C&iAO(0KsH{j(~XZ zV6E$ldaIvqpCXh5{7|;POL}L{_+R+9v5hll&YW5Pr*F{vY309a9P_4D;yKmxt68{* zTDf>-GH%ZvfUQ>2#A!>yq?QuyL(Gpl;UY=#PW65y2=&Lk&8MG!8k$eT2wd0tU>I80&#&vCS>C zZ*K^oXBzMo-C7<0A~~M59tNPi^Z2Fj7JhIw)cP`&g9iS%wPYCSdL+Z@j9}x^e+brx z*|dyH*Y8zyNxIM`M({q=u+=~Y579POEovf{69W`B|J;Hu{jf9QG@hT`V_*d7v(xgn zUcj^04eSif!piQW$?t+Rft}QI_1=c7s^UT(o0cqx<($#@%<`DqKejUeC`a+VbVV(^x<)gtDw>#i*QaDIp-_Hci@aUVR(SA8jvU zsu}QlL!)@Hee|4t8q+Pj*gi@C)I8c=#CoW~Hy8tXWZ|xPpEnhqG6%||atYJL#c&JZ zz}?@CtuitJ%viU&MHDGiN}YmS3pLHNGWJ0nZ>*!ZX1PUAE_z9<1=A@fAMp5PB)OHY z3!B#W$%e6VK9_39Mg91e$@!+)rx zxtZJs{lQ=`Al}ivf1L^tPz7RqL$0@cy?zrPWn?=N8y>!GAGK{Ks9ag9wmUxJ@7cAN z7Svl*_0xjeIPIhbMKFSX{1n@Kd-ynZ2)Iebv(g53-L%28 zlx`z$-~-57x$!sN<*`S7y28H0uio^6jfhJD1t@?%^kcQ=#zN22=_3WA43SEsDIEPI z@rO<6P0Qd-mejSfw_ksLMmvFk#Digq8b!&%%c!j*p{syu>I0*M9{4<>-&aF==~N^O!~Qo9A&qwOq(WNiZ8u%DaNK*!aX)yPix0%0N*C6 zt#)sJHh^NR-KvK9?XtNroV_$&!gwjZbT$lgJ`^{B%}DJYYNGwp3}<*B#Gu8L#G?9+NdHP_pIEO||pNJS7*K^7j5f zgFKTPs-~$8nOUY}=n8KcbO+;jo?|c6H#d0R7;IUAg6&)X zNoHkeQb3EE9lF3#8qwwlu>o?Z7D39F!P$RD<&?h76ze);kh6H?@6vZZ^w2{Z$rQ_G zD`$8Iz(=$Xv4^hU#|bsZ)`jE6xhWvFnu_IXhX1AW&2;&WN`tTK_&#&^C+b;@=0J&? zUGE)s$6?-WYYASiAvL(G8!Q8Lq9b)@epri-9NB6ms3ylAH!M2&g55j`gHbr4`mnzb z27;>YlS_U-kyWF(01*N9p{8v&wY@ax+-VelO1oq9&Jrn1P8y=+(qm3oaVwRIp0A!q zM{gsH)4P1b6jyK)>411eG!1|9J7AOF0c$4?Wz@Oz*u#iD&Q5d>LTO1G5SaIQ zwc~aiCkv`PK1L)CajJc?`HIsVr`11Imt}E)7TT+!7~ZHBQWRBF!%o?BvY}ox{ImSD z?2|+h!v>*ux&+1?{$sV!YMJ@O?%p)vqh4Ed#`2f$dQKE2z#QJneKWyDg`F;XAzCKM z%h637V6wIE)4;uHwoP^;Te|JHUfkE{27GjEvvDzXh4>9BpS24qdE498lMHHsj!q%-w; zn64fmjvEao7rYR9gF{1~suncr{>73g5iF~~OasBn$Pd;mj}7)(IIu-%YL0d_+Soyi z4(NGLvir_KSFNllzFH?Bs~0-ZG0(4#d|ldL%F4=BFu=*um7>z#L{>P^ZpSaQ+HQ-9 zlFg>YE0+@N`_uV9hYa42ilgPi^A=KdSd-;FQWG+;Z2iHtb8+dl2X)&57&=ta?(}rz z%$=G)YKr=nC;9$c!AJ464ZMz5pJbjADW7eg+B4yWKhAlV(Y4I z0+_n0+wczROb+7eZz{H0Q>tMZ%=ejL8R{7FrGJ2j@C+)T80|x^X#Y?J0K_y%6Va)y z)EqB5|JXn;>Mra#N?H?p@l=Fg{veEPUDaoExm<2mS9N<=#kOs`Vi~HDV4N7LVeL#_ z*Fh%yfeY_ytn64@+p#jM8kSLMw^vr$?TTR;>ZQec)NDreqN>~a+`)r$x~;31=0*4a zk!A3wf$+&%Ttz$40rU!VDw-_$5q5!di*s_-h{hVpX2xa(TikO?^vO~!t z99zLdf;E9``1QyqqBeOEBjQ~3AU9j4t~trYet&CnaREV5m_FYI4Q*~N7|z(Lsy?{0P11F4T1A8o=~{5wYUUxw#Mmk98OIJYcX)ZOzlkeeHqKJe9AR(L z{A`B}4}(5Q>NXhZu+H1=F}cYAHvWzzKIpQq#4@P;1^76OP;q=oIxMPj4O9mrZ`yFl z5B}ah$lkOIdFKJk+O&&j6}45h!9gfxFnHpLCkpnaZ9iZa^3JAJetsQIe0kb`iV-}8 z@>W#kP&C_kxEem2oJ_mEP*)0hsBtQyM~dQl$9US82*-!RVH6Eald(ZQKV-}_Cr^RK zoF@wQ`NKNlyuEQqHw91Ld+)t--R|6NjG5+b#V{=1YnqIG_K7E+uy4$g>IN9WZ?nu)|si08hampd<Fh)g$>`CHV-zrLU z*z9uBt8YvW!*i3v9_DYkg~uK|cegt_zG)QO!(;(nCrwEDaWEOO?#JV}qy(Y{tP5Q+ z*kG~~SA2q}eCJRcTqd_CWuu-S0LM%IrIgtvyYMf6Mwx~T1Oqq2aH|qUD+Kz|jY)SI z2_QW{zhBzFzhIZ_$tVbdEHe!1N2zB?2BR#P}ijo6|PvOJb5g(MiyTB zrV0-g27^Jb2mjyzIFqwQM@c)-GLtvIc=jyp0RG-i7SK*_KncYGC=3Pm>i85^Cw4yo zvc$gq!WpQzvB(i7{TUL~qYzQb+8Z&pO-fT{Cs4F5e`w8L z<`O&H^?&r&=<9?={n7AZ5mr@nyyh<-mG{2oBBpU{vfC;r~#xP0eXw2E%%$FY1lu(BF zLH6in_^XHCxuR>N|cTr&h#91Obvk zj_}j|$(B=xNY)!%9@hL@cFN#?^MBssKO;8)fp`rH1vp>zA`kAEDqZ)KH}*n_#MhuT+`H%dAF z;Icb8&HIVS*uIDnY#<%g&?=p*zP;b$W2Z0&lyfp&(^5MPZTa*fA$VIZxRyk+33{Rf?`S7bM{Q1+byb?O!15u!r!4TzJ zZrLE@xf-~OY$T@W0$ebSyFLPEOF-PjTZmf*fMFVa#`=cIS{Qq^rl`G*;;Ow&HixF1 zQK>!5csV+0xo117NEkn3KeeHq22QCy*8vx&*y$_1bFe0<8aY~!6h+D;f$05vUd}Fl%q;^F?G*R^Tn|U#-XU~ZQ?RrJ>KLA zhq&9ldZd>>pVN0j6CwKnr7Be)PLMm${vzr03H#zoC5l|CW1YHDRH-BlGCIbBayupq z2hDJ|dv$Bio;1y?YCcW(>}g%y?P4~16syumSF1pcyDowk;nT=OVT7Q?XN|^J(xw&( ze1nOGpqj?q6N8v--YA+=A^<_e?K*r}@Dt1Ptm#)xo2E7S0p+K8Sz*Ujdj3`CFN1ZY zMPkTl1;401e9$zfUo~xco@M=lYR8x&^V2*%|ElvJ3EG&zOp5(|bI=A7J@0{El)pFK zsO#X5}f3;JeVbq|y!a7v(uFc7M@WUcBw zJ6`}WC36w=#H1UO^b3pK_|sH7 z#qCZZ z!jld@a7=Q)`Z?iwm(juSJKTv$OtN8MtQ^Q1mD_bF!rOn{-h!{d2w9P1H%l7k*@`W4 z0}0m{aZm^L-*VZeqiE&HXqqX`CUZ@v3X&#H!^jx>BZ7x=V|^k7POd7tPujNt!3dSS zX5hGe9;L5aK2K0e$Aw$mSNyJvT68O zKmA6L8@0%AT>XbPjJL|>cjgTs2RoNz_zYHE(YVU_#Cak*H$zr+ze*zRX|@sG5(vK`)JYd9QwXmd;JR_2>cm5&c)4ITZH zdHsShUk9DXlHp=ppX)uvk}+-T_7caicxnFUzK1R~k=+%z$GrQC!kU>f^qJv?n`-28 zIiN%=xv@BIq>lVhlI3r`d3TOrPx_Rv>%D&tlYQvF$EOV=vc|2wosKnwn)#3!D6hKP zsON!k9dV|xco?Exy5Se`Ljs~jBPG-arsvNRM(N==R^?<*%WAxI=;Q>}><}NOl%17> z^1`W`Tqp;!7@r>8ujp(>;u%5IR`3Dt$3DQx)^BHxFCgB#p7v6Zi`od)0H9~aV??7O z0S3eAUB^YYPW}kWWmL)_*RPE>suZGpgMLu2`*2zC|sfN44TTBX)8olbV!_J~OJ(vUvR)50dKk ztNP&C4>c2~uGArUe7a9_n+D^2-De{O=IMLq_SLc`<+$N3hZO$YG#~wX!8dmEAZu!J z{_m7*M>^Xug7>4gw*EY377@rm-Alj$5bPTqJGy2_EErrGFJW+jc4wbYGnK5}olg0&-5?b}zlyDmI2D;oH zeo%i{f6%b61JLBaP+7|U!C}W-2d9%s26KJXVD97>GLjD7uw=NO1emJW-&nNpgB5QpT2v8R|o|ec<7(qZR#aG?K}q*LdAWx7j%EAt7w>e4?(0!=sDu2phBV71s`c z&9A8p%T>Y@t#D$=_gvB4(7u9{X#qrTX&AeotyZgWn2XpPoYU@+=Z;nQjqWoiLq1=_ zNimlzCRo(^KI^Jt9)?iOVN+2jMk7vci~+gScitGztgf!|J3EVGdltR!Fw3ju^ML2( zX6J(=y_2b^s}87&`PI$gYQ5FH%WcN>is(mkd+>g3s_X+0|&;$>rTaX0MmcFO^{SQI$DPzX*zAF7qKfF3wk zpx*RiYXuAMS}twxn3(~HqJ4X>+M5bO(J*)riP?}Db_UuWPrb~e5=MU|#1!aj?vX<9W&9a!P z7E}#_AcjvXYJJgnR5f3o4$7sHpLIG^emC9eWIGQZ*+03#_3O16rj``d@O@KN0^j$R zr%HMr&)vfNVT9_jE#W-RiKH9CWKswk=+JTdNaMywM&ig-Z>dIg0hW|pEe_mY$F6pJ zCiHzp5ES1JXWH#uOaFeVAgJlA6JE&;u&km&Wz4CSN+YsuN^|)##T<;l_p6mKoKn@P zFsxMl8C?+gO7}z@4WJx|!&Fux+oo!z(kR z=lBkZ;#5yKB=U8cILmSx#W|agV!gQV#**VGJf96$Oy=h*gXbD6yJj2Mj5H~|EppGf>AJUv}a=o>K9gBNAo znB67Imn_cgl;G(d_Sd#v+}^?h>?SJn`G8zThTfcG$KBMu4L7Ste12MheDx#QQOu#M%?^;g zx*^Xs%G0d??2C;?qdeWJG+Tx|2ew~h54EPBltY_L3v7VNH3;u?z3i2=UT3ZbflL3| z>2&65_+N?E`-~rE$DsBthgof{eM`fRvuIu~5Wj}FOOK0Eydsjayq3oQnC`^wxIHdu z(-BJ45><@IVTSbDc35AK1&+kv`9xD_k=R`Og&nQdR1qv@nplQp z7=LL+Bp8R4h_ebQB)FukCTZkgi&X>O=BNrkOKdwo*%AaB0&0ubx>d9tVHw0l5BtbD z;CW^e8!~JfblrY3$MljVX!bRjMjmv6y~$zbioTPY6)fO_BUC}iH|r>Y})~5xQo($hL+C*Mh5JmO}6k_AOD$Sgi?|cGp4r4B~=#*xpDH9M|So* ze*E}xu&m-81-oDu|EmVc`)|(JMcIN4OO~X8Y)u9)+P2P}I|n)6#8eA5mUL8RJYF-# zPw8G@f426r3(k|-_8A}cQ+U>!w!YJ3L(7$eZl8eCxyjFbJv<8axi9dC*(JjWGPDb! znlHk=!|J_)HsAroBJdFS%0K;cairhxCy8-6i7(HGFo<*Arfx+8C8DIr@Wl`oLe)|_ zGBegys$OIFD}=f(hwz{h7wz$85!pcoeZoYmf@CQrq+0>jObGNGRGNV?>O=+Q6%}v# zmrF}aAHKb^uu!>usIy-JhE(grx|Rf5r97r+S#Vyvkv9&9^7(w<2k0dA>h|Wv6f65x$#JS+=t(Kbac7$VABqVs4WTnnH`Ea8@MA8lhjXd zi5)V=Y4Ve7Qw>5mD6PM3kKw+4dAD6y+hl1WR73|s0+lHeJVwR%n}#&|I|0t%b(NGA z4+JV3ld&OF0lXD;no$%Vn1evQ&zs8`r!!4sI+vB-%zug@f=5{vsDqI$>qD;6a!^~ zPyQ1`LhbR{FCLdjG8jh;Ejq<72PhR{d^i8I)J=8Z1_IM4bhjJzD7@vO$co{r=a*9r zK_m)2k;-$)#@1F`H?Dk>hr#v2O08Bl6-7}D3QKNy9iU(+ilUh1TCH03T)^H<+}>Zz zyRW_WTEduF4|A5u!@I(|$r!noH_g0L65j=qguMexaV4-UNl-Q2P>{k7pcuNQ3X)|7 zm3ZlZhb4&){2krjYE2szKMdnXE5>xIwLcESIYThrK$oHGVvXh?D|l#CVALbn1*`xX z1Rx0<&4HMKVT2Vl7|hPv$)&Ya4YqjFbaUA|ja~Em-y6{-g_m;$#lJ^44D)%)pTb=t zyT)6ehxeeR=q*9^rL78S0>dO37C?M$9cDtLZ~))*k+_JIssS1DcM_SJmTj)A^cLnF zoAEp+a;vWRzvqIoCNRe7wb~LBI2FadlW-!(^UQYU7kVozrfr#2eq6b%m4i8r6WCI% zrgMx5aM{BcK98b#eGrq2omy#VZjnSIG`jxXA$q^4G5uH(!_J*I-0+5yoa01M?-%Vx zXKpqhyR-pM>7vNx>F?vePZ(Wx*lF0-H$< zqdKp`uZb@}oYG(bKg1m?$&oRBpJttMuNlUjji494S=*LHRZ~@75Upx;fdP=Ca7yJO zgnJ^w-PDv+J1qOqoXaK|SkS}Ri*Migaf z$LMckoc-;R1=FUDCT$(Olvc8ozc3WuHA-5k+hh&S1Sm)JkKu)STJDk1W#Rgh+8f0s zwMp{bt^sT0ylEV7O*}1@QOZug{N-t{LVT81>&o?4F-k@9EeO{4?v3hICQY0NR53(? zH3xd>%U^!F&S|}B{mNEL`BmTXe)9oAJ=(ihEDvChsDPOUll!fY-$MJ)b=>j6>7df_ zBT1KD7^ZR8N*S;LMm2R>FRDs+f;fM9o zXUU~-VD*=z3k&BY8qLjx_e9>sfpAo}geFnawIiO$aHX&G?4PHb!WP)JF<)D3CA4!c z4TPhT)?#hmux-x$2g;ghx|7nKY1)L5O9@+;amlb;9-R#h@XBHTt?wg5WxUON4 zH(!3=efQnhaJ|0gHsI^}AIrwOUU4E3iSb5G+ud!$OV<)a0=^)vLdu^#mkQRx^B>~x z%Npa1-AtyOwMXMQ+G zD=?%pJ-icLjUKUj2W{|eoj4)OEsMb0R8(8XFMc1e7hygHEfOFh^ zGM^;%Kz(L8hG-hD#c;E^wtHVosa>kkF_aC`zwF8N%8mp1>%-|C$t)pO4N*j`?DyCl zcJm>=4m$NTeAOY1);Ot?92^v{cjCyU=R15=v-f|Nl=`<=pvm+8a?qQJGkd{brh17s z8tYX{J!ooZhc?o2J6_@;u|~3#f)$04r!YNp`l`YZvDQt;M((iGOSkn7JZl;9HuA4V zl<3>S*iDy7y=7b_z}%&TAC+N;1(DlvyNR#2nGFep@_01Mz4isu`;VsQ=k1+vGh`@d z0<0b_$P z+VQY^88gj8*z@oqPFp86%>ivoygctQ{AV2GL;uN2 z84%g|C2D0W(@KxS_vvlYK5-qhgrw!4U}-URMy{)dPBr4p0usd_g!1L#5u@lBGcuaa za3Ms9&tdDc7{TY_RHbLUca~g+USj2Z3)LJFsHvb6#LbKUX|bCo(C0qIai8K!XU?3Fq%#~hIDPuG z(b(Bs@Xu~;Zc@uIO+_(H!=m3kefqS&(A?QD?m2Vj49A_3q)%IPvuNkOJD!G<{z7v# zGER>x4uh*!vZp&%%E7C;X4L4VjZjkZ9$_QxH6pY7s$i=v+l_RK=oPzQGw$0-pC+x4R_^5X()e-R00{!8DCbdsdg>VZm4P?X7!kSyG~KnMeE`T4SW4R`jq4 zL^t$zC98RGY=tgcsQ25wz zJa)KaZrqt11{**c4FXc55%^ThIuqLvEpDD~Bsz!*W(ZHxC?*yz4L)O{@S!`j_cqnQ z<2b^Yix?%vrHBwtS_EKLH1>omRp{fAl=iV!Qnw&_dUx3h_6WY8(vKJMF&}NCgFfE$ zhGvVEaazEQNQI7u>-l0x8}&$-EiL2SbXk#(nn)t9FC<7l%RtM$7c}j-Kqan68%+Ta zg~5{134-`+?~Z4GWMeoxi?N%V3(I!bkMjwf(}#M!cSo?c&ocQ$9-HavJ*!QA7^>|Z zR%_UHYpaSPhiAKM-+c#jCNK8tb%@$_73b^7OIh3B#jk_^g?6Dclt&7BVEik90q#tx zi^`jkuz}}9*d$RcENr}AJ?*|%90~3O+OsdEnYO%KSS19n&CNy2$pTjJqy)JRoS!Or zzJFUj2=Xq*CXis54TUn#*PAOlnuEXnWxrMv6pU?s!{g_d8w_2z?PJXyE6tj~mtk^r z91L?3m>9eHppv`I_r20o9xL;pT2 zwAHJmtz?`z4AG8oV|Et|xFhaARay|CSe%{qy_uQjTC?eEIX#$L7>j3W-BvZErlqD( zGUK^kOq$2@#>`B+?L$0NPqK(Ii35@A{m7hKMw#WpQ#I_x11K27T$sk)4g^Dw1umwq zmBDsO(EVv{ArxE|^fNpBP}Lse+IGLCZ#pJ$VpF&7FYsD)}4VF0rAivb<^M^ERJT zJ?7c0J8=)oFfL5|a{b1ZU+Mc7n*!Pb2kCL_%k9GNk+uRL?9gb)es zOhvbiJb-WeIjinH+%K7?D_6)0xH;};zrbqd{{$mAkM?=ygX!5kil-cjkD0>+_!BGA zGM%kQ_0%_HvsRDrs#q$JUeaZR#AM+)5mgNjW6yuLP^$D~$C1OYdqt6Gs^=;P(jAOv zM1EODF#hNbe-F~KCJ68uf#atuJ?1zza99+KySfR?@w}bS*$2`cOpq>DHoCg)%t~FQLFzE?EOTdf-Z{oIzr14av!ja4)N5dcGJ#S%^TX6%Wt7Gtqmz z^gFY`$1UElAL~>F93EHxs!h2dTUQFH^_SXI5Nnqj&JvH%pOB440$AMvNA)V1498xh zjQamL>!{z~ss?yc<~VS@IeAn?$PaNwAH;|x8ftj*g915AAz}d&q{s>W$;o|HxFTwT zWqPy*rx^>uUhVL#&kyxi3*aHd$(i?FN#P7X191(lM%hV6ur;`UsKC%e5Uql%kfH~OZ(C>&`f{O2l z-(`Bp2z6&7&_2T>lnxEs2C!{o_#Xe@Asm3LV8cQv=a`y-zTX3bkp4s;t_>B@7;ZQY z+zAdJylr9>(Y@lQ-;@vPCW*k-~ z=uO_lNr*ud!L}5`M=US03&2$C)sYCIOMY%X=>UCsX%dO z7sG0FcD4?O!@MSOzJ<4>{*1}h!{HLxm=SBWs#`C#8n0X)=Ek|0=Nb z=yS5B$pcE;zpe|fHdg_jrSwi&(`18E{wDs<;MXpbElLNnrpfJJO;~NN$}IawEpC4j z-Va}?bR+-C<$U+?m>OL5hb4uVQQNh*kwK_CxSZOq4agvpck5;FCg0HXE!}b8qpzgD zM>0v{H$DJ=1St*lp}?}hI>F=sH7*O|$hsBCb22~VIjV^Xc8I`GGS@yK+iacYy z=(^Ld4`5rCLS@Ph9=L&0W^3RhEsiQ!H0W;*78CLA|}|`=F|>FjI>T^D-E>$5fKqe@PW(u`@1;-clY7$kM_%wKFNj*9%V`VKW9O|ZII0TaOe&Pi3+-|c$Mb#hQhe#5I2KpJ8BkTH z5PU@6--yfLgPNl9^satv^)Bi%*Zl-uBBtTqwK_ki%@?OeIZ~obPV}uWpt*QZou6O5 z%QZ|Q@q(y*y$K1Rnc!!)kCBGzs5i@@lssQD@mfhKM1yA+H#Xx+Y{s2Xb0lZD!$9B1 zfr7Vfj(}_spUPmMU2lt~Q+7lQ!f?qYA8Irj4FW(Kjm9YZ#WjM-2>OA4j@(`@^ogm! zmv|-QQ(k^XJePjI!^Xj}NiGfuK@(&KdFA?_(p9^{{X*V63Nub>ex?&ja6tqa3CAuLvg@v` zudi=72@G1aL6i2@$2Z_Ydy3NNjX8%v*3LQ`uQ_q9Hl|4G1bZ6@2yDqN(OjPux~IF3lT>IUDWSM@iceRrK-tj2QJWh2X~?zlU0XvKqhs)3j8XyNmaj+n zU)+XsTq-bj)kk9Rh>R0J9}d{7Fa6g%k&kaiCV0Lf2w2oKWS@JbkG$9V*<~8 z7Io1+bOkyQ^%CjxYlpdl*FV?hWLz+vEq(#{TSr))y^sOjJp*vP%1Ph07)wh&KrP~{v?maeRLk*Asc*K z3lvEKP1!|(Ut!|!0I=1oWHJVGvF-3=(2ESjhs`@rojRp!rC!&4Hh1A=;8;KH)wS88 z1=4?Q_E7e}3^bC*H~H!>6Y#4(bqTHh>GlE7sF9Qa;DVdPvn-|UBNI#E@u5E&jaGS# zpsNo*yc-QgrL9AIc{W1`wvIE6%eC8=?I!R7a=N;kz>g2~=bVf_i%%mj^@x8{_f@Xg zZpLk5N3KqB@^!ub{OJ!0yd_QC?-<*HQC>O_a?_EV;d#B5T#?dKxJk#3vx}eY8LkbC zpR@vm0DWJ{6`6L;rC(h%rqVzVyzfe6hpdbLe?H#NvkxQ4c+hqI+QDrC=?BVtKjaKX z-_sx6j{I=o4?54aUBGW9X*AfxDx-+&Wn!GLQI9HPKvfu06jz6@^*`JW@b4AR1#JH9 z17(hAdqgD0(a7a+Q{Z})=_1Q;seFD+B(*GbXcDC!PA~j?>>dt1erWW1WHQ)@`UNqD z>!lpWCnldNymvj`da=%fUsQLoK1<^*^a{Q?Wl0M;13dn7*@-)qeP$3;tI4z*($bs* zk?I5W$V#@EdOoREr95xfjhsYQ{7{16@Fpo~R$Avs8%yM1NIC=E_6O6g>_dM0&zh*U zY$y7s^jtgH{?F}CVdRIAB#-Jk2%P0|bOQNMqhK!Rt=8R%1Wah)JQvLG#^&%$ioOJJ zzP#>5NImxZUbv3OY$6p3j!j6h;5dX7i?3h5e|8E*LTsl{kcjQCl0yGYeK$<_TAfE9 ze*?VP5~w@Hq9i)YtCOF7DvlsVyoDhXC$oH*46>E7v4m$h0A%gJE3nz6&wwcbNTPDy z@J%D_`u>;Tnh}`nf!P;yd{I{#%}u9m$$>hlX1+v{D=}AfHOaY(+Q6Xs|;JU z-1r*+uM~8R)2h|Q#cEaKG+mGjg)l4>Wc}NmGa4tdhBb0?8oIz~(})2h(;rWL%6IR& zh?@FAWX+2%+?np;=R&}e>X`8 zwQP1lo$J-?+v-t8U_|IM?&}(LLQwJ&cV5yA7*S-YEd+>RI;P5*WUjM4+EX;`n%P*Y<7 zPW8xv)l8}uMOf(;#zcx#&9!rLic%^2j;kuOj-_diozDOja!i_<%E}ej^$_l_&oEiR zvUrI~*5rAYYK7{QZXm@b>-)g9?Z3I7W{mA)dsPe>#Tkg=ewBkmoO+OmkO8Oe7Ae7p zdErvy$XQHZm&u-O2 z>J^GTOM)tn1x^l3o-J%?JY=_j#6$Qz@=ym|OTUuh{Y2{nPrQD8+=WGSsbC>_ecFZa zu%;^fFm&DYVtSp@6DN{m26#gQlW#A*@bR6TyYvuOr^Ggiyd1&&#=-({W@YR2vkhcAQ;p1=?+4xj6vid$vZTO#r8TMr4^J0Dz=km%1yv8;d*--L1 zyqOnyANuXm>b1x-^{V>~6;eHMV*vqK(y7!PH;@JNF*Zx@wIqy97^i7^Dki$W|LFIx zINqG6ziivBw-7f54EX}?zBaWMUC^hTZ_eH01D{uhp@=8nIE!*s7G@l`y?pUUY zB7vdF@R)o_yjPs4Wi1~=&`^SlJ`I}YmBRj9k4RhPZfKu}iN_Q38)6`lr@0JqvBylX ztRjj1xg`$POj%p_N}SN;o%55~hUCP;ww0p2=rz7U@?yHmu&8V_uOg9VqD~?|v;&hK zi3kieBe82W@{1NvP|a~X`Vtz~?-c#OlB8g^(^+`aSN(k6nU-#U{mRM;aqDZ?wRDPW zPS@-8Psy!0&Rb6qseY&I=V#{^DiOz3Zhwcub9QC!M4{K~?R50+#?`yqB}$dq1i5H? zjK=T*r1A*#8kx*wKuF>|poY`km&w%O(=?ejc^kKZWl(|i#_>_>nfbYIWr7*&)k6H6 zk}sl4RL^qDRLh=)AzVgpt%n_J;&tL1$pGt60B^k;Tu1uXILkz8#M}_JblnIn8{&JF z(;`M+PwFR5=y&ncWysfGFB`$SxiGRQC5A;qUhkkb44(5e&Ugg&`q#6ie2$2Z)ya;a zn>kpmVoFz*tJxBkEx|pFGfz0%tsuq6wT&`tI6@dj+gmt*CsC+FAm61VN+y>iF`ptA zx9l(pBPWLJN+5jUg%>Pq-7enPuy|2--)C8m*oC|!Qu<%k?|x(r9(>`27wkgbS+_s< zEx6AvYx3m!8y?vY2hoH1xfRznrUkB{z{P<3BN%RgV>lSM(z_GBZx}uV zKIj61^=dDMT}G_te9WnOL`FGXgpNXj?cul$5H;S0o$;VP#s!o4xqTXo=))#0O|~6C@ekprsIbXx*Ye#2B@@Lwq0s zb|Z)qZWMw^O(cpSLt0&FXq9*Q2VZ9vR@htkNw{f#e%^ArwHatZVoS=wy{$Il6ifw0 z_N`2M%es$GZmd?TBFxmfw8nACKEyKw_IB!NkNg1B%_T%2?{*Qthto;`+0*&xL;~Y^ zhXYfoRi8~aXZM=Rn?+cxo*2fRR-MBJO$*jhyTcVxpG?D!g>*SHGOY>yNhYz&5t@Ip zQ4q-3a!atnNZc07e_K4p2tJQ8bQSEFDaM)W9~xnNh;*UGB3oiw>Q<}3{xh88Ap~k( zPq_BqOMySAs>()=u(xDdTb5bs6;#KTtV%FvpZv1Df-_?34}bw@yrntXD*>OB^}a4s zF5vBu6^hl3jcT#LN>;}rs#7l%8Npg3d+ zk3j;aP{E}k2y!NpgdH#}<9+sya=EMt0=bD1QS7+0SM%K5(o!7PEP-%*p|DdBp?X|Z zRadvPe-W?oKHF0+mzl*JRaK8yK@@ft3Oq*ys}{#gOLH85^{m?wMZ#_-f}s5rT;-9c*)HE48WtykZ@nq%uc8|Z- zh~L{P98U0OR=9IXYFu|V{%)Z(uSb@DDaVtQr(1z20+5xLTPXiC@^K3vkj8QHz57sn zw{Az${9WA`1)~m2-3ACiet)zGa@N<+4e0Gz)|yScj^>aJmvAKtM~2RY9{8lq=GwZk z*<#W0dVz91-?R8NFdpi0rWZOSMe2q+W+1dhYN*Ej5nEN?gNu+;h4-`Ez>V2m3)QOS zb>B(ZwsUDV*;B+4D3#eaP3^BS&#TSSytBEgHbGxiwu@lwN_@dwST0r2iDU!Hv+7CX zqf%y_g#g?ob4NCAuLI*kzS&HYW;0)Sl~LWAt$><7!$}qVS_sm5JLBbrg+ifFSXfwo zm0GYA)|ppz-x5;mU1x;9JYPaX7y}xI9YAUCvydz(q@y4O$q|Hwkyi){KXK4tVZ6Y5 zR_N`GN~KNQ#4Ek)rtVl}ZoUQhI8d5i@TM_A zI_laLPk9(Bm0s_Fhp~5EZ^c8bZ(@FKuZOX>FkJ%d-C5H3bzr@T@GMeq5tFL-?Z9d? zGCciSe@0a`bwg1$RILKs_qBv=G9r>=MAM{WqIeB3ExvAgA2iS#K?Bs)jL*hBKhrrI zBqjDE6`J%s9$O0V5xS>B7#q)k*)+z}43GJEOWP({20DywB$F*&ku$ONl_c)Nt*FOl zOi-#-MPR2gurwKrM)n!g6BaaS#=oUp7XCuLN7q^*2?R~o>=NofwxOX&2E4}HS0Q!` zx1~7lZ|fkG!fi0-=fT~B=yxmxhwCoV@i0|x35gCuAx37xbxCBBknReB<3VI18L$Oj zOF`~dE)E(4msfr~0stY661#lsfCx|=JU5?qvzwp_N%|Su5X?sg)YI}0t3l%M&PwuV zK6XqQk4R}DX`*aPNJ_Z6aW8-f>N6bU4+fDjrmF5CSJMsc+c(~Lr)@T7DccP;V+1sVPz38BPO& zRkc&A5>Vz~JK-MZCD27byLHfCTwDx&A2>q=Y+#;K{G96oZp`jlmt|d(A5qnRpejFW zv;yL&0wSw~$N9}~f$xWbL^KnCH;n1&)~?wG-lfYjy<0I1bOehUV+aUG+-X{eQCBuckqW`lzCqFmPsv*{6zUFGi$JNaI zF&TKA1hb9D389bBqFWKlJU>nSeqZom=NJxD-7PBFJh-`D(xTg2%JK))R-o8W?`m4o zoo7qAZg-XRbb2<9z1OZY8$%znul3@1cALr7ZZ`*$nQvW^WlNSX&3ZD4!`bkIiRJDl z(Zq}-plBSL1a*L9mnZ+XwK@On;>&pm(phI%Oq|%4;^hXj52Z8Yq;SJdIykC3P6W6* z(=Rs|S0qI>F8BQIL_@0eLQ9mXraS!EM!p>i$Rnzwx>R&A^1LE}#zYt4A(<;XP zPkb~ejI?#85+>V89^hMX69SHBHjuI8hlVJVYgP6iStV>F>Dmi0xmSMFC41l?lhMdOK9chd@Bz>)%zD(O_hvPOZp%=5w-%O)UU-$CwbsEPy<28k#{ZhjN? z!bhzbvrV_Qh2&+1uRbooLsy$Wg5_~0t|!~gxT#*hbge0ajd1OR%s8qqT|XgLrf$9& z-f90(^tS7aN8|)P;IF8?jn4*_`ZLdz^{oL6(V>}%f>!MP;ipbrn87)pm+zw$$a5SK zt43rU}Iwf=xwJ|j1m@j4;xKhxOh`X>KMfrWif`2q7&#ex|TjK)7b;t6S3dd znZYF~=Lx=R=2wftcHAauk~I)byu|JzJ+Cf$Nxt0FHZ2Uv^!zjlLv7nsKGeNMr$oQZ zZM|ud%aQ5cw#$meBC}pW2qB_hEEX*WZ>Ers-jn(rJ`v#Hx#t2Ld}5h>k5`*s$S0l) z0mA1#@sIQKy;7-E>dnu?tKbuZc%tN#vpCpJxpJ{(OWlyq3lOm&P#BP^l1krIvbo5W z^`gsBnCh)(3qLpgs%dPhOG%I`%LlN4svWwAY+Uidjo{S56 zK>662tC210#04nL%lo&pW<~kv_z@0C3mQQjocTN(Y;KA^S?PsYIGR!NC)WH9*cAvaqt)Jc~TSj+@6gcPWhkP^*kYjOlOM22B)d-{EcOnU4-{j#j5NF z@KPQ<*}kQZ4_?U&49@!cr-pLmBmar+rj#;+)3Lqf3BQFrno1DTJP#qU-6v+`KIX(@ z8=Yg+GLE``MZ3PKcH4IH@yWs2gbxQ@>`b?YIw^e9$Y`s2+3TUTOp)idBBxfKn<=l? zw>R>6@Kfr3zYAlH!25MeAJcfr4_%MeQm9(`QhwLQpPgNqkvhvV$7Ch*Vf|5%&o?H@ zd)f5Cap0D17{Rk>hd%~LYAhvPdS^gVtp@(TL0jm=X2y0QI;WX+L(8a_!ZwdF8EZb< zlf`)(7_$kka!h#_-cZRkrvYLz#jtEy-uUljo8JU{Shg&WPsU&fuPj997>2||ohvhz z>0MwI%I#(=KVMdBZRRm1e^|p_Nvr-dzRvUjjEAH=N=qI#tJb?6=^s4zeUyjgP55se zOyD?IQhhF!*|(Z$UB~K=Pj)?Y3DJGNbRE^*EQ7fBdmN79|4=V7P>(t=%*YnT6XU%oGFI`_22LFg8 z-}ePPQkcc3c+mVH08jCk6o%BRXIpg~$5(~EPany@g}VL-cmguy(ZZ5}s|pw9g=vcX z)lL;`Y`aJ6A$xv#a?`E9#9w?tcqi3$knm!9ba8Sr zRJo2pMG?A(#(klvAy5n&q(lttfzwMwGUopX`OSdUp z7?9~jYkl^9Fvs7=bLgpYGC)nG<|QFSiYa&_Z231!NW2dXqp7Y=HTC?GKph{L#w4Ct zz~om#!;9E!N1ZBOSPYn^OG+@CpxkM|KzwMH^Z@+AdLanhGtK>lI>ptWKaCjXgK0vi zv3t`oNiu&#cuF!rum3)o&j$v5+f-tx04Ihs^b?IE8Kl^lO$KNGl*Y%e=)FTneth;K#!&_u z@l-weGtxtM@3a5saX*dgsKy++i}FNaK^ej}Atp$FXk^7H;<=I_f16DTF&@WZZ`} znmUP)=m1c|k?(%BhV_vk>DF@{S`gzwgXQ`LCc>Fogx>%|M3JXYnnuuQBK`yKCZYq> zHEm##BX_saCZF=cpe@K#LhVH3UR)o$5jC!NI_VO~Xy4cE>fEu3|NM?S?&$2o zn_I}v{YaA)PBZ4}R#@6p2vKK#zOL)_`T5RxGn~?3sOy$2n@o?QUSIbT8_j#EAuY~q zYs0b`$60?b`%rb=Ss`X!w+gS$=kunn%V%U=w-8>2o|>C3*|0<=!X!)tsAAGVfrQzs zfk5DzKJ0s4?|=XM->(z_6_xi(k|dR%eDX;ujfvPIWx##7;>+jHpD$osIFA8}XV0D; z?`*vGtU8Cp5JQ5AZ=yVk@*RUGVjrgga7f*Z%)^wQs_{h%_~d9ktv5k8j??F9x|J^Z zkIl`^Kvwgne(aW430UX>j(Z7wt>5pnTuwFB=c3_Uj`dab z&!R}6NB;;4ifZAl@z!7PkJdf^gd}yMRF(BGB~@Wvjre{ADNCGTG~m>*mdX=pGnOh5 zcmqmMkzR2EJr?D3YZ0h^kCRrfKWbbs~<>x0tzDqU)*{O*YdF!^DIOZ4`bq#6FjVjFA7HSodqqqLu>}`r+C_UA(DLq1| zZK*xSb={^5k%MO~2V)0gqX5`3HD?~midc}`Xw}wTw z)%|CN76V`#HdQUpvs7wtY@3;xnK^(R89NSECdvSeS*FHhg=?T%HpQ~ah2UI(m&cBS zWe597UYF;uRI88fL2rz}I$KG!y`6@s0U*RmlI%>gq1Fm{8}Ib36o=@(R;2-n^fJCam2jy6UivPl~~U}^gaLR zfDzNtRSm-F$^wz=^y~HS_`5cqP|KrA^;cz&FuV~tS&9eM|_bp4?uW9><{dZ-@uE(PB+qZ4o_SJ1)^*KlT3%Ccr zg_uQ?b5Ejc(9P&>JAGtA2f{O7qHYCzXe>gfSaFajp1_iPf?Gr(8F6oYp1f5v?D!oY zBoZ1h%@#o$Zu?u2dIaLjF1zgV%P*6pU%|Lv$587B;oYwa!hQs}-p3f%`<~aYV?ZZQ zo;>+4d;I&Ad-CMZCF!!uFTd=To8$9?~ko%T~AY8pEf7% zZ<-UZ-ZUpPZNhBcSFKjhMpsW(DwS%r0`R-%&!3O_6Tb^ksa7kWsMqV~qiYn5B!sqp z3nSQXay5Fgtxyt>b2+dq)b(D(;ch2Na)bgqn3UP#(Ygfh?&Xs8e)hUEP}ZRPtO9(?y?Bg%`={qlP!~4I|P6zu?ZVHgqMDw zND`Y2i*BIsrq$h-8&N8#mT+#6s%_hvWh$ZUUu80?nI|<`hn*ja#i>%bCXb-k*u z3)Ng+j~#!;*k~q>UN{nzGUnJIUxdo|GH~?h(W6H_0B`N)n{SRUP^LYSXy zgl)T&Q_Y)8wmq3+E@A-Q(W9Py=z+FS40L_bo+}BCJdnwnCzCdBz)z9iAc;Qs{Q8=4 zo&50K?|!$6{rq8q{n}9l2Ttkv>OAjbbn(9Q{XGlKKm@8K0ZvSkFQp# zKmTMdi+!qIjeSZEW1o^|Rr(%2)>$7-qg``#34~KlL#;--`o(}~6QC!nK%m^!d%y_y zc3*$}?jC+VmPO&>C1G^%rkf6q?|(S!24H0c=fHOR97(|grk^OjU5TNawtu+&Zo6*-=lMYum2aW?XeUr-K$l*=3Uhg^) ze7tozf{Qm6CgBh5lT@evE@o+>?E7@GZU~3>1MeNhVzEjrTdfwgz>{05Ws_QsShm`d zy+8v9yzjN~*4eXXuQmb9tIv9}77Q)0uD{*{u!eyqd&l$HICu}~DHH=g+p)Zv^Ai&j z6Q*nF3k%G4&FPHC@n-&KX=!Q6W~RG$uWK^<1L?7Xja;8yKWDwmoxF%z1hZU~=?C`j z-yhjbyc*3?zlipC6CCI4?BOhV8T=rM0$;$QkOS#pA?K&r}&qoHDMm0@a z(=`7eLFtklpH6+~uWu+-to({OsR^O3W|_gXGxK5S<|Ip522;Wef3lna*QWxhFP5n! zCvqf*HOSh6!*Jz{Cx+4tr6-*~K~efMc1F{7X+qP^oLRb&3GlwjZgi&Ie#Lh-tl5CR zT2v8%7DYM%0=two8-#}ei6n#S_y!1QFWGq-ic7X_+a-glEHG{U-w5O?HO}d}P9|z5 zfLWU$dYdy7z+_yfnnv||rutVlhqs%sRc=tj4+@3AH>knYs-h2cMIv#k{;rIP6jR;F z=N+9gt{I$i_N?@_3->w82a552Np&Jj!X%=XKx5!|>dDPgGJ>@H`uvxW&li< zo)Do-Der!L@2XTPy$S|)fdM`yy)1Rx%0FD|F>-x|f;$OyqLrn`v$s~O)vC95Nxr0d zt2K#*S&wSwFRImQ)fv5QEg@{X?l4}O#6q!`kVbO-PtT$iv}zT1-H35V>Q2xh(W0cA zGzm8)O`^hoC2AO$F`6hMk?%0!uervbQmoaZiE zQCXFyb#+%uw3lrX+kNVkEb1)G$Da={G0(^~?ZE-uTd&uds@Qi07=+-}O_1V5&6Mm`Kd;X5DPm z>j7m7I5NK!QZ!O$b;RKp!T44D(~?X<`cfP)=xHh&^IwDBhQ5T5pk#4d?k=zATVw@8 zr%9J~eZ5;Yn@Af3V`MLV^SK0*Ku!w8IdH#lwykIsRbop#H=lPs`tu{UKc82cfu{Qs zvGo+aO*ZQbW?~Pob=e;by$#UQVVloumO3gAR%L1#{IS9j7QubM6*r!JU)jUy2B?KZ#I} zMqWO#4kv-Wip!xPYC%0A!b}C_0Aji?>=YDfbAb4FRF8Nc!1pZ6k_nOQc|zuGiD;5F zuL7v^uh$Jj$MuGMx=Xo4>Dr^Sm2!bTNt!F3s5qDgZ<(8$BV^8&G$Pq^1c1zK?*ev* zQi*Fio-}HbWrL@$f^ubc3lvSm%-g{^{@prg4V_1*k%Wmcl+kH3ku+l>Suy8qfX2vY z(i5MiN(dzckp3pUwZl?f*=U_n6h)D>gQ|K^lNDvE)08!$?M>WyMVWV#y&BQv zWNGRdW*A?IgOtY1TDAbJWsAiKA&OvY6SiOj#i)(;p)2i1v*ms0dCG!J-bi%?jCax| zJwdJ)Dh3K={=BtieX1*5D}~?0CsV#pn_(CVx4Lu36<6%oS#?Dya+mvX{5bfR=Y(4C zT=RObs@vP@Gl1wDUt+TOW&i%<8nL8$cn`*(2A;m{TutD{$zfA z6!i1YdOT*R1fvI(tEu|tz?z}JPuj)lXY5`rVR=5&%SoMU%^@DWOmw)drD7T z|7f6SsOc*ZavcLfY0+0SrEval3Mx_j;)2@w^VBe#{ixS#yT*IH7~{C?_Rd^9xubw_ zVaH@V*V&%KS-_#FM3Im!LX(8rH(*0l2g_UR3DHs8ta`R;S!2ygvZKrKgmoMq`gjD^ zXPx9TGuw+qVy7d>tgN?NcHG<;8*f=Onsc!sN&DIqy$7N!vB8Y{_z|h2LzmQo|A{cs z`e;((`e<^n0pe3Pph@v z^w^&e8WZ|QeE%zTB>y;KkPT{F(De+Z#+CMBKY`5*&Bp-r3gxa4vJg9PcWM9r%P!l$ ze@QpEPW7E9PF!=%i4!~Z82!Bw(68FbxIroCNh{5=wABoDu)&>I1qo&Qe=e5F<2~C@Hp&ZUM({41(sdkNr zP(VBRxv^juWWX`^#vdVSFM0-IyFb{r<)bKXTS6r5_GF_m*={F7*j7G@@?prRtn%&9 ze?)lCp3SCh2@!_*D9Y0_N9?XfhiL74TzVxr%qIwP{h)ec2mBVuJcChOFd*q&T5$T7 zTW-NwQ+!Xq68bIWa&>aDGXBf3$SpURoUY{_n0rtr6UqZ~55lvz+;WQ+eatNO<wE_T|W+=DDS74)6jo@-J@js?*a}L0M zv%GMjaNBLSN!4gxRSm{S7qmfCZDEWU6f5`W@;qvzqv)T&??3nY%B5etxAxK=!e71U z#X`5W@FJMo&6e7&PAjrS2(CeQ<6ahXByOy+_UK}ou90ym{x#7`y|&-@K|8OolsXtv zD8w94Lyr`UE2+>)+t+Ye~~T$L#=wW)w*m47=42K8sHP>gD+ z96$^5q98EG>)HAfMY3c`k}c_VRk?C~dnJ7~!co&z*h)v>Wy$x2FaycRE|J{9H=IsT zn?cHsiFD##n>wLbnTwK!ur<_ zayA1l(tHjUHcu~8`r-5X86Gf-ERMNYR}qieDK`sbIjdE8Z`wF;+cs8VG^3}%DUGf| zDDJy`g)x~~k;k27(Mo(Yo9YAUKwv@86Ttx;JPP)v&Y&B^>-IFJ~=xZ6iw^Yzm~-~CaIxa>CfRSa&^zWk-G8I zQ512{rIDhBj5WR9!x?{N&z?Q1>lPLrNWwXlXlA|LZXbr|&O7f+cYyiwl3r8ykl08^ zzTnSNjavNelZ{%?DmaJM%{SkC^Wan& zOsh7!&=HR~bBhn7^Pj+Z^-SC1z2eT~jnWtd()*lQ%DU~;>S1x$auF<7G!-t&LP`(a zd_zs9MGk8uRj<*&Hv`cvJ9Zk&1^+RTS?o+SkCNJ1*tz4DA(ma`;`+<&l#2C=L;#L5 z-@+Uw7eiUgft0x+?t@2uPJBDABe{k~hW#c9Gw4b$4*P|?=|1Sywt*iF8!70Ob|kdl zr?pOib`6&kxHLlOpmJ+FcEQZHD$r^_I-+Lnx!(zd<(Nj0WWbzH3k=h-M1adcUZ2ap zVmb-ZXIKZ*G%Tmu2S6cI;W*e7Txqhd$ax9%ZIEn8+ydoM!vKY9uXsr89NE`d4%0)( zkmi-eKvN^vu8>kS6qBT*c^*Qkn*6V$uIj)L%z~_;iHGm7wqdt10pmnlKy~4{KBl5V z&0GW%<>{RBN8(oW%cfG`*A6zCCyAMI|6;XtZs!lNGm9Mu&pLQ&Rdm(a2++pplj$Kt zFCWCyjrjSXx)@egrM=BIrGMf&P84&dbsElzG&s6Yi?OtnzMw8-Vu5AzDl93UTRbIi zg{vqK}ijMpx z`B2a~6ljEUoXJx9q@8wP<>?gvGSpH(3_cDsYSJbyRR^ARWXZU>kgt?&&$A1)LjGn$ zlAT!u;cw8!~`J| z*Ie^GN1?}Z-Burq=yLrq?ND8R1nJ+Ceh{k4U1-lQ)tvhYl|q{+$~oHYEYVRy-EeLz zt*0spW79zT;VEy{fkf!ksL04QL>~O5CTW&tn^Xmo4=r1t@O^0)&>b8gskt7NIqH9j zdTuQN;5#Vnl6-$cx2-TwK&8u^^P-Lw1?vUFW_+g8nc>VnV_8J3`0R%G?yA}AV$ zVj(8F&I6&Ug#fsOZ^$c zX1pIo(O?usqtR#(MMy?yYZDJ(h%QHWpm%uK*eM+XsS|B)xNGYki7bWGQX=s8Q4?3Z zAH9;}eHYD=|I3`;iA=K8)1@__TWoGp4XMeH73;=1w|KLlRO0H3l@3a_T`|j#)oj;| z`Nlj>7X%M?f3Z|5anGftZe)u7lm+8|CYI&pv~xYnBK!1usAa=M0Nd*NzU_q?lq;K+ zlXEUc07c>0#hLbGtrnEal;#%ZX>W#17CadL!_v}{OS#wS<6e-n_c)CGID#=}u3RpZ zJ_gF=Ijy|o_5HSf2XVB>wbbhaV5gP|5{c7)D;J75GArsAIOR?Qi~<#7xF^EyiwPa- zk}P@$Oh0Eo8Z)KXEGi6J>)@94&iSAQi;=|*0FAP+e&FVt57a}ZQ2>Km(PE(DU@jNh zX|GRZXTQIzVkn9%=jKdgMg2PyEkP;De?qB%`TyX&>0FlKoM z(=3YpK&2dGMWLDvU~5!SFy@qw+@klP8aj(m80vO7M?z#6J>C!wD5)n62Tllr7|5A3 zr-Ya|{=lBqT^ zEj7Z*9kN8hk;OkUHUIhNpH~Qu3U?V`Tq#w6sLwI>xAFo9;HO?CKpaybSFktG3Hco2 ze>qk_S3rZI(Kl2t!IcNyO;`ELZ) zdueL+Q@pjXSFxqX*}Kr<@jEfc#C#n9pQD=|H||5wi!N~zp%l#GuO9}D zi`e&JW_oWVV#7Ca@AM4#{)EXjz`Aw}!{N&sU_Azibqpb&xYT61W^;ajtL3D=yR3Vx zZ(^&pf4OU;PoKo~POf5-w^N+kXQ6JINjBjrxKChsMFC zagI^W(r44_OH%l6fx$i9Zr1h@W&66WNTlj9{2NZpscv`bK$S>}-j6|^qx{vZKUg@>IGQSLSj|`DhJ+k6(&ohdSI%%b9P2|a8P%W zXC;?7`poRk?v(jf>+RNYY@stuvK`)qpL_vbiQX^Ap3QPsuVWbnMD$Rjn^{0&on(h_ z|B%U|F9NpxaIQ}WHraLbu=IE_ef3h|*<2vXiFnyxfTS#fx$TMbDJP@hqu%3sitMG^ zhu#)e0!g;N01%XOHAv3uT8KLgc?gqPbZE34f%sMc&QjUxjX9;6xRM+b>C@JdR%4o_iuDK5gSCR9Vft zH=<72Tu1#~dSn1`K#sqUjr=utaGY4*EX%!25+tPD)t@F*;DMdcfyNRW7RvXiE^9K; zmk(~$Prac>C+s-}c_C~&U%nNi(y}_Hbe;6ZAIq2*V-z6^-w*5Y3%1fH%gYf&)-;hiO{u{YC51VaQFpy`#%DjRw_5wJ zxZ)eXV+2C*H6hlx5S}>bUN4*LGd$@z!D+(G^xnQ1y`0o7U9;^qT~V#&`G75P>JXph zWz~4xfqDWS!%ox-qQ{?I{dku_#syqOE9rswCUr^4Ka2PX*|Ou^rd(qEXu^E0j~lR=J?9Z^Xf)vkPf2L??c5|57u z;HwQ4OK}fi{H2wZHH5%>P;HtfltT#BwpYHQpC9S8*CFE_<*UZv$bY`JhQjq-`_g*O z>*K}t(R0@7`Xa{<8ybaQ^n|7B+9UFNp7Do{wimGxyL$PAKK3}qkG$!#eliD)HZx$! zcjOSe|HzQ&55s~GhxtI@lpghvTU9Sn<^Y8@s1E>qFlh);g@;_NYzvDZ6hdO*mxu(V zo0N*pDHFK#ZH{I22BK^V^~5;l@sb3P7(zPN!cq`n>jX8ZKbVGfvQ z+(L~)L7i7^;Wj<577C3JNEM`De(4i7_h7)16dkoY`Th0Oai^g^_m;uuvY_lzr=iET ze352mhfbC7R!an>j-63+PNY=0+%819XQk)ylMF)8w1t#~Rb~Wb zTvJ2jsMi{WX)o0l&?d1)DQj?co?6<0OJ=x8jt|BI9&5RQ)!&FmYGVNFlnl>+K1AfW z7xW+q>q9ax#^OgoG6DY;+aCtT^-iMZ`cGm7xoYfxIEVP9p8H;I8XG=bc(svty1h zdt$myy1`6zKEj?(POk9#x^Z>+J`a=ezY^+Wcyjz!xM%!U50mR(&9iIqtFWh&|4^|X zhv(BT0l?QSe`Z1oZ(YL1Za_2$w;dG`9861BNzai_8^Y;H&u1jTnLf{+L?sBQW@rV= zN~r!iPXv}#&&@O64sW#dUw>~0Cp5M3zizwkXaxO*{05bB-FzaB`r;&(OY(P|wU4$x>VGsY z?Ziq~>@HxW--aZjBP1jyLLFQ5DXi(*7u-9LQR#v_T)Q`c7=)Lwe&fTkpCmtM#WT~2nRVhlh z#k0h-0}>3=-7E;*2nS}TmlNZexXp5gq%}QjJ59(kGK4}rQ2lrGRkQ)nA(%^(!v=lT zBlg1?PECc&8yg#|tE;WJP9^<%&9b!Xb&1yF*7(uGhY!DF^|ebD#`RV!u2U+16O;D% zCsUX=3W@FhqxZZDP#teR{BR$Fpl+Mb3NW5E?RpTvpFaHX!wo#brIhX=rN69uQ0o)__{Ow3 zvWAwWkp26KGCVZ;eyMUUR~~;)Bm4Ig{_Jgy>!p4Isrk|omac%6-DAc|K86YK;=x$dQQYl3u$^xKKF91cxsd{BL zKvL%e4FKjAE&7&B1U}H}1zZCg!u`hb6zOshsvlL<+)8tMGEEDs{WdeIPOVvXAD-ea zH~y>WAT?nVM$*m~mvEIloWhgXspq9bl@SQUQSe>76;WP+j)%_dTe-uqEYA4d`>u75 zri*wL!u@3sx%{O_`yM`i8A~gS8Xr9**DU5vbv|9T{*U4tt`-opSSR<<(OM2O9Nuj3MKN| zpnLu7xDMao9bX!l(39xj`FD$MW49?;|5$si6XIsZK$ZLnSNpl_yP-uLZ0Yih!pLGB zDecCJqGb2ZQB0ew)s2%h=8pxF3?&;d4EpYZ^k!PG?llA|`UyLZRusYIc*r`c@>p+CM^ z`g}5y7G~UY2}J8wM&3&HckZXY z_O-7)Yj8;sPWtBnYG8&{#hl7Jw$$#>K7jLR2CYfU==uFh*aHQu@WL#$tU@m<6Yv?? z^l0pA(-%x)&S~AU^wZPN0KRYDci;HeP8J^?i8nKQ%2u_2CdTaW9>chYoYzq(&{X?F zA990;-Vgl%5B?H;V!a=doYhKg{7z!*0O8mU-VTnZ2VcOcW*-M4J|pf)G0kfIb!zANUj0>T`c6^X3vtRR#7#jy&%o}kB=;nt zJrRJ4G2Me`Of-@NHWx%euBBIDpj*33v_n;%P72YOXh1BrXGe3|b*!eeW?e|Hk=F(N zpmj5G&T^Y!jP6WzO2)IY<+szPM^9YOr_zozq0-BOR(ZiESeexttU^fT;R?dGWf_3B zr!T%(7cdcn{Zijw@K}DzuNR7t-HC-{J9d0sQzOBAZiHA^INIa5+xE4`Nf!{hjvube zkN?37vLn9h@ZHzzo`RVjJI=eW*F8luiN4}_aRfuOK(h#(r{zI&Ozh*lraGMIS&Iv- zds@I4|KJO3?2?Sx1mlBWP017y3A zlzeqE=}j$HeypYmV4}-aOfy{~`KdS&NH>Nn1d~XL#i`l~33@%}*=L`Xq-P&`gZx%q zfibNMeV~rdHv2>7rfc1gF!e$%PQ8HH?J%FSb#iP%zd&L-N;1fJQ}lq{;6b9aC~Ya^ zeS5zUQ7VSL_br+S4+=_0nWKfwe@1CB)}lQ@(fu7obB~}@^fvphMW;IPi}*|2gAXGO zg(yKtNy5ZV!_>}f+vYY(lFrURMlzdeaMMn^nR=2}8byy9R0k067{Brz?|8?ISFn_2 z89bJa9|d@P{0Kb0s1Ra|f72u@@bJpY%J`ACOo#y|OwwmyUZfooY*D20LRl7jnF=~# zL7!6XHBZf{jR)kHDHEErrpvN&ZB~RvIxoNf=b?oTru)jJdwWTS?B4HZzmtr|;^1>7ChB_DLO4wzKj7GW?XC*O zB`SK6U^(R>(2S!9Mx)j1ac5A$gG>w?1gcNL#@zc%46>a^Eq6KFOLR6VfZ=QliguG$ z3&aJ-MSf@;ZKFMQt3%4|^e~wa!xC7PPI*?+_DYlxv;I%~9EgUVxxb{Y z$FDtoa(7!&Z7YOQXxXZmGP|9=mU}L}{AZW%gt}AIf9W~PV&;P$r>=cb=U?&-Aq?M@ zwU_*?OS$*K503w%-|rJP^XgFEGQVt0@w>v(taJKI{U8KNN|qW%3}N!VO2fsD>OK*s*5U9qOpFvZ7aIqhwU<|Y<(5}hTm}GAi5Hd z!`*dworoz|K`s)gM(I^@;Qac0C@slJnn^5QMKw{%JQSs`g^Tbb3$#)g@&%z%YOxzl z>kYAZP;7@ifE=$ZRAm)^k}d`C@O7%2OW9d?ltL}Fc0vO> zj*$|QZnj8KDJI-ua7z(O0(fRxwH&F`-owRzneSO$T_ujs^plkPjDHSTY4Fz%S1o}TUpu>!a?*vLhhGN%g~%Zr>$ zp%s66fYqwwq-wN)r7^lxm;$D`WujK*xhc!2RJ4)7J9UcMS&NSZ4F{%fQAB~9{! zn&uBj2qACl=UYF*Pr~zlgw^#J*fo2!Z+v&%i(ZSKLhnK!L7zZhL?iTj=#SA~^Ji2V z02MmYCq!ia3EeDSzkB;H-+1MPY{nYH4P@AbPaUpTvc5mkKkR1G?GaeC8_BqDlI-p; zFA11x+?x@IH}&)lbnWfIr?%Fqzj|Q5x?kL6`mv{e8Q&_)zKWZL-m0l@@6=acr;QzT z_*h)}8)!RvHF^_zKl)AdyV7E7mFWs{N5Zh?EC5gX9?hf~x6-Awp2o7-dAdX;nNmig z9F_GulF|i=NR0c3wkXaux<~|J((E$Wg$Su}W;3o6y7RD&HMUPuJ=?bp&W>XMB$Znx zCrHZP8f$jBY5|pnT&~ zpN!vYH!so(IQsZb5MW`{baF9yNwATbDR&LhdOtaUAOM=_!y}h zZbKd|?WZPK{bjpyetd326znGTE9cAo8u9+qGZBsmnUOgkca)aRu!PaBkV#OViag`S z|Cw7_@!=rpv(~AOUTLTaEnP~QG5maTa#E%fen}qgfRrcvx&OA=YE{9yE(}cr+DZ@H z=reORL{(yc)T-Cebpr<~?I~MAsemUZCnsrV@=N+^m}I$8_We1ON;8J00oHY6|Ln}u zzU)xTGu2i#N-~xh!c+5}D-gT7tvxsJ<3H$ZwSOID=ny(fw*pn~Dp>-lG>LaAfK`L~ z1Yy|dbesh^)$pVdCe@Otg*dB2XC6gi7Z-1Xum(}U%K05L8G?B~WtmRHVTS}K+M3}g zZ1;aI$6Gx9!+bs;7#P0)OPnth3dzj$o(9>6dX7QF?mv9d;lqKrf4mrfSAu$$u*ln& zd6Q*g@Yr#2?Dg|G*oO9?UzoH_@UJCAC6IPAsQ=Kv&s}V{(gwxfbc_8wOEXPp<~h&* z*z+wGhf}58_}z<=^6`1e%J|3d@oz<4mddrIrNuqD?~i}38BjT`>n8ruKkKVE9B*AW zS~cQko(ao+>NY_iFeQcAwzkyHg%Nmu18xuQ%VE507sk2q8=-HOBj^0`1ydRidp(>} zm8YIk)ExFJhThWcVQvkbMCZ{f;?bT`80n zc!a*BK~n@>aVAc4dhf>f{tJM8{eIuTKy}L$HyOIdVtfA`440Oca^;e1%TjQK&@$SOE~ny^rDhGiu!Q{~1W$G=9Qj5E{-;G0B|4YdesgoZ*AuhSLWBYr zqLQ=|CBoJ^cdG6XW_(BIpf=w0LLR9+=vQ6hb*6~*-ueXFGYCrr-wy2!dM4%t^cDWk zS)FsU&TT2^+@^;$tev1KG#Cs#|JIW^ok8@^pEzI zn`=QlF%iSJBI0u@*LRif`Qymi*H3j}pZ*r=fB4OxS?=afZxjzN3j3oUZDgYcs_;0a zcpNfrSbIX0%`)E>Hns*@l}n%PVEcujUT5Zh8X?bf;igqbLATtZUygcUn!$Zm?SG8^ zT^-gB#9q8%v%FAAMih1Vt1~najsGdQDK8l40Qw)DTTUO+n4bd2Uitu|id7i->828z z;=F}0waqoXoPZ8y(~1}$4*{JFJRYHk-TC&;Ac#pZ!U~tM35?h8-VThu3$Mc^26%-mlyhRi}9y!($R!%AskE( zk2^x8BUuQ`f%k|zVnGN9{yv?w)(5W2rV5V6<($8iFoTL^m|;}&QFECY(d2m0gIK5) zCjjkRg>et=;UJraRS8TXQvGY>HirwtsSEF%Ua(f4}ZV@WQCMBj;qzt~WsF{RL?=KF#bNit*Yvhf6f#&w^RLa;5%B6qW7VX;!KD#9Kut=(|lB>Q20iJ z0xUog_y(9qA*7!2X__S)x%}$0Hv+DtaOsy`#6f_IjsuElc&`Xt`A`tx<=tBL$fGqyo#^hSGZ8kO+jhEBmNep zI~~pUWhKWs*HlU3`M{I3VE>LC`vW5Tex6H`s&US9itPKEvojUKww3n{Z3TTm`1t2( z->#hm)O>)7DTHmO2jhL7qy?@}rS7OJYeLlmPjcc3SMmZ)6++nzQ-vF7lD98Dn7TY7 zs-#@9rJpge_a_bVPh0Hd5FL75)e+vw8I9Wi=W05&=)VFemS$&Cwhl|eaAIPrUi{}i zp2h$2*QMIj#6%dvwJb?zW=ll~{-uM){&V`;1|#@7YK}nx(O=4;0TLkg%ksw5Vuc?Q zRW}2r50S71x_HD*ddUR1hVGZ!>ZCpl^-8r^!r&$tGX8f93?WxKmyi<>#=}dj$ie`i z8s=CC%&|tH1tTy;GdZ+glt4YCOM+6%kc=m&9_R8h-o}fQ^SM~3PZ*N%Mhc)k zAppb^8UTIcGXQy&=)>MvLC6RurbCSl%|-mMpmWZ2z&C9>Ba7--9dk&WQc9@~&Q8y6 z+qP`)>MpaIuYX71-AkvKs#^DN;$UE@Dx01aIcUur`h)B}{C)oH#1J7r;s)1N-2#`T1&g--LY`0ZC|@B!_r zI<~()kry6a_Ay1>fEC{_*Dr9f9ZOz6^;+?HP17D&K2k4iz@T#hoLrsp`|>my0J@k* z7gg$E5;v++?kb7H=#0mD#)?LVK)sd2=cvKx0X=@PJlF%0(O}^6NP3~@ttUg2Sd}Rh z(TqY<6uGy97Ge^McB5MB+~O>54wnLD$=>kLYhLpj#$KZfq3eLg7pdz)=sy)(HE4FI zE5Yg4yyi8`ve;{MT^G9`;m@~A=(?^`p2=N=)uBq!@qIR2<1%adi0wSIPO;Tl($h~5 z@2BdGCyu{Mt!)arWZv;P_63htVy`F5!-fY-s{Xl{i7r2?zk+Z{)3gNgv-*GogVETF zuJlYQ`CLonwnG1_yvEPcW8hRYN8<7g*ec>jr#xm*gDUr0)@3IJO!0)sDsYKiYpU zhX35}_lZix#=s;h?P-i@_A)UH^1BB`x)M)7(p(AO-O6|*8giE*36^)_j*X2C&J8dO zzD8+q1!L?25KZyICYWrrIlf4e7h$VCb~;5T(6v@((@EdDCjZWQKLT4dJ zRx54SWyA?d!++nMIS_Y6;_JL$cX&VNyuPc>gKt|TmUUe`5Ln+1{)bYkk`tjMhhRqE zGfgw#^<8xqnvOH+7V=(eV`GCcU4PO131e)H7yoU`f6&l%X+pKW9X=O^p@PH9h)N@Y z;SnK3xazeDuaNf=*L`kdV}nx8HYjCWH=`x61ur2RP4MgO67Ic!298SLjxjph-}=Yb!Kc=^Znu#4CLQO{KmOxCn!y7?*X5X)|M)s2Yc0;XF7gOg>1;Jk z$r?n7Ne{-_i(!B!D`W!sx4JM`PP_K&^4pV>la8|lR^MK73wiGkZTlA67UGtmTn-Go zZ-M>EBe&!YO0nV}{uKj0H92`0{I(t4z7V$vVcWO({#%Cs`e9$elp6V44*!aw6G1Y9 z{k9L-f-;qp6s0yQ`L|?1xLg6N>YcKV?;$T3TZOl1i858 zpfW~q)7KCW>k%By27XGXa+sg0;rLx?hXiD zv%6nv)zBnbk@E?yM8eR7qmYHZc0ih~ILy?0O_U9Bya&>DJ(o>ckv!x#K!xHFU<^=fy}8>Xx=zUiQz}#HgK!z@2K0d1ccy|Cr(TjNq=u_ zSgOh zV^1ghoK;V+NhJxBbk?+G|5G0z>u{X)M02m_IR42^H{GPFH|@o`jSjLLo#U*!eB~Qe z67UBK{CS$)Y!Q*}I3PJ`9o^;NkId9Eoap)d1YhQofD!yQszsd3SglrTpW}Kq zfjl@(UalRxI2_!AKJ|)7mgOftj{IuR| z^_i+~e`ne^y?xiyk^h8#r(`kfO3$NGc)vsiawa*Yj~>A08L|ip)G_2W-2lg7s`Kl#n0D2_d zkNF^Z-tRv3~Xa7M*?j`(XPS$nyGr}188PnIBinQ`B6+j&Uj8p*i>1~pt zDpy~vsPG|Of5sA9NYelb>zPSe?Q&`C>ft!_F2Zj(AN*wnG}V7 zON6;Vf`No%WnbT`M}_~0wC{8umGyi|9molE_R!Bl*`GT5{WwmUGdBT z!A61LrFJ6aK+jl9&GF&cnQofOua@LoOKD;rKQ#=w4bW+wMRCoz47aLJa252T9}95* z1RA`l?9s&rEVH%+V!9YDcz(2XJZ66~nM`09{hTNd83Ft+qDO#-xL!P3zZf!0QJ3!q(2NH#n!Ku&VH4L3RyM=?IKb=CO) zb6Z!9?`l?Scp z)fD0jTdS3&wv{JcK@sX}%vBKZCJN;7bt-5X&TsK@pn}b5f^AP9Mxo}DP<~vIr03P= z{cL|oe|JRVsDdqn?R~ACvjR8}B51^&ns9ko5BLT8Bg^!@KbOW_zmPt(X|$lNYST4M zb042Nw!mK6N=?^b}=5G+_M4h6UQyMA6yUmPp|87yp>& zqb1Zu5n<2&HC``euG8OB3>_NRH!Bk8dJhQvsLj6HP;V&uY!)%|ZMN~+mG z>!xp`CLxk|)5WMnktN&xy@ux*ad2Hv$ z`il^KTPpe@ga_$H`*&=vavgsN&L};bup_C~_!J~P`f5`frVB=P=r?~921_&2 zb}XJ}zW)2XZvr1kX^>v``SkcVqwj^$4!~!-5yeL1oGp%Ft;KxYu2?HECv7z#JU0x3 znI`r~1vV#SW=M3qTw1dCrioE0L378aKF8h9XjvEnT*A1-8((3B8`~Tl0Jd7I3?D8m zEC{%CQNqH)nQH{K;6}NfgoTkW)$r%@b;%?x?kjc)ZG4V%-sWSA=O41*2x)2EdWh#O z>@(WAMsd>Zc0~^I17dgjt+;5lS}MJ=yL*MIEgd3khM*lse7}qn4|Iu!rlAhBk#*Vp zn%vtoh-9W4|N{hEGqb|lSC5e9xxi27j6g_9Zo!-B! zEMQ%|4?x5MApd3V>lCdip!dG}X}u__F)sbuIP(;gO&6yZ|JFG8SB9>J^ta_lZ&uf_ zHHI-7aBf9{5M+fe&Db0WfVSPAhW<4V)Nh8%Ngl-aoeui1`X+3s7hL7rOhx7HDJJ)D zs$zcIk^@l6UP+P!{Dfe)_-PjtxJ#x!yn9nsOo_bm3Xx1jb$Rdk4Mu4ZC)d}(lh)^U zaMNq!)xZ)7&qVrr0v6#BZx#nnFP_tJ$Mg6J7r5K=RSHrv)HKazidW%yZbeZfkVX}N zL7l^$&I4a#VZ|+1l7~M+unTW-F-eMX5|Vlcw`*Tw%7iHxbF+jHO2tFV|6bG7MVp$o zR8ih~`xLf{?>wkllC-JmCLF2FDT@}`P5Ef~td_iVji0VSdUeZF^4BFzgZlL1;&dG} z&7+J`T+`0fDyEc4zece8FD@p@IGHfdn-bjfZLLo)PI(@ql+}cj6QRZ^{qLRg^O=^4 za&$cgyF5~W#26p#f2u>7NZm}3Gz?0GQD=z~P2z+g2=1K|azHp{ujprsWk+%r7ahqd zi=ttA2Gf|#Vc|KW`1ErE=47TZ!!r%>vrmIYNZnB`9bS2!#O^(n9E~9$;r+xA0Of99HC{`apa6h%kcNVluJnaTvwdJQ#WpyE@+OlR z62ak@PJH+gXR?yrkrk~lgFMCadi*InTyRO0w7%pdLuIy|Ceh$x>Qp?>AXDHC;n5? zMfY&)PVrB7?%a_K!9Co{!;+T%?B1L_8h%6|@x4ncpZx2fzi_zi@=Ux_{KK6)cMi8* zUMH|8%YjX+2cx$n?VCkqE8_c=I4HJMd;GTOj6tWMJlYWvNz#Z<^Gqv_NIc`NHp$xS zuSbiRt8{=VjE93IovD|A5qMtw=dtI><-GXoWXfcoYP#cG-wxcOoUAb_O9ac5y;-Jg zX5XJs6Dd1-?vS>|djQIk`D%4O$;3@YzP$U?_^@Xu2lT@3F30WezVLc%993s6tVZ{}`OWu@dpo4p zV+ym<7xh}q|DqDB1Jv=HsLx9KG*!e;P=+KjQ(+pXzKR8WUnBr8TQc50nD}%QXP5<# zzU#g?r*u3+uv>o~yOhj?c=CHu{OIl5w}q<<*M8c;66I~J`AGKP+-BC&&nA|^r{z~p`ZD^Mh2 zh|ydk)}}i5XDgVemAwZZ+--FGgZ1kl$F+%nH^H)hF3|I4m;H?Cf_2+T&;cj{a> zii}2CTwdO9-qWY{`rh7dxU|&zMTYn9+G$lbHjYFgY$gpsT*({0NUVIj*IUw;_ii3? z{@>QZu-Cg$_xG;#g3st}H`?v%R~8=J-inHMK%C4jsfrueHbTfptr76XYaOE}(2H?& z`yu>FK6#cCNx^!66Zhr{lp(9&%BH$5Ob7ttL;txS$26XqqW1 zXXX2IJ(s;u-y7g)v_Z{E{L#g@HgTuJyl!$2c_F~LAmgklksr$QGpgnez+_;#<$Tv` zsrm7cU5|Yy$VmA+=f=Ksf>alPvRG_=aPrgai*~MGx?9Ro-MaJrHxnRQN^jF;8mp<{t6Ci{m2z04&(WAJX4ZPPY)h@a+J9fn?UtlqPYH*iSSZD2b_-?)|q9n@-5=FL+AXNxD_3A z793kurgRMe9UpZ@D>_ybQU>8{c;^H?E54O zlQ6}&?^D-DdXp}HNvhSCmgXkLf|c^}=Nnj#qbVF`KDzp*-vt~qc^2UPe~R|HdHSk% z2I@cT$%9To{+GYowFgDkt-%y~qX>y-$@@NES>nc%(07sMX`xvvJBLp@_T?5R+<_))ctc#>Ud`>6ZB- zlBU~K(?`$1Q2`O0puQNjP?bPbnZ(8%9b&-%5(VG8>cifxR4R>0qf$`}&bMt?w?Z~W zEbL&tyB^*KjX9R)s5%Cf*|w3lU6Yhk()Q35(XP=nWm4g|GBMQNR0XC}F>=V{#yqPWnYX;3xQTnGEW#(b}IfX|`1EH??# zNS9>vy;Dt3hy0M+!M3LtJJ|5^&nptYcAFW5!<^$r{}i@ zOjVuY<2c+@d9SfGSFIqwP0cBAC}mGEMG^GA-Pd_h(7t^$Tz6-Aq4c&z{2dBTI@4_* zz}#MD8Jr`y4#0k(RBZ##wQCCmJf3BZ{JKbEGBvxZj>+X~e5Vldi5d z=ug6j@uZ8`Kbb_OIF=+TcTq(zNIb=oo$v-EXj0wZh9XKebc(RI|D~d1N?*_6ZFiqcpGK(Nx+Y`8es?v?9g^ z4As;FH+AhaTN0PA7_z z3IrYJ)<#}+t}r3g`zg@G2m{aQQMdCm8pLK~Dn6pTAkbAoI+f}=3KZW59 z&}J*e`QME=>bhfWe6~{IkC}|n$e*fZn>Kdv^^Cqx;N%mz(2agFA>AC@-C+lzW~^q8 z6hrG&GOu4B0ato4*jIc`dhvJ$icrrk9O(G;Y38a{=}AEh(|G-jTTRz0SC2ZQ5GElG zkv*VfNE@1855)_jEK9FRAcu?ciPfEJe;g&svh+Fu`Q6163c2y*bI7F(Dm`8l5iD|UUOLGY?>#wTV5LvcLrX<+Bwa_(jU%%|+TlEE7@Ra? zB@WL<8~hqd&{O?JC-Y)2E9EeB1mhUaaw~aflv)7EG|okZ74V}}F{+?ex&R&&!j6hI zcwdqv$3CH5tdjCoqA@A)N!@pZuh2+&S!9~@1Dd8ew(sl81AV&PN#!Q zB`o`ot5{)3|1G369#JtU-*Tl2T)q4mQ@O1|{ThZpXUP~n5(WJEFDs7-+6$e)UczZE zkvOD5F(v7aP_ctls{nQ&oHpF?MbGR}KGZuNO}a0L^+$Bk%0#We(Rp%9BU3#`)H>vSB4J zqA!hlE21wfJXz(QbGm*|wR=3T?Y5)&3|Zp7mt`#sJjs-Yp@gWy+^6V%y+@IEvTV^T zj3IG(MU67|+En+^68-)OXrAIQxBteE zo+m4qGohBv&_P}2`o8b`k~*Zkq;j5uYi`VXyd~qM@_xoV1t{U(z!#i3tO~%&D zg*8Qy<+b^j*a&&|wi4yuBPJgbSMrGdX8xNua``pJOmpqXGZxomSy9&JUydWh$Lv{^ zq4=N~L$e*cJOWFqkq&toJjxS75Cklr2x($V-0qZsTV8;ltYnCb(nytmSCg;4lG)c< z{Ack2s1+YDvAQtd>&-7{U>KlHFH*4ywcm@<{J6a$YriY2SHH=(%iqLC9)bx~ z`sxh5Iy&jc5M1gj5Or;Ants;(DOGmxuGfh}W9l^X%lB}3dSS6%2dGaB@94-h89UlI zIy&-#!U05{-E2#&Df_I78^$2-Bw7|u8#ufI6jpoX+qp4 ziXG!PO{qTqhZLu!CM5_71MW^~)Q{f$^wUqb`?o+uuFRq1e<)2VsZ$L?mk2eZe)RKI zReen(UkB)^$dXp=CJOQ)wp&Zs%fKQG*ZO2@&mc<0vM+lD75hsoD1D;UYPFtOE}0XV zvQlugNm0*78x4oi@<85oxjD2Q?MKHE3T@k7%A!OH{zNr)?Gi>D(KTQ-nU4|d0$T24 zDrt;n0^6Awju>-*a}L~L%m-3p0#=%FS@_xbBCL%s1{el77(XL!#X-k|N!U65ChYVq zQ@cY^?$Auj8-LS8PRs)s<1p`y<#4dP9N_g{uh;9Zt*xzcM0^;GW?`W+Meywdy!wxs7W91*nTMjk2gEg(a z0(t>i^%cLf+Itp!<(-J)xruZmq@L^-c*v6s?wBle0o;(84mTw4&?-8LKr(LcR(;?@ z5k_ol6W(=j?*6&)nKJY!4XBgf?t5tf1Fbrj0ai4n1 zRpu4L5LcYO=9+6*mAi9oi%XD6Tm$!vNw8Sab9DWmg8-+pP(bA-{< zwkH1{U!Rl4eLFFN%-8I~q1)lH@h9QR@h9D5A6RZ#v8r)y+nOsgzBagfYJ3$K z>hYKWn}AJRX^&>$PCHi11?|)4RB!~tAyK(sUG@D+t5xxRwZ2~C4dR81*N29{&4nah zYei8jEq?RM&dljPI9<-kUW^ zQpD8cE>%8uWdGb8u3%v2yFMg$`1OP9^LD6J=l3|Oq~&@G)oK+YY?<}?vIESn*IO&o zQ~3gGVof7nE{CO^cy4b0aE^2=g@hXSc}m$d*^6Fh{XFIcLma0&GzRUGmR}EmgVP05q<_wVELW+9LEtz+5LPR>TXp@`Zwt!`RZ?}V3rFZ0 zw1M7V`**azN$S4GFRt!|R!afY6uKBFSWuI=09Zh$zZr*vL)5+{C%T<($2bv`OB0|k zcR{S;nYsyB)-_)yfo*V20*SidxH$>ub!L{TNcR}$O?V3RF~-<6IgtPq&BcxzN>ZM# z`k(HbyLMGz&K#C3_t@<0RRFr29Q8j6p(cu#fBoS z_`t^Bxf;&TNr;YFGlC(E3O-=;FX~d3oyUcqtZB+#8({i#X77WETW#T&3F+r6xc%u1 ziYxj4_=|s_KH3_<0PT~c%7ZdhHi}SGicC{RF_WyvBYA`Xb0H2$)WM>F@L-?-$5t4j zqyzfC0eC^Gx=`G}bQ%C@2Wn)k`f%LE^AQ$WgUv_{{W&#Dmo_xU0E{-j;*b`7uqG#i zWb|?fVWe+;7vBe8LM!Oe2$*zGvrRpY?jNfXm88;xfX$t53n<1pZ!sY$1Xz!Nb_#^RJrPVs_L5#C^NPNwSw~iH zGyo+1c?BfN69yQrVSs7n9{7$VNe7%Da4xf~DFDYVJ#grHH700gwdLjIWsOee->*1M zY1`pikJ2=>eSiqG-uB-Cv1;^RaTzZY0Rxt2K`-m z$mUux4eaGke%EPStOH~JF38PVt&oMQ^#}s_gU-DYAy~qmlb*Ubi5X_9$B@1-N(u!& zd6lK4>_Me@Y7Ln5+bL;P|8m(OgKlSkL&-M3XYie$; zP@bA6C-5XqgWHZj8TuXH9vTejPiYO8)%$|b+g^gA6=#ii z=(dfClH@?2gcN*KDuaJiIr~#BY4R2y8_>8oCfJLCY)xj{>+ZD~wtx^}FkKQST(InX zGg#1Tal7d)znQl!a8HO4VDi4fCT4AyupP22OOIlVu`F-r#C=<<)k+&G;|5n08OA*h zGnchmE!jRe8#GdzQ7rq$o;`co%)BOw0xxs=gj+b1w=IC`A>~-+d6i?zp(=o7=g$<} z6Q`r(Z1FWFYwsyBwLJdccE%NjmHz7f`|nS$<4VLL48R{sr#m}-_Wt|tw-Qe6y0q-2 zd-5|Nf?FDX$G62v-%>~A#0puINzSw>DdpR}gbv=6)WL#CbwV9l`iNkwRsrxH@3!0V zYq1bEG;NR%GtAvBMImZwj@ww|gn16fBH>`x@#~2$Etk!YtjnRYN(88uSM_Rj&h-a9 zsJsgF>%ZfGAw%6I0;mCYvar%YFzSfj7ve6Yw%*hsaNRFKKz`5SIC!pB;|hJpmc8{2 z_zm3U*%JsgQrh_H?18w4>d|52`!hzcO=BXJZtq4nNd&El)T-cr5M>!0e`81Wy=v9- z)#WQod22F1eQYw{baKn5&e>sCV62}RoH}*tdL3ht1@^H=-npVs4M3yH&TGr1Mx~tF z5d+jZ`Kmur?DcPRSXDCx@1N=S`xm${wJNhuwx_;k1S7PHZb1*Cw;<2b*`z^D93F$} zWT3ClyV4$pUIo-4{uisF)<};&0Y0X=r?N|fpXp8*foZv(P_=9C1=}wcUEKJe4V&tJ zy`(v5aW?V1Q^g{T?^R{2>xSo9=I<8RUrs5Rtt z*WnBg!w7e~SMYA7-F91)9?LTc4wyJlOz#TAt9I@*E?-%yRsjG9gB;UFIH#E9@0(tP zw3D7frrr4=!Tw`=vVSb5@N`@=2>G*4=v3M$v)GLXsch}9{-B|oSeI>}E=7ohEDY6HmYlbpG*|vX1bhVomNx;jH-44kY~Y>s~!mGP!)x&zkvPl7op+lM0%N6p=C8wkvgB6tS@v zcYODP=NcfwlqiF;Z4gX&cRYZo+FGbac)Vd4W*pj=4s7-c7BBjjKax~p%=?O{3hd7d z#^fnUCC2*&W70H?JP=B7M>Eyx{CUX=GVeH0rLhmC0Z5HUSW4P$$YH6j#R&`#N5Ep#0ZZ+GLi1^D>;MOB z55oFX7*2^(*!Q}6fK8EWGn?D(LJCFuT85b3cH3>Kj2E}v%*CkbgZ2>*FWk{_&F_t0$m_)2zO1zfLvk z0&-{JHK_Sd6&(k*kIzFg{s|`!2ddq^Z&P#^Hqaauh_-;$359M3Ikigr08%Bh*AabOU-bdN=xz{i={2GCgM-J}(?!Tu~BBR}N4d zXbgdpY*(TJE%7hlW&~w$09Z zb%6TgVM&sUTCkn;ZsTr%nYIpnQSQ6o-#Z|}eb?02bPn7? ztAJYQs83Hb=CE2)x)r4p^nVi)e8y9d7UePNW_jLq{@l59)e`l*c>J{AxR2}8t~iDS zT=~cmm|%tH*7%ZMD1|)^T+7|HM=aB*^k3gMP%=|odGCz{GE(=>>(?#^#4S%zViN~REI=^p>cOAloc?bpr1 zO$_U`w25cDnZ!;p&4|3QUywl@oNoAp;}plmjEVZ*Hz4I?DeKz)O101)bn|_DzD_4y>NFE5TN?{g zy3z>US;1{R{|?&Pdk(I{vtWyNNfnr3>$2b0ZNeEy(v%DZr0jX-IHfE~_xJkm&Ubw= zZ#r=USK!CgcE!ka9ms8M0rgh!8sIC33mivh5z0z*!q**r{3A>4vYlvQ5a>6GDQKwOw%B;U9L1)HC%lY8WYfJ2Ul zSl>4~7(p(dV9XQ+z~H7ZG)1BO3(RU-)peOjT=}nd&o5Y5J7pNBG;A%*?>-l~*Oi~TNyFRroA`Q3+7^U3S2zcq2OfxC z<}W#=7qA~JUVrm`k+a$IJh`mZiecvGBFm>Ir>2WvZPqRBJM~E#MfZ>knLJz5Q{0JUa&>ahq=PJ?+Z%ZJ51L#4-0XJTR258 zCi|&>!NAUkwHjHYRGccfcL;IVbY1ff*MOwbZb{D^^c#!3avbZ5;~jA|4E47I18cM< zx7FLNwB3HC>OR6A({{(qOZPDO_!}43JC0MEP>gP^4wM?N2T<+b0(+Ow?Ri zR;cGuMOM<19lukpRv&)az+!r4TNbdxwwY;xji(>3YN6g^G@8!hmyXwE9_>T-qGy3Y z4#}43$N8UCc;s(sCQE@4f#k;_o=Fi~DG)>yQL5CCE5%XW>6OC}* z=Sxw!B)w~TdOGK0U6v$_scv)5xvkT+f171Vs+8x>>9QnaMb#KKO%s6Wx~?+-(=;WW zDhifmNtc9>bV*XMqG(JqO%wR9LxjXMRhDE){;BXjK|_lWzlh%S)Ke~Qxb0)P4}q#E zQm%4rbEk?$*g@Sf)^|;sCIQ9-V?(1L>$)X`){EEnRh_XyIQSO#ku1(7j3v%33E&;K zURGwyW%yOXG)>l2s)9zTC3B21=CVbp1}arGS<@K7)V3*J`%eM zn#R==bST;0QrZ<;Bm5YA!?(e+SE1|Bz41)I&nj-_q9?Q9KoD{dM&A%c$RK)XP0SjK zuue=|vTuYzD6MyYMq1$Awdz>6zEE;^IA2h)ubH{!2~`u><7+akvDcnlU0q$>SZ(P? zaL;3+6~U-P`)kzUb@mskG#au{jLFK>-03+*8h`mK5dPdnn)T_wdiY^Y3H~9mAHX?L zkU(inP(eyex0DRdC1hSF(Zo%N02_!TKawd(X{3twIf}`#D3rw%_UA&aHATw=H+o3D zwaV^0mFxV__n!?ORp)t?KYpI|fb-T0{I@I)rf1g_u2`8GSzkq8J#|Xg!b^l;ags0z zX^N4o))@d~(_HmUl)e`A;IySvSq;vagTBXky$N7G?A&ewm^V6Gh+b8690?g42T$Ut z(G=)cXNcj%8)t*qVi9iZ**G_IXu71f1W{htU@-C^Dbb7yHC0m|qW(Ks0ov;5077A` z^}?f~3Zj+dRI(Wyb=0nxQvM;LpGS!xM68JFe?VscqYRsj0U99c!u5`vV;aQBukzm z^h`RP&+D6Pu5>NHRL;W{MS(w=cGhWp4_EGUXzbBmbR76RV{0sCT(vBfbH0UKW7=6~ z0^XI(1(-@+2`ZJxsEixA8QzLBcQ%~ykM5>uO3jev;5fy0SSp3`!cR|3O-=A&Iy+z# zW_T+y{R`cbbEMl&@9RrM&aw1_H7N;aTtf;AmRN;lrkiyWY)f6A+A4m&F0BrjwPh?x zj}q*CYUj*?0`}MTDZ5t2e@xZ&TC_Ojavn>vbT`pG4Abep`(5N>@GRCCww*RCiz*NX z9+OSfzyaJqL{l7#YMVh~XmGEy8B~>-#(i!x8a&C((cZ(zy-6M}x*kP3_y*S@>;{Jz zKuSM=wk;0xD4TJ|Zx?keVPb0cH}Uy(tYtX$|D5Pa36|A`?=?Py#U+f6UTY+G(N;n<^0-tJQ1hhfO|5;!o>FQgpKBZ80v zyvcee<#h*DEgF8>HaIWwacx9*2YTvKfVwsyxO+)5{(3I)ZLZylv*fdpyLBx{L5N`H z5%c>+as0pMti2CPQS6^rGjZtn`)2W+Ck*)4*ENNs=rD=3Ll8_PFD?CliLxs zI9qD_sh|37SH0rB+7rYxM>gcV^La3W#{6+b-FX$~xIxJJ-v!4XF|CDUve*Qy6`?qp zw9YL{QUf54w^{ZU4rXe37iNM=1PzP50Qkk`rT>t+9H=JzlB#+pm>Pu=2ESPJJWYZVHIE40j4M7P zSn3yj04M>~OurbLdLAIJsEP^zl3IwOsG#b)8Cz!Fz|uP8bu+Gq0YDJeV^hxqk`YXe zF6gG&`?XS0cTsE@&|^}gEINu*ot9 z0KBFno&w;#tdI4w7{1fU=VAOoHb|WIG1!qeEa<*Rk|_X~y4v)vW&)QRShcNiu`u0+ zuoy;=&o|2Be;xf|LojID?)Hf8U;pa)ZgCbD_w_VS5rHA90L&*cZnb(aM91qS0xQOt zI_&j2%%AIpLA#YMO5TJlfWj;?Ze<3@9#BJ`N}%I->S>9yO%uX6tIc|T&TN=dY$t>u zbZlh~P+@n{&Kg2!+u$@0AgM1AvU?0!(hunZT{imPsEv>Qck}ykW()n0F3H9{NJB>b zz+lE z999|PrvpFS>zlbnNVydf(|CsF~Q9k-7( z+O1cfz|ss-gZUMK!sdGlgKz#G_~0}GKNLELspS2z#l3y> zp>ulMT$;g1nEs*r8}Lt;zm&_D>YQ}&UP{0I_ZeommL;RzAbRn^>jFLER`c4Vt@)W} zt9%80CiMP!Ni9WV-b$}+CvNtP?-SUJz;n>SG~~k&rVgK8Z>4EK#?)jZBpqYcdnt`i z?Rq5FKd?)Fl9b^g4XAl{-y!kRYW*Wzg!i<+mo4bo?Az?!y^Z4M#*>Tm!Dq{H zrs@Fni9-cK<}!H0ICW*+(A*^ORJxFLpHS;&yL*CaEg%HQH)w5rEIUS74;tO6nE7|_ zhA;pmH&Q)5`35TXg65DlrAgWyNeX7#?M`a5&XM$sEN?;Hm?-z!a6!Ox7aw}uFh6JM zI?&|O-e!*wT_@xfN8}_QQ54mzb{txiN^`Pd*y9iG>q#OQWmmaziU4Ad1S>cSh|>Hy zY7asUrJ<u45%?^ybuAfY}QZ z3k&V+Yn0l#2C_Rr(l<13L!xP;BF3rvg=oB@d>z-pT}bwHv~^MYmuD?YJh7+{Qr%Gj z3GTig1%Z5}$2e3r&J7-!7u_@TyC_lp+SF8ZM5l}&QPd!xcO3}}S!rB-4cK~_OPkF- zdrqo>Un!T7v1fPnyr`gBreAkXox6B1;g!WwDX3NsGOp7@iY&0?xcPjbDvite8Xc#2 z#eoB}vs&5tduVIYXk72aceIQMp#pg=0&>vC)HiRJNk{}GyRvM(zY?BQq2zu0H^h%@9jHR-| zlC+(+vR1p*)wm@KLIXaa8$gVG)#v%=5qLYwxSEPMB^aUcLj~+;0MOakNiwEgpF+=i ztCgxmAyJ18&Ppq;b#c|>n(ud&X0)(av#5*#1AwejWm2JbkhnPjEK@sLT!@;AD=SrB zCtlSM1K zS3^*^GGzf!Evl5~gJ8Z~kw&#*O@&?JzF*ComSX6>t8)CGGcz*?w^uUUc>oEeF|I=Bf+Ykx`&SYvK%})@!T3u=r9)loPcdX z$=c!2se!>L=Ot>0%dqVSa70kVrP5v;A#IXR>!ICuhzZ_)k-MsBPnvRwouZ{TeJR%j zG#Gde2#!BXaxj;QEIXyp@nh@K6A61W$i%Zb}HjL)(Js)YI4_( znbp++`5l-u4esjd>S+6Zju3|RdT#aVfAsIK+n8Qm9jM>B^45u|!-uD48FN&bHNtx3 zjfYoPS66fMOwK0+m90OQF>QPYIzU^g)=o!6uxdC!gQbwF5rQ;O>PCpuIIRjp<6J&p zTqfj7!gK>av@aL)`P^*geez1aV0)<~%xg+xkGa+dvzwti1k(W?qLR#BR*I~tGf`}` zoaxTY=J(pM;Mt}EjDt4OzuFcMdZygoYS`s2QqahWSVlhcw6JRfjXJ!go8=kCBNxDh z-b3}V@w-$3vST0cPu{Q|3{V4IhhB}|9QOw3xd46+5Z{H?k<(xk)#6|P=$Ar6okqS z9r{}n%HOrk-Z*#N#Ef30M;F!?`ryBep_hL~A&l+T5qCSc-k z7nMc*F}W>Ph|nl_C14|``DNH`@$arPKVHY26&1x?QHel&lqc7H$M`AL_}`}9$I<>% z+=E|3HUGt#Rf;_D11X@Gav|CifmV|6j}o~$E`o0q7kXDV5a|wtps;7}o;^Eu?AY#@ zCO~1&p1pG>TwwxB2!U}WF<+^_j*#x8*G@(s-F3r>@%NxJ{$7O#n&#M4Gi9B+(3y9P zMopRO?*Bsrme)lCp~*dGRvedQq} zXi1jzfL)p+QrAX%g=w%%!wT8lD^O_JJGN*I$`#*yI2q(rQL&MwSO<+<#6H$fIiAnn z6GE7LOEXDGLCqv}qJ|oZxVeK6fnwwJ66@+((EcO>Q2AEw5pbi3dq(sniF;YrPuF<_ zx8NMpZAo=(VNe2~QV$#_kZ{O2b{uSRNg5)k2f`S;mZ7OYsEZvtkZ?|4E5jN5bEkgS%>UM8=NB`BE<|pJ@~m>H|vJ*DuyL8ZQIA%Z9RXC*8gyi0eWTeKg;dCPt1j1 z(G1Ra<#OB-W^Xh+Z`vXU#!e$%@t56)PNUncKV$0nSw&zglO78)JSV)}RzRhlzw~PQX%_HY1^>t|W?nL02 zSC=1I>cG9lh*3*CnsR2Bto_R0?fQa2%UT70o+#lvzT5tH!0WYt1qSdfwB&IOa~3}c z!uL3@SneW4f!6s1OB&AzXwDQy%Y>RWR|q~iX<7e`_`Zhc)=rvgGK`rcYl8X4V#yMC zQPt~p&j0%?R3YZLWs*Oa>nbtfl1bi%p=vLsY=w24RR?ZdPo4pNELO_$4|sc0TDtr` z9KOaVt$V>4+)I19kkBtEgb?Yb!O;bvBP3HC8X)BcNjhoUSc>)m%dOPMmu$FVA7HtQ zLieV9bF%P+;#GxbfQQc*Z&=n1(}$xU8it?&eZhNQZh=+xJhLsvFs)AQA>T3hc}`o`=P#oeIW@;S(qspoZ3RJ(RC*jJ4BbZehOyU_`BLtF!Pdq;$`NiHIJiqof=9?fh)OIWQwMJNlZ|PZvD*#&==^!RRnIxu8BF#7m)I+p`uJ;d2`vN_! zQ>vcsa+2CI@KV=@Ci0UhbWKX4O@6a!F%apbF{*1c-1Jf6ZCyrz;^`%zTZ?}^Es8HBT2+IZKCgki1G zC=?*h=R7Z;iz`!8)hdRGxV@B)H`%lhpul^OF16zcz}4#1R3*;kJujD!p-^ZvYO5}W z5fDuyFfg|AP^uHn_a-OHCCj{`RPESx+&8UKd2-V8HS#SQywmwx`bG_E(~hsEARjl| zHSx=6EA{B*WN2msAl3F(rBSZ6HV$q4va@!>;?dF$9qg# zotx`YLz?K!&sF(sg830%A?+)?{oQ3f)_6Gtx7spM4lO{{N9!^P>2P3mqw0w9A`toJ zng+NSwS5tON?j$YO?2zh&5GO;h(aikEzx9rh$LacDs2Aw6DLme&$V1Nf5m@@qUcmR zisRS>Fdu(-*(vL-R}j&PtWhq^&z+iVPWSu$z$aXMA1s_WaiV{19lkZ$-FiDaAbO~4 z56AO+TaZ!I!dMulVu(yNH~am5vOw+2EwVMj+ic}zk6XFS)RcmRDf$cN4H0s#nkv;fp zY0Lz?F%`~SbImn|aZRJq&;UxM|2~t}Xf)mn${XG=(rc-qX6xes@aah&b>(|i<2NWc z0>~r?)ld!vD5#P&$J7iC3Q2LFDQ@8IFTabs)*!t-;+V>Gs~9n_lf6+7;GMSg0FB7o zOZQmf%VSD>1%3_uDGXDhNTN9CPID>O;hnt=XL!$`F6dMD{C{p33Oo{5HsUO)3w;qzM`UHMng`~z~rHPMbG6MWELO35_8)x-8W4d0O zTjq??ZS!;Xt=D%Cr)%{Xxd%a)(=}k1WEoGy>Uu!AX`|A$xuS==*{(eKspX~oj z-}Op<+>@jm`@VedEB?!)A*G#q1jW5CAJ`ee?*LDd#IYWU_s5=NZ)bt6Z&{)pZTcMQ zu8&4BE-Q^X-js)}7WRwQR5>!^kzy(@FUpKfRNh^X1L55^z0mcR){GgO=nQEzTYLQ+ z&O~Nx<~L_Rcq(-vK3&z@w{>ba+k6&;b{3xopbA;;K2oP8gQ2jg7n~^he>s0(Q&}w3 z>xH5sw$mtmTmi^+X*+5R5qvEpZ98&Z0_cyG8V>A%y_nKnbkBJy&NEYxSeytA`INWUfd z4tKF=6K;Wc@#tmkvQt{x?wL@X)a_mEnLSaxX(tIO5@ag&^pA2LTa*IZsEsu%M#w96R7pFI z7Z>B$mde)Bb>rA|rM7>_NU04Y=B#1Zsm68k?t6045N6Igm>Js4z4ldV1n>d?p|XPkL-oCp7OlUYq&V7 zS*pfW!o)MROUJ0i@w|gpI^U${c7yp0bXeM$gC04YgxdV(xJ)5BFG{DqdBk6syVjBoRs*u+;#6l~OO$xIp0jB}<*$Z>xQb z|DeZ#hPCSkP$Mu|ncoy=kqR|L)%N@FvlJo9m_i4zVsu60s%*7n73)W6U%auuu2!p9 zw`8?it;&|h`?t`LjG4Gkxa~4PaS05D*KgZzpa7IPNwb+aOqG{<=?|y9rScIn8L!1# zDyO|#FD)-U^v)ZxEOPtbZvQ-d6a1p}sP#7MJFK6!If)r@7NaaL;$m+Ra;QmRex`ZG znjLL=0aFHlM^P(XloQG(T$jF1Ci}-@(`=aM>a6iM0$K zrvvc^@BAyhUJqovoCHF&iITLwE*ZuD+-|pp2$JPkLJw0W*XvX&0$-qENA#5Sg6rYj+A``%c*gAFU6s8(bD}?hxa1a>>7-oh?882B|G%t&Fd?W6M zjcHpwq&HH3iBN^CyVjrSHy6TZqSeHuBH4Cf|P;Yh`G>T30#NavRW0i-{XI|u{g zdK0rww{(A+&>ajM-8c4B(9O`6b%Q(m6RgqF(t z?%0@q>O=nu+sq-?1_M|h3>(?6y`SmHF#^T!QejZqjQK!?se~M0Lw8pmmt80tf z(L|T&>W>fpC9urN9{h3}LUh<`5;Z<5xh2gk9vQK5Y5*OSi}-P)stpLgq2tnZ<(R{& zY2Rz8x#0bkx6nkhUE!O~cuuPn3KqnXrXT*{cGig)SRS;|TXl444Jm)?d{9A*5Y_=YqYYmz{Hib*cwGfw3`T<0yP-T~CxsIM31i`uxAyT|x>}M3*|QsZ9#nR74-mtm70( zv|rcbEVt~7IPZqCCNBq2ct=aJM3U=A>hT!+qDzSEkY@U>r+D-1fdfdIx$9hX;K12y zk}VVE_kx#=hA8!urf=HI;+T}Pt-U6@{}F9y$G#5yvbQ;Z#fk;=ll50*HK|xY^RAL6 zsMSDb_trlnl5ec|9TpMC8*;VrNIxYiP zsbfk>>`c5?O9bX2EA?iO%#Sb+%#fo$;kPr6(VwYLX!bj0-19h2(py;2*{m70u(~@qtCJEp%S_vRIUj(T0M`cSoTq*2&R zsr~tE^`ni#MuFNNfA@X%d|W&KJP14v`y@R3xgZ=2_3za=A&&+SlD~cZA<-$96}DF@ z6`mbL;KZEH4@>mRYmuHp=|Eouu8wNWtEedpESiTw4+Q3VQcrGdNqbWDHZrsjJMd)fJVUqT%;1eLU)D z^t;DDNJ{I42P5RNIC6lceF(&;-XV-$Q`=?$syIjYn9o(M@t(m3N^3^SFV^g$I5Gp9 zs5sJ(DR~uoJTx~0Y+il_9^GeTXv4^VQ=};C(VOegHb*=6lkZsfSx?G)>TBz~rcOO$ zO{0}E@LlnEgKYkSj+Ls@QfhBuHU$QJWtY3W@a7dvOZ&0DalWDlxLG$K;$z|+@*i;v z14<0kJ4xC{fr=wR^P8M5B+3-kJfm$(lF5*bF~MvHt&&|Z^68b}temvQw*iu6?p>C|BMgDrM!p#(av$!-NytM>$d2=@1Dh-yEmLog0Jbg zr=C)2(lJ5RL57tw1drRiL9{-db=kaluO2`O>=9&l!W z)I-{DA_hNdjr=4JdE@D@NVWn&e{iN8#R^gVgEBBCRY$3jCcH3*!kJm3qq!LV!B-}H za=ar7;s08=8zl)E?w}+=J42Kt=x&T3y6g4XZ00KyzB38RLIMa;-G1|%x2r+`NKxLp zP%6Mh6Z-0Ixlr1?HmIIY5K7)}@P!1SrFZE$0)&T6GbX9dQ^7Ur5DcVNEMoCZ4em`t!zY>VC5h*=O08D;)Ka<8 z1i8FX>#IPQ*P&+l>jR7R`XXF|dSh#EyWVKjxA(Rhb=Vb-ac$lU;!>oA;~Gai9NCUz zM;Kq_bdawm9OF7dN2NIE3C2^hNzO3F5&l;Jc+I+(aTTb(ukeCv`IK;tnkTkK$A9VF zK}_L46c1{rF&K;`CfXCg9;cWyLWt}LV23$Qw8o&d*W2FNIk2{&w8QKlK^tE5ASa5F zI=@qfa8yA=DZvFA@r`1>KAtRjS_3wwTw2}U-db_w591F*=PZoVK==*fvvKj$hEDm2$ih^(?yq*wckPX-ZkkGTADb8+hFsfj(IL}y-eN^0 zW(-TJH1?Ii?n=-kJIip?L^7|TOlSu+3;=?Tfl-CYZ&rkBb7m-+P<=_HA#y(rCQHi@miLg0&0d;d4t1t(jS z{Oo>5Kbx3lbiGB%yY{+v^QOHB^feKt05|79U&(3fiTJZv-DfOY@li!8c>oWjqYqe` z7JPoF`*rNGDmhyYB|08?w5MQ!9w7ET?KIg?P+;zdx22bBa9O5oNHD1wzG*_b|1Jt!qhdGG9oBtlqy}SI2`#dfE!>2z>I^r zn(srYU8~vJxQ&Kuv|T?45Xlk@o2mCWP9bmh7r^(6q3iN)RokwqRE%ruT20%oS=>Tk zJHI_IgD$9nT-xdn!J-2rsS2x&8GgQeK%=u-@()-kcWOY zb_OwP_>8CIB6^B*P(M!Y$?(J(KQRc1ga5$augUB1|84&h-{nyTv^1ts&EsNCt&@%f z4lIy8)?6iUlZ#(-tw+vOD(_odT3Wmj$Im2{N-{ZoILopX_8v|cdxp}L`D=)sk;LX& z51(0FT3UQxrE(*wRFY@ncyi>Yhr?mB<(}Du(q|Z35&Vo5Klgfu9|G?fizjwHyaSQ- z4R{N{UjXTE78#mE_MzvQTh=&$1~!=H01kS27tLhX%#B^$=gbi)uV-0yh9Nkx@@Xb} zT!qecILdda5Y+Xr$;@Y^JTf^Y=Ef=7=geW{IO?y4!{He&R!)tXoT>fh{~dy$6xV76 z&I`3#NDKnMSpPyhK~l8_{r346VBu)T36Di!jalRk4g^9GfTKF@kh9*JCb=*mkY6O4 z^z%`T2Ye2^#beWaN*g`=XAk+-qEt2ST=cMC%VaV@o0}&G-Q|iGxK498+(cj_Db0Qz zf1M{WCMZb|{kKZdMp)Q^=TF-$Ai$X&5{k0ngHH;ncnbvL-1e!0MC&I9mdirKnC((e z9rbpF7*{^Q5+e~$ueHWlm`K*zJGr@uqOjpQUa(v!;LbsJ&bgBqvm{Pt3@I&qSQ4$3 zP3AfSB7iK`1GH%`rETEcCwii6V{B_J6fJbQip<9aT!SfCH1(3{~webXs{Z z_~+BG8L#Qnl}=|c=yVn@%v9u7o zZoE(`emkQWr<0Kfb7+!A?|CPPH+wLfx-*{hNOq#E@I!HmmsOU9uAOg%MO$wmw4;e^ zaFwop*9A{??*R~+dDrJRNoK}-6RFK%cDG*4;gWuUcq&UY;u~@4Wiw@>oHM-qK(_L0)4Nm_Yrc_zUdO}f( zZNW$5C`nV>GQqZ-n%UgPqoU@tYYT1Mq@H%-O6;_=X6=_H-#MGjU}^TF`b4GvVRl1p z%WZ$xkHvR5>A6T0K_tILMn~NfF$1gtIEO$s#EA(hnHzna3zkfepH2vDfrNFB{7(N0 zysDHU#8yVwE@g@n$|^R1*V4L1sg@lNO#cU*l*UsN&}7PohvKg zzSbn{)mx(n={Ussc*F+`2+pmYc6H@)+xo)-g{q80$b&Se(dlHPHAYkU@-6Qx%I*cM zMH^B|r?^pS*7%RBq_m3y@q$Rf5EJ!$x4{v0nbN5dTo~8&jKY-OJDJ4r{ZL%6DJ35B zD`-AAo&WiQ+n(p8+?SPZAVkoyIYY9jOAa+<5gB4cM7Sq0C7ftmdvO%{5rJ-Zc3{1) zsMgNVC&HLh>86p%#Cns=aHChPS4LS0bKiu`k8!5z)6tk6BN!3xNCZOBc$8AVlRGzW zj-O4r)q9@mMNfJu@r9}JJhMJeXNmKCDf9k~?~JR}sv|wAZ6-yaIl~NdZsXY2LVAJ_ z$G53hZ=KxSLCpzgb=-~(dOpKD5L;Dki@R5Fp7k6 zjbj+2z!a?BzwcJJXTIntHqoBEacpXT#GIPp%&9rTvUJ)$x?~OI=hDX|z&rhqTUS$h z@n7~h)U$0t8KsAUGH|!cDt`%bGPgHw(hQL+wbFd5hm(Q2ZKC+Dv14z@labl^(h|bEpZP_fEz%VM|x`S$0KfM2o75s`QJoz}NlKRDk0kO?hfywPOM z#$>Gc=*jRFP&h@^Jeh-*I_`m{Tj!X3n!+?6#syoF2PkU0WZ$yP0c%$~6!lD1SHR!1 z%9+o+Pzp`H1eV@k9tniGNQmJlcmI7ViQ2!TblbL4i_SYEz6+5Fm_c zyM!qP+MvrF9$I==kG^B?x87nsZGF!A|B-LzQ#B`yAIUA9lg*K=Mgfo1&j*0kG=MpD z%*ttW&>56?%(%+vKy3HYxqc6NmSx`e!zjn!7q#nKTMs?7wN>ZLrW8MmJ4f2{?!M>o z$M3n@^VGv|3o#=$Bqfw$}lO%S-JZ;qb7oP%f2 z-m4GKqRily&j@oIV=7_n7=-YnTiXb2Z#{|;GEN*;jAI-|-WQZg4uB2dSP|s=lu}HP zJOSX8N`V9*m{M-S&~*T8d4sLtd=9^YHT?Yl$W`m0b#)KQ9!Yjm!PE2Xt^S~_^}ctI;;v**=r_51xIf6sT9XQz*P+N&SY)bI#KHT}C`(d6`K-=-J@Aprldt&XMcUq+6;j7Zlz&**X zSV#9?r=4ZiH*st}9!F?Lz46p{uuwntRY4%q(Mg2fXlaR=Uw;E*+6vuj;Qhif9Rh2s zw|FVz^vNtj?*j9f4^R5O>0Mj<1Gaf7FH35()@^r<4PbrCWqY3|4hDmjI{BUYvJvx0 z{`t;mmB-#jOW7v;KrsLnTVC#z%=! zpq9L}(*Yl&0a*{QMDM&1H&v!x2Ea=1nj|BzMB&7^g;otG)IHkV+&p>xf1Mu4Orc-A zttC#fWDdLwZEl_f_x2h+002$_R%dRRV%hE*YnKFw%FmW~{Z*gej&;&{&vC>5<8c!p z4w21?hnVn_Lbr$H5D(`xADxf$K>&t8dB3sPeupXZBX5<>JA7?7{S)8ic_+wo&$9aG zuFHT|@X9OGm)pf6sN6uP(7U9z_Aj5@+(g&HqK1ruC0PMYaJf=K-Pg|GYcxJrK9r46KypIO;=r)^^2;w*{`mM8Ap~EWz{zd#Yo>5t=yTu7p;E;U z9yoB|-YQpjCy0OJAOGsbdb^Qb-RL$j@CB~;? zYtJfKJEM+Z&Ux#ib;XiD31vy?m;~xNiG3XdEv}E$fzuACZDJu2Ff#!mMaZnIi+jUg zwluRj4A~F20UeKX0Zp7ZzXb{?wtg!1zk0#(DT=DAf{QGF*WA+XCbywv{wKu@@NAw# zCoMOr#o#1O9zo}S;&~plGVMX)%t8|ktl6`eR?b_tDa^je2?^T8_9BS<7)-C1OAOpLA{wKqY>S+c&`{S zC8fEpEc8iThu_zJa>M~Ovq4lDuno20? zK4w=+bBOMzXTtt07WU8NM&ao_&Q$YB5`rDp953`XT2rjWmI!dCK z7gEedn$HCu_q}6jOig>-*J#Q+I5J3EBdViOhd2$|Y)s8Gn~i1z)0CP~`_pM*s?Jiv zd6Tdeo2e(R#mlg}Wa<1zx!A~-i?(a|e#=|{7Xd=tE-rW12K_rZK4-gs9ESIBe6H9I z2LQnF`9hnVKYu=LdRp7se}QAH%LV%Qc5EUU%%9HHdfeO8Hy7`B0fRJJ5DdV4pBY_! zKdq%i?8AmXi`a+D@ZwEB`{56NxP(C4-XUMxh_ylg>+q6w-FicMWzfKbX)j27=4aVp zG?)xqZlw8tVyQD|nsy2+Ze5y;-=#B7I%$8|&X9&A77PrDuxkCXLo*JHD?%wze6l?* zF$5HP)8;Tt2xi?Yn-DVpuL%?oE5(ZcK$rFbe@Q6K1w#lSCWKN#Fbyn@XZFX0Qid_3 zlwbfr)WGhQ?GyT>?4)kI?R`P;4^zqMmFd7#IDGa9;nv_vV0X=Lzt)rU4Ptzi%dM0;k+Hi!Acf;BATI8uLxSrU zlb$n-lp8H02jS`|UrI_{H#lWbfx2GsL(VIS&q*9RE?K|mO2S1t%Ug$m1H1}n{J&oD z3g%2hq#4I$z*^@41=_!S&c92-We2?ureBux|pd`=8(Etd~CMXU4Ss_hI|s zc`mST@4q)=n*{g&Kfmv{9={`6)6)*X`=|XYw6E6w%dO}iqiZRj#h?14g%oU?Q4`M< zeiA#RgJso{ zcyjhQ8#QCBK!u(~E`b%L2#BE^wj&XHH5by9y!)M46$z^8yTp8pzlTW1c~~205tNhvmh?eH-A(5ExMC9zVt8hfohel z%(}Cmf%x174P%VXfKo(agY!$1^DIiZubK~%=!g!GIQv-IFtu--rP&15{6aAd0UC`M zyL=H4hQ)%wl+NEdec+E%D#(Z7-SyN@Q$MZmeM{VEw%g_VAGpyN zmD}xRBj)_)H#Rny2d=|8UB%OQiE944u*JRk-x|ir=--u!lI*Um9ERoW_55GTMDm)? z9awi*hvOD1Lp-2GDQXJvwBZQvs^305h=z{@5l!UjG*@F2eJ1s3TKeGG7aiCcvw~8% zXj#IU|3w6)b|#$u@Qt*-w+jkg!|&DMzW)pCmC7NdUASJgK;e7;6_0Je+D;NF_3tCX znE~VR?DcZ?FZt_PISfB#0Y0%=NAmCRrM|2=QFEg3%Nb1_nGOJ*It-?+Q)|2;c@BS5 zZnw*>lT<3nabtY{xbHg`Fa1cV^uw#0u znRE@roGK)j(0oz|O*9pZo2UJI3JUi2gu?_=Xml1^#CMZGZ;=fbS_aB;0!w%TG(`mC&l zc4q=tIm^ychPG3&dN;Q0i}>++1mQ2wW>fqu7)(Rl`bEpNO1_@~Y0|TWSv?E0X`W}C zOLuO}%;)*E)`tjm=eN2*4pqc+Zhq(kCNa9&7nM3h===uULCK-{KM=8Pa9zGxqI1Bx zuUVe8!P^aTAN$9hz(kKCWG`vWM7iEhh`p{bB3Z6lTu| ziMhXBtv%I6Gx)Sbz@JYZ79Jl(W_DIrlQ_pfjwr>+>gvu9_0O$)R?*s0`TF&nJ8-}d zXS4)Kvul2`E-+HU&4JL{;-fWtQ)iE1kfe>vM#^&FOE9cmcJ0+fsv#?m67g(;{B=&n zhQ(5v+G$lN)WH6u;CjtioS(c7zwY*;RkH?GZim?9nDx$RdGTJQ$1|krr|frOE%Y#R z?m78VCrO)78;9dIHDO7xDf3Bz3zNdK+dT%;b=df7e0HDe5WP&OCIg}=S=OYZ_PIpw zQ=N4}HCe+;y8Z-*?%XY_>SXAq{fqwNBJO^v*dh#A;}I=JL>f!dv@0o z{J51#)Ns7~UC`~<6W?0b%nb}QEH}%F;SzpuaWgsK+TjGoMsb;dr_xsDt6alx>zVrW zZ)mM&D&(bC&l2*9+sQ}?j}ArYDEvq|t-O#{;Ztg+fBU~FrDpnQNemx_zZMd5_SF&( zuf?r7+^~E;)9LnVG2ChE9uqTRd8QM`$bGqamS-J!SDxn|UH81zAW4ElQp(F!`y_Lo zr!9uBreh+lo-+Z6s@jL1hM!$uU;k(xBuTL9c@HCm_NnUpp9Axp(&bdboL~IB!%o`O z%kt^^36&pUW0$0D-RFyNWmg>2A=05p@Fwq(ZIB^wzrcfw^3&m*vd-eyx{MF6D=*P&3tMxX-R&a9AI_A%V#w4E5ogPlJH0uNs7Bk5tWJ}X-B ztJQp`ErI!sh*(9-4`|LluJ9|UG5-4v+-I#|E#>pIemt*E%|4k>#Nr;NOcx(O!<^M{ z@?F$zv8@rp9@7=7KnOB_P{JeM{N^_c`FRbrr*B#jMorMey7{C=z&=zajQQKD#S7!d zLq_$h0LKp>KCByoLaB27VnHRAZeGcxCmTmS(*n^MKmE*EH?bI>mj@0ZzqWd9Uhp`% zltPMGpM~PA)YV5)t&L0LaSX*lI^4KD2*XRB*F4?Kg@*l#H=-M;wnY_GILa#Gz1b=stD8b>h z!&C9>_2|#F+jC%=uo6Q-pDWbk6);WsAUa+25Ny?1P%h|S<4c^N9q0tQ5m_^ZWSc=eIdxTE;VVih zJ&LUlqCUCtBFuXrR6o#}O6U8$4Ca9as&kffOjvum4O4J}Cdi8nfJ@zy{HZAwrT1%F zGXb|bqxv*}xh4$H%7rFz(l*Z!mp&HZvOV?Bo%HE$G324L%)s2OQl2Oi7fH4qgI%b1 z>PG3`EaVJ0Qx2qMv|S6DmI)M7XiBv3s@ePX`ez8hUtNCr<(Gf-uloJSyBBQ*lq7$E zTXyig>H}a`+*g{i0Nrj46vuI5unU|ybLLDR$eI3cVc(gzz!2Wg^CqBY4r^9!<4uMic}`V4-0V_AS7aQ0JlY72gAh^M_89o2E#+!{uE^7;>Xp z)#QH*N8^M2rfHh(g`5mzHPm+FAi%ObX#(^Ob2+@#f9*TyPV{V6-Q+*;kos&|&VR_d z@cL}}&#&7J6a(RA#`y$ETO<64txoizzT)T`J?cY!E2QG8$oO?QUpvP$i+zZa@WCXF z56GaThJ-pZ1#!;;lE!J)UMv$NMF{=Fz5+?ZheF$tmlW5j{-oN=#hd?(*!xM|ev~y8ZUs*{l8czw2G^VxM5YecNrf`LEXdH{X0SyQW(8 zVI<683su7aID8N5K@e5}1*YhAHnP^ba5GWW3w2WPp4D{^P78$tKvXzl;#eyI#xTc; zpDz->3)+j}7u&H;sUElYU#UA7CgPntj(XnF9XsO*z>a?9{`-wW!Cjf0TyYBpp=g@= zM^S28mYGJ=fSr|b*SFKbF#X$nYWhH})2SVpp5m%1Lp$0N!&qP@#mw5#`!g<;G9M4_ z!OF-|RwK!EbiMu$)ITVdN^TU`7IEyQ$r=WKUqh0x<5of^BU-L_s^Ga`u+VB!aGXNB zl+WAnx<;ds1B3h9yS^zVidMVQ3M#%2Vos5{V#=0P^ev+pmvbw^Dwvk6DY_mYgb+n& zYlQ1^dZwT2$#v-ND2B^@B1TXgO_F^@q6LMO6a@5@@n5@J!0v$FT4_A})6~4@{2?S| z8J*$VLL`3{UKW_-7p4Qm_uf*UX2plU6?)$>_{|7Llv1{0B|+1^fg{9uL}{D#31s}I z;&TTm43@euo1}|&+t@A8(tvje?G6FtO{@!yU6q!C=fE;{-0Sip*TG<#Vcqooxr+bd zZ>)kLs@~gJFi!+<18UAsK(TpG*1F3BV zv=^a`qH?7E8n zPB-GTQ}=MnUSOOnBw13eaRh2QW%tbNT%(O9E`3pwF#n~XXMA|zsF4N7b!q5h;noub zfGFR?O6cT_GlCEkMcPVEf9PI82Au;Ubpt{>yo}0&= zxPeuw^jn04&!y`tY#!AOL%&+re>gQ7jquw>$3Mz!)?g#DCJ{w3G9Hwk4=@+Ba>wE1>(9#DuIA zd{fuabzMtzs@o4>1HS+rcjuCdHRm)61r;(~UKf%iBg3+9;DB+tGOCoZlop7(cg}By zVME($Q_~F1k`5f3XDJK+Ed37v;Pr0+5AZ*X|7Z6*G2k|;qiGK(MGZNqCZw8n5`jVz zO^}r;Vu~AxK!LK7*8#ng-SHonFz$9wewpA}EeYAeb3`sz{J4rlW3L_my+tbdB6bz% z;q0Hb&7ACZ@muqS2rfLws@0|+?}1!&qc)&!_^t%&T>m=kMrYBh)JansU|A#_7?z<% z^`S^OUi=N5Yph&YDup@A)^$QOZ)!%KnesG3fa-RY^F{PU4XL&5uiM}6ilmvQCMk;M zIF9`V>>VKY7-aXV(IE^YsRI-Zu~*mzYd3E?&g%7GoDBJq)#NMmA-9f&M4C_>S1D_y zl2%q+N12dF$e>i3X_RD-X}XbXz*{_jni!%CMdl~Da|QFHFtEoQ`}HbpZrw8_jI5tag`lM z>2Z2&O%>cQlmflHr|23kl@?)HB)3g#W7@*>z38>*O*T>?dE-&iWnga%`v4fY+$Zwf zu$}(PGorqU6&%dBw-Ci^b|KO-)r4LjOF%g8v;Bg7_uc~XE)LT|kmgyW=E3ioUKEW- zQ3UHz)Y^!j2%$Myl|J^~UzSTX|wigAZ- z1K$Su)UnFTjR~pbsRT@$kJm6#Odcr zya!ToH}QYor>#1vpTu!kW+U@Q%}YD96&A~%U{UhI`8?;q6g9Y&{rU=4)rjnGAL?Kz8F{LD2yv{ly##NO%fu+ zRv3Vvlcn8GIGMhBkKx_vK5I%I#P;LntuR&ZrGP~6ZSB<9#1&=D`zm;<{Mg(_#1*dn zyXdXPcc5_}gmoh-8PtC%Zkatv#T#G3+kGUA=kdS0o?p;)aT9?yCGFbLPyxLi(ozTp z6h7Zz-feuM9fDmzdor`xsdT>iKHV&qw)sKel*>*K_}fZF+*;Ue2%#IC31RGNwJ@wf znmQcp;*PW>sVZTNsH)UTcigyZ7mh%*c1f1;u3b06U0TJh5&W%Q_pnFA;y!u>dKf*4 zJ`jVgNe~8IO*73rW*Q@Ef_z1|rW~oG!VVhI$=5~V>}t`U%98&sDhof@|DVxADQe$rxt{8X)zb(4_%{I_o}mP1Vg z42I5C8FSqL44M|6+TI+;{xDW@D>fJlR=Pk=!Ce0oGQAeQQAxj@JhreIZbz@}cx}v+D^`}~R zQdK97CHPCpd#=995LnuESn5eLt_YqPSngl#0rYYth<`6+tO3_T#7pk>_+R&y0To+V~ea zOVX9%_XMSo&3%KQWk9L;UQwa4mBVS$Ew~LDF@WnsA1K=M@lwuMc(>wimy{X((ffvr zsW~v~=ftd8FnM3sg1Z;M-mMDM>-0Q*kr1bwQ{4Vwc5o(>WVA_Xn^+E znW~L1?VpYM=FqKDgpf6{MLQmrMO%tm*j}i+?yn%c_1pMn_!4TNUFZSym`_02n_LdR zIk`>c9jT(!njO1rQ95|(z%Gz8S=tQ~bK%A0{%{g{SvZf%PB_4%zYv5C01N$AyWxzk z7gILo7EDkkW~b}|5dC;rW}`Q~=}ioj??!|jY_(c3V^+cv-D*=XCTl;}B$XJZ!I(@f zT`Ll7S@|dOVQAW(qy?U&1wWO9q3dH3%-P{f`jO0y8B&Xrd9fpB03?3Pcl-Sl6B82K z)?J`d<-mahYTVpaH5ij+nW#o}@ALt7B@@9F1=l4LglfU#B=8?-uTw&nb!Pao=6`O1 z7f=n5eWdA|gP@WJ=ZERVFid0RFHtQZzVNC#C9P=Q6h7L7Ucoz+_OY*3&`o`^)9Kt@ z)%=?;H}!p=0(AcT`SSc1D!GtPxzXXiM~xH;6BC63dEJDrMP}L3YI-d#7G0bE-@y1^ zG~K;v==YDg(C_#A{0eyFkw;>%t>OjEGW~TyX&m%r!N#{QN2zEn#xePgf+~AK;b&Qd z8o+q0kL|$--DcCAlnS`%Xl)n!dxk!zxlRw_!85^`V(frI$md0=WzaQD`-^*<4!B(e z$WK`|<4V6KdDGGkkfRuXzGZ@C=5c@h$>>+NKYWl%`4uKh+B(%IMf9InAer6Z%#M^C zn|)aG{9*^?MQ@{aGb;L?_V9_b(a7hGU-gchBJ8NHYki^;b`&TheWJ>M(D@jWvfa2X zD)S`;Z4@?}Kd?_(CLdVvm;Q|drADtJy3)PGgX8ryd%8~6bbSpz=UAz>CyAu!%Y0bK zktxGWSk@JZtTDERyA1j8dP;k2*1UGKmA2ZEY`uup$QIj4oCuRYMoOswuvOPT6U+|{ zd@Tf}3g?b0C{@I}!L*~C%Zg2=8hwbu@; zZWZ+CCL!O*B*fl(`?L!n2y=>%+B*k z+LH9{dlv@PBjOjbf(^Nw-*gKhocmG!pllzL3}7^pJvjTvgqo$yh@rQ_zL&+nZ>bCZZ|$PQ3G$}VMaz4E+j7G94}=p0+gG&-qWfmd#IxN^lF(>8|*myyES5Bmmz8JC8fs$_#`3HPJlCE7sa^{6(8YQX)=!Rh!%s~*g8q6 zh}2lDl=dM^yfsqU{i3E4@$p4rULI|>TG&q%P)Kk8o{FgWo-iQzK4%P1fp0&{Z%&(uC>zIgxY@=!}=^^qR{vpl{VyTkEA9{#Ol4=oy z^M_Ut{S@AHZ#|?+GAnUzkb?(_!Fh?vl6vT^|NWU<3+U-4u@OW53C_%i9JEfNt}dX8 zsA6oKENb@|6Z%Q({we2p&&y#R79^So=(O(BQ>0U0zLvgc zS>*QujDz2E4wz5g=K~~tEHS^2HU9o>&7mXcS|2!Omg(1}vB$UzZ*emgy>;DZWjGIX zk@j$!%KYor`0`&9kdKzzJ>vST2LOTa#oGDc*?=GZAl8CJ)mGVp?T_>8#kEae7} z=03fR0;;Q!AJSLh*v+UlRod~EPPCil&^iX2SN-?`doHRR{m6qr1%#< zh)oM*lR?;Ner&yVy?kbXDj`T%mvCj^Dmls;)3H%9cteFCir(0gOW%Pbp*p^3eg)Yu zB42nr6pK^$3>+ev51$GQnTg5=n!ZbVM~70x6SGUKcQQs_YP0DH0S5XO5YFmbZ0d&JO z`T1ZXo}HVWi>9@lr0LD%)T!B7!Elb2-_A`_P>{G`TU5s&X-~)+H|PJaaf92IPBAm1 zsM*Zt6-7}Ri|tuek@3-TJ||wa2TnN5XWnp5(td&MHlV}^&kuoK_c?V5iSYP_`<4NiW2Bs++>Rc zaj$${bwxVkEVxSB4D^}!*l5sNd;IwE<7@Tn2KD{sXfgx#Rz| zEX$J_vrJ|%RdO+QC6yV>v>1~;%afUIFTS9yGuhMLy(n6LrJL&a;74KYO@yrCGn%*D zatj3q_tcO2>xa1$JwYjD=Yx>{y9dH^a9*XK-1h78D^EADrNSM1z=hdr&3ZD?UG{Ay zAOobA0wqqW0UfZrj#Qwfv)?DE#QxkBY9GR5;qGI{j(za*cMRYEsHQdfIn@YY?_|9u zj)GQtr&qaQUwUstF`7eT*^;(&hm|*FRMT3vZ5hzC>6pGj(G$Gai{miU_kKvbHOisQV;S)yL#4gw`eXD^j)Q!ixRzCeuv-_VjurqU48?K8Q|R3MnlBmtQPrze z=DYOb0^j$i{Vz2ePw7P1Ad=%SSkG(3pA1)H%+Z*?VHkU+r>Cc#cIW17sZ{dY$!sgY zgme)jC)3fN+k+7bMm+_lmE?ksZJNXzy2d$&6R;}*aq$RZ0Yn;UCf(WEK^Pv@H0l|` zK{OMF;VqmiSf6m#io3bs8HN)j9X++Lt6C`dRMV(mC{zb9(dZ^oEtyIfV8L1!F3i=U zq}!OVDGlLrZH-ae*5Ujf=i04zxkVHDqK5(wZwE!4BZWR_mx1CZZjr9aalarQ){zjF zvNBu!rUGkm)q1AP{zA40>l=>nf~P|UqV%6 z+G#KuYIvkFZS1BOl4F`5V%NvD-C`4X21GHN>?NmQ9*irxInU+ecuEUtB4wT_PA)>K zu~%Tyq!+#P`tC~@1+?qRz<5&^>{o0XYG#|79{-q95fGJ6W?_$}S^fBB6{DaQ?Fi!g z(d>@!&8t3+-i#s6*-*z@d1E_e$y#OMuX6G@EB#;i#~5sa($okYSMtK0R1W$8MSX03oz;GE zoQl-$UtW3_)W?^meBS)7pY8n!;Ov9e!kl#%m#@C7baZaDlL>mY*kLyp6Kd-T8{-kL z{R~@opG+oB{;o&?cS)he$KQRSzCZE}q6LCnn3H$Wn|VC~#<4@7YFb&9z9M=as75jg*>faM3eln9m-^ORrd_v!NTX^p69dT9HssmH+NWf(A34FM+N zZIC%e?^qSHc%a>uXlLVX`VDzlB979Ef?@At+Ka-IWZV$Dc8(xL#k)nbVL}~e6$cqz z{h$p=tm|uJC&kM@h#o^vqIaP8p^u>F(Pz=OB=|Ifkfe!BrIUWvYH|JaD(xB!(u(4a zSp&f36tW216!|8hLpDJy6bZ2&(H;z=~(+pu?a0 zj(@u{p_s5D<`swe8R7?Om+6>tT|!mmM7YWho)p^&!6>cKaTxh*CeQ{)Z2NI@5M7!EZlTu!}og~)a+YU zzk@p5KE)&)#+-(M ze4odm45ANG>B;R5vm{g-ivKa|9sADZl_D8$NuV)S?ok9>@A<^XWcG4PMCy|&>tolt zB|cJ?Q)5N7(x2mI^ZyfRxA7E$8hp0h79#G7`kEgYS4=*WM=~?qEo@w0oJQBcwaWeT zQLL58=uea+wv^?1R09q?b8|2<=~^$+-_%_|l(rq0VMi(KxS&)FG;kd3%04mfBemlZ z;xb|YV@rvt#Y)9Me9!gP(jVzROL)*S*>wIP({>JP1Ev%^4#PM;$8J^wQ}t8|0+dpq z0GI*?AdbU<5^J?Lh^ekslq8f=AV9;=dK>x}`YHuLpqn}=Q;wHjy;~wj%lQR^H$p=) zr|And3^V#We6}NB4qi>*e~3yAHX0vndTj{F`VBFgu)7^^|&0 zwMyt6VnQZq1jfuLP9ChI+Pro;xR28-hNo?A2SPKmW6=?NyR+Hup3?u(a2^}E$jtbm zw5GXCuuh}o!*O~NINy7;B+&`$Y@3Naxux?za*$@8Cx}O}kkQMO&ehSVWI@+nhLD*J z{#9wVV4mr&tgN_^u!s^=U}`D2E3Ve+bk30ajpe76*6#0`)r!rSU8y#|yo=hz9=Jcm zAer-u0OR2O(|5i9o>%A}>c#M082!hcAPj@fA8X@kjf24EQgS;8@K->EjdqNx7r%@P z+gRw=_T$XgSFw<4)g`wD2f0kJO)-VaHPe%FV-g!mMDFS0uE-+@O||?8Vy-Yg%l|0` zc|}yVqtI8yttlt*smon$(wikDi8~&Q;Fx;>+M>+Z zo-ZU|$8~*6G42Hj0rD>H<00&$&muG_QVN7hndG|;nf}a9c3h65N89u*85@YSx*&%r zTA%n{JB-S;bXZ1lB<_3HNWgg2$w+sZ%6Kq_0r4E5DAv>D;OA;fG*KCpc;(=pMEHLT ziocKJm{7_a6f_6{8fb%4N@7e0oDV2o<9B>U0k1Y1fD3J9t}BTYYO!9kfz+AGI551* zlq+#}I=$|?>9mWbtJo@Lz%#|_1ngRUQ3*+;>t>cNFfbm&S~#I$~h`i3Zn38$42hLTAsKBo&dios-EIG?Hq1n?|bR0#VrH^*_z`V(Ug)qjbsKM+ki$p(Y%%&?Ld{fqe#{jksavGxrUammFhUU2@*9ha<3CdFGwv)Qdt zBmlySWr7FP;ze=vfBg2p#ebtTpcv-=ld<{#gal9u!Tc{F7#qUeC5SH~tuLY&mRv^6 z7y`%nvcrj)x{R19%nV`f)DZ4WFF2f-!f^)<;ZDJ50atBpZEbCBy@Ze*k$Uk>05`>6 zDt6|7fBp5>ulme2#5|PB9gKHm`j8>UW&Y~*jA3G277^pJsL5PIBIX(rX<}SvGiEc_ z5F5azDg7q`_%4j#D3G`awmBVsXkL=7M8en4I-VxI6-?x`QHmU z$%ggn4v$9BC>cm^1O`VXZt9OUerv52gCeCD5YM-x|7hpvqS|O`eKH(t znS+vyvsQI5;Xq2`KY;YNu`fRHP5S= z3z!iMw(NzKdfMp}!v(6fWR50M{w~8*6gHa+3!P3@b;Q$Hf|V`UQj*Keo($^^l`t3+ zp{m!-s4^GwWQhs8%YFM`Dx7My+gVs>Hp2)E_8k=n)(%rj(}iKt>7?~a*n5I|E!-J& zyFsH;X*^@X;op&b7uD>9hwKnzDWx*FYf=JP5jsQd#G z*4Iv#0=L0K;_lL>1Gelw;*fb>TQkeC<0I&*bq!BGl4aidB5L{Yj@X*7m&(-Gg3_J^>tRFofga15cLw?NRJJn)S(K zv@Dkn9|ys|vMjn|?qwNW4X8!EN|}0Z)m-43WVGT3WWnQJ%CKXoB(MGg8Zbj_FnaTB z5X@$6KN$qUO84gr{;2-j@7{dj|ARSPV40vV7_&dNYY`Mv(A|0s^2x(208_lIji(o=()vUwk=&B}8cgMqlPPYIjR~>1F zaU3>}z`t;ANA*UIao(s$_S+u{`n!%1J2D74-QdFSAs5{N^`8HzB4n!&gYIxhjv{4c znsF3IA2Ld*)HsEiG$$LQp?9gF29(eZEEtNRh`LC#w=wJvib2^aOA#jkVpUw&;tf;` zXaSub6zk)T*SQud(UeoL4dfD9``L9Tq z<1p3aFplDE#3_tgjzG^Rmi#Bm3exUz3EX2@p4Q&2#kUg%z$rk9Vh73{6UX&~HB&GG zxBp>)fl4oHXGRub#y)8KEm6mG41ko0V#{!lLAC0*oL~SY#_<&Ak6dge7w=` zfu9!jnsOX_-W(W7$}M|h#tZ=vJV_Uj!>_~^>NRCb3-Z=*(6AhBH3DCK32m0;T}o)M z>}U{vJd7~yIF<#(m(K(rVJa*&Ex<;kZT+blPB;w);|iS@LAEDtK3LU28&Ax_Ke2 zmuR%Lt-L!V3IY7YBr0+fB}28_Ie18AZekkMnhc7loIlwe6hpyKs2-?Cu#%MlYZ-*Y@7J{lL5*Oy6)6dTm2@eg zObX*9X;qLRO2KE*xDtx zO`Hr+*$&`3mg3zCW84es?Fd6$FW4#aTr0ISVVoX2LxD5DmN`a9Mr#u?{o1K36$X$h zYPpz5MZMG*l!Sbis-J6}`_)ws(u}K?#(-_*V!{mA#w*XO=>z&)0A>s@!GMi+7&U_P zBL6km0Gx5(4;?o&UR3eiyARJQjVuO;vqmK&m4gGgUM2EO2#yo_K5+o-O2dodTo}q6 z%f%RD#xMx(WAKa(hTU1FUyG}33|L#$xcP?3s}mXGOA+iNfkIS^H1x%jaXc1fk``gY z0U@E9`MJKfG122}G%iydlT>=KA5a(0ENr~`)vtc_mCGvt73iNib0!~lC#Ui-H+%3+ zAAIn5Os~7^uDd>Mw3iNdrWcPNKmH(%7pFUim)gds@4B9XNPTwazKCYf zHguSG9xlI9NstNwlvH3n7O4X4w*9B|segmbsgCr2QB(97W5?*X|5$$N6|Z6rS2?uu)PnhC+jW1 zY*I}HyHK66tKL!ubGXr)(M>ZT6y>iT(CcP(UDGVS-2;gxa+fi`yEw}oSQ+V6d+sak zcX!5pXHkAxv_1mAgSdPwPdWh{{WKV4((R+v-eSUy$iwDfCkhlHkV9Vd{b3mDwr!j- zFzC#(jO9XpI_J8)WfD^8UezLWF_%g@@n1dX(CA^`iK57mFk5974apVsxrrKZ?p<4{ zR{W$XYPwK#PG8N>Mt`jwZC0#b!L-J)ukD3WwcRt-pR27bV8JFc4x#Af8E9i;f*Lx^ zaebodB2(48K)7B}2;tfT2Iwbz;D-79OuhgmYMP*21-(hwE>?QzyGpUO*jF6#g_-9~ zZh(J62jc;a6QU^af&>^#`scj-VI~)~pl?xKp5yh}ocV}IjpfBxGWuRwA;kDwnG6N7*W&srIs}6FZ6HQppb9*MvruUNF?GD>Mk z6~}5IiyVc9R8>ENJH4o?U`f^K6!>g$VT$Uigq14Z^oqj<{+9n^5CkA=W}r))OL|~x z5)L$Ny4#olPgYgHP%KVP7K?yERVA;emqRqGzl0LHfbw^tmjo%{L64irbG<-b5d4CD z730KZOKbvj0gr8UFx{NG7-WC1asM6@6aS#>Nw$G6U*hqbac{%}^A+80F6T1l=5lzQ zUQ4pQUe%r1OEiCxImY#c6vinjb4&GR}P5LkBN%V}=@_d)0 z11@w%>d%jNfm;xSVd{oFu;T0;8zAYY!)xgSJN9`{RBbEBKgapsxs7-}yCr;rE-*jg z?z!@=SQj?aF(&NIn}k>SJj3Sajo<6aU^60k8lbuk^EsPdwu98_gVs$pD{Bw0PENQ<(%LbHdEX1nJ7UxpD! zAL@1=>hA4yIx1u4UCTx;XDr`kGN#V`liT-Iqfs3uax!(iKXTsq-Z zNOZi3FRWViOFwt3p4+;2i_-+Uy8d7Ztn=E=>lTzA{BB7hMCow>(0WiFen)suAw(&C zn-D8vvSrCEvZj~AS2gYCT;o+gD|{a4XRm7HZXV0P#{UT{Qj|l&d41WOX1;CX{{+V= zzTOds6eW64(@Iz`m2_O1sw1SotRC1btW`lBbW09!Eqx3bZgFa{lL}6GH+vbR| zV69`skEgqoI{s0Q;qWT!qL_-d6?s)}DAHw~5@DzkCM2plGcE}wNmJAo=QbLqS!U!% z9Tm2A8=2lQaM>vaFhI*J?G(?jP$r_ifv@YBkHU>{{f4N1O|#J6@PyjDEggLFxEgik!9-#ll@( z@79N4kFjA%=d^G1dcEHB)_;4uEA@K4zFn^$?e%)SqxE{d{ z4GnLCdro>9kf>l7fW^jH&x+=)UfT3?xpuXIy4pF%o6e@Q>6PbdHo;}1HJ`T{B_=lD zIcG~L525oJ&`omb;*Ad2 zkEZ1s3w!jAsT!AZwCF59mL{<_JoQeOq71%OcB;ZFn1g~a8N-?{CBjqvr9z=nT}MyUmnjK-xi)_&8VTCG+wW{fe6tE0`7U`99q##AW5c|K@hLJ0-Hgi?-4 zk*@@RlZ4_c?%lk}g#<&ek{1MXN(lxalw#5tgk0-#JrnH)00DAkf;EWF+`rv1Sd8`>wo!CQGI@EKjYvIr~b4v8nQ<=)98k46Id zIMM^8puj0!F`2B7?Wk*s$QPwp+v9o|4l$HDq~2tF5LcS$(T&4(#zkqp{E0#Sp4=9j zR;NO;e11(n7{(EDE87#tAoMtwF&59hoxU+(#&Cq_#eclGj}OE5&?;xG3*CdzgoGr{ zHKpsW8B7TCptXgSV0eVq87oTe8bTD+fPVOOou(IRwHkJu1lo3lS-WRDm>`xv zb{w|@k#KL>H>K}+erf#W87$BLBP_?RYg?Gp&%qcJ=F5R^+3^t{$gyJt{3EelmuBPm z@#DugjIv{>!q?L$de(D)Jb<-VfEt;@wup4FMUQz@uX-3gOGN7nPM08StwVuiHv*_X z>AKY^h+NAkz^YF@ukCI}P;=shp~88_Gp1ME$GR9al}qx_j6_`{LEG|^q_xq{-Srh z15woS177<+I*wkB9vMwWz-oj1yIG8{QGxDwOUn$P4{T#AYM(J1RyeQmE}?`Sry5RW zW0Ksl>`)kt08v1$zrjR$5~2=(Yw%-AILLDkCdgb~%cj2X`;A7=XGzru4;|+9b|2!waQMSedqSz;WwDF0 zC}iUGvLe4E)@4owKqCr#bJ;b-mo@5w7pkkGq052|HYK+CH9Pc=e-zBZ) zik;lshma9dA)Y(iP3Q7}Qw?lGBWhEeBzPP*TisXfjyfGk0Zh^$ z_ccXh?G4QD6gd7&j78cR4m;_EJU7|h+#5BoxB%I`Fc-kBaDC%Nec!!&&9N_wz|#Ix zBA-3Mor60+zV3O@>5MkQR$#IU`N(`>5PRYDifBJ#i}z#GHb6T?NW*eQebmhXucgmG z_oH*@z359g+Q6Mg4+Lsasx1Q$Vn~35?XL#};#!v{kG;a3X>Lr$!jnBFPCLKZjq#u> zS}JwNivIJ~Mnzhrr!dpi?58j72XG_ux|B>ph^`;8ZvJF<0=X{>bG(T(8I|Ml*pVlR zCipc%wt+EvRM@L_fcpP$9h^LQQkk(7ufflCB(S}AXS z|NGysD5E<}QN?yBqY5t1^PJm{XbiVAso3g0_uRuVX16-3#u(Lmk38~-&2y$CyB)CJ z)0DBAa&Bc9^Lze23`6X!bC^=gJp+8*!J%u>=}V(1qKv&sinCG*Ts?dCtdMWAafF4G z)s8oRktO@0L=^^Y8`BnlYu= z(5hsyhOKIqLNsq zGd4#3(h%*V*N~LkI)dFA+443I>udQ1*UG;sy1XN9%o0L|VPy=~uXj@7Mun!Ks zF2C%HM%NS&e5X2CIc?t*!Z9nfVJT&$;VS)h%$T}tVbQJ0xtsX)XcPxddQH=S>v;is z1$qN|Kl&WnM?XcsLY-$^J`ygeg>(;aoCQ_% zv?zJncXStO*@Uq*UKFVVg@lc5Fn`KclwW03@aJ$AApy;NozIV^{{t4T1Hf+ZIEjH* zbFCC@F;`JWb-mH3Yes2PMS^n?spkBj1z>eW-~nKlx2RG&ufP!i7VjiLJ*u@j?$G&H zmDbdwknF?^Fm;Zv?dhM&`SV`gh>lK8S#&=>DBv@Ly{e0E}MY zO3Ql4Dbd@ZIjdDqf)b^-bA0BO`rAXP6<<{b08F^83FTq^A;uDnlf|CKgs)oRHpfKw zx_ELVW1+SB8f?J^XR7P&0#ap&18qcm==o)j-6$Zus$<(|0i8h)qK`lWoc>@roJSY| zn7w@DXeKLCeS{e?jtS6)d*@}sRacB01P4|snD7E6PF94~4yBpF`UCbAb_C}X#|}ga z2D8r8mpso4*yB;*i5uD_VU9eKWAn=Xix2DCAp02@6E5X?mzveQ!v(MQ|13?$q-3l) zWCc(dr_&9Dw@$;h{18*A2v9$u3St(>o9#~UAh+3)`gX&dn}Ip4yxVGZ->Mz*@v;;` zE{}6w5;3zi_A`4ZM;mAxor{h{(^W6Rh3pZ!e_=FFL7=;*OM=mxj4&niuBWK>3$)Z} z?R8EG0zzRu8v+rXT%65te5njRlTXv0qf{2hureWTQMg<%oL~GHFGx|mNN&18w>-Jf zZYQWykK&{qTcMl`xIgKrz)=lJc2KunZXLq7+jPpo>L^5k71O6G1_IiemM$E}ELfJs zxQ87kv$MyUW!biBF)3N554=VuC2J|$w$AQHHAPfe3zeeS1IPCO@4@{4dzdN-pu3zm zEvw0SR|7~0?;6&Z26cM9WdpkE6(|D%c%(i8<^s3gM~SB_y3p}DE*zZ7tA{c&;Gq=4=TC6 z%(P;$4TA&y(vyx;$^4!JR(xOj4}bgH-xgw^_@89}agz7qg}Ck^**5I~>Z1*Gb-WC{ zko(bV(R4L zI)qCYVvD&zEbT28JT1(noCa*b1m}$52o;<<@VRn9F{OBZVQ%5l8IMZ^hbOG{NmoC! z&xE%Vex%BLK_*;&daHbIF5`nZEf3krlCWm`K73ueu4|F^i*Q{@$Z%F}Jo#6rbX|A6 zM%y89XP#%;hYs9kB}($=F<~=MEC~>B(oyPsyC`%eX`DvS^42C!5bC;2g0-WrEA8wA z48{>`776f~=?2JIs+?~U?%f0NkMxUs`qQ7DmanJW!*G<4Kl+8epaJxPLoJIs)3;~; z=`S(on8%F8{AbsHY&L&K@<02tKO48s5bOh`Zxm9>N4wj@wlA&=0p}6_d6sDYb9EAJ zQN%8O!&VmB_Ui;PpwqFX0>W4+fZoh<;us^<;ihATzl)4S@+m@IbUq!8Q8u)PqA4~7 z^sAx>&90eI7=~+6kFw|EDR^J_mdc7Wxhnh27 zm2rKjSCTm)I5xVLeDT<^V~fH&yE})TI+BmCC>VxH@-vbV^s;f3_xv$<-oL&1&2LuK z>GNLqJ{Vm6*0;XZ#TwQqyQXMalyao_tZ4TEd>FkRy%oI&eHcCGr4}0ZbW~d`k)x2z zaYqn>$C;K+&D4SrdqDHDcK?S{-~o!+hw0=D9X0cl5$_xaZCjEe z+qCBNVuOR_8wONKVL=evasYl`7Q}G-0hwu+oDP(FG;cx zhu8`OM2#Nf z+%Bo_viql8!u-_nPA)Gi8etNaZTrjIm`4H(R}8doSY&fDvU!zmNKplK5h{et&Wzx% zfWoSJ9hK>csGCXALHB5e8pHZKhFXWN!ax zD_`?{YW3^3Fe@`7Z#r5~w<%S8N#p(%s){K;Qo*}(pC4Ry*=3$f2r=Vh73bA1ooIA% zqU3UQsS(3`=GxP~KfAc(2aD#8yLFBGWm(E$c*CjXh+nPD<4?mLg3P@Id*JKnGQ@FB zM!~pxoT5TIt)eEZT`~Qyn~~;RZz2p!(7}7dU($ZBXPq@vqKg+hX4yme+Kev@V1IiWpM1D2;qgdr z4^M_DYRzObYSU+#dWWC)YZi}!V;&g7caTJDWl(r!nQ6762_VIcIF;P7XtlaeBwZkLGfo#>UjcNYqP7Z;Cci5n8jBB7gTM-~?` z6bid{ztSENzTFE9d5+zF+_t3w4;EyK>vYLaCHKPeB7jIJ^Us05w{| zj2q8SruL3%mz!o3j)pXVqtyYWiZNuczYX-q%-geP&z=*GVdQlyg<+N$Q?Mz$Q2}Qy z7+tt<;nAF7)D2w9WP~)$HwqhnVwXB4*H5UGrc6>j@>aB>W?siRJU6PPELE+f`-W{i z7cyT^JV1}OEw1Nr-^d&BaQS9{7I3XDzxADL3w5STBJGU4n#B|M3$a9@g6s%9{jH^1D(Ao8SM1q=vf^zH^ zIOmi(oFif_=7ytFL*pbhNt%RfT0qDozN+AA;SHT0;)-y_ShFjFX@c*^njy);+dn<+ zk;(*yyJC@y$Rn>Ti9)g7s}~DG3NV;XU^ecnY5`sjA&T-jUDJH`_~l1wRsRhCO9+v< z;iMngQpeyjku^oLjO`AZqRESd5LFiCECnCjUO3PSO~)yNe&6LRvR*sK0}A(ZJMz5s zQ_WPfS@b7MsAoz(gwS0Q<%S550-)elHj_>doI@8b9CAQs#q8|atjP4yp{4ffYJ2I> z7J^)^zIyY`tMweyDm8trFLS2>&~P{vhFQe8Xc~e}f(dcoEKD|vMLPzF?P9Uf|F7V8 zniq3*SDzkfPYO-ywyAD=u}#JXayF(nro)zKm{ger7c2nAY~uD0#&sRcNIJm?-@g(v zN(t@$4Kt9WDj~$ZsvyOv=ALC)Rm8Tc!oyr8pAUBNshZo?vYl4lzl4)vynTB#gz#7SQCrh4vD*X+8c0=1Cp!G=^33yKBRE=}z( z$P9^G=#X`LiIHm~KSWu?%}b-YaJZqKSVwRK2vnFnqA-Cd6*d>9bUINz5NUyB=RYQ2 zuKF@qp(io_GoPeVm5UG@Nh6dpte$Q8#qlMI(3X<7Eg1Ps^w8MthW+kySOPHy)GKenq7(hD4kO99!y!jK&4A$!aQ9sd@WjLy1 zx)xkGdrGng6=9_KwXg6w&hrj z2Cf=mIl@$bPkUJVrU5*fl^Iv#4hIDn-vo9w@cl&yr1Kr`Sdn zy1ZSxzBfzYtMyCSzQWK<)BaaS*Qh}#A*i7nmi8kl=fzPSkJa8)Ot@8DS#^i}1;ePh zw6u8N!2?ByQZOy6REavBV<$6OL)`fCn3LTu9`KCVQgocUxBNW?EwqSa(;WL&^d9sq z`gQbm>{^s$U{@=2;}K%_Kw3;|Fs_AeCds9gZ*0gGl_?^FXE9j{BC8XcN!;GXX33-$ zZpv9B(@89(T03Fy0`-}cBQIkAP0h8V4uuT#xBPa`vb5IiHa=&(R7qd98HZrT&+m-a zC>5F{sg#B~H*}Q{lJva^QFVjsA*HG$6^XF&YcZWq+QFRXKB&lN?H;`zrtm2U7SSTWQ<`7ew_=c|5s zK5q~5`Nn*~l5_dEk($42JI$nMm{<%MBYZZJ zG!c*o!Bb!Whq_9mP6)2GZjV)EHPv^mo{mFRo7r2S1qD|Nmj4 z(bY6f>oz7}Ss`D&&?hHOoPZ##7k!#{MsF+prlHY~=i`Yf#}fuzFByh|%ZDD9o`axP ztGO94SmlkoA`W{~zN)x4z)%V6hf6(>xRF_{nvvV=vtP;BS;l39TCZc#q`CE8382&) z#4gYi?w&8!dm+ytA@J=eY~Okpdb{tej4HjNSkIUtbj=gcj7CFR#1^H`t^6sU(CIAg zwi`z4&$iA(S`4$zEQU+Ar3vG-oOka(O2+@Dn?j$~*}4K|tjgdYY`}m6XDAhUFX=*5h zYPpZPg}ishbw84?R;!+w>KYyYbno81jNM4-tr~pM_&GO2L8GbZSF6?BM_l)eTgZDK z&1o8ujdZ&^j6c11?_Nr8WbB)CId1*W)(ERPjyC(7=tcBBxwtriRN9LMo6(Cfd*>jS zXvMAgBuXa|0y%51?BexJx6f~u@J-0zjo$yG7SqLci`4x&Bx=`szy2{hOP!VA5^F!|wPg{7i#3FhB@9d+Xt~YyddF2;5uT=eXp{cwfIU5EsGvZk$Mf0m0CW zx{s{*h?pgP;701P*ndC3ziliW3}g#iYf7_nu@3DSw*>w&jbp87**JDowL76H3MX1K zmj}*$iY2C~cO$_kj#Ku{YOCNGeqd2*QqAViLB-+}SmVESMWoxVh?vu#90DV#R2GbU zJ_2$yHKyY+#NZ$~&*Lg%cszhGs8<4C)m*^Z6h1eOs6Q8wLSbU6ScD1PZn=UvEmuql z9lJ)c#dmlQI*abH#4Mdk;|`U=Jz0KH>e^t9a8D()kS-n0t*nf>K+NbMMr~VfQtRYV5hV2=Im&6NC2uQN- z%6HbD!X+);_ee|kADsMC%>Vzb))tq%e%T6l)J4^Gf88TDwp7>G7v!pIwIf8f##>*p zqiL&)4xp2kM@b$;!&U{#8)H6a6D($}DJ2cEt_??z@aL7%6AP^fxnl><1;I zV4NndE)(1=J?&c<%AQ99sjd%Teg1VfxWs;yn}k~@W)_;_M*S;Zkrnt6(TTYb=yA@xq0R`7o}lGf3I0-`~YPB?!4agrw4nxBib z8HXEvIXzIYBH{UYEGbw4U`#zmjX#NYCtgw#AHdbvcsNPTX<_5QG=U$ZMTrL16%-S9 zQ?Nw+fGlE3!g@{uz_yGf$@e7*%Qgm(a{3*MPa8}3-uuR2XfXxP&SHg|9%u3@g~h@M zpLBzHre1ZOsg)?0X5gCFsXDX9Bb)Fzw}-z~k~~?GBrA|G_E!3r8cTsCNs{a>&G@Il zM@UXc>y7B`=o!ECI~EBqW>G$iPEFv50kn@ZT8>MnxX+4~Me3P>z8oB!;P0eQufV46 zZI_d|!T6v3HksSI$Ewx}dF$AT`rO_<)~4s63iWn6GYd(0-3j zSh*4rQY0}_EAd@+SRzG3)Jz_dTG5zi>yboj*52IqgSFkY-L-8&I;9?lAGfqMliN$? zeiH`pBC14rmuTHVgx+$+kLwsXndK#e!K1tfP!0aIw9;cH25~%1@pN~HKyDxV_51PV z6Wf*zuyzZHZ(CklTV57s&yb95%WEx#=Rz8SJGw#YDVr}dr+r3RC=C(Nq!rk7Q*`;b z+NrdGfPez}rGGJEH%;!GnPwK+5gR0Z2_-f?vl9nj5 znu6cn6#VNqzxho`BlI^HibcF_<#aZ`P`X8y6}@tldCR8bALx>UHw15sf}s8ggqMyU zJzCLaMZTr9FrS@X*@lb7h2Nw^lfH>14)BtGIX+@^u740iCmY?fdrZqX6dyXio>xXGEW{>5};k? zj4dUJB}GblD4GN(KrmCx(s>WnFJF zX%|-fE|(!2EgFImW9CnP#bEjXrFBT+RC`D=5rH3wL-WS}ySlonL|)VmQvrZ&7c=Kby0*v+H#;1nzo}3Ve3=q2j5^x zK3PjU4W#CjQszgJdy;#}N>*Q&WTV>8S44$c<8!11D$N6p)8cds;kmi}DuDcr(qC@E zP{zD0VURpUlH`0|mLyPo39ww^Sbj5>*Xi{=%O|ir8X04cj|8>|powr|Mcdbr0gT_0 zKlppSo|KnCk|nn&V~jb+SdxmaBugOYB}rN%>(9zCVv#FO(InW`*@Lj&%7u)+KGHJ* zmc}rbc|;V@NGdSe^xPxhC7j^3(g*^s*!5;IbP=#TU4J>jrRBdA@0s&o6E7~t#g;pB zhFGRmsWcYjX2*UtwbilLN)j9ZSz^*#Uw01_`3S0Xw8r222^?H#UI;LR%IZ`VpsJ|G z9J%VaP#tsy>?Urw%EC~YX;*)G^n&dUnnOp?tGwl(Qo?&Q{$iG@p2fV_6-$4&cTAo0 zKsq9&#?haOc!=;-47XCtgZ1%9h=b>@^q-%Ag6Es{?S}9`iS+cu@K?wx>-jOKrqd8$ zmH*)YR~XvoA30XOq}~)5;NmP0D#j^vNl#TLxa#zNZF)Jr2vmOQS@KVQ^;kWJxUZ@I zzTUr)r{tB#+`%&(a?JBrwmbM`4%M8Lwap=O$%8mu^({PI%22v3WdZ*8aHOD{LD<^u-~5ZyH6s-oOio0y=4sF%5Uz5jF*R-H%qDAA%p&vNbB zs=}e@i;1u>dgcyC5s5@&;nyvA&h>04gWxd%Q#1k2e8eyU%3$Q8m)m4<6BukkHCl>bTsBd7q8Dd>JqdTgm~pbv}?Q$ z1Ka)sPZ%&5U@ntCM~02@UU(DgqF10_^-N{QH8)=kb(-OAXzTu zHwP1hdyMpnCkkwy=Co}y_wfE_z*ZKwW?i=|F)`I{PfZBR(sc@^5T+{$qiw_AC+j0fCU~Y@0+XR;MLZwoOwUTSH^rERxOz37& zw=D7HPN&n%-TAUDU6_2e)9G|pxheG41J&{kc+JdLFoG|Qd3@1jm@_!UM}sf~xp|lh zR0nge?-!9YJMDI7fX?u6YD5#8#ZsX#t8+%~wa$z1R(bvB#9!; z$zbVWtBJMHojBo+i9^`O2e%g5)Nig6%;^A*Yz~JW5g1fG$g-fW01M&`hnq)WKshGs zO`oHo&i_OFx*(}9*g%$gy{fk1cHg!_i*qZqZ2#{U>PcYh zYOl9Mz`m{~O>|fzRN&CP(NPtrgUS7agli<8nkyaDMIH#ZvZCyRua zL=q0nfJfX)#fV#q>}TMG#Z4nO((QQ9ClV+=Hg=93%WDZUgG8WI89iapeN^|i7fOnU zHWwH0W6@IK6(KPx@>6^*QM91?NBmPBoLwg(MW`YRs_%7y&?0e%sh&IY^?aoLS#3ju z;sf>5nE0mE)m5WcrQ6ut z+$7>sg-;(ndbGLOde-LVW^=Ro)L$SgsuUEpq zPSH(F<)!0;2M=nq>WWLEDDvFc@t!ak48lQp#n;?p{eC~}hx+%({?g0&1BjY8kfOwS z!?_K1(yE921jaI4B>qfMmL-9!3Q-p|($nhys|9Y6N`a+A-}f7-?<>w#7}ioHflyFN zGAY?pE-Q{|ZCR6hxn;YrUf0^PHlNguV(1Sh@D=6zX~XyZP+OpsWKuGz6ab;1WTh5{ ziu0G@)lJLNdNTie*S7d&>z1YU(N#asi^-r52x){a;>MkL3|;HFLH)Wh14ZO2JG_oq zniVs~knTEzftoPH5J3G#gRV_@EYS}4aJw876T?2kc2Lx=*rB-$lxo{KTnT)NPb58c zo|jTTsQgN4$bk!aJg63O8;RN>IH&yVzt{TVxfZnWUzWRW$PXiZKGr8HuTci!(lc=3e`Z+ul1-Nz@Rg3TWABL$~{DcBBm;fLqkx}99jrO%!9)ehSxZ)}^Z-S%v+ zcIo!!7ttM#8bQ3dtU5p)}R8G5tMU~r=ktg0e8?MasTwj+vt`gH1n zRke|6mV7SMB~E|bZ5auOJkmu*v6zWm@*r%jx-mN8W*B=*X4Y=G<(6zOy+Xgg_uhM} zcjR|C7}wJy|DIqkuwDCRpJ(9szVmfYTX^{I%Sg^Xd$O=5_~%E`l1CsU9E`6PBe1t; z$=p0I;VO))wF<$$r|}fLQvjBIO|@RDZ5N?rM?VRryT%NeC7TfP6TAi3W@6E8ynk^L z7G7^FnsUEO%U|dVMc^w!h9G7dA$JOgjma5wh#k@hn;YgjO7YVfZiVjQZiw}PO?KhC z<1pQkFF~(BuSM^mgcAxlxee}mLN394@~E^7pdm#g2gU*h$jqfKOMsn*r}O|&+yyEb zi^gV4(5I;l$fOAIG$RSQ08GBHQIh_MMX}aeB@t8g(6Rk`gpD2Wt;#Vm?KxRe_iZPv z%J(%IjWX*~5_u~%Ib3BwFP`p8DMWFzo3xr~w;4yEQs?Bk{R`)EYv;?G;iPN|JHfCQ zYZf(|5mQMuvi;DBA+YQZ<^&f~-{Zr246zgXc96bpFvtUfrgn^_US?%BqY158M)lsm zYi(hnGXF=IQ)Oqk;@n+0l;D9LKjRG1x6*!(w#|lQoVa|*xgJ>ZxNZm;CvevYi2|Gs zQ2SI^ih`L4paiLdJvAwd(Zprm;Z*Otuj*VITUNid=%@Z$!_3x}u`R|pGmEX&Uf*(E ztJhy`EgHrdvuv}qWi0#ts#|8U)witp+U0t^ezUpeueZ0iottX#sYI- zE@jLZ+hSQZ_3yBInq=APv16-QMozoTvW;PkX4yZk+vbeAr)x{gOK+Ww$K(7+tyZfY z$;W-P(HF*utRzKwzckL^X;`vL&>?=EvX#wmPCIwB#%nA|TL0Ee61Il5cIMMVVHm3PWz9v8G$Vm8fq_ zF4AMkXoGtUgK$BlBNmN!N($gqMNBxTP+JWLz$CuKceKK72Q)6+QOz~02;qUr05|-< z?x5T4wgQWi1B8MG5-B^=G|)<#KvJp%^$B+&5?;&I#CwazW;2Ggmup&9H$9_t@jqFbT+cnO29gxV%ZeKR=UwOwqW zt5@J-QbGH6F)S`+0-KBX(|xq0x9O%cJ{_4yKH1h^r6tp2PB3P0FpKTO_;s5S4v^GV(!`XqQ(WqO)pp;4cN^)Y6(V^ok%r64= z-goe2#~D(#cp|CziBSr0tvebmFEZY#7e;Hs{7SFaJ>dZz`u@T{!SvW}>j{E)1t8&*YB3I=Am1qT=_p4DWB)M z7F|b;z%$idta76K8!rZ;Ayv32I|L!v9JP(o@6Lwt!;j;$A|#rDk1~}!8L%SR2T{t{ z^?K-NMVM5S3Ss9K6P68aQpyR(gi-m%{${_Oeo(W8pj1gllybv*-TwL*VHb3wdff&v zZ6H=z{1_;`$bE>SR=NqXWuVV;hJDA#1LS)rzBQj}aHnW$q@2R5+L{S%KJs~_cB z`GHR2sPocFwGvbSS_6);DQvDx9)@_!8lEJm$Enihknd4VDaC5bczvvxMH0n?hiXiu zW(IEGqU~$0xdx=P;rm9a6V!F-W`nvet2e0Y(ng)RF8fpP)ED&vW|5Ri8cgQVV6g;t z&ba{2^IdOu?(O-N9DNPdFh+(!gT)E#if^SdtN?SS!tc0f8+AYFP}lIwrqkl1J+GrdbY6t* z&E}UkU2v_!f7e}inO18x-@Wq4?(S~R62#Fzjmmt@e0O)(#z8TL)goq* zM%(RnPw|M05yu*tVrOQABWyesIiM3~V+=EY0UYoWhKq$u=-OalazAR4THu3!t+&q% zT%Je;_Zyu4EQ~H$C@?gd<^53#fnTczrz;wK)5XNGwc z6qz>daIJEXkJu<-sA4AFa{O*&+TIQ~CK!xuz~&krsA+phAro@lk>E}Y40GwfC3|;s zria2lP8zV77x&>JyaD-WfR4l!DD!#Z z_Sqy!({YU^mlVp{CX+H5O~6G=;fj*|)^3qpO#iU{a)8e{Qh~^_1E}} z^I@kmZ%8s(b%M=}jSbvfCEQ?>g#rPM3ME@_B`CoHrJ^%CTh%0qKk{z&2eJA=w3~^-XOm7|(Kyd{9o3jkX3Box`IHsCdElo^J5Lq&E#bVBoWHP}m)3{=F zbya8-^Mr9uCJI=F7d{OSDhwhb`Whl@H^-6|cLb&Bu3fu0A5#+FLC$v(l2R%T%wFTKn2N%falYcXu2VU0Fs3NxuPkpHKpzKV zDFoZ$Qh-~5E9153LwJ{KMNk+wu8&;LbEE48V+s_t7@Dmut=Wco_J_$b zR$+T~cKng;`0cNHm7fS0_VYI%82r^pnjP`Pz`GRkm3ln^h`SWg(zi=FwqzrZS<1TY zQ3I*haQc|XFI>1#dNJ2gzQ8aR@zj~^+p!=*@Ml|4{Z>`9zPe4gs%iHzU%PPOLV5ST z4W$cQ2qN(I?PsQ9fiZhzNF{X;#hJE+c=RVlH075D&Y_Mzl7>N_!&zj2_}0?swLNDM z^gjF9&lcR{?wf>(?8t&~(iBm?Hn9vw?s2y;{=L=FC{s9-Y-gFKG2Z()uSGBBbV70! z>PMI)86U15rky0@Mq+wpeu^ZwV$zOlW8oQVqz~6R+m70U5`5PR2N@(T+S&{;pTzb-Bb|(3 z#523K4IM={j-M9Z$&+PSP2uP1oJB)m7m=}L>O&*-$ z^*7U(P3#H|Ik=4lu1J!kVaeFRn67VUs-zl%oGX(rV1)#IS7h$Ua@yh~qQO55CR4?`fK58!8cY0idQcR%{oTRV`M_)N!~Z9p3H{4kdys zG;_M=AC_be-A7^LLlHclkWsuZTM0?yv_tI8wA&`R6*v}3sqo>c#kAGF1*&q(rE)`Y(;?6fXiEV90dK zH33Xl!4-??RxzZDj#Y5Q1n^yV`35HZ^I%N3o{8f)CX9h;f~#nn;vy3~be$1piIab_ z^tXbm&YGiHC&Gm8>g7S;cxd*A4{ z(apKj2@GUc@ISW)#o{E(ybt8flFpPf-{Xn29E?@IfXXnoSYu*B(t^T~--4p>hV~Bs z-nMN~JIAJakrN)2%W} z?X7pe``rZlSd1Y*1^se5B0gt!P4e(|mtG7zl-HdkPOfozTV11J{MFn^eBW_WemQ#Y zESpxxMUY}%U$bpn%s;TXxvA7OnlA?(yAEe6A~I%3j565A&uTpzy#zflmVq7SX_8JF zd*yN@T;P2(q?&>Vt}saum?)Kcr?|O}MWSG7sEGT^#atD%?=Xm|!b6V6r|vemo={6D zE%xfBu@=#xtAS!R#CX+<-;}tk7!MhyaP^@`=s3kmf=g~uVzH8`TQA0lQ4%5*!E=gC z@x?=|Fdb}~*>u0J|}zbz&do+#D}QDbq+!W2{sR=jnJd+5D| zdV8-1OUI8Nf8O+&ue;%f8-m4qLUM@*mxvtz4O=-_OW}sHC-uo;8S4uUJ5#_X5JBUM z1VST#h~w-pYuCz8yb76VN7&ul+ndfl`_rHPv`^!-O)2M@LrBDImW;A6IOWHlc>R{Z z_UFD8B;yt(l=L3m4j)p<;V7@nRo7U`88DsXNP!2=bUJM>SqbwABaeU><>^x7>{Cw) z5-Nxr?m9_Y=geR+r_lvvXkrS7J$w*dhi-&(3gno1{YXn+uJ9fgG?_kkP_!X|?6K)q z7>J^i(${k+MLdqjWf^JFw6AVX5>mm?OKK5Q+5_Zi#lkG<0aOXm*Y-`#p9fC^CX$0Y zI|mcNV0z%tFI5=_FVG#{h;h`taw&6a^m{zc=NMB zU)AweECP=^s1cExsZ^+G{hnqESetg1*0TcysoLXUZMy_fWo`WIKyO`AjeRo+(T77# z8DX;Jt32_&fMEY;KMPdJ|Dz`W4(Qf^V+F{3&w$;Zuu%kUMSM`|JMy!l<4PA zCHVoFJqEs!&J8!}CyDkaSlmXX^bIF%8X<*;3O3end4m{83F8St>=$*Z4z!fL-?A)~ zx!AmN!t-#N9&rqVUFk^SWyZ}s2lko`D8NU3->29OJ#l%wFyB6srg9ON$;Y}9!Two* z%u#a5Yc@MGb^Mg}ui$;~P1N)iJGVp={g0{NMrjITsYjF@`B36S&C_X5&AHux?nhx=3sO2aR~UBvw$sl2{#itj^&p2XP0KuISrn>5A`8c0iko zY_6UaoPw#3peBm{ec|eu_rZFo7gF7CF##0s*pa4^BH4;uaY)f|KuXgcJK|u#8;|4N zO^K>;addYa$3=!?BjtvPqLFbWt{v5*LFhXM?ISf^a}0U2@C(CAmnZ8Oy77%+bjh-; z$oUw>+qa)-4F>IzcpS)oI{7l{|0$cNk5qP9Pq{|d`Le&A*@r_vR|?c7<4bSG=b!~< zG)|hP9S5F2$SnAuTJMjS0D0XVjW!J*8Yri2V9<8XHPgNlY>&L)P&2=2*S}St^cPCsJ+CBQrtwXEJ0h>nV$GzCn(@O{3&L0th>W4d$C1DK0 zD0f_~;=k+GKv^N9$Y&!-31ue>ZP=1!kna$5bBBJtUiZ`-CO4IzzpFQ?^va-(8bi*6z~r@Hacd;iHJ`&-yPPDxv{ zoEP+ADiQO5*Y`=HN3BT&RWlYLBN5Su6|sB)o!{^>Rc}6V>y)udLZL*;Vrk*l@rUT2 zDW!J4P~K7UJ#fmlW7yz(rCprs)3vJQ=WPp2k#EG<5PqrXc`8vIJ6HFQeFBs;a&N)k z%HeU-G;@r}x@_C!yi>MwdE2maWy@9#sgO6ovh(?(@9UZnQle!`}sIRrRAnRtM?#xUf4}LH`)9cMl z2R`HrGc(sJW)|bwnccfqx(f@HGQjPOQ!QRhXH5XJ zIX7E8Zgo$-x!wnX^k9dAb?t{(fDM~UJS1}mL-d!X`o6uETo0eYaf+kSXtWui)bnmK zsGx=iro;1vhe_0T#KBl^v*$j1YJsJ{tDfGuvnlrl_uGV*VTg)o0?nZ=I*wVdg7DLM z2e_rs)auNs6>8=cU}|SxxoB~2bou4v8{Ww^3gCLJg)?UsTAm9)H9mgm6s+OimzC|H z^p+v*>oBP9>T$oJA}iM)9pwjac#~>el8i>9UdOmzZ!`=^;u`HAd`cyods~G|6=-5L zKq4#U!bb3D#4xqK$h*{`{t~9p0tBtA1QQrAK7r;7%^+yuKyWbmGn?^%E~}WP+pm_o z0l=I(yK-NjU!2f9qfzIk$(jADrr58L!wFS16l1X5N^^UnOh9y%Q&$@(9?$gW%%_Dq zKGE(>&7s{WpQ`YTp-rCWc??60?ZD&^kwn=C>f2Vg(@biSzI+g%s?=Jp*8wA3t1q`C zmAVme|Nm!!YVP0v_~ZNcn<`kp{42w59}u6Xpj=VvboTZ3?psl zTeDyY@2A7&(s?#7Se9ibd51H(6)u24kSxRCQhbT9grrpEtx<4k^B&}SNB^ZcaVOF5 zWrh}Q^UOjqL3|vaLx3JcZ$|G&8Cpu5`bpG(M3f4uw&JD(q}w|xG`QtVnN$JM9xma9 z1EcaUWMc{PbxUqJ^X_S1AdvtdB&aJS#Br}KqzU{(7>(9cCtex5PM!=M|S zb49@*aS;GF#GGF%h6h{-q3i2L|JU!uJuKV)zMae2)~6>9KHjw8`Fs@R$HOqpM^PU7 zVR%s2t%3UVDx3?0Lm;>WSW)Ku8ekYJs$gu*`uMev{}0aP?C;w=Q5|pU!Y4l9*0uP@ z2cvmv$59c}BKjQ>g$_J(d%kX#r8RgZ!zl~ruX^_%&tzAJ@xR@6+ik?M$ZcB)B2D99 zG@xlGIwE;_#vlCP2L^!g1Ac)z-($%jZabVlBEUJx}SQ83$##`53du^t++=^qlomXFT$9Bgs zbZRZGl;CUBp@nj}FuSLeH-P6jXuYv7aITqNsigUQ{QOtG@|9EGm0Bm~7r-zai*uQY zj!!}LOLYZZ*T{gpz02d9UY(@;?sd>*HJj-wGLbYB*R`TE&7>Z;Xv@1bOcjyls-HA? z-;z%_23I%$yT~19EnNpPzi{N}xxYhBI$BX~mWWE>VYoGJ`SczE$Ir}p33T0>WpR0V z0IqPuId}BP!aNz@swfy#&oX{JRaJcRcQ@oi%L}a;F0YMg3oWBJ#%K?k0;6_QB9ek$ zv~e%04IU_-_dKp&_eL25j;V7D#u3$-)ufUpR z2K|k@@|>(6#nPFs(l}~n{?k(?=(h~_Q6OofmG<2BN6{P6ThY5lr!gSOF^3Ly$gM!f zQKHKvT>*_s6OBeh(^k)la5^voaKy1^Zj&%lD730 zqc>nBijaOE{*e%(Bt)rHl4V)XN(!Y)aaosTdG3LW#o}_YxLg!Mh~l&mLX-}aN+t1I zQWDwMiW4hyr;E8oeG;+*HTmRmQB#Gmj z=uR|%mK5~yrxDrQONLQjuNnHcJNbs7x3aS0(+OFgp#F+1CqKf7EHk`)Ld$waG2CZ( zcF0-QW@*xiO(NiP1^V&o#jc@Tj0Kq;$eLxgEDx+t>6~eXUN@1qKW(-muoMpE4#$Un z=>HJf3XES2$xd`62b+Vqefdz^tZqU0kP#-i$GF}1YxwcCm!uu6M|<5syoq0?xNFxg z!?MJw5JP^)tC=Tec1%&mmiWxh_^n|2+4O$aK9ny(yjBR)Z^txEb-m}7N_O*=J9zHE z)=O!P?9RfbeQ8tsgS(=2Sid0X41I^>wJn)Jn37GM3?l4&)TI>uu#88L*S4{*)3kBC z1LWB6ICP+!!XK4|Qxc0`6wEgcFiVFQ1nf_SAXcgRA#jS~~56lT5%>)EX`lRBO%W*L2e!~0e{qjM_#U`t052i)R{e+1_z-?AEaR)`#76uXDo&tC zhjGZcPR^h^v;`{uYOKKw+NtjosB-0T2oP>ZX|vZJubI}vV>kbmq=u0eIZ(Wl>tLF4 z0de^1vp?`rMSANm_3ILuZm~)e1F*)s|G35%?~-Ym&z`|QMnk+Bz^7&Ye)t07XuHqt zoNKQoT;@>IrD-Kztgn{lx5Gr1Hd3Ck0l5{z#s&0vCuV6VB<1qo(tR&1E+*BA5ud0t z+v*^7Z3`*RxJML@FM8zE`uaK-)cVeb%LHdyEP}xWoo=d=FltSun5gKGzrB)qp?XEJ-0#5D?{)7@7HjHZ|AJ@vY-EX$dHsCN-&+h5_G zyvGQ*OSh$LZEbxuMJYI5+D732b6N0(zF_#P=O(W?=5w`QK%+Kvdm>8U!t=hsfX}tZzJU^NJP`&vT5ttx9a>?bI|dii%rdw}HA0jv7{Y8TPlUA?pR4IV z0r(86l_o_Crb;u}U(C@%5iMS4X3Zo4?z#m)l?ZG94EA)Q>!Ugc;#r71zQIl>vy_TxD=wtss2%`v&Rp+-x?h<5yIYn;x)(aJ)vM=5bSQ7!Bpqp_ z zR#3VN#W%hmxBMN6xP4XY2c?Tw+s`%6tF}Wu$c+5C7qRZO_rDFqeSZS)5ng3t{+=9{fL6un%jKll+^&kJOD>{>~hUY^L=9IO+cNm#pELAo{;wpNm=k z6&{q)7v4nNT8F`m`wzu`zZ+jz?kVd2^_Q$@f8zCo_Njj}^n<@vcwqVw$=H$oh+Gbs z)nJGwV~`o<}O*w)-V9LW7t^oYm_UYC7LVbKeo+WrybfIEPQIp{)6bV-E9p_Oj$ku&rpE0T! z|J{|V)&0a?>buQm({&%ARIIgB=p4X7_acQ^2(qeCM1v6h93j{|A4mI?ID|)yUiweAQU)9WKaG5ox&%!LAd_2I}`@U zXan)wFYopVbMX*stqtL=Um|4Zw9zIy>{B)BB~;wTD=;x-Ez7mdw6=y{A#YUN$e5G0zO>2zkMBO(mX&0CZb=KaCM($awgOP#nG2}{V~Ok>xs zbLV#LnwSk`VTq^-1I&q{;>pSOQbXvvu8YP}dvX%13gP(oT~h!E({;<7>dwkcQZ8d$ zE+@0w9G&vA8zUvH0Sr;YwjK?Vkp?KE=*dFQm@%=1xhucCv0B=|*af%$n|~i;OlNGz zA_Nl-=aA1=t3g$PmX&DOn6+!y zT7{Ep=P+!zIAW?>*JWKRCeCm;0)fGfx#@5?94k8d=p0caH3Bv~YVR+&oOS@ITW zu{pRnpV~SDEL;zP?nCTMUcIedbvVi^XDNLdNx(o*H!1)|-Y?Kq>3>7Y1z>!(#IS zNfomdbl;}|fY)iKUJ_kluOP4(8N=xel1S7v?R3`EFQXz=C{O)Cv}R zVMz*>#v$(#EQ`YOwXo6yt5(A5d{`5Kuk+wrJAAhiey9RJ#^5J6{Cfn1%Iz$0!PYoz_rs1^5KhAHFW`SM*trrSd9dqq*gXyQ1Yqwe z*#8b32*W`;{NaH^R*1#m&tnje!I2moy9p=q;M6QQOBaRAtP>~VH>__RdP{w$a8AMr8lzj@lVMW$`Q3QeRZL2C6a*RrR21c2xZ)su4vsgQ%7V)h>(bR6%tI zqIyzKg zxu|s&)W(O}hEcn7s6!Lfu^n=MjyeTV=LG6%Mcw+M?k|wXi#|GqK5l}%RnR8|=+hwj z+=aeqfWGvjo_5r08tNTHeezL19~xkwK~>P;0yJbR^2N~57ihQ@jWnZCUNkz0{5R29 z8ye?B<4>W92AY^efjVe%Pc$WjrWt5@S2P1Ovj&>wK(mjbxp`>54=uQa76s8_4_eX+ zE%l&0Gg@Xv%buVWUbJ#6TD=jiaigFM1;gm;S?HUA=-XE4yH@CX(Dy;~!&a2v75y|G z{hW(_j-b#@^j{bHWhE+zp+i!R#HMIX8Z zx?C1riK44sF_^yq7HEM5&tTy&SojMTnSw>ZVj(OZ!s0cs_#7;L2TR1o z5_7O*2us$$l5?=*+(*1=9Gv2z3LvH-j0z-|Sw zdkO4*2789E=M(I81bYYA`wI3sf_=wezYz9Yg8egK|2;Sm9C!l>( zPI-gV0-W9fXKcY)f8y*eIOhS*J%RJq;QY8ae*!K57j(geEpSl|T-*Z}f59bFaOnqJ z)&-Zxz!g<+Wer?a23Lo{)gN%p3tU$Q*Pp?Sd2mx0+*}7Yzrn3BaN7vnz5sVz!JU0@ zR{`9;0Qa=Py#en1f%~rD0q|f#Joo_*oxmgDkqvk>B_4}_$HU<9F?g~Do(hAfzToK( zcs2r_JAvmn;Dru&aRpwQf>)N{)fRa5f4sHfN9u+=~ifl*4 zLa6vCRN_4<8I4MYQK|K)bR$&eGAg$lmCt}G_)vw*sN!){={%}D2~`Q8s+CdI_o%ub z)o6ukHbXT}qgta-t;eW#WmJ1Ls*@PixsU1|NA)HlzX$n)sD5r#|2Aqc2{kN@8hKHp zFlrn`OVSEgGQ~-%-nG)M^rHodva?kJ|W9+n%Uh2Gs64Y9B!D z-=hv8)G;yYI30D$f;yc>o%^CL5va>?6aaOLkGj1_J^G@aSx~RSsJEfs=TV=YsBbgW zuQux63Js`^29835JZR8uG}wcNxM*ku8nze>pN>YvM?oJN8G%NI(I4BO?;0gtwxih(Ui?7+(Saa3I0_vaiVhn({2CoOjl###e=hpJ89F``ohXk^?nb9Nq0{lv8AE4o zqjTfX`Pt|~J9IHOy3`k4-i)psMprANYunNF>F7pvbmKm{8AP|LqucS(oeb!1Zgk&= z?uXHX^XTDb^k^u0oC-Zzj-KvE&rYKk8PJQ@=v6!P+K1i@L~o0ucOLZK(1&RBF*o|; zqEE-s=bq@xXY_Rx`ZgVX51}8I(a+`RS1Sw`i}4r}nAT%<8;dy(i?tt%-463S#^Nr< z;?>6DpT`oc#}dxQ5*@}8U&fLQ#F7@ql5WP5ZO7d6Sn_dLiqBZ83|Q($SQ1E=Bth6n2qJU zkL6yB<#n-qE|xzPR$w+(C>krg8!K`hE4Cdg;l)Y?vC{jovRgtipY)k{_#F z8LQG4tC|6;b{(s+7^_trtK-4yHN)yp#~O^o8hNnBUaX0WH64mI3t-KISc?d(Wp1qH zbF9^OtaS)$!-xIRvH%K;Lg8gl_$Mgh9~5~QMLmO}SD=_@Q0zA-?l4Mdj}kvYNxz`v zB$Uz_r5;CVFQD}4C}TFt?2odVqwK{fCxmhzL3tsRUxW%apu!(eQ5-66j7rX<<qBTGXwzi0={(we83p&FJ3l~oh0r|#bZ>QZ?+@s{Ai94x zdZ0IY@E-KgW%Td^=#k#&vCim;IP_E)db%@u_677@2t9uoz3>To@dfnKar9~wdhH+d z#s>6ebM$s^^v-znP6)lb0li-aeGo(+{((N8jy~;;J|B+0cmRF90)5jTeH(>-Sc85% zj(&Lo{r(90V=?-(KKd(w{ucWC7xb^tf1}a=X(%)pZE23SR!7^aqwRyyj>TwK8MGU; z=O46p4cZq(`}d=R!_mS0=$DNPkF0bIO&){xhxW{DNGYa>bjr)N6*2jGh<9;9D{`>KO zKk%S6cyI_0`3Db+!o!E-5g|MZJSq;4nUBZ(gU61?<7(q^FW?DTc;ak4@i3nB4W1l^ zr)|cZa9wbjuYDB#M(IVJWlS9lYhY}@8Hz!I4y|N2jh$)oYfv@ zeS))tI42C}4#s&=I6s67({NF5TpYl~hw<|Ic=;E2<#@dEI9|IQug}99R^W|ccyk%N zc?AyQA%i;qA9dji>Y|`7RZy3op{{&KT`Qojzd_xchr0Ozb?YhW_8!#j52!mIPet#mDK7Cs@mhd~&^yD{^#yyoDvyKR$zbb= zmU8an^H|6?J^wLU3B2U=F$3QG`0;U%oPX-$J~RBOk0%)Lw&jyQKCY;ElOJzkr}zHj zGdSRH`uI#z-mj0(W3&JKy_(Mqe`?f3$KqhJ>SSwqn@pI{$?y?vO;dlpf2BhvVy$Jj zX#|yB9jW$IZl8{AU_$Ba%%?Kh?)H2(*DhP{7#&`%(j#}8**0N9x45liBG!7>7I&^X z7ulY4`k)D2@vpzpmnxg)o~o83y0pbg(^w~wi4HZ2u>@rkiq%evVMUxVje6ix&z7EY z+H0vWIxzdUju8|5Kh}W#9l}=1Y(tp95YGMj8|DZai=l~LmIxj9Ok(t$#k(;y2&k|l zTAZSt6gl>h<0Y10O9vM^=_F5z3|YEqCr_3f?aw9SqZJQ->z>D#M~SyR%fQdL6#1VH7ilEbm{gI?aRv2BsyJKkO^CX1=}#lL5sF% zOYV2>ef*I8kz^_D_wKo0=bn4cx#ygFo)~A0X{^O;%zWs5`=%fI)X~=&;}d8t%`KHz z;y>8^bHI7i)9nKRWft-^BbP>PMH#=U3PidlLOF)XnAcQsq$U?%!oBf%WVc zS89#sAAJ1R#~4ff8EANsfmWNt!P<9^|K_8{Z9iik1=AS&_spxnA{8dz4 zOm+i3{=eG!25=P7|4XGvEn9vm(UM}n&s5P){d~(PJO3eHWezkyz)z$1UaR~W8^$aw z<*@ZIa(@qG?9SuwdyuVf-udPQNdsuRS74{t9sG+ZOh7I95XtiC{F1ZIulx0GJH{26 zKG1F|McUaw|GL2CM_ZUnSmGzR2%JTh!lPPGirVn}ri+ueI&GS#u+FdWw3$Rr`wKx& zh&Lf-7c#p8wAm-8{Yddz-DzPi;QExw`rJ%`a`fym@)^wap)H{$%riu6V8_ zuH13uzAF!0IdhP=Ns$B zw~Q|sPZ^&yE*g&;7mP=YlJQaFL&kfI86#~Z4Y&TL{v-V#^}p9&(Z8dATmP2+lK!;5 zSNHn9v_;0)*#G1AFnfl3d5K@<|Et`rykeWS{i)q=zsMQzxf!gnphIQ6?3;Z7jZ&PfNR8=jgflM?SO+^!#0xz}Kd9l5& zyYbU`QI_V(ldY2{Pk!ZO5^wSP%1OSC0c~plrwJF@Kjq%`PoI49fhX~s!Na|xppyng z>HM{z@g}y1v1q=K$;c;}N~WBuQ;kH$zwC6u1^^A{DC7%28cn5k?^2X}{^5u7c}3Z^ zJCz#MbY0sq791K1j>&g6cA#+Ut!Xg=Bh$CuS~w8%dU=EIHqG5!zDZEf#Ll|Hz^gJU z$wVd-SJjLYYiBdzOd^xZU|DJjhoaF;1O((VvIjI$3+O9k@}QZ)O5iYwu+Ac3gtza<4nt zt=Mfgk&Q&d8EgckVv9sDNT+wcU`u2Z$yAQW#x}7b+AJ7BZ>P#ntgfy`6t3&u)bT*T z)b-;jkJIV@^3~0n!|CViN@UgVbb3<9b=?dEj;Fl3&f9EztH;nG5JgyE6;^JP9R!_Q zmrg(;J+f}R2mUpSG@_wLZu>J=6G^o5Z&#ER5{);V`wp3o;PQ&&(6E#o6~6xf686uW?ZPe(JE2uw;O3WE}jghQ$`5u+26 zf@vw_vr#!9oS}|*Dw)fP=0Z3phW1-%@;e%A!oYBYuS$c|UWrE|90sT*>+;<~L$7XG zrfc-@HL_e^HfTP&c_bR=M*)yQvu06ITw7a{v#r8Je@V7bQQFwpkTt6i=S}h~#XbPh z!K4z4aMCPZBr<&tWMMvtWz*3VPF~+qXM2n##V5u>jSBr%e&O-QANP13Cljc(Bmpgr zYCkR(i?ZG=_Kc2lK03O`s`Fzs)$hk-Ot{0^?j69RX{dCVoQ*e4;*6%F9d&8Hr7hXb z-iN(OjhA*(+QhE4Bo~u#IMoac4QUGL3T*+~MGtM1Ob}@eZ4DcPV-j_nFS*@h z$zI$;W)jP{+M6V7A*P}wb|TSeuN#~jE0X9H93Pn1JulK+xBI1=$>g@sXVS#>dKX1^ zI8Eint`5lk4ahF|Hf0xtP3?PDDPNLmR%|>#wpQ)8{`h*Ld-7d~uKhhQiEou%XN&C6 z3EMTz|Arw(rn5-8mxYZd7bbO3xV=OY-XQ^(mOa`$K2$}NFzoe)!~QQQhYo%22l&m# zlYxNpL7Poc{^XW2;<~Oc{T=opb`H3SYveb8hXfBh0Oyz|_b47x0eG7PaRep&yX9xd zRSI83WjsM#b0kyHF6ncDGM|g(MaV!WhH%puajtMiW)Q6ShG#|0^OfV`;13XGtA)Gh-rG7i`iEG6Pnia3P1pb$$c zfNZCr>$^QyKJm~)6BJZf-kPf9)Ge=nonF57j)reTMq(1Nwqha*sWRJ8D{_y=DZ1 zMgnrUXaqx8N(cS}5`TOAzu(CmGx&@}FO1f}z z)`BO2q8bc)iU?gS5gZ*HL!nx)&PzQI&Fg)$+K1K#$K!Sj-j5D_ChSpzpOrAg>>nJn zI*8I7>=9;~b-o2#{kJTJ)6asjSgd5*^cG9;Ffa`*C;%-BR16P4eLR?)ou-P z6m^USUv10}`Vhr!qi<^^Vmj>aPqu1xH?eTw9;-kl+U zDT}^2@NyEwija?-V=wzcX9S@ktcVC45k!M(k$=e-_(vStQ>vz^PiYR-I005zcHn4! ze2kwkRIVGIRhMg3+@z(7(b?r=<9S@Hx2`=-i46)KDd?m-h`gJ~KwXB!QtuE7Lx^v= z_9MD?RaI9h9I*~;e`%dUJJ#8NT-Ko5l!u8#af^;_IdfvCF&q4tjCv?m0EsdQB_axn z9l(a3!dgd99LFLzE`=2}cF5@*bGls4Lor-_6uw467#v6PX#eM>OP36;z$&#l|B*gWX zj6~65&jDRcNEwZ3vDoBiG@IgbLESb2PBnUTI2dw2C%d0>hl0cYA_=l3TkwvI$RoXX z*DiIQrqT2@Jrd?VUqp6Ad_Dj;6LyyBysivkPbv0Z_6R$V++$zzdHA$wM&>SPp8-0- zNPy9<)Gi0a9#v>UpA*;>PQBcxKmkSwHYCA_gftIy+%Sj`s<&hv)i zf<8__8&9*3LmRsp`6P~@N~hV%GX==)B8HW|8gXfo%sw19jyFywxxsG83Nwe;SEQkY zGf3ryjqV<5@)9at^2F3UCg)+O``<0rZgj3SohNi-%IBmL=J7eLU~_6p?%wZ`!4wFIBpT!zkWzQOBM#iB}#F6q>J_-+kFIU7qP=5 zhC1LLxurqv4Xy+KZxHhxmiVuV2jqt<=sE;Lu^pV>9fL8zzygE5zilZXH{tcM2Ru6w zug}Rnke?ciy@gGe>FO91!l_>FM-cLQY(4!C#^$lKM>6je#8Nc&FS=A>O;l-V`Fx%xpkl0<;>^bOXH&5aeMj@BrkI8i8vvk0jZsR@bf$# z&$)mCj!AdDH=9*3{sp8+*uTiRh`4q)zWv{ho9%HC-%@NA9q`NjOBa%x zYK*QdIPD_UjAS!1h9rQz$TelJTlu6rbUx%(X>Xvbds``~^K?TPytoyt22Nid48#zyZP3)~ylBszU%~G>_zSqz`|$02lx4AJC?Lj1 zZ`!wSW@g{Mo9Lqn!FTT;AJJ92f+U@$DFMGRv^h9q5H<%FA^tEHAqyu#Ho`p?%-J~F zWV?c_*SH1SkoVnd!S;bM4mJ&Km*btVVV5je!{~prU=NG3?^>{*?POoH;DEqyW86-s zXus`N+_DMg?0!CO!3xuP&4O+07{6%2_JJ`Dc8dSA)$U|>DFqAG*a_u7EZD;)mA|lH zKbuh=x8MNbzo)j`H19lDY1Eb~=A-+~!?mTAaSHsP3I?b zlhem4^+vU}Y!(XH$*IgiqyVRL=EPP6a>$G+ChVa;i;hS4Z=oXnc9vggHdh+Zkkdd@ zmfM+}t1Z2Mp<<&yk2y#yy9bA8nKhY-)aN-?VGY!mP@3#fwjb}qqP~JZTxcQ;pT)C?r^y~e-@_O; z&(5Ny%<9`}zdgKZ9CrHSl1V7#F|1ezja5OliG*zdCTbD}I)jh+Ilvi=%7M0t8}4in z%M3>MSJ=~=x0N5njqCFw{af4p0(jd*3blbm|33UXZONy+MLLuC1VD0nKU)y8BFW9- zvedv{meA7x4ii{vc)Id2!8Mw-jGhL@(}*%^r%*Q0UV|LZ2-!}GovuK}b=2pv292G- z>^&&2Cs~|!ALTdkoqQMH%}pNTah~8~e4HnF%1eK!*Ti8=pQ<&7&aR+1x13(AoNlIt zw#U&VfHV%nKBd~WQdv&Vo^3X3%b}$j=2W&e`Wwx1J-tv~Je_Vd>$Ni#=h9^-TVDoi+@d6g8FEiOb_J@-r>f=Ubp7l~1xTy&%X+z9ubq=TzmP762da&Q8n}0^ zda5$mO7*np8k~ACopDPE)t2W;6pJX`LKFmbw+S!+;?7hX3+csby(~y@H|u58=gP~r S`O1A00000000000000000000 z00001HUcCBAO>IqfgAvW9Lm2G%TNV~eFq=~i4zf3wI2eYk+N|9e*gDQd+!}v8jUQE zEX&d)@q!?$NtP#COIg-qTqyYw=e4{kxRkyvy2R<}7XYY0oDw?^xRU?h>$LlXq|rzt z0WK)%V=#d@(1-Pv0ESL=Vh{O?)w|IhgzKZN7Q8OaGpWW1TrGEshnvVNTGXQY^dsG9|GjhVX32XKmtuFf8Ktyz@y_vrq zK!Ap(!86mnrHyPAqb!e zn% zb%`S^VHyIEuoDIV7=~d)bMsD5W`dln_FSSmijZ zN-V09GsYOlBAq`tetdNN`0hh zzLk{~w7&S2g(B8pq|;yn%maulSL$z`L?%+FI`zfvrKbyd!xui)X(E$6%YV_Aa`Pra zvh31Jvy6~Uo2BfVoSRd#Znx{se#z5tG2g#`2b7sCyL89?{rO^OS@0N!r_2k6=(?SPv7+O;I1D^Sc>ys3hOyf46D>6H z0pD@F{}~%$KLDn2>I+ZlRF_gwrAw)jJXcaBd6qZgG*09|WqBrq%2g(W@+nn9`BbNP zbUYsW1Ua|l5yuHQKO$SY-ud8oJoX84ZpkB#6L5Y+nws9hlKA6}uD9e7&I89eA}!9b z0^*N5x?Ur1*&G3g%&Y^7Uxz;I1;Fn0u_7&uajb=(aOmJjUWw|J^y~3clyKB-sCs~q z|9JrGN9z)sSi&{{H;GqH2%s;RJxYAe8bwNBJUD z3&Qt$RF6ZI$V577PoAX$D4(Z#RF5N>3U0f0iXX%tlh(#2|Eqf#}%tz?|W(%PCbw^6)RmS=fQVLY+C zyxeLno1B{_3(h_=P0r0<6~zz=GRBMfejl;lpD!zhS1vCvw>q8HvT2(95U|l9Zknda z7_XseLCzTV`|}9%{XU;!L7pf!!({-t>QPqW+9wjTlsz(uV@;<@>C{oa@I^QbtTl+J z?s-b5p&A}dl@qip1&1D^eCxXzGuv(RUGp8+on~1ziG+_+B@|N+3qo0yHln>W-p5|T z5?!}5|1PuLHW_>OR!*5W;*168i>NBmkS52r5p68&F{T7@c)1UtI>L_s>a;(uHQqZ4m8}XPR5l3B29O^L z;R~JWg5sY*CUPHRtUbN2PtdlJF>Bf&bjlFOo4mKpu_H<}TQ^&R8`R*o-Qi|K)6N$kPPOph`bH(As9j4Z5wHKVG!TFi{_RU7o?7n?x zp1E(|Y!uDz+jk}aG{NG&A&$U+2)4l)02rsSiNSB#nELj<(nKaQL~r1QYM?@}ip$Yx zG^&<*yp7oAJzULQlxm3M8W8GzgCH1UoA-)4WKN`73xXghD#2qZ7SwCFhO2N4z>U+G zC=d>#s8U>ZU#Lc2)u9??iJm~IItl(Th;qf#f=njz8|vI`?xKt4EUvsS!Q4d`&9!vB z1F`yWwTV`DoV{&|1Xw}<``|RV2A&V#(wrKN#1J4xp(vHP;L^}NrFv$9fc48KaYPYm zP8pKAhGbI|MVOlXD@hVfOE}Rq!gB>a$9c?qu8q&Sn6I9XpSbC!o2G7>s$#=f^csfI z@b*%=_qgFj9M-J&wVb1Wh(yzpYSngKJLVjpWxG8db6!0k7yC#UAv!_BSoCT*^WgX1 zgcLY}X^3G5I4<*L;gl@l>2AQmI6y%G34;k0HF? z_(ASVJrAAwB2Ba0c*C`KxuW0gB6hodEOSy-6h%=d7RB8;!fv-;TBGWNwmmbmY17P% zZJ$k@k9M<5NOx+gOGvj#x}7d*I=Y5V;3QThUZLy-I<^(%OA9f4-qG$(b2sUB3F&sp z>0Ls)0J=>6+Gbz}oC}YGC3rqSo(a$CTlPq`h=?kmlnol58F)HnDOHh*l#iCA(WW_F zunhG3j(WmRF|Q1ubj`uQJfi_$e4T>2UaLUllKx@_XT7+^K8$f3R}xwxJbQ zJ^I~OdM)a{D!)w^v4ht-?qM6wSWt@rj|%J05WufO2&VzKVHi3ntj*vi6FE?!-sjQM zMQfQjFXNNJ9`$#}zVK8SU{RG=pzC&qj14=ki%vM3&Z$ZWQ&`}z@EGMov)wj_+%UX? zZd(oXbEo!fX!D@2MJ(e4itR7}aOYYVDr1GU=9AzQ=5fO?bX2GuSo_@e(XfgGuJNNN z!fY&X1j^-R%^=HX8EJ6CwWfG}Vs;m=GV#NNnNJXWTN;VwFFQf1Sx;YV0K_+7o~=0#(O@d$=UJY~XbXzHWV&JTWU(5I z(HGH_>zcaNwydV6ccWMBjID)@~mc4(=DsL#rKiS%^%z?&j>7h z2)k+Yz6ML!34oazR7GIW@j)*E-66DRZZTC^j*r*OyQpc>yG)JRM;YU?;D#4xTW0a7 zO_AHJv0;oIXq-pz8;@glOIBjLfBP2SxW~`|nDm?pPt1HYVs|Z7zUaRm?&Q{((y_() zMd1F03`<;v=~&DxUnu1u#DlX4wYh0b*5df_^&&dsgLK`n54sP9_Pj6d%L=g#WcnX$@Y8D^OuHAhAYSDG=! zFl-^dP3p|uZ|>j_zmbelWnKK zY_=`4=ZRq!eRZfN_?`Jm=gV1a2Xq z!EhDT1puEB5qu8t)&s8=FhAqxJobX=IObuTh+D!om7h6j+QQ;Q{ICl+cV>n;bN28T zlB(e4HwMks2~Aan5Q4G87!yJWRn-!!&B393K0h>+GR=l1Y?Bl5Q7RxYIs54-C*r2C ztp;7^a884RiQTzGvM=ZjO3p|j1k+SiIF3+NjR_&-Op>n1=b?f1zg1SpHBPS`a3I<^ zlZ$#kvxLaP$nJ1W8aFU=;sa8uZZ@fJG-5aF<=lzxXhw6wWGMd}BQceMnSvXjW`Q%qw?-cn9i7& zj5Uc#FVJ9N5n7rJ?u$%UiZlq`5<_CN?}ws5A&9w}oF*H#>yn@rFcvh2fJR((-$lxI zEXks#Lm4MtvUN>Wg=Omor>#^<%M$j8Ed&5}{DU&a3`MalTX1if=FZJ#=guWcB!^1j^y4_^azjz9wF^hD9&_hr7o}Vdolu8w z(0Y0UX&~4G!iBwn+scfmGex&lrPI<`e787N{B9~zppDaT_FHm5{r~%K(7HafZ2~SN z3L!SI1Nz!g-ARD{7iJgXOp;{pNp&n#KojH8w7+3nz>qas=rK=WQ`5ULcDi?sJ`!g4 z2E~q8G<0Ntk&gCdh??BgoK9da{Ac^$(hS{!@i@F~*c%5;t$WQ9Vm(Qh2%h!B%eImHdQCQ1crO=O^02Qe(PqUzoeS)s@jvl}zr~+|X;aeHsquE^N%w<9N=U zID3sU>6otDwy@OP!jId%iPGt+VRM7H=@|R?zQVcl00Y-qi;lDw!esYx(l_GI@)CBy z9yrv-rM{7^l4mN%Yr5|R%xzJdS-flXVst4rzrgi;K6KI$XJU?cEXQiFNt&6Rao;bb z5hseriQ<`K(PK%Xq_YK<=X8v=jr%rE36lV{{(t>%6^$N%4jV(54?i+(BJeUU3z+Za z+>AfsdO_B}@YG_1F+G#c#bT)-<}0h~rPzaq9(w3ukAJn>Zo6$8D?jzr>@80{HGAE* zZQFKVMNj?u>#xkR*sqq^W#V7Kx~4@Z9ewoCN6+7T@4fdv{hfD8d+(j^gV2Xz5aRJ0 zO!J0#{QU^_{_H*?gBo_nQ3!!-^nmQwMG0xwS-94X4P{Eh-mjYIZpyC(wIJo^oMdha zgB0_wK}J;9Eu?cxs)XIf>ghC5I$a;zE&mIPTGC)Kg;-28S1id4mR9+kfevV3{J|DvtbC{|SG1F9%iTJ?3tpZYUvJCgmSN;QV9SLgCQ}nm8OnE0( zj@_ju)a})8wYA!F`Z!bBbH;!%Zal_Seh|Nke{7DLmsthtb8&*b!amBr%zn-O*XcUH z$LsNT0i?(Q0RVt`V4$9yge)OXm3NQek%?hB&Wu&z7!76Y&FDzuO~%9uj4&2lJj5ih z5I@l5yHg6qKq?1NA^*e>_K0OWC~%00;SePgtDYDQ8|ZaL$9m;{#>8PX$yg{TCo>7u zl|QOe5)Ne#XiXl!r8&JW?4CT`o}7wgeqyQICG13U&55mExV3fAR7;-N8qf9Q*02}J zZrDA3OLJPbC&G^GwIbQ-hHZrR+93Wv0&d+o3zgTOD8ys@h9m8&wpOW46^lPA2+ zU6-{&wNPHVOMTIbTo-QbHly}2Z^l+(ZfKySVm*hB#9ncP>q?w}NvcDl_V$;9FQAO2ZO2z2d2*wz z=%R@TZH%LhEflWrWJvDCuEbq@3K1f7(L)_Y6tS_V_~wlkxMAH&hsJGVPe6r&eyt3d S2VsmQO06}ypn7Xr@LvO5t4+KB literal 0 HcmV?d00001 diff --git a/index.html b/index.html index 0d9bca4..e878940 100644 --- a/index.html +++ b/index.html @@ -8,8 +8,8 @@ Help to Fit of a Parametric Distribution to Non-Censored or Censored Data • fitdistrplus - - + + @@ -78,7 +78,7 @@

The packageif (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes") -remotes::install_github("lbbe-software/fitdistrplus") +remotes::install_github("lbbe-software/fitdistrplus")

Finally load the package in your current R session with the following R command:

diff --git a/news/index.html b/news/index.html index d2dc5fe..8d2edf2 100644 --- a/news/index.html +++ b/news/index.html @@ -1,5 +1,5 @@ -Changelog • fitdistrplus +Changelog • fitdistrplus Skip to contents diff --git a/pkgdown.yml b/pkgdown.yml index f632c1d..9c7f971 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -6,7 +6,7 @@ articles: fitdistrplus_vignette: fitdistrplus_vignette.html Optimalgo: Optimalgo.html starting-values: starting-values.html -last_built: 2024-10-26T05:38Z +last_built: 2024-12-02T13:32Z urls: reference: https://lbbe-software.github.io/fitdistrplus/reference article: https://lbbe-software.github.io/fitdistrplus/articles diff --git a/reference/CIcdfplot.html b/reference/CIcdfplot.html index 0d6fe6c..5ed01f7 100644 --- a/reference/CIcdfplot.html +++ b/reference/CIcdfplot.html @@ -1,5 +1,5 @@ -Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot • fitdistrplus +Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot • fitdistrplus Skip to contents diff --git a/reference/Surv2fitdistcens.html b/reference/Surv2fitdistcens.html index 2dae0af..e80f606 100644 --- a/reference/Surv2fitdistcens.html +++ b/reference/Surv2fitdistcens.html @@ -1,5 +1,5 @@ -Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens • fitdistrplusHandling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens • fitdistrplusBootstrap simulation of uncertainty for non-censored data — bootdist • fitdistrplusBootstrap simulation of uncertainty for non-censored data — bootdist • fitdistrplus Skip to contents @@ -379,7 +379,7 @@

Examples#> rate 0.05458836 0.04622389 0.06476728 proc.time() - ptm #> user system elapsed -#> 3.993 0.108 3.995 +#> 3.981 0.096 3.969 # parallel version using snow require("parallel") @@ -392,7 +392,7 @@

Examples#> rate 0.05450354 0.04632331 0.06524721 proc.time() - ptm #> user system elapsed -#> 0.037 0.004 3.798 +#> 0.041 0.003 3.763 # parallel version using multicore (not available on Windows) ptm <- proc.time() @@ -403,7 +403,7 @@

Examples#> rate 0.05496497 0.04672265 0.06498123 proc.time() - ptm #> user system elapsed -#> 0.030 0.020 2.125 +#> 0.032 0.012 2.136 # } diff --git a/reference/bootdistcens.html b/reference/bootdistcens.html index 3d2327e..d77f4bf 100644 --- a/reference/bootdistcens.html +++ b/reference/bootdistcens.html @@ -1,5 +1,5 @@ -Bootstrap simulation of uncertainty for censored data — bootdistcens • fitdistrplusBootstrap simulation of uncertainty for censored data — bootdistcens • fitdistrplus Skip to contents @@ -410,7 +410,7 @@

Examples#> sd 1.129426 0.6853478 1.709083 proc.time() - ptm #> user system elapsed -#> 4.629 0.096 4.615 +#> 4.593 0.091 4.575 # parallel version using snow require("parallel") @@ -422,7 +422,7 @@

Examples#> sd 1.108123 0.6912424 1.673702 proc.time() - ptm #> user system elapsed -#> 0.003 0.006 3.370 +#> 0.005 0.004 3.346 # parallel version using multicore (not available on Windows) ptm <- proc.time() @@ -433,7 +433,7 @@

Examples#> sd 1.119044 0.7072572 1.656059 proc.time() - ptm #> user system elapsed -#> 0.007 0.016 2.455 +#> 0.006 0.016 2.422 # } diff --git a/reference/danish.html b/reference/danish.html index 4cf7f77..52d0204 100644 --- a/reference/danish.html +++ b/reference/danish.html @@ -1,5 +1,5 @@ -Danish reinsurance claim dataset — danish • fitdistrplusDanish reinsurance claim dataset — danish • fitdistrplusDatasets for the FAQ — dataFAQ • fitdistrplus +Datasets for the FAQ — dataFAQ • fitdistrplus Skip to contents diff --git a/reference/descdist.html b/reference/descdist.html index 42d9f40..d1d2bf6 100644 --- a/reference/descdist.html +++ b/reference/descdist.html @@ -1,5 +1,5 @@ -Description of an empirical distribution for non-censored data — descdist • fitdistrplusDescription of an empirical distribution for non-censored data — descdist • fitdistrplus Skip to contents diff --git a/reference/detectbound.html b/reference/detectbound.html index efc967a..89b90ee 100644 --- a/reference/detectbound.html +++ b/reference/detectbound.html @@ -1,5 +1,5 @@ -Detect bounds for density function — detectbound • fitdistrplus +Detect bounds for density function — detectbound • fitdistrplus Skip to contents diff --git a/reference/endosulfan.html b/reference/endosulfan.html index e791ce6..688a00e 100644 --- a/reference/endosulfan.html +++ b/reference/endosulfan.html @@ -1,5 +1,5 @@ -Species Sensitivity Distribution (SSD) for endosulfan — endosulfan • fitdistrplusSpecies Sensitivity Distribution (SSD) for endosulfan — endosulfan • fitdistrplus Skip to contents diff --git a/reference/figures/fitdistrplus_hex.png b/reference/figures/fitdistrplus_hex.png new file mode 100644 index 0000000000000000000000000000000000000000..3410472bb050bc9d8e5664aa02b8361fca07bc31 GIT binary patch literal 389854 zcmXtARX~(oyB$hOKqRG0kOt{S1cpZ96Qq%r?(P(%OS(&>l#UrX1*E&XyX(9&{^w#Y zKxSs|cR#V#de-*q2PH5T1_=fT1j3S)kyHhNkc@%XF4{BTH}|UP1|SfEpT+z49~@P| z5>&EM?|C@|czIdhvT=eyZ(^Neo8*z+y$FR$cJbj>zo_D;(|VnY8^9ZYt3oXkhU>-C zml7J&WAFN|^J66ez9gxB@FU7B#$-VDn*dF1=2>(8tvGSXKP z+SsV)+G*B;L_fqOxw+VU{2cAZ_sozUJ)2_?dF}d@v81V(p4a(6IMWSM*ZnJpxYGWg zRZ&AsvZ~+RBnRa$>N|tB?O$0;9y~rBl*SUDCjO{H&OYq=wdk4SgNiYibm46EDHyNV zJ*3p?wU{e%2W76U2KN<@oUBFjVmf=Kqy8DuztMb)eB3%~WC>K`+fyefg&h|8- zFHI6Dmp`=lS`a0;larX|%X6kdI#oFwKPlzE;!CNaLK$awYnSCMle3mto~8VM$8L{> z&s(x{ULdnw*mtzbdlk@5<7$wi{u8)4%o(B?Y#F z=~036c|~%Sr^vONyUbtd_XWEl`s(c^5vEsOPmk4mg}_GMSY!t%0Q(Tw@2&9h@U``x za9+E~Ad-6bU_a9@=}ugnaeIRl?74((l=qwQKNZ-HRsZ+#*BrtxlbA@KoOth!m(<_J z&u}Zca?4GW-<}*X6V>`6cS}2a!`Gqtzpr4~*1w4(l6?0tFxIe8!0;;_GTV2Du>7Bz zC-fnn;TIY3jHE1335;QTpe}H{_#G-l8mP~_<(htHoTUB3s@VkiCQGI!+Zo{JQrO~& z|NU`}pRcG&*pUf zt?G0H-G%^bIP$n;u;S@HxlEnJ8qAV;ho#Jus8Hc{(D3INYk#AG7i;Q zP04&91Nm;F%yadr!!O7am_=8MUOhSG5wJ&Y^>3^0?TkMk%ixSmo9%+Kc1dX zh8oDFD}y7Bp3YLuMU~SxCSXtD-!R}`{ME=UpF>mN zATYk6%5}WsM*VSfJ(rE&^R&$FcAS*z8;~-8(U|O%pSG3{AAS9$V}?{Kb;j{7CO-Ne zi?1v~gQ`Zfn51EW7n}n>V%e;f*lbN*SNqrLx8pjOu|rPZo>ls6n%F&F3+3c-hDAM# z$TIzn(`Maex%_x|l&?kya+*?39g@;bR@lG0jVyWfA1o9Di>Y8CsajyRjbdh&7nMeT zijy9fG>FG!)-l$96u8Dr`{Mm`1myK4dQRjR1V3U5wt18~>_=MRq$g*s|NK5XFVqIx ztrDDh`c$*>w3R^i`IGAA7dxLx(pVyN6kdg`d@75nZVA4i@3Uq9Nd4 zfr(>}9)e9Op2odWf)BO8xt9VI#xT#JC~DC@;i$(*2w6o?c1cK|m_7 zCn}Lwv8XGyQ_&xM@PuZR7pV?EqaHe@Uf8oR8H1j_X15JW+%x-icRTvay?-Bb<^E(K zr^x3e-e+9o7jZJg(QycVd|!AI=TPbhDnd}3e7=ADFQ<-I9h>^@wy$OB*5(WBkP?hA z{>5_K4vEKMw*`s-<|oFVn*6ALvWf~p5!gPws{<4rf7dvV#q8;y zAVXOlWd%5`ZikQ6Kd^xm7h+x+FNLMJ|M7LJKv$^1HAT zT{Gg~Wh#}26$XK@e=!mZ`44(`PrO>8u;qeNtF%C2xtWQT)>9D#l;A+H7vgge6(Y$> z9L;A{QO`f&{GXZ?*L{%cl0-qCLcX=>n_=e@N{?StTj@tqZ zuF?e+iDyDX@&lXV)~r^HP@<5{td)$^H&S)?J3pu6MjLzXace;!iv3$$D^@)ewFX^z z+w`LFg5B<{Kc-jE+Fy6Rmjy=f2u%F|kH8NhU_n&oB3+?a&*j0$%DDceuU_Mr${F?e zN@+ek$o6L&gD!a3`gG65qdZ>Gpiu=DlFV|O9()%W9%xnJrMe+)L%eT1Vm^*|RDREz zuKUqb0AAd%e9wVAziOX0e@y^SBNc!6J_`DdWj_2Y_nng{yw3qjn*<3x5}M)IdFgWc z*ShyPBd*8aAE3Sm$@qP%7NFuVF86sw;dw8I{O1v!H-C)mz$aSMBWKQ_Dhm0cQlEP- zYLe^tll!6JJ$OAt5r>yUM3BgPb~i2JEt~Xyk@~l+Y0N~t9Z9F2IhqR~8%d0#5c44v zP{as}MI^COGy{pMr@Fapn(wj4UmlCi470%_YM>bK%nM#eS3-{?OfCXD#hni{Mbe>m zro&fAYhb`%kJBw^4Tz zTuT~d5pMRlOK@G~n{|^$niK!Sw3XV`Py^m3v7Dg;yiRJM+}mdMi@4caPfa3~5W|XD z6u&yGt&&ANk6s5($lHMFYFWr~MTf~3(?Nv@h*&~?@7>S6o$gK{m~_pB1L<`VXtsZG z`PFb5s3O{Q@d({UQQnBKWO8lv;R$H-io0@_n)3~2RJ1KY>>9703B6~?2XkO zGcl>+)`ktN+~Eeo&MCp@E4&_Gg~;(=lr-$q>RP1eik+$QyWYRu{*n$7{?(x7{;M~ktnhjh977USjgO^VGcC3y`;R;7@Mf3L@e*D~!OQ@}QZ*TSO6fa%IQwEM7$4H7 zpyS6y{{$LJ_u?{M#U}iOXjgQ1vi+TXH)tvVL15A=i`e^yi$J9t(npJ4Yd(^cLDsit zz%qSsw4nITPNbyrXYg-W4t7-BLeT!#9H2mRIAI?MKA`lg8gUx0hE+mMc)Q(rV{EV? zum2H;P*f~_ZYnuxP|Z{yv=yz7Aaa4%7AcMJ;hjk^u3ySW;woYF=9yx`T5?Xqt}&8h zLC)q9)TO_)Aie-paVZ*526i7GP8BY{#rv3&d7EjiW(*hShvG6H>(Is74+4J2JOF$; zd-&g@NBQ^YX&OT^n{r-Y&2wiixqE2w%1=ZdSD)T3cLqE9OK0a}as51YnDRon=Iaj| z85iebjfIf^A^6xG;_dn>Fq1y74eqZO2 z4xAjdfowYbK1^0`?sFC0qssLI=(J?2|FQ`M|D#w-MG<6I4S@t7^iX?7 z;4m6O;zmOEh$p?bX7hzT@f`{op>26~XKce875vYA=203Qvh}~O(WfWx{u{mQd2eP^ zIXgq#oSVLMVoywZ(ej`LYgT{Y*ux=c6abCGC}=j{hYm#9Skp?kve37ZEByRtD`~&D z(P8{`O>0+-q}4meGU1IP5PxQ#5Z*Br=+DO*wQ9IxvKNtKIZ?Yzcb^sMN?%fs~B z3 zAp)#v4>g9`;dAcpg=3A(?9H$jJd4&hM&0M63lHK0CL;(k=Uc^Cq>*bQ~g833(`8qch2 z8lOelciTxEBx?ndHQIpNy_O;z53`y$Lkdtf()B&40=~@lE_EdF^&aiYnOOj=0|gu*uz~J zYx#CNwlm^+m1aU%@fyu7^=a3k-u2uA2nPEutf3}!kizR#D5prNUgc|~22y|yl*@fE{%-317!l zHoj2iw4TtqvqE#wwlcTSFwi0Yp+=_lCC25-IC*4o zvvQj51O$$7kSVUI{PL5bCGl?;VmNh4t+9RSLXuxr%1r1@lc;o%uo_XFRs8+;NZ(zP zZOB{%Wys9FkL_c@i%x3%Qc{RW#@t2sYZg#HmZHU{}H(^_u${M zIjVd9%bp>f=j4AqSzvjDhHqACP5Kf~FM31zyZIO9Z#srXQfP!4+zwr|+JjGO+K1!P z+P+Ie(Fii1Wil}=`nmV!C0cW_BHn8y5qOu^ueBhro_KJeUY+aXz+RErh!!!n=h7_~ z|M~i6R~}RtT5)G2PryiZ^#6_t9f?{0wo!}UbHU2RS3`?;YOoo3h!pxoY#QM*HbFrOhMC#O~ucNj{ zB1P)GZx=Zk5ktC?YAol(^9)BDIhyn$D_GYg@LDl6nw7A@y&T-_#P5-XJfFx9iLZ%S zjQf>DlHL=WJ&94TMV zINsi%+I5e=j)`b&#lT|G%jZ$>(lXpbOKh6UVUC$!8?BZRCLh_XTg9&PpBnMHl&WyM zC7y_g5VqL!7M8;6C~{97Ezwk7Cya!=_hoAo&|L0%g zV%I6<>oW(#p8q&_Not02fI5^P46o7*vHpqd-WDR7*Y^rG`o3Dcp=Mb)08YRbxrirg zlSFw=WJ*QW6H5zX#s+1q<#yR@4NZBeRCUn0K+Inesl=h>UL0F1D$jk%)i=$Hd={4q zv52arG=FuO@ujM+yqeO=gJr&cyYq8`9ww6$LZL&Gf#u1U>4l4>7{ZpCiD;A;%`RV# zTzz6vRREgx8W&y&lo4Ttp!~P}ydu!T&GJott1p1G0dkK=l({QwnC^8pY6fB|OwH|( zZd>08ybm5ZylXmE7iDX<)2;Nwh;?dH;dGVDW<{Cny!cfUlk3$?(bo2uz1O?#1lE|o z-F-L5a5Nisc3bGbDtKIWP5dre=Y9_zXu=D!>T= z%|AYXo?m(Uss(sBqCA<Sy_BM; znqIqT$74H?FTW8;CT2=utan_-t8BTl6fcTq%Nn{hD4=C(@f3!hsd4b`GV1_^{u4cX zeX5176DY$7ex!wtP0`8&n}6?Z0Y*i@@js*}F5MlMtfC{|;#>QNJyVW%H%-Yck z#wQOpm*Z!o{8^F8_SB#Ayfryw1DIv2?d&AwwAjrgaijTfd6%*!MwAG;tlQLNNh2Os zF!*RP6Wc^RW*Y?=8F0U>)t9Kyf^-29z6)dK}$G18LGF*Jcd$- zx|gvw+AmoxX7($vIsmH2!Tod|2(C0h$WF5%w(I;ufNAGx}Je!nH~d^F&?~z zDlwZN%#!=^%{&Tz&($*EXiKTT{a%)?NtO5mlo6&%nKNr|oMpk=O=2R1a}n)PK?Xkd z3~7sl7QYYSLGoM6*6FGz0K63Zs0w^eMQsPsDlbM99 zPD-5J`5*QWoimz?LRi92gPVf)e5}a7m264kK5L~`R$mr2$ z1~4gKAx~1ooAI8Z&40RoGe334Did*d8{XhGe%Avi&yf7R0f1{K!Bv(7lfS=rQPQ~V zAG`bRr#}ExREM9aHem^=U{@N%p_08hn@ddcCkHhqPk5!R+Vo0_1^=5}>ruzsy{e!5 ztrER5_)OV$LERAFfyZ~TgxUcM@>*`qS2SQSLHdg^GiTAF<{E&5m=e8hQIyVekxrXb^~ zm&$u+G@?X#jmx*Uo%BTJq6iU1+^a?|I`t36MdhQ;UYzojtq%)N2zAM#h>j`XpkK*K zFU$a7_*Awtf5Wy01ATyo)U`bY&o(5s zgioB9==+Pc5p3ui$2_@@lMSo=lF9YN2&aq=nrxrhGyQ$nH1^AV_fN#i{E4f$ z>l*Sh4JhI@NH%8LdRli&OjG!C(aX^bWu4ZMUgfjj&DNqILK<}ikkDa&e&KXKi*${- zknvo-bt?<1u^dHIQVJ38S}#F#_K9eo2n*)x*=X63w^{*Brcp{3$kh7FHQ>EoTD$8e zR*g(0i#rp+CL$YlJxrs8rNHX#lCt#vZgQwt)e-5E46Nn!tNarkaTeYWllDn1zyVek z0I-;f{$J`=67IMP{FbM8>Ljr{owgHn-PJnp&gat5iO@RKvXS;`Ao{g|1CHO76SOvHiKi~ z-ZeSHfRd?Cf*PrA<-!vcc-FmyZfVBmw;4j}c95X!8zhBKCd>@B+2=IsAkY?vy#MTu zVE?BKZGErIZX89K{Tn%HvlWxm_P8{oM3+#hw!0@(og7C)8KE3qi8ICyvOOJ zoMl6P*sCZqL`}{f&!|f%fh=!DQy>jmgPS@A;n{5DCs37J6KGZ<~q zOn;U2O6qlbKoe?tprMrn2Ow_iM3<%(=J;eU#w&jX}UB~b!pdiI!m(Ddu% zM~)A3Adp*;6kD#sy5h?)&F9lH)X|eXmM`Bs4!$aHirlv!ksV%p6Qc_b)(&!|uT~?i zGVj2ZnAc-6oo6<`8%rASOa|XtX)!0gU(QWlwdufFkGDX|XlDf9+u^@V263c#zOZ4| z#cC3I>M3b+*eSyj!2ngHEP9^dsKJyQhaecVN-#YOl4dLIM$Dc9JqPe02;SvRg) zzhZ8^rS2{YlRZ@k_gEg}SjT!ZpZLxt<7MXIq|+*?xIV|XF@}lwi0^20ZSRZzd$yj= zI1rg_Sheg4C1mfodTB~Rz5X{0Hv`+ENNBVeFXItRtHu4rLMox=YjEiU)HtnHE!<}I zzWVT-UxbK#lGSds@kB7|@BG}28Xzx_&^sC_Ww9lznsiLwDBS!98yqpiF$0(w7Bm9% zz+gk#79dEBy%{fBIu2qOpWe(M}GAEYflXb${pVgU zf2@47(@j#MXX-?%QG47FH=GkPv~m*t7xXXS>#rrIK#!QfI!YZDzKzCAIng1Z@8Cx& zQiMJmO1-A-1$!?m5uJOZ#?TVzRLksOLC~Yg*w#VC#W96HM~VrfD*sv}A}|tFD0k4r zW0Iuld!)R5v>B+aS;Z9*-5BxXV_$JmY_StuG^+Pj@m`iS}g=vKS}M?BJ%Z0`&!O#QO%t&xg(O{qkHae zk#bA`oZKje$9~WAmQpr17m!_ty3G~3#f%Qmlhd>+C&NQJSVgbsLf zBD-+H7IAep%X50)Q2Ag1fY8!1e^?s)xG@9N*li{MyrBjJFoPTN^8s2G^-8}O8hQE* z5q5Bll~eHztHh;yXM{X08ylh%cHK~hDmvBIXZoGPLw7|0 z4B}=Mx^4;I!o_9@wL`^aLMsxPsVrCofn*L!9U0tS7QLnf5&3+;zl`^s>Xy7CoP{G;@Manm#AG*RwYQcp`-9hr?QHX`Q^ z0X*B_u|j>Ucj-toC5h?IAZAmyAcfQ~W)S%iR*S|o{po2&t3qs@zsJJW&u8hgI)>*q z!v)>XaAg&aBP9UTysGk2bQ(gCUfqq$92%i)`99S_7KNLT9e1Fo2Z`{Pn;b*5lH7>Y zqO@1ppNM9Qj2&k*l~?NNs#QBm)=qo=P}CPFJj4fgi1!Z5Tpw^ofk9$qWA5W@swFml z?Zd18SLYnQ!_@PHyy$b{ zV?X7BaGj^56I74OB+!VkEI58mOsIlYDI_GPJvw%wG05eFk*lrGBB3ZM7^yU?`$Fic zytnm$XNqhAt zvF!YaBH8+>H{DmMsgOBWVMIEq7v8zOUWH56bFWDuYjKq;tEq)u0V87ccG!%&CPTcf zdYZ#GFr3GbWPWW8IbL{^*m>sTHsSiiMTj{bLSZiqkn89=#^FZ_-x^O;YL z4Dc!%`Y-AUoPPRDqU&C)K{bbt+(ePfa;Xl0nG*UCsW0F2;YVJ<-3T&n=$VBFZ#4d= z7&7ap1nw_H;Iw*cHiheIo)QYxPjQOE8(7Mv@w3*DyII>^)BWW&S;Q9+FZ7qzYULlk zj@{-4YqDs0mW#*UivN%=4P#Vd2k)AD@AFjEYDY3y;mmF2CP$$Nn&4IFujgi93=q?} zWpA0*=ee=|F`)?NMq7Q3U%Ceo$e6nW^uny@BG{OJ;|08R(Wb4$>XiICIVyDYEC=Q^ zhI`M;`nt*Oy4TYTj8B0Ho?CYwJ5ykuETqKynm^j~8s+Q0-7kyt6`}eU-UuY`1m)XKBNwHWy*HMUFz1$V z02jfe06>Vam0|3{p>q`Q5LA?hIzGKkTUd$*=0pHQbL?RcEbqoRR^Ez>xKtocngK+Xe7v`{PZss698ZY&dYCmr=_fSX2%MJ^wgq{dAj zqvxz|r#H=@4+nNJ$E^9d3GgF!AKdLSTK^Y4cW7BEXHstF9#`H5^Lj!(R=zi`6UcUVk@Mf9MpYk4Q{;B@5{fJMF#HO(KzM&w9PmQxSOo*5=lSo6Xo;?G=`e&TZ$* z+E?sJbHb}lA+i$VL?Y|$Vfqa7sG_mcyMo`jF9m0}^DDA;v}%b-Dt}**+HJM=7*cbM zNV}JzuaV)G7Wr7v-~F8ftWGKas2v{M6ol;?0)AW2-z}EaCwi_i@khPTIb<@(9$UI_ z5oD$SdF7x|B!E%wwj4`vw#c-0y!u9ZREyn)d8Zj)_E-A=fvjx65YYnHP*p~NIV}~H zPx4&bft-r)*L*ZRq1e`f@R@?kCa0)5KAL`_lhrhDgQ_o9BiqUMpTFo)>fn?_Ykzgz z30^F(-(){Ys)%J@o!L(|CLL5|TEPcOy8iOwqHpjU81Dn;Y3}`?d@|c;kHTr2EExc{YOEIw}0Dcxj*~Yq2o|tmKFLnY^Abq z*F+W5ox4#SZp)2L+NHYWitskRCpXIafDwaY6}TlE4tg!6J%6OGQyWnhQO4jCn8Jt| zA;%aHyk{Jh<~T}ETz{zQ?KSl?gYd+~7(0skHaw`H3V$V)dxBF|^R83i(3h>ZQrFY# zE#I@cL{Vf{RAk0%1>>3nA?}6B?EqFs*9D z<^@K1@q|ODyOK!NQQ0!>(l6Cj^*r?msbp1f`pP~3ID3`6aT=E2FuEJXZ~cgymj7rz zG0fkh-0Dz&Y#r*7XL69AT+u)m_AtfGc*~HuA-^}b@+)OX+9{IN_dy$zqp`fqVoss2 z%lcgE9m4v_Utws`8DV|dIW;g8*)HiAW&DqKUNGyG@5DVo0F_{jRZb4R*yGP``AvY- z{k3l%3A%(fHbc?$;-W)f%@>i>R1d+>DwlAyJP5M$1!8m{cvUt!bFR2W*b0qoCU73_ z+cc?p>0DL_SwTasTf#n&OsLe=PX&>4ekRlxlU+|8h7=P+A5~l2wB4#1Wb6Cd$JdLd zSW9%!*-j<0r^v)9kjlx0qCTHiJf&6(UB>rcE#Rr^NWUXLJZM2Uq^QRR@NiLjLF`w- z)yLW6qJd!RqmZhRV7P*n57#fSaYDcWs?Hrvn{T6i45+y>=SJmgNyc<9`b*(<#a6*F8uXZ@0sVviK2|Z$GMS9#?~x zwD>ahJl;9jar7shtn=KMKUhS1<NZ5?GiDt~-g@Cewhgc?T)`#Q^C8uq zh1NI=y~wR)$%9QXuR>u4r8&rS|8`^vH7wBcs13bS;-^pv| z*xD~!HJz*Plni@f=C{hxR&Km=qkMWS2k*Vvtw$04?xxEk=>8V;W(WDEH+c6{=>9%B zxK6C-@W1DUMG*jlhk`sWKb?dQGf@NwiYx$$cP4&-TM)b!NY&=Nx)Kz`Xi*XP-c#rXD z(3W&&FogrixS@vhcQHl2$ueZ(C5?}Fjx%6(z3GSehaH&T=MxExZ@HhoqSzt+Y1QcQ zmhZCGqdunp=mXns$Zv*4L>T94=f|{J4+MXo4@)8);mWh?hmSs-G#7a+RO`1R+f)QPqVL|{1x(^Z4TnGs0Ax1E_SjAPu`=GX z^()#X--z1}Ym7axNa-HkiCZq_Jb#I%#fTZiEErc<**7^~Hq}rZo1t_UDb(U1@JtIa zT6t`3 zO6doZQjZi5g{^So`XygxQg~j35ALw#fVH99CoeMD@4PqNRzg=)N*Q#IbpG@E5jTJG z0Ed~8o$s)#R?-NY?gar?L-8IC!pN zdYH-8nc#4TV|*_&X*~!5NAm+O6rX$z@%7EZ7YFWWaI{x!!}kKD*17#V+D{JSFMW$H z`05Yr^D$Ax+c>m9FduJ9_$Xz}Y1}4m|iI`pRd#bo8w+hz4%R+5NIx z!qk`npD-MRtjuhY3)2XVA69d@GG*R~xd^+D`wMl0WL3!hh-c!bP)CZNM>@#{jQRyj zfW~*aMa1j4wl_PsK+xYp-MI()m2#JQKq>OX;D)3g8E{``ZfT4HtdYIp;k$xD5*# zRt--(IfUbw|Koi)aIzWp{Ia5Os?*Hd*NMXKc&feb-zMjQe5Na z*)$@WD;i{FA&_$);puA(TLMHctjc>AYo zlcx*VI9GyJW=kaGMp{3w$Y=Bp@O}AW{=X@h3by4qLBJp2pu(Ddw-5LOlmUOh5ah)W zIrKSj#8h=yLg$Yq<~qyuX#&%0+~kC=^VS zo!^t&orl+7nU(IuaVnTywUZZEuC$d?N@$&re^fGFz3+lifCG4P!W*}*M1hKn%-uq zA(mGF@jVI)Tj(Z&Is=ozLN8UuXSq3xpU*Or2C6jlC`&3DI5t<6Nh;K>#w_2XB+iJ8 zT?R7uAM^Zr=%l2tA5*-kmeH)<@!SS1v-~(Q+z80WA9@a8Gs$mTN1@e#r(r=POW?Ra zctK2d7(h}z-0*arU!N4V&aU7+PjT?sVqT@ncqNh%`3~@Cs%i>3B7EQZBHX@#LB{k~ zXDdz`bUn^Tw$Nq+6(YcC6^vDt>`7RU;P|j2k;i|k|6UVs7-8X~L4(V!?8Rm{p{ zRbYvdUnb0CGIxOCz;t@;q(jORFU59fPxfx@?SvjXOJf0AWz8lAy;oGY@F)J+j}18D zppBfVuG(h<{1ntW`+M2gLY@!=S3)s_M7z=_Blh$CJjOg2@}5*Tp|rs~zuNw^y%cX3!H?5t#9k~nw2q?w(o%s z;dI@lgG$HY7a9$Q-nG69pO(!X^tPix2UC=6)E2z8509DIT8ox$H3AifjM-haI#-rm zJquIyup1wFGucSe1FZlodxpfMiOj%Nl2JsM?|~%(W-ibDM^9J_O&ka3f3E-beP`1hXRH7~jIK?$CD-h1xZ4QbWu1Zi0+I{&%0qNtf2M;(gBy?C8aa^;=e zv0l5KS_m$3jI50O-hMbMMQrQE3D}(u*J!9;O`_y)h!m-VWb9QL3! z^U&a$O;B&eDJntjNa$LJZEt$gOxo| z$=0XY%hBT3gjt6VTQAusKZR2$ZFDbBj!slooNY-gZj5MXKgc{kDFtq-+^@kB+2Icy z;JqFZId87HDL%3UnDZ6@SF=5stl0Ds7+BNmw!fbc;}R+pyFU>61h9#vxOROAvZ~+s z`}P6%)o$mR#W&&CVu8UyH$&uVX_VUb8P~09VwOD&4h!4JWhE?B^P+az-@4wob}F{$ zy5SG8FIvr64@*WYEucl?>ms*(bX-Gku$srw%)iOit2Q~fsHw>0ri@%&<Uu@jXC7VoNyT3F$JbW)TRNol-d5X+{Bt8%Tf= zKZdZY|2AvL#oQXHrwb|fm@(wfy*?WmA`h@)&Tb*6~@?%Qsaw@Hi0$UU@Wc* zKdK=Xie$+!QpJ^P!6fVcwIPux{vbbceF4N}>02GtIf56$G%FEa zBg5PzanGal3`6;mcg-E+f+&fo3Zv(1yIn8Rb*77FN_Xg{glv`I$#H zW+V@?6avc4#+dVfVnB)OvA-AsdeScy@Vti5jT&H*7yzaT)G!!GHUXo4uT$W{=~EP7 z<$tPulxOUN~|yMCN7C zxV=+RBLZDrn{|Y6FebReYZ=0Q@pkMsE|*)?b2hM?HVzr1)^b?YUp!A=y%q7;yoj{s zBzY^Vn^EZQkw@{Ki-@$W6OlrGnu5T!{8+X>Vi;AtE2Tp3Fc4`jw0UT1FHm z$4brICcRP3Y!%K1-DWE_$ef-A%c;xUrTSYuKn`Ir03S^S*Rn(p-P&W4kF*MPpIv!N zPbC8_7Cz!^J}+xrP64%P6gf0~1R{6io)Bg%e?C4Lu>MBH4!NDLhJ_L8i$Ne_3z1bp zN}Y^MS~-I>Ta3u1pBnkZzLUaN8gNC;PqVBa5;!DkT9ircMr*e(S;`-jbtAVYH$GX# zK-8mjBwyIZtH`pIGm1M?-aC*sH!);Tc2CHQ>2qGc!p>e3uB(80;~>_f+v^~CB(r;$coh)?b3CAqijuervm%v3v!Ld(Eh{W0ZcaD zD2Dsfu!ARtHD`47PbxO57b(MSH51#R-Cu7>7|&rS8V zH%(n`{LdF7z$1`+Kjz^A{^>@WvUmcxA zqw+%|iLfqu*v9+WJmg^9KRAFAMMqaqoONJCmVv~%+tXE4aWt>hxMP%IB^1Hz8uDU3qGvsHGWU#Nd~f<0VOX%&{@U4B_|KIWs?VEzp%U z&T1lkm1>8(Fl!jzk*}{`s6!m$MpLTY#-fbv*XDSX36mg@+iwliaHo zfOVacpHla!Yj3ctEOOpJ8{`laBNo$oG|japPeEIbRy_s1pr>jUt0#`9^rN$V(;oaf zTXb^+v2(i(p$yu>TN|nRu;8)K|{Ukz3i0(Q>? zT0)>(I(!>z+6~?cfjfC@m6QfH(J{?n_i{Ki7u=4+ju_n>@Ph^CWtE$}1A+w4A?KWu zFW8ju6li2+97c?25F({=OrqsiQ9jHT(k^LPO`ooZC9C`TT^2^vlKv+n$1tOBw@ooyDdyET&tOP)D=4=4JIkq}dKaXp`yRC5=8BUM>8d z^ZJBjOixC+nOz^7Sqob)Bd>?hBi5O6X5zG33f6ffZF!9jqhNjZ9E!f@>dFf$m}YG0 z3ul4)1@OIL!}k`a`}?LIGQ0c4m$`J4%YSTbUdi3K$GqHpGGO3#c_WS@=a>H9&g(|b zW%3JiispgLb>Y+qFa3++ zPaZ3`bj&h^ZiSgvO9QJhscOvhh3BK}TCdBc;#${oTV1_x_+$4GN`=_7k_Ya%@JDO! zr~-YI3AMiG-yT1mapc2Xww`bH@70R54no&6jIOq4M^D6iJ zf6TDa+>>BtA$8rSBee#RGqZvB|?&gUC@?@b9wb`PyE9pc$Rw zET8xBiSr)%eN;+X+9{mtssj#SuitLGoxtPVG0vbp_LWZ5FsIew<7O63cQAdl=Ie-P zWAI#^UWslfF(!|!!kEKG#va|uXgMe`C@vu>ysdY~R#uWu;AS(-A`h3Wwm|fLDiACk zFNuuS^OMT4XuY8(COFaQAWjlG-UIqIZ9C!XMRww{dKVXN?iyY|=jJ_?7X1pOgDQA2 z{gn3k_V1JUn-Hyf5bU-Ua6N9|1q#d=$9GEt+^>0R)R{iH;^3V%&v*|E6H#DC`kb>8 zn6vy0cFf8cJUmhy*kt_84x6(CAKqddix_UH(6hw3ol7}Tne>vSiAGQy)S^25|mteH_lZluY&~Z{AFLh8N~ zTLfrFu}WAj0n8pHTu@99 z=rrEA@5V}SnLB##+ek^oH11Z@AOHB!S!5R_A3-)TtgVAqiz?bp$L}}so{oREzTKr8 zWsMz%*KhL4{_N#-Yg%A5RtGh%_E|>^e^rk9(vM53kE{xrEDH+Jj$;0-4feY(4|DJk zwoaUl>38Dm1<5QPw`*=ZEQfR4v<1xZKGoEeU0}XyaN*6m$)xj{cXUw*l@PsZ3liG+ zq$N-I;!b!MQf(hjIZt-)jZRXTx3#oVzFL(1QmJ-3?-DIf`1!cfjTc}$IN;zFOoKV* zu!Ug9JkkrhR%>-VHnaZ-EitCohTfNAwezDYr+BcY3%H{j=P3>2nSx~3>B=zIw&EsZ z>aQh3)!BUGj-)5G#02f7m!XRP$b%C1Z>vRaqN(r~PryLle`1 z@-u2mR!=Ne1Et-=`WxaSt~=4Dk2GXLS2p-BXd_)`O`p^?;%gTe>mVoW3qlY}(4x_= z7o9SQqXhkBt~#APUWnuQIcvlEge^lpO>u5q-ii3$3BGB^?n^Cr;-Ex344C|1d*N+u z|6Ej0kfck??4kZDipa$W_@4s2sXqYDg%7Z;sz@*O6G5r4cl9%e4mQ}Iw0KLmGEuf7 z^`8Ov3fAjc3hM)>A-&n3C4A(Rb4F4&aSw?ZCMKkP%zdopG}x^14hhngTv|&ikEPV? zJmVz4HEX-IcunsG}c|nIc zTV;db7Jc&JrK|C3E9~*f_RZ4`yU}}75U5qzvmtO)IO$xyDMi8s?IwPIy*Jr%YO+5P zpYEVnIxC2wEG_P7BPxFoytj)ND!L*EJ-h8w;&8pyJy}`$DejZcWdYuaVxipJoa-d2Uw4b?>v9?9RrUH1!H`;qIOU{bj_zmangH z%b@o&?B!nn$J1GcMcqYfpOSb$5fG3Z=`QISkd#(XI;Dl7J7z>cq`RaOkd#`)l_wWD7xT($owv4pR&m$zA_Z6hL`O`@q8)Q1x%6861 z8VDV`^tdOeBa^~H-65dKdYZ7r^ZG$l3zq-?TQE9VRQ`f`n;_aPk)28k7<)SekTVlZ z^c4e~cl=Ka#uLIj%q>QLSL8NE;-9r|F5hEQtk&vugPRnBkkqMBdu#iWZsUjmXXfx_d0&%mx3;>J>eJfEl8KW$Z8 z?>C57%{Gk*2xYvWQ4ah5_C2{y!`rJ2|17P!CRjzqX(*juOM^_wb?M}bH+=zcrt5;0 zYj`XyQl#~8?i5%jxJ5seyDaW}BMd)EKDJD zo3^#V`i;|xJ|^8S>s>58aT))FYUsFmvj|Ld7(pj_okY*ofNaxDySUCwa<#Dj0gTf0CXVEZ<7s7P<{I`3wiwOEGHtrgFQ?nX*!&=Z@}>Lbn=Haj z8GdT$SGp_is?d;mSBb2B7CpJuo7I|4{b=_G^2gjF%AcKaH$eaF=ZA%(g`)Yg@ZDwy zUrw6G6&U@u0s$+rGAQRwAWDBZEDSj>f@TX@B_(2`s@V#h?1?u z4p>o%>Iw#KHSd!|IyHt>_ZX)P?IKCEnSeO^s~eoGOS}(h-xS+W=`i6A^e%3mK*icz zp;Mt+ss-g8S>JgcKd(k5fBA8PSgU@UOn&zq`e7;Lk*Cht)ed{4tC&?l`qr;My61Sk zxqm;#@h0310$G1*Y5DKF$>$nCu4$d0l%G*8Pf^sz8tW7pJ4k8f;xL@Z(kKzk;YyU7 zkG)K~?J_iI0S@CjE(s`d4DO<^3%?)29|UHGw!zAw~~c8iE)jdQIxJwu;abQE0hj+h<%zHOI54Wt~_b(MK^E1$(lpdZey z*X0~L6Lr+?&zh$e#(NB2zF!NUaeAZN9mmP-wcZVR$~mJjcI6IGII^P;2O$=oG8SL35O(oH^W4jnC+Vh)yaN!)8;5{Hx?$$wri zjNzd427XQsGuPovoA>LtAyD!kGxjwkN?`o&-hb88%92QBcTsu$x<4^E&yg}~;@f)R zi}HHLGbi7pLjhf@JjW)F_R6V@k~ShhE%ehYa`lmj1BZ*rWV zA(%?{bHnacmyFVa9*q9Z@s89+aQ{1y0|1)LdRM7$aR6`Ao#}!jo*_W#D?DUch>OwylAhN_+v+tb{`@ z-E3vMdP?)Nr&}u+K^mb6yK9~mexHcME5;IploA-_rQ+t)P8++u?@mIIkY0+ zllXHAK+R#-Dq`61h5mJjC@|2Ps>c#Q+w;Nx^<#&aE0ga|EdM_91qz>&H*5`sQ4ah0 z3tj&4!PQq}(T@$qciLQMIx10oz*e4&42Rqqh>D(!`^}o+rL3(06S0@0(hYE+QJ9dr zVRbY*-D8GM#M}K*u>sKOxo?dwa9D-L0_`Auw&`fwq(;NWq$!qbt~?*goZ;v1bS?3p z>3;8U@%3iTtb!;;vZ|(cT^*E_3b|(x$T*!Gy)f85*VT5L61}|{t13eY-0bMC+?;CX z8cBEe4G3cu%Qre+BVV9X4jfp!WU8IMxaxT1M8br9CVr{-!eR92`J<^$m^+Pp^Tc$-8*7B##nQ=Z`a? z2d9+ya7)O4B+(K`DFIX=bP$lqXAMC|p}FqDd{AgEp<5@%#~ddF2Nex|f%1uxMKg&? z-bs*BmO|L)Tkh~A{MiSy7MtDUv!=%1%DF0MwTJ+M#I9}v)FG=N8?XbK2m+*4?6Qzo zDD9FPQ0Bc>=S*=yy??sS@=(Qft)F`6Mw1<}*Y*RvX4!`!#m1ieA*p+!n_ z%Ja2rUiEcLO-m82FHQNzP>Jt(95nRikhr<8Ie7^l0Ic5Yb+CGrSfhvOsP^uy9Pzso zN5WSI2blkjD{O;325bcD8ecuJ;d1i^y!Cg0Lp281PA=UYf)UEoZE^?bmFXKOXijr6 zrMihg^A^rFN|?1&dc5FdrNQUpw>~>8M#8bMhmY^yX`u3^Ik*}$5b<=e)8@7UviERw zDNEsZAwlXxD;j5w{1fT|JzHlrVK%0U3z=KbU zi+8c~+_c-0raf|+U_-L#3(YGL8~xY=hP2LU2Lo%8p-q@9^p;;q;>7Mtd9km*y~!+i z6&8xbq0of&!VHuMU2<9Vc9&lGE$njuS6_Y2kTeNt4$JzgERtxNw&%okj_r6{ zin=b(D1#v}dz`f-q08=<;y4&S*WrFCmGG}eGQaEGFI#ex+ zHBG=HK7Rd}F4WQlQ1e-3>|PKUt-c!o*!x~R^VdRe#st9O77_g& z<3k7AV~BEQ(UalInw~Q-cmSinq1v|7tjNxym z%{GN>#oOYi@{h%H%4RB_@i~$J0kXT>;1(_<@KS2U^EMqd&_udG=I&e;O5}?&E=+*( z6KZKK`60>H{f1$tZNHR=*{^(9q80U_*Acrk;ahk9NiUpT#+#=pqh%M3xu2^d=o4u1 z`MEPc#YII6|2GmophmpXp#I_ez}rjqR>J#BbO6iPp$P|F(u=&)`)5t=&t3%`&)=hR z1f56kwzM0I27Z>;ZLZ+%kPq$Mj~M);xR9ex^$FH^<&9ovM&#HuYV4AcmNbN%V{-yu z8&$Y~!@U-zo_sEf{lrAo{? z4}>`oLvQ77_S?4NXtCa|sj-t!YjH1;t}sjD+Roe1NfR_TBXJ(_K`4v3gnxzHClrb9 zUgI(7i$ZoK`TsMBJq}_d-v3XdTAtB+zZu6`lJ8Q!kz0X2iA3URR))3++OAd&Hg&nNpdKMjN$I4f}I zRyFL~Y|{*Gd}#V{;g=6A+c&Gd-_{9~W%^kZyuF7GU2481shr*f)vOELmIa7A#80az z6XHAFHvW9QrD|~1kWIuOED5!Gl@N_svrE=sY~IgHD-w8?Z|A-cFPE)hvC_nBq!T&? z;{p3Zmr`?YX-sWl3=*vxEh;ioEQO3|&?RZ5uA3XtFADF1LH@#RJhxK#wY(X5rvF1r zygSAW2I>4CQ0zJCg{M4+df9gG+|-X)KQpY2VIxO@bfj^q@_B-);Y%37g@C*FxLKG9 zXCHKb=x?}k`eWb~@Tp^Zv|Pvgr2}W_DtX+KcDe^vFwmqO>R)gjiQWO>5#hw~WI<GXwy?#qVfo93hSQ?b+0fGu;GupTBnEKYlWEwV`bYvwg=2Z73Rd3@6jX!f z{pfZew{l42?jyo7HyouxD#fB0T7EvbE7K@zr^f#RTD&(rWh;Hyd!I$V0e_nF(N7n( zvR5J^e-3pm=L-G20FZRcb7@vXnptep%|I@ zwQGxW0r7h;701Xe?KTqDPI7^9I|IAxuFLUdG}8Px=0~AVvR=l?g=w_Z-)M#2<}cX% z_w(>ue&nVs?zVB<_x6MDbH4{YJhue4oFIL66bp7+)!lF_@#*0Dz;I?XJuGGSV>}Am zmSsD($&WW&AX2(ccbmGRYrJR*r58W$>Y|UIH6Y#8o~<7SBe2T@6#s(-)%-1gCZ$VA zZp_!bb?f;!8HsX!+zdfe9Xp@A0H~3yg2r-kzn3$*5s~ z`BS6YJAtjwd3pO<)ImzbhV7FP-E2bUv)gp0B`v$7T#);sSo#pdjPv<<&SAqo71ik4 zYEXT&_(;os%W$d9q*W%F_f7evyAJCJ8w1JgH6f-AYDyJH=664i>^6(3S``a_suE9*)plfE zcV;sZ4M7+h{s=my-DPU9t5D)A*`0Yc#u?3TYMZNyISf=`L|@8d_B26oJzq%uhhjK< zZOuag~vtJZ{7y zpq<+1Ju9mK-5Y^KG5-m={AXOGR6zLR7A|ulOQ_^&Vr>G40IF6B?QSp6P zgi^=HAM8{8?-PTbR-CrkUsDS_j}jao<$Gm>k9g**?c%_+MOv@sg7jX zg=)I0ifIhQm1jHplxYkuqqLz^1W0f}>uW*w+6>Zo)iv(YVr{ul5r8zniP(m|q9a%<(LkEEQ>s3mz!OzS=CWN1eb{iuGgoE4xdtVf=u z$HL&@KflFr&#ONL7f^TXI5f27sQiq%$tVWUk>rYJ_m`!jjb{R$v|e*ojdjfPW*AP0 z%!y|t_iu@fH6@3xV6uH<6bEVF)l09PwqQfQif0s4J$dwa zb&Lgdv+&J!boZNhtWb`VJ?AXkp6P*$(Cq@NXLE3Y(sEU9Ayxf8(H@X36Y|RIq&6)- zdzagrmTgrYw37TBL3txrcbNVmiSh1O6iuh1=Jcn`O~%Up>$*M9YtLB4tK20a&wjR6 zNc>G-U7tTI9UkAJgE9Fw-IsEy9~;9~mz|H3Q{Br;IxdvyPu)7Fg9_ztym@Ldl0c`w8!H3#2gW&3t;*kBfpVVMG<%i+Rbp=nqkxD$Z~1DM4b;4OzNF)>~E% znV)(_R0P^lQ!Zj>Mk~r;B$4BjOjmXyLIyy*N62NF-ds?vc96#CiKt3TxxYqBqpLE4 z4j;#4A}ZWv(W6;?5U_A2eqCim&W>FnnI+p-z*yIy`)@XB#hq;Yh0d1bgV{u9Ggz_5#7Rh`U=8KA-|gt6>C%|Z5qXMOhiR{TOQGcE(DLt=^SIdm1|MG(Ao7d%FrH#iLCMU zcAL~d4ecJV)Kl+i(0adc{Nf$Uy3KV50-Ssahv9`yBN$+z$86`wYgEZ8vdGuGmVum>pK5rI&fD_dOXP z+96CUt=~8hueNdyb(}Bd(VPd~R8H#yD2Psp4J{?bdQ|nH4S^DDTD~$T^Y1tjAMVy9bclwCismPNXh)V7fmiHXj7syTt;UbPuE8FxG z!Q~sQ1KH?YXX!%<106$cT(`mNd2a9n&bn?@!j*;wY)c$PVAVqP>%Kd6 z0;s1q=A;%(D1PBG>HmngQ7(`5nd7lr@9%+S4j(Bk&echf5;Vzwb*ueck z;7@|C{fWE447VeLr%z6I-IH((Lyy0fESZQJ_`f7B-aut z^;sVvjmPw5LoCN$OBzu>yjh%UY-*`q`&oUTU;aJU zQfl2$>B0N@{d~VhLg%c$-Jg56I7JPD9*xgq`;~kuUda#U;RrC}sE6N1hqJLq+-D7W z8VJ*(d%)4^JK+C0;E*H74oM+x#MNXCpOV2z;E-$7=m~}NeY9=(-XckVy+XZpf)z|! zi=nFz5#%07hKR`g(-&IrqaM+P${6%Q86){5RwhU)5pUTsvb_`*QKBpP4zOaUXN0(c zNaQ|+r!q}x)`(dF6{e||-lkvB4YR4}I-BlPc0&H$u}A->=&y*u-IZMq-{B$a(?11! zn__8#Hc(r)vLlay9&f|<%7;V;PGinCJN_TVS(F=~w$i|rl_g+2Ze z1yJY8o$wMTz68U|T#p%!-rz6}fui2mmr_5^%3{tGrx~5GWPpp@I`58_gwe8CH_j8f z*_H@`6r24lo9Pp#((|j`J75b3b3(}5`8(Jx)hb|UD(p=B^R7T5;_2f(*xU@JPdA}$ zt^K(1Y8dM9d8z(^|M9FQJSKLr#6mKwv!9J8b@gwnBlIgRsQ>lh6H^9@dMTB#qQsX3Noic zn{?*ONJWQES5`f}Ta&LjYm@5&)bZ`T$`UmQoC*>gH-;HlC%8$6F7jF?w&UroTbNq1 z)}QjP7k{pM?uZsCV;bA}B+xyoR89EnHsIE;L;&P6dJsl5Ur)__k{2oCRii^p`Qb(r z;o>t#4-;7Pm|r3hetoEJTT{mhb-k+(k0R1nl$iP}>UnqKo2;R@afun@8rPd^j`kq* zY-#Od-bxeT>{sqZ1Iod+99@q49)WoZmI z=aKfgaf8aehM3`2>7Q`i2nE3w40u9(5i7)Ls5!rEVNk|H+`iaFN<>@oX zr>}T(_T%F1iF!~?RE=6(A!bb0yJi_2eCoN+!0x_!KN;eAARp(($}>7g5=WThIM#H) zlZ5}G`5}~h`X_cWG_F9+lDD}SGl@lBhHpQ~{?@}4n0s3u&o*i3xc5@aIbvT0BLmU1 z$JQrEJW4SxlM8t4xs&lls1 ziL67~gU}!SanSvKAUpC_x?FJ#e+l&YG9c~lmKo-kap`?Z7vPte8=~Nag*cWzl<{n3 zez>pnpJ>7Py}~#zc|?H{KuEe4m#XqcM_E6jAp1HITb^KQ)EFLeg#wzOqM9y)TZLC@D5_$s;6t5=~@qoM2uzm z?Ib)Yq277XyxJimUZ&d~6>y8SU}Vh{DlL8fD~i-zqY&|RAR%33QDHH8zR11&L)%Vg zcQu0S;LgP?9I+}^Qx?cB{#(h?oNmvxqbQKN!Zn}gsM9Y1yWD>=R6ukVl>5$!3uDj)n` z851q&jJE&P?F)93Jif4jVj;!>FmJk>t-Tq?zo&Hht=mS+4 zB+yo4)R%_rbWBZ$F&DXfPb!VJc8#-qF&46t9fgn=AIL@pva)f1gOaOh|67t|6}`L@Yn+Z(7W8qX3^3DFhM`v-*`EJ|b8(>gMEYUG8c*gW z1Wrqg-3nrP5+EP;T74TVbw09)O##b&GJcD(;I)4s4r@VU!=(#7dE+-qE}!QgRQzj5 z{sk7%3rEP@<)StWx^VA*(du7rjNYz5q;K$E`kleis13vSIcYDsFl%B>#v+tgUi2I| z(s~GJ-lQv0^cRkvg*QoiU-7V|9^+)6Qz5Zz3H@ogE}`DXQ8rFLr@2pQK_%6NO1 z)?OHi1KEX#U`kPHv`Mjlq%9oI`=is-TMn5xJdyX3Ic8${d>IA;>b*T@S2_1MbtC15 z=lKj;CilmYi~VnV+T0+?hOKAHjXN`!16`(LsuOA{>#ITOH2A9{D+HpIAE!0@10{gj z3_ttcyu#EHeZh5tJ7GO?sQc>6a_3<>-Hch$FSY-EydwtLwl{8=9`3?$3EmUlnVDyb zC>pE`qv0g!!e;Dc-L~lZ%Pon5ieJn+U@{NkZt|s$-RL23=Ue@C5klEj_akvsdrZDk zbHRsy=6r94fH+LAW1d!)YhEgGG7tFxNE~;?fuo%?PiynPf9c_L{y=-4tWPlSDRgjU z(@QSuH!`(~dEu^lF^NQBsGj-;U#qJ&=n+kGG5P@T-$Gm_V>m0M?mgy4RTRd@Pz z>CnLbD`k5XpdN+sdnBLP8zPSeI1sDshb0szcGHX1~Gm?*Zl+vUiB?kpeP@iqpx6T#s{gfJXyurBpf(Ytj& zhwG&XSxotvI4~!~JfE8M%*E6j>#qp3ig5|tgS0je$n&%xWoMp9X}AICTenP--3%7l zgW(pF%!tVG*#dJW3ie}SgjMsWYiE_oQMhb*IgL0bOSmL$-)fo5xzJ=60x5u*KM6fd zRa1%7A|dQA*ji+--@kF~Jj@=AI>%R6yYy`JvAwxiqLMNZ%7^1el2q|Ya@%Y{$^F)X zR9i3qPSD&cbf1O}j3n)n8}qmp>)5M1*`HYc?8l_M_!X8SaMd?c{>=ahRy3ap!%@RE zl@LjA;`vH%vc%$1Ws9GdyWHf!=vYt{7*J;;Cv=|SWIDk7EM@&hPWj;+FTgQ`=_L>p z8E1)Nv{kLTMTkpX=*eq2WhGVVVF z1E&nXYUQ4qlk_+ji>oZhegYsG;Za7AA;joyLLQD4kS8kzk~1%LI{W7$bu4r8zP|i2 z$x*_2+5}={@wjEH6aH=4aRJx9ryBE(;C29~>e5MR&dG(*E_t6jetEo#I=DL3&DrN< z>-vfE)ugt^hrv!AED?GUC4Q&w@@!5dSQU%mJI68xy5QE~hFP<&R|&R>53!aAVK{y7 z;1?ne_(r#Ixn*CK@~2&X#H=s%grd{l_07q}Zz+YXL*7QBFJxb=ZbxtGxQbqVjJAD! zB*M8HE&iL!Z^>3I%k63T%~D60A}d4Td1!EA8q>kv<&^zcNwaDlP-9q*H&z?WSqbX8 z)ScbtHz?Z)HD=iiYfCE7e`Dc5pi`gA?Qj!KRi&@Zd;zIg7}cu$fhY!~dERR&3= zdOj2f%*Zo10#sc?+#Qy#g-`k#g$MfjZVM$0@~r;EH%@XOSN~2JJq+B?03c5Pib8b^ z^IUj@t?JgReJNf^*!{N$Ywu6Tk}UZL{w}X|K%vYyi1nR3o&KkUcfSTpjm((0y=s(} zXI~IHNV1+6r-@WvXvls!$x??3u^oTOrY<@bXNW`(Y?!kP2sHv-dd5)dsB|z&B`nn~ zgZ{WKnXg)+Q!r!e!k5fn3$KEf(4HTMM)^za8&c z8|d@x35dXr3GbXA(izjKhEv&}xU(IppA51XhT%_Aage*+HFD$~1D znyqw(V*1+`JZX0e8BfuBH_;>8Izl z@FL#r?-m8VwWJu`0dL=0w1DWTt1r3URyO-;E8vG-5jEOdnsnV@&^mt#&GJ9}gcRE` z1TNu3{#9;RRJ<)7G?c30P|-u{$c$>lS9H_D%+s@FK9TUYfIf-M_QpJk75BQ_`P-!}gm?JF;w zpjD3W)h48Jy(>$p&$)ChZrxH)eCX>Vv_hIa|Y;tti))Nkmy%UXkm!^#;@`K9Blye~miDes!A z7KX$O=J6CguFzd20BZpHgz$q3#S``t*r+jCgc2(i2{mJF9?qvL@{Nf@vcGyXSXc5C zyk?bnV$3Q+$A_XrelB}i50q$-2Wn=d7cVnJzIZGN;OS#11U<9TodR|?&{e{)zus@b zbCSPCGGyHy$&;1qsFpp?HJF|Gst|!!nYn0*SALOm$Pt05uXE4Ftvk7fZ43_v@Ps@J z6DRbN8@vYsQ)O?>PY%!PFXo<4yf>)ESM27#!!5M$UpCJ(-l_}7H{STI#^@Z|bNh@Z zTq(QP9|2@QRp>xYRY>ZNFLRCO`N>H6S=7AOP8sZe?B5DU(?70o#;Jh&w(#IcnbDni z_;`KmUphdvh#Eq8IN6Tem_q}2jDhz0Nl@q&-Xicax9lXQA)z4E;cX6K2aMT_;=OHH#N~vx9yC z3)OXDBd{5HLJkA$52rND)Skx4nmsJEcPz9iV9~1$b8S1zK0$73)3|cSYrz$r4?cNYZtF-Y1+i)7xM!M~0+#Q(1tT8+b!NU*Zy< z+`ux11~g^kJ^^0KIb{}ZTqh*cs4F8C^Ry8(oi$Lf1=dPpAv5sy$X+znyeKn9X0F9w zwz!-RLqx=g^UW#8bL7^ndwbjt#*mmKrwGR@DIH(P&v8IitkSQ>wW0zF?`~H=~S=PT}c5j0rpjB=$ zy+3T5x9_4GnhpbG*5*!l1irUDLFjOFVVZ$Sr|TU-fq36b1N{Hs?)>(*L|C6FTt9!I zV610C$4mT&)LkKi(*)bg&aVU{d8H$hnF`ZOo5}jPthp}XtAIT~2v5~W$I14$C%x@u z9=p1uVK$#iCSqJGQ>7t5Tu2nc!5@6(Af|YUtvb~W%(!CQ=BvM(mf!1u5P;&3CSe2E zqZg5o>YrK!!mNxzR7r~E^3)Y#R+XF>?I9Wi^ znTykAPow54zr}T_{xN?-8eG&J7>!Xo5{X)Ty|=#HKBJfR0vs@3znx6UoYm`Gl96O< z9%RCvKJ4g>6l+Zeh&c496@msw?WeG^l{c*Aag6-H+HUO)Jbc+}WrRIJqg zC)6CB3>0+sAgU}zkYTx)M&OlCP8*Yi{zQMn-5)o3YKm#nn!%zFY)~nD;4mvE-zMbj zFHzq-Icv=GB_iN+TFuPOU*D+%Z4bNCy%}!elh4aO*FAd!aZjVgA4Qan1@Ix)dIVt zD3mMkqdz7IM|m#cD2s5H8p^m;t?6||s}TVVW^~Q>O=;{54E@@+I9m;mS%so%=?EP) zZWofw*Vvhz`HWv*n^TJlxhKyHTrxM>!I$Dah9j(|FCP$VJTS=2pW-oB|XHf zCvH2P$#ufBN;-U544~&yd07+fP`=1U6PY1ZHie%nZJI178w2nk@X` zah!D1vm(FGDImgDRYt@J?jT4NDWaIk3IW6<+&BUtnn6S(p=Vl@A)7+ zU}Gq%tY>XtR2w6UZ>2-%-V2aqv2O(qgxbZ+sQ`;wt#9G$B&UuE3|b);9|Y=sy440+ zgCDLZi~!d={|YT3|Gz>@f_e&{T6va99ha(%n_RDetA$WB1K;%@c{&56aAU@c5QC5| z?c0}s>qf5Jo@mW}24N|nRxr^xQ=0VM65?)jJzyn>aB&*Jeb)jcus;a}F0qA=SXI_9 zY>1m^FYe9cB$~XbKji2sPm-+o(>Ti3$M0SsWy?sgdX5*fvy2g|7}v_6wU) zM-}qw!X{NZHTzB?phwp%^jH%t`-x}Ql#j2!~M5uD2NP(v-|_4(i9HzfA4y%=^tJ-vkMT{d76qTWk9 z<*L-Flurh%c%N3Qci+#tqqi_>_Ab$V->3O8Cn9EY4c&?Y-p{*Nx=a&CHK`uwQD`enqy>cin4eRw(dmiqqcOpb zzjXA-b&=lT`}BpsS_^b8exrV0dtb*RPyfpD^@$uS6+l!fAsc&m6pR$%f~1CVjX-^+ zY;t3v_TM4Eg02WWv*TB^qh=Zx?U7fzrzJlvs{yF;>HUlIipqL? z4TIr263b|b0hayg#89S2??cCdWb20yeye_)H6U}aC8B43gj;RtLR-|W0whC)I_84^ zj=7!fH1zPQPGQ{$p8w2*mp$`VT~epaz^io6*f-9?l#1jlhK+^BWY5>``7{V89Eh z6|5V9+O?jf-j`7<0>CnD`nUFo{<+IaobzQkzaGq*=T)Cy+v$q9J1cCX;O>;6I+ez6 z{sY4L;!JDe z%woxASKhWaLf2h)RO@yKAXCn-1-s|7Gps(5G718kjt^eI$sh|sR-lV&cM!~`wyjx&puibeG*=r8DA7~x)6+pa$p zHR~;&Is39Sk;F{M8cJ zbtSTmZ+vLW@2k#weHE|K+`!Sg_Rp3SyWVJ)(>jxDb%v7&*FPEmC;e$Gaxr8F^v>@j z0)V344=UORcYmi9Iv>~R>A!W7ta)g=xK1o+S?QhFFQ5Ng8r3CSa3j94lLtsHp!Tgi z!=5dduxLYB<!V!F)!eUnfwxY_%|LSSPX9aRoTD*w5<$4Sj zCyS$_*nwI#pYQ(!)4$_L%%S<6jdHO`gS*;|zF<&}>FisLG6_^mdp^ctVP#IJ+C?v> zxBAWtWZz_fZYm`pw>m$^Rzmhaa-8ffjPpK-WaU>2F>R{)`@ z?2~16M_B%vZ(wMfE7_m7eEk~X{Im7EYbmK1UH1?Z_~zJjekY`l1=90znKr)DWGnQy zhQ6-EaWkjd)$sXnZ(Rs7Yd>9~-~9PS_*Ac@*|BhZ5r{8)m;NmktZe@&BEt1+mWr3j ztdJkU5L_jxWGe162v(OP5(DMb;OzZh^)6iQuyp)Uy;J``^$zf;-YKU2Q<^@`H6A`$ z;^j;6SsL9IzlaRm3Lcp5J;yIkZk!y*GZ^bc2hE@N6K4=K9N`txFm6sal`7lWLby~s zbkhhbCf$fbN&GzK)q>DDpJDT7e9-pm8G$&8$C+@SB!32zSJ`IGHv+dA_^OqsU%b$F**8V(Ht~RVSLKxA^7@ z0ab#VrqQ7F-gT=3XIiPCv5q$F>%l^(Y`4Xv$3Si5xK7Kn>sSDx#TEP2mn2YqFi1A2 z)bv(pF7qjgElv?#r)z!^DA7)XK;VfUgq-^h-vT}sr=!nI~hbUk& zx^3Dk*V(3h_8#9xDbLF0!x4$m!?=0(*vdhcy8XYf>t_jD)0r{1Phb~UhC9{AxsFGu z7b0T0vSotGxSKrIV(mt?Cnfpe&oC@jW9Z>qdU^v)6Dlqkn7L=vdUp>mc=qsjjs{@z z5^tO@(iZ>U5$r{EaO0;OI`<(tTAs)~pTiGHM)vXmqoI3chzu&|9W;fW~%s5b#i@2NNU1znEdmd%*UkON%JNn zfepNpX`Y)4eOF~5Y%Ajgg*Hftv=WH^ss`Gdo2f;*!&*dK2Vn8CIMe^i){gXAkiBa3 z%l*c`Sul%I&s7i$%OOIPJu{gGl5JRh#g5X!&@;YV?U<{wDsSW7Rrd*se>2zlvCp1+ zGB!rhcDipRZDe_XmuA70^6u4893F=hLg`ps&p4@Pn~G(Y`-_Nialip*<;g{bn7q-N zdT@F2WhgMK%c5PbyS|fTz>9jMKl89>O1t0AwCw5gwPCfAPU4uSS}ai{ldMaL1KM|p zEazFVD~pY)?gSY<;#_c6bo-sY(57w+TXP}Q1VOTIe~fY{jv=~OME>jGuNayuL4hgM zr^tln{F2~~#zHEEjmQn(eQy>rj(vJ`lbF@!76X-@wO0G=g4-E*aKK;JT82J8=xTa4 z3kG&uPN~Vvypzj`@%8$=W3P@U^72D{>DX{>xwsfg%bJaCUV12CFh>o@KH8+mL~ffE z(2zqA{P{QHOP&@te@Jsyy^;OX1CQ0~R6CQbO$F;WbxZ|{Z38d!GC%3m$!lgNIakU6 z(8Z3YU#+vgN@hxEG$d)Ss~Yi(ixmH6M2yDaDkKW;N)8Z&$_x@?jKID`aC5ZZWH4o= zW;Q~$U8%1WfIa0?JP|Wd_=wT8=02SN^8$42YCrtoZs`4TanfSnF}jgj)i5)P{dY~! zf5RKfYw8amA#}h=YdEEm@dz3k$S>VJRk0TyV)m_lf~Fz*^RntRrDOe|>22JkEr$We z(v&v!n&10w%_-y&Ed<&RU>V*RSYui$@(JX^P_fPuM4hhJU!56E?XB9 zyIX8tMN8u5SySVCSeLee>aAkBw+)Dq1_iCf$^H&`o@YH;p7;C7|0*2)zq48~I+3>| zBHUbeD8-|`bRikYOq(c5G6!{rqW0eB#=R@gbm*^kud7J41W>s^y=Ar1v{dILsascH znc=iKFlkO(pgK__z3t>99QT9B+hZj;=I9LybUd8+$`YfG!|1HlmQQirpI8*iK9!i- zkEQ915V!nWo$4onH@rjly;7n{GBAn7P-y!vwrc92;P2$2c;+dG{U(jDQ@MH$7(IYl zTjF@Tr~J!K=~?j9WU?MBYrUew{e1~Y)uHK(uGoaC6Fhh<20h$WJo_ebcii{V{4-xC z9Iyc*agxfiPXbDSqN5VdCFLoB(e!i>E3yb?U-@@#n0-{ zvHzrtIMK0qf=ac2adH58bhR#7Zrpkbh>i-{PU*pVTrB~gIf&^ehmC#{F}~|s6T=LV zr2DJsB%4pXOeT=Ckd3z3ZCjZ+UrJ6UCql4J1L$g26gi+L*4TNfE5C zN=lRX^_p(fxT~eOzcrZxW@yS?60c=c2iK?wDWtHPYOUQq<58(t5hWllUL*DMJz7TV zO~m#42Rtzslp|N(iDT!(pZj;%TUIy^Me`-*`4?1U$GtZ(0PDUMhxTuLq;PHg!#u^! z>CBG$K7zsy!kIjHV8pW|c2&gE5y)jeFd`__xVEMaN1Z9x-PZs!F4zDW!ooc5I08@W z4Bw!Pi+fIkIbRoA7C@*%_);uS)68Q zpRKC-j@)DEW5`<|_n!{G8JdHL0^LUz-ygS}t=IQflY^D5rg?U#BSNy8Gz=+bs`{vt z>R+(PwC1#huyBCstJ)aU%Vzn+6Q*6<4~E%CN>_ZN}p1EBW2;qYs(Rl-&wO_1nDj&9cwA0;KJrQ zTXet15f4r;8N2B8jnL;&^@I7eZ9BCyj*U?$`i}2`yrmUEldx)P4a>&K&R!SRGMf$@CaoHxl zjuQ<)786EBY^Ts({@azrKN{D+4b(fPUqbV?5t*pF!?ig1UkPO4l-fNbty5`>meLJt zD|K2chR=3IPFK(-o$g!XVX3^5{CR%aRQhMSjaY^9rAVg-|ErNVHmYxbsjYHHm39(K z11k-vPH1QrH87_Y>JVLXr$1h?p4Hi{_4Qld2UJ<0y~dHd9=25g;n|gD=GmuPt@T}+ z&8ks?t3$EBUKb`Cq5btR6hO#tzOIJ_L^DBZE8EYvmF_^2S!daRn+6hcU*5%arI!Gy z>(%7LWU=y>RE_s7kyVVrOsOIs)xo4xfvH)hV?%3k3&&y29<_{hHgNlCA)E;~S%0jQ zZ@A89J}z$P#1eE)DEd>gMB@Ol7s_Yn=eVA^4@lM+guRbaUm!XIYW*;1TwNO=372x( zDI<)ILM>eVzv35z66m&u4h&&OQe0|FNQQeWkbs2nMQ?QySf)(mf_}TN>+RP{!kzOz zRt)}UzfKaN_Vh&nbXlVr$t2z>Vim%%lMEC*^z(%+j-9({H*rv%oR~^gN^b3%!QR_M z*hst}_TLo4)Y2V1-T7Gyj#K?)K;+IUl@092;Y?7}qh|NH-Mlo1h%Bdf?Ln=-;V4Uv$B?0u|o2w7Pn*_&fi zl*nEO+3Os8q;SrWBgZHlhjXms7{6z)@B8!n+ka0y9@lldZrA;x1aA%A3mH7TR_}WL zM={t`;jJM#aJ<^X#r}{APGZsig?%3UYW2yzc3*TmuEtUZzT-G^aGePOdU}L}c=JPW6;AZf&h zepi(V8e$Wsf&DqKF@6N6g&}C3cf1TA!c>b&2Y5LW@(G``aq-Y}0qqJ(T!ys4vsWnT z#K1Gll$4*eFm^(^_&^&NBWAv}_FeWePjH3($!H?pOGsijcj}oY&T-JN#P5f=*fXi7L-KG9kuh&$)e)X)|1AT1x;6uLV zQE`x9yzcQpCrnwYYSVF$F1J*1;rf$Y@%!|>?eGsWdM5mL7{FGk!q3FJ;$AK08p5^++yy(~!1SHC-C7ho>spiuvSVJ9xjd0GN#SFXqh zB(-E)W(7?qX%<;y0(!?XJVTs(Nl-~d%gx}{xtph26b!gtv4J<5$B_`y<|5`5T51)9p!9TDoNY4J-~DBQe684DZob5&o6-HVO6Uf7p%Ib~Fd z{31wYCRpKasmsqawQ2JG^(%LPxApt4P=sZ#jYCb|99aA^_6srr-#X=11qT>>KH+x0qR{kaIqI?g z{Aus7U&zkfpCaHfX5cfu?L!@EOa~|UQTPj-zf z(JJt|7oKd$8Vg79z(Rq@slcphy`^4G#vMLE?PR|O4KbI}++-yVDeSv) zS?;%F&FjQf8EBD!P5r0ZbSKa0wS@h;$hQwCXPy|rCtL%+LJkE;H+0w_USGd&nS8-9 zROPEH)oCV+iUom7iT7$rZXBhfGj2kCs`{NQvpmOI1N(@RzLw9v&q`n&B*K2K59R;C zrIV@(GeNdK@uLQ>tR-4F*|Mbk>!@x za&4%Lv26Q21Z*L9yMD05T+T>_Rnxw^`Ntrqc$-tGV!Xc6=Zsl4T-k+Zvbm*{SX+0s zvXF5jce15W2@Ce6?d~^GkRVeZvpSYghJIgtF-|UYh~`Y;AH@rt*a6N9R1NIQZUOPm zX9a@--JQQ)jv0@?;6@e>UZ%!lA?B2EnD`SVGHro`sX$hn6_}xe_s+h2q_{+jpggxJ zngH67N0GjwfR`4{)pwO*!5A$!i)pBzp8A=gV#~$gQ0bu}66-zFf-Bkqp2}Z?Zp69Y za0(e|20+Blke!kAba5!0YV`g*$KS9v3Uio*He4$1gN%9zOL{_WGNoFG{ub_rVJdQD zbus2V+`cf!{|;DV-<*_T8G60ANMPMSMfE3cdq~8u^OXG`15Q%?hs=u_TyZQ_#PcP% zz}JW6neqRK(|9W2CYY@lRjUMl))V6hDh!ZIyc;0dJ!|Ep{T5V1lI)@MWeefTg+7*v zo6U)#-5G`y+j@-KM7p#EE_MmaCl7OR5$VxYZy7%c&NhWDZiK)~fBJ9MR9Ps$*m@`E0gzPySbT43r8&P42NxPk*hij7>vY674 z38(z3Rj(pH=aNT9pNw#O+OZCz^a8)fed|BVY;ooKt=UD9#K7|Vxb(r)C+H<7KlGx^ zr{!_Ca0KbL7Z!gB5$A!OSZ5y#x(k*+)e5j#A%~6sKTE7?_wk%Rsfs<=7qG;3vOap= zQxen0%C!9MA*}Np%`fg$o~GSom4oH`RzaRtSq3Foyje6CrR%Re13jb8ZY`JS^}`<< zBdj}CBSSAEk;JHAF@rcGLNEFhZsDuf=JFw`vcz$Zbo+-fy0~NeGF|EItwo*4_L&mD zir%zMJ%ckJE2=fW{YfScuw9dRcE!4~8})Dw%{x7o82?j(KCRT_{A=6Xjpl2*mG;w6L5pZ~QX>&{o;;;KW-k{ebX9PE@=%>O2KpIc^I}T$S>`9H z#P%S+d7<;m_!*=i_Ay+J5|AuD`B~E#H?V`K)$2oD@L(yh$F^`)JLy~>W2Ar(UVRjk z1#pwKJLA$IO2YiLz?UM`DaA5OEMC}t%C4D)1T_Qx;CH(wNr`yX;i zV$ShzI18FGy+>u&8};b?eQH)<5=4wC3zRijU67twvdLBV{=4keOW%2oTy9*@5ldSS z=eJ$hc(EDNBK}=4UV3-o+j2K&ye&-=0lRv(D5T=C7?19qSr+g_Tx8a}KBv6&5b^mBmVfOuI5Oi?cBb%3Gsr16E>ouwtcU zgbkh@+o<4zwRjxQ{3o}Reh&h#B-bv#mXfnxm9>g-FztD(!%av{6a}@ZQcW0?PdwlrAOY_$* zTuxWMi18FCpmBTNt$tQGb8sk$X#clE3M)8;kJh4TMZKWw9TlmdEL*h%l$SRM2AKO&bwx1Nu;YLqaBhDf7^_Z zpgQM(U)*`&x1&q2^7Y~A#J5NuwrKxcAV_1LFI=UBPpn=eMzR^v**eQXHT!TpVpIXi z>bz15wPdp2XgpG}q*fJW)GJxl)%FKPhZ$mt_p^f*%?;MRB`jCOFK5$^nQaWbb|e&O zR2(vc-0l3+{P1+|v^sOE(aL9o;T!N6!zz_H3t z{EzLyz~$%O9w}ij_9f+2R# zb>RY6HbS7>zxr53B-ftzRnH#g?Q<>un?Jc29h1z0#gyqGmb>;H(GE3&2wU1mXF|@N z`I{KNtDhpnUR9pNWYOeR^KDJ&9MUZi;h`8RBYMCBCK2aO=%LkFBQwDjDFuL(@+cKX zTZucr&Kb3FJmMF;j&Rf4gtiTFmVA^Rj?=){rNeMoRYBst{BIjf_1nBq3C02Rfk0lz zWoUiJox$M`_~MmN`oZ8zUALhj2k(gK7pE?|GzxZukGQGzOZ1eVi#+%!pA>d-#51wn zD8<0^01duOOhKq%C45O}lV;E5v7|(x zGaS1PTu@2(bH45HtpZ|4JJj4pa{7%2(~?44S(CaFghAS`h}|)t zwPirsU5?@&c@@HP-&fhaUU|Si+B^<04OsN#WO?}QkGa^m@C!oZxeae2nv59@x<~TM z2raPfU>Ebc_|b^();F4CsqEn{!Pa@UKNp-eyN2uyO^-~zijuP}q^=L9zLTP@k6~hb z7sT_dZ#eyDIfa%!(Mae_!ow4*4(~-+8j5+^+D)%t1uotsp&e69S1Y0hkA&4U52fXP zEa+{OI;rt>%fD?JD(id6Y-R|#9F3XvC8>Ut=8f|?7_$kCk5kjgdT7@o#c;{`;^1rY z$(3jOx<`C?=nh}5lxuKjm&l^SbIaEppRcy@I_P|zZ^D0^A6N1Ffm*+#v^0%oA8^_` zfmi79mkrG3zlpexZarLrr~r7(xO$Glj?w}S+*MX!o{8cDWLx=v#sL`kVQBqrPq%C1 z?2lPXmR@RyK4jw$DAU{XbGr`0pK*!s6p)H3^DrhtQ{jyG%gJ`>`c;Bb@;F$hvoXf)lbhNo25WD6KUaoJ;Mu{NVGrXpCeMiz_ z*08;3S3vB@Ck^+utMeu1pL5Qe*uB5PiZTp#qPttxF<7;AuV7j{ zi`dWI>zt52BVf1Lr-Hxbn>-4@tkZ!H;%-{>@Wu@^VDa0R`i?OSPeuEB*ZvE3j)13@#nl~s&xtJCH9CWh#M zmI^Chix}F|JjOz)Y__L3|D#HzV(a*ZVXlp0w)&sTLnUfoDc6$HN-{nYB)%?p8#&)9 z+h-11T3?33?1N3{2}4O#KjT78@Uj_@R2CFqs&7^IZXjelv8a&>4j-hOBjM#Sj?P}6Dyx*{6^zmb>DKu7w2aE$qu-bY=)x`LN-=V=;7=B z-8rcKhi#=t$9_VEFzt{$Cjn z&2t2*CK7>fwcYRKA);Kn-Ww5| z<`Hd{Qp1uwNwP}h&+C8TI6s3hV0mQ-ne4FXD66bJqxFucWfI`Za*4_8oWJH<$6W{6 z?ru*7H)BFCs?8%)K-Up}mCs=Mi)moL5H@k(ch=w{e2ABezOMM7)S+~TfE?tFfYJT#4>p_Yjnu1DMZuH)Um`R$;uedqWC z4zsMSX)X*Bx;T}u5!9%Mfsl~3egOpy1`hdDZtk54(0QXR$6S=F30o>srbx95&3zi)P6xM9==-E>?yG<+>3>Q>cJ1)oHx*k35`rr!Y z7HBmlUz!kL%3FN$fB~!3qC9)lx6bmz$xAWIaEX-m>1>GLTUUX{mUmw0JIa0uzkFI0 z&UKBc*r zCwPvu+wo+mxD8Q|Xlh{(Q)9p=g}%boLzcs|<15=6@-cByPsR>8^l=rqN4ap1oot6P7rOBPVEtwHrQ*1-c8#+;LeG*(t z#Y)kol2q5Q9o}7Q_)P}7h#vN|Kk858)wn8xsU@8qzuo;ll9adlGmIJDZP>jPqobn{ zZA$prUgcfJC%)Y#MO=4_8kmj=wzUljTy(5vcbygLdytPs(X>XLSQyN^+H{)JbsxlO z;!0(c58kyMH$UH(kE$IQa>b=acVvqyea(dkj@WN2)p+QPBn8x-Jp(_(O*x+HZb;hA zt}ud=DwT+GszFE3AiQpHASPkjJrE0?A1K*WaeT5L1bzY52aS8m3X#ywWzYAlg&;yvfZ@p+%$%=XJC#5QtqkA%7v_D9Yw$2|Cl@s_92`?=dY6VBMf zBNF0dzR)!1WC*)7akt=Q;@`YaduUtW9vj^Z{H+|vIv?z|wiOAC8Yj!vmobqug9VMp zxicnP0si5&Ou1(kQ^JvFXC}|!Rbnz?9yv(Dytm%ouCdH85ew8n{8ZFU?Hh!zj~jVt z{FcE_D22>;JG1**==-r8V7EZ8qP=RHbf>!m3T|QszpePCxRfu#wBHGP5b2lG4B$tx zw*>KnoL*|+#oEWRXLSwR+9_3y8P9p3>5}v*d)^c)-_(lD?Mr_NtrGLNb~j&EdF!tf z4ulP=qA3?=IaAADC}O8HeyBji=dFQPp=Ac7t*kuo{m?n$Bf50 zsQY}7p~jf0&)fc%AG6M^vS{Z`L}e%@X(M3m6jIRmt=L zs;4w%;UbhZH?w_yLk&gu+0d6RY}>_JJ%;3}_U1*rRmj*j{)75dtWNo|5Y^ZA(1@~h zl)E$!D@=Jhx%pFJ^gn$)Fpl=Lc<|S(|I3K32Zem*i1p@$qhjUN2^DJ9U z0KYDOLv5G8n|YLh6eSL-Zz_231S!TS?Yd6#-T8Vpla&tKU^DVI2U2hK2^KFU*Z|z( z?fl@oH%RQ}w!SfYcWA1faTlChQvZW|Crj5AMAz2|-?;YD&xs(foWelE8csIBMd-n# z!!NCSgZpc{5)znkj%CHqJ3M67kn7le0viOs{zn5iwsWZ&O`V4|IL|%sCiLD0a-+UK||ez?0r|nl~hskz;(edyPoL6cNaLmb3eMYTqUhpx7t`l z@Bt63s%OY0s=1|!x0_T2$-b$dVgP5`%dRcNMSl*7|Lvx?jMv21?0PiNT4zC%kn)Av{cZxF!Ff{Us#@m% zfyA|xGPMw|;wJUfj=cXsV!K6w*9P?yFROw2;Wa6Cq~J!97-&YUw~_&G_B|TqJv-!v zoO&i0@Ah?;YgqMM3VB@-J#yS82&1Qw18+ zPKIe{T*s}+Ix5)6#W?z>V1~m63pSVb_M0sdq6}k7uoyZzKU|2qojJ1S-Sz_yYqV7H z-p{klg+aEE8doDH-@J2ax!o=(!3TZdDCTH)i&3%EdRLy) zwZ#$XQ#0VW=L3#=jYZ&U9eeOU4w(e(rJNiBaz7&B{Yw#JeyV%GG+?-4k$G}6cWA7t z*iIX-rk;hKeflfBR-<6?Zd25BudC;+M#1>w8&?^i`FFGh;-z0Xm6s@?T|T(Oc2lYs zZ0XGz^s*39?!ER0nf8 z|MSxB%HXXb*f812=eRU>n{SmpUKiZ5#2njnzTSJ1QKJTag)*?@q+cOf6{2T#92_64 zrh*7;oJJ=Vo0!`A0Sd|(vkwC2(aJk>YZZe1z#$V4&g zV4Row`i{NGa~^}nzQ@Wgy^0_IflM#}GP$%jLr#Sf|BYVdfj)5+M8NEaufK^X+ZW95 zz<+f(dg>$h?>O(eMDsiA`u>;zl-@(((ufSrGu_E_C(Rdr>t1Ka#?JIo`)G#n%?Zp8 z`J<#%;jg3*gV3gpjR%iZrwYTgc3$FUSV@#KKnOooe-Jq&kwh?HM{`^#s-~pGF(+v3 z#fGHFj+o?{SGp;TJZIsCQtpL|#VvaWp_vWlCW&o@6yM;w3JX}#BDGJp;^R1gVCobh z`^=nyV`E8BxE$JQfh>&tGSXS8Uhu@Dz+Wf%S77U|Q-XQWwvEju724nl{w;pNzW%1U z{k>na#awz?0`wv7+rCZsZp5`NkwkCv8>k11OP9YND;8$vuHdc#STt-}jSEx%?G)ZP zAH!!bzxy%bUtJ&dZ``4J4=Y0{5qGnOobGdf@dysP8L@XxzY=QQ>G<|PN4Bl>>5xGMDIMFg(en=tKW+ZV=?#YrkOzHaYilyEK zj<(gea}nRfww{<0Jf+#q@SiS+wb6a`zbHY!>Py!@Xin5|9uM6w7@=jNzlMIQEUCK2 z(cQrKg#Iq}O>V5etlgEVL)(H2ik7+p2tFGPiA{K^K+&f2cm)i+x6_*S=14+9>`y$Y z*rekkGI2KaAil7PMQrz^RY+8DWZD<%Mx>ri$(q#+tv&8v+PN}CjcpV{x^Pd{c)VaQ z^oLsaYcioG?=(xB7A1}Q+NwMGChWavnN_qXRps;Jw7)UqCY8uyA=UCE6zYjRyjw6* z8*;(clWSGML(yWkr-}X$%>r#JB+3^kRdwtu^#gu1|U(SJmXs>K?% zJ%~CdNl#`^x1HT2w1OoH3j)jYa{QlY-5s^Lw_+rFIt6_m73xMvOp^X5%Lp3fILj)O zXlLvRW#!;b+D>cKYY#fOp6^@TwWGjaddZ0}CbsEy{5`Qw1=e#P=p{FVJ?JxV_xkoO zI-iN-6-wvR8QJziVjU%hIYM4=-L{om2~^JCoLX`BJC2+tTqPJ7{QCQDS~XL6k*(aG zWUUL{w!NtRF^%x;k0z#v%H_cW;+c-{r-mBx%dgvdh5nb-XTZug3a@Ooz z_CmT9p?@UE3Jdq5gzmR^+|0EI(%98!SB4fPfL)J=t(K2Bj!`I`@yG=8j7s#;IQ1j( zZWu8_Ki4Imtl)31lT7Qg7{akCy1~WGRdnL)S2$vN;~TYKHesTpX+!wVODwXgRF|>@ z%|r(B)ywDC$yVCL#e*_;F;9#`E%FmA<6p^RQ1Xj-r7_y(g*ct_Wb=512TBQ__ipb) zxntb&wmi#5@J9D{MKjQ-8fR$Zu1(r1U_UK=#p5J}h5yYV7)I|0>4zvLKTl%^`_w_m z|1n&tLI~xl={I)TU%(P?Y_XS~(|N#OYz?)k^jjnR^A~RcTM!0qcL0K$v0(x^-MMuB zPA^TkcGaWV0ppy-^b7;2Z}@)qYa*w1Ry)jb;p&<{@LQ$ma>8BTyXeoFY zQz~BOs{z9+6o*fAM^9o*k$_ZIZ$@CGD|+&=+9 zT==O7DY%>_WO2tPmz$fP5%4&S@G9UH7 z+41s;S9(zMZP@kDi-vl6x8AQE2&k03ikiSq6TkWM{+u_nzP2BEkb2Hj;ii_B`Uwg} zhh*osR4Uo-1al)#8o7Djs{3CY9~YezyKSXDuv$BuYH)?Z|5j0!3|qdPS8QKjl5tyX z&?*Wfk_D^O>%biaU8(w%@0#9fMq3+wt!^z?z45|wAICrqt)0Ne=jP^>;|U}>I`^^O zykuccXxv;aUPR#rre&}1b4>S+Zqoemst^e1&lCOy446JtlL|1d6RqP0CI23 z_%hJQ0+Hsa;7?|93zFOWD8i29FigJ;+JR2==ot1fYN#F$GT_4~GmOwYFQHKzw{D5Y z6%TrjK;~y;59Szj7b^CAIVa9MTt?xaFkyl^1K!%vCTI*r#RxGLsRU@f`f~elTwZ5~ zP^91J;dQ6a=J8GhlYPMAjgpIkgJBN1#$4R<0o0q z@F$JTJRZH+=SsH{z-PHCUre69y3 zsI5ao{Mfi?JQy45-AH$8Hq*{kdY~vZ7C{WU5Uk0iBrc?Hu?N!R3%4y`gi@^p;S$t< zYtu$> zP^o=G?DVf}+>dQLg>VL&-~6 zw*+7bcF*987`MAc3%R)IpU|ZR*diNJMP<5$^?tsDqNaaJ=5d50^qP8;$H;I!x3#DN z_=N5{JF5OnX`q~=ti`O0+*pJ;-?l(0Fz3rlmaiorvUg^_yVay44MHvT3|*e;)FbyK z5jnm1-qc*pReyhqkCg})R10vFOwp5tZ1;H?F*Zar6J5HQ8bq2Mbf9c~KMJ@wxuh8; z4mR)Vy>|-~p6{HEyYPrbgZE^9;+=oj5NG!R=>o3Ph5aNy!4`hz>qY(MMDoki(R=*t@qND^!c5QRvoz&w1jDNIwT{|tepNhpig5G@Q%~-DZ}xHfobuRMDH`Y&(3VvU^!y;_Mtb0MCL*!l>_ERxi2n) zY`z7Y@;$m#$V>LE@y=QM1)iRLoMWvQJ??l(%|wkInzK(F3GtMV?tjGxX%EF7RAJ1H zRjpF=^Mf><*S$A`0)QRY@Br#g;Wj* z09+zK98Z)Uu$w%_@SpxkC5PAoo;E$|%Vxl#gcW8IFh*yDf@9`hopz>>jSGBlv-)2t z>b@J%YQ^DaN&lI#Ug?$T70JY?c3hXwaJ{@&EOp~<`g{W6&nn15F^eZj1Ma^(+K`j%P3F%uF-So53G@V4tb|HMnS!Y=l8>-*_TywJ$%4J>vsvUiXz+g*MC$ zBTEFLjH-|s%U5I*Lklk=FZXt89Be{g$H2c9;s{1|o&vOz@VnM^3JZC?T6Dbz41#uA8(9Cy|uJ@+KLq(m?dXvt=mvJRv)QLX$Fn z5Lv^xI+Vh<{9C~;Z^|p0Zb*dINJ#BVK&(zpa}H>|ZDqsXWhu$$2PC%gg?nYMn`+y1 z8ri5iRWh;t;o)eGy2`O=Kq<&`VrTisQpETiqIQ4PkMM2ffE4gt2-ter`|~^#?WM;q zGP+3}<6*Nu&If*t*?gDAfl!q+^}ye^6k0nBW^eF@t8m9}|6yV@4>^|b_@Z}F9YlU% zJl^v~rzk6U@nw7&u`Er1p^!F5g`}h4)UDY58$~CyAauxOFpZ zJ4roQRUppjH0uN0l`pq2s&TTj{qYSQAM@j%0jdgy%AXypHv=wh?{<@< z+I29ur>f70K>oM;VAWH{mOv`X!SE(vor5_j-O#AhwcdYqy!4dH)xdOTRR4Hq&^HKG zTX(8S!_`oIzY1(xWq$CoHYc_7Sq~B+#CC|1%+Ex}2_TW6^HQ8wgf|-r$T_&;D_lOkB zb1ZdG)-8n1Ab0DRH~r=QxJN0XFlSlU%&ZZKuKg3FznOsam81_4`geNZ4Mo08scJm~ z*H)>kHuAN5EnA~_6`7#^tiIHx-+lG`P0YW?Ka5s&FH;SHvs^eEB8$jmI)Mxw-n zj)O%M8_8BjQ|FP~b_1#k7Sv5)!t+jZ*m3E)oz!@CC3@#ZgFhzdu^l$2U zlK`pl4q5)%B&GlBq%On*c1`LFwccxYQF3VrkCO&!ebV5%UPNs)`(?}U?e}HIEJ)T; zm?#MU$rj7YFsR6mL@SDz%B>(=*^x}~*a%N*6~oY_FrLtpclv$bgtNjfF^JW$ZK{~^ zc|8f29G;e)O?iTl{Mc$D_=86fMk5Ahx%!Ngj{2Q3sH!M^Jn_xP{Me&y${nzn!iDIr zYKz`?J=D%GUhjtCFR>XnmXA`ZW~;MUi-Oj~J)=?yLD)CD0?gJNvQEiJ?;SfgSU_c)6ngI@i*)@tK>=&)wAte%FTVzGF z)!Mhe6^*2{D0IS_3{~QADHi{KL_2K>>RPONDzK_W?6rQoVIbGt?1q zY`-_jjLeZbhdxFO|GQ)!|C^g!2jEUN;43>l)(3A1Ws1W~w7ct43M_nfs7u+pvYCHR zD%91xLF;yvtS0LG-LoVIpI|ve8ei_sZ)fm9`wTD3yPE}$wrxge&SG#@#B&PLjk|Z@ zCKqcQlJ?UFYiM0H&6jlqsPhl1o!Q~>fvt7<_w};QU6InhSu1Oj+LOw`V1qUpMy_`a zkiFWmOb6&FT4))?EGU8wH=B@8!w zQ#1bQlHbLwzt$V?T9bq+wZAJx6~Fv0e06LZit)eTS8eEyFzH^2c5{c~OS!3t%k0v! zdus915WX11BHlEujHI!-r-dsO*XnTBX(sfPem3LxcsHJa$Z)eA*p!=Dx$nV46ajsC z6u?K{0PA>&$3F=6R8HfA*2yM~uRAQ`d#a>SJYr2+^ri(Ssk!?dmhQlI$5Kb_Elfwq zJV`Hfd>vlWN%95q;Q+X4_}TN`9@E4|2Tw>^bld9e4@;iJ*wvZ*Z;z`i0lI7&$T?Tb zrHxRqW=HNa*LECQK`1?%ZGb1#ExA9vEQ`;s<6GXvru&-r3(NQ4EHo%u-Ubv zxvuN$W^HYV4UWv!+m|6Ev zAHs$9uunSvOc8>)u+no5X`Nno%EF&5?n&-^VSEz9`2;ORERy4o6r@#U9rp3aeji;?Y#V*VW^HZNsj_QpQkv%xs~=9oGNuK_{(s47Jj)?X`u?dC*;RfZiZ z(bzM0%dYz|ESUsMTQMZI()4VYy=-O3X|U8+U%N6abpOEzqP1pN+u|nkdSGBaMTN{h zt&DEISgT{C=P!+p6c=cc&t5qxlg9gV{U&{s*}H+2ohPV%>JwvMLzCF9~ z+|7fkm`gP&w98Fc`c~)Jvm#O9L2X=#tI+qA&h=2zgu(o_>Yu9&mfK>C(+49<@^c zZI3^MJ#^A@0@SvV5g;|PT)Sk`e^kpO;F?mw8Zp_4pYk+dZIbV|YOO*|y-vMv1yfp* znH;y-yn!$)s90SOX|~m*ow;$pbAvZA>T2b(k9D_5=;(lhVD`(`~xJZjwN3wZ~o_X30ynbR|M3nBOUF0w3m z+h;>+w_gOEb!eP9-W*Jpvvq=+a<%@>ml_WB&Vxq#NA%G}7+J3CD-n%{O! zD}x(c8G20eE##Bt>IOZ~ahUxZG84 zOneonN&2Ag_jyFbr!ZrbL?^N`PP ztRGaE>sf98VGD#uXcRvSdS97dw=_JIcqdMD=^+}_b2uwA-Gqao5tjXSjPO6u(^|Ze zl*|Wq469Y7cr2KQM%(m>iARiC~(~kLW&Qz*dXicbk_gH0_l8 zRZ~6d5A{vg4~`0sRp%$YYpeoeqk z6j)iIBngTcV!F)raZtA!66#A>#F^)H?5YW_Re#~Z)SjGaaoqk{ciPt!4e9Crq#+(I2uPj)@{`4BK9mek|#?BJ-qF+2DcB>SR-1c|GcU|(}`xOZn>R7V%R=*_J;UVU92n5Nz!Y&}N4ubclR&~P1N7`RJ)|YB%KqusMzZSP28(pF@WDV5SM2@!0nt^i@pc+*gpfwe4J z?Q-qY9q}_E@lk}(Gk)^5yL)#9n%3eP@tu0IRqIK#Zw<&;oggTD2`-Lq5K8j^Y8S0u*9^ip1V=WX#+-M= zRp6v*1JVREwDOZ@4@Op!Xj|Ne7mH#K8;0yrexddD$2V!ouySJ&iu}`ZrxojICv+C^ zDW#a-)SS)%ZDK2*od4RHJWd*wxqx=vyZGe2{F6*CakXPRX6o13aA}vA77whQe8?5o z_UHetH#xBE0t>BY7?TCs2;H-6HhSM&wBc^%rgB;rZuCT0WZJkb+b;AEId@`x zF?TrW3MwjPfH`0_KW>NGmtctuLEL1tFcUc!ZPxcSn*ure-G`Q1Lt zHk9{wWN9pjv)a^3ilb|9cFBF+F)gIUarkGVFy@;CyHIPJ!8V1fJOb96$N{gAn{TYG zRzO8w;y2g}SwazK64UG;_=9l_;tD8+@}RQX72#;p#(_Q>l~9izKne_{4#vXRAY{vj zFyI1@12@yasrQ3z_3u&q6Z*Y>jFaP0$C{*TJXk~=C{jCpW-U^usI&T3y11CW5u=*B zX9biqL3=qX-?XA8UGx#WMk7Iz5`;wF*I&JQ>OOu6%0K$LA-3+Ea2c*4_-5V{N-7|w{B^j?V+C{$lzJ57AB}x=7 z;owteBlWEC8aCXM(%NXVQJ7$>#te#w(QF9DQ?8lQmp0l|eDh;Ljxb1p@Q9!?7nn( z>2SDfPTJV>J9jD6!Nvg~_%KDZt9puuJ_C2Gjy~xBTmbT`_+s$Me$o+#lC?}COWVq^ zQisO#fVJ|CZ0k6G>!H`KYIGyM-(5e{z{DpopBhpU2(y`!f~EoS*>0;;Qjy7PBec3vQ^U`p6mi2)d}Y;Z0iT0|%XM69pPNT|nTRrLEQA%pnTuP^8@=>91R zo^1=OLlftl2(u*Udi=*xSFVA;O0@^ikHr?74P{y#ho&8r)b7rQ@|sqQpBwp&dleEwVA=06edDDDe5 z2kNfd8D8jZo}Ax3Z(p;reU@T>(>NaQrzGzZ2=tGRZ#^Zun}e#(;#papu9QiGL}b;r zi>!EQG&Np*>BSWJHi+fUZJUiUVuP52BP$q&3S$?r8HMmD} zVO>k8;SOR?;i9Wh;Q~qE9#)KQv||#JB*H#css3g>qKlzQG2}tPh6G$1=9ek|`%Yj? zpXWO;VR?ZWSSRM#(KM~Voe$sOrzxv$kbNgtGlqFK0kZB(gSCf5q6AzW=R1U&GU9aWOmth2+EXND0PdmU5TBZSEu&_`GrQ_BlPn; zE)16zUQ*i%=xn%lMF^2Wxh_O}q$dOxI-k=q_iddX&=9Z=pZyHA?zRI-j6j^5HISJV zkO0KF*ixm}R|p5(6n+1Y4mM_xdO}$1s~vn=vvFQ*xlB9s&VOR{E)e3d#;B*k zas3m`PTmE#xzj5>>_Q7+ZQBNW319sF#e=~FaY0sYOrIiCjLQSTMheJE8lZFdb@XAqA;K6 zRs<*H*oAK8-`d4)ls3#my^D#E9~o#Ko*igU%XhUKjQb~5K`-|U-41`<{|w^`oIwD) z_|<@f5E#LKdsc}?u*onkHpvy!4mjJzvi~{T<43>}a1bc~a1#pC(T%|w&x)Cm>T-XR z_tagxv(DZd{{oazbRs1)P>Ad|nYC2fEk<-1?Ai?gxpw!|}~6nw1srovCa z6h9{VEnldy3Gep3`^2cxJ4HJ2*LQI4p9_EN3G&w6n(-QctUon`jBqAhw&8fL^+6re zjp0$9q2b2dUp74bxoUZ{)^1lARDXpjg%%^biLpB$@3B#jsF<6rN=@qAc6RtZ%-u5k z_;Ot9^f^wYd<<2a zJM@*zo#49F5V_iW5Sy-6M15??%f{FffizvdsKyYB(g)dO@XX!=l2hZv{?R?qp8kf1 zqd6j(yY@~8)9B%YKJqwH0;vX>A+J_J`AKmHzTHzY=^s6yLGGH{(L=uWYso;Yf-;a2 zP?-F5as$&5_y7o15I8AMiU$t4=-B~(i>bu8!cg?<&^?WR3_1{C&=mwAJlRT;r{K-r z!*F@)V~1`PmWz9VFS&q9_o$c^DLhFSL)xfI;iN(40$P{iS(N7{N5X5*FTlD%6ihzX zkH|0xi=BGV@KL%!AG%g--HFv^8D^%zF~H5MJ|UULh50TdGHBdR_QYeiKMW>HyVE;f zFOgSEMB3K}RT3pbTi4=RgH2COPi)PWPz=LZmArxEt)82h>fJ|L~Z30Vc2Y6lEKwbvrizhEHzp&zVvFV`l958E44W*xpkB#mWHvMfB09(?E1i+)@X`Kkup#P86Z zbw0214BpM@thm1XjENkgP4E#wNnM0CdF1fj&OdI6X@f!L2Zv80YB!NTBII+IV8?B@ z+E)AqD`ccV`v$IPQ4l#_n(f?Hv>^QpBkSeZqC;{Vi zNTP9PZq#j7e#S<<9cJlXE+ah}M&ydT{1ReG$FN2DLg0@Hmo7h+3daadS)yz&kO&J7 zyrO}~mOi)`8?s@mj%{heXFeEVS~w?^ktmy|DW^dr>1@e?4Mm(m%DV~)on~z;zLrUk zP=^S*eg7lE>TyZ5%X5*cV&0z=f_1ZU(s>7iU?6eO5LPXrr9K{SL*$=&BaiZ=!idJ1 zYQC|b6pke2H#6dIPCl`r7-L1-{y(1HGOX$M zjT=@GP$^M?k+V@6=>`?2C?(yKfgoL@H$YSr#-<{n#85ywMt8GOj_zi_fH7c<&S$^> zec#WU9p7Vb&*Qqz>pVZjT&}Xi8>_S{4n(^$F_wL-?KgJ0W6PP8+g0{RPVH_vJd&H2{tAl_?v zcI$aU3c-+91;zG%c#8+l@q(Oi`v6PeEj&XYM#4BoQ^d7dl^{s$aDfD3y{Y%iv*zcPdu0<^}5lCo8a5`Rez$M zjgc#AQC%~Yf%q66cI^uuBaG)G=zpL7%ra#=iPqj9LrU=C$_qZzFGxB`@ZP$WT6B^< zas^m#yN-J2b;Q{K_*?<%%k${}$m~q0{M>XXd7npCGWwjh5L8diolDIP5L>;-Qoi4^ zo)aL;ShRqn?j{}A$<9!E2pA0cpfv%{)tDE3yzaij6!(-G&~o(MX#>`|Cppnq=37E} z)>OD9F8W99jaR!%IHu>m%faX-%vl#b=({A+_VM$hvmfc#UIwyl`GO%2cjHmedI3~I zb&2(A5pfA7s)RJe?{hl5WpQ>F+-NcIrcn z1Nw}Ri`gmRXk|$NNc~}AVCv4JswieN32e3;6Lyi83CmfuZ`Z=>CH&3@nalkw^4oSbGwlO8kI^w&1G+(D< zj}tCOYRz9a=Idcj^q(oNrF#B3x#5R8-V4T40|V5M=rLBMPN#|W%ZGvQ#P`k*0*QXF zR8Nu|$QeoVw}sJD2?Fqx62+_&7|hjsIfQ0E-}&DnXV3L04-x5GXdMV@FbkqkT7b3h z>|XUqMrJK|s?(nBdw-Hiy8A7(t$o8gL(b7M_&3X%O`fX#1@Dh+$Kx`xvswyAvKU(J zc051g>C`8l@nZmQD>PPAIL5LOM59+mhzkDBv!ax!w}xkBRiIx*ii(VGZArD~%ns}B zj|z6ql(!2)V!sRS_dP3vfNZg@7cZUe&+Bo8rkU2V7R2kIt0N?ob}w?9A8q6u&waEX6+j;GjR#OX!+)VF76NYC!_4D#~_UocM! z=S)o9c&uyUMsjhXNl z<^^OjFiy9Jwi0?ZE%+Y`pJv{4RzV|Fp0yqFqy53suX_0B+t)B!|97P?49Z;J&)%8d zdhSD$`+lVZSqtf^1g1%cKV%<2GjlLS@3_$3ISX?_Y zUi|l-2Ki13AjZ6y!Dg^vs|M$`nG|9ZO<}bI32QOt6E8m_n_f4-t}eMw(?7u8y|pgi zN~Dg;N9E?j^PZ}}P*NLIaI2t#DuV}@FVlCTLjPLa-9U@@t=$BXK%dFqpGV4EOY161 zX818>%IEBAZfm-iid@+0w|$lHH2AFe^DNoU54={E&>FzB?D{LX$sufwu1OY@6t^?Fx`s zIe3~7uAy-MRvIhzl3v*>0Yn*)@#(xWAgT&~)0`O#Z_WwlEK7C2$2g@*c~bNRbfE0_ zYCccA=u3yi#LM^K7cW^-U(MNphR#(=PLZt`E(G&VOL?D_MKRXXTS6B{Jde#ht_!X$ z7v>ah5Q0UX{=412Yv-5d9fA-0k9>^9?;K3?*1NqP;EO$!Ep zrHZu>u<#u25{OkF>mt&mFaPCKyksRG^V0SorXkIg?9*MKlGh&NsUz(owT+Y_LJ+>z z$2|o!GeVFq z)J&rM`+QkG1H>Io`>`k&K=4vv5E_8a-XzZ!dSS%5Nq!eV*R3y9K5@0Fa&b=Z zoKgqoa>XXKqkdQI@HJQJ^7ZS=pB$Q?lfP|G!$bQgy6LnT04lTP);<2L&sB0o*DuA& z{?r}k_p;HMC=Tskol?)X&CT+Db!Aj68))!-a7dQ@>C*@AJ%tW^N;6PCS_!S7`O~~T zkIZgTw%52UA#*uAz(uc0^m7IJI03iNf|a^hI-UZ&Q(j)4QTySC#w@zyw7M(1yS}@inhj1N ziYzei!sj`-nV9l@&if`B$W)k+^=zyw_Q-*i*7n@L(G2c8vi|e%6))IEPFDtxfa)b4 z{dKsdX}1gbA9&&ZKk(vLJ^5q!eYc5}%Vg6A^_04Or-1%1?i`-sbP^kUi-~-B4X@8DHXq>FmL69eT2hF5MNc zfv8uuuPs&&`9)tnEfWi}Mm4$4sEIVG7hUtak|64Gd#x$zuo&pjS6o@Dmm3BD(2E#* zbJN~L2&pL?CHP80&wRG<}(UBmB%iBd&}Vu^F^e%rR!8nNGj z3X~5Y<9jFm1)0D4>XI0Y8{EtG!ucbp`s*Ns9F@PcB-$)tc)qd(lh38~2*w?fTh=Kd zv6J{uN%_`x;DqW>|2-twGIE!~!})Hk`@_y3n-N?N>Yjo_&FC7TSj&k^v6k)G$vE)} zkd5(L2*byK5EFD%;Ipo`aQsB?#;7iHNRA45@)Hxp*hbB7$W~9;*Rpf$?z(Wzsg|nL zo-B=~@m2r-+O(YQj}FcOZE6URmv`O-#`U$Pi8i*=lxH1PFBxg*lz!$VgW#WlJZi^?*4yxG>yg(gEs_Le6F`u~Iq{j^BQ*u^P1k1rH7MKe z$2bQVFq5X;$=%&`e4F&Sb;{rcMlCj;@xn0f(;jbg)2b`k@bpP<_$+4IV8!BS?4|38 zfgn474BuBtcg&*_$*}T{PT2y>z6qVvWpU1{sWWCQrb>XYi-(tOW^4*p{yZZr=-Z6A zkFDpf#$P`azYmf3szXv#-xr7R=F1xA@CSZsY1|$==l^D6(XD5aBH7gWFO+RJIw6;{ z7iWzN&^)6;VEf6q`-|>PG2`vPky7P4F=Mfl5#-#~ckZslw&ydt#uhpvR!_}fO2MjS z7`*t!L(?VuCn<|5v2{cjXA(yL%cHT@syH4`yKfZK^sGbM+kp$ypRU<`iIS6C{~W-2 zm1vaz&IX9`{8}~(35|V;Lym;4a97FME`p^@_ykQm&7B;X9mxd>H+b7_O^?yURkY(8;~;waVa>8ZeVII8aPUk!hMK}hTIolE@8-wg-*t}GYv z%R7ShQfs(-k#QfQ#;+DBVehB*N2h*fx$?F&In-SJo~Dpdtl=tL%qTMlze6J`s*)0W zsND|rK^q%u1{{HQLs=zz@EPb&?*aw1!iXgarY3epFLAoc&d&{e$4Kw;d=;ll}cAE zo*WN>YROz$GFM4h+UUGhu>+gs+oI&_3~_&zt?BZ1`kQfzduu?sw{qAlEM^Z~w)vR7 zicbkB_ArjgRavy9cp>1mOJ3=!XW1&Dl9jU*Sa!aZ7Ui{r)z4{54%Q!2RB011WD`cQ zHdHx8KQWw9F7|i~9*?1)h?qDTgv`oxf&+~*<7EG2N;$1ILb$s`Eh@Q#j%Ni4EpjJM zgSS>^9J3~BErU)E1UWrtdsN^v;wSyXim*>n)b^0sP9yQdEfe5$-5gyeYsj&8#Sl@O zXw6-yc3R-S{L9{ zlg*D~0ME@>iaa*U9YplDkr63z4J z;S{2Tr-%||{7CQR$v;WJk&K>ku3^$+Wvf#!GuO6G&wu<%xn7R2{@Kx!R3NTE$G8-rzx~HpF z7Nl)%p(&Bmum=$~#~E^#-g5_Mvx9S}5W@O=v+<#FAO-6`^`!#Wr4s@bY4?}Ey3#10 zjE_A}FyCdvG7H7iVd0?0^ZAU;M&QY?rU5qWWy>Ra2p8WQv9pp$CNnzJR*^_TuDiKWuD|FyxTv~RLVCfRzmdjmtMo4paPXvy>5xUjD;Bmjm_l2~5Pp4}|mHQEpi zvD_YG&77H36Cd{^rmge*7U2#dttYftS~U)RM3z9vuE8b&o1S(86Qck0hJ}KXM*6J% zHb6jcQ-yeNuN~uRUQCnW;qqFXB>lU5-z4&Ilhg2*1Aj1oRi?;okBF69>O4fR6xul zy}UA-{Sbh%!iK^hCG^6bLbEjJl)$V_objjLJ!_ue7J???rbOZDKJqW~vsF{YE32Ga zA6&Vl;UC|MM72wuVD{=BKj{D15Tstf>hNRm`l6fX7lueKgq}#GwJrrARom-lyU( zX@tKna?4`npW;FB-8k}o^dZSZkE}F1Kwipr_t^d9rBGxNA?b8$24kbzTys-^=osCu zd2gD@0C+S&ilkT4Cw|%q)3>&buxgFX zH61I`EjTZ>7wNs3=Us?2Q9dp6eB*f^2|RE#9CJ1P+vcVW-Q4HmtMVu8S^AMlI>`aU zzt64;R&3*P(AfF68w1srXYbWcalTw!Gk%A?nQr68Xz{=;7RpD#Y4(xi5Dout4**Am z_Z;b#=aATMu5mvFMOu6gI12Y0x1Yjp0tl>H!*8ySFz39_-Zi1qJ5>^RVgHzIYhxvw z`){gZ-N1&1_W4jho9eUx#SrdL9pQbjVo~?c_*JCyfI_2Bx>)!>^3X%l?QxmTT)``3_U$JR_45-huFDvRoy2eU3#%bP^M6&cP&Ix(_Q4RFwS1j@L zbMD@ojyXlakkSYFF_Yfae|M%{{1`<48XdfAySu2viaC*LP(dT{zaygR`X=8G9|>~x zEU@yBG}nfD{D(w;NWE!1mamPLhb~X=_iAq-84C{eTBu8l)l~673ERLf!rsYN!<2cZ z?h`YU8Z^agyUo4&V7V<3zPm7os)Jj=raewGJV_KkEEOFHdpdaMgTU9(KJe8V8;NJ? zd0@UUB>G*;sZ2uCT0^cHBi8C>7Wo_7DYSJC%W#r(mrri6ez&sP(!N~f=Zk{)h9X}M@Cu(n$??$e(r9@jMLoZT0y zJlAJGX_PaEc+knu7QwdmuN#NgGSV;8Nsr*Eq;3CL9igc-WLnTDhw76RiK4>~iUP>+ z6D7q-DLE@IZB!$E&O}fc6u*uavCr?hdcak>JURx_{*|Sw*@D{oR1YFgQF4wIEj$QN zP2$}@BeUcz#e{&x9h+k_(pxa7wqv{WSZ-sMp(p&C(QDf}qXzM5dXjyYfC^J{&4|vn=(%%u7OEL8y;*f(z$(YZXuw0>HUZwP zUwAA^I#(iBsC;aM6Bui#{drL*OM0r{jK5Xzl4YzT8eWYK5=vA#uUi~l(Rpr>rGyqYhqYm#6 z67Xu(qpA0`V{+CtHImO9lDqAa?^k_uD9uL-q(9!kBoF_=yhOcOk-Psc?Q~)e0*cuU zgYv841*vtfitdI$8JP|S>aF^dUD3MDsoK>atEgRs!>mjSD#{4_qwQe7edkQttzph# z{um#FiS`2TE&H?_#Mct{zoFoMwAMM6?-Te@E8>1naMeLvTQt!cKJk+d7qZ_Sc0Hn^ zZ}GP8d@L%&DtP*fJL*o&2xBq&*;v@&`aPLuvbmrt6dTWmJ)ick4$(~SO#3W=F=FBF zEn#h0JtMz2r1gRi>9m80(fCm!Yi{)EfUylxXX4 z@22o(!38@UXh?~8*MHJY6?A4zt#s#4>-v;)r;i!_RQKgY#z#n#bPH(ZxEq7aR3fug zXr#AH#aBIk^V)^Q$334h<+=Jv+P@=Jz&17yYdUt34&wxRzq}AOyd3n_%Mk@sM z5im&c|+`Y{aQRbwEn{SowLLJbGsjRpz$K_ zcbJYmDsxYwbMLUHQl;0R=j*I5)-2D{4Y|&2thR==m=pbyX1tXCt)BhhEPLpnG_~&t zMZ7q*`$x6-LvZ7xYstPMJG{cnP(^V}m( ztoU@g@YZ9ffJi1%LSewOhu#1$`TOE7Tl+HKQR^oL z@s5a)U6;Wjc(dg|z#x3Hx};?-99`x^jraZRo1*gWE0UUuoLR|9lOtDkYPrICi?1vD zF|D%f{AQ3wBKyo2l^6c@r0IB6Fwsnq?;L9WVh~SrYv7HNdJhUWkV>wn+K!3`fAw9Y zLzUD(v?D=j-EUAWy^%4>jA^=pJzsZ`nO}WJXZBM9aViQS3x`C7#cKRgET@Q;@G9$W zW_30oS80zV_B>g8c1J&J)Ve3C?cF9?$zZF+~G$=fh zy4N|q4?9PjzD&ANZm^U;LmY62Fy_ri+ZL7BY|B?aNNEw(9xW)28uC?f$;ZC-d||w! zJFVDBWCtWsK@@yJ0%H%MV?UQYFF zm8s_xC&(JI)>WZ7R>qYAtPltR)h>6)wOWQIpjQDn2SzNQbdY?o+WX9m9_x*K6~(|+ zCh*>q#h+vQU|Jb)Cz=%M+1aOXbOoy;`%sPzD}R4a@?3WPS34b6e{I}0xceq|25M0p z26JP?u3r5f|A3Hp-TufX=Z6p?mEUrzA&tG{b zz$cNso#0zLlP8zR1A;DJ9k5ZVQib0AW8a;DhG=r@o6wef>1KY!XDS&lxb|4p*k_9V z74!Fq*;e&u_(nD;1slM*QXTLkZ>~m;Oi_RFyMc2*UI5JtZn&d0q6Vf%wZNYDRIFKv`&t zfC$^0w_?1-&lYiPQ+xSb>nCg|m&}1KaeR>S$>Nd)>x|ECtlIwQZ{6cXCMAdR#{*4M zx$|)bGdzXxHC|f>#cR2-Z=@uaX4C23xsV$D`QD5I0VWx!tY@1m=doQ%j{dpD|J6{; zQCT~;vC`rPG-@$G!kUvM4?0 zCk!z?LIVdEUwvTfT`I~L(5TIX4fJA%)xjl3y`?9J2ZCGBE5J7QMrPE6dV$y4_A**WAW@-~IP3 zGK6$@B>(brLE5tKz9Ks9#E05D_MyMEAy*|AhL|DLpmg@DsVDLU0|Q20iSy0#mdDd9 z8>pGOxf4%MOfsm6m26!SL_Xj;p8ayi4{k$idIz+o$8C?dE&N~8Vr&wz z>22b@laKWmWg?mUrv&4K!cWsLbbbj(XfgFi5)6MD{=BWCC43m8Q$Hp(vP{d&R&sf& z)EMx1&9Z%C0pdnXJc$|5U2wnq;TbP~$msd|UDbqK9f7n#e%6rwCzQ)a(9+*1ffM9` z%@(fk;>M=!rpy|Q@6CkFuY#I2Z=bY0gZq4_Sw5qg72VM81IN;jw^&lVmAw*!G5IBY z`XNc~g9CTjKLdr-#0%FAL+18Y#f#|h z@WJn!bV&|y38g$6Ok0l(2#$yH##~Kkd^K}GJ7I(h#2#SdI&RsFQa&6v>RseL_Y%tp^fM#5Ib!Safu4Q|(-U52}9 zZ>*lu?mHB2uT7IA)qpA`79>MF2@jvQPmVyH5^3|B8V`;wWN^Eo z!K?kg!!I&0obQ4Ss%(TS>_C`Ehid?dD# zbZS-H;Pb+1Vz+6p4R6nZaLW51sY39lMyEboS71~6byudn!Aams9t5%RIo-L71l6OS zkurIS6jJ7UNk;8SckwjJkyle?e}$R^meir~Uh;w-7avr8rE!#;L_2<05Jsx}H@yoL z3*ld+xEifgZ{E>;AjKlKpEA?&b9P4Z6H`I;JTY$=pz+{sA8a;)-j72_{e{{5ajVbtKy-x1eIa|clPEY8Z!T~X$31C7rbcE(J`CZi^6D{=w-^> z*H>Wu#TQMo)iLQ6C=%7~C=fh;eKR{mAZh8d8yzFla`TilmzK*Y!nt(v#dxWDM z31aonRc7U1M~z#zUEh83u1ic`K~PHIj?0l4g@c_!aXzy@-o=Cf99WYif?cdR2f`{T zlU=Httxb%l%~iU=n}f!RR(@-luIY*H3jS+r!u4m{O-E--2XwmKX2;`;jXv_$-m8i8 z-nw-%tU4z7=dq4A@fcwM>U9$x3)1FV(`2UjC}{#b@IQf#As% zh2h#@b$qqJSE>md^%vG~J#5Q6P^Q8lx{a*pBr%s{(|KoQ(@k*#Jd4^Q@yYEza?wea z)k9{$)Ee6xco)*SZ^lrJSnuz&H2pzhL}D*bOx`|08oef$Hq z#crJXiusHA-T$OjPrM8`#n`5E8kL$dW> zL|s`yTH6X{q(%V3hxI*d&AcJ4kyQ2-`S3pXOISM2EL|POTYtALdB0tCp9OuM%wS!8 zz_Q%Z(fr%-zPOYQ_lxz*H21}oYYizI(~LgbezM&sBb6VC_&JJW(+Ej90R5q~I_B<} zA2J?Z)E~Az$fdVZyVEo6-JNRMA-i_0#YLAJEtDT6-{7qXCw|&%;~zP5SrZd8U6&P& z5V!W5qrEmyCInHzYaxgl_Zj?pU*tT|zH9N2>-dWr%#*klgqGi*-I-XWX6PQz{cqhZ z!J|r^EI%M;@4J^ZkZAqOfK+{3uFGIoIP+{0qe(~NEEUr~|}P?THBTkTy8r3B(zNv@1one^FH?yt!~ky+;D zSH4Qd@CAtmsqb=A^qs}hHg;0{j7K%V>N%G0WQR6t)7Oa8lV-$1w9Qm96i0H(_Kt9M zK63&w(QNk!R@bj;nFu>#aT-268$Tdb-xi_y=dde4hS%AUkCDV=wvzq1I{{DQJt= zsUjW>O?k(1&u6L`rG61=Rb6m3WAkg35m*V97G%CNm@;r`=nyzF6m8*Yu=8$6G9V(E zPyEflA6>=Z@1r<_vQ#mhW_GN;Q&);NDql#dRk|%+!Lkr0RS2*#a-sIDJX?|4UH@Hg zTNv5KKP7YikAJiOB1~31r9XZ(n{hFEMy`ly@3k)k-iLdI;mu^50yaR`aD*6>v$rm} z|DjMgF3-*XP^gCe#eC@ajfl?{!X|ag!q!l`y+gE8z&-(8ldpssRb1c=?zYN4A-llzXX}$j3?|#^QSqgc*cn*Z+Vi8?>2&=y-M+w zzpkip>?pqlA0=6^f^Q3%$nHj-yWO zUV=~zGOW5qR$6Z(N11|`wnbq#UYDkS$Gl)yjS=yFBS3w9a%W!J)=t|^qCCy5C|Le{ z+LmXOjw*H;_jGJ-=P+WN!zRfnQ`L{@In1?sS8lcFj3U{XDoAFOEZK_oF|NuD=ImSf zJ8dEppO;sPSTVZ0(u4BS_J+=fR`(;cmzlgR(KlC* zFXGA*dXsjYgj7u?yu`=2W$w?-s10P7j2=n<;NQ#NFHlBoH5vgU*`~JBJXd>FF!}#O zr#7pS2iya;Vj2QRh81Jy%P5b!=D0$}c1A4yr*)GQgZJcIosTw`+jt37-Fi`LnlZoS zuQd_*ro?cJ5-UbThL6+;0C|G*&NYD?W$6g#Q&j*XjSLUJ^fr!0r0_V7+_~@&CDu+3 zd}j8r5=tXH6F$;BZQLioYmFD}4e7*EzEwY&ld4jXHP}cJ6Ymaqb&|U!lG>(aSQ{l*(qM1-;rDT(oUl~M_jAl^-$KeD%sLEHJWCJ%&fa$G zZNt6iYL&yc{@GlkV=AlBk3#Vs7BOSo*oAJ{2;uj3`dRPjrEoz+LWBR@aV886(&-#8 zd|p*E0)3OxPFX7JGp3{-p2ub^LZ_^-aWBp@=J-2Iju3X<9r-u6{SzHn#cr7zdS3Jc zCDrK!DYIE>j$wKzs*r7qj8^F!{GGR6^VkE4ZMAnZlea;{yA1uN1i8g9Ne7|Y6~XbA zKYFTx-58<2DAma{35^-)H>r|_o%89HaBkI3bs0Q6P(v%Fg2A8PAav0 zA+cLLsR|&_uvz~<#+IuHHL1`fFI%C$Pn-9+Rg1Esie6y*Lqp!5#w%Uo3uG8FA)*e= z!gUtm=Yzq2F7JfWQi9L znR^?@{|3yfj3On?!YdoyuiKAPBgY%=pT{m|TfD-oN`mgRn+PNeNUz?GE>nkPA}hWs zA)dT&-Z|6xe#?(g^65?qT9E-NZ>rYJ2ZA_J6@|K3e=C4U(c;? zi~gvJl6egA7cab5=isY@CCi<@nhZD*2ORF2Agz^s*O%KMGq9~qs|{q0yWiEy!eY1W z(qhZK=0Z62#=wy-@NlrCe2B<|KK@?0)2cK-P8DZn!xB1uX{<1oD2rV!3BF?***2p` za)gec&j;vHi2BzEc-N=LurhNIVW8EyaR^qn%cybS2M{dx@HzeFO{P;>7tV!CbOmw% zq#;8&LZe4~ZIE!R>az$P)t8sWr6CLUPL;-`2*2-}K182oKjR|Y(738(0SZiRw z+LwZ=G`;InL`{ZM?fkE*xAg=QfGo||Y9Kw+o?MyZ<4#jq8b<4~xM=`CgTM0YGkG#) zL*o2m7}LYQ6^HlY40p`ZibCevd>ZBAI7kJnS-Tb!CF_VInJlbB_Sjn|n^QZGR}Vb- z-6s5zeVhO1X|{u0YG$y|r(#?-yTN$Q;ae54f5pQAcF7hpHc1kRg*{u|5O7VWc-Mu1 z5xcg-gmqhq=z&97EnTs{dU1iBT7C&%V}3k249kf~xC**bv;PRgin;UQ69P7fy#41} z6qWr&*SfW+vcfl+*INc_*z6~Jtr6~reg3mKY`F4HDA8uS@dj4|WgB+3Dp%fzM(N2t z0NHFWimOxy5!4g{_6x+P88q10N6;ZJXkAx{aG-ngJChwDBj4A6-dQ<^wFEYeh4nl> zTq)iW-zuL*>HE4X{pA%t)$q>gtUE!6t*gb&O4SBHG3&>1}Y>^Wak zihtObCb`a>aapI=Ww^e9QEj{FF*k{Jwtbo@*voa`!=*SQ6o}VvA$MGAuddc&M%{Gr z_Z-rzYuB2{L~dNM0-k#t+UQf1Jg?ltF(o>f`hjjz^1Ig>CQ#$pu=XiBE6l!3FcU5F z95O8w4RI;ZU$i8iv*)p|)nN4cC!MC7TWor9Ykm<6f0W(WCMEbZ@X)GO@8se9wIlfS zPTuZ^oyX?ItedR&a6B(`JKeX_;Fn=eNb-r-8pRq@cX&DW<*F;*8|D|f>Jghy%@)(5 zdg?|$42Y<-?I_sphg{r)VFvXYGN@}yD)08F3#u|a9su0-(ne0AmRySRCMIIn3MO-i z)qJ{!g_(p%s0I(9a4X^H$owA<=SQpJ&n5&}RgY~_j#}NVjWenB;DX8EE+ZVierrR; zcR6LFveMh_>_lCmHpn%2y2Cw+22w*YQv5gC1U|k2aR`Z(&CWNP3R*Nd=^t)^^BVuS zXL*Ay1I*YEGL_p%ne$+WLX^VK^nNdF86!lde6&W+(TX1HB2ud!tuoDl2)NbpR#ATj zkQI0{A8dI(mkt6{M)g>t@WM9d^BAsX3`9PgXZrU(75DP{4vk-hd&V$DQQa_~Tjtw(b#HT(eo{^pQ=Eo6S%|JJC&L{)vvy69TGPLb9&FeX zXqX?W?5astk6-X&%p0584$D!67dbL__ zTT}K&+KA}mUHM?jr%kYOqwN0sP&61*txS~*kej( ze%*&Qw0eHoFSvw1$kJz9p?lf7RWE!b-Eie~m3S6#Dc`)pmPO435aefu%VkA|I=!!9 zH#$`m7-*Eus!ZB4=iGDIo$Vyo5~Cb)l#^K*C;h_E^PDqAheqOxGd=R3zg%6)KU_DL z?udYT*C#iiK3@||^_cm4*14r7RliYr6mF2MkxebXM-9b{7(q}e=z+hz z5?r4IeK&U49Y&x4Q73l6YvVRzk0#y!e^^aPm;ZEM*ZnZ~=d;Tti@&()4qp2GeFF5S zA!;^7++Sb;(?3Vr@^LfVaw$P(Ma7`a1%Fxxp5Q}pTzxjzn9JJ|F1eSD{O%{M3>XQ0 zi6$LxpNGIw>)vB7T;2L}^7{r9S);H8=99qE8n2P}p5aBbsSm@4x zRY0yM_(GV^S#y022&-08$A+rfo`tRuU=4Y)Y*M?-=lI+z0+`0dH$mRcZD_3bBr@B7u8yb`Or8`9YHM!q zpQz{$d%=>zNE8+lDAJsVX}WN=EAX0#370F})ql7kkBDDZ%Z+yQ(!x~Lzl)Z+Te4W4 zKyyllwLMCXSbqSW@A?j3uh_!%6x??obx0PkS98qnS=@YaQQJ7jnl$hFC8|r`Y*>-& zp`^}xZ{Bi+Wao8(455xIh01PGnr@Bf!sd7h1hgU#h5(yxBv39th&vj1IS3V0l^g zBwaT-tABNcfLT?^2ymR@9g-@vv&W-EUCq!ao|8!<< z#g+EZFGq&u#E6A;jGC+fe3$$lSFu%mZBo6xM7WY1l2M3P8cb%^Y}_JDq0i#azeTarUlT zItKp;%KPndeK{NoNzD{q80UjKCw|U3`izd8;k!P^$9k9`5Pcpy3Kh!&0RSVeaeIhO_TZZ!TP~<`T(O4*50tdXFgoAiZSeelKdkksp0tk-T&5HL| z!!Pae>vPGK8A_~U4a`+kqEzSx=^2#_)7el`Z+rGveV1ZaJbdOX_MR#IFt}iTI{k2u z{~W&KLQ}%d`N`Al4J*WHN zY?|HJ-0yaP#`xfyW$Fj`dd}2>?Uw^vhgQY!J(npbeY#w=3o2xr@QSsDRW+L3UD*?8 z62Ufb(l3Me-hxNh%Io>#y9mP}I6n5*^gCixsi5tZ8AOdZ9paFV6s~6QpA7 zl90~Osk1=@(DCM`urL-#JDpP~FsiP?C!m-)s|u(NHIKQ< z{~gX0gSSMvgwt#mc|o>kOCJ0iw_%Ft{h&XjJSKt@44ZV+AMmtV56O}LZO?w}_w?6K zkw+Y7Iq2Dsu$_Ak7=K*Y&Gu15H&){Zf3q#yR@+xmj$aaJL>1UtYncCbJIL1DvUF!y zEb1YiPx&;Gt_D<3{N!A}-2T%6c)TNj(CcO~WnD+(yZi_Jc@uqg&RPu^fmMfF4?4Gs z2STLlXIuSRjks`$6k&kam*$9~Z?9xui(OI2XFYN(1Y4FoS77C#BDW3v;&c9_Qlwa% zLRDPD{{*gm3*ZU`bh!uX^Ql%=Du9K^%BUiD*)A?A#6VBC3(uw7Dj!S&PNY;vY`xZ(Z=m^oP8LjEu z@f4wd%eGrxqfn3${DZ6FdgLQ7kN~v)+iR-_hn|#brTLX41qh>}T&AuV!1VdqJ5p5_ zP@g!tBEF}F-BpUUbRUaKV^f1+<2L+!e*;t_Tf}KL6)Ot-%#Ot4ru+4IBaCg&OS>ZF za;`&(5%*?wz^Q73X={OYx~}${SxTH)iJb_gGIv**7iA47HIVlo$vR3&F^Q?z13zL??E zkVb@p?Cq8rc6U87)&{+b+sAH-{lp^N-dIm&ny`J~+CjwpFB01aoYA14>iGp|Sz-?! zG{{0vj39N${+M~Z`tU@^n;*Awoq9OB4L%SYLtEHb>`ztm{Q@r@(e){zs58bFhKF>oo$eg;re9uA2$_`CbxkYjX*U zfA}xV^MgQ{K?dnPTFf+OrmV2*JOO`h_&j8U(_YQNgY@3>gWA??a>oD@7Rh;0aFLtj z(P+G4!Ewp$3YH!2DP6#p!=GB!+QfGgrF!FeGK;X9HSk+`;ISSVA04is8%tHbDNf39 zYn_JSEm!9mqDtnfoRM(~N|&7~QiF6%YTBX({08}l_M93{AB^lz?PQOJDSm;>$y<4y z!?lGvOfmKh?e9!>JJj#{{zIsQBxcA~Dx&O#8S6`k%91_XFiCb3W8YPjF%+^d z*>~CZEsRiO9b?}H!wkmQx8XbQ?{R#7c>V>?l)} z)!g%F2QQ^*OlA&vx=9n97PFC=XXMaG-*>dN%ChP5Z~)u3{@JHfw7w3zb3xo0j^jrj z&K@gwE5=;Rjh_Vc8P(7Dug)Y_1X2NpdtNPVvP4GfogP_B?{j*RBDGVlNafr_PHC)A z?=d8nH`ssXtlPwjyL8%b6D=?J1x*9CSNIwRJE&Iz#*ncz(EKR6#`}t?;gbC-#QTA| z{6O8r5LXVM=2(#D%lh{QLY$S%66G{C46PzjSzg4DO6;_}#41dj6`^m%rr^ziu)JX; zo$A|58hQxVj+rX+Xp@VoD zu)Ud|;`r-u2di6-LkOJ1I;U{?+?xGgRz^jOiV<@c1<4=eJc1^t;)t{+#>Pp4i{t%~F z`RL(E9Z^p$|7-9~*VXen$VwUjt=@ES9?(&G$?@86hBR z=1Q)lnSG*_#tdL*VzKYLSe^B1S%%b5!}!1YB^hiHV79Q%*lT=F(2KF=AGBhWaMPT*=e?KTPvL}Te<~q zXIO(d*eWCcZyQMZ-!^dkf7?K1gSsu1(M6wsMfKfII)Mf}X~^yekd100FEzRN*RE#> ztR1+FlLvPHBY7^!`B1qRCM*aK1bRrEAK@FiLQ7f6c8jojA1W2u8l(Vh7PJ-?#b#Pf zD*-D;oB(?NNcX^I-~yvg)9^-2Z@fD!d#{wxb>G}VukeJpakba;OoY^Q3BHRX;Ga;L z(EPtm%(f3x&dhr2W?tM$J7=*Z2WS7>iSU?+Rs<*r1aHZGneHn;*o^5D&~W8W1R>9Tt|(Y0PVoAB&F^6V2HaEesn{W~ib z@j{0Mol)C1TCpGS?a(1z*=7=>`MgTk`vipxZEJ^&MR&B*o31SGl{813fv=|!3#waN ztN>L;!OGQYb?rN2pGL_{-WSi#N_5X&TBM0N=MCDw()6<4&jwtKwOXIFkltAA{;mKW zcmHH`ozNa+BYziaQ)t`nwnmhQE6Dmuc&TBCdsnx;0XxDZXSYpAuZDA(CH|cd5*VG002h7>9AoTH$CNa&eb~HHgzdqCskwi)s zxHa4cVHu~sGZm&=Iq++%Y045Z%?%$xGFE-?0$=cTsV2-xHCu()>7Kh9DiT6*Mh94W zISUz@y0ihdmvYmFx7}6mFn(--Z0RUvm3l_>ueSYLW(nC#2H=mO>ND!Hz)PA;DR z^!g1RnGbJ1?Du}9acm!>c4CT*Ynboj-)E#SoN!lkDV{IPG&gpUCJOq!EgxO|)+daP zFg&)s>F}qC&woJbdnC^P;0cx6I^Ac(GP>=LZ?{0`Q*wwU^T{v;xpcdAIbRQ22nSzj z6}d&W-Jm4Un?hvs#aM4S?Ms>`%pZL^$gTWad~~<{@HU^R>^ik@T+jZpOxndWX0`~; zU7RZ4YNz~ZpR_8dI4-5G<8~igj#cT)du^@!Pqp6TG1&6dQ2@^a>rS|g^NpCIzx&2s z88SmNP(1(UDGhW*msohlfiQcJ8Y+q0q~-asyHKF~CQoWTEuWJS#T#pr?9tE0fp7}5 z0Yh3^Mucu1FrjivtK$HRfQrZ3z(P-B&?;Rix#_I51iXRiD`&Z6 zaf$);Q|@r0c?Ua{X40|(f9k+%ps{r!Z!EuSz-mQHoPG8r3vgoo(a^&t6tv>EX=ykv z$Ju>G{W1_ zQ!84_u1or!LDyLsMPRbE)(o$f)okB2=;xnyoOS4KLNnC1OQLz&M5D#T4BD3|TlUCD zt_5dk@;u^yw@gn##?i!zw-@H8+znZ!!&C8s7(swrBsSB>K43T}_wc7CrG0{M9sOoBxQDMdc1NEEFQp+@9w_g2@LF*`Hdoj>&+8f4D1Pgp>ypO zxNvZ5$0-_}P#-BWdX><88CJKbG~1wN?As#qQ14>V$0mg*#;DEVUsTvi`Z;8h%I_0k z27gmx(!B=xJ4_Ds!P?9T>nNXm%4$Z+tOP_im9{5C#)Ejp1Ri@Z6@5BxA0fs;w(3Vq zj9fhLy=uWmr>O zbmMgHj`>T*C8L-NYK8T=ed%h(etvhwR8pJxB9j*wF@yE;(@o%!PHn1|^uE>2<#O0+ z&LhCfOF)_F7ULsN!U?G zXXauoiFwmnK+53agy~x^C5N#hA>9wd3&@jfB@lnbNx#<$;87%uh233jH0GdTc5#je z-Q47%q^S({jedIm++T5gBOtzx7;rvQkaIt(w_dDrq9>KNbym#YM2^!Yqt-2#DHyuh zh<|xXE>Y$_1TvY}{1F}yjLP)=?3}u?cN|C7RX)!+l09ry&Ue5(yk>CFtacDq&?eV* z_OqQ`R++51)ZuyWu47$lH88zX&YsUbFr-rY{XfUXOE2l}KY=i!l6ZfIT?%#EP9AW7 z&p-Kt^q0{+$mF-Y5SC%xDr{J)x87j&qjc}1id+w*GGkMKvg#0tGy6KhE%IN6eN;=K@=e?_&*;)|Q*NyOc{v(=Rz&`0dNRsSPvJ8=KJM{Y!Xm6=JeSU3sl0R@ncU(>JrlRB6rz)oFe*Rr2+_}43iy(S9 zn}dlJ8&vdFLQG2=kDYwx`EnTND_UD?*KeO?mL?u%8fi|Pi9tt_weGpZT@;BuYA@nl zU-Os&Ezm2+k%(R{P)Pta^>6pYUzBB zoJEN;y#YJIG*%-b6Y0eh1DJA91CQ0Au1B@j?Vo)`{m0Z8vAg2qrBJ@(JG@5?#B|JT zb!m7u)?r=|$C9cMST6rIU9RAZONK{X9-k)9s$iy8QGcH@Q`w#lc_z_{lFVE6I8lu; z2E|+-WR*rS$YeiMhI_v}bUAp6qKmlubm~s?`B~J`IF1yFSKe3e>@P%xvkWm>S*)sN zX66tE56YXN82)f`Mu`ArF1eEg@|kN41FKS0YDfsgf;T~lvYZ8-yRo(#y0o%%!ZnBh;-`l>%aH9E)9g%(gpo%kmP?(Tm}Eb)hEh=XjZj0 z>&?40)^x&0)?x%5+uN31C8qr5HIB{PV2k8%lJ2522HL|DO;GW@Zm)*?F@+i&RABf( zX<0%(hNTu?w2W@S(YuQPGVzc5`MHR7%@!HIPx;X-8=;0MXw&_-U~hH+lQ;pWV9w86 z=?3i!q(QPr&Aluus&SV*216WphREZa2O59ZwPS1unI@AlBx_=scZ$lXhB9e6SLxs8 z=YNap{1@-9OOtXyP2-*O!U4kL#anw?-*5QBm9C$z2l}|k28l8kGI})Sf{`2EhvA>* z4$`)^)A0L)Eo0Esl`&@frTO4Vp(WWlAGevqu--&K!NVn!t;i$Nxq5f2&^Q54DDi@B z(ofoFo)eC;H7~R`UPfet@GoRSRQ)CRDwtP{VMVflw}+4o9(63M$XlZ$m(WU3DJ5KT z>)J1a9nGdXdersE0V|7+-xke5Cgdh6l_y9uw$0J%8w%dxldyGf=)lF7ZBD*kULs@k z)t0x=I33P0vB+RfvT&oAvZF}V8K8$r4&_X^lV&kj?9~xGr^wVCZm`K~hOuIIb0$zW zX1CJ?@DVHE`IV{DY?s226uw0^v)<9$pW-cn*ClwL&|e!J-L>dV9{qHzeiW$*9FsMFT=k3&3)ci_h4WXhr6t;f2xBuPG@&sEVUFXKO675I3MacDiaS72*NQRqBiRlZjEMl zwt4{WwjJUH&yR!OOyV&no2X9sRXOUrjJm#iq!He1GElOnAXMZHVL(`_ENL^}lGo$C zkfK`eOFFtUuc8Ef+|MM`Io=wF_GYDa0-0SdN!MH7kb5Mr+(+LTlgJz8fCb zH-C4E=AM{?e!6XL|Nc?kEQ>#Z1(^;nR)+d&G$K!=0WJ8XuNPkr9dG-p7hxpFyl&33 zS&w+zSi8auGavsx4@`A5c$5D-5smx($FR~@2YYsiWHABsu5rR28Fb&6A1uz6)IlXZ zIRGA&RjPIzhRO$tIXX*b3aoEilO5APY!%HOEr=_;B|CGB%LQYPUHB6})@1TmSCDRA zq!g;oN6L_CPlm;P?ker@vZ8w1$=~F>DLovX&$<`G*(&_z2!4tEQ%qIm4GYhx7KAGa z_z#^)(WP{_mLKereteHPav6XnYF_v-@;N_`gjdabGXUZk%sq3)_8Cq~$?X!a10oa) zshc{G1nU(uYlkF89NXCjDnv2(Rf?5b@T{XI9B&Zu%VAJT~i zYxv-iIR@kEdTg~4!4BC-oHT*8Tb3?_5Ag5cixIFGaqlnpyYF_B=lI7zw!}pv!CTG` zNDe7W8|x4668KfeO?i*?Uk=Zk%C=kII*BJ)XhL|Z35Aa``;(^0<-~81>J|!ql1k)g zp(V7pA}}Ukecda~B|aMPmKav)aivQxQA8?f{^xmB@W1n)lFawFA%C@78*=$L+^L?6 zp1JbLP{orfxt^k(rL24gHeCQPhBQr!Ll1G8Qjb5Qkl}E$g}};91_NhW6a_A>E9(Wf zB8HN070Pm4sw`zhO%^aHJ(;j!G_YGrQ?q{J^(D0_@oQU2_?vq?MBQ{o-$uJgpe z+3=i_Dt_gwN9HZe@zMlm+~c_I915$ip>KA~J2OM@#zqzD5%JamhAYOSGy~rHvL&!Y z_oeT%fi7f#i1;Hq3{65#xiYF&$W4jgI|v&N!%p)fs|U-OMnv_WzKJawp}Khme|0F+ z79xkj$%T^fe&0)uM3*z@pz~hM5X1*Eq?7@Ft5JlM581dLs3sxp1p&b<&)x?95nG?_$HN{4q*Y~L|<#;weTN+wc5$!R(7%!=sT13)U} zfnGG=!8;e7v->oY_d{>R&RUc+yqzt39T7@%mk>It+>%jMSSR5&pR(BN75PHJih+*F zu>^|!y&J7#;^3jmDQ5BdFacSkSTEt(>CqD#rBBgLe5o?@5cjgHSA@OWuoGxG6MA{z@el@Z}e%bNyWmPt@860|)lVO#I*QqqMFY+M!(> zB&U`#3{c`f_~-los9K1BTBALRx1U{@6DuoJoH5^=U|-l#XA`GJd10+AZ+VUoq6^Dv;gQ#BjpN8bY+8gPr}$k9Fx~wzN4Ii z|6hBDfwO%qIu+DR4^^*Y$lp+OKIE^?2zV=3H6~~*U6OZTK2_T?eZhqqUfwB>_m$Y{ z6yxBiZsfa`)JsoJ6j+EkJZ=lnGCK^_EnVTfOD&X8w^{60uk&Ccadr@E@Y~nfhb=uNq zwqN~wZ;kpgOrK@%L9AN)bZ?+hV^*do6YJdYSZQ#CYNTY!8iW`B7O*6C+7R!y4<`&f z5iVv*%&nRd8>uce0`v_y?bCB2E{qP{v8VG z+L$&!$o_HP?$h}b?TcAkJ{WD$c(1HT0UMWhWm&E#BPvJnr}c(Q>x14K$}+>Fy}oJh zZ36X>BIi|GNB7i`pZbI~p6-c8iibZ%1{NrOS$X0n{>Vjqlj6oxz3P%UfwkeSCIQK0z2`;2QYa_IM# z>js+X5X3^&H3Td`h;;q4d|_u@r6!HIHZRa4q?TD?T;PE`Kjd$+CL_uy!X&hEyl4Ks zzuJXl?y3@DTCfD^t70tD5Tk7`)Xs7{Rw+N&L&Z4A^Uy3W*9`UD ze2bb&n)nNrQET759FmjZjgfKx3lVb|zvOkzQqQZe=G&v|UDhKpL2MMssdUzZvG!xh zi9=(v7?{&%KD*Zd3ZWpPoZRrz~T3B$BF{=LKEX~&AMlOp0hT0Ai>9tA6mzADM?7TJBj;$`cKsz&NX z3xwV(nYGWb{gaU0=ILwFb(|Ij^5+ax%0nZFPSPq~ zEirsXoLB0Im#XT6yBU@GYu@|X$yYHOWob@E!-k&5y1vTy;Tiu3K!$^2y}r~GXFztC zq=%Teg!XH*Z@4g?-81m_~?=){o{9;v^U09yf;nHmEY25z4-P=yEeHr($ zw%u;FRC3KOriX^9Uylx$H$2G5=mX_@!1cr~MapnSBdA#CAM~DWPPH1I?$^SM{028% zDkCYo{|h;L=gF{XMt=EYu`2nj(l*#1#Jb6RlkCV@}|1#|mZ%XaLv6p{m(VSJn#QfYAab4=5d8LSlm5!hVRn)n%^s;kRh zZ>Me>tA{6bBn6vfx$AV$d#LV@35X~XLnTK+tf;<25G2hdL8pFn<&xgY25qOln%!6* z;#4Q=nV($Y!3Pt|X>lO#NVsvsd_}3Wg&{>j^W<=`7+t~mr$~?`HX(Q!d!Iu=fNDM6 zO0m5GvXM20pZn~UfpnR@^wHcO*Z+&5n-_Q^)0x$*G4VO>@?58scvFaqe~~H&Q6&%Z zT-PpEVf{Z~rSo2lANqqyqv5IghW2%Ah{ra9r%`1*rf5)NrGN=I`Of#-`py6PBE*J@ z+y6VZ`(;g+LTIjKSpuG}vBZ3HLp6Y`EMu2v3}22sd!8AZ86{dnxfdwFteD9mDA)Nq zJkVr9Tv`^ydAgO-^6z|HORp69qULe1t#X>c68Q=0gDILx5)~;g`fT2a&LiI%2H(q{ z_;}R;5C_@F`%u&?#NjV$Wy4!wz;Ws!Ykq8#(Ijsi++N%OWb9{Ve;sY?XxGjZ~P=QvZfe-U_k>34FSIg zoeg34z^c}X(V6MjK=$a7ZJr}Y6DR-6l7M3Ac?RHa)MKE`gOhw6f#Y@yiPauLRtduZ ze9=7Im_>pOp^s`3po&3xm9L>h#N%_{6cTMI|lh;eP71Y(@O1Da|u*m zmk9mtvHD$$cXc_<;m2U$At)S2pk-S3Jjn%yhJIOknRv%U-fOR#<$dfTRTvs`-1GNv z?f#4U8$%yCgjg8vD+v^8SxJ=f-qGbwFagBnGVins!;`&H_R&}4 zA0Xj8WjZAzbHkrSNKsdos$SF~1P8Z(KM5aEY){x)U5BX)w{EQ-Qp$vZpKT0bD#+%@ zG&WGu@&<|YOkulMrzf4{C# z_0?zl{u1Nalhn8-*v}}c;6tyZ(?_HQ@xudB8q%MGQNOyIwJ#w&bV(nN?6PD0<7!n; z^T!D6lr@G_vzfYqV8- zghjW2*BDfxNSr6YBe>Z;9nW*>71dQL>$gCEdq8zx4;EB#Ja*}|cZbKV0}n?i+-5!O zv#S0`(o>_&TXfH`tWeHDE#{MB?tuwX;dMTragU__7QZUA^y0{-UakHgmCF$v9eGdYEeo7#A5lZeTi9y{$jh*t1>D zBf3b-Y}XfcGXsF>qL2Q!`&%gtnv+%O87k#>wYX2I%~s(gt?3Vc>*X1e#JzpgeZV*D zrx^qOjU}uY*E00WKW)^#Eh}V&)KF3w?$)`Y{^?gcG=AF|af)|sy5Zfr`R$0}t#qrv z`uuN8F7D4H)$ek#G2B3CWkva8ovVkX-yS&~yV3Sk?Lju;qo|tqozJoDTnNdA6h<7` z{|PXsc&*!eAq}#g;nGiS5S+5I7P_U3!*?MT=}vmnjzdNa&K*iBdJ?NaY;ub4AE=Y6H#iww?>@sXU~=9X-Jp`o@Jg*2$Dqeoout8xh$Vmu#FB>Uo=j%bnD&gH z8OMpph0QA(z0_%ffBPP=xD>FKAhze2ormm{OV~94lnR666U5xpAAO(K?-g>9xE=H1 zoqtKq!_8f-=C8pNvC!#gueuJ;CXm=SDksxCD^GFT)+Bss%w(JwuyJ>s_*;{BB4Fk8 zcsa3O!R`;`;kn_38T)Ir*pmhgXMJ8Tx$vhE-!+xlI{kXJaz!%LsaprXFF4rC-X>`+ zFaVLmSNV4=7!i~%?os*8szgg8H+8e@N3r?ZZO7}ssSM7wrvU0|uAxbwEGQgq>q>=j zYoh_}AsL4;N@T>osZez$PaNL*RG*takh0#eYsuQvIv=1Q#%IKTv==WlD`BN_M~!qpOd|(uALjtlwv6WynUj5-SSI!!sIT#UnB1#)5bfsozof4n2Pq`G<-I z8lkcioCIQ}I_<@_Gs4KR!wHvgv0hJZ*oTZ?lFIwtqJ9rJ$mNzWnGY#N*CFGi%9w64 zq6`q02=2Fzss$vkz=XPMs2LRL!I8kN3IW+dV(Ii7(-^}oj#v)_{L*A!h-O8={ui18 zz^p6Vh?zJQ;W@`a{A-tcff!JF#)k!cGB=>6 zG`M*1Ng?@l5pWCO&m(cHJ_a1*eYit`@=ppbvBt_rlRI~Lf0lj|@uGQq{d>z-980?pmvYtx%zNn5zNT{Xw}T~#=4ws8>Ib`@Tm7s% zfiXQjzFU=HT5ZirmjAhYmz0?+YZJmfLrtqAOyyu$9LJ#GfTQZ6q3$HW^JFusE2afm zm#?p88Q{H}v^#2ZV~J+K?QreZVcY(2!)~4Z)_q>_ljX^yy38Qqw#@>5gh}A?!zNk9 zi!YJV#Qo~wZ$0{c5~~pKKt2QkR;Wb>b(r!RzV?UR2MP*T+JgDcDrGL?2x}bIXIZp) z2dDuz@eUG89Zsnbg>ov1VUm>Qp-;!>!V5I{RRVd_IYH-654D^rWq{^cuD|&8jJG(n z{WBj6mHls^1CPiy7k=BG@@b$Wi|?OS^Z4}&y0?IM7ur8bmL@6(EdS_9G_3`fWh7LO zsb=0kHkY1muu6Xq|7;PEQ!4+lL*?qantdPIGW~H7%jsxHh>Pdm@a7zm<8*!-5}%IjL^7i<3*4SPLc?e z0Sb2%j&UA@GwW>(b9C-li#6r9N^;yN3G_)%p?jMUGg1Ny!D$*=CA7VXkap^?2eYJ5 znpAviN}#rZ?YH|Wdmd}km5&melr!!Oq+p&l1yxgFx1!8B(eJGE%Y*J9%^Y&?XYnTp z&Fu~jy59&tGn`%F1p|Nxt5ZBXVFv#6H`tm$cc$|>v5{$~xHKtv44l~gXOJb8?7cJp zPiVl+=u$yatQVR|zqHw6x%>c|j`nLCQUaRj2W{KIHS5W7r`jp8k+4A+9qLGdU%V=DA4en%vF0E~w$E017 zkd>aN+^ZzR*Fm;u0o}{4k#97f9ZGK(*~JJ+I2LK%k@W2YRb0Kf@yU*Fh;ce-WRo+w znLOcy?&h7}RhgmS`J-pm($B3McV;5cz24^(#wGFdZVLIkKR#3Ubb>WN#)}G2Vl#QG z`jioMCD#L@aI)Sx5jv&-9HhUYbYyy7?Dob`q8$2c+vgez2gmI~S~l`Pykfk-g6@X$ z1FCU9T{@HMMp*E)1y}kByS@!BY3$+ zn?0<{_{Y?rCARSa(#)HFMga=uQwz5kSL1&h4NBYpdyEgFJPbR3&JINmT*13_hdwOc zf<&oGXV7-g3-f1c`*C^8uokyQMP>McpFgNU}+y=Ho*urc=g|MrgXBdC(Fy{$wqwXG49Qy zIz&rrZMwne%xaL(tit#*y%pZH;DJ^=UTJ*CukTAHM#~j%FLVC)?b{VL7lTvef&p8O zcwmt>p7kuv>yzv~cl68*?BY59=_Y#XSLPR2#WU0>tk*hNVC(cG&S|vm-wtZt=aH-b zAcbtNy#1U<9^782$c9CU2Vq{3e_WhI2+r-M)%H%{StgIt#2(UY341~m)Wg&Y_9ps6 zB}MKWc(A?kq6Qi@q<_9raf@4~?6t7xS9Bp&m#XPqKT)F6a8^#!HqwXlk?=~^W-b&1 zAOJh9Bh8N`w)SnNA*I+oZ9Ypg&M_hjg07tC6l8#;lBO($t9U&ISYoLx$V*i_Ie~v4 z@6<%s5jlmPo8#ka+lNv2dCqLL<0Q=FUZe#w`G<8EkY;=KRZexXajlzQ}sHoW`h zWIQ+R^2$mey^yhZp4|d}!cNXmc>7xm(DyLMH}G~3`SVlb^}mt~@YyA9^LHa3H+d*; z@V6b^bc6ts8H(@B5890PJs{ztAnaPjXOpZ zA&A%d_`gO&?hgSy7W+T{T(por^j$gI71L_(*MzxzpMNpU1B!@5sk_w7;bn;;TfWw5 zSWtShxZi)dNPS7^BB>4m|aiBMI zkfGZ{4DxTqkDdp%h|C3#5^-+lV^|l3xz9{ePq;lTN;;CHpX@?rb{n1W8CK_b@ zXZPD4`S&|B)RINa+}(E$wk~TdJ0>v=E3L)ErY;j};Ljs!M&m7QS(C4gEYf&)(_{Cm zDz(SFg#%Z$G6Pgw^rzQ<@sk{4OTUOzK$Vi^_c+%zT1=lST8=fO^Uo$@$*b&w*l&H2Ny|m4+bg8Gw(_RP1KJ-xeJ&6FFXs*d4&={Mcus z^R$S*@Cbc+)a0{gOFd9ydZ{NXMB!ZCsf`U9wc??4Vo3dqr?~0)Q^<_Z2kM{9KnRlC zQK>jtK*iVTpuIY|gWrr#`MmixIWbu#;gX5)&ZE0yjjtgaUdeamCMGT;D0kVt8Q38H z+@e4%nOx8Gj#`_WeOyC3NO1liI2(d-g34B~IvNUvX?ifWY)$>wcDfMug7LEtYwvSV zA`-Pujm|MinLR?*>ko!j;`+!+nt-X)1A~28YEjTG^O)024uDdKB2kvdTjwn*{0e`k zEihdvK3snD?e{{~%R21?KR@h6tGv_k#?y+AO~TV_Z7(f-nZ}q!Fql->nh4EjT;xuU z=E~_w+CQ{j86X2Yk3c8Tzv_xfv}Cu-`G8ma&gT0*T$wwC-~|FS;$^Xti2{~)EHda@ z0h7~y@bRh^AKkRnE7#5`j-c=|b3M60uLf|CAkt=GZa)+!d9j~t{VE>=+XWF`t4u5W zTaZNcJYp#kIE_l*_qAfqp8BZ3_Yg`J<9mPPof%D{N&+&(bVl9!| zBn_2RSY52blH3a_I&x%eaoOT5>tytJ^H?hLN@2~*dnUP^$Lm>7@kqt-jL1bl)!q*s&$eq#XmVfbLM#XoP(4&oA6(hP?2rKUVDj$#~W22jhQ_uIttd zW>F(hN;S26@iXX%+k??PH0ouRbzRr)Gh0+qdXfk&Q0bk-0cAjoHYHq0?N#beq z?^gs^OdTskn7jcF933sGJK*-RN4Aj0Sn_P;i#%t|_uEaK1$SodW|=1uy)Sw@@!LPA zg!fRWhY#Ktes+F4JwR+M6Ea4<%`O2#DdX>iE?lzz)BQFbLCF^X)k{b|HW^x@_EDfF#|?x zLlggYR4O7}#M)kZ*Y9Zg1-eJ`oDfc~Bn8NcSyXpwzh|&Hr4wRoX>*ND#}>Q*-`L%Z z1jQzo2@l83x;a@w$d+aUby(@by=&izgp z-k2z_tWUAB+^c$Ia{mr-{gc=OQ)Hjki;a?9HWp3Z&!_f1tJZZNy?)k=!B&yM?_eH3 z)i+1(kIZZ}ozUdj%}UurL7ztqj;%6oL`F`0iOalrJ40?|?N+2V{rPmMBXHjp=JsRL z7UC?iHMc9le@ULab(C`SOaXlNt{001f)qfqjxH&mpRaZWp!w6DDpXB`!l(`ai}bT5 z>cbQkY9@m%zXinkn67sCSm<)^Pvn^dt=M}=pcjJAm~RE?xWI?jI9oGWAf+`L9_@6v z5Wk5;Iv``&IHYNx-^6b8jNmdwyJ;4t<=i z)I?7=g4^!>mrC$B>{Aeb!^LXJ3!j-WdTFhvh#zeTo(I_GRkx?9C8oWdUwpq7pp2A! z(~$Gyxu(6eM_;x4jMX0!g;Qw0aYDnPugY|zJKXx%XNHXBk34NZ8M7Oz@tque0jhb) zMrIrL;mh;+6G+;oP$88D z24ZE3eQ3qsV+a4HT$_A{E^*miMNUX)W%lv&ZyAkH(0(;T%$@l-h76MgFkv-x%~#8e zyyf<6Gw$Mw=#H|N)_%gG!uT54Zixu*G<%l!YPunt(kMUb3lAthM>pL`3)mVzOZt22Js-c>J%7xWul<*r&Z_mx&{; za?f>>PM(*XWPGZzI}v$RoZ~u&57^kYk6i!Tb?fVBdO9Q%$sY~2L=5+E7zuG!x0E0( zvXj~GnL1uyJ;|I7Svwk2GCod$Upr9}3VvjynIGN^BV{V`d7tew;=0RptsXp1O`x<; z>qm3x#78&a)tObxjdC6GRKG2aiJBRoLy+(3RAIs2kR<4{QkkABihXyw-S ze$>h})Dz!qFGsNq_U2A9YD`d74?azW84&PSHWF*scn z7@YE7c(VR4w0`U<-MBnwbo&*l=@WrZq{6TL~hblU;iQ!Wm3Y)+iGn`?M3b{VtNU3 z^15lckY=^^jxWy3;Q#PtBWs>7kBuiC?zTNT>w2D!FriP;ilN={F`vBabzQefEqc#O zQ!?4h@)_3G-fVj*bG^^MS*3Y;eeJvUpKyZ4<=sxE>*LA*8OL|C>w+VFOk@?zpt+IMDaO;C2TF=2RQfI-G_29voBO?NR1s2PM0K&+BIR#a~?{t zv7w#{rmqPf5~>;Clh&len&@%W^U6Xrs1=z!kG^=i#8;ntc|}ij1Yo;8@I=&;%(dGT;zMqoH-v9?d4t4v zij=IHCd)VZqPhii$ec)4u#W2 zh;=v51)EzOrfG`%r$t6{zO}rev{gn61>_<={KsOJ-^a`M;xLNE(eKDdUc?v4q$DYI*@Rh@6d|yiqlW!kg@qqtB4rtuyrEyb4;5y84MTpiH1I<(TkzsY^ef70N zjQ1}-ti7z+)42mM|KD@L3f9)Y>g2Gg$MAe*M}*(zS@q7~84Fjj)oPfa9m-?;ehH?1 z_o$z4Iz5KbJ;TTvFsAE0b-z)2W2J{q$l#aytG2ztr`fFhA69@id=+Wd&-u3FHFgNw zIkt_v)B7?BBc!vbLX*ur&f007uHJFB^NH$y_tw|Cp7K*tKmATt@JrNc>}(){`MyMH zihGzlhEv120!7YItU-6%jssaq#QVJEXI?Wr%AD351xr&Z{`@;W1FUB%Ej!T{BxBUq ziTA1~)fb9&GOQ?!O%ZOLmX1f615Ni@=~){W^|c}-0v5~GdU2ZHP+4?Imf0s22#SW z^GavW+o^GH$HB5I!ewyft<_xhU4=Bcw);nlnG!FYF_(Cb?H?7M6bClh3;oCpvWaW@ z=LIUam%74kYa}~?J;LAljQRY&^g8ioc@4iDlVMs7C`PEQ;uYTQb(-pE; zJlvnVm6cTf)763YJGuUa!kxe%Y-46H^O4CWpYM#u8(%a)MhyBQ+J7iRa?a*#)kjG4 zO`Ups|5HUaORW+-7x|a@Uy%c0<8}K8e5JRWk{C8l>irDoKom$(nfPf%&r&!L>8g)O zgW;7PsH0i=HJ1?B0rhcnO@zl?v(YNNP%`CRpf3*`avjCAMsJr~s|W<+tNA6+mmrn( z9@Tt66F;gDHR}{!4sf3hgyb3#Xv_Gx||xvn(WR6K1Vk~ z`H%kI=ClmC)1G289ujWX8}NQXmVUZsDb7}L3nktQmoZF{ku#iM#y39T+s=5A{l>5|7C~t~eX>wi6P@9X9L zy{-8MJPB@HwY;TxQl!MT`Klr>K#pPYUiXfbP`&VGHS4mz!^9U@f8qQcubSe&V>&m( zf&PRo&clR@svs#O=3~!}n9w6an3{`aC8ym2?m7l9iHyo@I_BM6)j55eJ+k=Pg?N%A z8ac~huG4x@bd=s67(8?O19uH;QvQ-H^J13~7ku7%!4te39^+g~jOp23QY1ei7d2ch zUrO6R?OMUSTPQ;9{v|jK=cDVBtB{r(dP7gCDjCK%vuw=9PoMrEtXX)eCnitoGNP2& z(VhN>8YoJLB=^D74j?bJanx|IWC3C=AErwuGI#k|G3pslnk4qMDM+k7Rf@CXwVYM# z=cahbm4p?h?3YujT0HIbPAiz~>EEUa>H-)+354Q9(+_AOEf+q1zh(<4M=@~pds$2! zY<-e0FOKL3BfnJHH|}KjLBk$+H96N$F$;Z4D&QXb#lx8KZ_(k^XYyJ8ET+8`Piwb& zyF$34=r)J6g+O6nl1-vE06FLap9y-@-QVQ&gy2`I-BLJ+Jvl#G{{1Wv|6ar?X8)zZ zIUm%x^|r^fqW$UyqB5-)GXJ-k{kcJXPR_xcP0oqrOyBL+wJ61;2bnJmJweG1sV!ZU zG4-HVuWfe%-4_?^lshRi9o!RX13Ul8O&C!e4ygL;b**BZQG}wvSgJXL{lyjV z_A(J`K>LIY*q)sn$#UEKmK4%F#Uc3QvvxTb$?`95!`n9#Y6x&D7lV4fS`u3t`ETQN zE5?8RWbbje7g}t|NEr?dxL}>nUx32zXW%5z-rik!E4rgkuDaO;e)@fvF z^iu;rA3aeM`*i$8gnPq>*2NUef3*WJK+k}D!r0MI&)>`R%aYq#kP0gm26uIa+rgjL zV9?s*bRAsFXRpB0eZjo)(T>&%4heaDd+Ym_7caR%Z(xl_5>{AF!Lc@{q9dXN3;EVDModaqDRjkz$*YyCriZ*etL+mc3NigtPhm_o7{^&Zt`mEne*B5siSE%P^ zhpk4eLEovo`tj0m3Km(?^_yrsWfLF_lqNa_wK@C%;qCcJ(0J2A_V98N)cwKr#!yIy zzC{qIECOA|ry*4}CkEu={X{#!xDngU!~al9U<4%gj{BD_w~X3k0|3QGnn{F;NG49q zz-chOYj6V%2^IOTF4$D%xN0!(5sl7Zl=q;u@jw-1e#K6lRUWu9egcxG^0swU#nxIn zC2?2vOB;CC&?Dt}-txFp&B4@Jq$6uEj0r!zIAi6q?Tb4~F)=1s!i0l28m>f%^Q5BM-+110ff@ z)!Nk+%73<=CNX?}{f_!`wH%k{&f845@_L15m!neX?%I-rOTZGV-fau8Xr%k+MSR0| z6z)7fF6euT**enaIPlALTnyW$>{$imZxmYR2!?5}Xd6)RNW7YmussxA@4L-CN`fo+ zmLNdXr4@RTk~u|jK55TJ&+pqJJN%CX;|Ea315eqm77EwSI&X>#J)U@;wnw3+h-k)_j5g>OyB3SMy?>#WbdRi8ybwVQq z!au{j9=K%CTgEW2yJS-4(rr7{_1|9fm3BhuoF>{MH|@cP&lAd4uAzp6HQFictf&_V?!LX$4qZ6ynlsVu#Fa#4mTCR z$By#jXRW;0bHnzQ+}>mj=z7KVoZVy2QsY+5CHN9(?35i{#pmrk1lIWkt?YbJDwsP@ zeD%f@aJJ|0P3K4RzdF6|)Mm6di_?sitOqv+iRC5XPctunzebBGnWmjJ*Km%eqF? zJ#@m%?dkq?1^6&{(F|65WV&FU<<;lZ^~0NoEgtLA*seEO7=L#0{mP$KJ!pd8frn~I z_gdO=;fx;Y7h*~R)~#Xc#j!#ckDM5QeqB8w1Tl?;iYZXBgbS|0B8{AqcZ z1UVzF7Q-!@n-nqi?_rh3UC~Ea+5+yGVX`bKBa-z|2*8?(y7bwYm)cMHi7tN#SmyiS-jslSY>GV-HsKtU*Dp(0DKTj+{Nk z)8IyJRl=Uh;r6#Tk}NzP#2PD>kikS{#^7Q(OJkiW}ac zPMvYlBx7388YRq~<ucE8MxSSj>sxfVMTw(l_lx~ z{pOlL3Z(8Spr)e(=KXI}DgS(dv|j&WK3akY66X2$e_5_m+zk(scv#rEc|ZH`2dB$S z6NP*%&6;%e4V*|mQtL?mNcNnB`pwMO$EAPS04%=is@~nLd0|8}&5rZ7s|~Q^_aGfS zWhXb?5h7>>?oHP@Y-a)b84*K8Ntqq_L>nk!ttw z=Df5z@SdTn*8aTYr>N+=humue0ZOEkEeoY7(S`22MOp#-t2}ZS;L=nkY~IK}?10W*KVE zzG35XDPbR>!*1BG^rabEyFOf#nsLcLOHoCifAon!)ZYp)jK~kd;6>|yAj$#I*_$G~ zByOslYG;03HrV3@m&u6gnP19v4jw@CGM_i;Gk_;#2AR<$n7a~sCMcc{XSmlkY`Y3VCXE|JO1X>d&PeD-v2rz z&EopSGmD2{?BT64`=4gRB?*v6gs z1y>JN0bsOHs&Qu>0nf|hg`?q%X~n^A37Ct(rb(_3Xu{hJ4p1XVz)3>R>Jg4=6v?G zbMLcRCr;1Fb1&1^OLO$n&b@d$rnvL-unx(X8CW^p!XEA*+OK2(K1<-(8-n0>F3h=+ zLET=c+IhKUE*GzBIkp&P7n=|wCTHxaQs)Y*_X?eS)ookQtv&&}QOUua(qli6%uNR= z)cn6Xa$uvpoN)+5?eF;&H)zNW2^xAcljL32HS?|A2ekX9L8|&pg}=$NL-M0BF%+x< z0IOx!t^ESY%t>%Z`GAbMC`v;G!%_}NZRP_7oLcHhp^LMy*G|g0Al`eZY{e>Qt@PST z64CrZAvuh7)VqSkrTfw*AA|G0W#iKZ7NAPOb`9I!z2+ol2r^}lTlNiEn*K}gNv|fg zPH^_;z3cdKq~IV@$XzK>NV;970TVu6=q9n$s#?`15j{jLeE1s1XyfoZ=Z-|Hty0M zLEn4^ag5)dKDH4iFV^~}O@`(et5EdHckz47x>%fk{t zXE^6!rG%sNdQV_X7FvWzEug>Id%)hT`D}sxdXF9cn<1NorG5FKkf;?6c_~Nn=X*}2 zVb!6^>`+mGf7CCD>ZIrT@HZ=-0{Zrpy2`IRbtEOsV0l&ZqNKWHwc;Ku9x>G|F)<7( zbF=E2aywJeTGpI?<4YjV#atQ+QDZ5!-?@)Gdf< zwX{C*swAq!T95)f)Aln|UJGy;aO1HEyy~Sav=+O+EU>q+}a|LQ86l zv|0Jon1tuxqm=`#?O%9wj<`!&b90@A(R;4{YM?$VwIDvoJT_FP8#FQ=@s&(eq%orCX|Ae)6F>AHc=_Dk zGHulB!*fG(lXBpKWpuG*%h!s#he7hBIR8Derg`avJ{WI#6ERDui8uEMtl%)iF%Qe) zPq7rKw1$yzXc&)mZq}IDXAj)<@V;%$jfy8aD-*>woY>)B+4a;lNSsQP%VskqOi$mFLI zvjbVgHXB!N2Vdr>&1mh*EAWqc1&gu;iriv>vV$g`)5x363i2M2%#fI zk4OJ;8yvT_rOE{s&=c=TR!5_2E8wlQl8}Cx%YX#t&`3k)`7)_a1_o}%N#7MNwMnj0 z-m1K#TRXkoMAx!>ArPxYZe|e04dIba&i7(9Cq?$fCuWumSi|Wj{IS~U_tMnjOg#GbD9&Z*pI({hohp(wUR>^CcxJ(a z!=?WiQJ$29?_2a;E)_XwB`h*nUu)o>xb&>7jz zqJX{eqGmCno~M98{0qq6+6ioSG@o|svn~sP7A|$jx@+(a^C8}o_!}ZUF05N6hz1HS zKlh^^{VvXLDw5&6-_Cv)e|o@RAhQL|d?$AkT%-4F6q0ng*~QNiz@Ajfn#aYTw(ncMNlEZv4h3V&!I25LVex(X6gEaJ5!`YaM~(%TWd}FX%0z1#WyvKc ziU_L)a&3V+|G5*fy>rN?cf#CL+-ke@TcR2yvyPvy=eLoL#(tHi9x>pmPegHC)#giL zWanKMVnj<4?%)Y8;S9s-CqA0H8j@dFbWFDBZKtT;ig`S#(JRuBLN%W)-q^7srUi)_ zxdSa{>kzX3u6Ilb5>_2!@kHx*Rv^wj!8zNLSq>Mcw_2)xv3c!(BbX=g$ols9)67Mq z(!GMYsqUG0_HLy^V0PyoHaDtu&C&Z=1$@=7@?}Ly!oAmvFB-Y7|1v{1!M8X=Y$7UH zT>i8XviMv&L{d;?O7~;`BaKuda8Pc}&`GB#P>+{ouFj05V3Z*ITO2t)GGJ7}j#0p> z%S}KR-9QPnUnYl^f@Br!@ssBKO1PHAdx@>QG=j#iukayInYs|ED7S#$SJw58;G^_h zdy#z}U~BHWqN7=t{;5+?2xOBN({xPc_hR~&PreTCTNyomCaq#-v4$m4tvh>O1$L!^*BY z)*^&tex53a!E+bxt$K{xE$Brv^}uGEZlQmH1%Lax9H%afUk=-khz;{rrdecPrN&2y zX(le=X!pLsl&S(vA33{!S=QV}%Ul=lZ>Re*{X4DkGOjwNpOCJeHy&Q_9%`L}@|Tpp zR_?or4)BhwqW_b%VYY2*vQ^|D3#Uxka~PwCcV6nV7CcJq^Sp_laO%OQU_a*z95@AH zm?jz;z;Y1ah<{(q%!RnpZ(>$~6%pfD^x8WKHgN|`TjQF-^^*>YJE&jGX7@CwfbMCP zrZ=oWE|lHB4}<)J3aIpdB8T-_7G-u$X*f>q3m^hN4iBqtO$mG;zt_$R+THAY4Qvdo zwx2GFlto}G0I=pGb!7w*D!qr$#Z;h(V>ywxR_~ zKyyCDEJY0Tbw^C=;Gyb-6sYt}8#NMl0(!5$_5ETfweer>iiRzlRY)zcS}q?FQ+X0ML9 zno1|o?E$UFxxx2_C z6QQ#X2xC%>TYvO_D5*p`mTx)->D01}V@M*iN==(s$WPp82GNNDg^1lm@L-LJTb|CW zxoN=*xP*abeF@~cE7tbZ0>MeOJ z^}Tw#B{lf!>o;!ip%{>-aU2T|MrGH|dwGe^h8i+=X}Ji{{}7y%inu|?geVXQ@YD>3i_I1%JCobd-55fCss4R?KnoDnl%QxRq8TU$={9_4UEIjW&9OJxdWKSy$x9jcoVfATBw}WS?Tk?4!Uj$QXH)8*RmL z`E1Z+jNj1UzqS#~rO1u*M6#UPxr70mzX>GUaUeh|h%M#6Ddfi7=+j2Qv8jsE;W|a= z>5t4fx)I;BMT%rLxXmuru9Po6QTxs$KFDuXbVkOzp3@Ce_pvuY(eIV&o=2~3(?j_f znl(lL03&nAOJP3`Iev(?Oqy9qQZ8eC}>ms)A!`E==; z)46MUlSE^uf34x|+i9~I<2M@!(`HLhQMNWz>JPfqlH(G8ie);ajC1S%M7{CJ_%CvN zT6pcWTOu2bue#1%eJr_(Lk93SF*L!}@IDNCPbpPC3_|U451L8{rfJbOl=PX{G0O5A zlOh4CTdAMKyBP>9J@eHzt8uKpmI*#qs5R75f3Ub28Y%Ulm~eJa1`xvOuj;l!(Zo`# z>U#*E)Cr@1g@TRuHdHNvmFk5MBkk2Oenbg#BQfv6PG&X%MsuHgx6rVDr|EEJPWxomP{e5RL2?IrJi%^L; z>pY;8l$?$V8asauR*V4q+``axW>{C&2AtBScvCB6IbQe7F_FbzXKk7!Lp8C8pm-G z)+npR7HZdt#>!8PZP`2aMzaT=mO^&kAq?VD%ZI71M`$`}#ZBFm+{3H2jh;LK;};T( z%Vho3oeT~N6@0oUz{+}0?Xd3jAekRD1UeqeVcnDjj%~c4vnbgB>Ls6()`9j*6-FK3@ypRG>6GE#;Qj`Lvc#4@nUi8r1fZp%32N=b7uqzvKixADF0w z6Lj)m5%-g|QWM2Ijs}9wx|-ysM{_3xMF`foUQBn6HLw8T zbk_h2BbQyNJ^HcfPT#`e)BK;JgAo^%Tf<}leol5onIa^fB5W9fpL4Swa@-1MSKJXx zUdxWt_xWKJ+`F?cN-cHw6g;LkHd}>L{N2TQ9_H=5WF6#WFr9pC@Q1I^>dL86&gokM zDl+dENcI#bkn=(nTFT0oLIoacTNHqG4+Nds;N;YVcir~*g~!)9M_V3VARs4S>;GGx zk{2DQJN!{6?0oS|dF9^0TV!8NehS=^8JimB(a~U|*XoPp%Uk~!^f133YTrUPu`YN6 zW#kV#MA*;O4(nvS*fGrCJO#;;`)nP}=k80Wc`y6;R^keekK~?|UY>?{r=nTEc1Y-I z2S)fDA4Rr{jbRQD4U#;@)GC7!f;gjnX^J-X7fsh!PF;mTH$n?8Fvpo?$IR%Q=x?_> zFIDISZ$0+TJ^EQ^!njt!-ZCSqMGcjEZcTJcp-O9ZEDy?`?i?V*=5+WCJKsR-?l}O!>N^WZ=e6l$W#CiKHeU>~L$B>fm`TJMy-N5) zSICbza-iYZiA)2tEu%2v*#>Qok5LcIMcT7*T(xYn68>Jk=w7lu4=6HTh&Hh|Ydw=` z{fK;b7xImK8O#UsrONidF)R(e%&&$_y)6GW;^3E@uiK%xCna}rsWRTwd>X2Pbek%V ze8L!IFv479^bP%eb9eqcy$YoqA((!ua{)uZ8!xmIOFV4Lo?DApyKboy2)rAM1L{sCSGER+jeSWxZR<9 zem#qls6QI;Bx$a+nN3sO(`x#-1b>pNw~?0^>DtXuoOk>ykYm$88wiq{K}6h~e~Pwe z_Z1$wJ4jodi&}02Ed)3#<$3j=uvX*PW6T{|1bF|bZBs1mNAeuW_>*#h+i_LCd!8)8 z^4(r52QDvy&k}Tb{7=-rp2Q0MpaHPCN}C?;=k-2OTB>_&q8D_*=RGfM8dpxclHM?U zfaSI0xhaCn2;?>h_ybT)2QXt|jw=$F-Af&pp*%m(5TVk0a3UMYiVtWg4hdkP((#3B z;iu|bYDRQvH!Yyu74j{^%a*gQrlZM+Jgl!O837#ed6Y2oe5~hFZ-y6J5P@9Ns~|F1 z_JfuQCcz(Gm7B9)cU0VtJ1`Hfw~`<1)4JNiotb$v($e!P8wEue8I!*e=GDs9KKHj) zn7(g8%Yk&o%B7T-I@y{Rl2}rOb2r!f$>q%r$gu6ygB&J3`rVJM8=shRIf8%A5(y#A zE1eqh^?3RN{E&;Jg&(#P5t+TdUk}``($g1dhAbAqOM+M8Rg!?1*+O0Y?G{)B6a2^@ z+93D159JtROyaP+NZ(JWhUvZ*3FwUNDP_?NqlV^;?^DHAilBbH^rOozQmCd0(V zCkp;aLAKr-J0vpby`il`dC>j3uaG^RlY$JR0-BEAjN-8sT7>tQckAGQLs%DG6%CnvZChKA$P!ZPY(IxRX zCdF4$z_UQ3gIjTR39}`XMM`IyyN$UEKNZvN(kxh5p%`1)CG@U94UL-z6rX+F6Z6C17d6LO3^uA1PN8|z1^eFcL}f798V zM&kTR4rGJ$FZieC+uTG`!j6&ij1yWPJPCdI~zZbZ^Yg*u^!6j{DE3`{rrD$Cx;dF zLxF4F{V-k!f)gV$CX|w9Mc?}b+l5Ru`JIcP=mX!)yb9H2c9}ap$Wfu%yI#=ll`PJn zqWruTH#_U6kWXSvsPI^g80PE$Gz?GP5zEhz(gWRo{#Hw|9VxIag;HvSX+^}ONwi0- zs^u92poIZ$YqR@@qBIi0d&DSHm&vQ$(rQxZ4e*CN95ghpD=^|N0K|SvnK-={I%p{= zpgTv9dIVY~OnTkqIA)X5evuHj9JRpy)uBYtHzaoAFUs$s{=cW$-_M5Owz5^mfFyIh z!hkS6DixC#7o;|K@3EsB+u}>{Am*X|UzY=yMyqqSzi=$(OY`8kptZ*7Ch67!nCs)uUo&GW8%0+s~Pgyr^j`f!^s{w_pfq8ReH^RoJAModV4RAok zO6wr?adRF(B=a;wnEi(KI&JwjrGEP=%}a*t?rQHY1BRRV{Wqe|aXb9Ap1y>BoGGJ- z;WGL46-XxiX=AC~FP1GukiZZjn4XTPM(@MpkZQnG5=8$4qkx@2qy(?WIwn+*tD!Um zvfqk#rp)tmZ=}_CVk$yUO=Mar+oR|0B6o0`~aWEy_yWtYq-4s+cN20|%}n@Okn@7Zw_39VWCBiV~iqzs2D zW27o+@g=H^%^0L*+V?Wts3B{YlbNA|Zr^iP2y|8MPcDyg-}o7RFj`71m2l;Y;Y&zJ zgQ!zT?U}M6Z)oQaFyfr zG!pXCcm1>tP|@XfNYgYS*2&Yr%zUmT^N79chSY;cUaRjx$Nt2XX|VKmUEHDB-EMQb zLm94+kndv1_B;&T`LKW$HAzK;iyEx|O;_{Bg?tp-)r2-C0QC2PWIhG~azkL)V6dcs z70LhrHNq0ebTuta;dj(#ZUZr&4hK#s^?S{7de4Frx`8eN31T$R43b+hb@mdIqK$6- z92IIdjO8dY+sB9%dI3s$fzZml4B^3=Nx!1HFB)v;jvu3qNxh*yD`|$EKr5B@D}~DF z@Z9g7+QwS^!GDLouZjAlS}?Sjx0~Y-m}3YWjf&UNV7?Tan)3f1X{*&1bHC+p!f5#i zz{JSqFpfLBbb6NPe2F?^$6BP(U=t4cgr3C7Gdm4SUnaA<$OwSw9=<++VUfqW)acdeDK)mbEoN> z!T@W=hTEwtXD8>u-C!N5VVuI95Ub9@s2s9oQXVEV#X04-_`705yM1{ZLKv*5L{oXe zXy!H3o}BC8HX!s#nb?lK&J;h*?l5EgWIaGdg`qsAYFx^)_vL zu%5$6t`4$RCrMJ?(3NH8Q991JwWS4JnVSB?DpIJ@Wf1lC5?5W)STaj{W5dBk8q z`|-B6G!T=VGt~v0ww42;S4zlbN^`#B;r(wL`Ql*^(<~;aIl`NGmMHj3*Gw3d_-m&t za@gs}W}69K6hjXLh+o)D;CW)k@3@`k*i>&JvzEBR{9e_>+Xj0=HtvEmkxopXG{T|L z;ZD{0MPGLO!S0Y?R$N&1PC(NO0Fs$)@H7&c_hnV6;_ER`n2W!x$MKrf>MD3wn!aF> zCSdb#>JB6Mg$jI=)4atq;rITt9gg3XTC;-GPnqS$9{94_ufU&T@fSnC0CSpGxG`YI zB5mIeawzw!K6>3t0rZi&sRXfapV^ln@?vmCFsJ%Eku2LU@v)_IbL{fNIdf7$;PYc)SJ7L+AGyl>7JpH zZ2sxE^uf5ysaJIHh_-LzJ)=po?2Ch?gAJnH95T&jF3qX}LtAW)Wh#2{ixYBz{0v7z zk2iigw9c(l{AFe}FvA||*Ias4UAT77LZ0A|p~lV(!sOR}9{xZ`AK1C~qOyZUeBo+Z zRS9?v?&gCa646Rd(&>+L82G9?c`^jGY7!r!LB%Of|?s#-|B`P zRS`fLH7?XFm-c&IHIR?|eZ>LV=>Z6ZZiw_4=syp5EBp=?Ty+U1B+>&<9hBRi# zHd)@fCUJsPsLiYL!#p(UY9Gtg(JC=Yf(B1ty(Lo17sw(KN@aMYFW13i3NU8bx;RfZ zRzHvmNZBvFKOc~C<*ov-9P%X!F;{6th=z)CMoCil7xeQ~MSkh>M8{+SAdXhNRyIO% ze~C6{l2<*>K;{(CLXR&UH-pl#sujkMD2i)Fnuf)4rUYfW6Mj88jNCrbamk{(!TwZ3 z)h}&m!2c0b)MG!;U5P9{nGzdsCkKDW70Y>>;i9-B+L$RM_(O_!?HQRR&n-=cq{sJp z3iC<|BFxjUyOIL20hEYtVofBO84dr|=Fg`Luk;O#%NRfAc6%)?TFZj&3M0x`aQ6pn zHF>=)TT5a;FoD+>B^qO(VVZR_e}k!hOx!@qG?}AN{CCXhc7Qe`R;lh8k-w+acT9gR z&m5oS{(K1&5oYHWcl?o8##~_yW>Tgzl;5eyi!xxB{X}StTgh^{4eDe;`Xhbi+dCYj z*K1gtaQu6{CU=K6*+)qzf$X`s={nYs`9t?>XMaFP)t-n+IO^zy3^@ZT;c01fyw{f|KA#w(DqP<{<;KXK{Rc|+-cjp(4MV2=GXHFmqPPFZ&D)i3ctS74f8 z>`fC5jHT+VD?R6Bt5Br^NfDl<`B0+t`TGdjiOqjm>hY|#2+B4}I>~=yg1_A} zm5s2GI93Ndts!Uw762l<)$f;^LY!rJ1V~IMfVu)C@uh?ao5(`KPgJmE?J^1&-Otu> zDrc7d+NB*g$AmK|euF?sdD-??bkE^BvkNPu#aPX|@u&O4{h}7NCt%+DE>-N(i`jf{ zI<~3Rsq3?N-P{zWyIk}M`a>R>UO0*Pj3PhS57TiihYSao)wxy=c=e0SuBTM&t5>$F zmi&?!LxD`;s4n4yXNf2O(EZZQDdnp8hc7qRW_BWJ#GWm^?AMRfLbA#CpN7~ruu6N) zQfJ`0^pJ7eyIMmhigHorP)%sLvE_Qsm1ZtAo2Y~pTNs->^{&Ksx}a%__YAq#_S4cb z`u+YK^8i_PMAe-wZc=Zwb=nM$18 zQEL;2p&gdnNQ@;&cPn8vvzw!cEu|0uySH1 zPmA;Jsh(;2&FXCWCtl*TH1Dt05gSX)-eUcH@BN?zz-oJwe8V$pOIb9J;W}k2wIRLr zN!=vwcJg4A7IFKne!Kk;jO)p}T^Q`$H{302p~!FRG&aH;ZxB{75=n)o)4=wD^+G>K zk{HXg1pzMR`YexxyL3Q9#;${)q2P58OII>{2hV3c)efYF?O5XfCxiH1;vO@Xe_VIE zy(<6b>>ewZ*#!bF|@UXj+nBn1yYr1Lx4uVn=>h&NJ!45{pvn zTOW_Nc_LL41Fa-M{wF!GqVoNwAC$``+)oJQ?ZclI9>2uygEL@AfDWOsJZ zzm!Y&cBH=x%U&81N4}2mZ4W~xCHjS?zoWzWnoh5FSBNJZ`-U&uyGQ13tV=wE%QRw# z`^Y#vB_*FQY7low88Vy4RrETQkN=W?k+MDcl)@@tF}EGG(jg8|w38NWU@QK@_T4K{ zuda#KUs`v|_QxSFlsZ;xhbA+?a+%~O@$_=?U$2t4xgk`%v#9Xm<5%45ePb_hFXxJv zM2wb#Er{WaP=?q`AFsTdz^q2dUIbaZV3<>*plMG!KfE|U#lADT**1X4x>?*Fy$3(& zChl~7etR>EG+)q{(>JSE^`w$GrGl@Xm`M`?Oo9kR*fN)?TQ4x){YrN%_+G|sUJa=S zdA$@4Un!MHU0)~GG2wz^Sd=MGR2~fgC?zoRw?w(eS(S!aOIa1tOr6+uaSL1pRY6bO zpgDV4YiZfx0NHGvB+#usi7s0Xs9uTYWMg{J$U|EdX|6MWxc~9glnRzDG7@J5_~%!M=NLI{ZQ&i$YbV&T{X8Hc=FDHURePKEH1plprs?oj@+WLS>i zc(H6n5)|YGPQOyW$I5|mG&$GcF|>V(T7G?3Esst#T}_*iZm^rELmyo6$Zc)(!#LWIU~?!kAhEL|I%*AARmzzrTz})uqFJsA{dePbp1DmXc|={o@O|O&np}dYHdmdc26z(kpVwa7SU;)1zX-*S4wZWfXq2RKhog8u+lz z4c4?RgZ8XG*DqwnR7xEEbIX>P=xwRK>t_BF)2VEopMw#+tW#lXxp|%MYgi(f_`C^5 z(&nWZw8d##ig_Nj^exv838hwvXumzmaa8d7=*~KoSo2!quZDC?L7!-<8W`v(6;<&C zRnW)4G82J-LfSZ}RzoUX9P(pUHoOds3unJL5)Oh%t!8i(s zY|*ka=_8G}P8R7JC;NpysY=}U#;Q{3oQwA|2kNH5=3mv@dEaAmiT=0hWkj0PxQS^+ zJm(uL(Wq51diA8VHMeXU4Z~{Jij+p`-;U?ua|?wLguzWdO%A=e24`zq_jCw)?V{#ph_6*eh)9=TuQ z+EI{*4wI?+_&<5eMW0knPl`gf!8|kt&<*} z=pD^9sfs!BrF^sd-6<|f&MFALKGfXuI4OUaKdoGyZSu3C`7w_%!xT}GaY*vv1e{dG z>10a{tU)IRH85*PNmu@6psE`xg_DA2yrxml=P`*Ih5K&yy_lk#hBDE)EAj6d)EjT) zAFtd+$_Wlo)%rm*veMZgfVP*V^1D;H$4_5Y5)YQqkFylI(VzbDhi zN^*Mzo<)z7k>a_d2eh5<9T+{we>J_IbcG_?kRJ}=*i=OmRL%G|n>g3H)r ztYN}Ka6eicHFoaB5_gC#uSeXnmBNf;n;98pT%~u;F^9qjK`*G~);8<61L1CFF^D^C zL$>h>Y&v?e1=dK|H5*Mi+>{kGv#bLJCS0dxGU?pv>!1 zK=D`+j_kA9t~%F-=cre}#%5hY6sf``hy_KGkN*j&mr=urOv<~OWLx=w@Dltkr93B0 z(2|ep5jsS$%U8JT`43Q;fE@90ex74DkrRt){=FqkhewIwloNi89S1Iu<|$Szv4ErI+JPsZ(FnmWpx+Q_*wO zwjX;9eKz!zDhSKFu^e8S9d(K{H*19M>Q4i#INgo>+$F2POns$Z&{+yW% zjkej&fEGn6w-3Z*CtcNgXsgCpvf#bWK*=OOB80zNqR^tUQnnJ!zU~qF_Zv z2h%22+U-7+v32RMvl~ZX(Q6v513?d38qCh*{Vjg2Hh~-FzqGNJ$53NlTHb2f*rE zZ^CXOUY}w5cGSj{m>QrEZrh1lv5=p_V zUPeH{<7YZvGEa1uC2lIM37ZM{3xKw7&_FT>j&%uRT+SB`**eHpMWyYKD7B(R%?aS-$*5*xzFie1?&p|25ao zQQ#{Bt2vTisSx{&Qcb9usccPI_qM|cX8j)^^%rf@YPL)#eZ5TmH^8)k&g&K{4PueZ z9g}#nUMS(QTc5UKZhiMEq4_q}v2gD*OPDxHv*`g>Fx{HO^WMUb_x_#yQn5+Al4~%T zzW67wlF@mEl{hueGtN50${#MCe@nZ*cKDO@yt6@o@UDvXK|0*k^vkK9OpSgRBuj*g zTLs9xdY?Q9pTF++wfku}187JZK#%v1Zr}!%vYbSW;w1@W4=wpfxV$psKbJT06TyCT z4dN%YWK|a|DznWl2j|+Xs`2Pk>$=$|mI5QFQc56n{$+=>^e@4m&O>FOp&QQiH3hXN zxl~%;#HkP8QRs{a#_bTyCy>nT;khk0f^xhAJ(pFyN#(~n@07X5;aS;Kjv{s9{@r!= z_{r(!VmUPC&2wyV4O0YHt`RR6k*g6tiwQJv7rxb>F2-t*Sl{xFJ)G}PwnqrFaz?o_ zkDr#W;b>j5O=$wSMpdYsdpTR7IbOIXvQ43O|(#hdzXkHpE9N350C8JP=#^n%fOistK3{jQB}5gS1s80o93 zb-Z7k`00gI!yx=7d`E1{?Gc~9p0Eh_Wy@WqKta1%fXfLg5A*yR-@LVF7-BcYvV(6%7S@hM?Ej)x$*-00W7S_$g_lKf;DYQ z*)RLi`-YV#GWOov+lI3H=%w%|`5XS}KX)~V+*onhjUbS{+q>yZ3YBi9Tg^m$;y{E~ zbHiHldoQ85u5{KH&>uGRE_l=N;MEYV)+GR#IFbp<^=mRXv|1WSh1cS3n%b>1QR2GO ziR^;lFaD;=0j&wF3=WuqN>9c0omuVWfEX$7FP5}Gb)w3tu9+S<6SNSA=e8_60I;2p z24)^NrH@@Hi@p1QAT~MPJNCVMpWBm1Q?b@+_;5ZApRW1lz1HfOjWe7ib@$lYR0BNOdU1kXC{rocq4zK{97F8Yw2%XSw=6T)Jo-D z7WF76ecW>v@4a=MRTIF7j@mdVX>OlMX40UlC)0G#|JZML#NBm_zig*>e!C=9G3U1_ zxNh={V%rU`M=h0Uk?$$}1N_Dr()J<@npyyy`Zt=F^kqgLL)*$$59+>E%z~v5Lb3+QX}mGLzMq5L%k^kWTow zpc`@ghXS)tgkjk3JQptGdp;>=@g0*@kZlvryXFH>w>6H#W(Jn;%DcS(dl}XVKr`t# z)7i=5TP7}54!kQ*H368IX(*S<)D0g@sD%_ox1NU+O9w`fAj1l)#P!&?4 zJ1`U+_kN)NoY=6Q)f!v$`l)vXp+T6||Il>a|7^c+8#aofXsh<>){i}E?=GEa?M77E_8bFe30}JhT{B6 z>TKn_c*dbUZ7z`*x+M+D+Z62cxMF}bCYFVw3KRdfD)iMKaOJwY+TYoT^DSo7q&l=T z@vI&30?MVoauU2H$e~+bPl7G$t_a_VdUE*Fle@gW>^PM;NXhNHyH0#y-u)FTc&GuA zng;70>r?%CMDL7cGj}HF*QUEQZ;WAl&wcEY`dX>XTX5xRt;HQ5HQpn#kN9GK$t_Rw zF%QlA<;u>8Cv-7OL9{Fx+e}n*$o5Cts?rhuM(?ULh$$C^2ujX7WqA3{6!2kcv|A@` zL^?MItKt1acEs8)b{cib#$#Z%8oSmy$IT6EKT_Nd4pwaQAIQFWE8<*YQ7)aE%yILd zrmyV{zzx5s;U|CEGh7ziB?+95Wx3*xt(l%A`hnXXML*&YTJE_){KidZ{=e8rr1ec{ zJKCU`8Nf1oCQJJ5Abga@gq?7g>rpQ4UJas$7MBA@R=k1jmx3{X7xxCEN zFxT(@*)eS@RS;QeCX6U>gf1|qDBBhIB!8JHzr^yM4e9lvl$6L>Xc;ZT<;;^YV?k^k zaMgReAAbw1`QqIIC}#kQtjJ+pzX2#}=Exc=B5Oo=4n%CuN8{64&)Lqp^8IgqbaUo-dD?Oy;IgvZp|LK`h)^d6u6upRoVbC=s(9#42P zv;ql*LK}~JDw2n5A9u=tDj3w!%{Jd$Ww6w!H_D6Y@&k zrseu3I%!|6^Lvx`r=t})Vk{R*qGGMDinkv4cf2}uEYtZQ4t_(?^2Z(aD>4OIMHpi; z%Wc;I$4?83+>^NUUMC3&BCIkzbZ#nl4S&+Jnsty-`kZvI|Mu$X$BI|d_YT;cLv2rt zq}IaRKQjJ!e@*y8S^sOSDI3- zQ80+3Fvx!_c5<Ukhzw^}!x zE_rYwP3bNJq_~i+#&2}ebG?ju=`iet-=0vv#ESA)mwaY=<094*Azm+RHlXN{DsKF( zqc0QxaZpk zXZO}Cf;Oj0g&3DzKi;ho_$?t<&R`j6K%9$SNf!7pql%Zr1x8yVQjIWgW|KUH+TehGyR6E>v3Eet$2G!SUfsz?u#B>aNZ6XX`oVl&6K& z%aEUyv&N0W(}v13V`li0U5xpZtZ#gEkjvy_(OSo}w+fV9{^^{R9UJ6iC z>{p!qeYME`E}KtUbw6w+ZIm(QHZTGlaJV}sm69xn1=TvMSp?^0{_3pqoD3M-I^|bP ztq*!+u<ANJgB&AU$ZNd6w z6cHVA`5cVdXl7zrhAe698aAX)wTv*{2Mb=X&mR0!`*<`XmLhUUE~-=hUk@#u4AzMR z7_|C%+09co^Zrdkj-wT=stirN6?S~3^2I&(H%`2|ZpD+A@gTXi_JAw=VT-Tn2NfJN z1-(F8YitfCg@fL2m=G5e{4>7O4MYY%u4lxrUF=0T30 zjC~REESvrDcRfc-1Rd%;WEgAR>N$Pz3<}~hq8}I}{FztQ(M+rcyqM>^P`8(-8gTD6 z3{!VM?jk2S<~|EPpITM&LSwgBJE?2Q$iUfs8rkwXKlbQq0410AI@RwU%V8o5SH+z3 zjOuyk%?@d(P)^A58*xYzr`6qIu;aq&yDY{Rd$ZpDrF=4q=Zegcoii>{+Wrf^vU%Lc zY+a$sKoe!rkDXhHyAHXg;L`Ki4yU{Jg=y~uuL8jDtvzjagMS+zXnD@BZih=GU0nm@ zL|#uc^yx^a-4|9`RXp|OQ*gMEC{g*zAI3xcEed8n!?|e|tG0WKRb33A&HSDm`8!)* zDxb1@NAy}w{&3v+Mw2pfhd`sQ0o3^my)y`HXhm#=OD^)_hZ097} z3L@_OT7%s3@O@tT&OcT$b%x@=vd{Aq&&#wy7~Z@5yB9&ChMm)@*P82ckx=YTh(;^7 zeW#;qNTF(B<)wA+7Q*H+FH_%Zn1?jJS1uuB0%IT5th0Vr3sl1)pdHPSr8|%G*Fz6O zr3G@P0)Ky_RKIWxw?5%|i0c(GqH$0hP{Dz^TbjQY6!}RFTgUtfmjA6qRx%3HwnaY% zI&*AdIa!?PDwuxz%;*Qt@J3gDy1tW~P7mOS5KdtH04$3NpHri?S(UoWeoR9v-^rCD zU9TNPFWv3feEM>KEv;nE7o1P6PZPO;|6R@~+>(JsrzK zpy|G&X7(z-N$?Ag%;m%N)1P~*_ukYEimj*mL0ZwVis%k?G^^1*E;CS+)8Zu4ln!{k z_||cc|EpwRcK2sd*A)i6cQ1r5RJU$=o0)cBRIh(S-Qq9FR}&+?EBp;+3}%cLnqM#H zJ{6V~2Kry}4ZBnv8uGjA$&sa$n5^y?e%QYA3#<$?wXMNs_s7SGqqo_+;Gl=U7ZrU{ zLq{#sla1JOwXQB?Oq@9m2TccKXq$HE0{j*g*-(sg#L6f>Y0#{m4@3*rxR6nqNhRJN zIRDFaWM6+%&@lD(&%Lb2Ed_q1U%x-4yT$zZQ5;2B?8SigfV`xqD|c1~Q&Q+``*n0KPMRt%WSBi!pin|2YY*6Gd$f2qY4hm?Y(H=d9yDdn`40hEdNg~Is}GoZJ9l6& zQL3zJ1-&A^%zSp?oYlpF($+EVVUPNL32RFdJ=PYS7%#no@<)3PySG@t^#7Jwo_anA zlnVlwOfBmgb9_zh@8JK#BdZPPMA{&hfy-HD1{m=|z2GEX8r4o%@WYe0D*?=i#Sidv;*7c zw9UsQ!FgqB^+7w}6k3nPVWQLSjat^qHDp%JTko`ceeqsXtI4^UcRy<`yGP_q+8yYp z@E6{I7xCE^=ilDpbo-Fp)ym)(PAIE;v17~>(^MxMlCWyg`&x6LCy#nLVe>wQQuD(O zP_hG3r)}QB!r8^Uo_VJzX*i3abL-n&daH}Uk|Hh0))dpz#3x~=DV#PnZ*v?=U2>Nj z)#wu%K}V{m;^*bsk02Ehz@R+!871jnConTOX-@edu#vEz%`4!pBT|KRvN`Ehc%40@ zZP)PN2h)AAzU%v+h_d+f5s~VqUyW@(Sl_4_7_1*=B!+A|-@=wa`Up=*vRx0JTm%-) zCrp?FE*@e;vAf)w@iU2Htb^i48RhLWy3x{&e6c30;s~!ykO0*3?isKh)_JtZE{kBc z59t}7Qtt+%4{I2`vli{(>hy)@6TYg8bsQ_I9UG$-0AMFU5o(9C(oYJ55o`fl!E$ck@Pa zR7~&uNp)LR*R2p(HHfzMLQ_*PY7VhrFD0-gdCh9`ll%ko9y?rG=j31%(3r5>6(vdX zAGIgJ)pNZhA)@?Yk8|^UXxxxBhj-fi9*ch*gwH}#XWX1tORvU1cIY4fySaX;9Y64x z!RyM^1N(;3h&v2jQeivjNUv47akmCzuPWhx5xG!Neifi+hHxgAcWPoocte?dcU59o zyG7YY#;TD{WiwM1wcRA(v|Xf>xTtY(r2qG*gO2TsK?pF6rQ7OD(h*~=p`AU~+W2y{ z?fc6FuU1cC!##d?K$A=a3t0+1YaZPHR@zn{qpnD~QMkcVr_c4;+J3>;cd%y2oSLj( z&=JjP$Zw(CqByAM^t!{3*u+ZmRQIDoJ%yxNw@MU7Hmn2L+e`#UgqO5I`kuErFv5=Uf6VfxIx!p+7Z8>N4 z=?qNY`e~_^=2c@7@>gr@To`WVCfWKBnZG zC8U-kt@OSCl+JZC1d<;9_LdgBrbfhq^HHeA2v2wD*HLTpH1pH7h@3ogm(C6u z=(gYDxi?Gdt(EIYM#ta328NPgz+Hu?+Pl`70PvJCrB&#~x5?PB5C)^yvsPS&B0k~@ zVIXhHZ$Q!fyQaW_G1!SEH4rV;SHZQQR)`W0%jE@fla=2T&SOGXX*RB!xM{^7pDt%i85j(d;N=j$|?f#n1 zosR=!tglvCv|yhh0N%R%(s;E-(gBTb8!DqfQmNM}e2n9Q;ii2~Co3iUHky3enDWSF zeedU@>#2OK)*6QeF-xUw;9=B?G=iXj`r!MH*A=a^?_!= zF+dyKmyIH)o%Z$Sggyp8kMYouHnwD2E|##7Q-uXxVO1rRomQxfSCwQ+VQH2pP=C=jOP_5l&lFu{L~mbJ_4vKamRC3b_=)y|;U@)fjdl={XFp8r=Yu z*sey=si0=AG~SDwMqq+=I!@q?!uAwd43#CtT4QBIq=A6m{zrSJGV}A`6n>MNY9NLC zuuqb^DaZO-{|?(GgDCRiOItBiIT?)rac}BhbDFdBpk%MB0-cmX+Etm5UXFLuSg)`q zjuOIncPjiTx$Q@1o9x8OVD0&Gb&klcCiZpWVuV!o;k1{BuQiG5Vf9UVuv~dg5{t@q zCUAjbl{zcOf5dPj|ILMU=c5=q3nanN`@p@Y4VKZ|EV^X9W2x6#0FJ#7aYB5q|_ z7(2uHL@}-h#V!;@s4_-lu}Z2D=SpGL-1pjb&(;otmCH5R8jC4(VJ+mY)AdLn+Z z6W;3yt4W;Qp=*Ht(K+6yru26)GNVPICAHQIb1vi)tEp|dlrQkY`ecB1)s&UNzpR~0 zZ`050QOtQz9ZBVck#x7+57)X2W&gIa>?7X-mEu&kTbB(l$W)dS?ac5?Q)aaif8L(A zWYBNw_{8)Qg@%;Yr@xF&SAT?Qy%)1%taY4ATyen!{|>A=N;+4glNGFgjZsx2t`e2f zUR$cq%}a!bg~#AZ*E^zsv-O{3DmW)}zKx=iGbY_iCQ`wzr~VcT&sl2jX^B?EvgbiW zv`M{^mAKEnliOjKhQO#%JAdJw6Cu=6L^j4d5s=Op&d*|;TP)e3bjjz4M8&ny~;hx3_YDq+LndUXvxxA?OUH%cXM=(1!fO0+Re%k zF={nxNvmxyDvIkmz8=E-`2V{AMpM2b&1yPZ;!{asL;Nza@E82NO>b6 z?xSqJ(ozQGD6aa4$-X52I=s-yr5^5(Q}XT7*Kr0o8**IL^t$;#N=h$na=x*8dIr40 zPa>cCpC=Fk+>?LRbZFE3FfC`(lMMDlCgK#EIH=jq7()-RzALZzRcsCqsmMZG^NNVO-PL?5+N*<{C)%E9Xcsz$~OQp8!ek3nwM^IL9TkMZiX~%s!vLC;sYN) zl7*Rv^BRf$!g6);p7-JHV_D9J7Rm<}9_O{F%##6avA!PBu0cZ1^RqaUfrGwPEaB&X zkK|paoo{4^((z|yPuDGIr+NEuhw~MON{8E&%m2~kmdDk7?k~FC zbS~1oUeK&RE*SO}50)gmC-kIhpt@mTW-)RjpOc!8-D0nDVb2GwSJ?08BS2}IuSAs( z{cTOui%0nrVBzZp6^1XM*^1#V1=tABGwUuxq58d7sjXxyb>|~FM2wEMiap^(tZk6W zEd6@16iiA+4oyAG+t?_E=aKltm6lQ0&dRzi7Cbw(Kv=VBmyZJxGtZLZ_XPK55!}4G z+Ql}MlAb?E*OqzF=*siAglg+B!Y7a-;Vc&@;8Jj@u0(AvH9l{|C*VT4FQjV~1D*S5 z*hYSCv)_-eY?~RziAJ3w)}4gIf=)XJ1NDwd3EHB;nj4qLn$!?MQ_?DnH*hUt!Oh2` zw9kDy;X{d% zvnjTja{tmYodbJu{L3&Ax_3fbW$(JU(Mn%D;{M})^rM61_ZO$xOekiknJ8w^mmRiU z5kfEw>Q&-vNIMFN_HkwG?EU)vrl+IDbe4{}r0ps<|HH4ZQQrOK8(++~)4ej>=oAud zbTjG=D#Bhpv3_89{22FZrq+F+vO8WNfxTlgxg03GI>nDqkH_>mq;GB{hX}{vHDQ>I zeUj?*-^b{j^U&pTKpnMkiGJ~aK`6qW7N&zy!~D*dmz6J{iB$liL|Dd&-A*3th2ZmB z$94WxW{n^?^%gH8i1?q&qP-aTVZ-WBfx1>eZq1bKQZ?o`;2z%G^a9#1bj6>_ZwOk& zPdIYXRp2M0iDUxR7PP&G9gefxjMwBWlKUPL8J=j(gBkGXRM*+0|FZ{YM7HpcYcOfC zSgU1pkPm{aXJ|H-t0wGMur8P5fORYhj^M`s>{~t3I~o!dxe_D+mdpuZgL4~bgL3A* z$DB`7a5;~-9;K_OMCD8!a)Z{|{N9stx^)BUCrZwbRS|Kk6-V4GL8iJKvZ!(ID-kva9&1ofPaKKfe_oQR6Oj|CNI zF-N+Sdfxxy-%K0H;9Z5Z8sVjf&p=5od@Gx$jUO$H$-ac~e|@hfJXZc!-D~MyGqjR5 ze1DcZV2Zy?eh_#w56gMbNCI)73qyYe%~oHT96vmBMbtDX-4^iSK`Ts{glyqA6*f*} zbSFFDjc0S|D;9dF;|`Gw@Fl}Vpdt&d-vi%~ zl5_$!cCzA$L(mo>lnrEB*_drMXm{y&>S6e0KJbgvUmd^!`8c9MU{I5@&^q5ygjJin zJ$X@_suaGT*w!Lt{Vt3))QOKDY*cyOxnF6=P}@4Gt&V%RJ#V5MncK%pHP^0MPl8mX zf1p7)FJ`eIJwFe&u;`c$4Ko6~X*~*_Ys6p}4ch5!G{YzN1UF6Y3i>stSEZ2V?zN^7 zM482a`9C9p>T+IKr3RvMsY7rcK*IPF?iU(k-8;W$7-SY@36pZkG&p_4 zFppJR?_+J&!FntuPS&P>t3`L(k!R(PJYDTBt3OyB>(u442xARF2j!ZJF1#b48^C0x z*1>@Fa(~+k?*+j}vA8nVvaPRq{=)pF_DH~yxd69j1sgbH^E=*Nb}<0cTeYHd)9&OE z#|*f&*T=k)Rb_nX6Zhsr1RUNrVo9jz5(Y`5*!#MZQG)=3Fw?4C7Yk*?auga52&Dow z`@;s=%@Pn=rE7#_-vXu6!`EF6c}TirQuosaxTGbuJygD6(A~QttaONm}cpQ!NY7L>Sy zuR-0_Pj!l~?C(Q64$sWyZ^Qu(7^e-_gHngaJ{k-mV0jF-=DEee!?`i<0#!!4uw&}u zurkk9K5i*1(&nQl(ALP%-NBzTpb5T0FSs}7Et&H#aO~NnT@uD%Dw;B!MBeI9#TOd< zWraYFJ!>tZKI%X}ms zZ$)mmM*05QBGFdS(pTFX$fm#6G5%JhsN_R%d@F~aG`gtyWnZK(G%dq)p@ThlrE*`N zRdr}CLNuKuHnWt;ynm2&s&U=CYCXzj+~yGRo1B(%WoMMpiRSdnmH+-cd=2|X zNuP_f)>HhKh0{nY-^{vC;{)B(-~$Wy_sFVE!qlYlSU|knHqZC7KYq9Eh**9`8v=iJ zv`*Ll6~qO_(Sf`Yx&?4_JrIxM?}0q|NT~Pmwl{rU#8y=p;G{*Ee)~?g$pAQq;bz@hnu9I*R-mP$RZ6QkmNrf@LxIb-yv1-2AyyS zg?aF-i~If2f);Nltd~#ju7Wp1<-}__t<6s*w9~m&NZgLNO3~epK*NY4sszkAV-P{r zH4zucQ8UzcBnd8#fA3p$4|PAdY4YRoG?ykf?IS-Q9%0uB(m9*24i~|8H*Iubfxhf` zb!_CO?Sd>r>tSC_YLb*Xza-6$U{h<_q1qZ|TFhF&fMpBukTXkH@S*`6AiP5 zdbR&jVX@Jbd)8uF#=vbZIng+v$qoW=hZAYJfeB;+AdFc=wPvD6Ec)g-?;LU9z7mRL z+}e4}CFPP%sx*1Uqn<9D*ar@gxPJ%_ja7}(MdPe5bmxb8u{0SdnBHaFpE5`C)qQEP zE{K?2!}EdD7*`}>ZJ!L1h?SG@BZ1q7`Bt(=a$fei8v&cZ{R9U%`WJyDZf=#GJ;@K=7W)E&_O!4m|3<%V|tvr((aX zaKFxo8l?qSaV77xOOn`?*T{>d?{nC4_nH#kE%VW2b~)>fo${qzkU1%Rc%B*P*RD4e zO*pRSTGF%E5=trCrhfSX7XjbY-rE&JDxq?#4*55+CN3UZaGGq`jf49dhDA6m{RMG^ zOPeD`tomH6xpikbh1L++<=txCc4(kgA;&)E6nEzxz)nh24Dv@v;no}7Ze*3~WMjqI zF`uoiN?@U)YxYIu_X<|jky7ke^A?PYZTctbcS9NfRW+#nom<-4m=Zp(aTYGscF#vz z%`ci-)n$S1nsn#$`!)J0D%2h}5%YH)A5~}%=j;GML2RYd;#N2i%xca)pZ3U z8Z6XI)o~{Wc3Ybnwj!?x^{H2@DlbgFYtKj>NA2f|1UrKrYtTyk)!bGb35s$XP-Q6= zq(nV1u7Xx$EiFt+;qjqmE2C-^n>4n*X4v~GDSky+HU5L2&UfVQT2*P&x0(sNHgBmJ zam|N-#r+U4v|Eou4sBUpO)sT9kuiemKnERPA99c&{3+5@<1e0ox|J}Z59l)8wpqe7 zk3LT=eLm8C+fU_-xP;H1)P$CSUZM7}9$-XwfULXT7rR7k4bc1{_u>WwhYWC1SzP5z?&fE35@CNm=&L0@0WqZwB~JOqfdjdxu*9i zw-BA`{|d>Lvhy_38~L6?KM^q07g|`g#D9>~a78B$(h*H0oPL}yfwoD~zGHDLK3LUJ zNZ;$sl_(q1uk8m*l>AM{BIe6&x2J+`k3DK|^Z* zaUqvhX5Hi$Oo!%*+qYhx(z|HlpSg0@g*t3;I$O$>w@KP_I(U_@;~ZvPp-u&yCN}_{ zfosDD!z&8nK#3TT%^wCD7tr0>$S59??d`Uk_AW~@@TG^gjKJ9iw1k-9y#hkoP>m?< z@+HCl;lRz%Ct~ukPK@n+a1q1GHq(mT)gMjBNd&OnZnz)IF;=bOjQ>tGJ2LUMj1f_> z3|9JVoLv@JW-k2aYRX|zae1|7jxr(TK-!8E#K}JwC*^~hv1u@#$3F^GFX#1>&`Afs=c3y3cGwrt z?AePiWE%zZAk*Xj130z^XV&@RFVG5oC@c)F#hHRJrH1hrtN+|nr@JK@Pujf=5@rFM z1`}z2%olO9RNv#j8(jFaNF3NF(AOcP^K3KhI zcZJ)36=SHu2)2ZA%TT$AcA0tVI?LH~$9fy*4(*Dix_Fn}U`RMYW!R=frIm0shXrYg z?1!ipmx@Je=0-nERY7+#Z?ZZ*RnZs>AL+ecLo+P-vuL1`W)#&6)#VByFZU-6hEKMc zULh8rpAH5aWtEOa%&rUtJbX93wkG0D@r?-`e3HlD*U4&Izs=-?k`QUOm-`&nVSfiD zOgruUk~=wF!WFKX9E($SdJ^GozYD!7bh1xi;n_yeoH1v0Oz6Y)GHVf4e0kfsMHJzZ zvO7f)zIq+-s$ZE$2;{W}F|+g)o6t$U!jkSazj5$Xxr(qM?n1MG=1bg08)>{14Gx1% zO4C!(I4UvT7fv{ts_Yf#c%x+xiAoZpx;<2P(Y6a4d%%>u{nc{1Ll zJ&8k?SNvV=Ym1bxGNugjRX64yzZ@8RUYwbJoeVB28C2|sXu@X}G{cfw>vLOTY5QqcZT5Z&^|qeQa8foS zkLzeZbuI+>@1`HLbur3fLfU7Zmd|O# zDeTQee+*uC(*suv3zHzW&8sCBC&27DJvixZHBmzN&wd8GfQ<3yhx@_F;;|~YW^mlW8$@rzY!C&J|Hkn$2|-^w z+0>?Oe%dMOHJFXb==&6Q+F}6McF}ZYUiMj8U3L{|p~1k}pX>zVqn?XgKZu4*CVCnU zDq?TTUDlSAS^Ih0DZkmJEDs;m`xxvryYLd^JZ%8 z^0?z=FfkzUn`~+kfD~{(AVTB%e4JdWZu8?g6i&l_0ehv)(WYV9sOA4PScFu}RliSJ zqg-t0=xDXK_EF+%;Ydtf?);T@7T>$)*A|kpeJ}UK0XV&CsT+u$EAm-*lPOmbbOZd4 zE5BSvirqnW$iM3OT0_5(g%TLFjZ)^q?_C@CR$~jLTkJ!=O;+P$~ng?0Y-BgJ?XES(8X{JGtzNh6$#M1@rS14T%d6P6~+(fR6}z4 zEX?ljws8JvmEqK-Y0B*4?KK0!9GjgeeH1g6l|vK87Fdc0)!DWLX~&z4a5cK~-?kmI zlgP8YY!pg!{#w7%`;kig?LR8vr@ZE-I7J;L8HuE|f6v^u?*I&pr?ySt8HV=pHvF!L zLaiBu<3!t!ypxhRhH`dKja&RKRkjCfJ@}HQpPH5ReO)Y&C&8au=py~J(_T;z&mZ}J zd4emg(7@OkT%R+1H|L`1hd_U1)?NLjF8@>q))!q;p<;Ngq_)3KwJWLDfV4rs(Bll@ zR{dEe6fb|A0a9{4IS;n8ncSi}*+5;EI#or|34Ebc5!8~`u?$E6_5JS;yODK|U*g_b zNeQ=455JM;O7W)s>0Y;x0>lPBs-fmNZlR_1GTj;0Ixi}ix@^cQD?PZXim5E2^!Yyb zDi9r2o3zTv8npA`JOJ0!r$P?3Ste*So`@)U4ItNi8Y5QtB5kWD5z6xkXvLGz=|~JJ zq;+5-ka$`wlOyX&#FEdRTV^Z5S+|r35P878q$YbWLxqD$h!FKQXMz^d~ut#h%S*dXLH`NUAcQV5H` zk(wm))8x>>;_Ka@4@vta^{<>m=ePyZPc;Rc65d;eEl+l`r=^l`Bz3V-LR^gCOVd~c zi_LRI%%>R|&cL=*akwT~8T@@8QGozi?ZEn6Aa zURE=XoXli!A~o0cNkNgmK~g)KEY=sz`=2J?wG|EIWO`J7T`gjqm3SCYS-uLAbDl2J z8)N|}ZO&fX1>=$#)2$}muXjfpDEF&p1X&gV26#4*51%TtNV+qs-6*0f`>n__uGxk* zC27Zl{{;Q~L&4 z(4g)AzR-hruPivre=mn!OUpB8?f@6%y=uHYe4zGT4e4x?JwN^UT#2P|}7U$X~p9j9jl4^NSts=sipH z*reX5zThmmgPJ0p1%CWKtrGTk2^9^tJ%%3oMVN{4tsbz7@Vo3+Bv56;d_;XPrlzb= ze*%6){HNj5>zAKi{C$JfLHB~RuHb9kYtL^)e7^ZnL_%7vTl4?B05?ri)6TS+UOY;E z@cKc){W}&65@qyKkD7))46!_T@Zjo5%YIKEe&_sL)n`r(nc=_fzXMkJZ>;|KOYq>d z2V7~=#y?SQ&BlWu6d7^#tpVqLT%E>uZHmCa=4XY|SmDoyPv04=trO(&2|`{u0&Nno zfD`E4XEb7s%~LIOd*O+yB|{SA;6?)hqH_uFW0Oxz68{*YS-vpp%Nour*7eE~>gzx2#iXAy>p5`pf zOWT4rW?8Uk7}-y1dnJWdSer?~7g01)yC*Z=Ciy4*E}Rhb*?CDQ4^V{|)%~rP`qaGx z5;?ZYCgk`k}+>-#6_5+kc9a)`4?!;QdAVJ}Jk?1B?q*(k;`}Xs*60o^?;K;vi z^H?^K5|-NlhyKZuN<4q@!I*VD(6jayoDTREiBcb{oBCAz{%*kFx(U7CFXF@mD55{B zm@d=0bYiroZqrVFD4MNH+shcIoAT&8{^i-*51#Q#X~n#6oq+ubH+Wc&-q`mIhCe;} zgT)38C>2u6g8l=DPTe@VDpAHIYW-NZ(H^B#Z+e&0lYQFf;LWAh`OpTFzCwV>8=EDG zqaN+DG^Z$cB#-`PZT{5I)`+hak<0Iwk58q6N7cr07q}62!1b7n0pPbPqci-xe{whA!*)2e8( zJeisGw0BEU0p%;RV_}C)B;$Qw7WWm~Z+18c=Ry}o8L?)v8a3!gK(Mh=p ziRut5aq+bfZPSUZQa?Jj4_-xLi?v8BE9F1ECEtJK$+?ZJv*z4&PhwzN_V&F~`d9s{ zw~c(ZlQ1DAILVi*dgStk-?yNx6b-24eo7^4ZsoXFGiCC;9c(iAq1w*llF^xvz5d{9 zP2%@GGvRv^4-w&}&;&1VCcd%ks{TXw!WcEpLRcxW4n*0djB6IbDD$>99t<{TrFh~; zD)3I7SVQRFc}byi`+xtn1>R25z{zAteUvG_{*yrcJIGhT2<_k_8Xw`}s6sx>5JzBq_~wJDFaR1sgQE zZ#Bp!=~{Lnf#vlljk#HVmRoUSl}l8({w! zy#L=vH(6$0r*x*#jReoUD}ZPLTkb+xSl_$UF7*Xza>A5nlXS6)>20|j`Rv|VX)W@s2o?Zj#QkGW)!T5X7TjE5_>>U&9|fn(*s=hjE$ zs|phzPZx9YhpxZe)&)mjo!y#DO?TZ*Qyb0LVJ=2l6ot>FTq?yzLuc9XDl*scl`<;A z!5Z3UEDL{Exmt>>U4na``RH8Y3Z0gp!Z#hXSDGd!paH9**0QJ+706|M@8+PgN8kKT zYz_a34~E|Ko+m5@yh^zg8c=2BKf0h~nZu5Wyo;;KGlPimUkhg#QUV_n(-)a&T$95Y zYT9`5g-I~(V$1#)pZ0-CQIq9de7>!(mON^iSUeY&|0@FziXrCrT=qHy#1)tBNyjf( zJ8b>*w_)8G@@IaEpKvJsDZM%Is0Q-r>8b!DAno;?UWfk3p3RPs8I1*F!uvI~e%;(y z@5dFNRu2nSbZz*Y!as@634Vb`zqQg41y#%@u}0cUL6elY z`;&oFzw&ZgJR*OD6*@8# z;-@1GhQ5$wNyj$EYb*jxGebW^yKmfr@8!SqCN?LT!RVKa=sh||ls zIteXB`<}=U*K)|d9pzzo61&3A`T{ua)Id~3t^>w_5~)CM>J z)ii%r);fEjaf)?=fF*Vg2BITJgW3>@);?XCI0Va4>xyyng2iiZOc(^T83{$*LFGoZ z!Uu(}aqn(qY~GKBN8Ry=e{p>Au&9!En(x2(a^?RxHTT?GcB9el^f7Sl5X3uDWXSZb zg%1PiqKKBa4D1Le88#t(WTbqv^M&G&2%nKLkI?z5AL*ehxjA!;SlB}&#tbH8sJ30{`8!*WTT~x!gQ+e=smCumJaQZ3xo!fejXHE|eej|Uo zyece@2rh@LI z=un_j4F?lS$wqy0nTwL*S{L$kDEbv(N~!LIZ--hIRurDzi$iHOofe@5KD|QbT^b3@ zU7f+@XYX2zh1})T+R>;s8Ird4nPaDthidy`pD9?C}c@_=YHNC_B7*Q3F!dFYm(k1dW`V16mA*)dZR(Ep(@ ztDx?Bc6o_fpTDXDEBn5_vu%tTrXaMq$0f~j^|0=Dph$A=$^m`j&1AAx5^cvOue`zC z);;2v209>8kZX9wcy`ml)gX`p1i z&rD=n*s}TVcd>KcPjk@JH;q)FKK4I|zXE4d_#x+<=4!HsZ08YVkJfPA^npJMvnyBI zw*Z}W82$|mRtp`}$kJN(2@Q30Cv*%brpMJOEU=cs8*<383H4VJ5WCTaD0y)Rc+hvX z%KucoWtj*`iq{6aH}QsK8XAAp+&q$yQI%#lvnMWKF1R&~3v%tR$x6~x<$_c1dEfK$ z$F%dv53}N0s|tL9!U_LJ(|Lxo{l0D7R<&AcwN|TWQL{EdTh&%ehrNkWTZ{xTs#>j0 z{jA!fwZ-0vo!Fs95Igo>5yW`%fAPG_>)glvz3$_@KIeHpr-O)dR(Ec4y`@H~OwhDk z5LK*Z1ky%^TqIAZ*Qy5TkxKT0<>l2P3nd&u#5ru>;rF5t#=WqRaC1&5 zn&ZaWKM$KkSW`_cIaIDG12OCk`}6FMu2G>N@j!JqLkDutgeg|G+8v5*$rSG+rrQ3V zx}=OZG|L->o;xg3M=(68(WfW zcMe)*JN?(lEJ9rO((2A?G013bv>d$0?(^&EYXV3HQ{|?)=%4m239$Kk%2ONa| zAWw@6_~sBKSzZNyqf&Djacd)`mZT?tl%(;t@t-=brNF%vpC%{_;bO|`A1U&4T7J8U zRI0lwk0Nv)t_BMz@8KJEhLcZ}X4{>80(emED(xdFn8!y6m<-4{Amvf>)NL?8>>VD2;2LSrTmpd>(%FLr(qY zJCUa&!7cKAWVPqY1xJAD2fb&k9|`F@nlwi{RZ{{9O_@S=3-uh1YfnM-Lh4S= zoJy)IRKhqv?Zgq)(?dVc2KJo5@H66nca|Hy)K55MZBvotjd+|_mqg{HA}(kLcFB7} z3JV#km*-sx5Bd&P&#T? z_$u0FIDhO94Huh1J(LTb$Zto{^}0Fyco9WEWvB;?y!tzTd?mR3Fs(RrJpCPkMNXOg ztwsILXIbH@bE=Z0`>QI%K9wX8#hT}V(y+M|l2~-*|K!Q=MU4X$mp~w-@rQ7fAEjfH zIZ$mW$l&Z<&(i5I)?A-&ps82oxm1kn=M*wN=zjgdiEuDo{Vx0@c;ReWf+wZ`adwB> ze-J>r{!+<-Y|N))%@x=0AOl!dcZGeAfF%vJkUd@ydiL|5vaJYrIky42Wc$Ef$aGPT zpi?!Qo3PH!{aTUx;I>5 z*1j|klo9!bMnLD>;3g7YY9{0*ULu|@HJe?Sr=?q!U~gRPjL2K?q-=Zw@ijxemajMM z9iqycdaEgvVoRC~Y^2sjLE35A3PuAI9%f80$WO!_s>q0cd}{bLVHr5 zhsrZ}#!#Yt;m9~14<1ht9#1i`Z28+*&cfX~Vd(|`*o#f0%z)*TD&OY8pdJ07m59~X z55~Ju8%@qz1v?b}rlr?nB}=F82SPU6h<3C4DaYQ=>|pN<>IwYxQb{DN60evqr5lV6 zv{fn6C9}=_+vEdRqS5mt=mhA!Rkll9psfd=he2Dc7Wwmx(9*p$s5QNt{jFK0(kbWt~0e=uFb@SUC^1 zB6*ToO4*~53wb$&VepQH$z$`7%6i$CRU))An|f9Q^nQX?W~!5upJ!^Ke_K!&4TJ>B z>L%tfew1`A30x};CVP|MK)yxwpB{|T`-7t6R>qTZ@6?RX@X?#i5El#MRlhc)*A_Zp z1xGwc{tVv59hETuM%S$sFlS7{>nQFj8=4$) zxJZJmTcwQjKw7MRmW`}M>XJvr+PTmM+FVPEAD9rFjS#xA$B!zDGKqcoF*SUzpffC4 z&!u~1JG$O&{_@q#hebVE!ZvG*t6OCiw%uubXY0~O+LTq-I824vRVz7o^t|*a?0AY< zw^gEyZ$C#kke=^iBa-wCpS})Rqb2ows+_qan2B>$*7exwiXCve|CQQeJ}lr4y|$>p z&{0xD)bQLMdpaKvh6czYd|!26FH44#DCftBJTnSor7z#Z*6kpqs*G+AGE`i?_A=LR|3HnDZ7gZ` zwZYWx1O);fDA7mhM?3;~7p%>g6a2JL*wUZR$+XkXRx48W_bnk>ueqfcVGxyjj$R$J z(4^3xh(PR50Oa?b*qiHiO( zYnIQv`a9||58J-dRu<~~f@=lw4qXgDD>hEXuPc`-J+}a~O9}JWRA&D5#9Vp9X43}; z6D+V@YzE)%L+Ix^Gp}#=r=EASCVLEjz$|F;1&PF{EH2I7?LXTK4c%(}{8I-){O< zzt+RQ*ZL^Znaa?O1<7zaEVa|5x2Noxt8x-}u1!Kgquz5FhfV6K6?T4! z>Bt8+@^~pLr8;uvr6;=@JnI_gdbc+U05(sB_FOvjczgy4v@h`OMrCF@IVKKejDqWU zrGtYX7fdz@=AI|67NKEtZuK!5y$%4m?}t|ae%j??rr!XLVFO(^2s^)S64sj|*ZDWr zc4@6{0obqc{GFa(6g~y`WBAF>oya|V=$4{laL}K=$tFwkXSAF259rpL?y4oReOy!5 z55}GAe71qhNA))qt@ecO8U=4}mXx~gYOnm1t-e8kDmFwQs(FhYUKjr}?RR=!%)u)` z9B$uT>v)_%{^Z8*{1-_v0?T`Q=ikfyGM zv2p{>WL*tLb_T}D@X#2MPT|1z>~Y_^*r@XwAr4^j8vZ3qY%){ zTDy_zm67uiSyjxek9pukCK-=cAQk__OU52RxgXh7=qF2v&fK&+94gHpyZ3Z|_YSNt z<1^}x@Xy&Tz*$L~#ZK8&sHw?HjqD`;Y;VWA7_N_2)V$S+D+G!6=l_E8w+oa)_}N4` z_e!p0KMzxJqSwZRE{x~a<@eOPZxoyFtk3EyE@w9&hLq2o3>$6Ji!tW9L1i2kvb6e% zS2od6Q~%YEv6LMYxQwwIP4vaqqDyt_q^`EVi4plx4|Hzm`NLS>0WqT2SZHQEs14uI zO>{qW%JSjQTDe=nYIxb>db;f_fpUeI692b~>4qP~SvKBdLRh({{}7EkC?}`_k3+QU zmxSGj9OqZYsU>u>$A26~U~@#cq{gIwCGB}3la%8)UD&gHe$byHB- zif$qt|JAeZ2RS7ZVoTQdKr1t>GxqvqebPR$AK^=3an*pa!MZ~8u3zr0s$Dd~CIhT_ zhAbSE>cV_{q|-+p5Y5*#r+54MMXs1qMM~leUu%W$cyY#l1u3Y@-=K*qS`vnca5}>` z>S>TVH~pr^E;i3QukGMrmSo*FYyWytOfds4E2CTl_M z(xBchw`%7$q-s#Xd}5%CBK|9DY=M78tCCQcPG2ZaB(#4E728)i*gfO$Pnnq$YHWfq zalnZZi^vrn8sN3|d^icM_oNqBf@{&#yBAAxnisd=+jl#T-6@}mz7b?#Tqfz`I&mL0;}Oa2)n1I=%W7r|Yw z{Hbx?CBE!CE9&j8{eK|vM{{9p?Y93MB!KC0M6Xn~F3Fh3_Urx!pIFJZe(#K+3ff=a zTdSCXT-)7#(y+gCXTm1|T7ZvT0^}pO-8<$=mU@R=_|9Gx*?7nHP@CYl9|brHeXC_2 z`S5fK8&JXrG3^hkiuJqSDw9BtCGdH)^96Ens1eUU<~4s}uNPa$pI5vi+_H4*Em&+p z$mI`W7NXzWe}B(^yDN5y-uD6EN3;&x~9A5vM6+*-7RpY z^5#<&Xem9p70%$R!4rlFYv&w8`RyDx_i0Gfe5h~T_wds2lDP~W@6T4GLCRK~Jo@(nQS{BvRsRKS-1+>(%CQ1mvu9Ejp|G4x^KH4B zY>L=xtFo0%u?#4-RR_B`EA_TV>{fszG9MP*u+g7s1gXX7f0-_;>m|((D{}#Oo7REu z$`KT~-O>BfLRM7;q^5sHpLAoP?VD5>sD(P@(R9CZZVtLn*8oS?NEvA(=v4K-vLBQI zf|~Wa#Fp)dII<$1++bRt3m-pmpf-gXqnucTvxCqv4OU@_i&Abkkc9`|_IXdciG?|o-wr3xm0#)B zFSTGSW3VyiM6$JgFGBYQTJa{)tR|>&K1G9256F#!ekFHUvPqBDUGSKxS@o0;xy|8>bCiz3TdUfs`{zxSHFoU$RGp9h5!bS}Dm1>TYr8 zXAfOvNh_Zl4vY<%ga>Z!OBQU77R7R}7zc+DXTr7d*m4#`l2etS?bt89=p0OWt0~`?bdiKGcJpHEd zb-gF|fu$^I+Z7+8689Jfmi%t|r5z0nHQanb+hmwvdyoc;pGKW>UiN@aQpI`ZKLK68 zlebv-m>VqXZh5t~gccEO)o!cXCi${Z=|t$!`I40818OEOGXf) zOvIU*ppp!iy7MXHwODD%VrJv}%s z79pS!2{)aOyKYk+vk!ELwF01R%ROd>A!nOt|0=HcSCZ{4F-UtARzB3@)1lw}Q+?eE zcVMW{10QZWl!t|(;ycN>jKk>_a~rQ^lM@ zM*U&7%Xbn}npmvP_HuOhhNPQKZ0Ny|IVo0g=0>{$C#7mthlt*@-|fB>vWL?P_i{!U zY%PpNU|*$Znda*5vL_%IkS1Q|jTFFHjUKMhyHQx#*{SzSY;EhfVGf^V{-O;fut*}A z`tChSTuN<;9mV60{*-Zek9#UCfFR5+7z#R8p+Vl9*5CicSGzR+X=f6o>w+my z;2)Wj>&)M()O{;r(w=XC?ft?ad5XMLRUgXNY^~Nq9O+G!0(<^dG3D6-$lJX5wqz{+ zyM;eH*ZU~kAQsB2Urw>2G6aSw&&@i0mJ1O1p|-cOm9WP+xVBc|dzJ1BCChoJw%Nqv z+_v)@^eadB@g%Z@!2`ejk!e}BG1s|xvBQ>2zJt)nkh;*&`tM7#;P+ym%^le(!uYhx_#GQr z`TPchUZaMTK1=)ANaDK9w&&0g%MBYZCg^{^S4M!z5 z(o*bdAn5TIz}4=Xn8n50m0ZDi#FVxxvf+Q2@M*-exqeA{XRdSi%=LfMsk0qrrU)zU z^E`0SL2C1z(aiW?hAcFe>r6Lj{>a2}bPrWfi|fDV7DZgjpEIjU4z2gnnr96W5=)8( zaCX$_IH?9gpNk|-M*v0KDWW~d{RzG;MbH-5hZlK1lwcSNa&86Wb`zjva`aI?A4oYH z358 z2DlgTL#|+f`)IdOd#8#OHzYa)G}hZyL=CDYX%%U(_Bj0=QQHNER*I?<(_T(+iAHb& z%5LBfjV-mRAlx z!RhOjyyp%>pgUeu1$IG84H*gbE^3(pi;dVyGz;RGXt;QB;}Jzw-(kb#xxcs!(Tj1Q`(xcPZO+#1`NRq_jPUC4*AojLmsgpX;YNXZ@Hl z8<|$y@;~H?=mK2w4~lKH?EPYAz_N8#(>H-VpT`y#@dZoSd9Z07wxwL%jEH2_O?(4B zEHI}RS*czZ{nKulKcZ2>eOW==Zl-v$71kILNilSrTb`}+yi_=ujoWyvTdJpq&}Iaa z(Eu+Lw;T03@kMcD{~bJTqD8vif6}9>DrxYmIBd?}PWoQn62jk#J7JJ)Fp{*&0!l$| zq_-Q%d@mj)N8nI*w6;p>a^2V-qU7f;i)5f8K@7qMjqd6gI87b7Kd z|MNNc-)LMP9ect&9X^*X^yG=jQIw=5ovMt1-j-rQ>jWPy)22Yjs#hF~KTJnX(#`Qb zT2;3&c{p##IBEprJ7$&N7nBs}#biJc2?W4Z%L=>-(qFu`u$WglPtithikwa;GUR4R zS!I)IcP%gp1sK4oK{^5159&W~ZWfi}`tl*Qk5f>SKcZIk9X-%gUt~d8U{>A>jwJcy)P-W)Igty ziKcq*do*s-j#qMW7kWaUtVbAoNP>$u%S(H^U+^b9uLsn_TX4T1bb7QTY+k&&O~GYa zU9IQ!E0|V|(6B7(E@PVh1BE9S5Guc! zHw(sTZ`nI^Y68t6cO`4UXXP6bR{>%#bY&N9n-9)_RVB(O)f#%J2ZN(*jb+(jGd?Od zi0VgRQn}{%>9hN~_Z)qS)i2qEkGY8fWnr0@uY|F}*Si`Xyg!oYPB~SHfhfUuUqHb- z7FVCRHO|Av?qY8fAEWpHm)Q>o9kR429hEr*V{W%7+l}SF@Sx002)7D6agRnl-I$EE zK0Eq$P7dC!{U!llb}~D?a4zYtFkLC1pW;IY;4VF8x@oPClCTSP*a?4~{1P=K&iVz% zQ!3tXkrM{$B8E$3Ep*JtH4ib7)@8sG?+qDQk>Kzr@;h4WzYlbuKd{e<_~feUF!Yw5U$4?lM-bkLP26d zG^G{)h2!D2fiWRcd$%_30wWB`rA_n7ADLfst9%0_FS`wAa1W1r{mD;@UAI@Kxigt) zg=~<0|8wcf40l1_-*!vJ9w$e|^$&zNOjtSjxa+`&nl=rSKg>+)oRPu;(p{I@R?d11 ztBtEylQA|gf$zxAe-zUetr+oslVy?e&ss`Q^3yaxGSx+gAD5l%qW{^OzjggYLL0=M zU>F?AwrGzq8>+drk~WBKTYfywZ)PIIF(IG6cFS<_0t8D{%C#b*x#iy z7Du7Nn}wp;Hu}V6I`tKQYMLHGSk&9*cL6rr1Xwe7m~}P&v9Qc#_XC(w@_I`dyeq_i z#w6_Y@y*m@8iFuHi=P~U>s$&8`_6_XFUi_4*DG%?7J=6kBy3WCqx(`42kA^%^)iNp z1YBxSobsY%SH}7Rr`$!{NAs0onk!a|y6O7pPX@;yV?_cMdfhSJo}!X&kow@W9(~Og z63?+&jH`SU!d17W1Y;Jy`h4nTXlgZn9Y-Vd-o;b|I-q&$H8qeX@orQG zLeGk!Xr%wkV~`marQ_ z>9?D4Q^^0uD)ZU+MT>@-Z)Wes-&p{m(og+P;~-6}+3lvtx8TX9^Xa`NtTM^k`y}%* zujK?ervIBV2kOvir^38CfHBsrpb6WdArS zv(LBtua~=xPx-*l)kv=lvTEk{q)r~R){G*_d6M3&;q5RQAbhl4fzPhEYPhU^;$;A{ zO*cj{eWb`^X)X1fms2#~j^yIN&_rSXIs5-+jyl_X5ya*7ptS!jtGCAI_1@p(9Hvya zq!K?-wXeiz&R#FDH)_F$)iyT(}n&T0ghHfkLF zcjwVT*$u0@_&@RfZ|G$1+i0KUsflQOr!4lmuKB3GsvE&`_m=O2@2=lBny{dxkMqTa zr$%Uv9~QRB;V3cil%7y!cl=$Cjv(h*zf{ zl6`gDvYSTSFXSiWFz+Qgod3hFRj#{;3_0iKF7`kDIT5Foq0GS?E$#N=z}z117^NAM zURyi5`j3)01GqMX++tgsC6>%_Me+bS^Tzg2O$>|&r3XrDjceYL>j`gquS%vqMu}GV zi&@#seuKUL7o!&&D8E%&^@%sjN&Rv)4p~yzZ?+jGQ@=Foe$M2kxw-~r+x{U9(6?5K9RA?o* zU^jPN>Al4tSJB^w&Y>Tk5@0H+tkq?6RIJ?4P0T)aEi?L(X$r1(dYKp-p!Pp;cj?4b zzWsXnU)OP0`lwf8L;VPn9RcVXAH-|= zSicpzx1d~>Hka6LBrt!XT$pr%avFOZOap8qM`KA<+`VsTa84hqWNZ~LUKy;15_w-0 zs310D5<@^k{K`fD#9q_f5HgthIR!5fPqVJutB^vj-Hb}~?Q&q&zA%-w%$^rWL*uw3 z=8Yyo*!^FXTa~tpqK)b-seyt)Ik3A?#waGFt4pQR1t&Y@4Yaj0w)(ktO8LeK!AD_a zv2N)_x_Knc{UeI1AJXr%0@dw#>{+_cKUG8rv&H!^+8-*?$4wzpYu3yki$_F{K*2Zg zCO4;q`|*C|0SpnYrNG&4XABSa=_8VwWX7x+>{|2N>6~ z%3n`@v5Grf{HA=#^l)5@N}Q%>4d0HqHQ~lQiVf86uP(rOW>9Awy`ZemfIw)^_NaDV zF$wdlF0zse0#9!X{7otC{_HRl*5T}+YYBs!)kkMRGB~oq3ALLId&kX|!LOG+Vt376 zVLt$Jno(!@bXd0|ddj4V8uHW4;O4uQ#GOG-c4sjYS4CX4*5dz=F4~y}$CvNwS}Qup zDPy{#PaOm9&d0i!6}gy|qX51#yg!56;gHLvmTJ%~WI;vqWOVFuA&l*Pq|zJa^Wt;W zDD-3*{HW4>-tMEB+tyM8F_29{g|IP65SJesp7uQ>fn6Q_!56ZVV627oY6SG{pCjOI zQ&EVw+AE0Do|4?^2iC2uK!rUa8eP3sX`^6({D(12c`0d4=)AVXksaAWi`Zd!$1EzG4zAp`Y9!4mIx_GuC482jP#X;S2b;0UoVytX3~9Rm zU{I?$;dM%^erT8qre!^YqDZ4NXtXJ`_G7al9QHdJvW0l*`-uMqs7~cZ4~*g%mmsr#@YAbVJ6~b*f$v?{nl?lrkr3 z1XJo=ztI-GH<7Oul}vO`BK_NUtZjImGoC7@@f7ostE;X(OtH!OweDDysqfK&Zg!b5 z9jNvvN0jeWF+I;KyR=thJ2O(RGptur6YRi!m~>&S6>#u53L1JqqokUu6P<%!UEvM(F`uFS`2M|TO?$L%Gem@zX;!No$$*0jCFESd{{Kps)2>OV{_}x<4?)=u8d&mRhgNV&SE!9{&QO8G4lY zUW(0Ka%3@E!DUYC%qbPtTr1G>;) zV_a&_^xEiHkT~$&oXDGn9HdmWUMA^j7^Tcll~H%@vEoj8)9oj0eDqigQ8vzc#s2KY zBV3-*6Hc$l_*6%)>9&`cZ{_!U@C`_^ab~q=)7051ojHYTN9d3@LJ^7~zrwZf40pC3 zv+C^3qD+;)-;E2gw`A&R(J?WV@>DHf>AjPvU-|t>NoL|>WYH7*C};l>0~Ph*gSzoQ za0BNi5z4B$4&k>IuYTrknNxBGGgX=Nt1713tUYY6C~e-5SCd-cGU7AOxQGO(kd>_o z2mCA#P6~(XUJO=V&Fl}z2A87+>@OK7&|S7gH*RO=SDfVt@en^IebT*IC~R=ZZZX6v zRI%miaxwE;Xrg%ON-4l;^6$`ZSje_ou3p{RmGln;r~unce5-Tk@V2;>qw0Pwsf!7A zg>jZD-6|U$Pt2RI<=Z8s=Xp$D#*#6m5z7De)5%%g_f3z5R|LMZ!yP(u|7h(X z;6%2WR|1*~{WWF5OeIOv1!` z+xfd@h%KIe5BkXBpi8a$1S|h1=&R^T8bC$KpS?3NiC2waUDDVN9 zw_%Mk9yVZ7WtnlfGhm7Aze!Og_$?=_jnQ{JM42;|y!V*N$A&FcU((=<_Zr53N>wsu zk2}Cla)?RR$xv(IFvVZvtMWy$p4CiLSxBYYA5)&x;&h>Y6k(isH|2Q2(I_F1*nDx}w8QgrY4NkBww zci{NsE;Y{PqW)j}FZkr`)zC;YnuI69QrenO;W^{cT06^T8r=C{m(W652$(XHg(2Yhz6anX>xNspNI!)e z_5YhX;+-140zqUP3+v|7&MCOlG<<jt5JVgy6%TaQPO5h7prhR%w?v*Ra!j?vinF9e2W5)+f&-*&~`LHchT0KXPD0d zwc|1&Z+Fj5)5_;tuDIgTMmNb1kD=5(Wzo|?PU^dk!)k_KRKy5t{$wPTE)!U&VR7wx zZq3r}+LVTo&8}(+NLT{!76^HM<<)Dl%6gyJdpKi2ZV7nM<(w8eFd?kQXJYNU-BInT zMNE(TC{`$g-r=1~EOyO}rfpun8M`iLQh)o=WWX05*2>F9w1Fk32Bf=v%phzG zTr$O?{mC?T$`JZKEBiz~B`TqpCG+@~;nIP`Rdu7cf&qX)P>V9W922b!+@dKIZ$zJP zh8*rkV5FZ(^4eWk7)^vvcv&u8-1+$eJSREQm1YB)3@OLSO@!E*p7|Rc$k=lI2fUF= zhFZJvXmdRk3z6P1mNS3hQhESSf(!9Y#glA@d9}Qa)(t1wB@{r{Jgp0_Tcec86OdZ^ zL?7-LS>?6u7syA+EHvMrCc|&i7+X&D)-I^*9V=?k%}T#BrsE8KXi3M3a-T_JB~Qd) zsP^`4C3iPK3{j2l`RDOH3mKdCYVjd^FjL{1H+v9F7e9|&)K4ncW=Wi)%9EDrLd=~{ zW++#0H%N>48m;YMepAMyN%_W-1B#h@1gvrdjZZAL0=k?NdawAV|Vv7(89~U|?0lr9b zh2)%;E#1}s_qgmLORmwde&TW zNSTI2p~HB<`Tn+|{gRcI=MNz!ODg_x$2X%&Do^mN$N}8qfKJe-w>4Za9Yb@EBJTz4 z&_Q!Tw#n|fZ;FIfhPK*+0vPy&XZ(VnREuqnAU#_9LBAK zuN};{Oonn&c^S>V^o=HK6N@1{+mOz*|7QVYu~@au3CR2LUBB<>FKF$dr^ISrG577i zFNsP*L63DGdiZj{-!y!Sl9i%xGCiz3Y?Wdz?(aj9cu#9#_GOaVGhW22U_8v+5Kwk> z*M+4{l5B$Sx(`=8VG=|s*57tjq-#|DAp%OEa(l~mS%cv~`Cxv$y-K}a-DI9$SXe#E_X8**fy zevnF7YKTNzeoAc#;L=)T^u2|M4!vEs(7ajZ>sekEZ@^s;sA_POOf1or0`VQB9{eiZk@s%mbK=85^G+U!UD>Iaj-u0=roz+rCHNQNmNXR9l$?F9vpx zTm7Yh6TJ!HmlJ8)a8a~!D<-%8Z`zA${}foM&5zQMN@cO~6^-HDub^7uQzj(-VN`-( z5YyjQ;CNdDev}fJ^=(eH$B-FmJ#Z;z;Uyke(AL!FZS~4CHkCK@6O36fGIdUGr+P;+ zr1ab;S4ktQ=6e8wwYH8F7eMNH>^1uW3i=C@3{HZQXg_} zc1AmleHpMSbGJ9=;l~`JDSP~O9O<2E6B*zZ_l6DvQ`3*qb@v7oRymi^o=s(2zYkA} zOF`UxgIk@h-{=akwowjkEYvJPdiun|(9ulS3JVRTBdFpTvi&FP?q(y_pxDJRAGiZ@`NyOXjxShBbyW*f^|iw7DU`{Y2=??!HoxK^^DSS5zq? z0o8$i>ijX6R~Rduy%HOnCJ$u76lxIiB_DI z{_kx7>8H>+!*_yWTUBmwrb9e>ooz>V>KDe2(B0vdLSuV zcyq``oWDc;idt07!p##ALd2Bf3ir=d2h;a%(P`>1`SM-(eZ6FAW^X;q%tEOSUHp4B zf0U?;xE%2v{B(UoxnAPefS5*XLL;-2v`r&I=)G`rkn_Aho%)yS%%u)-?vLr#z0V}l z3qQ_V;+HvP!Ac%3Ft)1bs$rKudCI-0YN_LPh4Thr zn*u9eQV}x}aK)DYf|S#-(+>zNT3u^dtfwmQWM>Se7V4*oGXR!k3KyI273K^U7PFt z5g#<&8PjD!EFD=)R9#Vf%Oym4wSto9{xO8$RF*rwINIzx;FzJ-4O$^AiW}}wtJt^f zB4}OBu3Fqsa_Pq_!R?PZ0ywwoVLSaV8eBS+T3894^SC=M&y@j&i<-oYvzLR)KCaDb zq2n3dJm#61u{|oj5pY?p%o|$2J-<@>AskJv_n{VF=g-^Fi=-bWJ+)Pb2lbbat}eFK zx`ucn!GGj4%{EEtP?oAWCj%o_lEN#Hj#{7FEwLA)Jpb6;JRPSU#UEC@!+=O_P&QhNQU z!*d7WwM{NPANo*>S8M#LW$XT?j5Rtu)Z)uh)ls?r%B1g(CN#m$)GPJKP69wO6==RWvc_O)IB?&xoo$&yw2)f(i#&>j|}{U*+ppQH@hxrxvOpyw_ES` z))#C)hI`_z>zDB83vTfi!m6w*q_a-*Vp(}NlJBYq^ocQgbbNUqt&Sn$G zQ|jPxjxfipRLU)&l*Ll!A89GT-`F7ImH2Ka(|Rr!Ta~jjYGz*%%|dH`j}fm5ws32| z%X)6#Wqv#I1iPCzleSf(m#@>Or}@@_J@%Aoq#9F;;-Q=YHBr>GFK& z&+yCL_OIo%%zw}2)(M_`+%=?JSl5bl)v9*9&A@X-H8=o?T=X3!(CM@Mh^?sQe~Rsz zY_+~LUb`}N)fPveszUoRr7cNU4{-6E1IMFwX6J&fsM8|f!$*f6lwz`CI?LxAz$yGZ5A~i$H57v&| zv<!P(c$(OHDd;leK!}B19VzN|D7NlHji!4GbX9yywY#^D zdVglBZGkQZio6B76`|doCZ>vK04YE3Jcn)L&)2S3RbRi9K2Y5*e0FmXaOf0Q@` z1FnT1|jC-!SBwc8+YQjlv$#n?WwdkWjRs2BX8P`P)5}B zDmtpakip8DS!`wCjy2llI-!f4v+&;8`ABTK3M<-;l+jDTn06Q?99&UiQmLML(MA(P%qMa zmbTdvAH*42{E6wl?!pkKo9_BK6|>*D^1(I+&KbD{R(l`3rkxqtdYQ3CPI>JYihL&` zz6nS}Jw<~w;qt!%^dFRMCI{ot<9W}4;fD6lWOd5|gU7G8t8#g&xC0H4I41BL-jbei zQ}le}<=b2uY{XCqD{JO?1FyTb$YTaAETn4FWlKOJnrz>;iJLoRKk|9HHz7vIPyIF( znh~Hzxt1Rnof&T9KC;q?5y&ouwyh!JGp7MYm`hw9S5#oYHLSfsC@m+!*lK3mBmD(w zjc94SE5-jwcw%`#c@p@=j8Amo#95{2num6|($AEUpAtYjYFYfHy2`ZPdiyRVE^+#l zQ_d;3A4*WqS=-g^B9o3)9SU~vC|al=Cy?{$rx72b?MBWKQ!Ce1+kHxxr<>s zs&pD)bjAFGc9s;er0&-8YjS+NV-iGn>eIPh3fVP*R-%1g`ekNh0ajr@saRpZm!Q!Gs2)5D-Ac=q{nHR)hvm%hDosmh~y-cF=>9BLw zGwCpiV&By`IX{J~Hl+Tinjt1o5)7ZIwAx-si}g1U zq|GEXMCshgvuC>Lb;(^=@^IAM)xjB{zH5iKAvDnTAnNY*!YH0$j2L^k83t)=QI$92 zpsBS|_f`SLTe9Gl`G9)y-9fozNDggBTubu?a4kC&@|$M)jQ`{NzW*Q$N4FQlEzirYO_oGy-d=*TFG~w zsG0RqX5T-B%+ki5w;g!xMYzuRi|VLeo6W41T>Jk(D1wyP5119W_K=PZJ{Wpsq4#R} zf2`w$LOFk22!giLrsHd70Q2=^`N_JCYOr4yJD;M*N2hmj>5<>Y`~%xMmRN>@#7liBOnwhm{TRIVre5kB07H1@hTRUL!ROioCQ>t~ZguZ?N& zevX`1Dr*dF!ia87l)3BpOpSO6Ni(Bi-<0%f8=+C+p-K=1EGdA~BQnxO9u9fxyytqU z`THE3@a8?)tE|93(v7hWuO}Ody9=fg`5oWBuqk*O$_d^-<9dhzvQ4i325I?R3^2xP zfBDXPlqy!Acj$TYP~tCdh|98{iL-R>Xxv#0<^KWIKq|juU5I}CN#~sH$HzC8IB0Fn z55_*WX+Mj|FH&f-RnOA#I;rh{UO=va)Ytp`fIPO_SUbC>Ykz3k+txs$0%XRGOwjXK zRM+i$3q;2pR_&w%m-yHs&!Hz5!X|v})OP5>N?ykiD9t)L=}t`NtipbN=~Hb#tsUR3 zt9Bh*dwgH3@BJ_iH2vzDsW7$K6|c6@wTUY5w~wz z+g-7{fBQf@>vd<~=qu*i6tY`!Rt7$SUdf<@|%FmoD*F@%_$hifem}zj_1Dl z1-SncKiJjjyyceb4#3MF_gY-|@zYGc9O&}3f#h{O@B6ztL6?(TuFt`)g>Pbf4`1M= z(s?{OBJ`!w<-t=a>!1Y zWpdJ?ZF?WBJvTC4c>H_4ZCyL*)<#8g^lzW}wk|rK$Mjld@nc?e?ifV;o+`L$W2}P= zC$+qYdt^8Wd9+x8@uaAA$AGc#0`|Z-^u%CN@32-0gZaT&b%x7<049A} zk2U9*!ak0ZZ0fr>W6JYYod?33$x=?uEsj zo?NQxZ^wyW`vgw;!iTWw_FL9e7B8Q}v3Ls}fJ<>O{aRyAPQW?;2x1=f^#lj|5%y*^ zA5)*^K3jb?`|u@ez2VI>P;-?N`9riq{(Me3ncx`G{B*X-?Ap{JkcM>YP2;(W5qXuu|{7)P&2AV`*E_ zZzi#-C*39Uc6#ICl4ViClLJR5u?jr>@$k6($dU%FRo||2CzQjoT-EMnt9eI*vGx`~ zz*14PykTXE86p-ur>)9w1Z=J_z zb7E~PAbapL4#=*DHhND$MEl_kV$7{>)M;|{=aZA2#sl#s z90?#+>h!;g3yN!s%bIMA;yzasZut~UP6`Hw)CqT3-8zWMfnUf`pTx-to7Tczj=2Ia zeE+j?^hw_=WLR0b^nw%chmU?8uD{|igpPuH)beWJQ*lD}hsy7nyLm9fco}oW2|s0- zp1_-=-+M{6Dru;JaK#<6kL0$Z@A=D$cF5X7$Rt1Nu(Q+q>d3KjYCZD6WR2xf;;H@X zf`!}@*Y&d-M34>6MmIzB@p`5}oqy}soqI?anutkfJ?QaIB~YwG4stXw3L~!wx`UYm zf|7o|*LMYS@a@5*f*CJP{?7VX9TpHZJqId3kI#cRz8*m109`G12PDp@ z^H^91d5&U~Oor>=s~7at$&(4(Ud92rl0Dl{UVj{<=zO|;vm^B&f&)t5-OT~O7$Aqn zqF*eJzYoYElYZ9u34JoT51in7+1Au%Ym8+q(s8?f`5whye*ustIdn~V(jXk5W2CPc z9x8qGuKOg6xy0)*5 zUs#&pvG2Jz9`}zYVBL=K?ET`-9k|ywK8;h)Klcv*TVhvXf4mhBz{hbM+`l>R^CXle zIov-m=7;a%M<+aS>3+Y55t;DUepiEw_|*fH95C?j%z=VGA9M1B_^tQ-91c2q_jgaf z@{tGP6;F6IZo6@RlA)KZ&HizWS4`W;K@hj^{335O06Z@+U4&fOXR4jFvAKVUHrNI@ zX`1IZxU_DxRP#8ytnj5C)y_~1f(2%$%#JfV(kg(d0|L)zDhX8r5dBvKBSz%J+81Y5 zxMw}8peNdoPkK-7=#}`?y9&CSrGbh9MQcMdx6xjf(}Ovmvm8*xm{8X79fm4+=1@%M zVy(bf+vj%X0b?C;AB=r&dZe!DCI7|ws;K?Ct?@czon`qd+tOVaKNqinT&?$d9lHZz zS^4)qAWwBu?fkkdnbc}~s&574ia@K*X92RW=_(lZxy|EsN9_Sh73j1!bWP+yi|Z)? zh&7=lEZCi=CW2Jf9N^_6EE<4m@`8JIe2lr!|BwaQc-O;$h!D9|zvSM-y+xCcFcuhN|q@O1~_p3VtT9_pG#5R*> zVqUkWL`;pV+ARpe5ro$>$UH_i#!SvUXi!q^fdY!}K6ciP|FP_K`U=Ln3z4%nE>oT9chbdVl))8gJQ`ecu%y9pB}0|r&%uWVI4WlqD5>66 z&s>mu|Dw&c24vMA%ad_{v9wB7g$y}?F~G5>PHyq~ zt^zYH`L`UTX55K;n_lC4WjXa)^LBcr-aV>z1*5$>vBlW0gsAo3uTh_?Q|N zRQ`xwqtGsx1O|EVkn3a2t-ggLi6oF0RKHPJ9Sn`Q>AA8#Vx7zUP?GOA6`^iE^A8SLy_* zC#xEM=_w-(l*V`~bCc-PF+10FpI`aXY2Oe0$@k;Gy!i!Kw_*2>vcB&X&%m31<2NxZ zG5#nM0UXydUsinddz}kL(zoHC^Eu`^oaF22R{L{a@5kx2?|N>kW9N|iu7hZu`M~^% zwpF}kF_Ug3E0tr+M{@EOWhU}L_{R)i`%$OICdGu&Bme#w>*6wH@KR%{GT|I2D1lM=YV23S*tTAu7)L>i%hpcPW z#X!1sFb*1@Wv_PzcpNbws{`ZS9Q&NHTn-wlEnTY$BGsCy0GX2&JTEH&?8N{$rKv ze{z3Z`kEuLns3x6t?2o*n(rytK8SN43i1Iajys9 z^QZp^uAl6KTfh8G!-qPttA4B1^p~`O+F(z%unVVZ4(V&X+69v_?7}?7g&H>+hAVgS zW8UGvstp1e_reZ5>33d(C;s>U)s=50_d55?Xy|p+9!qIVjrCS%z!v`(fY`V`IVMcJVTZZ(I==P z=s_mN$!r}^Fo|Cn9k|zVBBlSSGs z9UEhN#-b(w^`ZkH@{VWAxt(guo^=!VtqzQ}&9);OW1SM%CDW1H(l{79fUX3LYjVAE zFjn%GGk{gV*VDPqQ|L+oIj(vFMfFkxa)53Nwphmj$W=dHw+F~Nt_A8P19Hf_`+z*X zwrKa(xYQ%roH1nE;Wk?E%I=2snw^Yi@9JZz;Z!*BF%fy{q?vwqJPug`KY45#2rZU! zYx|WA=Oj&qGqpqHS&5^uG0$xdI6=^4U!=ZuzGnHsHiH!|IBxGL|8dHWLl$xAtB=I> zPhR6)u>m;z6`OI?f4v$DTWY&n0PqNG#m{34?sf+UVgPX2F(=_OXFe7;9CXC=hb=o2 z4l|EpucCMqIN$DR^&-K;IO@Sc#s=jYOVl2peqz6-k5V)#0DabNf2{`7?eWAAjqQk=)gBJIJ*fQ@q}si`PmQ5;=7g4sQB2B#x@ygXa2x!6>nKoqTSmanhqp8;>zh_xmJ(3 zgc8QChoZRsN*>R^=76CJEL_={{5V;yvh5x`siAt?TE~N$IylaR%w>;!1d985_f|oe z%BVmYnTtH`N?)Oc3XH?%DliU$JTDk~R{VPhj9K^GcId1Eh)kHXt*MNjm3Kf^yIwgU zt6Z^@vNAyCz(q@5haQ~I2gtc@9RSP%bXHH_|!~nP5>Z{$42YjI;cY>Pas0j%N zyRBuGZCLK#$ES1ZL2g^CW4@vep2mW*tcM6NRi3ecyS2Rt&35bhWSg$qYqD1VVtLFQ zxMfUJ>GqWWNacpd?}u-{_DJkFY*h!9x@F@<+i>@1UV(l8<+k$r0)QXJHvBwp!#(bV zKj=^~kcpSca#67!poO=N3OVz(eO+Ca;uh13t#0iMr+Icxz~w4+sP9SDEmG)#>n;=?`>cXJ8xMr66i$5 za~N(+RfOn{G=9sI8ufOHqE(W%SUu>-4;lLXl`K(bfol0PaC(2WTcqT&)m?%U;EJsD zh(~L=BD35l(ikfO9p996XuCBLt2v zM1~$1JNV27W8R*x0`*)N5Y=(y-Looi%S3u?m(@Qr*&GApCPM|ts*BYFat_+mcE$qR z9Mrd(^8xYz&~9IAxdAed--7|yS6;_FX1BkwK%M1o;|Rw%N#n})nv2eHo45H+D>U6W z-g-hL>zcbHqOV2j;lz)F^=K%}2>`#xx z=BLem<@`#^VXwXpN5AlDEZkZp%7KSr8-4+|;rKft5W9K5gYnrjAA^gJI~9F?7e1`= zLqM$6z?(^`)AL1+N!5K7x6AkF;}L$^D|`pbQ~H z!OI@~m$>x&`=FO(;=UEDbJC9Iq)NC2F4wxR_^kV7m5FVEPb>6r)lcfM{EYcIsme03 zzo<4*!avvkY~He>fJ`H34MdnoI0+wfG;P8vf>{wMCR&lf^(=EHJlc+DFS&KUf80s> zal&x$aTJCpKl)xm7&5*OPNaWUy&!$h!D2J+OtO2W%4kJ*3VJ0G4O?{D@3)*#t!N!Os;K)nWQFwXXp$+ND~{*DCK=eFa% zHXfbVF+gsXlL6WF*W?e|i&6pd7(P{i9QGDLdJeRwpY!p(USQ>GR@?BP&T%XP(7=`$ zo6oI})8*p1%X0RT5#t2IgGRP9Jrc&0H#<-qtepr3o55DW7!$QDUyzK- zh@E&>fl3a3Lw8)KB7I{4NFBr}c?oiI<1qOjmJBhWkm2z`6FV?mPQt_T3r(GUhhih+}mX9#4~a`83ynjeBp! z&!6*Rob{CV&ZWm$<(r>86@T=&KgZ42-bGNa$sOMnPuqB#Tz(=>xyx9|ryM-tH~o$M zwI|e~EsqntceNSzlRLq-6LK*3XvtO9lJon@*ZElT_-2(gIK1p+sFCyhRx=Gm*okJx z#S14B#N2jXLUeT5#+J@ zK3-t3IyqRY?+?=ABs6D%c@g)(*uRf~amW_O{?yJYO=ZFfYOQyF+&v!X&IaSyQSmo6 zVOJoS=gfnCot<$#s*}lgD2P+ZmhIwA~Hg*^awC>q_kZLH%1{695m` z6^NaDCj??U0K6NUa1Qpu)mXsKuiuDy_y0J_Rx^*x4L!-G9H{ZL_?OqOay8wR*XMuu zIdRB!mY#7cAMfK`nkopXlgNkO{R%w)oxg;;o%YpP>YJ;4{vRKPS3UU^*m~P0V0o-L z=~!c9jbp7=)Ky)0dl~;dxfekxC$G7OD#n;zt&d%*Ts-$RSBicYM5f+-?0c2dWd1*I zS@kny<3vPvQ9Of@2~8*sa=lH8I#uo|KILe%GBmEs_juN{x`*rKpp6%FmWd~6d^g0m zwJUP2qs()mvQ;|pdK0XJ@6?QClAH{>9xEW^VAg}$u>g4tjN`fkdJl|SE#3iBu&n~% zd~{QTq}7A57Y2ix?CO1*wenKg^F-F%I3Rm#Rh`uVvd^PJZ`A|KF>Bmeu9X2Y%e4D| z>`5w3@MJt}t+sZXtyUx+^t;Mc>xSouAA)9r(+>u9M~;Jt><`$1=DDsDY57^JD)1Q) z^9iZbWafZP6M*AkVIM2|#e+1KlUEDd&;&YvtI4MBDc|E|0$lS82jM$^d<2&E>hq=5 zltW&B1Md3#?_%*rC8KBV3dByk69TcF0RA4E@OtcpD_6Pgr$Nrd{sl5GP=B0{3XXl} zwgC4*{#pIY5(cz4{yH~v{#pkfEP;=%eLLx4=iyi0@vGSHkn1}dTUq(Y>z;%+{Nn$> z&K-;3H|#2AO*QI1MdfpBs{+u#PsjU;XRA&o;AFJoVa9FW;E3OSE_)m;7ayySclLdm zH;+AvRFxG6WbC}For3txB%)=UdrrG4u#q8EeYDBWzY0VkziULzH7c3)=nt&Fn%Jn3 z=0G8zVQt}!-d6!ey?}k3y=URLPTvsQZyaolW#((0^NvF3Tgg(?X;~?@C6=Y?ui4T# z7{{DANmLkaW3GuVr0oiftDPO~t0vjAz}Nw*3T`t%s1ECZxNbi*(ps$7DBthd%03`h zyG$97`3lcR1;`#uWc{%Y<^g1Fb7g?spJNB)2ufJ*Ro2(r4dPJs??+|(GG<-mG4uUB zd{y~p1rh9!bpE|$c`6%LAFzZTiQpB)?d$w%|GmWBv~~_A$8eYNqqd)n1dJ_v%D?@x z??4u21H&y3~m?Z?sY)W2na*dJ3afuor0z$2gcHazVWFU7** z+PrY(|NQRr@OQuWTnxjsuNsqFFFo#2^YA{0t9`Zp>!+PEU+8nr0U1Z*_vlj}JzS>*Tln+nMj*lI6Y{ivhSFdGR^gge2e#CY{(MJzNM zop-i!U#{HfzuK8;_e5|JJ;yaZmpY+#vTn7wp|FAb&YK&H%GU#j{!R?bqvNfETs8Q9 zFfbsF<7FKy31&jCz}Exgys&%YXusF`Rl6MK0Ars!-eKvH+a+p)$I23Bpc^BVdR@t6 zAB=tMYX!))4%QlwbquX-whP~d_h6z1yKLV*V*6Y%{SuMU;Zu@ub&e3bpRfTt#~@N;GTEF zTN9T6ybl}kI_!nZur`0dx=MmfS6=q*oYb8bPax_#^kfkCs#wx1EfHI-clHVFPqJU) zBxK%I8nABD?fBW(yaW$@=HJbbXTGv?`#PNSod1D;e*F{ZR}YCQzZY@`PWKX@HGi!5 zWDb&AagG40*W-4TFL3!iA?vl2*Hj!Vduo@OURNhFXN)7z$pl;b8K2;a@cPKAzq!+#e*=)#Clkyjx9=9R!{;$Gfv$Fedf8RJ zS}mqb*qlU${Mn!y*%>gl5UM%O8H!jmV_?h+VdY?~AX&+}yI{<_X56*{Mw20vF}004 zlRgWOsxtQcT27#{Mo&+Gv2l z*u^i(TEt-Txm=t`iBk^T{!Zc%`Gm$nM_q$o`@3Jl@ejOUUWJX88@_u8{^TcKi3>k| zKa<;wCmCxWHMaYu{ITgv?}z-t2|#{USbfJZD%U(bi1}YmNO}S=C&WG3*xKvOWA1yk zWyL#WHJQzXK_8BlV9jT6Qmh-Ew!LIq3gGl!?=v4*+=#v~ak_bxr zWDLc3?w12tFCKLDjKBTH8LM8<;O$oP<44l;JGitEw7K7YsUb3iyo5PVK5Sy(0} zT8_Vt8=S=P_Ptc?_;|eD7#^W>Ppj+ec~3z0Z?W8V`X*fbmZNd|i5u6n-GknK6OR2a zS7P0@J8=L8{1k4-Q?LaG+zECv0Qjd}f!M`s9*DK#r&M})qmLN$c}EY|ygf~HO)i*nJYPr)mm_)1)N`4I@rV=QD}R-Ld= zOw(iZ{XioEPfn&~Khk`l`U%F=%2&YJ>m^Q+uIv0UFZNB3#bW&2e)N}5hoSgh$vo)UZ5YVR;;=oS^jxH|sHHyX$dOpy-A@FBtnm90Owye7HnlHNI=>SsFhA zIU9@>0Ci0H8MIudmfz*5z@a%V)@Pmc$c4wm0oIuFk1hW}j?H*q@v@Sf0jmHR$acOG zKn`~kI#57v?PfsEvd5^#0GR^?waffw!e}RF=(wGp9^&8pVEinH&|4p@V-8$n9Y!z> zgbuw8KyA+PrP5)Ar4@#8FPRH!3{JrG8mS)PLx+-hrQe&F^8|hVAA3 zD=i;=)8lc@bAAWgZ*K_!_NC6XAedJtK=WJDYyQr7>VC!feDftuo}Lfrts_r%>5JCd zbLkpSRKBy#@$5$%vYvXrOSwlquGwV;0a-5?=mjmquK)7&inIcQkVsH{?#FjmSUFV? zqmESr05=?d{b`RhII(ab=>;g$g+D1BuIpw(GFC?*`997VxvpIX)(7|JdgqL?AJu9- z)B%jvYro|qdU2I^7kQDhj-3?uf-Rr3aWKxZR-ab`#;yK1LDrqOR>$q5M+U2)!RwBL zab2<v8d49gSOlaPKv3_rQ1EjANg3B~G|%E6&CiJQ`c?@UW8B?lE|kM zY$w7jCm&0z7H4m~P1aUsfw9Z(3z_>`3ozyv%+z39AG_!|qsp#&_U3D^fid^h1LM{% ziqHI;&#~L$EI^)EpIki9W*K%DkXc^ui^p35GTXSz$NHEDkSjay>DrSb(RUK5W55v2D%To>e}&D-iqI zofe3BX=|<}cd8^#!}p4;k8y#XUq$iRxsqc*=CfQ$mp!xP6C;}|nJM2!(tLZYG}4pC8K)ToJx12M)K1t-9X z$BQv)9A4r}qDhQX98g4=5t@dko0%T&+3$~i>#XlvYt=sI-aZXTZvB4U_nbXcty;C} zTi;q$d!Gy^nJ1R}2JXAB*ZYr*N7XnX_b21bSrU74Oy7ZL$TCM4SEZV89cOdBL$waF z$!NP-n@l>rLGk^it(q}3NgaIzp*Fdd2$W|$@@EDBZ69>bg8>(BgnZl+?92%LIsVSu zlmI4(O;ye;*aZ;CiEo?lFGu}E@t=!KSsyqcDY9l`n$FeYr&{lBU%MGkeb&Wz z;lfr-oJX;qi!r&R`UUNEItmT zdNSB6vl0dHBXIdi}(y^-rO z1=keQP=~s~wAaS-by;BL2eeEw&~ZQMm`K!Cru@X!!ds>+py}GN$)5#7JbAeEEus4U z4U>BSmD>|hKAtW0`#vfOjQaOPv5(+qM6L$LnGAhkoY| zYXOfhePGP(+x7gC4XYy~FUAwjt&A(A- z=S?z?L#1Z#u*+xemzRz8;a4>MHEh3L7Vdv`-D0_;lpB6!S6uwDb=Z2)(y7(iY;W<1 z*RRL>-h45Rp56JsAy&$NVku6=F8IOi9*BA03g~=b%EfRx-nC z-8I|>>&e%_ZF^$G_cNFjcqBJK{561rb*x#r>KZpDZP6kX_;ME7lIv4x5EZ$at9FrpvkXaViiG4O_ z-GIT;`k4W8jYwyJ%?fO=x5oYUQJ&(~u8$*bqfuX@XxpKifNk2?>){>|&BSLvAYUvq)j_ipzznY`Uj!71e zDVg8Rcuc;6X57$YrpJxwOYHe&jOtsq%Pn~J-@Fx%dfuleQN-BNwhcc0r!U55-~8OU zAA2r%?=eG@T*iMgzOKHq?)M{?%6*l`Fs{E`>p2o%KX79!8mRQMv@`>VN^mtw;=pH3K>cFz?dA_sWnd|W;D0CR8P9*7Z zAmU%RKC65@d@6T&;~YEW(pT;`lcKnSm(lh3yiPt5vS&;*`(HkD@XNaLW5BvC6uGoH zKr_0ZwDxOW_o`Pn3OIN6v@0>~_L>5H$A^)?A01FBa=9J4#XR`T>Z3;2hl zes-U|?dyDu+hEJJUO$YDeXCSiU0gD(SDmQ#GFe!La=&-z9NhD~wl=e8?NkcBm)lVr zYUG@bJpT}N)RnirY<{rUXlV=<%N?cMdbj1c=;H_A=KHRgYQ4vQU_IXR(hITcO*{Vw zodMuGSc>;!CBB2{t?o^y3;=oqZR9Q4E)PF%Yspi_2*%S{hYgu;&LUQ980tF zS>0J6#QiB}*eCk>%{SP%dd&5)1K;Npj&RqqT`6}o-`^cCkF3O1*2&8PUg{A)7xkI@ zvI%02#ye{!r=U(`TeSH4xSdVg&&Haws|6&Fm3kHNn*~nCID$<- z7&k$eDsMj+Tb*64Gl*l;B90IDX@FYbWpk>W6U^*IEb2EVoYvjRRzMBH30omW2U%2*m=7sh`KK%sU?A?%rlJ z53Jt$Ruk*DQT+2=<9@jgbga8ZSFiXw?SuN9?07k*yCfRXua{Bp z*C5!-!0qyw+m>G?LM%_#zcTiHTZb0Q?Oqz->OWkC%TL)C!xi}qSV&oY%{H9=oS))} zf4^~Rb@rA2#uA*0UGOa|y@OhoU(xovfNZtL5=^CJoD%M{)m7%Hl6FAncx`pQJD+n! zTBvARJo@>c!n5A~RxDkyrMKskmMed9C{B6eyKv=C4tH(Wc9Uejmi4M!K(8zH*s=Hy z1on?vAm+VBA69#QXzeTCvwlQ>#y)Z2?4(bY8fQ_Q&wPC0rI9~+-$y%dtc=VQ^5Us=k=7=-(tU2r$dxBIR!0py zvcSn32A?-FsG0K8W_c}h<7D;$uP3+)R=f&f8T@jO8EJzVXqKJ(m`$Qi77l~@z&Iz> z4AL1GGZ|bc80+Xo(&l9@SFla#Gfk}fVw2rpdzM9mkt~B#Tm7mLR_@Pu!pLOWWYn#C z<($nyuFLBw1HMc)>yKiA&8wC^`)I$Q`}vA&yK6>$YkmwPd15~n%k5FN?Y9J%d}tl6`?cMsTJPh(xCtLV z;e713e&@eE@oQLyk617RNmK|IS+jwesE1Jp%7}=38;|b-R!H=e?(H&1XK!SZ%>r!5ikVnp83j zGyZJtbpDGI+upx=9%~q zgBn*HUnZWdZRiXeWA3OLH$l{qMg`b}WJ zQV$aob+q008Ir>rOI$VNcI|k!^qRk@LRV-wXe*KdIw8w~b{qey(Z>O7afai5wC?Y92?wsyQBz+5XiIV#xs6`s9B@(BqKb zYGwgPMl5Q5u)e5Vy6O`hovmOO{4*?z_?*w>I94W+cevbVwp){<;4SNr@r&);$bxs& zrT?X8ehe_G&r4^;Z+-F_=l(V$nLXCnY_eotTsbbEGduJ1k#W4-nG>qTa@&{9PuLxo zoW2g**G%u1zgFBj#M@532q(Sg3N$-$c!vPKh~@YwR^A3b=sc|wAHh(v%-ih%_VfC4 z8WI>vH!ZTaVuaxgA70QcEsvJ8Aj#*_~8 zWaqp5mPhWP2V+0}lYlYnZ&E)HLDe9O@wmNBT@Ho^0KhSZV zr@AUwuGG48Rhco!XJPC8P<&wY@^))KkR6i^>|cP)HEj6fA(H9gZXVwtCcM`ufY3{|0{gse?MOO zt9X(5ww(Se-WS9)KNC5o6~kF(D9QjMmt-3>&bEJ?i2wi~07*naRK@;J*+a%_w8u7n zx`4u5v+%a)2d!?e1!Ygxj%tE!t|Zs_PJ4@UtiPpJ$Cr7oXO~q2>q-&H%(w__oz$)| zDN*NN28{=d30A)BX)1`XyY|Y?DXb?bY8+WROeA=lqGoeULw@l6GjROu6}O#FK+V>2 z;OmKl_DSJ{_inQR50m~gu_(vwrwB|wCG^^cuE4}hi~nvDt45S!z1CY@wO~SI6F387 zoxEkGp~&p@Qofh=N&x3UWoFb?_nn`OMv9rH4aO$JVhzgcD62B-UmlArl(T-a!&hwbIOFT& zI&5pb229>&8h2a+Gpmz5qOYxy`)b+yEP1t9c6Pbxn3cHbtOKy+uFIxYXVU^NKjm`# z`D-u1@-5RL>b_i$2A{-Ad>SjT`4`r1S~q)?%h>!eKLWZQf{lKTM-+$S80DPKUG=Xy ze*o4Tv;ilbdGgeL=otX6zUV;w^^@L(OTT|4luvogR9Uj?IEXRcTCOskWms0O(%lfs zgCeH+=VF1DUf7~Ofn$c~e<5{yE~?4ot_-2L?YV^Mj1zp`-$Wl`eJYD4p5L(iitlB` zZ1TA|&n=6Z8IR9<6@uvt-_!Oey>uvluu^MLS<1aUY5Vfk<(QE#zkP}a#ZLkyRN5cz zi8@|hJ>VJ|3JxG=S>jT2*-ppfp~h!YF(u*fOSd{%e~jqZWa6@m?%&uqRxP|!*-{D z95-gz3n7YTc?`YHsr~!sIEP{8s~*dIEUVG&NJhYPtv1Tv%l{ck@y?s|XJZ^~({x}- zIPSYWkE+{I`taD-uOnw;!#2^#H;k?-E%NBL?*3W;Ih0yVhh`gQXNFu+&x>Vemv#nN z|Ee{(^7Z?mT{1ao8dnayd<#DI?DO%+uWp`Nom|#qfHScYe~;z31=ExJ_9%*;HW%0r zV1&ChU&-|qgwwgCtM+!U_o{y9i1{?e)*&A7#INGS54{Gvt-5YO{m#k{|LMVa=hNSg z8#eACeO1oG{ZTLBF}WdaDDGK57~hcSsHt49*yi!sSfxET%z=xIaaVX(h}~z;BwO9~ zI7znX&_EH_dS=+`pT+jihVeC}^@7=@UA8+8*x2tC-yHc`Uzx|2MRsCEAl}O4pHBc? zZ$wR<&;TV6YUSQpsH0Xn%sIwE!JItyHxl#KeQu~P#W5;bF3-)_1UZapzernf#&{N|M;xc#O}D*Svy+2 zv*%V@se=MX6!Asts87k&6(M(w0XtLYvct32@Gu~uhq_1L|ZV6$=8XZmzp^qbwo znqMrpMcH!bQe1q-0l4vDyG*s-r+?~dyzd1+!`_>w|0B;AUN_-j1aohcb^v*4c zzKUt=pUvY%tW)1KKuLT<=B4saxE9qXGHNi!0ZUeF!81>J8yOZSCq<8Ukh_E_TyTweb@0T zb>)1PrSbBa|J4%tOyLQB#|vK(QQY_db7$r-^C|P(vZ$om2$vE%Gl?uCTSU$jr^d$Q zUe7aOVUtl4;&xg2J_m}CBx=A+jw2EDrG>4!@W6nMv9W6~87Vg0_1wO`ph79QL0nE= zV;?jSGD6HG5>8%>u4P^D3Z>GwxPrJ)Ft+ILqU1C?OZJ)Lo%E)BT5wN~3h|}4qI%Irj0#a5U86DVh&%hFU zN=~eQ1GL!g=9Xsbc;=ZKrvBoN?_w!FiWT@emSM_O-vNs6kqz=`?5H1C;}JUQ`igw^Q$>zit+98J zb7&otm3eJhRAOpbPE(D(xlJjr|KvNxt7v_+D_3S$Gn0o%^Gba@0DLxv8b+r3f-xgb zyyeg9A+{2y7Z0LNIX<6H9>BJxS+5F>vA?Vf)dRP!FBfQBZKfleC>nSuSo*5u1uUk8<&$np%)r>( zo!TPzMd_%X*;#+9I6?WFOeF9p!**TGVm!qZu{H^78ltC6xfO zJwIpS4^gsy{FcokDPs@^Vx?XiE3Y>V_Ga98@=W&QWqzP*NpUcuR>Xvet!(Xl&c>>{ zroP3}&jFJau-zSHepp6U@!BU-x;dsZgOiX&7aaK3-=Y|k9Ctp-eMc$VSIywk_wA2O zzq7|w>;0v#U5_)K^%ER&@vT#>_h08;UH|=Gh*#I|NHO2iePf@+zJ)#cQ0`_VM)>S` zpW%Q{qG5E75T|MJ$Uis-$G`Khux#b@#`yAckH9;B_iecRheyHugT6zd`|RVbv!KGP`Mb4?H&d&wqkZzTr>j}gC>R(fbBxJF#0+d4#;Yc9+0i~ zT}w=g8~UGv4%1=ga7_o>kxQnmy^(hh|RangH@n zjJ2e4M+}72WpkHXWIbvS!C{)!%=m1$Vn6cAfiaNVER!WgcGei*V|Bybt0bemd`kNP zx>geh)}tBGwQeys8CY}n{GYy2)_P;TQ!866(<(PSdRJWZ(F3sUfawJ{OSZRo{p&Bm ztKNJ$W@e`oNY4WJ0+!5|>(<3LJiryM%!ms0tcK=Z1^+?CQlKYwkLpkpnko>GQ(`>BAb7|8qKijN!R*w32 z0cCPl^9TQKw@=e#?104p)Z5VebS|I zGwd>;wq=veT?emh`5U+%51>ZjwZT2H_F3M>-NnQ|(ig6ENb-8RJ_vnaUGhjX=ow%# znPI5Zv4E?u4#rwb9~d+2Oa{i~_yB4fe2=blMbgXZgJ`bHFiWx5@XI48#j%TbSUNBQZ2kKdG~~+@n6lSdH%V;lJHn zv}Ci%)7^W-L%xd_ed1)SIrxgH_4nq@yW`a7yaC@l=T}MQ7U~CzB>iX^{f7D?E-~0) z9OC5a(6v+UZ%JOmd8^+Nnrn27pc**!}P%#7gg#&1TI@ZR8@cyX`=-`z|g^sWK<0=1`?_~ zZ|M3WfOd2z6C5^KzgotFro+s%PmRKxj&6rhJ#&VZi&CmM32KDcGTKVTB1!98VvfCF zlAnQ}-d6|X1`%-hnCdorhAs!=A=O_b;1?aR#CfpLx>XDXTN{1dR;Vk?JaIEHE?I#$^p65!Y?vmKrSoO^8;iAK=vz9k*N0ra^vdQ5kTgnlLNBZ&_KT1 zoqp79wO)hYG9%0Oxf0;{WK8Lg;RMtFNOYpz==@T=vw0L>p=`m5%_qeQOuc1TlaCiS z4opI+uOgjFsURsmLQxP&Md_|7J-S9Jh@^B61A`7}7|qB{BnFHgFkp0yjWKxm{h#Z( zuK$bsx?kO|&UMbY&pDqki2OB=Q=BRNI_lQX-GGs~;SQ?XS0DSrHY!JSsF(?cm>XK1 zIL%3(;P6ASXFcTpUl)BcNj}4>*_f|&Oq~x;ZKNkdpr=tMK_p$t-b#hhU2p6Bu_YF> z__`V@R4ef`!J@%yF}6fEE4kehm-SWsC?QYa@O)^$+dB?gH!09fc=Z^lU;Il_)>b)( zbLDy!kk>F2(<+@ab096Tb{^3ox!}KT2_6-uHIk%HA7>TKT0FWj6?gDh1ERZXR`6c- z_4uqHyO^2?1}+i@X?Qq)&|E?I*W}ZoD*vi!j3FnXbDlqtx7xP*S5nqlDF=UW!B&5z z>M*I%Hg1fOmh-*_b>6NEmkN$w zeWIgzU4XC?tM^0v?ZlE!!q30YY}%>(0+x1<<)m{66rySgS4wjJWh_%?>cwpJ9)^n< z4DW*C#}qm~DSWS6^Sb`ZeNrQaou4BEL!GGW;V&g0Z6<<`ti--!o^E*+P)z`>&RGHD-hddcJhZEeWh6H(|S+ZDdMZ@M07g_xqu1t-O@GRe!F?ZvP_H6Gx1p!N>Lk=;knSKQYb zcoUl5TG-8vElr)-U%#vGO0W|bdAt;2TKNW&W0i<{(L0?r?J>)7??}X}Y zs&me^$3qeCM;|AhVtLKQRg7plW7#Xa*Su!fcMdlH8-CC~QsBw~_+O-iO%e@FXai<>9svNhX1|GuN|p1VQi6GF7Kfj|XS)8*iK>vmL2 znfE`=i0Dw@7B{_Vq?iYKN!CmGCbn&v$l+=G;;l(^@~xoF7s;EN#Pwe`zYNzf7qY&b zo!;5)*$Z;TVzPTfC)JXPdxA?f6V^uFSo42AaYHT}u9WvtZbf$vUDky9Kj@AsDcDC$ z&3{ul7=pB^l(G`f3CEe#?!WN8^D66igYIOsP#f9C8ZrF6Y+$zi!pVf73zZ$yd3OUP z?i*-jS~W3oVh{UAL+cR9>>l=CU+ulo>Nj**%C~Ovzkh4^MqaJIL{D5E2w6Z3B+~pX zYO-`|x$%vrtdGS(%@On4e^c{Ji31lrI>c=kDzzTc#(2Meid8#?VnD9wub8yUPa`TO z2);ZLY~FkiCH*u=`u51&B=sdO5Og1MZk$aR=RmJago%pcwJ~B8QVMYxDscN;j934u#DfOr)3-gbn z8T9-%0?6XLj%=eZ_ zd=5&l3?(C2XUPh**)S0(IOLj&awEd+u;^O=0*gqe9>W4PF za<21se7~Emqtk|zsINq=@fGbWaljkT?tL*9NPtvDfz$tN#c(u*00nRy=G^DX_>Ft5 zg49n{-%TlvwCpR_U!W+OT@qkS3%7n8$)~%)BDazGyFNdQ@kR{+TZG>>j9x2rM4`{i zWt(wpQ@yG0x{%-<1(ulSOzsy!t@hRj4Ns<`2g}g$byz@qkwbCeAhwai#|YtzKtKK& zsDE$T3KjgW#i%_~hQea)M2caIM|9wW3KGMPmYIj_J6XB3qlXFFMmI^s#!wK zcxlCS^xPu<}Vn^a@uQl6$RQ=Ych&&%p`?$6FaAw%&&xD8;_Qk`6lzUmk z?_a>$Mx!IcmdY&9lQ-Lys?o-Z(W=l24jiQI(5td)VyjttxcsmTIfaFr2j&)uNFLD* z`~hEv`18@Q%~3Hi*^P|B?OvEZ%K7;0u6x=Hxxr(^)mal%=L9MeLv%jjee2h7UJ%U{ z!oWoakk!)NGU)bWwg9ZVDlB>(%43`T=@$g43p-dwp#N>q75t!TK> zrX)+)c=L(|r<<4L`W&ghI5+=jJ&|J1{dE9S&QQU+fK^P-EYBY>cas~0sx(Ye+4I-K zGWhX|bmGgxbb~};d5H@?rVY6nv;KkF{Q^3pv~LU4KADXQh63!@Nnyj@jg%53G}(J} zZ%^+3uF$=wz}RIz>_S^HDI!sraNA^Q*etd}rixAX{?L3=P$kH6nG7_!(l0!k^PD3_ ze`XG!&GzmNy`T!+mTiAivtyaPQl4&`EZL4z{8RD4B)3B@%(>hle987UO<+5`^&@Ue zG}b_S#0{wB+eybst1o`0Aa-aVa6&utBzCHVF!zGo^1N6_eGkihnoV-Ic$C{J(3FV@ z;9abO>El*<^Uf9qho~CFA4ofQo!8k1crK~cr6&XWH0|p*OLLfg&a>`Y#*3ZyZ#NsP zxl1KXtTipRT2&^Ee=wE*lbq-$WXDI`Tx5|?PLxi zJ14sPiBe;z$}pWd@Klff)9k8;AG$BW1K5Qsh9gF_V*H2js^_6ePbH9K3 z%Oei;%7wm(hvuhFyc4B3BS<+n~b?L(-& z0{Z;v95!s`iMW{6t-cEQIpgaF0O3X!X51`_-(hg&qk-83$p7e1{>_5kp(c9KWTkh; z`}O{y7QxWnKske;)BOF;pJC3W4?fglD9ItB6p@Z!j z-XpaD1V&~f$1oZ6STdUcY$DFhG;_5+%?lC6=$8W0zZI5s%zc5JG;R1#C12F1t-6F# zk)6?>eEJ*e-&R|0$rLtznI{T6taGM1kNq$$Pg9(?!RTm@h;MCuj#$uaJO(!&UD@TP z{py7eSE7206H-EA9-JOI$v)vWy+x>a3%njCwdC)_Vx;rt^ElrvR*ToJjK>g-Wt|RN=V&W_(_k|d zIVpUX;u5?MG(*NS7P2#ZCz{C8Q#J9yP^hakruwy+7b8(s=3Zh#Ib%|3(JX&!j&4A0 z=JV)v+fSZk`2|IF(qkL61N;{XiRwY-F+vPf6Xp?SxNxSeaetw|o- zm9k`CDQG&2!7Xp8vRbZBPA5U`*cyDimK(gX(dp}FIyBx5F0EYEa@hN{QS_iNp`_!F zE?4>G_|!-63E~bckz%jjoAJM*IGocZ8E(&F3*7#^ z`iU=_GHHf_)iL2yJp~bZ$%gcF1GQSCk?y)9QamX$qfM^PxjJ6&DQKOo??7LA$-Fi5m{Cai$LJDjg?Go!i)78(Cq_56>AA`{|sT24<_pU54 zZ}iOyYYekUvDU()pmpiroMp+rY;-|Vc=;>GG8&!zN~5)%wCv;du<(D`77&?E+|P-g zSNlyCQW=0I(SwU;57VQi8+Z2rG>7hNC``) z0n%4VyCn=c(yFpXp3Ha5lR7ElS5h6XF|2(vsV5vn2U!*}lqBT@o zUMDSlRkRFmaQrx<#BJPuXtjRrDU6F|BS$K#2K-N2THKR*9H?bUZWwY?KD;iPo+K@h zE=g0gW~1MvCt2Mjn+;)tb`e2*AI13Ea910$LPOi6Q}s*Gbm%xfevc@TwQ`lgRSujY z9d0#mUo!k{c2(=T1eAHMSDCg;y7*Si^{56tj{Q?SJ z_C{9o?9r$hXPBBP>v$MrEk6}fr47B8k{sGwAFVX@jtCMJY&dIaD?F*6PpmYG z+s69#0uy%!cSg|xDi$Smj$=Rfhpf+a*dSabe;xrEcp)zy4{G5H3ROk(siBv$!<0FF zjy0?)JtpPz^g&&uMEDN9*p;zYr1=I{*Tc&_1P)5jVoId!l9ZIIR&a|>%CJd@E6O`XjxFSY?R>MtR`d66F# zvbze*Wow-OO%9OH^+o?1?%D}yu2stpceZ2T;BYJavB;BH&q(5lq0E1Oz{gzltmtdL z_=dzOiJ^!Vb87-0B|W?ngZ0UVl}G>_>vd@`7K0R=M$IxByOqBIKAMN8)V#U#Ps=yQ zt*<#%Ch&r22H1M#cEi*Kj>0fIS$r(AxnI?egV z4+7?7jq8bKRa1E=<4x!c)bLjj@|J&>yfkL@lUn()5y@t&ydn}DnUh}H@JVN??xX%? z-OvTciu80e+(B$tt?~Lkju{O|QNg{n^4UN9U0>%Ent`U#fKmYDJ~S)UFL0dRag z;#KT1s&Pl%Ewsh)IV64Zd(=CaU8jefLAPGY16taMV5-ru@BI{*VJa$_y?eyEl&5zV zN3XjDjI*paC5@!7a9le$Rnv?glJ71>I(T=;T=`=^e5w@KUqa|A$Lhw{dChT1&e;*+q~V!!*<*| zDILxy!(GS4!M&W~G!`AtplFU5?8gTI;OalGJulXZJ_Tq!uSjbd!GIw4 zm#(l(fc$&Xgf=Qov)OCuz2IsQP_L9Ic!6__l0meQ#MAqHlay&us|9DMrXf~;<)Wj^ zZ#|#)T-TH{{lf&zBvq;@^LSb4D-5friDCI>d`Y!+>+D%sh_hlNUWc&ZVufCUR3J;D zvfv$!qC314hult_k7;Fk5|Qf!yq=b-X{5k-8SMvy*e~wzd?6H8G*48$Xu>z*5A%!f zIn^$91?fEKC%rq~&|&aYyZWB`(U?0reZ~&eFt}}c8+S!yXoDo%F|1-RT{&=}AvGnA z9&7f-nf2Z84Sgu5E7qP)wwwlD07^}NX1!x;`k)`+vrZsUD$z?3)C$v;k*r8X6|1wa z1`3FdL#6b3wqLp5oXurtn!P)rj)AHia&1#WpG0h%kR`nWvC_H+QG*69DEA~hcVwQ~ ze$z6k>x2;O3?He4bPlMBHGTF%oyaWa4s$UaY%~jO!v=9u5^7G=$tt*e=OoS39M@JP zsWYUFT*B^v%6I8$u6VnTG>b(#D8W9IB$U}V>TFsDBL|g5g?E(gq-1-m+6j8+2-`OL zVUy&=Xl1BOtH*UU;ubsR$SXpA)?UOBBjqyZNz?DW0O^i^+&420DmV*25r*(I-0onU z^R?Z{KFJQ&S2fz|sdtZ~w#`2}hrtNAwr$SSI9vp5$C^w-yQuWjXhBXL2o2QeH)z*U zF3$~0RPKE8zXpWK;108{QD}3;O1zQs{XB)jJb}H6@<;xoOJ&3EDtk4 zpE#M~Faa2O%EG70mCuUAI}g&+0OH{(OLoVDpJ*}S-hW&4MW``~MZ)%-XnTk)8GFuU z$$gBGyU&5EKcaM8tWxGUYRkRW>1xLrQ=}|F;;JKlkIJ3x38S2e3A0^Z`nB#IsY9VO z`*Y^4ASz2mhP3iVD|}pxRyWD2swIkN*i;m)GtAx^5d=c-M1o6PZ} zU;UwoGz2>G124&eeJ7!MUe2!n|J||ou9bKfsD{V3-)GJ!)3s*%d-XvB%AxzOKx&X#$9M!Yt3=RDDx4zhZSOyP~X4p3f z+1yT%R(14?T8yb=T{S<0JNj)>zd}ej!0PjgJmYPA2=G#WZ1QKZa zs>EHiw?vI!zG$>qjt^;Gv=~gZ%IrnQ9bQQ>$jTB)(aEn~4A;}moer9a66!n*2Wb>U zU&y8PDLXiPOaDM}p80Q_dc#-fuih#|M15#oRO5H6JSTl56HEKX`_rzc7{nRY&`i73 zO|a8nwJ-FIHmBlbr(!z4An(;n4C5sIv$qBB)HtDjodXpu?FCm7kX3xhkXq+aDgv#% zo7s5bES7mhySlOM&WB~r57lVEA6@&@=#OwHtsq$yZ2G27Cui%_y!I&aU&Y>k?gg9b zhsdg;cdzyB1XXPcc+yLHpe#<15~V4>M~y2Y=E6YR#!7l7q+MB*xlEtiD5>y3a)Q|R zu=QJ?W;H7_Tpq|^Sp$DhKChzPO?FfdeZgRn(aP32ED(69S=IC9^zvXrm@tUii!+Y6M1t3dw8+tS_zuXPhTRP7 zH&>l|CyybDBQTrv?@IVAnO$mDHS=EgcLlafXVcpIa=f$$I?X{HBEr7r6U_do$!338 zv2kcTgRbMb2BaNjDUW|}5H{3CCBwh;OLsgCbOGWuU)pvJ{7~QHbq_Ns8i1(ak?rn} z2-c958I8?RPq<~uBLG$9oa}nd8!R~fOFeMn$LX}Ft!VliLq4+`O1kHjaYmXZXXG(l*CZZBv0;qvE%p3PwV0 zI8oCWsA^(p=JeR=^TdKgDcM1Zm}IJ)H6v?Xjsp_YMBVNpe%pLoX!5#Hw&YFlH3Vt- z^;4uI6plT$_Ap4ww+!t4S4>K(jzd!0pY}9t{TMADK|Bf?7Gv&(wlCYc+sd@V7O|lU z%jFnEk1C>_lih&^+UFBSISk%u`)(<&(z+_YR}&(DLWrZIMLW*?fSZPfeIscXZ&1Xn z{~?=2FIR;L|5~9@0g5pLcLXc5KuB4HQsq;o+hiyNQ8`?lvS$jtO$4c$PsF{+X>HjJ zZBEYFc7K49uEJFgLJ!uTx=kJF#DtVJ773O2DI8e!Gj6p>iqR8zYjX~qsn-ync|*14 zz8zhg_)oTW?E8re)0@KfA=FTN?qTl4%<7S_@?<##N*SNt<261Ozj9)C(m&X(x{b5V z=wn$>`$uOt65Np9BHzd*kuUn^|r3 zj#>(Jm$SA)=lBmWwbiJb&_>)DWiD>m1DWE{S zCUwS1b>P#Li3kU2DtUEiyCX};ZiZa2n%VgAi9D@i_k*p%nu0YXV3iH5>sD?IKmon0 zlon@)X&lnN4P>BS^Dh&hUGLALYT|EL&R&nMj_G%QR-QS`WRVOi1mC_`yBTVr-|&Ol zCVXfuXw6Ns&$Lmz&$3!HJW-ehv2{gC$O^gDBGy;M<++Fh+rszSk4DV`C;E)D*XOF# z{^Jiqa91yPUs-|-7pa?^dk`3g%pER*m zY_74UGGsohXjJs4{&81~H$MnjcfCuPt2&Lg&~L6^JaMCUuqVAsodW3rfK@fHmQ5@s zFHF(>QhNg`joUu$tYz>pI44*IOZ+lSQS(07sp;pumgDDpdHB_i;j^rPK;|gFEIFh* z@1=I@&*Lj(i@Ybz@E!;D%(Pm*d)PF~q@Nli{nk{Y@}{d-I&51}K%4ez z&#IiQ2%GADL@|SKE<3$3@B_dNDsvP#^@cVCkwk0%2bz59zuNLr8+`levBn}=ntLBDgGXD_{uNxy zSbE0$)-*6t=%~H<#ryg?%yX!+k+7;u#=0Ik13L1HeIQreY4aZuucVUG1-{MHNEYLEs?w~5E5g&(FcyLVSGHhO z7|MwTHmZIL_4H-M0A~h`yqND9rZ~Wst?l;jTBqlpM>d`dTDyQQ@qwXRUfXlJDzUUY8!qH~ zJLt@q^$J!(9BlCgjbz!F$B7)akNZ!~XbWhv-qI8iYn)=~xibs$@!!qS` zvf$6FfmFX0u_U~w$naAY)bV@5bV&6Q>9S3l69b>sQ%JS*r`m|CG)~Xqq!u0f{$(~^ zK~V@&MTFAv8o$1*-;I~;lB!oy+4s;zLlA(&z?95yGu9G)im*U^B%x)oRv1k+)j-zRbPQ& zAZCo54)F22Ax)PY<@VW;M}XA4pj|DeSS|gFnjfDV^FGUB#YvaJ>T4L?$tn7ETaoiG zbPJ8-L3oN=IPec5Oah13YEsD|5HwKe;G%K|+O3)x;WcZ%MBOpO`JAJ*(?!f0hV|*9 zSA(F+9%RPDH0w)Wdkrt#jyt(aevSnl`cITzY_a*$GpJuQxu2+ASzu8ptH2CiNVX7DXl7ATvB1O;Dc`lP1?Vy^KIU)H9UJJbZWK8;j7eD0IY*urIgYNYaE=Qkz22cCLM_pQjHQG!Q^M^gDGE} zE{xMVOte>L%j=?l&w=>rU=I_EJ6?u|zhuJ+`cxg}9)!c3A0tzVh^)QYmvXQ$flP+2 z_O>L+;SIK*CJ`>;uda;>lmQw9n12`OT^so_+x#j!d)}+$KFu4!;!;_U#pm>$=|TqI z!M)al8n9@%4?7kApuP}fLFC35M#tpX!!FS%CA@(mno=M?XEu<(++1phS#`;U;MutIhM{Oj4Gu8By)}ll_xO z;kqwCx=EzVs5SRRFa>v`h3dsD?5PnWa_OuSL($St603XGC*$^wv39py2C($j9S*=! zRg)Z4vUa{V!~vFeBvt>bx)|21_Sig*9<{z8nedz?LLK{p$8D9&uR#6r7hfO8kW*Ud zt`1R_-cVcenZdggG?dI*fR9Ew9I?7xSC|Aj6Z&4Uzjk6UEy(|ZJUg$R{DMr!v+nsq zs9pw&X;e}1H!lV>V9{y1ab6j536CAtYs__>rhl|$)j z{ozY>T8x8g`Wl|m2tS6r=8Fka4cEwm^j(6Tk8W)?QiWR#Za z$(_1e;Hn<>4&yJ!y`q|58SP%%!l+EdY&@CXvq~yGZzR64WK8Ok!Gv}hPF_{_{?$}| z_6AU4V2J84mQ?QRRol6-6qDSE37qlx>c&ms<~94ir;@D1wCG38G#@6M<+Z4chw(l% z-R)q2M;t#7TdN5*tY4rlY`>8Lgz%4)w@OxfmcEh9;uR0wu0gd`N(OW|H0JN4tB3;O zm=gY;k$;<)_qrIlPrJ2k2%>}(!waric@f07%W)yWP@1EiZS3vKM|a@lNp1VvI5SbD zs!<4J^=6m{;-=jF9f)23Q7=J>&7wcTy9(Uc^VBDny0bUj&R zf>tK(C3kN52q}K{xo2h&(JkYm&#qsMd81VPEoJS_JFSU2yM#s_R|uyL_992`+)7WV zV}Zk8H@&KZck+!$K^o4Y^SkNVwSTlKj9X_PX=bA1J%o5(8iK7jBZF`(EUZ-tc_Sj+ zR+e*G5Z2U_GPa-OL6L{qTpZfyQ@a8{ewwbAp|r+7hg_a2*4+}Ds;-}#_dP2{0if2n zwUpK7fiDf}{2J@wpr_Nj%aQ80Y!{JdVM!>b)hq26;uVC;ii_b?iM`ITpK0E+n~;H< z(cJ;GIrJz?z92gO*@{pSHpLeh#s?d@C$yzQ#St6_|~nv+Yx<+H7irxqNRZr zYy9DEtl^iM+ADc-x<5^@d?-n{uOZ2{3Eu1$1^8jIiEBMJk(+2`0{tRpGL$LDkpR7OdTjr! zUnPe-6BAyt==cYlFQ=YK9h{{&-bh>r-c{RLfwa#kV?%vz{n8|v+ zNCRB#qB(M?JyTu;lTp(ccNMXxAoBy-B`SsiH%y&MVrpmM#L)1#rHQk)RxXYC%KSO0T9(~T~~9@ zMOt3kl@TR#MU4@J!`21Li)FXar?00Lvu+GKgBcwsO8NuV8&eRkyw7quz;E`ZZSK5T3^Hj_9cl}*@`_3Xa%$2+qWFb=e?z`lJMsLk})s9 z2i{RPbh_6rmP{s$DW3AR>Qx|w(X>HlKX>D`AtzFg8Y9@*hi?znTFYakB|}(uFIUI^ zGQ?uM%ruCzxgGH-uvMqICNQf5#m$-)u?m%+L96V1E#LZ=Gek4;?3h@B<-rCQmf(?CGa=1TP8)X2|gYJ)ZJ zljTT-;KN|39aiN2k=dO(8Wnp*sUeg0KXqIKOkF!5SKe&M=~x}S`G?p(LN{@^t% z(a-hA!f8*su86r@ZZK>T`J#jdhYCfQOfbziImY;BzqM_K+~)}mx+TZ-m>Eo|Yj zSf8Z1`fs6Eo>QPY@QMrv!eO%B)NZj%qI9>CbJG~QtIX>6U)q{96&O=U11sI zVw+*r6E#T@9L_mR2Ayh*E*IW4+s#RI;mh8?ip%Ry80$R+@!MIjP zqg!x(6&{62-V{aL?b8!BBA@hyRusC=`AxPCbgK2P%UwRid^;*uQ3I%}*GqOTC|D)M zRKjkQRW&n|DcB@KDq#$=b?7wMob*s^y)L*rE~S?}95VSD2RT;fkNsH)VHQU#%U-PX z`NT<5u9G(I>mB};qGwz)CW_zCX@{Oc(CsBN|12HzTm z#GRr)Z@>|D%l{7xQ1(d9vm976H;N6D$zMB)7dS%ZN+xw#?G8`Fv%F^V{oD%J+dUpv zn~}|lJi)5$$Oy6$ZzIrtn;*^bZ?Sw=hfVj-2&shcS;p>V-j~N}IGL*5FQx}|DBnYh zvu$U9fM~Gv(SM>E71qK0)8TsTM`tHOO~EI|?hK$G6;|;%=bM0 z!Peu2GC!L-H6xm$O?OXo8x)gI0=8Sk&6hp2{Y`D(91^wyb?&L1VAMpVIgpeMF&E9f zeI&lWkW#}%nzW0pX~6;h!J<=HSQVdwCBt7k-b(m#6HT1GAQN0p@ixj~Eqk}AD_hbw z?CQYMp28DfhPRflR}O3ib6zZIp28t8g?avzE0sD!a~Nr|Y0s;JOB;CZw_UFKQ=NEDRf7#E zH;*DiKj;mL(CgOo>s*gU3K1@s6?KP6E+^o zW^lE;Ya-a2eC_1gy<4vsv(9r^zgoc*KBgP$K-dN*VxSCdK#DcW_<9zA5&!qM4Pv%w z4Jw$aC)6LgI8fPhf*d3mmcmJ9avPzNGz}9SKJfVNFV%pwvMjp)Dbl^H07DrpQkagR zY%U>TZY1Ev>QIX6avxC#Ns(5*tLFAH2*f%5}<(JdULZNj$p`P3~Eumu4T+rjgneowWUd6NDyG++j7 zgVlG*t0;#ViTuD9+$b-z4|O&NZ~{s;oMp$GVgR^)$BaaCD5m*Q1(Q)-MUnLLGOK5B#yfsJ)b+LU3gsayX2uaRPjq49MW<#CRN&TO4(r zSFZFO#$=&6xcM#dK_cO;K_P0*nU9i_<@B(SH|VBGWAM|jc7>BhJ?UX0S>_@|YXdOf zA2EyWaP@vrNBSFR@5?XJ-p9xaf|~O)TA)kUH^lktF^u%{L!Ulpv&7@Br~0DKwwEVu zq8HK>4PvTB6CdT$o0jLwjy*L`&(6ev`$mVClt-oHvuw@Hz{WdaEco8Ukd>~j2Ic-4E*iMuXxp|)#Qn)UM4(3HRT6#OE&fD&!mGt+QvV}1t#@T*wIo?QtVx!+RD9RjGW=0r zrKNEWbn&B4c`j10`exiR7f)1@;xW4-;c!hmaFN>Zazgc>C)8liBTlvs06ZyZ#ei+q zc=T272S{eAIK8vr@!msXV1`7NZU0W@z!xk%Gn6m_0<+(-xKA|~HeYE31BKn#S1V_x z4{Gd_ve%TGJD6)&uX_0L*G`@h(@$V!*`Vm+j#41>?U9c2PT;DyTCq{Cem9L;R277u z|D#{LYR_nn6yls(H1Z_R2f7lX_ds@WLVBnfoV9TrKI}Wq+fzwho|@TB$-$iZU_=a9 zd9NnA?M~TIBXY}0E@%H~#^4Kb)Is83|Ng$v3t6ReAuN_&CT8&C#|VHyl8`gXzuyCu z=MIC7ER|={z#i_DCAq0Z-BmiGY@qs_jY4q`W>$VB#t&bQ)ajg7*Zt)KXKm%Bbesej zhG#u~9Mfe6kPi?#EB{n4tHfcscm{YoH5x{#RJVBrBE_i+pa6}V6f!84&SVN7y<$KA zEsld`HBlEbPve%0zgBJn`7QrB=&vfiJa-CCKBX)ZWrZ2+y4kXs!5^Uv4%F*4vavuG zdCw{<^SZ|pvsju=Wvhf9@XkF7oC)m5WluvRs5v-W)w{2yb!CDDwOlCK!e^Q7jBl*2 zU3}BJ+-uAq86%my$b91}RyH%(j}dTk(burex>4t%;)s+iXMBH&Ux$l!s~rrQqbdR z4^u5jkeZsOHr0GDUV8nXFeTio5F)E?<3z4h&+0w|x29)Pt7*D) zvrcY-{kNN+$Y2=*7r0xLJw<-GygswuJB30Hm{f;3_K5}0%{7ejb4(0lXf#KDgbd*4 z*Tv)_)9zNq6=Y~xi`^Nz9^wIn92`o&Ta(|JZBjO?y#GCQZ7Gjd@NeyPd44I0Lo=8A zd$umxSI7vOuWC{*)`mCwU0WHn*MjC+w)Sek8;IWQN^yo%NJIs{P5;~Um+%?cjCo#2 zoAFzj@?QNU=H(yw_oEcp` zQdTYNQdSL@e2Sz#_3mcoqTT!hzT*N!csPL)5AQ?cwOV16QwmLIW2VcN5oE|*i)VoA&cpdf zon&Y$^HgCS&ti931EX4G`R`u-&Q@IOyV#*&CO1 zP6{X$>mc>;`F=Dj+_Sk?)YDN3q}L;iC5k`JA^uTjuExxKidaXx0i}{F7KvSA@%pKQ z**e?Ja~^nf5;aDOWN#}I@#WBLK6ejWLHI=9Lbn`I8i`?Qpd`&9N6*7*TG(zSQqtcT zka0-$I6X*Lyc$SBSz7|Gk&c#)=c@IY->mLz{h563%XxkcG5hyTm zce7b`+m3}r=qR#QlHwKQY{mce?0()zXK7065!M0?v74(1zhb+?wCfj>rGC{Ex3V=+ zlp+}trPEZD5e$iME`T>zDBW*KnFMct>8gSUoDJWc@Aypz~(Vf)`dW;2VY%4#hypdhZE&}Bt|H!klAnBI+~ zVT>JiIlw5`Bplh+G&A+k-*dZg7UtV8uQv>#7uC3`>}+&}OB4&S$HbuzLHXrFrsqk9H!{v%2q<@s%*`u<1dEa|f{EV>Iwc64G4}rjG`y`L<>B_f zs}lvTL~4Fk`w>mH%MTYX3A%)V1ly<+b=7NQl3E=$v*;|Jw@|{(xYqe_56t->CRh}j&2cm?5q&aq>^XSlsQPsN14E#1bj2z zfl_cffEJx63T6-jW+@eqFej2v82SM%`DhuFT|Om>vnf_Vpmr zD&JS2eP7p<>uj-dWcUe1ZbVp`Tcg?x*haPGA*GTaQ@BK}J}H<3J>vJP?8=0w4vXgvJF{&@(Bd-Nf_TJ7pU)7jlQqZZopb1;v87A974*} z1AA*3WeJ=n82JA(`$Cb~t%miCWCM&=;Ay5_JSWh&U)Wmy5Yd#SpWE>-^Io;YYsn73 zM_C{O6N5U|O=U_S-i0C;;J#?Evyg}`k*Qr2k>FEu6MoM!S*%2De&#Pq;ewB4Y0I$9 z_9z9o!IXXYZl+RUS>b}fa6UE2-!-HvGVh>#S$lQ$KN!nyqgo`+39dJYo;C9-8HVw6 zlWyVnlnsb08Zeb7>|Bv=Wv8^%)$c*;c-evMFRXV4^4G(noHsE-^^T)+Mhqdne7S8Zly3Q%13;Qr~<`KH)Mj9qElMigPlqpy< z=sSu#e;|USBtRmRnAroc-AMZ(%cnvr!~a`Z>vgvFtcAM>IxI#Ra(ho-ZRYx;ZN zKR5vu1W{r%f`Ihs9{5&7MOwPMV|0foIT#_GiiC7XcWpEx4TAxrM@Wp({Py`i9=|{K z*Zw@`bueVnzjQiKQ=z)y;oRF%~q6Az^GKF6*cv1tR2L%`iO<>b{3=xQx5?OEyn% z9)*F6&hw#r67OZ0TZ67NmL*|z>-DbRT?5!xLRcoRJ1e$rc}K;JNBDC33-SWUbB|lJ zYPLEUULO`|H!RmO=3NYLt+1$G$T_uo(Yd!@2#wtM^hNp#Ea*w4p71GOzfny-A(9uL zCkKX<%Pd33>R2kK(KV>^|KQ!iAzJ?Fm?p^VzxuYTzC%s*xb-0G3vB@`MMnFsoL^6S z?&*Y;08R?+EXJII0ArWLu(hF$e3|(*uh=pB@A3}}79*Z^>pk=+Iz9FQn<156>aVRD zsmcD*014cfbEOhFNF`=bK@%Bqa3WTYOQYJ*xNhGW6{M!xsE|DinulO`{@sngX<}R+ zf6V>rj*tLN&}Jz2B(H1XTFb@lyZgi9JoWp*Af2u3FpUny#OHXk>~P!5vin~*FU>gW zo-vO@LbHi-&-}ol*|v{8467&ds1gb$kFYZYQb~*gk4J2PfM$LU5AVtuH9>qrG?>wR zo(*i*PPelQmhjAd#;1}ni}{`{=<&c4Hm^eVRzynJ=oA+qufD4(YZ_mHUuY#>(@3-d z;}@0Ms+BPV1A;V*Lydo4%rh1M@7FSs&q`mBa}G_Ak2mhi#UAGRiv{2Q-l+^kZa|mZ z^5hM|Ic66Zyq^HuV zn{=l;synQ6*1D$V)+!5I(jaNkR|#`e6uIipXeb?hWKSN;9NWC0OS73zsf?F{j-cbW$hC+1On%g6c+U9Hui{8@dB=7Nx>R!*O zb24mUOgpHR@9zdA}40W2!k^eUO#75@eX6~-1#x$=rB*1R!96kwR^Oy$%W$PMrg?fZ=N=arv@?q0N+Xh zXTy1uXW^ZpWVlfo-+)A-;aq_G#8(v0E@D!qs{975Fy?G7tY9;;wgteCIah_Bo7Kmq zttJjt3i8ExBpf9@(>0JEe~#yiN@<*UM0uJ2)l*rb@Nit>|FQ3}G%@k3ViBuTwqwW9 zumZxrm}~5*LMwW`ei5PQlo!dW{i=c>qnQBwFTC0u^}5>r@CMQitL$$^HT`If*lbNA zfD1?Qr(B2|J<7(lj7r{3XZ;z2$(*KY-!(HL7+Yytjeb)(jTbs|;Pa9QTeN?@Z8~RM z6s?LGg;t_9_r|Xp*}^#Ogkq+`K1Cr5=oa)8AK0WYAbm3Cm=$wfosL1iFA%k-e{ zPIC?m#o6PGkkSi@agqW?>u;jnW3mflh!h}eLpNhT*OIdd!3ck>oLkRRg#}YoFjB+Z zqpa<5N>F;E9HySB>cT!w!gEBHLR)UvcU|Zs`V@Zq`;^_i_;18og<8Vzf8yGLq?hK~ zkck(7iZIA0ofq66#6>_e=L-Q(Iu)O*evt-Bn1xTL$^t1$iiQ5dc1U9E&?@UrbY`jK}{c}5Ss^cJn5*N3s(^L)4yv1b0X zRIhT+BwIoewOcy|9ZX<%`iA8(QM%l0clzRxk+!mH%<&g}FHIanGK8uUZax=v19|;tF{hqvVnulGD+7fz zl?0>jgG&7%)=J}c@;VXa-g@WZ^EKzmMWqYhc`HVh;+kSZQ`C9yQx2dc%_=GEx zbV-?~-P!l!r>RU9k#eaZJf96T;f9d*T(oUobnrkT|fj5iINOy zQr*XpUZW%6bSs^RpR?ctMhY?afkOz6?#Os?LMBKqWl*~Lp69a z^lQ+m9R`L0NDm`Px*i~6Y_2Qm>nlD7l}nhYIJ`yqJr83Uj%rP565KqQjtnqK^?2n{ zKxcd?C64PiI1~2B@w$j>><{km3w`ghpVn1!A0gh#MsuDX-dX4Daoi8DGBP%pWumW` z8t$0!WZ6oF3F^VhyM;}+v}jIctoFPab~3ppc$iVo*!8*y%2O2^3Z#T)-iw8RW*RVN zvfnZTy)z#plKURCWE60RY>~ntSKm~}UN)+b^B29o&8{dtNNa);q*? z6Lww3b^AC|r`5yfi+&_CLY0+Ucvk8hz3}i&%>{{Xu64I}tXlscYPqr_)?)v~(bhfHgKtFxaFmegZj>4`WQNnpkXDrP&mDreZI zs|LrPeGTE0IFJzk`|y&nd#Wm`RLg0jMH*OlV*cMFH*qU`6@$XR8fVhZhMlZ!!yq$x zfz~}QHrR!LW#N!tOIGV-9#ZnA3N{QH_e>Y+8j2nc9DRL058MbXZ&~3*KWb@25bhLs ziTNVvvHOKnH5h!ODlh)Kfx?T8qR9iYXTk@%O1Z6v;mz#)OSbhjx8(xNi@>f?v#3_H zXYrYaH`4UFmZ-buWu|p@Zro>ZBE2}$Y6pRH9#^@&M`(pfsL!hN@cPuaS_2blGsEw> zmMy-HyIe=eJVfHa7g56Kc2?PR7+qH5snthK&UD`DKQOknE}M@8;G~`^*Y%~F9W~kD z%05NB0lW2X%Ph98y?JgMSZ|6Q^bR@i`&M7&KB~!3qk22+n{0k=a%CKj@}H-}x2y8{ zYEvc+IYx&RQOykC+frIIzRdVnlD0t7V0#!or<+PWZLye3D;)V(y1D3JC^+zNNWIc( z9xVW63`r@txUFXTW+841j;K%vh+5m%vx=KMoNbto+nScu`O3ARBU@?etgagmrEpuM zQ=VKB*l2S<-4o(+f$8=e0EhlzvD8ZMP$NoLr^KkVir?*<-1 ziQocQrgna1;mu_$r|5i?7OqY^xu|Dm1DP!5Za#WJcKH)aCvRO;*EVHtdRrMWc4Sl$LF84MFYb6FSy_$S$35jjQV)q`^B0J?%SVn+8&)ypYWo z)XI8}g>I~fcZHWbm5uyzg+353)HncoU=QkZnTu$(0L15#b{xc%9Zt#rHu}JSz7Rv` zt9rbBJ}>!7HlhLh1rR!A+7>a7ghwvNxaRnYxqvrF1FZP zjD3|rVoW!`f49{rnk)NSNOre*Yj29hi#QZ3-%1L`W+wk*v6ZI*9vg-_59c}mY6Pbv z^9$hf78Y{{$;~g6=5QCRJx?$B=i-IIKnyf{oQu!TXd0(~wai6pI@}hToQ~{FJ>bi` z=J{Q}|EzX~`3=qTQm?c{mX1qLV1l#y4K+?k+3BA6y~J@h)0EfSrt&D+@xOf_+Q}*l zZ}uDY_05{kts#GF^HI+y-&0gW6hSl8pW()G_>tjQrzvJAHIJCt8lOHXv)cjeaVR;c zv8>j>7>w-O_Eq{ucV7>j^CRf$i#8%dgge9oWV16BEi@lT2@Y5MHer->YeJuF=^ZDo zBkc|OukY3{M+~I1^xK`KB#se!)BH6Ig1)5f*2{q$sIS#4`Y#$7<|p(vLd=W+&%VCA ze^Kt5Et+vZLB!aacLJYWk&7qs`34viphVBt_F5_MJnGu{<#l}4&_1AkJAIyR(MK>G z!8-X($}5GRi}1|2<}VzzU?J=%KI!bxVE_MT0eal(yLphG11#hYzG5Zm8QfSadvn@H zespupG0<9IjR+d()686*m_}imWR1SI@9B6M?C*+}I(p8`1=rpW?j;e+2k&!UuZv|B zU#`WsceFE7NP0i^mI>k3PfQxJ2;nRq#nS7qnk8Y5zT|8XTwR_wtb8ViXwO>l$gSM3 zTDCnI`WbbyZ^${$(7_OymaSAs7|xhGGGOAyq6Fz|78Y0(n4aT0!03`VgWkp=+#y6 z1iB17LU^aJRG)JSiG&ch&;t$S4R#Bho!7!nD^A0OC^Y4PwpMxOec?6WPE9;SBob?} z{+zhfp{wFFE?huO((fbdtP-2h$9T*Zeow^Q$4xS3<@F(bi0*f~Cx;0=c~}}x5uVU! zUPVDH<_)KWiv5|j6v`5l0*ctZG{bTc<6p0=e$Ou%NCqy8_&L0gre(dK`Lm|yeQPY! z?8JmMflHS0>4UHO-d`cH40hEr_*CYm zN4TDpCfXU)4xn0}%CTZLMs*Rq)IZ{KV@ucmnk6uo7DaO4WwVI$(%X<{d4Z9w! zma)y=VGIM>?c!X*xmSYR;00xj?a(IppMM%B|ML;-ag5=Pi3V}DaN*v{>AL6 zGfC1r=z9tY@J`m^()-J^cD1a7j+^zJk4tJ6Me&?JIQJ9!o)be^!=ELy{$XW(DWWJu zt*xjXWQ&qNYaF|*Kp9*GZc&*A5fv?O{kj~RzhatgT}EA2U^@a>2$b$->m_edRsAF4 z95aj{jhvBgb-3}g(-@vj+@^C;SHFX;;m6fKb^cMMU#T|h+=tb1$i}{ zvA)y;4I451Xe-S1Nd^cQfHd~&l)V>< zU|=&K4LhNfQA4qcEh%r}%<8l^su57RNmV3%$+;T%Ww2!^uK>_kCB&OsT)*zf_H6S-xcY4XD@AF8K4Cgn- zrV+=gPU&azE?EApw8fX$t8ugJ6UV#e^3OFeqi#KG5pJOE@i>kRh$qO7-`f3z!%cUB z3QwH=$Tse(?KMk7UEl`iKwv__b0En;rD2v}ieiGygpDL1GC`0m6-POJ?p)-}44&eyw*EEveCoAEdt>NWMDf9bPy7nRq<*5p<5(?vH~ zX1MZ}cXxaj{I@}f-e2#L73OlL{lKNLAjlzdEKT!igW|jZoax$5kXp%3MU`zXRVW)% z1E)uxh^Vc?Q6W?^XC{(QJJ!PJvi(1)QT@jr(NK8)Hzb4$e3ci>tR~k|q{mX@dM&lkWe59F4-qyxV$8Di88rAM zA?-d?%$8JF7<<_luV|WQTufJdm0K5Yd87pb&yJW2gE0%S` z;mtLP2s6hiuMkx!oU(KN0tY!5(K+Is{@Bd|oOkiX!`)@NBGBtIbk;(KUMo955q4xh zgnAyU0TRD22Hg5u@B%S3YbJ|h|LUPDR{DV3`fk?D3B?0y=nzV*$sHkqbJ8(u>HNLu zo_lXZ)M6A)|8m4Q&JWwqEz|=vPkf(0{#81!+zD*r)$t=SKIo(@4q4`%TuNlR^XWyJ z_~zI9mJ*xf@ZKrgag8vcs;uXFd6u2}k>vtu)Rk@4J}8vEP*E__ti)lhuaxNELR z7xsahacPcff8ERQk^-Zw51KWj*7aok@i~ix0o=?zu{B|9pXFuk8Eo7n&iZo-LNE}) z8FTUbyGAii2b7!LC%g0m_oI)Jv34$CF8^PVnImypYVy4EcGCY` zbyqiND-g$z-!cF>S|M+J#1;rSBPnrwN0}bwQGNxMJ(5&|EXkF2GVEV1*=!pmrAH%`eaq>2eAicaZ9<80lR1KYLdM$>AFi_^6>ivK8*4w^7e9w1Q^{;G_F?N^$MEz z;wadDnwRzG8CgJVrd;S$$?d#F^EY*kS&+f>N4Ue_pR(?%4fbRE3iUodH+1hB8Q5Ty z%)G_*w2?=4na!BWMx=b&Ycjj6-#>C{7o@hk>bhG1LxaeHt>_bb0uYItWfoE#C$zvH z!fR3fdf#RpIg;i3`o-ZVT}RY~*1F9DXA$x@j*IGsMrrT)-E|2{>VL|stR@IWLry4F z;y>4*_P>~hL0*SYL&aR7|8WC7Tra!Qm7a|{U1J>N@x_w25rn>Z+2>PuSZSirTu`ft zlC1mb=K!17hfyBr*mzq1*Ea+4)Lm6YFf?R3_Cu-VsZiKuQ6lBAyd2OY`Vq9wr8Zli z#7}@4>iFMi{@`B>0DayD7#_~~Dy#Gep3C#3oH={O*MNmp3DIiO*{=pD#woQNO8`-;Sm22QS42fn+kDP5T-96wxj*nerZG z)w{OP>)M1^sH};d4qzHr7hvGe{E-4Hu`&(fk~_L*{gfe1^mh6vXtr-c z_l&Cu{WC|)7Y*~~WuGHM7uz`3D?hg`-Bg;!d|zUcTTYFZ_(_(3GI%yHAiVW=D|0Qo zfzVLcCn`)m&7fcLt#uX+*Y$t|q}%oSNZ{_3t@7;cK5PZuc^0xlBEcf%)BmJqjUBjQ zezmE$pZ#)iVx$;!NdqW%5@7=?Wq4e*#J7z@e>njdcvu$8KhH_$9o5huxCSoPNwZZz zRRVq;+eo$WLknVSqq3MG2jvv>>R3n_Rl}7TwftS7dAPr2ku9*1t!VB! zgT@zYewP~#CbHsA$B;db6XDYe?#6RflbdxCv$NW6=s;33Nvz&x zy#RyFr=JycSl+cb8Ig~A{5vk%FUq8vZ#3oB*yle+82*~v*z~upU0V_Kh)NwA9qi2L zA71t(TE1lnfm)7TdrwS$RlB3khC#$TDd?$e9i zc23M5Rb6vqD^fjLf>Bqlovw7W-gI8@BTs6GJlG>a?9ne!G zs&om)t_9zJF_zC&RU-M~=IxHnPW5Nsv9Q`QRS@VSB*_!%AE$b-u!bpNABdjF1mFrC18oP0Mv^ou$+G5B8&EqW~kSLJrIWx9AH zAcd75itx-NytUEde{`Quyp}L6m*wG6iyFN1A3ZE!aZ1w=@`DxwAdXVcWDZN$N zKc4U*nv!&yWp9BaNZgI7X>E)K!lcp)atV&bb9}-o)$VN@XYLMIRJdL0KVwnG9G~sq zBUDYf3$#`kolWq8(?6drHg@Z+gA@v}cCgKg~JYavf+ zjafnqn2*{##APQ2@Cl*5VY03h1o*w>^@{=;#_1&G$h3fa-=)VCzI_@XqYn!dz^DNHnBiyQ+z`{cz)h}Z9zEEf~o z`IW!k1^e;yvf>6mD=kqB@K==i%E|xP9}5oL?5p{2ws^1~&ipt@Gn9s3$BaZ34*1_5 z`GkzEpNrY@SZHzLU^el1z-$lzVmA5T&jXY@=g9QYL4+Va<0?%vx$|tX;`YifXXaw7 ztW&UJ&T*&BZ=r#RekFj#_YvyCl|iW0-PWLV@>t2{-*Mg&1xcVeM|OhMe^j%pCAQTH z%H;ka>UFOw4DVvXzIdB8oBF!r~%*^qN)IR zBB+$mJn{p}BiG?zc)xvn^ox3B`lG6?UxL$SMk&^2djQKSt`C2@=T53!HjGK15bzSl zD>0Q(UI33>7%?A+;B=Y&iR*5xl{+eKd%d{LPc4K05aK6F8HJx3;GI=@88LP_vp zlb>e-x=>Yp-13bqIQ3_vBL|-u{)oT2!QEz_>YROYWUX*ao+8pE656HMjNz%AIAu{bLJX|b+> zEzKf;ZeJSX2gH2g`3`z+Kb1xF zM~P9JmD=7}Gx(5V7PWOh{hq^mFg+;##r&`;Iea;*V~gt3cWoXd7iaxPV7=-ex%SF2 zyr6>Hnh%3eJFnK#K|L%&E28*k6PY~3Bri;+bB74${6O)D$87mJO5fK?40hFizHz`? ztm=XdO?`+HEL$f6H!VK{<8-|u0W#Xla|T|&h3*yItKB3_8J9K!2v=DXSrG?JJ9{;K zI=cW`WWxkd1pM*6y#)nPk!Xl4%cNG>EP53X-_6vp^= zpkS??&R4yvXLh%4Yq(WfK^wN?hqs5i&g($r9Fog7Uy>Rt4H3>$ZXC_YsUX=r3QOYr zmLCJWLv#Mz7JMCJNS9gnTko=B4~oJR&yXx`K)tnYXuOEav_Lf`v$2d*m0m>O%JRxK zMX`zfJ6(DtnDFRl#b!p_+1#9_jiThxMRhe!M2p^TPAC3fT6*4*bu(m;R&Vc%j--_2 z8@mS)=HIkU+f_KbsDU`jAuy<+O-J=H^K7v~-10b<@=ey(`))lvOZXlGJ}cq%r4HiO z|HlZ@Jck7uCQB;=fGfze&MHt7&Kuxm*D#(rjeP;wB4jMIJapXA?j>$IL0hTh6^>US zK4>>t*L!78%j<>ytlnMQ{#R#7A{<&28L0(fIfM7y{yVHx#W!iI%{zPl{UePBN|_t# z?zOkJVzX5j)5g}SLXK&S{KtBjwGD$#{vPx*NeFyRlBt)wfB?t^xc_D>{hEQ!X257m zgX-5`33TWZWf@$`GS4$@tc3g(c{bGAN_wg?bkKGSXBjQ`%C;*z`suN4vh zvJadVuUMODIrt{xDg5tn`Wg~_E%_kdXaYVD{lNY9TzWHjCa3|TRB2N|_FY6?VE;5x z@lcNi7!Y}G_!yq*%IH_Qyw>o?Y|r|#-G-GXC0vcz5|fu`d}lr^3D#bAsM*$E5B$U` zpjU>VQSN-Y#U8jI;Zo>oJE@d)*bl+iiMJ8&z`N92qDgo7QZ7h?7-mHeI!n4)vBQa7 zA~vo+@Q3rbdcX9b>kuE4e97vV;x=b

K>;PP%4K&F8l^OBi+<1y+!!Q zn2R!j+VJuUNiooZ8Hne^B_qbU>9N{#5tB!NHe%e06%v;5H#*RzXteAs!=odHln&PfUtm!a;i}v?2?aeJ%auu}~o53H7G0)@Q7HwhcG>);} zWhE*8fWo@=!5WMrwJ$EC^yA&GnC=Wk;!1J+OJ#Yl&^qlVK0|EeOjo5@|28RZIZTeu zl!emuv#~eZ6y$xV>k#?Ks_S`}dafyM>FUycTByj;F!- z$toz^KnG=Jja-zOo}(c7Y8Cy zLJpt8g|^BrtAtCezWa$Pel`rRiKBS#TH%p;%&YFh3~gSDaZ|eo*O0&Fcya@G2`rr* z6AjJo9`pwA!|v|C;*`=>S45QHzs_Nd;g$v=@|BGI$u7E#P=Bk1vxICQ8vziN6<`k9 zfLbpZ#xqG$X9J_lh}M6z@?Ij| zeEO1~JFdUewOwzW^%9yI5t2SR_y3jG`s$FHwbHPICd3ugNmbf&*OAI|vm>ax$Xa1| zH0tgXG*-of3!ai;cS^hRw*0}Lq2B_i^1VtsnxP0L6AbQG=?D6#_-~KBKAOL~1_xpV zmm`=KOP4fQEnTcd9G$${YXiO2eA*RIOs-{1e|SPRvY~>uzY0T^53`kOOaZPbOn;4e zHk#0GEW&4uvQCrsH4g2i9`;y|plMW0mfl*k4Wh)rk&nbMc{;ms$DjPtHlCV^bvbHh zq2h*$A$;DE{MZc~Osmkp#A>YvDBzV+S%^kGFRPs64cG!$GBtg!n=zy`zFllA(@Df+yA0-=AhqxV?bo)XE&#-1s2AY688|B-xq&3@L;uh=pK%J@-FJ zn-U5qgz0vdf0voW!z2xZJbG{>1U8DfIw3>@)D53zL!k@{ksY=CsTNUM{|Lr7t{EBUVyx5H`{H{U6+Q=R{IIt*cb#UUqbf&0m>_B>jRqAZ~B=O!^EZZrqoyhlOhJw5H!=DI%-)lVf>6_8N?$TRY; z8dphb%$aFD{;GVPTq(ancs-ui&YX2-(uAVPn*Y1f7t&6ybi3-1UlEnE)4<%hKe-P% z`~89X)}O&oDOBFj0?OD~Bs`|u@F}@r&?{bEU`a69_a*^-0M81!uxLB5y(J&k5m>Iv z^Fg`sfp5O!&{=sGl)rm9N}4tLM#WchDc2v3k3?`sc;qz41!i)fiw`B-P^Ii^GZDGL zrJM7)%4@o9I7*+2&hfW8xNq`S#?fPXj9N?yQ!RfCmyf3#o|bGijJu2?Ad*6dheK}< zW+iCq7yU;ob+emXRex7@NYyb087-c2?D~>-=roDwXa0R&M*PSs%$`o?C8u6+#bccQ z%1Iam)we4(39(#t+!fVn_5Ij)`NcRKl2%#~GFq13WoGL){d~8qH3jq07wx0=aqp^Y zZe2S}2$r|F0x7Ng{$anhtY+W7t7&&K&M1fs`9?5g>2t6-ICgVe;nVm@SHWirj{4|?m{wv zyk)anOs-*R_2IJ>{o08c`HiSR*j>XXu9JFVV}cN3DTVTepS7+dBA1@6u-A4SF%oO}u-)GS&9Yln#ThkDOUUglyH^Mbd|vL?Jz z*l}62pIB0-?1vipj#1COZkoy9JjhbDa&>C^1Figy4*i_VKP^1jJV|F9L*F^WgZfej zy$T0{R(>c1CrfR$;LiEB79VvREUYrsDTWO)6Y=2Xqqqz+#joSAR=CJaU<~95!Lq<* z9MTqF=3f?Y_A6#(3!Aq%JdSJ1Ce~|k=QTOW9be^VX#+-ftXJ|y7^k~5?}-NP%6G6x zglwEjDM9nTYJJ@Nc~6uqmh?quvFz2=7OHm7VFGA7nSdL6zsQWxyp&9OjsJ(ieEtZ;iw!DBn6> zx2#c4`;WIgy?~{@9x^>i$TajE^fWsb#U3UIHIGIP{9scdhbJ5ck(z@+l>?|3cpFrl zd%g+>?B_^%fn>twjLC4~$0lFmBuRbH(@i8-XnR@O$tAr=*Hv|+$nYcMz1VrrB*Lf` z8RJt|ba<8&tb^q`OwbFu0;6}7KJ~}zOto={uy~e`U|Ijl-FZAYo`ce+-?QH3h zMu{dV?hcc;-(n;ubdC23%aB9z;qL5Rg)d3&VhDV?6N9q7Iy^suSh zw+9lZq{=8=?&`Uh^G}$v>hkQJ%Tawf| z7}W4}ngR~7bqL0<+LB~#S>NlyFL4~nuy@zKBac21v*77HE5bmD9es(i*IRX&rWj>L zTci8P$}?l8Qd7VLROh5egwPyKJX;qCNhNRn8D(Q@z7(_2@&8BTTD) zvuVsf&hxFwY~sDzL49j2!rI;Z@Zx*5^2sHb!6EHLl%DO!M+3(4GZrEyuvQ59L07ll z+-!>An}v8fkk{wx;%-bQ~* z`bGmmew_1lQegDSCUh-h>p)jGB$;!5JQ#Srt8Q30R}>ae`4OH`I%cQUbVHkS*?E*o zVFDFF9dV5C86(q<0#8J2(ITA}Cry|I^oA;mT#*I0UFpXh4B$dp&XYfxjasuW)Y|6m z4YFK{_1FxG_XkQ@TO0E1m7bQr>H|Y7w=S_Bzb~*uB~70;$lhEXqRT)ZytFXYW2-pOW**M;9&a~EX(&qlxf7%5AA#e45d~#aVxIedoi}0*UdI)=UwYt%&<2{dW zAdJ#i;7`sL3{EuZDXsOVJqU5dJ#?pQ_g9~yTv5$qu^n_ z#L7=*EjOUYawg%sPgF9gxGp=&Zb8A?-Bbf&9OfOW*CcNuJqkUQ-aQVo&b#zpXmai+ zx(i0hh@&Iq1F05gRc(JG3Nwls+V)KMV|(uc#s0vt^x+aSMY%kyLM4oCfenT9qH7rX zchQ001$!7zAo5%fAAFiEZJU_s&oNk{MuVkj9@|7n?jr^IjS5zt!c+@N6f*9&nyefNTCV9`G=TC3us$o_?c*Dd(^?#M$Ke zRhBWKN<1>u)>-ePjW@q}*FR!o@QIaqmq_b|nzHF@-jNrUb&=q#Iw@1n4%N5DOSWNcS05q@DKb-351RcM?u4b4ajUfs73nm%H&n>$j&z^B0q!lxWZv zzdCe(mJ8Q*bR9S3J~fDA-EMHmJA&#u6!#8W6Qz?u(Wb<*?2i*qah=>Hn&l7PBtk7e zHM~ErJ_-ZlLz3m40tg{IKT6YunMH*y*wd?BoENmPkb7p_EkDqIi zZ*nt-RaRbY3TK}k+*I$zb3~nI&w3j*Qc8Rp4fB zf})l>TlppbU?p#95VR+H(7h4HsJEqW);jbn(Va(YI_&M{e_|h-6VqS~owau(?=3)u z;AyHs`IFaId#jh;2c`8DgpLh9)Q9G`X4bxB3h;uVi=n{(X1}+;Q)Hn$5?XxN_{j2= z*ZfUPZ^LQ}+LZdq%%zz67@;Ocn_ALJb?}vhoP(S~*5!BiSg#B1O?e}jbSht-L+-9< z&1-o4m(N0zX_CM24$ncVEUt3mH-}0hn&%JT&IYXBkzo$_%RW2C2>z|272P+&6}>q9 z)kyiN-7jq&8l5*mlMJ){Rzpcx#Fk|+$VPa`u!=h0U>AVi&OQx?7`@?48A4(#aK>d1 z;E7GTt;|k8f8XX1S~3+6z3rZ3I&Q1mf)^|||50@DLilBpK`pcJni3K#4Ps$kK^3VkR5Y zu7QcYvF;U7NUpBE$_}@6isDpp=<_h^J>FRIpu#`#nu;9qdGeA7SpC`}p$)DTH-mU~k`P2KDl}gz#Z5gk*c!3U%4-)UzRi15*JCusT z4K;`n9#qZbi`wudJBs=cF+|k*!d8worF^5=k`RM>+eKq*!>PVRFtgr znE7kr;jHKUMpXsI881}!5~j_{V@*2a0BF4*@ez(pyMNw*T&$i70~-fESm&L_IZ8+K z>FNCb_>Dbr+my`Do>#B-NAvf%V=J#p0utef!LBAsx+yc+&@r8Fu#R_qVSlaF>0I_( zne8X~Kk>g62X0QCGL@Nw&cG`7;5P`lQ;+*2gI$sijwUQ0p?;MH_VvS)cvVE6we@7^ z4R5@=ENkmeo9@t}SGn{F^G|GFQ*r#QdbXRbj>fw0gnsnd8=DJ${S%IUx7JPn?!?Q3st!Gc zhotBzne`em4wOkMV$8#|(@NdLi++tT13@{9qt+JN%CSJ4rO)BmZB{E{N*f=|Ar|Y$Me*xz=$)wTNQHlK)e_5zM#iS+G8! z3jd<<)D4Q3i6k8m2n(?Bxh|(>=EepjEvvT&DS_eJ6`}A+|nCIgGjVkts_?rW%M6|yB?d>nGeWe_4x%Haq z&^?SLwfBlx6N76PlO#QiQ-44H^rg-!4`Y0p+l!9ao3}}gHQVH_yzy`PY-a4>FJ}io z?tbh@rf10oAYb^Uc;8kFm-aa!oPfn*FkHBvd#&!O4no8Lrjb|u5ILcjchbR9ZJLdCqMNV zA9fN)H${H@$-Y=J69TrTdac?*`J`|aZMN7mW-H^seHJFA*fBnbc;)dp^Cbmgy(2rU z3GSGEq~X$CSG*93X61BVQDDT!?xqw05b(;qx()J|3fj&Y=hv5NA^KINaswmduwE~+ z8pWB_j+JcrP;#)Mn6PzJ^Ve_*$tyvo6R(}ZC98Dmx9%jXlCkiI7UPl3_65(Kd>Z1q z7|lG~LTnjKJfei0&SFn~j$$s&GaGAsf zR5Qk2&HUz(i{5CW7wn_r2(x z!_@D}$ybhw7p#1pa%;d6;ko3Xu3}{?e(=KYMkm!v8&qlkFB}JsbG^gG&DHp?4qPX3 zk@(IF;weqERm}CY8+nLl2nRne+6vC;$N!I}vv6zrd*3*SfI$cdj7AU;>1L#;fJ%u- zr;P5=or=U5A}yr?0+J&~_eKus(J^3jkB;9y-|PDQ3GeHi^FGhHpZk72anZeGJn|R2 z(L`|F>e=aN%`bIwdH%L*h`!IN&XH3wfa>KuZwYD`;RMT-FsfzU)$L>`gyfQ?<*f13 zkds+h|IL-^LH9zC)Nbpwa0NNUk)d0fbUpYY@`!h`;`WG2HBq)ouuk4NEwvDQ@T`jT zRZtm=zP#3{Mwal+_SGvk@(pUX%gMx#5T2HZM55w^L>t*I)mgs*L+*JqDTK|Ks+yA) zDW{=t>umIUY_Y;L3r)GfUM?;gn>eY|HNpHETVwBDOXn-r;XW9;6VPR~CPnp@!NafY zu|7it@C<>m3eLwkVV#IPo~9(mFKCT;`WRsOaXa5vvo*f|xf-V-0i96OLULc^9v-c1 zb%_B$o;kfmb;)(a#?%&%Rpbz&6p-Y{E5x)u-+oga9r%Tm1Z~OkN%HAFeWH!WcspRm z*ZT?PVqbFrE+4T1@pcx8$N2Z^#D4YL+6 zOhjy|-(8WWCboj~t0PnLGml&0F>ou`1bOPZ-U*$;6^jjI!4kVbNeBiq- z9bss~oH|(+jROY&m$en`na1n-2oY|%?8b7N z^@F(yCwREwx>@Ry(*K5oPH|fvOWsl#eU0#`2jEzddwBgI&sF_zQ=GSWUAhjLjZxEa zQ!#j#R3R?4b*C6Gz<=X{&OK!fZN0trS?mwFqCp>{6$l8%U$IE6xr3*iIykshxFd(l>9^iwzUp4M{!p*oa$y(&nOK^G4!f5%Mt@H7#QNZ8A=c@M*-So_<`n zMT*SF(w;$Y0n!nn)jKBUEN|`(mV$y;1`~A7p;8`-ri!dqaaYNX)-9{doz{IA=K3pnf*2R#Tr&Vo&R;^HdSd^axxk!wJ6v`Ubpl@?#ovGiPTTw&|PMj>C&VlOYotnDe~V?_^C{ULcyr9V&3NY?ygHG(PbBT9+pl_tnb?{KIQDi@+-n$>TcO z+AY!U<-c(3N7;7^u=X-4ozY4GH@Zns}Gx^)u@tb29-pkv~w13u5e^QzubT!%x zUM8Ki-1w6|rZ#GSqsNl7{9Z+tM~H9ub0v8k?`>Rd*N$bNOYsN`B49EnDl28k(~1sS zZ$mR>20LLN1NosR>g-^9O7Y9My-TLd>l>}T+PR7XHuE^O+jXA({M!Qxr?Z>6*08Q;;=wlAHY*#cA=U-bt(YhyGDSUlplNU|ipx%4 z{yb5Q~l&P7*s() zSV!QuCE3aWr7I(o`0?DQ^O_GAM8;@X!DNC06HM7c-9D-0{j|TFyv?edqva?+$yavn zgkjN+DH|(9ccSob>rr zlyb*+V+-r0d|?VmkoqwEM>t-U;lAX*PYg;WD|VOS5lp^^IGGKTorBpvBt#7V8iGD0 zDhe0b6Fm+C^Lg<<1@Ngq2Lyy*~w76dy>JJkj_0zi=U@;I z5~;7BKRkGbAS`RV?`W=5y&9a{T0|Z{$q8fOp9)LOzVvEZy3T30SF8>2?j%?Y+hc|P z&)qj3-$H9FS^8tkRiV8tj9+aos`AyQd(JO+Z-H#dMOykDOES+2fw+``mII1=VlUSC zvokx-q*Nba{Jn=Ry7XKgImAg+ltO3YC*KA6hg)|u5tmkPKsqLyJ!to6j`I2KTjSmNs*X%%n8MAc3|;y=uc2^d6{!t;FxRjqg|#{^aVl#MOWkLvGym^$l;lYyB@ zTBaut4iqS5KE!^z;$Yal&&Gvs42qXW9oTQ$_Ao=zk1I!H!#-4^lQWfX$mz=hBv%%I z!ni18 z#W@Km)X50p%}7*4okJLd>?1~gupQFZ+J40g_gaTrkzr=iY9@Ofgt4*o$V6q z72rk++6F?KZUmPg@_j`FJE?rh0=VSMUNkXjc#;7^gna~ZmmkfQNv@S01sWPdXs2L= zy&pt|^udzvFdvkscQniTOCzzknxg1~G<)MNc4T>Bd3qtz?_eHqf3}6qZ7#>DEkd!y zdig->`^oL$gUk!DE!c7kmr@QVNqVz8>14u)fB#S&Z;PMjBoYDG5$zGPwp$G2Q*%ib zf4v^%;6L@IG~rpzNBa0w-jvB9CAOd4OFVO7lQ#nkcq{M?#{?_wN$|~|U+3T~BJ=|h?dqf)VyLE!7*XOXg|9_%ytsQs2XkH2V#3n?MKL@p zC<+GlyD-H@)3LlsSXf9+LB z(5WMBCsZ@jea9}&(r_X$?w(nea#DgYIIKY2G;<@0-6@yC}lSpb;#SpRzD}MoaxGZKA_(H#RTKz6$u# zxF9p|J>Q5g*W`t7NAc-ayr`oF7RNu*mBdViFq!K|B2&+ZT-M=qZ8%|-1uq4T<0nV|T^8H*Ns+-n0TKriHD7+Ono$!;%67OLod{H1XEQ&3Z-}y~zM1o`_n0R5Z9dgY zI@dqMr5bMSSF-bbkx@h%$(^SE}m=5i8Pg?}Ee3X9)}L>a80T+BZ$A zm8*H6f3<4-KR@|y{l+r}+C4IxtVz&BsM>euB+`?!Lg^W8RBq+T&wJ&YHxhQ|zq6L> zAwkDo16?@X%eBmeh~R{fu||Pu%ak7rV9g~HaR<-<3o>52K1zeC^Km2k3v}IdE5vN* zX1iu`#qs+kpk8eyR6C{Q74&-or#(yXAd4ntnqylOe)@Lw?%7e)c#0p3V1R19Q>@A8 z^NF81oH2I0&vZ1PAvYTFds?=f1>QXz%GD^cjhI9)-FWpiU;U{2cOzgmop(?c+w7fG z29UmrA4N~rc*|&VOl^329IYzGL5$xny}w$oS!9~yp;r*B9>zmn*pezowakpfsRbmWgd(V)*d)}a9-1~+FCd&~$S9<3z z?NYy&-9kwUoj^(%-d-EB{h;ZjqqVcwwZXrR2*;cPRg6I3h@QXz3r>#G_!YR}^ zUF3WZnVW)UlS&ioXq>_{U{Z)w`m!R`&}j9sYHYE`-om-x>Km_8`RpLB39u=}%eUi! zHXiS8k~7W?3{{xB##mLI98_=&54HJ9*1MfO;cVEj`X6dzTZc7R{Wv5ct90>s=&o=> zibQV1+3B@s8K$?q$42z;#~`+kqVq1w7s$da zE#B4toibxo8cKowH zl$i(F=e$2DOfPBVz(s%+yhm}LUYtwAIu64ng;6$i7Rf!@D1cIw756;RoR08BAQ{3* zns6saaCv)R%%UVGH^KcM=77y@efp!rm5;icP)BLsr%5iCt*Kha>+<+VKK36BHC_$W zU;lWdX_ix(NhdUODBtvHG*0>=eeS63W};m*5HvmvcQ3~rT<2dB5uE(EOvO4G85**T zZt~XEZ;gytiyf^Q)&|SR-*?T&${)%4?tIDVOy4^rEitMp(&uQDarSedExSS+yKS(U zhBlTf>hUx8OYVky_O8xH9j8}pH7QGlW>KcvT+a$)m(M??a_{~*13o7BB~)*Y*b!yJ z5Y#A8?nTcG`lGhOt=rWTk=S+*a&Bwy$&qAV$jGF5W`1F7gIXuWxA-Q;DlBL+ch}YF zmARyLYVm|(Ub*%H`?=S4f{&`%9_Qi2crSD{c?fG!Rx=n(R7ZmiKbYS;5-W~;)f7G4 zTZLeCjg5GeEXIf&@(`{sT`1y=r3KX8K^TfOtd}U{+56B!l<>xY82$lx#W?RFW9Fg= znX+!hCGg$irh|lg$i-|uK7Mv;pn*?7VUFu2ePwXIkCWTovbiZoH3R79kfTbfHAU zk!g+y0c58)2^^!J-Brrwf2Y$rk!kXLhW~y7ilP8V%U^#R}=BiLYssPG^T}8o-)^WfN4DxfX?R~6 zh4I{@bZ{>^B->*M=N1a$_OA>O2cFyxpbS zXnD~>EW|b$W}lC()rjQB06teGY@xDKiGTAuJ6*hdG#PaF+-z^3ZSXzFHScYL{~L zghKVJ514~4rC?UbjuwZ&@6}%2^Ki`isN1DTc^i$RS;hK};)2dopjT**`*UyD6Hkam zgb~9zq-Z#J<)+&1;5gQ2u|aVmDz4lNZTpDwZ23Xvp~t0V_p4e*0FkSy(1mxW0Ucvo z<@e3PS|t)j;hlHWT5941ETF!_n3i#s2wI<}b$K@=q>M~}_U6U`A@f>(0T zkR7jlaXLQdx#|b{f!l-d<9NZg(=u6#^v6}4Z1KM&!q#oE+@w#E^u2O`eQaJnFBa0i zGWd19;Ykw1H<(^gT z{`8LxlB>_BAp-};aU!Vo%x4GQD|Y6X_GMBzraPd`& zDQRInD&MY=W-~U+)1tq*trw5!VumIjJTxUNbU!|OR^UIw z00A{(+R}1>8C(qb{JWh44;}+_=_?GSfQ7?M#TXDb<1d)vv{>Gjk~Q9u=#NEK@yOrm z#76Ah3Yxhe>4VXfE_38NQt-WhAn<<+dP=EC*x7!CJU)47T$siuBd5`Zf&4by@_uQ(<5@bfV^B77N{|EQWaIU z9!bd`l6G|!t8Q^q<1S!kn2<{_F%~dlOO3Mfsj!MlM4glTSID^`v;v9M^!bC^zKs62 zf~kS<8SPV41vPyar2+CKG+E}e)mh8fXTs&h1 z`fOD(IQ<6;c#JnvE0(nQ70oWekaO|O*Sl5Co9rUJUYX81@+wZ-b&v}m#&aWzm%n+0 z65D27gHKlW6L-6|vWd$7l7`x5n?>^p1=IYmwAz|ZWuHd939aJ4_V;8xgLBMCc+n*V zda%xY`P&zMIQBZ3?K0}MxY<#I3D0#E$yzb?L{|#3`NA>Cen~y4^ zNEJj`y}HDA8*ziHzut1K%znINIeJ~P6fBV|v#jl6aqo5qR$@%l#eiH_$oYH(wmUzt zfA@7Ds5ST{zR@urbhA^gKIUPi!$Rf#ZQ4y&u)N4klKR$+mBY%Yfv_0b))L9~((PpFfZx$hd14brXDO0ef$Uy2ZsBiBv;QGo&{V zAzkwsbf>4Ckr9L~A!pI>Yqb9IO+>uP%r@XrujE8tQNwg3MN?!sMUc2 zH%opJ?gs7I%$^0{_g!3z!Nyd6?#+xt)5_$8y)!YRGn198>5;nRVU7VoogN>$E<5*(*c<$`Q0y zEI`63k3kkhKG9MjPQ5f>6JyEo?t7BK3Nc*&`|L$##csegf4YBri`?}nd-9N~jMLlm z<}>OC1aH4hBVD#n7H#+v^~Q&}(+jm(ND)B<$f3qKwb5{x3-M+1Jp{K-FhJkjSM)w0 zAlqBqj3feIDjgZ_eA|6HR8Z}CDJFeVTfX#p&jhNQkz+Den`7AWppNDA`Kv0slBIhX9S9sUMl4yD=#@4KYNGi~mlT|w8Q^<;6H<6g{ z`OtRkwUD9GHK;LfWjOm(n~Yez?QukuTxhaC}_Z8g*ezbSUw2uDy-|7fhp|6y$aB7*e0arf(~?d&kZHhCQjAW@uZ+S-*dUjYehu>HkaqQtmGjXLh{5Wsr+r_+65gWq(10w z`if(~h=;tuOf4~T!JSJJe*S&SOH@3;3QJyQA%-5wN&O-h# z@>y|l;9)MR*!Wk;qrYbug6f0@=^QRx*X8E&*c&rb{t2-hr&3pc;j)#L>rY1FK!696 z3F57m0?S)A?GIa98Iv)ADk(km9w~j&c?t?m$FZna{C-qt9Bh6n~ zzZz@$3hc_R09J6#JUc(~0N?fkVl-nvy%P`jAIN9hLNB_QPcHvin2BO*vOTI|qs~ts zB-gxH_3~ZH*_D_i=Q5=|zXNw27sK^D$-thpHG98-2W@bd4wjCg{6F6V2 zmX5SlVyFmZ)B?3`*u6=|cfAX9Z^V^&OC@!^CAp2X#6Psyd4+{u{CZ%d;qCc**+3M4 ze50QKD7X}=f(N)Uyl$fW`?NvBoDE#W@(bS;lc8QfuB$jSWqb^S)eP;d)mI5D-$+&N zydBD~b~s9uKKN6ISjf1r8ve3`01p;6>><9&g- z(s0n`PtV%TUs5jZxR6>ZV@fl0){mM={?4L3v-oD$93}2=RK^}{&sAz?z76#e4e$ES z0pq9M-MvkcHEe>q4K77)ZFH< zLmk#^et5aE!w?N~zAvgsQ0ei0%3ne6?@92RrY^Nre>8VxDVBwc>+`fC`RBvyb#|M8 znaJ6b^qk7&h4gXGzY?nZwkt=Ca-3nMTLkXk=DlX)3EUY!EDVOlISSMkG@8h;u4>U5 zBJD~NjGw2D){ks?Pa^U?c74-)-ZoRFFZnJQT`-5V$)ikh8C7O_BERB^E^J>3Dq0%U z4N4pZ1{i~zE;O%}kbHC*3XQ{p(i+T=4js6NsA5?zfHFOl7|D ztuodQqHlDBF?+Kr&B-OPnp-|@OSeJo;`U1(rn+o~FoXREi0Pr0U=Q>2G`7>>&GNQ1 z`k`tIry}wk1Jp1>$r`??aryh1w^lA-Hn>JUUVEzKVUUGMZ-|z^V1BcdbJts`U}EI# z4LMn4tdHl$M)Snt1ZJpX2iZbB#BW|>x3l{;W#EmM>v8Os1S_$vjx*P_{ghyL@*uJ) z!!7H;201U_a#N}bJ|(3)d*k$w`8r1*1HZK;Cz%Obw>YHYnU7oN1c%1eR`ShZpP!|@ zjj{CO?$0dYs=?{m*3JkR@tfAN2+8hkNgrjUE*`VBGV`67p;?ew8a}~WT<~p9*Us@A zIZ7>OgNNL2!B*-D$&c;~-^|cXn5L9YIskLGlRyOHBEl_fiu$Nm#z}U0WDBc!x%Id! zz);?d&`LfwXka2`2R7xKB92|+^iwbMO69u=^_4>*gT}MeOym9YD&1oon@=CJK&T}Y#%R380x-% z0G9WMhzJsyzfOLsMS=+aitm8N&w+*4usrH5viRyJFIUmj2Q%r`?<}Qm>Z0|7pG9&u z(#JV~4C@r~cQ#{QcOxa8_H3-O2JT)#?>+0y`X>;9`7p!IEZzh0@U zTqb<>>D7DDViJY#ocR6Nvc_O?nh=K$T!Cf*tV5=^%DALgZBM%|_)Vo+sKLZ}olHazXEk2&%)sT_iT|8~fb4cw0xV5b)-Bm6N-d0jbSn@9NcSSud2D zGw#>!1wJlyApIpMw$Yh(n`J*O^2WV3>wmt!l_IT@J zSg9xTG_d0&&ZFk7g3%` zjh1Qs;flJdFV0=FzbX{)ZMBrs-t0C|`#BFf47Q82pb91dd{f>Uov#aKpTs8Sx0naM zqER_#8erFauaWk*x7i3kRN*JK(1|!DH@pn5|5N0H*YD3B@M-Ok@k2CJua?ZDm-Htsd$}n^JOTE#T>(Zp z1BFoT12Oo)vyk0j&egdSv?-VKVBy@8@eheAwbf!`0#NxalBSES+feQO3PwlCvn$pc^8@<^ie4O(DwX_V$a`Q^HyZ<*|6NH1KS+J zokGwtXpPgQK_Tgpx27_)J}&J}KT#;!x7=`M&6Kr#2>EAg;^HrJDevJ&)T5*K@8_ck z1$xVF^I6Qyuc*8g4s?LpezX^5_$r(#(S#?t{55NDVCiym)AQ?f%arEv@}?Pt_IU60 zfKg_Il6E$Zx99;;@!XJtskmR)dHe-!y)zyM+2ZZX977Zs?^l&$8HKTFk@5SnDGH*M_u8Nq$`|n~~qI&WyW>FjKyCE5q zisPOf-itx#@JY*qY{~#?a)k)mGrp3xSFsJ0bv%QqHntK*PDLClF&+{wuYXg+N|a-H zG-o2W6dI_sSXwI@E%|mc+Ii_qJ0G^>wd$^>4elCT%T)wbCKB8XK#f!T!f+{ax9mL} zkzz+x5;yu=bqbyfq~vbLe6C&iBIs`U#fDnN?oaY&QNw|;TJn~o_=2TkwjjQigN-Vf z*XX;-Mma&X2C)f_?;pI@IKi>Z-DmNK#O0jXg&Wf!c<|vL7lZjv%QeH}7{!`gA=g6B zE{@Bf%Ehj-p9z{N?-pViON8`-3GOyJp2)@XuE82gT2FqL>pfk@#MSsVZ$CJejZ`3l zJQe7j0DdX^g?62Yh+J86WL#90Oo~@FwOe65T9URESycGx`0DkfI?+kZHiND1$y7RQ zzRXnY#!hzx>Pcki?wJa)Oo z@eHH*x9?G0#x}c+2$i>wAdl;X)wUV^T=_&nL3{3=lKB&6f=suER35jEqP?#Lv)$b~ zM5n0$1Iuvzd5Bo>GN13NmTAnj`B|HRAHPk*Z7u!sVf@vzvNLo>#nL4y$4N6s0;We= z_Ej2Rkx8hUh*FuESvzi%iofct-c|i(c^6uKe-fuYHb=4P;&^)`>y=|jmx-JxObqrJ z5aqO}lnu-a9MI0SQon8d03Ky`;OZZeE|GnYQz3J8?85zm zWUdxF#`+xnCVTwkh{^=zh|^MNJmxnGV7m_%zbkqFV*k0yf@ww7I&-E% z?f`-9=Y>8Mp0M^b-RH7|RH46pnA*_+9q+_$KJ_(?n}&hS09Ts>_+)&EQokPH;%)_vJ;bn1Ax?}r2Qjrcf);W41fu3<}Tv984K zgR9OjLh&@;ac0}o`QK$yuE@O{TF8-i2+1nEaQb(M9OL|7msh+@yeh!cf5)cj zBm`z(oqgGA7tfU4yKD&Y$Mt~TAiA-lZH6siL{KCBuILAFlQ{JZgt#+-mT~HPI-So= zpAH=NO3MQ7B=btzTxPwB@1T;hr1S7A&M&jf$K@f1_u;*uF-ZN5A?N5Yug+oL<3Ac+ zdofnMHse@kQlL(@ zsg}Z;&URP5ECipt#}cAyKfEoP9&t!jGf(>jOwa80i=zNv%|6bV`kQOF0MxE6*nXOZ z=wgY4=h_fbC2DZzzwk85-7OB3*AI-2imwkoH=d=`E*M8Johv;ZZW81p9kL~P9~4y@ z(E2faJ{2y@V#*Pm5*IIMl#|8h9(O=qRF6I3UH-DH;Te^NlYBqBOdeCnvKgb2y#dty z%2}sBKOfoR=(o4^%^|vB^$?M^W2>~UgSFkjgd4vFbzfl|Cb z&NZegBT5qbqF1)n{}d4ho_!MYTt7nOCCjWJpUKBSeFz!elt&qDK7@^T35TX@4Jz(Tz9!VDGi5eP` zOSb`R-XXXl;lraWyRHN9x2xHosG^v04R~%8OI+P;p6bKJ4SH83F62g9vc>;X$f?5y z4N>NC`NVQX>+C~(zKN4smAeLFj){J*+JO5QF~N4~UNR3@>G$$dWp7rzWUVB$y*gXP zFJix2$5wg!z<2C~SZ$r@^#>DN^pV>tav@5CX@b_|iKR@dOk*Y0aFMlaK@O(41b9*| zYtlz7-7`(_4_LJAzanOmDidsi@^1aB4=on3iR*U>s9wFAZJjI?b;W9mZ9IHpx$_Rf zIq43h9FZ{75x{XZB>yG4pib6Xp{G$R;)N6fDU~UH1`NnwFz2M@Oo9Wsf>Ehn zwg86`1Vg#33M>ylaYaA2{;`MVs@3qXW4UCP=kuz-ExV~!H~GFMxz<8%8Po00Q&rx9 zSGC?N>79JR(w%HCCe>`(6Z4A}DgimBqN`RG>G}6P4LS=Or(nYZScltEHptGAv@ojU zc&McqNs%o0PS51?aCfVQUGYgR<`p_I6xM5V6_F2Hpk>D&&QcI5Xnuqk?|U*{?e4qQ zMmnj`y(WFJ3DLD+)*-?4&2EFcy)|T5W)6fZ7{i1pn7@iPnIdt@q!P8rg zpPr`k0aIg+uKf?f-J5O=_<3D++xB1k!G*KxTSa~(Fa1tz^650JkWOIx%YzqP%ry$3 zApb=78EHA8)abiUCOQe*36qOTb_g`Yy4sNoabl?32Po-VcV@2(fuVQYjU01cI|uMh z8tn3Bs|+bqONd3AC&dsJvyL*XRE$da=r7QilC70qv zwE+iknwgw*{cox)E8Dfvne0b7#_WU7LpOGsB={ zwq<6oAL2L)2A`HjOOkh-cgn1qDvni^W`xmtpBZyMwg8?)kRT#pM66FqW$(gnnZH=7 zimFbY1$ZuU6Jvan!V<@S521^wprzB#s*bjT0Hg>>_gys-5OLVsHfb4qe9|n*hZrIG zaJKWzUd!Km;iKwrGAp2JW~9bopfWC0#WxFMjl790PQyx zB)@wHP%XWdEVT?dYRsyLfZnu+_t`!fId%0HDZA+MgI|=R+F$uFr|cFBKu%hQ4xs*{ zroCZCA$reSKNS zvj_P=h1)I!bd@*Oo-2VNlD~C5ug`3r53(dQD2kRqU3zOSFZ&sVeEe0?Z%q-tz0aY~LFIn(@ zMTmt{ZVuk}`cnV<2qnS<&wv`B@JPmw%7y~NVbpy6jbau9&JIGHZQTWglOuIpq;>ccv#*D4lR}3q;!C&t%ZvD176T6?r1@ z@Dj4#qA!T#RcxdSn5(~=8~_-0`6g_3kd|BXX40=jgaA+!zSe)5Qi6_5MBPky9Og5p zu)?q1{`UhN=Nam3u4BIc8O(>13a}H}jqw~`Rk#;2sA&mxQ=K}3>h7@bGq0pNJvsh$ z*(E71qaV!C#cQ^rIl<#i!Z#NMO_0I$WsZTDZaikx0lE#@sy=RGksd*7nO#5C)~MBC z;~UmyoyT&}FrxP1C+{`W(?MfETl#I2ZU$>-*hHyKazg4F`Cac8j+k}Ef2=^BxTF5> zD|7JrL?TXBR*9JixfgY1Bp*{7rE70~znXi~UlL2SnaWmbU4TG7TKLs=XsIy@Y6g?- zdcr=+8?55){Iwl`d6CW?T~Gjr?w0>9VYqjdo`vfjNfN^C?@omtatt+oOyfhAsrsn4 zOFxe&78DX9X*j?uTf?LX2a@N)Yf(>CaNaqUovE}R+tcWs?_%I*08fq=Ha-G(7(PH% z5Hf6ljNcb@9(O@5oz_b`$GFb2w4}o-AF@ia#2|~3=(%dAmD4=?k#@i`5gJmrn68|F zQ6bw;H3+-?LW^#)T?@XaBWWKQAwoUH#p4^P@Jj9|+%WL6lj&gb+?cM~Ho}=4dW#$P z9d<3p4;S}E@<>7~1|Ylo1$<3=>iVR-tqVs~HUZQ}N4+u46D}RRuJzOfIU!RXyZH*zxi=PhXVDuazpV`J|^2k7sIqq})GA(>HL%WLzb!tDZ(@iPvTkZ7{`i$*gQ9aL(^nR!7o&pv9In!Ko{Rh3^VEEVAd9Cw3sd$6j8L{$@^8 zxqjVq(+TN*0%2@q-kjGXWbbcp0(7JKkYBRYb0VGDnjwi zuYSo26xcRiU+ z(6xo;p3A&ScXVSL!m+Ne)}5Y3N)bp0%dXKy`BzJ3Im%xqi8 z%kZHWDA zyb_qVZ`FNpwxVxg!eF6AhJfMD-2SQ+P(nP^&KacpKcNQrlhHt7C{=WCTN=v+`sJ*j z)r%wVQhjhB9!6@dgiG}a+YHdJp=;QY7iR-j2wxs)F zynN?BwjNq-7h#Vzj}$|-lHsueTOIc;2uEos%R47;E|~F5OYHJQv00?I9q8_uA8ssC ztM3j+JIf#DKsgi}*M5|5#&n$Auj2ff^THbK`?V+Vcj}KgLD4~$SOuAPrGX&PK3V$G zK%Ya|pC|5*+RwE;b&~j27SR2_ECqLtcADP#J&f|{4rV}({y5UwMnm|{Pd}*dnnq^( zq*z%smM5ESdU>;RM8t(TCL63Y$vsR-yrc$FU>>bm^`+fiu|)?m4{V{MOA*UZ>s7#C zCpd-IW8FQc?W@_0{^t9qOn;+m2Hi%ydB>x@kmI{`Ze+{kDR4oD(lWnd+0Z6q#`tkt zqz5Cn#Hul!UqA-=Ln(U}Xrm#XV_`8ycdJE-c<{UIX{Hl(P^`$BN4d<2bofj42kyg$ zJNO)aktl}*fHUya|Dp|$`#la)+(;9zxKg!i=xiw3G*f87tmLO4tk*g9Aq%`@%Sj)H z|9-8%qwlJHd1{wmI<;>qf18|3YtQ+_)$(~Y_PFCpexv^TEDC>3sK9u_6`!V}H0d3}pFc2--fdU2m+UAhOR ze{e|oaEI_EwVk@PgSyB)r~7$blGJd$)YKVojJw~RY3qRczPZ&+Hq-GPvvF?$9|4S? zcjL6+5rZ41H zVfJQHf4Rk2%3|#W&`0V`&^4G;QoU`6p9_Q?)=Q+;!z_A)< znTF9T$&+`1YJ)dk=;t~vieZ@+sS0*Q@&fWKUVnQQ{T`J6KG)@_DeKc3+o-Zo4#NF; zq#JbqF6lrV4`Wfy&8Z@Z$;Gr|n7Kr6IF`uINJ_BJu(HcUr$&<6g;i=IzQxhQUU^nI zpHJ=NZNye}LgmV#zYe(s!*?!;XaieI> zm#W&dRaEW0f==4n)ZSv$-g{NGv=tP!iB`>;F=B5LgrxQef*|%t?D@(2^ZkB)c;*kt z^|-J5T<1FXIidRo)A2y7=!xFg>RXYZ)Khg~bBmTsZf$l@*6+O-W-6l-Z;WT_CwQbI z&R)nfs^NOgCFf`_;hATmb)VpLd6}J?4V}6OG0U~)k589f0(GmF5;?6>0iDs3P^028Nlz4mU7z>vP!+3v8vy5T?WZ6u*3I{MO?v8 zcLB79is$l<-H7Uit#>V#8VS)-$e2FkQo>2^0_ad8g6RK1yF%~xI+u4B_n{0MV8&PR zl;+pxiLtRE(#eVYWwktbwNKl`{CTFN-bX8kLhOUtb=N^w)@Lz=-K$~0G8}YO{&Mms zhwOBe^HESes$6$Moo!q30!DX31&SCmhm#0S*MDFaujx26Q02?{ARo^TGuWQ?hf*FI zbzy&@j=0Oji)pIXMuyWXMkD$QGWT@`tA9^q&SrsCsj>&H3Fy+6Q2?fm^Y=0x3nNnOBTp7qc*!?yFMsX&;jk(Wkh1Mp z^Z)!58v0e_Sb-VFTzO`<{t?M$uec9>CSXlb3M{_CJ9vCOy`5v6rz=Rpdy}-0GW0-d z`b&y6`P8~=ZNhYVweU!7-(g!yQ1-xL%a!WsI9brOWhi6^Wz}6d#($v{IF_hw+oQu7 z4Q#KY9SI6Ca6J|f;E0HQ^N?LFXj%JK-|U2X$ETz0_RkuXZFE~zP2>n;tpJU+f>9(( zVz2SdZtnEuz=<7tI30KcuKC-a;STLbqt=JcEB2BErm3UUI{a7vnwWJAf8RRvR3Nkn zPg8m;y=##^{Oi6tp%q|K&Xsj|nNsX?KA)q;4;g{r%LEo?^eUGI+*WjFDO1StIrU%N zYwr5ipCln&tXGb6)EAyU(MRQBK^hI&BBbm~c`|2UAEPhK}2%yfX%0Kr~ zZ%&#H!^$iQjYjoph*a=lpS`i^@bjzCXNaid={ zFVW2bp{!7++Wu)fGRn3C?(H6wEsJ(2oh*$3&h`ZtRwzc||EVS{ELQ@)r$6fJ5Ydvmqc(c5GOyeEWdc>-%+!xROHT_*C>N?a z*Qe>Y%hF`a%?cDG(uE6Nay-6VlGO+Dbi-dn*v$V0O42XhwMnZKk(Gtbp zUDGLII}{g)kArxxMEmf?s^GT%&eKpS%THIQPXd-WH3KFdY6RK{Uf&v<1G;$ z$Zs|O;b8PN@oX*Ewl5xGqeV#HdrM*KC z1Eo%LYG#!3?^PRjh{ytCm+lLZFYexto8O@T3JDfuhk{d0Z~E)4TJ8o?IzTI60+Q|a z!4*3*E*~4Y@}8hB*1B7mPRj}EI)xH7NeB%}!@cttt_x|ICp_GF!!v_u{yx)lOE zQY$lhCvrqTS;^}|>1x>Q?)>8I_SM|mG)dEos)MYoSZebHu4HDm&P7Bx$Dov^$y8R) z3jm0Wts~~IS3`Ld!w9IhndoF9*wwH!8Zw9>0G$-gcNqpjQD41B0jvfpCMq?_?!%$( zU5$^cNRC#1hggV1pAUcxxIlj_9Q;87!2?qiFcrJ4uAxK<4y|A4MY4Gt+CeFLR``e5 zW8Z+H1pmkFA}%USOGjDAKGI7-z|!4q>-}b^ji5@D^IYxvSCJw{gUAh9?&}7n{eM+$ z-veA~96FZ7ioF+Z!XBr!|IE!Kd1Sgw!u>1Ayi(#?0;xM5{hIzC7!S54zhj4bR*|r0O=$ji7ECt(c~z29NXW(%d-X3%6B=G7-&^8QBYoezH1% zXhdr>X9uX(#CmolCOOX7jrt{9oxrU7s^;c{GiK2GrL(ytS0$fvJ0q9uTA-C^1QI4# z`LU)oqDAT#a9HFBV9K@bvAY)Nu<`C!Ju0v4>CstJPxunr*d7wRm^&JfAwZ1p-Y}ng zzPberKfqepJ2`n*^ZrT256T_OHA6Y@m$f10rJmAxJ;e~~Q0v})0YBL{Ie;hoQVOg> zwkI}2M^h);@Js$UjOcoo31XKLm^PzRw%&ObabT09Tz1lIOsaLv30lHC`h++P{`}cx zvNzEsA7?+FR_t1qjs19VVx>qYbsD_g9a`<#wbjg{v42MsD->tW#A4v2KNobAb2WX= zBDTCrmqK@6I20P|uj+emGQy{?T2w5W0xb#uPMCDdT1JjCUqE&ThlwarY%!nb{80kn zdOu7MeyxnCzDh$>pPdU;ukK;K8RsbxP4~MVqm=TKKEsTTuEw-YYmy#efV> z&zmBtd9Wd8rmK%7I@$B$%DxC^GprDm?()>uSf3e*gOIGFwvM%}QKM(z>h`!xDUTil z%e7$R^N$DpTZw@&;}TL0a9Dp11*&Yul-y^b{D>Bx-hlGCSiDYyy6A$@r9S7lhYsWL z4%o$M?fLD(s+uNe_p)L0yh~OeuCNM(z^HW5%e1HilFzMKNab&8qOHcG8fV$KCUg32)zgl;BicDE;=q9`&Us}#Ki6e{ zU2M1W>Sl@gFkQ#UmslVQn|KLP1x6m<-JWkE^Ves3+rb}cMXr1I54v4=BIiF*r+o$7UG5v2 z_;J@JLhp`kC}}gihLGEobGPGf*2L04rDt9wl0b5!rC6_SK5E`H*73NT`{vcU!wwv&_M@U^L{WpDuE4=>COJH zHZ#EsS(I#z7@OCiQ%*W)s?o~!PuhY&w~!^}5fe>e=*Jcj9n8slQ`bImthFXO)7rf| zX<2Sy2OPo9D$To2I+0nj3ZVSVimCOVjoVafw5+N)(#Y-R#v?}r35jja6Ea=yik8Ea3BZj)@Mq5mgZ z&k63{iK2{}b{>F1{?^E`LvI+2B>%7gnDN!h1u^U)@C>eFP6%wRfG> zgoNu(BT~E*><|QBHORuGGB)tB1O~C~Z**;OS>=_BpNSwBU#q>YpxDLnZyMniQ!oi- z=VkQorl{Xd7PH--SbLQ{N4VNV)t~u9wYCN0o$i^B2v)&c`9_c$X1S|5xmD3-wrr1s1EvsQ-;xMOj{F#2D*_Q=IR%Vb?RikF|NM|Gc z+&1eZWxO2g`2f$VU7^9g2ugEAS(_W=^rR}Uw?5>`o`V^bT~N->>Z(*bwPtP|PYqiQ z8D9UX$d+++YbC(mCMTYS7|@-aMt15n9Ub}2lIHpkQtWqEo(=mA9RjlFzfcwDTvO`) zv4SUp-THJ|DkBqI4hDN6e9A9M|5W1#`1%LD;}`wd2wg{iv_t4wEUp~fru~#A+xCPq ztZISzH9C^Y?N^#}N2P(N_MceVI${)_ROgQ2a?k13&XvVy^vSQg)_ndlJ}yD?w=R*} zwdq=0#`*BG@ZhsEVFcWLA6*v~X@6@NBe$DB<${7}4rPDmU26FJ`XYNz_n^hI4pl2< z#ZR6bD(o9C<{dtBY;*&_Y_b2DTm@dI4=9l#pS}sPSjT* z*Q>n#SN(nu@lG1y(t@$^mxI@07L(XGhDTlsR*}md!Ya3z{P}vAC$DG)NOp*zPZ3(r zTqEj{dt`8iz$%!r6Q262%KSeqK>dloIPwc|cx|%a_@M?y4c1?1mE;ko1Lt46x=T1! z7PU^sJZFo*e*$Nd3K`0oOm$A5ShKL6SVmN$I7t4T=-;0juvtN1R|J^IH3W6Kra>wv zh=Q)^20rq#DIFJ*R*s8(n&~eFEJ3+0EbNzj|9Ab+GI|EhxNo4W9JwueIiuqSpF#yY z=C5k+xOpMPc)rQqbTK|dn3#Bd{ATm`icR*g=v@A4dB(6>D{#4vh5^&?2ztf6+pB`d2?lX-p(KQ|wRR@wyxZ0pRaJ&

    ^iOd(9Gg?v&G3W4>D;y8 z7@``F(h;nzO9kYvdmAm0?is2g;vXepp@R66U^l$6>?-U^DQw#I`uC-kC8gZW%--wc zk1Gf;_>_$o}?CWkG5_SN;Sw$n061Y?WW0a=HFHxK)z zad{_^pt53TGPyry=aPN@=5ztR-=G@Cb1a&-y|tZqHY!=@G&G?Do?>qF(V{h~x_Db6trN z|H6SyaPu3$D4;228J&We%2g@kS}=7C4sz1e_JF^3Q&qDJcYNC*Zl)S!l|0-rsoWEl zQh7aiTCm~34eCGtj>cX{^I|SFrw$U6FX_c6^?07)2lm4L4tJbWYgQ&MYz%Nty5ZR8 zWKeyQ@bZ56dA9PN|MyiKNeXT{^Ur2DDF$vDS{qu}9%5Yr4$Y)I!w&idX5rHMES@oS zM@x_|5GnMtp+PfBpP0>K_XE9Odbh*qr>m4%FTVZI$eg&Z9F`DsUwn_ODjC39o9vjd zD{nKjbBHGoJ}jh!RVDffpPwphcT=&Pe&Gry$?K-Rd}y?siM&F`IZLc=C<*Lz=0(_Z zaI0LMeM>{5(TnAgp|=+B=4Jn~^}{$yz z<{DOr!%c!K5o9fV8J@C^3RMZcbG&PI$DiL$_i2w-4+s?Gg{QA%s5`r7Y|t9*1a;7L z>fK<^XJeG%NMcmr``X&TPjt9gUAJ{i*Vm3s41lxEyfkWU zq?q#UDp7YIExry}`BTILPImyrU(|7uyI7n>q~ARmwwOx{u5woZtxm1RHLo4a<&3h5 zlhqBmMQmB73a0RCm|DtGf%eu=35YS+Tup{Gsw?Bm(|5OC6%x41b)vOFNE6})d_dBx z`@66~t-=}^Fne5qRb9YQ#U`ghG@b#rAb4O-F45{SLd>DV@MGluL_t#BnG`-rq$^Mp z+5DyBzYjr-##a`*i5$P>ImaaHPW86C)T=qhXy(8u8@33Bu^=+0YrT@bL**pLqVvYH zD@0QQ?_6`;fw)kjGCExPcm5F<_|DWuC#>xWmeLq83v%nzL@m z+vX9hU}VbaNG*G`-fVG;WV|xis56O})PTVa&o5uzV&ucY|# zgMu;=Mo?4}-E$V!i<+6=o5NOBuPejwF$4BQPwmQU2OA6HrwlMg-I>L}DDUHPPh)zR zuTEG8HO=73d`tHjNh9^ezV~+TY(mAIlsADx*CmALIMvP8jI~U#{E@@ufI4*fn}Id> zL(?$n;!tGdE#&FZ#QM$&Yw8mHKk4wCut{K}*H}6vVr)l#%MvY7zp@`8u#QEede?{( z{{KL5BF2~fpO3UfZZe=7%(yUAxpG{^H492E{%LBiEK!r@8ET)svg4!%{wbW5k~Z@+ zwXjuCdnebkHa$A`mUer%OfestpGF*04axX@c|ANZ1Bt?yx^Deis~4DVqL{VU^i4)g zwd6b!f)DN}n(3#cI{a05mAQNF>oyd5X;i!Jm7#4)Qzs`me}D{*QRQ0&>TeS0O{_vW z4to>JCYW>OxCE*&R!I3>u#V|j8Y-j`5#=76$6hLii}Usu_t|ypdW<}MM4UZ>C-TUj z1$#c~AFKyS3NWjxYf*Kze|I4c8vL#i;BQp-_Q^K|ve#@$up-H9tl5AOlRg&zS9ohG zNIom|IwJ^iC95e3UKdIIZdy2y3z}Q3uF0C)DSx=00Pn8Z+i9QT{J55KpTkSlf|49kG>c!>+Tq>z{OL!$k6;6O~oUpkj6de zQRCLRkk?c(CfEHhxbrvtbAwi@z|Spv zA%>8;&xnR+kjgdjhDF1g%4b3dwliAK9s~GDgHl==#Y}2PX3pAe-w0O&OVz=nb0Zxs zD^KbuT&SsF>t+%COK13^QeLTKac{uyBcY1^Lp4)*rAHw}$%uwx_oe5FPnUkQtr;tNugr zqW{qW$9+8hR+>~v0j<95HwQNhAg9UQEVnKZuXE3G82YAx_uuyKIQl%DkgbMl+p$G> zCMtOEzObdr3`Q>7@Bc#xt&IQcdgdODoJwG8sZar4o|s;@W5WDBl}=M1C%vG)-8Xj3 zZ_ObT(AQ5C6&H%^N&7xn(`bCf<-J?>wT!=5AV>c?PZM-)nB1*eExSKLpZ?5`VfI9g z40~S+dc$5I8USdpyqp@GG-KivN&0gSa|#~#v|-8=Ky zRXF7g9Q2Nqj5+K0Q;@_}qhCh_q!<{8hG;!PD08!h>SR~p>$7Mf+PhRd{`%qxXsSjn zN{RbdpkICNTVz0~IpS3n7z`k}xU+RJCQY(r#lmVHZpPyJS@C8hmy-e1!7(arb}1i# zg;!G{SajC}B)(w!-VyidEI~{wb|K2enYiom4|;KMp7Y%eXVU&-3J|)iLcq z*Az*lO`5}akRek6ghVJjR*VKJ zMduLBedjj%$LwaRT21!H3H~ zLByW@Qm^@-xG*ZkTQ~bM({ndanfE=|AivoBl% zXg*{hA>_n^E210r5?$1)=Ksl|P5=ZVP{&QNtOY2f6V6lf`tV}Oz%=t9N7_#xel__A zn#!WIfUIP4yr1B02d3p6Yuf)CSdkrgQR1_|(B&g-MPLir-nuBa&=XyO*S@iH zUj>A32J43I2(IJij{}YmUk5d7)!iwK2g7D- zYQ%)K4=Pb;uD~gPvMyo$ve}25t7VLwEFf&Jow(!J-L7fc(I+-T z+BpeR@-`Od9@65KQQzhG3s(RbJ{6)?I^=|41Y-5|sw5x1Se`Z4B-C;MhG5JDee?9ON zH2b9fE!bo$pguXaDm|J+2!klD6!A!j#K|H>$17I!YT*W* z17r&#lf>YUG>B;J%5IjvU6T5XIP|J3AUJe~@~^9Q#=Jde_L;Y&#JE%DyIM z*Y{JKG;BxGYiWIFQ4p<=s(VsEekyGlG;;w8a4aoY46o1?b#9{oF-U90WIEC>m?4 zh^=6Ap-UFDU3V?2@-ps|iv}vdYCLOQo5qj`qcp-~+H9Nv7yF|O_u2IY2bKsV%d!Q!g zv!fQBSzGfUnb;kfZiA)y!$Y5AV$v7PFRe?-tHiG2`l;u72$d^YiHYl}k z^Is<^)k=KQtLt7#UlJ>>pZ)3e_CmyGET%J=q~SQ|(}15A&nB|hm!US(`ZAmm6xEb4w*nc(B!%Effn%-IQO>87 zdhwn6(o0E|Q4&I~EUXPZPR~}K_P-(q@GTxpMtlzmkXNuT8(9{BaHxKor#aW|5wl^t z=F!CF-H+68li;$S?FhI-ES*`a-#jCeNFa5%TiC9|$|4~a{uQg$L`HXFJV?5@Gs{{pP%J#d|s1EW%@U>CYD?V zx}T0{yu3IKt0$2}UXYsX)aGMaYV4Dw^fg7Mfmb^Q%^wBZKQbRY6yivWHXj;_U6s9C zT4w28-|NP%R5dHR_D02I$6A^(Iz4PMcgJ7p@$}k*p8~CbIm)dDO-i02&l1rIRsY86 z>oNnT;75ruibk84HJ62v+izF%!!#+LgjlK6_k?T*(~0u};Ez=j-kxo-NV&zPDz>Qz z;v5&4A)oxE6qS<(gr*$xer?-5y&6Ch;>Sp87aY zt4JrN!m8@|RaXWQiCx#8$ym~kuVZ6n`Q6-U@+ZzYt_IB)MWU*C=amGpFCe45|>$ z{k&Os?w(Hjm(M%T8^hTwO|fXU-G#D~O9(*I^Jj8*-@O#j#FoWXRa7U$bs^5?$cSAQaihZapLLHrh)DCc%)dQ6Us}ygTtJ{Qnx~d|nf+lC=e0 zk0*_2FmL5CXrN{HKj$xVXDkox%?nPOtz6%T`iD>lgmycOhP130liH12agjt=m8MjB zt&E&brrNf47RU;GSzYUJb0{zG+Um_N$jf|U8r~c(?nC2m&l_o5w%tJDQ-@)aTVu`2 zC*)A{)>}$+nt_kc3rHQIr9&SjJ)-^@O(^+tUw(hs;f!j0Zz)@U@=I~%8l53Ir7y?x zuxd}8o{B(SshINa%2amG1RbW2EHRgyIjhx6P?x+6PH9o?(LJ}}X z(lIY#jPw<~LSM-`k~V2U{`nz&qB2h4N`L>ea-Wdb*j1Z_m`=R7e9)A3yer2F5nZ~M zY5$k~)g?zmff2OivRu+@VN+f~Jkn+b11WcOs&3(94qpGOj!%8VzbF*ENzPE?-T5 zKd}`O?TKaukD82JZU27fj$TY^L3aPIg#Y_^%@<;zsHnO|v%Ns`pP{Q+c6wdn;Ys)4 z$%y+A{tJuvv#%9P(kbs@Hqf7(Ifuy1>s3I7g_wV)yHj!X&)@bxd135NhTo*9xCcym zvPqhbd!#uz#uR2{&(T+zMyx(rczDzcxLaYCcVuDS=HYe2=8fc<0lWU$?X&eLx;3^S z3&(aL%LLk*2fJFb@gxnA+HgHCcGoD1+ta`8&UTu*1>aK+4Nl#aJJ^v_UfS_Un@Vv{ zF^%qgcQ)*ey_f44@IFIMDt-LfSDfCEsBdxGVcQ1x37?YhV24TexHD)P2F#yp>i@Cv zd-!+E%GkV}o(Vk~ajHK`^KAQ2_})Y^O~Wf4{OTkOV!c7&_=OQFasU z;gAv|PT~eG;IIW!!!Htj2x5wK!?$_f`k_vFH8x(>P<7%2NeTM%2~tt`l(p6)n3phH zCEknQUC;?40PAjLi@!g-z=Z=u#DNUi`5%+*77J(V$M}{o<)YV_Vn{{#Tk3^ zK~ajfXfJ>jxcX;`j*_(^8QQby9@-;}3W{lE6jSw;woOf9>Uqk?4~lxHJAVJgyF=;x zUxnbN`hJLQ8O1QQ-WW+nvd^BdOaHk2>Q{+|+CuKdToG?X2j!w4bJrWX69Hh8fYlt3 z{8JB9pxBmukU(mj($)AqS+(DZj^U}me%URRegtuu{5HV2p)6#%NISNn7)t(Guzg}c zHv8hiUhVp;kCjgJ-~`|XQ|*Kvw(WX_2x*iZMFBHG~Ot8%qhR!g{Dcxcgkmq{jHeEg}DISrUmTNGR>hl zxG*ny1v%a_<(o*4aaq?;5N?2exhPEvnR)nL`QL;`=-`%?b1GgwR6CFlqWY_?7sz9k&5Q>+2B{4|_H5(}QG(Pf?TT+<#-ZX0BX*$%|_}}JoF8yx0 zKr2&cywj?a&Rl4hMb$~!&QJ>dN{P)-FSjndiUt%Mm(NCJ^z$~%6k=u?2m(NEchXxyd4!Yo{e`+pFhZc;u5CJs;*0NRLO3TP4de^;=EqGlxp$i zbaWnElr!_$NWo~h$tO(j(>dkcXKB^6wUzxswk{ zj-R!sA{*#Pk^ojBKz$hWn(A;|d$sS3QC#H% zh%n4``JMTQ<(SzdJ>U7z?;xR~^!a|j zm%?~`1|rPrw0BC!J&_b`D0{9glM z0M;@x-(sAN#fk)#x1YFCr_{u%VKXgAV<0?TfGjhr?nR8Czelz7IZ+h#%c(rw&-HCw zD}y=}Fhz_U_=}h>z*n(i}dnofav<(H|1#V z2#&GF;DelLyDZ(=gI(t^!pWo^$`qkQ(E6Picd+lpy!`^Othu(7*?!cS?P#qLsjz!9 zs>nIra1RU!&Nl)@zI;Lz`_N4&qg-&qPQRa?Rf$*gew;fbmE+%-_f}RCM`C=&mE&=I zbgD#$DJ3F_YU|yy)z+Al^1zb&uVk?nrs%)-Z%dc9ZV|)D)=#(x`d3nOMY(x+a$^ym zHGicxf~gbrSn~b_q$-`a9{g1cdF0!fAx+3I*`0OC4-E0|({(VwnFOjqQf!SB6xAa5 zdX*SwCo^p`z3`>lr#2L*!V-kaM?_($=rX#qnV6+G6LItprMeS|{R~E@39bEi-oh7T zo#1{-62re{wI(m9?$A`1Sg!56>(N4IOMWY_qMuldxENr{tCB29w5 zyYHp`ve_mDQS~c5Wn-;f4v3bR^4!5bA?xVlbB&~jwGMYiLokwenf*M(k^GVfu%|)1 z`CS03lLAHm|M1IE+JJocyg6VsFU zR{fLzrv(@Z5wrA*aFvFJUpea7&uUZ z&FAb*2Tu95VxJl&8L-izX#7tm1{UvKz>@>berjw4TJxumKA>FJKtA!M%_2Ni25KW; z1J|Rx<1TU<6bOGAwumM{gcKZ=Y7g)5&WK@i@)*=1t?2`b=o@qRDseVHBx5O>@-}TY zt!tB5aQ#1Y%E1_bv*vb81YdFC+w!xZ?szhykKWG?1=fqVOiKKJ^HtyVar@=I#>b(d{hus z`Fu28cSae?O&(0Umr+pbZl?sd*KwFKyd64w-u>0cqG>TLJEs-cbE4@-t;~;;G}CH! z>26_v)McwbsQeebI$?Fr{K~WN-P0~j-H3~M2&i*b{&0RCbbD)T?s~|f;XH154f@)H z#Mzo$$A*+cXm3#pC?S~dKJ544uHIN|KjIQF4*nY68}(B%eQ@&&ipz3_R8JHRc*KLn znK$nR+i-d+kKRQoIIGeoQ`XiRzST$?^9pZLgVe@+-~LiI)y+LCRA2uu$4TYr>%$Bw1V%(D-h zZ(p?;B10P}utuu{D(M)NVQSeLyjGAlr8N6C7M$tLe~6kmV^|YWZ9;KEQWXFpHzddE zKZ=&ykhHc?5>&<?}U z67GA2E-TmKdfVHBwB01Mp6mo)_y7)C1ePXFrpvacmcJ}Loj$>OaDK^+)$JY`9lGZG z-*Rc%r-!(~$O~DI|A2n;m+ITMBzIFDo3tb)8g3Z#*>jgdVfvt%cN9w6L?(60O(0ew zrd^w_n+@V*zH}!!5`56mTJmg5VtI2XE{*FBH*T}V2_W&lyCmTLqLjathmOmTo^1=b zYR-!oZxihQ%{(bFx+$_S*Cj6eKd2G7Qq63}5hFQ6${h;`ivNKE8&f^R^HjvSH*LHe z;oy0e5cUs|FXTkDac(r%2Z%9b?5;Gg2b%>AYT5)q*3bAHmM$k#pJ;IPAeok%c!KUp%=~FIsFky!UXTuw7$B_5+UC?>Viuhv>@fvm8xtHjKuVZ`DNI0t9)* z>%>c_W|=zL@DspU|Y@X zH-E4CCmNp97xL;z!PliW9q9F6G`x0h#C7mpra@+VP-TU_bh2Dpw)bYuztKil?&#o! z!&Og9DvfJUoa-KJMdWRq8{l;HOAaEvP+mGDOa1mjG(@2Hqu|?Hz+2J1+?PahThJiP zI!#cW9>zBn*44POM>&Dl7W&*>y~$%#=RPe*5=I%T?kSDH=a~{Fz?(KMH@}i-WU(9b&Nd! zw$fRR`B9X(uuu2R-(tj&AL?-VfD;+#CJtZOMm#}wCg zDYJlmE1N^xj%jOKO&2<4ewM0)CO-X3Lk$|{|9QW%Y-oWp9877y!9!%u!c>3aGlW)! z!dF9H9;G|gMvT;Y{|XEK?~@bLTnc38d$^*f>!sZB^OcPxgoAQYe8(z%7NHkO^FD7YH^q@0-JU`*GNMKl(z>wTqB3Qekec(yL6)i z)dpzZ|KATKDC#_s34g-Q8pK7)aUi zovYC%3Mv34QHftY<eW)Uf3cqIH(%Zm#mY`ev-*3PH=dankxBt22M9 zcZ#|=827HX`G^i&jBhJxnKBCQ)yT43JE?K^M~0Tj^$PHEozJ?6Sl+RYUoeT2%r@w$ z(l@gTjIfHz8;Q_Eo7Dm%isSaI#mbTkE%|KX#5<*!Om#Cly|07CGP#q zor9xOl})oNL%&5cgs+*Ef-03@G1Nh_DpWMg^GRS@)%X%lJV*Gvb%VPBp032=xuXrN z{BiaiyG26*V|#w`>%i=jL=38~r{fYWij9H@kOqiI^ zK1^tS5k2&S)BH!tt&wSEagO(x@Xp|uTTX;~kH8dyTl~tAQ6xE>Zjs6ovU3U0VJew3 zk$#yP`VulE1 zU-@ETjO#?04pzofV4zgCYLWRS9pkG|Y_Yw_KgerpWUe4h8o5@qqY1 zrBojcRR?F?$_)}<&q`yQA>fZGw#qBWLPKTIcIx5PZ-Qp7#^;OIMw4H>$5`FOx61W@ z?^QpNl>^0pj`EJ2yq)#ee(~Q6jAU;|nmbd`-0Bu1$iE}<)dSO0O z+J8}g$LF>9B!;cl!nEnQJn!>0?H5l0Vi4bDNY%q87vy=PO?iQxsCZ?x!&JdgVGz&p zGI6g57x{PG`@aQxx3q^Pkpv^^iPA(>KOXHxaHd-l{u6CEf7p6f?!-|4hPTjCj4N71 z&I05vXC2exIrB9=&(dJPG@I@FKt#6hwVPwkk%iiTakcdB(a-L6wB=5=2Ki9j30?!}Qw>kT8eCfAUs28>z#d9R@=yWg<5B5||}E zt%{$yqW`K`^1)?W~Jo~I>*Y&c)yf!1AUmi3dg zfL~oawtUw@MmCs?6h8@(q7_fLe++JyvFi$As_zFKLOXZ&7Zd2Do_W>tNRex^|x0GmkhP?wi)} zBT4z0fR5+%Rlv2M`#RCD02K8&wER;R&bQW0`S53kZcl(6P)R|h9S-^L(2C-ULa{_XVkCe4<-Xz#aSBz{EQ zq}HJP%+B>MWLUOVMTLbCCG5@hn5J9svz2Q%PVowmbHgDA#1+GQYDRojkP-s ztXtT1=0oY)eKCg9E!gR$wul=+l6gWsbe_-3Nl7<3*$2kn(erG>t+x}1q|YyF!p(6{{Ki6xU>B+BOq5%KKm_J!>i4U5AB@VpWQp zkudBK>S`}91W#OD^leLQypjpWUap=rURora^Ca5a1G&)IJlKB$X->f>x+0wb*0CmJ zn2*&noK_595+YDZX?9`mt?-GRl3R8#Oc^(tNQ7zJQ&rH1=$S$XXurFr5j8r&%v^10 z3Idg|-t2l{?dt*~#nS#KC62_$-=#akTs|A`Y;>$MiLfqGo}z<=&?DYu(tz61RiCoL z2bisUCFofRjfq5C5Rm0DCsSQCDOR{Xh=4s=POb{0E(>UXKj7=N;cD{fhNf)4b6Ek> zC;cJ>%Z6fTcxZLHpbl~C>%N+tFVOG6EM~@v^^Kiek${^|*jrC~DL$ZgnYZ4&@{eqX z!^2$dpy6N%y*u}?Z<;En#`#yuPG!#M_6Q$S;CwpC^^ugN<4Y)A0?v&pdcgUXs4uuN zkLt8^<)?%S+76S~hNjoxyc99YLw&3EbDJ`Cd@%c3Onsow{eAY2B&^$np?fh3;Xifz zDWWpu9-8q;QpsubO~ej^|I;Sba^W?q1-A7p;LyY6ziJf~lMAQHSYNN;mQS}LUiS_5 zqlQC;OA$k2o@Gi+{3#6ECL$4WQiSS|t0wJ0?|0lz9DmlgN&jkCaw>!G)Z$MqoMY5g zKK``L(fM0;kdv7g_eV*>qH3d2^=1tYOMLFZv#Iha5**I3x-@@v*IaUqt%jYI<)w@$ zt583&bGdjOlFWBSc_(!>$dCgAv?aw8$hp(2{C}-c0x5YJU^`^d-jtm6Z<)GKLe67% zU~EC@X%2df?W1%sKCZJt-^Oj^O@cxDkg3oB4q12veb+#EnptIfGo8o zF4vUtbi-V^i7q_hOaB+GU8B_(5psFolspxg0tCn2m(Ody>kx#v z-vvdOTfdGKoct}6b+q&SU5P(S+{_w3z*xgkwaxuR_Qj6sidDE_54y4GO_pMV)_Ru4` zVv_gOe?K17-bubgtPwQVququRQPuI!?yk-Mq3JCAn*P7H4^jr9A4EnY2uPPS3{mM4 zl&;Yz-J?sT#^^4UmIm4AW|JBs-7#Rm=!OyZzQ4!g{ulN>ud@@^^*j~M=j(n#ll`{Z z>pHZo`$R42%2~g^7HP9}jRsy4*Z8EuejQKXKN7!G4HahT*e}O-1Vd{Rz(z3Ny6SMf zbrKCO%IvQ7w71Y$vfldgA-_k_U?C#5s+kPah9%@P3BJfD$(H)tbCW&NIXRLWTY2Zk z%-pe0;V{?5XfjGvTwJ{I^2ZAn&696&5&p0Vl=W9<2}Y3qH+JBATkX0YgB}*`Av%lt z*9p?eMY+50+jnweR@~ZG5xr&mSFfE@tQ?yxt^~QLuLGgD;GB#<_Caa6>%-=h%ey%) zOcl;|M|HwaD$glODt2or4)YSd+j|Hj8rczzU#N_+V*9d=CdbbDSzL=BppU)l3+r?f z)APxm)P46(T=JS;yk81ckc~-{=@{Y=>)iF`I->P4RHNfDkzvgAVCm7O6Ihe|YdUNE zI79$gg8cBg%O*Z3ub|F+{TP4Kt7-KqJy-mGbs#Y8=%RAo*uM9r%cr$sdPyNa)@Ge4 z!O4lzw}~ZJsqf8d{`dy2`S;fyHH`i<3@B^9f8qdg;>i4OMr^4oxIt9`wTSvJHw-T_ z7<~T;$U&f}MPhxH)84VVkw3{_{Nfv#0ZZmsIE(w07UbBi$aWD;JHn zvKz_06B)OGONr%_W7p2CF%a+`}m$|x+8$1n7}mfYI!N}!#DfH z)}#4&8@c$LtI}W922{i8SyJKhaJfzK-}6wml38K?w-FhWl%8suoSaZU0>S!WpM2by z46Ry4JuNvnGRe`hM5KI9jX6ax9#u}1K%z85X=pY(vY2>wlb0y$I#sdfG!?ZqL%wA_ z@7`nGe*>aaFhk0om$?H*gf(Cy25KEV58(E#!9q(qnZG6KJ--i;6o?{2Vj-bZJY!TW zp;LYY&CI9ou^Yy0cSjzIrZ2_7Tw15Hq?z@#A8xzL$6_cT&jW`AKVJlgr+n}QR^%J| zSthw^&jWu2?bfoB&K3wb=C5^K12!>rXtg|4iMXEZ&w$MRNqmOR6^w{c_$&Q>iCrpEh-iuZK)Km8twup>n@*$?|#zoY44WQGV|S9s8U zx2eOMD!B`!_3(y3)!5V=Zm^tj=eZnU&U3VufeJFHcUu)IY#{`t2GLP2OUp)ud?Tyf ziT*m1cM&%x*li$qOSoh1*BhSFkomT3{I?lo9F|) zD_-H<$`4*{o9?h*Ic;8UDO^9b|G?cvnr^*6v=T?{N}I}vqPuUC;EVS zP8LzCG5gFh#Q9Nv{}y{Ab40^%QJ$_+V*PSk`q;T+`!L@b&0IC)sl5;IEI#fhl#5z< ziF4uRi8uS~>=8LOhRfB>`5@<~O4sf|)*ti+RwrrSAfPR`IzJu0cG|gD9*k_a%th&^ zrP_GL_{d^7i>ViN4v3{-!!oL3ix^ekXF1EjLwR)Jv5W_!pu7x&dB>bsi|M?ZsT~nVY^Z)gg^uq_f&NGOVK1+WQiow=qj9CHL(}a(li`l zBgDRc__<()qev}qve9?ACF)a!s0|$;;J*=~?1<^PxSi6w1$ zluN=2vpcm?7Es;AyX434%!P(en=a9o@er!5JYhl24;JapwO1wRQ2mao1VTz;%9&C1(30HE!}o!|N&VCNw~oWYuSKkF&zsZ9iJ~mdCG2Ah1ZFsdawiRbu@xqT-?MHgI$u6qxY{3IpQK7tX(6p8f*<;#J^09_7CXG8V3i^Ad-Ay4&2y!o#4aoUw!#T6 zSrOSsKC5%>p^HgD{~INZSxY{FgNX8)?Wq@!Fn8=}ZC>NgQ+3GJFp-Xkr}+XbQx!XKwuY#pN4=!M7s3an}Jqe~5RJY2WZb;UC(A z)Pl!3PG1yieg3%{F|W*4eiqrk<_!9Mj`x`B$twlh3QDUCC`cxl%&rjSAmrbFI%$z#4Z-_uqR;@TZy58L))6py z2*!U9{O(5gW~|9rG6mHXr7g$xiGty`^Dk=tbpkW7T!+m7uX{~*)(O2E#8Z{Bnd=Uw zexr0B7AtPhL?rHt3-Xx6KsVQi;&!8SD50M7SZRv^7jN^v%BVzxbZw_f4GCpU>*@qy zfUn+DlCy8a5yBna@Gyy}%>Uv^5!+20TJa%xf)(Pd?Ga?~I7sMqfRMhjIQzYM)HPpb znwbUD4jz}Qvp3eLR{U*3rmCG$J7-Ah6tpvEc;)^|$UOYn)GFb%=Y0ISVa9HBeA_zS zoqQ5mY09nHVZUJhACjqv6nC!#igr|&y&X0P`4 z!Rg9nnXL)fo>I*^*JB@~~iX z5hS@s<%8XZ61l^;Lc_s&w4g$>kQ;lo=SVl>Y->sTbP%zIzJ$p zFKtU?CisYZ$1B_ES8Q;5T3rnJw~^Kltm*Y>YB*3!U@!X37`wf`w;TIWA6Hyb2Z-<@HE3}%K(1E!>h2G%PXMnNMahz%QeV(+H~ z*aCvq@k)@X=Vy(*(avcin^%Cvm-ue2hy_E2m?@k*EDdv~9xxC>6|}#OfTR5a8BN@I z%pZ7bv9+~s5YIOqzP&i>x99u6+^EJ24V*RqzD|%lgw%=9z&aF6?MxRF}5P zTg_k&?5N8-TEULBEK~j{ypfe z{pDUk&vLYujx8^0Y!J*d`zLLc4~E)~*tLYD1eESHZ*O_?Z45K@h10sNf2KOBs+*-0 z&9lgoV0i0u)F?0WSdH@kYXKlVN>X))WuC*E2{p$new-HPrWuaM&bAOV@VwDH59O`r z=it>@(Q$o!^oUa$BXaxTJdlW$v<{(aEoH-Lx&sj?De}@yTatMR2riEh#+=XHn-CoC znx+Ch(_4n6*-sxR3fT#ok%@XbG!EgZcmc@4fpe>rZt8ex0P@PXe@csg>R{aT$H(6@TlJLUd43kU^zGiVw45@|`@?Yr+}cf|DE zd}VB@VYK+JSA%r>Io|=osa}kB7)M%L82UxM*wLcrunRnwp|bj&TFcrqHpGq23i_+! zt88=D<+kwm72G>B7cHKC#a0W^iW}(i3E!vjaP<*q4_y?PoC7db2;lT)(ax?~35LKy zEYn!dpWfxw@5C}Cd$xG(zPa&IUw)PFObP_&p}u9-jCkVpnh6VVH?TM4cO(A@c!C1Q zFICYXm#!xCwSEKhvMGYj!=O!=h>nqk*c`_gsC(s5h3Q24AH9OHrz04~3yYMJ7#cx&Z zXXvihS!_8At$_Xef2QR-w|>lU{k?P%yaV)9#%9K582?bru>*r#X%nklg5QXqVvk9i zO_|6bZ9lKhvVYowhFHrg`}qFi{Y>>9Q#tRWWbr!@U=QkdxVt9M@dI(XPUm!buI7dM z!P8*MId?`Mt~!=L|5Z)QDsQ@ZEK_;BdBPFx*bhy0gVq>4BAQRjNCNyEIUu$qHO@Ai z$=u1{_DFo$&Z(10k|Chx)tb~*Un>4HNwYA(D&>g(gC?g)zSO5dzVt0yJicotM8s&o%&*^)bmH$a;raxWrtV%We-ZBJ#$53&(-H!YHj8FTLnT ziQ~j+O7`D=mufqjS+lZ-<>dn_8nDf%mrbZk=&4CXHpUgf(kPEs)8`i^<2$_!Xs66A zsAcR3%Ji;Qn!k}PlVawKbdA`r*41jw+o~1h>Y>mIhFCGYg0o;DQ?y2a_0`okroy_{ zkc=Mb@6#2sBDlQMu%+DPIrH)ed3(d&r5nuAPUEK?8W9(Qh}!*wBz%Qnfx=6(%N{Tx z9%fNoUx0FWt;H3r@-u!?v=0y6uJ=n*0s@3$KRxd>#7W;%pWkQdYE(h?TH^JjYRZ-p z!Suf9TyUM}Qs7v*GGnZuYb&L8`$`1~cZe`kvsZ6471c<1uG@z5rsl5vIGcAdlVMm$ z?`4l(4VPP>nCqB0KHZh9%xdR;r3u&|*{U`g$a&eeKXSBb#%ant)oMAc^2O}5tCun4 z+T}j~luVpENkOcaP}%5y(KuZ*GN`A}9Bb1Q)Rt$6Y(F*d*Y!Cw-C)fmzm%L(agpWf zRVE+*gmS9()$qg(os6Cv!R}?uP0~jj1XPtjO$@~BF5}#sNBuQQCKQBYUBqY4{Z?LP zCRZ{l;QGNzGK_Rh|oUZN<7Hvk|LD%@Z~MZULTJ}p2~;@e-|dc<9>*YC+C^7 zJb_b(?=+kIGd?3t`G&alrlS^s+xfM&=(sND+bM4?Q(sY>R6t9nVEj7v7c!aYHs?uX zRXchOmv4Kpo-R(YCur16W$!c}eq43+AKn(IpW`-5O*2E4@5Q3kH(Xy1@?GWIVIa(l zpMx9y(CI0|2KA3r;dtpyBQ^bC^C7@4Xw6_4BKfIJoGcFF z;HS14__wO!zA+2<-+-!ASkhu3q@c|%yHsJ+n0dEEntSlW-icDc$nGDa`l8#b?erv^ zh~2v8^y^rn_mK*?A9(96orP8Ro&jj$lStd|BR#4UD5-Oh{O^xc*k|YY9{mvb#U*0? zi?8d}^+usW+K}*H^Rc>dI_su-AN%HV=fl8Q+Y{3<6mXWw)9R?%Bbd>I^i!*~v?RTR^(SQDpw%U|E5mY$H=C@5 zT$U{V2KJZ=kq0hwlA<`!Nn(U}M%oMDTjGYmUsBIhbBkpd~nq)}K-01eo(q&R17{TD#Rt43RZ7H-}o05ypRzLT`=5wJV z_+!m)qikWn3eMOc8K=0;sN-pq4W5tP7wdM40tc%-OtSr;etvT8I9Te@jhb!kvC_R3 zMnYz^u{q1uNObEGX?c+D)cQ2WPtMq6%bp{aZfON%&@`qhcC;g-Digv}@IcyR{?dXN-!ymA7Rw+hv z*rJ)SsCBzcM<1@S<3s&^V+bB9y6)=KD)DjtlRq5=SH5DB_;=vh++IUt)5c4hc&9yFr3#BI&G9O~4ZD<@3Js{B^`dNI3;j#e)8k-Qt1c;K>6>uGn-UBij?s^omsBX??nZdx~H zAIr;W+nl4j%FdaN)`x=~&aTBwC+6Zy!r76B+_8)dRVmjoAw)yJsPlpwjoFocMffkW z{#)mpoogEwnzVq6t_Qbs)CQvZ!PjBi{uYLxq(@HLNi9G3N^(_S_s4f2Pg*S+_H?J_ zTNDDfDTl_*AHUaSp-8)**ITR4HwYK^kZIfHfr%6=n34TuGg)7oe22SlAc0vsqeyC( z&c#|8eY6*u`vAnA=?}USz^tAXMfb`q4jSbA48}ug%sqo*yMB6Tk66=xqcM^4{abYs zzMV>ylTv>(M6EEgodcRbY8}T0r=wjwJhJ%|DKjno$wf9B0h5OLelxL)IKAIhyD8@9 zn9ufUQCiwz1QyaE8dDJSxB3b1D>_b^r7yO%BencuOmSk2Yhe+4st#pd+b=<~ zB%UJMAgBDB-VYv6XvBC57MwewAa46xkW(Tf4n-`Z)>&uctV=h50fSpYg_BDqosZ?_ zih?=!y#Ee$NlFu%5zmE^5TH6^FC<~h{9q~W^JU!*34Eb?&OG0?r^aYlH?&||vLHhe zfg{5!Lu1rtyoJsPrb!S+@9=Kp4e`>d-q5h{3^`hiYOz59LEV(M^VmsNZsT%L_WvGr z@0H_TZx*#VIloj6{s-Udo%`SS)@*!ifh=0( z_|{53m~^rP+B`z!CwlU0Hm6vD@V@!B4?@HRDdt5oq$a2sc&wcMxw+_iie0tUpyz=8oQ6%po;Sr(h0T$Gtf@If|QAlXRy~#2pNK0 zWQO4U@1J+4y(wLdp@aBU5>xQ@i{7hl{1L%-6>}jh6cA^tv)X&27drEiRI3bl8%$TR zYknZ<-u%V$)>cjziZPX%smxKl%I>H+k)iK0Tp0U?0%GKH*Q#n(RhPrz3bPWHX4QU9 zFP6$qwpNF~KVCwp6+DM-JRtI2?WUR!t6%h%C{E)WiVxA|X$zfbBw=vFe{TC)Wsj6{8-i2_wUO*(dDCUYv#cfqn@iu z4_v_LrQmT@`UcsxEKkufk4A+&sZJprFBmMfU-r59?=h zrjC~oQHMOIcJC+qSCalru)~w%-{O(0tp~DwWNJI~Sj`<(!Fp-Z3Wk}7&B(52&Lqhq z=3`0Mzz$g3#(bTrx7mBG&hw^r$eiE8icdO0_1#JUZwZV6lo0D%^*c!(!|f-e>HZ<& zq5qnyV?1EzlH$CQs`rzOGk(K}oq*!}f^cD(uSR)FN5d2d z&n?mKowS5fEaw8R1&(|PMd0?|mGRJZq?CaZle8sQ-r2{58#ndy)#1hmw5v*{Uxp>A zMLqXO0nZycvu{Bpi)W7IwbxBujXc5Nn|`-14|`I^q*$kpNf%5M>uCIfVMV;3xdN9V z29lrGw@i=DShrzqcCCu>FbUhWD1+Vcu0Kh`P5L!S>nG*9?qc6^$=;>{KXX%k_OLkg z(JfcJOFR2a2cZdOJX(fqr;(VkiMual6;zZLHto>`|GF7CnRUeu9JERFE(O}A<3{EN zvY_lu$e=WA@ljfq+}Wwk?h#C?enM7>PcS|#b*?Z7Gq?sz4<3Q39_JjObSj}?PISz@ z?&yEp(@*AdNq%2ms610~?>l&*{LNxUZejGXcaLW>pHSC& zy}6yNpHN7}@4Ej!c^}EnVnX8U2OXj2o^Jvdp0E~_0BHeKRenNt@v`|D*MSl* zsSu^I1`ut?yO#jmSwMv3LjZmr19!WpY$X`{@O-%~J@VP-^qIuBdFxE{Z3ETd~pK&vuJj2^|eTXUmsE}j_Hd)M6jfbh!P ze(NBh{;qTdn=gN2!d?gfu1Cu;*vk=(wegoE$nqArkF14BG&i8m&?Kf!tZxKMjgm}zCV z>N*fOsR8j+PIWI&g{5ByXWJ-kJladuyHptvxo5#{*xF|hLdm)9KE5K}X296zG+$vW z>{k&l_@xxi&YntFO+O#YOJC7JCw%%Pd+t?2YIIG|uaz&Z>gn?{lr+If7O8v3TcQwN zMPL7VnO-mZ}zt=uP7Z9 z0UzjCDj@*!&!+N3vvAYc{P=~N?=pObECgeI@wT(}pCgsEkLT+)xy;Hkn6e^B&;X;g z)`tkE`FgmVFaXPx>iQ86@JLw7LX3<*_)3Ph3kOvUn8mue`57^IzCH&r&h6R$R(bk& z_2D)3EyrG0;&k?J^zy$5Nz#ru3NWLi+rqzfPb~_f2^FR0t6#EHoeXIA&gl{MHv1>b zx@Qg`Q#%D`8U2)^z<6HYh_jjf$uiNuo}yYGNdIV#A-V14zYSL5*aVu+VEZw&9v_8zzom$+=xpHFur|m_|u^hV758C67+xG zDR6#zm$G*5Psc$|yoE++i4c?K6vg33^=47!S%G9R8=U8a;X<1tODHYjYweN-3R0Hxn9WR{+yuHR#SBKm` z%lb?oa7|>&jn8mDe^|r&FGkm`ccaIag_~S+aX%J^$ zMu*mNJ){F1kC6~O`q2;RU#^#z&?iDaYnATtz-RpGe~A82_i-|mMC0W5Nc15OFlKi; zv_yCd-zy#9CB_n~{F?^3SA3Un-N~+L<@4sn0X`XZ2@`hXO(mgsRTEd(CGnx19R;8Vq4n5{yc|Lc!yr`YN zgkDY7MX2JjbieV@Np}}auWsH?kzpBLS%dJ5o>3F9Cbrgl=0Nx~f11G4T-1<$0wp9; zPIMr`qx!Kmo;XQ8&crM88uOHIyRljVTkt0AA}fi|Oc;V;%q?_(V6Cd&tbO9z@mBfH&LmMyDlwru)ls_e z!m#~(%XOIU=v;l3rcHr4I8`QneODm|8|wQ`j|=@!5)-?AY;ns7KQ_{IlKHuC?k^1Q z8R31YpTA)pWV@%wXtt<@^)5mj(_fYB>Y)0XzmKH*qqVKpnhkG1XAVqUy>xvI@n2Mv z(#f-X*)n3FgsOgJD{fk=>>YJXQK;BhO^9Rj9${MPM-kVNH?T|cSo2o8XZEy6yaHn2jC#8OI&S%KhiAe*}`J?CnSkcQc8-@(Tx}^~FOSfSV z9-az!bd#^5zofa(+w*h*=7dL66oO32OY=1rEl)r@3XKd9btZJUI~9NMV=6TAz?;t^ z)L|4Kb)$1^`3K*FIrj|O_vR* z1Z(=RTV9*)8TyHIM-Or@IXjV|dovQYlwtS&_+@;o3xq7>OjQH@TGA_FSMI3I@>HqL zMU8mTAj0*nx60A$D8yeGMt6hoA3l|LRxL+Ton5Ksn^;M$nd{Zt8u51(f51AsruyRc znj@soM)oCFFr=Q`GB_jH&slXy-BqQwXAt-0Fx{VLPc)z+`s*!J-Tusfno+Wq_zqK( ztWNlC!YDWbQ`YX3>)52_5##@}b!pblQ2#UEyi&Z})^;nbOaF08IE&F8CF1QV$gLw4 zmbMo0BDmFWK5!wJ5|WJYFN>rYng^Lm%g?dVK42vJ;$IuMScdog)ztUfCL_j2s7Xmc z-Vnp!zvtwTCi4%Z@^lCyYkj=1PgDm5qoom=yscz8no@nn-3bO=KWl}=Tl1ni*DOBV zilm{bYA7vw89<2NF#74#R+qELrH}bDujhF9$R2Hjh7tjI-flW!W1{SOJr}tYpg!l& zsPYHppT($D8>JL)+rI6EX}41YxBdo;UPXB~89Y6xcO{2;!JqSg+#ozH*CP;J1uxho zlF{Brhsn=IyM};Q%**9(rk|p2`}8EY#sq5Zt+e?Cv(JC8r?S{=miO18{9>yjBA~^+1^0O}51wggO`L~95ei|<3 zI*>0ZxMH$7(s^A!o#KDu!Kx}o)b;^3`8UUW+KHoR`?selT-TO<6i$eGi{`$OePW&d zpA9nsKz%)Z{$;=8V=jFd|d9J zev94Ju>T%zX~cIzhr+@4v}49`otfi;ax;TSn(|VXyX9krMdj{7YbRW^zCIY3bp!YN zc+iQ?xI>{#VgGao1e>i)#+`w7o-uQl9=)L^O#@-o^a3I*%k8?(wE;yaCmCm%AzSAY z8Q~psQ$kWNMv<0HzfZs8_=48d_!!W<^PbG{R#2U>Ka_OkMvU+G!G0X?SF1@!lY?#% zqU`1mj0VeEdI_|nR&g|-%9u1)42-jq0kBC7};8QA!r9zNs2Iql5OJE z7tdqYVr?xw>d5(niO*Nwg73GfX)|eDF)n_QNBDth1|PW1{a9-l?We{XX;*E0*MFU# zpZf?B#I#PKn3)I)dLNM&;XX+z?$Jtbbz?;sPtO`rCCbX6QYE!#YkGp;O8&#T^fj07 zb>q({xHT5H$ryle_5xjL4B}Q;&GePN5-7{!oUaoL)m@7j_!{F|kbC_^n4oifkF@0O z>XjBQE;VCz_mI2iUlO>A5RX#ZJeS^#rs`aiV&}8PQr$tdVI+genv=-D^`XHB{Y(#( zQ6H39+^dmsb(}L-AFJ)$rf!|MTbJm7K&jMXevo%05EO#FWAT0~k`& zcMLh5i+yYH*Q1VZu22ZC62lbBnJ$pBL6Im|H0D>a^}Gw^(^MVZy-78e^xMN})coN2 z*9P9Pxjj69Op!%69>f@A%6?6X;1C9T40L+9$?cVmR1K3svMr4`BRCh{?l5sX7x*sY z(K^dcp)?S&Up+VZVYS?O6Ud-XBIEFq!|jz)kdybt^nHSyc>6-`$2v+TQY`U zwU_TXA*0`3eg?@JTm9u#g-ea+Zo|x-!x)TDmf#yXQlP z)|u{#UC~L79Z~AlEj0EWSmsx4__?k`^hvI3E1`+YbX4_X&6D#57$xppfJkQlW}TlI zci|#??bzZ58V0jUYZ>FovKvM9zKyof_dyT*6(KX}wK6kxnWW-Bji9buTc_vxGMqnn7Vn~^^yT@Anem70&-qCXG$6iG@984Vp+2J-ql_*Y0K zpSNr?9t-Vzx>+2nwYC}Z?Y$G~_cXDb>!CsKH2qUTuPeTR^<-M;DxdSk?Ihr#(vzRo zjbA9phARd-j|Q!lZbfrZOWR=*bFG$rBox!&^28Ry}a%3k~FW! zc)iKsO(S|PUj{PGJK-pAjbtYwjp4sfSoMizdECC%@pyAZ^0Mk7JacxfI! zWX}5sc)LjV=d(u-3Y`UnlyoV3R`65e7koD3uI|EZnG6uk2w7Ig%hzMS!96qaD|J|x z|EomC#ARPuEw!J0vb+0l-gl1VVw-}YcKuu2#a&atF?g=(m&S<+ZXOs!_T@w_?A43M zR?Uwd92Bjt{(mh%oUPbHMVj~bd9=+xiN)v@ovID;l;;8S^|tZb7XJK@6DUggoLz&g!Y!38SG8%zAPHoE<6bg$j`}MlR6%h3#hh32_qSg8I}N^ zRSg}Ub5C9ibDB-9+`^rErh;uB!I;}H!EXNf)m2@*!5)H)ftUW9`g(8)KBlqSQ-Yyf z)5i#{Kk?PSt^z+Tfn#-Oh=o@e)MOj}saOUOaaSnfNrha?cO zuA8HZWw*?^3XCvu2{JsAROMbhyf0lo@oyT^3LCRDuQYTRT*hJ^ zGDb63(YFL4I*@-<&?1mGive|)9o z_tT-as0PzaEPw0>C4^4gV|a&{34-(*ZUt35eAqt&PA9S+`B_N}nNdL9Lzn^xE-PC0 zFS{g%rbJm2np-!w)lVAU#vi-01d~fx-pL*C;9M<3uAias`}@$fJQ9dKue5t6A9$lN zHBYIhK5-(A#g~|}pAx}G3!fF4B!@Jw_vHGLqQTBVrd{PB)JDytY+ACo$$oXI#lVS2 zSHn^9@@|h^F4p#FJpwl-rmxl(#Rb$JvdD$dy4*C_`*hS;kEDoc`$!%xxpoV8v^-hR zf<0mhVyag;>(y)ijQW!AKEPB{1rWN73T^c;mh}w`pr^h&TR3!l^G>?iqIQl+0GISF zn}^9_&{LJk;ju#Kjf&v8jd8iE0^e5EZ2O*(7fX?9KD$E67dEn3f*4?FbrkNcl^N4V zrUsv9kA1o`HVk`jXOm_3#!Qu};WK6biJsDG)p;D0ig;M1ty}UMgfO8q47>{Pcn!+$ zdiqR{PWc70tHz}SEn8xfcGDa^yJfcolkpxR=i1q`iQ8@b7{Ab`>=@x3>1S@gR1oRM z!F#p1!_5^MR8)8xi>m-^NkfT~NN4?^XX6HalA55Gv=l+F?)ozxT<+8lB-_R)LoJ-f zGN};0i1OB~l9o35Jj^>St#4w^5-w%#1Z4Iuytw(ok)7QBBV@rtDk zwrMSlCk_~&cA?7VEoxB;-a~Ll2NGAKwJk|rfMfqmAGzINm~gpBX9h)Am*loiL@bVR z=;N7Dr=-nlWjrs5^#p){R)$GTHmzgJK}yJued_k(;KX5E^pFCW^zNwz!tsDWzh$TM zo&ci%0suw+w7s<_M9;*8+mH82Gu^X>O+JJ$`O_Fjc7Y*)p3g2qHQpy?IMay3d#Z(@ ztv(9^t*hP=%R6w>oA6*!t0mvYn^-RGJQ7@QQzIII4m-S0qiXp&c;kz3`x&n`I;^AP zN|i`J`Fex`JTv0beYKB(F|f##xH`|baBML|I|Pj~5xPTiNv8T#iQorzV#C76u7jG! zLndnkFzn86!u+=`cXatT!APszu^3F;#rkXZWQAP`9Hr)k_x&5u(K7gtcOnrt<_Z5) zS9Y8@)8;t~_tn5}^zU}%x$Z-jm@bUEY7$s&xAg*kjUIYu3C2j%^?)6JasHa7m*Qo) zIuhO~6yS8!$XX3hpZyf#7yR3=-?(C<$iKg}S$Ze^E%W!pfl0EwL6@HG5XmOOQB2|R z*Ks?_&>-gUlg{A02sT#zg;aE()SmWO2#0!GHoG;Mrv{v%9lmQm`hv|fs{+Y7iRxgy z`&r#8=905*3vuz(#B~L^Q%c|KyajqK5a*YQb&F`D-TZDtzc&z`NH&XJx>cS{-4&8fV=l!@aDWQv&TE9jbI!TU%(l zf6Wt70JeP>9}R!_Wn9Epp9|SF+NkA-Lh%Q%kHD)99jQ%-<)e2Ql!#rwW_^Ruj}(Z# zkB!xBnged6fx+TJlt{WPCv@Qpo(OlrIuEmDB(83~zs+sTBJZnU(5mVDL0SD_Zv@%c zg=gmjN!EQQ!JP|xlfTuhG-8f3cd?Fc1M*8`IExOu2SJ8QdIw$XgN0_bPs{RSka`D+ z^?^GduQLHiy=u)*$?_w=3>-tdb{qc7n8{(b4ou%(eI1!=lMJ*Ms}J@H_V4DbS^?HW z$1&ax3iohdFA@!Xht2+Vj6HQhK2Xx@Zcni(3mKNKxF*ea^tP?*v(bzw_?yd_@@yCU zr>yO~vSiff29jn{dL@~bX8WjPDj6~=L*dnPcbLIVNmd+r0&HGkw=xBc$!(F2)+V3o z!^ybazs*@|eqtT=fV1Wq*otbApBHSY-_(8Pwb3yd3>qw2k~5$1k#1O;)Sk_9A=JKD zfY2tlNF8`WzZGfcGPyT-1?!8{3Wy2$1pJ*vY{dY0nPn#8q3hc%9 z^elA{7Sy6FDLtm8=YA5b6 zb;f&25&XJ0o>vvM_)^NioFG|4uK2&ks!jD5Y*zCToutvRG*$PbCgeR_^$K>T{1&}E zl%2)4sZO5|!+wA{8#uqrLzf2YzuuT_JZg{qy8ammr0zj*AH^?Cs>bPSzhHRNY}x+d zSO7SZBxV>?H+)r}O=BG%d8mCk!`shI@{M%_#Y ztofm5ZGxbW=UcXhYyYX&=PmQ^zQnTG!MC4#y$Zjw1TGC8zJ9c{S9Pjs5UhNrbi6&l?!ZklXSmPssU5o5;G7 zsvm#6^>X@^kW2n$Ms@E6v7^0cq5;{C2r+9}#aqxkboe2HoY${o&r68XZEMt;|Jk8< zAkp1pxaDw4sBO_Y46988ZqPAv`vDu>+Q>RbQ`3xUyTiM7jU`gZ2~Ww zME;v6ZS2s(*_eCZcph~_M`4u6n=2(oT)u?Wa57y24I!Z~MDtAC+=)w=3JGHT_4v(_ z@gIz2=h3cCnB%qI8x>aHNZH+b*Hp#k2wU5dtm=)|95pI9%)hmz7F|37{Y60G->9)l zpSY_?A0^Qfvzd_ef`Sk(yw>^^`2LM!&C?bhuuDrYn6wjhC)P?)O#ws<_0yT8tei?0b2@TI zRIBRcKuz^gXaY6i*tK%}ff#*Y%}Kl7M`r?`{E>KOCd7+Jj^bQ2-gC6y=iKG~LW0YK zj++o9GLABi?}?&0tBuP(f3Ig2)kgQm0F56f9javVd>2W0+Ndh0$B+6Z%p~p3Qo8DMP&%r6_|b5)mZK zo&%Un@M6zl>T(}k8|j?ElrPplHoN>KL7sf%qKY3Jof`lmx&{HLg!MSDZ)kd)=9N1 z#82IRIFwfgA|<-@7UHrjuHK#T%(^q9CpdzLr0ZSO|5DLsZLi+6G2A$}k`gq9!|YdyG?Se6!osi2d4 z^Cy`G))<)g_|(q9rBSXvV*;G74d46R3Y~W4o5d!F_zUjf*2UC4>Uc*J4_&wU(9F4T z*+reVLmC{I!2GIrk`H3CW;+;PiyV$E3f=2#&_V=}WHK3lDHQII)Ehk*zqzmebOhs1 z@ZIoPxf<{;P+)tYf%@45DUP#5SlaH1ml)eXj#LajHeN8IBn%&=Kb9I_9q8GPT~D&RFLncIuWI7 zeX_CFbxu;>XA4TwX*estSooA&VEzKR)ctlI#(bvkcmj{kZ?rQ@Emjm2nuMJf1FD0AMtQy0ekV&hzFT zzr9Vcw}Lq(F!G7A-~45Kpb4wRoYgb3*Ff2!8Er#Nw(bCkw{EX58Tw(09SJ%QszGEe zMlMK*xOpnHVNON%kuYGavvDE2YkJ=XWE49V&qZ94y8RkSp0Oh-qfOFlE#{eSf zhDZfp0A#9a?&}AER>Eq(<43sRC@MUZz|^8he%^o3uKG03WCEZfTcLZy5* zs4{EQ=G3HX@VfTHnXV5>30ja)o7THz?mS5UNtI>#;b)=A^td*>+$***fKn@qn|n3U zD%l9L5WiW1v#kwt=fy9Fi-A(!s{Lf=CZMD{b(U>F49&9z;7)n{ zE`*F@fIo0b|Ic8*8-^`W@|PD1ow_@^?y~zSy!c@Tm@nnppdT}d>V;Yh2>fl(gg?pA z4;MuVFOm}mLzXsyR+*d~b*igf_Hl`j1~MZ;F1Vzi0irF(U;x^& zk%x zhFX#}-u)y1#_z}!r%-Q@_s3lz!kyFZHQs_P0f5YLHT1;54!xPKhodYyi*NMhLFRED z=flXAW_5v1?{p``f&^3i;ocrOiJ&QC0+X6=>;1-?CE19UyDcBcltDt9wCzmAL^feBW&JW)uu)-G(lu0Z=~_TV~GDp0RD zW8#e(5I+fJDYkL%;Ll=KX8#lM%Vo-0^AK<^FK_I4${x{>*0JF_OY$K+`tvyIM3R}o zw#*jRU)CX0JMQa}_r;5-9BgzxN5}Xr{q4H%{Vm@HQFcBr>1!68R`>Hk5mgRN+LexH z*5vPN-0*&y!DY%|$y(Z;M-1Ii*l*-PGaoDhL zl%s(qrfF+;nmc$*z0?362|w=6#3NJx$>bUfCb@K2Kt~T781}0&T_%Lr@1hM3@@)en z&{lJtis<{5O~!pWJ29P#B0nygRcBSjQD(3M4CUgvdOKHCWPSKtJYLbo{-2^44-wVAzE z(m{@s>U$`>4~g*vFJAv2uAGky1uZ0YccOd6`b#gEama8Ak(4hq;!5C4!w2rYOV%Fp zYz8IR1N5nVpG}26F?84FnB=36 zX)|c_3p;T#W8v1N1AFz{BMv67Zzz1*|9#u-3C8KOgw;gfPoEF*?UjG~t|kV%KE@+1Df+8V|n4A)g9zu{tiws zmZO*xSbiE$P|WIgi#0JZdwx9er)-(*XVp+t#g9ps_ei<3^+L}bRrHk|XRa4m>O9OW zz8YfMINWx5z|i+Z4Lh)%E1vG`@1yGQS7j-PQ=i%1Z|0loAJUNJb9~fvMOAyB8+f;DK zM7-kx1>D7QS^RH|S=jRJUpIQPcdQE^7aq6X@KU_khkvKNK>amzLIXVxDHH~Zr&~^K- zy>}sNV7up)t;2Qq(0`o!YmvfX;cB`k2f5XYSoik7{j;*-LTv1quRF8h`4{DWdL5<` z)i~lp#*55pa%T#?pLok}DjP$6u9gttB!57W?bjQu_i&z`r{l?$Okx(4A#S{z%bcB& z;~R*U$)uA9)Jpx8f3^Xc=YO|CM2M z>JPJLtIVzPM)O5^$Y+j~V1KxwC$vS91ns zkuLh9b<;4c#agjc6qwnh$w;?(;_6ar$V*oBLEBx+*8V#YE72>i{kd-sv-st%0e@ z)&oxe_*viIuy1;pw^3$Xqv@<$A9|z^k5mP!t+R^wxBczF4Vzw}k7iOVxv>zu|TtDdjKK(lt3GRiUc+CGE~)XKO?Ok94)$ zl>Ld0S5j?!z6t2ptIU0-olNnriu&}1Scc%x1s|JD=dlrdWq7i33DtdNON+pah`97X z(`NAE=Y-FW{brHOhvDAJ?Gc(? z_1L;GI0Bps3ASIU#(?G9j8K*qh&r6bfI=Rqx=$vdz{-P68iFEv!(l0_)j7 zp5>mG^nI$~*^=IDt)#6yth4B)SM9RHO}vXY_#|2(h#FS*>vA8xv&azJicCuarBke3(3DTE~nnv zxj(1lbiPUz>81SL?~GB>R{3eqRw@@{Kk1HD`zGOF^!g#T&9}sDw@Hww$Ute26U8cj z|9NuH>m}ZEP^WkDPt%ReLZx@{nNlwM{*)C%n)yJ2JO!4xBCH)8d z@#U_Z0HK}c-G58Vsw$yZJDa>$+}^p$sTfVTXk;DaIC}K@SAMs<9IyA={*a6Me3_>! zw<|=91gY!xzG)fKrXX~q_UiZma%(OkeoE5T?aPcma8{8u3_EiL9IbEdZHI2@Hf=+_ zhb`1&Fejb(nsY6Ie9Z}bm1+>axN$3Wi27aGPUujk$Mx>>*y?#aDZQM@!Tm`%I*)+4 zaL^X2(tdQsqI&O_8t)rzawOXAFdqd&jCQx$#G%%YNEk;LEucCY_U&@{|Fz zhjbHn&U`DU0b}-n5eIibj)0${NXtFVc0#~XV&XHk;Ae?XTtf=*)%G6Aj zk=1994t$F6_vh0RS?HeWt2Ny-JnO+fDj$tv-G{u&i>PK~(cm$55j^sEz62lz7)BBE zz;7X>PTra8S0utQ!yAV^{=(AHF+cN@S!{I#LM2~Ly0|Tyxax=6<F1)H zSvrgEEq}jD_`WA0ij8&{z$zbwux#P1JBc^Hdnn+rP@0vumA__Hki!5p*Z^6iryO_Y z6{wDZscRoHJ>UUmt%xG5Mp~urO z@9|E}N~fuXr_GEWm1RrwH=w!^*ImN-oA#)XQ@P z#)auwXX7fBgC(ayAhi#9qZHXWdhgfZH-d__u9KuD=zzl2Q?DJP6hj?LvAZjsLD~m$ z!9!h_Gj4B>^Gs+?+PbNgA8N~+NQ76)N}u;1E-3uoO^Qo|+tJLE4h->h7_jbv4=CZL zxy7gq1A`lgQ?DZRq0YIuUV7hvuO!8>ZCKrxH(dbfXT+HmT4>4uhS~bINRT?SEV+O; zH`7-(Gq$RTPyPR7B1LJENn@AW9)?1GoMb!3->5%r z96o)fCWVCz>}pC#hOTI?VvFUUYpgz&TQj0gUJ|c4Tw-~cd;vr46qQecn1|~aa0O=! zOF#H@Cu2l*Uv)O#(y}yBfb+zk<_L%`$edi6i4Z1VH?EWX+Wp*%_6#7^ojBWZ-pg^{ zk1qsO{v}0%vB@ZWgE=}c6QiP)eF*`E)!L(lII`jqibp3FUUS&{NTu@y-^ue{sn+!B zcG7kOx-gn>zv%)dd=spCSlJy=Sh`BMDnu}K&?vA3(fTl`9-d;!380zi65V`a36rsT zvy4~I`D8>|m@UTyBqoh!d}jGfSqOexllMIKYS5{MJG9!ByM&JmQjoNuuqMItN`#sY z;U~zT{phmkjY*|^)Lwuah}%`!uY}~=x7Wi8Euq@$mUcrgPyS(oCFNrGpD0*$*`S;@ znfUPw9Qx`;(EHmPZxFJx10`Zg%bvHI#<=XipDu*9jh(fIxV{}zc;aTND=63TGiC6l zpx?(Y78Cb^vX_I^8-vv51mS71VDgayB9d1IuliZVO^^FggU+4$N-wV^iCv=((}J}^ zk98mBF!NLIpC&)d}Ppb1(6$mZ6rF)Bd3)Vy1qkZT1&GIO0>M?YnZN@YfMBuC%oP5<&Q%xCg!e`T{gB!fR_i2R=>}O z81cO^r+`0t`nv@-3h%nhRNiMbZ4@lHxJI0LP}|?;#7|N6{L8#}AuY3*yjt@wqFjks z!#A!RWF!i88kgARRC@iFP!3mC;H!;KT66_c!2!M_DZ)U zQl*?T&87WVe#_SaJxzW^!rjFvz4Q&``+artGz|SGP-^&L8QFQ0l{UwO$I{tt7syrP z2gD}ro1KQ|KQUGUTjZnGkmiCA)}W3Vvi;0%tRF=q^7%omgXerChC>fO;mn9&tbaZ~8mEy=R z`Mm~+r+HLbxDy9!yIW;G`JPw2RHw9%?>xk@{FeUsJ};1j zEOS*&tK2uqT%n$J5)9|A$j!pP@-W}n;oEOjQ2}rvgG+U&M0P|4Hden(fZ&b>ii*eE zs@Q}cjQ725(S01P4dL-5F{H(`zE4TB+C)WPW9Hd>w|#NhI~EZW`Q;zit!%}#-GjGG$iXqk0+8TLk!zvXdcpmh) z4=-UEI92xHg~VO}yxI(7Gzr;`Rjg{c@N=)@_c7BOUc6uKa;3+hKO|QWwKZ9MioyGa zGTTCIUVemf3OWQg=^R|yU^u>Aw`3Ul&9z+1o5%lS4-Bd${C;jcPt$P{b>3x{mj(<~0WR3If zskeObQ(n$A_E4<^C5;Tnw>!T#yzDMlblsYZ^jvU1T(y}C{js9J7ct+--N;2TFLa{8 z4d`2mR+G}gR`8q_5TNdE2J?4RB$Z)XS~HoeQMug0Ck0$pk$4-3&Oi#P{s-^0|B+EM zKE2tKO>LE|!8R+2Fko`$&~FA`JXG88`nb)R)OF|3UQ17vwvp{sA7LvS?2Y zJ2b-_l&1KhG2lP-A=3@IPq4cj1^-<98$D+6hY+gW0HxwgoIz(!uWFvGZY(z@P~9Rm za}b@cikT8smT^ZOnImGvrRhyG_e`6l*dPtaE7x6ruHWuxy^S^P_R1{me=FZ%&JR=< zZ|^!>Yf%b1Yhekpusm!RzZ?+GBzA+mQ2`<~FLVqD*6Ro!+@!iv7bh4AlgE7vB%@Gi zCfLw;8S{`w1}etmKNJxD%g1Vq%d+>JyFE<6lI{5XYml3vj^A3-z04VpL;{h1fe@8YY3Npt%9&)BgOJfDwPSMAeo1)D956&4ov|e_w(!J7|chN9n5T?kIEb0&R-_V`e5BKP$^PH=RRjWiDGdxpV%v4zXG;a|z zvTv&)VC@}kl}n@a{xw1hdHD`XZ8E#zxYfPj9B6XVD#+HxX8(!MMZpD-B1`6S;R27h z76p$JLM3Ri)4t3RvskRRXJ!)H5swBr=$7VLHnZMx)7GJ+L6#?B2RB1>O~_zNz=1@QWyU*Y%zP3bqGJWcRB*GrGn@xtvQWVA zP8rZbV`MY?-K=Mzvymx_$}V-1rNZyBs;^#E1vx&j?pX(&G3$1$%dH)o$%@xy#;^ft zO+eeMs$rd4+Z#reM@RQHBiI!J4*LUf)YDAdoeNW9RX^Iozi!MP$bVQ6K>#Y7F}C0{ zzKtBP4DtaaL=%@(i+sP{3Nl?aO>)+?>~TN0mDmbL?r*vpH&ui-c6)c!vOhnCvjE)5 z3(sk?@orzrpc?djc^c*d$)d;>2WRc(pD9iOOVzHel^e|iDT;U9_Gw#SKH?~LMs+Gz zpb6P&Z{RE`aPWu7lrH;z`=Q+YS5SM};9N$0E0A`2*htZ0Nb1a*y>W7ytK-fX;@M~4 zecyaN5!QOUU>}6KV`$81+5`qbCnK3H^ZyXf>a5f$V7XG;tl2@X=O1Ol zx>AOHw09XdF<{k*kjNfv)y8=H+thgikx`;NA_2?TG!8d>)PfsMu9S-D@HbnOb5L%| zV6X>T>$I>KH>#A&PPI(-ZTOqm=^icJMlO5@(N~c~%*6eA)3K-8Z%w`$?T{DhoW%n^VvDP;d0E?#>nY;crQ@dd zcaf|puDx}ra_{cj*}n($e*LUiMI+x|kg6Aa2cv{t(%_q!;pQb@k$V4kkDiH?8(799 z+P4c|>TOGd`MeKPI#ayOp^rYg>7)Ln!na!A*J2Y0=R+?4bV6Nv&=_IjeX7VT^{ab6@3z%$!d?gdgJ&$XG5{#R(a=r!V>|0 zsKH6oey7%PdR9EQD+wR~mc5#=r7`+bjMa2rFUPJ0Hv=CZx^?SY5$$ytWCG<00Hnx#?Hj>M4h*LB4k8Bf)-;+H5~eW4l8dk4 zetO;9)@s~LRnaeW=L)_?uIgvVAPVxWy88y4#~KM)*0ze7+AlWT|BfH(OmD26ZJX(C z=JgT8E#__^zm(mQMA6fA+yC7x`X*d<1sWH3iHPFBPPdP7-4{Z2mfs&ma0Gz%f@K-T z5=~*T9}ZeNj4ZfLedS4(RSE@z?y3ubTgOK^+OKiVt?#!KH$&V4uC#bgrZ#akNi;Sy6MlDSg0L5XSU#`a}0LR z74YU3j8Xej=q~J_nB3<$9N{M4r8xiau4-{1oAl~F?s&~tuKu0I*-QqVU|aY>*(BVg zVUGOvL$39a2zaGFPFg@|?R-d_fiFaT_=FN3y*nSKl&DT1py7(IVX94B?f^^R zkf$XHrU%KXxhuwJ`xk}uw(=INS^Bot#C^7CES{UFWYESc5q&v7=?K49PRy}d9?yyO zlKpI}B1Hf0=T}8Q)ghCGn^l=(`Eu0@84S^9wf*hu=)H#y=bFt| z_*a`M16%TYvjHu|XU=>sfvjW}MEZtxdj|gR1By*B62RmK#(E|Hjb}_-Qs8M$S~j}V zBDlM6Rbu^#<>@tPMlFb6CCs|e8St3sLACU$jFrA;~o^UBC)A|`#`-D#$*C1!F~=^6&x z98>60dE#eZet5hi3#02yrm3%`#(UZCzV}W`NPvr@A^HkDQcLwv%f!(RF!wY2^U4Ni z69KhtY0I`F{-A%InN==rQr~O6&nezx<>wcl7wp-dERaD#?Y${Ghja`}%}E7GRQ#p! zXj)?4oF1{pSnC~b-ri`U()B9p5<>95#ezCve~uChvHN)^iKcK4#hnf)lHAeC&fDI0 z0fH0h1jw|WI`7dm)e-80Y*N(qs?47VC#EUKd@_!AtS7P}&!gQPeqTSGaH}F9<4B1M zp(jdAb2$%|t0x1;h|HeP7hT&DioKV2gEtQkB+|vkfs)I-k%g#Ix`bC@?M~XyTki0$ zv9f&emrhT*QEv0yntiCj&|;~v!KM7_!A-w(WKC)wN^<$DK&Gi!xz9Q_&ZVh3#_AZz&QeL+|S>%a3o6FxA80_C{ij+f6A7N4QQiA^1~| z%9&9X5?7M-@ZCMn!a=0CnxO>6G$)3o#Lt8&lZmTbL5-2?aAEg87QXR4Q$bGQnTj&Q zv?xDmAhRZ~aw25d5O58E*dO0QCvp3~d_JAIIXZd%LA_#?$NrWe@n_W+UBdaF1}f~f zI*xDxRRH$u11p`A`Nk5O?)TUbkelZfHkZn9a^h;OpNj#ijn#aI$d`q zC%A$V!zv{HP(_Rbe`dIW51IS?*GK4FpEaKIW4$S8kKR!!(5!=o5taEmq+oC#Kr^2b z0_oTP9F+_whU(jDHpCW3FgvvNv4#t1Oz{1(H}coT^6N_1#2XpJ`@XmKtX#|(OS##Q zhrNA(evay6f zL1D$4aMn-QoM)5ZID;0I1K(yg!7Y=tABfDGaj%d3dnFQ&!Je|?jF&Z#t}gAe5)|OvFO0X;pmR- zo>KkEvrNWj%zk~KwDkGX0q-2Bcd9}Z(ljg?fNfVv%8UMLRuw@8?imkd>$Mwj} zFfXdyf{nLk9cJM3@1|8)!55VDs^NmF(o%#a`{-2jpSbVxe~@Gis$`-lCEv9% z1b1v8uOrDd?9oSti*;+)eig9yc`_}BP9Nl)cwgCb&P3A@Lmqzg9PNi0Cis?1ag0is6ta^_R!JXkG{%FA!HL{~Z;?(t2&CKco7s?`3G7TMQpq3ch$2 zqmC)pd-P>@5_SZv=lK;fx~nvaAwb+9AAg1+Bt?x))kgzdSlCaD!7D9RSVRhGFIbL6 zaK|IodfJZiOUu#Tew;L1B=1HKYCT zO&UwDJqGXX?gJ_ULmvLtDd`Y+*tU}pZg1S%>=DnEAHRuJbsW)}XwfwwTI{i(UQ z9uzi|2B!&f|}tt6KwVAAH2|2 zy6sf0iT}|VZJvom-?^-`>6V!k^Vi_D&NNNM@gfmfpX$qx62dueCUhvF0H-J%Pj*k| z=A=i}a`|2@0uhhnoLYLV^24@JAKGqczYuu0KWFuMEwmK-FZ56zGw#CKvDO_P<`{f# z!tf%yx(+B&ivw1fPc4M8v7b&@r#Bj?B$V%cR4`l3VKZd3W_{rj>a~-TRzJECA8bnB zp+p|#JaBq<*!SUt44FQdn~sIGk^FC2U?*w{EtNhPm_28I>zyi&W(eC76%DIy5oI47 z`=B}P;ji-8s+?0ZYxFe-<-}1$i)L%ZlRe@w=0nCt6u$SeiySAz=>^18qb}NjA2u5Wo#(hQ^z=-cY7ja@RsrR!)Nwx%3l6-80R+yVSXS> zO3KjTNzDD4WHq#!nIRd!HEFo<@++Pi!P_?PAGzf zaG1jyHi9O{t52K4?C#%B_K@rMUz)8}MXQfvrAwVkH)NIhyJ&$jISkHqt zV_?IMRbws#Qg*+h0!ek(wvx*#%N({YD?@i3pFp17MS9UbA?ynw_P!lFNB{FBmPbPj zGL#T9xa5ghE~;Xq;`IGm4}=HBF3~VRwC8h~NqBwK=CYadbi?E8K!)b;YBRm1BViq$ zKa%SylNh>nfZn^2;x!y$m@MezydZL{3?kx{5{9^^Q6L0`Qc#<$}>%4 ztI~kb04;yx$RQu>z3GO^ii`1V$|pD7!tl=3M+{Qk1|_pK=Hej}jwp`M#c3@CTn?Mn z2Pk-7dvm(U=7O1Hw&?TdPQYaw!CU0r$QroHT`a3rPyV@&%GMc~XgWh_V(ejU_F>UI zIZ9C6-gV7PGz#d}O@n50CiDB@85}Yk6*L5<1FV~6d8BrpeaMjxw{0F$+)h6z6ABC8|aydx~hRsPjx4zQSya6xaS`y z#>e^Vk1gX}+dQXBHjY1&)~_h>0`Jl|y0f_pCR#TH1SQ_@7_h_$);9PmiP;>bQ^#Ai z#umYQ=CfNrCk=O*G9;=95vjh?UDbT)hH+3}m zL&isG(M0>u%O6qkz>E^>MGWFHg;^0_ zt2$a)6c={VMWbr&@Uk*O%EP-!_H)0&n>$a+Y=lXG%c{%s@qD+qt9RYB%x+{%5^I?} z9#ogUh;go#uyXnH^#E^gu6O0yM$sgr52rM1!naEs1&*MtBY76YZ>iu;(7OF?yi=FU zmaoVgH>|(96^NK)IWxQwX4I@d=o)UX{kY(1^UphmIkV336^D`2j~6ty-Uys4rkU7* zEpmNs=$38!pIm-=(N9yEAMs@LzV`oX0n$Gk8i@W%~rp^s|z&-3KG6>GwGqS&d$|pB8 zmmqAYsc91d+IwFoO~y@ZqbUj17=`CPOG2or<$-C)+h`D|{6Oh_HzRt)hBmhRlmC0Re*LJLj)FQ7R}|+R&yghIYIlA5L<~s3m5z91@ZW*x+Td{vI|Ycjv7!>G=e>K%+RU^|vSDnfdp^d2ogr#9bi7i_eD9zwc2VBp-P@evNL zS-AyQO;yhN;~2U|3<>+;C(s>Gh5Tw7la}bxUb?&3B9C3$Q8aVDHZBe0PYNE4ysJ_> z0E~H`AL8M40U1dP?``(tuK~7R6rd4MG`T}5a+Axxu-4Yy^QW%XXy4MmfP&c;OB`Q* z^||o8Ri_}veG}&_7G0efWp>9&xZdx@{wQapgV}G7@xmB&oQ(?M&h{6i66NvM=YtM+ zJ-%1}>M7IUftKfrLjQ_>z-!{{bzI{`mF^mf8ZFHgFG0MmmM@5bEYgm7%Ih`9d(yR`39o-!FPMlz;6nJ*g0nSJDJJuC-4&E?f}h=xzL~?s%kXlV#bB zHqy=%ot}^}$u4Jxdwz6oo#^?_z@!xP>xCM&l=N(*iMWixH!r2Pi$&n&H&v5m=x#r> zkDgHbA~qJde-tPdMkD4*;luE`g}TX#x*3r*N}W}qkXT;orzzQA>+ ze>tP{RoX0Z3dCJ8b0YCY9i-POeT2h)L=#>JfKG&+7=c|wdjB5XY!lDe_E%O=Iw}LB z@QerFh0RJtyOs3O{WnQ;XomFiSJpMdUw9kB6jx!sgFzwNC%$uE&TFOinkwy!vu=dg zPwk%AoRVt-&g+9;rz;iQcrzOge!weTw^R{PaaRcG-`0n|O5OZ`V<^F=L4 zK#Z-#*&UBg)b<*m{NUe9+w6Qvab#IHt_T!j{?*gJkLQZ5ofsZS0GvNZ3R`<~SyQS| zUR^5cQJRThud_r_WUVj7{J-t+|K8j^T~JahyqGP?kEDtO6$LKUvSjL_wh ze>CDRtXdn)wRPB38MLN@o)yQ@fJ~!sVZmzF|N8y4EI5?dWd()s%!@8GKCG7j$^HZ$ zK^!bM{!9&y!pL|ugf3<0^D&E^A;dy89C1?N5R0*D4%@-i1tAC4^4+E3_FLuxSI2Ir z=Qm%vZnU_NLRf&+MO7lJhwj{&AvbSTNZkn;K~}qaCSm`P-xBe#=wo!iP~ zEYt`9uw~pO|B-;0eqn)Tdlw%DcsXOgEx*Ymj!z3tW77D8h*aG@6gqxm-=&}Jin4PA z%QG5E?<+?uS76o!4jVHFrLs%RH>h*udU+{;iGw?}*wa}%4;BSlx!^Wx#s@61^N6`a z>O&LM9jS%9ZW%0l*K6IG;hR}!qxF*G-IK$}7_|>q&PJTc(}7me-}E|8&HjP+29}29 z8_A>2i#cqdPXzS<0V-3W*ap5B9>I?closIE}4h=0l$($XsLM_6y`M7D$P&=c^ z{H&!Ij%#-ecm@@VarR1m{fa{p?-sQomg0PWatzD;?Vh>*tMIzWn*jDLte3LX64A@< z*-?y7wXBdTOtO}}|Ew*|z5!jv*RY@a!{~P@1ceUnITX)k&3%*auvtpi`tEyU>hpfL z39E6Wn|hk3vY<~9R94dNljq@ZcH={I?tlr)o@R{#$8^qjHv0VL*Zm(1gY|f0LgXdD zbR2t@X#I{Xea!dk6B7@%R=X@6)noBWN525G|GvGzc}@^As>)6)aPQW{P{q4yRYO2= zLHi@kE#<3!TxS!fTVsPePwYNfFry5uco{DsS95lefvY#WQjX?q)qBqBnNl0sQrz6` z?VgSvM}HTzGB7!T29mw=l{`L$tz2+FtNrcV_cMz8z}|{AJ|Fz7KQhrdkg7jGUH5rs zc(U0Ur0Qj2#o8Ir$_7H1na*++33NX9mOQ&EYA-r*VVk+#De+L3o|PVVn45|G7H!&4 z(PfIAZJd9jcu;9#zsqJFOOF=~c!vI*?~7z*xbAmb=sBQN6MA!oYBfS!EUr3m2GUQ?cT3B0*BkNb+myu2#9 zZa-u@_pwsZ>d}t?oWz&mcgiEV6ar6faJk<4p3g>lPPY4^|j$clxv*|{W$hNhl@vsO>zJwR8^5pfRd)xCVcqn&M9nt$W*?x= zFms=->A!+vA!_Ia_fPwjeZkVo4S_p3(nr}>l?61m0#o}OGFGV0w(3IaUuOL~l1;9g z8uxBnZ@rnFh*X{@$U0HUfiR*6s50r4kL>WE`6^W7*pf3hc}#5F7R+p>KP0@vO*bK)0-`R6~pcVPh*+?t)H=uz$F zJ{Qbu?IN3%c9U<=$XASTZ`t7b(4(9rZ}-t7_VoVY7y~oWq$mb{fuiV2xhd$2#_4)H zE>VrTQ&;It|8i2)3<%8)ED5sohUNhlym!34b<^=j&a*zDvBjjXQLnJ$UsXc7e9@ zqKnsnM{)p+nXlAn=Oj%Xe`RM@EE^$cLoPd=fShIUK$PyORi`d44h%!gt`x zTeIkqVdjtMu@BQ2e$*o^#CAvfI{dnp_yY7Yu{DCP!9|5fBX9vzwmVaPJcB*0h>Csj zmew)k!mk~C#E;=+1U2U)cG12RDza3dGjv!1>$+_k)#27hlEN#8KaPQMyh8S&Y-_$DecW;)mz3)!*Pph#*4k#|IMm~JX`$ZEk zf1pvtN9fZfGRohvN&Mtd>c+mJG5dzx!AIN{|+(z3M8x>iG5!klM!$tViVZ8$9i!Y7_(ynGWTup9GHt zl76pBoL{D>?)5FmOWb9%;h@(1(ha_Pi;2Y-;la{X@M6|9Zs5;I|D>mUeuu)1+c9U? zxK8^@yw(^GDqT8E%-cgTT3&mCZ8RoUC(mYl2`LvM`goe&SlyF0esASyGnO$`D#eCq zVZFE&Oj4LRMZ;_fDd-W{lMAi|8(w3i3gk#flK`)PrH}<9w|MBHZ~NC@5hmE96egpVqiZ=UeTXfUij|w)x%X%R4|aT>f4<2 zHByvt)4VSr<#YH<;b(1;S^;SXeBsMlj`e72sMSkl5+pv|RGi+hFr)I%%Q{Twt{V7_ z?n1RL9o-CKBf{x*nNc_k%uWE_-gHVh4DR^iKvU7Z7%bo$rS;SS%ME%r1XH*4k&QkL zU6l?C95*T4wTuz@8MF!Bd%&R9a9@)_tr1>fWL@dyL+Jn7x+9=~MGTsBPTk06__gYL zRo|9-lZO~;-9UEjuJQwa!T$2l!2Ft%&|OxxV;%_cRufBX@(}UYF}_r?Xw;%Gy?j&v z@7b|RXV!el>CuuSvBj0M8OzWu6*KA?pQ#$9I{e4s)#(&;uZid*$u+6BRS7XdlqiTK zX4ZY7uES2sh>f>^Id)$+01Mt@hr{cuwcFR*({^t z=63J4?j(R7KMdKT{ZPE~?;B+)hpmIPkkXmEogmdptIVvl3vafWIn$LLoxYC+si^bE zH|3Y;S@&3}H_^?X74w_U)HlQAEB})p{dW(ZpASE)2EiEkY5+5*=X3P3r|S7Q>)~-VBg1khfbDkHrwN)|CzkYz2Mk-^6ZVRRAEcEv zZ=-_^d#bhqUF-Jlg%llcS2Uzr+>vY@;E>ntt{49_W+U#GM&}_aHs$R9^$PM?*wzzy zbNT|{{39P1guR)kLQ44Sn+8$`$C+~%GV!NH4a>rn5-05@!@g?aba{fJVrLS}Z@m9M^ zfH*>XSSqJ=3M6G>eDXSJ0^ocFYaq(I8zIAy$5(I5*w|dlpJ7i;BBaaj0(c#{N+;Hy zscI7%S-Hu<5@)z;Bk!f{(rYgtxbjOpgHS7SdI#m27vi4_gAY92 zk^iB8%>2I@cdd&NEC&f*GfD;-w>sKdE$O51 zBgj8%=Y>>dEOXynyWPi2SJ*#<%4+-=M&ELD!v*l;XZFY1<+H!!yNnlVzm482Oye%r z)T4E5?e8*x=;D*o=o{{^?D%OzHZ^rc(C#1gO`afs?XfqP8SjX<>YtCUe!kRj`uf%_ zdx-AKvU{$N<&FnGiyd$V%lFNXnW0zK40KJ66nHRB%%yH^h!Fuu89cwgs}`Nqcz^pA zw(miU$y^%8WlhCzpIMwlR0A9SUCuv}7%5LhO4$!zJ?JO0?v@1%y_g;ggEb#n$j4~Z zu7){SxVLAUB$QN|_neA@(rn+4?`5t;LoUTSfXzQjcW}!kVjPFWZxmJ#P%uC>^xd&bQdzexQaq|8gX?n zhVP~9RLnE~RT+s&wKYR@(1I)Sy4cR#VA^@q?;1F7J6Z(tjAhg3eo0Xmv(e z&+{!df~F!e!)(-^sRUV%??M2{xL7d(Q)Zf4kKJ6>zNu^^pBaGfTb`P~#(voU(R9{- zP5tj12N5QUOhu#yLs3yWBt|0w1|i*@(gQYXw1P;BC=H5qgLLP}O{8HkV8Cb?qw~A> z=kfUdaQ=YvIOp8we%<$VJ+ChMgLT{GXOl|%C-EskqN<{)+S1j;jNh8gC;cE&0;GHO zF#90KjDOo{@wBc`$GrRlUK^I^+h9`(>CfEGrKl$_c^$(=ngO4s<=zE#gZK z)#I^ z3@A)~#*)~$d;fO6_`L6@628T#$9*Tkm;2>xsgZsoUD>7N#Nw|b#LcqPnF$Mvq4mpv z>9_s0E>izB091&}&@vqDV>4%S^HqsRaPyzd{Rtw?bSO;s>>P)AD~-7Vsg~H!M#$?Q@fxGggQOrkG0#^Tsb<=C zA5=||8KA>mFoks}H;_uDB~V5ao3>O(CszRcK6KDt#3sVjwtXn|-jijF=+jhmrhf&g z>pOC5Wxo3%P~ic*@UHRF?>D8brzr0CC5g6XDy9ZIIs!fJzJZ`l16rWF`BBf&UlY@a z{IfOj#W&YDpO^wR>iVhdK*W-)3zz#V^?Ea+Gs@9{YNNvZ1*e-Hm2YX!4rCsvwk$vk zD`NZjGdutJqyqz5X0zWgl=z9S9wMcM!XNWZ!fA_x=pFhwCp>tOIb?@&ie@`NVx+w~ZEkVlZIRx9X z(6U*$wxA`qwa6H`MnFnu+q6_9+am||qM3rtXG;xp(ZcGzd*+o@W;R!c>?fZnZYNw# z`2M%J%|d65gA@@SMtfs3H-Aeuv?e|@A+NgTo^EV$BFxlZr!_0KlvOy!%lXtWw$xe= z^?3$om}m1{1LUe$RUkhnJ+s~S9O%7&Qu-G4&@SoXxMfElG-V% z;l792ww?KGty=3@axYTCoVr7%5-YLj=SjQ!ppWuTmlf@Cb|zDM61&g1e*>*Ii~cyk z%xvh%<(%(}Ker1WEz{TUonB9TngLBEJsbU!5*`>}WBPO|C@Zqt`zn(dO%%`E9a~jjDjCat@!MI4O2U zpVTmt!QldIl@V#f4iDZt-%S#IFw*Yb^b>h;We?c)#loe0PCZ)pyh-pRD2Bfb8LU^< z?kQKg|AIB{;Pjz;4zi)1ydJq3RLt9bvP=fCyT=W251bK+(Wyr>oilMXE;js>si0x* zdUc_{t{%YmdSm_HB*8m0X1QhQ_uYO!hm#_YgflKFtBs-_QHCP}dDP28cuaG0OWfn> zwy$inPm8j09|c+QY%4LMUd0ZNC!Ri$UGl$`S`dmq{J?Bbx;g5lZ7mDV+!(uDo-XWj zK6^2w#C4Y1!!mvB4K;FT$<(v4y0my7zEwswHBIYSRYh!Y5QwV1G(i_&nfK1d#9{F6 zI5v~8al;WhcRo<+igPXNx!Z z|I;XwY=OqcJ_HshE~l&=wFTl)sd?^csi0smo|2@;^nU2I1fwvSuKDJkn;-rB{^a35 zMGR3LzL)`?iD3;_Nfj^Zo81>v4Yvab$HLd{w&cbF=Cj|cNpY% z3-o6@3EWU9`upl_I0oDQ9GGA-{-*h!_P|1sF&m?#Zr8YXt&U=XXsHhi^Y>@*M-iEy zK0|f=R)5bb8Hei$DrhCj+W8UZX(=+f&H9l=cE()4{#m1oTo0N`Ujt*+Va^D>rh?CB zcU&a#P``s3u9*9Hj8&2P3cSkrLi2X7ErVgZtF6ryz4GuXr22Dc-?RC`^?gFNfY_cCoaKXClG&nae+MG9lA#d?5C?-wS z!C#-6@yzvQJ}ppk&D3~5Lwy0Z(?SDi*DMaDK69}hti0|}D!lkbyIo>x zT2PW^oH*d9y1q=&Cj$)q}#CY3P zrup*$;(|)QDzT2eRhYD~(b6HClMmIGI|)Z4=d4#QCG844yB^msvM7kI`!Cc~Ax@aD z<_vAqX{tLb{aS@xd$)8?@g5Ra)84=>uH+cufddXVcH5|5SuF@|xVWls3X*5DWsEU^ zXEXRL+)+S8Mr ztcP%gRx#^Kn1xo1_w%t|(voZo#719p#Qlsm^t=)%lry0ior{SI-Q5tU09tSeQ7=5f z-+Y`l|4QScXIS-2CX{3^qWoXM{|7NM8EYy2jX7DyLC3mX+r|RV!)E#MpF2e9+9bId zH>w!dU#5BfIW?*&Sc90>ZX_2lM^#l_^L2vcBK@ysXfa0|lI2^wAFQG1Z_TX@K~|Ro zzD@g!)%uf*S;GA6wK$RBXkwZ`G9Zpg+|fPr-E~tRKl=^*3psdN(kPQ?`+*KIM%V)( zy*dpqe&p<=p1e7ZR9fZP{rf)Xtn2rq*TYMQCKWk?=#p#uXIs|heNVBC@V@J23|2j^ zhbg_~m;ok6?5_!h88`PV$cI30KkOY1malg9Y)kc`5W^`8V#311P(-2Yo{rRn*I~(6 zk}7tt*Lm%&iF0R5B)c7vDWb?)Pq6{9Y1r5I{jK_(m=A9LS$r~_QQF|XlJyO?)Vyc}I zrBBY`SMl|lLpdabq-$r{FnWr=3OIO?ughUl_(tPOK$w7R;(%|fjQ=wpU6}wXTWu{&J;+ZbjfDNjliHE3->^`0+!uW2QA+v zhP>I}rjQ>uBLQi?P0W6rXvJ%Z<4g1`W_z}Z#$yw|g>U=ZExpVZo9~ar2Kl)_Y}e{S z3A^i~PUeF!CElAo2{lsUv4nh!im$3j^Coid`5{RtHr55%>FUi^6Li=Xd1-k&=8}H{ zy1VQs;G+?45=T~l^Na4aUMXZ#^Dgk*uhcAmgoY_vj*uba-9fa}!ZAQfUk-86gvdsXYU zF_!lWO!OWkWnP?5ksxTgjYCVd@9@)J)f;Qqi^s-reOg`%E#TP**H%9OoK1Z5#*wxG zQ9Hcz3NHb=nAs>_gQIQ=PVGYme>XwmXnGY4vil%r7E{7Oo9H*|`G26jD87)3aij%l zTY$XtsDPr^0P7@~;xtT_n+}X7Jox@8r)5;$@ocWtvBXzX1<4mvpz5e`J3ll!g-WuaZK)44hbI9WI4m#FE#Jq(#AczEM;eW{w$KRc=dlI)~WoS)0t}@!XmZ zP~O>AeJ~D2QzA>&1+U$HFgc#CI_{BZGk>xynWP`vq+n-n6gVG<$ULM;n`3}4>(fzJ z(k5QM3{UVCCttcHfTGu@s+B79crj^PDqJp%ssNi^KZ6~mZ!)C1+)pr2Q#+0QUP@)s z-Y|f8W24K$sHUuALuvE?CIk#6yke*PiixM7onzP#D{V)ZZzhaz{$z2V{qf;vT0%@! zEhZRDDuGU*9Y$SrS`NUCI;Io6XftVG-IDsPSiywXonj?I;N*`*S;>EOf;2EN{-R}i zw5*fhpR-(~mmW3{c;$*qk!>cM$(;M+nI@l?NgahV3uHXysR?)Cy^mShFe5@%`a9NS*zGTQ2P3EDIxz zJseGaS2lYYo~33lK9WbC|Fv=a=>(JX$VR;NFkURtTac=!UWJD!1!Hl+r%5%cHe4wG zg}ic@aT=)~uTkSqF@?BosjQ6CJ@wdJHGmd%U(Xd?I7ez29)-_ytk4w+qSn)>yEj5b&xa0)}vf_ZZQOK*^3Vkz>PA z@fymV<>R^itJJ^NhiQ>3X|E!Q2I?_Hk%Mh7bY>g>I34afz}E?olGPhA;?Pq0IGI~1 zwYrpR)4Xx)aoC|g{^MEcjY%%m?s$he`>W9wA?zWvm`=*!XuZs!v>&{S(3LJwA9dtD z8y1$ND#>i9G!(;Q6zJs&HAGMhwsn9##B0nDmlGcz(7GyVjkgcbp zO>5vQN>m_nd9n`uT_@3hZg01lzk@z-_o?ya_z0gJ9%RX4TW;-3AuH`)9)zDRNmj)1BvcGgMv>ZmZMzg}_l_~*`?Q67Cvgx|%$Lr;@248)L`Xz0Rx&oY4( zByC9}ZIJWoZm&0U!*I)zxg@|uN*Uqk>Uuyv=Aee-FwVS(;bB3iCsD*BoLickt6#u{ zAf?3dr6k8fNwa5d=HRNpMfCZj6vh!JEs42m|EkA@**vKe@fB|Fysoi!G%Sm9+e91! zba34?5tOo;OZzr0GCMGB-P%~$4 zRDFX`2m!39uyzzZ{DXV{&Rjbw_5AOolJM+>2XjzN(QfO5@LmS;$=QlPZ{^46oN>963%V^9a<^>Jz^DeO?h>vMPS_n z<%@BNO%LwYF}nu>VFxqR1|oJw=?Vsq_|A>q@aQ4NX?Q~&!XL`@u(K-%(r1TwE6N&k zlFUH|on9+eH+$LI1Rq*OdVI~M75z-J5eI7{|8(>sRt3{J16GD-=sB0~yZ&i6ik#Ke zKT;#_8C65<$&?d4UKU$FQYug@*5;3KySBJhxcx{tWve|DlKA9C1|WWl)?2a!H3Kr< z(65l1va=StE1#1vSJzUg*cZs}Z0l!pp^dhBv9wi!>bFgnD-hi;O#4mvZG3FhX7=yi zL^RpgOrw~4XijC#X^QKLaff1P2G^fY`|sX3GyZsxywyt~Gfs(};Rq=oZ+%);(?DP{2BK=3+QfH`UXF`PH?G(o z19qYYN@UM}NvVVF8b1qezfoGzh_dk!fp|Bc!dV%Xp;AC0K8oouLmiiNNN?|U4CI&8 zClOQ5JubC*@&1UJH+_8uf9o^0({qaL2Y!W$0_Qf(L2vzjCVuKp_dYNSyvBdEK1MQd z#T>8@K6zY`Z%eHtPQvkCc_9^Ft;0-21jxh>b+Bzmm$=rp6_SF5<w*JK}h>>c= z&TKFs(46zMTA0f&aUjf3ndFL}_>X20D-!E|=noOln`HKwug_*wS8 zNp{&8yOcX+IgCH}rPgRN6~oJ8GBO=0@VU=y(&+ktnmGq`=PAuqj(%1|mW0)IdmXCD zVVT2;XP`&OQRvOiZIkEGksfd33g@4U>=^U^6G>j}(rK#v>H7igdZ-E2v6_HA9s{0pICA^>&?;qT)F~gB423?`tLCl9 zlMt5SrlM-8GfM`I%~GG9^8@b3cj<}dM4UeioZB*uh^qfd{p6teGJ%;eb)xlcd?uhk zbpo3v_!|1)sjx><+?$hooFIcp1n11_$9Qq}m8l-hK||g0m%0f- z50PLjahw2mx7*ceNrWGFV+9^^S5Fj==Cor7iQUla(J@XO$=Abx9oXjdPz* zY>|_H(7I_?G2FY&vWHO6QQ?fP&bZG2iK+V;eNiU1uy+)=bA_C3RGNY7*!;p8pGm!( zqxRXmVR+|J{tc?Th9pr7ooKU|E$LE^GwH$FbP#uG`;DC;)Mz=C4e9avd7d#%RxCo@!LND+02ryTMVG%n9S0*C*D~0oY0mAi zZK3M4D@pGjnr$Ym8oKl1MandAI#-kyT8-t&WKJpvm-iEuQfpnVt)9#CPK(n&+8-!| z_j*5aSZBPiUm#p3&Il$U9z4$H8DzfLArAQkQ^76Sc1A3IZYQzEAIqeceHg>pjEk}d zB74mRExYIfj~%_&_|3wI0ow}7o?=cL^K6LIS#*0imIU-bTve4<*W1ZkaXYa)3b+I@ znuFN4_=W9x^++tJvY3((g8dPF6#k3~bFKv-0IwG1F{l5ArWa*sY~WElw_Y!dX7$=u?wgm%m*o?UlR=pj&-QZ&X3=xk#KyoEVXAWbxSg|6oKoD>d_4U z5=-wh`IGt{d|#ioP~_iiy*?K7qDv*UlYYvyl;vJmrR4jv2JRory*7Rs+hLpe2}_Ko zCU#q6v&!mGyT*`SRI=4XW0S&*N4VbrbI9Mt>ikt|h*tBG$oxscITyWJqr}y^b`>}t z--cMJ-o;CflE(3W&71S_oqq_RI@cAOo<0@XGJeU!LhyQx?_VTR>%y0NvwQ7)O6}CJ zx)Cy!8&gkAhq7Ojs@B?+Q@;?|T;TXABRhMtGF1(sj4m#dk92_TK0Y2_C&YI(t(-h2 zzjLlL*t?H(Ae>e{8Ce4Euun+Ne#wNYZ>b)W%iCfFL*l%} zLIl+apD%74q+9WJfN!QRSpO*npS}61-k$0-6EyFCJ&Q+Ag`j3L)T*Ex3Pl-<*D3mm zsI1d~3*0fYwK$~gA*c97t}AFfz}0Wxicmoep!Le|_JxYkWZw*zAfuq#lsybMKQ6ku zi8ph9p0Phz1muq1`0gm1|2!W~>nv*R9}&O|5K< z$``#;6$wo4n_Jrf>kmrLu-nLks||9HxDD)+1jgp77Layub_N}1IkLj4l1WZ7-SIWw zR5QhrpX|Aj^Th>uG-)rR8jYC5jbvGs1kDBRA0KS;IP7=tWZ&ePWxB~`XQG?UC}oNN3(L#| zQfkX+dxicJSNh%l7Wc!D>2`;R9J#*BRN{NY;`^a=^`V}sgaP+Lmj=m@w}FyxI^IM= zhYJJfeRrSRN5BS{*+dq(b_CW1P2hqM@!iw)CXbmLxlN^335UOg9#}mGhBhPtH@^Om z_nm*0MdjDdf{hp%W4)7YJJYd;eP%;56v4sf(`CMu+}JMIEpX879+!6va?I&t1TMX- z_1W70rfuWrey0$*DD~|2IjHeS2iP35XXJC&!$!hn+y{jzL#bAXd1vKm-UrKXS#Ca! z8_KymB@6P%zp@Ds+fk8(MJF@;so>b#sdZ{KR>opJ(0=$;YusQa)&*X5YNSd zx}07<9&Yt#IAoJb3k3PJ;R9C{k1@h1B%-F`k1b`7s^``4NLl;Ik0t>6svgKXEcRp$ zwn2S7Ib#|EY(2PU>^GLsmi3dJ{*NRnUJusAEr3F)Sr38SrfX?>Fz>24mTci|6tS}% z(N|0CE{Fqp%t^NL3MFevn%fCg3*PGx7bQCrIYsVe%^C$^wDKwq7VNw-ZmX+yRV0U0 zS`Twlm{RuAR?_rtU8R#5q!%4yL+Jt5a9!58DFL0J%@}_v8nj0-xdDD#?cUCLdGO!# zT>Ytezd>P4DwTe4^_Q=)hHN=3#)vwy;lJw;#rMVwV*EZQT%FDY6HqH-Mz zs~AMq&PNit0kwQs;e{X>@T9$`uOT+d!inhl;?hZYLeA7#_tOWqa{zzPx{m{ErcZ9e zU`9@<0;@#0lSMl=2{xI6`D&J-bw*zF&E(Qzb^7)7K9{=h!G!qd%F=H98N@!XMQZGM zhsD-WhLw5`5_OhWx3<4CTj2uJwz#S|TJR}L$+#_frW}KDnM62-anGG)9~a3=fPa{s z8iS<%&hhp1XH5aTvUQ$M1y3{?*&sbnebBLxw5VemfV=hP2W)-JexkFvp|`46k)024OzZvRrOZ|2NvkTn6T z<9LvwklRSf+=kj}khsCjxH@c^VHNPiPdu;0>-J!!@RY8|ioUcuE{5C#bHaL2g`4r{ zKa7MI+F z>QqQt-QHo&Ppdr~FlFj0YT`VYZWU|Iep-+DH!3o;zUS%~miridDE^7n4f zJvBq0trRW3`}5DdcnU*4<$4PeEwXI>Uc&^b`B(>n3jVgv_q(jUyaQG>C8Pe#jk?&J z+KFFa82_#36?uN(m~y6LZ$5ycFf`*hdU!JC4|6~EugnU}v}!f*_YUS|=&kOB&A?A- z�&l{3Rb)`96xB;X;OdQ3+ht$hLDNNWa}-@;XqcUrHCR;fTJOD{-cxVKiKNb8`H- zBb&iKkMf-k+b+(p?R8@)qi;ZE9}LW_ohWljE46;v2y|>UlRPsx9V388Fsif z$a(%3iwk)-7fKAc?A;FY-xyGDGiyGuIbH>~GI}A-th0=p*HWpj|LmvLWm+|wqK-1U z`Z=$^pT+l2!W=Q*1#t=Je(K^)Dk+CwRig&P1i^cItFfEyxVa{VysHyaC(I6$^bOgL zI>Dgoor+sa^7#}wjd5BKOi;1izxq+y1qYH2fR-{Yz0Bg9gWPz0=Wt}j)CXO;QdS93 zwC}7}#`^DTX5dfv)RgSkvn4__Arb}y(+Blqe`(ydUKQamr_{J^$)wPI2JVG06oo#I zjPmfsgE-n7%HP+v!8W)2HSmvCmS~+()t1UnyvcGKN!;%mBvI;SmF!5%!*bYw|O z;29<-$2a-*xOMb3wG_nma)A};Xs5#pHC!A!_{(DuYrKJTo!Oo{pOkqO!zaRoSOriq%J z7iY90G-@y&F*Axi$?Qo6Q(*S@LhA@)0JnhwQ9*32Bb@bn$L7P|+`*{r@Yus$Ro7_r^Z&1^qc|IS_ z-TcBe}WQ@<~&Ge`Y!}oMsf9!a+*J-(|nk6#^ z3vPUK$lj_mptRHxVDZIKF}tymIGJ+Iw+ob6IRWyfnFngH8JPI7reqitlt9q@aBCBm zX2axm5)&|e@%%3V<`EUW&%kN?bQ6}*Kr3SC(S}UZ4cD)T@?mIv4du0l9P2*G^X4># za+SXD6MCCo9NB~#6I4yqf?;$nJum!+d}&QTDh#_GN_HrGacvg^f;!Z#^3p<+9m1r$)(8)R|L(z4*b^8z^Mwb-g|T(!kXUwe{+_j!yqJ zJHr|3n)m=_IcU~!zkJksR$1U~`iTkU&+WAji&f(b#T%RGkDvqVXdBH~(J*HXlih?@ z@)hg()4dsUIv(=`y=eE`N+r3THdZD1V9`**v;GD1CdAj0LF8TH&|MDNd#tqZXeFwz zZl{@1k_1-*MC!p&smwBc|FSrJ84|c~SaOk+L20fq@<126OO#e`r24PrMV*}h7guqy zgD#Wev~?sF$zrE#Gg;rx6Yb5D&(Wq{UUubJ=limjT@id2 zGdjZ(twAC658$^VHIz<1Y78Q^45;>0^jD;r?g?d_uuampPQrjLg)%}&B_=h^FZ2Je z1z@p}?%s~7j?*wf>(_>cM^R?~(KB7)u=+yd05x`*B_35VuLG=SE3Qof@>}Bx$+U zuBjAxHm^Hn&sl819kkz=^6o#g78<gyliva8|d@gq6i zJ8_q8V74Q(LknzeFd07;g`PR=0VcRt5mk-lR4(;i%c@FdaKCRMpo|z<@%$8ir=IN9D?M=m=+OHVl;v9j*!F2=NZ#o z@olT>p@Rh(tx_R(e^4Cp<|`BmyJxIerSa%&J|?n=%#59@fF)m+$R;A2i0`9WJ(ivL zuBmI@L2qo(asas35&5Cr`V6I8ZdC2$>@x3)J&;1o1Nl1eSL;lSc)Fa@tP2_^VIE_QhU0_~W9$r!B%=D((f)=0 zVJM+auyadRMw;$$>uQ;uR;D3u6=*h92NuVr`w@<1F|i3Uf)qVYqm{z9ifMb!{h1g& zRwP29*_{+-f&S1x1iVSoJ+W(vokI5~3Oi2yf0IF2!qOnb!zRt9MooagbpLIfdG++> zuVyR0MqMW|nDVffuV>5@)8fI>64`)`hoSb@Dmq45U6C4eEqs#PH2ZRxhfVTyZz=w8 zXtHxbQBiB_l};nq+tf^|{Po5R8pWBnGTx6%(Hk0DZGx?sQi*EN4S_(P^fAIeIiZP( z^psqz@NT>}Yf%S7BHkNrO_g-K(;DN0VpTl2{S;#~+H@S_+BbL%3y8QA>ZWqpq1 zWg8K`fLSyyxCCY!O*~rS;Y`<_xL~x)Q4q9CrvO(_4Cg1^mnvL(T&9r#RBZkGaFKt=svdlA@rC*+gMw=|dm;O>(j z#=c!%oie+*w+IWh8L>NT_#&SMIC3Q#O26*|9V@4imr#lK}YhB};@iX-JkUmlCU60U!g#n(^h^Kc zq1Y$ZrV@R<#@}rXRwANbr3UCtbMa_}XFbVx@SRm!arH@);>sMIe{8TRzJJa1yDn@I z6ls{A#y(H@w$d-p_OpEgGfkU*8RRrE2`B(f)X;6z^i)*3!?UCQg`UYk%5^g8__YS= zP$Njox4Cd1)7xcrcoi*LQ>xYCBG&iapPLeBv^PwKiWa}3#AnE)dr(cDQyM12KjH*X zJp}0tJ2jW`ZCxT!vjisuo-D|>F8DNb;8ODR=|R`4PpZk|q;voSBff;&vGopZg`AHj z0ZdtcRz33A;(A;hrTNFO{fBFaDKpzBkQR7L65XNhRIEEr+soAndaD@><;~xqv@#ZA zB&y92#DmO`c!Cq=&%ot|J4Z~OBr z?Ui}kU6h~WzV1pir0o^i9gp$YpYUKy7n@pG2iS8Q=WA@`|1mo^c45i$6}BisEAJv0 z3X#NB;Q){ANuL$9_xoj}4*c4BenY3|)-Mm$L?502d=@BwyxawS+(pc{mi8`a4-n6U zc-&rkrO>)G2+f%oSY2W_|6OyQ?n8$vJj`RP0A)JNEzeH8miUwLOZ~?%*Rea(VKwoU zItGn!mmW|{FH4n()B$run?U?z0J$2%>rzUlc531fK2%9HCmXXWj~?BXf!WE_IJEs% zV)wH6&g3eI!fW{5IZsPgI@$Q_8{1?h;@Pb4&7cqc!a5n%*mE%%lkg(VHJ**wvZ6Cl z`UmdrrL9F^p_RP1Nu|ZIK6zg^PR6F69$H@8=EgM*P~6$bm3dWNQ)Ira9ZB~q%7~dp zm-A}6;khbLnTie?+-I{Dqyn&&2Sp1|Sq1t=wtJ6N3Ny;yu3f|~yp>`GZn_wz@5!Wn zlI_xR3$&d0JT({UeaI~v5ovnHM;gnUNfqNtwOmenn2zUD`MOB+_x8h$_2#;trS1B+ zwYGMFeeuc~uF?XuhLxd+BP~%Vu1Jh@0T<^uWracn$h`N%kLC%kbCJSx)xb^)X^N2m z;z2q{9`EQO2Lv!4^%O)ct_RVun6chr&gIu9i@eW5vuFxDguijlIFZeoejxq#WS6cIfw!d$czlN zo)3AJAbafQ78ZOHg~JzRjs%S`Om{2e@LM%u%Uu%#csk<e_qkqtzWpSM&Q>5=?$9T-tBPju%V~4Hof*5wuuY3ytDeEen&~lz zVeQ*H8_cyQs-$&Hp3k#Fy#ayfGax7*>`X|m$5b@csrO{u-|*olL;JR(Rz~_D^ zyKF@O*)ArHfVljkA0v#_?w;EHOrtF?9a z1Nu3#79xjH-N`T2|Mo7>BcP0XNO^WCcz3dPyBjyx08}=s8YZ{mBQG66T)4m<7mWcS zu`P~PRlXi`rEFCC1oc?Y9lcG=GN*=ynh-Ld zmxRE62zo9jZxmz%+v^8hm%0{yD9bVXU=7}gbu)0z+52cF^nOZ%4Y@qL{Op>fDY)A% zartNGK)=R71UbKyvdOL6NtVr3p4yK6ZxZ`+AvImMK$G3pc3Kl{tjoivObn#oldIw1 zZChm3+}16_U;N-kJ1?$ca$olW}QMlq`=vpW3;qRY=_umZ>ucByue`_z}Tzuc>ypRxoS>$GF(;@`Mbn&#XYu&%dz>eh04k)K~r%^ zI{e{uiD^>e&zpAh0p~pmyNfaE3!dk+8DWwYK&kMm%mW?c<7ux2A6cTt=2WeZKGL`v zgV6usAWKorR3sX@@lFg%R7|Z~<`jd@j~;wF z`N`G!)NQ))f8Twq)|@Ow&2(tarWEuOA=yt|8xg9eFA`Sv5AD-hfG@QudpF%m0lgOtYT7|k=5}%`tl|A9{lwBxL0PKW z!Td*KMP7NSX^PUc_eTp#mVPsfl`i9)oI#=~a*v(3@RXWjvS}ty$O~C{^2%mSdPXc# zCDg^Cvc=iwP6Hswvm2*6Udc~g@bY%%%KVHU4$fb@mf*Ig5fzpkeRXI-aBIXN==Jg} z>I-V{o%6(xWg8;uqIl(V?WKdZyVM%QNyTn+@Q>rk>pawx9r2=B#(#CZuZe^cFB_uO zY+X9%Q>FUtONIHlkdi{9p%?DSwh)`Z0chJqv}_(kROU#1cYo8$cqBV%sAg2Ocy9Ud-?3hwH*QA= zGI(q<)hVN7EA@GZk|zNC)?lO85*&3RWkdf*XKApyzQ2ov}q*&qJ}skc$i|c zjfHS=Ovct~PMo^{?4||oP1LPc??_k%w)wP(bmn0%KuuTC3=9205=Ry#sn&)v-|YDu zYx~LVQ%IC~cVRnYcT&~{Z4a|P=IHm9NqM9w^o&?5uq}O^O8WZpZ*`~n!P=efhESbCmL^vL}!->PNL1WfZq^?m-9IUmG}3cB>c4ztq2M&%|D&ApEhS!;ez-s`yL zQG9AWZ9Lb(xLyU*!AkqlVLcvIOffH8xZ3gC=ZBX0{wTozt#MW3l#HZj-HfATC4^fu z&2ZC<*x_6G3|VJ#SK%_cSfESlP8JS+1~( z_G?_UO@)pFyP>x>?C0e_*<=@6G?w~zRdem)FQ>kH2R6JD``uDh;e%>#M0`t~@s*8h z*m}%j#o(GN&?+K7Hd4zRI+K*rrEMtGeC{`;_T-0CtH>L?cOBhOeX)-scAj2fT3S@A$f@^H}Ft>vRTqu~MvH z?-BLIP}h1guQ~6Vx)eAY^CIPMdl&E=ua9>00x{6?EcdQDhB{n5QSlCZ2NIi>{kbG< zH~GQGt#%D;VP<0FKME^8=aS>qxBuM`%&Q50tA_(l@Z3Zzn{VPwMBfuDl_84Z;AnOl#p^2Ha@ zV?Vk2>ol%UX{2qJU#_8?qaE4UDNCeu>EpuaMs0H?p~c`70Z$+DVM7Z5y8d6)@Oso} zVQpxi^9%bUPr?dSLGX2{&cs`Kpyol8(42aa7_t$|-yweae|PJzcZp zs)NJ}@)@o)psH@uJkiy)`ba|0V;mS4OU)=85Qpmpgj0Fqj>3OUl6l3zAS3)gTO z*cibXv~KR@unn|LQuanz3LN@f{ClwJg!zPRvm-EKYHu}c2qA-N7o5(SGCrP=NiEl# zwS6i(**UJryc8Eb1hq`8l&PS{X)=A#u?rWj2uSJqe0`FuPTRy=VARPMu0Zi_AU&hl zr?iE(=WVyqnRA8r(tR}WU*5r%Q+aJL`v6G{9dBzTmJfml~!w*4i!{lRlR+C`w@j;u2 zHKt0bTIM(f*>w*7sXIMYE*`%cFCHGmN1>h_30(}k+C)9_!{FrMs8cnwoZ7^Ad`q)h zuTU)Bqrmf}&-}i(zt>_to21|R$LByDQBO?9tmA>9+;H0a3F3{gD&n#}iz7bR8-7`c zyqQ$Wm9Lwudp0OO(z97z5Q`X7ZWEF%veNfq6RfG3kf@$5oW7oTsS>qa`bT2@{ERwb zFtAtZkIaB5u_BM+tsZQt^!vLOJeu1hPIK#P$8kO>1Gpx51$FR(w1F1JT zTL0L5wT=40LsBv~2YfOK>2wsrKR{PJ*3_{LkWMVIJ&EyqI}&tL&#`fepBrF} zTf9mqVWPCRKcyFsMX(AW)p3UB!ldu~E}>Jav{UeySBiB)X8l>yuHDr=hL%H<f}@!9KsPTdb?a(hUMn$(j3SskU^9Jt279Y^^aEotb%bxo#e{lw0}Ib@B`F0egTx?w zr3uemwS8)8*Jrn-HUhkyk7xa!z87xjn8Q6k*|0C@3|)^#ecfNvE>oUe|6~teIQlTW zW%@u8?vkZUfF2>gHN$}v;k5ViBdqzUXBjz0o?j^rNDlXJW%fNwRgv~$ky$SNSCbD% zuYHnz)%SHOQuqVibyaXrPizQ0PBVHj4OcYr)VE|f0H&K{pR}fzI?^Cf+a{^QRIWou z3O|7cYyiBbswSrG_tgSOUg7%L^(+1xh!`e=wBPT7=cU*AJkea3x@kOfu}BGbNp|@I zcq5~3dS5))t2IUT`H!GLpq;Rq!-{9IMXv*EPn&{xABsw!8U=_# z(pTDkJ1Fv25^U^Pq%1UU+-wM+Gx5kX#AE{1T9=-#(sPzG1b&QD#TgJn4;ngf7Y1d6GKvW;t1}2{<9=GcQ8I6!PPXee8|d^xiID% ze8jhTU$vyBqhuF_8j|FZWBvJwXMyQV@h9v zcV&|oimc8Li~rj&j|F`~U9|g*`42$1ugzWzbPzVb*k`aGV)O9bR_N`2&5zEZ;#sWD zcf+@TE-f5;N%4QPuHJXEbCSYoKjSF(WSa7EYvSB46F}H)yr*wPn*lPmaS%l!g%+A1 zy#F>u=d>t;!w)@`Bf`B~EyU1m9y}cyAHJr|9&PJdQMcYG(7f_$%+rc99x2#LKmO{A zl2jk8=K$y-=On5Y8>CDxv)gv!2*fF4#{-I_rHB4n#oYs#j80X=Rj=saSE2P3XppFR zp|{YxzYP)`O-VL&>!AiqasqEl?cwZ-|@p2e%Vz496Vi4!yflH(^PK<=+ zU|h3#nd<;T>bN=n3p4!F=Q9j7NM$}BJx|7|+rJ2(`ZFlRdaQ%3K4N{ zV8Uw7vcjT0(26rgS5f!-qMTx5@%*xds=DLU4__}8$^ysLpi;P{PG$G@0D&{xjNlViWJ2w)Xq_ewKnO(yc$glqJ&l*erh_(Asq{HY>DEoh$@dmL0XhRY^-*_h$zYGBmS1htb_9X*!i-EmPBP+Q~IS$L_zhtdo zNlQI>R2{}4=ECmv?=uc>Fu|W6=jaj)BqK%%RuD|9@^HZqAch-GN_;tJUowY7#%fK_eA0b#f!k?(4q(MUI8F5zs$PnXUMn4f< z0RXg0LQ3WFcThTf&1?`F!MD)_E$7qGz(6{XwnstzT$X{c>=2{F`_}u`eB~akx}g0#C<8S>2_4{YYN&ZFMGW&cD(W? zwLPa2>rk$**;!@u%5ZwFG0c)RUbg!oGuYn|b2pj6i(QH{drEJYt5S?%(LpfztdxU> z{SHAW}rn$mZOp>^G5g1Zj~?Z?Bg1Pxn>VGlRN+#K$QGv=frn+$mzxZV|Sb z3`R@r@AP#nK{|B~={}8CHc}xg)*=WU)P2GeR{V6e5YWWfLTBF&>MJz?n+8KIWpynE z`kd#+4aHCamXUMN;N=gt?wXv_Xj{a5hgzBDq{y>3D%;m!DWixjBUD&MQ zfD|)x_liFHlw{3%zt-sP{r@b0&ko3#J@A-WaByn97`5`-N!gOaXEj%|TGAy!G+Hl2 z)3s)jyK9o{{OA~%Ct$((%;Hr;F=VYes(PPJ1`s+D)%INnXQf(}5pI_ZNiK2{vy95n z@A|2l9Lc<5A$+Jz(yp;;2r7GMXlqD{m%g)Hx;H;$xLF;M@y(XcNq|}0Dqp#w13O)TMGY6U3C>W^ z>?i4PU=Z<6PKqtJWM!sex`Dl$`IJrHwIo=T7bH#!tza@?t6Zi`gonbstFYLc`|ioYHNVB_FEKAoa7) zcBe^9?NC(E!yb0X#$5>!=mXp6enYn7N}&Sh*$wu`>LuAjpD>7p zuG&fVj$M2GkQ;f3Yr+KSv~dI8H-p<8Utme9J}CB}jHL5+?oWqGdmO(a%r(WKSusECc>$;RBdYzn<(tH$@+Fpy4_ zg<&J04_Mo5%~9K89XpAi>!9iS%6VkEx{R2~MpI6-5qFzcZmQ@ZITU7U25+|<9_=2zue{w~&;IH~eQAt} z_jF}0S^4czVR5Xo5W;C|0&_@K_56AN0!Pm|qzhk}2wE;JMTt7I93tKhEEm{6= z^JnLJFO&aSv28F?WPk;h;WvQUZgRe0>ZpsFv$pL#ZFiOmp{z)S^s1K3o?q`*6xb^A zQw1b9&vPCwXl8{S)lJnIUeXWuv}{%3uJvSj#xyrmKm%_W0ksU};y^`eqYxcKsBH_l zqbPZ!%`utM_B^DD-i0ZLLC)H%w+Zr4Y7{Z@%!pL2EXB5EGdEl_vZ2+)1i??XiHCb$ zX3wecK{wJbdIX55Ie?a*bhjyX+a>xuB$jR*`NB=LTC)%3m8?eq z+A?tJ^31z(%NXLG$NLXH1zw7oA&D#p8SbNPmJS)xrGK|m%&@5^up>Ku!~J`!9w8sH zw}BbN<*Eu8D4|15r=1*Z!YcWgJCYU|(HLuJET9+&qUauPR5!vW)pA!L7c^efC z{dX9~j`Jkno+FKHPG>g%?{!0J1tQtcX>}gF<-%*qo3eJ*+`?x1!3eMK!QvxJuE85S+i6I}(nK@ZP=9;LJj`LjXqB{w_hWb-I$f5Z>%td^B41B9zB zQ!N1Tuy`EZv_j4L-lDakss$=sr{skEuxh9b<4Qy??8>MEac0D%&FglS_d{eitAdd( zAJ8I^e)-h}&hXTjS#WYj@Tb`Zp@(Ar-yEI6PQ5o0KhuYSzWOt~$HV~UKLy5v#nu@u z9i_N;Gy0pE<3ym~(Fn>*f!g5GRMd&2}#nfCN@`BjlH} zcHHD7vpdP>^C;G~1~lNH(cT{(v`qg2HF7L1J_1=XJ@1As+TUj%ux|8zWwE$=28eNuqd?S3-rKE9!hzK2@*cIcQSq(Ni z=pY0}lqAv&VST)BQchS@V|lkxTa3W)0ctnR=FR1dgnxS;Wp#)7S>a9aNZnsG*i~hhjIm9^37Ohis zS-T*KEBrd5ns-o)2Lyjk$$#Er^47!Sw)ZaGSNO!a=^xyT)J$Ap|D{PcyxMnm)HLnr zttkr-KLa_oNbs7Vr&8JAJJynD0DDRT{3r%7y;ee1U6!^$*$MkrkKKG^2SM>3l*Qt# z;do~AeocxO3RLvVzwZ>*-1hgJv;Q{5_4gHX-ctUQ=+*7Ir273vUu~E!D{{oo1ync@ zAE=^}xHM<;G5e7Z!U4ZB?`vTXi9OkWui0fq38z@a72p&yZIjN_Ml)Jz5i{!wR;2NZ zl*V;yzCHW%V#|F>ozRkYUF*I9$0(V1MEG+`R#uDt(G&OtZ|R`>(&k~H;c+j~u7!P1 zc_#BmuxHSp(vC_(Ze-j!~5j8k#S+FUu_^DaXn6O&7PojXM*^Il6qtQ&=PrkXHm z+;^N1=f+e|{8(2mrSG8}6o-=GhTT!5I-v$&yfZ`pSt(Ob8l^bil~ZklJ_(vlwUi$P zMn^~O*SqFwt*9DXaiSn~%g$7`6eYzGi3s86UV0#ECbQ3{YJT1cZXHbIl-n5c@g*0FUs8U$5ZisAd5gRHXigJJ+B*9;mPBqggQTfGT1`o@#mkX?w9 z#|6w&c7fla6Oy{hd#`FlQdw`G{tgvZ1Jq$Jq4VLu;gz6qKigD%qYat_e)|j#h$&0g zP9}-=Z+W+5moFN=RJKBnx`Oze0ABwpzR5i+w)AXNK~S}~hud72I%us60X@vY69cVs z5r-8)2wr$nej`D;u{Q|*-9NE%)OnLixMyubvTwYs(`j+|%KS5Ceuncwy6t&fOKd5@ zB6izk)-n^@p^t{`em?A1u>5e)vi*3<_v}FwsZ5Zqo1(-8All>T=&%YJQ+~Kln;DHC z|MzLk0%0uhDYm55O*EYgF^j$+*n^?KvLWovY$M>LW#G$|96DRIL>UB%y}JSq#L);{ z=3%NCuBn8nJD|Dat1(lz+Xt9nc69loTI1S|7*!x7uTLj5QdYt=OfYdKTCP}9 zB3;PhjmX>KnYH&NCr+xGMT5KZr>3sXRkI2=T@mJD_^*z!sr4DFruI|b$6&gVfC}=7 zBK5}p6x50-<5ng#Klks)Vjb66u@x`gzZ=7toJs#M&Vi&673@0#cy00O{d_H*?aQSw zULnPHuU?gKf)U9;IFhfb-eQT;85|h+6&`Y+JSy(oZ0~uyjJ42Rq|bWlUe#x#1%WNC zy4yj9clPQShIVaQ&P}3cVT80H(B4k9PKba6yhC)W*H1+NATX7_Ve~~$l(p7Vij6+Q z{bfk#k@AY$d>O2EEAv(`-sn*5Ik_;$`XAd??Z*P>Wh*z>JQYzgOO91v821Q0W!*@OMPt_dbo9S0--W zE-LNj1FGZ~H3H)%AE~KY&1P3_8_6C@uaJlK{QGm)6gdTCbc*@xFz+y#ftK@cR_prV zg<`n~zDyFsv9sm)P9W>Z`L^+wI9Kym--qQnqI}AZcjPlReTxyy zQA?iMwB2kwtA*A&+{-q883=kY2ouk$>A$gp)Qp2eN9+oVoZ z%F@t~!M2qEgGk|cH2G~(|8M|489-kR>dQRJ`W}MOi zS#d?~@9r@U;M>lroQYnR>O7mCql5aTUi9X)May>J>!7rx*y^Lf=(hTZiq43Og&( z{XPE~$6sc!XSn}jsa#UC)a^fpWse#CM@BKvUoss05LT)y+aGWqT1l7yWIg@uV^#&T z;UAHFGZe_FGJN&doxw8jdhheqdjiS8x#L9m-;58hOYDwM&Y!!wPciA|?S!2OFZ#bc z;K^4`)ZsW?6CM?LS#nbf!gp=vph5uy@)5L%DNoCEvNw{X>e>`lK~@&zo7l23&yD%b zV{{rB%}|Y(cC*h!%KZ+^;EfMzXSd!pZ5~*~vNx>>d#=0^W_%_3e%rg6O>9|q4lV3D zUj~i!B75xCQE#n&HQk?4nHxTEn;X_TnETF-EkB4g`5`93HNqP;;XXayn;K{t`TXuE zfXL)ayAo3rM_f(Vm!F>4TZ8|hLWf~YM3?JqjL|&a-INMH8DC}k${Irb;|cA9RH!Fm zl21i#8MGqg3IR0JB#v=3DGa@ZA2uA|T2Rzn@{{>CG9bdXH_T)S2nBg(8Oe|(;`^K} zVaX>GVn;Yr-a8XTuLf5d{I$nTVj9!tXqFZ%8eLpyoMJ0@UPS~Wcl;NPoKT8W;zrT} zN+J$Tw`7fCmEkIT9)J1r$Wx5=)v$>IIXpgO*K~h=>PYu!?9}IAQ`!sW!zD%;zfYQ8m>?|Yxj_^(AXQ4n5>~(aRt8Wb_ z=*N&&{yLEbOFZ9b?0aoL8(=9%v)6xNE5}dtWD5=oi+lUz+=4SXq$Qt3@-0u6ayj#V zUAAnNUs{=(JAzWsc4~_Lmd$F|@UNm_kl~SLU*jov56+(8ZnAe(UW42PZ28pEq1vr* z%*WDtnn!qh-XJP<@!5SN;)A+~)p;lTT%|(OrI3@d6`S2Tmvm1g(6#WNkXA)r&kK%~ zI#R082H5W!ZIxmyRKb|#ZKPzUP&1bQvi9jDYsM*oS8?f#S8@K0EQ{I3yBfaoCF_ij z!zVVmbgQ>ZD(|VWb_Yk)Q>%f|Jm6zNjmr3cLX-jt?#ZkJBGwtQzFFL>@Jn(RW{zQU z@nK(La#2A{i{J#hT8{-MO=GP;KVoxgKuw`jcH{H`QLBZlLU9<$d%BSyoogx_q7WoL zAdVX;3p6@;R54^Y1mSKLb1(H#OfS|lxHWDc(-P!y-PxFt4IT-KovCBfyi#AWsza(5 z3z(ha;!3VZ%O;&%-T@1eOaS7E61A<>l)%GXxAot$I&$FyyQSp;!U0gr#g(UZ!%FLw zKX-}e0phZ(X)n27&3d)2;cTtCfd}5W*p5=ybggRNHg?QFU9R)j05VZ(01oUiUJU%| zspnWkX1(sbRF~{oJd+sZ^Xb=c#xc8g^xMWspAbuP=)gnR0yN<7_esM8x=gdi4Pz_Y zCjwQX>C;+j(h&_7J*!Dtxrt|qHEag2o2eN(ql&Mb6dw5?b9=^bNijWr9qZ^Y?&#eZ zD>@=Ky4CEW?liqoYQ$R$y(C=9h*EZ=28LpD(&K*13ALjv zHyA48Dp=yF)cI#Jl`rp&|5)f$gE>|G7!}gp2;NFF#AWw?8Om#>uCeh)??EitDqwV` zas4t2 zd6s6e1#Pt%Bb%I7X2~ax*!!@~qK{b#CTq{x<0fvGcgXeT2ymh@gvH9It!9QzHRc4W z{!-thA=Q!tzBBwYa$+yv8U6!3V2x4SO<6lQ7YS-BFb(D#yJghS+Bjgc`BnoGZScK! zXO2V;Ugh%~vQY5(q8Npz0bAdJo& z(g^IED=f^x8p|EC#Xy>}=M_^hL8})hUIynDup^wSEqfWwa6w@s8k7X$DjQrc*CF z5lbEyXd5)LJJKNH6Hh4#Nc^x0Uo=mh47U-^VZ?8?(BasS-gY^!|JUZs(Vjh4ZC zbo=S`{x*GFnWW!ppgRh=bZ1wlpMqW2uO!38hZOh7fM6fd%Lm+HER%A`qUcb=NAfxpb-3ukG<{rd3~5 z9ZqvcBRYyY&sT9z4rhy*3&u)mP;*8(o~Rh*8=dqWw`GsWY9tPLp!Yp~_7?VEyn~fl zdgZ}m9ZUN5>5GL+0JzBRgZ+N#anCYgJf*S!6WlGkIkxXFsMGG8c9 z2>6QcxQA)rIUvqplSLa6Q0es2RhD0d73W;b37IISy85KO+`oAaIWry&`6DHgb}4uvNbt8K&pIm!@;!2TUC-f@rU~vv~zeoK0AvQ3y77k8H$w59oyv-!7Ho$!+A& zYaWcQE2q6_PRIicP>46s;~5S8z>wg81vSoeL^|AqH;V?KfS-!o zXW_%uBv2ekFPFYn+&Ot-fFQM z3vqzez?#wS5ahiUNLf!se zS^GOHA)VlQ7W)<_6B=|BbX=7c%>~(i>P6X0M|E9OJr`Eo&r)7IKD1SMOY)($J^D}# zvPM0TWnhm@y~EXzqpRk}L8>(B@+hd>8rvOuf}faRYhat3foV+b!r#nM=WO{^j$`e{ z>-<`uXJ@zyJj$4-U8tp$|Ijd%?he-OqgoZ)DrG4{`!!0u{_j%e?lS+-U9nchcT3p5 zjkdA2>9)|5!goF%MJ%;S*KN~~0;xNp`Ii>^c#7U39kF=BC+SrBam)szI#!dwo*lQ5 zZ*Q`99J!OkfYBsEUzCzzst)d8U0Ioa{6-(qs4{v1;o&1PqJ4Ep*M5}m+Jre@`q|Kl zXIcQ-;HJ|Udcm1YZ=N`(Cq@wJQ=4=9n8@jHbUS+AqlaKFgBTZF|{ zNu;?b#OO0Fqx_TanGhLW_EPfCil2ocTTjGzXiN;HgrbOHx1id9&tL+rQf{*LwCThG zN(UbaS&69rq&Vy~q=0M9YdLvwy3}YHosL($b)W)^4GP(4>nbC_p?K-pi}G*Yo&DVq z2JPccmu;4@t41u`Y!VFq0NWfH-$q)1o>|P)PBi3T6D}=S`&Xvs=&sNN;VT`n@6XBD z47wp36&Y?kkmscVck%NMC4;@Dsq~;i-_T8-mt~}bpinZwd|#x4xc7L2@3vs=Z0U?l zCJ~(+Lp+@*DHY9Wr%q3471x^ar4eH+iMC4&0;n-|+?niBSGUF0S z@GRiW*m*?DGKAeLAoisIDi3AV)4TYIA)xd(f8ocsX?TJCW)Vonp3g!M*u9hsR=5>r z-^|$^`od3iBB*Xgi(kCsO08;QlaxTPQHM|UUq+h1^EkEpM}|Pby{b|&D)qp?#>d_) zXl(plk^gen3KBd3-}B$twTYZMYV~Q}BflNd9AWP_N?^<;%4B?)Mv?)B<$__}NKMN;vHr2Izr= zlfjVF(5dAguwI!YR&yj5%<*pz9BO$&WsXk;(6oKG|KUe@b&hWXb(K-(Q#l9k6*jV2 zbuvG*WEouj(04@H4qMSLO|LC_H+uQij9&H|O5bza+g>3`z9%K`^S)fHyVO*qPqAGy zHMQ!i-7hg&=K6Ea@_0j4xMab#%{*{d-}HnU4A(tfCdIOjxH(ycFqy~_UxYpX|15x& zszBt)LTkwcXOTx+#mq^`Mp&6FWCogmVHW1w%&d_|RBLq-rCcE{pAeDXxDvIEy!D$F z1(TwV*~bq&5C*sl8D6vT+;zVsjKfNI7remC)l%Ebi)Pwthiq5RM;gVgEcpJMM!M*X zpes=mSu5PIEcaPwJj~F4ll!X<+lMn>KHa8k%0*|=;!3LdKX$an_zh3zeY3=t8WQ11 zZC!um{E+HOL$&=6&Ml!U@EyJ}8Z*F+ujAS!n13#{s%KU_3d`gb$d`HB8nh*LDsLHf z;W-N8#;=S2y_mAcThP|xn9B)IIYjZikG|vHAKCw^b@kQO9*^|TDRUb!61i?OzSUxW*sT=kQkO4%pGg7Ae%+>4BmS0wq2Mz&ck_%FJ!V@4D?n-2kw9g3 z-T~m8Y;zOut%F?gAqyT@R!g7F<$YPP6+JmZUt5p=P0V{9uFyN%$oc0>pA3WPM_};% z0GKL`w*5%~G^&#nF;zYQ-X1)=q1Ofvj7>Z6cywZa>kmq4lh#la^1oLNw^{K^rQu4i za`7(FBJpXfJvbgB5anmD9?Zq2i>?4y2=!L^6$~$hg%|}Y|4TCeXb>ZxKd3d5g(>sZ zNJ%b516Wuucw-kAJBFQ4Z}?CME9>0Gy-xUxIhAlgIF{oNNV1CHhR;Ozsa`$y#;wmj z`6ge8&0}tTdtl)zhVMVI%l=sj7o2h$uBZ59>Z4gToO>Sgb}S`qe{)>)4ZsDw3U*>-8FU zfoOPb&g=C)5UsvrlVzZJPw|a`55tG?5&sm;lE?1Dk>M5LA_vn9_vH73TP__kNAq$H zwDCxZWmvcocfHgGXS74jOFwQV>1zwbwUv@SgfV3# zcJvds>r@KzgGvZ!(ru~%SHSO8Spxl{{eonR)WJ zxd|)OLa5qAZ&-4DqB@Z}n_y%+^UYog&VDJBJj|gQIdEVZe$n#us(zVXpbJ3%FO9y4_h6FapWwws7n&n|rRTyoV zB3GeMsjT(xhPq#hJu)#azmW|zlrydyd}W4Fa2J);6cDmHdUgD>+{knuH;S*`HKb5K zU@kT^98I$B;?fnsMfKhA)WpQx<{fi-m4YmFx8#yPELW*RImL)rXbdLz+qgFu)74Q{ zt4E4%@_;pbGxBq0_^3{Zc_|&&pV{$TZj;_snq10rw&uhnq~eJ z?axEJ|J*N4M8K|MdHzyYRkw=TXGYOpfe#P;3O*C=Lr2f<|NdLhcH>s_V!h&9!<0Gy z`Cc9!!&OrzCTYlSSM#0i@U_0!rXBZG@q=SJ)Kz@ZwnFyf`Psl=aLK>IjHzr{fG-Gt z%&#<~Jgx9EH*>5%MrrD1STMOl<;85-mSb2e!KFoH_UE^BQ+&R3;AeYCqU;OS+O8?9 z#LgXGu{QxzFJ(!R_+dxulcAxbrjF!nRSIYFosro>hM#Rz41?qbSI=>?DXn5oZ&`kU zwep?p;}dW?65)>|5Z|6Yz;!T;os`F%cqBdD^66s1SgCYeHF9wQScQgHD8 zU5u!PtrE;8i&ee^(!@QgJkh+04|CqJx!)ZI7d%>DvHeJ!>kr`@CBOya`~K{5+(A~& zFRuZX(Y*B%+yvY+COg610mpE-yDQ{c9?cJJ+`c5xx8%~IVJ@IY+lc()mERc8g1kAw zaf?Mh2kaAId%HN(fxpT$xlhljOSU)>>>s2+7PAu1X??F#DhV0wxr@qFGFFkqWbstf z()u!kebR5+r%Gb2x;`{1FI0J*Vej@|+x-+b_wIzd_8ZaZKt%&-KV=V5>2Q-`dvZpP zE(;$Rw;x=}T$$0r)}1-oYUqNWK{j%!W;RMgIdvS)e}=-ognVgCBzwIOSGI+KPyZ2O*boVvWI!}%R%G;&}RU#yA?W8fn2lm%a zCvR7(L}dSfV@p+Q9I<|9{4%7&lE9RNF4%$&9`jb9WBWP97#>*g4U`o(l&-_@l8B4i zFTF^uwoxr^e_Gpehu#G9{XE*QI2ViuE9ey0xhS7YM?6hG{*Z9uY6;$MF*P~K<7f9d zx{tWt5)-vef9mnNdUILEWd}#t+Z>a`mTwD|i*T<*wpi3dU=1AHwJHj_R|hs)$1+Q4 zJVw}^AzhHmaq|QGMw{`Ye>Y`V+evh`@(Sb69v=Gvx@~f(_j4Pv=2J=m-F*ej_iqcj z-w-rn8@G1ljz>c8W@OPu1?I!sL9k+u*r1*)h8?Ek&}d2^TMuU;1E)11udivR&~4Ei zm)JhW$$SKbc$YoEPxa1ASJwQlIV-8!H`p}R2$Cy~<|q~L?HgNj#Qo&5T=BgtC3Pv@ zP$9N)Y0d!jr=^>FWo&rSj#=uv0HyJ5Ud=9xDk1KuD;ha{+7}^JX0zRUr+%f?A=dw!h}Z3KB8QwkbW;U3mN)9 zhWkn;x_XX#za;ux$#`Axn=;2_MXVoPW|xBJpiLs+FLe$pXg0#3xbBMDG|5(f0k*0I z=RHFCuMs~n-^t}Yx(!&@#l*iZ1~II$r2zwPU*(Q6{liOFqq!087($Wtod=}0^t%U( zg#1&mfZmj7o7l+!8U+$2r$KKSGgZ~Jztu~WyB2E$AJ%(3|0DQ&!2KCF3H>H1a?}|? z+TYpvbfu?8sv~Ne8b2?<=jg>&|9g&fD4K79B1+D-dRnE}?B`t5t1fg)AqHM~ z#kr0ASDuPb?fvSc#BIb*twlRajk+ieRd8_Q3*j__#~ZTT$TWU_DFFOEj8`e)vXnn?PdhZ6_P#__t1){d> zS+MDZHMK}$Jy&IrVTo6d2wpLSUPoSMk_T$GatKXC0obv%(SXFuzY)I+^BI(B2B6zv;2H|R;qP45h@FT}Z4A3ZB%XJNn+0udg zdt2%UtjgXPbH^2gLGs9>y<9i33#_wvB3LsUSGynN))K&Xr$rqNsK@;Uak~;1oT||` zz?~^an??aSOSX3A*pxT;BH`VUwfB0ff0D(22afxev)c2pj$>0+G$mm_lh$^vvvr(* zgWO#E%O-F+i7RrZe93xLN8?)WM2!;+OmC)m+STup^7QM%vdaSc=fy{I*dkwJ^%{+I zXKlK@Z$2W#AxTt$^HST`UI>Cdp~h8T?(E8V|K|k_cjApz)0**yto2z%m92~kt(37@ z{jAGpSXkE^%%@fL(i@vHOi38-;uaI7@!I+7tJ9u5wCKDR`v=Lzz`;=m9q)f z9=ADXCMc;N-FE|0ZrfwuNk3B+3qLh%mxnFu<+?LG3u%m7!erh^7V`TKVs}6%?eL9n zr-ltudv6y~yK)O;!jRUyZ;{Ep`sVL4C8ft+%5q~Eb+0%j*|oOT3sG#5Rw(=DqK{#K zPRHvjwW9qke@O9CC*_Rg<_{sT@i(m0Ot~&Y!qMD!!0W8B!K1|wq5GqS^Kb6W zcYl=V_B>1hOtM05NUQlwxzEFJApmb9z`8nM5RFxkmGb`O)-yLQBkhM*;^yR)9(r0@H7 zI?c$vQ2d-f${TfSy+zS4DN*QVJp5Y00&CqB_bx3Du2_|u|B;Ky&9GK!BX-68sEQLG z@=90?;5N5&eg%6sy&&!08&SpH?WsCNV}7BIpeXp)b^gdik(tiw$w{cak&5b#*1cmc2O7X2fbNV#aeHG}a zeN>l|kf2bJL>@+FEHA#;8!9iM9C}zQbAZF(anho;-E7{Cn17llzDNE8*!*nO_%S`QY*Mhvd^Q*Lx1gEm-O`{;Px*q@+A?; zqt$_kYMaRL+0bI|`XmF*@JyYCH~h}+w_fr|EYCk-uOWOIoXtccJfX76t3RsDerve8 z_|CjVK(IT(4U_dWhu!|;deLR|j@h{=H07a2p|<_~5Kx#hbLlIAw_=%QB|kT*bl^1m7XS^A|4crDAMbI0TB zxNpk;lCM0mn2m_dQsl+3&Eu@GY85G;>GggSYC0DD*zO0oFcBx(-bm3HmThLKw6MfK zNA1?Xd`_%O*r+t=-1(WY3D%Mj7o)#ii!+?eKKu?joWG{s`6esirZ$Lp9|$M&17TDA zKx!dmY$}7;8g8bz)e1bAt^FLdsq`smbD|S4i(o(>Zv_3I?|Qg_M3A->FQB+~r6uBK zBfWd`B5zkFOHYhD4>tD|#s5Jbk2O1O8(ABV$!p-_%q%TcqGMjB?`b)ZaPb!y^SsO zoR~o0DQ&?y3sEVT{i4z0tpsUs9(!SHw+2)eOP=D(r7LA z5;?2S^5ff!xfK*p@fG7HI2Cvk_StcSgF7QySLw0R;WNp}J~6-$>M(rcsFUf0xdHhw zG2o}^BtxHj%^phA;)dp5a=Y}Oa-+;U zC@+&nGy6Jj#y4}}JKc$#hl*JuE{fmmXT>{wbL1E;G_6>HK~$Ew28UL%pup!j1&U{A zIZL!jU4gP#q4BVuE@px4T8D@=n4nP76hwM5Z z%+W-r>5G!9D)WnQ#ocX8$;j;U0x|=BO4gdC$L0H5#5jnbWol#q6dYP|FP%O$CX!)i zuV{`J!d4{F8XCOgctTj--WH~CMAN-T#wai_0fQE1s!z}xX+iI_*XxkP5vTgbrN-I{ z1o|yncKPtndKIjzJG1CZyQZyyFoPalk}6-^N|*L+fYXVI$Kw@|sYfyy$8=4^{syjl}Tv9H~WJFd#Q@qD)%AcMZ8ng7l7P>bVndW;knCWp$z znU;s_Rgg%X@B`q(h5!~!h}dDS-(xq>b%4wT=g?R^@q9{vF3`EBr`v04^>eE9G_X|% z5@~BL+;fpE%tw++oB=`B+*IZkUK#xa*D8j3b!ws|YxqZtm)b|Z0i6SaN8 z{e`&gOQ&oS-6Q^Fbvf)`8D%=)S+xmsQ*3=;M6+LAWBZ00t;O^8Ats+dtrZBe`3S9W zo=tydWZho+P-~uf?t5{#ZkY6<3T?Pz_&2jn@t}vtd-kj2Zxll}Ii)UK>e)4>4=&{Q zT+&){EL7hnd}le8+&ogz(}1AAd9%i7DoUvlD-V+1!4HLbJg1}0hho|?BeU8Z<0(k#v5R-A#h zMr_8wHK$aH$OE~^ov*G@o6jO*^vn%1HbENFIP*aI>Z>dH_{Pi^oDP?btFHhP(6)G4cBps!><}gj7lg`QC z`wMdsC7&kne`Kj&HF86Yqfs&z14Z4KN9Tk^=97DAuWf^iN8bmz`+&xFJjs@0zC14)E_dPiBDsoXp ztqTenJeN(1`WmROT!iQiOz|CKs5J8gpUYZCTtp|&-^%c)Yn1Fi=LIO4a)%Le%lM=w z|Js*^v#BH*DR1RkI6^w1OiGsM^%qy3)G55wj8=GnY-FM}aR=WGAB$hrx%y52C8!TYC z(}yAciE4QI{{XZ=OTTS>6pYs?!Fdh5*ho4IPS>)-*Z`SVbr~SDtD*9ewj~4H=NbX=EAQt#hbG)}2&!PIp&)4Vs+#akOw4SZCB|j0h*Z(auf|ucbNz?4*S* zF`i|_R2*d4j#`RK!70UW4g7hIx`K@L>I!h6r}E8)KiuEwx4mgg*CIO~u?_=R)@D{S z+!Dq$mKEkTI|U=0ue>Xu$L!cqP##sg{o&Aps8@$@v~hWLqlG(pF@898EGfGwyoM^1 z?!f!b9rKd;;Z!!e*iLq27ma^4E=+g)c~7RmWxnC(1~}MbXD`pW%~Q3hw{y#yr~blh zF$VXcSet#DJjSFSmF<{5;}HY5sH_jn5!QWm;yqdJ2^`jSFU&3XgM5arnU&0HaOpcw zA!90EQS?+6wt2D zE9TR5kP}LM1lCvAvp=m~Nc>XF^yBl+VIX)FeBuOD^ifG)uN=03?CUraugAssW4sXe zW8dNGb}A#V@8$tVT|eOHug`GI_Ab{kSI=4~#)fjz2!f zrZKLxZ((dgDD;H{tpx*4<;;d6S74k)F{sxz**#<>3E*Yg8H^dc>QR9V#=#?Ya(G@0 zq8?Pb;_eK_!MhP`2%K8wDgz$#dM7YuuowZ%_6_*I-}`ht{l`u%c(dEWAn4BV?t65n zwta>ZHC-7o$A9?ir{NjTS?^h@Klp#XjCcS08#{okGVBg0sJ1#I4fJz%%K=%(>;ZE7 zY!b+Tyjvb;Kz58!_jKLDP6QjqVKIwX+sc6$to6o%YSwb*3fHd;X|&aQyf!%)f%M(O zytaPN|Fu)_8?S%*TAcd&SMJ2GKkf4EzWt>q?s!FBy3S(y;V|!ahg}UN)Rc2 zD9+U{_i3#;3Dx>@ndeoW`rd-tjc}USAqqL_t(QtkT;E#o6;vZyHXYj_E=|sB|8I5e zT(Z3#)d&6%`8;pI@OM}+YN5#SJ-CqeEFDno4sO5PSj{{ z1))a$G_?VmFlTLaPH3NVfZj7PENBP}^YKQaw>pl9G+xL~OBU!em^@GQzjHjG*y6Tx z)pE!I*(dN+{5CGed+{S_1AmpWaUZb%`T<8>Kg0g(fuk1RrvPmK4Zu-1&M+JV+~kEi zF^E=R%tAQbb+EwV_l7YQSPzVq%2mNwl_VBa9ZD9H5iH~mV(k2RA2k@O8ONeO2B->* znNt-Q57bxNmY>s|3XIv=;fF_q`|E<XRuFGH2ZJufUi&#QDt_Wx#+--t?ry9E`OQ z+*#axQQ+WJ;s9KOpp88uNo)>pV_cv-KhAlIDp#Gcg#!(XGG3&qLZIyK4 z`#gu)3~S5a#zuZ#iygAK$Bsh0)I}wTk0q)O>hpHoU)Tkn8Lv-;?aaa8``mb>P7~WR zl>rV4!nqB*>8Q)eOScEXlW`lv^`#H_=wke84vg-?Ram;a!q!afEcdO?=?uh&czF%m zPDfh}(?T!Qp;phsJ&AB~1KU(4rvF)HvyH8FhGl86-{A)UlMS8$X+A-tCS=1~V*iGB z9UGtHK4VJ@JKC|l2#VatP)?~XM4et@l5M4iF!AYuR>+1XA!dlR9qAwU$qD5^+NsQ; z8s>V&eZ=?njs3wGm+$k7%lh_Zu(^(lHta)kG9`Oq;{p=_*5QzK)~W2XuwAT*S>g!8PVV^LU)!Y)^gcZKsLvK|Ayz{^|%<9qw}V) z5!nCj2&(p9Kidwr2JGMdchvO*_S`iBqZ$fzCV0u(Z$4%d1NOmKkKq9zhN{!p2V;i; z2IE-r492mjy8&Z&444Y#&$Kd?c@5EaFpemHT=1y1t-)C5<*&c3z&H%n&S1>^W&_7y ztYaxaDi|xx?S2O1#*^%XqWgVe`jt!v5|4Hebg(z(32GpjyRz7A;69XieT z*xu^#NA4|FnyFF@XS?dVsEju%U+(c#yEE=Pa6%*!2Wo3H0SpK7hsu%0?QmvX7e3<{jAe%31;0Jva()#ZTZr z-FFdg`qmjZ>iPkXxgOJw)(yjAhim`!GwlC1Fl;JFWAW)i70|-W%7EG?na}X@B7`#f zxXMs&>jJ=lUV(8aRx2$V!UnnZUK_I*dlfL|9X17HR$HAnJ8TZdaZG{%Hw*Ot&)j#% zOIB4`uUqf+>+aV%=O)wSCMO93X5d2%V;WI0jB!*@Kb=v`X&iJ6BZG>{XTUV-U={^Y z5y_xH6NJWYpqtKtPOtmb{r?NQ9v?7^78JAGIgLOWwL) z<+mtyFPcF5zUW>U@TFZ5Aj@3oMKmy`V;;P$`)v=%dV4a@XR*r$a@7t{`=TIDUtgab zGKOh;mibY)SAAke6CttX;=G;X8f_(5VCR`?;xV!bu*h-29P`N&tEC~BF2060*s~JuQG}7xF()1cmB$L zlMJ!gyM18^&oymBwx3gL>x%A|D8NglH&o9=ex=ZJnC{b~9Y3Flm{;DpIC zUwO4f`+n``?Hu7?Dz%5_w&=%g=R~9?tu|=ayn<^lp3k?P1#9LS!)po)5z5XD)?UGkv`I1@Ke^ZA2R7Wr%$rcL_ z#!R3r7)vmfI)#_iweg5aGk9=$aYw{ptj(khjMH^C%VELTW{=a}@NU4EjRRnC^mPeH zu(S0mNj$mOK_-XGL`x>N24hfJnfas%i0xwydubfI17jISo(NoiRWR0ddNB5H>gf!| zO`QC?8QAxr$ryRb!DL#4aorN_d;*ot{bBdSSJ%8dK#m-ueyb>2dL%%$D=(=$T{Z(c z)MzH@4v?$2U5F{q1JkOI9TozCab+na?XNjba)ThkTBA<;tXpmCYU zizz1KqQ!7f8|sO5!ZyHT;;G4~9XHXi+~07U?CMME=sMToC_e{gzxGwV!R>lR<4?J{c;0J2&@nkSB!M2Rm99DMjOP^lX~NbzPDsuBmLj zg+29|=0qYsnAC`5c?$dXVLEDq1+TA^!mEbbKWY7}uh5t#8g8%({H3+O(zk5%}-Pxg_wO4nNp zc|P(O$`}<|G)({KU^K#LyX&i-f3Fab{rrlPF>lkH-A`C)vcw=EU>AY0CTXCfdib04 z*$$N))nrajEj_3*;XN4p--hW9j0GTLFiry$5ddxAnmpOeSHrX~a?0sGAyjr1Ob90j zzcCo=-nL+@r>X7}55{_8r>m-fG24~^dJM*Ce9OQXfi66{mNZ!XV=zAToH?%5BQGfk zOuE!7Bi(K1V0%EWF6MZW6kHKBn#9wFtUy)-4g+_CtuyRyo?}7 z8|w*UUes>uco>;1?%Mk;dCg&eUrb|&-7+3!vqdkdgI``?am-_)1atL-v++6*VmwPD zpX^V29np@l-X@2*&%M(Y+vjKP3f*)f(|a8$CCb#sbJ*JfYoecrPIE$Mri(V_u!M&L zFwx77By$0+pAIe}(pg7*vMN*ks{3ARRs5fm5t4{VSLnXiS$D>>G&|(8%zK_cqH_*w zLsBmLC+0oLUwVeboWv8`T$Q7fyq@pKtY!Od!jE0LPab>7AU49Q2fAN)cZA)Fu6ORl z`tsOv8&fU%2bI$p+FsNAK_{iTbF=w9^W1u8@8kNmIh73RW4zv*L^lkt7pcUhaUyp_j|J*k~PmcR2j z>2??W_=G(}wu^|azywFgqIA)m2#l@6Dg$F7H3CR6kb=%Lx${O`2F5ls+$|Vua;c4k z+w6%Fz+fx`Y~8vzd~UI<-g)96#^k|Rx7mZ6xLGMX24g-i?a>d)XYQT|j8kthZ~*ZN zg}`Sy7)$%?7MV0bH5mI<1rNr0LPlVG(2-M}7Tw5$_iV)XE`1P7|NJ-}zGXWGCT5s8 zrymohj>7n<{dnZwtsxi#Iks)xg-siVf>O)I9oYK#u2w6c=5s2!9FXfsX2z#8AhXz& zV6o6>uJQclWP}D}`{q#5tQ?T5iq1Z3ry#4HJGM1x{L06S%atF&O`2LV5pNZe-qQq! z`@rIm#b61HQc%NAn#YBcTdB=3_vjYLU)gR~kZaB&*As1LY)wpL60TbZs|lC3UH)X} zlZ<_8Cnq2zNuUEjjcbCxq9cE2KdRfw#E@~F5uMxTG0*myw87rZTbjIHY=>#bf^74a z`CLEXPGxJGN^J*ZT$prPe{Ykfo?_}+JYUf^pDe}(FFG*O0V;EZ^31%Ru~FiOx9nRo z&q1B^iRBWSt3=!^p2PaN8r>ESHZ)l-xS}Qe8oN`wxW8>ujde-iY#G{ri~Z?7*JW9I z);e)taKHJ*0qb#ir;ffV;f4gXtdG@;@6Ob5%NsoQ%KX!~&Hk9@zwJ-)8+PZBpDULC zai7E0#rpF+=3dg{6>TGwcFTC%jsRIcrf1M^s%*m{Q0J9SK^|l z-GO{a>fgB`$JS*{ti+J2Edt~J_r|68e{X!Kq;zkqcJ3rPT1Yp5TusEPEwM|Vy9Hzh zU+uO!0W!B$1A7F>qF3zfyO$V{^}PPk2Oh1+cHp3go&=`ARWGuM*X(TN+o#W4kMv+Mlkp$;aXx?{2YkRj%1}ZE=VD#U|~G zGW_li>z}-xr0+JV>p8Z?uxcElf~OOu_3iDz7<68;pQ$W88TdZl5+fa*mMmjw2gd0W zgR#`ZW!Pzv>d#DjG6Rmq|C^Bd;!IJR0%%-EuwqmNOYD}R;*+XIUBrR zHQ?_W2!+ZQZIXVFtE7xH&P{kn$Sp~$R}oNI;Ir*W*9g261d-3#pgS%*1HYCT$XfM8 z_ZFY$>^;`Cr;`j1H*whq?%N%V*)r42XoCCD>#OgPutVA+XDs;>nwjmswj7Y_3YUyIl$6 zxgmj--u}jOFky7VU7yO(_@3It$;Z^s`q?GB`e?63>kMy^({o6VZR|G~zqJoaW6FV9 zvRC7iW>wVp8cvyQsYykB8%0;ImyD|VXWu)cP6(CWlVR|z8Q zGr&C8bL6mdQ2RK+F1C$i&*l6_d~orPgPP)g`Ax8LdqS%$`O7>l*`}0jmGB#;Wv5ta!7K=4{HlTSj z+lJKX`^WZ(Dpu;+hWI~*!EB>DbEgEjEqjYMn zCLh3qVM&ArH6|;{a}nKCf8B$Dgs7L27VsnLS5=oNu6Z-ujyee#UaCH0ld>J{7Y(A8V1>u39kgC%y*JmSgve0`a#vuK;(HP@xfts+8(^}Nkn zBrNHWh!)$R?fVE9a&=B{Je7&oJsNlA>PVj^J^!lx2k0tG-YrI?`Z&-k%1gdR@CMLC zGhqSiVI}h0O;`-dTE}HPxZS#))kXUi(Ot-M>Z7#M<;OvP=Kw&@Q6Y1Be(FF@XfWoCv=G_^dUjwe2=QMj!-FxqMIGJC zy?V<|x?Z2xV663th_mi54aV#!EEseBRlqnAMSe)p8oU6Ogyn1xoxs@I6K7rIyKQf0 zFt+W{b|s+H4vf)io7H>b)KRY9PO@(GcBiSq`_gw={bk(I^O2pT4fHz&ZTwmP z1chKqG@tZb$<#d>XE+G+c9(SKeu#0fn%J#Ej41N50oLBuXM;z*wJKBB+n~U=qg=R8 zbR7|H>O`Urx~zXq_M>guzDP=R6)fPFbHz3QI?sKqUs4dY(vzfmV=T!=Jr{DNchZkD zX_$1-oTSY~Y|9o4~_9J4FM zykg6Jt)H3}p2wn-n#>_Irt=(Sj8FJ)v4?ew5>Z?~>*w)6+cgJo{PGu`H<7(DUhte| zJg~e7rLZ`l5i&5CO*#nY`^E`y6&U`>#!uX6BzrYS;-t)g2Y6Fj|c!G3s~Qub%XD2IEwYJ(n`WWpxM`_w~7IJIbc@=@~JrACFT6#+_{3&LIY4 z`?`O79fKAp2YOj4O1fGn+YXS^c_s=?D#MrC9U!N2BZr@)+4`pexsJr7K|6UF1HY2Q zcvO@Z5lmT4vt!y0+AVf)nMuctj*7myYl}sZbZ=^B!`y4Jo!hJH(#NCm>PC`|c2|a8 zrL)Pl__-L+wM@p!9KHUm369{tZ@XVfj#m`z7+S2-He|Qv>3*^Hm&&s%0yYq2Oyui$ zHw%}`TfWNtX;n$~&Nxv~FDXa9JFrW5zAzPpIVu76Fofl}8>G7Jm2U_3#Hh+&Vx|K~T z$F4|oeSFmK0@6C$AYWGclbuN!c4d#po0GjfU*y_&VI<;9FxRl%RrgsH0fEKTZdEyj6wHz24ivh-Gi}| z6ak!-PGBs0YB08|F%}R(*Oh^>T;s>ru9T(z?GB7>7E=c>5s9>8)V(yDfDg8%RMw%8 z&S0#8&)Rm{YQoVYZ_%>zFxe2``?)(2GTSX6`>nLS0kZT%V=&UL-PrPcuP6R_t+M(o zrt%nAe3jDWcp58RcGfyy%!7E9D8h88YjQNvm>|g&oe`F~qvt>C!&!M-56pEIw;9i? z&oixKpOTX7+1ZIkrCCe-nV|nmM`rtOLj=Ip|RVq`mLOzpH8MR?s7k+E0S++n6uVSQeWF2 zB-i7v6T4zZ7Av&P>iT>#hv&QQ8y!$uU+?FP=Y`$}__C#Z4nk!YPUKILa8fAM!4ln9 z#v!d;6vs6G>7GYac*~-#G$~`jSjbVC1It?zJX*!F4Y&7zuCc&kpBJ;)I4i|@zvtEK zRQ{Y8mu)kinrsyHlw}xoo9y#ydUe6-*Y0!Fht}wme!5MZo%OxP{V)4&^A?fO{bB)z z!6II*upoif{q{vHu`LLRY=WQB0c0$=6g|G>eQrr+ovm#}KS$+f1hQ#jXRY?7HtD~4 z%W*_OF$Y%d2qNHZ(SmIiv4G5aX&q%NtaV!Udb@2lm7gIS08V)MR2=)1>6kWi45myU zjmgu;VB(Yk^!GQgY5fq^uicIfYj$Ay1CQgH@2$X+D<4NZKe`9dSlGpI+t^9i{*Q6M z;~tNr?@R53h4jb+$f=w{24(<9J$nlFIADCyXVXSw;?x05nlgZiQ$}OLqydZ`(}yja zcVg4}9oVpL2piXJ$NDuxSigD))~?)+KmBe!mR~yrUX6-Zf2C*Qtv>0B^g+a;AwP+&YY_s*luwmT}Y*@Pk8`kW=(j}X4)#WR({_b5tf28@-G@m2ewHWTpsv>&c zmXT=72{f*Gg3r9g^9()K5tdr4<+9lhS+t!O_Bh;{3)bhx!Y_#7XGaVCnRDD|9Qo9# zm@#)OCQl#0q^YBK&-DRJoIHT>lLnACb8Ol$gpKQluyObJuV1qR>sAe6#nP>~>3`N? z`=dZv@Rh4EkhbX+V(FWByrjJ{hxq;^c5G5f+JEbLVaJk>>tG{q&9#)!)V8A&UEB}a zAN$=wQG126Xnnlz));I1l;>K+qgaDx(Erwkc@8v?);c8-tTECjd#wJt{oYpWe6qZl z`>_n40o{*$ZM&Etor#{~v_7koZ>XM3tPzmK1KbT5=WW5oXB^p3%1C=T7z4iJtAcU6 z3Cy3%3_2kUQV_yb?u{G*;0j)Q{hC1h0L8Abm3~Ul9h`^W~l&!l87)Qjq ztlgHNc`#0ZcfeWW@y3rGj53)#wfajv4mo-nK6~e}ZLhig4{LDQYaT!xs5J2G56;0E z=gzNG?;o#PiEq4hS?hZO^z%NmH;z4hX6b!%7L2P@4*)#-<$K|bbLX|aX3M6Xc;|_? z^#sTVoIL?gd+|IRf5z;#KR+;eS`lnuf8dA{XX5|7bZ@L!_Bj6d>y^0j(&boj!w%)Z z>#lwl5)%O?%%{D12A=cky(^V^=S^#I=_?*+l?^v{(tM2wh!Bd2`}N`MH_XQoPnwSX z4w-_{V><7<9XDYV#!VQ7ISb?SD<9s1Ki#kf*Zkm7-1d!)t$xtSE{jic&0eRE!-bzY zw6y-g@zq_`ygkR`b9Wx!@w%^Fd_Qjd%6eFgJ@egj@QfEO=(@fg+lTSCqi+U}2hZQe zAn>fW&BnQ}*c6MmAFsf#FMA9-9uxia z8*1%t82+0+Cm&Lcw3G|iWBpTxi*G&(16|lU%e$X@3pU=n3uV5h5L*{uIl);M&B4?E zp=9!u$cQCQ=;{beGbDCt98?Ae3$~+K z7ds(hFxD#~+`&wkU=8SRBWb*!_S`*i&)e7G+n;*?*MDhktL=W3M*EM!iG3q( zgJTE#yDD??^ifV9Ajgck)y8Q1wq3hFX`ITD<9Q#Mk8@tOH)hUt`)AvT#WN=11oWtWNjkCk?*vhftfY%dievJ>6_*(w!PjD<~Z%2 zXW-ed+6#*gom#2hPBJjiheM8@jzf-~j_18*U;O%q%kj;RK8SU9$`98wm(u)&SFE)# z@%5a~2245KXUQ@9jp9?pu{>ce3v7~I^H|&6K*C#ef(x|s{vevoVAWj~gK`SJ1nZO0+fo8yRbp2y{-{@}$N;qe}GL?=G3d(uCX7xUxz zy$;p4V^`d7WdnjZYW0Cu`X`em$1w8Ne^Do;YwZ%K951fqrQ0MN#TTE_U~qks1@}fP z%hZwah(QB-3suxdndt+2t$kLg>^i-*khQe{WXDBMVl6((k0f_>GuTD?SRl3A%=8@= zjH7@lvOC-N$)Y6SDEqNOuY>&M&m-}SL$9Jq6#d)B|q z!6nz8)Kf6VGIp>Z=fC|ReEN=KvG*C{5#w7`NpsuI$zIEpGK%eJb{k~uF#h4=3-Mpq zo`PpQf6oyQ#;hE$cq-oe#bfaCKOTk22lhiSJc)M7@$QGerEve1VRd(6v9Et}hKqi= z7_WZUA(ex%LbKlcCPx#~7Z2dWzdHhNc>iIwgE1@PCk@~Y?>`J5z3y<#I$9D;{sk&I z1h?#J#>faiAIn%#b<0pxvi$XYC-S-NpZBU$zkOSv55D;M+v$r3aPiH@;QxO5s1Xmw ztc)G($5}7l3tztRR6P3w^AXMIJj5i+b#hpa;NMDU`_UekzLDfE*K7OIu12-PqRfnS zlmGyokN8BSGar+(i%*(&^7gNrB#<=Wvt?)IeT;`KyoV5)1(|7*Ib0-B! z!t-rh;M>E`^5tOe?GyX_h>jf#t6!5o$6Q7F!j4Hb={g`Q+rr~XRJ8rbgK?3CwnnZe zaT#!15RAcC%FZE%BXzPNHG}J;SG;qZx&`BS@Z!H|;I;8o0b{u?melrSJpTC%52PHJ zM%%jVme1;7TpZ&uDtiXS+h7?OOMu7a)C1$chu9!joRiPuRWG+ZN$|Ip?g6=fas%(b zdNKa_!;3Lt(m+sFZ?fpHsrbZyABR0puR@%?lsT#Rt)JCOwr{(#I|-~4A6(dn_x@@z z&VR>27#OJWO3)r;@o_Wo*{e^)Aw}y^BwrdA?FNz>aqD z)G4$6oGQ0e!TWQhv5HSc`ycMh@Xm|4+bRh znzyc%V`TvcTHoV&VUJ64mpO$F_W0gT32D|puAKvT9XPN}micz5YxQm?=5EoBta4nn zHq3qG6YcU><8@^FS?j;}-3vwe`ek)dAB!*J-A$}Bw@>;Zm7AV5-euaR+a&GbU`+>F zvS`yDJ7TV=^{(Sbx(>)}a1$vk4$T@z^k%?#LKXjyUJN0Dl0I(1m^(eyrhlMVw}}lW zZ(W~`Ngee`L%j5^ovj5DIo9q~%Gy}VRvOVVU~klViCC{T`V2}macvMP?J3`0#bBJ; z*;M!HZ6KD|mV+_t=L5V9b-?)VBR1(!2FBIIf^n~J6YN#kiM0ddz6m+rvuF9g001BW zNklcLQbu11!din_Hk^&sOWjKT%~b~sLb00*`?avXEsWW4u_$70H~@@HwEXgTfd1^C!6j@tcZ)+mq^Zo)6^k<|_P z@oC|Ees#kpqS9Z>mCBw!igsPaepN7`Y#eh_roS$=Q75Y71T>cwi{-NZ4Tzd6+Z8jt zdRSZ?)t9yUFX^T0)STG|lw1zaO)q*JrLXmQJ^wPuLX2Jsi`#Aah1GzQzfw*#mSW4c z{03cB?*(BMSI9Ehm95BmaqyPz_GGIZjQKZUxc*EH0@UEhfHztlAx2q7f;bz&WFZQK zqCnH?p^dMxT0s8vFxFmRLaVq_N^JNF(fW=SjmZffFV4x3g|Bu5l??irX zQYTOTH_o-XcLTt?E?bN}_n$N}3bmwqAHD8N`{D2t%3s(slF~n_fw%qp5jg&p$>zMi z@iSKu39BOfJDz$j^gidTf9IbTkfSzXjS-vknfa?VOLJ$|(WCLUPaTf_{@(nfol{lkpmGi+sDwzY5_<3~Zj(pDBAO`?Z5aew)4h~Y1 zU)zOkgG~&Vb58M@x}1h-t52vG*K&f|b8Y$S1EVs&%h%ZRx_>kEHyX!`u3`SDvBL+y ziObuzx7Rrf`b+y*9{}g(dS7B4sklN`9@KaxJP>K}V2z+@IxUXiAE=s#^Ynh z!e&UATrwEj2eczFjxsY`TR9kO0O}Tu|31GfBZtaFL3s8ebZ!7lGeGGuuAJ7(eO&)>5~9Z!r*oH800U4A4+ z&+^Zb_7(v3E(uAvPkYhn8Sk8fLyqcgWy~hU>dg5?hfTrR|5)u!p1sPz=svvjvq$34 z=TC$ta29K|{d)4(z2xdQ>6if)nEglzu%VOWP#Rq)ET3}JsWu0g^NZ+FY%N7gXi7Lgh>NE+i)EjGqxXZ{`jHj zn*fMk@mQ*H-jb#6MQ`D<*>kC@jwBo9?e|dvm#^2~%XVLMTYtp6bR8$WdTLK^DBU?U zjO7n*!o7c9hd(Pu&B+(NVSfzH?}I18Hen^NmiK{=_78eY8_=X! zbG}q=G)|W5=(;o?jJ8@wI4*Xgc}lup^Cm6&UP(fy>-k>0qa-RzKE-3``_pr7nkD@=jOJ=I^~N6 zoutz1w%U^?{i^shpfYK+k{sFdgN1y(eOrnRY=p3~^Nc5-4h%U<%fGj!9Wi%sw)^$= zFlF(e2xucXSDqEn;M6&z!vNt4O{A9w9eYm7wb>TVy4Vbiv{eUD5-4S@b2?KKWrea< zf5Tm8C#iMvQNIcpdqC1)ECHl;yt}VxX4p9tZ8a^V(a6wOahpi9nPa$vvrEp9_d{#D zt1b-FJfC*$Y_@GP+&)A}I{ zj_=2$DWfZoTUFscj_|9Fuzi*R{t+*ljD`Dj|9e$QHg6olw=cO5*Z+7m9=>k7(>X(i z*~bmw>F3SElb$}ORvpiJ@gDf@dzYcPRUBZB9L?dptqo?4tnzlOZsr*PG>6L)Ue(9w z)gRlh^!J;ph)pIwkuo$ij16mcVCT*zrp_3Hu_N#n&iQ*yz(0O$FI@hHrEUEjliuDW z5vZmryZ8PR|6hDdNkgqK`zC*EWJKDc6RFR9Nxg*CZP%~Dm0x)Xw_dpcJ0A0aWZz(h z<6bfir=L3qN1Z$igX62copb!80i1im0(|p=zbZeL%GaMoJU_6>c*{}5Z*y|F=&$1W zw6Xx&ujB|3w*gutp8F(CGj)ywJX?M$38vlJ_uC{k2P>L8+ZIOpdfi3ynD?^Y5(wMZ z!l(N!H?;3ju$+|p-(!JKN=H9+k;9h7W9rA6b&WZy$1S#18^aWKHVhqKRGa@@Bd;dw z(UK&ua5U(QCnOOV>xNnKUmj4%bsP{{Lu2R9#^Mb}AGd9doku4yw#H!zl{JnCILg3S z8)>wX=(8;-y-xS9pLn2qd>N^4B(UK+%K#N9qkI%p4lY8R4w;qTp^2e)J;Qdd%!}L$4+7-_mZeu-(#^96BUxH8J9;#ad@<*>b z0!N%!?fI=Qe(Wy%@I8;1xuLU(odEgeAK$yXA2KbGEt_}Z`d_cW&%d!8_gu3HJJ(Xr z4K%Uf_;GmJOXuM9vln36tg*H0(Gs`O&y{*ak+G7?WsE!XrQQFSagqn`*? zxzU@%e$3Y&Ct-T2-x5Xb$1+S`Jb)ultp1GP*FUoaUwiXI!MqS(KfE=^^hu$m8Rcs#0moA~%pFk$2sIjLvxg z)BpJ;SUU-=9`-u!j7YbrgZVizeXoDcC<5a$qmP`8^h2b(=)-Nv%zxV0eL3aEBmoq^ z6h#N1dNnD5N&+lE#w~s3VAf_Uq&&$`L?k1sm&ddWjCG%t1*MWj@d0I_v+XC_Cp@Oy z$0H%qANH-DoT0IO-y69o%M_+sHIXUbV$iL`bJgo(0eRA*{+^zH`TZ{+!AlSM4L))1 zUAXgmn^iEDBL{{WSpM5>xa`#r;w1}zgYR8-f5#QQZpAw9GG|`5Y&(AdJr@r=vigfW zzWtvMV8u<_l)hR7yV7`Falv14*~jmy+;1nJIgi>P4ozd7B|G(`d?jDbi`ZmT_3^5@ zlH9E%JnmJtZ`*}W|Lg5|)jq$+ciyqQ6^sGmZ#Le)6F>j>qj<{^H{jjp-GKFLd_dKW zj2_dESASrCSTg5#EhGY4ukRwiEAxVhcfGPD0PsAl7m+Enz9 zsy07XEPEVZfAe3RZ!5}_AcEVyVaZOs*D>>Y6co5Jx^|D%iJLT-0sV!5AQ8 zpm8sHMoHfz*Xrav<4PH}FsEgEvikBP7g`73RIZqk*!+M$DVU7w!Bc5J*Y zHK_&}H+vtKpUUChW3Y+(o^_LYy&VD8uJm=$R>-xuNQ?zwz-t2H3^ z5=2CfQlHn)m$FW0y>=c3Mpu9Je6yM3vX9+`|Nhs9VFBL)dP|QX;7hN10QcUq5pVz0 z5w+@Nu{Iv#L~rC%w?`)%CQYgKaR1gVJ8|6=t6DmdOQ=Ji;+atGEvx&s>?8^YL$ z4UC^M3bPiD!Tf#3W7A{1(CbXMvgWa^c>gPI!F@m5j50q9F#a`h^Viqojeoul@Bije z*!Q6B$2iH+C(p*@14m)QlAU0(rg6Eqm9m7NNoP(Rq!!f;W`%EPoQihYl-=K}6?q>j zb-;dJo?^>`pJ@d zO#tF8N$e1!y3=IkevgAboAv1yjIH5D##YuB7L@$Nj{>hasM2k*6WERq1B)F~DJAM> z52E(o-rTGOW3jt*J_2K%A?-~x(M|Rk0dhTQpXEwA*!i5~v~%X8j{M*&f5G?ubva}# zQ@vI@Z`Z%QWECb(yAyBv;Nn_!N9B7ADEn=`Zp*bni=GeD7WZRR#UFs)vU%t36-2#t zJR-K*(G+9%=bP443dm;69>n0nK5Sh!4C_foHvX9Pz-9Uxg7@hfk-yol&~U&3wFd_i*u-wzh=bkfk;a%vX` zxZEdxG^D(yJDU1NjzwHe+U5C}?bn=ogm;)lW9JuaSZu$?oJFpXwrE?;s9ja1>$|k` z;$v*M)@Zze6i4mkN}%Njg`?+qB5N?#$5SU~^iN7-u;dJaT}9D`*whoTc zanU_*mBGUOECE8A)D0>_bdSgk>nb{GT=U7oj{ z1O#^bUj{w4J{#k+BiP-q0+fC-*KxMmNn0@1wwQFbiHPbV8cU|pwpaR%on779@^CEd z{IrDS$QMt-{JkbruJNv0*Win(Xk<9>lr+9RVFh(W{{U$n(Mc>z z@Hn%|+2tmxK0_8svzg=Dm)?ihEdCv?`@(AELtZ=4C&I*NPF9yMf8uT|xpEWvJ>K5L znO{4`MV;&J+=-98{?=OCdQUlXo;#-Tx3~7$47+Uc(l5WNBkad@K%sr7C1IMk)DQ02 zUWG8rxv4X&uaF&Z#1sgB&@YZ)zYvkw=2qaIJJwaIZ{fZZsQ)!rV2&ZaPwddn0UbPf zPNexL)IFYeiv0=y;69M8Y4%?Bmx5cKBkB6Mtu_&?`DOH<_iGkA0S;`%P9uBeppADn z=>$gN9=bm)Mxb@Q)+g~U{d|>NZ_D(Fa$6?bik;Kk4sRQtXQjHMYm+_^hxV~Urs^!? z@ZCtoEsJ_}fsL03KB*IRYHEO1TAYvv2D_r6S63`?;E5U4sddiO@pK)iBu#+Q^$8?E z_wcs1NIyGi_0a3V^BFyHoJ=nLicMssHmk#-K-A~9&pdI};&n>jbl;{vuwcv+P4`>$ z<|OIksJ+s@Xp+ZZ%)O!Q8G*iRTXhMW1!KGN(C*IqgkDXFSgnxNhXiuP${uv;9SYY_TFo6W{-@hw$VxtG^fnyGxcnY{Jg}UdB)BEu+W% zI(o3)cFTAC*I{_u$v0tmt0sNiKNgGAT)X)@>+s6^exopyueClRLxwdErRS2EFmN#9 z>#rlazd87^bKhpLd*8B{=Q+Ok(L3?|cQ1!-8<#D;#`8h@r`}}ax*hn@Cmx23hkXE_ z`-uH^1|EsKzP}lNzG)2>A2*{?{d?^{3Hv>B9Pazs=3u`240APMvT9dbyj|53ooz3U z$n(k4b4_=>_wCh0a|exEIdTk)u8z;goH`q4ymcmi{i#*p`IQ;HdEPQ-_~V0iqE51n@&n+Pn)PJM)n6O@ z>1_g_a-ty7I(|F2_OIRRs;~DQZ*{Vh-cLRYB-T+`Fy?!0e=^CoI?3ON?6e1CX;TjD z21C$QHuJ&(a~$xMgRutXW~+u6jO`LRYn8rfFt+0&cgWirt?0h)C0MZp#0CcKz_=c{ zNF$zVvqZHsAongl;N|1RnCy3G^=DPqJhlzjUb-4N*g2H}vJUD2O{VVs@n$@*WJ9HG zcp_=s1d!9#jO$)~P-M*? z{=-&yywcd{iIB#$Xijj;>xd;(abh5S$_Vj#CGo&_F1-&wc=sbzzTNF%2?VIl-rJt! zcfWW9&CLy9_k2!xDfsJghDl!1AV(9w_|{)52V?+n^i!wp-YzM^nBNPo6%qw=TK{^Y zgAGflH6M~b<@$PU8z*%*pap<+t9Mi{FUxS@Cyu~zPk97i{lH)F=naw_;Q&Q@YVp;Z z?%#<`_wS+-Bb&8&7jrnz>GZv}-{oxbPxcMa`dN0dZ^2=mIM;6nw|>HoGq+PGFn!?0 zdfNm`+%Gxlq30s&k$j8Rffpu(-z6=)KiB+6CrUIP`!EMCjFF}}$hi8!`>d4RMVJj23HI#dSFx@Vc^+k?gZeYwrCHOxu`u9z&n|9Lc zzedfRqKR=8jlO1hsQM@xptumWuYZPUiHDVVW_=y29cN5x3v4tm&v`+nx1 zSj+Ea4WfLeJ+}esJeK6U!ncA+gN1q6@_QHXE0iq2g{I^`t`p%YUWD)E5u85Kd+~=# ziD*7pHA_=Rwc@-RUn1IWohpu0I2Wt5FaD1*HXK_(kWajff9e>aOu08f{B$)L9zHs{_ESWfOS zdZ&)Q2GUmyH`X*ymSd-;!&^=M!nhU1n=Y(eWruADT2OrC@R3VT?I8Pr%FMVTSc3n>CLB9n0q}bX@*uw|`9gQts5v>eCMOYR^z7sk|*AZJtu)wbvsSdb?PVpa%q15tj&#V#YEyiQm%Py|L`7buH|%6RmM3dKPOrr z?s+j7SvKbk9fZ4KB=ZcnVYqVB37h&)eOE=M zMaQ>U=pyZ>=A(b~t9E?mYl!>ccq2QIwsgv!kneVV^rV?X?=tXCcz8fE0(>+-iUe$DG__D1keR$1z@j@qY|bS$|3$J;t|LZFAKlH41{ z)*C*TxYC3rxyqT!A6z8A7HsC6wLvLo2*Y^s^f*L`j)`o(zqwmk6MdUx%urxhb80dB zIOf~KC*X0_AFO!UHBxW+$$2y7XDQ@8HUYXL$r1pE*ICMDVA^Do$EP=lM|SH^yuAB5!n zo{t9Wd%GP+mp5Y5>KoTlQ;jXezdQB{0R zV8Pyt0|oqpfRAvdAL~$jbw{17X+laW_7~=LaN4G(l~pvd4O7JhKa~JYKPp17^?K{& zQIN5)vGtFKNUy}JRP^8NF#e*@(LwX-ja+6Dip@O+InyW15IfwZCiC`h-%IKQZM6*^ z;s(a5ZvNefdez`^%aq2z6o2wJ;L*-6t-k z3?3=R%-i99uiqZU@+zIto0VHpy~V+&*-F;41=v6=^Dm87l9DdcM2HluPH*w@^7u@T zj_uuyX7i}5jpSv3e=6a8M1OBupqM-+&FJ``Wje-x8gxIIwn=06OS(^>e}&@2D81Uj zbkqwIeB(^ms&aj@kXCTj3l|FX>*vW)1r=rzv2#px@rCrqzLw_v#JHjj+M56K0_?0e zm}GCOT?7uhd(ForkBq2HYsR}ejsi?`t-YGoSyu-{w!hcggoxZ{42L;g_U?ce`39Rm zw#qw@XSz3SGd2Qw=wuFdWZGA^?&Lhfa%UoW z81C_^^d~%8Te6T4Pv7Ky6pMQ~^v?eZp`Nhv)zxtFSw|Kv?UBuxjln*BrOD?QBEc>+ zWQ4VGGyU6g=59_*J563}|J;brGrG0kYt9E(`i~f_&_R~7fD@NC>%@LV$<LvMW*G(P>L zw%rN~SpBgnAENh}bIZ~2$z1Jw2P6A^J;#!JiQdIC{c3OP?i37lW>AmIKg~ese+#xP z&5g;OIcfhR?88Y^t*qm5sT4u0{2N%mXkI&)@CcyoKbhs=98RrAy!9DKn)sgE2(i;| zDR2W@BK5f*suPGL)b{q^VvGMZ&N$-57`&@7{_H$aZfR;YqvjXWWN@WI6y=&<8S@i> zk?L<%5}&A%jeHgb19oEbww=E{-cOsmIUxTJYsjK9l;dNOqW%QYX5ch-^! z4c*X@?H+uc+Ow?H<0F&fe?X_Q_yO;%gr4n6^HuwvtZiRFcZ&Y^E9*6$zi&d^c8^J_ zl+V?^Zdl=8yDpU&XLZ%9{X=3bNl3ZJe0Xzk%^9#)P(0dWrW`EW?t>_i#ZdT;$vob& zklqz?v>u0Ku6{yYW0NDp5Z8p%OIS-ZJy8ov3O-oYa#d5h5!<2^}+p#jDh0d zb(@2vCo^u^B@l&KUs3;hZK^km@!9_=Ju<7cWw(7+8%3_1_uX@wf_}i&Xxid2_U1qv z6$_9+kL2+Oo#w*#c9|DG;nBdR_jyiRyPivfj>4UVNJUoS>CkJj-QMbd0DY!{nWmZ2 z6w1E$=}$v1vpfI3CGB>qRp?(rdAgN%Vt{tvk1HzRe_MccJEvzJgjs60F?gU4EfO`V zU^D*Apt7P=c{-32@X5+(s450N^%QmLKHpHkYWy3S_6@jP?s$1$#*)xmnW8S?x+B}V zqx5ZAP+PZ;G>OIZTg&MQ>V2rY!*&F^C{(a#L97-90M0bEhC(677C;V$CMae|%*w0F z=P^QH$c+!>-ixfgTLanr+!0J?zkIo@EP&q0^?HhOPQ+h>G3oW*#LanqpyH4i*9MnI z44BYF9A2M67*^I)|J{H;r?L|;uwrsjBmed>J~dYU@eKnrJ_wX-iY`_`3OcNB9Mz>^ z*f7*NqkGHsdr}0DYV{=9SnpPwBeMN!F!`3suSQW;vZL1oQw^qAkcw9jlWe$;m4@pB7N{{jgf!gAQLJEx>E38qVCsPd$sR`OBtF73em zUlh}dC_-A_jLnIu<%fp0qCZ$9vHG@W;I`*}DfczwYlYyTE1=RA_f+SWqiAm-GUt=; zf=v3T+TnAb;kLKM*9h8%8Ta`qyZ@yi>NBA&Z*uJs!+q$c7LZ1vIZ3pBC3N-gc@BDtj>l^|v(H{TG3*8% zxSyRu=RXymUb`E?e+*~I&{cwWZOwhqQ5ivNPEKx=YM5ugZl<9BC=JCQB{?`HH5; z3m6ii-GFseh;PWn_fJuMxG!@mza8*G&40gor|`gacQ*0PD{e*`8e@h3Y^fiz+RnD= z?@iLg`kSolH&+xZu@r~MWzW1*_^k@___)Otd-b$x-Pp%XMUy;M)>`!B{KYw?ubd%c ztjSPUBL|tA*$*yzYqu!bbnd9~()6W2vEg-S{@l^il{L6(H-!(U>b|{;YTBy)qCE-w zpyag4P1a0pC&=ojppF>>VBH>V-W=dzb*c;L`f;?|gLLoVA8osXfq2~w3+;K6=xQ?Ln4MDCj9A={SgBY^c zJg#yg>S)svTMFM0Tm%HgOts`%eLpM03+AP#vCjyHnrjxAO;+a0mQ6*$qlRuC-Kl?~ z(*Rg(R|Lh&sj~*eUbe1}=%2UK`@rnsex-a;ESi%)orn$!dzinoZ&2?1xK_>jXCsl- zSHfajAJ_|FoI}>Q#fNrrMP*mO8yfE4FhME%#9B|8rv%^E|Wa`|ed|SpFUQP(5P~?wQBpK!5L@x zzbREcoincbkYNX|a+Q1-?SX8%C(eAB=0{I)nyydNAi{-B_O1AZibg^38QwD4gD>> z{(a%XsjH4ltA?MVnv=RM1hhg^Bq3B})SDqz; zTufNU=*m%?v0q4a5+|df)4)TP{$L=99RVBHIKRnYxX69N57htXHZ*ysPmfxO0 zuGEpp?{D^`jt%<=7i<~+n@(kokKNl+mE-p_y_HT1w@`xyrPOu_o-;Hw8tD7w%@X4* zX*-PIq;C-T_C0htLuUi#UYbt&R^IZo;Pi5XpfuTl_~F2q?0ycb?4_2Z*T19*G3TK- z{JC|*CbC?fO>2)rk8mo<6|54eYm9E=&HUA^y*?TWkbH@wR!86fl8Hnm!UaoaNP z^=uhKEF^zE7fc5P+4g>G*+K#ppNp>z><(aTMbuQnEc&H{n6zhC1m%7)PK|_)_*k+- zX3iqctyiwywBPx9K8O440Le{*drLz~SQ5{5lZ-x@7I#xH-KQ2wPB5WZ)ktUMzDG|5 zP9xZjr&0>M$_iw+v=9@SsJ+EKD#z!L2v*$6Z2ysDUwsvfNZ< zE3<>2rS>k?DqxqkpLHJX+WDSp=_EwqUzwRGQlDuK$@>=NdO-Eb~NVA)WIy*4~ps&+gzFtS1r0It>N6GQsDZb)u{8Q2iy?Fa?%&KReS_L zfIMY;p&t5^Qvj#iuZyFus{N>YVIi3}5vHg#J-H>?w1_fL3q7yxSM1OF2&G&8233yb zUb#;uM;Cl@lY=nHDtpw(IzvnpU}3RvlXlb)hv2^!RH@=IVX)nlXow& zdi%2W0{@CvfgWTuX}NUF)1oCqlQl!G`io10XxJBh6NpCZ$+Wmw0{=e<;X zd6k=#@o!Eg+8w~ar&-8m;rDuz;1l2HultcT??sB8s4MqS66ckbNzovMZM_vYafwRz$wv%=6M6QRY3lQ*-1`E@8xjO z$i{+}m%4wfmI~j8%>0?)RE)Pz*4^H-o~uX@Vv!`PrvP9(-Ng7$2{Pw?6#Z@Y`bf*g zi^9?&4lmBABmB()|EIwMFV$|?KP9*B$s!#m%8mfz_H@E7wMU?hc;?edxQpun+kNzN zvT|M*26Uo@dwp^Bma5noxb6>KIED?a1p!S}VbX?kFaQL5DNnE!5!c2@u zZaKDzMarE9_*c_>#e3VG-6r#JcuETB_-bmnh)D^8cS+Ih<>kav;07f^?mTlC=DuUi zlbV|il-A;IaaCQdK6_(r8<(2;4r{02^VaV8D?|&GB$^Vst6Ma}-YMbs<&=7*@^X}| zQu`|PnV!gOUg#djvHAlze|QVHku~7Ti$OxDuQQe^UM}EveCUB6htH9p(46DS_p&q@ zHr7!^u(HTG9uyI#s445D^d6c6#)^~RCcZ^#v->mSosZwT)`rMbuG{4~slZ}ys zRUBJ(P&PQI z+B&1>66OM7h+&qgYY)1!$dip1BCU+jNrssc2coC7`wulGj%A*__vSa=t3KV1ulkys z6V2Pqk+MZ5?DLWGOlJHx!N-&G6fLDQ#nb4d{E9t5xoR z`2KPc7Q!H!HFk^*eq>^WupXm}6}E}_LI1HJL`(!qOk#A`Q>^fRD)-kVJd_Ce+rjBJ z+U?z<{Xk-tD-j<`<(3^$cVPG~`T={3YYlm*+T7o?yp85oO-MvP{0}H5%eTo?77dyl>PLLu#y{u(hsh))9Q3f^_&TLX1mmLMv9G~ z)5}%q6DBWP=-0G2TfHvc6|-(P7~P#wxUqkSPbiC8C7+F39e&Z0Y6vvd0u_6|IB>j_ z0d2^6NC^+In9lc-Hj3IqBr7!5|JXIU>RibppmiYA%L1sjHQ_VuFK36L^t zf86M>|7JsbfsMj)ANev8w$LjYein3LCw{FwkMJ=+ihW>nZJfEQC52X}$tzV35zczJ`Ikf|QWy=EExZ!gI zx&ZbLS~%?OWhj3G;N74F&AI5Ih{>B|z4$4~%oK~KxN7d;DK0}Bd}MKwZCQ^>4olL$ z$>qOI@{V3gnn+wdFUxwnM`* zD>DqN_D*JtQ7~)FW@-=0-jhxjom-dgt+st*YXaTMbg4&FHx_PW?D+T&^`B9^0%&^Y z0o>B<$+0sbwwmuXggU4tG-THJk?pP-5Gs+xz;1N|I2Yc-YC!O zZpGDB4N85_+u5No{q z3SKiPjy?|*(~Vb3g{dioIed?ld^b%hVUtzNThnPg?xjk#VvH@cNct6J+04Brt49*O z>f@V*n}<1cw(TkZXkoS^#jQ%ApM%J1SFjRA`?rWYxnkW9m8!EWyF1IcDejl)1}_dGzNDNI-zU8 zP}IDFS9SDDrjri(U?t=raX*IR1;Ug4PYOSyJUonAukW`cEF0+Z&VVy-(@R3P0yN~y z^kI6n^3+pJIpwXp>Z*InpDjzpvHD52->8KMW>0NxG*$~C4D?Ll5?(5a%yKD6Vnu3*8jlEGlAbKWe%8|-x8}*uSvwFP_C=zrP zt#{dyY;Gzki?V!9yzM%6@0_n7I~Up>nNXlHFr-Ql)(N9w-411%6eT7ML{X8O=+-0n zw<4l{`v|~3`5o2V?pPbV-_ZsWX*1k4x0`2cFa2+fKdb=G`wCs(g&qavNE3?*a z#Fu`65`S68nSJpZFU)#~=okomdU?-{@zsjtU>l`9lV{UiluT+8b@rdXo}!Mq9rsQz z*D4ws$COPT88>3K0M(?dW2*ld+=6}TH1Lk0#o!;uE0&`Dg{p>&+p}B*J+)rZg7~9B zm+dY9unDXqLo>ay82P9O&Nfo`V@i!h;FxpSYs{>t9b%w(16 zFSBq_%_Hcl;(z_DtR>{z%1w^{q7ytj4<)wr;+B)H_FDCNEk{i{h7Mz1Gr?aMGAmLq zU*DYP#jxMwAged(SS#k_kEXaariXT71)9x=nwVMXb0&+;_US^VsWJ)9of(#6vD2m81P@X#&X z`m(pxTz0tZiu?R8;=LRTT`m78BjW^p+WxdE zuMWwdz9!0vOU;_MW6S3U>&y5Vey+6p8S#sy`M50}J)aM+ZZNT=#MfWTZaoUtdh&>B zdi06svq`!_a~@VSI;?|DGxtTy5$X+F94#X3>Q(5;>1o|tq3cD~MBxGMlgp?KsN<;` z|AUt2Zr(NNKjl-q4uv%t&`?}ADOy(fVQ5Sb@)fdR+S-}_?4#q4Sq(QI-YxMuw#F;^ z(!C=L{$t6`J1kI^+a}q!ZRVm^fT93;c2l9FkbdG&@RRmb-vbd=Zf$v?`=e$E+f9OE zzfm&3l0TQS4Av4CD3rPmd0ibgH9EVNJ{q>(J%nZ?U)sq=vJOvNmm_C8b6#JVK!1%6 zjsEWjMm?r54#6ZIIzO1y=HB+E>c`K1ONQvnH1)isP~rKIqT;-*T6nHZA}g4G3Muhx zdB-?3J+mvWc`;QexL-ssB}f9vSPpe%fa%QKc%0W3!-f@{%7}HgprYZ>Tn?Mdgilgd zX}X(d*1ZR6p4^5ApE8@#0c9c6o_EL#X69UB`gkCJ z5FN)zm&86Oxr%S2MtYfrz=t45+o-l4{xsjNE>C+hHuPhGDt%XaJSSyDr+C2Mr`*G? zS>_w9vR>8Kzk(oLuuy(^PwRK)05WTRWi*(@`6S3G`d(I)_9 za)-327XQm7>^use*10?IMM((*`Af-US|{m9cwn~W-p5muPRq*GYOlRsL#GUUUgld9 z6+h#QMFr4(w9>Mu_0cR9$(7XxBF$EQ1v3216=kD7?4-g{+P?p-@|!^icu7Kdr6f)@ zSHxzEh5IhHtJ<)whEG}S+G;`AG8(PGxi0u_p@K$SK9@Ss4hS02u+2kB=~zxRxl3C5((3p{QeKWW?Ol+gUm2-Y1I z(3@k?XxxWw2oM~}H zdi(ENb4nvdqFoAssnPQ(X2^* zolxE&K}}Z5J-Z_|gO1XB;3r5y0g_#7VgRP6Zq)Yo%_s*mcA+!swmyorSI`<^5$CV2 z_uo-3{bAPA_cuK#I!0devb(MP_T-4tGAJb)UvO6hKWs5o@%%{u**%RbJ&9ZCxWi!# z{kw3-Ntd8(no9f9q?TrtdKAwrGCY0tyZgH`+pXJy?0Dkq=~tDLO99;!cm+ zu;~7Vzx^jPI+W6v6|XXt^Kqs-hCzZTmnf+1g{7pQmy!N+5K)lwOO6pK9ICns?n&u6 zmXy(wr!PI^^2PH!n)BE=uoaZftCo-Enk&9E!j5S@|Jl0e>vs2peA=Qh3qE@)QiQ4m zp{_Ah@R9yah?uVX(urE|o{1ThX^j5{08KajI{81E9XE)^RsYf|XA}J^_ka4-u@j&$ ziEUydb&cVyeDWhz0Za?UDyiGgx@>piiaEl&CHyHBQnsq(d2ly{^uJnP)p#Ut99Jv} z2xus{G3QIvl3f+9HI?_h#|L9cB)t(ao}!gg-AX69pXslcoOH}n7XD$e%l=*GAN{YseEhT0C^A+v4P4_uzQcUdy*A0T$~t9s)<#(E!s`jaS(8xg&4 zk8gBF7ovw-Pkl?iXi6>aoQ&ULe$4*t?eptJWpBfUjw!XO7e)=jpdp1DLK`%?H@t~P zsrnS4Kny0ne}!4+iB)2<<$-mxwaR!#Hm3BM#0AtZVcYWY)rhb}R$c0&;YR zhBqw*WRAZmIm$i7a#5N^wMxoFpJmope#dB8aLn`_Y5{*bUH4xJ9ZR0>HwXjL^)nkp zy39<%t2ktgFvX8zbNXX!t*;gK~8mrpjRKe&g=qFU2WW)HktkJ*dY zH_(_5X9G=UT9jcmCUyz&&J-Elw+=)o;= zhON!3Ccsr3#Qf6ot|MH zO#snRr<2t)FYcT_PTxOal4VvcnQmBk8Y`UbL(i`toUCP_vC3nma9{N5$Z~*6cyMX) z)kCWq>}j5L7vIgNHiHKLv`G^%WnomPGB~;_<2K;eTZqJq8+>Ys6xo`3TUsr zG`_R0FCUm*1Tta6UpvcVS37}vwUao=?1WKf)@nFm-YGwLb3+N?Ls1&#pRF%J)E+v7pMFWuh#;^n6|_H04r z8tNL)Fwe}(I&+bl8r;?R5hgry%*2X-hiaf-pi0=>cbK3M|H{Nmk0r(zDig+v(^vaE%|Fv5 zH+$-(*$0la(DlVO0ruu&h|$N|UxDeRQJHm>o$!)xv`dkT%FRssts6u3EpWD?CgBs| z2Fs(xtZ!K2!YnBfvQocmX&!vN5-|5^y<>5@n;xGYoNQhXqlP@GF)girn?6x`l;E;d zNDLJl4INuJ!|QQ(Rdx`>(&iiPqPgesR#XzhvThIQl!7Gku0JVxZHh|*JIZUC*Mj0j z!ACn}EkgTS!ekI^b0$}$3zpL!M znHu+u7R)0ioWlLXQ;vj8U+OU%32UuLV&_{)bL`X(GOu_O9X;XYGDJJ(s1Nb3K3{7Una^Q!YI#ec;GRwzUe3g&W4@ z3l|J-tTbVEZG@iH@|T;@Z4?ai;@TR*Mxak=Ec?Ze{>IjozX^PSo?b>Xluw`c#bx+z zOK)A@$JLk{hW=J|z{VMps)Eb3pZ9nu>0J#45TprbaZ`$ zM^-Cuxc<{^w=qSiT2b|bmSn&rYCKt27sPUKMRgNQ&l1pF%*KLb;Ik;1W{Gvz4t^|h zpQZRcoIL0%E128_`krb4Y1Kf5?AT1o%iEH--hV-&DY1pGygZ7-23X@7!)W76i9bR7 z`{Bb;H?Pe!{eJLIC2j1DPIY>QU5br{4JxO;#bZ5}f-22F*OJ8QFk+16UW{PcYD6|S zY=av32@jZjvDC{v+RKW_Odu^gzl;I?RzBN7P4y;-fHJ&tK#}jhGOhvFt^N|FEf~k5 zUWaw*Up)YMrE)act*mG0J<{;?F0dn+ECWyACw;Y64WeY$@Quf~8Rb|K#u0frA|#5u z_LK9tKm|`huc4}UwhBS_xwY*1JagI?4_ZFT9;zSTkoXyxjm4d>lHhcK9KPE*sC7Rc zoLpv=@A&o*LIf7GP<3mS?*n0Zi!wy7w}p5pt9aS)?re*ZetDyGdTH6L!3ODor*Pnu zysf9=sh4v(d|uzcOVJsJtmrJF2EK3c8%Of##JHaES_;J$0SRUob`bj^)4ATtBeR;3 z;LTo7c@I!w9p%5lud-*~e+-m}j3j?iSNC@X34^2LiXhzM{DvxK8nebG`-1_TvirA< ze`%g0uxG%H3d@~HU4=o#B5my;%fcTZl^MGqdka4C^KE{diRGNWDh{4px`!;^0NUy|hKfHO-Ia$H> zUH1}YOpHyU3v{37Pz>UE*jA&;-S#0nJU;S2KI8od?bHA?w~00}i8QW}n&idtUB>AX zckBVC))&n zIArsog66?xF%-G&wtUWYzu$gQbkITW+=N>6*t4x{HjW`jj&BMvrd`^FMfC@tP64pslc0TsbxJIp~@+2xfuhXe-Z|tpgjQ;;=yLD-c|7ANlF za4i3EEH>-)t5im6ku>HZ7VJ}LB80(BO?QDC01MqKMhUFl{Ue^!HlXft?WsNx zo?clShy;P8q|3oi3Ne<-QU%fXwThf45snWuc%SM)g9diB*#Q%~q;?UgbNf>#Z=sY2?<;x(4w8M5-aWRoa69Q}gl%-ZRIh! z;a^QnWSE5M>iAbjif^OV<~a;r&##Z`hmJ-O4u*bY-PWw+A1=@WKR1@DNP~C;f5)Sz zRI3N!>FD7_(rGP2_8dW~k~{rPNGZ0JvF4D=-f>wE_Wvs30*EIf7P7yg{Vn`IVdrF= z(u4+U+y?3Rys0`Ls^4(F@%dhN%FVOrf+Y(1k(m!N0I3)6zMxDFSl-`byZuA# zOP%C@A70ri)B<3DDu%vV!0Znx)76QP@$8)~m5LLKjz!OM(8ABs*oEt|fL*ze%C>2g z>(w->M-E!wCs3OZ_h<>jOKpd#t`8t$B2S92C*}p$hY~VSr;bB|0AsNCI*)ljJt#X8 z;{w*Jkm|c*^|Ld-+acfzPeaI7Y4&6UnYT(vDtUGs?(Wpg_E3uU$C7N*Ib1}R6n0`5 zQ9V1u*Q-DafuBM%4-+%oT6E67wq?MB2rPu-Uh0YU&q~^@A2Da#2PN$E=TU#2@aO`M zsFz)n^Q1$kLyei8Yjb!vdi+_dmOeH5>*fKJcOL$Zf09??_Q&;fIm61OqBB_$X5LTU z+K(4UMEm|2cLXX)8ITmq%U-wer;~D40UCUE=#_}(KOaC4W|aw<=)6}$-CWevNDm}Osmn@{C;sB z5E7*eyehzsFMtVC)T15EXSOLK=TyM`X{)gAg$qY|(q{}fhcBW5 zrv^>LvVLM#D}qJ9RK+FCfd& zZA)+O4kX!SXG;;T4XFYg3q0zLWd18sx7UqW+4J41j|J#FJ^WIXnqmOM;E-0n-*TIu zDa=*3Zuy7cI6F>c`vRwU(QaJ3hNd+IwfDINz}#)#sD(?G2%yx)F4skKTWdln@)uwcf+2-(*od zVMhF7&gAJh*MCo(OGn^@@=5gOyNAKeS@R!Bm%Y5bz8?~L84FP`cW=5U7T+>nx}{=u zcLngveppwj(Fwcr2-pdHuenm8zUn=V_vzIoA_O?~!sYvSb2qHpc|em}zl_FKaR1*?!{V7kScW{9CF7jZB43I=KI866!KwFpYJ5AzsX z2%P5%fs5j$z->x=e>n&uz0@_`fb_o5on1Zf+RK(@JhP{dr*xqH(C*dcJaBE*w=^w% z^~DC){@TCg76&}WDzp@|XQ5|)Z;|;us-YfAoO*pyD%y^AI1y#xCk5;Ge&0!!Qj!wu z&r*bb(DX0Q=<(d-+gYweC5nn=>)5#{@S&#HV?E1{L)Zu}tlWh1#8%8-%CLLU)~E-J zoO*fR;+M#QxL8K1B!!<>Xh*5bP`2uxWZc47DoZzOXQAoPuX%{s0v>8O4c6cD!^cJV z2t3gaiod7?hR*W{(8Gn9FXamxOzL1Vi*8U{`)J;rUvU%p&wsOOYB3#lON#4<#J}P$ zZR_FQX`RceX{qqO<+jUTgBfl?K3tAxg>XmC*p)JyH{19i-7fpdGa50-`+0;0M|!T^&I zM?k11{#Ac@yV52tEOfx5Diu z3lfsf?g&$#=2jCi#Kwv1MM&U1+wUU52Z2kz1sUMkszNw6>?gzHa)S_MD_`0XaaRU* zi@=>P&4rYQedcOmXTb~7C{Jfd$G>t)wrF-m?`6vUm+i&LqGd3}-=l|^t(8WAM{0*% zpAML!{PJr#sn$oX(Mkq>IE!ocVA|Wg2T&5~@%Fel>D@5HrwM`=PuwqNDA(*QgV`}6 z+=D%=zJLkf;zSEfKdLU2e$IwX?Fo7-=j` zUWpmb3$RGuvgWh^#aIsycE9bMaj%nJw)D`qynS5Q*(kkb;r*I_&$yu=b}jh_-_sAex%ad@^db zJ9Ke@xOj@{2pRl_tFJ@>B=z=4JYO%Lk`v>d$4r?cq}a137}GK1dD#oYQ4m+X6+j2C z5>}VrVu!%Pl4)nOdp~8T*Fbg|3yeK!hy)YY1aM&g^_9`019(3KQkA)NXHi%o0N8cC zqqkEF3F^|s|2oYvdC(!Ni?sqB&b`%+6{Xmohrx-P-?*x*2-Rk|3HqGY@bsvenJpS>X~=LH~A|FA?sTBO{crZfp0nh`H_9iRGh35E zHO)?5#)%EOv6IF%ch#&OmqeCR&AUDM;Iywcm0MY@_{PR(2Vr|YYU6nZT`)X`(=@WX zCNZK@l)dd`#z|SiN4f7W#_1Ld1{G3ft*9PAqgX<#J*gg0guaAs6@}scA5CxJ*Yx*CkEfnWGTQ)~ubtC6E zYcza}%B=8;tN0K_Q7KVr@h8A_KVk&_|0=SA*A2n zaN!8pJ=6C-phWE^Fpy10I!alyV>4C3_Pam5qtNhjtAE&eGaj!CwX5y2#qY*01M3!0k8k**@0*!X;q54HRx=Oo-uFW* z?U0kJI&rVa$1Wp92)!f@#Ry&gkzcqb;yye^~CRz;KOqj|eg}a60Pm z=X^@YBn9t{0GGZLgitc7UsR5q)aoRuf`RNSX}oGlH)J5+ensLzM91TRIv-NyLj!4^ z*X*5XdMH^}m>H#wE5VEvo7%LT{-V9&)S~g0>zc*abPnt7WIhsHnFKk%z~x%bpo?GaNrYK6ZlQnj%?nHtNLA!qzEgT8 zr5JjzFKbDx^}odL@cwFeUvn-*hrKeGKH6<2g3Gyy*q{=ToNjemR8^e`Tjf!Eu1oaz z+{oW0bOup8H62n4Lhzz%E@mdz2NCY;n}O-#OJ#LD?=)xNnE2kNlRoyn)MozzL%#_x zGy+s=-}EEW&}9pMyuP;&re#!WqO6EoDv!oX_UiU(=Pwg-H&_tR0LIvR#1$J-36H|R z2zNZHD(hFrtp1Ji(7j8RG&8KksYV)J1iHD;OxW6^VAY75!)nA{h2Z}^wO(6#4d!V2 zowEToCTS~n=+JCWf#u%$h^zbcfvC8<(`3}HxK(De=j)p^>q95@`*w?#1+;tPLA7#mXgZdclW}(67b7DSl1tq#-kzPH z&Kj-HSvUH)%5L(+_wskh*sncF>JAwu(aJ_mW9~+Ero8)!uxc*-sp;K1d2!shsJQcP zTDpjst>S5BHF?Wk7Sm0l=#p@RMqcK7mPZAwMpHksFW)Ryei-{3_4js_k5(%eHZ^H&(EuT5AMXx z757v4Gk%%ZCYv(akSq?`(X91-n%Z}XI!euyG;L_HOW*TBiZvYnu145z(4*aF>hBvl z7=svu)n|{lM8!RQ2Gy9wh0A}k;#VCO756fkJe~JPM$L;0#|f&nQn}g?H+Yerd~won zWnxwynxImixauB=VYocxD_Yel78 zIZK8P*TzfI{Fwo<6`Xa62UTAu+Xu04&M3%8HUDGD=&V5ur&gm7KEuq5J`vRe))~2( zItDG}?xuZZ>ZPLR#&*~Yzqf1C{6^*WH`k}a>B$nu^Y*UGsF?o#NlpiZ6^nnJA@Jra z;)e-e-_M7XyGdG3sVSA9CPv84F@H1fk^uk@=SRU>D_nR7WwMBn+KjDbxWDl%u#%s^ z!(5QYZ4?beur7kqYr%8{-WMgnNs8>I4C1JP3OzgFrRRlJ>pz8559QsPS-pL8PnW=r z>+u^l14fxac3`#4-|$zxX?w?JTie9nbETpeQyeW20Vn0W`6PPVVAl#t$|tIE@y4eE zqk~u4bL~=mB;0!m?U$U*FM_@J@{^wXrg{DrPj|cN>>a01aiMy$cdC%C zv-3Q+|KRQ`Ok=T%8J^iB{ZZ)%N$~}nD43F!0gy-p7*EW9n0<$C{K7QtV4{K)k z)fhu;d5%r_CUY4q7C+LyjJ?mke?gxU%HuZ@ZdMcu+7NT*olLaj4ZsOC(6Kmo z|LlqgOpoCdEquw-h<;v!i6`P;XD0wnMfPQ!*gyN5)56Ci5hD76)@WdOu=j`}t=qB! zjz#A3RR_AqZ|U#wol=R9V|Z1d1bQQTSqe&7_0HtR^i;mOCw~n4*v4jlZ)Y)U7Qdzk zt7{kXOM$;&q+mkDX{z*9|4$2GAI}Y?M1QAA!waz#mJ7OvrY9eBC-&$I>2gJQb5*}{ zr=+}q1?NLhYmDFb-m1vOZjdw#x47|rtJG$qEiLv}_J56ZVnTW4Q>#Pc{5&5pUg=Z^E2|fb2!wD&vA}*U9(jJUNKHAbI;WM(WTxq)*Hd8LYtTb7t|yi?igbsioO) zK`2@dy=Y&+o>`?4PwFa;O~np0dTlR}ZaJQ7@h7^fFZf`OS7U622KRghQi2CqJxgbc z1l6bav>V%`+_up6?z<5AXEMBlr0~-RMxEfy5=BKzbwPR_3Fh0u9XP3d!}k_@w!eeSzx>!`I7{ zO@JZ8$N16(cxiP$NCTP0ld18IDfg0?Yz01Pcm0*735lV?sTItjxGM9k=0KIOTKbyhJ&chw!lY!->|lPWIS_u776hl)CqxDbkv%UAC4p2f zerbJSa4$RQ@KakYj|t?^EK`ibls96Cri2PY{SeYje(UDz_7m7*Pt;b6ls=4=Qs(&xyYIezvtc=rAMAjGlrql&qJ&J>vAT@#pzHjd=d$mVCrT3EwU8dyBa@(IB$ z3ohDwusq5l3UR!|7qRX4Y-Z5*s!grkyKs`9v=P% zRm}FbBA$|8$o|ITe|e6%ow(CX;c!I}(PgMqv0q4!*+s;){-I>YdLWb69|gGXg*>Co z^~f!e8}GW=bA4m*$fD4Mp#=Ys`salHL;<#Q{o|CX(iNF->=5gQy;0( zid8Rl>vDha!>Q?lDhjzqQl4n@g5c5kiLe(a%Z z^Y7DfvOB>-<6AKn7*=J_MVq!YdWbWmPQveZ)`50}eixh5a_LV!>m!SoO(A&z)@Ka{ z+7SbRl3qPmy9x7Um2wPHG2JbPd<({7ivkTh(wPj>b@rF7M2mG=H*0BoKy-tZ!}d~S z%d{=KyM0R2iX81Th7b$5uobyuQyU(oS9CX*yxoTq0v~}Mu>@h+rq0Wxd9q9(H*uqt zE@>|Vfi#XS0svHJ9x()cnJ}Zm z{^9~&IlKDMZJiR2F{MQ|U4o-HB@*O; z_I1<7q{8T5+EtGHEPc;&sbUQsZi|K5g{VAaeV;=WpF|+Ew09lr-{^tg)CMUg_T51Y z_ne%mcFb=Bef2JdxwUG?>4QWpsYQ=b5z0HwMNDf$?5`XO7bXaG$kQrMvINy6Dzvvj z-kA^210QkU*81XHBkXoy{1Yr@x^OatbJR1srWJJ?2KdmQDU;^#gw0G|tEFH;r29OH zi5_kwIMB9MB;aye?|29nkI5g*Ws#Prs{lj_iN6UI@@aRN_f7Y`{Bx$?Qg@K~5RQPW zRyHi4^%`9Nw<~PC&p3C^JQMC5wE0sPJ@9(h%0yVxAwvsnDyqfaa;0d#7&F;7gTuIE zE&M^~LCLWU*Xstmu{mlvqLZG7MDZ)pm%W-9)fnS~uGTPQ-=0qw5)#?&x#Ws`jI{f! znl%%^ku|bXN$p^A9q^swvEO+G>0Qm0$nOU%f$(iqUesQ1dRs@+V`RNpN@}}Mesd!cEUZHAyA6OP5nS3`-2JLoMzAE(5+2C zMHwu=+_#}+c`=X@!Y*YgCi=cv;>pH`z5v3bv37=^#Ey;Sp_lDezkny^4J$go8cocQ zPYSLe3IbK+Q%u{V&LpLTpkKyrQk7Q#N9Ql$fZcG;(Zi&F@MqPXQo)y|X=CTVfqz^d zAe5?#N7W%p*2Vy+FXLCM3;xcTcF0P^r2!2qoueSi%q}tlxlp;2ExnrNn|AKv7V;NOTIl7DQi66a1TkK{BE|ebqO~e;KT8k4|u6SZa;69YdiZ5--GUM zbg{eY^JWPiV2gSPPM1zSW_s{VO<`|HT_3aY?tm|Lyp7?`BF|pCde`0IPSG(n8NB^3 z(lzmJe02p$eDhU}pI^NBbj>4>D~f1wtB)ULg<-|@wS zk&>hUM(*upGHkm5B?NfP*1k&pNfihe7lD=$@;<8mqmvLwd2eonOFaKECzkDyvV6&Ax*Pyb^X1HzF#->8K4Kbk}JPGw^+E+d?)VD z0AG@iGIr-k3V+Gu`fq1!lP8p#{DFl}AOGi!B+P%L9Ky1~xEYhdg_7B1^X z+rVIZo{Z5qa4Po6w99Osssn5m0!7nhfuCVT3E+o>2gMFsof$Q#%RH~!620Q+>4z%k zNemvotpFbmRTC3QguBj*8d0fD8Zohw%4n|b5~756HR zVhlOX{s}D)n)wW>o$hF8hvnuFqj(LpB4UjuV^EKSXGXU$nPh49-n|Z@`Xv%9eHNICf@=pFOr~Bg04vZe?CtZl-qi;M4 zARKD_sj$~0N8rK%NPKb-9OMiIE6f6qt<6GB432+`DGIw8eIrMbUZ+OlTw0R|o&^Ef z1?#CM`jqrj_Kg-qRc{C0 z0QVne8&|4VP5wzs(!mYpv{{|J{@~Q9cndSABtJR_EJ0YsU4Ht3I_Sf`in8>&d7iTX zVO1dgc0k!5XMAqfckE zc3=MLKtg=I4E#J$u*c&b2S)SsXH{j``D1eH<@WC zJh1O|vwOZ(YDLzF9YA;t{R@Zr$$slykhCfwTR4x!+*BLaQeiFM;euC{yjYHhz06~URXngt!VVzTtSR#xLp zAC0{h`Xs@YrG%euWzT(#6#02zP86%~5L3d*0%4*uWUuXA)hcIuV9a{RO$%}6ybvMp z?S29b0#1%m5eMnw-E~0(yvU8BVJSCw78#N&0Io0eAbaY^kZkyN!0u9L#Qg=m`*ktR z)n|V`{vfEp1YDLKb%B0nlcR1QvLUxO2I*~e#dyp|)I8&k~TiGM(fV7z2DdYo*$(|h5OEU) z52p$upWS3sc^6pG!?4 zP20$AR3J{&Dth>X!u|X--Sx^N80ADFLsi+N%ZtOx@rmH&437_6B#Xec{OE14d~c$vbI5mV{`#+pUxMS{A2+?sj2R3( z$kL4&HK*csp2kX6ks61YhHHOG)AWPoz0B~|qVyE7DeIY+!G;Y1X~YaqBV3@8|Cw

    >a)ITdLG29kd=Ng4J>6l=+X(O*nZe1xG^0hk4y7diTNgXl1{VnLgd6^zJUuB zJYpGUK|xFS+KdyFFl(5QN`1F%RU+yzT^>d1@2z>F$4gDBB@2do=bD_BW0Z=!+4fijXovZUptk%3B ziRV56djqWIw#(gbU59uzB7+8h-jAhOf3(A29rEVL`M;92Cg*^#cSHod>uvx-c`|DH z;e+!?f-rH0kZBt0oJwlL1{Eo`TPF5u#y8VHiGqi=k6c;h$(d!e3V=oOO*Kd#GZ{rP zLv0wP+JB`sJg`2<tIklsFjkqvfUO*R(UnRhnvzBEwcn%%FRH?NXhWa<>&q zOgZQff=S1f(DFCkI>Fd|r#$CqTgpDOf1~YTZV< zf8Mn2?^#h@&W1=_{X~RsnhX=3Pi*ykjS)rpTuoZdE?O9>?3}17-N1Dk zY=spy<3lGNZ6MY$y`X^R=uIw8Q)hKg?ljDa<{XB$IK+v1=dPjP)%D?|IBZ+TRAy zOeKLBp2{k=)sCB;t@n_sX-}Vk1#Lu4(jGk}Q3Z1mVO%6&3=sQnV=|;f%n_?cT3jXP zfIpYvwa89oCJYG>iam}VA;c9)_qiDnC`1^d5vNGI< z*@|M-kGprzl0H_+FnE4262-~tO3aLZ-S7$3)99iE`XvO#DjR;p{a5Prr<6pj;Gebv zu*A@={{j@{X%o$kA;m#Js2@N&YtC&VY_|Q;Ab!@Ajz8ThO{*~BAi2Mvw%$Z_g?~P5@O=ql9 zOVs4=k*^+Fhvt-&v&ie@uj5TM$FY>J^H=^>0VN4xj1y-3?7gHDQ23%Vm+qB_-tp*{2L$zHOs&zEJ zyr2G2enoa3hA&3~mgzmb;mTM`O%%O#s(cJ}Cnhv0%MiLrgqTB{KT-&P>q>mTS1 zl#PKa1aq6XM|70pUWn@7XVgDl<46K2c*_achfJ04dFne}BK*hf-FM=rW|{|TNXv{r zZk?d)$97@DLy@YEcxci0?nE|&a_23pctuh?p4Z68;Jf-MsNK9N%znwR;AeS=H-&yP zPzV42dUpI8Xjf>G{hkRi*{DR?It~ZS~x|0P1bfur^66pmTzd{xXDSi{};Yh4FR; zd2I$RInvByubf%$&zJ>v*eTL!H~c;G3uhnC*I)OfiOkhz+zLk6Ka@lucdvm@rI3>h z2%+fYSVA7_#H6eWHBorSLvA&5;1UjIq%v+s-~F@xKRauVLECkv674t&;63}siY^mo!?k_^g?dHAW6{N@+Cpmhv{XItB@s9#tuYpz%nsF>+yhr#hEvCm2= zJn&_H=rJbn-AH%Y+)pO9z|%o-kVie$=`KoAfwr~RS4yZe6P`i(eY=VCDc>M<_;H<; z%19GCL_}mC0E%Wg1ybt5)fTnL3k9PX#W+TEwkak0Np~C5KBj|w+f^Z11Mh@=^Ee zovuEd8+7`A4YUB3n}JOJ&fqr*T9h15)ZG3Ko#a63qb* zYaxJQA}YX@vLjF(X*JX9id*}|cp1*4C!V;kJDA=4IMfxElU|8I?Y7z9+2M5}j=68~ zO>%~{-EQb0BY4qUQQ=3pi*pp`x8CEQmXUrk$Me7Q6<6fD zBgzX_5d&$stb|1{AE?6|+$YP#Ov|wtK|nTp(kWwhxOOkkkwJEfar_+ESeV@xBIv&q#{S#H7!TSus*hBRec$PR7aZMhFfOjNFpYW1cUZ zqB165Ds&(BQ`B-5S1h9OP6X>U6*G~Ed;RY{KV7UsGNKO_wF@69zrSY4IbD^IZai2%IB zaN2B03YU&>y?n<-J0OQ(e_Q(?Ju2$0pHoidBO$;Rx zzMmPAE!{#R+n|@xb-sa18x8a5y^A?Q`L%n{az%0~FYE4G7w|>^3hBpx% zV?s>QbI!6+K*nMw@s|FAsbwwHRy=n1>NN!f6P9UMW6hvu=+@su-`i^p@D zDU~Fy>{cp07_4EgG=0k`F=M16#bDstfO|5o6Zp=KU(|0E6P~o;aUo?i0RGTYIwSKT z_YzPa#pp^@rThg6LWSZG34Hu8HDc#RuGoLAyX=zY7vBnb-3ObE6 z7?{oYMUPn|sBjUhAJ<~(;bvzd?wVoWZ9(TKl^mF66;VZx6*LoJ!8MyDA~AY#7$%yQ zeXgq2`UsadlNcQ@{g~fPvHwpCP?`(MT!vy~c$~U{|GlzgtduZ5yi^f2vrWkL)Xy+? z8Uj%AT5jMtkpHDGSsmJ_>B_z2pA=UI#rUl=^uNAjK^;`!+C*hDP+~0?IxfCi3x8^7vKVM z5&9)io(bdo)pmU4kq-f8I88j~XbOJ0 zydkY-H;~{RCTbyUpb(wmah++DVwP(&p9#Ao;pbf*p-ov$c|)0a3C`}Pi<-o7DvO%E z8cVqai?}9_?dTo673tIo-BW2kGJAHYC8vE`9N{ijgvz;exlu^RnFRzV)H!@cypNpy z)w|gs6-%>~WZl70n@&UWp(V`mOf%!KGsfFO!tNcHz`FVQFP%tK^@>?ZUe)hrK7Cp7 z&lxK@VV{aBtQFe)if(Kjf)tM{8XrnqVFFGQQQw#y4V_9%>-);;2>X7CMj4+lX&JkI-D9;1bc?mEBXfuMR%d9SoZO!?jTK-|r?P?05?MZrdKV zVk}mj&0bqusNe9k)yHxDtLv2`!+bL8Yn%#0=7q~i^*~5n`rVN%b~ke+?dybF+zxf)47TF-?}Mq z5It|2s&wE*($E1w`r_0aq{7XN9d-eH0wUaO?Wj-5XfxYQMLxFk_-Ze2p;jc)ywR=dX#5lhaKa@i?^ zcWrrdec;RJ+ZCI_g9@DPFe<>L_E6g2yBR+jmXX#Ow_Gu2ccjIp9>hnIvV9BoNCV)7 zF0m|Xy?oNc^6w_~@d9DKO^wW*C1*kYEf%y*d+58nepW;ut6W{~4#VI-?K!kayVIw6HU39K>Se9cW>~H_ug=b`d^>2eEjvxCpc+* zgc&1}IzYrwGzGXUV{=*LMLj#$3z)AuR}frf^8yXFeP)iQ!S~yL|Cd zD-hzlL(R#Ost5Doz^i8_&)%L@{-=4D;8?AAl!joN06eSn`vs}*kY94=Mc&3MHX|gu z-T`Row$*5__8(w&T?vOX9wx3vK_vwa9pBkgLu`9O*p@$Pw~35pjZXXuMHNwa)razK zPM{J|+%=2H&v(7KM-!jD|1PraQOAu~!u@wy{g5$X*Um3GzG)DuTMJICIrSWRJyXoE zrCmCx*M{Ple3dKF@gVxJwS^8a)y zxXzvYdg@uSRj?%6sot0=J(~t{pdpG=R}Nw(HKg7SQOtD!$> zY5?_vO$RpC@z8yK-(ZzG377Tkqdqa+1&IP5!38h1A^L(W3i@&bo3EnL9)H6%<2Su$ z9t0f4RDb`)cct4YI?mpk)YVX{ym{!!qDD?D0?c+;@{g~%PF-WSv7)~l-l1-p5UV|? zHMw>n#Z9l=&9rpqqKnK(q=FLA42PY^+kQD>Lry*ToP5eRR^fgMIOQOfU?1 zUKgS&$l122DX4F^Ziju%0{@?^MOBr#N+S+_n{tc9pHvIW!y;@No_WrQdB*g<2Yyv) zzy|YLrnaR;G+NjkE-5Q?E!PLzpQd5T%9;3w_(Sr*KHY=HXIQRk9DQxd_wnW@XP$d_ z%4J4!jkO?DBJpnoF-(>h@SPXbjLSF+m#A4+a@q@1jyy}+d|PhwC70;+=akE*+Lj-I zJ)8NOxdmb}w@eV{NAPOBk#us^{|-_JOv#X7l3?wH6O_gP_|?#c;g(VaSes5$riQXrhJqzpb1zkEQK(6H^JvfB zYh)e?<%*zD0QZ^KtJK*VGYMp^8TpKUitb%^o&PAK0@jq6k>MnsliLeDTZ!(CSyxGG z0}jEB%Q_T0YytTQTcl`c)cakEv4W(7u#5j{|5IMwON4fZAEs<{KRgJ&{qHX zsC$x(jScV%~ZPs1o+Be}? zKUA)XChUzA$!(mPwPu=V0n~bi>~2EK2;`Sa*iE1{3aAd9YoH z*B(xFheg%;I56s##03|j&ITM8&+GBrJ&RDKqyFxIA#aNnN_{#ub-(HYPKW06&Wo(3 zM79R1=|E6W2KkQu=c&x|wt*NbeyEbXdmQgwgiNSf)EV5|WcQ>O;YRW>vg>xh<%N@I ztyCcJFw(30Ip^OQA~8C{yhu!#l9AjGdK>-1)8N!={nYISQm*Zugl(OZ=trxZ=fG}w zluaHCt6*M)Hnl``%U#$ZF{>xrC~tm0jc=rh{m^E+kS*(gvPg2aRy(X+4gJH)&dw#* zxIA&ESCRd~8mL=Fr3GemkjVLf5X!f{W7=okb|5~Xs1?DWeu*O-*>ru zO6N!3{PHgEcqwC&qw2sKtSW^lA_RYrWEY0?ys3;TyHwn*Z04n42M!!aPdrH!c^^@r zJ6;(v|6#5@vHJK|RFu6B87Xx0?SjxVqQlx1Us9yWlA;7No!aRdKUDF9Ab%A_bJ;ZS zlBu5^l6m&jq2X@SpMC~5of53NG0A0j)3epXDvc<|!U$Mjdc}MSA<0Z#+qRu@#EZmE z@C=22$)Y~s$s*;A+i8C+Pi1kwk@aK(>lZ*bHN+P!MEIdnqKzZdcF;SccE{bt2Rx*L;pv?A4jed6A?*xP=O zCJQbHMYLtg#l9?!__i~vTFF=AWYt6GGwYq?d5;-u%i_1nrR0sU29z$Nh7sW&U1r6{ zulf?>@Cfl>3KVe}9ohIM&8O(tVoDsL*&TDTtz?if!YXtDM_!#iopa~sB7xK^kbow#YMb2~KN_OE7_(NPbO z9wpA;5!!Y{y6@@5JLR9w2|p69whw42A=Coe#7IjwE1@ha8cK#(Y%u|ds8P%sDdogS z1vvpn!1zjOApG-h08_1SLS(Yk$)!uH&P7kF4yrA$e|H%B(w~k;_pp-c1;}??={V7A zN&D)v5xj8D(B*^vWNyQL?sO)h;z{T%Me8g@9XDqWva3W2(aXQ^jwEQT{fo#{FvF)%=NH`N zSrP4w`egM)Y%tl1F_PgjWk-1X*HsU#sPgw`Ksc5Wb*qIAFF_H69Wc5N2t%Xt~FNf=lV*Z^Z1j~ z^q+7g@t~`|`=B+PY@1FWUbW)DK8tc`6S1w)iXL04;FDRuD!#v(!ekwYMw@#~jSngqoo=_z>kidDg*ugI(G$Ht`W@G9gq>@&kAym|g?0_;m?!!OZk`{V37> zlbLh(t`}?UE{yCff)Ec+!cyu249rz3L(Dyy+|{pRA^E~}vV|1AaS^A)w8ms1_&pP} zc-%?hZH<;j!`-02yo8!QmvS*VW9uU(kX!kl0tuAZ<+m1GDz$7M5O5lfXQf%pf0pzFkBEq2bRs0wezGl17mhifH(1E2Dju zdm6R&pzoS6+wrG{)5Zr!VGR!&^TW*E;4a)EIub5*PIz*O3Ge4v3w3kmNdmNAD zdgMz&USxTT4mK>jld5);xOy@T6&NV69 zmd(&d24Kg{&s-0O9blAs4=+-viZ~sd(37iVctTt$78T#}7XQ!-@_iJV=dd>!YUlhnw4VHTvdn!}2LJW0 zjw7a9WI5$WNFEoSj>Q2CX6qpNv?S!@Ku`s9G)owOAj z)bFmnS)7IC$;gnb#=E;lPK>tiK!O`F`gWy%bs1}$+Lgz^nXAI>P-Uy;lWOR^esM1i zsKeJvqo{?V_pzeY*++<+7N`YG;eW!ijN-r_>VyE#kz0Xf4c>1lGlJMu_KfbGC9fam ze`6km28a{yyHgf+@n1(^}Xm%;;XtRh$@omIN>QG z8eb&8{(Egv{@V%50ki9(y?n3qLr@?5t~Mex;yI8*Fedm0wc`;9KZ}DlA)gt%dEZRT zqdWKR#}6y<2z0U$#SRq#->sX3?QPGoS(@{rMT5}4%8FQGopV4Qw}N-3p-?^`AB?$> zxz=Us60dIIOsH6OOV9GLaWn+C{7FjP6768z)!dr&Vc5&>Tjwld!2Lq~QKyve=RBDs zMCUsa2`yMa9ei5`&MfWlb&sV6U(jw&@O4kx-up}i`fJk)KF${~)W_$!7Lg%5VtjUr zZ*YvI-ln9kyZ^87jdF{gy+$fTsr`ARK*sCF3lnwc$bEo058+b{naBNbE6jLf@}Q*6 z)zhRuZVtx^*yt(L%`n$N32yMR5%oR7K%_Fy24lrqDqVPy&)MU<4R_J2u52jld!n=P zu#Z+sGJAD}u+G2CY2dZS&)Z%-Pqzu}Yx9hbrTnpJ=z@w-#oD0Jb z6E`WxYq0z@+|7P&-p<_Use_=N=k5)miiD#IAU|xf^kB*HZ3% z-eL=j>&X>Yu7U$0)TERRxuZ7}KelL>`xbBI&|=jreB@|#ws^VO9p;yy%q%B8)OTsf zbF<+0|MP$J)C1{bcfI(shqm8qr{S+$S`rlzJy=~3{C&IQ-oq31x=*Nb&DEoga0Az5pEIGPUJ_yjzJ2CnN1CO%x zy*As$B0Nq@Q;f>X>?F)MCB*&am={is#4B#47{2NXx14+cfuseFRznxW@kNkz?dX)Q z0>&p@efBfhKGnG4`{{2S{<%n+3s${7UNuFxY^m9%%A+N`<4^4DHm`$PX6sKfb zIjj5TlVs85LI{VDyZDCEYOCoITL*{CR{=jJ>U9k=67(`LM~K7>jd`m? z^;Pe7Cz-`uCKF_A$gU`x%+?4+S*eyjwt(1E+Oy#z`Wt7%<8mwm?Fx_=Gc!{C? z+vJ&MW;^9Qu)npu%wgxgo!(69EergUj}8Mx^3O5fkl+Hzip&c^h-PiM*|!5@Tq)gx z<}CdYe`XJO_$n_B*Y=3HO0>!xOnMy+Yq*-oq25ExyrV&BD1{um|!Ik*_prM5H#A zC9RE*Lx?{SNOGQ9?u{k-v`STDyu|rE|6BH4v+!}AUkSNjY7v$6Jod&ntGI)YVSZ8Z zc0XFOdZ+m_TJS8|X-o3|q3JB#ntY?iuc&|`qM|fIK|(3%8la@Kgh&qhk4z9{l!wfA9MT?Adkg-gBS(KId~jCmnd~YG3`L8e8pY7HRCZ zBH=(VWITR6>DB%(SgiMACr8{QStq^wW#k_KK-Aq!mW<@|3L~B_$NK}cIR=_lVc5 zwWj^~iI2PyJC}cDSvYtlPUEf)kBJ7?`zFSPNX1y4oRLQ@fw4xV7 zKnmBjf(iA;(n^~7BWzwE6E3p4BxoxiH2xfG$2j%x8q1Ux1OAGC?%%Hrpx-&)C+$=dm_$2aPe&!ghVCpb|(l`pNR;^YkaYKci&JevUHBfL~K0Ij|{g1l(~VUnBEoQ_9xJpH%T8LeKwjiFfB zSaQxYX`~At;L3hKeCLU*>>#b!(r<0wTc;KuO6`zZGU`Oh%4bqFre!bmNLI`Cee)-c zDJ~UgcaR;ff4vVPV#jd={=|}?A6~u@bh#%Y1=2Y?uyBpjamS>uc{b~OwAkjq zm>WVjGaf?wI+E07+>^3wH}6CCzLdb-{61av#$@FQ&curwa~x*fxRQei3hfkBDm(bY zVu>16IKQ-&Pb*Vm3hwX4qy<|>iOY>gX0$||gIAQ9)14OkB2IKvj&hC*nO~%@Mbuj` zgDD={m57&+R)`SBupY7El)9^E?qumkDfL0n;?lYq>=z?}(>?hNLznqU-7grmRSe94 zAEkAj$an0Jh+;AdI~O*M-yS+H!JMPL%JmFB645Mn0xge!W7C#txJ2s14>JOQHDtx* zWR!dt>y7Uz?Kq8N*xB&3hvAFW_Xc~@?0!8j73y1lZj{L5&6HE_Ft9tG9&oY*^FnP4 z6Ud~Qe2!I@i z#Sa}}RxaRsp*5?pA6P#t)E7LZ~fz^dpn6HG_ti^e7Y-70GpyscOyQLN0H(8 zl13P!1ZW1^QxzQvh0EL0Y}P}pB%d>n8+cOg^ftKZtKjTe?Y{NgVdeCS|E$sRFD!q~ z&d*m+u(DIBWyCA?@!r8&?zE{R2_-IZS(*(p(^U>{h~E!Svz0Dsze>yFnSTM=<)zgv z<2m$Tm^kZdX`;4*BG0)Y@KG}9RsD*ddof{a`ftKqjfN{;mN)1HcYDO~Hsm?@?~>O6 z#ANX3F2ECY%{Qw_;8yiSYo=<=xBOCUyn&&TTO^IikhBthaUu z$gj46#C-YNLjY0;3V@8=e_?)(I+mD@WC1W9$wXiu+R=&%$49wkTt6;kUVjEPTM>}D zsUcM$t|qdzF@DRmtPPRvvy*}OJqEIF^#qD~0}7S(>7wwTDoL;V?-240p9yMAuB9&1 zMpK3m`;1oyA@&PA>cGKgq?*OqrgHNHQA2u^Ct)U*P+s1__slY?DC$m}I1Nh+^F9Fz z=yGHITGk8@H8bN`0cj`@CytwVcaL5E3+1_CfS9ROv{WtHd>ze-BDS-(v}GFc)wC)q zZ@#r|SBp1r{Z?(QL~;6aRqLvd@gyEqjtpq6Pz)4n97QUrhGm&pUH%S9hualDOij5W z_gQa9Jxaxd@p}r%bO^<5biF3~yH3dE0)1 zivo2EK8YfzR}bVwd-xg1q9iIl)yj4Yk2?di%XzdEB>#^Efd2rS*x5O_+6}SudX7^Y zjsJR1dCdAj^wC-J5RL3nRPG1OPMCuGNF7ZU#z|O)efBQu%}&#o$w&C{lJbq0Gx>C5 z!)%3j9=P0YKdKI9Y91VSPzmB;j$HeR)^CYAoUbf+g!nc*v7#0a{fPPLS6(;yKPYVM z>;g9vwA*SyK@)Lde}Uet$6t@9hqMv+SwdQEQxkc38d<1ngzGT2rxL#DOhd}@0+QGW zsnnWHhM-1Fs!xUf)2ha;kwltf^Mp^*vrzxHo^R#o&R=zhc{D@lj9*T+#h)@99LZ5= zzYQCRyo1POsCX7dZxaoHo~?Gzj%g9_*};jgP!+_YE>Z8rKTn_MfpF60;MczvTt<^L zwEeL$ikt4ms_dGX4t82INv3*n!2w(<5S*+Y6LcE_hJ4rYX3!bZUc`q@}^lZajgYwwGyO-Uxj8RpO<=Rww zCe*F*Jsn1LFUEajNBPX<_Dl85YNf{)BKtaK&QaPfBAk6Xc4rS-XZ4K^+5?|CKL|Vv zr9U@w81{UKZdmuMZ2B2_pQe922dKO0G7)z1R<;rREAM2FTbW5Tufubpp68>b3%_#r zYM0*gc>as0;Ba0&bmXfmDW=X?mtkD;IuYOLKbwqx`;C@*rT%b;zQ6pgeZ;SkfJy6t zCuB_BFk|boBVb38-`-_YxTB|cXYuZGxyW?m;SPA1TxU_4yF+4LB``G5f+%+`ymbx z_#~+Dk@3q&_pO*^8;ZUfutK{+=D|_NeU`hkj68ZxiEKt)#yWvRG>aFsd|Dc5C6lzv z4GO;$%jv0C1c|I0HZF$Q<$9s$w;PXVUNP9BiC%Af9nfAwt;;rB0qD^)+l2nnf3P4J z#Qu~RUj{47X?6#pxZpdx5Rt|I!d@}!1%}$b@nY7B0PRQ%NC{I8ssiP)7;l)X=p5}^eSKO;4tmC_8K^nhNgTiZ7LY{kxn;bzGMH|)l zNUJkqp5rggy4So%2sHLKijsx480Ok_`KU-S9zl#XbLB0@zyY{iEVuJ%trJTF7@g>&^~^_3Vi`PA0^Y0q*W+tL z?PE?LOUF|GC2y)KXF2fw`G?WJ1|xU2IqrigV|_+smrI@~bAU%eP*!suoyJmA)>7>! z@(@urwT=w!bxov~kDYM)bqKGcOY3rgx&eK|Fg$otw{zBJszO5mfJ-H<*<;aFq}5-n zW1J4}A26og>vEYq)CB;;a33hCEZ)Ts-|VQfp*eZBRy;PpEB>UuGy4~`or6hsc@dlV zgv=Y$oD1FtJ(s(Cw`)9OTC%vDg38SAM7R>deV9x{;98_kyLVL2= zE4#y58{p$SE283ojj9cAlY7j{=nuo$8SFH7B!}*4tR!ZSeClF6$$EJDoZdxqc0hx| zvbsAnLhN66`)oelCb`p(!77ogGVZ#_oSD$0wVn%2r#9WfNZvoLa}0B6kw^mnk2#rU zWvczYP!^qkd_jpJ-1vfo>o)%K&Mru9D##p@6#^w+{z(-29M~zqenAlU3ixcD7vwjZ zUGw`59j2gQHN*S@4@s?3!)@Ol(WO45W*7OL5dtNy>P8 zC-`doOY$LFnxnfg_^S7^>29^P1cYXbg(S18{BD#=vxB_;?U{i~&o~}_zJTmRU#6lT zT-y^#{@kf&2R~sIFA&Ipxfz*^E~%9@Wpp*gmP|;_aBM`)Y2`7$--J#Z=b=nfy4;tJ zZ=+pERW?fYj?}lJ#Xl9dQhv?qNFhX z9)8&osn`_q9cjVXR&bT6H-X@!a>J!b$7jfC|d32@7#4PhMsnWHy3*x z>JU*Kcq$U4Xdf9A*qEI(yXeFjG7v$s4Cn+C}2{^ z5SbqcFJHxVNj7bqke+2zWiXT&`0M9=Kr$VTNLY_t)_&4AD4WDw%{M4IbbU<8Mc8NT z?WXTL{4@Ar(@vNpTc4fQXZ9pt&^gi}J_lo-WpzJ^7A+#hQW#+}_~S6?;Ow6Vup-|U zv|(uDB_22<01%JK)M;>X4dR$?S5#YFQ5-ipzMrMH+;6s$AHjAY8CT@l(o0?X><(1= zL~~plR+S6?4%v_y_IT=rQ9UcuAa&5=%{XB8v`hHzX)fyvWD~s>Jt^;=7f_2jRIAdY zN=~5OwHsckYCE9GcYZdTR$1+ML=?F~3-+7Sw-kE|9a5$OPxl;~#2BWPQ}6X>?WVod z=%`7Bbo0V^qsT2^CU`xMmoAI+Vur0@s|L|)6g|v-5&~lgmkFC)n*L!&1fS(%R|j~A zjD2h0xB8x+In(G_r1xrKL9sp8KboJ{S`XUpzTpXNWU8N>J?_#zIe?u`|EC@;>1#U2 zC=Sg2S8P(0BJ}(bZsQKyZ$NXFfp+@^WGE3(y~_#K4R3YGN+>otZ|lxmTCKeHa~;c? z+#f_GH*%IwC7UhDnP|^rK@ZE)P1jA!0z@S%LzE;>W2?U9`27>6KrCcK_7%L5cUVY7 zRcUE!`|#O9R&o+4Ou;6LQCmXke`Psq4f2A=C{*1e{b$z7Y}5&jY@3?T%7kWI&kb!> z?>|%rh|URiuEno+D7i}NSc0{7j!PcxHN54%HgurCWCRJ)_OME%cV*Hdza|_D&#da$lAgz#Xl>;3gU$TUrDEPTEKe{0>g~1f{!e!0g4=YN7OqS?ej^En zE6<_h>pIQ74&7)hjg_}GmbR@PDU{q+!3%r2#yU%S6YT}Q8t+P{#Ep_^{4GEPyidH8 zHPPIc%|aGy^6GcxOtq64 z8kC&|yxM`4GS(RAry0%JOl6)4H(w33NlDXWnj#8bT8+$nsqTCcazM#0{%|z=nN1Mi z@=9(TYlS4ZXP7;_31u#O{`l;;S!2$-TM@#wrWpHUxwS)6Hi3#}z(la!w=~!V`~bm6 zx^|xo8`91WaO;biwgi7`|1HJlBEmOW&bkbIGD2PTTcYdJ#+f-?okrqdK=r>BmKc4lNmh!>tTD~N=>>jJ1?x;no@!2C zh_7MT{j@B*d57ZX=N8g~&`)?_6>sB>lKy*y!bSHLfwZ-wY;@<87pUW0cjO5|Ny3d;M$|=tVwBpEVysaI|$wkvyH|+)bF_ zEhV_jx!hT4(|2pbzUq>Id2`6$^6<;Q-6N7*yM05r{MCFaBKXdyHj@aRCGvdZh@50D z)A1M;Q^~?V!(O&48P7APtxxO_;8fOP8%jG~Q3Y|IUWe@Me5bSUC~Xe|t89k+0rHVj zl0Acw&;-b<;(=qIUH99pv!KDrlpfv{dCywqOLrtqTV@bIT5ZG8Tcd|{MTsmR@5&|v zcaLV$WmhDHxBgFyIuNdRx=3u0UoN14fXbfpz0}UoIgjMtmPuw6tPK)~zJrtJDelL< zc2?VI>o;Wh=?eAlgUtSb=cPKnqt=>huBtGnRsX6yx9!VQm-rB(GDst`63T@DCD8|r zMd7l*vfE13s1>4^6a$8?%aS|M+v3-AJUOWxfQ&iqHuFHNHEYsuRA~KxD=$VCPaG>E z9(!(H8biL)#FLnohbax{y0UtKJgkeIkwq;yTC-8GHyIkwh}Iu1%s>SyvkCZU0Sn88 zyjFI0obkJvDs)k$$?jn5;oMoN8eu^QogJ8T%0(vvQ<|zTLQ~U8$es|BRMtGB0e|gy zm&3}g&-?}2o(e(q_KcdAS+}v!GzTVAAb!)64;9>6oh^LVE~*kEl-8XuoP_*#5_&4A zKe`V*bTyvz?88QYRnx1Nj8wkx!v}X(ze|0Ma8B-;>Ul-4l32X;a#k?VtI<56kaKwk zG!c80gE{Fk4y(A!iMpr7_+s8*^rxfRnG8O^yxGJR5M7~oHLz=VZb5g}3VXNV;jZ<) z*3NUNX=%PFQ+M2+6GP%VYQ4ACDEo zN_?XIs!v@qwX|uMFYwPVB8zw9Tft~QX>G)rS+Z8HuigT>d zi*)GcA;>Uy2^WYop#VbVXu>Cj`zFKEbk5j3-;78wkcpjuKUyM(TL&x{8v%O0c`RwW@*TKnJvm4_b zX{v{+eZ^B|t&XK53T5Ao0q z@U=+gA7l=R$1lyb_Y!YTKY0cXxqZa4SkAQ(9#!p|=IDE){D;07Yr$@=b^_Ep&rlon ze*I!cf-U+9<98Z3;Myw0a%R6FemQUB1ubEkx~NF{nV*l_PIxt!G4d|(1Je_JBeG*sp;+*NOo@*>}*w~p3$>2?6#{-;|fe$7guN# zkUe*6J8XbW6I`=A=WzjcJt##Or!c(1$pwEEvzVR+yqG__-1D~~>}MZTKFj$xmE}fB zuAZk<MNLOVXZP ztImQ^Tu+csXY@#70}dWgt0%9#5vp9U{xhBaaBC-{uLxrotnX2Tsf!u239Ggg*MH>r zP)D5D%0HBiK81&N(3Q~&``mWmi%?bGKHXfkan=Wsz#b$O$_YM7piuM`Hkj_DOb!x# z+2EZnp0#Pr!Gv~!Q=QnwH)i$}$vbMG73V?cb9MbzvOeyaH6mW?p0LCm3#rd%9ZgM_ zUoreQLl?bIOVW8QR~as&Pi&O%s*?=%)maqqfPg@v9Xh4rL)T!}PRo`HJj@CCeEdtDo4VpseD zWwlx)-f==p{My8>?QH%jTtF(0Cq`4D*{I)hZoI+{9_>y%Mblr?Fw-Ft$Z4}@oos~8 zAP2P+9orR~e_=rL*GrOZw?4&R&fluW-c${=J()2P`lIghH;aiPcvCu(jrnxkv%;b<4P6(K~?-`~>yT_f> zJCR@$OL1>UG}OUsuO`raD{RjvB9WQm5v@ zV6Zg^_w~Ff_ft?f-qwv;5uDGTA;j{+JlIn1W?Xva$>MXD1A ztvpuh_Kfo??0Q5I(x&VjKf&_ zkF$9S-u!R%1pSLW=GwhYMEa?5EN*IAK%=#*0*KE)Y6I!I>G9T0F@cQU3Px)|6W);BY+d+n{9YaeXUSj~?K6tItVZkzxty4G+DA0_#6auP~b*B9rQ$_xXy zgUi!rR7gXl0iptEtG<>&HGg!rI8xjfPX!w;o*c5e(CyMeO<(N z{Qw6NX_L8!7&z?6?w^8X3^MLUJxIfn4MEz%}=U-wP<7wu;sj;~U9tOBm5wIP^uhea`HS5%V&;7js{| zY-f5xjRZ{%s9viJtFKE%x^^uZ_3sY(lxpnRU3A$SpJjHxs4L*go;aiNM=YWSKpzYE zq*v9p^B=C8aKoq8Vg&W-hqR?$4pG@bzv7%r*NV$XPCr}YyKLN!+Rc^rQ>R_&wo|MjEVLL7Oan=Uk0A*rng=iPLF$9-S^ zraanNfgV5L(a@qataVtVWG**26mK%fRL5nVch7d5>Y`fe;OM_`fC@7n#+$*H@ZY|l zN^Z*r(|q{)RPLMdT3C`C>{Wldbunb<#Y_w)2DSZg2E`mJ>OF$pD(!0^_`A$ne$6X_kv?=?C)g~&+N0^6gur$WlcQ|r}gth zfz99R3lY*a5;cc;*8^7d_mh&`*N$-_y~a}uFKY_!+!ig(cu|gsm>aNQAj^r8cc?97 zp)$4gdC3kgv%dTGSJI|9ZOZYEyx!&p#xCMdGDjaxl5GG#xeIP;?3SPB92Mrz(Q|ug z*x$)ui^)pnB@u4Z_xUfLPH_e(Sfm$xk=Y4WBvsiLvWd9jA4x0VBmL-6OI$#;ElcVU zGlD0>Og=sT^9U%Pe`F*1!F=SjNbKH`4boRK;?&l>-6dE!-^P#f_GIV}#;i`Xjg#ei zVs@k+KB`KO=#zQ~`p%9z2AS_BN_+&}LsBlD`Y^JzDPn4w+J%(S6KEENU5{qyr9tL%y7=IFwI+UQEh!@yQ*7bD0IUH;( zzRA$E{ukcYIFdi-firfsTlLV2wsxMI2uZ*F)6kKB@6DFWi+!2NBJwASio?fA(DYfrmN79vW_BgK_=H8DHGgi#4vQt>*|APcEf7!Nc2 zS~EkSDtW(WGdF8{6W=ZRR?33@?!ba0{&zs~)E<82ZO6oa7s|z*=NytbR_Y}g zmC8?0i4R1$5#}uN6VW=NVcJfwkAoAXx1&F(s`uZAPIfb&C&%d<1~jW@qbL^i=)(?L zV#ei8g*bR(?*K>Q(0?cYT2Riit%wkx%4Vlkd4vo?n*bwE7Rq`~n#1po$A>m~j+|8U zRXSSXOVx97NPs+MM};75tZ3(H9k*442rYUy3XxO(hmk&N$%XcltdIY?l+W=&vBkfc zv{87qyMo(l(Y2#ARO)6eh<%gEM!}ZzG9fuGAky;s*lSTzMEpR2e!e(RQCmF4uV+2G z?nX=3%Y>DAObzjxoP_8#NRT##>iQQO_!rrt^cxL?9_&Uu!#FBEBTCE-bLE(nN-A=Sr~^HP^=J z8MsV9tede^?6u*1@)Ee&u$8$fD}m{5#f-}-5^wzo$g!x0_nAp=mf39-(_fH0|GqG= zae4~4x%!SsgjGA+-k;%Sjx1idmITw{$4}-aSk$(GFdSW@H3si$`bF+)#WXTOr{9S7}@oEa-t-sZL%w6dA zE3*hoIn^>7dda+FN5{q3-*@KVwb&SE_d&$%{(^mlcaWqEH=kP{MNY0-8Sd}xZXa|J zXWcB=PT17v!h5hB5_b3qcg&VpN&Uy*8DqWcmD_JneYla*hz%=Oh2vYFxoMEHD7Q) zACt;n%w3NY4E(uKmdap&D&w&)t>@jNFN=BI+Up*FXv+d0i&t)YRP#)0OF;A7M%ob4 zvo?$FLmwRDl&i(O2@c`KN>;G5S6YMhhOMp}HUbC5JePW&-cGMP~-yepAi5Z#ul zWQQRLA3!_2O9K(*uO>np`KDfwc50?D$67Kibx!s8uE(lO+FzxMtaka)fv@)skAZn( zf$!}RZ$8PnH%PZ++g{eyPsLKG>Cui@N)&h~215OXv-<)7?u*N}aK4ViG^( z+}ft>9Y+E_hMo#BY(9Bii*AI)Q?iYBZp^R!5PXESdIFM&C#~7yhf}jFq%bZfRY|q? zu$(~0i*cL}gq?kiO|RRdtr=99;nJ#v9V1@xQIIkKbe&(cvT+fy>82TE$Um1~aj(CnXC@b!!Pqr?&7F`mv*OWh%rdzJeEOBF+85%Rk$wFm zv9|dhv*N^_32(nfnR3(lG0!e=bi_GU)})7mLQrJmZMn2vVr?`NZ%4BEcP z?6=b?B%hs8^$vFp-)^)B0;M3`QWv8SC z6!AFfp83QNvpWo>oy}i97Xj|J{1rJqW1xy<5)La6Q$}|T>PV=YORT^hHrHP@-WVA* zf&fzfmB-ew%!Q12J0MGcE2VUufFDldnAhL7^+{2=Alq2p4_!Fxs3hLFGL4=)(4xlg zyk5BIXld`Y4{r9fH*a2|dp~MQ4UwMu;zhbA|BwX>ri1h#tKwC|tUC>C)oqJ&i}l z=Y)}&(-Xr-lv2b{b=Lf!igmAOew4uz4^vMFW{p?$K4_k-Ug(zarGCMSCs&G|%q?NGt zN;O@Q8;*~FVPC)jS|&8a)z)9C*W=IHPKVDqVrRHt;q zUu4{F5dWm*=CO!0ElcjvErpZgDGkK67mPaA*k$F_PGg1S9TA&gR^LVoiUfq+`(@`Ny9xu?>hiYF`1V(h1&H{_9hpdltr-I!cf2YPI~!)W3gi_ zcFVWWrPa{SxG;XM=#SEQ^MO}?q{{6ZQ$5E5ed|;sQC_TaIy$dN? z3)~29jsEKU57OWFb&&V0G1;bhzU7~3X!}f}$&~yl*zhyO<0sRIDPeUSZfzpG%a|GUOh1z}mN9U^ZlRF~Mz;Ej#hW!%8{7 zMR+WAJap7b$I^WzEOO5tR#!_>rG`U9uqk?SrzNIG*Z=|o-AGzrn6jJ{6@uy`vQn&1 zw2&t17#F_v;`4!%VrUH|+5s7S(y%w>yH^E6`#>B?x6Vy&MlF}nY!a9$nXo#e zp#zVo!&LC#n||HXzWqH=!#+Bi!*Si`x!l|Gxs}n@qv)PZ5|Wz<-&5qN(1b5ph^*`I z#A*NGpfd!BT(Q{AqmKMakP~~FJ!!Al^wNhiXu`G&zFkWaFmFAXNT*T4L%$X#_!mHUz=GM@#8_t9ZL zm2Q4(kfQ*as*$LgF{!0kd_MNI2<#9i$?f@iD!w35X6mI=e~W$jm|nuqfKn;uxSjR) zP&m^z$F}0*Ar=`aXJWxaZ&f-Bu{xWHZ%ZCtH-r?Olkc!q;!ZWa{K%IW4Zkt7m>&M@ z>0sCj3$jGjg=y)Zi2lyGJOhTt+cC5Ww@Lpl6EUaxY8^nNL1#)|bsZjRjn!$p>2f&0 zPbYuB=-PU}l%>6-Hwe>jv&5e_&QC{8gu&si})=Bl!`^ zpOh20{KPNA1fmE#{JWtrCupH5?RiYs!#gzNSw6KoxAG9sJ>ZEZ*E`nmF`1yRXQ4%| zuP?^^i19b_v`+Ua`a}1bumzsyz&0kY{~bViL5^~^oV?++p}X>L$ch&(va${be$+R8 zHW^7A>XfLA`&@}v*WaFh39bG~q6j-#P(z$8VK-g)v=;P5SSX@EI3_--_FQ)>;T1!PHRrFN>I z|0WEa`~kDWXFgQCCA2;E1pGvL;czP!9ure)@HP~$IYvvN70~E965j0OCi)K7eOt~hmz{j#mNYkryq=Yb+d8YPan_p7f;a(2fl#@ zXbngfe$)s0P#6E0xMw(ek&Fxa6Fo@Y;IxGsbyi;%&*0M7cER-G)1v2r$yWybjx`yP zvni8oZSCkO0Ti{iSo+8OQy21DdIoW|reYEIK0H*AcTH?AR@=zAJaArtvQg@$!5@q( za1FS2u~km??Ex^jdrCK4p7Gu&fC&e*_Ci(*UMn%T3WFWS8`+Sdli2;C3SPm2+5~NM zCFHZQU&;VQ%0Pg^!+azSyYk)pV;Ts$#3J;jFs&a^E_Ezk6(LNK6BRJ?p_@6b{hd5a z=|q_}Lt>hTu{Y1Lvngs2|B$P7pUV^%={_1#HMVzgv8oRE%cgR?;y~@=7kfJFKj`@$ zBjf0JXgYNvKQ1|Q6anl0w#qg0Lzp>IVu=-Ni;`8HcPP|AZLr*4$RX_!eE--wWEbCO z030!2lQMJ+SiSx1Det@px6djD!3=21q7CnkCK5(Snob1B#A3q#ZF{M>;*X=+MefSY zR9vzuETx);DtEfu*I+h%H#$6JnS;A@hlBFnZG+BSeiSmVM{YcPb>-Wla8x;e*WAa! z5r_DR0bP_1cVlO-IhO7)1o;C-0{ZGjP+VhRGW1_0!2DL5`a1B#KfEoK#Mm^vQlm|0 z!*+7N#}7f%ux-H?3W9v`==PbceC`nQ4`ZfMeb?X1N){lFb!n`28pah*&Eg z1vwn8u-~Zt&!Q0SExh-{+?>oTPfyxf#k|%vqQvnEv+kaPyp>HSkHlK96-p6|A|5rX z(#)4RQIb`3X%RfxQQr%DD&NUNT&TvS8hhBmcVDj~Mc2dMXM#>1CstL0d>=b4eR-P0o$}wunkG3v+ zdg*c!y}MoeVty9lXv&+$VtPPN!gpBnBIS-b`AHSykwS1qgwuQBy<@#mOCRj7&1V{a z{q=nvP|%DV#brrNX*bEUx)RwGz*;rI37o)_Coun;x0-y%9DZYd3nvG z{U&Q$Of@d?)DXxPscEEPFmu&?!R*ApG-qx&IaF74VJ2M2g&?K{|W-er*i%@E@we-UtSEFh%v_og~6dW_yNFmO_2k zVwXk4pMUbz<93fxN;SmxToCcABII;nX-s_6jzW4G{Ve~x8o;tdH(($y2r~qLB^eju z_FlnkPN2?yclUKuou4$YluXHu@gAh|8X_*f!h$cu+HyMo@)LR8ZDQEcs|KU0S+=wd zhx5YSRU2V2-?b`u_hR1Y>L`&CMOea3g5>#!-^3rq5~A)tMz-$p0!Ud7&CZY9sLuOw zGD~Q`XNyK=`w82}{tKN?GOm^}jIyl((shKtRUm14%# z!1ARoiN6JsGeOh7a{gT%Cbm`uB~7)9_Kc>3ECvE(&BM@ndv2bA(2*_0 zy(2sW6p4saOc6~krfy@Ss0dW`w5n8M;in?Ri@^P%T^!R6=*(OIblY~_kca!F-~GN| zZD+rX@T-B7x!J>C?tpnIPV1=!UKKpb)LhN{RON`Vxxiv9h460NA*G6o@~+leIESAj z^`<4Sz9L07Z z5rtSZjo;ZGFVWq}qwk@$C)Jw!mj_X!_S=)1QYEU_?dY|=f=MkklWswLDn(pSG- zYG)>m_kXe+$c%;-N*E&YbEkP7gWAa{F{esQP`6On@YC1Fx7k@qt00yiuFlyMCfhf@ z8|`bY5fRXuvI9rcew@YMS+h;6c|a=lduSFCa}vy>G(4Q|?ky8v{^9!A{`)DE!_UDu z{+8Y1`Z?!<=sW52#`t>MiXTBv=={rglC#Vb@FjFmbD`edIN{9aegs)X+w_r~L$iQ+ z8D^MO5iODZw!b#nJX(?q86L?d`SK%e25hLX&tdMfUFxjTW;*N3T++kO?Sl0f zd`EL%m-sBnhUrXLxvfrZhH9Qf=$}g9PJ_6*L-kjYq}W21qWZ4s5E_ja9-yZMhV|d~ zC>iwD=7iZ39^ORWmgUoEm4s>Zo9GM2vb{~J=D7Jx`3S^>5h34k$$mTnw{SEqcm&&b zeHVs*5z_KNYg4!zp1%1iOA$g1vUU`EN{6{oo^kTfr0BpUUea;G!Aww|;{-f2~x4^$BefN&8Wa8IJ%qRj$|j$|yJo_afL-$PWP)U>Na zEBq}-D6&R)du!MMDf@>hrE>Aat$O~WPML(>f-w!lwYK4Wagg_yKt(c+`v{%cz{L*R!E-%FVStvzA^n z4z)^~wQ-7D7Qn{-zsK$p(nmFkesPgvL}lo|HOgYxft5W7;mt2ee3Tb zk-(Uax=*OC0-HwZDsgqenUi{p;h|vBNjyXOr9%Z6pzk0xbtJkml?=r?WL)qkRI*{r zQSYbB3P%w~KXt&3RjxTe-!pB(+wp;Nly)#C<)Y^rd2s~^G8vMtnhzhxN40IEy{tIDNw{LnZf0p()R@I4`zrA8a#eCn6``*Tfd(#erblr{ zj7_RZ(AH!j9JFJ?k`_Z~RZPzO1Kg1x9wd-MB*f{0UnmvVy^^`(kBWimvcBl)4xuTR zPKxlUdYC;)DUq>=qP?A(T;jy<5DRvl84PRIzHOFc;F~z3} zTGcSQ(oS2?K#hY1x7Bs5*=Cut5B&0x*d5_xMGEZ&;3m6P=A^W-mtPl6Pd*c;FL!MQ zbb-Q`%dBm(kVItg%iOkFYxtMs51;4Lm!L$31qFc#?ow_`W`ufPLA8*Ve0swAJcKas z&cQ!u|Bx3rNk}9(>HoI?s*f^#QTq)2XT_cy6#ysYO=$U4?@Zk(GPvDr8Iqb4)jenL z#1D{Q#JvEaTx%ducm-;r7N_v`o8q&AdryPG#2k|p=Nyo!=s=}g6nHSI2SAUDY^a;C zKgdPiu_VK42Z)6qs$Pp_cv{no7UvM*lHIE?+A@5QLww^(nDi$C*U-no{xTii{D;9(9{68k-(zw`t2==D0luPzirtvni0a{mY`|Pkgf81?w*4x^GB1>P_=ATf1>f>|edT;K}Yr zcy>LmNjn1nA|Wf-!FZJJ_hw<*!Fd!|o2LD{vlExS@I z4q_TQinWpF&Dmu-JOrGRhj(f?mIUznorH?!nuWb`Wo*-i=U8aZ#=yv1K2K&n^T09s zI|FT`?qB?UiQ>?lD<0=&1QsS|$^B(y4q@)547?=8+ zH_8?6GgPloQvZ*vw|;2)4cq=DBoskF35g*!l?DZ=4HOuSV9<;XDe2shQ0XyJTIueN z0n#8K-QA3C7=veg?&tnKKYag%_jaAvdA^S0I4^UIFJM4t;wlr*yBqM_XKVaY@|`mE z3F#-zB;e5|MU!2rghq`$h6OcFSBcP%OLM#H(Dx^D$9qAxc-%9^tZGUp8goy#JewAy z%7bAE1hih)mAiqJK-y7Gt1fQ;CSxGsGhEQd9dOs!j1ZECdNQa7pY<7s2oxI5O>HGv+~d2F;($OP%TXVfv6ta%RQTH=)p*d{-L6VgkCzmSvvw2Ps7u? zr%ZW`83=1lxu>^s^2=j}uG+WKQkNP;^_#+a{w)IyfLq7WyGHZP9%FK>=<$rp%6bVo z8^tkunC|#MtvC6yOd*^%0T*lRDeh{;>DkwRKm8^jR(h3!T%9}cDHrnEL7e?xRNt_H z8!`&Jt~U2TBH{@*`{rkxnj-%X)VItIi|m$|Fh^mA{Q@^Rmq}L6Uy2CPd$G2-2BX6( zO6Ch}9^juCb5cPba$6d*4B%^0I*1{2qOU?dDOXgh-+3MWM*F$gyh*DHAF|>HO#UGW z7&AftM4ZXGaQm)lRFJuB2ws#zi_-hohyCU>8l9nuRmCHdzmyJ7`kE)%(pqXU-iEUncKRoZ_@o5gc3Z3DMAbGuD&BJ$Dek2XOVg zadtbsbbK$SX6Idx`&)uG$>TC`s`R}5(B>AG7KBz@x zs&6amD!{O3Dv{Au?lFIs;$dg9!w+MCWI-Fa9%NS^w`Y*cDbWQbNQoqAaot>eoGwQ-x3FPB@DSX zm)8&!d=s;{^_zlcFGY{e)zkioX6w50gh*Z7YLNd~$cNFDy|^70(FdGgvUL`!u@cVs zTo=tsK^s;PK_NOpGn>gq)|wv-#Qt<_Q8n#Rk`j}`$Da&e-WBB0LWPgc-E;~@_ovK> zH3zSvy*8W(<3#m~AQdHHvFVHd>jfxx9TrRbHnvu5smod_KTy^`ld@eqCX^duzf9hp zeHT+qq;rvb@{~C~KSXP#?8Q1n#d}{9*?d&u8@uDO#j>x8de;8j_%yc_ivmR z*lQ_6{hSK-#oW6@5V^mmxqAqa;f8l+PoWv;=RBJ(tZ|Va1oLNngP~l~&pnz6eDOtk zO+ClDAjHOT?smiTcJz)dV*TGt`9xCN*zZ7`J8Du`7%)akb*~C8cVu-<_1pHw(>VL6 zeea=QWMYp@qzh1i|DtSIC34BF=(NjtxLi0G0iI$)w!}ed9AVLOZ71E85Y( zgwV;NM4BecHOyeYF=5&eJdg8@Wnwdo#>&u-5WiS}sXS&g)Yp9uRc2x4f4TV ztAZ7cnW!IFa&$%~l*#HKjTCYm{`{yC_!y}reqOJd>A~Zf@@(wkRA`t7VOG_^K3k+t z9N&DC|IgtCt%^MzWAK>S;`mbsiOGYdd65@=)Pq_bH~y=q-Zg*B1hUkr5gGkYd(BDH>XD>)!+GExJ7x2sNv(Hh81# zn#6}PuB^FjWs??TY(tSZ4PS|8w3?{Y9|ghuSswb*_5velQ^$Aq1;ryXP*zRG7T9Jf z$DoJ&|FpIkD1U`*h3wEWd4-VRP0=&`4sDAfzd7vsbH->3&c8gIEtaAMM z)lI%-YWfeO=_4g`6NK-xQlYBo)R%FyDf$ZkWHnH$J5C*5k!z<{?pT_FnoXzpwvnDE ztg^)*b`R)_U|n1hN*O=z|5-vJhs)$3vO2B10s!=s@pSp z=)#>hLs>93s+hQw&n=ZycU{WrIw;}L0ZR$B43YAu6o88WiP{-Piv$4UOac?n)rR@m zp<4F^aawy_%qN-K>wO#2pQNib*@)e<>DRwlPFHP0qJ+c~;&80}N0N?DqjSe)Oh;mT zsFNoaV(;J^>H->TFi#QuX?RWoPeEBT?#ZG7|&l{BE#`-;RUlJL!; zgMeB>1z_kS>BN!_4Ia?@(`j-eLgp#!=c&0Iz{N3X+sbLsR?Cmrz22WbjYGl;bbvgR8yT@?w4WEf5|h;4F00F7w5tQt!wK+ z9B{H#a4B+49V-s{V}ot;ej4#r%zj$HAM;BoZ}NPSqXOJXmv_(;h95e*rc9I#-^Su*psg)-p@1m_t07B zpZa=loy7f`k z`ovll%;fj9!6vrdX<_&m&Z1dy_Fm4*nB2ErH^~?QY>7OVO)17Unc3L@&sAh<+!(!?P^hcV|eoi7MFl)=zC=+{thd0z3;~1QrHjqlVKd0>r5E( zaK;qOl%ImMx92%A@u4QobSfQYhCnYjI)@VRSR2Yi!+SeFi5d=MQy@|jcgjzfF~;f_ zQv;7To~h7!LIm-pT<#$7d^cO*gm-$bjW5vhmZX;MUz_n>6Kp=d>J2}Yg;%QrWBydl5^LYt_!YZms|3Vvj>tl zHg)NdD3Ak)sDx4~4mHu!!&mt{s~nV+@n*>g!^Ku2AX>I8z{&UbR71Z2Mj0eGqy?(# z?$+n!NQhcK7-0K+vJsK5yT?qI5F90pg%2y7tr4 zcYj%8apU;Q=QzmW)%jdhM0VRWl)9c7f6(|lpN$tQ9o`5DVaYSSiikEAF#gA+E_a3l zGY6lm!B__0$CP>tlavewKghCd#O%+U*^x-Fnljz_Urnac9i6#Fixoajo(Wosr~AlW zXRw%JhHV~!NddH6z*njdgkU3Am$Yqk$9#MNBrYEOxq3$o%(qTGIC!8}#F4HHfgOh; zm>!UuG%`h{Kmf*n`ls}uand4*d=>K*d*_rEa|m1T*>Y*o0|%Qj&`+%Yat-6F#~;c4 zg0xkn;CsM2iQ9E-TbS0 zBAo72^UO`(D|@E&x~|bH^KJx^5jCL#S=wHr=a~}K@3HSrgz|H`@mQ{_ll{J=-yaaG zg^htfS@fV4fVw%@-Mo3pO5rx7EDa@9sjV)rKLMSuSNG*JjhOeQV4Z~zo=vUzWMiq@ z-=!+N4949=S&VklctSB@XVe5Y4)wsMHcH7oulcMQWwjif*f})^=&944`I?}q5K05?CgmL4)UJz(HMuebWz4-INh34QiJxy5 z0El{bCw8m095UeFl%J3c8n!y-U|AFG0fj32^*SMNruga!Fu|mzj}@h649-c?hld9V z6J1H@B{(ftJ;vEk{@0dk_HWVw)kwb?YTkKkesG3ecY|@Z+zGqemeJaZRGTZTXC~SH z^)wT&k2}uQRr$X#aE=e++AoirzG;0_-UVzm2f-tI2$Z>dvrOt;K~$a1T=-RorL1sM zL87m#?V_uLCx6Y1HeDT7nrj=Td%QbI6AH!gwOpggr3r~2!KaQ;AtXV-Ek^mvs(8+_ zHhDG#+4*(rl&>!%4c;@P*bcbhtqtx$-d7Iexl2y;Z}cWr+mY{YiUx$PL6~#bmYXpH(}z_?T@|UnIv*Ez8Yjn% z*b`&cE8A0EtIZ36fyyHEo%c1U)Ie8DiMt&T3EXQgmzQ^qc$uj7Et{Xl;$6yVmy~(6pE(VJ#3-eK_sgq)kX)oCA zXIzIPx&rAoycplbg)ej51j?-NZX!rzW{Qbq>`AT^nnrOY+8NX&aHvR_ORg-q7MYE_gwLE*Bh zN+Umu^b9^gQ3oOytLyJnzw~5Nm=2&E*$a-7sXI$46(Jf~1rY&JH$Tmz4F zod&x6At25HZqP=`YypnR)v{_}Z<*Z1t+F%J^WfG*rO1Ve!{v#Hi6InlOOLY^%MTCc z#J0#LbZan?|3558K$O?m%|NT)l#}Hg)!r7xPvLGu??E|~%e^Qs78y1gcC-4(<7t&g z=L_JPVw0L=ZTb8>3q&uXJ%dKED=YVW&2q7mXa-p*UdO+^Ji6e zGdtR>>DAZ4JN~1sYf8=Og2JXOzfZCoo@WAk$n>`Ad153Z70J42GIkI(mE?PG0-O$l0Krb;MsW5Z)4HV&Q9;%v6NKJSe>f=%`tc`kEQ z*QcNx$?N04!3eOu9n4fLXh4DQ?6ZfZCIgpU_pGTU4IKJgaUfKhuMUCsn#!)ycTV1( zX<2(UFETL`bF)y+gw~$8dcCpT(+b&*nQzzu_k22|*Qm$#&U|qwnSf&RaT5)IPVan( z4L9y&K7J@nqidC^9`Dia#;CmwxS+lD=nvEcyEUG%9ko8j2{-jCD;+DQ?D(bZHP)MM zm#-(x+t@bc3^!@r8MLKK^4wkn-5`;%&rwHPK@?lVOKjBiT?e#Fws!CeJ~3W1*cNQf zO}-WZBg8|Srax$9cz$75MWn?o*y8$G2^NT#UI4{R5XfguR~a!YxN_yqVs2w~3S zSNT6G2jr-;2D4p^r^_vg(3oc^ip{3(%%GNcFPph2^?B-GKj&@eSKWw(C`VBY_Y%3Z zCY2$IR-{Pa!-f=3To2)0ZwcE@2+->FZoE(B|}nfaJBL+rFs+iaAx zM6NSOsGfBXX6&FD=CFOU!lP_YLviL^f$l(BUEEmO%*~g=L^w*;$mz|D=9Ly>w>)p* zRf2@~xxwaujVM-xZM23>sd4$n&f;_mLC!nlb8DQfppw5W7A>aN$B8JIYsLOz-|q0) z%m2sYdUlz-&gkTO)vn&erDVK}!W@W;cnCBHM$b-;jZ^+y9(Z_XOSVec<{z=ZMhTxD z+jHgBSSf$r?4%C=QL2aG%_3`jlAU657nD+lvviB0%_{xq7M`p^0y^Zb=eyn@vu+FZ z8|gQ`D}&?H)6AnntnQ*sKD6YvJoa~@MI`>2qsvLSI~kq`NFOlJxgYb7YZAhAw`f;E z@j(o17-8@k#gyUR^l6WqXi&(NA2!JMh9twp)3y{Wr>%DCP&7U`RO4eYzmv_qM}YyE z!aSAEwNiUfKPQhE)=n-<`)wX9BsvZ1v~5u%q_;KY`5nTNyDGnRt}x^SV#4g4d@ikX z-LAN>{inp92%l>PT!)ky<5v+-5JRgMyX(GAK`vH*-E$Q=ORV8iUF+?hBYVHnRBV2L zi82_w9KdbRfYwW=eQ*Pk^QL9%S!6K$=Xd_k;=m?|swVgFj$?Y0QvL9gJLA468`;_v zF}3cSPi}MCog2oaZJwze><^CI%QV4!BY4L8fH5EUXb-Vp2CR?BCRCTb))Fr-*Dh=# z|HvI{K^JX1MNHEiO}w^9qz)5-Aq~)M4o}h}rE`cN2ft`O` z|3*b>C-vFTPP*?L!u$FZ0=FPs-~MT;s8v;j>ECwoIPU_k91dw_KG&vWted0n!*-3e zt6_iqS5zMjq27-5+~>uf98r*IQot;6hM!EM?6VGsSKj>$1h|8|ved#C64=!;$3#vd zy9ZNl+Z*|$fpj*z^Kx*7*2Wb<5WMxBNA$rM+o&u7LB+t0s%IrHR8t@N;Lk+3X|l=% z$WPF^+SXq~gf9Q3O{c;5B-N7#Br=oBzKUgB`<9M;p4t!vR#j{5>Yug~h65Q!In0a|<;e z5xi!q>>uy~9;b392|AlNJlAU$A=ewGNZ^fAbTsI23@qYq)u%Zcxt|HbTIm&g){M_GEQF>1{^Q&0-#--^&Y}` zF{x|%Fc^=y!$D_?9p}=+MvFhL(Uat#tHfKlnoRUx%#Xj9A&9b!OTADc__fVNv-#$Z zaj{208-OX;ZQKQW#4G9pgsiXE_bR{EvatOmme%eXNtq+Nz#p#2&+yPj4rM0HvS?`A zyc5(Nya%)aHiGhcw&=3C!tT!jH6~BpGP2>}|1Q(6HOlf|4lLA|FDqesAQ#zQnh)84 z*Y3LTaRdO_%icczak~4a|Kn`UaMe+*AUX*BsxPVY(pCZzH#Hl(Dzbc7jwIm_tl4bC z_%Z8VNa`mgPR{JD%L(tEzb;C|3=?&+|8e!$u@T?i+k3pVkT^mX?h0k&Mn?Db7{`Grj9IPQQU??K)~ERD z@>E&kMD4Bt=$*x1)r7B|>w>FP2a1!(6nuJ*n{IZ6Z zKyn^Va-EGgf;>Ot8^$_DhmAitnqtebVg6~(e6ZTS_oB364nFIqF-7)Q)WLM2L0QYB zn?Y;Pn+}<*F;&B-t1>)c6GQ}@O=914?YIcNfi+=|$0HxS-ffgQs$w4ZZa^>p;kX$c zNENn)i}jiN^{sb~XuYh}OZCyfC6DfUxL&`p+=H9tx@`~Qj;-(Qv^aii`R5XOe*3+~ zaLL5~xY-nrn_>SyZWewr-ko36__nT6|Xkfb8I z^Mkvwlp@z;XrYTZYJ#SEUHk?46|KgH0dg)wpr3l}Z6V!9ckOIJPcH@ePYmz%n$)k$ zpTAz@v%R;_5g_>@GbnvtA+@R>LtHWF9z67gzelPGIF5RNJC3os)fex_TG3)V=E_41 zC#5xH=3u~k)Xvfw3wlc%(74##WJmDVK4;R$#b=EjILZZprEHvZmd5*>!&JnEG;!;q z`1*$D)U4jJV!jV7iYCu18=Eu_%onJ*)Q*9gp2t)yCzxV7+4b$s-!p?8i}iG_U7QzG zdan%4yl@w3dg5&M4M6m!xld^0laAH9TLq^OXwlE4H7R*%+J>EI!6u_gI5CyiNBC#sO&!A zz;Dmo2$( z%%7^VVlAW`4C_iKF3ALyHhG%=((TqF66}*xmT-kB*88eM45JF4{4MuvlX(d*4x=vn zaJ|AiZ8*3rj*PP8MoW0;v6iG}n6AL599FvhV=b=(F)4ktkSu4hdf_h`mc4;%DMLSl zJB3cUY@oj0gHGn@r`fY4K~oY{Z|%8NjzjZ)OThO-9IyXbm`!XdPzwreh0|j4Zwz7A zLr~-q1&g^9 zrOy=y-`c7hi_8`NBc2>esW*2%gx}dOQxacPIGiVXp+d`Sqe^h7{+5*FX$>#l{ZFW| z)-+2pWr6)tFuvIlrr|(n4mEtI&Jo7ubzm?iUMo(sqbnDRmY!IdgQqFMTiNmFsAdpS z=8;w;FR4^r^^m@rbE>udm_4OS|A*=;t#*Mjgky2vV8{zN7&+q3`=>1G3v094R4q># z!oGRb-hI{K-PC!)+K1+27ZWFtedlJNYhAy!nzysT`iTjDaGnv9hmW+E)1#Z7rSa9@ zqln(q?T;F9i4p&UwA}tL(oz$?{V)kJ|9zO=Tcnj8cvZhsoBw~2R+7F~G54vxKhWm) z8VmoNgSkxa+UWhEm2ljXK=4=j71p~hs+c5wB}1-ygy)i{=a&}|ko7Ru82L*osho@z zD3m5^QA4eOjX#jeH-Dx`AlKX!fSG<-B_s1sPVgVE+7{jOjwJPwm!KElPuDE1R#RfO z+AbM@{?|WOS2kZt#Y&zDtBROcUQ>|_=$trV%z7;6hSb#`PIjLxoXR>qtRL{+QLqi#K6HC^HwE3WiGmZqV%@mx$~X(VVT$QX zbEH}+FPr?&lBZn6E1{FN# zOL&FACmgK)9_oDfA`haq;Z-3L2L$4(E4}SkNPB+CwYQ1ypAbc-4eYEZ&j)(14X8p^ z-cgq;Ufh`;flIWEeH)GUEzH1^uD)*mKIVHt$%wxG-npU$mK{7R=(sd#H`=ZC12}2 zEX2O{`J(F-?_ne7Bt6mu6aP>YJ`-okyyRFwLZ_oLaMqnjgb4^rc7 zl8Fe7E~CS#rHYA%H!0H2tET21*lP!~rZ`VURlFJ`YBO6>=M|4cWS-uuMlB~;NYA|QQq7z8d*zu6vjav*0Xv1E5&zO zQDX5B`)^!*{2O(+H&dlsG?9zi+pdy<-$e!Xb!PLOJ4kY(U3yw0icI@C8qD>1)h z7eW4BcKnR8vuU|yXV84=RlV9ooMYq3527rB4i^)tfNBaZ@MI?|Bb%iPtRX(eSJrfY9R77LC0s568PWM&YTFO$VFo^g3{Q$8rlLd3+8u!3q@^LXM3m&z#<2dKkC z^2;*UB|*>FfXUhP4OV7WWU`^NMaDst4RZ3ubN51aVzcoj^x|$-zMT!*6qK~&}tsn ziOKTdW-KUxQS$uv>MXTj9+5r0%lDJ{^de;6?&I2;Lo<5Kx1u#(ieef;+l}_D#tM&k z-^A$T$T|in$xuJd&!wtgkX_J+qAiIKpG}h|tFH3dNVh%se@m+mLGeB5Nv00}o$AS% z6(LuQBpZ0K^p1&Dggsn?Ri1yoW-hA=Mq21IdvUVc4xxh}r=diJQ|9`egyfZW$FJ&( ztgkYi zwqWvfJD4Qu_dr%9^4=I|!^Fl%PMLow*OocTYf@ELjS=D^Tq+*~r{I-TI4xbyQ%}Wd zs-*?%qWZx6UTSVI@y3P5ILq#jT#CsvX)cSVz!TAM+KS}9(d5Pic0llk$2Y|C;p>qT zGpjU2f~79d#B~Dq*xC>9^CpjJpJ-h3FMWya-rjFE#JRx^D z`KWTJCq&iU;Gk}l06XeaItklqdZIaKcXMqDuVHm7Zq!w^NvU@rriJTG2%ZoB5-}i| z9Ly&P;NNFNFJ+LssTQ6yjUw}YjQ(~x3XBU^=3sbH185R5vxR)o&tnU5Hxz3e-bO5~ zbcN2gubJ}gu}n)S{99`fw^#W1sPvB~TI?|AD_BsR2$loEEi(JMu|6U9*Ol6t>R_}KR}}z9aGdFkgqA%`hc4jfUj|U4c+Gb@C+IqNVj#k4^0x{f021qhU)7e z3-_A8K3J2gif_v9Ni){+u{GJm3dsFxOwCFKJCklNi^-3i=yk3I=PtX44ypc$y|lTLBhQiJ%ti0UE<~P93Zh?oj&GXr&9n1y_%az zxm(m)t##WGE2*xjHEm8?$Q^;}M;jg8-iuM}rbK#;&G8Yt?L9=2@t%uAqaJw!HQ9C8 zfw>RKbQcWgkAhqSUU_ui&j&fKvVmDiXe3CCnx&{eRE6l{C)2PTnAlh8DUqkU!QNGm zS=m+;HrtG^>i$|rP?@PPTN*b$oLQ+_$w(YUMltzFaB(7&WTL-nDr$;>640g?=N_iY z^099$47Q9)yYo79-NqSTnV{``KggonM+t0Y{bF~G>RF|Gu6Yq6L}lp*v5F?9W0!A~ zVWoDV(LuFwm;`%Luv~?qc-&f0v?_*ryj8%QI8#ysY&g-yyz~!PrK4G3XM3~<@hhqhQf_jJ|$=8w5VH) zu1@XUTal^3)a(}VFO&>pfc53$w6|gFpO7|^Z66wUw{2bQ@P-f9FBkD8a@VS|y5A=5%P)xjO}d`K<%j&gDvS$Pdo`q|6qPC`_ebg{GO(rUs8!96 zL1)UlI;(D+sG3QC5+%{~1-1FXtA4jk-NAF=6_rgopi3Y=t-8swW>1SbN3rkL<~TRZ z<+wQxj6b*u>k0-2UtwP-w}x|tjvVoM1)ul*iUepMbDvQHR;!UtH*>T{napSB%IgO> z9`j`WW+?m7gt);}lTDb>5&Sqb?AyJe%k-fOcuMPkfoa55nx4Vsi$M>kGXB>UI52g6 z3#M*ta5{JJNk@#s)bbBvw_ZY;Yyw#s?CWqK0^ z`hHevR;+KJAwsABswFz;udxLZvM~pAND6>z9=DSCOjM&a}`k)fiACsAAmUql{jP9(>-b*4T zLqhB?#0mpXC{L61coB5Ej@n?Ncd2KjV0h=1X?vAIkN+|;sp+Zj8G;71i|#BbO0*-k zMeO=;zu76?k$Y!q!fbINsd8G`YBQek2+X@bWtX$y{+2aQY3Sr}l&jXsO6l9wxK46x zS!ka0%0E~)4n0-yzdmfbH6o<0ToLr)0DX+^e-m!+n_KYIebLgRX6u~lY4N@hjss8q zMU9%_wpbeJePQH~HmvE+R{FEDxE@H)_M2Td6*fn@9SxxcKfW642%~QH5QEH1tF(Zx zx;YP?m|A1S?lrsa(i2vqsz8S*i(h2Z&KZuLEbUWjr{1_IV0C8YBe#yuegL!b>dhxX zwy`tH+zMN2CLz=2S^xJ8W?iRSIdPm+=b4iF1cY$9*%BC}?JVrz0oW9Ph=xYGf7zR6 z7mLi({p&xGq|?OLHScg}q8+ykEI4?O=)$ZN)11AcnH!-W=+GnaX(Ftl0*iYlg~FJ{Y58?t8E?2gSG?Uqx?cFZaU8Y z57+DJ-Ci@jYW+$1xL~kP#r))@SL0;Z1?PuP8r?g%_%CjTQCa)0nkdd=eAaKE8|y3~q%-r%qx>}c?7pCuMXK%iN%@|%Wsqwm z3@fx~CO)&7hkqyDI(*LwmzhrK4z{^ZuDP0|NaW<{ib4bnZ2Okun%?Wpy)K6vw@unHu97(JuG=uvqyuNClP4n} z2ydMfz3W$2ifhDN)41(zK8`r;9oln2e6=#K$i-*$-?Rf2b=qz2+3jv>!o_zHp<@1W z9e=hhr43wmb{s!r#WS9)x6kr^Wz@Vup7~lhPEa={Jl0q15b=QAjC4Q$aWRm<0m_v%99%un_x=GtWs%J76$)efO%eNLDb1T zmE*JANm549xn^*x&HFlzb(YA2<*OrTV{-{CQR9{7TdH&%G@2e9lD zgYQrx!|TrdvMa{dlxj2HV(jc%Cd`$+pEhb+rmWGCq#-vC5m~{5S`RhFaiYYx+Fjv1 ztxMeilT0>%(P<*zL8;n(@sJ+k2b2)m#lT9H}+_ex4kHoxYtRdyqTzk0MIN+Y}zMSRO32K=dOn z`QH^4^L%+Y_*D)v^H5&d*)r1Ptea9o=iSe1Yc^W3I3kjicPx~no#Sm2pM8UYoVoo6 z<7|cQU$WBgKVEr4?3#JF-CsUSIsicpji0~N8gQxpb&m}{7gc9BMw_s(R9gD;FQ&|f zRGBZbH))LEVjHXyOV=W!v4=>#D9z{&XNJh@JA(tmJvX0)h70n~yBn{QW&$Pa`V+o{ zfAg5T9!l4UqE_m1D3=A?t=w5j_d8E9 zIhkk+qfy;=@>*yJS$(SBkzzDYEX$gL-s+S<7_$CWgOBXkU~RGSFyncj5n(;;tM3vG zm!y*^jZe-!Q*nve-Q@FaTuB9NK+lKT&2QVxkoUOrk0aG9&AHW$x~5k+5L7fb7Tuq& zd)76XzlS8ivTfOM)YSJ&AMHlX327^5_;DYDW8_jL2_6ixotw+{rNP7 zhzhKXfKa{M3?vfJq`kmqmypCiw3Ysw=3TI7IAFH<+6=pp8@W^dtgaBb zus*fesbn+T@aoc|VbB<3?+Jud=Vse#g%|r)D#Y2^27$!V*Y7faTs8>@?7a0efwgz# z%1cxodMUw`Jrj4F#Iu~VyFtSJC43gF@iuP+g9C=bm3H1Lg*v1Mg+Y(8bn+>uQJ}Kj z`O4#?qMfK~dH#NVT_??zTmCrZ3(0tnBW#T?V+YT6B@Sw#pZ^=)196G{!iu!jzqS2` zyFpDA;$B!E^nlHP@b9uGZY#%^=+(cK)!o~cC=%Y6NhMOP8u>}{%(cTT&2mrV>;^y5 z%ss=y`_A*&&C;Qo&du>?si^lzU4)XbK0#|yfHgVA_p;!r?`L-2CmWuL6+B|!_}cN! z9|*%fC@6iPRr_U2_(ldphli@Nv$3H%f~rAe+BQc{POA=<^GU9pu{OFTV!9duOA0i8 z8cbHrBnYB)lGqFNSLEK6KaT8ALFO;vjj7u;BRF}9ck*vXIFmJ;vFy_TY9HFt_9QmQ z%<~TkuLdqfh~*63OME?7fc0*@gN3&6RScYRCAJRK*1?X=->Ld03J=QprUD{Jrpg96 z8Djpf2q$#9biej7?&}o9;UP#r;5t;oVZS$HZ#D_%7xp}A&1|X}Y`j|D@iH!~i2UyZ zzA-)R-;=1}e|?2xX}w_nqAT*_zOI=s>brHPsYOLq+8aPH~m0I#!FMho{ROS5ugcKAc30UYLIMMSV zte&)EA0%V6o?a^BM#@@RqqnAgr_Fq)ieK-xb?W?BuO!bB7zK9Kl zzop9Pty&NW-pBy$iLg3iIdcoujQ9I&s*qI2FHXg;c|VS|btM+QuL?y39skuW@KWEk zKY#jgetkP;M>D~a?dg)O$i+7-sX_`z)Irue?FK|)oCNmaM+%0g!ycl!eJ2HpLJs|Z z1TtI}lXBJ-suov=7X+p)i+Ws*Y&%BZ@$%l}C4mp=H{GiI8!%D*mgWsOi8G4fDDJu< z$;UANzrma8wrd|!l&;I3Ki;Yx^t~l*H?;rb3d6NUQLW-`!GVE@u8g}PFx%c>pzvm3 zFnDVTDnI_~ExP;{c)of-GKunFp$FfkX*u8NA`ns8>hIiSA6`Fxwaj=OkB}=V=3)J9 z`c)Hy^cP{*IQ3^s;@+J4%+S$BT$h6KR(V*l2h81;tnW=x3YRaigQ}6ae7^Fp()$OW zBN_vR2J{$QC#hoYDKKZ}B@vcbCUg3Br@ZzvTD_xX7pL4Tkvnc{o9;ZN z2p+O+Jc(m>Vmi+XOXWI-iIbloL6Q(-W?!!YzBYDU>gH&g=XnUj&jW2{i~5qpBeDW8 zuWaFd2J2eeAFSWoHa%EkT0mpzA~t1A*2K(6+>)rLiB4)t?{PNwYcBu({<9s>b#Gs? zhfQWo+|OJ0!l$f=cJa9bFG%ur(+qB5pdqfOv`5MPk9~oWh6q%-a@U((cng_g{bpsJ zxJ3L9$v@V}bYBJdz~8-><$*-Z1ih(_;cjEp-6K+ecMan3B7m^azpDw>e{>JQKHz6l zE>(lS&qKo8ZChSh^U1w)*0G^h+vd(ywgP_b^Ea!1YG{vNW@`=Kn_M^i~>*a@+0-5tF2#m$K)gFtg27`F;FMm*>s+R zDgdbXlxQ6FuS!j45m#RNp+gH=V=E)UDB3=P*yBx#?Jv^~tx9b^8Qu2x_}~1UmX1>1 zYr2~0*L7th1qTOMUC}A8+7C#wQ2=T+i@4!$6C%G|FIFm=!!0pEYBz8G8xlEQ2%RYC z;M#GVC9Ig@6VATiaVGQXf6yApxl(O%+W;BD)%0TdGdov(c7~G)ZjU4tAH35ol8=&E zy;s1Gl0ubRyOl&vG1VDuf}sDavOC$7>aXsvP)B$ikz)uGqADC;S-aG1eP zpjeO&d`!PT^*WLSUuHT*{<~w5V#zAk(O;#zn=+gKPAXig$1i#j%1z(xL9FV*WE0*= zCE9|8n7Cc8n5U9+KG)XqwVLKocRwv@=%`7n!D9AQRPyUsIxDIuRm`8Mg6`zgWNeQ< zvro**!FQ~}O-E^e7pb*oETj$BdVj37NK09lSIr|}-m3t0n`z%H9tDyZNtQf1mcCwI zow%GFhqrzWlNJV=3fj$!9y^8bj`t^{43-=7x0eMnmeV@rJk_?XtFcKouEhZ!r)=e| z-&%}sHg0N0VQXjPyxyC?|HD5@dRq}tuEGj@!K2{uLgtO)>y_KBi9C-9$}n*ki@`IE zr*@*H9=jb(O{u9g2B(|eT=H!%|H*&(gUWx1xB8*%8)*SIj3=#kz!eQn=?Q$gwtl(e zz{NoPPgvLRy|39^f6%#!%O~8cb=F|M>9IdvLgV*Iv#$HV-Ysc#q5GNESdr+5Z2}WP z0lg5}7xDw%ey_|?e{$|z<}CO0V;QV%(xqG_J}AzU92=#UwY5^)*;=tG6aY8?n-rBS zkKcI3Pk?m818~2ktLjCkF``M9GA~Iu1i_1T^!9MzSrQ1hZ5|TzQg3PmE@4O+I^q5D zJN}fSHz%!n!Mky%HNRE;Pjc8B{S#SJuX|%-qYth#FeGvvxLYJU*5@s==%E5`HLxPJ zMws~RDPByTwDvK@B~>kONmX3;O?}&a7tUmGC&7>G_d?e|WuuLUdAL;|Q<6NmRUlkk z_0{t=CepxZSbrqxJ0c2FW$otpVH5h4K(ok&c0VV8!hX|9gKE&dUhnqL{-%H6 zVtNZNsACli9_V=0Wly($K8E0RitJ9G8NEg}d)`+l-Z9y85X6*WX;$JuBT*#otO4o_ zDiu?6->`4kPtC>*F{OK6Vj}q7iStz|DL|^hYGk8^?#4#y&Y35wjDg|WJ=cx^TNJ;8 zPN`YY>T+pyUD-4-GNlMY*>dP-S4?Ki^POU+A~$n5)H@EhukLzmY;>A6dgW^Uz;XeMKKh|)EA-j*M1N` z__;hn04R@ z?LW`c*X(&Uj_RKAB65yNuC;FlH?=)XEQ=-jZC?6Ox{d)4P(TvugFSbFvb^bOKi9pG zXjSyUS%mw_Sz&~p3@EhQuL{gMNcpsXRTf}*U~92XKdb3sO0)iOd*EE0)q!e7DJ?yZ z^+1B!wqQf$iE?^oZQ`V+)NjSq+WjcuiBYC!;K)BF`O%Z zYj+QEgI%p;_31k9)nvBi&*mdBnjnn1C=44mM4vq_d6Je)Nb5l;`WzZfjBwp_)X=8< zHC(?O+Q1Rf*K_){<&6*#5xvwKDPW6`?f5&%-o0mGEuZd-6&8|Z2$lS-JrQf(qR=0KpA>CgOJ;7v!K7J@(Wxw+1NsC@ag7; zIs~WD2HVn^dNuG^T}_!JNlXP%impMEe}suk{=k{dPoToz9qhVYALRS$y*|F{vHfq| z?8Ac$(s=ql1o<)IsxRz9$w$8*{4exYZl=FASzBdFH!M!>qOiRKYRPn&P;TS zvGwC~Y}K}nI@`PAenu3+l7k6>-$I6&>rH-rx=Fl4;HO4Yn6T11>*N(C(+P~?F zt#(FmoPBM3a{`&IAeU2oO&N?)6TOTorZkL0B?O^{;4KnqyXY%R2w|1BmsxVkLlZ#C zM9;jenJpudggs^O2S)_^q(?{;hw7;d2$Z^@Wp3J!+j__*!;Lhl`t{&1;x%GnCT%#F zrFMEgh;W{nmFYijb>7ujChpbv%LE`;WLoQXl|JpH>EnJrQT+1gaRkol@?V#G=-u^a zQ^ZH}Rm#Oa&Sb@e+x1-LB!>fmYB&sIH)E6`9l-bR2_ydH;H(KV7*b;N70JoDWc4fOvN4 z2Gc#ZIWe^n%?n1fh!JPmTq8S0A^c5>evOn(ji~na?CC_#6YtwpA*@yarx6VW;}!zq z+N~6Dd;|0p=qaS@lMOBxoe$_yd{JwHb#YwVnl`noC~=GPuc4x&T5PVCsT+dL#LMk} zHPdQ5fIQbamR2H?2SsL~7tMzCyou@<2^4&E%I3IFh!)L_-ZV*zEk<-b38XD4=wviCZ1A zv@XJN)j>QwU{jfTKCqhYRdCH(#q?z^h~!*G&E8yzS@Q6?hvUU~=JQSRShqDfoJNcb z`KLx7QOGcr#Nf^j`Tx{m6mF6vxCQSmV>81(?j!WVf@|iG`?nFJDDg2UaP+mO2sb{Q zRp6g-fNBUlc8KkwRq!X%Ag}YIZ#irUQ?bqzzGMWi4+QcA@uZu@pYx4B>V5OD^XhA} zL>~2JJYH4ch9JOe$OavB=NVH25(B8#whF|@d42iY#Ti(eh1EslMyI*=~# z9gCRM3nmHf*+aY^s|JUgaEm!8QbQ4C4`9Dm3Wk3~e z)GZQHN{7;dfHVlw=}SmU2uKT3(sAfRBVE$nC0&Q^?(XjHI5c+-e&4jJP*4B&A3U>Mwuy%Uy69#IXl#6mW3>~_vPar3C50BLwbr?a zb#F`YSC1!+S2W?pKc9(ipa#I>VB5bBjldKSMH~~)LO*Bq^4sk@ZhoJ5J}OAFa~HKd zP5Y2c|L8^tL8o#q{j8b zkgSxlvyMk6L>x)UCfQo~#ee^c4#XQAZH*%){a~5UBP*(>cK7Q?lUPRHe(zRBUNR+R zPhXcas^qZ0Kt+|Dn!0%jbI@S&he)2a_|B64{{UYz`C-zPA?-{;q+JEnW5x=5MEr zP-co2AN@(uq;WlU-p}r+5||M2B-#qmO(v5n1@#H9Xzoor6iCb$Dp4 zKj5_f0Eg`ZBcbTb-b+tw%(oxVi^Y$G6=LofvE{Fo;`ST6p z{G|Wa3!w4NXb1${j!$Pt1Gmd>fF|z$qpSckG#FCBK?9*%V~fO3;=k$!YOf~3p;wfU2=^vpD0tz}w zIVBRJg!}DZlCemGCw4vxumuNbiC)n{&s;z5-X)w&kXQfILsdQ?Hq_6OPFh@&z?L-h zDY3w%*zOe^;f?T@J%aKj+B}|Y!;Ie2_gqU?w6^V?+JM9v%T*uy^y4l?4KRnCJRgS} zg1&>~WhY};?VKgxp;@j;_{};Y?NIZ+r0&+9r}k7GZbFjsq}Ri|Qlsd+gAb|}vj{Tu z2_{D<*CjpwXk!w9jO@Pq7m+nH4->&HVL(h!JiI^DmhI2ixo_PcGwkM$zW{?J6!%cR z_J%6z1lUbRXQxUC8aOo$AJQN)m=Zn3NG2Yt8WSR%@xBhi(p#S9QHw(Er$Z$+0;bB! z8Ew?#h!jz2P(PJtc5y#eZiIXwkI)BXv0nB;Zdqpqc@7K)pt!BJ=HAriu_rfsd6s!6 zRhu{S?e_!nyIvTAPzM@a%>s?C)J+VQpFbE#=DOV0yY(;Zv`BE)%e!kR@v1u0Zo(?H zMuv95o9haN??La;sNsuw>AeuKsMt{Y6yviUq{alqIi)}WW}zu!Y1TxnDv;4g)k>WX z1UvA3m{|FU9Gl4=N<~P56KlYYK0FJ(`-DSj$|$nom?b#*@G1g1F*j6nemxcb<`PsF z*d<@N@as=_)ino18w6gKU@wYRsU{0M_>lK74Mp%={s77))gt4ZPjWI%Y*y*lWuKY}T|tEABW>*9UtQkFQHGc;}a^@2}6`9pR0&;2rYICsHmvO3&xg+ zWAP^tMr^hDT#K!^RJWmkYN41Qr&{nMw-FJ0?eX_UHFt4QPzQSECx~F3jTxtf&@zhIQZwTPrcP1T`G2qx4VlkLD3z4RfeBhbLr~HFuv_AOZrVQipAe+M zMb8i@EgS1FI^(LB@9&J(!Os^jFL&{?vv_s)s*B?~nZJy}t#IWv@-oWE%#rJq_X?xd zyAx3gZn}y&hGE=~MXBV|qPq)umgS$kA#q>JQKG=bhxj5-jyOPX%+Zx4+}z$}ymlL= z($F0pDUArCEgM&6OsT4_w`VeUo{`0xsev244pI=AM+1YdKX*z8+4uEc%S(p$hg>ha z!pY~&7@f?h1(F+^Jl!s0GK+WKPPu=KS+hTT%-;bYSzb8|-(S;<-P1VzAv4u?3f||T ziXGgKz17<^8>c&Gm+p%w%y{hEfew%6lmS@}pw)H7S(UyJZQ>%s4^rqqS-}0W#1fQx z)94MjYD!_^-k22CSyHPuut(1Bdj=S^9WI2QdYXX}hR0;ZNz&Z}IUpMaGl~JdLR>#8 zBA^q40z^0jp%h}$V*+^f1;h5k4-usG?4dsXhi#e6Pb7ySRIXP_R}G=jV+ogRLS3Wq zN2Xe_DDTVbHp_EqX?f+%$W5taG;j)YFJ+s85WjS|-FsZV1rHuqKnlW~DSljWMrA~g zvVC1Hc~JWaras!Q4UMc%Cg#Z~QMKU%U$9sxEf0Tpc4A1 z-G7F4EO8HOwtYS&HXjsy!8X?M(3a6@4ZeISU-|Cx;C}tNzyc`Y%@~3)a7f7|-#qz_ zz+>mK+4IRCt9b^*+OM7|cs|# z0nlkTYgx!%X5s-aj%=vod^D8@?^&NDIxQ{y?E{}(a3P>doXQg1=z&fnKBRL>;9TY# zS5SpYO;+JAzsjT1r>6WUE1*Wy5WCkqe)5HIYnqqRWvzUV$_8{5YQWey``T%vhjueR zyJX~cId}jQ!(t$K>+uBUEs=o{>Fnrj+2_O)3k}fX*bS&64Kp7WI5+y8>_)Zj@c*dv zC7|%&O}x)XsFvYI*?iycJ)a&W!p0I5>5muiWrbr7sX9$Dwlm-zJoE!j>prmrrFh$( zb}ZME$a)P1@24OvIowwIStY{WR=1A1_mr#Fl@%Cpcg7|-i}pO~(Wvya_@L2uPnu9d z9YTKb?XXE%_N^RhFKp~&!9b3(KwKuWZ+fHIJ7Ad$9x9Ph!xsz=XCzNU6b$1KOOwpM z8!SL;K_@_!C%Mhwx8FJcSa{1Z=w@IzK}rh9bu&P7;AZB3^OJz%+VISWH^n{xh3}77 zK;e;l{U~_PTsCA4!MoiCI_I(hDRL3*K$vO4& zc*{hzqyJV=RunIjO2*AWD3|;Xuaiy|j#LC^@Q&`I|A13!nZpn6AAyotf;hb?VwaWg z1FOJ8rcXD;|HuT-g38%lk|!Q$elh}` z-cQRGXD`WNP5EtrkuSjV5=#ABUcK+!moZ$bV>eKsy~Lf<+rkeLkYQM-#6HC*2v*j4 zvgt#T#JijTh-fpFRh+NH`!^$c!;T#tg6F#9UVXd5)_6B(sokmK%(7f z=>q+av-m5@`UpH)o$usbqk@Pd32-wg z2|kyT*y%}f2u=_DkTTIenMQq83Dy_>)N89HCSMKLWcjLaB7@ zNhGVF+@5nK6xQRAyZs1YaLfka-;SnLbvxvVP0j-P!p0MmUMs;WbbOQCDabB>$kWHf z@+^JD1}5=T*8sDWp5_u8vj<%dN!-M5y&R3#Nry_588H$sx-{u6Se z7E+7k`AFi2CAnLC_b(K)E4{(AvU!%gRu(VPm~s_C!e7lK@u>*rN@ju58!l|@)efFe z!A*y|aI>BAavJi7vhMfYl}#v9(x9LNF`RIlpWrJk8gon1@NLo8z0CUvWy#cvxRRnP z5i7Q5gW&i}m9H#IT!r#|#BUXn!$)q*FRi8Ji=~N4d4w#`L=lzm2Cl@GrXOfz`nZaJ zpLO)bZr-oz{P|%tJ$YxC#hyUSauxQN2?L@E01zF22BMo9^#49RZea1Iiyi1O)4Y73 z{TK@Ln7Oo!1jH<+f_Vpg4;30*+?JG-+b^L=+6q1ho|#BWACkDE9;a{#F(%Bi+PprZ z``fA`dSvgA%&ja-w$`2bN4S&C4*CBqwxm50(b^lve?_B4SQWQU` zK_(d>8LU^j4*jp3RBff*RG)7M;d9TQMPZLpEWuJ1L2`R&dwI}n#!|#`ABittK9d>7 zcGEJxFQe9QqYq6E#^o!^iV(*%6+aI*taGU=DERecyjZ)P;$m?btX!_WohVgS9 z@UM7X^JB70(3PhxXANYHm$!QXK6anQoWa5$ z)VeO6{O(=`WE#elCgYjD59uDcd{vU;S{K=g{`Fg+B^4R^J$tI0Y4vc!@6c>@B_rst z(Ot;;dfnh-7pu||=AB;?Vn|yOd)Kdg4hbmddG7L3%7Oz&MVc3drj=+oYMX(S$@(fb!t>r?U*!0l|pvAu?-tKA!=-Ynw!F9fvw|9NL{mMF0 zm{$Qp+f$=u?^4TcsH2ZFZ_hWz`Kol<^3=LcHcm?pE7rL?gnHX}2HGK@pkm$$QXHdM zPGV)HUEXT63gF{iYAD^*Kl$8EB29l?S8ICxXr+H-&p$rDNuX%Kc1-YCtSnoRRR3Bx z8U)2I!Dml3KXMp(6xdcuA3q8S04D{S!(s0Ez6HwkaRy>A)PRPzQmZ4VaE@u#7i9wR zNcf1W8Sfm@i78OpZqggAjNM&ipZ;qctQwpNa_DrsOCprxK&!ASJTAbxd3RAg<(Y~% z;?wb|H4DB|j`*Xmm{;G8xa(~$?=U*@{HIo-`NOGv?@Bq4jJy>Ov_oREb{g4H#$|$O z6xYaw(zxE-kgt~WR`}S{i77=mk!|OCueZ>YQ5IQF-nF(&#o*)D&>K$X9!AoDm#feq6l5ZI;_EA2!HALuXVOHl$+^~;y@Cj&6F6j_4ei*bZiP5cZR@GS_ei!OpRZX?-8#WQXOi1+Kd9i|| zUM-nd_ovQ0X=FT`XH-|NP!CRD31NQndvDfk$^t0WZr+QPXPjV;Q1xM=vm!b6Xl**F zCI?+b$)ixzPI2{D?yIx)$QUw{psyNcRvFpz6>ZhS7O~QHGn6Bff^fx+3%14k%Evov zK6TO_@I2yL?<$IlEBS7-k!YvPU;Mo4-9%F?n=uEIgxQ= z>*vk!FWbJ+&UeH9)oK?DDM^wty|{ra*EBnA^gjIC_u`206q=qoem&M%yJr@y&%nQ$UQIyEl?~iAM4OP6N}aG?&N?@Vf_yET z&f66{IU=Iv=4R9Fk|#{x@<|~4Zx8nv?}VAaoP`BVi_+hA(Ggp3YIbpRL)0JvMaFi` zFyDkTt zk-0+!V>q!pBi-vTWIMaqP7#Of2sBN;8S*a(70WK1B*1qDTgu5Su6G9D#jv>uUwA%(I8F8s&}uEoK3%WTD*2n6|$@>(QMr16Fk!&sFhCA-S?+DdOR)uz`AYiIu9yO zeH%SALl-Ly-!%CW${v=AaGvNCZQIfowdtJ1U zkr`6({xq%n)2%y^?+sMkulZxD>{k&iW-%a8dBMTK2@oGUR-|r_6(h52ln3&ZR-!_N zn~-kR_ghnZ=GRμ6-}EItSJudHjjx+%sn8%_+{{Snvts1X&nQnxe5#@@tdfRzif zet?i-ETr10z8_3q>59ntzIM9nI`uiW8H4XlgOwGkYVm{N%@IbpDdX;&xLxyGb*1)-kDe!| zE_!Li@@es7lqf&FB?#=UpJ=w}D_XS_b31R^ilFUgN2f<=qYgs(D1ybI&(|Pjx*?Fh zd~L97)lyD^%T_6_41{`TAw{!6ly-#wKnAwi9 ztAE^v?>F=2O9c|H-T~lyV7YMOQ(;-oONXUNn?Ey=K?-&9Pn>sZ~?v)>Aj)s2{C}a6ZlVSh# zqL>Lj{BXrN{b1k7dhR)c$$sNPNr-GUnLn?)zDwLHKamCW6fTi7i%(rNk*^CjH3iZ0 z^PbjN4%J3AS}ZTr&n4M230YTn=W6iP$?hF;n-qI8*~uVx7gr=+f4ZB^*Qll35>Pyr z&D`&-!Rx(ni);H5Jq5OFOd_%*#oXc5WFbTFu%pGGID9V@tQ%e@6dW%%k3|{I%3H} zAb&3r_hy&|b_u-(LWzjgpJmte-+J}OC8n(tq~9OR70~>2?-n$4Wx;pxRCXDdJ2i4V zZF6j5nEw_UsVp_aw!!Wy6b^>pLt`X?w z@X|L*T6#s{h-uYTSE-3NzT}v+squZ!A4m-Qz#RY{COg2l7!mu{R(~n-txAtgZQeEjOxczho2V)dAoJjR9WGsBH`&hfVR3F=pg6^DK6G4)^g#q@HoIt2#kCEy8K)@Z@Xt7yvsduy`U~A zJbh{r$-M4tSaTPCRhZp*{AgB}8o&`T2$mz<3~I$YC~?Eya-pfOG}?^PnCzViI?~{+ zgU$|kp?O(-r}UD+@`)b(4m6W1(mYIORmChv+4V#wmeax1KX(uoWcIviqOfSor>%qd ze@V1Lp!0LznwrR4XO8VkvO5oj50RbUu{pTlIoC3t5!eYPl`Etf)kU>;(E zySN)E-T6q0Zt*dhnR}4U>cK4ZMQVnz&c1!k0q2j@UVGa*9xan@leg0rQHwsR>UV<- z6UTvcDBmg(FUKdv1qH^d-UVqna7}R6=%9vQ)o&0)8k&MgREc*ScOHFec}YlIUxh*i zn!O$igVVIW(|2s9mLQG0qv5VEk`ahtA;QjA;h!?n=%uYWp+bDQCfd_Xna^A6WHY?g zxKk`{gpL8r5AeBn9<*%>K1#`|soxlF0mcG%Z6|p&XMy`|Pj}S5hNhJv8IEDB1JR@L z<|U|*Tf~!gpaz4UcSqWB@QswrSY1_6&fdo|+CAp(A+wTV{E^EBlu?5kmv(5p3z-#K z1|9u?tcV?-3a^p8gVRae8q@tH-wUl0i_$x@z=ld$J>|xQ9ton!l8)?fzEw7R`^2;o3atR2wUH>y1gp&g;pq@nLHo zh0Rm1(xewZ#%26j&TA9xTo*M1nyKF%ma%HlFFjcC@h_$z@zR*kCD#?Pca**@fXAaoOjdsEx3EC@uK4q*2$3W}fUP=pZg?shrWg z0mqt}UAauI;uFQ>6s4p=W+^KDsFKjV_mjb?44{h44M#;F@gf6ppW%DF}Hx)?-ot-2G z?bvPxNouavA8Sb%hqM$xJEkZ5FQZVTOp!_TS&ux)h##F9ck)WpF!T~|yBBrTV_cRF z>|5fY;Mx?CL&)ge;oDJV_nYPJN*1oM<`z~Bz2*_E?wkze(fnrZIM0VkmUgpUINZB~ z55E76!}ZP+Nsuome+R;LF*hu1TO9=-_IyjoY@QoPeH=bqP!tRb*Z$)zn``OAlxy)@ zQ*)ww)SWM@{(bV8a)T9o*u2(AA-ep=pe)JBOSDRG&1RC4ULdNic@ps=SA&?4pg8S? zqX0p(VUJzcSkf0)Qhae`U1OsT0vC0-tS}*=e4UW-wxF-E2nBO9wac!qF0PJ#ufq@s z7;DIj@n$QbF{9<`c!|t};z5Go=>;8lj&HhbC;%fpy=pvo=n zl1Hcah>lmgw|vWJ(>BGaO8K3!1DXQetBfX{A|$0|WZd;xM1W`piUEKdB|c*QY^zm~ z``V~V_qOP}tgBZZyarHZRM$xg7uuWNnE%29N9sD!<=Y$qwH=NHnh?G6iBBmm1M=0z zcUiJsA3S*?{Rut(fGXc)CK}vK*IbTiaei^l)NhM9(%JKU=%luZcWVVq~@=&sP#}wku85ucy9`eKB*B zr591sO|mZcYF?{rSdg}IkVC|x5MUVK4wmsLb2j&BO&wu4$CF>n0Ee=|^r`**>6ejr z>_*xc(PKgO{cfs>ykLE}By43JVJjQX)0&gG92X{yIqe;3*+XxrV@uj#apjWMT#KEj z;i1%g(lLwotGJSrV*6qka;IxaWg*wnxXFFg`-xE}7pT|*E7ZJ7&a;t2wLq_4Pje#E zX-5@OD;~Djmg$z(>gRFi&Y&|KQ2wG6x8t^U^>q~2n}^8JJM7I4;^M;0XVNeQvsBI8 zRJx{ztu7i!cq!6rQf#@Eu{RLgm@Q>GqrS%ToWrzJJq6NqmZ+S>;+18j!x+6XlP=af zv-;|!c0=g+BmB!CG0>vs@H;?7_&DKZ(h~@PFjHXEDR_0Gq6CIw5MMm+VKO?)nM+8I z<->RO=8?qzk4*8sTO5L8`8rk|=)m*A@i0)J*O&b=arzY3fXT5k@hAH0accJQ=AA47 zafyN3MP>p59$~$;$An1prLuN?53wYsEGyS}Y>rM`TH`}&t8C=8n7SkZ#+|_nUOsR* zq4HJZG&F|SIma(w}Sg=n#)#Q=p*y)nW0Xt zor;`K{6i zb%O&4?BdF#uIEPZy8r71$g8g9^uIrWbOa6ENT;c@zV5zFL4KxH$f*r3up_$5Bxn6` zT5&ZXpDCP|+78dQZ9l#DWAjTiKBU`~;BB#W=V7;)b#tmlz>?Im55rY)a9+ zOWN5QH}!8Tn9RV+T{=x|4J91r1of)Czb$ZO|KsQ$!c(YORBus?RT2_w4cP4vuNP3?%t6QEugc6+>=gtMTR5YAR+vK4X8QTZxol>q{V|MTxkpSc^LN?{nI+Z}i~mx^W&45l(hVuJf01pP z*o3PW58rAlGUda*B&TeCyTPS=jqDW>>3DvN$)9_5U?fycv|7xpNpc+%B(ELZvIZBH z9Pj)lbKmJ*UlBq(L#&KZNC9D3o&e=UZ%2NPb3^`@s_ER-vWZ>CmZ%~HYW7mz7 zO`v}2#E7N*dz-9wNh4mMTgz-)lti~;rt?M1B&=UyoYhY3)u)#yFKAL))H))AixTXuVm;(U16v+17;21hi$md!eR1?;U2lYq0P$K4|;x^t?$* zj;%rNQPI}rO9;6rn4F0tpIBmj#WqPf%_4r*REp8j+C@rlPg zkm@RwhdUn7or^f97Y@-1V=qc|C@pnt+1AvPN#35u(GwBhOt<&P8EHcfdm-D~4w~D% zzw1Z9Ne``CTlfkLEL1Q z%8Z~3W>ni>Hcw75WUjK#{a9{nU5jCh;{T*^i6)TRaK9?~Tj46H35G4ws7g^MnHUYR z)oxGu&mMXky!cZgnm~rhTb4_g1X0q5_}~Uz8Dc|RBfkb(Z%xu3+>Rs-RE{2w{hM5OwH5{lXp)E_(qZe?nd|+FxXz(%A<46kLsb_7AbS^? zvd^7-ZUHs+Ti4rvTr4!1-;<*6?a;}bmUIWmvu$$rTIYJ&7h~FfihHww-hHR7+m}Qt zQeUp@FHK&EL@NXkt>*>mfqUL{Nx_GC7o5|N3Po0Divi>YIOSNeD}QVGbRX8LR;V3A z(74i(za;)_vq6=E)7@?@ZYMzHpk^~H9#=RFfFz^k(lo*qSN&KV+s-@p>PBu6h&ZOv zJuzbR;_>W2jo~1C8T6mU;GbRBUE?irI^KlCvlmwR4i`70`xygvg07akMBIlp3e~Kn zck!(A@g5Fa(V3d+g%%oZq26Tj_rbS6Uwx3`%|Qj#W5S`n*M0>AYrp@zUD6M^RJmX7 zURw*&`I!pV0a`dZE89{JObh?~+n)j#$dcsL>`oot)NI=Vu(iG-@ViM!%f8{Ur)M@( z3*QIQ8a#T7HMx@Pe79t(E258eK}pGq#<`SpE2vH~H2sjoP5Tg)7z@Mh480&*5AIPt z%Y`2$i5cCsg0)rw!m4+&qef_-inUdr@=LSmqDqH{D!KTiD%_`Qy^a$|s5v7|TYQ7+>Ggi|fPn zFvWsUouSU$N*~UL+xt2=7B>*XB1j3tB9H1B@S_MPm3#g=-w1M|Rg^a0q5#P#?z>WT z=eAZ8ILEuO(Qfiy*qgUGQBF7d15e-FMyu=Ss{~I!`oU5AH8DQ@X4z%u`d>gUi+c$Q zFY1Q!P;|LMd&6$TZ6uML9_c>)Gp_EdV6FCOe9OLh(+jP290n6TNpRO;i@VLrM`yi5 zX21P|dy^)YSN~Z?^0?KJ-Ikj%iq|dv8XnbVWE@*~2ObFacDLzl>CPLm3s7+;rH%4T z+U^@uV`dAK#MQ5S<-r3n!FWSZeEogPmjX_B-mSVQb))sCA+l(ejgty#+ve=C|xych)|I(>PuzD;q#7v|+%g@TW&^tmwvCHk!otLD3NN3Dn69CC3fg!NXDT$617j5l#IBxyiJ$Is&U z_EkS8T+cIefeCW~y!Y~u9kgcblGoGCr@wY)2knUtp=(Y@VQvq3K}#mfHq8@BGge~+ zu^lV`>v#vWc2f)(RAlu_+k7>7?fwgx==X5g3r3HJUAK-Q&vz5GwDouqpN;j&jWeq) zNQLb#VPE&|Km#BzWDB^8{OM3QVRPQkI)>;2!)gsH4#BlFU3)}wns&A$J3ftxrkeTP>yS|7A!axCNi$R)gK{miVzda}lSs0~Ml-V!D~S6i`*s}rgd=GPsAn@j(a zpb2kUz#cp*JbbSApgjD;G5Z!}!Yd#D!qz}pol3`Yli>I@Z7P`h8L^c73+EdE_qY+> z@N=IVV7V~Fx3xPZ0`wj9Kn0dU#zPrduQCm9+V%xGFR5_41+Omc67kN@bt|l-T)g@A zMZ4;|?-wQwkBFQ#LtZzLg^?3rXg+DKu@{jh8E=ws78gnYkg^@N zfxk=MjU@@wuW9K;m$=Zm6JfkM{2dt`tF)&^D)8fpPKfSa6wr8^2cLH}lJL~?PQYa* zac29AAviHlau{%bG6f?I{W&nwaHj+Jk2F|1dB3FpJD|VU*-2X0$T6LwHcq%z*Gb5_ zUE9k`;94xnAvi1x;qmjoi%iYHaa28@(8 z`TPgBV}U2uyXSIvA!Z2}DFHWVB->2WO3iJ;IPHWx1sk4Z{;Ec$~`|>SjprNYPG`zFwKNNv4t%@wIp zXci9>11Q{4Rm@y#S<5&Z^#wg89GvS;!`HY(Wm^-l@`su}o6GS>nz@qx6Wa(}D?#~fiA zL-kJCIZN8Ct^<`cGWz~p@)$`_&Nq%IH5z5hqAlE%55N3cEum*iQ*@dEkr!$P0PHK( zX2jf}?pXyM;=ZC!ck^!NMZhHu7I@ycgs9Ir)b;KB>NoXvA^h7AA>&O`f$zRr%_mw` zllO;MqS5RU6F>2UAF6(#Q&@bIUBs9ss+3y@Rf2D+f4Aq%f0Jvjctk8c|dZrNP=A+obaEIZrIX1t7RhmRkihF7ZymoJ@l+mFh6 z^KSU;y2srf&gO-ny{+M7>P7#H1#*7zvP+&TJ?^dVE_*|dk368CjzmU6`!$VCze^Af$ckb_Z*d^I_iSBY%3JV@Y3taE_+t4qBL9$ zxW`3hxGkR=CAk1X+cR0(m89^GNFIlO>GE2nhbDaYRk!Il76++6M`!BSxiz@>0|P>E zxA$7lE+PMkm5du4S|e}0p-nhEEuZhmz8e)>+eQcrJN!NS`Rk%Q{U2_&FR3C#>>@@3 zoU_(WY^dQay=T=x6jxB|OZAfZ0_Uue8OCvrevAg(Y@ZTgT#S(Gd4sI&%sO^6W3E($ zwB3JEVJiqJDmA|%`{4Iq8f`NiWCd=;hd4DfPk+Zj*2MGo^1eat+URYqWPP=!!#1tz zZ33O*0EdVii4Dd#F6`VS@&%oRE}vc6wEdl$-7AIB_8?8?h0VItID_b~%H~~d5>Qkf zbBXr+0OcfWi+2QdR}XT>L43)POUU@PY;j%)lM+9gwo7egRHW%hc!~bA(>dS4l{bP( zK&v>yFnv#zMxXB<(1!@sYdfE?w}^ZhKRX9t5)%}AGo4opzhg#lTP@~!xa>iEn@M|~ z1J|x_;WM99v%f!xTE{ycU{GhNd=E?`DW1;2b>*v)3zBBNZG&R-iE(hsI?1$<@Xb%M zIsEDP?Q@v2A@ z0R!{M7sz+(--7HB52Y%wV%-5~{$%+-Nvbw#AJ~OaYzz>loqZF`dj%%$8~p%61Dp!1 z=#{@W0CsJw>ThdJGWgreY0qJIOGLooJT?G~YcnJ$)%3D;KKYcCn5|7#)HnWGycBMO zgq0%IJhsb>I5gKq8YzR0H~+(#2%js*B378A*DaEYz%4uLHYn9f-N?^^SnunpNiMsd zwO_LEH&wm+;BL_>D$ZXVTt_4M0d=&7^tl9eb`_W$Q`(6Cz^Xg|Jw85Y3H(b12x&2< zTvNydbA3bwlqxG!STYCk1sqzUrK=X!H$feAIc-3QlQKmFHaY@=bfQ`;0-qq<9ICXm zfNj7VKG5ry1Z6&74AwHx-&7obSEA%Y_*oMRdNTWkflBH5-uA=p49p z&Ba}pq&MXb)DsFG=IkK&8@HNdo;kt!^on_XX!Ci1Af{bmX(VALd(KHR1F07vdjHoL zzF5#qKNw-8UVDqM8j*`>@Ncs`i^1?2l|QyIX|(TfrG3Y}fxKC9GZ|!X2}$7akzGOt znrSOqvYa7@qK0dC@Uqngao&0q_j(9_=Jex!c}Z)r>?^X376N9DZ=s1NO33Ij`o^kV zOUc;vpG!$1=I=jG9;chiiulc2xaNUoQIX}HR~j<`w-n3&yg2Q%UCPUfZB(24 z;t+;Vof9*y52}uXAM)lCgXg-KrctU^y*txjWxL>R;J-&QMx3qDyUCki_MLQ}Mln1xmp|&4TSWzK^_MUq&ufLE zdh<3NQ0EDQS}(#pVn<)TRA4bnuE(8X*7m-qm~TxR!j_}WG!5Occ4>c2-_4=+Gad(H zEbO&)^rvo?d@u(RvKjK{TDM@5JXsO<66M)(yawPdin{b4Y~qq?;_ z(!@3dk1;hJ*X2CPCRX=`#Qtlka2XQ0EVj)2Oj}{UOsz8AALXAkx_xj;TeEaNa5JrJ zN<4=V@OB*pk;>O%qEWMS@wd(mOM@{nIatyo3bA|!q1b@$Uw`dSJLKT12Lvg|;hwZb z1RO_if3@QM6wCg^Pj)K}tqSG8EczEWpNS-(K5f3PUwV5}BxJHhNDm~l>K$Lhey~>_ zIS zv?}u_YH%izJfU0is5^@|0_#Re5R;=kf1&upPscG5QuC(OQ9+wH9cn(#UBmW$R3Nt9 zs+!AZ!MinGfK|)sZF$CJ!fLCYcNtndk z_G3DNhHs>cp7AQ*vE80&5kkWdD)MwU&vYMwl`=3f?-j_0Z|l2)j8IT<$H(cP@e+d$ z`SFw=*oq;Yp% zL?%yG^Cow80+4y*s}Z^B1J}R?EE?q0-vCm50U*_%BFyySo29qJA-YG%?Y&0L@y|Gw z0nQrs+CyG#Tg1~Z51Zttyz*VjZxbtX7cn)Aj=ff$Y1zlLX~k6|S39`J>DJ2{Y36VC zLJ!m8^R4VaQ3N_U#Nrx=?+Ij=#|^GOnG|mbvfm6xaRG9K@~c%2`G%noY0`}@wAkrx zoD+($F2&jej-tpbxLk*X&scm>YOmI<*h}U$D_di)sR=w zL|{R(jc26d^eVC8J@iJLwVtjZ4!D_r+#M`_H3?B;#)!?40JEO^)2&#cCE*aM|D4ed zV^UM*Z=B?p4Ylp1a-gT7EvY$tIuul7lj_X)&PDe2E^g7GO5vq;b5|8|3;R^v>P(Fs z{mej=>>UZdYfgBjFRMlM(wxraw%sW=NuD4YfJrGB07QyRCDc&uv=OlVZ`pm{!J@~i zCV)tzHQJBSAG(hHZq(&DMFiBEt(Y`Fx%1&=;8+!;njNFWgz4o57QZ>~(Rt`StMB!Q zl8sw0CD9?R>I}tkGGu}je!+FUVApQmjQGNQn(Ots*=Mq-{ zxac!I3>RUQ>zPU9SJz%a_k81_oiGHdb8d}YoY0Iv#iEJq258$4I(%Ys@(<&hlSv2iV)hgZ(RPr>DI!n82f8C3NoUALt)7@3Vwo z*?u0wGvZyuCO=L}q-%yp{#*Zp&b?2-uHfB^yh?F$^G(8~w{n7azc^uX1md$khq4`# z5>mbVdgb9#@rT>{e+nw63t?27$O!ILC>%SN<=2`&cbfYN+{SMkj)DV)hDuf!KJbMo z2H|+f{Y`Bo>u-#BX@PFPUn97J;!OCN`4Y3lKsrVV>jij<*e*_J{kn!p3h{Qw$m0I= zp+irxnLQ-*wFh#QE)h)My-8`88K}s_!ypZ6+=2PKQu<&F*{k%KviY3%&R{f;k=%3| zx}9>(*NVRbvfyj&zkT<0`|pdw!-86hOg@sP{Je={!r)b1JI^s*Ng_f6?RrFU>3Eqz zJ}oYO+)qoL?9ZzOS!fbZrmBgqT^Y0acf$qSo3j1ZPoRsdA*~c*PDjh*=kA+@iiem0 z9^j6Vb*qMBQ`l=^MJI|VKX$5HorM`B1J1#=oBy7VtEsTt`D-wMO2Q_*;vHzTqa+7D zD|?mQ%>OuSON4`qA;q^{{PyZf&dqrpElevFae$5Qn?evSBj?xx`B1hZykxyF>Yg`~ z#RMB;AKZZ!c^OA1pY*M6kdSL|sP|E9;}C{-ztg~QWl}MMC6Qhb7dRwdeYTeJiW1Pe zK5=tgBUV08_rS9ZQAb-1rZN%ax{=Xz5kJc`SV=H8CgS*-&aCMaB%BNDZf< zN1};rIVJVNP){W+)qd+N5%YwyT@sNL((W27+x z+c6RF?=|X39jt!TB&`f%b_lt`?*@*#_OHnR4xahaW_HOdAcu%Bb6DA_1&^Av1IDKLef2r6fSpYxEcP0v?%C71-+}>kFP9xg z6ac%>^Bu*E<0V_&Ku1paltTuTvBS&>Fl!Al@P9mY5MJt{Be7p%SU&zJj+Kfjx$;2c zhf_Kkxwa#HD>rLf^6+umu6D}pg{=T%>|{nr4(guT?dDizq2EwFV%A?O2XQlck(_Z% zzBQb%Xfj-_Ak18s&=$b3T>3_aZQap6N~3+*_%6gnLn6hE=idT;r+N-Rl$i}brupXG z_A`s_Kl2pCnG%+G7HnDg=}kCnq2P;=mcChWxNnCq!p*b|kz(lJ;~G1I%h9#T2Aqz6 zb~EIwrVl)&SK{S}@kMjijRG0xKGI*Q>|xoj?Jwe6*Ymk^o;_05>50;)@fL3Uo?8KWrX6(Gv z(-K;IgJ}87a~#gPAMW5nbZ=#~Qo?ul zkIAS3MlIc1jk4%?h!RG;i;SaQ$?Hgj;g$$bFqVtCQSD@SP@r|bz)50IXFh;`|E2X% z;{U~G%AC@hE|5t5ab%V5-cjLW*O3Wk3uHF=Yf*D3w%Qw}@_|aRgM*QM*>_#JO19aQcdx;I03EI$^_jkaSlX&dqPrzY-y#UIJ(>PBBi ztJl2#d%49Ns?2(pdgweDF}mMkmyBw&QhH}Bt%{id=wBQch`=F=*xj7Pu5=;3skwf1 zW<5uZd*d+c0vzEsv1h%&M>?d8G<#`l=Xe^uLJ0rvQ09?rxLd5|*Q1IWAmOc0o6J|5 z))2BX%%&p|E}4WAS`~AM?PbK9cuWLkNdDm#!>*n!emk9ZiDI{ZG(n!pV>}46oT11MD+#~4XBN1YtW!UxVkatAPTw(NhVD##zMOC#-Yu0WdOzpK zB#-DaHpq86K>kOe(vgO`pP{*PCis+!&7)%b{RM1hsXKNxd4PVvQu8V+3;18`&5uE+ zJ;ebig@f-{;ajQR+xS}zAAJs|ZjSycdx4I#bKOD_YJNWt|BxY*JM$DfLzw#79iY&X z%rb9jOb!Xk)CkZKH;ECq<|lsFr!RDo=;HE8Xy*#3C+z&k`2fT*|qk+Vs!%7<&s&9O^Bzl-fj77F4Un$WR{ zA>24Wh+{e9nU@WWD@*lMv$4tR+j5*V*VdlXQa%r&{4AAS$zOBxnDf$z2}$p89)+8RE2Il99E0aI$f8Gp*HMN&KWNt=oI!wUxoAT+&>?@N~0QiODtV z)`DFow^bqxi)6SXH-*79bJ-l&!rz3K#RL|{_;)ifVYuzwJ?&dJ{oIFSI3*jS*DM9P zt4gg8q^h?+1X2F^!YLb4ze{_O2P5y=PtSC9H= z$kpez-ghu9509FB^}J|7uTy?G7k>+1yYcJZAf>*!%ZAsZ_vF-qfCR8Qd_J@IYLJ<_ zp{L_FhJ-ohS^Msp}h@#K-r?R`` zEV7|UUm{j#x;Gg(6QfiZUlxs$UcR=B$6BA`S!bT(0?Wr+5ubFc$|pog)Ty`MAHA~W zwBBxCI60E?|{Z3~gKl_KHsFnIA1KV0GUo87XZ?!N)RoF}xjKPSaJHy>cFg zeo_CRgS8n_x$>Eqf5=6H1*M6$LHAzN0ZxI{&%hH|31Z`=&nXuUAB~3d-gVy^megRml(ZEjha+_5U@i zGM{rLFO7Tr83c43))Hwr_|UZ7oe)MU^J%cVuIQfMt{m<)Yy=+?rt~XaO*Uz? za8`Nv_ppgu?Y2vM=L09alyo^QXQ8cpczpY@mjl#4=Uz?(9IxO^7x|Sg zP{`_Wxc#q_y+iFYhyw#MYj&2=+`hw;$y+i0{flnu2~|h)66y+!dX#FAYw^E3*S)J> z)ICB3tRx%_h8r%41#^EJxWZ2?y(N9sqRE!D-m#vHPd)dLlePD?psGuA>wK!z&-&g& zq2?#rPPR!;xXs?`QT0KFt|g#b}1yanA#cAUjvlx z>#b}Gl-KNV6>iSYE?5G&+X>%0<7o;WEel2^)_f6x1$){zA*iNv1{$j`Yyw--QIFi_xUqD*nPKKIX zH8mLO?Z(qASU+k2k(!P1^Z;NGDiabln}&lfiZ9)1j6~;NO|z74FRJMx?SLIG4@b4y zus46}7*9*~<486*HHO)P5(0+J^tilj!-PD9NX9N86nh%$rvbuii|gq7vR(mWaJJ}c zY%MK(q*0+Fv%gPlv&q7KLfyRfo$C>{2J0Kv20*z0aJbY7dx4!ZFINOa@&g%`WRGWC z1qkZiyKW0>i{_WJRBGvow*Gz~5WXy$$AUnW%x61(K$EWRJSILQg4_5=9PvEUR;fNx zzY8`W4ETcij|zp*lPSpm!cnvSjRO~jss=@d6q4N(pfMKi$wTM57<1b@g0Kh^U8MQm!jO$3xCc3PG3U?y}0Nc;`u-{$R5H*{2Vj3m{)P5NW0GRB5JGZn+5y zkZ!GVv89+g$h~c?kC)hPbQw@$zgE<7H#;*wc7aV)2Pc;VngiD>nx|c#q{ZGh0?g!+ zj{Hg!82g8+yJjJn4~;vBV*g2Zlu|{m+=~k2s&_dlG%+M99!TEOqU*A6r)?V{K8w2{ UqCwmV{G>2lZNm$NT6VPm0TJ9vumAu6 literal 0 HcmV?d00001 diff --git a/reference/fitdist.html b/reference/fitdist.html index 6040afe..c10981e 100644 --- a/reference/fitdist.html +++ b/reference/fitdist.html @@ -1,5 +1,5 @@ -Fit of univariate distributions to non-censored data — fitdist • fitdistrplusFit of univariate distributions to non-censored data — fitdist • fitdistrplusExamples#> ## #> #> -#> Sat Oct 26 05:39:31 2024 +#> Mon Dec 2 13:33:35 2024 #> Domains: #> 0.000000e+00 <= X1 <= 1.000000e+01 #> 0.000000e+00 <= X2 <= 1.000000e+01 @@ -749,7 +749,7 @@

    Examples#> Solution Found Generation 1 #> Number of Generations Run 11 #> -#> Sat Oct 26 05:39:32 2024 +#> Mon Dec 2 13:33:36 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit2) diff --git a/reference/fitdistcens.html b/reference/fitdistcens.html index a2f68df..f99402f 100644 --- a/reference/fitdistcens.html +++ b/reference/fitdistcens.html @@ -1,5 +1,5 @@ -Fitting of univariate distributions to censored data — fitdistcens • fitdistrplus +Fitting of univariate distributions to censored data — fitdistcens • fitdistrplus Skip to contents @@ -439,7 +439,7 @@

    Examples print.level=1, hessian=TRUE) #> #> -#> Sat Oct 26 05:39:40 2024 +#> Mon Dec 2 13:33:42 2024 #> Domains: #> 0.000000e+00 <= X1 <= 5.000000e+00 #> 0.000000e+00 <= X2 <= 5.000000e+00 @@ -486,7 +486,7 @@

    Examples#> Solution Found Generation 1 #> Number of Generations Run 12 #> -#> Sat Oct 26 05:39:41 2024 +#> Mon Dec 2 13:33:43 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit.with.genoud) diff --git a/reference/fitdistrplus.html b/reference/fitdistrplus.html index 8213b56..ae7081f 100644 --- a/reference/fitdistrplus.html +++ b/reference/fitdistrplus.html @@ -1,5 +1,5 @@ -Overview of the fitdistrplus package — fitdistrplus-package • fitdistrplus -Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam • fitdistrplus +Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam • fitdistrplus Skip to contents diff --git a/reference/fremale.html b/reference/fremale.html index e599a95..222f3c4 100644 --- a/reference/fremale.html +++ b/reference/fremale.html @@ -1,5 +1,5 @@ -Fictive survival dataset of a french Male population — fremale • fitdistrplus +Fictive survival dataset of a french Male population — fremale • fitdistrplus Skip to contents diff --git a/reference/gofstat.html b/reference/gofstat.html index b89927e..f3a415e 100644 --- a/reference/gofstat.html +++ b/reference/gofstat.html @@ -1,5 +1,5 @@ -Goodness-of-fit statistics — gofstat • fitdistrplusGoodness-of-fit statistics — gofstat • fitdistrplus Skip to contents diff --git a/reference/graphcomp.html b/reference/graphcomp.html index 99894ae..13e91bf 100644 --- a/reference/graphcomp.html +++ b/reference/graphcomp.html @@ -1,5 +1,5 @@ -Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp • fitdistrplusGraphical comparison of multiple fitted distributions (for non-censored data) — graphcomp • fitdistrplusGraphical comparison of multiple fitted distributions for censored data — graphcompcens • fitdistrplusGraphical comparison of multiple fitted distributions for censored data — graphcompcens • fitdistrplusGround beef serving size data set — groundbeef • fitdistrplus +Ground beef serving size data set — groundbeef • fitdistrplus Skip to contents diff --git a/reference/index.html b/reference/index.html index 1aac788..6521891 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,5 +1,5 @@ -Package index • fitdistrplus +Package index • fitdistrplus Skip to contents diff --git a/reference/logLik-plot.html b/reference/logLik-plot.html index c2cdc1b..58ac638 100644 --- a/reference/logLik-plot.html +++ b/reference/logLik-plot.html @@ -1,5 +1,5 @@ -(Log)likelihood plot for a fit using maximum likelihood — logLikplot • fitdistrplus(Log)likelihood plot for a fit using maximum likelihood — logLikplot • fitdistrplus Skip to contents diff --git a/reference/logLik-surface.html b/reference/logLik-surface.html index e5e6571..1b7b439 100644 --- a/reference/logLik-surface.html +++ b/reference/logLik-surface.html @@ -1,5 +1,5 @@ -(Log)likelihood surfaces or (log)likelihood curves — logLiksurface • fitdistrplus(Log)likelihood surfaces or (log)likelihood curves — logLiksurface • fitdistrplus Skip to contents diff --git a/reference/mgedist.html b/reference/mgedist.html index 862a896..d9bdb13 100644 --- a/reference/mgedist.html +++ b/reference/mgedist.html @@ -1,5 +1,5 @@ -Maximum goodness-of-fit fit of univariate continuous distributions — mgedist • fitdistrplus +Maximum goodness-of-fit fit of univariate continuous distributions — mgedist • fitdistrplus Skip to contents diff --git a/reference/mledist.html b/reference/mledist.html index dfb6017..a63e8da 100644 --- a/reference/mledist.html +++ b/reference/mledist.html @@ -1,5 +1,5 @@ -Maximum likelihood fit of univariate distributions — mledist • fitdistrplus +Maximum likelihood fit of univariate distributions — mledist • fitdistrplus Skip to contents diff --git a/reference/mmedist.html b/reference/mmedist.html index 0bc54a6..b4dd5cc 100644 --- a/reference/mmedist.html +++ b/reference/mmedist.html @@ -1,5 +1,5 @@ -Matching moment fit of univariate distributions — mmedist • fitdistrplus +Matching moment fit of univariate distributions — mmedist • fitdistrplus Skip to contents @@ -501,7 +501,7 @@

    Examples#> $memp #> function (x, order) #> mean(x^order) -#> <environment: 0x560153fdafb0> +#> <environment: 0x559a12ac20f8> #> #> $vcov #> NULL @@ -586,7 +586,7 @@

    Examples#> $memp #> function (x, order, weights) #> sum(x^order * weights)/sum(weights) -#> <environment: 0x560153fdafb0> +#> <environment: 0x559a12ac20f8> #> #> $vcov #> NULL diff --git a/reference/msedist.html b/reference/msedist.html index c569e06..516cae5 100644 --- a/reference/msedist.html +++ b/reference/msedist.html @@ -1,5 +1,5 @@ -Maximum spacing estimation of univariate distributions — msedist • fitdistrplus +Maximum spacing estimation of univariate distributions — msedist • fitdistrplus Skip to contents diff --git a/reference/plotdist.html b/reference/plotdist.html index d4bb725..3089551 100644 --- a/reference/plotdist.html +++ b/reference/plotdist.html @@ -1,5 +1,5 @@ -Plot of empirical and theoretical distributions for non-censored data — plotdist • fitdistrplus +Plot of empirical and theoretical distributions for non-censored data — plotdist • fitdistrplus Skip to contents diff --git a/reference/plotdistcens.html b/reference/plotdistcens.html index 134856c..b59ca7b 100644 --- a/reference/plotdistcens.html +++ b/reference/plotdistcens.html @@ -1,5 +1,5 @@ -Plot of empirical and theoretical distributions for censored data — plotdistcens • fitdistrplus +Plot of empirical and theoretical distributions for censored data — plotdistcens • fitdistrplus Skip to contents diff --git a/reference/prefit.html b/reference/prefit.html index baa6900..d25ac6d 100644 --- a/reference/prefit.html +++ b/reference/prefit.html @@ -1,5 +1,5 @@ -Pre-fitting procedure — prefit • fitdistrplus +Pre-fitting procedure — prefit • fitdistrplus Skip to contents diff --git a/reference/qmedist.html b/reference/qmedist.html index 6d2940d..bd40612 100644 --- a/reference/qmedist.html +++ b/reference/qmedist.html @@ -1,5 +1,5 @@ -Quantile matching fit of univariate distributions — qmedist • fitdistrplus +Quantile matching fit of univariate distributions — qmedist • fitdistrplus Skip to contents diff --git a/reference/quantile.html b/reference/quantile.html index 1751299..9c7420d 100644 --- a/reference/quantile.html +++ b/reference/quantile.html @@ -1,5 +1,5 @@ -Quantile estimation from a fitted distribution — quantile • fitdistrplusQuantile estimation from a fitted distribution — quantile • fitdistrplus Skip to contents diff --git a/reference/salinity.html b/reference/salinity.html index 5586130..bbe6ef2 100644 --- a/reference/salinity.html +++ b/reference/salinity.html @@ -1,5 +1,5 @@ -Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity • fitdistrplus +Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity • fitdistrplus Skip to contents diff --git a/reference/smokedfish.html b/reference/smokedfish.html index 715d6b8..32a5264 100644 --- a/reference/smokedfish.html +++ b/reference/smokedfish.html @@ -1,5 +1,5 @@ -Contamination data of Listeria monocytogenes in smoked fish — smokedfish • fitdistrplus +Contamination data of Listeria monocytogenes in smoked fish — smokedfish • fitdistrplus Skip to contents diff --git a/reference/toxocara.html b/reference/toxocara.html index 9c4a02a..f774074 100644 --- a/reference/toxocara.html +++ b/reference/toxocara.html @@ -1,5 +1,5 @@ -Parasite abundance in insular feral cats — toxocara • fitdistrplus +Parasite abundance in insular feral cats — toxocara • fitdistrplus Skip to contents diff --git a/search.json b/search.json index f012934..c1bbe5e 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-know-the-root-name-of-a-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.1. How do I know the root name of a distribution?","title":"Frequently Asked Questions","text":"root name probability distribution name used d, p, q, r functions. base R distributions, root names given R-intro : https://cran.r-project.org/doc/manuals/R-intro.html#Probability-distributions. example, must use \"pois\" Poisson distribution \"poisson\".","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-find-non-standard-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.2. How do I find “non standard” distributions?","title":"Frequently Asked Questions","text":"non-standard distributions, can either find package implementing define . comprehensive list non-standard distributions given Distributions task view https://CRAN.R-project.org/view=Distributions. two examples user-defined distributions. third example (shifted exponential) given FAQ 3.5.4. Gumbel distribution zero-modified geometric distribution","code":"dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q, a, b) exp(-exp((a-q)/b)) qgumbel <- function(p, a, b) a-b*log(-log(p)) data(groundbeef) fitgumbel <- fitdist(groundbeef$serving, \"gumbel\", start=list(a=10, b=10)) dzmgeom <- function(x, p1, p2) p1 * (x == 0) + (1-p1)*dgeom(x-1, p2) pzmgeom <- function(q, p1, p2) p1 * (q >= 0) + (1-p1)*pgeom(q-1, p2) rzmgeom <- function(n, p1, p2) { u <- rbinom(n, 1, 1-p1) #prob to get zero is p1 u[u != 0] <- rgeom(sum(u != 0), p2)+1 u } x2 <- rzmgeom(1000, 1/2, 1/10) fitdist(x2, \"zmgeom\", start=list(p1=1/2, p2=1/2))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-set-or-find-initial-values-for-non-standard-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.3. How do I set (or find) initial values for non standard distributions?","title":"Frequently Asked Questions","text":"documented, provide initial values following distributions: \"norm\", \"lnorm\", \"exp\", \"pois\", \"cauchy\", \"gamma“, \"logis\", \"nbinom\", \"geom\", \"beta\", \"weibull\" stats package; \"invgamma\", \"llogis\", \"invweibull\", \"pareto1\", \"pareto\", \"lgamma\", \"trgamma\", \"invtrgamma\" actuar package. Look first statistics probability books different volumes N. L. Johnson, S. Kotz N. Balakrishnan books, e.g. Continuous Univariate Distributions, Vol. 1, Thesaurus univariate discrete probability distributions G. Wimmer G. Altmann. Statistical Distributions M. Evans, N. Hastings, B. Peacock. Distributional Analysis L-moment Statistics using R Environment Statistical Computing W. Asquith. available, find initial values equalling theoretical empirical quartiles. graphical function plotdist() plotdistcens() can also used assess suitability starting values : iterative manual process can move parameter values obtain distribution roughly fits data take parameter values starting values real fit. may also consider prefit() function find initial values especially case parameters constrained.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-fit-a-distribution-with-at-least-3-parameters","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.4. Is it possible to fit a distribution with at least 3 parameters?","title":"Frequently Asked Questions","text":"Yes, example Burr distribution detailed JSS paper. reproduce quickly .","code":"data(\"endosulfan\") require(\"actuar\") fendo.B <- fitdist(endosulfan$ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) summary(fendo.B) ## Fitting of the distribution ' burr ' by maximum likelihood ## Parameters : ## estimate Std. Error ## shape1 0.206 0.572 ## shape2 1.540 3.251 ## rate 1.497 4.775 ## Loglikelihood: -520 AIC: 1046 BIC: 1054 ## Correlation matrix: ## shape1 shape2 rate ## shape1 1.000 -0.900 -0.727 ## shape2 -0.900 1.000 0.588 ## rate -0.727 0.588 1.000"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-there-are-differences-between-mle-and-mme-for-the-lognormal-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.5. Why there are differences between MLE and MME for the lognormal distribution?","title":"Frequently Asked Questions","text":"recall lognormal distribution function given FX(x)=Φ(log(x)−μσ), F_X(x) = \\Phi\\left(\\frac{\\log(x)-\\mu}{\\sigma} \\right), Φ\\Phi denotes distribution function standard normal distribution. know E(X)=exp(μ+12σ2)E(X) = \\exp\\left( \\mu+\\frac{1}{2} \\sigma^2 \\right) Var(X)=exp(2μ+σ2)(eσ2−1)Var(X) = \\exp\\left( 2\\mu+\\sigma^2\\right) (e^{\\sigma^2} -1). MME obtained inverting previous formulas, whereas MLE following explicit solution μ̂MLE=1n∑=1nlog(xi),σ̂MLE2=1n∑=1n(log(xi)−μ̂MLE)2. \\hat\\mu_{MLE} = \\frac{1}{n}\\sum_{=1}^n \\log(x_i),~~ \\hat\\sigma^2_{MLE} = \\frac{1}{n}\\sum_{=1}^n (\\log(x_i) - \\hat\\mu_{MLE})^2. Let us fit sample MLE MME. fit looks particularly good cases. Let us compare theoretical moments (mean variance) given fitted values (μ̂,σ̂\\hat\\mu,\\hat\\sigma), E(X)=exp(μ̂+12σ̂2),Var(X)=exp(2μ̂+σ̂2)(eσ̂2−1). E(X) = \\exp\\left( \\hat\\mu+\\frac{1}{2} \\hat\\sigma^2 \\right), Var(X) = \\exp\\left( 2\\hat\\mu+\\hat\\sigma^2\\right) (e^{\\hat\\sigma^2} -1). MLE point view, lognormal sample x1,…,xnx_1,\\dots,x_n equivalent handle normal sample log(x1),…,log(xn)\\log(x_1),\\dots,\\log(x_n). However, well know Jensen inequality E(X)=E(exp(log(X)))≥exp(E(log(X)))E(X) = E(\\exp(\\log(X))) \\geq \\exp(E(\\log(X))) implying MME estimates provides better moment estimates MLE.","code":"x3 <- rlnorm(1000) f1 <- fitdist(x3, \"lnorm\", method=\"mle\") f2 <- fitdist(x3, \"lnorm\", method=\"mme\") par(mfrow=1:2, mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points=FALSE, xlogscale = TRUE, main = \"CDF plot\") denscomp(list(f1, f2), demp=TRUE, main = \"Density plot\") c(\"E(X) by MME\"=as.numeric(exp(f2$estimate[\"meanlog\"]+f2$estimate[\"sdlog\"]^2/2)), \"E(X) by MLE\"=as.numeric(exp(f1$estimate[\"meanlog\"]+f1$estimate[\"sdlog\"]^2/2)), \"empirical\"=mean(x3)) ## E(X) by MME E(X) by MLE empirical ## 1.61 1.60 1.61 c(\"Var(X) by MME\"=as.numeric(exp(2*f2$estimate[\"meanlog\"]+f2$estimate[\"sdlog\"]^2) * (exp(f2$estimate[\"sdlog\"]^2)-1)), \"Var(X) by MLE\"=as.numeric(exp(2*f1$estimate[\"meanlog\"]+f1$estimate[\"sdlog\"]^2) * (exp(f1$estimate[\"sdlog\"]^2)-1)), \"empirical\"=var(x3)) ## Var(X) by MME Var(X) by MLE empirical ## 4.30 4.36 4.30"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-distribution-with-positive-support-when-data-contains-negative-values","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.6. Can I fit a distribution with positive support when data contains negative values?","title":"Frequently Asked Questions","text":"answer : fit distribution positive support (say gamma distribution) data contains negative values. irrelevant fit. really need use distribution, two options: either remove negative values (recommended) shift data.","code":"set.seed(1234) x <- rnorm(100, mean = 1, sd = 0.5) (try(fitdist(x, \"exp\"))) ## Error in computing default starting values. ## Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : ## Error in startarg_transgamma_family(x, distr) : ## values must be positive to fit an exponential distribution ## [1] \"Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : \\n Error in startarg_transgamma_family(x, distr) : \\n values must be positive to fit an exponential distribution\\n\\n\" ## attr(,\"class\") ## [1] \"try-error\" ## attr(,\"condition\") ## fitdist(x[x >= 0], \"exp\") ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 1.06 1.06 fitdist(x - min(x), \"exp\") ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.914 0.914"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-finite-support-distribution-when-data-is-outside-that-support","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.7. Can I fit a finite-support distribution when data is outside that support?","title":"Frequently Asked Questions","text":"answer : fit distribution finite-support (say beta distribution) data outside [0,1][0,1]. irrelevant fit. really need use distribution, two ways tackle issue: either remove impossible values (recommended) shift/scale data.","code":"set.seed(1234) x <- rnorm(100, mean = 0.5, sd = 0.25) (try(fitdist(x, \"beta\"))) ## Error in computing default starting values. ## Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : ## Error in startargdefault(obs, distname) : ## values must be in [0-1] to fit a beta distribution ## [1] \"Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : \\n Error in startargdefault(obs, distname) : \\n values must be in [0-1] to fit a beta distribution\\n\\n\" ## attr(,\"class\") ## [1] \"try-error\" ## attr(,\"condition\") ## fitdist(x[x > 0 & x < 1], \"beta\") ## Fitting of the distribution ' beta ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 2.08 2.79 ## shape2 2.50 3.41 fitdist((x - min(x)*1.01) / (max(x) * 1.01 - min(x) * 1.01), \"beta\") ## Fitting of the distribution ' beta ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 1.77 2.36 ## shape2 2.17 2.96"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-truncated-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.8. Can I fit truncated distributions?","title":"Frequently Asked Questions","text":"answer yes: fitting procedure must carried carefully. Let XX original untruncated random variable. truncated variable conditionnal random variable Y=X|l= low) * (x <= upp) } ptexp <- function(q, rate, low, upp) { PU <- pexp(upp, rate=rate) PL <- pexp(low, rate=rate) (pexp(q, rate)-PL) / (PU-PL) * (q >= low) * (q <= upp) + 1 * (q > upp) } n <- 200 x <- rexp(n); x <- x[x > .5 & x < 3] f1 <- fitdist(x, \"texp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=min(x), upp=max(x))) f2 <- fitdist(x, \"texp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=.5, upp=3)) gofstat(list(f1, f2)) ## Goodness-of-fit statistics ## 1-mle-texp 2-mle-texp ## Kolmogorov-Smirnov statistic 0.0952 0.084 ## Cramer-von Mises statistic 0.1343 0.104 ## Anderson-Darling statistic Inf 1.045 ## ## Goodness-of-fit criteria ## 1-mle-texp 2-mle-texp ## Akaike's Information Criterion 127 132 ## Bayesian Information Criterion 130 135 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points = FALSE, xlim=c(0, 3.5))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-truncated-inflated-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.9. Can I fit truncated inflated distributions?","title":"Frequently Asked Questions","text":"answer yes: fitting procedure must carried carefully. Let XX original untruncated random variable. truncated variable Y=max(min(X,u),l)Y = \\max(\\min(X, u), l) ly>l+1y>uF_Y(y)=F_X(y)1_{u>y>l} + 1_{y>u}. density (w.r.t. Lebesgues measure) since two probability masses P(Y=l)=P(X≤l)>0P(Y=l)= P(X\\leq l)>0 P(Y=u)=P(X>u)>0P(Y=u)=P(X>u)>0. However, density function respect measure m(x)=δl(x)+δu(x)+λ(x)m(x)= \\delta_l(x)+\\delta_u(x)+\\lambda(x) fY(y)={FX(l)y=lfX(y)lminiyil>\\min_i y_i u= low) * (x <= upp) + PL * (x == low) + PU * (x == upp) } ptiexp <- function(q, rate, low, upp) pexp(q, rate) * (q >= low) * (q <= upp) + 1 * (q > upp) n <- 100; x <- pmax(pmin(rexp(n), 3), .5) # the loglikelihood has a discontinous point at the solution par(mar=c(4,4,2,1), mfrow=1:2) llcurve(x, \"tiexp\", plot.arg=\"low\", fix.arg = list(rate=2, upp=5), min.arg=0, max.arg=.5, lseq=200) llcurve(x, \"tiexp\", plot.arg=\"upp\", fix.arg = list(rate=2, low=0), min.arg=3, max.arg=4, lseq=200) (f1 <- fitdist(x, \"tiexp\", method=\"mle\", start=list(rate=3, low=0, upp=20))) ## Fitting of the distribution ' tiexp ' by maximum likelihood ## Parameters: ## estimate ## rate 0.333 ## low 2.915 ## upp 20.899 (f2 <- fitdist(x, \"tiexp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=min(x), upp=max(x)))) ## Fitting of the distribution ' tiexp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.947 0.982 ## Fixed parameters: ## value ## low 0.5 ## upp 3.0 gofstat(list(f1, f2)) ## Goodness-of-fit statistics ## 1-mle-tiexp 2-mle-tiexp ## Kolmogorov-Smirnov statistic 0.92 0.377 ## Cramer-von Mises statistic 26.82 1.882 ## Anderson-Darling statistic Inf 10.193 ## ## Goodness-of-fit criteria ## 1-mle-tiexp 2-mle-tiexp ## Akaike's Information Criterion 39.6 162 ## Bayesian Information Criterion 47.4 165 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points = FALSE, addlegend=FALSE, xlim=c(0, 3.5)) curve(ptiexp(x, 1, .5, 3), add=TRUE, col=\"blue\", lty=3) legend(\"bottomright\", lty=1:3, col=c(\"red\", \"green\", \"blue\", \"black\"), legend=c(\"full MLE\", \"MLE fixed arg\", \"true CDF\", \"emp. CDF\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-uniform-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.10. Can I fit a uniform distribution?","title":"Frequently Asked Questions","text":"uniform distribution 𝒰(,b)\\mathcal U(,b) support parameters since density scale shape parameter fU(u)=1b−a1[,b](u)f_U(u) = \\frac{1}{b-}1_{[,b]}(u). distribution, maximize log-likelihood likelihood. Let (xi)(x_i)_i ..d. observations 𝒰(,b)\\mathcal U(,b) distribution. likelihood L(,b)=∏=1n1b−a1[,b](xi)=1a≤xi≤b,=1,…,n1b−=1a≤minixi1maxixi≤b1b−L(,b) = \\prod_{=1}^n \\frac{1}{b-} 1_{[,b]}(x_i) = 1_{\\leq x_i \\leq b, =1,\\dots,n} \\frac{1}{b-}^n = 1_{\\leq \\min_i x_i} 1_{\\max_i x_i \\leq b} \\frac{1}{b-}^n Hence ↦L(,b)\\mapsto L(,b) fixed b∈]maxixi,+∞[b\\]\\max_i x_i, +\\infty[ increasing ]−∞,minixi]]-\\infty, \\min_i x_i], similarly b↦L(,b)b\\mapsto L(,b) decreasing fixed aa. leads minixi\\min_i x_i maxixi\\max_i x_i MLE uniform distribution. notice likelihood function LL defined ℝ2\\mathbb R^2 yet cancels outside S=]−∞,minixi]×]maxixi,+∞[S=]-\\infty, \\min_i x_i]\\times]\\max_i x_i, +\\infty[. Hence, log-likelihood undefined outside SS, issue maximizing log-likelihood. reasons, fitdist(data, dist=\"unif\", method=\"mle\") uses explicit form MLE distribution. example Maximizing log-likelihood harder can done defining new density function. Appropriate starting values parameters bound must supplied. Using closed-form expression (fitdist()) maximizing log-likelihood (unif2) lead similar results.","code":"trueval <- c(\"min\"=3, \"max\"=5) x <- runif(n=500, trueval[1], trueval[2]) f1 <- fitdist(x, \"unif\") delta <- .01 par(mfrow=c(1,1), mar=c(4,4,2,1)) llsurface(x, \"unif\", plot.arg = c(\"min\", \"max\"), min.arg=c(min(x)-2*delta, max(x)-delta), max.arg=c(min(x)+delta, max(x)+2*delta), main=\"likelihood surface for uniform\", loglik=FALSE) abline(v=min(x), h=max(x), col=\"grey\", lty=2) points(f1$estimate[1], f1$estimate[2], pch=\"x\", col=\"red\") points(trueval[1], trueval[2], pch=\"+\", col=\"blue\") legend(\"bottomright\", pch=c(\"+\",\"x\"), col=c(\"blue\",\"red\"), c(\"true\", \"fitted\")) delta <- .2 llsurface(x, \"unif\", plot.arg = c(\"min\", \"max\"), min.arg=c(3-2*delta, 5-delta), max.arg=c(3+delta, 5+2*delta), main=\"log-likelihood surface for uniform\") abline(v=min(x), h=max(x), col=\"grey\", lty=2) points(f1$estimate[1], f1$estimate[2], pch=\"x\", col=\"red\") points(trueval[1], trueval[2], pch=\"+\", col=\"blue\") legend(\"bottomright\", pch=c(\"+\",\"x\"), col=c(\"blue\",\"red\"), c(\"true\", \"fitted\")) dunif2 <- function(x, min, max) dunif(x, min, max) punif2 <- function(q, min, max) punif(q, min, max) f2 <- fitdist(x, \"unif2\", start=list(min=0, max=10), lower=c(-Inf, max(x)), upper=c(min(x), Inf)) print(c(logLik(f1), logLik(f2)), digits=7) ## [1] -346.0539 -346.1519 print(cbind(coef(f1), coef(f2)), digits=7) ## [,1] [,2] ## min 3.000684 3.000292 ## max 4.998606 4.998606"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-beta-distribution-with-the-same-shape-parameter","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.11. Can I fit a beta distribution with the same shape parameter?","title":"Frequently Asked Questions","text":"Yes, can wrap density function beta distribution one shape parameter. example concave density. Another example U-shaped density.","code":"x <- rbeta(1000, 3, 3) dbeta2 <- function(x, shape, ...) dbeta(x, shape, shape, ...) pbeta2 <- function(q, shape, ...) pbeta(q, shape, shape, ...) fitdist(x, \"beta2\", start=list(shape=1/2)) ## Fitting of the distribution ' beta2 ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 3.24 4.26 x <- rbeta(1000, .3, .3) fitdist(x, \"beta2\", start=list(shape=1/2), optim.method=\"L-BFGS-B\", lower=1e-2) ## Fitting of the distribution ' beta2 ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 0.295 0.312"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-estimate-support-parameter-the-case-of-the-four-parameter-beta","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.12. How to estimate support parameter? the case of the four-parameter beta","title":"Frequently Asked Questions","text":"Let us consider four-parameter beta distribution, also known PERT distribution, defined following density x∈[,c]x\\[,c]fX(x)=(x−)α−1(c−x)β−1/CNf_X(x) = (x-)^{\\alpha-1} (c-x)^{\\beta-1}/C_N CNC_N normalizing constant α=1+d(b−)/(c−)\\alpha=1+d(b-)/(c-), β=1+d(c−b)/(c−)\\beta=1+d(c-b)/(c-). ,ca,c support parameters, b∈],c[b\\],c[ mode dd shape parameter. uniform distribution, one can show MLE aa cc respectively sample minimum maximum. code illustrates strategy using partial closed formula fix.arg full numerical search MLE. NB: small sample size, latter generally better goodness--fit statistics; small positive number added subtracted fixing support parameters aa cc sample minimum maximum.","code":"require(\"mc2d\") x2 <- rpert(n=2e2, min=0, mode=1, max=2, shape=3/4) eps <- sqrt(.Machine$double.eps) f1 <- fitdist(x2, \"pert\", start=list(min=-1, mode=0, max=10, shape=1), lower=c(-Inf, -Inf, -Inf, 0), upper=c(Inf, Inf, Inf, Inf)) ## Warning in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, : Some ## parameter names have no starting/fixed value but have a default value: mean. ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced f2 <- fitdist(x2, \"pert\", start=list(mode=1, shape=1), fix.arg=list(min=min(x2)-eps, max=max(x2)+eps), lower=c(min(x2), 0), upper=c(max(x2), Inf)) ## Warning in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, : Some ## parameter names have no starting/fixed value but have a default value: mean. print(cbind(coef(f1), c(f2$fix.arg[\"min\"], coef(f2)[\"mode\"], f2$fix.arg[\"max\"], coef(f2)[\"shape\"])), digits=7) ## [,1] [,2] ## min 1.36707 0.03395487 ## mode 1.367072 1.955289 ## max 1.644537 1.956234 ## shape 0.0005813856 0.008506046 gofstat(list(f1,f2)) ## Goodness-of-fit statistics ## 1-mle-pert 2-mle-pert ## Kolmogorov-Smirnov statistic 0.69 0.0584 ## Cramer-von Mises statistic 28.59 0.1836 ## Anderson-Darling statistic Inf 1.2787 ## ## Goodness-of-fit criteria ## 1-mle-pert 2-mle-pert ## Akaike's Information Criterion -99.7 265 ## Bayesian Information Criterion -86.5 272 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1,f2))"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"where-can-we-find-the-results-of-goodness-of-fit-tests","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.1. Where can we find the results of goodness-of-fit tests ?","title":"Frequently Asked Questions","text":"Results goodness--fit tests printed given object returned gofstat() can access described example . Nevertheless, p-values given every test. Anderson-Darling (ad), Cramer von Mises (cvm) Kolomogorov (ks), decision (rejection H0 ) given, available (see FAQ 2.3 details).","code":"set.seed(1234) x <- rgamma(n = 100, shape = 2, scale = 1) # fit of the good distribution fgamma <- fitdist(x, \"gamma\") # fit of a bad distribution fexp <- fitdist(x, \"exp\") g <- gofstat(list(fgamma, fexp), fitnames = c(\"gamma\", \"exp\")) par(mfrow=c(1,1), mar=c(4,4,2,1)) denscomp(list(fgamma, fexp), legendtext = c(\"gamma\", \"exp\")) # results of the tests ## chi square test (with corresponding table with theoretical and observed counts) g$chisqpvalue ## gamma exp ## 1.89e-01 7.73e-05 g$chisqtable ## obscounts theo gamma theo exp ## <= 0.5483 9 10.06 23.66 ## <= 0.8122 9 8.82 9.30 ## <= 0.9592 9 5.27 4.68 ## <= 1.368 9 14.64 11.37 ## <= 1.523 9 5.24 3.74 ## <= 1.701 9 5.73 3.97 ## <= 1.94 9 7.09 4.82 ## <= 2.381 9 11.08 7.50 ## <= 2.842 9 9.00 6.29 ## <= 3.801 9 11.93 9.28 ## > 3.801 10 11.15 15.40 ## Anderson-Darling test g$adtest ## gamma exp ## \"not rejected\" \"rejected\" ## Cramer von Mises test g$cvmtest ## gamma exp ## \"not rejected\" \"rejected\" ## Kolmogorov-Smirnov test g$kstest ## gamma exp ## \"not rejected\" \"rejected\""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-reasonable-to-use-goodness-of-fit-tests-to-validate-the-fit-of-a-distribution","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","title":"Frequently Asked Questions","text":"first versions fitdistrplus, available, results GOF tests (AD, KS, CvM) automatically printed. decided suppress automatic printing realized users difficulties interpret results tests sometimes misused . Goodness--fit tests often appear objective tools decide wether fitted distribution well describes data set. ! reasonable reject distribution just goodness--fit test rejects (see FAQ 2.2.1). reasonable validate distribution goodness--fit tests reject (see FAQ 2.2.2). fitted distribution evaluated using graphical methods (goodness--fit graphs automatically provided package plotting result fit (output fitdist() fitdistcens() complementary graphs help compare different fits - see ?graphcomp). really think appropriate way evaluate adequacy fit ones recommend . can find type recommendations reference books : Probabilistic techniques exposure assessment - handbook dealing variability uncertainty models inputs .C. Cullen H.C. Frey. Application uncertainty analysis ecological risks pesticides W.J. Warren-Hicks . Hart. Statistical inference G. Casella R.L. Berger Loss models: data decision S.. Klugman H.H. Panjer G.E. Willmot Moreover, selection distribution also driven knowledge underlying processes available. example variable negative, one cautious fitting normal distribution, potentially gives negative values, even observed data variable seem well fitted normal distribution.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"should-i-reject-a-distribution-because-a-goodness-of-fit-test-rejects-it","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph > 2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","what":"2.2.1. Should I reject a distribution because a goodness-of-fit test rejects it ?","title":"Frequently Asked Questions","text":"reasonable reject distribution just goodness--fit test rejects , especially case big samples. real life, soon sufficient amount data, reject fitted distribution. know model perfectly describe real data, generally true question find better distribution among pool simple parametric distributions describe data, compare different models (see FAQ 2.4 2.5 corresponding questions). illustre point let us comment example presented . drew two samples Poisson distribution mean parameter equal 100. many applications, value parameter, Poisson distribution considered well approximated normal distribution. Testing fit (using Kolmogorov-Smirnov test ) normal distribution sample 100 observations reject normal fit, testing sample 10000 observations reject , samples come distribution.","code":"set.seed(1234) x1 <- rpois(n = 100, lambda = 100) f1 <- fitdist(x1, \"norm\") g1 <- gofstat(f1) g1$kstest ## 1-mle-norm ## \"not rejected\" x2 <- rpois(n = 10000, lambda = 100) f2 <- fitdist(x2, \"norm\") g2 <- gofstat(f2) g2$kstest ## 1-mle-norm ## \"rejected\" par(mfrow=c(1,2), mar=c(4,4,2,1)) denscomp(f1, demp = TRUE, addlegend = FALSE, main = \"small sample\") denscomp(f2, demp = TRUE, addlegend = FALSE, main = \"big sample\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"should-i-accept-a-distribution-because-goodness-of-fit-tests-do-not-reject-it","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph > 2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","what":"2.2.2. Should I accept a distribution because goodness-of-fit tests do not reject it ?","title":"Frequently Asked Questions","text":", reasonable validate distribution goodness--fit tests reject . Like hypothesis tests, goodness--fit tests lack statistical power sample size high. different goodness--fit tests equally sensitive different types deviation empirical fitted distributions. example Kolmogorov-Smirnov test sensitive distributions differ global fashion near centre distribution. Anderson-Darling test sensitive distributions differ tails, Cramer von Mises sensitive small repetitive differences empirical theoretical distribution functions. sensitivity chi square test depend definition classes, even propose default definition classes user provide classes, choice obvious impact results test. test appropriate data discrete, even modelled continuous distribution, following example. Two samples respective sizes 500 50 drawn Poisson distribution mean parameter equal 1 (sufficiently high value consider Poisson distribution approximated normal one). Using Kolmogorov-Smirnov test, small sample normal fit rejected bigger sample. rejected smaller sample even fit rejected simple visual confrontation distributions. particular case, chi square test classes defined default rejected te normal fit samples.","code":"set.seed(1234) x3 <- rpois(n = 500, lambda = 1) f3 <- fitdist(x3, \"norm\") g3 <- gofstat(f3) g3$kstest ## 1-mle-norm ## \"rejected\" x4 <- rpois(n = 50, lambda = 1) f4 <- fitdist(x4, \"norm\") g4 <- gofstat(f4) g4$kstest ## 1-mle-norm ## \"not rejected\" par(mfrow=c(1,2), mar=c(4,4,2,1)) denscomp(f3, addlegend = FALSE, main = \"big sample\") denscomp(f4, addlegend = FALSE, main = \"small sample\") g3$chisqtable ## obscounts theocounts ## <= 0 180.0 80.3 ## <= 1 187.0 163.5 ## <= 2 87.0 168.1 ## <= 3 32.0 73.4 ## > 3 14.0 14.7 g3$chisqpvalue ## [1] 7.11e-42 g4$chisqtable ## obscounts theocounts ## <= 0 14.00 5.46 ## <= 1 15.00 14.23 ## <= 2 15.00 18.09 ## > 2 6.00 12.22 g4$chisqpvalue ## [1] 3.57e-05"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-all-goodness-of-fit-tests-are-not-available-for-every-distribution","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.3. Why all goodness-of-fit tests are not available for every distribution ?","title":"Frequently Asked Questions","text":"Chi-squared test available distribution one must conscious result depends definition cells observed data grouped, correct definition possible small sample. Concerning Kolmogorov-Smirnov test, proposed continuous distribution, critical value corresponding comparison empirical distribution fully specified distribution. distribution fully known fitted distribution, result test subject caution, general asymptotic theory Kolmogorov-Smirnov statistics case fitted distribution. Nevertheless, one can use Monte Carlo methods conduct Kolmgorov-Smirnov goodness--fit tests cases sample used estimate model parameters. method implemented R package KScorrect variety continuous distributions. asymptotic theory proposed quadratic statistics distributions (Anderson-Darling, Cramer von Mises). reference book used subject (Tests based edf statistics Stephens MA Goodness--fit techniques D’Agostino RB Stephens MA) proposes critical values statistics classical distributions (exponential, gamma, Weibull, logistic, Cauchy, normal lognormal). asymptotic theory statistics also depends way parameters estimated. estimated maximum likelihood Cauchy, normal lognormal distributions results reported Stephens, propose results Anderson-Darling Cramer von Mises using results exponential, gamma, Weibull, logistic distributions. user can refer cited books use proposed formula estimate parameters Cauchy, normal lognormal distributions apply tests using critical values given book. R packages goftest ADGofTest also explored users like apply Anderson-Darling Cramer von Mises tests distributions. time sure case parameters unknown (estimated maximum likelihood) tackled two packages. Concerning development package, rather develoing goodness--fit tests made choice develop graphical tools help appreciate quality fit compare fits different distributions data set (see FAQ 2.2 argumentation).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-use-goodness-of-fit-statistics-to-compare-the-fit-of-different-distributions-on-a-same-data-set","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.4. How can we use goodness-of-fit statistics to compare the fit of different distributions on a same data set ?","title":"Frequently Asked Questions","text":"Goodness--fit statistics based empirical distribution function (Kolmogorov-Smirnov, Anderson-Darling Cramer von Mises) may used measure distance fitted distribution empirical distribution. one wants compare fit various distributions data set, smaller statistics better. Kolmogorov-Smirnov statistics sensitive distributions differ global fashion near centre distribution Anderson-Darling statistics sensitive distributions differ tails, Cramer von Mises statistics sensitive small repetitive differences empirical theoretical distribution functions. mentioned main vignette package, use Anderson-Darling compare fit different distributions subject caution due weighting quadratic distance fitted empirical distribution functions depends parametric distribution. Moreover, statistics based empirical distribution function penalize distributions greater number parameters generally flexible, induce -fitting. Goodness-fo-fit statistics based information criteria (AIC, BIC) correspond deviance penalized complexity model (number parameters distribution), smaller better. generic statistics, adapted focus part fitted distribution, take account complexity distribution thus help prevent overfitting.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-use-a-test-to-compare-the-fit-of-two-distributions-on-a-same-data-set","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.5. Can we use a test to compare the fit of two distributions on a same data set ?","title":"Frequently Asked Questions","text":"package implement test two nested distributions (one special case one, e.g. exponential gamma distributions) likelihood ratio test can easily implemented using loglikelihood provided fitdist fitdistcens. Denoting LL maximum likelihood obtained complete distribution L0L_0 one obtained simplified distribution, sample size increases, −2ln(L0L)=2ln(L)−2ln(L0)- 2 ln(\\frac{L_0}{L}) = 2 ln(L) - 2 ln(L_0) tends Chi squared distribution degrees freedom equal difference numbers parameters characterizing two nested distributions. find example test. test can also used fits censored data.","code":"set.seed(1234) g <- rgamma(100, shape = 2, rate = 1) (f <- fitdist(g, \"gamma\")) ## Fitting of the distribution ' gamma ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 2.025 2.66 ## rate 0.997 1.49 (f0 <- fitdist(g, \"exp\")) ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.492 0.492 L <- logLik(f) k <- length(f$estimate) # number of parameters of the complete distribution L0 <- logLik(f0) k0 <- length(f0$estimate) # number of parameters of the simplified distribution (stat <- 2*L - 2*L0) ## [1] 23.9 (critical_value <- qchisq(0.95, df = k - k0)) ## [1] 3.84 (rejected <- stat > critical_value) ## [1] TRUE"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-get-goodness-of-fit-statistics-for-a-fit-on-censored-data","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.6. Can we get goodness-of-fit statistics for a fit on censored data ?","title":"Frequently Asked Questions","text":"Function gofstat yet proposed package fits censored data develop one among one objectives future. Published works goodness--fit statistics based empirical distribution function censored data generally focused data containing one type censoring (e.g. right censored data survival data). Build statistics general case, data containing time (right, left interval censoring), remains tricky. Nevertheless, possible type censored data, use information criteria (AIC BIC given summary object class fitdistcens) compare fits various distributions data set.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-cullen-frey-graph-may-be-misleading","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.7. Why Cullen-Frey graph may be misleading?","title":"Frequently Asked Questions","text":"considering distribution large theoretical moments infinite moments, using Cullen-Frey may appropriate. typical log-normal distribution ℒ𝒩(μ,σ2)\\mathcal L\\mathcal N(\\mu,\\sigma^2). Indeed distribution, skewness kurtosis functions exponential σ2\\sigma^2. large values, even small σ\\sigma. sk(X)=(eσ2+2)eσ2−1,kr(X)=e4σ2+2e3σ2+3e2σ2−3. sk(X) = (e^{\\sigma^2}+2)\\sqrt{e^{\\sigma^2}-1}, kr(X) = e^{4\\sigma^2} + 2e^{3\\sigma^2} + 3e^{2\\sigma^2}-3. convergence theoretical standardized moments (skewness kurtosis) slow future, plan use trimmed linear moments deal issue. moments always exist even distribution infinite mean, e.g. Cauchy distribution.","code":"n <- 1e3 x <- rlnorm(n) descdist(x) ## summary statistics ## ------ ## min: 0.0436 max: 20.3 ## median: 1.02 ## mean: 1.61 ## estimated sd: 1.89 ## estimated skewness: 3.49 ## estimated kurtosis: 21.9"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-choose-optimization-method","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.1. How to choose optimization method?","title":"Frequently Asked Questions","text":"want perform optimization without bounds, optim() used. can try derivative-free method Nelder-Mead Hessian-free method BFGS. want perform optimization bounds, two methods available without providing gradient objective function: Nelder-Mead via constrOptim() bounded BFGS via optim(). cases, see help mledist() vignette optimization algorithms.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"the-optimization-algorithm-stops-with-error-code-100--what-shall-i-do","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.2. The optimization algorithm stops with error code 100. What shall I do?","title":"Frequently Asked Questions","text":"First, add traces adding control=list(trace=1, REPORT=1). Second, try set bounds parameters. Third, find better starting values (see FAQ 1.3).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-distribution-with-a-log-argument-may-converge-better","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.3 Why distribution with a log argument may converge better?","title":"Frequently Asked Questions","text":"Say, study shifted lognormal distribution defined following density f(x)=1xσ2πexp(−(ln(x+δ)−μ)22σ2) f(x) = \\frac{1}{x \\sigma \\sqrt{2 \\pi}} \\exp\\left(- \\frac{(\\ln (x+\\delta)- \\mu)^2}{2\\sigma^2}\\right) x>−δx>-\\delta μ\\mu location parameter, σ\\sigma scale parameter δ\\delta boundary parameter. Let us fit distribution dataset y MLE. define two functions densities without log argument. now optimize minus log-likelihood. don’t use log argument, algorithms stalls. Indeed algorithm stops following value, log-likelihood infinite. something wrong computation. R-base implementation using log argument seems reliable. happens C-base implementation dlnorm takes care log value. file ../src/nmath/dlnorm.c R sources, find C code dlnorm last four lines logical condtion give_log?, see log argument handled: log=TRUE, use −(log(2π)+y2/2+log(xσ))-(\\log(\\sqrt{2\\pi}) + y^2/2+\\log(x\\sigma)) log=FALSE, use 2π*exp(y2/2)/(xσ))\\sqrt{2\\pi} *\\exp( y^2/2)/(x\\sigma)) (logarithm outside dlnorm) Note constant log(2π)\\log(\\sqrt{2\\pi}) pre-computed C macro M_LN_SQRT_2PI. order sort problem, use constrOptim wrapping optim take account linear constraints. allows also use optimization methods L-BFGS-B (low-memory BFGS bounded) used optim. Another possible perform computations higher precision arithmetics implemented package Rmpfr using MPFR library.","code":"dshiftlnorm <- function(x, mean, sigma, shift, log = FALSE) dlnorm(x+shift, mean, sigma, log=log) pshiftlnorm <- function(q, mean, sigma, shift, log.p = FALSE) plnorm(q+shift, mean, sigma, log.p=log.p) qshiftlnorm <- function(p, mean, sigma, shift, log.p = FALSE) qlnorm(p, mean, sigma, log.p=log.p)-shift dshiftlnorm_no <- function(x, mean, sigma, shift) dshiftlnorm(x, mean, sigma, shift) pshiftlnorm_no <- function(q, mean, sigma, shift) pshiftlnorm(q, mean, sigma, shift) data(dataFAQlog1) y <- dataFAQlog1 D <- 1-min(y) f0 <- fitdist(y+D, \"lnorm\") start <- list(mean=as.numeric(f0$estimate[\"meanlog\"]), sigma=as.numeric(f0$estimate[\"sdlog\"]), shift=D) # works with BFGS, but not Nelder-Mead f <- fitdist(y, \"shiftlnorm\", start=start, optim.method=\"BFGS\") summary(f) ## Fitting of the distribution ' shiftlnorm ' by maximum likelihood ## Parameters : ## estimate Std. Error ## mean -1.3848 1.355 ## sigma 0.0709 0.108 ## shift 0.2487 0.338 ## Loglikelihood: 8299 AIC: -16591 BIC: -16573 ## Correlation matrix: ## mean sigma shift ## mean 1.000 -0.885 0.999 ## sigma -0.885 1.000 -0.886 ## shift 0.999 -0.886 1.000 f2 <- try(fitdist(y, \"shiftlnorm_no\", start=start, optim.method=\"BFGS\")) print(attr(f2, \"condition\")) ## NULL sum(log(dshiftlnorm_no(y, 0.16383978, 0.01679231, 1.17586600 ))) ## [1] -Inf log(prod(dshiftlnorm_no(y, 0.16383978, 0.01679231, 1.17586600 ))) ## [1] -Inf sum(dshiftlnorm(y, 0.16383978, 0.01679231, 1.17586600, TRUE )) ## [1] 7761 double dlnorm(double x, double meanlog, double sdlog, int give_log) { double y; #ifdef IEEE_754 if (ISNAN(x) || ISNAN(meanlog) || ISNAN(sdlog)) return x + meanlog + sdlog; #endif if(sdlog <= 0) { if(sdlog < 0) ML_ERR_return_NAN; // sdlog == 0 : return (log(x) == meanlog) ? ML_POSINF : R_D__0; } if(x <= 0) return R_D__0; y = (log(x) - meanlog) / sdlog; return (give_log ? -(M_LN_SQRT_2PI + 0.5 * y * y + log(x * sdlog)) : M_1_SQRT_2PI * exp(-0.5 * y * y) / (x * sdlog)); /* M_1_SQRT_2PI = 1 / sqrt(2 * pi) */ } -(M_LN_SQRT_2PI + 0.5 * y * y + log(x * sdlog)) M_1_SQRT_2PI * exp(-0.5 * y * y) / (x * sdlog)) f2 <- fitdist(y, \"shiftlnorm\", start=start, lower=c(-Inf, 0, -min(y)), optim.method=\"Nelder-Mead\") ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced summary(f2) ## Fitting of the distribution ' shiftlnorm ' by maximum likelihood ## Parameters : ## estimate Std. Error ## mean -1.3872 NaN ## sigma 0.0711 NaN ## shift 0.2481 NaN ## Loglikelihood: 8299 AIC: -16591 BIC: -16573 ## Correlation matrix: ## mean sigma shift ## mean 1 NaN NaN ## sigma NaN 1 NaN ## shift NaN NaN 1 print(cbind(BFGS=f$estimate, NelderMead=f2$estimate)) ## BFGS NelderMead ## mean -1.3848 -1.3872 ## sigma 0.0709 0.0711 ## shift 0.2487 0.2481"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"what-to-do-when-there-is-a-scaling-issue","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.4. What to do when there is a scaling issue?","title":"Frequently Asked Questions","text":"Let us consider dataset particular small values. way sort multiply dataset large value. Let us consider dataset particular large values. way sort multiply dataset small value.","code":"data(dataFAQscale1) head(dataFAQscale1) ## [1] -0.007077 -0.000947 -0.001898 -0.000475 -0.001902 -0.000476 summary(dataFAQscale1) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -0.00708 -0.00143 -0.00047 -0.00031 0.00096 0.00428 for(i in 6:0) cat(10^i, try(mledist(dataFAQscale1*10^i, \"cauchy\")$estimate), \"\\n\") ## 1e+06 -290 1194 ## 1e+05 -29 119 ## 10000 -2.9 11.9 ## 1000 -0.29 1.19 ## 100 -0.029 0.119 ## 10 -0.0029 0.0119 ## 1 -0.00029 0.00119 data(dataFAQscale2) head(dataFAQscale2) ## [1] 1.40e+09 1.41e+09 1.43e+09 1.44e+09 1.49e+09 1.57e+09 summary(dataFAQscale2) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.40e+09 1.58e+09 2.24e+09 2.55e+09 3.39e+09 4.49e+09 for(i in 0:5) cat(10^(-2*i), try(mledist(dataFAQscale2*10^(-2*i), \"cauchy\")$estimate), \"\\n\") ## 1 2.03e+09 6.59e+08 ## 0.01 20283641 6594932 ## 1e-04 202836 65949 ## 1e-06 2028 659 ## 1e-08 20.3 6.59 ## 1e-10 0.203 0.0659"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-scale-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.1. Setting bounds for scale parameters","title":"Frequently Asked Questions","text":"Consider normal distribution 𝒩(μ,σ2)\\mathcal{N}(\\mu, \\sigma^2) defined density f(x)=12πσ2exp(−(x−μ)22σ2),x∈ℝ, f(x) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left(-\\frac{(x-\\mu)^2}{2\\sigma^2}\\right), x\\\\mathbb{R}, μ\\mu location parameter μ∈ℝ\\mu\\\\mathbb{R}, σ2\\sigma^2 scale parameter σ2>0\\sigma^2>0. Therefore optimizing log-likelihood squared differences GoF statistics. Setting lower bound scale parameter easy fitdist: just use lower argument.","code":"set.seed(1234) x <- rnorm(1000, 1, 2) fitdist(x, \"norm\", lower=c(-Inf, 0)) ## Fitting of the distribution ' norm ' by maximum likelihood ## Parameters: ## estimate Std. Error ## mean 0.947 1.99 ## sd 1.994 1.41"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-shape-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.2. Setting bounds for shape parameters","title":"Frequently Asked Questions","text":"Consider Burr distribution ℬ(μ,σ2)\\mathcal B(\\mu, \\sigma^2) defined density f(x)=ab(x/s)bx[1+(x/s)b]+1,x∈ℝ, f(x) = \\frac{b (x/s)^b}{x [1 + (x/s)^b]^{+ 1}}, x\\\\mathbb{R}, ,ba,b shape parameters ,b>0a,b>0, ss scale parameter s>0s>0.","code":"x <- rburr(1000, 1, 2, 3) fitdist(x, \"burr\", lower=c(0, 0, 0), start=list(shape1 = 1, shape2 = 1, rate = 1)) ## Fitting of the distribution ' burr ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 0.968 1.06 ## shape2 2.051 1.16 ## rate 3.181 1.63"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-probability-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.3. Setting bounds for probability parameters","title":"Frequently Asked Questions","text":"Consider geometric distribution 𝒢(p)\\mathcal G(p) defined mass probability function f(x)=p(1−p)x,x∈ℕ, f(x) = p(1-p)^x, x\\\\mathbb{N}, pp probability parameter p∈[0,1]p\\[0,1].","code":"x <- rgeom(1000, 1/4) fitdist(x, \"geom\", lower=0, upper=1) ## Fitting of the distribution ' geom ' by maximum likelihood ## Parameters: ## estimate Std. Error ## prob 0.242 0.211"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-boundary-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.4. Setting bounds for boundary parameters","title":"Frequently Asked Questions","text":"Consider shifted exponential distribution ℰ(μ,λ)\\mathcal E(\\mu,\\lambda) defined mass probability function f(x)=λexp(−λ(x−μ)),x>μ, f(x) = \\lambda \\exp(-\\lambda(x-\\mu)), x>\\mu, λ\\lambda scale parameter λ>0\\lambda>0, μ\\mu boundary (shift) parameter μ∈ℝ\\mu\\\\mathbb{R}. optimizing log-likelihood, boundary constraint ∀=1,…,n,xi>μ⇒mini=1,…,nxi>μ⇔μ>−mini=1,…,nxi. \\forall =1,\\dots,n, x_i>\\mu \\Rightarrow \\min_{=1,\\dots,n} x_i > \\mu \\Leftrightarrow \\mu > -\\min_{=1,\\dots,n} x_i. Note optimizing squared differences GoF statistics, constraint may necessary. Let us R.","code":"dsexp <- function(x, rate, shift) dexp(x-shift, rate=rate) psexp <- function(x, rate, shift) pexp(x-shift, rate=rate) rsexp <- function(n, rate, shift) rexp(n, rate=rate)+shift x <- rsexp(1000, 1/4, 1) fitdist(x, \"sexp\", start=list(rate=1, shift=0), upper= c(Inf, min(x))) ## Fitting of the distribution ' sexp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.248 0 ## shift 1.005 NaN"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-linear-inequality-bounds","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.5. Setting linear inequality bounds","title":"Frequently Asked Questions","text":"distributions, bounds parameters independent. instance, normal inverse Gaussian distribution (μ,δ,α,β\\mu, \\delta, \\alpha, \\beta parametrization) following parameter constraints, can reformulated linear inequality: {α>0δ>0α>|β|⇔(01000010001−10011)⏟ui(μδαβ)≥(0000)⏟ci. \\left\\{ \\begin{array}{l}\\alpha > 0\\\\ \\delta >0\\\\ \\alpha > |\\beta|\\end{array} \\right. \\Leftrightarrow \\underbrace{ \\left( \\begin{matrix} 0 & 1 & 0 & 0 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 0 & 1 & -1 \\\\ 0 & 0 & 1 & 1 \\\\ \\end{matrix} \\right) }_{ui} \\left( \\begin{matrix} \\mu\\\\ \\delta\\\\ \\alpha \\\\ \\beta \\\\ \\end{matrix} \\right) \\geq \\underbrace{ \\left( \\begin{matrix} 0\\\\ 0\\\\ 0 \\\\ 0 \\\\ \\end{matrix} \\right)}_{ci}. constraints can carried via constrOptim() arguments ci ui. example","code":"require(\"GeneralizedHyperbolic\") myoptim <- function(fn, par, ui, ci, ...) { res <- constrOptim(f=fn, theta=par, method=\"Nelder-Mead\", ui=ui, ci=ci, ...) c(res, convergence=res$convergence, value=res$objective, par=res$minimum, hessian=res$hessian) } x <- rnig(1000, 3, 1/2, 1/2, 1/4) ui <- rbind(c(0,1,0,0), c(0,0,1,0), c(0,0,1,-1), c(0,0,1,1)) ci <- c(0,0,0,0) fitdist(x, \"nig\", custom.optim=myoptim, ui=ui, ci=ci, start=list(mu = 0, delta = 1, alpha = 1, beta = 0)) ## Warning in fitdist(x, \"nig\", custom.optim = myoptim, ui = ui, ci = ci, start = ## list(mu = 0, : The dnig function should return a vector of with NaN values when ## input has inconsistent parameters and not raise an error ## Warning in fitdist(x, \"nig\", custom.optim = myoptim, ui = ui, ci = ci, start = ## list(mu = 0, : The pnig function should return a vector of with NaN values when ## input has inconsistent values and not raise an error ## Fitting of the distribution ' nig ' by maximum likelihood ## Parameters: ## estimate ## mu 2.985 ## delta 0.457 ## alpha 0.466 ## beta 0.237"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-works-quantile-matching-estimation-for-discrete-distributions","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.6. How works quantile matching estimation for discrete distributions?","title":"Frequently Asked Questions","text":"Let us consider geometric distribution values {0,1,2,3,…}\\{0,1,2,3,\\dots\\}. probability mass function, cumulative distribution function quantile function P(X=x)=p(1−p)⌊x⌋,FX(x)=1−(1−p)⌊x⌋,FX−1(q)=⌊log(1−q)log(1−p)⌋. P(X=x)= p (1-p)^{\\lfloor x\\rfloor}, F_X(x) = 1- (1-p)^{\\lfloor x\\rfloor}, F_X^{-1}(q) = \\left\\lfloor\\frac{\\log(1-q)}{\\log(1-p)}\\right\\rfloor. Due integer part (floor function), distribution function quantile function step functions. Now study QME geometric distribution. Since one parameter, choose one probabiliy, p=1/2p=1/2. theoretical median following integer FX−1(1/2)=⌊log(1/2)log(1−p)⌋. F_X^{-1}(1/2) = \\left\\lfloor\\frac{\\log(1/2)}{\\log(1-p)}\\right\\rfloor. Note theoretical median discrete distribution integer. Empirically, median may integer. Indeed even length dataset, empirical median qn,1/2=xn/2⋆+xn/2+1⋆2, q_{n,1/2} = \\frac{x_{n/2}^\\star + x_{n/2+1}^\\star}{2}, x1⋆<…= low) * (x <= upp) } ptgamma <- function(q, shape, rate, low, upp) { PU <- pgamma(upp, shape = shape, rate = rate) PL <- pgamma(low, shape = shape, rate = rate) (pgamma(q, shape, rate) - PL) / (PU - PL) * (q >= low) * (q <= upp) + 1 * (q > upp) } rtgamma <- function(n, shape, rate, low=0, upp=Inf, maxit=10) { stopifnot(n > 0) if(low > upp) return(rep(NaN, n)) PU <- pgamma(upp, shape = shape, rate = rate) PL <- pgamma(low, shape = shape, rate = rate) #simulate directly expected number of random variate n2 <- n/(PU-PL) x <- rgamma(n, shape=shape, rate=rate) x <- x[x >= low & x <= upp] i <- 0 while(length(x) < n && i < maxit) { n2 <- (n-length(x))/(PU-PL) y <- rgamma(n2, shape=shape, rate=rate) x <- c(x, y[y >= low & y <= upp]) i <- i+1 } x[1:n] } n <- 100 ; shape <- 11 ; rate <- 3 ; x0 <- 5 x <- rtgamma(n, shape = shape, rate = rate, low=x0) fit.NM.2P <- fitdist( data = x, distr = \"tgamma\", method = \"mle\", start = list(shape = 10, rate = 10), fix.arg = list(upp = Inf, low=x0), lower = c(0, 0), upper=c(Inf, Inf)) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced fit.NM.3P <- fitdist( data = x, distr = \"tgamma\", method = \"mle\", start = list(shape = 10, rate = 10, low=1), fix.arg = list(upp = Inf), lower = c(0, 0, -Inf), upper=c(Inf, Inf, min(x))) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in cov2cor(varcovar): NaNs produced ## fit3P fit2P true value ## shape 50.16 57.14 11 ## rate 9.76 10.92 3 ## low 5.01 5.00 5 ## mean sq. error 526.46 730.64 0 ## rel. error 1.94 2.28 0 fit.gamma <- fitdist( data = x-x0, distr = \"gamma\", method = \"mle\") ## fit3P fit2P orig. data fit2P shift data true value ## shape 50.16 57.14 1.498 11 ## rate 9.76 10.92 2.289 3 ## low 5.01 5.00 5.000 5 ## mean sq. error 526.46 730.64 30.266 0 ## rel. error 1.94 2.28 0.367 0 ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced ## fit3P fit2P orig. data true value ## shape 15.144 15.490 11 ## rate 3.623 3.679 3 ## low 5.000 5.000 5 ## mean sq. error 5.854 6.874 0 ## rel. error 0.195 0.212 0"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-compute-marginal-confidence-intervals-on-parameter-estimates-from-their-reported-standard-error","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.1. Can we compute marginal confidence intervals on parameter estimates from their reported standard error ?","title":"Frequently Asked Questions","text":"statistics, deriving marginal confidence intervals MLE parameter estimates using approximation standard errors (calculated hessian) quite common procedure. based wald approximation stands sample size nn sufficiently high, marginal 95%95\\% confidence ith component θi\\theta_i model parameter θ\\theta estimated maximum likelihood (estimate denoted θ̂\\hat \\theta) can approximated : θ̂±1.96×SE(θ̂)\\hat \\theta_i \\pm 1.96 \\times SE(\\hat \\theta_i ) SE(θ̂)SE(\\hat \\theta_i ) ith term diagonal covariance matrix estimates (ViiV_{ii}). VV generally approximated inverse Fisher information matrix ((θ̂)(\\hat \\theta)). Fisher information matrix corresponds opposite hessian matrix evaluated MLE estimate. Let us recall hessian matrix defined Hij(y,θ)=∂2L(y,θ)∂θi∂θjH_{ij}(y, \\theta) = \\frac{\\partial^2 L(y, \\theta)}{\\partial \\theta_i \\partial \\theta_j} L(y,θ)L(y, \\theta) loglikelihod function data yy parameter θ\\theta. using approximation, one must keep mind validity depend sample size. also strongly depends data, distribution, also parameterization distribution. reason recommend potential users Wald approximation compare results ones obtained using bootstrap procedure (see ) using approximation. look loglikelihood contours also interesting Wald approximation assumes elliptical contours. general context, recommend use bootstrap compute confidence intervals parameters function parameters. find two examples, one Wald confidence intervals seem correct one give wrong results, parameter values even outside possible range (negative rate bound gamma distribution).","code":"set.seed(1234) n <- rnorm(30, mean = 10, sd = 2) fn <- fitdist(n, \"norm\") bn <- bootdist(fn) bn$CI ## Median 2.5% 97.5% ## mean 9.41 8.78 10.02 ## sd 1.73 1.33 2.15 fn$estimate + cbind(\"estimate\"= 0, \"2.5%\"= -1.96*fn$sd, \"97.5%\"= 1.96*fn$sd) ## estimate 2.5% 97.5% ## mean 9.41 5.927 12.89 ## sd 1.78 -0.685 4.24 par(mfrow=c(1,1), mar=c(4,4,2,1)) llplot(fn, back.col = FALSE) set.seed(1234) g <- rgamma(30, shape = 0.1, rate = 10) fg <- fitdist(g, \"gamma\") bg <- bootdist(fg) bg$CI ## Median 2.5% 97.5% ## shape 0.0923 0.0636 0.145 ## rate 30.1018 9.6288 147.323 fg$estimate + cbind(\"estimate\"= 0, \"2.5%\"= -1.96*fg$sd, \"97.5%\"= 1.96*fg$sd) ## estimate 2.5% 97.5% ## shape 0.0882 -0.0917 0.268 ## rate 24.2613 -143.3660 191.889 par(mfrow=c(1,1), mar=c(4,4,2,1)) llplot(fg, back.col = FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-compute-confidence-intervals-on-quantiles-from-the-fit-of-a-distribution","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.2. How can we compute confidence intervals on quantiles from the fit of a distribution ?","title":"Frequently Asked Questions","text":"quantile() function can used calculate quantile fitted distribution called object class fitdist fitdistcens first argument. called object class bootdist bootdistcens first argument, quantiles returned accompanied confidence interval calculated using bootstraped sample parameters. Moreover, can use CIcdfplot() function plot fitted distribution CDF curve surrounded band corresponding pointwise intervals quantiles. See example censored data corresponding 72-hour acute salinity tolerance (LC50values) rivermarine invertebrates.","code":"data(salinity) log10LC50 <- log10(salinity) fit <- fitdistcens(log10LC50, \"norm\", control=list(trace=1)) ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 1.047296 ## Scaled convergence tolerance is 1.56059e-08 ## Stepsize computed as 0.113883 ## BUILD 3 1.085037 0.943438 ## EXTENSION 5 1.047296 0.760789 ## EXTENSION 7 0.943438 0.617018 ## HI-REDUCTION 9 0.782878 0.617018 ## LO-REDUCTION 11 0.760789 0.573266 ## HI-REDUCTION 13 0.623518 0.573266 ## HI-REDUCTION 15 0.617018 0.573266 ## HI-REDUCTION 17 0.585623 0.573266 ## HI-REDUCTION 19 0.584177 0.573266 ## HI-REDUCTION 21 0.578139 0.573266 ## LO-REDUCTION 23 0.577473 0.573266 ## LO-REDUCTION 25 0.573373 0.572282 ## HI-REDUCTION 27 0.573266 0.572282 ## LO-REDUCTION 29 0.572444 0.572282 ## HI-REDUCTION 31 0.572321 0.572229 ## HI-REDUCTION 33 0.572282 0.572210 ## HI-REDUCTION 35 0.572229 0.572195 ## HI-REDUCTION 37 0.572210 0.572195 ## LO-REDUCTION 39 0.572196 0.572191 ## HI-REDUCTION 41 0.572195 0.572188 ## HI-REDUCTION 43 0.572191 0.572188 ## LO-REDUCTION 45 0.572190 0.572187 ## HI-REDUCTION 47 0.572188 0.572187 ## HI-REDUCTION 49 0.572188 0.572187 ## HI-REDUCTION 51 0.572187 0.572187 ## HI-REDUCTION 53 0.572187 0.572187 ## LO-REDUCTION 55 0.572187 0.572187 ## HI-REDUCTION 57 0.572187 0.572187 ## HI-REDUCTION 59 0.572187 0.572187 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used # Bootstrap bootsample <- bootdistcens(fit, niter = 101) ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.537616 ## Scaled convergence tolerance is 8.0111e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.698650 0.537616 ## HI-REDUCTION 5 0.634460 0.537616 ## HI-REDUCTION 7 0.579447 0.537616 ## HI-REDUCTION 9 0.576254 0.537616 ## LO-REDUCTION 11 0.559725 0.537616 ## LO-REDUCTION 13 0.541980 0.537616 ## HI-REDUCTION 15 0.537976 0.537616 ## HI-REDUCTION 17 0.537817 0.536886 ## HI-REDUCTION 19 0.537616 0.536609 ## HI-REDUCTION 21 0.536886 0.536609 ## LO-REDUCTION 23 0.536785 0.536542 ## HI-REDUCTION 25 0.536609 0.536542 ## HI-REDUCTION 27 0.536575 0.536529 ## LO-REDUCTION 29 0.536542 0.536526 ## HI-REDUCTION 31 0.536529 0.536524 ## HI-REDUCTION 33 0.536526 0.536522 ## LO-REDUCTION 35 0.536524 0.536522 ## HI-REDUCTION 37 0.536523 0.536522 ## HI-REDUCTION 39 0.536522 0.536522 ## HI-REDUCTION 41 0.536522 0.536521 ## HI-REDUCTION 43 0.536522 0.536521 ## REFLECTION 45 0.536521 0.536521 ## HI-REDUCTION 47 0.536521 0.536521 ## LO-REDUCTION 49 0.536521 0.536521 ## HI-REDUCTION 51 0.536521 0.536521 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.527456 ## Scaled convergence tolerance is 7.85971e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.631319 0.527456 ## HI-REDUCTION 5 0.623182 0.527456 ## HI-REDUCTION 7 0.563846 0.527456 ## HI-REDUCTION 9 0.550055 0.527456 ## HI-REDUCTION 11 0.544128 0.527456 ## REFLECTION 13 0.537017 0.523952 ## LO-REDUCTION 15 0.527456 0.523952 ## HI-REDUCTION 17 0.525857 0.523952 ## HI-REDUCTION 19 0.524449 0.523902 ## HI-REDUCTION 21 0.523952 0.523612 ## HI-REDUCTION 23 0.523902 0.523385 ## LO-REDUCTION 25 0.523612 0.523385 ## HI-REDUCTION 27 0.523492 0.523385 ## LO-REDUCTION 29 0.523446 0.523383 ## LO-REDUCTION 31 0.523386 0.523383 ## HI-REDUCTION 33 0.523385 0.523376 ## HI-REDUCTION 35 0.523383 0.523375 ## HI-REDUCTION 37 0.523376 0.523374 ## HI-REDUCTION 39 0.523375 0.523374 ## HI-REDUCTION 41 0.523374 0.523374 ## HI-REDUCTION 43 0.523374 0.523373 ## LO-REDUCTION 45 0.523374 0.523373 ## HI-REDUCTION 47 0.523374 0.523373 ## LO-REDUCTION 49 0.523373 0.523373 ## LO-REDUCTION 51 0.523373 0.523373 ## HI-REDUCTION 53 0.523373 0.523373 ## REFLECTION 55 0.523373 0.523373 ## LO-REDUCTION 57 0.523373 0.523373 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.454225 ## Scaled convergence tolerance is 6.76848e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595417 0.454225 ## HI-REDUCTION 5 0.556873 0.454225 ## HI-REDUCTION 7 0.500293 0.454225 ## HI-REDUCTION 9 0.488374 0.454225 ## LO-REDUCTION 11 0.478085 0.454225 ## LO-REDUCTION 13 0.463377 0.454225 ## LO-REDUCTION 15 0.457625 0.454150 ## LO-REDUCTION 17 0.454225 0.453426 ## HI-REDUCTION 19 0.454150 0.453347 ## HI-REDUCTION 21 0.453426 0.453241 ## HI-REDUCTION 23 0.453347 0.453208 ## LO-REDUCTION 25 0.453241 0.453179 ## HI-REDUCTION 27 0.453208 0.453162 ## HI-REDUCTION 29 0.453179 0.453148 ## LO-REDUCTION 31 0.453162 0.453148 ## HI-REDUCTION 33 0.453152 0.453148 ## REFLECTION 35 0.453149 0.453146 ## HI-REDUCTION 37 0.453148 0.453146 ## HI-REDUCTION 39 0.453146 0.453146 ## HI-REDUCTION 41 0.453146 0.453145 ## HI-REDUCTION 43 0.453146 0.453145 ## HI-REDUCTION 45 0.453145 0.453145 ## HI-REDUCTION 47 0.453145 0.453145 ## HI-REDUCTION 49 0.453145 0.453145 ## HI-REDUCTION 51 0.453145 0.453145 ## HI-REDUCTION 53 0.453145 0.453145 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.545611 ## Scaled convergence tolerance is 8.13023e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.668602 0.545611 ## HI-REDUCTION 5 0.667351 0.545611 ## HI-REDUCTION 7 0.600961 0.545611 ## LO-REDUCTION 9 0.575566 0.545611 ## LO-REDUCTION 11 0.554511 0.543154 ## HI-REDUCTION 13 0.545611 0.543154 ## HI-REDUCTION 15 0.544152 0.542342 ## HI-REDUCTION 17 0.543154 0.542220 ## HI-REDUCTION 19 0.542342 0.541881 ## HI-REDUCTION 21 0.542220 0.541881 ## LO-REDUCTION 23 0.541883 0.541838 ## HI-REDUCTION 25 0.541881 0.541774 ## HI-REDUCTION 27 0.541838 0.541774 ## LO-REDUCTION 29 0.541800 0.541774 ## HI-REDUCTION 31 0.541776 0.541774 ## HI-REDUCTION 33 0.541775 0.541771 ## HI-REDUCTION 35 0.541774 0.541770 ## HI-REDUCTION 37 0.541771 0.541770 ## HI-REDUCTION 39 0.541770 0.541770 ## HI-REDUCTION 41 0.541770 0.541769 ## HI-REDUCTION 43 0.541770 0.541769 ## LO-REDUCTION 45 0.541769 0.541769 ## HI-REDUCTION 47 0.541769 0.541769 ## HI-REDUCTION 49 0.541769 0.541769 ## HI-REDUCTION 51 0.541769 0.541769 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.490659 ## Scaled convergence tolerance is 7.31138e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.644823 0.490659 ## LO-REDUCTION 5 0.555530 0.490659 ## LO-REDUCTION 7 0.554656 0.490659 ## HI-REDUCTION 9 0.516549 0.490659 ## HI-REDUCTION 11 0.505856 0.490659 ## REFLECTION 13 0.502485 0.488463 ## HI-REDUCTION 15 0.492428 0.488463 ## HI-REDUCTION 17 0.490659 0.488463 ## LO-REDUCTION 19 0.489534 0.488463 ## HI-REDUCTION 21 0.489086 0.488463 ## LO-REDUCTION 23 0.488718 0.488347 ## HI-REDUCTION 25 0.488463 0.488347 ## HI-REDUCTION 27 0.488411 0.488347 ## HI-REDUCTION 29 0.488357 0.488347 ## HI-REDUCTION 31 0.488356 0.488341 ## LO-REDUCTION 33 0.488347 0.488341 ## HI-REDUCTION 35 0.488342 0.488340 ## HI-REDUCTION 37 0.488341 0.488338 ## HI-REDUCTION 39 0.488340 0.488338 ## LO-REDUCTION 41 0.488338 0.488338 ## HI-REDUCTION 43 0.488338 0.488338 ## LO-REDUCTION 45 0.488338 0.488338 ## HI-REDUCTION 47 0.488338 0.488338 ## HI-REDUCTION 49 0.488338 0.488338 ## HI-REDUCTION 51 0.488338 0.488338 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.444794 ## Scaled convergence tolerance is 6.62795e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.546766 0.403067 ## HI-REDUCTION 5 0.461208 0.403067 ## LO-REDUCTION 7 0.444794 0.392330 ## HI-REDUCTION 9 0.403067 0.392330 ## HI-REDUCTION 11 0.396380 0.391167 ## HI-REDUCTION 13 0.392330 0.389343 ## HI-REDUCTION 15 0.391167 0.389343 ## HI-REDUCTION 17 0.389390 0.389280 ## HI-REDUCTION 19 0.389343 0.388805 ## HI-REDUCTION 21 0.389280 0.388759 ## LO-REDUCTION 23 0.388805 0.388748 ## HI-REDUCTION 25 0.388759 0.388705 ## HI-REDUCTION 27 0.388748 0.388705 ## HI-REDUCTION 29 0.388716 0.388705 ## LO-REDUCTION 31 0.388711 0.388703 ## HI-REDUCTION 33 0.388705 0.388701 ## HI-REDUCTION 35 0.388703 0.388701 ## REFLECTION 37 0.388701 0.388700 ## HI-REDUCTION 39 0.388701 0.388699 ## LO-REDUCTION 41 0.388700 0.388699 ## HI-REDUCTION 43 0.388699 0.388699 ## HI-REDUCTION 45 0.388699 0.388699 ## HI-REDUCTION 47 0.388699 0.388699 ## HI-REDUCTION 49 0.388699 0.388699 ## LO-REDUCTION 51 0.388699 0.388699 ## HI-REDUCTION 53 0.388699 0.388699 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.731663 ## Scaled convergence tolerance is 1.09026e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.919495 0.731663 ## LO-REDUCTION 5 0.782163 0.731663 ## LO-REDUCTION 7 0.781055 0.731620 ## HI-REDUCTION 9 0.740843 0.731620 ## HI-REDUCTION 11 0.731663 0.728435 ## HI-REDUCTION 13 0.731620 0.725155 ## LO-REDUCTION 15 0.728435 0.725094 ## HI-REDUCTION 17 0.725736 0.725094 ## LO-REDUCTION 19 0.725155 0.724861 ## HI-REDUCTION 21 0.725094 0.724740 ## HI-REDUCTION 23 0.724861 0.724718 ## LO-REDUCTION 25 0.724740 0.724708 ## HI-REDUCTION 27 0.724718 0.724683 ## HI-REDUCTION 29 0.724708 0.724683 ## LO-REDUCTION 31 0.724694 0.724682 ## HI-REDUCTION 33 0.724683 0.724682 ## HI-REDUCTION 35 0.724683 0.724681 ## HI-REDUCTION 37 0.724682 0.724681 ## HI-REDUCTION 39 0.724681 0.724681 ## HI-REDUCTION 41 0.724681 0.724681 ## HI-REDUCTION 43 0.724681 0.724681 ## HI-REDUCTION 45 0.724681 0.724681 ## REFLECTION 47 0.724681 0.724681 ## HI-REDUCTION 49 0.724681 0.724681 ## HI-REDUCTION 51 0.724681 0.724681 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.541366 ## Scaled convergence tolerance is 8.06698e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.717668 0.541366 ## LO-REDUCTION 5 0.630994 0.541366 ## LO-REDUCTION 7 0.623956 0.541366 ## HI-REDUCTION 9 0.572190 0.541366 ## LO-REDUCTION 11 0.545899 0.538964 ## HI-REDUCTION 13 0.541366 0.537345 ## LO-REDUCTION 15 0.538964 0.536393 ## HI-REDUCTION 17 0.537345 0.536331 ## HI-REDUCTION 19 0.536393 0.536199 ## HI-REDUCTION 21 0.536331 0.536081 ## HI-REDUCTION 23 0.536199 0.536051 ## HI-REDUCTION 25 0.536081 0.536043 ## HI-REDUCTION 27 0.536051 0.536033 ## HI-REDUCTION 29 0.536043 0.536023 ## HI-REDUCTION 31 0.536033 0.536023 ## LO-REDUCTION 33 0.536024 0.536020 ## HI-REDUCTION 35 0.536023 0.536020 ## HI-REDUCTION 37 0.536020 0.536020 ## HI-REDUCTION 39 0.536020 0.536020 ## HI-REDUCTION 41 0.536020 0.536020 ## HI-REDUCTION 43 0.536020 0.536020 ## HI-REDUCTION 45 0.536020 0.536019 ## HI-REDUCTION 47 0.536020 0.536019 ## HI-REDUCTION 49 0.536019 0.536019 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.549754 ## Scaled convergence tolerance is 8.19198e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.708643 0.549754 ## LO-REDUCTION 5 0.592929 0.549754 ## LO-REDUCTION 7 0.590926 0.549754 ## HI-REDUCTION 9 0.557629 0.549754 ## HI-REDUCTION 11 0.552007 0.547783 ## REFLECTION 13 0.549754 0.545919 ## LO-REDUCTION 15 0.547783 0.544040 ## HI-REDUCTION 17 0.545919 0.543532 ## HI-REDUCTION 19 0.544040 0.542252 ## LO-REDUCTION 21 0.543532 0.542252 ## HI-REDUCTION 23 0.542537 0.542252 ## HI-REDUCTION 25 0.542396 0.542252 ## REFLECTION 27 0.542309 0.542183 ## HI-REDUCTION 29 0.542252 0.542183 ## LO-REDUCTION 31 0.542209 0.542183 ## HI-REDUCTION 33 0.542196 0.542183 ## LO-REDUCTION 35 0.542188 0.542183 ## HI-REDUCTION 37 0.542183 0.542181 ## HI-REDUCTION 39 0.542183 0.542181 ## REFLECTION 41 0.542181 0.542180 ## HI-REDUCTION 43 0.542181 0.542180 ## HI-REDUCTION 45 0.542180 0.542180 ## HI-REDUCTION 47 0.542180 0.542180 ## LO-REDUCTION 49 0.542180 0.542180 ## HI-REDUCTION 51 0.542180 0.542180 ## HI-REDUCTION 53 0.542180 0.542180 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.531956 ## Scaled convergence tolerance is 7.92677e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.702911 0.531956 ## HI-REDUCTION 5 0.640171 0.531956 ## HI-REDUCTION 7 0.587243 0.531956 ## HI-REDUCTION 9 0.585068 0.531956 ## LO-REDUCTION 11 0.566618 0.531956 ## REFLECTION 13 0.538422 0.528758 ## REFLECTION 15 0.531956 0.523607 ## HI-REDUCTION 17 0.528758 0.519983 ## HI-REDUCTION 19 0.523607 0.519257 ## HI-REDUCTION 21 0.519983 0.518754 ## HI-REDUCTION 23 0.519257 0.518236 ## HI-REDUCTION 25 0.518754 0.518134 ## HI-REDUCTION 27 0.518236 0.518133 ## HI-REDUCTION 29 0.518134 0.518022 ## HI-REDUCTION 31 0.518133 0.518014 ## LO-REDUCTION 33 0.518022 0.518014 ## HI-REDUCTION 35 0.518020 0.517999 ## HI-REDUCTION 37 0.518014 0.517999 ## HI-REDUCTION 39 0.518002 0.517999 ## REFLECTION 41 0.517999 0.517998 ## HI-REDUCTION 43 0.517999 0.517996 ## HI-REDUCTION 45 0.517998 0.517996 ## HI-REDUCTION 47 0.517996 0.517996 ## HI-REDUCTION 49 0.517996 0.517996 ## HI-REDUCTION 51 0.517996 0.517996 ## HI-REDUCTION 53 0.517996 0.517996 ## REFLECTION 55 0.517996 0.517996 ## HI-REDUCTION 57 0.517996 0.517996 ## HI-REDUCTION 59 0.517996 0.517996 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.457849 ## Scaled convergence tolerance is 6.82248e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.573577 0.457849 ## HI-REDUCTION 5 0.547401 0.457849 ## HI-REDUCTION 7 0.502497 0.457849 ## LO-REDUCTION 9 0.480847 0.457849 ## LO-REDUCTION 11 0.458322 0.452116 ## HI-REDUCTION 13 0.457849 0.451269 ## LO-REDUCTION 15 0.452116 0.450611 ## HI-REDUCTION 17 0.451269 0.450306 ## HI-REDUCTION 19 0.450611 0.450244 ## HI-REDUCTION 21 0.450306 0.450145 ## HI-REDUCTION 23 0.450244 0.450100 ## HI-REDUCTION 25 0.450145 0.450100 ## HI-REDUCTION 27 0.450102 0.450086 ## HI-REDUCTION 29 0.450100 0.450079 ## HI-REDUCTION 31 0.450086 0.450079 ## HI-REDUCTION 33 0.450080 0.450078 ## HI-REDUCTION 35 0.450079 0.450077 ## HI-REDUCTION 37 0.450078 0.450077 ## HI-REDUCTION 39 0.450077 0.450077 ## HI-REDUCTION 41 0.450077 0.450077 ## HI-REDUCTION 43 0.450077 0.450077 ## LO-REDUCTION 45 0.450077 0.450077 ## HI-REDUCTION 47 0.450077 0.450077 ## HI-REDUCTION 49 0.450077 0.450077 ## HI-REDUCTION 51 0.450077 0.450077 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.516736 ## Scaled convergence tolerance is 7.69996e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.651724 0.516736 ## HI-REDUCTION 5 0.617368 0.516736 ## HI-REDUCTION 7 0.562123 0.516736 ## HI-REDUCTION 9 0.549954 0.516736 ## LO-REDUCTION 11 0.540531 0.516736 ## LO-REDUCTION 13 0.525588 0.516736 ## LO-REDUCTION 15 0.518110 0.515387 ## LO-REDUCTION 17 0.516736 0.515387 ## LO-REDUCTION 19 0.515975 0.515387 ## HI-REDUCTION 21 0.515516 0.515275 ## REFLECTION 23 0.515387 0.515207 ## HI-REDUCTION 25 0.515275 0.515165 ## HI-REDUCTION 27 0.515207 0.515149 ## HI-REDUCTION 29 0.515165 0.515148 ## HI-REDUCTION 31 0.515149 0.515139 ## HI-REDUCTION 33 0.515148 0.515136 ## LO-REDUCTION 35 0.515139 0.515136 ## HI-REDUCTION 37 0.515138 0.515136 ## LO-REDUCTION 39 0.515136 0.515135 ## HI-REDUCTION 41 0.515136 0.515135 ## HI-REDUCTION 43 0.515135 0.515135 ## LO-REDUCTION 45 0.515135 0.515135 ## HI-REDUCTION 47 0.515135 0.515135 ## LO-REDUCTION 49 0.515135 0.515135 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.674578 ## Scaled convergence tolerance is 1.0052e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.841103 0.674578 ## LO-REDUCTION 5 0.744070 0.674578 ## LO-REDUCTION 7 0.743356 0.674578 ## HI-REDUCTION 9 0.701895 0.674578 ## HI-REDUCTION 11 0.689791 0.674578 ## REFLECTION 13 0.686399 0.673820 ## HI-REDUCTION 15 0.675812 0.673820 ## HI-REDUCTION 17 0.674578 0.673210 ## HI-REDUCTION 19 0.673820 0.673103 ## HI-REDUCTION 21 0.673210 0.672911 ## HI-REDUCTION 23 0.673103 0.672862 ## LO-REDUCTION 25 0.672911 0.672862 ## HI-REDUCTION 27 0.672878 0.672825 ## HI-REDUCTION 29 0.672862 0.672825 ## HI-REDUCTION 31 0.672826 0.672815 ## REFLECTION 33 0.672825 0.672809 ## HI-REDUCTION 35 0.672815 0.672809 ## HI-REDUCTION 37 0.672810 0.672808 ## HI-REDUCTION 39 0.672809 0.672807 ## HI-REDUCTION 41 0.672808 0.672807 ## HI-REDUCTION 43 0.672807 0.672807 ## LO-REDUCTION 45 0.672807 0.672807 ## HI-REDUCTION 47 0.672807 0.672807 ## HI-REDUCTION 49 0.672807 0.672807 ## LO-REDUCTION 51 0.672807 0.672807 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.693852 ## Scaled convergence tolerance is 1.03392e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.808416 0.693852 ## HI-REDUCTION 5 0.797287 0.693852 ## HI-REDUCTION 7 0.738246 0.693852 ## HI-REDUCTION 9 0.719156 0.693852 ## LO-REDUCTION 11 0.714526 0.693852 ## HI-REDUCTION 13 0.704153 0.693852 ## LO-REDUCTION 15 0.703373 0.693852 ## REFLECTION 17 0.695792 0.693741 ## HI-REDUCTION 19 0.693852 0.693306 ## LO-REDUCTION 21 0.693741 0.692769 ## HI-REDUCTION 23 0.693306 0.692713 ## HI-REDUCTION 25 0.692769 0.692579 ## LO-REDUCTION 27 0.692713 0.692579 ## HI-REDUCTION 29 0.692588 0.692558 ## REFLECTION 31 0.692579 0.692518 ## HI-REDUCTION 33 0.692558 0.692518 ## HI-REDUCTION 35 0.692533 0.692518 ## LO-REDUCTION 37 0.692531 0.692518 ## LO-REDUCTION 39 0.692525 0.692518 ## LO-REDUCTION 41 0.692519 0.692518 ## HI-REDUCTION 43 0.692518 0.692518 ## HI-REDUCTION 45 0.692518 0.692518 ## HI-REDUCTION 47 0.692518 0.692518 ## HI-REDUCTION 49 0.692518 0.692518 ## HI-REDUCTION 51 0.692518 0.692518 ## HI-REDUCTION 53 0.692518 0.692518 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.519774 ## Scaled convergence tolerance is 7.74523e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.664789 0.519774 ## HI-REDUCTION 5 0.589088 0.519774 ## HI-REDUCTION 7 0.545458 0.519774 ## LO-REDUCTION 9 0.544449 0.519774 ## LO-REDUCTION 11 0.530123 0.519774 ## HI-REDUCTION 13 0.522030 0.519774 ## LO-REDUCTION 15 0.521075 0.519718 ## HI-REDUCTION 17 0.519774 0.519268 ## HI-REDUCTION 19 0.519718 0.518894 ## LO-REDUCTION 21 0.519268 0.518795 ## HI-REDUCTION 23 0.518909 0.518795 ## HI-REDUCTION 25 0.518894 0.518795 ## HI-REDUCTION 27 0.518821 0.518795 ## REFLECTION 29 0.518808 0.518785 ## HI-REDUCTION 31 0.518795 0.518784 ## HI-REDUCTION 33 0.518785 0.518782 ## HI-REDUCTION 35 0.518784 0.518781 ## HI-REDUCTION 37 0.518782 0.518781 ## HI-REDUCTION 39 0.518781 0.518780 ## HI-REDUCTION 41 0.518781 0.518780 ## HI-REDUCTION 43 0.518780 0.518780 ## HI-REDUCTION 45 0.518780 0.518780 ## LO-REDUCTION 47 0.518780 0.518780 ## HI-REDUCTION 49 0.518780 0.518780 ## HI-REDUCTION 51 0.518780 0.518780 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505864 ## Scaled convergence tolerance is 7.53796e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.672302 0.505864 ## LO-REDUCTION 5 0.589360 0.505864 ## LO-REDUCTION 7 0.583160 0.505864 ## HI-REDUCTION 9 0.532805 0.505864 ## HI-REDUCTION 11 0.513600 0.505864 ## LO-REDUCTION 13 0.509560 0.502226 ## HI-REDUCTION 15 0.505864 0.502226 ## HI-REDUCTION 17 0.502842 0.502226 ## HI-REDUCTION 19 0.502747 0.502100 ## HI-REDUCTION 21 0.502226 0.501965 ## HI-REDUCTION 23 0.502100 0.501962 ## REFLECTION 25 0.501965 0.501888 ## HI-REDUCTION 27 0.501962 0.501878 ## HI-REDUCTION 29 0.501888 0.501862 ## HI-REDUCTION 31 0.501878 0.501859 ## REFLECTION 33 0.501862 0.501850 ## HI-REDUCTION 35 0.501859 0.501850 ## HI-REDUCTION 37 0.501850 0.501848 ## HI-REDUCTION 39 0.501850 0.501847 ## REFLECTION 41 0.501848 0.501846 ## LO-REDUCTION 43 0.501847 0.501846 ## HI-REDUCTION 45 0.501846 0.501846 ## HI-REDUCTION 47 0.501846 0.501846 ## HI-REDUCTION 49 0.501846 0.501846 ## LO-REDUCTION 51 0.501846 0.501846 ## HI-REDUCTION 53 0.501846 0.501846 ## LO-REDUCTION 55 0.501846 0.501846 ## HI-REDUCTION 57 0.501846 0.501846 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.413930 ## Scaled convergence tolerance is 6.16803e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.557263 0.413930 ## HI-REDUCTION 5 0.540686 0.413930 ## HI-REDUCTION 7 0.475171 0.413930 ## LO-REDUCTION 9 0.456539 0.413930 ## LO-REDUCTION 11 0.429062 0.409608 ## LO-REDUCTION 13 0.413930 0.409608 ## HI-REDUCTION 15 0.412507 0.409608 ## HI-REDUCTION 17 0.410374 0.409608 ## HI-REDUCTION 19 0.409774 0.409216 ## HI-REDUCTION 21 0.409608 0.408982 ## HI-REDUCTION 23 0.409216 0.408843 ## LO-REDUCTION 25 0.408982 0.408836 ## HI-REDUCTION 27 0.408843 0.408826 ## HI-REDUCTION 29 0.408836 0.408814 ## HI-REDUCTION 31 0.408826 0.408814 ## HI-REDUCTION 33 0.408814 0.408809 ## REFLECTION 35 0.408814 0.408807 ## HI-REDUCTION 37 0.408809 0.408807 ## HI-REDUCTION 39 0.408808 0.408807 ## HI-REDUCTION 41 0.408807 0.408807 ## HI-REDUCTION 43 0.408807 0.408807 ## HI-REDUCTION 45 0.408807 0.408807 ## LO-REDUCTION 47 0.408807 0.408806 ## HI-REDUCTION 49 0.408807 0.408806 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.662481 ## Scaled convergence tolerance is 9.87174e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.726804 0.662481 ## HI-REDUCTION 5 0.715502 0.662481 ## HI-REDUCTION 7 0.664010 0.651208 ## HI-REDUCTION 9 0.662481 0.650567 ## LO-REDUCTION 11 0.651660 0.650567 ## REFLECTION 13 0.651208 0.648906 ## HI-REDUCTION 15 0.650567 0.646197 ## HI-REDUCTION 17 0.648906 0.646197 ## LO-REDUCTION 19 0.647688 0.646197 ## HI-REDUCTION 21 0.646622 0.646197 ## LO-REDUCTION 23 0.646541 0.646150 ## HI-REDUCTION 25 0.646207 0.646150 ## HI-REDUCTION 27 0.646197 0.646130 ## HI-REDUCTION 29 0.646150 0.646119 ## HI-REDUCTION 31 0.646130 0.646119 ## LO-REDUCTION 33 0.646122 0.646114 ## HI-REDUCTION 35 0.646119 0.646113 ## HI-REDUCTION 37 0.646114 0.646113 ## HI-REDUCTION 39 0.646114 0.646113 ## HI-REDUCTION 41 0.646113 0.646113 ## HI-REDUCTION 43 0.646113 0.646113 ## LO-REDUCTION 45 0.646113 0.646113 ## LO-REDUCTION 47 0.646113 0.646113 ## HI-REDUCTION 49 0.646113 0.646113 ## LO-REDUCTION 51 0.646113 0.646113 ## REFLECTION 53 0.646113 0.646113 ## REFLECTION 55 0.646113 0.646113 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.556899 ## Scaled convergence tolerance is 8.29844e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.782408 0.556899 ## LO-REDUCTION 5 0.660493 0.556899 ## LO-REDUCTION 7 0.639391 0.550746 ## HI-REDUCTION 9 0.582261 0.550746 ## HI-REDUCTION 11 0.560411 0.550746 ## LO-REDUCTION 13 0.556899 0.547400 ## LO-REDUCTION 15 0.550746 0.547400 ## LO-REDUCTION 17 0.548108 0.545007 ## HI-REDUCTION 19 0.547400 0.544818 ## LO-REDUCTION 21 0.545007 0.544505 ## HI-REDUCTION 23 0.544818 0.544450 ## REFLECTION 25 0.544505 0.544176 ## HI-REDUCTION 27 0.544450 0.544176 ## REFLECTION 29 0.544305 0.544156 ## REFLECTION 31 0.544176 0.544101 ## HI-REDUCTION 33 0.544156 0.544101 ## HI-REDUCTION 35 0.544116 0.544101 ## LO-REDUCTION 37 0.544109 0.544098 ## HI-REDUCTION 39 0.544101 0.544096 ## HI-REDUCTION 41 0.544098 0.544096 ## REFLECTION 43 0.544096 0.544095 ## HI-REDUCTION 45 0.544096 0.544094 ## HI-REDUCTION 47 0.544095 0.544094 ## HI-REDUCTION 49 0.544095 0.544094 ## LO-REDUCTION 51 0.544094 0.544094 ## HI-REDUCTION 53 0.544094 0.544094 ## HI-REDUCTION 55 0.544094 0.544094 ## LO-REDUCTION 57 0.544094 0.544094 ## HI-REDUCTION 59 0.544094 0.544094 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.602798 ## Scaled convergence tolerance is 8.98239e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.754891 0.602798 ## HI-REDUCTION 5 0.691000 0.602798 ## HI-REDUCTION 7 0.638621 0.602798 ## HI-REDUCTION 9 0.635769 0.602798 ## LO-REDUCTION 11 0.620563 0.602798 ## LO-REDUCTION 13 0.605715 0.602798 ## HI-REDUCTION 15 0.603087 0.602488 ## HI-REDUCTION 17 0.602798 0.601819 ## HI-REDUCTION 19 0.602488 0.601819 ## HI-REDUCTION 21 0.601947 0.601819 ## HI-REDUCTION 23 0.601940 0.601795 ## LO-REDUCTION 25 0.601819 0.601795 ## HI-REDUCTION 27 0.601795 0.601764 ## HI-REDUCTION 29 0.601795 0.601752 ## LO-REDUCTION 31 0.601764 0.601752 ## HI-REDUCTION 33 0.601753 0.601752 ## HI-REDUCTION 35 0.601752 0.601751 ## HI-REDUCTION 37 0.601752 0.601750 ## HI-REDUCTION 39 0.601751 0.601750 ## HI-REDUCTION 41 0.601751 0.601750 ## HI-REDUCTION 43 0.601750 0.601750 ## HI-REDUCTION 45 0.601750 0.601750 ## LO-REDUCTION 47 0.601750 0.601750 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.558332 ## Scaled convergence tolerance is 8.3198e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.761450 0.558332 ## LO-REDUCTION 5 0.590417 0.558332 ## HI-REDUCTION 7 0.582868 0.558332 ## HI-REDUCTION 9 0.564589 0.556985 ## HI-REDUCTION 11 0.558332 0.550727 ## HI-REDUCTION 13 0.556985 0.543300 ## LO-REDUCTION 15 0.550727 0.543300 ## HI-REDUCTION 17 0.546025 0.543300 ## LO-REDUCTION 19 0.545146 0.543300 ## EXTENSION 21 0.544510 0.542726 ## REFLECTION 23 0.543300 0.541935 ## HI-REDUCTION 25 0.542726 0.541935 ## LO-REDUCTION 27 0.542406 0.541935 ## LO-REDUCTION 29 0.542112 0.541935 ## HI-REDUCTION 31 0.541981 0.541935 ## HI-REDUCTION 33 0.541978 0.541935 ## LO-REDUCTION 35 0.541948 0.541935 ## HI-REDUCTION 37 0.541943 0.541934 ## REFLECTION 39 0.541935 0.541932 ## HI-REDUCTION 41 0.541934 0.541931 ## HI-REDUCTION 43 0.541932 0.541931 ## HI-REDUCTION 45 0.541932 0.541931 ## HI-REDUCTION 47 0.541931 0.541931 ## HI-REDUCTION 49 0.541931 0.541931 ## LO-REDUCTION 51 0.541931 0.541931 ## HI-REDUCTION 53 0.541931 0.541931 ## HI-REDUCTION 55 0.541931 0.541931 ## LO-REDUCTION 57 0.541931 0.541931 ## HI-REDUCTION 59 0.541931 0.541931 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.595932 ## Scaled convergence tolerance is 8.88008e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.674920 0.595932 ## HI-REDUCTION 5 0.646873 0.592481 ## HI-REDUCTION 7 0.605194 0.592481 ## HI-REDUCTION 9 0.595932 0.591583 ## HI-REDUCTION 11 0.592481 0.588242 ## HI-REDUCTION 13 0.591583 0.588242 ## HI-REDUCTION 15 0.588810 0.588242 ## LO-REDUCTION 17 0.588363 0.587807 ## HI-REDUCTION 19 0.588242 0.587715 ## HI-REDUCTION 21 0.587807 0.587715 ## LO-REDUCTION 23 0.587732 0.587671 ## HI-REDUCTION 25 0.587715 0.587653 ## HI-REDUCTION 27 0.587671 0.587653 ## LO-REDUCTION 29 0.587656 0.587648 ## HI-REDUCTION 31 0.587653 0.587646 ## HI-REDUCTION 33 0.587648 0.587646 ## HI-REDUCTION 35 0.587646 0.587646 ## HI-REDUCTION 37 0.587646 0.587645 ## HI-REDUCTION 39 0.587646 0.587645 ## LO-REDUCTION 41 0.587645 0.587645 ## HI-REDUCTION 43 0.587645 0.587645 ## HI-REDUCTION 45 0.587645 0.587645 ## Exiting from Nelder Mead minimizer ## 47 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.509626 ## Scaled convergence tolerance is 7.59401e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.596233 0.509626 ## HI-REDUCTION 5 0.578223 0.509626 ## HI-REDUCTION 7 0.528795 0.509626 ## HI-REDUCTION 9 0.510375 0.509626 ## HI-REDUCTION 11 0.510266 0.502343 ## LO-REDUCTION 13 0.509626 0.502343 ## LO-REDUCTION 15 0.502422 0.501147 ## HI-REDUCTION 17 0.502343 0.500002 ## LO-REDUCTION 19 0.501147 0.499858 ## HI-REDUCTION 21 0.500176 0.499858 ## HI-REDUCTION 23 0.500002 0.499858 ## HI-REDUCTION 25 0.499929 0.499858 ## REFLECTION 27 0.499872 0.499856 ## HI-REDUCTION 29 0.499858 0.499835 ## HI-REDUCTION 31 0.499856 0.499835 ## HI-REDUCTION 33 0.499837 0.499835 ## HI-REDUCTION 35 0.499835 0.499832 ## HI-REDUCTION 37 0.499835 0.499832 ## HI-REDUCTION 39 0.499832 0.499832 ## HI-REDUCTION 41 0.499832 0.499832 ## HI-REDUCTION 43 0.499832 0.499831 ## HI-REDUCTION 45 0.499832 0.499831 ## LO-REDUCTION 47 0.499831 0.499831 ## HI-REDUCTION 49 0.499831 0.499831 ## HI-REDUCTION 51 0.499831 0.499831 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.490916 ## Scaled convergence tolerance is 7.31522e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.608547 0.490916 ## HI-REDUCTION 5 0.590807 0.490916 ## HI-REDUCTION 7 0.534275 0.490916 ## HI-REDUCTION 9 0.515659 0.490916 ## HI-REDUCTION 11 0.511434 0.490916 ## LO-REDUCTION 13 0.503283 0.490492 ## LO-REDUCTION 15 0.490966 0.490492 ## HI-REDUCTION 17 0.490916 0.489661 ## LO-REDUCTION 19 0.490492 0.489661 ## LO-REDUCTION 21 0.489789 0.489513 ## HI-REDUCTION 23 0.489661 0.489386 ## LO-REDUCTION 25 0.489513 0.489386 ## HI-REDUCTION 27 0.489405 0.489386 ## LO-REDUCTION 29 0.489398 0.489377 ## HI-REDUCTION 31 0.489386 0.489376 ## HI-REDUCTION 33 0.489377 0.489374 ## LO-REDUCTION 35 0.489376 0.489374 ## HI-REDUCTION 37 0.489374 0.489373 ## HI-REDUCTION 39 0.489374 0.489373 ## LO-REDUCTION 41 0.489373 0.489373 ## HI-REDUCTION 43 0.489373 0.489373 ## HI-REDUCTION 45 0.489373 0.489373 ## HI-REDUCTION 47 0.489373 0.489373 ## HI-REDUCTION 49 0.489373 0.489373 ## HI-REDUCTION 51 0.489373 0.489373 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.392680 ## Scaled convergence tolerance is 5.85138e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.514414 0.392680 ## HI-REDUCTION 5 0.480591 0.392680 ## HI-REDUCTION 7 0.435361 0.392680 ## LO-REDUCTION 9 0.410461 0.392680 ## HI-REDUCTION 11 0.394678 0.392680 ## REFLECTION 13 0.393386 0.391126 ## HI-REDUCTION 15 0.392680 0.386541 ## HI-REDUCTION 17 0.391126 0.386541 ## LO-REDUCTION 19 0.388645 0.386541 ## HI-REDUCTION 21 0.387160 0.386541 ## LO-REDUCTION 23 0.387032 0.386540 ## HI-REDUCTION 25 0.386569 0.386540 ## HI-REDUCTION 27 0.386541 0.386468 ## HI-REDUCTION 29 0.386540 0.386453 ## LO-REDUCTION 31 0.386468 0.386451 ## HI-REDUCTION 33 0.386453 0.386448 ## HI-REDUCTION 35 0.386451 0.386447 ## HI-REDUCTION 37 0.386448 0.386447 ## HI-REDUCTION 39 0.386447 0.386446 ## HI-REDUCTION 41 0.386447 0.386446 ## LO-REDUCTION 43 0.386446 0.386446 ## HI-REDUCTION 45 0.386446 0.386446 ## LO-REDUCTION 47 0.386446 0.386446 ## HI-REDUCTION 49 0.386446 0.386446 ## LO-REDUCTION 51 0.386446 0.386446 ## HI-REDUCTION 53 0.386446 0.386446 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.568157 ## Scaled convergence tolerance is 8.4662e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.683653 0.568157 ## HI-REDUCTION 5 0.653178 0.568157 ## HI-REDUCTION 7 0.598967 0.568157 ## HI-REDUCTION 9 0.584430 0.568157 ## HI-REDUCTION 11 0.578937 0.568157 ## LO-REDUCTION 13 0.573214 0.567749 ## HI-REDUCTION 15 0.568157 0.567749 ## HI-REDUCTION 17 0.567849 0.566894 ## HI-REDUCTION 19 0.567749 0.566894 ## LO-REDUCTION 21 0.567012 0.566860 ## HI-REDUCTION 23 0.566894 0.566818 ## HI-REDUCTION 25 0.566860 0.566791 ## HI-REDUCTION 27 0.566818 0.566791 ## HI-REDUCTION 29 0.566803 0.566791 ## HI-REDUCTION 31 0.566792 0.566789 ## HI-REDUCTION 33 0.566791 0.566785 ## HI-REDUCTION 35 0.566789 0.566785 ## LO-REDUCTION 37 0.566785 0.566784 ## HI-REDUCTION 39 0.566785 0.566784 ## HI-REDUCTION 41 0.566784 0.566784 ## HI-REDUCTION 43 0.566784 0.566784 ## HI-REDUCTION 45 0.566784 0.566784 ## HI-REDUCTION 47 0.566784 0.566784 ## HI-REDUCTION 49 0.566784 0.566784 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.536183 ## Scaled convergence tolerance is 7.98974e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.654355 0.536183 ## HI-REDUCTION 5 0.570541 0.536183 ## HI-REDUCTION 7 0.539877 0.535975 ## HI-REDUCTION 9 0.536183 0.528907 ## HI-REDUCTION 11 0.535975 0.527394 ## HI-REDUCTION 13 0.529032 0.527394 ## HI-REDUCTION 15 0.528907 0.527394 ## HI-REDUCTION 17 0.527511 0.527394 ## HI-REDUCTION 19 0.527431 0.527174 ## HI-REDUCTION 21 0.527394 0.527115 ## HI-REDUCTION 23 0.527174 0.527115 ## LO-REDUCTION 25 0.527117 0.527084 ## HI-REDUCTION 27 0.527115 0.527079 ## HI-REDUCTION 29 0.527084 0.527079 ## HI-REDUCTION 31 0.527080 0.527077 ## HI-REDUCTION 33 0.527079 0.527075 ## HI-REDUCTION 35 0.527077 0.527075 ## LO-REDUCTION 37 0.527076 0.527075 ## HI-REDUCTION 39 0.527076 0.527075 ## REFLECTION 41 0.527075 0.527075 ## HI-REDUCTION 43 0.527075 0.527075 ## HI-REDUCTION 45 0.527075 0.527075 ## LO-REDUCTION 47 0.527075 0.527075 ## HI-REDUCTION 49 0.527075 0.527075 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.478880 ## Scaled convergence tolerance is 7.13587e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.582598 0.478880 ## HI-REDUCTION 5 0.547802 0.478880 ## HI-REDUCTION 7 0.500623 0.478880 ## HI-REDUCTION 9 0.489762 0.478880 ## HI-REDUCTION 11 0.484249 0.478880 ## HI-REDUCTION 13 0.480427 0.478880 ## HI-REDUCTION 15 0.479060 0.477658 ## HI-REDUCTION 17 0.478880 0.477270 ## HI-REDUCTION 19 0.477658 0.476846 ## LO-REDUCTION 21 0.477270 0.476846 ## HI-REDUCTION 23 0.477037 0.476846 ## REFLECTION 25 0.476909 0.476751 ## LO-REDUCTION 27 0.476846 0.476751 ## LO-REDUCTION 29 0.476756 0.476728 ## HI-REDUCTION 31 0.476751 0.476721 ## LO-REDUCTION 33 0.476728 0.476717 ## HI-REDUCTION 35 0.476721 0.476717 ## HI-REDUCTION 37 0.476719 0.476717 ## HI-REDUCTION 39 0.476718 0.476717 ## LO-REDUCTION 41 0.476717 0.476716 ## HI-REDUCTION 43 0.476717 0.476716 ## HI-REDUCTION 45 0.476717 0.476716 ## HI-REDUCTION 47 0.476716 0.476716 ## HI-REDUCTION 49 0.476716 0.476716 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.632103 ## Scaled convergence tolerance is 9.41906e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.806430 0.632103 ## LO-REDUCTION 5 0.667493 0.632103 ## LO-REDUCTION 7 0.659661 0.625722 ## HI-REDUCTION 9 0.632103 0.625722 ## HI-REDUCTION 11 0.629033 0.620735 ## HI-REDUCTION 13 0.625722 0.620735 ## REFLECTION 15 0.623144 0.619777 ## LO-REDUCTION 17 0.620735 0.619777 ## HI-REDUCTION 19 0.619790 0.619494 ## HI-REDUCTION 21 0.619777 0.619267 ## HI-REDUCTION 23 0.619494 0.619246 ## LO-REDUCTION 25 0.619267 0.619246 ## HI-REDUCTION 27 0.619266 0.619202 ## LO-REDUCTION 29 0.619246 0.619202 ## LO-REDUCTION 31 0.619205 0.619202 ## HI-REDUCTION 33 0.619204 0.619196 ## LO-REDUCTION 35 0.619202 0.619196 ## LO-REDUCTION 37 0.619198 0.619196 ## HI-REDUCTION 39 0.619197 0.619196 ## LO-REDUCTION 41 0.619196 0.619196 ## HI-REDUCTION 43 0.619196 0.619196 ## HI-REDUCTION 45 0.619196 0.619196 ## HI-REDUCTION 47 0.619196 0.619196 ## HI-REDUCTION 49 0.619196 0.619196 ## HI-REDUCTION 51 0.619196 0.619196 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.617735 ## Scaled convergence tolerance is 9.20497e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.755738 0.617735 ## HI-REDUCTION 5 0.659942 0.617735 ## HI-REDUCTION 7 0.628864 0.617735 ## HI-REDUCTION 9 0.622427 0.615215 ## HI-REDUCTION 11 0.617735 0.613065 ## HI-REDUCTION 13 0.615215 0.612070 ## LO-REDUCTION 15 0.613065 0.611427 ## HI-REDUCTION 17 0.612070 0.611427 ## HI-REDUCTION 19 0.611582 0.611237 ## LO-REDUCTION 21 0.611427 0.611206 ## HI-REDUCTION 23 0.611237 0.611205 ## HI-REDUCTION 25 0.611206 0.611164 ## LO-REDUCTION 27 0.611205 0.611164 ## HI-REDUCTION 29 0.611176 0.611164 ## LO-REDUCTION 31 0.611173 0.611162 ## HI-REDUCTION 33 0.611164 0.611162 ## HI-REDUCTION 35 0.611162 0.611161 ## HI-REDUCTION 37 0.611162 0.611161 ## REFLECTION 39 0.611161 0.611160 ## HI-REDUCTION 41 0.611161 0.611160 ## HI-REDUCTION 43 0.611160 0.611160 ## HI-REDUCTION 45 0.611160 0.611160 ## Exiting from Nelder Mead minimizer ## 47 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.622215 ## Scaled convergence tolerance is 9.27172e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.810537 0.622215 ## LO-REDUCTION 5 0.708024 0.622215 ## LO-REDUCTION 7 0.701509 0.622215 ## HI-REDUCTION 9 0.647897 0.622215 ## HI-REDUCTION 11 0.628038 0.622215 ## LO-REDUCTION 13 0.624931 0.618775 ## HI-REDUCTION 15 0.622215 0.618134 ## HI-REDUCTION 17 0.618831 0.618134 ## HI-REDUCTION 19 0.618775 0.617998 ## HI-REDUCTION 21 0.618134 0.617916 ## HI-REDUCTION 23 0.617998 0.617692 ## LO-REDUCTION 25 0.617916 0.617692 ## HI-REDUCTION 27 0.617750 0.617692 ## HI-REDUCTION 29 0.617744 0.617692 ## REFLECTION 31 0.617714 0.617677 ## LO-REDUCTION 33 0.617692 0.617677 ## LO-REDUCTION 35 0.617688 0.617677 ## LO-REDUCTION 37 0.617682 0.617677 ## LO-REDUCTION 39 0.617678 0.617677 ## HI-REDUCTION 41 0.617678 0.617677 ## LO-REDUCTION 43 0.617678 0.617677 ## HI-REDUCTION 45 0.617677 0.617677 ## LO-REDUCTION 47 0.617677 0.617677 ## HI-REDUCTION 49 0.617677 0.617677 ## LO-REDUCTION 51 0.617677 0.617677 ## HI-REDUCTION 53 0.617677 0.617677 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.460247 ## Scaled convergence tolerance is 6.85821e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595625 0.460247 ## HI-REDUCTION 5 0.566459 0.460247 ## HI-REDUCTION 7 0.518363 0.460247 ## LO-REDUCTION 9 0.492034 0.456962 ## LO-REDUCTION 11 0.460247 0.449228 ## LO-REDUCTION 13 0.456962 0.449228 ## LO-REDUCTION 15 0.449274 0.447361 ## HI-REDUCTION 17 0.449228 0.445644 ## LO-REDUCTION 19 0.447361 0.445527 ## HI-REDUCTION 21 0.445886 0.445527 ## HI-REDUCTION 23 0.445644 0.445527 ## HI-REDUCTION 25 0.445548 0.445486 ## HI-REDUCTION 27 0.445527 0.445486 ## HI-REDUCTION 29 0.445486 0.445475 ## HI-REDUCTION 31 0.445486 0.445469 ## LO-REDUCTION 33 0.445475 0.445466 ## HI-REDUCTION 35 0.445469 0.445466 ## HI-REDUCTION 37 0.445468 0.445466 ## LO-REDUCTION 39 0.445466 0.445466 ## HI-REDUCTION 41 0.445466 0.445466 ## HI-REDUCTION 43 0.445466 0.445466 ## HI-REDUCTION 45 0.445466 0.445466 ## HI-REDUCTION 47 0.445466 0.445466 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.676910 ## Scaled convergence tolerance is 1.00867e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.785600 0.676910 ## HI-REDUCTION 5 0.776660 0.676910 ## HI-REDUCTION 7 0.717414 0.676910 ## HI-REDUCTION 9 0.695463 0.676910 ## HI-REDUCTION 11 0.693745 0.676910 ## LO-REDUCTION 13 0.684729 0.674914 ## HI-REDUCTION 15 0.677731 0.674914 ## HI-REDUCTION 17 0.676910 0.674914 ## HI-REDUCTION 19 0.675815 0.674914 ## LO-REDUCTION 21 0.675500 0.674914 ## LO-REDUCTION 23 0.674947 0.674914 ## HI-REDUCTION 25 0.674923 0.674858 ## HI-REDUCTION 27 0.674914 0.674842 ## LO-REDUCTION 29 0.674858 0.674842 ## HI-REDUCTION 31 0.674852 0.674842 ## LO-REDUCTION 33 0.674843 0.674840 ## HI-REDUCTION 35 0.674842 0.674840 ## HI-REDUCTION 37 0.674840 0.674840 ## HI-REDUCTION 39 0.674840 0.674840 ## HI-REDUCTION 41 0.674840 0.674839 ## HI-REDUCTION 43 0.674840 0.674839 ## HI-REDUCTION 45 0.674839 0.674839 ## HI-REDUCTION 47 0.674839 0.674839 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.509735 ## Scaled convergence tolerance is 7.59564e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.677654 0.509735 ## HI-REDUCTION 5 0.571044 0.509735 ## LO-REDUCTION 7 0.536958 0.481215 ## HI-REDUCTION 9 0.509735 0.481215 ## HI-REDUCTION 11 0.489270 0.475982 ## REFLECTION 13 0.481215 0.460989 ## HI-REDUCTION 15 0.475982 0.460989 ## LO-REDUCTION 17 0.467155 0.460989 ## LO-REDUCTION 19 0.462470 0.459736 ## HI-REDUCTION 21 0.460989 0.459736 ## HI-REDUCTION 23 0.460298 0.459736 ## HI-REDUCTION 25 0.460048 0.459736 ## LO-REDUCTION 27 0.459883 0.459736 ## HI-REDUCTION 29 0.459798 0.459736 ## REFLECTION 31 0.459750 0.459727 ## HI-REDUCTION 33 0.459736 0.459712 ## HI-REDUCTION 35 0.459727 0.459712 ## HI-REDUCTION 37 0.459713 0.459712 ## HI-REDUCTION 39 0.459712 0.459709 ## HI-REDUCTION 41 0.459712 0.459709 ## LO-REDUCTION 43 0.459709 0.459709 ## HI-REDUCTION 45 0.459709 0.459709 ## HI-REDUCTION 47 0.459709 0.459709 ## HI-REDUCTION 49 0.459709 0.459709 ## LO-REDUCTION 51 0.459709 0.459709 ## HI-REDUCTION 53 0.459709 0.459709 ## HI-REDUCTION 55 0.459709 0.459709 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.591495 ## Scaled convergence tolerance is 8.81396e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.694478 0.591495 ## HI-REDUCTION 5 0.689410 0.591495 ## HI-REDUCTION 7 0.630600 0.591495 ## HI-REDUCTION 9 0.608476 0.591495 ## HI-REDUCTION 11 0.607354 0.591495 ## LO-REDUCTION 13 0.598496 0.589559 ## HI-REDUCTION 15 0.592005 0.589559 ## HI-REDUCTION 17 0.591495 0.589559 ## HI-REDUCTION 19 0.590293 0.589559 ## LO-REDUCTION 21 0.590100 0.589559 ## LO-REDUCTION 23 0.589652 0.589559 ## HI-REDUCTION 25 0.589625 0.589550 ## HI-REDUCTION 27 0.589559 0.589531 ## HI-REDUCTION 29 0.589550 0.589530 ## REFLECTION 31 0.589531 0.589523 ## HI-REDUCTION 33 0.589530 0.589521 ## HI-REDUCTION 35 0.589523 0.589519 ## HI-REDUCTION 37 0.589521 0.589519 ## REFLECTION 39 0.589519 0.589518 ## HI-REDUCTION 41 0.589519 0.589518 ## HI-REDUCTION 43 0.589518 0.589518 ## HI-REDUCTION 45 0.589518 0.589518 ## HI-REDUCTION 47 0.589518 0.589518 ## HI-REDUCTION 49 0.589518 0.589518 ## HI-REDUCTION 51 0.589518 0.589518 ## LO-REDUCTION 53 0.589518 0.589518 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.685105 ## Scaled convergence tolerance is 1.02089e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.831025 0.685105 ## HI-REDUCTION 5 0.779278 0.685105 ## HI-REDUCTION 7 0.723822 0.685105 ## HI-REDUCTION 9 0.715486 0.685105 ## LO-REDUCTION 11 0.703496 0.685105 ## LO-REDUCTION 13 0.690733 0.685105 ## HI-REDUCTION 15 0.688502 0.685105 ## LO-REDUCTION 17 0.686844 0.685105 ## HI-REDUCTION 19 0.686292 0.685105 ## LO-REDUCTION 21 0.685741 0.685065 ## LO-REDUCTION 23 0.685126 0.685065 ## HI-REDUCTION 25 0.685105 0.685036 ## HI-REDUCTION 27 0.685065 0.685033 ## HI-REDUCTION 29 0.685036 0.685017 ## HI-REDUCTION 31 0.685033 0.685017 ## REFLECTION 33 0.685021 0.685017 ## HI-REDUCTION 35 0.685017 0.685013 ## LO-REDUCTION 37 0.685017 0.685013 ## HI-REDUCTION 39 0.685014 0.685013 ## HI-REDUCTION 41 0.685013 0.685013 ## HI-REDUCTION 43 0.685013 0.685013 ## HI-REDUCTION 45 0.685013 0.685012 ## LO-REDUCTION 47 0.685013 0.685012 ## HI-REDUCTION 49 0.685013 0.685012 ## REFLECTION 51 0.685012 0.685012 ## HI-REDUCTION 53 0.685012 0.685012 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.599833 ## Scaled convergence tolerance is 8.93822e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.679547 0.599833 ## HI-REDUCTION 5 0.669670 0.598766 ## HI-REDUCTION 7 0.619864 0.598766 ## HI-REDUCTION 9 0.601289 0.598766 ## HI-REDUCTION 11 0.599833 0.595275 ## HI-REDUCTION 13 0.598766 0.593197 ## HI-REDUCTION 15 0.595275 0.593197 ## REFLECTION 17 0.594349 0.593131 ## HI-REDUCTION 19 0.593197 0.593056 ## HI-REDUCTION 21 0.593131 0.592630 ## HI-REDUCTION 23 0.593056 0.592630 ## REFLECTION 25 0.592768 0.592625 ## HI-REDUCTION 27 0.592630 0.592601 ## HI-REDUCTION 29 0.592625 0.592570 ## HI-REDUCTION 31 0.592601 0.592570 ## HI-REDUCTION 33 0.592573 0.592570 ## HI-REDUCTION 35 0.592571 0.592566 ## HI-REDUCTION 37 0.592570 0.592564 ## HI-REDUCTION 39 0.592566 0.592564 ## REFLECTION 41 0.592565 0.592564 ## HI-REDUCTION 43 0.592564 0.592564 ## HI-REDUCTION 45 0.592564 0.592564 ## HI-REDUCTION 47 0.592564 0.592564 ## HI-REDUCTION 49 0.592564 0.592564 ## HI-REDUCTION 51 0.592564 0.592564 ## HI-REDUCTION 53 0.592564 0.592564 ## HI-REDUCTION 55 0.592564 0.592564 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.499725 ## Scaled convergence tolerance is 7.44649e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.640893 0.499725 ## HI-REDUCTION 5 0.608286 0.499725 ## HI-REDUCTION 7 0.549849 0.499725 ## HI-REDUCTION 9 0.536574 0.499725 ## LO-REDUCTION 11 0.526483 0.499725 ## LO-REDUCTION 13 0.509528 0.499725 ## LO-REDUCTION 15 0.501752 0.498303 ## LO-REDUCTION 17 0.499725 0.498303 ## LO-REDUCTION 19 0.498585 0.498141 ## HI-REDUCTION 21 0.498303 0.497898 ## HI-REDUCTION 23 0.498141 0.497898 ## LO-REDUCTION 25 0.497996 0.497876 ## HI-REDUCTION 27 0.497898 0.497875 ## HI-REDUCTION 29 0.497876 0.497866 ## HI-REDUCTION 31 0.497875 0.497863 ## HI-REDUCTION 33 0.497866 0.497861 ## HI-REDUCTION 35 0.497863 0.497861 ## REFLECTION 37 0.497861 0.497861 ## HI-REDUCTION 39 0.497861 0.497860 ## HI-REDUCTION 41 0.497861 0.497860 ## HI-REDUCTION 43 0.497860 0.497860 ## HI-REDUCTION 45 0.497860 0.497860 ## LO-REDUCTION 47 0.497860 0.497860 ## HI-REDUCTION 49 0.497860 0.497860 ## REFLECTION 51 0.497860 0.497860 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.582547 ## Scaled convergence tolerance is 8.68063e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.739057 0.582547 ## HI-REDUCTION 5 0.671122 0.582547 ## HI-REDUCTION 7 0.620619 0.582547 ## HI-REDUCTION 9 0.620032 0.582547 ## LO-REDUCTION 11 0.603066 0.582547 ## LO-REDUCTION 13 0.585978 0.581524 ## HI-REDUCTION 15 0.582547 0.581524 ## HI-REDUCTION 17 0.582306 0.581270 ## HI-REDUCTION 19 0.581524 0.581270 ## HI-REDUCTION 21 0.581296 0.580965 ## LO-REDUCTION 23 0.581270 0.580927 ## LO-REDUCTION 25 0.580976 0.580927 ## HI-REDUCTION 27 0.580965 0.580915 ## LO-REDUCTION 29 0.580927 0.580908 ## HI-REDUCTION 31 0.580915 0.580898 ## HI-REDUCTION 33 0.580908 0.580890 ## LO-REDUCTION 35 0.580898 0.580890 ## HI-REDUCTION 37 0.580891 0.580890 ## LO-REDUCTION 39 0.580891 0.580889 ## LO-REDUCTION 41 0.580890 0.580889 ## LO-REDUCTION 43 0.580889 0.580889 ## LO-REDUCTION 45 0.580889 0.580889 ## LO-REDUCTION 47 0.580889 0.580889 ## HI-REDUCTION 49 0.580889 0.580889 ## REFLECTION 51 0.580889 0.580889 ## HI-REDUCTION 53 0.580889 0.580889 ## EXTENSION 55 0.580889 0.580889 ## LO-REDUCTION 57 0.580889 0.580889 ## EXTENSION 59 0.580889 0.580889 ## HI-REDUCTION 61 0.580889 0.580889 ## EXTENSION 63 0.580889 0.580889 ## LO-REDUCTION 65 0.580889 0.580889 ## EXTENSION 67 0.580889 0.580889 ## EXTENSION 69 0.580889 0.580889 ## HI-REDUCTION 71 0.580889 0.580889 ## REFLECTION 73 0.580889 0.580889 ## HI-REDUCTION 75 0.580889 0.580889 ## Exiting from Nelder Mead minimizer ## 77 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.591532 ## Scaled convergence tolerance is 8.81451e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.760876 0.591532 ## HI-REDUCTION 5 0.687641 0.591532 ## HI-REDUCTION 7 0.667766 0.591532 ## REFLECTION 9 0.623998 0.571125 ## LO-REDUCTION 11 0.591532 0.556044 ## HI-REDUCTION 13 0.571125 0.556044 ## HI-REDUCTION 15 0.566313 0.556044 ## LO-REDUCTION 17 0.557740 0.553203 ## HI-REDUCTION 19 0.556044 0.553203 ## HI-REDUCTION 21 0.554297 0.553203 ## LO-REDUCTION 23 0.553805 0.553201 ## HI-REDUCTION 25 0.553215 0.553201 ## HI-REDUCTION 27 0.553203 0.553080 ## HI-REDUCTION 29 0.553201 0.553054 ## LO-REDUCTION 31 0.553080 0.553054 ## HI-REDUCTION 33 0.553073 0.553053 ## LO-REDUCTION 35 0.553054 0.553051 ## HI-REDUCTION 37 0.553053 0.553049 ## HI-REDUCTION 39 0.553051 0.553049 ## HI-REDUCTION 41 0.553049 0.553049 ## HI-REDUCTION 43 0.553049 0.553049 ## HI-REDUCTION 45 0.553049 0.553049 ## HI-REDUCTION 47 0.553049 0.553049 ## REFLECTION 49 0.553049 0.553049 ## HI-REDUCTION 51 0.553049 0.553049 ## HI-REDUCTION 53 0.553049 0.553049 ## HI-REDUCTION 55 0.553049 0.553049 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.560681 ## Scaled convergence tolerance is 8.3548e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.681649 0.560681 ## HI-REDUCTION 5 0.655570 0.560681 ## HI-REDUCTION 7 0.607463 0.560681 ## LO-REDUCTION 9 0.584576 0.560255 ## LO-REDUCTION 11 0.560681 0.555462 ## HI-REDUCTION 13 0.560255 0.554575 ## HI-REDUCTION 15 0.555462 0.553442 ## HI-REDUCTION 17 0.554575 0.553314 ## REFLECTION 19 0.553442 0.553182 ## HI-REDUCTION 21 0.553314 0.552740 ## HI-REDUCTION 23 0.553182 0.552589 ## HI-REDUCTION 25 0.552740 0.552589 ## LO-REDUCTION 27 0.552704 0.552556 ## HI-REDUCTION 29 0.552589 0.552556 ## HI-REDUCTION 31 0.552570 0.552548 ## HI-REDUCTION 33 0.552556 0.552548 ## HI-REDUCTION 35 0.552551 0.552548 ## HI-REDUCTION 37 0.552548 0.552546 ## HI-REDUCTION 39 0.552548 0.552545 ## LO-REDUCTION 41 0.552546 0.552545 ## HI-REDUCTION 43 0.552545 0.552545 ## HI-REDUCTION 45 0.552545 0.552545 ## HI-REDUCTION 47 0.552545 0.552545 ## HI-REDUCTION 49 0.552545 0.552545 ## HI-REDUCTION 51 0.552545 0.552545 ## HI-REDUCTION 53 0.552545 0.552545 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.691915 ## Scaled convergence tolerance is 1.03103e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.860556 0.691915 ## HI-REDUCTION 5 0.801679 0.691915 ## HI-REDUCTION 7 0.744326 0.691915 ## HI-REDUCTION 9 0.739412 0.691915 ## LO-REDUCTION 11 0.722397 0.691915 ## LO-REDUCTION 13 0.698927 0.688279 ## LO-REDUCTION 15 0.691915 0.687937 ## LO-REDUCTION 17 0.688279 0.687839 ## HI-REDUCTION 19 0.687937 0.687499 ## HI-REDUCTION 21 0.687839 0.687363 ## HI-REDUCTION 23 0.687499 0.687363 ## HI-REDUCTION 25 0.687458 0.687363 ## HI-REDUCTION 27 0.687383 0.687363 ## HI-REDUCTION 29 0.687365 0.687347 ## HI-REDUCTION 31 0.687363 0.687340 ## HI-REDUCTION 33 0.687347 0.687337 ## LO-REDUCTION 35 0.687340 0.687337 ## HI-REDUCTION 37 0.687337 0.687336 ## HI-REDUCTION 39 0.687337 0.687336 ## LO-REDUCTION 41 0.687336 0.687336 ## HI-REDUCTION 43 0.687336 0.687336 ## HI-REDUCTION 45 0.687336 0.687336 ## LO-REDUCTION 47 0.687336 0.687336 ## HI-REDUCTION 49 0.687336 0.687336 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.459809 ## Scaled convergence tolerance is 6.85168e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.601866 0.459809 ## HI-REDUCTION 5 0.551544 0.459809 ## HI-REDUCTION 7 0.501147 0.459809 ## HI-REDUCTION 9 0.494955 0.459809 ## LO-REDUCTION 11 0.482140 0.459809 ## LO-REDUCTION 13 0.466706 0.459809 ## LO-REDUCTION 15 0.460665 0.458807 ## HI-REDUCTION 17 0.459809 0.458807 ## HI-REDUCTION 19 0.458951 0.458712 ## LO-REDUCTION 21 0.458807 0.458595 ## HI-REDUCTION 23 0.458712 0.458531 ## HI-REDUCTION 25 0.458595 0.458527 ## LO-REDUCTION 27 0.458531 0.458499 ## HI-REDUCTION 29 0.458527 0.458497 ## HI-REDUCTION 31 0.458503 0.458497 ## HI-REDUCTION 33 0.458499 0.458497 ## HI-REDUCTION 35 0.458497 0.458496 ## HI-REDUCTION 37 0.458497 0.458495 ## LO-REDUCTION 39 0.458496 0.458495 ## HI-REDUCTION 41 0.458495 0.458495 ## HI-REDUCTION 43 0.458495 0.458495 ## HI-REDUCTION 45 0.458495 0.458495 ## HI-REDUCTION 47 0.458495 0.458495 ## HI-REDUCTION 49 0.458495 0.458495 ## HI-REDUCTION 51 0.458495 0.458495 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.546488 ## Scaled convergence tolerance is 8.1433e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.674401 0.546488 ## HI-REDUCTION 5 0.640320 0.546488 ## HI-REDUCTION 7 0.584994 0.546488 ## HI-REDUCTION 9 0.571998 0.546488 ## LO-REDUCTION 11 0.564160 0.546488 ## HI-REDUCTION 13 0.554473 0.546488 ## LO-REDUCTION 15 0.554300 0.546488 ## LO-REDUCTION 17 0.548616 0.546341 ## LO-REDUCTION 19 0.546697 0.546341 ## HI-REDUCTION 21 0.546488 0.546305 ## HI-REDUCTION 23 0.546341 0.546248 ## HI-REDUCTION 25 0.546305 0.546235 ## LO-REDUCTION 27 0.546248 0.546231 ## HI-REDUCTION 29 0.546235 0.546222 ## HI-REDUCTION 31 0.546231 0.546219 ## LO-REDUCTION 33 0.546222 0.546219 ## HI-REDUCTION 35 0.546219 0.546218 ## HI-REDUCTION 37 0.546219 0.546218 ## LO-REDUCTION 39 0.546218 0.546218 ## HI-REDUCTION 41 0.546218 0.546218 ## LO-REDUCTION 43 0.546218 0.546218 ## HI-REDUCTION 45 0.546218 0.546217 ## HI-REDUCTION 47 0.546218 0.546217 ## LO-REDUCTION 49 0.546217 0.546217 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.481520 ## Scaled convergence tolerance is 7.17521e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.599929 0.481520 ## HI-REDUCTION 5 0.587969 0.481520 ## HI-REDUCTION 7 0.528572 0.481520 ## HI-REDUCTION 9 0.508383 0.481520 ## LO-REDUCTION 11 0.504648 0.481520 ## LO-REDUCTION 13 0.492677 0.481520 ## LO-REDUCTION 15 0.484624 0.480430 ## LO-REDUCTION 17 0.481520 0.480065 ## LO-REDUCTION 19 0.480430 0.480013 ## HI-REDUCTION 21 0.480065 0.479871 ## HI-REDUCTION 23 0.480013 0.479719 ## LO-REDUCTION 25 0.479871 0.479719 ## HI-REDUCTION 27 0.479796 0.479719 ## REFLECTION 29 0.479764 0.479718 ## LO-REDUCTION 31 0.479719 0.479706 ## HI-REDUCTION 33 0.479718 0.479706 ## HI-REDUCTION 35 0.479706 0.479706 ## HI-REDUCTION 37 0.479706 0.479704 ## HI-REDUCTION 39 0.479706 0.479704 ## LO-REDUCTION 41 0.479704 0.479704 ## HI-REDUCTION 43 0.479704 0.479704 ## HI-REDUCTION 45 0.479704 0.479704 ## HI-REDUCTION 47 0.479704 0.479704 ## LO-REDUCTION 49 0.479704 0.479704 ## HI-REDUCTION 51 0.479704 0.479704 ## HI-REDUCTION 53 0.479704 0.479704 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.362732 ## Scaled convergence tolerance is 5.40512e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.454957 0.362732 ## HI-REDUCTION 5 0.440590 0.362732 ## HI-REDUCTION 7 0.389880 0.362732 ## HI-REDUCTION 9 0.374580 0.362732 ## HI-REDUCTION 11 0.371778 0.362732 ## REFLECTION 13 0.364796 0.360495 ## HI-REDUCTION 15 0.362732 0.359902 ## HI-REDUCTION 17 0.360495 0.359849 ## HI-REDUCTION 19 0.359902 0.359024 ## HI-REDUCTION 21 0.359849 0.359024 ## LO-REDUCTION 23 0.359201 0.358889 ## HI-REDUCTION 25 0.359024 0.358889 ## HI-REDUCTION 27 0.358948 0.358889 ## LO-REDUCTION 29 0.358904 0.358889 ## HI-REDUCTION 31 0.358891 0.358881 ## HI-REDUCTION 33 0.358889 0.358881 ## HI-REDUCTION 35 0.358881 0.358879 ## REFLECTION 37 0.358881 0.358878 ## HI-REDUCTION 39 0.358879 0.358878 ## HI-REDUCTION 41 0.358878 0.358877 ## HI-REDUCTION 43 0.358878 0.358877 ## LO-REDUCTION 45 0.358877 0.358877 ## HI-REDUCTION 47 0.358877 0.358877 ## LO-REDUCTION 49 0.358877 0.358877 ## HI-REDUCTION 51 0.358877 0.358877 ## HI-REDUCTION 53 0.358877 0.358877 ## LO-REDUCTION 55 0.358877 0.358877 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.533882 ## Scaled convergence tolerance is 7.95546e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.647255 0.533882 ## HI-REDUCTION 5 0.622845 0.533882 ## HI-REDUCTION 7 0.569033 0.533882 ## HI-REDUCTION 9 0.553738 0.533882 ## HI-REDUCTION 11 0.548768 0.533882 ## LO-REDUCTION 13 0.542760 0.533882 ## HI-REDUCTION 15 0.536437 0.533882 ## LO-REDUCTION 17 0.534650 0.533882 ## HI-REDUCTION 19 0.534093 0.533742 ## LO-REDUCTION 21 0.533882 0.533695 ## HI-REDUCTION 23 0.533742 0.533641 ## HI-REDUCTION 25 0.533695 0.533608 ## LO-REDUCTION 27 0.533641 0.533599 ## HI-REDUCTION 29 0.533608 0.533599 ## HI-REDUCTION 31 0.533602 0.533596 ## HI-REDUCTION 33 0.533599 0.533596 ## HI-REDUCTION 35 0.533596 0.533596 ## HI-REDUCTION 37 0.533596 0.533595 ## HI-REDUCTION 39 0.533596 0.533595 ## LO-REDUCTION 41 0.533595 0.533595 ## HI-REDUCTION 43 0.533595 0.533595 ## LO-REDUCTION 45 0.533595 0.533595 ## HI-REDUCTION 47 0.533595 0.533595 ## HI-REDUCTION 49 0.533595 0.533595 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.571933 ## Scaled convergence tolerance is 8.52246e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.668545 0.571933 ## HI-REDUCTION 5 0.667817 0.571933 ## HI-REDUCTION 7 0.600808 0.571933 ## HI-REDUCTION 9 0.588120 0.571933 ## HI-REDUCTION 11 0.581290 0.571933 ## LO-REDUCTION 13 0.574816 0.568417 ## HI-REDUCTION 15 0.571933 0.568417 ## HI-REDUCTION 17 0.569443 0.568417 ## HI-REDUCTION 19 0.569199 0.568417 ## LO-REDUCTION 21 0.568570 0.568341 ## HI-REDUCTION 23 0.568417 0.568327 ## HI-REDUCTION 25 0.568341 0.568297 ## HI-REDUCTION 27 0.568327 0.568296 ## HI-REDUCTION 29 0.568297 0.568293 ## HI-REDUCTION 31 0.568296 0.568286 ## HI-REDUCTION 33 0.568293 0.568286 ## LO-REDUCTION 35 0.568289 0.568286 ## HI-REDUCTION 37 0.568288 0.568286 ## LO-REDUCTION 39 0.568287 0.568286 ## HI-REDUCTION 41 0.568286 0.568286 ## HI-REDUCTION 43 0.568286 0.568286 ## LO-REDUCTION 45 0.568286 0.568286 ## HI-REDUCTION 47 0.568286 0.568286 ## HI-REDUCTION 49 0.568286 0.568286 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.594867 ## Scaled convergence tolerance is 8.86421e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.733016 0.594867 ## HI-REDUCTION 5 0.706121 0.594867 ## HI-REDUCTION 7 0.643515 0.594867 ## HI-REDUCTION 9 0.627810 0.594867 ## LO-REDUCTION 11 0.619071 0.594867 ## LO-REDUCTION 13 0.603980 0.594867 ## LO-REDUCTION 15 0.598576 0.594867 ## LO-REDUCTION 17 0.595741 0.594430 ## HI-REDUCTION 19 0.594867 0.594359 ## HI-REDUCTION 21 0.594430 0.594328 ## HI-REDUCTION 23 0.594359 0.594247 ## HI-REDUCTION 25 0.594328 0.594225 ## HI-REDUCTION 27 0.594247 0.594225 ## HI-REDUCTION 29 0.594242 0.594223 ## HI-REDUCTION 31 0.594225 0.594222 ## HI-REDUCTION 33 0.594223 0.594217 ## LO-REDUCTION 35 0.594222 0.594217 ## HI-REDUCTION 37 0.594219 0.594217 ## LO-REDUCTION 39 0.594218 0.594217 ## LO-REDUCTION 41 0.594217 0.594217 ## HI-REDUCTION 43 0.594217 0.594217 ## HI-REDUCTION 45 0.594217 0.594217 ## HI-REDUCTION 47 0.594217 0.594217 ## HI-REDUCTION 49 0.594217 0.594217 ## LO-REDUCTION 51 0.594217 0.594217 ## HI-REDUCTION 53 0.594217 0.594217 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.621944 ## Scaled convergence tolerance is 9.26768e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.764670 0.621944 ## HI-REDUCTION 5 0.709613 0.621944 ## HI-REDUCTION 7 0.657822 0.621944 ## HI-REDUCTION 9 0.652043 0.621944 ## LO-REDUCTION 11 0.639335 0.621944 ## LO-REDUCTION 13 0.626730 0.621944 ## HI-REDUCTION 15 0.624247 0.621944 ## HI-REDUCTION 17 0.623147 0.621944 ## LO-REDUCTION 19 0.622320 0.621872 ## HI-REDUCTION 21 0.621944 0.621753 ## HI-REDUCTION 23 0.621872 0.621746 ## LO-REDUCTION 25 0.621753 0.621716 ## HI-REDUCTION 27 0.621746 0.621701 ## HI-REDUCTION 29 0.621716 0.621699 ## LO-REDUCTION 31 0.621701 0.621699 ## HI-REDUCTION 33 0.621699 0.621695 ## LO-REDUCTION 35 0.621699 0.621695 ## LO-REDUCTION 37 0.621696 0.621695 ## HI-REDUCTION 39 0.621695 0.621695 ## LO-REDUCTION 41 0.621695 0.621695 ## HI-REDUCTION 43 0.621695 0.621695 ## HI-REDUCTION 45 0.621695 0.621695 ## HI-REDUCTION 47 0.621695 0.621695 ## HI-REDUCTION 49 0.621695 0.621695 ## HI-REDUCTION 51 0.621695 0.621695 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.568834 ## Scaled convergence tolerance is 8.47629e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.664628 0.568834 ## HI-REDUCTION 5 0.646727 0.568834 ## HI-REDUCTION 7 0.596693 0.568834 ## HI-REDUCTION 9 0.579577 0.568834 ## HI-REDUCTION 11 0.577361 0.568834 ## REFLECTION 13 0.569635 0.566510 ## HI-REDUCTION 15 0.568834 0.564766 ## LO-REDUCTION 17 0.566510 0.564754 ## HI-REDUCTION 19 0.564766 0.564351 ## HI-REDUCTION 21 0.564754 0.563966 ## LO-REDUCTION 23 0.564351 0.563966 ## HI-REDUCTION 25 0.564053 0.563966 ## LO-REDUCTION 27 0.564025 0.563956 ## HI-REDUCTION 29 0.563966 0.563943 ## HI-REDUCTION 31 0.563956 0.563938 ## REFLECTION 33 0.563943 0.563935 ## HI-REDUCTION 35 0.563938 0.563932 ## HI-REDUCTION 37 0.563935 0.563931 ## HI-REDUCTION 39 0.563932 0.563931 ## LO-REDUCTION 41 0.563931 0.563930 ## HI-REDUCTION 43 0.563931 0.563930 ## HI-REDUCTION 45 0.563930 0.563930 ## LO-REDUCTION 47 0.563930 0.563930 ## HI-REDUCTION 49 0.563930 0.563930 ## HI-REDUCTION 51 0.563930 0.563930 ## LO-REDUCTION 53 0.563930 0.563930 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.551090 ## Scaled convergence tolerance is 8.21188e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.739905 0.551090 ## LO-REDUCTION 5 0.667719 0.551090 ## REFLECTION 7 0.655222 0.540546 ## HI-REDUCTION 9 0.586407 0.540546 ## HI-REDUCTION 11 0.556422 0.540546 ## REFLECTION 13 0.551090 0.538485 ## HI-REDUCTION 15 0.540546 0.536176 ## HI-REDUCTION 17 0.538485 0.532491 ## HI-REDUCTION 19 0.536176 0.532491 ## HI-REDUCTION 21 0.533341 0.532491 ## HI-REDUCTION 23 0.533289 0.532388 ## LO-REDUCTION 25 0.532491 0.532241 ## HI-REDUCTION 27 0.532388 0.532118 ## HI-REDUCTION 29 0.532241 0.532041 ## LO-REDUCTION 31 0.532118 0.532041 ## HI-REDUCTION 33 0.532075 0.532041 ## REFLECTION 35 0.532048 0.532028 ## HI-REDUCTION 37 0.532041 0.532028 ## HI-REDUCTION 39 0.532030 0.532027 ## REFLECTION 41 0.532028 0.532025 ## HI-REDUCTION 43 0.532027 0.532024 ## HI-REDUCTION 45 0.532025 0.532023 ## HI-REDUCTION 47 0.532024 0.532023 ## HI-REDUCTION 49 0.532024 0.532023 ## HI-REDUCTION 51 0.532023 0.532023 ## HI-REDUCTION 53 0.532023 0.532023 ## LO-REDUCTION 55 0.532023 0.532023 ## HI-REDUCTION 57 0.532023 0.532023 ## HI-REDUCTION 59 0.532023 0.532023 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.533546 ## Scaled convergence tolerance is 7.95045e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.823126 0.533546 ## LO-REDUCTION 5 0.599163 0.533546 ## REFLECTION 7 0.569045 0.513700 ## HI-REDUCTION 9 0.533546 0.513700 ## LO-REDUCTION 11 0.516383 0.504567 ## HI-REDUCTION 13 0.513700 0.501713 ## HI-REDUCTION 15 0.504567 0.499515 ## LO-REDUCTION 17 0.501713 0.498896 ## HI-REDUCTION 19 0.499515 0.498896 ## HI-REDUCTION 21 0.498987 0.498456 ## HI-REDUCTION 23 0.498896 0.498456 ## LO-REDUCTION 25 0.498566 0.498456 ## HI-REDUCTION 27 0.498482 0.498426 ## LO-REDUCTION 29 0.498456 0.498418 ## HI-REDUCTION 31 0.498426 0.498413 ## HI-REDUCTION 33 0.498418 0.498406 ## LO-REDUCTION 35 0.498413 0.498406 ## HI-REDUCTION 37 0.498406 0.498406 ## HI-REDUCTION 39 0.498406 0.498405 ## HI-REDUCTION 41 0.498406 0.498405 ## HI-REDUCTION 43 0.498405 0.498405 ## REFLECTION 45 0.498405 0.498405 ## HI-REDUCTION 47 0.498405 0.498405 ## HI-REDUCTION 49 0.498405 0.498405 ## HI-REDUCTION 51 0.498405 0.498405 ## HI-REDUCTION 53 0.498405 0.498405 ## HI-REDUCTION 55 0.498405 0.498405 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.471266 ## Scaled convergence tolerance is 7.0224e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.614025 0.471266 ## HI-REDUCTION 5 0.573752 0.471266 ## HI-REDUCTION 7 0.515562 0.471266 ## HI-REDUCTION 9 0.505746 0.471266 ## LO-REDUCTION 11 0.493995 0.471266 ## LO-REDUCTION 13 0.476865 0.470594 ## HI-REDUCTION 15 0.472096 0.470594 ## LO-REDUCTION 17 0.471266 0.470100 ## HI-REDUCTION 19 0.470594 0.470041 ## HI-REDUCTION 21 0.470100 0.469945 ## LO-REDUCTION 23 0.470041 0.469929 ## HI-REDUCTION 25 0.469945 0.469895 ## HI-REDUCTION 27 0.469929 0.469878 ## REFLECTION 29 0.469895 0.469875 ## HI-REDUCTION 31 0.469878 0.469870 ## HI-REDUCTION 33 0.469875 0.469866 ## HI-REDUCTION 35 0.469870 0.469866 ## HI-REDUCTION 37 0.469867 0.469866 ## HI-REDUCTION 39 0.469866 0.469865 ## HI-REDUCTION 41 0.469866 0.469865 ## LO-REDUCTION 43 0.469865 0.469865 ## HI-REDUCTION 45 0.469865 0.469865 ## HI-REDUCTION 47 0.469865 0.469865 ## HI-REDUCTION 49 0.469865 0.469865 ## LO-REDUCTION 51 0.469865 0.469865 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.574463 ## Scaled convergence tolerance is 8.56016e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.663001 0.574463 ## HI-REDUCTION 5 0.653992 0.574463 ## HI-REDUCTION 7 0.592792 0.574463 ## HI-REDUCTION 9 0.578317 0.574427 ## HI-REDUCTION 11 0.574463 0.568349 ## HI-REDUCTION 13 0.574427 0.568349 ## LO-REDUCTION 15 0.568410 0.566687 ## HI-REDUCTION 17 0.568349 0.566687 ## HI-REDUCTION 19 0.567061 0.566687 ## HI-REDUCTION 21 0.566968 0.566687 ## HI-REDUCTION 23 0.566731 0.566684 ## HI-REDUCTION 25 0.566687 0.566631 ## HI-REDUCTION 27 0.566684 0.566600 ## LO-REDUCTION 29 0.566631 0.566600 ## HI-REDUCTION 31 0.566617 0.566600 ## LO-REDUCTION 33 0.566609 0.566600 ## LO-REDUCTION 35 0.566601 0.566599 ## HI-REDUCTION 37 0.566600 0.566599 ## LO-REDUCTION 39 0.566599 0.566598 ## HI-REDUCTION 41 0.566599 0.566598 ## HI-REDUCTION 43 0.566598 0.566598 ## HI-REDUCTION 45 0.566598 0.566598 ## HI-REDUCTION 47 0.566598 0.566598 ## HI-REDUCTION 49 0.566598 0.566598 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.577943 ## Scaled convergence tolerance is 8.61203e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.727232 0.577943 ## LO-REDUCTION 5 0.632311 0.577943 ## LO-REDUCTION 7 0.622652 0.577943 ## HI-REDUCTION 9 0.589416 0.577943 ## HI-REDUCTION 11 0.580794 0.577943 ## REFLECTION 13 0.578638 0.577525 ## HI-REDUCTION 15 0.577943 0.573829 ## LO-REDUCTION 17 0.577525 0.573829 ## LO-REDUCTION 19 0.574806 0.573829 ## LO-REDUCTION 21 0.574733 0.573829 ## LO-REDUCTION 23 0.574081 0.573696 ## HI-REDUCTION 25 0.573829 0.573696 ## LO-REDUCTION 27 0.573726 0.573691 ## LO-REDUCTION 29 0.573696 0.573680 ## HI-REDUCTION 31 0.573691 0.573671 ## HI-REDUCTION 33 0.573680 0.573667 ## LO-REDUCTION 35 0.573671 0.573666 ## HI-REDUCTION 37 0.573667 0.573666 ## HI-REDUCTION 39 0.573666 0.573665 ## HI-REDUCTION 41 0.573666 0.573665 ## HI-REDUCTION 43 0.573665 0.573665 ## LO-REDUCTION 45 0.573665 0.573665 ## HI-REDUCTION 47 0.573665 0.573665 ## LO-REDUCTION 49 0.573665 0.573665 ## HI-REDUCTION 51 0.573665 0.573665 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.593863 ## Scaled convergence tolerance is 8.84925e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.722320 0.593863 ## HI-REDUCTION 5 0.661061 0.593863 ## HI-REDUCTION 7 0.613907 0.593863 ## HI-REDUCTION 9 0.610663 0.593863 ## LO-REDUCTION 11 0.598769 0.593863 ## HI-REDUCTION 13 0.595552 0.592856 ## LO-REDUCTION 15 0.593863 0.591836 ## HI-REDUCTION 17 0.592856 0.591742 ## HI-REDUCTION 19 0.591836 0.591648 ## HI-REDUCTION 21 0.591742 0.591557 ## HI-REDUCTION 23 0.591648 0.591520 ## HI-REDUCTION 25 0.591557 0.591501 ## LO-REDUCTION 27 0.591520 0.591501 ## HI-REDUCTION 29 0.591501 0.591492 ## HI-REDUCTION 31 0.591501 0.591491 ## REFLECTION 33 0.591492 0.591491 ## HI-REDUCTION 35 0.591491 0.591488 ## HI-REDUCTION 37 0.591491 0.591488 ## HI-REDUCTION 39 0.591488 0.591488 ## HI-REDUCTION 41 0.591488 0.591488 ## HI-REDUCTION 43 0.591488 0.591488 ## HI-REDUCTION 45 0.591488 0.591487 ## LO-REDUCTION 47 0.591488 0.591487 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.678440 ## Scaled convergence tolerance is 1.01095e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.871752 0.678440 ## LO-REDUCTION 5 0.775639 0.678440 ## LO-REDUCTION 7 0.761189 0.678273 ## HI-REDUCTION 9 0.707565 0.678273 ## LO-REDUCTION 11 0.678440 0.673744 ## LO-REDUCTION 13 0.678273 0.672030 ## HI-REDUCTION 15 0.673744 0.671650 ## HI-REDUCTION 17 0.672030 0.669533 ## LO-REDUCTION 19 0.671650 0.669533 ## HI-REDUCTION 21 0.670168 0.669533 ## HI-REDUCTION 23 0.670008 0.669533 ## REFLECTION 25 0.669798 0.669397 ## LO-REDUCTION 27 0.669533 0.669397 ## LO-REDUCTION 29 0.669496 0.669397 ## LO-REDUCTION 31 0.669417 0.669395 ## HI-REDUCTION 33 0.669397 0.669394 ## HI-REDUCTION 35 0.669395 0.669392 ## HI-REDUCTION 37 0.669394 0.669391 ## HI-REDUCTION 39 0.669392 0.669391 ## HI-REDUCTION 41 0.669391 0.669391 ## HI-REDUCTION 43 0.669391 0.669391 ## HI-REDUCTION 45 0.669391 0.669391 ## HI-REDUCTION 47 0.669391 0.669391 ## HI-REDUCTION 49 0.669391 0.669391 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.655526 ## Scaled convergence tolerance is 9.76809e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.824594 0.655526 ## LO-REDUCTION 5 0.708727 0.655526 ## LO-REDUCTION 7 0.704422 0.655526 ## HI-REDUCTION 9 0.667883 0.655526 ## HI-REDUCTION 11 0.660097 0.655526 ## REFLECTION 13 0.656252 0.652702 ## HI-REDUCTION 15 0.655526 0.650238 ## LO-REDUCTION 17 0.652702 0.649788 ## HI-REDUCTION 19 0.650238 0.649788 ## HI-REDUCTION 21 0.650017 0.649600 ## HI-REDUCTION 23 0.649788 0.649600 ## HI-REDUCTION 25 0.649649 0.649600 ## HI-REDUCTION 27 0.649608 0.649571 ## HI-REDUCTION 29 0.649600 0.649563 ## HI-REDUCTION 31 0.649571 0.649557 ## HI-REDUCTION 33 0.649563 0.649557 ## LO-REDUCTION 35 0.649558 0.649555 ## HI-REDUCTION 37 0.649557 0.649554 ## LO-REDUCTION 39 0.649555 0.649554 ## HI-REDUCTION 41 0.649554 0.649554 ## LO-REDUCTION 43 0.649554 0.649553 ## HI-REDUCTION 45 0.649554 0.649553 ## HI-REDUCTION 47 0.649554 0.649553 ## LO-REDUCTION 49 0.649553 0.649553 ## HI-REDUCTION 51 0.649553 0.649553 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.540986 ## Scaled convergence tolerance is 8.06132e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.730514 0.540986 ## HI-REDUCTION 5 0.660050 0.540986 ## HI-REDUCTION 7 0.602342 0.540986 ## HI-REDUCTION 9 0.600412 0.540986 ## LO-REDUCTION 11 0.579842 0.540986 ## REFLECTION 13 0.547923 0.534959 ## REFLECTION 15 0.540986 0.527758 ## HI-REDUCTION 17 0.534959 0.526600 ## HI-REDUCTION 19 0.527758 0.524574 ## HI-REDUCTION 21 0.526600 0.523747 ## HI-REDUCTION 23 0.524574 0.523747 ## HI-REDUCTION 25 0.524056 0.523434 ## LO-REDUCTION 27 0.523747 0.523434 ## HI-REDUCTION 29 0.523460 0.523366 ## HI-REDUCTION 31 0.523434 0.523309 ## HI-REDUCTION 33 0.523366 0.523282 ## HI-REDUCTION 35 0.523309 0.523282 ## LO-REDUCTION 37 0.523299 0.523273 ## HI-REDUCTION 39 0.523282 0.523273 ## HI-REDUCTION 41 0.523275 0.523273 ## HI-REDUCTION 43 0.523274 0.523272 ## HI-REDUCTION 45 0.523273 0.523272 ## LO-REDUCTION 47 0.523273 0.523272 ## HI-REDUCTION 49 0.523272 0.523272 ## LO-REDUCTION 51 0.523272 0.523272 ## HI-REDUCTION 53 0.523272 0.523272 ## HI-REDUCTION 55 0.523272 0.523272 ## LO-REDUCTION 57 0.523272 0.523272 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.542328 ## Scaled convergence tolerance is 8.08131e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.624966 0.542328 ## HI-REDUCTION 5 0.622533 0.542328 ## HI-REDUCTION 7 0.569658 0.542328 ## HI-REDUCTION 9 0.549146 0.542328 ## LO-REDUCTION 11 0.547839 0.538020 ## HI-REDUCTION 13 0.542328 0.538020 ## HI-REDUCTION 15 0.538800 0.537240 ## HI-REDUCTION 17 0.538020 0.537240 ## HI-REDUCTION 19 0.537351 0.537127 ## HI-REDUCTION 21 0.537240 0.536949 ## HI-REDUCTION 23 0.537127 0.536880 ## LO-REDUCTION 25 0.536949 0.536880 ## HI-REDUCTION 27 0.536883 0.536871 ## HI-REDUCTION 29 0.536880 0.536867 ## HI-REDUCTION 31 0.536871 0.536864 ## HI-REDUCTION 33 0.536867 0.536862 ## LO-REDUCTION 35 0.536864 0.536861 ## HI-REDUCTION 37 0.536862 0.536861 ## HI-REDUCTION 39 0.536861 0.536861 ## HI-REDUCTION 41 0.536861 0.536861 ## HI-REDUCTION 43 0.536861 0.536861 ## HI-REDUCTION 45 0.536861 0.536861 ## HI-REDUCTION 47 0.536861 0.536861 ## HI-REDUCTION 49 0.536861 0.536861 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.574089 ## Scaled convergence tolerance is 8.55459e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.678888 0.574089 ## HI-REDUCTION 5 0.629012 0.574089 ## HI-REDUCTION 7 0.588701 0.574089 ## HI-REDUCTION 9 0.583413 0.574089 ## HI-REDUCTION 11 0.575819 0.574089 ## HI-REDUCTION 13 0.574131 0.572233 ## HI-REDUCTION 15 0.574089 0.571700 ## HI-REDUCTION 17 0.572233 0.571333 ## HI-REDUCTION 19 0.571700 0.571333 ## LO-REDUCTION 21 0.571492 0.571267 ## HI-REDUCTION 23 0.571333 0.571209 ## HI-REDUCTION 25 0.571267 0.571209 ## REFLECTION 27 0.571226 0.571199 ## HI-REDUCTION 29 0.571209 0.571183 ## LO-REDUCTION 31 0.571199 0.571183 ## HI-REDUCTION 33 0.571185 0.571183 ## HI-REDUCTION 35 0.571184 0.571181 ## HI-REDUCTION 37 0.571183 0.571181 ## HI-REDUCTION 39 0.571181 0.571180 ## LO-REDUCTION 41 0.571181 0.571180 ## HI-REDUCTION 43 0.571180 0.571180 ## HI-REDUCTION 45 0.571180 0.571180 ## HI-REDUCTION 47 0.571180 0.571180 ## HI-REDUCTION 49 0.571180 0.571180 ## HI-REDUCTION 51 0.571180 0.571180 ## LO-REDUCTION 53 0.571180 0.571180 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.622694 ## Scaled convergence tolerance is 9.27887e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.778554 0.622694 ## LO-REDUCTION 5 0.675776 0.622694 ## LO-REDUCTION 7 0.667924 0.622694 ## HI-REDUCTION 9 0.632848 0.622694 ## HI-REDUCTION 11 0.624374 0.621744 ## REFLECTION 13 0.622694 0.620256 ## HI-REDUCTION 15 0.621744 0.617610 ## HI-REDUCTION 17 0.620256 0.617037 ## LO-REDUCTION 19 0.617831 0.617037 ## LO-REDUCTION 21 0.617610 0.616888 ## LO-REDUCTION 23 0.617037 0.616844 ## LO-REDUCTION 25 0.616888 0.616707 ## HI-REDUCTION 27 0.616844 0.616664 ## HI-REDUCTION 29 0.616707 0.616631 ## LO-REDUCTION 31 0.616664 0.616631 ## HI-REDUCTION 33 0.616634 0.616625 ## REFLECTION 35 0.616631 0.616615 ## HI-REDUCTION 37 0.616625 0.616615 ## LO-REDUCTION 39 0.616619 0.616615 ## REFLECTION 41 0.616616 0.616613 ## HI-REDUCTION 43 0.616615 0.616613 ## REFLECTION 45 0.616614 0.616612 ## LO-REDUCTION 47 0.616613 0.616612 ## LO-REDUCTION 49 0.616612 0.616612 ## HI-REDUCTION 51 0.616612 0.616612 ## LO-REDUCTION 53 0.616612 0.616612 ## LO-REDUCTION 55 0.616612 0.616612 ## HI-REDUCTION 57 0.616612 0.616612 ## HI-REDUCTION 59 0.616612 0.616612 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.520485 ## Scaled convergence tolerance is 7.75583e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.685045 0.520485 ## HI-REDUCTION 5 0.664394 0.520485 ## HI-REDUCTION 7 0.595387 0.520485 ## LO-REDUCTION 9 0.578105 0.520485 ## REFLECTION 11 0.540794 0.508911 ## LO-REDUCTION 13 0.520485 0.508911 ## HI-REDUCTION 15 0.510062 0.508503 ## HI-REDUCTION 17 0.508911 0.506635 ## HI-REDUCTION 19 0.508503 0.505636 ## HI-REDUCTION 21 0.506635 0.505636 ## LO-REDUCTION 23 0.506336 0.505547 ## HI-REDUCTION 25 0.505687 0.505547 ## HI-REDUCTION 27 0.505636 0.505517 ## HI-REDUCTION 29 0.505547 0.505472 ## HI-REDUCTION 31 0.505517 0.505472 ## REFLECTION 33 0.505488 0.505453 ## HI-REDUCTION 35 0.505472 0.505453 ## LO-REDUCTION 37 0.505458 0.505450 ## HI-REDUCTION 39 0.505453 0.505450 ## HI-REDUCTION 41 0.505451 0.505450 ## LO-REDUCTION 43 0.505450 0.505449 ## HI-REDUCTION 45 0.505450 0.505449 ## HI-REDUCTION 47 0.505449 0.505449 ## HI-REDUCTION 49 0.505449 0.505449 ## HI-REDUCTION 51 0.505449 0.505449 ## HI-REDUCTION 53 0.505449 0.505449 ## LO-REDUCTION 55 0.505449 0.505449 ## HI-REDUCTION 57 0.505449 0.505449 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.609325 ## Scaled convergence tolerance is 9.07966e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.809806 0.609325 ## LO-REDUCTION 5 0.696989 0.609325 ## LO-REDUCTION 7 0.683483 0.608640 ## HI-REDUCTION 9 0.634254 0.608640 ## LO-REDUCTION 11 0.609325 0.605687 ## HI-REDUCTION 13 0.608640 0.603439 ## HI-REDUCTION 15 0.605687 0.602172 ## HI-REDUCTION 17 0.603439 0.602172 ## REFLECTION 19 0.602502 0.601343 ## HI-REDUCTION 21 0.602172 0.601343 ## LO-REDUCTION 23 0.601385 0.601185 ## HI-REDUCTION 25 0.601343 0.601175 ## HI-REDUCTION 27 0.601185 0.601166 ## HI-REDUCTION 29 0.601175 0.601143 ## HI-REDUCTION 31 0.601166 0.601143 ## HI-REDUCTION 33 0.601147 0.601143 ## LO-REDUCTION 35 0.601144 0.601140 ## HI-REDUCTION 37 0.601143 0.601139 ## HI-REDUCTION 39 0.601140 0.601139 ## LO-REDUCTION 41 0.601140 0.601139 ## HI-REDUCTION 43 0.601139 0.601139 ## HI-REDUCTION 45 0.601139 0.601139 ## HI-REDUCTION 47 0.601139 0.601139 ## LO-REDUCTION 49 0.601139 0.601139 ## HI-REDUCTION 51 0.601139 0.601139 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.604200 ## Scaled convergence tolerance is 9.00328e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.780605 0.604200 ## LO-REDUCTION 5 0.646963 0.604200 ## LO-REDUCTION 7 0.646568 0.604200 ## HI-REDUCTION 9 0.611875 0.604200 ## HI-REDUCTION 11 0.606789 0.601630 ## REFLECTION 13 0.604200 0.601227 ## LO-REDUCTION 15 0.601630 0.597507 ## HI-REDUCTION 17 0.601227 0.597030 ## HI-REDUCTION 19 0.597507 0.596149 ## LO-REDUCTION 21 0.597030 0.596108 ## HI-REDUCTION 23 0.596149 0.596105 ## HI-REDUCTION 25 0.596108 0.595985 ## LO-REDUCTION 27 0.596105 0.595984 ## LO-REDUCTION 29 0.595985 0.595953 ## HI-REDUCTION 31 0.595984 0.595944 ## HI-REDUCTION 33 0.595953 0.595936 ## HI-REDUCTION 35 0.595944 0.595936 ## REFLECTION 37 0.595939 0.595931 ## HI-REDUCTION 39 0.595936 0.595931 ## REFLECTION 41 0.595931 0.595927 ## REFLECTION 43 0.595931 0.595927 ## REFLECTION 45 0.595927 0.595924 ## REFLECTION 47 0.595927 0.595924 ## REFLECTION 49 0.595924 0.595923 ## LO-REDUCTION 51 0.595924 0.595923 ## HI-REDUCTION 53 0.595923 0.595923 ## HI-REDUCTION 55 0.595923 0.595922 ## REFLECTION 57 0.595923 0.595922 ## HI-REDUCTION 59 0.595922 0.595922 ## HI-REDUCTION 61 0.595922 0.595922 ## HI-REDUCTION 63 0.595922 0.595922 ## LO-REDUCTION 65 0.595922 0.595922 ## Exiting from Nelder Mead minimizer ## 67 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.545196 ## Scaled convergence tolerance is 8.12405e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.645332 0.545196 ## HI-REDUCTION 5 0.624446 0.545196 ## HI-REDUCTION 7 0.577959 0.545196 ## LO-REDUCTION 9 0.558642 0.543442 ## HI-REDUCTION 11 0.545196 0.543442 ## HI-REDUCTION 13 0.544819 0.539722 ## LO-REDUCTION 15 0.543442 0.539188 ## LO-REDUCTION 17 0.539722 0.539188 ## LO-REDUCTION 19 0.539675 0.538899 ## HI-REDUCTION 21 0.539188 0.538778 ## HI-REDUCTION 23 0.538899 0.538778 ## REFLECTION 25 0.538806 0.538694 ## HI-REDUCTION 27 0.538778 0.538679 ## LO-REDUCTION 29 0.538694 0.538673 ## HI-REDUCTION 31 0.538679 0.538667 ## HI-REDUCTION 33 0.538673 0.538666 ## HI-REDUCTION 35 0.538667 0.538666 ## HI-REDUCTION 37 0.538666 0.538664 ## HI-REDUCTION 39 0.538666 0.538664 ## HI-REDUCTION 41 0.538664 0.538664 ## REFLECTION 43 0.538664 0.538664 ## HI-REDUCTION 45 0.538664 0.538664 ## HI-REDUCTION 47 0.538664 0.538664 ## HI-REDUCTION 49 0.538664 0.538664 ## HI-REDUCTION 51 0.538664 0.538664 ## HI-REDUCTION 53 0.538664 0.538664 ## HI-REDUCTION 55 0.538664 0.538664 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.833819 ## Scaled convergence tolerance is 1.24249e-08 ## Stepsize computed as 0.147026 ## BUILD 3 1.035586 0.833819 ## LO-REDUCTION 5 0.888919 0.833819 ## LO-REDUCTION 7 0.885229 0.830422 ## HI-REDUCTION 9 0.842238 0.830422 ## HI-REDUCTION 11 0.833819 0.828858 ## HI-REDUCTION 13 0.830422 0.825698 ## LO-REDUCTION 15 0.828858 0.825698 ## HI-REDUCTION 17 0.826280 0.825698 ## HI-REDUCTION 19 0.826177 0.825643 ## HI-REDUCTION 21 0.825698 0.825595 ## HI-REDUCTION 23 0.825643 0.825394 ## LO-REDUCTION 25 0.825595 0.825394 ## HI-REDUCTION 27 0.825467 0.825394 ## LO-REDUCTION 29 0.825467 0.825394 ## LO-REDUCTION 31 0.825432 0.825394 ## REFLECTION 33 0.825400 0.825377 ## LO-REDUCTION 35 0.825394 0.825377 ## REFLECTION 37 0.825379 0.825376 ## HI-REDUCTION 39 0.825377 0.825376 ## LO-REDUCTION 41 0.825376 0.825375 ## HI-REDUCTION 43 0.825376 0.825375 ## HI-REDUCTION 45 0.825375 0.825375 ## LO-REDUCTION 47 0.825375 0.825375 ## HI-REDUCTION 49 0.825375 0.825375 ## REFLECTION 51 0.825375 0.825375 ## HI-REDUCTION 53 0.825375 0.825375 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.562230 ## Scaled convergence tolerance is 8.37789e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.680158 0.562230 ## HI-REDUCTION 5 0.602472 0.562230 ## HI-REDUCTION 7 0.564518 0.562230 ## HI-REDUCTION 9 0.563541 0.554159 ## HI-REDUCTION 11 0.562230 0.554110 ## HI-REDUCTION 13 0.554159 0.553898 ## HI-REDUCTION 15 0.554110 0.552900 ## HI-REDUCTION 17 0.553898 0.552852 ## HI-REDUCTION 19 0.552902 0.552852 ## HI-REDUCTION 21 0.552900 0.552732 ## HI-REDUCTION 23 0.552852 0.552732 ## HI-REDUCTION 25 0.552750 0.552732 ## HI-REDUCTION 27 0.552739 0.552725 ## HI-REDUCTION 29 0.552732 0.552719 ## HI-REDUCTION 31 0.552725 0.552719 ## REFLECTION 33 0.552719 0.552718 ## HI-REDUCTION 35 0.552719 0.552716 ## HI-REDUCTION 37 0.552718 0.552716 ## HI-REDUCTION 39 0.552716 0.552716 ## HI-REDUCTION 41 0.552716 0.552716 ## HI-REDUCTION 43 0.552716 0.552716 ## HI-REDUCTION 45 0.552716 0.552716 ## HI-REDUCTION 47 0.552716 0.552716 ## HI-REDUCTION 49 0.552716 0.552716 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.567536 ## Scaled convergence tolerance is 8.45694e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.695738 0.567536 ## HI-REDUCTION 5 0.631433 0.567536 ## HI-REDUCTION 7 0.585193 0.567536 ## HI-REDUCTION 9 0.583188 0.567536 ## LO-REDUCTION 11 0.571104 0.567536 ## HI-REDUCTION 13 0.567575 0.565866 ## HI-REDUCTION 15 0.567536 0.565596 ## HI-REDUCTION 17 0.565866 0.565258 ## HI-REDUCTION 19 0.565596 0.565137 ## REFLECTION 21 0.565258 0.565093 ## HI-REDUCTION 23 0.565137 0.564990 ## HI-REDUCTION 25 0.565093 0.564960 ## HI-REDUCTION 27 0.564990 0.564960 ## HI-REDUCTION 29 0.564977 0.564956 ## HI-REDUCTION 31 0.564960 0.564952 ## HI-REDUCTION 33 0.564956 0.564947 ## LO-REDUCTION 35 0.564952 0.564947 ## HI-REDUCTION 37 0.564949 0.564947 ## LO-REDUCTION 39 0.564948 0.564947 ## HI-REDUCTION 41 0.564947 0.564947 ## HI-REDUCTION 43 0.564947 0.564947 ## HI-REDUCTION 45 0.564947 0.564947 ## HI-REDUCTION 47 0.564947 0.564947 ## HI-REDUCTION 49 0.564947 0.564947 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598115 ## Scaled convergence tolerance is 8.91261e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.685973 0.598115 ## HI-REDUCTION 5 0.656843 0.595745 ## HI-REDUCTION 7 0.609328 0.595745 ## HI-REDUCTION 9 0.598115 0.593421 ## HI-REDUCTION 11 0.595745 0.588876 ## HI-REDUCTION 13 0.593421 0.588876 ## LO-REDUCTION 15 0.590219 0.588876 ## HI-REDUCTION 17 0.589412 0.588871 ## HI-REDUCTION 19 0.588876 0.588710 ## HI-REDUCTION 21 0.588871 0.588520 ## HI-REDUCTION 23 0.588710 0.588520 ## LO-REDUCTION 25 0.588632 0.588520 ## HI-REDUCTION 27 0.588538 0.588520 ## HI-REDUCTION 29 0.588522 0.588516 ## HI-REDUCTION 31 0.588520 0.588512 ## HI-REDUCTION 33 0.588516 0.588510 ## LO-REDUCTION 35 0.588512 0.588510 ## HI-REDUCTION 37 0.588510 0.588510 ## LO-REDUCTION 39 0.588510 0.588510 ## HI-REDUCTION 41 0.588510 0.588510 ## LO-REDUCTION 43 0.588510 0.588510 ## HI-REDUCTION 45 0.588510 0.588510 ## HI-REDUCTION 47 0.588510 0.588510 ## LO-REDUCTION 49 0.588510 0.588510 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505212 ## Scaled convergence tolerance is 7.52824e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.622560 0.505212 ## HI-REDUCTION 5 0.593863 0.505212 ## HI-REDUCTION 7 0.542925 0.505212 ## HI-REDUCTION 9 0.530756 0.505212 ## LO-REDUCTION 11 0.523453 0.505212 ## LO-REDUCTION 13 0.512755 0.505212 ## LO-REDUCTION 15 0.508668 0.505212 ## LO-REDUCTION 17 0.506145 0.504948 ## HI-REDUCTION 19 0.505212 0.504948 ## HI-REDUCTION 21 0.504954 0.504835 ## HI-REDUCTION 23 0.504948 0.504802 ## HI-REDUCTION 25 0.504835 0.504802 ## HI-REDUCTION 27 0.504817 0.504791 ## HI-REDUCTION 29 0.504802 0.504791 ## HI-REDUCTION 31 0.504793 0.504787 ## LO-REDUCTION 33 0.504791 0.504786 ## HI-REDUCTION 35 0.504787 0.504786 ## HI-REDUCTION 37 0.504786 0.504785 ## LO-REDUCTION 39 0.504786 0.504785 ## HI-REDUCTION 41 0.504785 0.504785 ## REFLECTION 43 0.504785 0.504785 ## HI-REDUCTION 45 0.504785 0.504785 ## HI-REDUCTION 47 0.504785 0.504785 ## HI-REDUCTION 49 0.504785 0.504785 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.636106 ## Scaled convergence tolerance is 9.47872e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.809384 0.636106 ## LO-REDUCTION 5 0.676363 0.636106 ## LO-REDUCTION 7 0.672086 0.632637 ## HI-REDUCTION 9 0.637653 0.632637 ## HI-REDUCTION 11 0.636106 0.628258 ## HI-REDUCTION 13 0.632637 0.627448 ## REFLECTION 15 0.628258 0.624736 ## HI-REDUCTION 17 0.627448 0.624736 ## LO-REDUCTION 19 0.625787 0.624736 ## HI-REDUCTION 21 0.625041 0.624736 ## HI-REDUCTION 23 0.624947 0.624736 ## HI-REDUCTION 25 0.624770 0.624725 ## HI-REDUCTION 27 0.624736 0.624681 ## HI-REDUCTION 29 0.624725 0.624656 ## LO-REDUCTION 31 0.624681 0.624656 ## HI-REDUCTION 33 0.624665 0.624656 ## LO-REDUCTION 35 0.624661 0.624655 ## LO-REDUCTION 37 0.624656 0.624655 ## HI-REDUCTION 39 0.624655 0.624654 ## HI-REDUCTION 41 0.624655 0.624654 ## HI-REDUCTION 43 0.624654 0.624654 ## REFLECTION 45 0.624654 0.624654 ## HI-REDUCTION 47 0.624654 0.624654 ## REFLECTION 49 0.624654 0.624654 ## HI-REDUCTION 51 0.624654 0.624654 ## HI-REDUCTION 53 0.624654 0.624654 ## HI-REDUCTION 55 0.624654 0.624654 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598797 ## Scaled convergence tolerance is 8.92276e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.744383 0.598797 ## HI-REDUCTION 5 0.723447 0.598797 ## HI-REDUCTION 7 0.656835 0.598797 ## HI-REDUCTION 9 0.637572 0.598797 ## LO-REDUCTION 11 0.629464 0.598797 ## LO-REDUCTION 13 0.610929 0.598797 ## LO-REDUCTION 15 0.601248 0.596826 ## LO-REDUCTION 17 0.598797 0.596826 ## LO-REDUCTION 19 0.597047 0.596415 ## HI-REDUCTION 21 0.596826 0.596287 ## LO-REDUCTION 23 0.596415 0.596287 ## HI-REDUCTION 25 0.596298 0.596245 ## HI-REDUCTION 27 0.596287 0.596245 ## HI-REDUCTION 29 0.596245 0.596236 ## HI-REDUCTION 31 0.596245 0.596229 ## LO-REDUCTION 33 0.596236 0.596226 ## HI-REDUCTION 35 0.596229 0.596226 ## HI-REDUCTION 37 0.596228 0.596226 ## REFLECTION 39 0.596226 0.596225 ## HI-REDUCTION 41 0.596226 0.596225 ## HI-REDUCTION 43 0.596225 0.596225 ## HI-REDUCTION 45 0.596225 0.596225 ## LO-REDUCTION 47 0.596225 0.596225 ## HI-REDUCTION 49 0.596225 0.596225 ## HI-REDUCTION 51 0.596225 0.596225 ## LO-REDUCTION 53 0.596225 0.596225 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.537674 ## Scaled convergence tolerance is 8.01197e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.653704 0.537674 ## HI-REDUCTION 5 0.585139 0.537674 ## HI-REDUCTION 7 0.542221 0.537674 ## HI-REDUCTION 9 0.540913 0.530789 ## HI-REDUCTION 11 0.537674 0.530789 ## HI-REDUCTION 13 0.532024 0.530442 ## LO-REDUCTION 15 0.530789 0.529873 ## HI-REDUCTION 17 0.530442 0.529522 ## HI-REDUCTION 19 0.529873 0.529388 ## LO-REDUCTION 21 0.529522 0.529304 ## HI-REDUCTION 23 0.529388 0.529304 ## HI-REDUCTION 25 0.529308 0.529297 ## HI-REDUCTION 27 0.529304 0.529281 ## HI-REDUCTION 29 0.529297 0.529281 ## HI-REDUCTION 31 0.529286 0.529281 ## LO-REDUCTION 33 0.529284 0.529281 ## HI-REDUCTION 35 0.529281 0.529281 ## LO-REDUCTION 37 0.529281 0.529280 ## HI-REDUCTION 39 0.529281 0.529280 ## HI-REDUCTION 41 0.529280 0.529280 ## LO-REDUCTION 43 0.529280 0.529280 ## HI-REDUCTION 45 0.529280 0.529280 ## HI-REDUCTION 47 0.529280 0.529280 ## HI-REDUCTION 49 0.529280 0.529280 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.450901 ## Scaled convergence tolerance is 6.71894e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.572860 0.450901 ## HI-REDUCTION 5 0.534733 0.450901 ## HI-REDUCTION 7 0.496393 0.450901 ## LO-REDUCTION 9 0.469331 0.447074 ## HI-REDUCTION 11 0.452130 0.447074 ## LO-REDUCTION 13 0.450901 0.443557 ## HI-REDUCTION 15 0.447074 0.443557 ## HI-REDUCTION 17 0.444266 0.442732 ## REFLECTION 19 0.443557 0.442643 ## HI-REDUCTION 21 0.442732 0.442356 ## HI-REDUCTION 23 0.442643 0.442340 ## HI-REDUCTION 25 0.442356 0.442309 ## HI-REDUCTION 27 0.442340 0.442241 ## HI-REDUCTION 29 0.442309 0.442241 ## LO-REDUCTION 31 0.442272 0.442241 ## HI-REDUCTION 33 0.442255 0.442241 ## LO-REDUCTION 35 0.442247 0.442238 ## HI-REDUCTION 37 0.442241 0.442238 ## HI-REDUCTION 39 0.442240 0.442238 ## LO-REDUCTION 41 0.442238 0.442238 ## HI-REDUCTION 43 0.442238 0.442238 ## HI-REDUCTION 45 0.442238 0.442238 ## HI-REDUCTION 47 0.442238 0.442238 ## HI-REDUCTION 49 0.442238 0.442238 ## HI-REDUCTION 51 0.442238 0.442238 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.589111 ## Scaled convergence tolerance is 8.77843e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.717457 0.589111 ## HI-REDUCTION 5 0.684726 0.589111 ## HI-REDUCTION 7 0.631972 0.589111 ## HI-REDUCTION 9 0.618775 0.589111 ## LO-REDUCTION 11 0.610954 0.589111 ## LO-REDUCTION 13 0.599362 0.589111 ## LO-REDUCTION 15 0.593630 0.588706 ## REFLECTION 17 0.589111 0.588481 ## LO-REDUCTION 19 0.588706 0.587541 ## HI-REDUCTION 21 0.588481 0.587434 ## HI-REDUCTION 23 0.587541 0.587434 ## HI-REDUCTION 25 0.587527 0.587381 ## HI-REDUCTION 27 0.587434 0.587355 ## HI-REDUCTION 29 0.587381 0.587352 ## REFLECTION 31 0.587355 0.587343 ## HI-REDUCTION 33 0.587352 0.587334 ## HI-REDUCTION 35 0.587343 0.587334 ## HI-REDUCTION 37 0.587334 0.587334 ## HI-REDUCTION 39 0.587334 0.587332 ## HI-REDUCTION 41 0.587334 0.587332 ## LO-REDUCTION 43 0.587332 0.587332 ## HI-REDUCTION 45 0.587332 0.587332 ## HI-REDUCTION 47 0.587332 0.587332 ## HI-REDUCTION 49 0.587332 0.587332 ## LO-REDUCTION 51 0.587332 0.587332 ## HI-REDUCTION 53 0.587332 0.587332 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.546616 ## Scaled convergence tolerance is 8.14521e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.639771 0.546616 ## HI-REDUCTION 5 0.631414 0.546616 ## HI-REDUCTION 7 0.572790 0.546616 ## HI-REDUCTION 9 0.558815 0.546616 ## HI-REDUCTION 11 0.554291 0.546616 ## REFLECTION 13 0.547684 0.545760 ## HI-REDUCTION 15 0.546616 0.542904 ## HI-REDUCTION 17 0.545760 0.542904 ## HI-REDUCTION 19 0.543665 0.542904 ## LO-REDUCTION 21 0.543137 0.542534 ## HI-REDUCTION 23 0.542904 0.542482 ## HI-REDUCTION 25 0.542554 0.542482 ## HI-REDUCTION 27 0.542534 0.542471 ## HI-REDUCTION 29 0.542482 0.542464 ## HI-REDUCTION 31 0.542471 0.542445 ## LO-REDUCTION 33 0.542464 0.542445 ## HI-REDUCTION 35 0.542453 0.542445 ## LO-REDUCTION 37 0.542451 0.542445 ## LO-REDUCTION 39 0.542445 0.542445 ## REFLECTION 41 0.542445 0.542445 ## HI-REDUCTION 43 0.542445 0.542444 ## HI-REDUCTION 45 0.542445 0.542444 ## LO-REDUCTION 47 0.542444 0.542444 ## HI-REDUCTION 49 0.542444 0.542444 ## LO-REDUCTION 51 0.542444 0.542444 ## LO-REDUCTION 53 0.542444 0.542444 ## LO-REDUCTION 55 0.542444 0.542444 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.789032 ## Scaled convergence tolerance is 1.17575e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.927519 0.789032 ## HI-REDUCTION 5 0.900760 0.789032 ## HI-REDUCTION 7 0.837902 0.789032 ## HI-REDUCTION 9 0.820546 0.789032 ## LO-REDUCTION 11 0.812873 0.789032 ## LO-REDUCTION 13 0.799799 0.789032 ## LO-REDUCTION 15 0.795274 0.789032 ## REFLECTION 17 0.790478 0.788608 ## HI-REDUCTION 19 0.789032 0.788288 ## HI-REDUCTION 21 0.788608 0.788164 ## HI-REDUCTION 23 0.788288 0.787982 ## HI-REDUCTION 25 0.788164 0.787982 ## LO-REDUCTION 27 0.788009 0.787933 ## HI-REDUCTION 29 0.787982 0.787930 ## HI-REDUCTION 31 0.787935 0.787930 ## HI-REDUCTION 33 0.787933 0.787924 ## HI-REDUCTION 35 0.787930 0.787923 ## HI-REDUCTION 37 0.787924 0.787923 ## HI-REDUCTION 39 0.787923 0.787923 ## HI-REDUCTION 41 0.787923 0.787922 ## HI-REDUCTION 43 0.787923 0.787922 ## LO-REDUCTION 45 0.787922 0.787922 ## HI-REDUCTION 47 0.787922 0.787922 ## LO-REDUCTION 49 0.787922 0.787922 ## HI-REDUCTION 51 0.787922 0.787922 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.604981 ## Scaled convergence tolerance is 9.01491e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.706696 0.604981 ## HI-REDUCTION 5 0.661801 0.604981 ## HI-REDUCTION 7 0.618024 0.604981 ## HI-REDUCTION 9 0.608587 0.603795 ## HI-REDUCTION 11 0.604981 0.601194 ## HI-REDUCTION 13 0.603795 0.600142 ## HI-REDUCTION 15 0.601194 0.600142 ## HI-REDUCTION 17 0.600994 0.600142 ## LO-REDUCTION 19 0.600230 0.600041 ## HI-REDUCTION 21 0.600142 0.599902 ## HI-REDUCTION 23 0.600041 0.599902 ## LO-REDUCTION 25 0.599962 0.599902 ## HI-REDUCTION 27 0.599905 0.599899 ## LO-REDUCTION 29 0.599902 0.599892 ## HI-REDUCTION 31 0.599899 0.599890 ## HI-REDUCTION 33 0.599892 0.599890 ## REFLECTION 35 0.599890 0.599890 ## HI-REDUCTION 37 0.599890 0.599888 ## HI-REDUCTION 39 0.599890 0.599888 ## LO-REDUCTION 41 0.599888 0.599888 ## HI-REDUCTION 43 0.599888 0.599888 ## HI-REDUCTION 45 0.599888 0.599888 ## HI-REDUCTION 47 0.599888 0.599888 ## LO-REDUCTION 49 0.599888 0.599888 ## HI-REDUCTION 51 0.599888 0.599888 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.634935 ## Scaled convergence tolerance is 9.46126e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.738426 0.634935 ## HI-REDUCTION 5 0.682008 0.634935 ## HI-REDUCTION 7 0.643565 0.634935 ## HI-REDUCTION 9 0.639372 0.632130 ## HI-REDUCTION 11 0.634935 0.631442 ## HI-REDUCTION 13 0.632130 0.629993 ## HI-REDUCTION 15 0.631442 0.629993 ## HI-REDUCTION 17 0.630247 0.629993 ## HI-REDUCTION 19 0.630163 0.629831 ## HI-REDUCTION 21 0.629993 0.629831 ## HI-REDUCTION 23 0.629831 0.629746 ## HI-REDUCTION 25 0.629831 0.629746 ## REFLECTION 27 0.629764 0.629726 ## HI-REDUCTION 29 0.629746 0.629722 ## REFLECTION 31 0.629726 0.629719 ## HI-REDUCTION 33 0.629722 0.629714 ## HI-REDUCTION 35 0.629719 0.629713 ## HI-REDUCTION 37 0.629714 0.629713 ## HI-REDUCTION 39 0.629713 0.629712 ## HI-REDUCTION 41 0.629713 0.629712 ## HI-REDUCTION 43 0.629712 0.629712 ## HI-REDUCTION 45 0.629712 0.629712 ## HI-REDUCTION 47 0.629712 0.629712 ## HI-REDUCTION 49 0.629712 0.629712 ## LO-REDUCTION 51 0.629712 0.629712 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.636291 ## Scaled convergence tolerance is 9.48147e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.846800 0.636291 ## LO-REDUCTION 5 0.726408 0.636291 ## REFLECTION 7 0.711578 0.630505 ## HI-REDUCTION 9 0.651561 0.630505 ## HI-REDUCTION 11 0.636291 0.630148 ## HI-REDUCTION 13 0.630505 0.624840 ## HI-REDUCTION 15 0.630148 0.622782 ## LO-REDUCTION 17 0.624840 0.622782 ## HI-REDUCTION 19 0.623436 0.622782 ## HI-REDUCTION 21 0.622867 0.622517 ## HI-REDUCTION 23 0.622782 0.622396 ## HI-REDUCTION 25 0.622517 0.622349 ## HI-REDUCTION 27 0.622396 0.622349 ## HI-REDUCTION 29 0.622364 0.622334 ## HI-REDUCTION 31 0.622349 0.622324 ## HI-REDUCTION 33 0.622334 0.622316 ## LO-REDUCTION 35 0.622324 0.622316 ## HI-REDUCTION 37 0.622318 0.622316 ## REFLECTION 39 0.622316 0.622316 ## HI-REDUCTION 41 0.622316 0.622315 ## HI-REDUCTION 43 0.622316 0.622315 ## HI-REDUCTION 45 0.622315 0.622315 ## HI-REDUCTION 47 0.622315 0.622315 ## HI-REDUCTION 49 0.622315 0.622315 ## HI-REDUCTION 51 0.622315 0.622315 ## LO-REDUCTION 53 0.622315 0.622315 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.582153 ## Scaled convergence tolerance is 8.67476e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.824125 0.582153 ## LO-REDUCTION 5 0.696517 0.582153 ## REFLECTION 7 0.661103 0.558560 ## HI-REDUCTION 9 0.595303 0.558560 ## HI-REDUCTION 11 0.582153 0.558560 ## HI-REDUCTION 13 0.570357 0.558560 ## HI-REDUCTION 15 0.564325 0.558560 ## HI-REDUCTION 17 0.560689 0.558041 ## HI-REDUCTION 19 0.558560 0.556733 ## HI-REDUCTION 21 0.558041 0.555238 ## LO-REDUCTION 23 0.556733 0.555238 ## HI-REDUCTION 25 0.555650 0.555238 ## HI-REDUCTION 27 0.555468 0.555238 ## REFLECTION 29 0.555387 0.555224 ## LO-REDUCTION 31 0.555238 0.555192 ## HI-REDUCTION 33 0.555224 0.555192 ## HI-REDUCTION 35 0.555199 0.555192 ## LO-REDUCTION 37 0.555194 0.555188 ## HI-REDUCTION 39 0.555192 0.555188 ## HI-REDUCTION 41 0.555188 0.555188 ## HI-REDUCTION 43 0.555188 0.555187 ## HI-REDUCTION 45 0.555188 0.555187 ## HI-REDUCTION 47 0.555187 0.555187 ## LO-REDUCTION 49 0.555187 0.555187 ## HI-REDUCTION 51 0.555187 0.555187 ## HI-REDUCTION 53 0.555187 0.555187 ## LO-REDUCTION 55 0.555187 0.555187 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598240 ## Scaled convergence tolerance is 8.91447e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.753555 0.598240 ## HI-REDUCTION 5 0.685536 0.598240 ## HI-REDUCTION 7 0.633685 0.598240 ## HI-REDUCTION 9 0.631778 0.598240 ## LO-REDUCTION 11 0.615777 0.598240 ## LO-REDUCTION 13 0.601414 0.598240 ## HI-REDUCTION 15 0.599726 0.598240 ## HI-REDUCTION 17 0.598321 0.597880 ## HI-REDUCTION 19 0.598240 0.597695 ## HI-REDUCTION 21 0.597880 0.597650 ## HI-REDUCTION 23 0.597695 0.597635 ## HI-REDUCTION 25 0.597650 0.597609 ## HI-REDUCTION 27 0.597635 0.597591 ## HI-REDUCTION 29 0.597609 0.597591 ## LO-REDUCTION 31 0.597599 0.597590 ## HI-REDUCTION 33 0.597591 0.597589 ## HI-REDUCTION 35 0.597590 0.597588 ## LO-REDUCTION 37 0.597589 0.597588 ## HI-REDUCTION 39 0.597588 0.597588 ## HI-REDUCTION 41 0.597588 0.597588 ## HI-REDUCTION 43 0.597588 0.597588 ## HI-REDUCTION 45 0.597588 0.597588 ## HI-REDUCTION 47 0.597588 0.597588 ## LO-REDUCTION 49 0.597588 0.597588 ## HI-REDUCTION 51 0.597588 0.597588 ## HI-REDUCTION 53 0.597588 0.597588 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.586047 ## Scaled convergence tolerance is 8.73279e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.696613 0.586047 ## HI-REDUCTION 5 0.648203 0.586047 ## HI-REDUCTION 7 0.603319 0.586047 ## HI-REDUCTION 9 0.596396 0.586047 ## HI-REDUCTION 11 0.588705 0.586047 ## HI-REDUCTION 13 0.586515 0.584502 ## HI-REDUCTION 15 0.586047 0.583804 ## HI-REDUCTION 17 0.584502 0.583250 ## LO-REDUCTION 19 0.583804 0.583250 ## HI-REDUCTION 21 0.583486 0.583250 ## REFLECTION 23 0.583298 0.583194 ## HI-REDUCTION 25 0.583250 0.583166 ## HI-REDUCTION 27 0.583194 0.583154 ## HI-REDUCTION 29 0.583166 0.583151 ## HI-REDUCTION 31 0.583154 0.583147 ## HI-REDUCTION 33 0.583151 0.583144 ## HI-REDUCTION 35 0.583147 0.583144 ## LO-REDUCTION 37 0.583145 0.583144 ## HI-REDUCTION 39 0.583144 0.583143 ## HI-REDUCTION 41 0.583144 0.583143 ## HI-REDUCTION 43 0.583143 0.583143 ## HI-REDUCTION 45 0.583143 0.583143 ## REFLECTION 47 0.583143 0.583143 ## HI-REDUCTION 49 0.583143 0.583143 ## HI-REDUCTION 51 0.583143 0.583143 ## HI-REDUCTION 53 0.583143 0.583143 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.542604 ## Scaled convergence tolerance is 8.08543e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.708938 0.542604 ## HI-REDUCTION 5 0.641696 0.542604 ## HI-REDUCTION 7 0.587335 0.542604 ## HI-REDUCTION 9 0.585664 0.542604 ## LO-REDUCTION 11 0.567827 0.542604 ## LO-REDUCTION 13 0.546935 0.539218 ## LO-REDUCTION 15 0.542604 0.539218 ## HI-REDUCTION 17 0.540628 0.539218 ## LO-REDUCTION 19 0.540234 0.539218 ## HI-REDUCTION 21 0.539636 0.539218 ## REFLECTION 23 0.539455 0.539126 ## HI-REDUCTION 25 0.539218 0.539126 ## HI-REDUCTION 27 0.539168 0.539111 ## HI-REDUCTION 29 0.539126 0.539110 ## HI-REDUCTION 31 0.539111 0.539095 ## HI-REDUCTION 33 0.539110 0.539095 ## LO-REDUCTION 35 0.539097 0.539092 ## HI-REDUCTION 37 0.539095 0.539092 ## HI-REDUCTION 39 0.539093 0.539092 ## HI-REDUCTION 41 0.539093 0.539092 ## HI-REDUCTION 43 0.539092 0.539092 ## HI-REDUCTION 45 0.539092 0.539092 ## LO-REDUCTION 47 0.539092 0.539092 ## HI-REDUCTION 49 0.539092 0.539092 ## HI-REDUCTION 51 0.539092 0.539092 ## HI-REDUCTION 53 0.539092 0.539092 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.532740 ## Scaled convergence tolerance is 7.93845e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.686241 0.532740 ## HI-REDUCTION 5 0.613358 0.532740 ## HI-REDUCTION 7 0.562992 0.532740 ## HI-REDUCTION 9 0.562265 0.532740 ## LO-REDUCTION 11 0.546388 0.532740 ## HI-REDUCTION 13 0.536847 0.532740 ## LO-REDUCTION 15 0.534518 0.532453 ## HI-REDUCTION 17 0.532740 0.532333 ## HI-REDUCTION 19 0.532453 0.531994 ## HI-REDUCTION 21 0.532333 0.531994 ## HI-REDUCTION 23 0.532082 0.531994 ## REFLECTION 25 0.532027 0.531986 ## HI-REDUCTION 27 0.531994 0.531936 ## HI-REDUCTION 29 0.531986 0.531936 ## LO-REDUCTION 31 0.531949 0.531936 ## HI-REDUCTION 33 0.531940 0.531934 ## LO-REDUCTION 35 0.531936 0.531933 ## HI-REDUCTION 37 0.531934 0.531932 ## HI-REDUCTION 39 0.531933 0.531932 ## LO-REDUCTION 41 0.531932 0.531932 ## HI-REDUCTION 43 0.531932 0.531932 ## HI-REDUCTION 45 0.531932 0.531932 ## HI-REDUCTION 47 0.531932 0.531932 ## HI-REDUCTION 49 0.531932 0.531932 ## HI-REDUCTION 51 0.531932 0.531932 ## HI-REDUCTION 53 0.531932 0.531932 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505690 ## Scaled convergence tolerance is 7.53537e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.630551 0.505690 ## HI-REDUCTION 5 0.620169 0.505690 ## HI-REDUCTION 7 0.561481 0.505690 ## LO-REDUCTION 9 0.541900 0.505690 ## REFLECTION 11 0.517142 0.503370 ## HI-REDUCTION 13 0.505690 0.502368 ## HI-REDUCTION 15 0.503370 0.499488 ## HI-REDUCTION 17 0.502368 0.497714 ## LO-REDUCTION 19 0.499488 0.497714 ## HI-REDUCTION 21 0.498375 0.497714 ## LO-REDUCTION 23 0.498112 0.497714 ## HI-REDUCTION 25 0.497776 0.497714 ## LO-REDUCTION 27 0.497733 0.497651 ## HI-REDUCTION 29 0.497714 0.497633 ## HI-REDUCTION 31 0.497651 0.497633 ## LO-REDUCTION 33 0.497649 0.497629 ## HI-REDUCTION 35 0.497633 0.497629 ## HI-REDUCTION 37 0.497631 0.497628 ## HI-REDUCTION 39 0.497629 0.497628 ## HI-REDUCTION 41 0.497628 0.497628 ## HI-REDUCTION 43 0.497628 0.497628 ## HI-REDUCTION 45 0.497628 0.497627 ## LO-REDUCTION 47 0.497628 0.497627 ## HI-REDUCTION 49 0.497627 0.497627 ## HI-REDUCTION 51 0.497627 0.497627 ## HI-REDUCTION 53 0.497627 0.497627 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.376971 ## Scaled convergence tolerance is 5.61731e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.494204 0.376971 ## HI-REDUCTION 5 0.445798 0.376971 ## HI-REDUCTION 7 0.419724 0.376971 ## LO-REDUCTION 9 0.391149 0.371340 ## LO-REDUCTION 11 0.376971 0.371262 ## HI-REDUCTION 13 0.371340 0.369929 ## HI-REDUCTION 15 0.371262 0.367303 ## HI-REDUCTION 17 0.369929 0.367303 ## REFLECTION 19 0.368709 0.367092 ## HI-REDUCTION 21 0.367310 0.367092 ## HI-REDUCTION 23 0.367303 0.367011 ## HI-REDUCTION 25 0.367092 0.367011 ## HI-REDUCTION 27 0.367011 0.366948 ## LO-REDUCTION 29 0.367011 0.366948 ## HI-REDUCTION 31 0.366963 0.366948 ## LO-REDUCTION 33 0.366959 0.366942 ## HI-REDUCTION 35 0.366948 0.366942 ## HI-REDUCTION 37 0.366943 0.366942 ## HI-REDUCTION 39 0.366942 0.366941 ## LO-REDUCTION 41 0.366942 0.366941 ## HI-REDUCTION 43 0.366941 0.366941 ## HI-REDUCTION 45 0.366941 0.366941 ## HI-REDUCTION 47 0.366941 0.366941 ## HI-REDUCTION 49 0.366941 0.366941 ## LO-REDUCTION 51 0.366941 0.366941 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.426218 ## Scaled convergence tolerance is 6.35114e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.587314 0.426218 ## HI-REDUCTION 5 0.524047 0.426218 ## HI-REDUCTION 7 0.469487 0.426218 ## HI-REDUCTION 9 0.465189 0.426218 ## LO-REDUCTION 11 0.449697 0.426218 ## LO-REDUCTION 13 0.431507 0.424190 ## LO-REDUCTION 15 0.426218 0.424190 ## HI-REDUCTION 17 0.424801 0.424190 ## HI-REDUCTION 19 0.424329 0.423941 ## HI-REDUCTION 21 0.424190 0.423857 ## HI-REDUCTION 23 0.423941 0.423785 ## HI-REDUCTION 25 0.423857 0.423785 ## LO-REDUCTION 27 0.423803 0.423768 ## HI-REDUCTION 29 0.423785 0.423757 ## HI-REDUCTION 31 0.423768 0.423757 ## LO-REDUCTION 33 0.423764 0.423756 ## HI-REDUCTION 35 0.423757 0.423756 ## HI-REDUCTION 37 0.423756 0.423755 ## HI-REDUCTION 39 0.423756 0.423755 ## LO-REDUCTION 41 0.423755 0.423755 ## HI-REDUCTION 43 0.423755 0.423755 ## HI-REDUCTION 45 0.423755 0.423755 ## HI-REDUCTION 47 0.423755 0.423755 ## HI-REDUCTION 49 0.423755 0.423755 ## HI-REDUCTION 51 0.423755 0.423755 ## HI-REDUCTION 53 0.423755 0.423755 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.668259 ## Scaled convergence tolerance is 9.95784e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.833265 0.668259 ## HI-REDUCTION 5 0.755189 0.668259 ## HI-REDUCTION 7 0.703265 0.668259 ## LO-REDUCTION 9 0.702614 0.668259 ## LO-REDUCTION 11 0.681122 0.667199 ## HI-REDUCTION 13 0.669508 0.667199 ## HI-REDUCTION 15 0.668259 0.666696 ## HI-REDUCTION 17 0.667199 0.666585 ## HI-REDUCTION 19 0.666696 0.666308 ## HI-REDUCTION 21 0.666585 0.666308 ## LO-REDUCTION 23 0.666311 0.666308 ## HI-REDUCTION 25 0.666311 0.666238 ## LO-REDUCTION 27 0.666308 0.666238 ## LO-REDUCTION 29 0.666258 0.666238 ## HI-REDUCTION 31 0.666238 0.666233 ## HI-REDUCTION 33 0.666238 0.666233 ## LO-REDUCTION 35 0.666233 0.666232 ## HI-REDUCTION 37 0.666233 0.666231 ## HI-REDUCTION 39 0.666232 0.666231 ## LO-REDUCTION 41 0.666231 0.666231 ## HI-REDUCTION 43 0.666231 0.666231 ## REFLECTION 45 0.666231 0.666231 ## HI-REDUCTION 47 0.666231 0.666231 ## HI-REDUCTION 49 0.666231 0.666231 ## HI-REDUCTION 51 0.666231 0.666231 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.507589 ## Scaled convergence tolerance is 7.56367e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.645362 0.507589 ## HI-REDUCTION 5 0.636714 0.507589 ## HI-REDUCTION 7 0.573306 0.507589 ## LO-REDUCTION 9 0.553533 0.507589 ## REFLECTION 11 0.522282 0.493601 ## LO-REDUCTION 13 0.507589 0.493601 ## HI-REDUCTION 15 0.499004 0.493601 ## LO-REDUCTION 17 0.496977 0.493096 ## HI-REDUCTION 19 0.493601 0.493096 ## HI-REDUCTION 21 0.493333 0.492416 ## HI-REDUCTION 23 0.493096 0.492416 ## LO-REDUCTION 25 0.492728 0.492416 ## HI-REDUCTION 27 0.492467 0.492416 ## HI-REDUCTION 29 0.492456 0.492410 ## HI-REDUCTION 31 0.492416 0.492408 ## HI-REDUCTION 33 0.492410 0.492394 ## HI-REDUCTION 35 0.492408 0.492394 ## LO-REDUCTION 37 0.492400 0.492393 ## HI-REDUCTION 39 0.492394 0.492393 ## HI-REDUCTION 41 0.492394 0.492393 ## HI-REDUCTION 43 0.492393 0.492393 ## LO-REDUCTION 45 0.492393 0.492393 ## LO-REDUCTION 47 0.492393 0.492393 ## HI-REDUCTION 49 0.492393 0.492393 ## LO-REDUCTION 51 0.492393 0.492393 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.572406 ## Scaled convergence tolerance is 8.52951e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.654488 0.572406 ## HI-REDUCTION 5 0.652801 0.572406 ## HI-REDUCTION 7 0.594692 0.572406 ## HI-REDUCTION 9 0.583929 0.572406 ## HI-REDUCTION 11 0.578655 0.572406 ## REFLECTION 13 0.573347 0.572264 ## HI-REDUCTION 15 0.572406 0.569228 ## HI-REDUCTION 17 0.572264 0.569228 ## HI-REDUCTION 19 0.569907 0.569228 ## LO-REDUCTION 21 0.569665 0.569094 ## HI-REDUCTION 23 0.569228 0.569018 ## HI-REDUCTION 25 0.569094 0.569005 ## REFLECTION 27 0.569018 0.568988 ## HI-REDUCTION 29 0.569005 0.568953 ## HI-REDUCTION 31 0.568988 0.568948 ## HI-REDUCTION 33 0.568953 0.568948 ## HI-REDUCTION 35 0.568953 0.568946 ## HI-REDUCTION 37 0.568948 0.568945 ## HI-REDUCTION 39 0.568946 0.568943 ## LO-REDUCTION 41 0.568945 0.568943 ## HI-REDUCTION 43 0.568944 0.568943 ## REFLECTION 45 0.568944 0.568943 ## HI-REDUCTION 47 0.568943 0.568943 ## HI-REDUCTION 49 0.568943 0.568943 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.571957 ## Scaled convergence tolerance is 8.52282e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.744999 0.571957 ## LO-REDUCTION 5 0.627196 0.571957 ## LO-REDUCTION 7 0.624647 0.569140 ## HI-REDUCTION 9 0.582690 0.569140 ## HI-REDUCTION 11 0.571957 0.569133 ## HI-REDUCTION 13 0.569140 0.565188 ## LO-REDUCTION 15 0.569133 0.565188 ## LO-REDUCTION 17 0.566382 0.565188 ## HI-REDUCTION 19 0.566173 0.565188 ## LO-REDUCTION 21 0.565529 0.565175 ## LO-REDUCTION 23 0.565188 0.565098 ## HI-REDUCTION 25 0.565175 0.565098 ## HI-REDUCTION 27 0.565102 0.565092 ## REFLECTION 29 0.565098 0.565082 ## HI-REDUCTION 31 0.565092 0.565078 ## HI-REDUCTION 33 0.565082 0.565078 ## HI-REDUCTION 35 0.565078 0.565077 ## HI-REDUCTION 37 0.565078 0.565076 ## HI-REDUCTION 39 0.565077 0.565076 ## LO-REDUCTION 41 0.565076 0.565076 ## HI-REDUCTION 43 0.565076 0.565076 ## HI-REDUCTION 45 0.565076 0.565076 ## LO-REDUCTION 47 0.565076 0.565076 ## HI-REDUCTION 49 0.565076 0.565076 ## LO-REDUCTION 51 0.565076 0.565076 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.481535 ## Scaled convergence tolerance is 7.17543e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.591242 0.481535 ## HI-REDUCTION 5 0.586433 0.481535 ## HI-REDUCTION 7 0.524878 0.481535 ## LO-REDUCTION 9 0.511136 0.481535 ## LO-REDUCTION 11 0.489333 0.476667 ## HI-REDUCTION 13 0.481535 0.476667 ## HI-REDUCTION 15 0.478756 0.476667 ## HI-REDUCTION 17 0.477855 0.476667 ## LO-REDUCTION 19 0.476896 0.476351 ## HI-REDUCTION 21 0.476667 0.476283 ## HI-REDUCTION 23 0.476351 0.476283 ## HI-REDUCTION 25 0.476329 0.476259 ## HI-REDUCTION 27 0.476283 0.476250 ## HI-REDUCTION 29 0.476259 0.476236 ## LO-REDUCTION 31 0.476250 0.476236 ## HI-REDUCTION 33 0.476240 0.476236 ## REFLECTION 35 0.476238 0.476235 ## HI-REDUCTION 37 0.476236 0.476234 ## HI-REDUCTION 39 0.476235 0.476234 ## HI-REDUCTION 41 0.476234 0.476234 ## HI-REDUCTION 43 0.476234 0.476234 ## HI-REDUCTION 45 0.476234 0.476234 ## HI-REDUCTION 47 0.476234 0.476234 ## HI-REDUCTION 49 0.476234 0.476234 ## HI-REDUCTION 51 0.476234 0.476234 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.534200 ## Scaled convergence tolerance is 7.9602e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.625720 0.534200 ## HI-REDUCTION 5 0.551147 0.534200 ## HI-REDUCTION 7 0.550163 0.522786 ## HI-REDUCTION 9 0.534200 0.513641 ## HI-REDUCTION 11 0.522786 0.513641 ## REFLECTION 13 0.517723 0.512005 ## HI-REDUCTION 15 0.513641 0.511127 ## HI-REDUCTION 17 0.512005 0.511099 ## HI-REDUCTION 19 0.511127 0.510393 ## HI-REDUCTION 21 0.511099 0.510375 ## REFLECTION 23 0.510393 0.510316 ## HI-REDUCTION 25 0.510375 0.510159 ## HI-REDUCTION 27 0.510316 0.510159 ## HI-REDUCTION 29 0.510176 0.510159 ## HI-REDUCTION 31 0.510164 0.510144 ## HI-REDUCTION 33 0.510159 0.510139 ## HI-REDUCTION 35 0.510144 0.510139 ## HI-REDUCTION 37 0.510140 0.510138 ## HI-REDUCTION 39 0.510139 0.510137 ## HI-REDUCTION 41 0.510138 0.510137 ## REFLECTION 43 0.510137 0.510137 ## HI-REDUCTION 45 0.510137 0.510137 ## HI-REDUCTION 47 0.510137 0.510137 ## HI-REDUCTION 49 0.510137 0.510137 ## HI-REDUCTION 51 0.510137 0.510136 ## HI-REDUCTION 53 0.510137 0.510136 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.549906 ## Scaled convergence tolerance is 8.19424e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.666903 0.549906 ## HI-REDUCTION 5 0.603833 0.549906 ## HI-REDUCTION 7 0.562408 0.549906 ## HI-REDUCTION 9 0.559067 0.549906 ## HI-REDUCTION 11 0.550021 0.549679 ## HI-REDUCTION 13 0.549906 0.547204 ## HI-REDUCTION 15 0.549679 0.547006 ## LO-REDUCTION 17 0.547204 0.546728 ## HI-REDUCTION 19 0.547006 0.546674 ## HI-REDUCTION 21 0.546728 0.546671 ## HI-REDUCTION 23 0.546674 0.546611 ## HI-REDUCTION 25 0.546671 0.546610 ## LO-REDUCTION 27 0.546613 0.546610 ## HI-REDUCTION 29 0.546611 0.546602 ## HI-REDUCTION 31 0.546610 0.546600 ## HI-REDUCTION 33 0.546602 0.546600 ## LO-REDUCTION 35 0.546601 0.546600 ## HI-REDUCTION 37 0.546600 0.546600 ## HI-REDUCTION 39 0.546600 0.546599 ## LO-REDUCTION 41 0.546600 0.546599 ## HI-REDUCTION 43 0.546599 0.546599 ## REFLECTION 45 0.546599 0.546599 ## HI-REDUCTION 47 0.546599 0.546599 ## HI-REDUCTION 49 0.546599 0.546599 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.581946 ## Scaled convergence tolerance is 8.67167e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.704669 0.581946 ## HI-REDUCTION 5 0.629002 0.581946 ## HI-REDUCTION 7 0.591742 0.581946 ## HI-REDUCTION 9 0.590439 0.580275 ## HI-REDUCTION 11 0.581946 0.579959 ## HI-REDUCTION 13 0.580275 0.577405 ## LO-REDUCTION 15 0.579959 0.577405 ## LO-REDUCTION 17 0.578203 0.577405 ## HI-REDUCTION 19 0.577739 0.577379 ## LO-REDUCTION 21 0.577405 0.577237 ## HI-REDUCTION 23 0.577379 0.577232 ## HI-REDUCTION 25 0.577250 0.577232 ## HI-REDUCTION 27 0.577237 0.577223 ## HI-REDUCTION 29 0.577232 0.577220 ## HI-REDUCTION 31 0.577223 0.577219 ## HI-REDUCTION 33 0.577220 0.577219 ## HI-REDUCTION 35 0.577219 0.577218 ## HI-REDUCTION 37 0.577219 0.577218 ## LO-REDUCTION 39 0.577218 0.577218 ## HI-REDUCTION 41 0.577218 0.577218 ## LO-REDUCTION 43 0.577218 0.577218 ## HI-REDUCTION 45 0.577218 0.577218 ## HI-REDUCTION 47 0.577218 0.577218 ## HI-REDUCTION 49 0.577218 0.577218 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.492827 ## Scaled convergence tolerance is 7.3437e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.617351 0.492827 ## HI-REDUCTION 5 0.613171 0.492827 ## HI-REDUCTION 7 0.543053 0.492827 ## LO-REDUCTION 9 0.527563 0.492827 ## LO-REDUCTION 11 0.501574 0.486890 ## HI-REDUCTION 13 0.492827 0.486890 ## HI-REDUCTION 15 0.489578 0.486890 ## HI-REDUCTION 17 0.488561 0.486890 ## LO-REDUCTION 19 0.487394 0.486523 ## HI-REDUCTION 21 0.486890 0.486523 ## HI-REDUCTION 23 0.486573 0.486523 ## HI-REDUCTION 25 0.486535 0.486465 ## HI-REDUCTION 27 0.486523 0.486465 ## HI-REDUCTION 29 0.486476 0.486465 ## HI-REDUCTION 31 0.486472 0.486463 ## HI-REDUCTION 33 0.486465 0.486461 ## HI-REDUCTION 35 0.486463 0.486458 ## LO-REDUCTION 37 0.486461 0.486458 ## HI-REDUCTION 39 0.486458 0.486458 ## HI-REDUCTION 41 0.486458 0.486458 ## HI-REDUCTION 43 0.486458 0.486458 ## LO-REDUCTION 45 0.486458 0.486458 ## HI-REDUCTION 47 0.486458 0.486458 ## HI-REDUCTION 49 0.486458 0.486458 ## HI-REDUCTION 51 0.486458 0.486458 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.489471 ## Scaled convergence tolerance is 7.29369e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595300 0.489471 ## HI-REDUCTION 5 0.582442 0.489471 ## HI-REDUCTION 7 0.526612 0.489471 ## HI-REDUCTION 9 0.507384 0.489471 ## HI-REDUCTION 11 0.504877 0.489471 ## LO-REDUCTION 13 0.497241 0.488735 ## HI-REDUCTION 15 0.490921 0.488735 ## HI-REDUCTION 17 0.489471 0.488735 ## HI-REDUCTION 19 0.489121 0.488623 ## LO-REDUCTION 21 0.488735 0.488463 ## HI-REDUCTION 23 0.488623 0.488463 ## HI-REDUCTION 25 0.488477 0.488450 ## HI-REDUCTION 27 0.488463 0.488439 ## HI-REDUCTION 29 0.488450 0.488432 ## HI-REDUCTION 31 0.488439 0.488430 ## LO-REDUCTION 33 0.488432 0.488427 ## HI-REDUCTION 35 0.488430 0.488427 ## HI-REDUCTION 37 0.488427 0.488427 ## HI-REDUCTION 39 0.488427 0.488427 ## HI-REDUCTION 41 0.488427 0.488427 ## HI-REDUCTION 43 0.488427 0.488427 ## HI-REDUCTION 45 0.488427 0.488427 ## HI-REDUCTION 47 0.488427 0.488427 ## HI-REDUCTION 49 0.488427 0.488427 ## LO-REDUCTION 51 0.488427 0.488427 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.688720 ## Scaled convergence tolerance is 1.02627e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.821253 0.688720 ## HI-REDUCTION 5 0.747123 0.688720 ## HI-REDUCTION 7 0.707236 0.688720 ## HI-REDUCTION 9 0.705874 0.688720 ## HI-REDUCTION 11 0.693771 0.688720 ## LO-REDUCTION 13 0.693004 0.687222 ## HI-REDUCTION 15 0.688720 0.687222 ## HI-REDUCTION 17 0.687861 0.686848 ## HI-REDUCTION 19 0.687222 0.686848 ## LO-REDUCTION 21 0.687014 0.686782 ## HI-REDUCTION 23 0.686848 0.686772 ## HI-REDUCTION 25 0.686782 0.686748 ## LO-REDUCTION 27 0.686772 0.686748 ## HI-REDUCTION 29 0.686748 0.686743 ## HI-REDUCTION 31 0.686748 0.686737 ## LO-REDUCTION 33 0.686743 0.686737 ## HI-REDUCTION 35 0.686738 0.686737 ## HI-REDUCTION 37 0.686737 0.686736 ## HI-REDUCTION 39 0.686737 0.686736 ## HI-REDUCTION 41 0.686736 0.686736 ## REFLECTION 43 0.686736 0.686736 ## HI-REDUCTION 45 0.686736 0.686736 ## HI-REDUCTION 47 0.686736 0.686736 ## HI-REDUCTION 49 0.686736 0.686736 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used #### We used only 101 iterations in that example to limit the calculation time but #### in practice you should take at least 1001 bootstrap iterations # Calculation of the quantile of interest (here the 5 percent hazard concentration) (HC5 <- quantile(bootsample, probs = 0.05)) ## (original) estimated quantiles for each specified probability (censored data) ## p=0.05 ## estimate 1.12 ## Median of bootstrap estimates ## p=0.05 ## estimate 1.12 ## ## two-sided 95 % CI of each quantile ## p=0.05 ## 2.5 % 1.05 ## 97.5 % 1.20 # visualizing pointwise confidence intervals on other quantiles par(mfrow=c(1,1), mar=c(4,4,2,1)) CIcdfplot(bootsample, CI.output = \"quantile\", CI.fill = \"pink\", xlim = c(0.5,2), main = \"\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-compute-confidence-intervals-on-any-function-of-the-parameters-of-the-fitted-distribution","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.3. How can we compute confidence intervals on any function of the parameters of the fitted distribution ?","title":"Frequently Asked Questions","text":"bootstrap sample parameter estimates can used calculate bootstrap sample variable defined function parameters fitted distribution. bootstrap sample can easily compute conidence interval using percentiles. example uses bootstrap sample parameters previous example (FAQ 4.2) calculate 95 percent confidence interval Potentially Affected Portion (PAF) species given exposure salinity (fixed 1.2 log10 example). complex calculations especially tranfer uncertainty within quantitative risk assessment, recommend use package mc2d aims making calculations easy gives extensive examples use bootstrap samples parameters estimated using functions package fitdistrplus.","code":"exposure <- 1.2 # Bootstrap sample of the PAF at this exposure PAF <- pnorm(exposure, mean = bootsample$estim$mean, sd = bootsample$estim$sd) # confidence interval from 2.5 and 97.5 percentiles quantile(PAF, probs = c(0.025, 0.975)) ## 2.5% 97.5% ## 0.0487 0.1470"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-we-choose-the-bootstrap-number","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.4. How do we choose the bootstrap number?","title":"Frequently Asked Questions","text":"Generally, need choose number bootstrap values high original sample size. search number mean standard values become stable. log-normal example , enough 100 bootstrap values.","code":"f.ln.MME <- fitdist(rlnorm(1000), \"lnorm\", method = \"mme\", order = 1:2) # Bootstrap b.ln.50 <- bootdist(f.ln.MME, niter = 50) b.ln.100 <- bootdist(f.ln.MME, niter = 100) b.ln.200 <- bootdist(f.ln.MME, niter = 200) b.ln.500 <- bootdist(f.ln.MME, niter = 500) d1 <- density(b.ln.50, b.ln.100, b.ln.200, b.ln.500) plot(d1)"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-personalize-the-default-plot-given-for-an-object-of-class-fitdist-or-fitdistcens","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.1. Can I personalize the default plot given for an object of class fitdist or fitdistcens?","title":"Frequently Asked Questions","text":"default plot given using plot() function object class fitdist fitdistcens hard personalize. Indeed plot designed give quick overview fit, used graph manuscript formal presentation. personalize () goodness--fit plots, rather use specific graphical functions, denscomp, cdfcomp, ppcomp, qqcomp cdfcompcens (see following paragraphs).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-personalize-goodness-of-fit-plots","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.2. How to personalize goodness-of-fit plots ?","title":"Frequently Asked Questions","text":"default plot object class fitdist can easily reproduced personalized using denscomp, cdfcomp, ppcomp qqcomp. similar way, default plot object class fitdistcens can easily personalized using cdfcompcens.","code":"data(groundbeef) serving <- groundbeef$serving fit <- fitdist(serving, \"gamma\") par(mfrow = c(2,2), mar = c(4, 4, 1, 1)) denscomp(fit, addlegend = FALSE, main = \"\", xlab = \"serving sizes (g)\", fitcol = \"orange\") qqcomp(fit, addlegend = FALSE, main = \"\", fitpch = 16, fitcol = \"grey\", line01lty = 2) cdfcomp(fit, addlegend = FALSE, main = \"\", xlab = \"serving sizes (g)\", fitcol = \"orange\", lines01 = TRUE) ppcomp(fit, addlegend = FALSE, main = \"\", fitpch = 16, fitcol = \"grey\", line01lty = 2)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-obtain-ggplot2-plots","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.3. Is it possible to obtain ggplot2 plots ?","title":"Frequently Asked Questions","text":"argument plotstyle added functions denscomp, cdfcomp, ppcomp, qqcompand cdfcompcens, ppcompcens, qqcompcens enable generation plots using ggplot2 package. argument default fixed graphics must simply fixed ggplot purpose, following example. latter case graphical functions return graphic object can personalized using ggplot2 functions.","code":"require(\"ggplot2\") ## Loading required package: ggplot2 fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") dcomp <- denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), xlab = \"serving sizes (g)\", xlim = c(0, 250), fitcol = c(\"red\", \"green\", \"orange\"), fitlty = 1, fitlwd = 1:3, xlegend = \"topright\", plotstyle = \"ggplot\", addlegend = FALSE) dcomp + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Ground beef fits\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-add-the-names-of-the-observations-in-a-goodness-of-fit-plot-e-g--the-names-of-the-species-in-the-plot-of-the-species-sensitivity-distribution-ssd-classically-used-in-ecotoxicology","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.4. Is it possible to add the names of the observations in a goodness-of-fit plot, e.g. the names of the species in the plot of the Species Sensitivity Distribution (SSD) classically used in ecotoxicology ?","title":"Frequently Asked Questions","text":"argument named name.points can used functions cdfcomp CIcdfcomp pass label vector observed points add names points left point. option available ECDF goodness--fit plots non censored data. option can used , example, name species classical plot Species Sensitivity Distributions (SSD) ecotoxicology.","code":"data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV taxaATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa f <- fitdist(ATV, \"lnorm\") cdfcomp(f, xlogscale = TRUE, main = \"Species Sensitivty Distribution\", xlim = c(1, 100000), name.points = taxaATV, addlegend = FALSE, plotstyle = \"ggplot\")"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-code-censored-data-in-fitdistrplus","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.1. How to code censored data in fitdistrplus ?","title":"Frequently Asked Questions","text":"Censored data must rpresented package dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. type representation corresponds coding names \"interval2\" function Surv package survival. way represent censored data fitdistrplus function Surv2fitdistcens() can used help format data use fitdistcens() one format used survival package (see help page Surv2fitdistcens()). toy example .","code":"dtoy <- data.frame(left = c(NA, 2, 4, 6, 9.7, 10), right = c(1, 3, 7, 8, 9.7, NA)) dtoy ## left right ## 1 NA 1.0 ## 2 2.0 3.0 ## 3 4.0 7.0 ## 4 6.0 8.0 ## 5 9.7 9.7 ## 6 10.0 NA"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-prepare-the-input-of-fitdistcens-with-surv2fitdistcens","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.2. How do I prepare the input of fitdistcens() with Surv2fitdistcens()?","title":"Frequently Asked Questions","text":"Let us consider classical right-censored dataset human life: twenty values randomly chosen canlifins dataset CASdatasets package. refer help Surv2fitdistcens() censoring types. performing survival analysis, common use Surv() function package survival handle different types censoring. order ease use fitdistcens(), dedicated function Surv2fitdistcens() implemented arguments similar ones Surv(). Let us now fit two simple distributions.","code":"exitage <- c(81.1,78.9,72.6,67.9,60.1,78.3,83.4,66.9,74.8,80.5,75.6,67.1, 75.3,82.8,70.1,85.4,74,70,71.6,76.5) death <- c(0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0) svdata <- Surv2fitdistcens(exitage, event=death) flnormc <- fitdistcens(svdata, \"lnorm\") fweic <- fitdistcens(svdata, \"weibull\") par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcompcens(list(fweic, flnormc), xlim=range(exitage), xlegend = \"topleft\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-represent-an-empirical-distribution-from-censored-data","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.3. How to represent an empirical distribution from censored data ?","title":"Frequently Asked Questions","text":"representation empirical distribution censored data trivial problem. One can simply represent observation interval y-value defined rank observation done using function plotdistcens. representation can interesting visualize raw data, remains difficult correctly order observations case (see example right using data smokedfish). Many authors worked development algorithms non parametric maximum likelihood estimation (NPMLE) empirical cumulative distribution function (ECDF) interval censored data (including left right censored data can considered interval censored data one bound infinity). old versions fitdistrplus used Turnbull algorithm using calls functions package survival. Even Turnbull algorithm still available package, default plot now uses function npsurv package npsurv. package provides performant algorithms developped Yong Wang (see references cited help page plotdistcens). Due lack maintenance package forced rewrite main functions package, using another optimization function. ECDF plot also implemented using Turnbull algorithm survival (see ). can see example, new implementation NPMLE provides different type plot ECDF, representing filled rectangles zones non-uniqueness NPMLE ECDF. Indeed NPMLE algorithm generally proceeds two steps. first step aims identifying equivalence classes (also named litterture Turnbull intervals maximal intersection intervals innermost intervals maximal cliques data). Equivalences classess points/intervals NPMLE ECDF may change. Equivalence classes shown correspond regions left bound interval (named L following plot previous toy example) immediately followed right bound interval (named R following plot). equivalence class may null length (example non censored value). second step aims assigning probability mass equivalence class, may zero classes. NPMLE unique equivalence classes non uniqueness NPMLE ECDF represented filled rectangles. Various NPMLE algorithms implemented packages Icens, interval npsurv. less performant enable handling data survival data, especially left censored observations.","code":"par(mfrow = c(1,2), mar = c(3, 4, 3, 0.5)) plotdistcens(dtoy, NPMLE = FALSE) data(smokedfish) dsmo <- log10(smokedfish) plotdistcens(dsmo, NPMLE = FALSE) par(mfrow = c(2, 2), mar = c(3, 4, 3, 0.5)) # Turnbull algorithm with representation of middle points of equivalence classes plotdistcens(dsmo, NPMLE.method = \"Turnbull.middlepoints\", xlim = c(-1.8, 2.4)) # Turnbull algorithm with representation of equivalence classes as intervals plotdistcens(dsmo, NPMLE.method = \"Turnbull.intervals\") # Wang algorithm with representation of equivalence classes as intervals plotdistcens(dsmo, NPMLE.method = \"Wang\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-assess-the-goodness-of-fit-of-a-distribution-fitted-on-censored-data","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.4. How to assess the goodness-of-fit of a distribution fitted on censored data ?","title":"Frequently Asked Questions","text":"available method fitdistrplus fit distributions censored data maximum likelihood estimation (MLE). distribution fitted using fitdistcens, AIC BIC values can found summary object class fitdistcens returned function. values can used compare fit various distributions dataset. Function gofstat yet proposed package fits censored data plan develop future calculation goodness--fit statistics censored data. Considering goodness--fit plots, generic plot function object class fitdistcensprovides three plots, one CDF using NPMLE ECDF plot (default using Wang prepresentation, see previous part details), Q-Q plot P-P plot simply derived Wang plot ECDF, filled rectangles indicating non uniqueness NPMLE ECDF. Functions cdfcompcens(), qqcompens() ppcompcens() can used individualize personnalize CDF, Q-Q P-P goodness--fit plots /compare fit various distributions dataset. Considering Q-Q plots P-P plots, may easier compare various fits splitting plots done automatically using plotstyle ggplot qqcompens() ppcompcens() can also done manually plotstyle graphics.","code":"fnorm <- fitdistcens(dsmo,\"norm\") flogis <- fitdistcens(dsmo,\"logis\") # comparison of AIC values summary(fnorm)$aic ## [1] 178 summary(flogis)$aic ## [1] 177 par(mar = c(2, 4, 3, 0.5)) plot(fnorm) par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcompcens(list(fnorm, flogis), fitlty = 1) qqcompcens(list(fnorm, flogis)) ppcompcens(list(fnorm, flogis)) qqcompcens(list(fnorm, flogis), lwd = 2, plotstyle = \"ggplot\", fitcol = c(\"red\", \"green\"), fillrect = c(\"pink\", \"lightgreen\"), legendtext = c(\"normal distribution\", \"logistic distribution\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"quick-overview-of-main-optimization-methods","dir":"Articles","previous_headings":"","what":"1. Quick overview of main optimization methods","title":"Which optimization algorithm to choose?","text":"present quickly main optimization methods. Please refer Numerical Optimization (Nocedal & Wright, 2006) Numerical Optimization: theoretical practical aspects (Bonnans, Gilbert, Lemarechal & Sagastizabal, 2006) good introduction. consider following problem minxf(x)\\min_x f(x) x∈ℝnx\\\\mathbb{R}^n.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"derivative-free-optimization-methods","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods","what":"1.1. Derivative-free optimization methods","title":"Which optimization algorithm to choose?","text":"Nelder-Mead method one well known derivative-free methods use values ff search minimum. consists building simplex n+1n+1 points moving/shrinking simplex good direction. set initial points x1,…,xn+1x_1, \\dots, x_{n+1}. order points f(x1)≤f(x2)≤…≤f(xn+1)f(x_1)\\leq f(x_2)\\leq\\dots\\leq f(x_{n+1}). compute xox_o centroid x1,…,xnx_1, \\dots, x_{n}. compute reflected point xr=xo+α(xo−xn+1)x_r = x_o + \\alpha(x_o-x_{n+1}). f(x1)≤f(xr) 1.2. Hessian-free optimization methods","what":"1.2.1. Computing the direction dkd_k","title":"Which optimization algorithm to choose?","text":"desirable property dkd_k dkd_k ensures descent f(xk+1)1k>1, initiated d1=−g(x1)d_1 = -g(x_1). βk\\beta_k updated according scheme: βk=gkTgkgk−1Tgk−1\\beta_k = \\frac{ g_k^T g_k}{g_{k-1}^T g_{k-1} }: Fletcher-Reeves update, βk=gkT(gk−gk−1)gk−1Tgk−1\\beta_k = \\frac{ g_k^T (g_k-g_{k-1} )}{g_{k-1}^T g_{k-1}}: Polak-Ribiere update. exists also three-term formula computing direction dk=−g(xk)+βkdk−1+γkdtd_k = -g(x_k) + \\beta_k d_{k-1}+\\gamma_{k} d_t tt+1k>t+1 otherwise γk=0\\gamma_k=0 k=tk=t. See Yuan (2006) well-known schemes Hestenses-Stiefel, Dixon Conjugate-Descent. three updates (Fletcher-Reeves, Polak-Ribiere, Beale-Sorenson) (non-linear) conjugate gradient available optim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"computing-the-stepsize-t_k","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods > 1.2. Hessian-free optimization methods","what":"1.2.2. Computing the stepsize tkt_k","title":"Which optimization algorithm to choose?","text":"Let ϕk(t)=f(xk+tdk)\\phi_k(t) = f(x_k + t d_k) given direction/iterate (dk,xk)(d_k, x_k). need find conditions find satisfactory stepsize tkt_k. literature, consider descent condition: ϕk′(0)<0\\phi_k'(0) < 0 Armijo condition: ϕk(t)≤ϕk(0)+tc1ϕk′(0)\\phi_k(t) \\leq \\phi_k(0) + t c_1 \\phi_k'(0) ensures decrease ff. Nocedal & Wright (2006) presents backtracking (geometric) approach satisfying Armijo condition minimal condition, .e. Goldstein Price condition. set tk,0t_{k,0} e.g. 1, 0<α<10 < \\alpha < 1, tk,+1=α×tk,it_{k,+1} = \\alpha \\times t_{k,}. end Repeat backtracking linesearch available optim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"benchmark","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods","what":"1.3. Benchmark","title":"Which optimization algorithm to choose?","text":"simplify benchmark optimization methods, create fitbench function computes desired estimation method optimization methods. function currently exported package.","code":"fitbench <- function(data, distr, method, grad = NULL, control = list(trace = 0, REPORT = 1, maxit = 1000), lower = -Inf, upper = +Inf, ...)"},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"theoretical-value","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution > 2.1. Log-likelihood function and its gradient for beta distribution","what":"2.1.1. Theoretical value","title":"Which optimization algorithm to choose?","text":"density beta distribution given f(x;δ1,δ2)=xδ1−1(1−x)δ2−1β(δ1,δ2), f(x; \\delta_1,\\delta_2) = \\frac{x^{\\delta_1-1}(1-x)^{\\delta_2-1}}{\\beta(\\delta_1,\\delta_2)}, β\\beta denotes beta function, see NIST Handbook mathematical functions https://dlmf.nist.gov/. recall β(,b)=Γ()Γ(b)/Γ(+b)\\beta(,b)=\\Gamma()\\Gamma(b)/\\Gamma(+b). log-likelihood set observations (x1,…,xn)(x_1,\\dots,x_n) logL(δ1,δ2)=(δ1−1)∑=1nlog(xi)+(δ2−1)∑=1nlog(1−xi)+nlog(β(δ1,δ2)) \\log L(\\delta_1,\\delta_2) = (\\delta_1-1)\\sum_{=1}^n\\log(x_i)+ (\\delta_2-1)\\sum_{=1}^n\\log(1-x_i)+ n \\log(\\beta(\\delta_1,\\delta_2)) gradient respect aa bb ∇logL(δ1,δ2)=(∑=1nln(xi)−nψ(δ1)+nψ(δ1+δ2)∑=1nln(1−xi)−nψ(δ2)+nψ(δ1+δ2)), \\nabla \\log L(\\delta_1,\\delta_2) = \\left(\\begin{matrix} \\sum\\limits_{=1}^n\\ln(x_i) - n\\psi(\\delta_1)+n\\psi( \\delta_1+\\delta_2) \\\\ \\sum\\limits_{=1}^n\\ln(1-x_i)- n\\psi(\\delta_2)+n\\psi( \\delta_1+\\delta_2) \\end{matrix}\\right), ψ(x)=Γ′(x)/Γ(x)\\psi(x)=\\Gamma'(x)/\\Gamma(x) digamma function, see NIST Handbook mathematical functions https://dlmf.nist.gov/.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"r-implementation","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution > 2.1. Log-likelihood function and its gradient for beta distribution","what":"2.1.2. R implementation","title":"Which optimization algorithm to choose?","text":"fitdistrplus package, minimize opposite log-likelihood: implement opposite gradient grlnL. log-likelihood gradient exported.","code":"lnL <- function(par, fix.arg, obs, ddistnam) fitdistrplus:::loglikelihood(par, fix.arg, obs, ddistnam) grlnlbeta <- fitdistrplus:::grlnlbeta"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"random-generation-of-a-sample","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.2. Random generation of a sample","title":"Which optimization algorithm to choose?","text":"","code":"#(1) beta distribution n <- 200 x <- rbeta(n, 3, 3/4) grlnlbeta(c(3, 4), x) #test ## [1] -133 317 hist(x, prob=TRUE, xlim=0:1) lines(density(x), col=\"red\") curve(dbeta(x, 3, 3/4), col=\"green\", add=TRUE) legend(\"topleft\", lty=1, col=c(\"red\",\"green\"), legend=c(\"empirical\", \"theoretical\"), bty=\"n\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"fit-beta-distribution","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.3 Fit Beta distribution","title":"Which optimization algorithm to choose?","text":"Define control parameters. Call mledist default optimization function (optim implemented stats package) without gradient different optimization methods. case constrained optimization, mledist permits direct use constrOptim function (still implemented stats package) allow linear inequality constraints using logarithmic barrier. Use exp/log transformation shape parameters δ1\\delta_1 δ2\\delta_2 ensure shape parameters strictly positive. extract values fitted parameters, value corresponding log-likelihood number counts function minimize gradient (whether theoretical gradient numerically approximated one).","code":"ctr <- list(trace=0, REPORT=1, maxit=1000) unconstropt <- fitbench(x, \"beta\", \"mle\", grad=grlnlbeta, lower=0) ## BFGS NM CGFR CGPR CGBS L-BFGS-B NM-B G-BFGS ## 14 14 14 14 14 14 14 14 ## G-CGFR G-CGPR G-CGBS G-BFGS-B G-NM-B G-CGFR-B G-CGPR-B G-CGBS-B ## 14 14 14 14 14 14 14 14 dbeta2 <- function(x, shape1, shape2, log) dbeta(x, exp(shape1), exp(shape2), log=log) #take the log of the starting values startarg <- lapply(fitdistrplus:::startargdefault(x, \"beta\"), log) #redefine the gradient for the new parametrization grbetaexp <- function(par, obs, ...) grlnlbeta(exp(par), obs) * exp(par) expopt <- fitbench(x, distr=\"beta2\", method=\"mle\", grad=grbetaexp, start=startarg) ## BFGS NM CGFR CGPR CGBS G-BFGS G-CGFR G-CGPR G-CGBS ## 14 14 14 14 14 14 14 14 14 #get back to original parametrization expopt[c(\"fitted shape1\", \"fitted shape2\"), ] <- exp(expopt[c(\"fitted shape1\", \"fitted shape2\"), ])"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"results-of-the-numerical-investigation","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.4. Results of the numerical investigation","title":"Which optimization algorithm to choose?","text":"Results displayed following tables: (1) original parametrization without specifying gradient (-B stands bounded version), (2) original parametrization (true) gradient (-B stands bounded version -G gradient), (3) log-transformed parametrization without specifying gradient, (4) log-transformed parametrization (true) gradient (-G stands gradient). Unconstrained optimization approximated gradient Unconstrained optimization true gradient Exponential trick optimization approximated gradient Exponential trick optimization true gradient Using llsurface, plot log-likehood surface around true value (green) fitted parameters (red). can simulate bootstrap replicates using bootdist function.","code":"llsurface(min.arg=c(0.1, 0.1), max.arg=c(7, 3), xlim=c(.1,7), plot.arg=c(\"shape1\", \"shape2\"), nlev=25, lseq=50, data=x, distr=\"beta\", back.col = FALSE) points(unconstropt[1,\"BFGS\"], unconstropt[2,\"BFGS\"], pch=\"+\", col=\"red\") points(3, 3/4, pch=\"x\", col=\"green\") b1 <- bootdist(fitdist(x, \"beta\", method = \"mle\", optim.method = \"BFGS\"), niter = 100, parallel = \"snow\", ncpus = 2) summary(b1) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## shape1 2.73 2.272 3.283 ## shape2 0.75 0.652 0.888 plot(b1, trueval = c(3, 3/4))"},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"theoretical-value-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution > 3.1. Log-likelihood function and its gradient for negative binomial distribution","what":"3.1.1. Theoretical value","title":"Which optimization algorithm to choose?","text":"p.m.f. Negative binomial distribution given f(x;m,p)=Γ(x+m)Γ(m)x!pm(1−p)x, f(x; m,p) = \\frac{\\Gamma(x+m)}{\\Gamma(m)x!} p^m (1-p)^x, Γ\\Gamma denotes beta function, see NIST Handbook mathematical functions https://dlmf.nist.gov/. exists alternative representation μ=m(1−p)/p\\mu=m (1-p)/p equivalently p=m/(m+μ)p=m/(m+\\mu). Thus, log-likelihood set observations (x1,…,xn)(x_1,\\dots,x_n) logL(m,p)=∑=1nlogΓ(xi+m)−nlogΓ(m)−∑=1nlog(xi!)+mnlog(p)+∑=1nxilog(1−p) \\log L(m,p) = \\sum_{=1}^{n} \\log\\Gamma(x_i+m) -n\\log\\Gamma(m) -\\sum_{=1}^{n} \\log(x_i!) + mn\\log(p) +\\sum_{=1}^{n} {x_i}\\log(1-p) gradient respect mm pp ∇logL(m,p)=(∑=1nψ(xi+m)−nψ(m)+nlog(p)mn/p−∑=1nxi/(1−p)), \\nabla \\log L(m,p) = \\left(\\begin{matrix} \\sum_{=1}^{n} \\psi(x_i+m) -n \\psi(m) + n\\log(p) \\\\ mn/p -\\sum_{=1}^{n} {x_i}/(1-p) \\end{matrix}\\right), ψ(x)=Γ′(x)/Γ(x)\\psi(x)=\\Gamma'(x)/\\Gamma(x) digamma function, see NIST Handbook mathematical functions https://dlmf.nist.gov/.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"r-implementation-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution > 3.1. Log-likelihood function and its gradient for negative binomial distribution","what":"3.1.2. R implementation","title":"Which optimization algorithm to choose?","text":"fitdistrplus package, minimize opposite log-likelihood: implement opposite gradient grlnL.","code":"grlnlNB <- function(x, obs, ...) { m <- x[1] p <- x[2] n <- length(obs) c(sum(psigamma(obs+m)) - n*psigamma(m) + n*log(p), m*n/p - sum(obs)/(1-p)) }"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"random-generation-of-a-sample-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.2. Random generation of a sample","title":"Which optimization algorithm to choose?","text":"","code":"#(2) negative binomial distribution n <- 200 trueval <- c(\"size\"=10, \"prob\"=3/4, \"mu\"=10/3) x <- rnbinom(n, trueval[\"size\"], trueval[\"prob\"]) hist(x, prob=TRUE, ylim=c(0, .3), xlim=c(0, 10)) lines(density(x), col=\"red\") points(min(x):max(x), dnbinom(min(x):max(x), trueval[\"size\"], trueval[\"prob\"]), col = \"green\") legend(\"topright\", lty = 1, col = c(\"red\", \"green\"), legend = c(\"empirical\", \"theoretical\"), bty=\"n\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"fit-a-negative-binomial-distribution","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.3. Fit a negative binomial distribution","title":"Which optimization algorithm to choose?","text":"Define control parameters make benchmark. case constrained optimization, mledist permits direct use constrOptim function (still implemented stats package) allow linear inequality constraints using logarithmic barrier. Use exp/log transformation shape parameters δ1\\delta_1 δ2\\delta_2 ensure shape parameters strictly positive. extract values fitted parameters, value corresponding log-likelihood number counts function minimize gradient (whether theoretical gradient numerically approximated one).","code":"ctr <- list(trace = 0, REPORT = 1, maxit = 1000) unconstropt <- fitbench(x, \"nbinom\", \"mle\", grad = grlnlNB, lower = 0) ## BFGS NM CGFR CGPR CGBS L-BFGS-B NM-B G-BFGS ## 14 14 14 14 14 14 14 14 ## G-CGFR G-CGPR G-CGBS G-BFGS-B G-NM-B G-CGFR-B G-CGPR-B G-CGBS-B ## 14 14 14 14 14 14 14 14 unconstropt <- rbind(unconstropt, \"fitted prob\" = unconstropt[\"fitted mu\", ] / (1 + unconstropt[\"fitted mu\", ])) dnbinom2 <- function(x, size, prob, log) dnbinom(x, exp(size), 1 / (1 + exp(-prob)), log = log) # transform starting values startarg <- fitdistrplus:::startargdefault(x, \"nbinom\") startarg$mu <- startarg$size / (startarg$size + startarg$mu) startarg <- list(size = log(startarg[[1]]), prob = log(startarg[[2]] / (1 - startarg[[2]]))) # redefine the gradient for the new parametrization Trans <- function(x) c(exp(x[1]), plogis(x[2])) grNBexp <- function(par, obs, ...) grlnlNB(Trans(par), obs) * c(exp(par[1]), plogis(x[2])*(1-plogis(x[2]))) expopt <- fitbench(x, distr=\"nbinom2\", method=\"mle\", grad=grNBexp, start=startarg) ## BFGS NM CGFR CGPR CGBS G-BFGS G-CGFR G-CGPR G-CGBS ## 14 14 14 14 14 14 14 14 14 # get back to original parametrization expopt[c(\"fitted size\", \"fitted prob\"), ] <- apply(expopt[c(\"fitted size\", \"fitted prob\"), ], 2, Trans)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"results-of-the-numerical-investigation-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.4. Results of the numerical investigation","title":"Which optimization algorithm to choose?","text":"Results displayed following tables: (1) original parametrization without specifying gradient (-B stands bounded version), (2) original parametrization (true) gradient (-B stands bounded version -G gradient), (3) log-transformed parametrization without specifying gradient, (4) log-transformed parametrization (true) gradient (-G stands gradient). Unconstrained optimization approximated gradient Unconstrained optimization true gradient Exponential trick optimization approximated gradient Exponential trick optimization true gradient Using llsurface, plot log-likehood surface around true value (green) fitted parameters (red). can simulate bootstrap replicates using bootdist function.","code":"llsurface(min.arg = c(5, 0.3), max.arg = c(15, 1), xlim=c(5, 15), plot.arg = c(\"size\", \"prob\"), nlev = 25, lseq = 50, data = x, distr = \"nbinom\", back.col = FALSE) points(unconstropt[\"fitted size\", \"BFGS\"], unconstropt[\"fitted prob\", \"BFGS\"], pch = \"+\", col = \"red\") points(trueval[\"size\"], trueval[\"prob\"], pch = \"x\", col = \"green\") b1 <- bootdist(fitdist(x, \"nbinom\", method = \"mle\", optim.method = \"BFGS\"), niter = 100, parallel = \"snow\", ncpus = 2) summary(b1) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## size 57.33 57.33 57.33 ## mu 3.46 3.24 3.72 plot(b1, trueval=trueval[c(\"size\", \"mu\")])"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"4. Conclusion","title":"Which optimization algorithm to choose?","text":"Based two previous examples, observe methods converge point. reassuring. However, number function evaluations (gradient evaluations) different method another. Furthermore, specifying true gradient log-likelihood help fitting procedure generally slows convergence. Generally, best method standard BFGS method BFGS method exponential transformation parameters. Since exponential function differentiable, asymptotic properties still preserved (Delta method) finite-sample may produce small bias.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Introduction","dir":"Articles","previous_headings":"","what":"1. Introduction","title":"Overview of the fitdistrplus package","text":"Fitting distributions data common task statistics consists choosing probability distribution modelling random variable, well finding parameter estimates distribution. requires judgment expertise generally needs iterative process distribution choice, parameter estimation, quality fit assessment. R (R Development Core Team 2013) package MASS (Venables Ripley 2010), maximum likelihood estimation available via fitdistr function; steps fitting process can done using R functions (Ricci 2005). paper, present R package fitdistrplus (Delignette-Muller et al. 2014) implementing several methods fitting univariate parametric distribution. first objective developing package provide R users set functions dedicated help overall process. fitdistr function estimates distribution parameters maximizing likelihood function using optim function. distinction parameters different roles (e.g., main parameter nuisance parameter) made, paper focuses parameter estimation general point--view. cases, estimation methods prefered, maximum goodness--fit estimation (also called minimum distance estimation), proposed R package actuar three different goodness--fit distances (Dutang, Goulet, Pigeon 2008). developping fitdistrplus package, second objective consider various estimation methods addition maximum likelihood estimation (MLE). Functions developped enable moment matching estimation (MME), quantile matching estimation (QME), maximum goodness--fit estimation (MGE) using eight different distances. Moreover, fitdistrplus package offers possibility specify user-supplied function optimization, useful cases classical optimization techniques, included optim, adequate. applied statistics, frequent fit distributions censored data Commeau et al. (2012). MASS fitdistr function enable maximum likelihood estimation type data. packages can used work censored data, especially survival data Jordan (2005), packages generally focus specific models, enabling fit restricted set distributions. third objective thus provide R users function estimate univariate distribution parameters right-, left- interval-censored data. packages CRAN provide estimation procedures user-supplied parametric distribution support different types data. distrMod package (Kohl Ruckdeschel 2010) provides object-oriented (S4) implementation probability models includes distribution fitting procedures given minimization criterion. criterion user-supplied function sufficiently flexible handle censored data, yet trivial way, see Example M4 distrMod vignette. fitting functions MLEstimator MDEstimator return S4 class coercion method class mle provided respective functionalities (e.g., confint logLik) package stats4 available, . fitdistrplus, chose use standard S3 class system understanding R users. designing fitdistrplus package, forget implement generic functions also available S3 classes. Finally, various packages provide functions estimate mode, moments L-moments distribution, see reference manuals modeest, lmomco Lmoments packages. package available Comprehensive R Archive Network . paper organized follows: Section 2 presents tools fitting continuous distributions classic non-censored data. Section 3 deals estimation methods types data, Section 4 concludes.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Choice","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.1. Choice of candidate distributions","title":"Overview of the fitdistrplus package","text":"illustrating use various functions fitdistrplus package continuous non-censored data, first use data set named groundbeef included package. data set contains pointwise values serving sizes grams, collected French survey, ground beef patties consumed children 5 years old. used quantitative risk assessment published Delignette-Muller Cornu (2008). fitting one distributions data set, generally necessary choose good candidates among predefined set distributions. choice may guided knowledge stochastic processes governing modeled variable, , absence knowledge regarding underlying process, observation empirical distribution. help user choice, developed functions plot characterize empirical distribution. First , common start plots empirical distribution function histogram (density plot), can obtained plotdist function fitdistrplus package. function provides two plots (see Figure @ref(fig:figgroundbeef)): left-hand plot default histogram density scale (density plot , according values arguments histo demp) right-hand plot empirical cumulative distribution function (CDF). Histogram CDF plots empirical distribution continuous variable (serving size groundbeef data set) provided plotdist function. addition empirical plots, descriptive statistics may help choose candidates describe distribution among set parametric distributions. Especially skewness kurtosis, linked third fourth moments, useful purpose. non-zero skewness reveals lack symmetry empirical distribution, kurtosis value quantifies weight tails comparison normal distribution kurtosis equals 3. skewness kurtosis corresponding unbiased estimator (Casella Berger 2002) sample (Xi)∼..d.X(X_i)_i \\stackrel{\\text{..d.}}{\\sim} X observations (xi)(x_i)_i given : sk(X)=E[(X−E(X))3]Var(X)32,sk̂=n(n−1)n−2×m3m232,(#eq:eq1)\\begin{equation} sk(X) = \\frac{E[(X-E(X))^3]}{Var(X)^{\\frac{3}{2}}}~,~\\widehat{sk}=\\frac{\\sqrt{n(n-1)}}{n-2}\\times\\frac{m_{3}}{m_{2}^{\\frac{3}{2}}},(\\#eq:eq1) \\end{equation} kr(X)=E[(X−E(X))4]Var(X)2,kr̂=n−1(n−2)(n−3)((n+1)×m4m22−3(n−1))+3,(#eq:eq2)\\begin{equation} kr(X) = \\frac{E[(X-E(X))^4]}{Var(X)^{2}}~,~\\widehat{kr}=\\frac{n-1}{(n-2)(n-3)}((n+1) \\times \\frac{m_{4}}{m_{2}^{2}}-3(n-1)) + 3,(\\#eq:eq2) \\end{equation} m2m_{2}, m3m_{3}, m4m_{4} denote empirical moments defined mk=1n∑=1n(xi−x¯)km_{k}=\\frac{1}{n}\\sum_{=1}^n(x_{}-\\overline{x})^{k}, xix_{} nn observations variable xx x¯\\overline{x} mean value. descdist function provides classical descriptive statistics (minimum, maximum, median, mean, standard deviation), skewness kurtosis. default, unbiased estimations three last statistics provided. Nevertheless, argument method can changed \"unbiased\" (default) \"sample\" obtain without correction bias. skewness-kurtosis plot one proposed Cullen Frey (1999) provided descdist function empirical distribution (see Figure @ref(fig:descgroundbeefplot) groundbeef data set). plot, values common distributions displayed order help choice distributions fit data. distributions (normal, uniform, logistic, exponential), one possible value skewness kurtosis. Thus, distribution represented single point plot. distributions, areas possible values represented, consisting lines (gamma lognormal distributions), larger areas (beta distribution). Skewness kurtosis known robust. order take account uncertainty estimated values kurtosis skewness data, nonparametric bootstrap procedure (Efron Tibshirani 1994) can performed using argument boot. Values skewness kurtosis computed bootstrap samples (constructed random sampling replacement original data set) reported skewness-kurtosis plot. Nevertheless, user needs know skewness kurtosis, like higher moments, high variance. problem completely solved use bootstrap. skewness-kurtosis plot regarded indicative . properties random variable considered, notably expected value range, complement use plotdist descdist functions. call descdist function describe distribution serving size groundbeef data set draw corresponding skewness-kurtosis plot (see Figure @ref(fig:descgroundbeefplot)). Looking results example positive skewness kurtosis far 3, fit three common right-skewed distributions considered, Weibull, gamma lognormal distributions. Skewness-kurtosis plot continuous variable (serving size groundbeef data set) provided descdist function.","code":"require(\"fitdistrplus\") ## Loading required package: fitdistrplus ## Loading required package: MASS ## Loading required package: survival data(\"groundbeef\") str(groundbeef) ## 'data.frame': 254 obs. of 1 variable: ## $ serving: num 30 10 20 24 20 24 40 20 50 30 ... plotdist(groundbeef$serving, histo = TRUE, demp = TRUE) descdist(groundbeef$serving, boot = 1000) ## summary statistics ## ------ ## min: 10 max: 200 ## median: 79 ## mean: 73.65 ## estimated sd: 35.88 ## estimated skewness: 0.7353 ## estimated kurtosis: 3.551"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"FIT","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.2. Fit of distributions by maximum likelihood estimation","title":"Overview of the fitdistrplus package","text":"selected, one parametric distributions f(.|θ)f(.\\vert \\theta) (parameter θ∈ℝd\\theta\\\\mathbb{R}^d) may fitted data set, one time, using fitdist function. ..d. sample assumption, distribution parameters θ\\theta default estimated maximizing likelihood function defined : L(θ)=∏=1nf(xi|θ)(#eq:eq3)\\begin{equation} L(\\theta)=\\prod_{=1}^n f(x_{}\\vert \\theta)(\\#eq:eq3) \\end{equation} xix_{} nn observations variable XX f(.|θ)f(.\\vert \\theta) density function parametric distribution. proposed estimation methods described Section 3.1.. fitdist function returns S3 object class fitdist print, summary plot functions provided. fit distribution using fitdist assumes corresponding d, p, q functions (standing respectively density, distribution quantile functions) defined. Classical distributions already defined way stats package, e.g., dnorm, pnorm qnorm normal distribution (see ?Distributions). Others may found various packages (see CRAN task view: Probability Distributions ). Distributions found package must implemented user d, p, q functions. call fitdist, distribution specified via argument dist either character string corresponding common root name used names d, p, q functions (e.g., \"norm\" normal distribution) density function , root name extracted (e.g., dnorm normal distribution). Numerical results returned fitdist function (1) parameter estimates, (2) estimated standard errors (computed estimate Hessian matrix maximum likelihood solution), (3) loglikelihood, (4) Akaike Bayesian information criteria (-called AIC BIC), (5) correlation matrix parameter estimates. call fitdist function fit Weibull distribution serving size groundbeef data set. plot object class fitdist provides four classical goodness--fit plots (Cullen Frey 1999) presented Figure @ref(fig:groundbeefcomp): density plot representing density function fitted distribution along histogram empirical distribution, CDF plot empirical distribution fitted distribution, Q-Q plot representing empirical quantiles (y-axis) theoretical quantiles (x-axis), P-P plot representing empirical distribution function evaluated data point (y-axis) fitted distribution function (x-axis). CDF, Q-Q P-P plots, probability plotting position defined default using Hazen’s rule, probability points empirical distribution calculated (1:n - 0.5)/n, recommended Blom (1959). plotting position can easily changed (see reference manual details (Delignette-Muller et al. 2014)). Unlike generic plot function, denscomp, cdfcomp, qqcomp ppcomp functions enable draw separately four plots, order compare empirical distribution multiple parametric distributions fitted data set. functions must called first argument corresponding list objects class fitdist, optionally arguments customize plot (see reference manual lists arguments may specific plot (Delignette-Muller et al. 2014)). following example, compare fit Weibull, lognormal gamma distributions groundbeef data set (Figure @ref(fig:groundbeefcomp)). Four Goodness--fit plots various distributions fitted continuous data (Weibull, gamma lognormal distributions fitted serving sizes groundbeef data set) provided functions denscomp, qqcomp, cdfcomp ppcomp. density plot CDF plot may considered basic classical goodness--fit plots. two plots complementary can informative cases. Q-Q plot emphasizes lack--fit distribution tails P-P plot emphasizes lack--fit distribution center. present example (Figure @ref(fig:groundbeefcomp)), none three fitted distributions correctly describes center distribution, Weibull gamma distributions prefered better description right tail empirical distribution, especially tail important use fitted distribution, context food risk assessment. data set named endosulfan now used illustrate features fitdistrplus package. data set contains acute toxicity values organochlorine pesticide endosulfan (geometric mean LC50 ou EC50 values μg.L−1\\mu g.L^{-1}), tested Australian non-Australian laboratory-species (Hose Van den Brink 2004). ecotoxicology, lognormal loglogistic distribution often fitted data set order characterize species sensitivity distribution (SSD) pollutant. low percentile fitted distribution, generally 5% percentile, calculated named hazardous concentration 5% (HC5). interpreted value pollutant concentration protecting 95% species (Posthuma, Suter, Traas 2010). fit lognormal loglogistic distribution whole endosulfan data set rather bad (Figure @ref(fig:fitendo)), especially due minority high values. two-parameter Pareto distribution three-parameter Burr distribution (extension loglogistic Pareto distributions) fitted. Pareto Burr distributions provided package actuar. , define starting values (optimization process) reasonable starting values implicity defined within fitdist function distributions defined R (see ?fitdist details). distributions like Pareto Burr distribution, initial values distribution parameters supplied argument start, named list initial values parameter (appear d, p, q functions). defined reasonable starting values1 various distributions can fitted graphically compared. example, function cdfcomp can used report CDF values logscale emphasize discrepancies tail interest defining HC5 value (Figure @ref(fig:fitendo)). CDF plot compare fit four distributions acute toxicity values various organisms organochlorine pesticide endosulfan (endosulfan data set) provided cdfcomp function, CDF values logscale emphasize discrepancies left tail. None fitted distribution correctly describes right tail observed data set, shown Figure @ref(fig:fitendo), left-tail seems better described Burr distribution. use considered estimate HC5 value 5% quantile distribution. can easily done using quantile generic function defined object class fitdist. calculation together calculation empirical quantile comparison. addition ecotoxicology context, quantile generic function also attractive actuarial-financial context. fact, value--risk VARαVAR_\\alpha defined 1−α1-\\alpha-quantile loss distribution can computed quantile fitdist object. computation different goodness--fit statistics proposed fitdistrplus package order compare fitted distributions. purpose goodness--fit statistics aims measure distance fitted parametric distribution empirical distribution: e.g., distance fitted cumulative distribution function FF empirical distribution function FnF_{n}. fitting continuous distributions, three goodness--fit statistics classicaly considered: Cramer-von Mises, Kolmogorov-Smirnov Anderson-Darling statistics (D’Agostino Stephens 1986). Naming xix_{} nn observations continuous variable XX arranged ascending order, Table @ref(tab:tabKSCvMAD) gives definition empirical estimate three considered goodness--fit statistics. can computed using function gofstat defined Stephens (D’Agostino Stephens 1986). (#tab:tabKSCvMAD) Goodness--fit statistics defined Stephens (D’Agostino Stephens 1986). Fi=△F(xi)F_i\\stackrel{\\triangle}{=} F(x_i) giving weight distribution tails, Anderson-Darling statistic special interest matters equally emphasize tails well main body distribution. often case risk assessment Vose (2010). reason, statistics often used select best distribution among fitted. Nevertheless, statistics used cautiously comparing fits various distributions. Keeping mind weighting CDF quadratic difference depends parametric distribution definition (see Table @ref(tab:tabKSCvMAD)), Anderson-Darling statistics computed several distributions fitted data set theoretically difficult compare. Moreover, statistic, Cramer-von Mises Kolmogorov-Smirnov ones, take account complexity model (.e., parameter number). problem compared distributions characterized number parameters, systematically promote selection complex distributions case. Looking classical penalized criteria based loglikehood (AIC, BIC) seems thus also interesting, especially discourage overfitting. previous example, goodness--fit statistics based CDF distance favor Burr distribution, one characterized three parameters, AIC BIC values respectively give preference Burr distribution Pareto distribution. choice two distributions seems thus less obvious discussed. Even specifically recommended discrete distributions, Chi-squared statistic may also used continuous distributions (see Section 3.3. reference manual examples (Delignette-Muller et al. 2014)).","code":"fw <- fitdist(groundbeef$serving, \"weibull\") summary(fw) ## Fitting of the distribution ' weibull ' by maximum likelihood ## Parameters : ## estimate Std. Error ## shape 2.186 1.667 ## scale 83.348 40.272 ## Loglikelihood: -1255 AIC: 2514 BIC: 2522 ## Correlation matrix: ## shape scale ## shape 1.0000 0.3218 ## scale 0.3218 1.0000 par(mfrow = c(2, 2), mar = c(4, 4, 2, 1)) fg <- fitdist(groundbeef$serving, \"gamma\") fln <- fitdist(groundbeef$serving, \"lnorm\") plot.legend <- c(\"Weibull\", \"lognormal\", \"gamma\") denscomp(list(fw, fln, fg), legendtext = plot.legend) qqcomp(list(fw, fln, fg), legendtext = plot.legend) cdfcomp(list(fw, fln, fg), legendtext = plot.legend) ppcomp(list(fw, fln, fg), legendtext = plot.legend) require(\"actuar\") ## Loading required package: actuar ## ## Attaching package: 'actuar' ## The following objects are masked from 'package:stats': ## ## sd, var ## The following object is masked from 'package:grDevices': ## ## cm data(\"endosulfan\") ATV <- endosulfan$ATV fendo.ln <- fitdist(ATV, \"lnorm\") fendo.ll <- fitdist(ATV, \"llogis\", start = list(shape = 1, scale = 500)) fendo.P <- fitdist(ATV, \"pareto\", start = list(shape = 1, scale = 500)) fendo.B <- fitdist(ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) cdfcomp(list(fendo.ln, fendo.ll, fendo.P, fendo.B), xlogscale = TRUE, ylogscale = TRUE, legendtext = c(\"lognormal\", \"loglogistic\", \"Pareto\", \"Burr\")) quantile(fendo.B, probs = 0.05) ## Estimated quantiles for each specified probability (non-censored data) ## p=0.05 ## estimate 0.2939 quantile(ATV, probs = 0.05) ## 5% ## 0.2 gofstat(list(fendo.ln, fendo.ll, fendo.P, fendo.B), fitnames = c(\"lnorm\", \"llogis\", \"Pareto\", \"Burr\")) ## Goodness-of-fit statistics ## lnorm llogis Pareto Burr ## Kolmogorov-Smirnov statistic 0.1672 0.1196 0.08488 0.06155 ## Cramer-von Mises statistic 0.6374 0.3827 0.13926 0.06803 ## Anderson-Darling statistic 3.4721 2.8316 0.89206 0.52393 ## ## Goodness-of-fit criteria ## lnorm llogis Pareto Burr ## Akaike's Information Criterion 1069 1069 1048 1046 ## Bayesian Information Criterion 1074 1075 1053 1054"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Uncertainty","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.3. Uncertainty in parameter estimates","title":"Overview of the fitdistrplus package","text":"uncertainty parameters fitted distribution can estimated parametric nonparametric bootstraps using boodist function non-censored data (Efron Tibshirani 1994). function returns bootstrapped values parameters S3 class object can plotted visualize bootstrap region. medians 95% confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations (due lack convergence optimization algorithm bootstrapped data sets), number iterations estimation converges also printed summary. plot object class bootdist consists scatterplot matrix scatterplots bootstrapped values parameters providing representation joint uncertainty distribution fitted parameters. example use bootdist function previous fit Burr distribution endosulfan data set (Figure @ref(fig:bootstrap)). Bootstrappped values parameters fit Burr distribution characterized three parameters (example endosulfan data set) provided plot object class bootdist. Bootstrap samples parameter estimates useful especially calculate confidence intervals parameter fitted distribution marginal distribution bootstraped values. also interesting look joint distribution bootstraped values scatterplot (matrix scatterplots number parameters exceeds two) order understand potential structural correlation parameters (see Figure @ref(fig:bootstrap)). use whole bootstrap sample also interest risk assessment field. use enables characterization uncertainty distribution parameters. can directly used within second-order Monte Carlo simulation framework, especially within package mc2d (Pouillot, Delignette-Muller, Denis 2011). One refer Pouillot Delignette-Muller (2010) introduction use mc2d fitdistrplus packages context quantitative risk assessment. bootstrap method can also used calculate confidence intervals quantiles fitted distribution. purpose, generic quantile function provided class bootdist. default, 95% percentiles bootstrap confidence intervals quantiles provided. Going back previous example ecotoxicolgy, function can used estimate uncertainty associated HC5 estimation, example previously fitted Burr distribution endosulfan data set.","code":"bendo.B <- bootdist(fendo.B, niter = 1001) summary(bendo.B) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## shape1 0.1983 0.09283 0.3606 ## shape2 1.5863 1.05306 3.0629 ## rate 1.4907 0.70828 2.7775 plot(bendo.B) quantile(bendo.B, probs = 0.05) ## (original) estimated quantiles for each specified probability (non-censored data) ## p=0.05 ## estimate 0.2939 ## Median of bootstrap estimates ## p=0.05 ## estimate 0.2994 ## ## two-sided 95 % CI of each quantile ## p=0.05 ## 2.5 % 0.1792 ## 97.5 % 0.4999"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Alternatives","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.1. Alternative methods for parameter estimation","title":"Overview of the fitdistrplus package","text":"subsection focuses alternative estimation methods. One alternative continuous distributions maximum goodness--fit estimation method also called minimum distance estimation method Dutang, Goulet, Pigeon (2008). package method proposed eight different distances: three classical distances defined Table @ref(tab:tabKSCvMAD), one variants Anderson-Darling distance proposed Luceno (2006) defined Table @ref(tab:modifiedAD). right-tail AD gives weight right-tail, left-tail AD gives weight left tail. Either tails, , can receive even larger weights using second order Anderson-Darling Statistics. (#tab:modifiedAD) Modified Anderson-Darling statistics defined Luceno (2006). Fi=△F(xi)F_i\\stackrel{\\triangle}{=} F(x_{}) F¯=△1−F(xi)\\overline F_i\\stackrel{\\triangle}{=}1-F(x_{}) fit distribution maximum goodness--fit estimation, one needs fix argument method mge call fitdist specify argument gof coding chosen goodness--fit distance. function intended used continuous non-censored data. Maximum goodness--fit estimation may useful give weight data one tail distribution. previous example ecotoxicology, used non classical distribution (Burr distribution) correctly fit empirical distribution especially left tail. order correctly estimate 5%\\% percentile, also consider fit classical lognormal distribution, minimizing goodness--fit distance giving weight left tail empirical distribution. follows, left tail Anderson-Darling distances first second order used fit lognormal endosulfan data set (see Figure @ref(fig:plotfitMGE)). Comparison lognormal distribution fitted MLE MGE using two different goodness--fit distances: left-tail Anderson-Darling left-tail Anderson Darling second order (example endosulfan data set) provided cdfcomp function, CDF values logscale emphasize discrepancies left tail. Comparing 5% percentiles (HC5) calculated using three fits one calculated MLE fit Burr distribution, can observe, example, fitting lognormal distribution maximizing left tail Anderson-Darling distances first second order enables approach value obtained fitting Burr distribution MLE. moment matching estimation (MME) another method commonly used fit parametric distributions (Vose 2010). MME consists finding value parameter θ\\theta equalizes first theoretical raw moments parametric distribution corresponding empirical raw moments Equation @ref(eq:eq4): E(Xk|θ)=1n∑=1nxik,(#eq:eq4)\\begin{equation} E(X^{k}|\\theta)=\\frac{1}{n}\\sum_{=1}^{n}x_{}^{k},(\\#eq:eq4) \\end{equation} k=1,…,dk=1,\\ldots,d, dd number parameters estimate xix_{} nn observations variable XX. moments order greater equal 2, may also relevant match centered moments. Therefore, match moments given Equation @ref(eq:eq5): E(X|θ)=x¯,E((X−E(X))k|θ)=mk, k=2,…,d,(#eq:eq5)\\begin{equation} E(X\\vert \\theta) = \\overline{x} ~,~E\\left((X-E(X))^{k}|\\theta\\right)=m_k, \\text{ } k=2,\\ldots,d,(\\#eq:eq5) \\end{equation} mkm_k denotes empirical centered moments. method can performed setting argument method \"mme\" call fitdist. estimate computed closed-form formula following distributions: normal, lognormal, exponential, Poisson, gamma, logistic, negative binomial, geometric, beta uniform distributions. case, distributions characterized one parameter (geometric, Poisson exponential), parameter simply estimated matching theoretical observed means, distributions characterized two parameters, parameters estimated matching theoretical observed means variances (Vose 2010). distributions, equation moments solved numerically using optim function minimizing sum squared differences observed theoretical moments (see fitdistrplus reference manual technical details (Delignette-Muller et al. 2014)). classical data set Danish insurance industry published McNeil (1997) used illustrate method. fitdistrplus, data set stored danishuni univariate version contains loss amounts collected Copenhagen Reinsurance 1980 1990. actuarial science, standard consider positive heavy-tailed distributions special focus right-tail distributions. numerical experiment, choose classic actuarial distributions loss modelling: lognormal distribution Pareto type II distribution (Klugman, Panjer, Willmot 2009). lognormal distribution fitted danishuni data set matching moments implemented closed-form formula. left-hand graph Figure @ref(fig:danishmme), fitted distribution functions obtained using moment matching estimation (MME) maximum likelihood estimation (MLE) methods compared. MME method provides cautious estimation insurance risk MME-fitted distribution function (resp. MLE-fitted) underestimates (overestimates) empirical distribution function large values claim amounts. Comparison MME MLE fitting lognormal Pareto distribution loss data danishuni data set. second time, Pareto distribution, gives weight right-tail distribution, fitted. lognormal distribution, Pareto two parameters, allows fair comparison. use implementation actuar package providing raw centered moments distribution (addition d, p, q r functions (Goulet 2012). Fitting heavy-tailed distribution first second moments exist certain values shape parameter requires cautiousness. carried providing, optimization process, lower upper bound parameter. code calls L-BFGS-B optimization method optim, since quasi-Newton allows box constraints 2. choose match moments defined Equation @ref(eq:eq4), function computing empirical raw moment (called memp example) passed fitdist. two-parameter distributions (.e., d=2d=2), Equations @ref(eq:eq4) @ref(eq:eq5) equivalent. shown Figure @ref(fig:danishmme), MME MLE fits far less distant (looking right-tail) Pareto distribution lognormal distribution data set. Furthermore, two distributions, MME method better fits right-tail distribution visual point view. seems logical since empirical moments influenced large observed values. previous traces, gave values goodness--fit statistics. Whatever statistic considered, MLE-fitted lognormal always provides best fit observed data. Maximum likelihood moment matching estimations certainly commonly used method fitting distributions (Cullen Frey 1999). Keeping mind two methods may produce different results, user aware great sensitivity outliers choosing moment matching estimation. may seen advantage example objective better describe right tail distribution, may seen drawback objective different. Fitting parametric distribution may also done matching theoretical quantiles parametric distributions (specified probabilities) empirical quantiles (Tse 2009). equality theoretical empirical quantiles expressed Equation @ref(eq:eq6) , similar Equations @ref(eq:eq4) @ref(eq:eq5): F−1(pk|θ)=Qn,pk(#eq:eq6)\\begin{equation} F^{-1}(p_{k}|\\theta)=Q_{n,p_{k}}(\\#eq:eq6) \\end{equation} k=1,…,dk=1,\\ldots,d, dd number parameters estimate (dimension θ\\theta fixed parameters) Qn,pkQ_{n,p_{k}} empirical quantiles calculated data specified probabilities pkp_{k}. Quantile matching estimation (QME) performed setting argument method \"qme\" call fitdist adding argument probs defining probabilities quantile matching performed (see Figure @ref(fig:danishqme)). length vector must equal number parameters estimate (vector moment orders MME). Empirical quantiles computed using quantile function stats package using type=7 default (see ?quantile Hyndman Fan (1996)). type quantile can easily changed using qty argument call qme function. quantile matching carried numerically, minimizing sum squared differences observed theoretical quantiles. Comparison QME MLE fitting lognormal distribution loss data danishuni data set. example fitting lognormal distribution `danishuni} data set matching probabilities (p1=1/3,p2=2/3)(p_1= 1/3, p_2=2/3) (p1=8/10,p2=9/10)(p_1= 8/10, p_2=9/10). expected, second QME fit gives weight right-tail distribution. Compared maximum likelihood estimation, second QME fit best suits right-tail distribution, whereas first QME fit best models body distribution. quantile matching estimation particular interest need focus around particular quantiles, e.g., p=99.5%p=99.5\\% Solvency II insurance context p=5%p=5\\% HC5 estimation ecotoxicology context.","code":"fendo.ln.ADL <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"ADL\") fendo.ln.AD2L <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD2L\") cdfcomp(list(fendo.ln, fendo.ln.ADL, fendo.ln.AD2L), xlogscale = TRUE, ylogscale = TRUE, main = \"Fitting a lognormal distribution\", xlegend = \"bottomright\", legendtext = c(\"MLE\", \"Left-tail AD\", \"Left-tail AD 2nd order\")) (HC5.estimates <- c( empirical = as.numeric(quantile(ATV, probs = 0.05)), Burr = as.numeric(quantile(fendo.B, probs = 0.05)$quantiles), lognormal_MLE = as.numeric(quantile(fendo.ln, probs = 0.05)$quantiles), lognormal_AD2 = as.numeric(quantile(fendo.ln.ADL, probs = 0.05)$quantiles), lognormal_AD2L = as.numeric(quantile(fendo.ln.AD2L, probs = 0.05)$quantiles))) ## empirical Burr lognormal_MLE lognormal_AD2 lognormal_AD2L ## 0.20000 0.29393 0.07259 0.19591 0.25877 data(\"danishuni\") str(danishuni) ## 'data.frame': 2167 obs. of 2 variables: ## $ Date: Date, format: \"1980-01-03\" \"1980-01-04\" ... ## $ Loss: num 1.68 2.09 1.73 1.78 4.61 ... fdanish.ln.MLE <- fitdist(danishuni$Loss, \"lnorm\") fdanish.ln.MME <- fitdist(danishuni$Loss, \"lnorm\", method = \"mme\", order = 1:2) require(\"actuar\") fdanish.P.MLE <- fitdist(danishuni$Loss, \"pareto\", start = list(shape = 10, scale = 10), lower = 2+1e-6, upper = Inf) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced memp <- function(x, order) mean(x^order) fdanish.P.MME <- fitdist(danishuni$Loss, \"pareto\", method = \"mme\", order = 1:2, memp = \"memp\", start = list(shape = 10, scale = 10), lower = c(2+1e-6, 2+1e-6), upper = c(Inf, Inf)) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious par(mfrow = c(1, 2)) cdfcomp(list(fdanish.ln.MLE, fdanish.ln.MME), legend = c(\"lognormal MLE\", \"lognormal MME\"), main = \"Fitting a lognormal distribution\", xlogscale = TRUE, datapch = 20) cdfcomp(list(fdanish.P.MLE, fdanish.P.MME), legend = c(\"Pareto MLE\", \"Pareto MME\"), main = \"Fitting a Pareto distribution\", xlogscale = TRUE, datapch = 20) gofstat(list(fdanish.ln.MLE, fdanish.P.MLE, fdanish.ln.MME, fdanish.P.MME), fitnames = c(\"lnorm.mle\", \"Pareto.mle\", \"lnorm.mme\", \"Pareto.mme\")) ## Goodness-of-fit statistics ## lnorm.mle Pareto.mle lnorm.mme Pareto.mme ## Kolmogorov-Smirnov statistic 0.1375 0.3124 0.4368 0.37 ## Cramer-von Mises statistic 14.7911 37.7166 88.9503 55.43 ## Anderson-Darling statistic 87.1933 208.3143 416.2567 281.58 ## ## Goodness-of-fit criteria ## lnorm.mle Pareto.mle lnorm.mme Pareto.mme ## Akaike's Information Criterion 8120 9250 9792 9409 ## Bayesian Information Criterion 8131 9261 9803 9420 fdanish.ln.QME1 <- fitdist(danishuni$Loss, \"lnorm\", method = \"qme\", probs = c(1/3, 2/3)) fdanish.ln.QME2 <- fitdist(danishuni$Loss, \"lnorm\", method = \"qme\", probs = c(8/10, 9/10)) cdfcomp(list(fdanish.ln.MLE, fdanish.ln.QME1, fdanish.ln.QME2), legend = c(\"MLE\", \"QME(1/3, 2/3)\", \"QME(8/10, 9/10)\"), main = \"Fitting a lognormal distribution\", xlogscale = TRUE, datapch = 20)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Customization","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.2. Customization of the optimization algorithm","title":"Overview of the fitdistrplus package","text":"time numerical minimization carried fitdistrplus package, optim function stats package used default Nelder-Mead method distributions characterized one parameter BFGS method distributions characterized one parameter. Sometimes default algorithm fails converge. interesting change options optim function use another optimization function optim minimize objective function. argument optim.method can used call fitdist fitdistcens. internally passed mledist, mmedist, mgedist qmedist, optim (see ?optim details different algorithms available). Even error raised computing optimization, changing algorithm particular interest enforce bounds parameters. instance, volatility parameter σ\\sigma strictly positive σ>0\\sigma>0 probability parameter pp lies p∈[0,1]p\\[0,1]. possible using arguments lower /upper, use automatically forces optim.method=\"L-BFGS-B\". examples fits gamma distribution 𝒢(α,λ)\\mathcal{G}(\\alpha, \\lambda) groundbeef data set various algorithms. Note conjugate gradient algorithm (CG) needs far iterations converge (around 2500 iterations) compared algorithms (converging less 100 iterations). also possible use another function optim minimize objective function specifying argument custom.optim call fitdist. may necessary customize optimization function meet following requirements. (1) custom.optim function must following arguments: fn function optimized par initialized parameters. (2) custom.optim carry MINIMIZATION must return following components: par estimate, convergence convergence code, value=fn(par) hessian. example code written wrap genoud function rgenoud package order respect optimization ``template’’. rgenoud package implements genetic (stochastic) algorithm. customized optimization function can passed argument custom.optim call fitdist fitdistcens. following code can example used fit gamma distribution groundbeef data set. Note example various arguments also passed fitdist genoud: nvars, Domains, boundary.enforcement, print.level hessian. code compares parameter estimates (α̂\\hat\\alpha, λ̂\\hat\\lambda) different algorithms: shape α\\alpha rate λ\\lambda parameters relatively similar example, roughly 4.00 0.05, respectively.","code":"data(\"groundbeef\") fNM <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"Nelder-Mead\") fBFGS <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"BFGS\") fSANN <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"SANN\") fCG <- try(fitdist(groundbeef$serving, \"gamma\", optim.method = \"CG\", control = list(maxit = 10000))) if(inherits(fCG, \"try-error\")) {fCG <- list(estimate = NA)} mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values = par, ...) standardres <- c(res, convergence = 0) return(standardres) } fgenoud <- mledist(groundbeef$serving, \"gamma\", custom.optim = mygenoud, nvars = 2, max.generations = 10, Domains = cbind(c(0, 0), c(10, 10)), boundary.enforcement = 1, hessian = TRUE, print.level = 0, P9 = 10) ## Loading required package: rgenoud ## ## rgenoud (Version 5.9-0.11, Build Date: 2024-10-03) ## ## See http://sekhon.berkeley.edu/rgenoud for additional documentation. ## ## Please cite software as: ## ## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011. ## ## ``Genetic Optimization Using Derivatives: The rgenoud package for R.'' ## ## Journal of Statistical Software, 42(11): 1-26. ## ## cbind(NM = fNM$estimate, BFGS = fBFGS$estimate, SANN = fSANN$estimate, CG = fCG$estimate, fgenoud = fgenoud$estimate) ## NM BFGS SANN CG fgenoud ## shape 4.00956 4.21184 3.93636 4.03958 4.00834 ## rate 0.05444 0.05719 0.05366 0.05486 0.05443"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"otherdata","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.3. Fitting distributions to other types of data","title":"Overview of the fitdistrplus package","text":"section modified since publication vignette Journal Statistical Software order include new goodness--fit plots censored discrete data. Analytical methods often lead semi-quantitative results referred censored data. Observations known limit detection left-censored data. Observations known limit quantification right-censored data. Results known lie two bounds interval-censored data. two bounds may correspond limit detection limit quantification, generally uncertainty bounds around observation. Right-censored data also commonly encountered survival data (Klein Moeschberger 2003). data set may thus contain right-, left-, interval-censored data, may mixture categories, possibly different upper lower bounds. Censored data sometimes excluded data analysis replaced fixed value, cases may lead biased results. recommended approach correctly model data based upon maximum likelihood Helsel (2005). Censored data may thus contain left-censored, right-censored interval-censored values, several lower upper bounds. use package fitdistrplus, data must coded dataframe two columns, respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. illustrate use package fitdistrplus fit distributions censored continous data, use another data set ecotoxicology, included package named salinity. data set contains acute salinity tolerance (LC50 values electrical conductivity, mSmS.cm−1cm^{-1}) riverine macro-invertebrates taxa southern Murray-Darling Basin Central Victoria, Australia (Kefford et al. 2007). Using censored data coded salinity} data set, empirical distribution can plotted using theplotdistcens} function. older versions package, default function used Expectation-Maximization approach Turnbull (1974) compute overall empirical cdf curve optional confidence intervals, calls survfit plot.survfit functions survival package. Even representation always available (fixing argument NPMLE.method \"Turnbull.middlepoints\"), now default plot empirical cumulative distribution function (ECDF) explicitly represents regions non uniqueness NPMLE ECDF. default computation regions non uniqueness associated masses uses non parametric maximum likelihood estimation (NPMLE) approach developped Wang Wang Fani (2018). Figure @ref(fig:cdfcompcens) shows top left new plot data together two fitted distributions. Grey filled rectangles plot represent regions non uniqueness NPMLE ECDF. less rigorous sometimes illustrative plot can obtained fixing argument NPMLE FALSE call plotdistcens (see Figure @ref(fig:plotsalinity2) example help page Function plotdistcens details). plot enables see real nature censored data, points intervals, difficulty building plot define relevant ordering observations. Simple plot censored raw data (72-hour acute salinity tolerance riverine macro-invertebrates salinity data set) ordered points intervals. non censored data, one parametric distributions can fitted censored data set, one time, using case fitdistcens function. function estimates vector distribution parameters θ\\theta maximizing likelihood censored data defined : $$\\begin{equation} L(\\theta) = \\prod_{=1}^{N_{nonC}} f(x_{}|\\theta)\\times \\prod_{j=1}^{N_{leftC}} F(x^{upper}_{j}|\\theta) \\\\ \\times \\prod_{k=1}^{N_{rightC}} (1- F(x^{lower}_{k}|\\theta))\\times \\prod_{m=1}^{N_{intC}} (F(x^{upper}_{m}|\\theta)- F(x^{lower}_{j}|\\theta))(\\#eq:eq7) \\end{equation}$$ xix_{} NnonCN_{nonC} non-censored observations, xjupperx^{upper}_{j} upper values defining NleftCN_{leftC} left-censored observations, xklowerx^{lower}_{k} lower values defining NrightCN_{rightC} right-censored observations, [xmlower;xmupper][x^{lower}_{m} ; x^{upper}_{m}] intervals defining NintCN_{intC} interval-censored observations, F cumulative distribution function parametric distribution Helsel (2005). fitdist, fitdistcens returns results fit parametric distribution data set S3 class object can easily printed, summarized plotted. salinity data set, lognormal distribution loglogistic can fitted commonly done ecotoxicology data. fitdist, distributions (see Delignette-Muller et al. (2014) details), necessary specify initial values distribution parameters argument start. plotdistcens function can help find correct initial values distribution parameters non trivial cases, manual iterative use necessary. Computations goodness--fit statistics yet developed fits using censored data quality fit can judged using Akaike Schwarz’s Bayesian information criteria (AIC BIC) goodness--fit CDF plot, respectively provided summarizing plotting object class fitdistcens. Functions cdfcompcens, qqcompcens ppcompcens can also used compare fit various distributions censored data set. calls similar ones cdfcomp, qqcomp ppcomp. examples use functions two fitted distributions salinity data set (see Figure @ref(fig:cdfcompcens)). qqcompcens ppcompcens used one fitted distribution, non uniqueness rectangles filled small noise added y-axis order help visualization various fits. rather recommend use plotstyle ggplot qqcompcens ppcompcens compare fits various distributions provides clearer plot splitted facets (see ?graphcompcens). goodness--fit plots fits lognormal loglogistic distribution censored data: LC50 values salinity data set. Function bootdistcens equivalent bootdist censored data, except proposes nonparametric bootstrap. Indeed, obvious simulate censoring within parametric bootstrap resampling procedure. generic function quantile can also applied object class fitdistcens bootdistcens, continuous non-censored data. addition fit distributions censored non censored continuous data, package can also accomodate discrete variables, count numbers, using functions developped continuous non-censored data. functions provide somewhat different graphs statistics, taking account discrete nature modeled variable. discrete nature variable automatically recognized classical distribution fitted data (binomial, negative binomial, geometric, hypergeometric Poisson distributions) must indicated fixing argument discrete TRUE call functions cases. toxocara data set included package corresponds observation discrete variable. Numbers Toxocara cati parasites present digestive tract reported random sampling feral cats living Kerguelen island (Fromont et al. 2001). use illustrate case discrete data. fit discrete distribution discrete data maximum likelihood estimation requires procedure continuous non-censored data. example, using toxocara data set, Poisson negative binomial distributions can easily fitted. discrete distributions, plot object class fitdist simply provides two goodness--fit plots comparing empirical theoretical distributions density CDF. Functions cdfcomp denscomp can also used compare several plots data set, follows previous fits (Figure @ref(fig:fittoxocarapoisnbinom)). Comparison fits negative binomial Poisson distribution numbers Toxocara cati parasites toxocara data set. fitting discrete distributions, Chi-squared statistic computed gofstat function using cells defined argument chisqbreaks cells automatically defined data order reach roughly number observations per cell. number roughly equal argument meancount, sligthly greater ties. choice define cells empirical distribution (data), theoretical distribution, done enable comparison Chi-squared values obtained different distributions fitted data set. arguments chisqbreaks meancount omitted, meancount fixed order obtain roughly (4n)2/5(4n)^{2/5} cells, nn length data set (Vose 2010). Using default option two previous fits compared follows, giving preference negative binomial distribution, Chi-squared statistics information criteria:","code":"data(\"salinity\") str(salinity) ## 'data.frame': 108 obs. of 2 variables: ## $ left : num 20 20 20 20 20 21.5 15 20 23.7 25 ... ## $ right: num NA NA NA NA NA 21.5 30 25 23.7 NA ... plotdistcens(salinity, NPMLE = FALSE) fsal.ln <- fitdistcens(salinity, \"lnorm\") fsal.ll <- fitdistcens(salinity, \"llogis\", start = list(shape = 5, scale = 40)) summary(fsal.ln) ## Fitting of the distribution ' lnorm ' By maximum likelihood on censored data ## Parameters ## estimate Std. Error ## meanlog 3.3854 0.6741 ## sdlog 0.4961 0.5669 ## Loglikelihood: -139.1 AIC: 282.1 BIC: 287.5 ## Correlation matrix: ## meanlog sdlog ## meanlog 1.0000 0.2938 ## sdlog 0.2938 1.0000 summary(fsal.ll) ## Fitting of the distribution ' llogis ' By maximum likelihood on censored data ## Parameters ## estimate Std. Error ## shape 3.421 4.321 ## scale 29.930 20.210 ## Loglikelihood: -140.1 AIC: 284.1 BIC: 289.5 ## Correlation matrix: ## shape scale ## shape 1.0000 -0.2022 ## scale -0.2022 1.0000 par(mfrow = c(2, 2)) cdfcompcens(list(fsal.ln, fsal.ll), legendtext = c(\"lognormal\", \"loglogistic \")) qqcompcens(fsal.ln, legendtext = \"lognormal\") ppcompcens(fsal.ln, legendtext = \"lognormal\") qqcompcens(list(fsal.ln, fsal.ll), legendtext = c(\"lognormal\", \"loglogistic \"), main = \"Q-Q plot with 2 dist.\") data(\"toxocara\") str(toxocara) ## 'data.frame': 53 obs. of 1 variable: ## $ number: int 0 0 0 0 0 0 0 0 0 0 ... (ftoxo.P <- fitdist(toxocara$number, \"pois\")) ## Fitting of the distribution ' pois ' by maximum likelihood ## Parameters: ## estimate Std. Error ## lambda 8.679 2.946 (ftoxo.nb <- fitdist(toxocara$number, \"nbinom\")) ## Fitting of the distribution ' nbinom ' by maximum likelihood ## Parameters: ## estimate Std. Error ## size 0.3971 0.6035 ## mu 8.6803 14.0871 par(mfrow = c(1, 2)) denscomp(list(ftoxo.P, ftoxo.nb), legendtext = c(\"Poisson\", \"negative binomial\"), fitlty = 1) cdfcomp(list(ftoxo.P, ftoxo.nb), legendtext = c(\"Poisson\", \"negative binomial\"), fitlty = 1) gofstat(list(ftoxo.P, ftoxo.nb), fitnames = c(\"Poisson\", \"negative binomial\")) ## Chi-squared statistic: 31257 7.486 ## Degree of freedom of the Chi-squared distribution: 5 4 ## Chi-squared p-value: 0 0.1123 ## the p-value may be wrong with some theoretical counts < 5 ## Chi-squared table: ## obscounts theo Poisson theo negative binomial ## <= 0 14 0.009014 15.295 ## <= 1 8 0.078237 5.809 ## <= 3 6 1.321767 6.845 ## <= 4 6 2.131298 2.408 ## <= 9 6 29.827829 7.835 ## <= 21 6 19.626223 8.271 ## > 21 7 0.005631 6.537 ## ## Goodness-of-fit criteria ## Poisson negative binomial ## Akaike's Information Criterion 1017 322.7 ## Bayesian Information Criterion 1019 326.6"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"ccl","dir":"Articles","previous_headings":"","what":"4. Conclusion","title":"Overview of the fitdistrplus package","text":"R package fitdistrplus allows easily fit distributions. main objective developing package provide tools helping R users fit distributions data. encouraged pursue work feedbacks users package various areas food environmental risk assessment, epidemiology, ecology, molecular biology, genomics, bioinformatics, hydraulics, mechanics, financial actuarial mathematics operations research. Indeed, package already used lot practionners academics simple MLE fits Voigt et al. (2014), MLE fits goodness--fit statistics Vaninsky (2013), MLE fits bootstrap Rigaux et al. (2014), MLE fits, bootstrap goodness--fit statistics (Larras, Montuelle, Bouchez 2013), MME fit Sato et al. (2013), censored MLE bootstrap Contreras, Huerta, Arnold (2013), graphic analysing (Anand, Yeturu, Chandra 2012), grouped-data fitting methods (Fu, Steiner, Costafreda 2012) generally Drake, Chalabi, Coker (2014). fitdistrplus package complementary distrMod package (Kohl Ruckdeschel 2010). distrMod provides even flexible way estimate distribution parameters use requires greater initial investment learn manipulate S4 classes methods developed distr-family packages. Many extensions fitdistrplus package planned future: target extend censored data methods moment available non-censored data, especially concerning goodness--fit evaluation fitting methods. also enlarge choice fitting methods non-censored data, proposing new goodness--fit distances (e.g., distances based quantiles) maximum goodness--fit estimation new types moments (e.g., limited expected values) moment matching estimation. last, consider case multivariate distribution fitting.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Overview of the fitdistrplus package","text":"package stage without stimulating contribution Régis Pouillot Jean-Baptiste Denis, especially conceptualization. also want thank Régis Pouillot valuable comments first version paper. authors gratefully acknowledges two anonymous referees Editor useful constructive comments. remaining errors, course, attributed authors alone.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"geometric-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.1. Geometric distribution","title":"Starting values used in fitdistrplus","text":"MME used p̂=1/(1+m1)\\hat p=1/(1+m_1).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"negative-binomial-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.2. Negative binomial distribution","title":"Starting values used in fitdistrplus","text":"MME used n̂=m12/(μ2−m1)\\hat n = m_1^2/(\\mu_2-m_1).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"poisson-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.3. Poisson distribution","title":"Starting values used in fitdistrplus","text":"MME MLE λ̂=m1\\hat \\lambda = m_1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"binomial-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.4. Binomial distribution","title":"Starting values used in fitdistrplus","text":"MME used Var[X]/E[X]=1−p⇒p̂=1−μ2/m1. Var[X]/E[X] = 1-p \\Rightarrow \\hat p = 1- \\mu_2/m_1. size parameter n̂=⌈max(maxixi,m1/p̂)⌉. \\hat n = \\lceil\\max(\\max_i x_i, m_1/\\hat p)\\rceil.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"logarithmic-distribution","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.2. logarithmic distribution","title":"Starting values used in fitdistrplus","text":"expectation simplifies small values ppE[X]=−1log(1−p)p1−p≈−1−pp1−p=11−p. E[X] = -\\frac{1}{\\log(1-p)}\\frac{p}{1-p} \\approx -\\frac{1}{-p}\\frac{p}{1-p} =\\frac{1}{1-p}. initial estimate p̂=1−1/m1. \\hat p = 1-1/m_1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"zero-truncated-distributions","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.3. Zero truncated distributions","title":"Starting values used in fitdistrplus","text":"distribution distribution X|X>0X\\vert X>0 XX follows particular discrete distributions. Hence initial estimate one used base R sample x−1x-1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"zero-modified-distributions","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.4. Zero modified distributions","title":"Starting values used in fitdistrplus","text":"MLE probability parameter empirical mass 0 p̂0=1n∑i1xi=0\\hat p_0=\\frac1n \\sum_i 1_{x_i=0}. estimators use classical estimator probability parameter 1−p̂01-\\hat p_0.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"poisson-inverse-gaussian-distribution","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.5. Poisson inverse Gaussian distribution","title":"Starting values used in fitdistrplus","text":"first two moments E[X]=μ,Var[X]=μ+ϕμ3. E[X]=\\mu, Var[X] = \\mu+\\phi\\mu^3. initial estimate μ̂=m1,ϕ̂=(μ2−m1)/m13. \\hat\\mu=m_1, \\hat\\phi = (\\mu_2 - m_1)/m_1^3.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"normal-distribution","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.1. Normal distribution","title":"Starting values used in fitdistrplus","text":"MLE MME use empirical mean variance.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"lognormal-distribution","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.2. Lognormal distribution","title":"Starting values used in fitdistrplus","text":"log sample follows normal distribution, normal log sample.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"beta-distribution-of-the-first-kind","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.3. Beta distribution (of the first kind)","title":"Starting values used in fitdistrplus","text":"density function beta ℬe(,b)\\mathcal (,b) fX(x)=Γ()Γ(b)Γ(+b)xa−1(1−x)b−1. f_X(x) = \\frac{\\Gamma()\\Gamma(b)}{\\Gamma(+b)} x^{-1}(1-x)^{b-1}. initial estimate MME ̂=m1δ,b̂=(1−m1)δ,δ=m1(1−m1)μ2−1,(#eq:betaguessestimator)\\begin{equation} \\hat = m_1 \\delta, \\hat b = (1-m_1)\\delta, \\delta = \\frac{m_1(1-m_1)}{\\mu_2}-1, (\\#eq:betaguessestimator) \\end{equation}","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"log-gamma","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.1. Log-gamma","title":"Starting values used in fitdistrplus","text":"Use gamma initial values sample log(x)\\log(x)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"gumbel","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.2. Gumbel","title":"Starting values used in fitdistrplus","text":"distribution function F(x)=exp(−exp(−x−αθ)). F(x) = \\exp(-\\exp(-\\frac{x-\\alpha}{\\theta})). Let q1q_1 q3q_3 first third quartiles. $$ \\left\\{\\begin{array} -\\theta\\log(-\\log(p_1)) = q_1-\\alpha \\\\ -\\theta\\log(-\\log(p_3)) = q_3-\\alpha \\end{array}\\right. \\Leftrightarrow \\left\\{\\begin{array} -\\theta\\log(-\\log(p_1))+\\theta\\log(-\\log(p_3)) = q_1-q_3 \\\\ \\alpha= \\theta\\log(-\\log(p_3)) + q_3 \\end{array}\\right. \\Leftrightarrow \\left\\{\\begin{array} \\theta= \\frac{q_1-q_3}{\\log(-\\log(p_3)) - \\log(-\\log(p_1))} \\\\ \\alpha= \\theta\\log(-\\log(p_3)) + q_3 \\end{array}\\right.. $$ Using median location parameter α\\alpha yields initial estimate θ̂=q1−q3log(log(4/3))−log(log(4)),α̂=θ̂log(log(2))+q2. \\hat\\theta= \\frac{q_1-q_3}{\\log(\\log(4/3)) - \\log(\\log(4))}, \\hat\\alpha = \\hat\\theta\\log(\\log(2)) + q_2.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-gaussian-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.3. Inverse Gaussian distribution","title":"Starting values used in fitdistrplus","text":"moments distribution E[X]=μ,Var[X]=μ3ϕ. E[X] = \\mu, Var[X] = \\mu^3\\phi. Hence initial estimate μ̂=m1\\hat\\mu=m_1, ϕ̂=μ2/m13\\hat\\phi=\\mu_2/m_1^3.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"generalized-beta","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.4. Generalized beta","title":"Starting values used in fitdistrplus","text":"distribution θX1/τ\\theta X^{1/\\tau} XX beta distributed ℬe(,b)\\mathcal (,b) moments E[X]=θβ(+1/τ,b)/β(,b)=θΓ(+1/τ)Γ()Γ(+b)Γ(+b+1/τ), E[X] = \\theta \\beta(+1/\\tau, b)/\\beta(,b) = \\theta \\frac{\\Gamma(+1/\\tau)}{\\Gamma()}\\frac{\\Gamma(+b)}{\\Gamma(+b+1/\\tau)}, E[X2]=θ2Γ(+2/τ)Γ()Γ(+b)Γ(+b+2/τ). E[X^2] = \\theta^2 \\frac{\\Gamma(+2/\\tau)}{\\Gamma()}\\frac{\\Gamma(+b)}{\\Gamma(+b+2/\\tau)}. Hence large value τ\\tau, E[X2]/E[X]=θΓ(+2/τ)Γ(+b+2/τ)Γ(+b+1/τ)Γ(+1/τ)≈θ. E[X^2] /E[X] = \\theta \\frac{\\Gamma(+2/\\tau)}{\\Gamma(+b+2/\\tau)} \\frac{\\Gamma(+b+1/\\tau)}{\\Gamma(+1/\\tau)} \\approx \\theta. Note MLE θ\\theta maximum use τ̂=3,θ̂=m2m1maxixi1m2>m1+m1m2maxixi1m2≥m1. \\hat\\tau=3, \\hat\\theta = \\frac{m_2}{m_1}\\max_i x_i 1_{m_2>m_1} +\\frac{m_1}{m_2}\\max_i x_i 1_{m_2\\geq m_1}. use beta initial estimate sample (xiθ̂)τ̂(\\frac{x_i}{\\hat\\theta})^{\\hat\\tau}.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"feller-pareto-family","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.5. Feller-Pareto family","title":"Starting values used in fitdistrplus","text":"Feller-Pareto distribution distribution X=μ+θ(1/B−1)1/γX=\\mu+\\theta(1/B-1)^{1/\\gamma} BB follows beta distribution shape parameters α\\alpha τ\\tau. See details https://doi.org/10.18637/jss.v103.i06 Hence let Y=(X−μ)/θY = (X-\\mu)/\\theta, Y1+Y=X−μθ+X−μ=(1−B)1/γ. \\frac{Y}{1+Y} = \\frac{X-\\mu}{\\theta+X-\\mu} = (1-B)^{1/\\gamma}. γ\\gamma close 1, Y1+Y\\frac{Y}{1+Y} approximately beta distributed τ\\tau α\\alpha. log-likelihood ℒ(μ,θ,α,γ,τ)=(τγ−1)∑ilog(xi−μθ)−(α+τ)∑ilog(1+(xi−μθ)γ)+nlog(γ)−nlog(θ)−nlog(β(α,τ)).(#eq:fellerparetologlik).\\begin{equation} \\mathcal L(\\mu, \\theta, \\alpha, \\gamma, \\tau) = (\\tau \\gamma - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - (\\alpha+\\tau)\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) + n\\log(\\gamma) - n\\log(\\theta) -n \\log(\\beta(\\alpha,\\tau)). (\\#eq:fellerparetologlik). \\end{equation} MLE μ\\mu minimum. gradient respect θ,α,γ,τ\\theta, \\alpha, \\gamma, \\tau ∇ℒ(μ,θ,α,γ,τ)=(−(τγ−1)∑ixiθ(xi−μ)+(α+τ)∑ixiγ(xi−μθ)γ−1θ2(1+(xi−μθ)γ)−n/θ−∑ilog(1+(xi−μθ)γ)−n(ψ(τ)−ψ(α+τ))(τ−1)∑ilog(xi−μθ)−(α+τ)∑(xi−μθ)γ1+(xi−μθ)γlog(xi−μθ)+n/γ(γ−1)∑ilog(xi−μθ)−∑ilog(1+(xi−μθ)γ)−n(ψ(τ)−ψ(α+τ))).(#eq:fellerparetogradient)\\begin{equation} \\nabla \\mathcal L(\\mu, \\theta, \\alpha, \\gamma, \\tau) = \\begin{pmatrix} -(\\tau \\gamma - 1) \\sum_{} \\frac{x_i}{\\theta(x_i-\\mu)} + (\\alpha+\\tau)\\sum_i \\frac{x_i\\gamma(\\frac{x_i-\\mu}\\theta)^{\\gamma-1}}{\\theta^2(1+(\\frac{x_i-\\mu}\\theta)^\\gamma)} - n/\\theta \\\\ - \\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) -n(\\psi(\\tau) - \\psi(\\alpha+\\tau)) \\\\ (\\tau - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - (\\alpha+\\tau)\\sum_i \\frac{(\\frac{x_i-\\mu}\\theta)^\\gamma}{ 1+(\\frac{x_i-\\mu}\\theta)^\\gamma}\\log(\\frac{x_i-\\mu}\\theta) + n/\\gamma \\\\ (\\gamma - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - \\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) -n (\\psi(\\tau) - \\psi(\\alpha+\\tau)) \\end{pmatrix}. (\\#eq:fellerparetogradient) \\end{equation} Cancelling first component score γ=α=2\\gamma=\\alpha=2, get −(2τ−1)∑ixiθ(xi−μ)+(2+τ)∑ixi2(xi−μ)θ3(1+(xi−μθ)2)=nθ⇔−(2τ−1)θ21n∑ixixi−μ+(2+τ)1n∑ixi2(xi−μ)(1+(xi−μθ)2)=θ2 -(2\\tau - 1) \\sum_{} \\frac{x_i}{\\theta(x_i-\\mu)} + (2+\\tau)\\sum_i \\frac{x_i 2(x_i-\\mu)}{\\theta^3(1+(\\frac{x_i-\\mu}\\theta)^2)} = \\frac{n}{\\theta} \\Leftrightarrow -(2\\tau - 1)\\theta^2\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} + (2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{(1+(\\frac{x_i-\\mu}\\theta)^2)} = \\theta^2 ⇔(2+τ)1n∑ixi2(xi−μ)1+(xi−μθ)2=(2τ−1)θ2(1n∑ixixi−μ−1)⇔(2+τ)1n∑ixi2(xi−μ)1+(xi−μθ)2(2τ−1)(1n∑ixixi−μ−1)=θ. \\Leftrightarrow (2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{1+(\\frac{x_i-\\mu}\\theta)^2} = (2\\tau - 1)\\theta^2\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} -1\\right) \\Leftrightarrow \\sqrt{ \\frac{(2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{1+(\\frac{x_i-\\mu}\\theta)^2} }{(2\\tau - 1)\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} -1\\right)} } = \\theta. Neglecting unknown value τ\\tau denominator θ\\theta, get μ̂\\hat\\mu set (@ref(eq:pareto4muinit)) θ̂=1n∑ixi2(xi−μ̂)1+(xi−μ̂)2(1n∑ixixi−μ̂−1).(#eq:fellerparetothetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac{ \\frac1n\\sum_i \\frac{x_i 2(x_i-\\hat\\mu)}{1+(x_i-\\hat\\mu)^2} }{\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\hat\\mu} -1\\right)} }. (\\#eq:fellerparetothetahat) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=(xi−μ̂)/θ̂, z_i = y_i/(1+y_i), y_i = (x_i - \\hat\\mu)/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)). Cancelling last component gradient leads (γ−1)1n∑ilog(xi−μθ)−1n∑ilog(1+(xi−μθ)γ)=ψ(τ)−ψ(α+τ)⇔(γ−1)1n∑ilog(xi−μθ)=ψ(τ)−ψ(α+τ)+1n∑ilog(1+(xi−μθ)γ). (\\gamma - 1) \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - \\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) = \\psi(\\tau) - \\psi(\\alpha+\\tau) \\Leftrightarrow (\\gamma - 1) \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) = \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) . Neglecting value γ\\gamma right-hand side obtain γ̂=1+ψ(τ)−ψ(α+τ)+1n∑ilog(1+(xi−μθ))1n∑ilog(xi−μθ).(#eq:fellerparetogammahat)\\begin{equation} \\hat\\gamma = 1+ \\frac{ \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)) }{ \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) }. (\\#eq:fellerparetogammahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"transformed-beta","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.1. Transformed beta","title":"Starting values used in fitdistrplus","text":"Feller-Pareto μ=0\\mu=0. first component @ref(eq:fellerparetogradient) simplifies γ=α=2\\gamma=\\alpha=2−(2τ−1)∑ixiθ(xi)+(2+τ)∑i2xi2θ3(1+(xiθ)2)=nθ⇔−(2τ−1)θ2+(2+τ)1n∑i2xi21+(xiθ)2=θ2 -(2\\tau - 1) \\sum_{} \\frac{x_i}{\\theta(x_i)} + (2+\\tau)\\sum_i \\frac{2x_i^2}{\\theta^3(1+(\\frac{x_i}\\theta)^2)} = \\frac{n}{\\theta} \\Leftrightarrow -(2\\tau - 1) \\theta^2 + (2+\\tau)\\frac1n\\sum_i \\frac{2x_i^2}{1+(\\frac{x_i}\\theta)^2} = \\theta^2 θ2=2+τ2τ1n∑i2xi21+(xiθ)2. \\theta^2=\\frac{2+\\tau}{2\\tau}\\frac1n\\sum_i \\frac{2x_i^2}{1+(\\frac{x_i}\\theta)^2}. Neglecting unknown value τ\\tau denominator θ\\theta, get θ̂=1n∑i2xi21+xi2.(#eq:trbetathetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac1n\\sum_i \\frac{2x_i^2}{1+x_i^2} }. (\\#eq:trbetathetahat) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=xi/θ̂, z_i = y_i/(1+y_i), y_i = x_i/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)). Similar Feller-Pareto, set γ̂=1+ψ(τ)−ψ(α+τ)+1n∑ilog(1+xiθ)1n∑ilog(xiθ).(#eq:fellerparetogammahat)\\begin{equation} \\hat\\gamma = 1+ \\frac{ \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+\\frac{x_i}\\theta) }{ \\frac1n\\sum_{} \\log(\\frac{x_i}\\theta) }. (\\#eq:fellerparetogammahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"generalized-pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.2. Generalized Pareto","title":"Starting values used in fitdistrplus","text":"Feller-Pareto μ=0\\mu=0γ=1\\gamma=1. first component @ref(eq:fellerparetogradient) simplifies γ=2\\gamma=2−(τ−1)nθ+(2+τ)∑ixiθ2(1+xiθ=n/θ⇔−(τ−1)θ+(2+τ)1n∑ixi(1+xiθ=θ. -(\\tau - 1) \\frac{n}{\\theta} + (2+\\tau)\\sum_i \\frac{x_i}{\\theta^2(1+\\frac{x_i}\\theta} = n/\\theta \\Leftrightarrow -(\\tau - 1) \\theta + (2+\\tau)\\frac1n\\sum_i \\frac{x_i}{(1+\\frac{x_i}\\theta} = \\theta. Neglecting unknown value τ\\tau leads θ̂=1n∑ixi1+xi(#eq:generalizedparetotheta)\\begin{equation} \\hat\\theta = \\frac1n\\sum_i \\frac{x_i}{1+x_i} (\\#eq:generalizedparetotheta) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=xi/θ̂, z_i = y_i/(1+y_i), y_i = x_i/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"burr","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.3. Burr","title":"Starting values used in fitdistrplus","text":"Burr Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1. survival function 1−F(x)=(1+(x/θ)γ)−α. 1-F(x) = (1+(x/\\theta)^\\gamma)^{-\\alpha}. Using median q2q_2, log(1/2)=−αlog(1+(q2/θ)γ). \\log(1/2) = - \\alpha \\log(1+(q_2/\\theta)^\\gamma). initial value α=log(2)log(1+(q2/θ)γ),(#eq:burralpharelation)\\begin{equation} \\alpha = \\frac{\\log(2)}{\\log(1+(q_2/\\theta)^\\gamma)}, (\\#eq:burralpharelation) \\end{equation} first component @ref(eq:fellerparetogradient) simplifies γ=α=2\\gamma=\\alpha=2, τ=1\\tau=1, μ=0\\mu=0. −n/θ+3∑i2xi(xiθ)θ2(1+(xiθ)2)=n/θ⇔θ21n∑i2xi(xiθ)(1+(xiθ)2)=2/3. - n/\\theta + 3\\sum_i \\frac{2x_i(\\frac{x_i}\\theta)}{\\theta^2(1+(\\frac{x_i}\\theta)^2)} = n/\\theta \\Leftrightarrow \\theta^2\\frac1n\\sum_i \\frac{2x_i(\\frac{x_i}\\theta)}{(1+(\\frac{x_i}\\theta)^2)} = 2/3. Neglecting unknown value denominator θ\\theta, get θ̂=231n∑i2xi21+(xi)2.(#eq:trbetathetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac{2}{3 \\frac1n\\sum_i \\frac{2x_i^2}{1+(x_i)^2} } }. (\\#eq:trbetathetahat) \\end{equation} use γ̂\\hat\\gamma @ref(eq:fellerparetogammahat) τ=1\\tau=1 α=2\\alpha=2 previous θ̂\\hat\\theta.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"loglogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.4. Loglogistic","title":"Starting values used in fitdistrplus","text":"Loglogistic Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1, α=1\\alpha=1. survival function 1−F(x)=(1+(x/θ)γ)−1. 1-F(x) = (1+(x/\\theta)^\\gamma)^{-1}. 11−F(x)−1=(x/θ)γ⇔log(F(x)1−F(x))=γlog(x/θ). \\frac1{1-F(x)}-1 = (x/\\theta)^\\gamma \\Leftrightarrow \\log(\\frac{F(x)}{1-F(x)}) = \\gamma\\log(x/\\theta). Let q1q_1 q3q_3 first third quartile. log(1/32/3)=γlog(q1/θ),log(2/31/3)=γlog(q3/θ)⇔−log(2)=γlog(q1/θ),log(2)=γlog(q3/θ). \\log(\\frac{1/3}{2/3})= \\gamma\\log(q_1/\\theta), \\log(\\frac{2/3}{1/3})= \\gamma\\log(q_3/\\theta) \\Leftrightarrow -\\log(2)= \\gamma\\log(q_1/\\theta), \\log(2)= \\gamma\\log(q_3/\\theta). difference previous equations simplifies γ̂=2log(2)log(q3/q1). \\hat\\gamma=\\frac{2\\log(2)}{\\log(q_3/q_1)}. sum previous equations 0=γlog(q1)+γlog(q3)−2γlog(θ). 0 = \\gamma\\log(q_1)+\\gamma\\log(q_3) - 2\\gamma\\log(\\theta). θ̂=12elog(q1q3).(#eq:llogisthetahat)\\begin{equation} \\hat\\theta = \\frac12 e^{\\log(q_1q_3)}. (\\#eq:llogisthetahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"paralogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.5. Paralogistic","title":"Starting values used in fitdistrplus","text":"Paralogistic Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1, α=γ\\alpha=\\gamma. survival function 1−F(x)=(1+(x/θ)α)−α. 1-F(x) = (1+(x/\\theta)^\\alpha)^{-\\alpha}. log(1−F(x))=−αlog(1+(x/θ)α). \\log(1-F(x)) = -\\alpha \\log(1+(x/\\theta)^\\alpha). log-likelihood ℒ(θ,α)=(α−1)∑ilog(xiθ)−(α+1)∑ilog(1+(xiθ)α)+2nlog(α)−nlog(θ).(#eq:paralogisloglik)\\begin{equation} \\mathcal L(\\theta, \\alpha) = ( \\alpha - 1) \\sum_{} \\log(\\frac{x_i}\\theta) - (\\alpha+1)\\sum_i \\log(1+(\\frac{x_i}\\theta)^\\alpha) + 2n\\log(\\alpha) - n\\log(\\theta). (\\#eq:paralogisloglik) \\end{equation} gradient respect θ\\theta, α\\alpha ((α−1)−nθ−(α+1)∑−xiα(xi/θ)α−11+(xiθ)α−n/θ∑ilog(xiθ1+(xiθ)α)−(α+1)∑(xiθ)αlog(xi/θ)1+(xiθ)α+2n/α). \\begin{pmatrix} ( \\alpha - 1)\\frac{-n}{\\theta} - (\\alpha+1)\\sum_i \\frac{-x_i\\alpha(x_i/\\theta)^{\\alpha-1}}{1+(\\frac{x_i}\\theta)^\\alpha} - n/\\theta \\\\ \\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+(\\frac{x_i}\\theta)^\\alpha }) - (\\alpha+1)\\sum_i \\frac{(\\frac{x_i}\\theta)^\\alpha \\log(x_i/\\theta)}{1+(\\frac{x_i}\\theta)^\\alpha} + 2n/\\alpha \\\\ \\end{pmatrix}. first component cancels −(α+1)∑−xiα(xi/θ)α−11+(xiθ)α=αn/θ⇔(α+1)1n∑(xi)α+11+(xiθ)α=θα. - (\\alpha+1)\\sum_i \\frac{-x_i\\alpha(x_i/\\theta)^{\\alpha-1}}{1+(\\frac{x_i}\\theta)^\\alpha} = \\alpha n/\\theta \\Leftrightarrow (\\alpha+1)\\frac1n\\sum_i \\frac{ (x_i)^{\\alpha+1}}{1+(\\frac{x_i}\\theta)^\\alpha} = \\theta^\\alpha. second component cancels 1n∑ilog(xiθ1+(xiθ)α)=−2/α+(α+1)1n∑(xiθ)αlog(xi/θ)1+(xiθ)α. \\frac1n\\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+(\\frac{x_i}\\theta)^\\alpha }) = -2/\\alpha +(\\alpha+1)\\frac1n\\sum_i \\frac{(\\frac{x_i}\\theta)^\\alpha \\log(x_i/\\theta)}{1+(\\frac{x_i}\\theta)^\\alpha}. Choosing θ=1\\theta=1, α=2\\alpha=2 sums leads 1n∑ilog(xiθ1+xi2)−1n∑ixi2log(xi)1+xi2=−2/α+(α)1n∑ixi2log(xi)1+xi2. \\frac1n\\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+x_i^2 }) - \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} = -2/\\alpha +(\\alpha)\\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2}. Initial estimators α̂=1n∑ilog(xi1+xi2)−1n∑ixi2log(xi)1+xi21n∑ixi2log(xi)1+xi2−2,(#eq:paralogisalphahat)\\begin{equation} \\hat\\alpha = \\frac{ \\frac1n\\sum_{} \\log(\\frac{ x_i}{1+x_i^2 }) - \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} }{ \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} - 2 }, (\\#eq:paralogisalphahat) \\end{equation}θ̂=(α̂+1)1n∑(xi)α̂+11+(xi)α̂.(#eq:paralogisthetahat)\\begin{equation} \\hat\\theta = (\\hat\\alpha+1)\\frac1n\\sum_i \\frac{ (x_i)^{\\hat\\alpha+1}}{1+(x_i)^{\\hat\\alpha}}. (\\#eq:paralogisthetahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-burr","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.6. Inverse Burr","title":"Starting values used in fitdistrplus","text":"Use Burr estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-paralogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.7. Inverse paralogistic","title":"Starting values used in fitdistrplus","text":"Use paralogistic estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.8. Inverse pareto","title":"Starting values used in fitdistrplus","text":"Use pareto estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-iv","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.9. Pareto IV","title":"Starting values used in fitdistrplus","text":"survival function 1−F(x)=(1+(x−μθ)γ)−α, 1-F(x) = \\left(1+ \\left(\\frac{x-\\mu}{\\theta}\\right)^{\\gamma} \\right)^{-\\alpha}, see ?Pareto4 actuar. first third quartiles q1q_1 q3q_3 verify ((34)−1/α−1)1/γ=q1−μθ,((14)−1/α−1)1/γ=q3−μθ. ((\\frac34)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_1-\\mu}{\\theta}, ((\\frac14)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_3-\\mu}{\\theta}. Hence get two useful relations γ=log((43)1/α−1(4)1/α−1)log(q1−μq3−μ),(#eq:pareto4gammarelation)\\begin{equation} \\gamma = \\frac{ \\log\\left( \\frac{ (\\frac43)^{1/\\alpha}-1 }{ (4)^{1/\\alpha}-1 } \\right) }{ \\log\\left(\\frac{q_1-\\mu}{q_3-\\mu}\\right) }, (\\#eq:pareto4gammarelation) \\end{equation}θ=q1−q3((43)1/α−1)1/γ−((4)1/α−1)1/γ.(#eq:pareto4thetarelation)\\begin{equation} \\theta = \\frac{q_1- q_3 }{ ((\\frac43)^{1/\\alpha}-1)^{1/\\gamma} - ((4)^{1/\\alpha}-1)^{1/\\gamma} }. (\\#eq:pareto4thetarelation) \\end{equation} log-likelihood Pareto 4 sample (see Equation (5.2.94) Arnold (2015) updated Goulet et al. notation) ℒ(μ,θ,γ,α)=(γ−1)∑ilog(xi−μθ)−(α+1)∑ilog(1+(xi−μθ)γ)+nlog(γ)−nlog(θ)+nlog(α). \\mathcal L(\\mu,\\theta,\\gamma,\\alpha) = (\\gamma -1) \\sum_i \\log(\\frac{x_i-\\mu}{\\theta}) -(\\alpha+1)\\sum_i \\log(1+ (\\frac{x_i-\\mu}{\\theta})^{\\gamma}) +n\\log(\\gamma) -n\\log(\\theta)+n\\log(\\alpha). Cancelling derivate ℒ(μ,θ,γ,α)\\mathcal L(\\mu,\\theta,\\gamma,\\alpha) respect α\\alpha leads α=n/∑ilog(1+(xi−μθ)γ).(#eq:pareto4alpharelation)\\begin{equation} \\alpha =n/\\sum_i \\log(1+ (\\frac{x_i-\\mu}{\\theta})^{\\gamma}). (\\#eq:pareto4alpharelation) \\end{equation} MLE threshold parameter μ\\mu minimum. initial estimate slightly minimum order observations strictly μ̂={(1−ϵ)minixiif minixi<0(1+ϵ)minixiif minixi≥0.(#eq:pareto4muinit)\\begin{equation} \\hat\\mu = \\left\\{ \\begin{array}{ll} (1-\\epsilon) \\min_i x_i & \\text{} \\min_i x_i <0 \\\\ (1+\\epsilon)\\min_i x_i & \\text{} \\min_i x_i \\geq 0 \\\\ \\end{array} \\right. . (\\#eq:pareto4muinit) \\end{equation} ϵ=0.05\\epsilon=0.05. Initial parameter estimation μ̂\\hat\\mu, α⋆=2\\alpha^\\star = 2 , γ̂\\hat\\gamma @ref(eq:pareto4gammarelation) α⋆\\alpha^\\star, θ̂\\hat\\theta @ref(eq:pareto4thetarelation) α⋆\\alpha^\\star γ̂\\hat\\gamma, α̂\\hat\\alpha @ref(eq:pareto4alpharelation) μ̂\\hat\\mu, θ̂\\hat\\theta γ̂\\hat\\gamma.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-iii","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.10. Pareto III","title":"Starting values used in fitdistrplus","text":"Pareto III corresponds Pareto IV α=1\\alpha=1. γ=log(43−14−1)log(q1−μq3−μ),\\begin{equation} \\gamma = \\frac{ \\log\\left( \\frac{ \\frac43-1 }{ 4-1 } \\right) }{ \\log\\left(\\frac{q_1-\\mu}{q_3-\\mu}\\right) }, \\label{eq:pareto3:gamma:relation} \\end{equation} θ=(13)1/γ−(3)1/γq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac13)^{1/\\gamma} - (3)^{1/\\gamma} }{q_1- q_3 }. \\label{eq:pareto3:theta:relation} \\end{equation} Initial parameter estimation μ̂\\hat\\mu, γ̂\\hat\\gamma , θ̂\\hat\\theta γ̂\\hat\\gamma.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-ii","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.11. Pareto II","title":"Starting values used in fitdistrplus","text":"Pareto II corresponds Pareto IV γ=1\\gamma=1. θ=(43)1/α−41/αq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac43)^{1/\\alpha} - 4^{1/\\alpha} }{q_1- q_3 }. \\label{eq:pareto2:theta:relation} \\end{equation} Initial parameter estimation μ̂\\hat\\mu, α⋆=2\\alpha^\\star = 2 , θ̂\\hat\\theta α⋆\\alpha^\\star γ=1\\gamma=1, α̂\\hat\\alpha μ̂\\hat\\mu, θ̂\\hat\\theta γ=1\\gamma=1,","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-i","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.12. Pareto I","title":"Starting values used in fitdistrplus","text":"Pareto corresponds Pareto IV γ=1\\gamma=1, μ=θ\\mu=\\theta. MLE μ̂=miniXi,α̂=(1n∑=1nlog(Xi/μ̂))−1.\\begin{equation} \\hat\\mu = \\min_i X_i, \\hat\\alpha = \\left(\\frac1n \\sum_{=1}^n \\log(X_i/\\hat\\mu) \\right)^{-1}. \\label{eq:pareto1:alpha:mu:relation} \\end{equation} can rewritten geometric mean sample Gn=(∏=1nXi)1/nG_n = (\\prod_{=1}^n X_i)^{1/n} α̂=log(Gn/μ̂). \\hat\\alpha = \\log(G_n/\\hat\\mu). Initial parameter estimation μ̂\\hat\\mu, α̂\\hat\\alpha .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.13. Pareto","title":"Starting values used in fitdistrplus","text":"Pareto corresponds Pareto IV γ=1\\gamma=1, μ=0\\mu=0. θ=(43)1/α−41/αq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac43)^{1/\\alpha} - 4^{1/\\alpha} }{q_1- q_3 }. \\label{eq:pareto:theta:relation} \\end{equation} Initial parameter estimation α⋆=max(2,2(m2−m12)/(m2−2m12)), \\alpha^\\star = \\max(2, 2(m_2-m_1^2)/(m_2-2m_1^2)), mim_i empirical raw moment order ii, θ̂\\hat\\theta α⋆\\alpha^\\star γ=1\\gamma=1, α̂\\hat\\alpha μ=0\\mu=0, θ̂\\hat\\theta γ=1\\gamma=1.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"transformed-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.1. Transformed gamma distribution","title":"Starting values used in fitdistrplus","text":"log-likelihood given ℒ(α,τ,θ)=nlog(τ)+ατ∑ilog(xi/θ)−∑(xi/θ)τ−∑ilog(xi)−nlog(Gamma(α)). \\mathcal L(\\alpha,\\tau,\\theta) = n\\log(\\tau) + \\alpha\\tau\\sum_i \\log(x_i/\\theta) -\\sum_i (x_i/\\theta)^\\tau - \\sum_i\\log(x_i) - n\\log(Gamma(\\alpha)). gradient respect α,τ,θ\\alpha,\\tau,\\theta given (τ−nψ(α))n/τ+α∑ilog(xi/θ)−∑(xi/θ)τlog(xi/θ)−ατ/θ+∑iτxiθ2(xi/θ)τ−1). \\begin{pmatrix} \\tau- n\\psi(\\alpha)) \\\\ n/\\tau + \\alpha\\sum_i \\log(x_i/\\theta) -\\sum_i (x_i/\\theta)^{\\tau} \\log(x_i/\\theta) \\\\ -\\alpha\\tau /\\theta +\\sum_i \\tau \\frac{x_i}{\\theta^2}(x_i/\\theta)^{\\tau-1} \\end{pmatrix}. compute moment-estimator gamma α̂=m22/μ2,θ̂=μ2/m1. \\hat\\alpha = m_2^2/\\mu_2, \\hat\\theta= \\mu_2/m_1. cancelling first component gradient set τ̂=ψ(α̂)1n∑ilog(xi/θ̂). \\hat\\tau = \\frac{\\psi(\\hat\\alpha)}{\\frac1n\\sum_i \\log(x_i/\\hat\\theta) }.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.2. gamma distribution","title":"Starting values used in fitdistrplus","text":"Transformed gamma τ=1\\tau=1 compute moment-estimator given α̂=m22/μ2,θ̂=μ2/m1.\\begin{equation} \\hat\\alpha = m_2^2/\\mu_2, \\hat\\theta= \\mu_2/m_1. \\label{eq:gamma:relation} \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"weibull-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.3. Weibull distribution","title":"Starting values used in fitdistrplus","text":"Transformed gamma α=1\\alpha=1 Let m̃=1n∑ilog(xi)\\tilde m=\\frac1n\\sum_i \\log(x_i) ṽ=1n∑(log(xi)−m̃)2\\tilde v=\\frac1n\\sum_i (\\log(x_i) - \\tilde m)^2. use approximate MME τ̂=1.2/sqrt(ṽ),θ̂=exp(m̃+0.572/τ̂). \\hat\\tau = 1.2/sqrt(\\tilde v), \\hat\\theta = exp(\\tilde m + 0.572/\\hat \\tau). Alternatively, can use distribution function F(x)=1−e−(x/σ)τ⇒log(−log(1−F(x)))=τlog(x)−τlog(θ), F(x) = 1 - e^{-(x/\\sigma)^\\tau} \\Rightarrow \\log(-\\log(1-F(x))) = \\tau\\log(x) - \\tau\\log(\\theta), Hence QME Weibull τ̃=log(−log(1−p1))−log(−log(1−p2))log(x1)−log(x2),τ̃=x3/(−log(1−p3))1/τ̃ \\tilde\\tau = \\frac{ \\log(-\\log(1-p_1)) - \\log(-\\log(1-p_2)) }{ \\log(x_1) - \\log(x_2) }, \\tilde\\tau = x_3/(-\\log(1-p_3))^{1/\\tilde\\tau} p1=1/4p_1=1/4, p2=3/4p_2=3/4, p3=1/2p_3=1/2, xix_i corresponding empirical quantiles. Initial parameters τ̃\\tilde\\tau θ̃\\tilde\\theta unless empirical quantiles x1=x2x_1=x_2, case use τ̂\\hat\\tau, θ̂\\hat\\theta.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"exponential-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.4. Exponential distribution","title":"Starting values used in fitdistrplus","text":"MLE MME λ̂=1/m1.\\hat\\lambda = 1/m_1.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-transformed-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.1. Inverse transformed gamma distribution","title":"Starting values used in fitdistrplus","text":"transformed gamma distribution (1/xi)(1/x_i)_i.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.2. Inverse gamma distribution","title":"Starting values used in fitdistrplus","text":"compute moment-estimator α̂=(2m2−m12)/(m2−m12),θ̂=m1m2/(m2−m12). \\hat\\alpha = (2m_2-m_1^2)/(m_2-m_1^2), \\hat\\theta= m_1m_2/(m_2-m_1^2).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-weibull-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.3. Inverse Weibull distribution","title":"Starting values used in fitdistrplus","text":"use QME.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-exponential","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.4. Inverse exponential","title":"Starting values used in fitdistrplus","text":"transformed gamma distribution (1/xi)(1/x_i)_i.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"general-books","dir":"Articles","previous_headings":"3. Bibliography","what":"3.1. General books","title":"Starting values used in fitdistrplus","text":"N. L. Johnson, S. Kotz, N. Balakrishnan (1994). Continuous univariate distributions, Volume 1, Wiley. N. L. Johnson, S. Kotz, N. Balakrishnan (1995). Continuous univariate distributions, Volume 2, Wiley. N. L. Johnson, . W. Kemp, S. Kotz (2008). Univariate discrete distributions, Wiley. G. Wimmer (1999), Thesaurus univariate discrete probability distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"books-dedicated-to-a-distribution-family","dir":"Articles","previous_headings":"3. Bibliography","what":"3.2. Books dedicated to a distribution family","title":"Starting values used in fitdistrplus","text":"M. Ahsanullah, B.M. Golam Kibria, M. Shakil (2014). Normal Student’s t Distributions Applications, Springer. B. C. Arnold (2010). Pareto Distributions, Chapman Hall. . Azzalini (2013). Skew-Normal Related Families. N. Balakrishnan (2014). Handbook Logistic Distribution, CRC Press.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"books-with-applications","dir":"Articles","previous_headings":"3. Bibliography","what":"3.3. Books with applications","title":"Starting values used in fitdistrplus","text":"C. Forbes, M. Evans, N. Hastings, B. Peacock (2011). Statistical Distributions, Wiley. Z. . Karian, E. J. Dudewicz, K. Shimizu (2010). Handbook Fitting Statistical Distributions R, CRC Press. K. Krishnamoorthy (2015). Handbook Statistical Distributions Applications, Chapman Hall. Klugman, S., Panjer, H. & Willmot, G. (2019). Loss Models: Data Decisions, 5th ed., John Wiley & Sons.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marie-Laure Delignette-Muller. Author. Christophe Dutang. Author. Regis Pouillot. Contributor. Jean-Baptiste Denis. Contributor. Aurélie Siberchicot. Author, maintainer.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Marie Laure Delignette-Muller, Christophe Dutang (2015). fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34. DOI 10.18637/jss.v064.i04.","code":"@Article{, title = {{fitdistrplus}: An {R} Package for Fitting Distributions}, author = {Marie Laure Delignette-Muller and Christophe Dutang}, journal = {Journal of Statistical Software}, year = {2015}, volume = {64}, number = {4}, pages = {1--34}, doi = {10.18637/jss.v064.i04}, }"},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"help-to-fit-of-a-parametric-distribution-to-non-censored-or-censored-data","dir":"","previous_headings":"","what":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Please note! Since January 2024, repository belonged lbbe-software organization. avoid confusion, strongly recommend updating existing local clones point new repository URL. can using git remote command line: git remote set-url origin git@github.com:lbbe-software/fitdistrplus.git git remote set-url origin https://github.com/lbbe-software/fitdistrplus.git fitdistrplus extends fitdistr() function (MASS package) several functions help fit parametric distribution non-censored censored data. Censored data may contain left censored, right censored interval censored values, several lower upper bounds. addition maximum likelihood estimation (MLE), package provides moment matching (MME), quantile matching (QME) maximum goodness--fit estimation (MGE) methods (available non-censored data). Weighted versions MLE, MME QME available. fitdistrplus allows fit probability distribution provided user restricted base R distributions (see ?Distributions). strongly encourage users visit CRAN task view Distributions proposed Dutang, Kiener & Swihart (2024).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"the-package","dir":"","previous_headings":"","what":"The package","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"stable version fitdistrplus can installed CRAN using: development version fitdistrplus can installed GitHub (remotes needed): Finally load package current R session following R command:","code":"install.packages(\"fitdistrplus\") if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\") remotes::install_github(\"lbbe-software/fitdistrplus\") require(\"fitdistrplus\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Four vignettes attached fitdistrplus package. Two beginners Overview fitdistrplus package Frequently Asked Questions last two vignettes deal advanced topics optimization algorithm choose? Starting values used fitdistrplus","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"authors--contacts","dir":"","previous_headings":"","what":"Authors & Contacts","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Please read FAQ contacting authors Marie-Laure Delignette-Muller: marielaure.delignettemuller<<@))vetagro-sup.fr Christophe Dutang: dutangc<<@))gmail.com Aurélie Siberchicot: aurelie.siberchicot<<@))univ-lyon1.fr Issues can reported fitdistrplus-issues.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"use fitdistrplus, cite: Marie Laure Delignette-Muller, Christophe Dutang (2015). fitdistrplus: R Package Fitting Distributions. Journal Statistical Software. https://www.jstatsoft.org/article/view/v064i04 DOI 10.18637/jss.v064.i04.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"cdfband plots empirical cumulative distribution function bootstraped pointwise confidence intervals probabilities quantiles.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"","code":"CIcdfplot(b, CI.output, CI.type = \"two.sided\", CI.level = 0.95, CI.col = \"red\", CI.lty = 2, CI.fill = NULL, CI.only = FALSE, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datapch, datacol, fitlty, fitcol, fitlwd, horizontals = TRUE, verticals = FALSE, do.points = TRUE, use.ppoints = TRUE, a.ppoints = 0.5, name.points = NULL, lines01 = FALSE, plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"b One \"bootdist\" object. CI.output quantity (bootstraped) bootstraped confidence intervals computed: either \"probability\" \"quantile\"). CI.type Type confidence intervals : either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. CI.col color confidence intervals. CI.lty line type confidence intervals. CI.fill color fill confidence area. Default NULL corresponding filling. CI.logical whether plot empirical fitted distribution functions confidence intervals. Default FALSE. xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot, see also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datapch integer specifying symbol used plotting data points, see also points (non censored data). datacol specification color used plotting data points. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. fitlty (vector ) line type(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. horizontals TRUE, draws horizontal lines step empirical cdf function (non censored data). See also plot.stepfun. verticals TRUE, draws also vertical lines empirical cdf function. taken account horizontals=TRUE (non censored data). .points logical; TRUE, also draw points x-locations. Default TRUE (non censored data). use.ppoints TRUE, probability points empirical distribution defined using function ppoints (1:n - .ppoints)/(n - 2a.ppoints + 1) (non censored data). FALSE, probability points simply defined (1:n)/n. argument ignored discrete data. .ppoints use.ppoints=TRUE, passed function ppoints (non censored data). name.points Label vector points drawn .e. .points = TRUE (non censored data). lines01 logical plot two horizontal lines h=0 h=1 cdfcomp. plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). ... graphical arguments passed matlines polygon, respectively CI.fill=FALSE CI.fill=TRUE.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"CIcdfplot provides plot empirical distribution using cdfcomp cdfcompcens, bootstraped pointwise confidence intervals probabilities (y values) quantiles (x values). interval computed evaluating quantity interest (probability associated x value quantile associated y value) using bootstraped values parameters get bootstraped sample quantity interest calculating percentiles sample get confidence interval (classically 2.5 97.5 percentiles 95 percent confidence level). CI.fill != NULL, whole confidence area filled color CI.fill thanks function polygon, otherwise borders drawn thanks function matline. graphical arguments can passed functions using three dots arguments ....","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. if (requireNamespace (\"ggplot2\", quietly = TRUE)) {ggplotEx <- TRUE} # (1) Fit of an exponential distribution # set.seed(123) s1 <- rexp(50, 1) f1 <- fitdist(s1, \"exp\") b1 <- bootdist(f1, niter= 11) #voluntarily low to decrease computation time # plot 95 percent bilateral confidence intervals on y values (probabilities) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", plotstyle = \"ggplot\") # \\donttest{ # plot of the previous intervals as a band CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.fill = \"pink\", CI.col = \"red\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.fill = \"pink\", CI.col = \"red\", plotstyle = \"ggplot\") # plot of the previous intervals as a band without empirical and fitted dist. functions CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"red\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"red\", plotstyle = \"ggplot\") # same plot without contours CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"pink\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"pink\", plotstyle = \"ggplot\") # plot 95 percent bilateral confidence intervals on x values (quantiles) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quantile\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quantile\", plotstyle = \"ggplot\") # plot 95 percent unilateral confidence intervals on quantiles CIcdfplot(b1, CI.level = 95/100, CI.output = \"quant\", CI.type = \"less\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1) if (ggplotEx) CIcdfplot(b1, CI.level = 95/100, CI.output = \"quant\", CI.type = \"less\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1, plotstyle = \"ggplot\") CIcdfplot(b1, CI.level= 95/100, CI.output = \"quant\", CI.type = \"greater\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1) if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quant\", CI.type = \"greater\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1, plotstyle = \"ggplot\") # (2) Fit of a normal distribution on acute toxicity log-transformed values of # endosulfan for nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, with their # confidence intervals, from a small number of bootstrap # iterations to satisfy CRAN running times constraint and plot of the band # representing pointwise confidence intervals on any quantiles (any HCx values) # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(endosulfan) log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) namesATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa fln <- fitdist(log10ATV, \"norm\") bln <- bootdist(fln, bootmethod =\"param\", niter=101) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.844443 2.190122 2.565053 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.334340 1.697255 2.099378 #> 97.5 % 2.531564 2.770455 3.053706 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlim = c(0,5), name.points=namesATV) if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlim = c(0,5), name.points=namesATV, plotstyle = \"ggplot\") # (3) Same type of example as example (2) from ecotoxicology # with censored data # data(salinity) log10LC50 <-log10(salinity) fln <- fitdistcens(log10LC50,\"norm\") bln <- bootdistcens(fln, niter=101) (HC5ln <- quantile(bln,probs = 0.05)) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 #> estimate 1.11584 #> Median of bootstrap estimates #> p=0.05 #> estimate 1.120901 #> #> two-sided 95 % CI of each quantile #> p=0.05 #> 2.5 % 1.045539 #> 97.5 % 1.191979 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\",xlim=c(0.5,2),lines01 = TRUE) if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\",xlim=c(0.5,2),lines01 = TRUE, plotstyle = \"ggplot\") # zoom around the HC5 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1)) abline(h = 0.05, lty = 2) # line corresponding to a CDF of 5 percent if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1), plotstyle = \"ggplot\") + ggplot2::geom_hline(yintercept = 0.05, lty = 2) # line corresponding to a CDF of 5 percent # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Provide function prepare data frame needed fitdistcens() data classically coded using Surv() function survival package","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"","code":"Surv2fitdistcens(time, time2, event, type = c('right', 'left', 'interval', 'interval2'))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"time right censored data, follow time. interval data, first argument starting time interval. event status indicator, normally 0=alive, 1=dead. choices TRUE/FALSE (TRUE = death) 1/2 (2=death). interval censored data, status indicator 0=right censored, 1=event time, 2=left censored, 3=interval censored. factor data, assume two levels second level coding death. time2 ending time interval interval censored. Intervals assumed open left closed right, (start, end]. type character string specifying type censoring. Possible values \"right\", \"left\", \"interval\", \"interval2\".","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Surv2fitdistcens makes data.frame two columns respectively named left right, describing observed value interval required fitdistcens(): left column contains either NA left-censored observations, left bound interval interval-censored observations, observed value non-censored observations. right column contains either NA right-censored observations, right bound interval interval censored observations, observed value non-censored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Surv2fitdistcens returns data.frame two columns respectively named left right.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"","code":"# (1) randomized fictive survival data - right-censored # origdata <- data.frame(rbind( c( 43.01, 55.00, 0), c( 36.37, 47.17, 0), c( 33.10, 34.51, 0), c( 71.00, 81.15, 1), c( 80.89, 81.91, 1), c( 67.81, 78.48, 1), c( 73.98, 76.92, 1), c( 53.19, 54.80, 1))) colnames(origdata) <- c(\"AgeIn\", \"AgeOut\", \"Death\") # add of follow-up time (for type = \"right\" in Surv()) origdata$followuptime <- origdata$AgeOut - origdata$AgeIn origdata #> AgeIn AgeOut Death followuptime #> 1 43.01 55.00 0 11.99 #> 2 36.37 47.17 0 10.80 #> 3 33.10 34.51 0 1.41 #> 4 71.00 81.15 1 10.15 #> 5 80.89 81.91 1 1.02 #> 6 67.81 78.48 1 10.67 #> 7 73.98 76.92 1 2.94 #> 8 53.19 54.80 1 1.61 ### use of default survival type \"right\" # in Surv() survival::Surv(time = origdata$followuptime, event = origdata$Death, type = \"right\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 # for fitdistcens() Surv2fitdistcens(origdata$followuptime, event = origdata$Death, type = \"right\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # use of survival type \"interval\" # in Surv() survival::Surv(time = origdata$followuptime, time2 = origdata$followuptime, event = origdata$Death, type = \"interval\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 # for fitdistcens() Surv2fitdistcens(time = origdata$followuptime, time2 = origdata$followuptime, event = origdata$Death, type = \"interval\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # use of survival type \"interval2\" origdata$survivalt1 <- origdata$followuptime origdata$survivalt2 <- origdata$survivalt1 origdata$survivalt2[1:3] <- Inf origdata #> AgeIn AgeOut Death followuptime survivalt1 survivalt2 #> 1 43.01 55.00 0 11.99 11.99 Inf #> 2 36.37 47.17 0 10.80 10.80 Inf #> 3 33.10 34.51 0 1.41 1.41 Inf #> 4 71.00 81.15 1 10.15 10.15 10.15 #> 5 80.89 81.91 1 1.02 1.02 1.02 #> 6 67.81 78.48 1 10.67 10.67 10.67 #> 7 73.98 76.92 1 2.94 2.94 2.94 #> 8 53.19 54.80 1 1.61 1.61 1.61 survival::Surv(time = origdata$survivalt1, time2 = origdata$survivalt2, type = \"interval2\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 Surv2fitdistcens(origdata$survivalt1, time2 = origdata$survivalt2, type = \"interval2\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # (2) Other examples with various left, right and interval censored values # # with left censored data (d1 <- data.frame(time = c(2, 5, 3, 7), ind = c(0, 1, 1, 1))) #> time ind #> 1 2 0 #> 2 5 1 #> 3 3 1 #> 4 7 1 survival::Surv(time = d1$time, event = d1$ind, type = \"left\") #> [1] 2- 5 3 7 Surv2fitdistcens(time = d1$time, event = d1$ind, type = \"left\") #> left right #> 1 NA 2 #> 2 5 5 #> 3 3 3 #> 4 7 7 (d1bis <- data.frame(t1 = c(2, 5, 3, 7), t2 = c(2, 5, 3, 7), censtype = c(2, 1, 1, 1))) #> t1 t2 censtype #> 1 2 2 2 #> 2 5 5 1 #> 3 3 3 1 #> 4 7 7 1 survival::Surv(time = d1bis$t1, time2 = d1bis$t2, event = d1bis$censtype, type = \"interval\") #> [1] 2- 5 3 7 Surv2fitdistcens(time = d1bis$t1, time2 = d1bis$t2, event = d1bis$censtype, type = \"interval\") #> left right #> 1 NA 2 #> 2 5 5 #> 3 3 3 #> 4 7 7 # with interval, left and right censored data (d2 <- data.frame(t1 = c(-Inf, 2, 3, 4, 3, 7), t2 = c(2, 5, 3, 7, 8, Inf))) #> t1 t2 #> 1 -Inf 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 Inf survival::Surv(time = d2$t1, time2 = d2$t2, type = \"interval2\") #> [1] 2- [2, 5] 3 [4, 7] [3, 8] 7+ Surv2fitdistcens(time = d2$t1, time2 = d2$t2, type = \"interval2\") #> left right #> 1 NA 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 NA (d2bis <- data.frame(t1 = c(2, 2, 3, 4, 3, 7), t2 = c(2, 5, 3, 7, 8, 7), censtype = c(2,3,1,3,3,0))) #> t1 t2 censtype #> 1 2 2 2 #> 2 2 5 3 #> 3 3 3 1 #> 4 4 7 3 #> 5 3 8 3 #> 6 7 7 0 survival::Surv(time = d2bis$t1, time2 = d2bis$t2, event = d2bis$censtype, type = \"interval\") #> [1] 2- [2, 5] 3 [4, 7] [3, 8] 7+ Surv2fitdistcens(time = d2bis$t1, time2 = d2bis$t2, event = d2bis$censtype, type = \"interval\") #> left right #> 1 NA 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 NA"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap simulation of uncertainty for non-censored data — bootdist","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Uses parametric nonparametric bootstrap resampling order simulate uncertainty parameters distribution fitted non-censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"","code":"bootdist(f, bootmethod = \"param\", niter = 1001, silent = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bootdist' print(x, ...) # S3 method for class 'bootdist' plot(x, main = \"Bootstrapped values of parameters\", enhance = FALSE, trueval = NULL, rampcol = NULL, nbgrid = 100, nbcol = 100, ...) # S3 method for class 'bootdist' summary(object, ...) # S3 method for class 'bootdist' density(..., bw = nrd0, adjust = 1, kernel = \"gaussian\") # S3 method for class 'density.bootdist' plot(x, mar=c(4,4,2,1), lty=NULL, col=NULL, lwd=NULL, ...) # S3 method for class 'density.bootdist' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"f object class \"fitdist\", output fitdist function. bootmethod character string coding type resampling : \"param\" parametric resampling \"nonparam\" nonparametric resampling data. niter number samples drawn bootstrap. silent logical remove show warnings errors bootstraping. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bootdist\" \"density.bootdist\". object object class \"bootdist\". main overall title plot: see title, default \"Bootstrapped values parameters\". enhance logical get enhanced plot. trueval relevant, numeric vector true value parameters (backfitting purposes). rampcol colors interpolate; must valid argument colorRampPalette(). nbgrid Number grid points direction. Can scalar length-2 integer vector. nbcol integer argument, required number colors ... arguments passed generic methods \"bootdist\" objects density. bw, adjust, kernel resp. smoothing bandwidth, scaling factor, kernel used, see density. mar numerical vector form c(bottom, left, top, right), see par. lty, col, lwd resp. line type, color, line width, see par.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Samples drawn parametric bootstrap (resampling distribution fitted fitdist) nonparametric bootstrap (resampling replacement data set). bootstrap sample function mledist (mmedist, qmedist, mgedist according component f$method object class \"fitdist\") used estimate bootstrapped values parameters. function fails converge, NA values returned. Medians 2.5 97.5 percentiles computed removing NA values. medians 95 percent confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations, number iterations function converges also printed summary. default (enhance=FALSE), plot object class \"bootdist\" consists scatterplot matrix scatterplots bootstrapped values parameters. uses function stripchart fitted distribution characterized one parameter, function plot two paramters function pairs cases. last cases, provides representation joint uncertainty distribution fitted parameters. enhance=TRUE, personalized plot version pairs used upper graphs scatterplots lower graphs heatmap image using image based kernel based estimator 2D density function (using kde2d MASS package). Arguments rampcol, nbgrid, nbcol can used customize plots. Defautls values rampcol=c(\"green\", \"yellow\", \"orange\", \"red\"), nbcol=100 (see colorRampPalette()), nbgrid=100 (see kde2d). addition, fitting parameters simulated datasets backtesting purposes, additional argument trueval can used plot cross true value. possible accelerate bootstrap using parallelization. recommend use parallel = \"multicore\", parallel = \"snow\" work Windows, fix ncpus number available processors. density computes empirical density bootdist objects using density function (Gaussian kernel default). returns object class density.bootdist print plot methods provided.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"bootdist returns object class \"bootdist\", list 6 components, estim data frame containing bootstrapped values parameters. converg vector containing codes convergence obtained iterative method used estimate parameters bootstraped data set (0 closed formula used). method character string coding type resampling : \"param\" parametric resampling \"nonparam\" nonparametric resampling. nbboot number samples drawn bootstrap. CI bootstrap medians 95 percent confidence percentile intervals parameters. fitpart object class \"fitdist\" bootstrap procedure applied. Generic functions: print print \"bootdist\" object shows bootstrap parameter estimates. inferior whole number bootstrap iterations, number iterations estimation converges also printed. summary summary provides median 2.5 97.5 percentiles parameter. inferior whole number bootstrap iterations, number iterations estimation converges also printed summary. plot plot shows bootstrap estimates stripchart function univariate parameters plot function multivariate parameters. density density computes empirical densities return object class density.bootdist.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 181-241. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # (1) Fit of a gamma distribution to serving size data # using default method (maximum likelihood estimation) # followed by parametric bootstrap # data(groundbeef) x1 <- groundbeef$serving f1 <- fitdist(x1, \"gamma\") b1 <- bootdist(f1, niter=51) print(b1) #> Parameter values obtained with parametric bootstrap #> shape rate #> 1 4.015562 0.05365499 #> 2 4.214437 0.05762101 #> 3 4.176366 0.05807901 #> 4 4.119164 0.05944029 #> 5 5.013486 0.07194809 #> 6 4.461409 0.05807600 plot(b1) plot(b1, enhance=TRUE) summary(b1) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.12112272 3.32325118 5.11745944 #> rate 0.05518452 0.04684843 0.07170367 quantile(b1) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.16733 42.32692 50.91831 59.15298 67.62801 76.88308 87.67764 #> p=0.8 p=0.9 #> estimate 101.5208 122.9543 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> estimate 32.71222 42.80078 50.98942 59.25093 67.5939 76.42124 87.17521 100.8405 #> p=0.9 #> estimate 121.5466 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> 2.5 % 27.77396 37.42586 45.73489 53.96687 62.26638 71.30894 81.64618 93.9737 #> 97.5 % 35.67197 45.22459 53.97730 62.58326 71.31751 81.30652 92.96508 107.7329 #> p=0.9 #> 2.5 % 113.9634 #> 97.5 % 130.6715 CIcdfplot(b1, CI.output = \"quantile\") density(b1) #> #> Bootstrap values for: gamma for 1 object(s) with 51 bootstrap values (original sample size 254). plot(density(b1)) # (2) non parametric bootstrap on the same fit # b1b <- bootdist(f1, bootmethod=\"nonparam\", niter=51) summary(b1b) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.08546931 3.47931694 4.71280030 #> rate 0.05561944 0.04797494 0.06302539 quantile(b1b) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.16733 42.32692 50.91831 59.15298 67.62801 76.88308 87.67764 #> p=0.8 p=0.9 #> estimate 101.5208 122.9543 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.25183 42.25577 51.01738 59.05788 67.47548 76.95389 87.65113 #> p=0.8 p=0.9 #> estimate 100.8612 121.7738 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> 2.5 % 28.77577 38.76800 47.17181 55.16178 63.29618 72.15077 82.21068 95.20268 #> 97.5 % 36.49366 46.74605 55.27953 63.37110 71.62773 80.58611 91.32593 105.92939 #> p=0.9 #> 2.5 % 115.0083 #> 97.5 % 128.1651 # (3) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for # nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution, what is called the 5 percent hazardous concentration (HC5) # in ecotoxicology, with its two-sided 95 percent confidence interval calculated by # parametric bootstrap # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") bln <- bootdist(fln, bootmethod = \"param\", niter=51) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.811067 2.156258 2.529461 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.187935 1.634263 2.095273 #> 97.5 % 2.276507 2.563692 2.917189 # (4) comparison of sequential and parallel versions of bootstrap # to be tried with a greater number of iterations (1001 or more) # # \\donttest{ niter <- 1001 data(groundbeef) x1 <- groundbeef$serving f1 <- fitdist(x1, \"gamma\") # sequential version ptm <- proc.time() summary(bootdist(f1, niter = niter)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.02609408 3.46463055 4.71706986 #> rate 0.05458836 0.04622389 0.06476728 proc.time() - ptm #> user system elapsed #> 3.993 0.108 3.995 # parallel version using snow require(\"parallel\") #> Loading required package: parallel ptm <- proc.time() summary(bootdist(f1, niter = niter, parallel = \"snow\", ncpus = 2)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.02321963 3.45598967 4.80078519 #> rate 0.05450354 0.04632331 0.06524721 proc.time() - ptm #> user system elapsed #> 0.037 0.004 3.798 # parallel version using multicore (not available on Windows) ptm <- proc.time() summary(bootdist(f1, niter = niter, parallel = \"multicore\", ncpus = 2)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.04947721 3.47970416 4.71828189 #> rate 0.05496497 0.04672265 0.06498123 proc.time() - ptm #> user system elapsed #> 0.030 0.020 2.125 # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap simulation of uncertainty for censored data — bootdistcens","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Uses nonparametric bootstrap resampling order simulate uncertainty parameters distribution fitted censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"","code":"bootdistcens(f, niter = 1001, silent = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bootdistcens' print(x, ...) # S3 method for class 'bootdistcens' plot(x, ...) # S3 method for class 'bootdistcens' summary(object, ...) # S3 method for class 'bootdistcens' density(..., bw = nrd0, adjust = 1, kernel = \"gaussian\") # S3 method for class 'density.bootdistcens' plot(x, mar=c(4,4,2,1), lty=NULL, col=NULL, lwd=NULL, ...) # S3 method for class 'density.bootdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"f object class \"fitdistcens\", output fitdistcens function. niter number samples drawn bootstrap. silent logical remove show warnings errors bootstraping. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bootdistcens\". object object class \"bootdistcens\". ... arguments passed generic methods \"bootdistcens\" objects density. bw, adjust, kernel resp. smoothing bandwidth, scaling factor, kernel used, see density. mar numerical vector form c(bottom, left, top, right), see par. lty, col, lwd resp. line type, color, line width, see par.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Samples drawn nonparametric bootstrap (resampling replacement data set). bootstrap sample function mledist used estimate bootstrapped values parameters. mledist fails converge, NA values returned. Medians 2.5 97.5 percentiles computed removing NA values. medians 95 percent confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations, number iterations mledist converges also printed summary. plot object class \"bootdistcens\" consists scatterplot matrix scatterplots bootstrapped values parameters. uses function stripchart fitted distribution characterized one parameter, function plot cases. last cases, provides representation joint uncertainty distribution fitted parameters. possible accelerate bootstrap using parallelization. recommend use parallel = \"multicore\", parallel = \"snow\" work Windows, fix ncpus number available processors. density computes empirical density bootdistcens objects using density function (Gaussian kernel default). returns object class density.bootdistcens print plot methods provided.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"bootdistcens returns object class \"bootdistcens\", list 6 components, estim data frame containing bootstrapped values parameters. converg vector containing codes convergence iterative method used estimate parameters bootstraped data set. method character string coding type resampling : case \"nonparam\" available method censored data. nbboot number samples drawn bootstrap. CI bootstrap medians 95 percent confidence percentile intervals parameters. fitpart object class \"fitdistcens\" bootstrap procedure applied. Generic functions: print print \"bootdistcens\" object shows bootstrap parameter estimates. inferior whole number bootstrap iterations, number iterations estimation converges also printed. summary summary provides median 2.5 97.5 percentiles parameter. inferior whole number bootstrap iterations, number iterations estimation converges also printed summary. plot plot shows bootstrap estimates stripchart function univariate parameters plot function multivariate parameters. density density computes empirical densities return object class density.bootdistcens.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 181-241. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # (1) Fit of a normal distribution to fluazinam data in log10 # followed by nonparametric bootstrap and calculation of quantiles # with 95 percent confidence intervals # data(fluazinam) (d1 <-log10(fluazinam)) #> left right #> 1 0.5797836 0.5797836 #> 2 1.5263393 1.5263393 #> 3 1.9395193 1.9395193 #> 4 3.2304489 NA #> 5 2.8061800 2.8061800 #> 6 3.0625820 NA #> 7 2.0530784 2.0530784 #> 8 2.1105897 2.1105897 #> 9 2.7678976 2.7678976 #> 10 3.2685780 NA #> 11 0.2041200 0.2041200 #> 12 0.6812412 0.6812412 #> 13 1.9138139 1.9138139 #> 14 2.1903317 2.1903317 f1 <- fitdistcens(d1, \"norm\") b1 <- bootdistcens(f1, niter = 51) b1 #> Parameter values obtained with nonparametric bootstrap #> mean sd #> 1 2.148176 1.2301856 #> 2 2.359487 1.1144722 #> 3 1.886811 0.7960468 #> 4 1.983487 0.9941790 #> 5 1.912052 0.9906398 #> 6 2.189226 0.9088450 #> 7 2.287131 1.2049569 #> 8 2.288832 0.7645444 #> 9 1.787691 1.0077846 #> 10 2.893830 1.2229467 #> 11 2.569893 0.9597859 #> 12 2.343772 1.2402711 #> 13 2.645568 1.2934746 #> 14 1.942141 0.5982854 #> 15 1.932680 1.0077309 #> 16 1.824771 1.0653955 #> 17 2.983895 1.8018944 #> 18 2.347785 1.3994097 #> 19 1.845464 0.8555560 #> 20 2.427059 1.5893095 #> 21 1.948223 0.8705864 #> 22 1.692356 1.0223265 #> 23 2.275639 0.8147514 #> 24 2.148972 1.0345423 #> 25 2.348520 1.1739100 #> 26 1.893396 1.1106869 #> 27 1.911591 1.1574565 #> 28 2.610027 1.0803468 #> 29 2.080525 1.3340362 #> 30 1.985938 0.9870137 #> 31 1.742953 1.0956522 #> 32 2.549440 1.0330325 #> 33 2.268481 0.4832085 #> 34 2.144250 1.3228431 #> 35 2.184267 1.2698264 #> 36 1.821893 1.5316162 #> 37 2.085662 1.1654912 #> 38 1.868720 1.0912928 #> 39 2.138497 1.1356628 #> 40 2.119477 0.9868753 #> 41 2.153767 1.1818298 #> 42 1.933517 0.5773863 #> 43 2.074073 0.7280150 #> 44 2.421981 1.1254148 #> 45 2.486787 0.6096348 #> 46 2.030623 1.0934793 #> 47 1.938514 1.0258803 #> 48 1.678181 1.2224439 #> 49 2.339840 1.3061770 #> 50 2.278660 0.7921537 #> 51 2.195027 1.1382020 summary(b1) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.144250 1.7050054 2.831765 #> sd 1.091293 0.5826111 1.574886 plot(b1) quantile(b1) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.6655064 1.179033 1.549321 1.86572 2.161449 2.457179 2.773577 #> p=0.8 p=0.9 #> estimate 3.143865 3.657392 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.7210295 1.215519 1.593624 1.854354 2.14425 2.418499 2.691487 #> p=0.8 p=0.9 #> estimate 2.961351 3.394931 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> 2.5 % 0.1683713 0.6922166 1.066910 1.433480 1.705005 1.996046 2.241195 #> 97.5 % 1.5718878 1.8638800 2.141966 2.479624 2.831765 3.146062 3.482325 #> p=0.8 p=0.9 #> 2.5 % 2.472445 2.753590 #> 97.5 % 3.883480 4.463155 CIcdfplot(b1, CI.output = \"quantile\") plot(density(b1)) #> List of 1 #> $ :List of 6 #> ..$ estim :'data.frame':\t51 obs. of 2 variables: #> .. ..$ mean: num [1:51] 2.15 2.36 1.89 1.98 1.91 ... #> .. ..$ sd : num [1:51] 1.23 1.114 0.796 0.994 0.991 ... #> ..$ converg: num [1:51] 0 0 0 0 0 0 0 0 0 0 ... #> ..$ method : chr \"nonparam\" #> ..$ nbboot : num 51 #> ..$ CI : num [1:2, 1:3] 2.144 1.091 1.705 0.583 2.832 ... #> .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. ..$ : chr [1:3] \"Median\" \"2.5%\" \"97.5%\" #> ..$ fitpart:List of 17 #> .. ..$ estimate : Named num [1:2] 2.16 1.17 #> .. .. ..- attr(*, \"names\")= chr [1:2] \"mean\" \"sd\" #> .. ..$ method : chr \"mle\" #> .. ..$ sd : Named num [1:2] 1.206 0.984 #> .. .. ..- attr(*, \"names\")= chr [1:2] \"mean\" \"sd\" #> .. ..$ cor : num [1:2, 1:2] 1 0.135 0.135 1 #> .. .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. ..$ vcov : num [1:2, 1:2] 1.455 0.16 0.16 0.969 #> .. .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. ..$ loglik : num -20.4 #> .. ..$ aic : num 44.8 #> .. ..$ bic : num 46.1 #> .. ..$ n : int 14 #> .. ..$ censdata :'data.frame':\t14 obs. of 2 variables: #> .. .. ..$ left : num [1:14] 0.58 1.53 1.94 3.23 2.81 ... #> .. .. ..$ right: num [1:14] 0.58 1.53 1.94 NA 2.81 ... #> .. ..$ distname : chr \"norm\" #> .. ..$ fix.arg : NULL #> .. ..$ fix.arg.fun: NULL #> .. ..$ dots : NULL #> .. ..$ convergence: int 0 #> .. ..$ discrete : logi FALSE #> .. ..$ weights : NULL #> .. ..- attr(*, \"class\")= chr \"fitdistcens\" #> ..- attr(*, \"class\")= chr \"bootdistcens\" #> NULL # (2) Estimation of the mean of the normal distribution # by maximum likelihood with the standard deviation fixed at 1 # using the argument fix.arg # followed by nonparametric bootstrap # and calculation of quantiles with 95 percent confidence intervals # f1b <- fitdistcens(d1, \"norm\", start = list(mean = 1),fix.arg = list(sd = 1)) b1b <- bootdistcens(f1b, niter = 51) summary(b1b) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> 2.175510 1.729164 2.788799 plot(b1b) quantile(b1b) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.8527461 1.292676 1.609897 1.880951 2.134298 2.387645 2.658698 #> p=0.8 p=0.9 #> estimate 2.975919 3.415849 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> estimate 0.8939584 1.333889 1.651109 1.922163 2.17551 2.428857 2.69991 3.017131 #> p=0.9 #> estimate 3.457062 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> 2.5 % 0.4476121 0.8875425 1.204763 1.475817 1.729164 1.982511 2.253564 #> 97.5 % 1.5072477 1.9471780 2.264399 2.535452 2.788799 3.042146 3.313200 #> p=0.8 p=0.9 #> 2.5 % 2.570785 3.010715 #> 97.5 % 3.630420 4.070351 # (3) comparison of sequential and parallel versions of bootstrap # to be tried with a greater number of iterations (1001 or more) # # \\donttest{ niter <- 1001 data(fluazinam) d1 <-log10(fluazinam) f1 <- fitdistcens(d1, \"norm\") # sequential version ptm <- proc.time() summary(bootdistcens(f1, niter = niter)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.146743 1.5792689 2.877993 #> sd 1.129426 0.6853478 1.709083 proc.time() - ptm #> user system elapsed #> 4.629 0.096 4.615 # parallel version using snow require(\"parallel\") ptm <- proc.time() summary(bootdistcens(f1, niter = niter, parallel = \"snow\", ncpus = 2)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.144793 1.5914352 2.899763 #> sd 1.108123 0.6912424 1.673702 proc.time() - ptm #> user system elapsed #> 0.003 0.006 3.370 # parallel version using multicore (not available on Windows) ptm <- proc.time() summary(bootdistcens(f1, niter = niter, parallel = \"multicore\", ncpus = 2)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.163302 1.5524788 2.874380 #> sd 1.119044 0.7072572 1.656059 proc.time() - ptm #> user system elapsed #> 0.007 0.016 2.455 # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":null,"dir":"Reference","previous_headings":"","what":"Danish reinsurance claim dataset — danish","title":"Danish reinsurance claim dataset — danish","text":"univariate dataset collected Copenhagen Reinsurance comprise 2167 fire losses period 1980 1990. adjusted inflation reflect 1985 values expressed millions Danish Krone. multivariate data set data total claim divided building loss, loss contents loss profits.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Danish reinsurance claim dataset — danish","text":"","code":"data(danishuni) data(danishmulti)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Danish reinsurance claim dataset — danish","text":"danishuni contains two columns: Date day claim occurence. Loss total loss amount millions Danish Krone (DKK). danishmulti contains five columns: Date day claim occurence. Building loss amount (mDKK) building coverage. Contents loss amount (mDKK) contents coverage. Profits loss amount (mDKK) profit coverage. Total total loss amount (mDKK). columns numeric except Date columns class Date.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Danish reinsurance claim dataset — danish","text":"Embrechts, P., Kluppelberg, C. Mikosch, T. (1997) Modelling Extremal Events Insurance Finance. Berlin: Springer.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Danish reinsurance claim dataset — danish","text":"Dataset used McNeil (1996), Estimating Tails Loss Severity Distributions using Extreme Value Theory, ASTIN Bull. Davison, . C. (2003) Statistical Models. Cambridge University Press. Page 278.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Danish reinsurance claim dataset — danish","text":"","code":"# (1) load of data # data(danishuni) # (2) plot and description of data # plotdist(danishuni$Loss) # (3) load of data # data(danishmulti) # (4) plot and description of data # idx <- sample(1:NROW(danishmulti), 10) barplot(danishmulti$Building[idx], col = \"grey25\", ylim = c(0, max(danishmulti$Total[idx])), main = \"Some claims of danish data set\") barplot(danishmulti$Content[idx], add = TRUE, col = \"grey50\", axes = FALSE) barplot(danishmulti$Profits[idx], add = TRUE, col = \"grey75\", axes = FALSE) legend(\"topleft\", legend = c(\"Building\", \"Content\", \"Profits\"), fill = c(\"grey25\", \"grey50\", \"grey75\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets for the FAQ — dataFAQ","title":"Datasets for the FAQ — dataFAQ","text":"Datasets used FAQ vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets for the FAQ — dataFAQ","text":"","code":"data(dataFAQlog1) data(dataFAQscale1) data(dataFAQscale2)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets for the FAQ — dataFAQ","text":"dataFAQlog1 dataFAQscale1 dataFAQscale2 vectors numeric data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Datasets for the FAQ — dataFAQ","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of an empirical distribution for non-censored data — descdist","title":"Description of an empirical distribution for non-censored data — descdist","text":"Computes descriptive parameters empirical distribution non-censored data provides skewness-kurtosis plot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of an empirical distribution for non-censored data — descdist","text":"","code":"descdist(data, discrete = FALSE, boot = NULL, method = \"unbiased\", graph = TRUE, print = TRUE, obs.col = \"red\", obs.pch = 16, boot.col = \"orange\") # S3 method for class 'descdist' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of an empirical distribution for non-censored data — descdist","text":"data numeric vector. discrete TRUE, distribution considered discrete. boot NULL, boot values skewness kurtosis plotted bootstrap samples data. boot must fixed case integer 10. method \"unbiased\" unbiased estimated values statistics \"sample\" sample values. graph FALSE, skewness-kurtosis graph plotted. print FALSE, descriptive parameters computed printed. obs.col Color used observed point skewness-kurtosis graph. obs.pch plotting character used observed point skewness-kurtosis graph. boot.col Color used bootstrap sample points skewness-kurtosis graph. x object class \"descdist\". ... arguments passed generic functions","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Description of an empirical distribution for non-censored data — descdist","text":"Minimum, maximum, median, mean, sample sd, sample (method==\"sample\") default unbiased estimations skewness Pearsons's kurtosis values printed (Sokal Rohlf, 1995). skewness-kurtosis plot one proposed Cullen Frey (1999) given empirical distribution. plot, values common distributions also displayed tools help choice distributions fit data. distributions (normal, uniform, logistic, exponential example), one possible value skewness kurtosis (normal distribution example, skewness = 0 kurtosis = 3), distribution thus represented point plot. distributions, areas possible values represented, consisting lines (gamma lognormal distributions example), larger areas (beta distribution example). Weibull distribution represented graph indicated legend shapes close lognormal gamma distributions may obtained distribution. order take account uncertainty estimated values kurtosis skewness data, data set may bootstraped fixing argument boot integer 10. boot values skewness kurtosis corresponding boot bootstrap samples computed reported blue color skewness-kurtosis plot. discrete TRUE, represented distributions Poisson, negative binomial distributions, normal distribution previous discrete distributions may converge. discrete FALSE, uniform, normal, logistic, lognormal, beta gamma distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of an empirical distribution for non-censored data — descdist","text":"descdist returns list 7 components, min minimum value max maximum value median median value mean mean value sd standard deviation sample estimated value skewness skewness sample estimated value kurtosis kurtosis sample estimated value method method specified input (\"unbiased\" unbiased estimated values statistics \"sample\" sample values.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Description of an empirical distribution for non-censored data — descdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-159. Evans M, Hastings N Peacock B (2000), Statistical distributions. John Wiley Sons Inc, doi:10.1002/9780470627242 . Sokal RR Rohlf FJ (1995), Biometry. W.H. Freeman Company, USA, pp. 111-115. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Description of an empirical distribution for non-censored data — descdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of an empirical distribution for non-censored data — descdist","text":"","code":"# (1) Description of a sample from a normal distribution # with and without uncertainty on skewness and kurtosis estimated by bootstrap # set.seed(1234) x1 <- rnorm(100) descdist(x1) #> summary statistics #> ------ #> min: -2.345698 max: 2.548991 #> median: -0.384628 #> mean: -0.1567617 #> estimated sd: 1.004405 #> estimated skewness: 0.6052442 #> estimated kurtosis: 3.102441 descdist(x1,boot=11) #> summary statistics #> ------ #> min: -2.345698 max: 2.548991 #> median: -0.384628 #> mean: -0.1567617 #> estimated sd: 1.004405 #> estimated skewness: 0.6052442 #> estimated kurtosis: 3.102441 # (2) Description of a sample from a beta distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # with changing of default colors and plotting character for observed point # descdist(rbeta(100,shape1=0.05,shape2=1),boot=11, obs.col=\"blue\", obs.pch = 15, boot.col=\"darkgreen\") #> summary statistics #> ------ #> min: 3.937372e-36 max: 0.8890347 #> median: 5.660314e-06 #> mean: 0.04094397 #> estimated sd: 0.1281058 #> estimated skewness: 4.368522 #> estimated kurtosis: 25.02241 # (3) Description of a sample from a gamma distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # without plotting # descdist(rgamma(100,shape=2,rate=1),boot=11,graph=FALSE) #> summary statistics #> ------ #> min: 0.0753002 max: 8.631328 #> median: 1.627968 #> mean: 1.989657 #> estimated sd: 1.443636 #> estimated skewness: 1.509842 #> estimated kurtosis: 6.691933 # (4) Description of a sample from a Poisson distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # descdist(rpois(100,lambda=2),discrete=TRUE,boot=11) #> summary statistics #> ------ #> min: 0 max: 6 #> median: 2 #> mean: 1.98 #> estimated sd: 1.377892 #> estimated skewness: 0.5802731 #> estimated kurtosis: 3.037067 # (5) Description of serving size data # with uncertainty on skewness and kurtosis estimated by bootstrap # data(groundbeef) serving <- groundbeef$serving descdist(serving, boot=11) #> summary statistics #> ------ #> min: 10 max: 200 #> median: 79 #> mean: 73.64567 #> estimated sd: 35.88487 #> estimated skewness: 0.7352745 #> estimated kurtosis: 3.551384"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect bounds for density function — detectbound","title":"Detect bounds for density function — detectbound","text":"Manual detection bounds parameter density function/","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect bounds for density function — detectbound","text":"","code":"detectbound(distname, vstart, obs, fix.arg=NULL, echo=FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect bounds for density function — detectbound","text":"distname character string \"name\" naming distribution corresponding density function dname must classically defined. vstart named vector giving initial values parameters named distribution. obs numeric vector non censored data. fix.arg optional named vector giving values fixed parameters named distribution. Default NULL. echo logical show traces.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detect bounds for density function — detectbound","text":"function manually tests following bounds : -1, 0, 1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect bounds for density function — detectbound","text":"detectbound returns 2-row matrix lower bounds first row upper bounds second row.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Detect bounds for density function — detectbound","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detect bounds for density function — detectbound","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect bounds for density function — detectbound","text":"","code":"# case where the density returns a Not-an-Numeric value. detectbound(\"exp\", c(rate=3), 1:10) #> rate #> lowb 0 #> uppb Inf detectbound(\"binom\", c(size=3, prob=1/2), 1:10) #> size prob #> lowb -Inf 0 #> uppb Inf 1 detectbound(\"nbinom\", c(size=3, prob=1/2), 1:10) #> size prob mu #> lowb 0 0 -Inf #> uppb Inf 1 Inf"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":null,"dir":"Reference","previous_headings":"","what":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"Summary 48- 96-hour acute toxicity values (LC50 EC50 values) exposure Australian Non-Australian taxa endosulfan.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"","code":"data(endosulfan)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"endosulfan data frame 4 columns, named ATV Acute Toxicity Value (geometric mean LC50 ou EC50 values micrograms per liter), Australian (coding Australian another origin), group (arthropods, fish non-arthropod invertebrates) taxa.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"Hose, G.C., Van den Brink, P.J. 2004. Confirming Species-Sensitivity Distribution Concept Endosulfan Using Laboratory, Mesocosms, Field Data. Archives Environmental Contamination Toxicology, 47, 511-520.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"","code":"# (1) load of data # data(endosulfan) # (2) plot and description of data for non Australian fish in decimal logarithm # log10ATV <-log10(subset(endosulfan,(Australian == \"no\") & (group == \"Fish\"))$ATV) plotdist(log10ATV) descdist(log10ATV,boot=11) #> summary statistics #> ------ #> min: -0.69897 max: 3.60206 #> median: 0.4911356 #> mean: 0.5657595 #> estimated sd: 0.7034928 #> estimated skewness: 1.764601 #> estimated kurtosis: 9.759505 # (3) fit of a normal and a logistic distribution to data in log10 # (classical distributions used for SSD) # and visual comparison of the fits # fln <- fitdist(log10ATV,\"norm\") summary(fln) #> Fitting of the distribution ' norm ' by maximum likelihood #> Parameters : #> estimate Std. Error #> mean 0.5657595 0.6958041 #> sd 0.6958041 0.4920032 #> Loglikelihood: -48.58757 AIC: 101.1751 BIC: 104.8324 #> Correlation matrix: #> mean sd #> mean 1 0 #> sd 0 1 #> fll <- fitdist(log10ATV,\"logis\") summary(fll) #> Fitting of the distribution ' logis ' by maximum likelihood #> Parameters : #> estimate Std. Error #> location 0.5082818 0.5901708 #> scale 0.3457256 0.2917097 #> Loglikelihood: -44.31825 AIC: 92.6365 BIC: 96.29378 #> Correlation matrix: #> location scale #> location 1.00000000 0.04028287 #> scale 0.04028287 1.00000000 #> cdfcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\"), xlab=\"log10ATV\") denscomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\"), xlab=\"log10ATV\") qqcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\")) ppcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\")) gofstat(list(fln,fll), fitnames = c(\"lognormal\", \"loglogistic\")) #> Goodness-of-fit statistics #> lognormal loglogistic #> Kolmogorov-Smirnov statistic 0.1267649 0.08457997 #> Cramer-von Mises statistic 0.1555576 0.04058514 #> Anderson-Darling statistic 1.0408045 0.37407465 #> #> Goodness-of-fit criteria #> lognormal loglogistic #> Akaike's Information Criterion 101.1751 92.63650 #> Bayesian Information Criterion 104.8324 96.29378 # (4) estimation of the 5 percent quantile value of # logistic fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # parametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(ATV) bll <- bootdist(fll,niter=51) HC5ll <- quantile(bll,probs = 0.05) # in ATV 10^(HC5ll$quantiles) #> p=0.05 #> estimate 0.309253 10^(HC5ll$quantCI) #> p=0.05 #> 2.5 % 0.1891451 #> 97.5 % 0.5457214 # (5) estimation of the 5 percent quantile value of # the fitted logistic distribution (5 percent hazardous concentration : HC5) # with its one-sided 95 percent confidence interval (type \"greater\") # calculated by # nonparametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(ATV) bllnonpar <- bootdist(fll,niter=51,bootmethod = \"nonparam\") HC5llgreater <- quantile(bllnonpar,probs = 0.05, CI.type=\"greater\") # in ATV 10^(HC5llgreater$quantiles) #> p=0.05 #> estimate 0.309253 10^(HC5llgreater$quantCI) #> p=0.05 #> 5 % 0.1860103 # (6) fit of a logistic distribution # by minimizing the modified Anderson-Darling AD2L distance # cf. ?mgedist for definition of this distance # fllAD2L <- fitdist(log10ATV,\"logis\",method=\"mge\",gof=\"AD2L\") summary(fllAD2L) #> Fitting of the distribution ' logis ' by maximum goodness-of-fit #> Parameters : #> estimate #> location 0.4965288 #> scale 0.3013154 #> Loglikelihood: -44.96884 AIC: 93.93767 BIC: 97.59496 plot(fllAD2L)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit of univariate distributions to non-censored data — fitdist","title":"Fit of univariate distributions to non-censored data — fitdist","text":"Fit univariate distributions non-censored data maximum likelihood (mle), moment matching (mme), quantile matching (qme) maximizing goodness--fit estimation (mge). latter also known minimizing distance estimation. Generic methods print, plot, summary, quantile, logLik, AIC, BIC, vcov coef.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit of univariate distributions to non-censored data — fitdist","text":"","code":"fitdist(data, distr, method = c(\"mle\", \"mme\", \"qme\", \"mge\", \"mse\"), start=NULL, fix.arg=NULL, discrete, keepdata = TRUE, keepdata.nb=100, calcvcov=TRUE, ...) # S3 method for class 'fitdist' print(x, ...) # S3 method for class 'fitdist' plot(x, breaks=\"default\", ...) # S3 method for class 'fitdist' summary(object, ...) # S3 method for class 'fitdist' logLik(object, ...) # S3 method for class 'fitdist' AIC(object, ..., k = 2) # S3 method for class 'fitdist' BIC(object, ...) # S3 method for class 'fitdist' vcov(object, ...) # S3 method for class 'fitdist' coef(object, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit of univariate distributions to non-censored data — fitdist","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. method character string coding fitting method: \"mle\" 'maximum likelihood estimation', \"mme\" 'moment matching estimation', \"qme\" 'quantile matching estimation', \"mge\" 'maximum goodness--fit estimation' \"mse\" 'maximum spacing estimation'. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). may account closed-form formulas. fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. use argument possible method=\"mme\" closed-form formula used. keepdata logical. TRUE, dataset returned, otherwise sample subset returned. keepdata.nb keepdata=FALSE, length (>1) subset returned. calcvcov logical indicating (asymptotic) covariance matrix required. discrete TRUE, distribution considered discrete. discrete missing, \t discrete automaticaly set TRUE distr belongs \t \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\" FALSE cases. thus recommended enter argument using another discrete distribution. argument directly affect results fit passed functions gofstat, plotdist cdfcomp. x object class \"fitdist\". object object class \"fitdist\". breaks \"default\" histogram plotted function hist default breaks definition. Else breaks passed function hist. argument taken account discrete distributions: \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\". k penalty per parameter passed AIC generic function (2 default). ... arguments passed generic functions, one functions \"mledist\", \"mmedist\", \"qmedist\" \"mgedist\" depending chosen method. See mledist, mmedist, qmedist, mgedist details parameter estimation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit of univariate distributions to non-censored data — fitdist","text":"assumed distr argument specifies distribution probability density function, cumulative distribution function quantile function (d, p, q). four possible fitting methods described : method=\"mle\" Maximum likelihood estimation consists maximizing log-likelihood. numerical optimization carried mledist via optim find best values (see mledist details). method=\"mme\" Moment matching estimation consists equalizing theoretical empirical moments. Estimated values distribution parameters computed closed-form formula following distributions : \"norm\", \"lnorm\", \"pois\", \"exp\", \"gamma\", \"nbinom\", \"geom\", \"beta\", \"unif\" \"logis\". Otherwise theoretical empirical moments matched numerically, minimization sum squared differences observed theoretical moments. last case, arguments needed call fitdist: order memp (see mmedist details). Since Version 1.2-0, mmedist automatically computes asymptotic covariance matrix, hence theoretical moments mdist defined order equals twice maximal order given order. method = \"qme\" Quantile matching estimation consists equalizing theoretical empirical quantile. numerical optimization carried qmedist via optim minimize sum squared differences observed theoretical quantiles. use method requires additional argument probs, defined numeric vector probabilities quantile(s) () matched (see qmedist details). method = \"mge\" Maximum goodness--fit estimation consists maximizing goodness--fit statistics. numerical optimization carried mgedist via optim minimize goodness--fit distance. use method requires additional argument gof coding goodness--fit distance chosen. One can use classical Cramer-von Mises distance (\"CvM\"), classical Kolmogorov-Smirnov distance (\"KS\"), classical Anderson-Darling distance (\"AD\") gives weight tails distribution, one variants last distance proposed Luceno (2006) (see mgedist details). method suitable discrete distributions. method = \"mse\" Maximum goodness--fit estimation consists maximizing average log spacing. numerical optimization carried msedist via optim. default, direct optimization log-likelihood (criteria depending chosen method) performed using optim, \"Nelder-Mead\" method distributions characterized one parameter \"BFGS\" method distributions characterized one parameter. optimization algorithm used optim can chosen another optimization function can specified using ... argument (see mledist details). start may omitted (.e. NULL) classic distributions (see 'details' section mledist). Note errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1) ... argument. parameter(s) () estimated, fitdist computes log-likelihood every estimation method maximum likelihood estimation standard errors estimates calculated Hessian solution found optim user-supplied function passed mledist. default (keepdata = TRUE), object returned fitdist contains data vector given input. dealing large datasets, can remove original dataset output setting keepdata = FALSE. case, keepdata.nb points () kept random subsampling keepdata.nb-2 points dataset adding minimum maximum. combined bootdist, use non-parametric bootstrap aware bootstrap performed subset randomly selected fitdist. Currently, graphical comparisons multiple fits available framework. Weighted version estimation process available method = \"mle\", \"mme\", \"qme\" using weights=.... See corresponding man page details. Weighted maximum GOF estimation (method = \"mge\") allowed. yet possible take account weighths functions plotdist, plot.fitdist, cdfcomp, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). parameter(s) () estimated, gofstat allows compute goodness--fit statistics. NB: data values particularly small large, scaling may needed optimization process. See example (14) man page examples (14,15) test file package. Please also take look Rmpfr package available CRAN numerical accuracy issues.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit of univariate distributions to non-censored data — fitdist","text":"fitdist returns object class \"fitdist\", list following components: estimate parameter estimates. method character string coding fitting method : \"mle\" 'maximum likelihood estimation', \"mme\" 'matching moment estimation', \"qme\" 'matching quantile estimation' \"mge\" 'maximum goodness--fit estimation' \"mse\" 'maximum spacing estimation'. sd estimated standard errors, NA numerically computable NULL available. cor estimated correlation matrix, NA numerically computable NULL available. vcov estimated variance-covariance matrix, NULL available estimation method considered. loglik log-likelihood. aic Akaike information criterion. bic -called BIC SBC (Schwarz Bayesian criterion). n length data set. data data set. distname name distribution. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. dots list arguments passed ... used bootdist iterative calls mledist, mmedist, qmedist, mgedist NULL arguments. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. discrete input argument automatic definition function passed functions gofstat, plotdist cdfcomp. weights vector weigths used estimation process NULL. Generic functions: print print \"fitdist\" object shows traces fitting method fitted distribution. summary summary provides parameter estimates fitted distribution, log-likelihood, AIC BIC statistics maximum likelihood used, standard errors parameter estimates correlation matrix parameter estimates. plot plot object class \"fitdist\" returned fitdist uses function plotdist. object class \"fitdist\" list objects class \"fitdist\" corresponding various fits using data set may also plotted using cdf plot (function cdfcomp), density plot(function denscomp), density Q-Q plot (function qqcomp), P-P plot (function ppcomp). logLik Extracts estimated log-likelihood \"fitdist\" object. AIC Extracts AIC \"fitdist\" object. BIC Extracts estimated BIC \"fitdist\" object. vcov Extracts estimated var-covariance matrix \"fitdist\" object (available method = \"mle\"). coef Extracts fitted coefficients \"fitdist\" object.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit of univariate distributions to non-censored data — fitdist","text":". Ibragimov R. 'minskii (1981), Statistical Estimation - Asymptotic Theory, Springer-Verlag, doi:10.1007/978-1-4899-0027-2 Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit of univariate distributions to non-censored data — fitdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit of univariate distributions to non-censored data — fitdist","text":"","code":"# (1) fit of a gamma distribution by maximum likelihood estimation # data(groundbeef) serving <- groundbeef$serving fitg <- fitdist(serving, \"gamma\") summary(fitg) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> plot(fitg) plot(fitg, demp = TRUE) plot(fitg, histo = FALSE, demp = TRUE) cdfcomp(fitg, addlegend=FALSE) denscomp(fitg, addlegend=FALSE) ppcomp(fitg, addlegend=FALSE) qqcomp(fitg, addlegend=FALSE) # (2) use the moment matching estimation (using a closed formula) # fitgmme <- fitdist(serving, \"gamma\", method=\"mme\") summary(fitgmme) #> Fitting of the distribution ' gamma ' by matching moments #> Parameters : #> estimate Std. Error #> shape 4.22848617 6.64959843 #> rate 0.05741663 0.09451052 #> Loglikelihood: -1253.825 AIC: 2511.65 BIC: 2518.724 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9553622 #> rate 0.9553622 1.0000000 #> # (3) Comparison of various fits # fitW <- fitdist(serving, \"weibull\") fitg <- fitdist(serving, \"gamma\") fitln <- fitdist(serving, \"lnorm\") summary(fitW) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> summary(fitg) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> summary(fitln) #> Fitting of the distribution ' lnorm ' by maximum likelihood #> Parameters : #> estimate Std. Error #> meanlog 4.1693701 0.5366095 #> sdlog 0.5366095 0.3794343 #> Loglikelihood: -1261.319 AIC: 2526.639 BIC: 2533.713 #> Correlation matrix: #> meanlog sdlog #> meanlog 1 0 #> sdlog 0 1 #> cdfcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) denscomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) qqcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) ppcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) gofstat(list(fitW, fitg, fitln), fitnames=c(\"Weibull\", \"gamma\", \"lognormal\")) #> Goodness-of-fit statistics #> Weibull gamma lognormal #> Kolmogorov-Smirnov statistic 0.1396646 0.1281486 0.1493090 #> Cramer-von Mises statistic 0.6840994 0.6936274 0.8277358 #> Anderson-Darling statistic 3.5736460 3.5672625 4.5436542 #> #> Goodness-of-fit criteria #> Weibull gamma lognormal #> Akaike's Information Criterion 2514.449 2511.250 2526.639 #> Bayesian Information Criterion 2521.524 2518.325 2533.713 # (4) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view # dedicated to probability distributions # dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q, a, b) exp(-exp((a-q)/b)) qgumbel <- function(p, a, b) a-b*log(-log(p)) fitgumbel <- fitdist(serving, \"gumbel\", start=list(a=10, b=10)) #> Error in fitdist(serving, \"gumbel\", start = list(a = 10, b = 10)): The dgumbel function must be defined summary(fitgumbel) #> Error: object 'fitgumbel' not found plot(fitgumbel) #> Error: object 'fitgumbel' not found # (5) fit discrete distributions (Poisson and negative binomial) # data(toxocara) number <- toxocara$number fitp <- fitdist(number,\"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) fitnb <- fitdist(number,\"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb) cdfcomp(list(fitp,fitnb)) gofstat(list(fitp,fitnb)) #> Chi-squared statistic: 31256.96 7.48606 #> Degree of freedom of the Chi-squared distribution: 5 4 #> Chi-squared p-value: 0 0.1123255 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo 1-mle-pois theo 2-mle-nbinom #> <= 0 14 0.009014207 15.295027 #> <= 1 8 0.078236515 5.808596 #> <= 3 6 1.321767253 6.845015 #> <= 4 6 2.131297825 2.407815 #> <= 9 6 29.827829425 7.835196 #> <= 21 6 19.626223437 8.271110 #> > 21 7 0.005631338 6.537242 #> #> Goodness-of-fit criteria #> 1-mle-pois 2-mle-nbinom #> Akaike's Information Criterion 1017.067 322.6882 #> Bayesian Information Criterion 1019.037 326.6288 # (6) how to change the optimisation method? # data(groundbeef) serving <- groundbeef$serving fitdist(serving, \"gamma\", optim.method=\"Nelder-Mead\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 fitdist(serving, \"gamma\", optim.method=\"BFGS\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.21183650 5.72702767 #> rate 0.05719298 0.08257022 fitdist(serving, \"gamma\", optim.method=\"SANN\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 3.85618448 5.225475 #> rate 0.05179296 0.074932 # (7) custom optimization function # # \\donttest{ #create the sample set.seed(1234) mysample <- rexp(100, 5) mystart <- list(rate=8) res1 <- fitdist(mysample, dexp, start= mystart, optim.method=\"Nelder-Mead\") #show the result summary(res1) #> Fitting of the distribution ' exp ' by maximum likelihood #> Parameters : #> estimate Std. Error #> rate 5.120312 5.120312 #> Loglikelihood: 63.32596 AIC: -124.6519 BIC: -122.0467 #the warning tell us to use optimise, because the Nelder-Mead is not adequate. #to meet the standard 'fn' argument and specific name arguments, we wrap optimize, myoptimize <- function(fn, par, ...) { res <- optimize(f=fn, ..., maximum=FALSE) #assume the optimization function minimize standardres <- c(res, convergence=0, value=res$objective, par=res$minimum, hessian=NA) return(standardres) } #call fitdist with a 'custom' optimization function res2 <- fitdist(mysample, \"exp\", start=mystart, custom.optim=myoptimize, interval=c(0, 100)) #show the result summary(res2) #> Fitting of the distribution ' exp ' by maximum likelihood #> Parameters : #> estimate #> rate 5.120531 #> Loglikelihood: 63.32596 AIC: -124.6519 BIC: -122.0467 # } # (8) custom optimization function - another example with the genetic algorithm # # \\donttest{ #set a sample fit1 <- fitdist(serving, \"gamma\") summary(fit1) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> #wrap genoud function rgenoud package mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values=par, ...) standardres <- c(res, convergence=0) return(standardres) } #call fitdist with a 'custom' optimization function fit2 <- fitdist(serving, \"gamma\", custom.optim=mygenoud, nvars=2, Domains=cbind(c(0, 0), c(10, 10)), boundary.enforcement=1, print.level=1, hessian=TRUE) #> Loading required package: rgenoud #> ## rgenoud (Version 5.9-0.11, Build Date: 2024-10-03) #> ## See http://sekhon.berkeley.edu/rgenoud for additional documentation. #> ## Please cite software as: #> ## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011. #> ## ``Genetic Optimization Using Derivatives: The rgenoud package for R.'' #> ## Journal of Statistical Software, 42(11): 1-26. #> ## #> #> #> Sat Oct 26 05:39:31 2024 #> Domains: #> 0.000000e+00 <= X1 <= 1.000000e+01 #> 0.000000e+00 <= X2 <= 1.000000e+01 #> #> Data Type: Floating Point #> Operators (code number, name, population) #> \t(1) Cloning........................... \t122 #> \t(2) Uniform Mutation.................. \t125 #> \t(3) Boundary Mutation................. \t125 #> \t(4) Non-Uniform Mutation.............. \t125 #> \t(5) Polytope Crossover................ \t125 #> \t(6) Simple Crossover.................. \t126 #> \t(7) Whole Non-Uniform Mutation........ \t125 #> \t(8) Heuristic Crossover............... \t126 #> \t(9) Local-Minimum Crossover........... \t0 #> #> HARD Maximum Number of Generations: 100 #> Maximum Nonchanging Generations: 10 #> Population size : 1000 #> Convergence Tolerance: 1.000000e-03 #> #> Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation. #> Checking Gradients before Stopping. #> Not Using Out of Bounds Individuals But Allowing Trespassing. #> #> Minimization Problem. #> #> #> Generation#\t Solution Value #> #> 0 \t4.936206e+00 #> #> 'wait.generations' limit reached. #> No significant improvement in 10 generations. #> #> Solution Fitness Value: 4.935532e+00 #> #> Parameters at the Solution (parameter, gradient): #> #> X[ 1] :\t4.008339e+00\tG[ 1] :\t2.759167e-10 #> X[ 2] :\t5.442735e-02\tG[ 2] :\t-3.841725e-07 #> #> Solution Found Generation 1 #> Number of Generations Run 11 #> #> Sat Oct 26 05:39:32 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit2) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00833889 5.44012540 #> rate 0.05442735 0.07867032 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384394 #> rate 0.9384394 1.0000000 #> # } # (9) estimation of the standard deviation of a gamma distribution # by maximum likelihood with the shape fixed at 4 using the argument fix.arg # data(groundbeef) serving <- groundbeef$serving f1c <- fitdist(serving,\"gamma\",start=list(rate=0.1),fix.arg=list(shape=4)) summary(f1c) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> rate 0.05431619 0.02714888 #> Fixed parameters: #> value #> shape 4 #> Loglikelihood: -1253.625 AIC: 2509.251 BIC: 2512.788 plot(f1c) # (10) fit of a Weibull distribution to serving size data # by maximum likelihood estimation # or by quantile matching estimation (in this example # matching first and third quartiles) # data(groundbeef) serving <- groundbeef$serving fWmle <- fitdist(serving, \"weibull\") summary(fWmle) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> plot(fWmle) gofstat(fWmle) #> Goodness-of-fit statistics #> 1-mle-weibull #> Kolmogorov-Smirnov statistic 0.1396646 #> Cramer-von Mises statistic 0.6840994 #> Anderson-Darling statistic 3.5736460 #> #> Goodness-of-fit criteria #> 1-mle-weibull #> Akaike's Information Criterion 2514.449 #> Bayesian Information Criterion 2521.524 fWqme <- fitdist(serving, \"weibull\", method=\"qme\", probs=c(0.25, 0.75)) summary(fWqme) #> Fitting of the distribution ' weibull ' by matching quantiles #> Parameters : #> estimate #> shape 2.268699 #> scale 86.590853 #> Loglikelihood: -1256.129 AIC: 2516.258 BIC: 2523.332 plot(fWqme) gofstat(fWqme) #> Goodness-of-fit statistics #> 1-qme-weibull #> Kolmogorov-Smirnov statistic 0.1692858 #> Cramer-von Mises statistic 0.9664709 #> Anderson-Darling statistic 4.8479858 #> #> Goodness-of-fit criteria #> 1-qme-weibull #> Akaike's Information Criterion 2516.258 #> Bayesian Information Criterion 2523.332 # (11) Fit of a Pareto distribution by numerical moment matching estimation # # \\donttest{ require(\"actuar\") #> Loading required package: actuar #> #> Attaching package: ‘actuar’ #> The following objects are masked from ‘package:stats’: #> #> sd, var #> The following object is masked from ‘package:grDevices’: #> #> cm #simulate a sample x4 <- rpareto(1000, 6, 2) #empirical raw moment memp <- function(x, order) mean(x^order) #fit fP <- fitdist(x4, \"pareto\", method=\"mme\", order=c(1, 2), memp=\"memp\", start=list(shape=10, scale=10), lower=1, upper=Inf) #> Error in mmedist(data, distname, start = arg_startfix$start.arg, fix.arg = arg_startfix$fix.arg, checkstartfix = TRUE, calcvcov = calcvcov, ...): the empirical moment must be defined as a function summary(fP) #> Error: object 'fP' not found plot(fP) #> Error: object 'fP' not found # } # (12) Fit of a Weibull distribution to serving size data by maximum # goodness-of-fit estimation using all the distances available # # \\donttest{ data(groundbeef) serving <- groundbeef$serving (f1 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"CvM\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.093204 #> scale 82.660014 (f2 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"KS\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.065634 #> scale 81.450487 (f3 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.125425 #> scale 82.890502 (f4 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"ADR\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.072035 #> scale 82.762593 (f5 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"ADL\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.197498 #> scale 82.016005 (f6 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2R\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 1.90328 #> scale 81.33464 (f7 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2L\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.483836 #> scale 78.252113 (f8 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.081168 #> scale 85.281194 cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8)) cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8), xlogscale=TRUE, xlim=c(8, 250), verticals=TRUE) denscomp(list(f1, f2, f3, f4, f5, f6, f7, f8)) # } # (13) Fit of a uniform distribution using maximum likelihood # (a closed formula is used in this special case where the loglikelihood is not defined), # or maximum goodness-of-fit with Cramer-von Mises or Kolmogorov-Smirnov distance # set.seed(1234) u <- runif(50, min=5, max=10) fumle <- fitdist(u, \"unif\", method=\"mle\") summary(fumle) #> Fitting of the distribution ' unif ' by maximum likelihood #> Parameters : #> estimate #> min 5.047479 #> max 9.960752 #> Loglikelihood: -79.59702 AIC: 163.194 BIC: 167.0181 plot(fumle) gofstat(fumle) #> Goodness-of-fit statistics #> 1-mle-unif #> Kolmogorov-Smirnov statistic 0.1340723 #> Cramer-von Mises statistic 0.1566892 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mle-unif #> Akaike's Information Criterion 163.1940 #> Bayesian Information Criterion 167.0181 fuCvM <- fitdist(u, \"unif\", method=\"mge\", gof=\"CvM\") summary(fuCvM) #> Fitting of the distribution ' unif ' by maximum goodness-of-fit #> Parameters : #> estimate #> min 5.110497 #> max 9.552878 #> Loglikelihood: -Inf AIC: Inf BIC: Inf plot(fuCvM) gofstat(fuCvM) #> Goodness-of-fit statistics #> 1-mge-unif #> Kolmogorov-Smirnov statistic 0.11370966 #> Cramer-von Mises statistic 0.07791651 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mge-unif #> Akaike's Information Criterion Inf #> Bayesian Information Criterion Inf fuKS <- fitdist(u, \"unif\", method=\"mge\", gof=\"KS\") summary(fuKS) #> Fitting of the distribution ' unif ' by maximum goodness-of-fit #> Parameters : #> estimate #> min 5.092357 #> max 9.323818 #> Loglikelihood: -Inf AIC: Inf BIC: Inf plot(fuKS) gofstat(fuKS) #> Goodness-of-fit statistics #> 1-mge-unif #> Kolmogorov-Smirnov statistic 0.09216159 #> Cramer-von Mises statistic 0.12241830 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mge-unif #> Akaike's Information Criterion Inf #> Bayesian Information Criterion Inf # (14) scaling problem # the simulated dataset (below) has particularly small values, hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 0:6) cat(i, try(fitdist(x2*10^i, \"cauchy\", method=\"mle\")$estimate, silent=TRUE), \"\\n\") #> 0 1.876032e-05 0.000110131 #> 1 0.0001876032 0.00110131 #> 2 0.001870693 0.01100646 #> 3 0.01871473 0.1100713 #> 4 0.1870693 1.100646 #> 5 1.876032 11.0131 #> 6 18.76032 110.131 # (15) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for # nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution (which is called the 5 percent hazardous concentration, HC5, # in ecotoxicology) and estimation of other quantiles. # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") quantile(fln, probs = 0.05) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 #> estimate 1.744227 quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 # (16) Fit of a triangular distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # # \\donttest{ set.seed(1234) require(\"mc2d\") #> Loading required package: mc2d #> Loading required package: mvtnorm #> #> Attaching package: ‘mc2d’ #> The following objects are masked from ‘package:base’: #> #> pmax, pmin t <- rtriang(100, min=5, mode=6, max=10) fCvM <- fitdist(t, \"triang\", method=\"mge\", start = list(min=4, mode=6,max=9), gof=\"CvM\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. fKS <- fitdist(t, \"triang\", method=\"mge\", start = list(min=4, mode=6,max=9), gof=\"KS\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. cdfcomp(list(fCvM,fKS)) # } # (17) fit a non classical discrete distribution (the zero inflated Poisson distribution) # # \\donttest{ require(\"gamlss.dist\") #> Loading required package: gamlss.dist set.seed(1234) x <- rZIP(n = 30, mu = 5, sigma = 0.2) plotdist(x, discrete = TRUE) fitzip <- fitdist(x, \"ZIP\", start = list(mu = 4, sigma = 0.15), discrete = TRUE, optim.method = \"L-BFGS-B\", lower = c(0, 0), upper = c(Inf, 1)) #> Warning: The dZIP function should return a zero-length vector when input has length zero #> Warning: The pZIP function should return a zero-length vector when input has length zero summary(fitzip) #> Fitting of the distribution ' ZIP ' by maximum likelihood #> Parameters : #> estimate Std. Error #> mu 4.3166098 2.3777816 #> sigma 0.1891794 0.4062398 #> Loglikelihood: -67.13886 AIC: 138.2777 BIC: 141.0801 #> Correlation matrix: #> mu sigma #> mu 1.00000000 0.06418931 #> sigma 0.06418931 1.00000000 #> plot(fitzip) fitp <- fitdist(x, \"pois\") cdfcomp(list(fitzip, fitp)) gofstat(list(fitzip, fitp)) #> Chi-squared statistic: 3.579708 35.91516 #> Degree of freedom of the Chi-squared distribution: 3 4 #> Chi-squared p-value: 0.3105704 3.012341e-07 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo 1-mle-ZIP theo 2-mle-pois #> <= 0 6 5.999996 0.9059215 #> <= 2 7 4.425507 8.7194943 #> <= 4 5 9.047522 12.1379326 #> <= 5 5 4.054142 3.9650580 #> <= 7 5 4.715294 3.4694258 #> > 7 2 1.757539 0.8021677 #> #> Goodness-of-fit criteria #> 1-mle-ZIP 2-mle-pois #> Akaike's Information Criterion 138.2777 153.7397 #> Bayesian Information Criterion 141.0801 155.1409 # } # (18) examples with distributions in actuar (predefined starting values) # # \\donttest{ require(\"actuar\") x <- c(2.3,0.1,2.7,2.2,0.4,2.6,0.2,1.,7.3,3.2,0.8,1.2,33.7,14., 21.4,7.7,1.,1.9,0.7,12.6,3.2,7.3,4.9,4000.,2.5,6.7,3.,63., 6.,1.6,10.1,1.2,1.5,1.2,30.,3.2,3.5,1.2,0.2,1.9,0.7,17., 2.8,4.8,1.3,3.7,0.2,1.8,2.6,5.9,2.6,6.3,1.4,0.8) #log logistic ft_llogis <- fitdist(x,'llogis') x <- c(0.3837053, 0.8576858, 0.3552237, 0.6226119, 0.4783756, 0.3139799, 0.4051403, 0.4537631, 0.4711057, 0.5647414, 0.6479617, 0.7134207, 0.5259464, 0.5949068, 0.3509200, 0.3783077, 0.5226465, 1.0241043, 0.4384580, 1.3341520) #inverse weibull ft_iw <- fitdist(x,'invweibull') # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting of univariate distributions to censored data — fitdistcens","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Fits univariate distribution censored data maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"","code":"fitdistcens(censdata, distr, start=NULL, fix.arg=NULL, keepdata = TRUE, keepdata.nb=100, calcvcov=TRUE, ...) # S3 method for class 'fitdistcens' print(x, ...) # S3 method for class 'fitdistcens' plot(x, ...) # S3 method for class 'fitdistcens' summary(object, ...) # S3 method for class 'fitdistcens' logLik(object, ...) # S3 method for class 'fitdistcens' AIC(object, ..., k = 2) # S3 method for class 'fitdistcens' BIC(object, ...) # S3 method for class 'fitdistcens' vcov(object, ...) # S3 method for class 'fitdistcens' coef(object, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"censdata dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution, corresponding density function dname corresponding distribution function pname must defined, directly density function. start named list giving initial values parameters named distribution. argument may omitted distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood. x object class \"fitdistcens\". object object class \"fitdistcens\". keepdata logical. TRUE, dataset returned, otherwise sample subset returned. keepdata.nb keepdata=FALSE, length subset returned. calcvcov logical indicating (asymptotic) covariance matrix required. k penalty per parameter passed AIC generic function (2 default). ... arguments passed generic functions, function plotdistcens order control type ecdf-plot used censored data, function mledist order control optimization method.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Maximum likelihood estimations distribution parameters computed using function mledist. default direct optimization log-likelihood performed using optim, \"Nelder-Mead\" method distributions characterized one parameter \"BFGS\" method distributions characterized one parameter. algorithm used optim can chosen another optimization function can specified using ... argument (see mledist details). start may omitted (.e. NULL) classic distributions (see 'details' section mledist). Note errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1) ... argument. function able fit uniform distribution. parameter estimates, function returns log-likelihood standard errors estimates calculated Hessian solution found optim user-supplied function passed mledist. default (keepdata = TRUE), object returned fitdist contains data vector given input. dealing large datasets, can remove original dataset output setting keepdata = FALSE. case, keepdata.nb points () kept random subsampling keepdata.nb-4 points dataset adding component-wise minimum maximum. combined bootdistcens, aware bootstrap performed subset randomly selected fitdistcens. Currently, graphical comparisons multiple fits available framework. Weighted version estimation process available method = \"mle\" using weights=.... See corresponding man page details. yet possible take account weighths functions plotdistcens, plot.fitdistcens cdfcompcens (developments planned future). parameter(s) () estimated, gofstat allows compute goodness--fit statistics.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"fitdistcens returns object class \"fitdistcens\", list following components: estimate parameter estimates. method character string coding fitting method : \"mle\" 'maximum likelihood estimation'. sd estimated standard errors. cor estimated correlation matrix, NA numerically computable NULL available. vcov estimated variance-covariance matrix, NULL available. loglik log-likelihood. aic Akaike information criterion. bic -called BIC SBC (Schwarz Bayesian criterion). censdata censored data set. distname name distribution. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. dots list arguments passed ... used bootdistcens control optimization method used iterative calls mledist NULL arguments. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. discrete always FALSE. weights vector weigths used estimation process NULL. Generic functions: print print \"fitdist\" object shows traces fitting method fitted distribution. summary summary provides parameter estimates fitted distribution, log-likelihood, AIC BIC statistics, standard errors parameter estimates correlation matrix parameter estimates. plot plot object class \"fitdistcens\" returned fitdistcens uses function plotdistcens. logLik Extracts estimated log-likelihood \"fitdistcens\" object. AIC Extracts AIC \"fitdistcens\" object. BIC Extracts BIC \"fitdistcens\" object. vcov Extracts estimated var-covariance matrix \"fitdistcens\" object (available method = \"mle\"). coef Extracts fitted coefficients \"fitdistcens\" object.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"","code":"# (1) Fit of a lognormal distribution to bacterial contamination data # data(smokedfish) fitsf <- fitdistcens(smokedfish,\"lnorm\") summary(fitsf) #> Fitting of the distribution ' lnorm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> meanlog -3.627606 4.706165 #> sdlog 3.544570 4.949219 #> Loglikelihood: -90.65154 AIC: 185.3031 BIC: 190.5725 #> Correlation matrix: #> meanlog sdlog #> meanlog 1.0000000 -0.4325873 #> sdlog -0.4325873 1.0000000 #> # default plot using the Wang technique (see ?plotdiscens for details) plot(fitsf) # plot using the Turnbull algorithm (see ?plotdiscens for details) # with confidence intervals for the empirical distribution plot(fitsf, NPMLE = TRUE, NPMLE.method = \"Turnbull\", Turnbull.confint = TRUE) #> Warning: Turnbull is now a deprecated option for NPMLE.method. You should use Turnbull.middlepoints #> of Turnbull.intervals. It was here fixed as Turnbull.middlepoints, equivalent to former Turnbull. #> Warning: Q-Q plot and P-P plot are available only #> with the arguments NPMLE.method at Wang (default value) or Turnbull.intervals. # basic plot using intervals and points (see ?plotdiscens for details) plot(fitsf, NPMLE = FALSE) #> Warning: When NPMLE is FALSE the nonparametric maximum likelihood estimation #> of the cumulative distribution function is not computed. #> Q-Q plot and P-P plot are available only with the arguments NPMLE.method at Wang #> (default value) or Turnbull.intervals. # plot of the same fit using the Turnbull algorithm in logscale cdfcompcens(fitsf,main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, xlim = c(1e-2,1e2)) # zoom on large values of F cdfcompcens(fitsf,main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, xlim = c(1e-2,1e2),ylim=c(0.4,1)) # (2) Fit of a normal distribution on acute toxicity values # of fluazinam (in decimal logarithm) for # macroinvertebrates and zooplancton, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology # data(fluazinam) log10EC50 <-log10(fluazinam) fln <- fitdistcens(log10EC50,\"norm\") fln #> Fitting of the distribution ' norm ' on censored data by maximum likelihood #> Parameters: #> estimate #> mean 2.161449 #> sd 1.167290 summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 2.161449 1.2060732 #> sd 1.167290 0.9842019 #> Loglikelihood: -20.41212 AIC: 44.82424 BIC: 46.10235 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.1350237 #> sd 0.1350237 1.0000000 #> plot(fln) # (3) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view dedicated to # probability distributions # dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) fg <- fitdistcens(log10EC50,\"gumbel\",start=list(a=1,b=1)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. summary(fg) #> Error: object 'fg' not found plot(fg) #> Error: object 'fg' not found # (4) comparison of fits of various distributions # fll <- fitdistcens(log10EC50,\"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1518291 1.2058724 #> scale 0.6910423 0.6530058 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05097494 #> scale 0.05097494 1.00000000 #> cdfcompcens(list(fln,fll,fg),legendtext=c(\"normal\",\"logistic\",\"gumbel\"), xlab = \"log10(EC50)\") #> Error: object 'fg' not found # (5) how to change the optimisation method? # fitdistcens(log10EC50,\"logis\",optim.method=\"Nelder-Mead\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1518291 #> scale 0.6910423 fitdistcens(log10EC50,\"logis\",optim.method=\"BFGS\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1519961 #> scale 0.6910665 fitdistcens(log10EC50,\"logis\",optim.method=\"SANN\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.0238931 #> scale 0.5261822 # (6) custom optimisation function - example with the genetic algorithm # # \\donttest{ #wrap genoud function rgenoud package mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values=par, ...) standardres <- c(res, convergence=0) return(standardres) } # call fitdistcens with a 'custom' optimization function fit.with.genoud <- fitdistcens(log10EC50,\"logis\", custom.optim=mygenoud, nvars=2, Domains=cbind(c(0,0), c(5, 5)), boundary.enforcement=1, print.level=1, hessian=TRUE) #> #> #> Sat Oct 26 05:39:40 2024 #> Domains: #> 0.000000e+00 <= X1 <= 5.000000e+00 #> 0.000000e+00 <= X2 <= 5.000000e+00 #> #> Data Type: Floating Point #> Operators (code number, name, population) #> \t(1) Cloning........................... \t122 #> \t(2) Uniform Mutation.................. \t125 #> \t(3) Boundary Mutation................. \t125 #> \t(4) Non-Uniform Mutation.............. \t125 #> \t(5) Polytope Crossover................ \t125 #> \t(6) Simple Crossover.................. \t126 #> \t(7) Whole Non-Uniform Mutation........ \t125 #> \t(8) Heuristic Crossover............... \t126 #> \t(9) Local-Minimum Crossover........... \t0 #> #> HARD Maximum Number of Generations: 100 #> Maximum Nonchanging Generations: 10 #> Population size : 1000 #> Convergence Tolerance: 1.000000e-03 #> #> Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation. #> Checking Gradients before Stopping. #> Not Using Out of Bounds Individuals But Allowing Trespassing. #> #> Minimization Problem. #> #> #> Generation#\t Solution Value #> #> 0 \t1.475558e+00 #> 1 \t1.468136e+00 #> #> 'wait.generations' limit reached. #> No significant improvement in 10 generations. #> #> Solution Fitness Value: 1.468136e+00 #> #> Parameters at the Solution (parameter, gradient): #> #> X[ 1] :\t2.151910e+00\tG[ 1] :\t-1.894464e-08 #> X[ 2] :\t6.909672e-01\tG[ 2] :\t2.323783e-06 #> #> Solution Found Generation 1 #> Number of Generations Run 12 #> #> Sat Oct 26 05:39:41 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit.with.genoud) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1519105 1.2057751 #> scale 0.6909672 0.6528594 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05106547 #> scale 0.05106547 1.00000000 #> # } # (7) estimation of the mean of a normal distribution # by maximum likelihood with the standard deviation fixed at 1 using the argument fix.arg # flnb <- fitdistcens(log10EC50, \"norm\", start = list(mean = 1),fix.arg = list(sd = 1)) # (8) Fit of a lognormal distribution on acute toxicity values of fluazinam for # macroinvertebrates and zooplancton, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution (which is called the 5 percent hazardous concentration, HC5, # in ecotoxicology) and estimation of other quantiles. data(fluazinam) log10EC50 <-log10(fluazinam) fln <- fitdistcens(log10EC50,\"norm\") quantile(fln, probs = 0.05) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 #> estimate 0.2414275 quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 0.2414275 0.6655064 1.179033 # (9) Fit of a lognormal distribution on 72-hour acute salinity tolerance (LC50 values) # of riverine macro-invertebrates using maximum likelihood estimation data(salinity) log10LC50 <-log10(salinity) fln <- fitdistcens(log10LC50,\"norm\") plot(fln)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistrplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Overview of the fitdistrplus package — fitdistrplus-package","title":"Overview of the fitdistrplus package — fitdistrplus-package","text":"idea package emerged 2008 collaboration JB Denis, R Pouillot ML Delignette time worked area quantitative risk assessment. implementation package part general project named \"Risk assessment R\" gathering different packages hosted R-forge. fitdistrplus package first written ML Delignette-Muller made available CRAN 2009 presented 2009 useR conference Rennes. months , C Dutang joined project starting participate implementation fitdistrplus package. package also presented 2011 useR conference 2eme rencontres R 2013 (https://r2013-lyon.sciencesconf.org/). Four vignettes available within package: general overview package published Journal Statistical Software (doi:10.18637/jss.v064.i04 ), document answering Frequently Asked Questions, document presenting benchmark optimization algorithms finding parameters, document starting values. fitdistrplus package general package aims helping fit univariate parametric distributions censored non-censored data. two main functions fitdist fit non-censored data fitdistcens fit censored data. choice candidate distributions fit may helped using functions descdist plotdist non-censored data plotdistcens censored data). Using functions fitdist fitdistcens, different methods can used estimate distribution parameters: maximum likelihood estimation default (mledist), moment matching estimation (mmedist), quantile matching estimation (qmedist), maximum goodness--fit estimation (mgedist). classical distributions initial values automatically calculated provided user. Graphical functions plotdist plotdistcens can used help manual calibration initial values parameters non-classical distributions. Function prefit proposed help definition good starting values special case constrained parameters. case maximum likelihood chosen estimation method, function llplot enables visualize loglikelihood contours. goodness--fit fitted distributions (single fit multiple fits) can explored using different graphical functions (cdfcomp, denscomp, qqcomp ppcomp non-censored data cdfcompcens censored data). Goodness--fit statistics also provided non-censored data using function gofstat. Bootstrap proposed quantify uncertainty parameter estimates (functions bootdist bootdistcens) also quantify uncertainty CDF quantiles estimated fitted distribution (quantile CIcdfplot).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistrplus.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Overview of the fitdistrplus package — fitdistrplus-package","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":null,"dir":"Reference","previous_headings":"","what":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"48-hour acute toxicity values (EC50 values) exposure macroinvertebrates zooplancton fluazinam.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"","code":"data(fluazinam)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"fluazinam data frame 2 columns named left right, describing observed EC50 value (micrograms per liter) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value noncensored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"Hose, G.C., Van den Brink, P.J. 2004. species sensitivity distribution approach compared microcosm study: case study fungicide fluazinam. Ecotoxicology Environmental Safety, 73, 109-122.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"","code":"# (1) load of data # data(fluazinam) # (2) plot of data using Turnbull cdf plot # log10EC50 <- log10(fluazinam) plotdistcens(log10EC50) # (3) fit of a lognormal and a logistic distribution to data # (classical distributions used for species sensitivity # distributions, SSD, in ecotoxicology) # and visual comparison of the fits using Turnbull cdf plot # fln <- fitdistcens(log10EC50, \"norm\") summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 2.161449 1.2060732 #> sd 1.167290 0.9842019 #> Loglikelihood: -20.41212 AIC: 44.82424 BIC: 46.10235 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.1350237 #> sd 0.1350237 1.0000000 #> fll <- fitdistcens(log10EC50, \"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1518291 1.2058724 #> scale 0.6910423 0.6530058 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05097494 #> scale 0.05097494 1.00000000 #> cdfcompcens(list(fln,fll), legendtext = c(\"normal\", \"logistic\"), xlab = \"log10(EC50)\") # (4) estimation of the 5 percent quantile value of # the normal fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # non parametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(EC50) bln <- bootdistcens(fln, niter = 101) HC5ln <- quantile(bln, probs = 0.05) # in EC50 10^(HC5ln$quantiles) #> p=0.05 #> estimate 1.743522 10^(HC5ln$quantCI) #> p=0.05 #> 2.5 % 0.3021309 #> 97.5 % 13.5760675 # (5) estimation of the HC5 value # with its one-sided 95 percent confidence interval (type \"greater\") # # in log10(EC50) HC5lnb <- quantile(bln, probs = 0.05, CI.type = \"greater\") # in LC50 10^(HC5lnb$quantiles) #> p=0.05 #> estimate 1.743522 10^(HC5lnb$quantCI) #> p=0.05 #> 5 % 0.4182488"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":null,"dir":"Reference","previous_headings":"","what":"Fictive survival dataset of a french Male population — fremale","title":"Fictive survival dataset of a french Male population — fremale","text":"100 male individuals randomly taken frefictivetable CASdatasets package","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fictive survival dataset of a french Male population — fremale","text":"","code":"data(fremale)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fictive survival dataset of a french Male population — fremale","text":"fremale data frame 3 columns names AgeIn, AgeOut respectively entry age exit age; Death binary dummy: 1 indicating death individual; 0 censored observation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fictive survival dataset of a french Male population — fremale","text":"See full dataset frefictivetable CASdatasets http://dutangc.perso.math.cnrs.fr/RRepository/","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fictive survival dataset of a french Male population — fremale","text":"","code":"# (1) load of data # data(fremale) summary(fremale) #> AgeIn AgeOut Death #> Min. :23.87 Min. :30.20 Min. :0.0 #> 1st Qu.:47.29 1st Qu.:53.82 1st Qu.:1.0 #> Median :63.95 Median :69.49 Median :1.0 #> Mean :60.34 Mean :67.00 Mean :0.8 #> 3rd Qu.:72.00 3rd Qu.:80.23 3rd Qu.:1.0 #> Max. :89.17 Max. :97.11 Max. :1.0"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":null,"dir":"Reference","previous_headings":"","what":"Goodness-of-fit statistics — gofstat","title":"Goodness-of-fit statistics — gofstat","text":"Computes goodness--fit statistics parametric distributions fitted censored non-censored data set.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Goodness-of-fit statistics — gofstat","text":"","code":"gofstat(f, chisqbreaks, meancount, discrete, fitnames=NULL) # S3 method for class 'gofstat.fitdist' print(x, ...) # S3 method for class 'gofstat.fitdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Goodness-of-fit statistics — gofstat","text":"f object class \"fitdist\" (\"fitdistcens\" ), output function fitdist() (resp. \"fitdist()\"), \tlist \"fitdist\" objects, list \"fitdistcens\" objects. chisqbreaks usable non censored data, numeric vector defining breaks cells used compute chi-squared statistic. omitted, breaks automatically computed data order reach roughly number observations per cell, roughly equal argument meancount, sligthly ties. meancount usable non censored data, mean number observations per cell expected definition breaks cells used compute chi-squared statistic. argument taken account breaks directly defined argument chisqbreaks. chisqbreaks meancount omitted, meancount fixed order obtain roughly \\((4n)^{2/5}\\) cells \\(n\\) length dataset. discrete TRUE, Chi-squared statistic information criteria computed. \tmissing, discrete passed first object class \"fitdist\" list f. \tcensored data argument ignored, censored data considered continuous. fitnames vector defining names fits. x object class \"gofstat.fitdist\" \"gofstat.fitdistcens\". ... arguments passed generic functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Goodness-of-fit statistics — gofstat","text":"type data (censored ), information criteria calculated. non censored data, added Goodness--fit statistics computed described . Chi-squared statistic computed using cells defined argument chisqbreaks cells automatically defined data, order reach roughly number observations per cell, roughly equal argument meancount, sligthly ties. choice define cells empirical distribution (data), theoretical distribution, done enable comparison Chi-squared values obtained different distributions fitted data set. chisqbreaks meancount omitted, meancount fixed order obtain roughly \\((4n)^{2/5}\\) cells, \\(n\\) length data set (Vose, 2000). Chi-squared statistic computed program fails define enough cells due small dataset. Chi-squared statistic computed, degree freedom (nb cells - nb parameters - 1) corresponding distribution strictly positive, p-value Chi-squared test returned. continuous distributions, Kolmogorov-Smirnov, Cramer-von Mises \tAnderson-Darling statistics also computed, defined Stephens (1986). approximate Kolmogorov-Smirnov test performed assuming distribution parameters known. critical value defined Stephens (1986) completely specified distribution used reject distribution significance level 0.05. approximation, result test (decision rejection distribution ) returned data sets 30 observations. Note approximate test may conservative. data sets 5 observations distributions test described Stephens (1986) maximum likelihood estimations (\"exp\", \"cauchy\", \"gamma\" \"weibull\"), Cramer-von Mises Anderson-darling tests performed described Stephens (1986). tests take account fact parameters known estimated data maximum likelihood. result decision reject distribution significance level 0.05. tests available maximum likelihood estimations. recommended statistics automatically printed, .e. Cramer-von Mises, Anderson-Darling Kolmogorov statistics continuous distributions Chi-squared statistics discrete ones ( \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\" ). Results tests printed stored output function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Goodness-of-fit statistics — gofstat","text":"gofstat() returns object class \"gofstat.fitdist\" \"gofstat.fitdistcens\" following components sublist (aic, bic nbfit censored data) , chisq named vector Chi-squared statistics NULL computed chisqbreaks common breaks used define cells Chi-squared statistic chisqpvalue named vector p-values Chi-squared statistic NULL computed chisqdf named vector degrees freedom Chi-squared distribution NULL computed chisqtable table observed theoretical counts used Chi-squared calculations cvm named vector Cramer-von Mises statistics \"computed\" computed cvmtest named vector decisions Cramer-von Mises test \"computed\" computed ad named vector Anderson-Darling statistics \"computed\" computed adtest named vector decisions Anderson-Darling test \"computed\" computed ks named vector Kolmogorov-Smirnov statistic \"computed\" computed kstest named vector decisions Kolmogorov-Smirnov test \"computed\" computed aic named vector values Akaike's Information Criterion. bic named vector values Bayesian Information Criterion. discrete input argument automatic definition function first object class \"fitdist\" list input. nbfit Number fits argument.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Goodness-of-fit statistics — gofstat","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Stephens MA (1986), Tests based edf statistics. Goodness--fit techniques (D'Agostino RB Stephens MA, eds), Marcel Dekker, New York, pp. 97-194. Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Goodness-of-fit statistics — gofstat","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Goodness-of-fit statistics — gofstat","text":"","code":"# (1) fit of two distributions to the serving size data # by maximum likelihood estimation # and comparison of goodness-of-fit statistics # data(groundbeef) serving <- groundbeef$serving (fitg <- fitdist(serving, \"gamma\")) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 gofstat(fitg) #> Goodness-of-fit statistics #> 1-mle-gamma #> Kolmogorov-Smirnov statistic 0.1281486 #> Cramer-von Mises statistic 0.6936274 #> Anderson-Darling statistic 3.5672625 #> #> Goodness-of-fit criteria #> 1-mle-gamma #> Akaike's Information Criterion 2511.250 #> Bayesian Information Criterion 2518.325 (fitln <- fitdist(serving, \"lnorm\")) #> Fitting of the distribution ' lnorm ' by maximum likelihood #> Parameters: #> estimate Std. Error #> meanlog 4.1693701 0.5366095 #> sdlog 0.5366095 0.3794343 gofstat(fitln) #> Goodness-of-fit statistics #> 1-mle-lnorm #> Kolmogorov-Smirnov statistic 0.1493090 #> Cramer-von Mises statistic 0.8277358 #> Anderson-Darling statistic 4.5436542 #> #> Goodness-of-fit criteria #> 1-mle-lnorm #> Akaike's Information Criterion 2526.639 #> Bayesian Information Criterion 2533.713 gofstat(list(fitg, fitln)) #> Goodness-of-fit statistics #> 1-mle-gamma 2-mle-lnorm #> Kolmogorov-Smirnov statistic 0.1281486 0.1493090 #> Cramer-von Mises statistic 0.6936274 0.8277358 #> Anderson-Darling statistic 3.5672625 4.5436542 #> #> Goodness-of-fit criteria #> 1-mle-gamma 2-mle-lnorm #> Akaike's Information Criterion 2511.250 2526.639 #> Bayesian Information Criterion 2518.325 2533.713 # (2) fit of two discrete distributions to toxocara data # and comparison of goodness-of-fit statistics # data(toxocara) number <- toxocara$number fitp <- fitdist(number,\"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) fitnb <- fitdist(number,\"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb) gofstat(list(fitp, fitnb),fitnames = c(\"Poisson\",\"negbin\")) #> Chi-squared statistic: 31256.96 7.48606 #> Degree of freedom of the Chi-squared distribution: 5 4 #> Chi-squared p-value: 0 0.1123255 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo Poisson theo negbin #> <= 0 14 0.009014207 15.295027 #> <= 1 8 0.078236515 5.808596 #> <= 3 6 1.321767253 6.845015 #> <= 4 6 2.131297825 2.407815 #> <= 9 6 29.827829425 7.835196 #> <= 21 6 19.626223437 8.271110 #> > 21 7 0.005631338 6.537242 #> #> Goodness-of-fit criteria #> Poisson negbin #> Akaike's Information Criterion 1017.067 322.6882 #> Bayesian Information Criterion 1019.037 326.6288 # (3) Get Chi-squared results in addition to # recommended statistics for continuous distributions # set.seed(1234) x4 <- rweibull(n=1000,shape=2,scale=1) # fit of the good distribution f4 <- fitdist(x4,\"weibull\") plot(f4) # fit of a bad distribution f4b <- fitdist(x4,\"cauchy\") plot(f4b) (g <- gofstat(list(f4,f4b),fitnames=c(\"Weibull\", \"Cauchy\"))) #> Goodness-of-fit statistics #> Weibull Cauchy #> Kolmogorov-Smirnov statistic 0.02129364 0.114565 #> Cramer-von Mises statistic 0.06261917 1.854791 #> Anderson-Darling statistic 0.43120643 17.929123 #> #> Goodness-of-fit criteria #> Weibull Cauchy #> Akaike's Information Criterion 1225.734 1679.028 #> Bayesian Information Criterion 1235.549 1688.843 g$chisq #> Weibull Cauchy #> 35.76927 306.99824 g$chisqdf #> Weibull Cauchy #> 25 25 g$chisqpvalue #> Weibull Cauchy #> 7.517453e-02 2.364550e-50 g$chisqtable #> obscounts theo Weibull theo Cauchy #> <= 0.1547 36 27.86449 131.86592 #> <= 0.2381 36 34.87234 16.94381 #> <= 0.2952 36 30.58611 14.10775 #> <= 0.3745 36 50.14472 24.12899 #> <= 0.4323 36 41.16340 21.90706 #> <= 0.4764 36 33.55410 19.88887 #> <= 0.5263 36 39.57636 26.45041 #> <= 0.5771 36 41.67095 32.12597 #> <= 0.6276 36 42.36588 37.99145 #> <= 0.669 36 35.03524 35.92961 #> <= 0.7046 36 30.15737 34.26649 #> <= 0.7447 36 33.82481 41.80511 #> <= 0.7779 36 27.74805 36.41317 #> <= 0.8215 36 35.88169 48.69182 #> <= 0.8582 36 29.58833 40.27626 #> <= 0.9194 36 47.80044 62.45332 #> <= 0.9662 36 35.04387 42.03891 #> <= 1.017 36 36.19084 39.23047 #> <= 1.08 36 42.46698 40.45810 #> <= 1.119 36 24.49715 20.76625 #> <= 1.169 36 29.68482 22.91028 #> <= 1.237 36 36.49226 25.22891 #> <= 1.294 36 27.94301 17.49247 #> <= 1.418 36 51.25543 29.00440 #> <= 1.5 36 27.82405 14.64740 #> <= 1.65 36 38.72011 20.11799 #> <= 1.892 36 37.73807 21.69844 #> > 1.892 28 30.30916 81.16036 # and by defining the breaks (g <- gofstat(list(f4,f4b), chisqbreaks = seq(from = min(x4), to = max(x4), length.out = 10), fitnames=c(\"Weibull\", \"Cauchy\"))) #> Goodness-of-fit statistics #> Weibull Cauchy #> Kolmogorov-Smirnov statistic 0.02129364 0.114565 #> Cramer-von Mises statistic 0.06261917 1.854791 #> Anderson-Darling statistic 0.43120643 17.929123 #> #> Goodness-of-fit criteria #> Weibull Cauchy #> Akaike's Information Criterion 1225.734 1679.028 #> Bayesian Information Criterion 1235.549 1688.843 g$chisq #> Weibull Cauchy #> 6.532102 303.031817 g$chisqdf #> Weibull Cauchy #> 8 8 g$chisqpvalue #> Weibull Cauchy #> 5.878491e-01 9.318101e-61 g$chisqtable #> obscounts theo Weibull theo Cauchy #> <= 0.0264 1 0.9414531 111.941831 #> <= 0.3374 123 118.0587149 63.070591 #> <= 0.6483 222 240.3305518 167.852511 #> <= 0.9593 261 252.4491129 318.542341 #> <= 1.27 204 191.1128355 165.083876 #> <= 1.581 111 112.9380271 62.221846 #> <= 1.892 49 53.8525607 30.121634 #> <= 2.203 19 21.0847217 17.463676 #> <= 2.514 6 6.8505892 11.335604 #> <= 2.825 4 1.8602036 7.933114 #> > 2.825 0 0.5212296 44.432977 # (4) fit of two distributions on acute toxicity values # of fluazinam (in decimal logarithm) for # macroinvertebrates and zooplancton # and comparison of goodness-of-fit statistics # data(fluazinam) log10EC50 <-log10(fluazinam) (fln <- fitdistcens(log10EC50,\"norm\")) #> Fitting of the distribution ' norm ' on censored data by maximum likelihood #> Parameters: #> estimate #> mean 2.161449 #> sd 1.167290 plot(fln) gofstat(fln) #> #> Goodness-of-fit criteria #> 1-mle-norm #> Akaike's Information Criterion 44.82424 #> Bayesian Information Criterion 46.10235 (fll <- fitdistcens(log10EC50,\"logis\")) #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1518291 #> scale 0.6910423 plot(fll) gofstat(fll) #> #> Goodness-of-fit criteria #> 1-mle-logis #> Akaike's Information Criterion 45.10781 #> Bayesian Information Criterion 46.38593 gofstat(list(fll, fln), fitnames = c(\"loglogistic\", \"lognormal\")) #> #> Goodness-of-fit criteria #> loglogistic lognormal #> Akaike's Information Criterion 45.10781 44.82424 #> Bayesian Information Criterion 46.38593 46.10235"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"cdfcomp plots empirical cumulative distribution fitted distribution functions, denscomp plots histogram fitted density functions, qqcomp plots theoretical quantiles empirical ones, ppcomp plots theoretical probabilities empirical ones. cdfcomp able plot fits discrete distribution.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"","code":"cdfcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datapch, datacol, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, horizontals = TRUE, verticals = FALSE, do.points = TRUE, use.ppoints = TRUE, a.ppoints = 0.5, name.points = NULL, lines01 = FALSE, discrete, add = FALSE, plotstyle = \"graphics\", fitnbpts = 101, ...) denscomp(ft, xlim, ylim, probability = TRUE, main, xlab, ylab, datacol, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"topright\", ylegend = NULL, demp = FALSE, dempcol = \"black\", plotstyle = \"graphics\", discrete, fitnbpts = 101, fittype=\"l\", ...) qqcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, plotstyle = \"graphics\", ...) ppcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"ft One \"fitdist\" object list objects class \"fitdist\". xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot. See also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datapch integer specifying symbol used plotting data points. See also par. datacol specification color used plotting data points. See also par. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. See also par. fitlty (vector ) line type(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitpch (vector ) line type(s) plot fitted quantiles/probabilities. fewer values fits recycled standard fashion. See also par. fittype type plot fitted probabilities case discrete distributions: possible types \"p\" points, \"l\" lines \"o\" overplotted (plot.default). fittype used non-discrete distributions. fitnbpts numeric number points compute fitted probabilities cumulative probabilities. Default 101. addlegend TRUE, legend added plot. legendtext character expression vector length \\(\\ge 1\\) appear legend. See also legend. xlegend, ylegend \\(x\\) \\(y\\) coordinates used position legend. can specified keyword. plotstyle = \"graphics\", see xy.coords legend. plotstyle = \"ggplot\", xlegend keyword must one top, bottom, left, right. See also guide_legend ggplot2 horizontals TRUE, draws horizontal lines step empirical cumulative distribution function (ecdf). See also plot.stepfun. verticals TRUE, draws vertical lines empirical cumulative distribution function (ecdf). taken account horizontals=TRUE. .points TRUE (default), draws points x-locations. large dataset (n > 1e4), .points ignored point drawn. use.ppoints TRUE, probability points empirical distribution defined using function ppoints (1:n - .ppoints)/(n - 2a.ppoints + 1). FALSE, probability points simply defined (1:n)/n. argument ignored discrete data. .ppoints use.ppoints=TRUE, passed ppoints function. name.points Label vector points drawn .e. .points = TRUE (non censored data). lines01 logical plot two horizontal lines h=0 h=1 cdfcomp. line01 logical plot horizontal line \\(y=x\\) qqcomp ppcomp. line01col, line01lty Color line type line01. See also par. demp logical add empirical density plot, using density function. dempcol color empirical density case added plot (demp=TRUE). ynoise logical add small noise plotting empirical quantiles/probabilities qqcomp ppcomp. probability logical use probability scale denscomp. See also hist. discrete TRUE, distributions considered discrete. missing, discrete set TRUE least one object list ft discrete. add TRUE, adds already existing plot. FALSE, starts new plot. parameter available plotstyle = \"ggplot\". plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). ... graphical arguments passed graphical functions used cdfcomp, denscomp, ppcomp qqcomp plotstyle = \"graphics\". plotstyle = \"ggplot\", arguments used histogram plot (hist) denscomp function. plotstyle = \"ggplot\", graphical output can customized relevant ggplot2 functions store output.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"cdfcomp provides plot empirical distribution fitted distribution cdf, default using Hazen's rule empirical distribution, probability points defined (1:n - 0.5)/n. discrete TRUE, probability points always defined (1:n)/n. large dataset (n > 1e4), point drawn line ecdf drawn instead. Note horizontals, verticals .points FALSE, empirical point drawn, fitted cdf shown. denscomp provides density plot fitted distribution histogram data conyinuous data. discrete=TRUE, distributions considered discrete, histogram plotted demp forced TRUE fitted empirical probabilities plotted either vertical lines fittype=\"l\", single points fittype=\"p\" lines points fittype=\"o\". ppcomp provides plot probabilities fitted distribution (\\(x\\)-axis) empirical probabilities (\\(y\\)-axis) default defined (1:n - 0.5)/n (data assumed continuous). large dataset (n > 1e4), lines drawn instead pointss customized fitpch parameter. qqcomp provides plot quantiles theoretical distribution (\\(x\\)-axis) empirical quantiles data (\\(y\\)-axis), default defining probability points (1:n - 0.5)/n theoretical quantile calculation (data assumed continuous). large dataset (n > 1e4), lines drawn instead points customized fitpch parameter. default legend added plots. Many graphical arguments optional, dedicated personalize plots, fixed default values omitted.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"*comp returns list drawn points /lines plotstyle == \"graphics\" object class \"ggplot\" plotstyle == \"ggplot\".","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"Christophe Dutang, Marie-Laure Delignette-Muller Aurelie Siberchicot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"","code":"# (1) Plot various distributions fitted to serving size data # data(groundbeef) serving <- groundbeef$serving fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") cdfcomp(list(fitW, fitln, fitg), horizontals = FALSE) cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE) cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, verticals = TRUE, datacol = \"purple\") cdfcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", ylab = \"F\", xlim = c(0, 250), xlegend = \"center\", lines01 = TRUE) denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\") ppcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlegend = \"bottomright\", line01 = TRUE) qqcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlegend = \"bottomright\", line01 = TRUE, xlim = c(0, 300), ylim = c(0, 300), fitpch = 16) # (2) Plot lognormal distributions fitted by # maximum goodness-of-fit estimation # using various distances (data plotted in log scale) # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV taxaATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa flnMGEKS <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"KS\") flnMGEAD <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD\") flnMGEADL <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"ADL\") flnMGEAD2L <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD2L\") cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), xlogscale = TRUE, main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\"), verticals = TRUE, xlim = c(1, 100000), name.points=taxaATV) qqcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\"), xlogscale = TRUE, ylogscale = TRUE) ppcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\")) # (3) Plot normal and logistic distributions fitted by # maximum likelihood estimation # using various plotting positions in cdf plots # data(endosulfan) log10ATV <-log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") fll <- fitdist(log10ATV, \"logis\") # default plot using Hazen plotting position: (1:n - 0.5)/n cdfcomp(list(fln, fll), legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\") # plot using mean plotting position (named also Gumbel plotting position) # (1:n)/(n + 1) cdfcomp(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\", use.ppoints = TRUE, a.ppoints = 0) # plot using basic plotting position: (1:n)/n cdfcomp(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\", use.ppoints = FALSE) # (4) Comparison of fits of two distributions fitted to discrete data # data(toxocara) number <- toxocara$number fitp <- fitdist(number, \"pois\") fitnb <- fitdist(number, \"nbinom\") cdfcomp(list(fitp, fitnb), legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"l\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"p\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"o\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) # (5) Customizing of graphical output and use of ggplot2 # data(groundbeef) serving <- groundbeef$serving fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") if (requireNamespace (\"ggplot2\", quietly = TRUE)) { denscomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") cdfcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") qqcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") ppcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") } # customizing graphical output with graphics denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\", addlegend = FALSE) # customizing graphical output with ggplot2 if (requireNamespace (\"ggplot2\", quietly = TRUE)) { dcomp <- denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\", plotstyle = \"ggplot\", breaks = 20, addlegend = FALSE) dcomp + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Ground beef fits\") }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"cdfcompcens plots empirical cumulative distribution fitted distribution functions, qqcompcens plots theoretical quantiles empirical ones, ppcompcens plots theoretical probabilities empirical ones.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"","code":"cdfcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datacol, fillrect, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, lines01 = FALSE, Turnbull.confint = FALSE, NPMLE.method = \"Wang\", add = FALSE, plotstyle = \"graphics\", ...) qqcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, NPMLE.method = \"Wang\", plotstyle = \"graphics\", ...) ppcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, NPMLE.method = \"Wang\", plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"ft One \"fitdistcens\" object list objects class \"fitdistcens\". xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot, see also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datacol specification color used plotting data points. fillrect specification color used filling rectanges non uniqueness empirical cumulative distribution (used NPMLE.method equal \"Wang\" cdfcompcens). Fix NA want fill rectangles. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. fitlty (vector ) line type(s) plot fitted distributions. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions. fewer values fits recycled standard fashion. See also par. addlegend TRUE, legend added plot. legendtext character expression vector length \\(\\geq 1\\) appear legend, see also legend. xlegend, ylegend \\(x\\) \\(y\\) coordinates used position legend. can specified keyword. plotstyle = \"graphics\", see xy.coords legend. plotstyle = \"ggplot\", xlegend keyword must one top, bottom, left, right. See also guide_legend ggplot2 lines01 logical plot two horizontal lines h=0 h=1 cdfcompcens. Turnbull.confint TRUE confidence intervals added Turnbull plot. case NPMLE.method forced \"Turnbull\" NPMLE.method Three NPMLE techniques provided, \"Wang\", default one, rewritten package npsurv using function constrOptim package stats optimisation, \"Turnbull.middlepoints\", older one implemented package survival \"Turnbull.intervals\" uses Turnbull algorithm package survival associates interval equivalence class instead middlepoint interval (see details). \"Wang\" \"Turnbull.intervals\" enable derivation Q-Q plot P-P plot. add TRUE, adds already existing plot. FALSE, starts new plot. parameter available plotstyle = \"ggplot\". line01 logical plot horizontal line \\(y=x\\) qqcompcens ppcompcens. line01col, line01lty Color line type line01. See also par. ynoise logical add small noise plotting empirical quantiles/probabilities qqcompcens ppcompcens. ynoise used various fits plotted \"graphics\" plotstyle. Facets used instead \"ggplot\" plotstyle. plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). \"cdfcompcens\", \"ggplot\" graphics available \"Wang\" NPMLE technique. ... graphical arguments passed graphical functions used cdfcompcens, ppcompcens qqcompcens.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"See details plotdistcens detailed description provided goddness--fit plots.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"Turnbull BW (1974), Nonparametric estimation survivorship function doubly censored data. Journal American Statistical Association, 69, 169-173. Wang Y (2008), Dimension-reduced nonparametric maximum likelihood computation interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402. Wang Y Taylor SM (2013), Efficient computation nonparametric survival functions via hierarchical mixture formulation. Statistics Computing, 23, 713-725. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"","code":"# (1) Plot various distributions fitted to bacterial contamination data # data(smokedfish) Clog10 <- log10(smokedfish) fitsfn <- fitdistcens(Clog10,\"norm\") summary(fitsfn) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean -1.575392 2.043857 #> sd 1.539446 2.149561 #> Loglikelihood: -87.10945 AIC: 178.2189 BIC: 183.4884 #> Correlation matrix: #> mean sd #> mean 1.0000000 -0.4325228 #> sd -0.4325228 1.0000000 #> fitsfl <- fitdistcens(Clog10,\"logis\") summary(fitsfl) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location -1.5394230 1.706269 #> scale 0.8121862 1.352708 #> Loglikelihood: -86.45499 AIC: 176.91 BIC: 182.1794 #> Correlation matrix: #> location scale #> location 1.0000000 -0.3189915 #> scale -0.3189915 1.0000000 #> dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) fitsfg<-fitdistcens(Clog10,\"gumbel\",start=list(a=-3,b=3)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. summary(fitsfg) #> Error: object 'fitsfg' not found # CDF plot cdfcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol=\"orange\",fillrect = NA, legendtext=c(\"normal\",\"logistic\",\"Gumbel\"), main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", xlegend = \"bottom\",lines01 = TRUE) #> Error: object 'fitsfg' not found # alternative Turnbull plot for the empirical cumulative distribution # (default plot of the previous versions of the package) cdfcompcens(list(fitsfn,fitsfl,fitsfg), NPMLE.method = \"Turnbull.middlepoints\") #> Error: object 'fitsfg' not found # customizing graphical output with ggplot2 if (requireNamespace (\"ggplot2\", quietly = TRUE)) { cdfcompcens <- cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol=\"orange\",fillrect = NA, legendtext=c(\"normal\",\"logistic\",\"Gumbel\"), xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", xlegend = \"bottom\",lines01 = TRUE, plotstyle = \"ggplot\") cdfcompcens + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Bacterial contamination fits\") } #> Error: object 'fitsfg' not found # PP plot ppcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found ppcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE) #> Error: object 'fitsfg' not found par(mfrow = c(2,2)) ppcompcens(fitsfn) ppcompcens(fitsfl) ppcompcens(fitsfg) #> Error: object 'fitsfg' not found par(mfrow = c(1,1)) if (requireNamespace (\"ggplot2\", quietly = TRUE)) { ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = \"ggplot\") ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = \"ggplot\", fillrect = c(\"lightpink\", \"lightblue\", \"lightgreen\"), fitcol = c(\"red\", \"blue\", \"green\")) } #> Error: object 'fitsfg' not found # QQ plot qqcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE) #> Error: object 'fitsfg' not found par(mfrow = c(2,2)) qqcompcens(fitsfn) qqcompcens(fitsfl) qqcompcens(fitsfg) #> Error: object 'fitsfg' not found par(mfrow = c(1,1)) if (requireNamespace (\"ggplot2\", quietly = TRUE)) { qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = \"ggplot\") qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = \"ggplot\", fillrect = c(\"lightpink\", \"lightblue\", \"lightgreen\"), fitcol = c(\"red\", \"blue\", \"green\")) } #> Error: object 'fitsfg' not found"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":null,"dir":"Reference","previous_headings":"","what":"Ground beef serving size data set — groundbeef","title":"Ground beef serving size data set — groundbeef","text":"Serving sizes collected French survey, ground beef patties consumed children 5 years old.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ground beef serving size data set — groundbeef","text":"","code":"data(groundbeef)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ground beef serving size data set — groundbeef","text":"groundbeef data frame 1 column (serving: serving sizes grams)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ground beef serving size data set — groundbeef","text":"Delignette-Muller, M.L., Cornu, M. 2008. Quantitative risk assessment Escherichia coli O157:H7 frozen ground beef patties consumed young children French households. International Journal Food Microbiology, 128, 158-164.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ground beef serving size data set — groundbeef","text":"","code":"# (1) load of data # data(groundbeef) # (2) description and plot of data # serving <- groundbeef$serving descdist(serving) #> summary statistics #> ------ #> min: 10 max: 200 #> median: 79 #> mean: 73.64567 #> estimated sd: 35.88487 #> estimated skewness: 0.7352745 #> estimated kurtosis: 3.551384 plotdist(serving) # (3) fit of a Weibull distribution to data # fitW <- fitdist(serving, \"weibull\") summary(fitW) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> plot(fitW) gofstat(fitW) #> Goodness-of-fit statistics #> 1-mle-weibull #> Kolmogorov-Smirnov statistic 0.1396646 #> Cramer-von Mises statistic 0.6840994 #> Anderson-Darling statistic 3.5736460 #> #> Goodness-of-fit criteria #> 1-mle-weibull #> Akaike's Information Criterion 2514.449 #> Bayesian Information Criterion 2521.524"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":null,"dir":"Reference","previous_headings":"","what":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"llplot plots (log)likelihood around estimation distributions fitted maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"","code":"llplot(mlefit, loglik = TRUE, expansion = 1, lseq = 50, back.col = TRUE, nlev = 10, pal.col = terrain.colors(100), fit.show = FALSE, fit.pch = 4, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"mlefit object class \"fitdist\" \"fitdistcens\" obtained maximum likelihood (method = \"mle\") loglik logical plot log-likelihood likelihood function. expansion expansion factor enlarge default range values explored parameter. lseq length sequences parameters. back.col logical (llsurface ). Contours plotted background gradient colors TRUE. nlev number contour levels plot. pal.col Palette colors. Colors used back (llsurface ). fit.show logical plot mle estimate. fit.pch type point used plot mle estimate. ... graphical arguments passed graphical functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"llplot plots (log)likelihood surface(s) (curve one estimated parameter) around maximum likelihood estimation. internally calls function llsurface llcurve. two estimated parameters, (log)likehood surface plotted combination two parameters, fixing ones estimated value. (log)likelihood surface, back.col image (2D-plot) used nlev > 0 contour (2D-plot) used add nlev contours. default range values explored estimated parameter 2 standard error around mle estimate range can expanded (contracted) using argument expansion.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"","code":"# (1) a distribution with one parameter # x <- rexp(50) fite <- fitdist(x, \"exp\") llplot(fite) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fite, col = \"red\", fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fite, col = \"red\", fit.show = TRUE, loglik = FALSE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # (2) a distribution with two parameters # data(groundbeef) serving <- groundbeef$serving fitg <- fitdist(serving, \"gamma\") llplot(fitg) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # \\donttest{ llplot(fitg, expansion = 2) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fitg, pal.col = heat.colors(100), fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fitg, back.col = FALSE, nlev = 25, fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # } # (3) a distribution with two parameters with one fixed # fitg2 <- fitdist(serving, \"gamma\", fix.arg = list(rate = 0.5)) llplot(fitg2, fit.show = TRUE) # (4) a distribution with three parameters # # \\donttest{ data(endosulfan) ATV <-endosulfan$ATV require(\"actuar\") fBurr <- fitdist(ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) llplot(fBurr) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fBurr, back.col = FALSE, fit.show = TRUE, fit.pch = 16) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fBurr, nlev = 0, pal.col = rainbow(100), lseq = 100) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # } # (5) a distribution with two parameters fitted on censored data # data(salinity) fsal <- fitdistcens(salinity, \"lnorm\") llplot(fsal, fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fsal, fit.show = TRUE, loglik = FALSE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":null,"dir":"Reference","previous_headings":"","what":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"llsurface plots likelihood surface distributions two parameters, llcurve plots likelihood curve distributions one parameters.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"","code":"llsurface(data, distr, plot.arg, min.arg, max.arg, lseq = 50, fix.arg = NULL, loglik = TRUE, back.col = TRUE, nlev = 10, pal.col = terrain.colors(100), weights = NULL, ...) llcurve(data, distr, plot.arg, min.arg, max.arg, lseq = 50, fix.arg = NULL, loglik = TRUE, weights = NULL, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"data numeric vector non censored data dataframe two columns respectively named left right, describing observed value interval censored data. case left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution corresponding density function dname corresponding distribution function pname must classically defined. plot.arg two-element vector names two parameters vary llsurface, one element llcurve. min.arg two-element vector lower plotting bounds llsurface, one element llcurve. max.arg two-element vector upper plotting bounds llsurface, one element llcurve. lseq length sequences parameters. fix.arg named list fixed value parameters. loglik logical plot log-likelihood likelihood function. back.col logical (llsurface ). Contours plotted background gradient colors TRUE. nlev number contour levels plot (llsurface ). pal.col Palette colors. Colors used back (llsurface ). weights optional vector weights used fitting process. NULL numeric vector strictly positive values (classically number occurences observation). ... graphical arguments passed graphical functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"two function intended called directly internally called llplot. llsurface plots likelihood surface distributions two varying parameters parameters fixed. back.col, image (2D-plot) used. nlev > 0, contour (2D-plot) used add nlev contours. llcurve plots likelihood curve distributions one varying parameter parameters fixed.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"","code":"# (1) loglikelihood or likelihood curve # n <- 100 set.seed(1234) x <- rexp(n) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4, loglik = FALSE) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4, main = \"log-likelihood for exponential distribution\", col = \"red\") abline(v = 1, lty = 2) # (2) loglikelihood surface # x <- rnorm(n, 0, 1) llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), back.col = FALSE, main=\"log-likelihood for normal distribution\") llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), main=\"log-likelihood for normal distribution\", nlev = 20, pal.col = heat.colors(100),) points(0, 1, pch=\"+\", col=\"red\") llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), main=\"log-likelihood for normal distribution\", nlev = 0, back.col = TRUE, pal.col = rainbow(100, s = 0.5, end = 0.8)) points(0, 1, pch=\"+\", col=\"black\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Fit univariate continuous distribution maximizing goodness--fit (minimizing distance) non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"","code":"mgedist(data, distr, gof = \"CvM\", start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. gof character string coding name goodness--fit distance used : \"CvM\" Cramer-von Mises distance, \"KS\" Kolmogorov-Smirnov distance, \"AD\" Anderson-Darling distance, \"ADR\", \"ADL\", \"AD2R\", \"AD2L\" \"AD2\" variants Anderson-Darling distance described Luceno (2006). start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. silent logical remove show warnings bootstraping. gradient function return gradient gof distance \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"mgedist function numerically maximizes goodness--fit, minimizes goodness--fit distance coded argument gof. One may use one classical distances defined Stephens (1986), Cramer-von Mises distance (\"CvM\"), Kolmogorov-Smirnov distance (\"KS\") Anderson-Darling distance (\"AD\") gives weight tails distribution, one variants last distance proposed Luceno (2006). right-tail AD (\"ADR\") gives weight right tail, left-tail AD (\"ADL\") gives weight left tail. Either tails, , can receive even larger weights using second order Anderson-Darling Statistics (using \"AD2R\", \"AD2L\" \"AD2\"). optimization process mledist, see 'details' section function. function intended called directly internally called fitdist bootdist. function intended used continuous distributions weighted maximum goodness--fit estimation allowed. NB: data values particularly small large, scaling may needed optimization process. See example (4).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"mgedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. gof code goodness--fit distance maximized.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Luceno (2006), Fitting generalized Pareto distribution data using maximum goodness--fit estimators. Computational Statistics Data Analysis, 51, 904-917, doi:10.1016/j.csda.2005.09.011 . Stephens MA (1986), Tests based edf statistics. Goodness--fit techniques (D'Agostino RB Stephens MA, eds), Marcel Dekker, New York, pp. 97-194. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"","code":"# (1) Fit of a Weibull distribution to serving size data by maximum # goodness-of-fit estimation using all the distances available # data(groundbeef) serving <- groundbeef$serving mgedist(serving, \"weibull\", gof=\"CvM\") #> $estimate #> shape scale #> 2.093204 82.660014 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.002581367 #> #> $hessian #> shape scale #> shape 0.0159565105 3.639558e-04 #> scale 0.0003639558 9.522745e-05 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 65 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.623 #> #> $gof #> [1] \"CvM\" #> mgedist(serving, \"weibull\", gof=\"KS\") #> $estimate #> shape scale #> 2.065634 81.450487 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.112861 #> #> $hessian #> shape scale #> shape 122.668263 6.509057 #> scale 6.509057 7.599584 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 127 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.975 #> #> $gof #> [1] \"KS\" #> mgedist(serving, \"weibull\", gof=\"AD\") #> $estimate #> shape scale #> 2.125425 82.890502 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0137836 #> #> $hessian #> shape scale #> shape 0.1158157367 0.0007180241 #> scale 0.0007180241 0.0005332051 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 67 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.393 #> #> $gof #> [1] \"AD\" #> mgedist(serving, \"weibull\", gof=\"ADR\") #> $estimate #> shape scale #> 2.072035 82.762593 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.006340469 #> #> $hessian #> shape scale #> shape 0.053243854 -0.0013083937 #> scale -0.001308394 0.0003140377 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.837 #> #> $gof #> [1] \"ADR\" #> mgedist(serving, \"weibull\", gof=\"ADL\") #> $estimate #> shape scale #> 2.197498 82.016005 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.007267475 #> #> $hessian #> shape scale #> shape 0.060343316 0.0021420124 #> scale 0.002142012 0.0002184993 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 65 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.415 #> #> $gof #> [1] \"ADL\" #> mgedist(serving, \"weibull\", gof=\"AD2R\") #> $estimate #> shape scale #> 1.90328 81.33464 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.04552816 #> #> $hessian #> shape scale #> shape 1.31736538 -0.041034447 #> scale -0.04103445 0.002056365 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1259.112 #> #> $gof #> [1] \"AD2R\" #> mgedist(serving, \"weibull\", gof=\"AD2L\") #> $estimate #> shape scale #> 2.483836 78.252113 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0385314 #> #> $hessian #> shape scale #> shape 0.44689737 0.0161843919 #> scale 0.01618439 0.0009217762 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1265.933 #> #> $gof #> [1] \"AD2L\" #> mgedist(serving, \"weibull\", gof=\"AD2\") #> $estimate #> shape scale #> 2.081168 85.281194 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.1061089 #> #> $hessian #> shape scale #> shape 2.10614403 -0.04170905 #> scale -0.04170905 0.00299467 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1256.313 #> #> $gof #> [1] \"AD2\" #> # (2) Fit of a uniform distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # set.seed(1234) u <- runif(100,min=5,max=10) mgedist(u,\"unif\",gof=\"CvM\") #> $estimate #> min max #> 4.788260 9.568912 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.001142423 #> #> $hessian #> min max #> min 0.02906956 0.01461523 #> max 0.01461523 0.02570923 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 59 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"CvM\" #> mgedist(u,\"unif\",gof=\"KS\") #> $estimate #> min max #> 4.664535 9.463995 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.08 #> #> $hessian #> min max #> min 43.06566 -33.35097 #> max -33.35097 -61.06933 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 29 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"KS\" #> # (3) Fit of a triangular distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # # \\donttest{ require(\"mc2d\") set.seed(1234) t <- rtriang(100,min=5,mode=6,max=10) mgedist(t,\"triang\",start = list(min=4, mode=6,max=9),gof=\"CvM\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. #> $estimate #> min mode max #> 5.051036 5.796428 9.391579 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0006428299 #> #> $hessian #> min mode max #> min 0.03051858 0.03248860 0.01522501 #> mode 0.03248860 0.03821007 0.01800899 #> max 0.01522501 0.01800899 0.01593900 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 106 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"CvM\" #> mgedist(t,\"triang\",start = list(min=4, mode=6,max=9),gof=\"KS\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. #> $estimate #> min mode max #> 4.939094 5.813200 9.248592 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.06191245 #> #> $hessian #> min mode max #> min 158.93759 158.9436 70.39038 #> mode 158.94358 199.0473 70.39510 #> max 70.39038 70.3951 106.08995 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 268 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"KS\" #> # } # (4) scaling problem # the simulated dataset (below) has particularly small values, hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 6:0) cat(i, try(mgedist(x*10^i,\"cauchy\")$estimate, silent=TRUE), \"\\n\") #> 6 Error in eval(expr, envir) : object 'x' not found #> #> 5 Error in eval(expr, envir) : object 'x' not found #> #> 4 Error in eval(expr, envir) : object 'x' not found #> #> 3 Error in eval(expr, envir) : object 'x' not found #> #> 2 Error in eval(expr, envir) : object 'x' not found #> #> 1 Error in eval(expr, envir) : object 'x' not found #> #> 0 Error in eval(expr, envir) : object 'x' not found #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum likelihood fit of univariate distributions — mledist","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Fit univariate distributions using maximum likelihood censored non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum likelihood fit of univariate distributions — mledist","text":"","code":"mledist(data, distr, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum likelihood fit of univariate distributions — mledist","text":"data numeric vector non censored data dataframe two columns respectively named left right, describing observed value interval censored data. case left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution corresponding density function dname corresponding distribution function pname must classically defined. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see details). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. optim.method \"default\" (see details) optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying MLE optimisation (see details). weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MLE used, otherwise ordinary MLE. silent logical remove show warnings bootstraping. gradient function return gradient log-likelihood \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum likelihood fit of univariate distributions — mledist","text":"function intended called directly internally called fitdist bootdist used maximum likelihood method fitdistcens bootdistcens. assumed distr argument specifies distribution probability density function cumulative distribution function (d, p). quantile function random generator function (q, r) may needed function mmedist, qmedist, mgedist, fitdist,fitdistcens, bootdistcens bootdist. following named distributions, reasonable starting values computed start omitted (.e. NULL) : \"norm\", \"lnorm\", \"exp\" \"pois\", \"cauchy\", \"gamma\", \"logis\", \"nbinom\" (parametrized mu size), \"geom\", \"beta\", \"weibull\" stats package; \"invgamma\", \"llogis\", \"invweibull\", \"pareto1\", \"pareto\", \"lgamma\", \"trgamma\", \"invtrgamma\" actuar package. Note starting values may good enough fit poor. function uses closed-form formula fit uniform distribution. start list, named list names d,p,q,r functions chosen distribution. start function data, function return named list names d,p,q,r functions chosen distribution. mledist function allows user set fixed values parameters. start, fix.arg list, named list names d,p,q,r functions chosen distribution. fix.arg function data, function return named list names d,p,q,r functions chosen distribution. custom.optim=NULL (default), maximum likelihood estimations distribution parameters computed R base optim constrOptim. finite bounds (lower=-Inf upper=Inf) supplied, optim used method specified optim.method. Note optim.method=\"default\" means optim.method=\"Nelder-Mead\" distributions least two parameters optim.method=\"BFGS\" distributions one parameter. finite bounds supplied (among lower upper) gradient != NULL, constrOptim used. finite bounds supplied (among lower upper) gradient == NULL, constrOptim used optim.method=\"Nelder-Mead\"; optim used optim.method=\"L-BFGS-B\" \"Brent\"; case, error raised (behavior constrOptim). errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1). custom.optim NULL, user-supplied function used instead R base optim. custom.optim must (least) following arguments fn function optimized, par initialized parameters. Internally function optimized also arguments, obs observations ddistname distribution name non censored data (Beware potential conflicts optional arguments custom.optim). assumed custom.optim carry MINIMIZATION. Finally, return least following components par estimate, convergence convergence code, value fn(par), hessian, counts number calls (function gradient) message (default NULL) error message custom.optim raises error, see returned value optim. See examples fitdist fitdistcens. Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary MLE carried , otherwise specified weights used balance log-likelihood contributions. yet possible take account weights functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat, descdist, bootdist, bootdistcens mgedist. (developments planned future). NB: data values particularly small large, scaling may needed optimization process. See Example (7).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum likelihood fit of univariate distributions — mledist","text":"mledist returns list following components, estimate parameter estimates. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. used fitdist estimate standard errors. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. method \"closed formula\" appropriate otherwise NULL.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum likelihood fit of univariate distributions — mledist","text":"","code":"# (1) basic fit of a normal distribution with maximum likelihood estimation # set.seed(1234) x1 <- rnorm(n=100) mledist(x1,\"norm\") #> $estimate #> mean sd #> -0.1567617 0.9993707 #> #> $convergence #> [1] 0 #> #> $value #> [1] 1.418309 #> #> $hessian #> mean sd #> mean 1.00126 0.000000 #> sd 0.00000 2.002538 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 43 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -141.8309 #> #> $vcov #> NULL #> # (2) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view dedicated to probability distributions dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) mledist(x1,\"gumbel\",start=list(a=10,b=5)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (3) fit of a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) mledist(x2,\"pois\") #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> [1] 1.539478 #> #> $hessian #> lambda #> lambda 0.5882357 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 4 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $vcov #> NULL #> # (4) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) mledist(x3,\"beta\") #> $estimate #> shape1 shape2 #> 4.859798 10.918841 #> #> $convergence #> [1] 0 #> #> $value #> [1] -0.7833052 #> #> $hessian #> shape1 shape2 #> shape1 0.16295311 -0.06542753 #> shape2 -0.06542753 0.03047900 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 47 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 78.33052 #> #> $vcov #> NULL #> # (5) fit frequency distributions on USArrests dataset. # x4 <- USArrests$Assault mledist(x4, \"pois\") #> $estimate #> lambda #> 170.76 #> #> $convergence #> [1] 0 #> #> $value #> [1] 24.2341 #> #> $hessian #> lambda #> lambda 0.005856175 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 2 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1211.705 #> #> $vcov #> NULL #> mledist(x4, \"nbinom\") #> $estimate #> size mu #> 3.822579 170.747853 #> #> $convergence #> [1] 0 #> #> $value #> [1] 5.806593 #> #> $hessian #> size mu #> size 3.518616e-02 -3.987921e-07 #> mu -3.987921e-07 1.282598e-04 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 47 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -290.3297 #> #> $vcov #> NULL #> # (6) fit a continuous distribution (Gumbel) to censored data. # data(fluazinam) log10EC50 <-log10(fluazinam) # definition of the Gumbel distribution dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) mledist(log10EC50,\"gumbel\",start=list(a=0,b=2),optim.method=\"Nelder-Mead\") #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (7) scaling problem # the simulated dataset (below) has particularly small values, # hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 6:0) cat(i, try(mledist(x*10^i, \"cauchy\")$estimate, silent=TRUE), \"\\n\") #> 6 Error in eval(expr, envir) : object 'x' not found #> #> 5 Error in eval(expr, envir) : object 'x' not found #> #> 4 Error in eval(expr, envir) : object 'x' not found #> #> 3 Error in eval(expr, envir) : object 'x' not found #> #> 2 Error in eval(expr, envir) : object 'x' not found #> #> 1 Error in eval(expr, envir) : object 'x' not found #> #> 0 Error in eval(expr, envir) : object 'x' not found #> # (17) small example for the zero-modified geometric distribution # dzmgeom <- function(x, p1, p2) p1 * (x == 0) + (1-p1)*dgeom(x-1, p2) #pdf x2 <- c(2, 4, 0, 40, 4, 21, 0, 0, 0, 2, 5, 0, 0, 13, 2) #simulated dataset initp1 <- function(x) list(p1=mean(x == 0)) #init as MLE mledist(x2, \"zmgeom\", fix.arg=initp1, start=list(p2=1/2)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'."},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Matching moment fit of univariate distributions — mmedist","title":"Matching moment fit of univariate distributions — mmedist","text":"Fit univariate distributions matching moments (raw centered) non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matching moment fit of univariate distributions — mmedist","text":"","code":"mmedist(data, distr, order, memp, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matching moment fit of univariate distributions — mmedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution (see 'details'). order numeric vector moment order(s). length vector must equal number parameters estimate. memp function implementing empirical moments, raw centered consistent distr argument (weights argument). See details . start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization . weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MME used, otherwise ordinary MME. silent logical remove show warnings bootstraping. gradient function return gradient squared difference \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matching moment fit of univariate distributions — mmedist","text":"argument distr can one base R distributions: \"norm\", \"lnorm\", \"exp\" \"pois\", \"gamma\", \"logis\", \"nbinom\" , \"geom\", \"beta\" \"unif\". case, arguments data distr required, estimate computed closed-form formula. distributions characterized one parameter (\"geom\", \"pois\" \"exp\"), parameter simply estimated matching theoretical observed means, distributions characterized two parameters, parameters estimated matching theoretical observed means variances (Vose, 2000). Note closed-form formula, fix.arg used start ignored. argument distr can also distribution name long corresponding mdistr function exists, e.g. \"pareto\" \"mpareto\" exists. case arguments arguments order memp supplied order carry matching numerically, minimization sum squared differences observed theoretical moments. Optionnally arguments can supplied control optimization (see 'details' section mledist details arguments control optimization). case, fix.arg can used start taken account. non closed-form estimators, memp must provided compute empirical moments. weights=NULL, function must two arguments x, order: x numeric vector data order order moment. weights!=NULL, function must three arguments x, order, weights: x numeric vector data, order order moment, weights numeric vector weights. See examples . Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary MME carried , otherwise specified weights used compute (raw centered) weighted moments. closed-form estimators, weighted mean variance computed wtdmean wtdvar Hmisc package. numerical minimization used, weighted expected computed memp function. yet possible take account weighths functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). function intended called directly internally called fitdist bootdist used matching moments method. Since Version 1.2-0, mmedist automatically computes asymptotic covariance matrix using . Ibragimov R. 'minskii (1981), hence theoretical moments mdist defined order equals twice maximal order given order. instance, normal distribution, fit expectation variance need mnorm order \\(2\\times2=4\\).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matching moment fit of univariate distributions — mmedist","text":"mmedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function (appropriate) name optimization function used maximum likelihood. optim.method (appropriate) optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. method either \"closed formula\" name optimization method. order order moment(s) matched. memp empirical moment function.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matching moment fit of univariate distributions — mmedist","text":". Ibragimov R. 'minskii (1981), Statistical Estimation - Asymptotic Theory, Springer-Verlag, doi:10.1007/978-1-4899-0027-2 Evans M, Hastings N Peacock B (2000), Statistical distributions. John Wiley Sons Inc, doi:10.1002/9780470627242 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matching moment fit of univariate distributions — mmedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matching moment fit of univariate distributions — mmedist","text":"","code":"# (1) basic fit of a normal distribution with moment matching estimation # set.seed(1234) n <- 100 x1 <- rnorm(n=n) mmedist(x1, \"norm\") #> $estimate #> mean sd #> -0.1567617 0.9993707 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] -141.8309 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 2 #> #> $memp #> NULL #> #> $vcov #> NULL #> #weighted w <- c(rep(1, n/2), rep(10, n/2)) mmedist(x1, \"norm\", weights=w)$estimate #> Warning: weights are not taken into account in the default initial values #> mean sd #> 0.08565839 1.02915474 # (2) fit a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) mmedist(x2, \"pois\") #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 #> #> $memp #> NULL #> #> $vcov #> NULL #> # (3) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) mmedist(x3, \"beta\") #> $estimate #> shape1 shape2 #> 4.522734 10.219685 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] 78.19503 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 2 #> #> $memp #> NULL #> #> $vcov #> NULL #> # (4) fit a Pareto distribution # # \\donttest{ require(\"actuar\") #simulate a sample x4 <- rpareto(1000, 6, 2) #empirical raw moment memp <- function(x, order) mean(x^order) memp2 <- function(x, order, weights) sum(x^order * weights)/sum(weights) #fit by MME mmedist(x4, \"pareto\", order=c(1, 2), memp=memp, start=list(shape=10, scale=10), lower=1, upper=Inf) #> $estimate #> shape scale #> 4.560420 1.464763 #> #> $convergence #> [1] 0 #> #> $value #> [1] 4.474863e-13 #> #> $hessian #> NULL #> #> $optim.function #> [1] \"constrOptim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 534 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -80.49091 #> #> $method #> [1] \"default\" #> #> $order #> [1] 1 2 #> #> $memp #> function (x, order) #> mean(x^order) #> #> #> $vcov #> NULL #> #fit by weighted MME w <- rep(1, length(x4)) w[x4 < 1] <- 2 mmedist(x4, \"pareto\", order=c(1, 2), memp=memp2, weights=w, start=list(shape=10, scale=10), lower=1, upper=Inf) #> Warning: weights are not taken into account in the default initial values #> $estimate #> shape scale #> 5.656722 1.630818 #> #> $convergence #> [1] 0 #> #> $value #> [1] 7.397593e-14 #> #> $hessian #> NULL #> #> $optim.function #> [1] \"constrOptim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> [1] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [38] 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [112] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 #> [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 #> [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 #> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 #> [260] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 #> [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [334] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 2 #> [371] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 #> [408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 #> [445] 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 #> [482] 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 #> [519] 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 #> [556] 2 2 2 2 1 2 2 1 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 #> [667] 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 #> [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [741] 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 #> [778] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [815] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [852] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2 #> [889] 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [926] 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 #> [963] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 #> [1000] 2 #> #> $counts #> function gradient #> 999 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 119.7362 #> #> $method #> [1] \"default\" #> #> $order #> [1] 1 2 #> #> $memp #> function (x, order, weights) #> sum(x^order * weights)/sum(weights) #> #> #> $vcov #> NULL #> # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum spacing estimation of univariate distributions — msedist","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Fit univariate distribution maximizing (log) spacings non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum spacing estimation of univariate distributions — msedist","text":"","code":"msedist(data, distr, phidiv=\"KL\", power.phidiv=NULL, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights=NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum spacing estimation of univariate distributions — msedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. phidiv character string coding name phi-divergence used : \"KL\" Kullback-Leibler information (corresponds classic maximum spacing estimation), \"J\" Jeffreys' divergence, \"R\" Renyi's divergence, \"H\" Hellinger distance, \"V\" Vajda's measure information, see details. power.phidiv relevant, numeric power used phi-divergence : NULL phidiv=\"KL\" phidiv=\"J\" , positive different 1 phidiv=\"R\", greater equal 1 phidiv=\"H\" phidiv=\"V\", see details. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MSE used, otherwise ordinary MSE. silent logical remove show warnings bootstraping. gradient function return gradient gof distance \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum spacing estimation of univariate distributions — msedist","text":"msedist function numerically maximizes phi-divergence function spacings, spacings differences cumulative distribution function evaluated sorted dataset. classical maximum spacing estimation (MSE) introduced Cheng Amin (1986) Ranneby (1984) independently phi-diverence logarithm, see Anatolyev Kosenok (2005) link MSE maximum likelihood estimation. MSE generalized Ranneby Ekstrom (1997) allowing different phi-divergence function. Generalized MSE maximizes $$ S_n(\\theta)=\\frac{1}{n+1}\\sum_{=1}^{n+1} \\phi\\left(F(x_{()}; \\theta)-F(x_{(-1)}; \\theta) \\right), $$ \\(F(;\\theta)\\) parametric distribution function fitted, \\(\\phi\\) phi-divergence function, \\(x_{(1)}<\\dots0, \\alpha\\neq 1 $$ Hellinger distance (phidiv=\"H\" power.phidiv=p) $$\\phi(x)=-|1-x^{1/p}|^p \\textrm{ } p\\ge 1 $$ Vajda's measure information (phidiv=\"V\" power.phidiv=beta) $$\\phi(x)=-|1-x|^\\beta \\textrm{ } \\beta\\ge 1 $$ optimization process mledist, see 'details' section function. function intended called directly internally called fitdist bootdist. function intended used non-censored data. NB: data values particularly small large, scaling may needed optimization process, see mledist's examples.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum spacing estimation of univariate distributions — msedist","text":"msedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. phidiv character string coding name phi-divergence used either \"KL\", \"J\", \"R\", \"H\" \"V\". power.phidiv Either NULL numeric power used phi-divergence.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Anatolyev, S., Kosenok, G. (2005). alternative maximum likelihood based spacings. Econometric Theory, 21(2), 472-476, doi:10.1017/S0266466605050255 . Cheng, R.C.H. N..K. Amin (1983) Estimating parameters continuous univariate distributions shifted origin. Journal Royal Statistical Society Series B 45, 394-403, doi:10.1111/j.2517-6161.1983.tb01268.x . Ranneby, B. (1984) maximum spacing method: estimation method related maximum likelihood method. Scandinavian Journal Statistics 11, 93-112. Ranneby, B. Ekstroem, M. (1997). Maximum spacing estimates based different metrics. Umea universitet.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum spacing estimation of univariate distributions — msedist","text":"","code":"# (1) Fit of a Weibull distribution to serving size data by maximum # spacing estimation # data(groundbeef) serving <- groundbeef$serving msedist(serving, \"weibull\") #> $estimate #> shape scale #> 1.423799 80.894950 #> #> $convergence #> [1] 0 #> #> $value #> [1] 3.789824 #> #> $hessian #> shape scale #> shape 0.792656647 -0.0043440632 #> scale -0.004344063 0.0002995895 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 59 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1287.97 #> #> $phidiv #> [1] \"KL\" #> #> $power.phidiv #> NULL #> # (2) Fit of an exponential distribution # set.seed(123) x1 <- rexp(1e3) #the convergence is quick msedist(x1, \"exp\", control=list(trace=0, REPORT=1)) #> $estimate #> rate #> 0.967625 #> #> $convergence #> [1] 0 #> #> $value #> [1] 7.516802 #> #> $hessian #> rate #> rate 1.066843 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 12 2 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1029.544 #> #> $phidiv #> [1] \"KL\" #> #> $power.phidiv #> NULL #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of empirical and theoretical distributions for non-censored data — plotdist","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Plots empirical distribution (non-censored data) theoretical one specified.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"","code":"plotdist(data, distr, para, histo = TRUE, breaks = \"default\", demp = FALSE, discrete, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. argument may omitted para omitted. para named list giving parameters named distribution. argument may omitted distr omitted. histo logical plot histogram using hist function. breaks \"default\" histogram plotted function hist default breaks definition. Else breaks passed function hist. argument taken account discrete TRUE. demp logical plot empirical density first plot (alone superimposed histogram depending value argument histo) using density function. discrete TRUE, distribution considered discrete. \tdistr discrete missing, discrete set \tFALSE. discrete missing distr, \tdiscrete set TRUE distr belongs \t\"binom\", \"nbinom\",\"geom\", \"hyper\" \"pois\". ... graphical arguments passed graphical functions used plotdist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Empirical , specified, theoretical distributions plotted density cdf. plot density, user can use arguments histo demp specify wants histogram using function hist, density plot using function density, (least one two arguments must put \"TRUE\"). continuous distributions, function hist used default breaks definition breaks \"default\" passing breaks argument differs \"default\". continuous distribution theoretical distribution specified arguments distname para, Q-Q plot (plot quantiles theoretical fitted distribution (x-axis) empirical quantiles data) P-P plot (.e. value data set, plot cumulative density function fitted distribution (x-axis) empirical cumulative density function (y-axis)) also given (Cullen Frey, 1999). function ppoints (default parameter argument ) used Q-Q plot, generate set probabilities evaluate inverse distribution. NOTE VERSION 0.4-3, ppoints also used P-P plot cdf plot continuous data. personalize four plots proposed continuous data, example change plotting position, recommend use functions cdfcomp, denscomp, qqcomp ppcomp.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"","code":"# (1) Plot of an empirical distribution with changing # of default line types for CDF and colors # and optionally adding a density line # set.seed(1234) x1 <- rnorm(n=30) plotdist(x1) plotdist(x1,demp = TRUE) plotdist(x1,histo = FALSE, demp = TRUE) #> Warning: arguments ‘freq’, ‘main’, ‘xlab’ are not made use of plotdist(x1, col=\"blue\", type=\"b\", pch=16) #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete plotdist(x1, type=\"s\") #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete # (2) Plot of a discrete distribution against data # set.seed(1234) x2 <- rpois(n=30, lambda = 2) plotdist(x2, discrete=TRUE) plotdist(x2, \"pois\", para=list(lambda = mean(x2))) plotdist(x2, \"pois\", para=list(lambda = mean(x2)), lwd=\"2\") # (3) Plot of a continuous distribution against data # xn <- rnorm(n=100, mean=10, sd=5) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn))) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), pch=16) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), demp = TRUE) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), histo = FALSE, demp = TRUE) # (4) Plot of serving size data # data(groundbeef) plotdist(groundbeef$serving, type=\"s\") #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete # (5) Plot of numbers of parasites with a Poisson distribution data(toxocara) number <- toxocara$number plotdist(number, discrete = TRUE) plotdist(number,\"pois\",para=list(lambda=mean(number)))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of empirical and theoretical distributions for censored data — plotdistcens","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Plots empirical distribution censored data theoretical one specified.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"","code":"plotdistcens(censdata, distr, para, leftNA = -Inf, rightNA = Inf, NPMLE = TRUE, Turnbull.confint = FALSE, NPMLE.method = \"Wang\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"censdata dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution, corresponding density function dname corresponding distribution function pname must defined, directly density function. para named list giving parameters named distribution. argument may omitted distr omitted. leftNA real value left bound left censored observations : -Inf finite value 0 positive data example. rightNA real value right bound right censored observations : Inf finite value realistic maximum value. NPMLE TRUE NPMLE (nonparametric maximum likelihood estimate) technique used estimate cdf curve censored data previous arguments leftNA rightNA used (see details) Turnbull.confint TRUE confidence intervals added Turnbull plot. case NPMLE.method forced \"Turnbull.middlepoints\" NPMLE.method Three NPMLE techniques provided, \"Wang\", default one, rewritten package npsurv using function constrOptim package stats optimisation, \"Turnbull.middlepoints\", older one implemented package survival \"Turnbull.intervals\" uses Turnbull algorithm package survival associates interval equivalence class instead middlepoint interval (see details). \"Wang\" \"Turnbull.intervals\" enable derivation Q-Q plot P-P plot. ... graphical arguments passed methods. title plot can modified using argument main CDF plot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"NPMLE TRUE, NPMLE.method \"Wang\" , empirical distributions plotted cdf using either constrained Newton method (Wang, 2008) hierarchical constrained Newton method (Wang, 2013) compute overall empirical cdf curve. NPMLE TRUE, NPMLE.method \"Turnbull.intervals\" , empirical plotted cdf using EM approach Turnbull (Turnbull, 1974). two cases, grey rectangles represent areas empirical distribution function unique. cases theoretical distribution specified, two goodness--fit plots also provided, Q-Q plot (plot quantiles theoretical fitted distribution (x-axis) empirical quantiles data) P-P plot (.e. value data set, plot cumulative density function fitted distribution (x-axis) empirical cumulative density function (y-axis)). Grey rectangles Q-Q plot P-P plot also represent areas non uniqueness empirical quantiles probabilities, directly derived non uniqueness areas empirical cumulative distribution. NPMLE TRUE, NPMLE.method \"Turnbull.middlepoints\", empirical , specified, theoretical distributions plotted cdf using EM approach Turnbull (Turnbull, 1974) compute overall empirical cdf curve, confidence intervals Turnbull.confint TRUE, calls functions survfit plot.survfit survival package. NPMLE FALSE empirical , specified, theoretical distributions plotted cdf, data directly reported segments interval, left right censored data, points non-censored data. plotting, observations ordered rank r associated . Left censored observations ordered first, right bounds. Interval censored non censored observations ordered mid-points , last, right censored observations ordered left bounds. leftNA (resp. rightNA) finite, left censored (resp. right censored) observations considered interval censored observations ordered mid-points non-censored interval censored data. sometimes necessary fix rightNA leftNA realistic extreme value, even exactly known, obtain reasonable global ranking observations. ranking, n observations plotted point (one x-value) segment (interval possible x-values), y-value equal r/n, r rank observation global ordering previously described. second method may interesting certainly less rigorous methods prefered.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Turnbull BW (1974), Nonparametric estimation survivorship function doubly censored data. Journal American Statistical Association, 69, 169-173, doi:10.2307/2285518 . Wang Y (2008), Dimension-reduced nonparametric maximum likelihood computation interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402, doi:10.1016/j.csda.2007.10.018 . Wang Y Taylor SM (2013), Efficient computation nonparametric survival functions via hierarchical mixture formulation. Statistics Computing, 23, 713-725, doi:10.1007/s11222-012-9341-9 . Wang, Y., & Fani, S. (2018), Nonparametric maximum likelihood computation U-shaped hazard function. Statistics Computing, 28(1), 187-200, doi:10.1007/s11222-017-9724-z . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"","code":"# (1) Plot of an empirical censored distribution (censored data) as a CDF # using the default Wang method # data(smokedfish) d1 <- as.data.frame(log10(smokedfish)) plotdistcens(d1) # (2) Add the CDF of a normal distribution # plotdistcens(d1, \"norm\", para=list(mean = -1.6, sd = 1.5)) # (3) Various plots of the same empirical distribution # # default Wang plot with representation of equivalence classess plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Wang\") # same plot but using the Turnbull alorithm from the package survival plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Wang\") # Turnbull plot with middlepoints (as in the package survival) plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Turnbull.middlepoints\") # Turnbull plot with middlepoints and confidence intervals plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Turnbull.middlepoints\", Turnbull.confint = TRUE) # with intervals and points plotdistcens(d1,rightNA=3, NPMLE = FALSE) # with intervals and points # defining a minimum value for left censored values plotdistcens(d1,leftNA=-3, NPMLE = FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-fitting procedure — prefit","title":"Pre-fitting procedure — prefit","text":"Search good starting values","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-fitting procedure — prefit","text":"","code":"prefit(data, distr, method = c(\"mle\", \"mme\", \"qme\", \"mge\"), feasible.par, memp=NULL, order=NULL, probs=NULL, qtype=7, gof=NULL, fix.arg=NULL, lower, upper, weights=NULL, silent=TRUE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-fitting procedure — prefit","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. method character string coding fitting method: \"mle\" 'maximum likelihood estimation', \"mme\" 'moment matching estimation', \"qme\" 'quantile matching estimation' \"mge\" 'maximum goodness--fit estimation'. feasible.par named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). may account closed-form formulas. order numeric vector moment order(s). length vector must equal number parameters estimate. memp function implementing empirical moments, raw centered consistent distr argument (weights argument). probs numeric vector probabilities quantile matching done. length vector must equal number parameters estimate. qtype quantile type used R quantile function compute empirical quantiles, (default 7 corresponds default quantile method R). gof character string coding name goodness--fit distance used : \"CvM\" Cramer-von Mises distance,\"KS\" Kolmogorov-Smirnov distance, \"AD\" Anderson-Darling distance, \"ADR\", \"ADL\", \"AD2R\", \"AD2L\" \"AD2\" variants Anderson-Darling distance described Luceno (2006). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. use argument possible method=\"mme\" closed-form formula used. weights optional vector weights used fitting process. NULL numeric vector. non-NULL, weighted MLE used, otherwise ordinary MLE. silent logical remove show warnings. lower Lower bounds parameters. upper Upper bounds parameters. ... arguments passed generic functions, one functions \"mledist\", \"mmedist\", \"qmedist\" \"mgedist\" depending chosen method. See mledist, mmedist, qmedist, mgedist details parameter estimation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-fitting procedure — prefit","text":"Searching good starting values achieved transforming parameters (constraint interval real line) probability distribution. Indeed, positive parameters \\((0,Inf)\\) transformed using logarithm (typically scale parameter sd normal distribution, see Normal), parameters \\((1,Inf)\\) transformed using function \\(log(x-1)\\), probability parameters \\((0,1)\\) transformed using logit function \\(log(x/(1-x))\\) (typically parameter prob geometric distribution, see Geometric), negative probability parameters \\((-1,0)\\) transformed using function \\(log(-x/(1+x))\\), real parameters course transformed , typically mean normal distribution, see Normal. parameters transformed, optimization carried quasi-Newton algorithm (typically BFGS) transform back original parameter value.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-fitting procedure — prefit","text":"named list.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pre-fitting procedure — prefit","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pre-fitting procedure — prefit","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-fitting procedure — prefit","text":"","code":"# (1) fit of a gamma distribution by maximum likelihood estimation # x <- rgamma(1e3, 5/2, 7/2) prefit(x, \"gamma\", \"mle\", list(shape=3, scale=3), lower=-Inf, upper=Inf) #> $shape #> [1] 2.57829 #> #> $scale #> [1] 3.559245 #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile matching fit of univariate distributions — qmedist","title":"Quantile matching fit of univariate distributions — qmedist","text":"Fit univariate distribution matching quantiles non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile matching fit of univariate distributions — qmedist","text":"","code":"qmedist(data, distr, probs, start = NULL, fix.arg = NULL, qtype = 7, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile matching fit of univariate distributions — qmedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. probs numeric vector probabilities quantile matching done. length vector must equal number parameters estimate. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. qtype quantile type used R quantile function compute empirical quantiles, (default 7 corresponds default quantile method R). optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted QME used, otherwise ordinary QME. silent logical remove show warnings bootstraping. gradient function return gradient squared difference \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile matching fit of univariate distributions — qmedist","text":"qmedist function carries quantile matching numerically, minimization sum squared differences observed theoretical quantiles. Note discrete distribution, sum squared differences step function consequently, optimum unique, see FAQ. optimization process mledist, see 'details' section function. Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary QME carried , otherwise specified weights used compute weighted quantiles used squared differences. Weigthed quantiles computed wtdquantile Hmisc package. yet possible take account weighths functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). function intended called directly internally called fitdist bootdist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile matching fit of univariate distributions — qmedist","text":"qmedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. probs probability vector quantiles matched.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile matching fit of univariate distributions — qmedist","text":"Klugman SA, Panjer HH Willmot GE (2012), Loss Models: Data Decissions, 4th edition. Wiley Series Statistics Finance, Business Economics, p. 253, doi:10.1198/tech.2006.s409 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile matching fit of univariate distributions — qmedist","text":"Christophe Dutang Marie Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile matching fit of univariate distributions — qmedist","text":"","code":"# (1) basic fit of a normal distribution # set.seed(1234) x1 <- rnorm(n=100) qmedist(x1, \"norm\", probs=c(1/3, 2/3)) #> $estimate #> mean sd #> -0.3025734 0.8521385 #> #> $convergence #> [1] 0 #> #> $value #> [1] 2.427759e-10 #> #> $hessian #> mean sd #> mean 2.000000e+00 -2.784663e-14 #> sd -2.784663e-14 3.710520e-01 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 57 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -146.1278 #> #> $probs #> [1] 0.3333333 0.6666667 #> # (2) defining your own distribution functions, here for the Gumbel # distribution for other distributions, see the CRAN task view dedicated # to probability distributions dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) qgumbel <- function(p, a, b) a - b*log(-log(p)) qmedist(x1, \"gumbel\", probs=c(1/3, 2/3), start=list(a=10,b=5)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (3) fit a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) qmedist(x2, \"pois\", probs=1/2) #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.25 #> #> $hessian #> lambda #> lambda 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 1 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $probs #> [1] 0.5 #> # (4) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) qmedist(x3, \"beta\", probs=c(1/3, 2/3)) #> $estimate #> shape1 shape2 #> 5.820826 14.053655 #> #> $convergence #> [1] 0 #> #> $value #> [1] 3.889731e-12 #> #> $hessian #> shape1 shape2 #> shape1 0.002714767 -0.0010963293 #> shape2 -0.001096329 0.0004477195 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 89 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 76.02016 #> #> $probs #> [1] 0.3333333 0.6666667 #> # (5) fit frequency distributions on USArrests dataset. # x4 <- USArrests$Assault qmedist(x4, \"pois\", probs=1/2) #> $estimate #> lambda #> 170.76 #> #> $convergence #> [1] 0 #> #> $value #> [1] 144 #> #> $hessian #> lambda #> lambda 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 1 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1211.705 #> #> $probs #> [1] 0.5 #> qmedist(x4, \"nbinom\", probs=c(1/3, 2/3)) #> $estimate #> size mu #> 2.518966 182.313344 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.1111111 #> #> $hessian #> size mu #> size 0 0 #> mu 0 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 37 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -292.5969 #> #> $probs #> [1] 0.3333333 0.6666667 #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile estimation from a fitted distribution — quantile","title":"Quantile estimation from a fitted distribution — quantile","text":"Quantile estimation fitted distribution, optionally confidence intervals calculated bootstrap result.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile estimation from a fitted distribution — quantile","text":"","code":"# S3 method for class 'fitdist' quantile(x, probs = seq(0.1, 0.9, by=0.1), ...) # S3 method for class 'fitdistcens' quantile(x, probs = seq(0.1, 0.9, by=0.1), ...) # S3 method for class 'bootdist' quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = \"two.sided\", CI.level = 0.95, ...) # S3 method for class 'bootdistcens' quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = \"two.sided\", CI.level = 0.95, ...) # S3 method for class 'quantile.fitdist' print(x, ...) # S3 method for class 'quantile.fitdistcens' print(x, ...) # S3 method for class 'quantile.bootdist' print(x, ...) # S3 method for class 'quantile.bootdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile estimation from a fitted distribution — quantile","text":"x object class \"fitdist\", \"fitdistcens\", \"bootdist\", \"bootdistcens\" \"quantile.fitdist\", \"quantile.fitdistcens\", \"quantile.bootdist\", \"quantile.bootdistcens\" print generic function. probs numeric vector probabilities values [0, 1] quantiles must calculated. CI.type Type confidence intervals : either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. ... arguments passed generic functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile estimation from a fitted distribution — quantile","text":"Quantiles parametric distribution calculated probability specified probs, using estimated parameters. used object class \"bootdist\" \"bootdistcens\", percentile confidence intervals medians etimates also calculated bootstrap result. CI.type two.sided, CI.level two-sided confidence intervals quantiles calculated. CI.type less greater, CI.level one-sided confidence intervals quantiles calculated. print functions show estimated quantiles percentile confidence intervals median estimates bootstrap resampling done previously, number bootstrap iterations estimation converges inferior whole number bootstrap iterations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile estimation from a fitted distribution — quantile","text":"quantile returns list 2 components (first two described ) called object class \"fitdist\" \"fitdistcens\" 8 components (described ) called object class \"bootdist\" \"bootdistcens\" : quantiles dataframe containing estimated quantiles probability value specified argument probs (one row, many columns values probs). probs numeric vector probabilities quantiles calculated. bootquant data frame containing bootstraped values quantile (many rows, specified call bootdist argument niter, many columns values probs) quantCI CI.type two.sided, two bounds CI.level percent two.sided confidence interval quantile (two rows many columns values probs). CI.type less, right bound CI.level percent one.sided confidence interval quantile (one row). CI.type greater, left bound CI.level percent one.sided confidence interval quantile (one row). quantmedian Median bootstrap estimates (per probability). CI.type Type confidence interval: either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. nbboot number samples drawn bootstrap. nbconverg number iterations optimization algorithm converges.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile estimation from a fitted distribution — quantile","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile estimation from a fitted distribution — quantile","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile estimation from a fitted distribution — quantile","text":"","code":"# (1) Fit of a normal distribution on acute toxicity log-transformed values of # endosulfan for nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, followed with calculations of their # confidence intervals with various definitions, from a small number of bootstrap # iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 bln <- bootdist(fln, bootmethod=\"param\", niter=101) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.206058 1.615810 2.040136 #> 97.5 % 2.372660 2.617113 2.937556 quantile(bln, probs = c(0.05, 0.1, 0.2), CI.type = \"greater\") #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> left bound of one-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.299871 1.64396 2.126053 quantile(bln, probs = c(0.05, 0.1, 0.2), CI.level = 0.9) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> two-sided 90 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.299871 1.643960 2.126053 #> 95 % 2.297746 2.565286 2.894080 # (2) Draw of 95 percent confidence intervals on quantiles of the # previously fitted distribution # cdfcomp(fln) q1 <- quantile(bln, probs = seq(0,1,length=101)) points(q1$quantCI[1,],q1$probs,type=\"l\") points(q1$quantCI[2,],q1$probs,type=\"l\") # (2b) Draw of 95 percent confidence intervals on quantiles of the # previously fitted distribution # using the NEW function CIcdfplot # CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"pink\") # (3) Fit of a distribution on acute salinity log-transformed tolerance # for riverine macro-invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, followed with calculations of # their confidence intervals with various definitions. # from a small number of bootstrap iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(salinity) log10LC50 <-log10(salinity) flncens <- fitdistcens(log10LC50,\"norm\") quantile(flncens, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 blncens <- bootdistcens(flncens, niter = 101) quantile(blncens, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.057448 1.138889 1.239646 #> 97.5 % 1.203538 1.270419 1.355852 quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.type = \"greater\") #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> left bound of one-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.062249 1.145186 1.245616 quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.level = 0.9) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> two-sided 90 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.062249 1.145186 1.245616 #> 95 % 1.195896 1.266786 1.346183"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":null,"dir":"Reference","previous_headings":"","what":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"72-hour acute salinity tolerance (LC50 values) riverine macro-invertebrates.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"","code":"data(salinity)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"salinity data frame 2 columns named left right, describing observed LC50 value (electrical condutivity, millisiemens per centimeter) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value noncensored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"Kefford, B.J., Nugegoda, D., Metzeling, L., Fields, E. 2006. Validating species sensitivity distributions using salinity tolerance riverine macroinvertebrates southern Murray-darling Basin (Vitoria, Australia). Canadian Journal Fisheries Aquatic Science, 63, 1865-1877.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"","code":"# (1) load of data # data(salinity) # (2) plot of data using Turnbull cdf plot # log10LC50 <- log10(salinity) plotdistcens(log10LC50) # (3) fit of a normal and a logistic distribution to data in log10 # (classical distributions used for species sensitivity # distributions, SSD, in ecotoxicology)) # and visual comparison of the fits using Turnbull cdf plot # fln <- fitdistcens(log10LC50, \"norm\") summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 1.4702582 0.2927558 #> sd 0.2154709 0.2461943 #> Loglikelihood: -61.79623 AIC: 127.5925 BIC: 132.9567 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.2937484 #> sd 0.2937484 1.0000000 #> fll <- fitdistcens(log10LC50, \"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 1.4761562 0.2933442 #> scale 0.1269994 0.1604527 #> Loglikelihood: -62.81293 AIC: 129.6259 BIC: 134.9901 #> Correlation matrix: #> location scale #> location 1.0000000 0.2024688 #> scale 0.2024688 1.0000000 #> cdfcompcens(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10(LC50)\", xlim = c(0.5, 2), lines01 = TRUE) # (4) estimation of the 5 percent quantile value of # the normal fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # non parametric bootstrap # from a small number of bootstrap iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(LC50) bln <- bootdistcens(fln, niter = 101) HC5ln <- quantile(bln, probs = 0.05) # in LC50 10^(HC5ln$quantiles) #> p=0.05 #> estimate 13.0569 10^(HC5ln$quantCI) #> p=0.05 #> 2.5 % 11.08712 #> 97.5 % 15.50325 # (5) estimation of the HC5 value # with its one-sided 95 percent confidence interval (type \"greater\") # # in log10(LC50) HC5lnb <- quantile(bln, probs = 0.05, CI.type = \"greater\") # in LC50 10^(HC5lnb$quantiles) #> p=0.05 #> estimate 13.0569 10^(HC5lnb$quantCI) #> p=0.05 #> 5 % 11.31157"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":null,"dir":"Reference","previous_headings":"","what":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"Contamination data Listeria monocytogenes smoked fish Belgian market period 2005 2007.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"","code":"data(smokedfish)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"smokedfish data frame 2 columns named left right, describing observed value Listeria monocytogenes concentration (CFU/g) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"Busschaert, P., Geereard, .H., Uyttendaele, M., Van Impe, J.F., 2010. Estimating distributions qualitative (semi) quantitative microbiological contamination data use risk assessment. International Journal Food Microbiology. 138, 260-269.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"","code":"# (1) load of data # data(smokedfish) # (2) plot of data in CFU/g # plotdistcens(smokedfish) # (3) plot of transformed data in log10[CFU/g] # Clog10 <- log10(smokedfish) plotdistcens(Clog10) # (4) Fit of a normal distribution to data in log10[CFU/g] # fitlog10 <- fitdistcens(Clog10, \"norm\") summary(fitlog10) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean -1.575392 2.043857 #> sd 1.539446 2.149561 #> Loglikelihood: -87.10945 AIC: 178.2189 BIC: 183.4884 #> Correlation matrix: #> mean sd #> mean 1.0000000 -0.4325228 #> sd -0.4325228 1.0000000 #> plot(fitlog10)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":null,"dir":"Reference","previous_headings":"","what":"Parasite abundance in insular feral cats — toxocara","title":"Parasite abundance in insular feral cats — toxocara","text":"Toxocara cati abundance feral cats living Kerguelen island.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parasite abundance in insular feral cats — toxocara","text":"","code":"data(toxocara)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parasite abundance in insular feral cats — toxocara","text":"toxocara data frame 1 column (number: number parasites digestive tract)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parasite abundance in insular feral cats — toxocara","text":"Fromont, E., Morvilliers, L., Artois, M., Pontier, D. 2001. Parasite richness abundance insular mainland feral cats. Parasitology, 123, 143-151.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parasite abundance in insular feral cats — toxocara","text":"","code":"# (1) load of data # data(toxocara) # (2) description and plot of data # number <- toxocara$number descdist(number, discrete = TRUE, boot = 11) #> summary statistics #> ------ #> min: 0 max: 75 #> median: 2 #> mean: 8.679245 #> estimated sd: 14.29332 #> estimated skewness: 2.630609 #> estimated kurtosis: 11.4078 plotdist(number, discrete = TRUE) # (3) fit of a Poisson distribution to data # fitp <- fitdist(number, \"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) # (4) fit of a negative binomial distribution to data # fitnb <- fitdist(number, \"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb)"},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-12-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.2-2","title":"fitdistrplus 1.2-2","text":"NEW FEATURES website bringing together resources related fitdistrplus package now exists github.io following URL: https://lbbe-software.github.io/fitdistrplus/ BUG FIXES mgedist() may suffer numerical issue Anderson-Darling GoF metrics. GoF metrics now take care numerical issue, log(0) 1/0, properly scaled sample sized avoid large sample size issues. Thanks Ethan Chapman reporting bug. default starting value gamma distribution wrongly computed rate parameter. Thanks Wendy Martin reporting bug. mledist(), mmedist(), qmedist() may suffer scaling issue objective function properly scaled sample sized avoid large sample size issues. mledist() now takes care numerical issue, log(0).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-12-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.2-1","title":"fitdistrplus 1.2-1","text":"CRAN release: 2024-07-12 NEW FEATURES fitdistrplus git repo now belongs lbbe-software organization modify add initial value univariate distributions provided actuar. create new vignette regarding default initial values. add generic functions AIC() BIC() fitdist fitdistcens objects. make gofstat() work fitdistcens objects (giving AIC BIC values). add calculation hessian using optimHess within fitdist given optim. compute asymptotic covariance matrix MME : Now theoretical moments m defined order equals twice maximal order given order. add new argument calcvcov order (dis)able computation covariance matrix method. graphics function *comp() now return list drawn points /lines plotstyle == \"graphics\". add density function bootdist(cens) objects. add DOIs man pages. BUG FIXES scale parameter fixed, startarg function also set rate parameter. leads error calling density. add sanity check plotdistcens: following code plotdistcens(data.frame(right=smokedfish$right, left=smokedfish$left)) raised error via npsurv(), thanks R. Pouillot. bug fixed using breaks plotdist. solve extremely long time taking lines descdist. add defensive programming input data (check NA, NaN, Inf values). correct links man pages URL DOI. remove use plot.np vignettes.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-11","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-11","title":"fitdistrplus 1.1-11","text":"CRAN release: 2023-04-25 NEW FEATURES add print argument descdist function allow plot skewness-kurtosis graph, without printing computed parameters BUG FIX use deprecated ggplot2 functions updated use deprecated BibTeX entries updated bug fixed drawing CI lines CIcdfcplot ggplot2 called bug fixed drawing horizontal lines cdfcompcens","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-8","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-8","title":"fitdistrplus 1.1-8","text":"CRAN release: 2022-03-10 WARNING FIX update URL fitdistrplus.Rd replace (class(x) == XX) (inherits(x, XX)) replace dontrun tags donttest examples rd files BUG FIX fix error t-detectbound.R producing “failure: length > 1 coercion logical” reported Brian Ripley","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-6","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-6","title":"fitdistrplus 1.1-6","text":"CRAN release: 2021-09-28 NEW FEATURES new function Surv2fitdistcens() format data use fitdistcens() format used survival package new dataset fremale order illustrate Surv2fitdistcens() support use ggplot2 CIcdfplot add taxon names endosulfan dataset new argument name.points cdfcomp CIcdfplot add labels next points","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-5","title":"fitdistrplus 1.1-5","text":"CRAN release: 2021-05-28 WARNING FIX reduce testing times test files","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-3","title":"fitdistrplus 1.1-3","text":"CRAN release: 2020-12-05 NEW FEATURE take account fix.arg uniform distribution BUG FIXES add loglikelihood value uniform distribution (mledist()) correct usage triple dots argument llsurface() fix error ppcomp() qqcomp() raised large dataset","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-1","title":"fitdistrplus 1.1-1","text":"CRAN release: 2020-05-19 NEW FEATURES add internal functions cope problems lack maintenance package npsurv remove dependence package remove deprecated argument Turnbull plotdistcens()","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-14","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-14","title":"fitdistrplus 1.0-14","text":"CRAN release: 2019-01-23 NEW FEATURES add new estimation method called maximum spacing estimation via msedist()","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-13","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-13","title":"fitdistrplus 1.0-13","text":"BUG FIXES fix issues coming noLD (–disable-long-double) configuration R","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-12","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-12","title":"fitdistrplus 1.0-12","text":"BUG FIXES bug fixed qmedist() fitdistcens() raised error checkparamlist(). bug fixed testdpqfun() assumes first argument d,p,q,r functions exactly base R.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-11","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-11","title":"fitdistrplus 1.0-11","text":"CRAN release: 2018-09-10 NEW FEATURES update FAQ beta(,). improve graphics discrete distributions denscomp(). improve automatic naming legends xxxcomp(). harmonize outputs mledist(), qmedist(), mmedist(), mgedist(), fitdist() fitdistcens(). automatic test d, p, q functions fitdist() raise warnings. improve test starting fixed values. add new default starting values distributions actuar. change default CDF plot censored data, using Wang NPMLE algorithm provided package npsurv (plotdistcens() cdfcompcens()) add two new goodness--fit plots (QQ-plot PP-plot) censored data (cf. plotdistcens, qqcompcens ppcompcens). add part dedicated censored datain FAQ vignette. homogeneization xlim ylim default definition plotdistcens. Removing name first argument calls dpq functions order make package compatible distributions defined non classical name first argument (resp. x, q, p d, p, q functions). add possibility change title CDF plot plotdistcens() using argument main. support use ggplot2 cdfcompcens, qqcompcens, ppcompcens. BUG FIXES bug fixed concerning use gofstat chi squared df <=0 (error message blocking functions) bug fix mledist() bounds set (NULL) censored MLE enable correct use non-equidistant breaks denscomp histogram plotstyle = “ggplot”, prohibit use non-equidistant breaks probability = FALSE (adding stop case).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-9","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-9","title":"fitdistrplus 1.0-9","text":"CRAN release: 2017-03-24 update FAQ linear inequality constraints.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-8","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-8","title":"fitdistrplus 1.0-8","text":"CRAN release: 2017-02-01 NEW FEATURES support use ggplot2 cdfcomp, denscomp, qqcomp, ppcomp. BUG FIXES correct legend qqcomp ppomp large data. correct weights mmedist. correct name Akaike gofstat. correct use trueval plot.bootdist. correct vignette truncate (inflated) distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-7","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-7","title":"fitdistrplus 1.0-7","text":"CRAN release: 2016-07-02 NEW FEATURES keep JSS vignette pdf. start FAQ vignette add datasets (?dataFAQ) . provide likelihood plot/surface/curve: llplot, llcurve, llsurface. provide parallelization bootstrap bootdist bootdistcens. provide graphic (e)cdf bootstraped confidence interval/area: CIcdfplot. allow use constrOptim() mledist, mmedist, mgedist, qmedist functions. add possible pre-fitting procedure: prefit. BUG FIXES add invisible() graphical functions. bug fixed concerning use weights censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-6","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-6","title":"fitdistrplus 1.0-6","text":"CRAN release: 2015-11-30 BUG FIXES automatic definition starting values distributions llogis invweibull now working.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-5","title":"fitdistrplus 1.0-5","text":"CRAN release: 2015-09-21 NEW FEATURES update starting/fixing values mledist, mmedist, mgedist, qmedist functions. update graphics bootstrap procedure. add argument .points cdfcomp. add argument weights mledist, qmedist, mmedist, fitdist, fitdistcens. add argument keepdata fitdist, fitdistcens. suppress warnings/errors fitdist(cens), bootdist(cens). BUG FIXES defensive programming plotdist, cdfcomp,… simplify plotting curves cdfcomp seq(xmin, xmax, =1) used.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-4","title":"fitdistrplus 1.0-4","text":"CRAN release: 2015-02-23 release JSS publication.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-3","title":"fitdistrplus 1.0-3","text":"CRAN release: 2014-12-13 NEW FEATURES new generic functions fitdist(cens): loglik, vcov coef. vignette updated version paper accepted Journal Statistical Software. add argument discrete fitdist order able take account non classical discrete distributions plotting fit plot.fitdist cdfcomp calculating goodness--fit statistics gofstat (add example : fit zero inflate Poisson distribution). add S3 class descdist print method. BUG FIXES fitdist can handle non invertible Hessian matrices.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-2","title":"fitdistrplus 1.0-2","text":"CRAN release: 2014-02-12 NEW FEATURES plotdist can plot empirical density histogram, density plot superimposed. strong warning added documentation function descdist problematic high variance skewness kurtosis. BUG FIXES bug fixed bootdistcens : argument fix.arg now correctly passed mle.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-1","title":"fitdistrplus 1.0-1","text":"CRAN release: 2013-04-10 NEW FEATURES gofstat can handle multiple fitdist objects. plotdist discrete data slightly enhanced.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-0","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-0","title":"fitdistrplus 1.0-0","text":"CRAN release: 2012-12-27 NEW FEATURES update cdfcomp add denscomp, ppcomp qqcomp functions. add argument Turnbull.confint functions plotdistcens cdfcompcens order draw confidence intervals empirical distribution requested. ppoints now used “fitdist” QQ plot, PP plot cdf plot continuous data (used QQ plot previous versions) enable Blom type plotting position (using default Hazen plotting position can chanfge using arguments use.ppoints .ppoints) many changes examples given reference manual. vignette removed, transformed paper soon submit journal. add four data sets : fluazinam, salinity, danishuni danishmulti. add functions calculate quantiles fitted distribution, 95 percent CI calculated bootstrap : quantile generic function available fitdist bootdist objects quantile generic function available fitdistcens bootdistcens objects. BUG FIXES correction formula CvM test Weibull distribution. elimination CvM AD tests normal, lognormal logistic distributions : formulas previously used (given Stephens 1986) use exactly MLE estimates thus results approximates. make arguments xlim ylim functional cdfcompcens. bug fix closed formula mmedist lognormal distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-4","title":"fitdistrplus 0.3-4","text":"CRAN release: 2012-03-22 NEW FEATURES posibility fix xlegend keyword (e.g. bottomright) cdfcomp cdfcompdens. improvement new vignette. BUG FIXES correction NAMESPACE file order enable correct print summary fitdistcens object (correlation matrix, loglikelihood AIC BIC statistics).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-3","title":"fitdistrplus 0.3-3","text":"NEW FEATURES new function (cdfcompcens) plot cumulative distributions corresponding various fits using censored data set. add example scaling problem man pages.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-2","title":"fitdistrplus 0.3-2","text":"NEW FEATURES new plot empirical cdf curve plotdistcens, using Turnbull algorithm call function survfit{survival}. new arguments function cdfcomp : verticals, horizontals xlim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-1","title":"fitdistrplus 0.3-1","text":"NEW FEATURES add draft new version vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-0","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-0","title":"fitdistrplus 0.3-0","text":"NEW FEATURES new function (cdfcomp) plot cumulative distributions corresponding various fits using non censored data set. add two data sets : endosulfan toxocara.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-02-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.2-2","title":"fitdistrplus 0.2-2","text":"CRAN release: 2011-04-27 BUG FIXES elimination NON-ASCII characters vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-02-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.2-1","title":"fitdistrplus 0.2-1","text":"CRAN release: 2011-03-18 NEW FEATURES new fitting method implemented continuous distributions : maximum goodness--fit estimation (function mgedist) (moment available non censored data).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-5","title":"fitdistrplus 0.1-5","text":"NEW FEATURES new goodness--fit statistic added gofstat, corresponding test : Cramer-von Mises distance. new fitting method implemented : quantile matching estimation (function qmedist). moment, available non censored data. moment matching estimation extended (function mmedist) enable numerical matching closed formula available. BUG FIXES correction bug inserted adding argument fix.arg prevent print results goodness--fit tests.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-4","title":"fitdistrplus 0.1-4","text":"CRAN release: 2010-09-16 NEW FEATURES component named dots added list returned fitdist fitdistcens order pass optional arguments control optimization mledist bootdist bootdistcens. bootdist bootdistcens changed take account optional arguments defined call fitdist fitdistcens. argument added fitdist, fitdistcens mledist, named fix.arg, giving possibility fix distribution parameters maximizing likelihood. Functions bootdist, bootdistcens gofstat also changed order take new argument account. new data file bacterial contamination censored data extracted Busschaert et al. 2000 examples corresponding analysis dataset. BUG FIXES correction bug print plot bootstraped samples using bootdist bootdistcens one parameter estimated maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-3","title":"fitdistrplus 0.1-3","text":"CRAN release: 2010-06-02 NEW FEATURES new data file groundbeef (groundbeef.rda groundbeef.Rd) new use dataset examples. new function gofstat. Goodness--fit statistics computed fitdist may computed printed use function gofstat. new function, whole results computed printed : results tests printed argument print.test==TRUE continuous distributions Anderson-Darling Kolomogorov-Smirnov statistics printed default (complete results returned gofstat). modifications descdist : three arguments added descdist 1/ method, choose unbiased estimations standard deviation, skewness kurtosis (default choice) sample values. 2/ obs.col choose color used plot observed point graph. 3/ boot.col choose color used plot bootstrap sample points graph. modifications plotfit : minor changes performed order facilitate use argument … personnalize plots (see examples plotdist.Rd) modication vignette BUG FIXES correction bug plotdist due redefinition previous version parameter “ylim” plot histogram theoretical density function (problem infinite values theoretical density function).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-2","title":"fitdistrplus 0.1-2","text":"CRAN release: 2009-12-29 NEW FEATURES deletion mledistcens modification mledist order maximize likelihood censored non censored data. possibility choose optimization method used maximum likelihood estimation (MLE) distribution parameters using new argument “optim.method” mledist. possibility specify contraints distribution parameters using new arguments “lower” “upper” mledist. possibility use custom optimization function MLE using new argument “custom.optim”. moment matching estimation longer done argument method set “mom” set “mme” fitdist. renaming momdist mmedist. calculation AIC BIC criterion maximum likelihood estimation distribution parameters change default number iterations 999 1001 bootstrap order avoid interpolation using quantile function use argument “log” (resp. “log.p”) density (resp. distribution) available compute loglikelihood. BUG FIXES omitting name first argument calls density function maximization likelihood order enable use density function defined first parameter (vector quantiles) name differing “x” (classical name density distributions defined R), density function dexGAUS package gamlss.dist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-1","title":"fitdistrplus 0.1-1","text":"CRAN release: 2009-02-16 Initial release.","code":""}] +[{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-know-the-root-name-of-a-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.1. How do I know the root name of a distribution?","title":"Frequently Asked Questions","text":"root name probability distribution name used d, p, q, r functions. base R distributions, root names given R-intro : https://cran.r-project.org/doc/manuals/R-intro.html#Probability-distributions. example, must use \"pois\" Poisson distribution \"poisson\".","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-find-non-standard-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.2. How do I find “non standard” distributions?","title":"Frequently Asked Questions","text":"non-standard distributions, can either find package implementing define . comprehensive list non-standard distributions given Distributions task view https://CRAN.R-project.org/view=Distributions. two examples user-defined distributions. third example (shifted exponential) given FAQ 3.5.4. Gumbel distribution zero-modified geometric distribution","code":"dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q, a, b) exp(-exp((a-q)/b)) qgumbel <- function(p, a, b) a-b*log(-log(p)) data(groundbeef) fitgumbel <- fitdist(groundbeef$serving, \"gumbel\", start=list(a=10, b=10)) dzmgeom <- function(x, p1, p2) p1 * (x == 0) + (1-p1)*dgeom(x-1, p2) pzmgeom <- function(q, p1, p2) p1 * (q >= 0) + (1-p1)*pgeom(q-1, p2) rzmgeom <- function(n, p1, p2) { u <- rbinom(n, 1, 1-p1) #prob to get zero is p1 u[u != 0] <- rgeom(sum(u != 0), p2)+1 u } x2 <- rzmgeom(1000, 1/2, 1/10) fitdist(x2, \"zmgeom\", start=list(p1=1/2, p2=1/2))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-set-or-find-initial-values-for-non-standard-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.3. How do I set (or find) initial values for non standard distributions?","title":"Frequently Asked Questions","text":"documented, provide initial values following distributions: \"norm\", \"lnorm\", \"exp\", \"pois\", \"cauchy\", \"gamma“, \"logis\", \"nbinom\", \"geom\", \"beta\", \"weibull\" stats package; \"invgamma\", \"llogis\", \"invweibull\", \"pareto1\", \"pareto\", \"lgamma\", \"trgamma\", \"invtrgamma\" actuar package. Look first statistics probability books different volumes N. L. Johnson, S. Kotz N. Balakrishnan books, e.g. Continuous Univariate Distributions, Vol. 1, Thesaurus univariate discrete probability distributions G. Wimmer G. Altmann. Statistical Distributions M. Evans, N. Hastings, B. Peacock. Distributional Analysis L-moment Statistics using R Environment Statistical Computing W. Asquith. available, find initial values equalling theoretical empirical quartiles. graphical function plotdist() plotdistcens() can also used assess suitability starting values : iterative manual process can move parameter values obtain distribution roughly fits data take parameter values starting values real fit. may also consider prefit() function find initial values especially case parameters constrained.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-fit-a-distribution-with-at-least-3-parameters","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.4. Is it possible to fit a distribution with at least 3 parameters?","title":"Frequently Asked Questions","text":"Yes, example Burr distribution detailed JSS paper. reproduce quickly .","code":"data(\"endosulfan\") require(\"actuar\") fendo.B <- fitdist(endosulfan$ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) summary(fendo.B) ## Fitting of the distribution ' burr ' by maximum likelihood ## Parameters : ## estimate Std. Error ## shape1 0.206 0.572 ## shape2 1.540 3.251 ## rate 1.497 4.775 ## Loglikelihood: -520 AIC: 1046 BIC: 1054 ## Correlation matrix: ## shape1 shape2 rate ## shape1 1.000 -0.900 -0.727 ## shape2 -0.900 1.000 0.588 ## rate -0.727 0.588 1.000"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-there-are-differences-between-mle-and-mme-for-the-lognormal-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.5. Why there are differences between MLE and MME for the lognormal distribution?","title":"Frequently Asked Questions","text":"recall lognormal distribution function given FX(x)=Φ(log(x)−μσ), F_X(x) = \\Phi\\left(\\frac{\\log(x)-\\mu}{\\sigma} \\right), Φ\\Phi denotes distribution function standard normal distribution. know E(X)=exp(μ+12σ2)E(X) = \\exp\\left( \\mu+\\frac{1}{2} \\sigma^2 \\right) Var(X)=exp(2μ+σ2)(eσ2−1)Var(X) = \\exp\\left( 2\\mu+\\sigma^2\\right) (e^{\\sigma^2} -1). MME obtained inverting previous formulas, whereas MLE following explicit solution μ̂MLE=1n∑=1nlog(xi),σ̂MLE2=1n∑=1n(log(xi)−μ̂MLE)2. \\hat\\mu_{MLE} = \\frac{1}{n}\\sum_{=1}^n \\log(x_i),~~ \\hat\\sigma^2_{MLE} = \\frac{1}{n}\\sum_{=1}^n (\\log(x_i) - \\hat\\mu_{MLE})^2. Let us fit sample MLE MME. fit looks particularly good cases. Let us compare theoretical moments (mean variance) given fitted values (μ̂,σ̂\\hat\\mu,\\hat\\sigma), E(X)=exp(μ̂+12σ̂2),Var(X)=exp(2μ̂+σ̂2)(eσ̂2−1). E(X) = \\exp\\left( \\hat\\mu+\\frac{1}{2} \\hat\\sigma^2 \\right), Var(X) = \\exp\\left( 2\\hat\\mu+\\hat\\sigma^2\\right) (e^{\\hat\\sigma^2} -1). MLE point view, lognormal sample x1,…,xnx_1,\\dots,x_n equivalent handle normal sample log(x1),…,log(xn)\\log(x_1),\\dots,\\log(x_n). However, well know Jensen inequality E(X)=E(exp(log(X)))≥exp(E(log(X)))E(X) = E(\\exp(\\log(X))) \\geq \\exp(E(\\log(X))) implying MME estimates provides better moment estimates MLE.","code":"x3 <- rlnorm(1000) f1 <- fitdist(x3, \"lnorm\", method=\"mle\") f2 <- fitdist(x3, \"lnorm\", method=\"mme\") par(mfrow=1:2, mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points=FALSE, xlogscale = TRUE, main = \"CDF plot\") denscomp(list(f1, f2), demp=TRUE, main = \"Density plot\") c(\"E(X) by MME\"=as.numeric(exp(f2$estimate[\"meanlog\"]+f2$estimate[\"sdlog\"]^2/2)), \"E(X) by MLE\"=as.numeric(exp(f1$estimate[\"meanlog\"]+f1$estimate[\"sdlog\"]^2/2)), \"empirical\"=mean(x3)) ## E(X) by MME E(X) by MLE empirical ## 1.61 1.60 1.61 c(\"Var(X) by MME\"=as.numeric(exp(2*f2$estimate[\"meanlog\"]+f2$estimate[\"sdlog\"]^2) * (exp(f2$estimate[\"sdlog\"]^2)-1)), \"Var(X) by MLE\"=as.numeric(exp(2*f1$estimate[\"meanlog\"]+f1$estimate[\"sdlog\"]^2) * (exp(f1$estimate[\"sdlog\"]^2)-1)), \"empirical\"=var(x3)) ## Var(X) by MME Var(X) by MLE empirical ## 4.30 4.36 4.30"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-distribution-with-positive-support-when-data-contains-negative-values","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.6. Can I fit a distribution with positive support when data contains negative values?","title":"Frequently Asked Questions","text":"answer : fit distribution positive support (say gamma distribution) data contains negative values. irrelevant fit. really need use distribution, two options: either remove negative values (recommended) shift data.","code":"set.seed(1234) x <- rnorm(100, mean = 1, sd = 0.5) (try(fitdist(x, \"exp\"))) ## Error in computing default starting values. ## Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : ## Error in startarg_transgamma_family(x, distr) : ## values must be positive to fit an exponential distribution ## [1] \"Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : \\n Error in startarg_transgamma_family(x, distr) : \\n values must be positive to fit an exponential distribution\\n\\n\" ## attr(,\"class\") ## [1] \"try-error\" ## attr(,\"condition\") ## fitdist(x[x >= 0], \"exp\") ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 1.06 1.06 fitdist(x - min(x), \"exp\") ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.914 0.914"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-finite-support-distribution-when-data-is-outside-that-support","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.7. Can I fit a finite-support distribution when data is outside that support?","title":"Frequently Asked Questions","text":"answer : fit distribution finite-support (say beta distribution) data outside [0,1][0,1]. irrelevant fit. really need use distribution, two ways tackle issue: either remove impossible values (recommended) shift/scale data.","code":"set.seed(1234) x <- rnorm(100, mean = 0.5, sd = 0.25) (try(fitdist(x, \"beta\"))) ## Error in computing default starting values. ## Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : ## Error in startargdefault(obs, distname) : ## values must be in [0-1] to fit a beta distribution ## [1] \"Error in manageparam(start.arg = start, fix.arg = fix.arg, obs = data, : \\n Error in startargdefault(obs, distname) : \\n values must be in [0-1] to fit a beta distribution\\n\\n\" ## attr(,\"class\") ## [1] \"try-error\" ## attr(,\"condition\") ## fitdist(x[x > 0 & x < 1], \"beta\") ## Fitting of the distribution ' beta ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 2.08 2.79 ## shape2 2.50 3.41 fitdist((x - min(x)*1.01) / (max(x) * 1.01 - min(x) * 1.01), \"beta\") ## Fitting of the distribution ' beta ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 1.77 2.36 ## shape2 2.17 2.96"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-truncated-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.8. Can I fit truncated distributions?","title":"Frequently Asked Questions","text":"answer yes: fitting procedure must carried carefully. Let XX original untruncated random variable. truncated variable conditionnal random variable Y=X|l= low) * (x <= upp) } ptexp <- function(q, rate, low, upp) { PU <- pexp(upp, rate=rate) PL <- pexp(low, rate=rate) (pexp(q, rate)-PL) / (PU-PL) * (q >= low) * (q <= upp) + 1 * (q > upp) } n <- 200 x <- rexp(n); x <- x[x > .5 & x < 3] f1 <- fitdist(x, \"texp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=min(x), upp=max(x))) f2 <- fitdist(x, \"texp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=.5, upp=3)) gofstat(list(f1, f2)) ## Goodness-of-fit statistics ## 1-mle-texp 2-mle-texp ## Kolmogorov-Smirnov statistic 0.0952 0.084 ## Cramer-von Mises statistic 0.1343 0.104 ## Anderson-Darling statistic Inf 1.045 ## ## Goodness-of-fit criteria ## 1-mle-texp 2-mle-texp ## Akaike's Information Criterion 127 132 ## Bayesian Information Criterion 130 135 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points = FALSE, xlim=c(0, 3.5))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-truncated-inflated-distributions","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.9. Can I fit truncated inflated distributions?","title":"Frequently Asked Questions","text":"answer yes: fitting procedure must carried carefully. Let XX original untruncated random variable. truncated variable Y=max(min(X,u),l)Y = \\max(\\min(X, u), l) ly>l+1y>uF_Y(y)=F_X(y)1_{u>y>l} + 1_{y>u}. density (w.r.t. Lebesgues measure) since two probability masses P(Y=l)=P(X≤l)>0P(Y=l)= P(X\\leq l)>0 P(Y=u)=P(X>u)>0P(Y=u)=P(X>u)>0. However, density function respect measure m(x)=δl(x)+δu(x)+λ(x)m(x)= \\delta_l(x)+\\delta_u(x)+\\lambda(x) fY(y)={FX(l)y=lfX(y)lminiyil>\\min_i y_i u= low) * (x <= upp) + PL * (x == low) + PU * (x == upp) } ptiexp <- function(q, rate, low, upp) pexp(q, rate) * (q >= low) * (q <= upp) + 1 * (q > upp) n <- 100; x <- pmax(pmin(rexp(n), 3), .5) # the loglikelihood has a discontinous point at the solution par(mar=c(4,4,2,1), mfrow=1:2) llcurve(x, \"tiexp\", plot.arg=\"low\", fix.arg = list(rate=2, upp=5), min.arg=0, max.arg=.5, lseq=200) llcurve(x, \"tiexp\", plot.arg=\"upp\", fix.arg = list(rate=2, low=0), min.arg=3, max.arg=4, lseq=200) (f1 <- fitdist(x, \"tiexp\", method=\"mle\", start=list(rate=3, low=0, upp=20))) ## Fitting of the distribution ' tiexp ' by maximum likelihood ## Parameters: ## estimate ## rate 0.333 ## low 2.915 ## upp 20.899 (f2 <- fitdist(x, \"tiexp\", method=\"mle\", start=list(rate=3), fix.arg=list(low=min(x), upp=max(x)))) ## Fitting of the distribution ' tiexp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.947 0.982 ## Fixed parameters: ## value ## low 0.5 ## upp 3.0 gofstat(list(f1, f2)) ## Goodness-of-fit statistics ## 1-mle-tiexp 2-mle-tiexp ## Kolmogorov-Smirnov statistic 0.92 0.377 ## Cramer-von Mises statistic 26.82 1.882 ## Anderson-Darling statistic Inf 10.193 ## ## Goodness-of-fit criteria ## 1-mle-tiexp 2-mle-tiexp ## Akaike's Information Criterion 39.6 162 ## Bayesian Information Criterion 47.4 165 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1, f2), do.points = FALSE, addlegend=FALSE, xlim=c(0, 3.5)) curve(ptiexp(x, 1, .5, 3), add=TRUE, col=\"blue\", lty=3) legend(\"bottomright\", lty=1:3, col=c(\"red\", \"green\", \"blue\", \"black\"), legend=c(\"full MLE\", \"MLE fixed arg\", \"true CDF\", \"emp. CDF\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-uniform-distribution","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.10. Can I fit a uniform distribution?","title":"Frequently Asked Questions","text":"uniform distribution 𝒰(,b)\\mathcal U(,b) support parameters since density scale shape parameter fU(u)=1b−a1[,b](u)f_U(u) = \\frac{1}{b-}1_{[,b]}(u). distribution, maximize log-likelihood likelihood. Let (xi)(x_i)_i ..d. observations 𝒰(,b)\\mathcal U(,b) distribution. likelihood L(,b)=∏=1n1b−a1[,b](xi)=1a≤xi≤b,=1,…,n1b−=1a≤minixi1maxixi≤b1b−L(,b) = \\prod_{=1}^n \\frac{1}{b-} 1_{[,b]}(x_i) = 1_{\\leq x_i \\leq b, =1,\\dots,n} \\frac{1}{b-}^n = 1_{\\leq \\min_i x_i} 1_{\\max_i x_i \\leq b} \\frac{1}{b-}^n Hence ↦L(,b)\\mapsto L(,b) fixed b∈]maxixi,+∞[b\\]\\max_i x_i, +\\infty[ increasing ]−∞,minixi]]-\\infty, \\min_i x_i], similarly b↦L(,b)b\\mapsto L(,b) decreasing fixed aa. leads minixi\\min_i x_i maxixi\\max_i x_i MLE uniform distribution. notice likelihood function LL defined ℝ2\\mathbb R^2 yet cancels outside S=]−∞,minixi]×]maxixi,+∞[S=]-\\infty, \\min_i x_i]\\times]\\max_i x_i, +\\infty[. Hence, log-likelihood undefined outside SS, issue maximizing log-likelihood. reasons, fitdist(data, dist=\"unif\", method=\"mle\") uses explicit form MLE distribution. example Maximizing log-likelihood harder can done defining new density function. Appropriate starting values parameters bound must supplied. Using closed-form expression (fitdist()) maximizing log-likelihood (unif2) lead similar results.","code":"trueval <- c(\"min\"=3, \"max\"=5) x <- runif(n=500, trueval[1], trueval[2]) f1 <- fitdist(x, \"unif\") delta <- .01 par(mfrow=c(1,1), mar=c(4,4,2,1)) llsurface(x, \"unif\", plot.arg = c(\"min\", \"max\"), min.arg=c(min(x)-2*delta, max(x)-delta), max.arg=c(min(x)+delta, max(x)+2*delta), main=\"likelihood surface for uniform\", loglik=FALSE) abline(v=min(x), h=max(x), col=\"grey\", lty=2) points(f1$estimate[1], f1$estimate[2], pch=\"x\", col=\"red\") points(trueval[1], trueval[2], pch=\"+\", col=\"blue\") legend(\"bottomright\", pch=c(\"+\",\"x\"), col=c(\"blue\",\"red\"), c(\"true\", \"fitted\")) delta <- .2 llsurface(x, \"unif\", plot.arg = c(\"min\", \"max\"), min.arg=c(3-2*delta, 5-delta), max.arg=c(3+delta, 5+2*delta), main=\"log-likelihood surface for uniform\") abline(v=min(x), h=max(x), col=\"grey\", lty=2) points(f1$estimate[1], f1$estimate[2], pch=\"x\", col=\"red\") points(trueval[1], trueval[2], pch=\"+\", col=\"blue\") legend(\"bottomright\", pch=c(\"+\",\"x\"), col=c(\"blue\",\"red\"), c(\"true\", \"fitted\")) dunif2 <- function(x, min, max) dunif(x, min, max) punif2 <- function(q, min, max) punif(q, min, max) f2 <- fitdist(x, \"unif2\", start=list(min=0, max=10), lower=c(-Inf, max(x)), upper=c(min(x), Inf)) print(c(logLik(f1), logLik(f2)), digits=7) ## [1] -346.0539 -346.1519 print(cbind(coef(f1), coef(f2)), digits=7) ## [,1] [,2] ## min 3.000684 3.000292 ## max 4.998606 4.998606"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-fit-a-beta-distribution-with-the-same-shape-parameter","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.11. Can I fit a beta distribution with the same shape parameter?","title":"Frequently Asked Questions","text":"Yes, can wrap density function beta distribution one shape parameter. example concave density. Another example U-shaped density.","code":"x <- rbeta(1000, 3, 3) dbeta2 <- function(x, shape, ...) dbeta(x, shape, shape, ...) pbeta2 <- function(q, shape, ...) pbeta(q, shape, shape, ...) fitdist(x, \"beta2\", start=list(shape=1/2)) ## Fitting of the distribution ' beta2 ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 3.24 4.26 x <- rbeta(1000, .3, .3) fitdist(x, \"beta2\", start=list(shape=1/2), optim.method=\"L-BFGS-B\", lower=1e-2) ## Fitting of the distribution ' beta2 ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 0.295 0.312"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-estimate-support-parameter-the-case-of-the-four-parameter-beta","dir":"Articles","previous_headings":"1. Questions regarding distributions","what":"1.12. How to estimate support parameter? the case of the four-parameter beta","title":"Frequently Asked Questions","text":"Let us consider four-parameter beta distribution, also known PERT distribution, defined following density x∈[,c]x\\[,c]fX(x)=(x−)α−1(c−x)β−1/CNf_X(x) = (x-)^{\\alpha-1} (c-x)^{\\beta-1}/C_N CNC_N normalizing constant α=1+d(b−)/(c−)\\alpha=1+d(b-)/(c-), β=1+d(c−b)/(c−)\\beta=1+d(c-b)/(c-). ,ca,c support parameters, b∈],c[b\\],c[ mode dd shape parameter. uniform distribution, one can show MLE aa cc respectively sample minimum maximum. code illustrates strategy using partial closed formula fix.arg full numerical search MLE. NB: small sample size, latter generally better goodness--fit statistics; small positive number added subtracted fixing support parameters aa cc sample minimum maximum.","code":"require(\"mc2d\") x2 <- rpert(n=2e2, min=0, mode=1, max=2, shape=3/4) eps <- sqrt(.Machine$double.eps) f1 <- fitdist(x2, \"pert\", start=list(min=-1, mode=0, max=10, shape=1), lower=c(-Inf, -Inf, -Inf, 0), upper=c(Inf, Inf, Inf, Inf)) ## Warning in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, : Some ## parameter names have no starting/fixed value but have a default value: mean. ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced f2 <- fitdist(x2, \"pert\", start=list(mode=1, shape=1), fix.arg=list(min=min(x2)-eps, max=max(x2)+eps), lower=c(min(x2), 0), upper=c(max(x2), Inf)) ## Warning in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, : Some ## parameter names have no starting/fixed value but have a default value: mean. print(cbind(coef(f1), c(f2$fix.arg[\"min\"], coef(f2)[\"mode\"], f2$fix.arg[\"max\"], coef(f2)[\"shape\"])), digits=7) ## [,1] [,2] ## min 1.36707 0.03395487 ## mode 1.367072 1.955289 ## max 1.644537 1.956234 ## shape 0.0005813856 0.008506046 gofstat(list(f1,f2)) ## Goodness-of-fit statistics ## 1-mle-pert 2-mle-pert ## Kolmogorov-Smirnov statistic 0.69 0.0584 ## Cramer-von Mises statistic 28.59 0.1836 ## Anderson-Darling statistic Inf 1.2787 ## ## Goodness-of-fit criteria ## 1-mle-pert 2-mle-pert ## Akaike's Information Criterion -99.7 265 ## Bayesian Information Criterion -86.5 272 par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcomp(list(f1,f2))"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"where-can-we-find-the-results-of-goodness-of-fit-tests","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.1. Where can we find the results of goodness-of-fit tests ?","title":"Frequently Asked Questions","text":"Results goodness--fit tests printed given object returned gofstat() can access described example . Nevertheless, p-values given every test. Anderson-Darling (ad), Cramer von Mises (cvm) Kolomogorov (ks), decision (rejection H0 ) given, available (see FAQ 2.3 details).","code":"set.seed(1234) x <- rgamma(n = 100, shape = 2, scale = 1) # fit of the good distribution fgamma <- fitdist(x, \"gamma\") # fit of a bad distribution fexp <- fitdist(x, \"exp\") g <- gofstat(list(fgamma, fexp), fitnames = c(\"gamma\", \"exp\")) par(mfrow=c(1,1), mar=c(4,4,2,1)) denscomp(list(fgamma, fexp), legendtext = c(\"gamma\", \"exp\")) # results of the tests ## chi square test (with corresponding table with theoretical and observed counts) g$chisqpvalue ## gamma exp ## 1.89e-01 7.73e-05 g$chisqtable ## obscounts theo gamma theo exp ## <= 0.5483 9 10.06 23.66 ## <= 0.8122 9 8.82 9.30 ## <= 0.9592 9 5.27 4.68 ## <= 1.368 9 14.64 11.37 ## <= 1.523 9 5.24 3.74 ## <= 1.701 9 5.73 3.97 ## <= 1.94 9 7.09 4.82 ## <= 2.381 9 11.08 7.50 ## <= 2.842 9 9.00 6.29 ## <= 3.801 9 11.93 9.28 ## > 3.801 10 11.15 15.40 ## Anderson-Darling test g$adtest ## gamma exp ## \"not rejected\" \"rejected\" ## Cramer von Mises test g$cvmtest ## gamma exp ## \"not rejected\" \"rejected\" ## Kolmogorov-Smirnov test g$kstest ## gamma exp ## \"not rejected\" \"rejected\""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-reasonable-to-use-goodness-of-fit-tests-to-validate-the-fit-of-a-distribution","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","title":"Frequently Asked Questions","text":"first versions fitdistrplus, available, results GOF tests (AD, KS, CvM) automatically printed. decided suppress automatic printing realized users difficulties interpret results tests sometimes misused . Goodness--fit tests often appear objective tools decide wether fitted distribution well describes data set. ! reasonable reject distribution just goodness--fit test rejects (see FAQ 2.2.1). reasonable validate distribution goodness--fit tests reject (see FAQ 2.2.2). fitted distribution evaluated using graphical methods (goodness--fit graphs automatically provided package plotting result fit (output fitdist() fitdistcens() complementary graphs help compare different fits - see ?graphcomp). really think appropriate way evaluate adequacy fit ones recommend . can find type recommendations reference books : Probabilistic techniques exposure assessment - handbook dealing variability uncertainty models inputs .C. Cullen H.C. Frey. Application uncertainty analysis ecological risks pesticides W.J. Warren-Hicks . Hart. Statistical inference G. Casella R.L. Berger Loss models: data decision S.. Klugman H.H. Panjer G.E. Willmot Moreover, selection distribution also driven knowledge underlying processes available. example variable negative, one cautious fitting normal distribution, potentially gives negative values, even observed data variable seem well fitted normal distribution.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"should-i-reject-a-distribution-because-a-goodness-of-fit-test-rejects-it","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph > 2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","what":"2.2.1. Should I reject a distribution because a goodness-of-fit test rejects it ?","title":"Frequently Asked Questions","text":"reasonable reject distribution just goodness--fit test rejects , especially case big samples. real life, soon sufficient amount data, reject fitted distribution. know model perfectly describe real data, generally true question find better distribution among pool simple parametric distributions describe data, compare different models (see FAQ 2.4 2.5 corresponding questions). illustre point let us comment example presented . drew two samples Poisson distribution mean parameter equal 100. many applications, value parameter, Poisson distribution considered well approximated normal distribution. Testing fit (using Kolmogorov-Smirnov test ) normal distribution sample 100 observations reject normal fit, testing sample 10000 observations reject , samples come distribution.","code":"set.seed(1234) x1 <- rpois(n = 100, lambda = 100) f1 <- fitdist(x1, \"norm\") g1 <- gofstat(f1) g1$kstest ## 1-mle-norm ## \"not rejected\" x2 <- rpois(n = 10000, lambda = 100) f2 <- fitdist(x2, \"norm\") g2 <- gofstat(f2) g2$kstest ## 1-mle-norm ## \"rejected\" par(mfrow=c(1,2), mar=c(4,4,2,1)) denscomp(f1, demp = TRUE, addlegend = FALSE, main = \"small sample\") denscomp(f2, demp = TRUE, addlegend = FALSE, main = \"big sample\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"should-i-accept-a-distribution-because-goodness-of-fit-tests-do-not-reject-it","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph > 2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?","what":"2.2.2. Should I accept a distribution because goodness-of-fit tests do not reject it ?","title":"Frequently Asked Questions","text":", reasonable validate distribution goodness--fit tests reject . Like hypothesis tests, goodness--fit tests lack statistical power sample size high. different goodness--fit tests equally sensitive different types deviation empirical fitted distributions. example Kolmogorov-Smirnov test sensitive distributions differ global fashion near centre distribution. Anderson-Darling test sensitive distributions differ tails, Cramer von Mises sensitive small repetitive differences empirical theoretical distribution functions. sensitivity chi square test depend definition classes, even propose default definition classes user provide classes, choice obvious impact results test. test appropriate data discrete, even modelled continuous distribution, following example. Two samples respective sizes 500 50 drawn Poisson distribution mean parameter equal 1 (sufficiently high value consider Poisson distribution approximated normal one). Using Kolmogorov-Smirnov test, small sample normal fit rejected bigger sample. rejected smaller sample even fit rejected simple visual confrontation distributions. particular case, chi square test classes defined default rejected te normal fit samples.","code":"set.seed(1234) x3 <- rpois(n = 500, lambda = 1) f3 <- fitdist(x3, \"norm\") g3 <- gofstat(f3) g3$kstest ## 1-mle-norm ## \"rejected\" x4 <- rpois(n = 50, lambda = 1) f4 <- fitdist(x4, \"norm\") g4 <- gofstat(f4) g4$kstest ## 1-mle-norm ## \"not rejected\" par(mfrow=c(1,2), mar=c(4,4,2,1)) denscomp(f3, addlegend = FALSE, main = \"big sample\") denscomp(f4, addlegend = FALSE, main = \"small sample\") g3$chisqtable ## obscounts theocounts ## <= 0 180.0 80.3 ## <= 1 187.0 163.5 ## <= 2 87.0 168.1 ## <= 3 32.0 73.4 ## > 3 14.0 14.7 g3$chisqpvalue ## [1] 7.11e-42 g4$chisqtable ## obscounts theocounts ## <= 0 14.00 5.46 ## <= 1 15.00 14.23 ## <= 2 15.00 18.09 ## > 2 6.00 12.22 g4$chisqpvalue ## [1] 3.57e-05"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-all-goodness-of-fit-tests-are-not-available-for-every-distribution","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.3. Why all goodness-of-fit tests are not available for every distribution ?","title":"Frequently Asked Questions","text":"Chi-squared test available distribution one must conscious result depends definition cells observed data grouped, correct definition possible small sample. Concerning Kolmogorov-Smirnov test, proposed continuous distribution, critical value corresponding comparison empirical distribution fully specified distribution. distribution fully known fitted distribution, result test subject caution, general asymptotic theory Kolmogorov-Smirnov statistics case fitted distribution. Nevertheless, one can use Monte Carlo methods conduct Kolmgorov-Smirnov goodness--fit tests cases sample used estimate model parameters. method implemented R package KScorrect variety continuous distributions. asymptotic theory proposed quadratic statistics distributions (Anderson-Darling, Cramer von Mises). reference book used subject (Tests based edf statistics Stephens MA Goodness--fit techniques D’Agostino RB Stephens MA) proposes critical values statistics classical distributions (exponential, gamma, Weibull, logistic, Cauchy, normal lognormal). asymptotic theory statistics also depends way parameters estimated. estimated maximum likelihood Cauchy, normal lognormal distributions results reported Stephens, propose results Anderson-Darling Cramer von Mises using results exponential, gamma, Weibull, logistic distributions. user can refer cited books use proposed formula estimate parameters Cauchy, normal lognormal distributions apply tests using critical values given book. R packages goftest ADGofTest also explored users like apply Anderson-Darling Cramer von Mises tests distributions. time sure case parameters unknown (estimated maximum likelihood) tackled two packages. Concerning development package, rather develoing goodness--fit tests made choice develop graphical tools help appreciate quality fit compare fits different distributions data set (see FAQ 2.2 argumentation).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-use-goodness-of-fit-statistics-to-compare-the-fit-of-different-distributions-on-a-same-data-set","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.4. How can we use goodness-of-fit statistics to compare the fit of different distributions on a same data set ?","title":"Frequently Asked Questions","text":"Goodness--fit statistics based empirical distribution function (Kolmogorov-Smirnov, Anderson-Darling Cramer von Mises) may used measure distance fitted distribution empirical distribution. one wants compare fit various distributions data set, smaller statistics better. Kolmogorov-Smirnov statistics sensitive distributions differ global fashion near centre distribution Anderson-Darling statistics sensitive distributions differ tails, Cramer von Mises statistics sensitive small repetitive differences empirical theoretical distribution functions. mentioned main vignette package, use Anderson-Darling compare fit different distributions subject caution due weighting quadratic distance fitted empirical distribution functions depends parametric distribution. Moreover, statistics based empirical distribution function penalize distributions greater number parameters generally flexible, induce -fitting. Goodness-fo-fit statistics based information criteria (AIC, BIC) correspond deviance penalized complexity model (number parameters distribution), smaller better. generic statistics, adapted focus part fitted distribution, take account complexity distribution thus help prevent overfitting.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-use-a-test-to-compare-the-fit-of-two-distributions-on-a-same-data-set","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.5. Can we use a test to compare the fit of two distributions on a same data set ?","title":"Frequently Asked Questions","text":"package implement test two nested distributions (one special case one, e.g. exponential gamma distributions) likelihood ratio test can easily implemented using loglikelihood provided fitdist fitdistcens. Denoting LL maximum likelihood obtained complete distribution L0L_0 one obtained simplified distribution, sample size increases, −2ln(L0L)=2ln(L)−2ln(L0)- 2 ln(\\frac{L_0}{L}) = 2 ln(L) - 2 ln(L_0) tends Chi squared distribution degrees freedom equal difference numbers parameters characterizing two nested distributions. find example test. test can also used fits censored data.","code":"set.seed(1234) g <- rgamma(100, shape = 2, rate = 1) (f <- fitdist(g, \"gamma\")) ## Fitting of the distribution ' gamma ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape 2.025 2.66 ## rate 0.997 1.49 (f0 <- fitdist(g, \"exp\")) ## Fitting of the distribution ' exp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.492 0.492 L <- logLik(f) k <- length(f$estimate) # number of parameters of the complete distribution L0 <- logLik(f0) k0 <- length(f0$estimate) # number of parameters of the simplified distribution (stat <- 2*L - 2*L0) ## [1] 23.9 (critical_value <- qchisq(0.95, df = k - k0)) ## [1] 3.84 (rejected <- stat > critical_value) ## [1] TRUE"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-get-goodness-of-fit-statistics-for-a-fit-on-censored-data","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.6. Can we get goodness-of-fit statistics for a fit on censored data ?","title":"Frequently Asked Questions","text":"Function gofstat yet proposed package fits censored data develop one among one objectives future. Published works goodness--fit statistics based empirical distribution function censored data generally focused data containing one type censoring (e.g. right censored data survival data). Build statistics general case, data containing time (right, left interval censoring), remains tricky. Nevertheless, possible type censored data, use information criteria (AIC BIC given summary object class fitdistcens) compare fits various distributions data set.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-cullen-frey-graph-may-be-misleading","dir":"Articles","previous_headings":"2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph","what":"2.7. Why Cullen-Frey graph may be misleading?","title":"Frequently Asked Questions","text":"considering distribution large theoretical moments infinite moments, using Cullen-Frey may appropriate. typical log-normal distribution ℒ𝒩(μ,σ2)\\mathcal L\\mathcal N(\\mu,\\sigma^2). Indeed distribution, skewness kurtosis functions exponential σ2\\sigma^2. large values, even small σ\\sigma. sk(X)=(eσ2+2)eσ2−1,kr(X)=e4σ2+2e3σ2+3e2σ2−3. sk(X) = (e^{\\sigma^2}+2)\\sqrt{e^{\\sigma^2}-1}, kr(X) = e^{4\\sigma^2} + 2e^{3\\sigma^2} + 3e^{2\\sigma^2}-3. convergence theoretical standardized moments (skewness kurtosis) slow future, plan use trimmed linear moments deal issue. moments always exist even distribution infinite mean, e.g. Cauchy distribution.","code":"n <- 1e3 x <- rlnorm(n) descdist(x) ## summary statistics ## ------ ## min: 0.0436 max: 20.3 ## median: 1.02 ## mean: 1.61 ## estimated sd: 1.89 ## estimated skewness: 3.49 ## estimated kurtosis: 21.9"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-choose-optimization-method","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.1. How to choose optimization method?","title":"Frequently Asked Questions","text":"want perform optimization without bounds, optim() used. can try derivative-free method Nelder-Mead Hessian-free method BFGS. want perform optimization bounds, two methods available without providing gradient objective function: Nelder-Mead via constrOptim() bounded BFGS via optim(). cases, see help mledist() vignette optimization algorithms.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"the-optimization-algorithm-stops-with-error-code-100--what-shall-i-do","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.2. The optimization algorithm stops with error code 100. What shall I do?","title":"Frequently Asked Questions","text":"First, add traces adding control=list(trace=1, REPORT=1). Second, try set bounds parameters. Third, find better starting values (see FAQ 1.3).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"why-distribution-with-a-log-argument-may-converge-better","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.3 Why distribution with a log argument may converge better?","title":"Frequently Asked Questions","text":"Say, study shifted lognormal distribution defined following density f(x)=1xσ2πexp(−(ln(x+δ)−μ)22σ2) f(x) = \\frac{1}{x \\sigma \\sqrt{2 \\pi}} \\exp\\left(- \\frac{(\\ln (x+\\delta)- \\mu)^2}{2\\sigma^2}\\right) x>−δx>-\\delta μ\\mu location parameter, σ\\sigma scale parameter δ\\delta boundary parameter. Let us fit distribution dataset y MLE. define two functions densities without log argument. now optimize minus log-likelihood. don’t use log argument, algorithms stalls. Indeed algorithm stops following value, log-likelihood infinite. something wrong computation. R-base implementation using log argument seems reliable. happens C-base implementation dlnorm takes care log value. file ../src/nmath/dlnorm.c R sources, find C code dlnorm last four lines logical condtion give_log?, see log argument handled: log=TRUE, use −(log(2π)+y2/2+log(xσ))-(\\log(\\sqrt{2\\pi}) + y^2/2+\\log(x\\sigma)) log=FALSE, use 2π*exp(y2/2)/(xσ))\\sqrt{2\\pi} *\\exp( y^2/2)/(x\\sigma)) (logarithm outside dlnorm) Note constant log(2π)\\log(\\sqrt{2\\pi}) pre-computed C macro M_LN_SQRT_2PI. order sort problem, use constrOptim wrapping optim take account linear constraints. allows also use optimization methods L-BFGS-B (low-memory BFGS bounded) used optim. Another possible perform computations higher precision arithmetics implemented package Rmpfr using MPFR library.","code":"dshiftlnorm <- function(x, mean, sigma, shift, log = FALSE) dlnorm(x+shift, mean, sigma, log=log) pshiftlnorm <- function(q, mean, sigma, shift, log.p = FALSE) plnorm(q+shift, mean, sigma, log.p=log.p) qshiftlnorm <- function(p, mean, sigma, shift, log.p = FALSE) qlnorm(p, mean, sigma, log.p=log.p)-shift dshiftlnorm_no <- function(x, mean, sigma, shift) dshiftlnorm(x, mean, sigma, shift) pshiftlnorm_no <- function(q, mean, sigma, shift) pshiftlnorm(q, mean, sigma, shift) data(dataFAQlog1) y <- dataFAQlog1 D <- 1-min(y) f0 <- fitdist(y+D, \"lnorm\") start <- list(mean=as.numeric(f0$estimate[\"meanlog\"]), sigma=as.numeric(f0$estimate[\"sdlog\"]), shift=D) # works with BFGS, but not Nelder-Mead f <- fitdist(y, \"shiftlnorm\", start=start, optim.method=\"BFGS\") summary(f) ## Fitting of the distribution ' shiftlnorm ' by maximum likelihood ## Parameters : ## estimate Std. Error ## mean -1.3848 1.355 ## sigma 0.0709 0.108 ## shift 0.2487 0.338 ## Loglikelihood: 8299 AIC: -16591 BIC: -16573 ## Correlation matrix: ## mean sigma shift ## mean 1.000 -0.885 0.999 ## sigma -0.885 1.000 -0.886 ## shift 0.999 -0.886 1.000 f2 <- try(fitdist(y, \"shiftlnorm_no\", start=start, optim.method=\"BFGS\")) print(attr(f2, \"condition\")) ## NULL sum(log(dshiftlnorm_no(y, 0.16383978, 0.01679231, 1.17586600 ))) ## [1] -Inf log(prod(dshiftlnorm_no(y, 0.16383978, 0.01679231, 1.17586600 ))) ## [1] -Inf sum(dshiftlnorm(y, 0.16383978, 0.01679231, 1.17586600, TRUE )) ## [1] 7761 double dlnorm(double x, double meanlog, double sdlog, int give_log) { double y; #ifdef IEEE_754 if (ISNAN(x) || ISNAN(meanlog) || ISNAN(sdlog)) return x + meanlog + sdlog; #endif if(sdlog <= 0) { if(sdlog < 0) ML_ERR_return_NAN; // sdlog == 0 : return (log(x) == meanlog) ? ML_POSINF : R_D__0; } if(x <= 0) return R_D__0; y = (log(x) - meanlog) / sdlog; return (give_log ? -(M_LN_SQRT_2PI + 0.5 * y * y + log(x * sdlog)) : M_1_SQRT_2PI * exp(-0.5 * y * y) / (x * sdlog)); /* M_1_SQRT_2PI = 1 / sqrt(2 * pi) */ } -(M_LN_SQRT_2PI + 0.5 * y * y + log(x * sdlog)) M_1_SQRT_2PI * exp(-0.5 * y * y) / (x * sdlog)) f2 <- fitdist(y, \"shiftlnorm\", start=start, lower=c(-Inf, 0, -min(y)), optim.method=\"Nelder-Mead\") ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced summary(f2) ## Fitting of the distribution ' shiftlnorm ' by maximum likelihood ## Parameters : ## estimate Std. Error ## mean -1.3872 NaN ## sigma 0.0711 NaN ## shift 0.2481 NaN ## Loglikelihood: 8299 AIC: -16591 BIC: -16573 ## Correlation matrix: ## mean sigma shift ## mean 1 NaN NaN ## sigma NaN 1 NaN ## shift NaN NaN 1 print(cbind(BFGS=f$estimate, NelderMead=f2$estimate)) ## BFGS NelderMead ## mean -1.3848 -1.3872 ## sigma 0.0709 0.0711 ## shift 0.2487 0.2481"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"what-to-do-when-there-is-a-scaling-issue","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.4. What to do when there is a scaling issue?","title":"Frequently Asked Questions","text":"Let us consider dataset particular small values. way sort multiply dataset large value. Let us consider dataset particular large values. way sort multiply dataset small value.","code":"data(dataFAQscale1) head(dataFAQscale1) ## [1] -0.007077 -0.000947 -0.001898 -0.000475 -0.001902 -0.000476 summary(dataFAQscale1) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -0.00708 -0.00143 -0.00047 -0.00031 0.00096 0.00428 for(i in 6:0) cat(10^i, try(mledist(dataFAQscale1*10^i, \"cauchy\")$estimate), \"\\n\") ## 1e+06 -290 1194 ## 1e+05 -29 119 ## 10000 -2.9 11.9 ## 1000 -0.29 1.19 ## 100 -0.029 0.119 ## 10 -0.0029 0.0119 ## 1 -0.00029 0.00119 data(dataFAQscale2) head(dataFAQscale2) ## [1] 1.40e+09 1.41e+09 1.43e+09 1.44e+09 1.49e+09 1.57e+09 summary(dataFAQscale2) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.40e+09 1.58e+09 2.24e+09 2.55e+09 3.39e+09 4.49e+09 for(i in 0:5) cat(10^(-2*i), try(mledist(dataFAQscale2*10^(-2*i), \"cauchy\")$estimate), \"\\n\") ## 1 2.03e+09 6.59e+08 ## 0.01 20283641 6594932 ## 1e-04 202836 65949 ## 1e-06 2028 659 ## 1e-08 20.3 6.59 ## 1e-10 0.203 0.0659"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-scale-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.1. Setting bounds for scale parameters","title":"Frequently Asked Questions","text":"Consider normal distribution 𝒩(μ,σ2)\\mathcal{N}(\\mu, \\sigma^2) defined density f(x)=12πσ2exp(−(x−μ)22σ2),x∈ℝ, f(x) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left(-\\frac{(x-\\mu)^2}{2\\sigma^2}\\right), x\\\\mathbb{R}, μ\\mu location parameter μ∈ℝ\\mu\\\\mathbb{R}, σ2\\sigma^2 scale parameter σ2>0\\sigma^2>0. Therefore optimizing log-likelihood squared differences GoF statistics. Setting lower bound scale parameter easy fitdist: just use lower argument.","code":"set.seed(1234) x <- rnorm(1000, 1, 2) fitdist(x, \"norm\", lower=c(-Inf, 0)) ## Fitting of the distribution ' norm ' by maximum likelihood ## Parameters: ## estimate Std. Error ## mean 0.947 1.99 ## sd 1.994 1.41"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-shape-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.2. Setting bounds for shape parameters","title":"Frequently Asked Questions","text":"Consider Burr distribution ℬ(μ,σ2)\\mathcal B(\\mu, \\sigma^2) defined density f(x)=ab(x/s)bx[1+(x/s)b]+1,x∈ℝ, f(x) = \\frac{b (x/s)^b}{x [1 + (x/s)^b]^{+ 1}}, x\\\\mathbb{R}, ,ba,b shape parameters ,b>0a,b>0, ss scale parameter s>0s>0.","code":"x <- rburr(1000, 1, 2, 3) fitdist(x, \"burr\", lower=c(0, 0, 0), start=list(shape1 = 1, shape2 = 1, rate = 1)) ## Fitting of the distribution ' burr ' by maximum likelihood ## Parameters: ## estimate Std. Error ## shape1 0.968 1.06 ## shape2 2.051 1.16 ## rate 3.181 1.63"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-probability-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.3. Setting bounds for probability parameters","title":"Frequently Asked Questions","text":"Consider geometric distribution 𝒢(p)\\mathcal G(p) defined mass probability function f(x)=p(1−p)x,x∈ℕ, f(x) = p(1-p)^x, x\\\\mathbb{N}, pp probability parameter p∈[0,1]p\\[0,1].","code":"x <- rgeom(1000, 1/4) fitdist(x, \"geom\", lower=0, upper=1) ## Fitting of the distribution ' geom ' by maximum likelihood ## Parameters: ## estimate Std. Error ## prob 0.242 0.211"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-bounds-for-boundary-parameters","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.4. Setting bounds for boundary parameters","title":"Frequently Asked Questions","text":"Consider shifted exponential distribution ℰ(μ,λ)\\mathcal E(\\mu,\\lambda) defined mass probability function f(x)=λexp(−λ(x−μ)),x>μ, f(x) = \\lambda \\exp(-\\lambda(x-\\mu)), x>\\mu, λ\\lambda scale parameter λ>0\\lambda>0, μ\\mu boundary (shift) parameter μ∈ℝ\\mu\\\\mathbb{R}. optimizing log-likelihood, boundary constraint ∀=1,…,n,xi>μ⇒mini=1,…,nxi>μ⇔μ>−mini=1,…,nxi. \\forall =1,\\dots,n, x_i>\\mu \\Rightarrow \\min_{=1,\\dots,n} x_i > \\mu \\Leftrightarrow \\mu > -\\min_{=1,\\dots,n} x_i. Note optimizing squared differences GoF statistics, constraint may necessary. Let us R.","code":"dsexp <- function(x, rate, shift) dexp(x-shift, rate=rate) psexp <- function(x, rate, shift) pexp(x-shift, rate=rate) rsexp <- function(n, rate, shift) rexp(n, rate=rate)+shift x <- rsexp(1000, 1/4, 1) fitdist(x, \"sexp\", start=list(rate=1, shift=0), upper= c(Inf, min(x))) ## Fitting of the distribution ' sexp ' by maximum likelihood ## Parameters: ## estimate Std. Error ## rate 0.248 0 ## shift 1.005 NaN"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"setting-linear-inequality-bounds","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures > 3.5. How do I set bounds on parameters when optimizing?","what":"3.5.5. Setting linear inequality bounds","title":"Frequently Asked Questions","text":"distributions, bounds parameters independent. instance, normal inverse Gaussian distribution (μ,δ,α,β\\mu, \\delta, \\alpha, \\beta parametrization) following parameter constraints, can reformulated linear inequality: {α>0δ>0α>|β|⇔(01000010001−10011)⏟ui(μδαβ)≥(0000)⏟ci. \\left\\{ \\begin{array}{l}\\alpha > 0\\\\ \\delta >0\\\\ \\alpha > |\\beta|\\end{array} \\right. \\Leftrightarrow \\underbrace{ \\left( \\begin{matrix} 0 & 1 & 0 & 0 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 0 & 1 & -1 \\\\ 0 & 0 & 1 & 1 \\\\ \\end{matrix} \\right) }_{ui} \\left( \\begin{matrix} \\mu\\\\ \\delta\\\\ \\alpha \\\\ \\beta \\\\ \\end{matrix} \\right) \\geq \\underbrace{ \\left( \\begin{matrix} 0\\\\ 0\\\\ 0 \\\\ 0 \\\\ \\end{matrix} \\right)}_{ci}. constraints can carried via constrOptim() arguments ci ui. example","code":"require(\"GeneralizedHyperbolic\") myoptim <- function(fn, par, ui, ci, ...) { res <- constrOptim(f=fn, theta=par, method=\"Nelder-Mead\", ui=ui, ci=ci, ...) c(res, convergence=res$convergence, value=res$objective, par=res$minimum, hessian=res$hessian) } x <- rnig(1000, 3, 1/2, 1/2, 1/4) ui <- rbind(c(0,1,0,0), c(0,0,1,0), c(0,0,1,-1), c(0,0,1,1)) ci <- c(0,0,0,0) fitdist(x, \"nig\", custom.optim=myoptim, ui=ui, ci=ci, start=list(mu = 0, delta = 1, alpha = 1, beta = 0)) ## Warning in fitdist(x, \"nig\", custom.optim = myoptim, ui = ui, ci = ci, start = ## list(mu = 0, : The dnig function should return a vector of with NaN values when ## input has inconsistent parameters and not raise an error ## Warning in fitdist(x, \"nig\", custom.optim = myoptim, ui = ui, ci = ci, start = ## list(mu = 0, : The pnig function should return a vector of with NaN values when ## input has inconsistent values and not raise an error ## Fitting of the distribution ' nig ' by maximum likelihood ## Parameters: ## estimate ## mu 2.985 ## delta 0.457 ## alpha 0.466 ## beta 0.237"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-works-quantile-matching-estimation-for-discrete-distributions","dir":"Articles","previous_headings":"3. Questions regarding optimization procedures","what":"3.6. How works quantile matching estimation for discrete distributions?","title":"Frequently Asked Questions","text":"Let us consider geometric distribution values {0,1,2,3,…}\\{0,1,2,3,\\dots\\}. probability mass function, cumulative distribution function quantile function P(X=x)=p(1−p)⌊x⌋,FX(x)=1−(1−p)⌊x⌋,FX−1(q)=⌊log(1−q)log(1−p)⌋. P(X=x)= p (1-p)^{\\lfloor x\\rfloor}, F_X(x) = 1- (1-p)^{\\lfloor x\\rfloor}, F_X^{-1}(q) = \\left\\lfloor\\frac{\\log(1-q)}{\\log(1-p)}\\right\\rfloor. Due integer part (floor function), distribution function quantile function step functions. Now study QME geometric distribution. Since one parameter, choose one probabiliy, p=1/2p=1/2. theoretical median following integer FX−1(1/2)=⌊log(1/2)log(1−p)⌋. F_X^{-1}(1/2) = \\left\\lfloor\\frac{\\log(1/2)}{\\log(1-p)}\\right\\rfloor. Note theoretical median discrete distribution integer. Empirically, median may integer. Indeed even length dataset, empirical median qn,1/2=xn/2⋆+xn/2+1⋆2, q_{n,1/2} = \\frac{x_{n/2}^\\star + x_{n/2+1}^\\star}{2}, x1⋆<…= low) * (x <= upp) } ptgamma <- function(q, shape, rate, low, upp) { PU <- pgamma(upp, shape = shape, rate = rate) PL <- pgamma(low, shape = shape, rate = rate) (pgamma(q, shape, rate) - PL) / (PU - PL) * (q >= low) * (q <= upp) + 1 * (q > upp) } rtgamma <- function(n, shape, rate, low=0, upp=Inf, maxit=10) { stopifnot(n > 0) if(low > upp) return(rep(NaN, n)) PU <- pgamma(upp, shape = shape, rate = rate) PL <- pgamma(low, shape = shape, rate = rate) #simulate directly expected number of random variate n2 <- n/(PU-PL) x <- rgamma(n, shape=shape, rate=rate) x <- x[x >= low & x <= upp] i <- 0 while(length(x) < n && i < maxit) { n2 <- (n-length(x))/(PU-PL) y <- rgamma(n2, shape=shape, rate=rate) x <- c(x, y[y >= low & y <= upp]) i <- i+1 } x[1:n] } n <- 100 ; shape <- 11 ; rate <- 3 ; x0 <- 5 x <- rtgamma(n, shape = shape, rate = rate, low=x0) fit.NM.2P <- fitdist( data = x, distr = \"tgamma\", method = \"mle\", start = list(shape = 10, rate = 10), fix.arg = list(upp = Inf, low=x0), lower = c(0, 0), upper=c(Inf, Inf)) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced fit.NM.3P <- fitdist( data = x, distr = \"tgamma\", method = \"mle\", start = list(shape = 10, rate = 10, low=1), fix.arg = list(upp = Inf), lower = c(0, 0, -Inf), upper=c(Inf, Inf, min(x))) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in cov2cor(varcovar): NaNs produced ## fit3P fit2P true value ## shape 50.16 57.14 11 ## rate 9.76 10.92 3 ## low 5.01 5.00 5 ## mean sq. error 526.46 730.64 0 ## rel. error 1.94 2.28 0 fit.gamma <- fitdist( data = x-x0, distr = \"gamma\", method = \"mle\") ## fit3P fit2P orig. data fit2P shift data true value ## shape 50.16 57.14 1.498 11 ## rate 9.76 10.92 2.289 3 ## low 5.01 5.00 5.000 5 ## mean sq. error 526.46 730.64 30.266 0 ## rel. error 1.94 2.28 0.367 0 ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced ## fit3P fit2P orig. data true value ## shape 15.144 15.490 11 ## rate 3.623 3.679 3 ## low 5.000 5.000 5 ## mean sq. error 5.854 6.874 0 ## rel. error 0.195 0.212 0"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-we-compute-marginal-confidence-intervals-on-parameter-estimates-from-their-reported-standard-error","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.1. Can we compute marginal confidence intervals on parameter estimates from their reported standard error ?","title":"Frequently Asked Questions","text":"statistics, deriving marginal confidence intervals MLE parameter estimates using approximation standard errors (calculated hessian) quite common procedure. based wald approximation stands sample size nn sufficiently high, marginal 95%95\\% confidence ith component θi\\theta_i model parameter θ\\theta estimated maximum likelihood (estimate denoted θ̂\\hat \\theta) can approximated : θ̂±1.96×SE(θ̂)\\hat \\theta_i \\pm 1.96 \\times SE(\\hat \\theta_i ) SE(θ̂)SE(\\hat \\theta_i ) ith term diagonal covariance matrix estimates (ViiV_{ii}). VV generally approximated inverse Fisher information matrix ((θ̂)(\\hat \\theta)). Fisher information matrix corresponds opposite hessian matrix evaluated MLE estimate. Let us recall hessian matrix defined Hij(y,θ)=∂2L(y,θ)∂θi∂θjH_{ij}(y, \\theta) = \\frac{\\partial^2 L(y, \\theta)}{\\partial \\theta_i \\partial \\theta_j} L(y,θ)L(y, \\theta) loglikelihod function data yy parameter θ\\theta. using approximation, one must keep mind validity depend sample size. also strongly depends data, distribution, also parameterization distribution. reason recommend potential users Wald approximation compare results ones obtained using bootstrap procedure (see ) using approximation. look loglikelihood contours also interesting Wald approximation assumes elliptical contours. general context, recommend use bootstrap compute confidence intervals parameters function parameters. find two examples, one Wald confidence intervals seem correct one give wrong results, parameter values even outside possible range (negative rate bound gamma distribution).","code":"set.seed(1234) n <- rnorm(30, mean = 10, sd = 2) fn <- fitdist(n, \"norm\") bn <- bootdist(fn) bn$CI ## Median 2.5% 97.5% ## mean 9.41 8.78 10.02 ## sd 1.73 1.33 2.15 fn$estimate + cbind(\"estimate\"= 0, \"2.5%\"= -1.96*fn$sd, \"97.5%\"= 1.96*fn$sd) ## estimate 2.5% 97.5% ## mean 9.41 5.927 12.89 ## sd 1.78 -0.685 4.24 par(mfrow=c(1,1), mar=c(4,4,2,1)) llplot(fn, back.col = FALSE) set.seed(1234) g <- rgamma(30, shape = 0.1, rate = 10) fg <- fitdist(g, \"gamma\") bg <- bootdist(fg) bg$CI ## Median 2.5% 97.5% ## shape 0.0923 0.0636 0.145 ## rate 30.1018 9.6288 147.323 fg$estimate + cbind(\"estimate\"= 0, \"2.5%\"= -1.96*fg$sd, \"97.5%\"= 1.96*fg$sd) ## estimate 2.5% 97.5% ## shape 0.0882 -0.0917 0.268 ## rate 24.2613 -143.3660 191.889 par(mfrow=c(1,1), mar=c(4,4,2,1)) llplot(fg, back.col = FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-compute-confidence-intervals-on-quantiles-from-the-fit-of-a-distribution","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.2. How can we compute confidence intervals on quantiles from the fit of a distribution ?","title":"Frequently Asked Questions","text":"quantile() function can used calculate quantile fitted distribution called object class fitdist fitdistcens first argument. called object class bootdist bootdistcens first argument, quantiles returned accompanied confidence interval calculated using bootstraped sample parameters. Moreover, can use CIcdfplot() function plot fitted distribution CDF curve surrounded band corresponding pointwise intervals quantiles. See example censored data corresponding 72-hour acute salinity tolerance (LC50values) rivermarine invertebrates.","code":"data(salinity) log10LC50 <- log10(salinity) fit <- fitdistcens(log10LC50, \"norm\", control=list(trace=1)) ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 1.047296 ## Scaled convergence tolerance is 1.56059e-08 ## Stepsize computed as 0.113883 ## BUILD 3 1.085037 0.943438 ## EXTENSION 5 1.047296 0.760789 ## EXTENSION 7 0.943438 0.617018 ## HI-REDUCTION 9 0.782878 0.617018 ## LO-REDUCTION 11 0.760789 0.573266 ## HI-REDUCTION 13 0.623518 0.573266 ## HI-REDUCTION 15 0.617018 0.573266 ## HI-REDUCTION 17 0.585623 0.573266 ## HI-REDUCTION 19 0.584177 0.573266 ## HI-REDUCTION 21 0.578139 0.573266 ## LO-REDUCTION 23 0.577473 0.573266 ## LO-REDUCTION 25 0.573373 0.572282 ## HI-REDUCTION 27 0.573266 0.572282 ## LO-REDUCTION 29 0.572444 0.572282 ## HI-REDUCTION 31 0.572321 0.572229 ## HI-REDUCTION 33 0.572282 0.572210 ## HI-REDUCTION 35 0.572229 0.572195 ## HI-REDUCTION 37 0.572210 0.572195 ## LO-REDUCTION 39 0.572196 0.572191 ## HI-REDUCTION 41 0.572195 0.572188 ## HI-REDUCTION 43 0.572191 0.572188 ## LO-REDUCTION 45 0.572190 0.572187 ## HI-REDUCTION 47 0.572188 0.572187 ## HI-REDUCTION 49 0.572188 0.572187 ## HI-REDUCTION 51 0.572187 0.572187 ## HI-REDUCTION 53 0.572187 0.572187 ## LO-REDUCTION 55 0.572187 0.572187 ## HI-REDUCTION 57 0.572187 0.572187 ## HI-REDUCTION 59 0.572187 0.572187 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used # Bootstrap bootsample <- bootdistcens(fit, niter = 101) ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.537616 ## Scaled convergence tolerance is 8.0111e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.698650 0.537616 ## HI-REDUCTION 5 0.634460 0.537616 ## HI-REDUCTION 7 0.579447 0.537616 ## HI-REDUCTION 9 0.576254 0.537616 ## LO-REDUCTION 11 0.559725 0.537616 ## LO-REDUCTION 13 0.541980 0.537616 ## HI-REDUCTION 15 0.537976 0.537616 ## HI-REDUCTION 17 0.537817 0.536886 ## HI-REDUCTION 19 0.537616 0.536609 ## HI-REDUCTION 21 0.536886 0.536609 ## LO-REDUCTION 23 0.536785 0.536542 ## HI-REDUCTION 25 0.536609 0.536542 ## HI-REDUCTION 27 0.536575 0.536529 ## LO-REDUCTION 29 0.536542 0.536526 ## HI-REDUCTION 31 0.536529 0.536524 ## HI-REDUCTION 33 0.536526 0.536522 ## LO-REDUCTION 35 0.536524 0.536522 ## HI-REDUCTION 37 0.536523 0.536522 ## HI-REDUCTION 39 0.536522 0.536522 ## HI-REDUCTION 41 0.536522 0.536521 ## HI-REDUCTION 43 0.536522 0.536521 ## REFLECTION 45 0.536521 0.536521 ## HI-REDUCTION 47 0.536521 0.536521 ## LO-REDUCTION 49 0.536521 0.536521 ## HI-REDUCTION 51 0.536521 0.536521 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.527456 ## Scaled convergence tolerance is 7.85971e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.631319 0.527456 ## HI-REDUCTION 5 0.623182 0.527456 ## HI-REDUCTION 7 0.563846 0.527456 ## HI-REDUCTION 9 0.550055 0.527456 ## HI-REDUCTION 11 0.544128 0.527456 ## REFLECTION 13 0.537017 0.523952 ## LO-REDUCTION 15 0.527456 0.523952 ## HI-REDUCTION 17 0.525857 0.523952 ## HI-REDUCTION 19 0.524449 0.523902 ## HI-REDUCTION 21 0.523952 0.523612 ## HI-REDUCTION 23 0.523902 0.523385 ## LO-REDUCTION 25 0.523612 0.523385 ## HI-REDUCTION 27 0.523492 0.523385 ## LO-REDUCTION 29 0.523446 0.523383 ## LO-REDUCTION 31 0.523386 0.523383 ## HI-REDUCTION 33 0.523385 0.523376 ## HI-REDUCTION 35 0.523383 0.523375 ## HI-REDUCTION 37 0.523376 0.523374 ## HI-REDUCTION 39 0.523375 0.523374 ## HI-REDUCTION 41 0.523374 0.523374 ## HI-REDUCTION 43 0.523374 0.523373 ## LO-REDUCTION 45 0.523374 0.523373 ## HI-REDUCTION 47 0.523374 0.523373 ## LO-REDUCTION 49 0.523373 0.523373 ## LO-REDUCTION 51 0.523373 0.523373 ## HI-REDUCTION 53 0.523373 0.523373 ## REFLECTION 55 0.523373 0.523373 ## LO-REDUCTION 57 0.523373 0.523373 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.454225 ## Scaled convergence tolerance is 6.76848e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595417 0.454225 ## HI-REDUCTION 5 0.556873 0.454225 ## HI-REDUCTION 7 0.500293 0.454225 ## HI-REDUCTION 9 0.488374 0.454225 ## LO-REDUCTION 11 0.478085 0.454225 ## LO-REDUCTION 13 0.463377 0.454225 ## LO-REDUCTION 15 0.457625 0.454150 ## LO-REDUCTION 17 0.454225 0.453426 ## HI-REDUCTION 19 0.454150 0.453347 ## HI-REDUCTION 21 0.453426 0.453241 ## HI-REDUCTION 23 0.453347 0.453208 ## LO-REDUCTION 25 0.453241 0.453179 ## HI-REDUCTION 27 0.453208 0.453162 ## HI-REDUCTION 29 0.453179 0.453148 ## LO-REDUCTION 31 0.453162 0.453148 ## HI-REDUCTION 33 0.453152 0.453148 ## REFLECTION 35 0.453149 0.453146 ## HI-REDUCTION 37 0.453148 0.453146 ## HI-REDUCTION 39 0.453146 0.453146 ## HI-REDUCTION 41 0.453146 0.453145 ## HI-REDUCTION 43 0.453146 0.453145 ## HI-REDUCTION 45 0.453145 0.453145 ## HI-REDUCTION 47 0.453145 0.453145 ## HI-REDUCTION 49 0.453145 0.453145 ## HI-REDUCTION 51 0.453145 0.453145 ## HI-REDUCTION 53 0.453145 0.453145 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.545611 ## Scaled convergence tolerance is 8.13023e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.668602 0.545611 ## HI-REDUCTION 5 0.667351 0.545611 ## HI-REDUCTION 7 0.600961 0.545611 ## LO-REDUCTION 9 0.575566 0.545611 ## LO-REDUCTION 11 0.554511 0.543154 ## HI-REDUCTION 13 0.545611 0.543154 ## HI-REDUCTION 15 0.544152 0.542342 ## HI-REDUCTION 17 0.543154 0.542220 ## HI-REDUCTION 19 0.542342 0.541881 ## HI-REDUCTION 21 0.542220 0.541881 ## LO-REDUCTION 23 0.541883 0.541838 ## HI-REDUCTION 25 0.541881 0.541774 ## HI-REDUCTION 27 0.541838 0.541774 ## LO-REDUCTION 29 0.541800 0.541774 ## HI-REDUCTION 31 0.541776 0.541774 ## HI-REDUCTION 33 0.541775 0.541771 ## HI-REDUCTION 35 0.541774 0.541770 ## HI-REDUCTION 37 0.541771 0.541770 ## HI-REDUCTION 39 0.541770 0.541770 ## HI-REDUCTION 41 0.541770 0.541769 ## HI-REDUCTION 43 0.541770 0.541769 ## LO-REDUCTION 45 0.541769 0.541769 ## HI-REDUCTION 47 0.541769 0.541769 ## HI-REDUCTION 49 0.541769 0.541769 ## HI-REDUCTION 51 0.541769 0.541769 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.490659 ## Scaled convergence tolerance is 7.31138e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.644823 0.490659 ## LO-REDUCTION 5 0.555530 0.490659 ## LO-REDUCTION 7 0.554656 0.490659 ## HI-REDUCTION 9 0.516549 0.490659 ## HI-REDUCTION 11 0.505856 0.490659 ## REFLECTION 13 0.502485 0.488463 ## HI-REDUCTION 15 0.492428 0.488463 ## HI-REDUCTION 17 0.490659 0.488463 ## LO-REDUCTION 19 0.489534 0.488463 ## HI-REDUCTION 21 0.489086 0.488463 ## LO-REDUCTION 23 0.488718 0.488347 ## HI-REDUCTION 25 0.488463 0.488347 ## HI-REDUCTION 27 0.488411 0.488347 ## HI-REDUCTION 29 0.488357 0.488347 ## HI-REDUCTION 31 0.488356 0.488341 ## LO-REDUCTION 33 0.488347 0.488341 ## HI-REDUCTION 35 0.488342 0.488340 ## HI-REDUCTION 37 0.488341 0.488338 ## HI-REDUCTION 39 0.488340 0.488338 ## LO-REDUCTION 41 0.488338 0.488338 ## HI-REDUCTION 43 0.488338 0.488338 ## LO-REDUCTION 45 0.488338 0.488338 ## HI-REDUCTION 47 0.488338 0.488338 ## HI-REDUCTION 49 0.488338 0.488338 ## HI-REDUCTION 51 0.488338 0.488338 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.444794 ## Scaled convergence tolerance is 6.62795e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.546766 0.403067 ## HI-REDUCTION 5 0.461208 0.403067 ## LO-REDUCTION 7 0.444794 0.392330 ## HI-REDUCTION 9 0.403067 0.392330 ## HI-REDUCTION 11 0.396380 0.391167 ## HI-REDUCTION 13 0.392330 0.389343 ## HI-REDUCTION 15 0.391167 0.389343 ## HI-REDUCTION 17 0.389390 0.389280 ## HI-REDUCTION 19 0.389343 0.388805 ## HI-REDUCTION 21 0.389280 0.388759 ## LO-REDUCTION 23 0.388805 0.388748 ## HI-REDUCTION 25 0.388759 0.388705 ## HI-REDUCTION 27 0.388748 0.388705 ## HI-REDUCTION 29 0.388716 0.388705 ## LO-REDUCTION 31 0.388711 0.388703 ## HI-REDUCTION 33 0.388705 0.388701 ## HI-REDUCTION 35 0.388703 0.388701 ## REFLECTION 37 0.388701 0.388700 ## HI-REDUCTION 39 0.388701 0.388699 ## LO-REDUCTION 41 0.388700 0.388699 ## HI-REDUCTION 43 0.388699 0.388699 ## HI-REDUCTION 45 0.388699 0.388699 ## HI-REDUCTION 47 0.388699 0.388699 ## HI-REDUCTION 49 0.388699 0.388699 ## LO-REDUCTION 51 0.388699 0.388699 ## HI-REDUCTION 53 0.388699 0.388699 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.731663 ## Scaled convergence tolerance is 1.09026e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.919495 0.731663 ## LO-REDUCTION 5 0.782163 0.731663 ## LO-REDUCTION 7 0.781055 0.731620 ## HI-REDUCTION 9 0.740843 0.731620 ## HI-REDUCTION 11 0.731663 0.728435 ## HI-REDUCTION 13 0.731620 0.725155 ## LO-REDUCTION 15 0.728435 0.725094 ## HI-REDUCTION 17 0.725736 0.725094 ## LO-REDUCTION 19 0.725155 0.724861 ## HI-REDUCTION 21 0.725094 0.724740 ## HI-REDUCTION 23 0.724861 0.724718 ## LO-REDUCTION 25 0.724740 0.724708 ## HI-REDUCTION 27 0.724718 0.724683 ## HI-REDUCTION 29 0.724708 0.724683 ## LO-REDUCTION 31 0.724694 0.724682 ## HI-REDUCTION 33 0.724683 0.724682 ## HI-REDUCTION 35 0.724683 0.724681 ## HI-REDUCTION 37 0.724682 0.724681 ## HI-REDUCTION 39 0.724681 0.724681 ## HI-REDUCTION 41 0.724681 0.724681 ## HI-REDUCTION 43 0.724681 0.724681 ## HI-REDUCTION 45 0.724681 0.724681 ## REFLECTION 47 0.724681 0.724681 ## HI-REDUCTION 49 0.724681 0.724681 ## HI-REDUCTION 51 0.724681 0.724681 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.541366 ## Scaled convergence tolerance is 8.06698e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.717668 0.541366 ## LO-REDUCTION 5 0.630994 0.541366 ## LO-REDUCTION 7 0.623956 0.541366 ## HI-REDUCTION 9 0.572190 0.541366 ## LO-REDUCTION 11 0.545899 0.538964 ## HI-REDUCTION 13 0.541366 0.537345 ## LO-REDUCTION 15 0.538964 0.536393 ## HI-REDUCTION 17 0.537345 0.536331 ## HI-REDUCTION 19 0.536393 0.536199 ## HI-REDUCTION 21 0.536331 0.536081 ## HI-REDUCTION 23 0.536199 0.536051 ## HI-REDUCTION 25 0.536081 0.536043 ## HI-REDUCTION 27 0.536051 0.536033 ## HI-REDUCTION 29 0.536043 0.536023 ## HI-REDUCTION 31 0.536033 0.536023 ## LO-REDUCTION 33 0.536024 0.536020 ## HI-REDUCTION 35 0.536023 0.536020 ## HI-REDUCTION 37 0.536020 0.536020 ## HI-REDUCTION 39 0.536020 0.536020 ## HI-REDUCTION 41 0.536020 0.536020 ## HI-REDUCTION 43 0.536020 0.536020 ## HI-REDUCTION 45 0.536020 0.536019 ## HI-REDUCTION 47 0.536020 0.536019 ## HI-REDUCTION 49 0.536019 0.536019 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.549754 ## Scaled convergence tolerance is 8.19198e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.708643 0.549754 ## LO-REDUCTION 5 0.592929 0.549754 ## LO-REDUCTION 7 0.590926 0.549754 ## HI-REDUCTION 9 0.557629 0.549754 ## HI-REDUCTION 11 0.552007 0.547783 ## REFLECTION 13 0.549754 0.545919 ## LO-REDUCTION 15 0.547783 0.544040 ## HI-REDUCTION 17 0.545919 0.543532 ## HI-REDUCTION 19 0.544040 0.542252 ## LO-REDUCTION 21 0.543532 0.542252 ## HI-REDUCTION 23 0.542537 0.542252 ## HI-REDUCTION 25 0.542396 0.542252 ## REFLECTION 27 0.542309 0.542183 ## HI-REDUCTION 29 0.542252 0.542183 ## LO-REDUCTION 31 0.542209 0.542183 ## HI-REDUCTION 33 0.542196 0.542183 ## LO-REDUCTION 35 0.542188 0.542183 ## HI-REDUCTION 37 0.542183 0.542181 ## HI-REDUCTION 39 0.542183 0.542181 ## REFLECTION 41 0.542181 0.542180 ## HI-REDUCTION 43 0.542181 0.542180 ## HI-REDUCTION 45 0.542180 0.542180 ## HI-REDUCTION 47 0.542180 0.542180 ## LO-REDUCTION 49 0.542180 0.542180 ## HI-REDUCTION 51 0.542180 0.542180 ## HI-REDUCTION 53 0.542180 0.542180 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.531956 ## Scaled convergence tolerance is 7.92677e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.702911 0.531956 ## HI-REDUCTION 5 0.640171 0.531956 ## HI-REDUCTION 7 0.587243 0.531956 ## HI-REDUCTION 9 0.585068 0.531956 ## LO-REDUCTION 11 0.566618 0.531956 ## REFLECTION 13 0.538422 0.528758 ## REFLECTION 15 0.531956 0.523607 ## HI-REDUCTION 17 0.528758 0.519983 ## HI-REDUCTION 19 0.523607 0.519257 ## HI-REDUCTION 21 0.519983 0.518754 ## HI-REDUCTION 23 0.519257 0.518236 ## HI-REDUCTION 25 0.518754 0.518134 ## HI-REDUCTION 27 0.518236 0.518133 ## HI-REDUCTION 29 0.518134 0.518022 ## HI-REDUCTION 31 0.518133 0.518014 ## LO-REDUCTION 33 0.518022 0.518014 ## HI-REDUCTION 35 0.518020 0.517999 ## HI-REDUCTION 37 0.518014 0.517999 ## HI-REDUCTION 39 0.518002 0.517999 ## REFLECTION 41 0.517999 0.517998 ## HI-REDUCTION 43 0.517999 0.517996 ## HI-REDUCTION 45 0.517998 0.517996 ## HI-REDUCTION 47 0.517996 0.517996 ## HI-REDUCTION 49 0.517996 0.517996 ## HI-REDUCTION 51 0.517996 0.517996 ## HI-REDUCTION 53 0.517996 0.517996 ## REFLECTION 55 0.517996 0.517996 ## HI-REDUCTION 57 0.517996 0.517996 ## HI-REDUCTION 59 0.517996 0.517996 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.457849 ## Scaled convergence tolerance is 6.82248e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.573577 0.457849 ## HI-REDUCTION 5 0.547401 0.457849 ## HI-REDUCTION 7 0.502497 0.457849 ## LO-REDUCTION 9 0.480847 0.457849 ## LO-REDUCTION 11 0.458322 0.452116 ## HI-REDUCTION 13 0.457849 0.451269 ## LO-REDUCTION 15 0.452116 0.450611 ## HI-REDUCTION 17 0.451269 0.450306 ## HI-REDUCTION 19 0.450611 0.450244 ## HI-REDUCTION 21 0.450306 0.450145 ## HI-REDUCTION 23 0.450244 0.450100 ## HI-REDUCTION 25 0.450145 0.450100 ## HI-REDUCTION 27 0.450102 0.450086 ## HI-REDUCTION 29 0.450100 0.450079 ## HI-REDUCTION 31 0.450086 0.450079 ## HI-REDUCTION 33 0.450080 0.450078 ## HI-REDUCTION 35 0.450079 0.450077 ## HI-REDUCTION 37 0.450078 0.450077 ## HI-REDUCTION 39 0.450077 0.450077 ## HI-REDUCTION 41 0.450077 0.450077 ## HI-REDUCTION 43 0.450077 0.450077 ## LO-REDUCTION 45 0.450077 0.450077 ## HI-REDUCTION 47 0.450077 0.450077 ## HI-REDUCTION 49 0.450077 0.450077 ## HI-REDUCTION 51 0.450077 0.450077 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.516736 ## Scaled convergence tolerance is 7.69996e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.651724 0.516736 ## HI-REDUCTION 5 0.617368 0.516736 ## HI-REDUCTION 7 0.562123 0.516736 ## HI-REDUCTION 9 0.549954 0.516736 ## LO-REDUCTION 11 0.540531 0.516736 ## LO-REDUCTION 13 0.525588 0.516736 ## LO-REDUCTION 15 0.518110 0.515387 ## LO-REDUCTION 17 0.516736 0.515387 ## LO-REDUCTION 19 0.515975 0.515387 ## HI-REDUCTION 21 0.515516 0.515275 ## REFLECTION 23 0.515387 0.515207 ## HI-REDUCTION 25 0.515275 0.515165 ## HI-REDUCTION 27 0.515207 0.515149 ## HI-REDUCTION 29 0.515165 0.515148 ## HI-REDUCTION 31 0.515149 0.515139 ## HI-REDUCTION 33 0.515148 0.515136 ## LO-REDUCTION 35 0.515139 0.515136 ## HI-REDUCTION 37 0.515138 0.515136 ## LO-REDUCTION 39 0.515136 0.515135 ## HI-REDUCTION 41 0.515136 0.515135 ## HI-REDUCTION 43 0.515135 0.515135 ## LO-REDUCTION 45 0.515135 0.515135 ## HI-REDUCTION 47 0.515135 0.515135 ## LO-REDUCTION 49 0.515135 0.515135 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.674578 ## Scaled convergence tolerance is 1.0052e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.841103 0.674578 ## LO-REDUCTION 5 0.744070 0.674578 ## LO-REDUCTION 7 0.743356 0.674578 ## HI-REDUCTION 9 0.701895 0.674578 ## HI-REDUCTION 11 0.689791 0.674578 ## REFLECTION 13 0.686399 0.673820 ## HI-REDUCTION 15 0.675812 0.673820 ## HI-REDUCTION 17 0.674578 0.673210 ## HI-REDUCTION 19 0.673820 0.673103 ## HI-REDUCTION 21 0.673210 0.672911 ## HI-REDUCTION 23 0.673103 0.672862 ## LO-REDUCTION 25 0.672911 0.672862 ## HI-REDUCTION 27 0.672878 0.672825 ## HI-REDUCTION 29 0.672862 0.672825 ## HI-REDUCTION 31 0.672826 0.672815 ## REFLECTION 33 0.672825 0.672809 ## HI-REDUCTION 35 0.672815 0.672809 ## HI-REDUCTION 37 0.672810 0.672808 ## HI-REDUCTION 39 0.672809 0.672807 ## HI-REDUCTION 41 0.672808 0.672807 ## HI-REDUCTION 43 0.672807 0.672807 ## LO-REDUCTION 45 0.672807 0.672807 ## HI-REDUCTION 47 0.672807 0.672807 ## HI-REDUCTION 49 0.672807 0.672807 ## LO-REDUCTION 51 0.672807 0.672807 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.693852 ## Scaled convergence tolerance is 1.03392e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.808416 0.693852 ## HI-REDUCTION 5 0.797287 0.693852 ## HI-REDUCTION 7 0.738246 0.693852 ## HI-REDUCTION 9 0.719156 0.693852 ## LO-REDUCTION 11 0.714526 0.693852 ## HI-REDUCTION 13 0.704153 0.693852 ## LO-REDUCTION 15 0.703373 0.693852 ## REFLECTION 17 0.695792 0.693741 ## HI-REDUCTION 19 0.693852 0.693306 ## LO-REDUCTION 21 0.693741 0.692769 ## HI-REDUCTION 23 0.693306 0.692713 ## HI-REDUCTION 25 0.692769 0.692579 ## LO-REDUCTION 27 0.692713 0.692579 ## HI-REDUCTION 29 0.692588 0.692558 ## REFLECTION 31 0.692579 0.692518 ## HI-REDUCTION 33 0.692558 0.692518 ## HI-REDUCTION 35 0.692533 0.692518 ## LO-REDUCTION 37 0.692531 0.692518 ## LO-REDUCTION 39 0.692525 0.692518 ## LO-REDUCTION 41 0.692519 0.692518 ## HI-REDUCTION 43 0.692518 0.692518 ## HI-REDUCTION 45 0.692518 0.692518 ## HI-REDUCTION 47 0.692518 0.692518 ## HI-REDUCTION 49 0.692518 0.692518 ## HI-REDUCTION 51 0.692518 0.692518 ## HI-REDUCTION 53 0.692518 0.692518 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.519774 ## Scaled convergence tolerance is 7.74523e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.664789 0.519774 ## HI-REDUCTION 5 0.589088 0.519774 ## HI-REDUCTION 7 0.545458 0.519774 ## LO-REDUCTION 9 0.544449 0.519774 ## LO-REDUCTION 11 0.530123 0.519774 ## HI-REDUCTION 13 0.522030 0.519774 ## LO-REDUCTION 15 0.521075 0.519718 ## HI-REDUCTION 17 0.519774 0.519268 ## HI-REDUCTION 19 0.519718 0.518894 ## LO-REDUCTION 21 0.519268 0.518795 ## HI-REDUCTION 23 0.518909 0.518795 ## HI-REDUCTION 25 0.518894 0.518795 ## HI-REDUCTION 27 0.518821 0.518795 ## REFLECTION 29 0.518808 0.518785 ## HI-REDUCTION 31 0.518795 0.518784 ## HI-REDUCTION 33 0.518785 0.518782 ## HI-REDUCTION 35 0.518784 0.518781 ## HI-REDUCTION 37 0.518782 0.518781 ## HI-REDUCTION 39 0.518781 0.518780 ## HI-REDUCTION 41 0.518781 0.518780 ## HI-REDUCTION 43 0.518780 0.518780 ## HI-REDUCTION 45 0.518780 0.518780 ## LO-REDUCTION 47 0.518780 0.518780 ## HI-REDUCTION 49 0.518780 0.518780 ## HI-REDUCTION 51 0.518780 0.518780 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505864 ## Scaled convergence tolerance is 7.53796e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.672302 0.505864 ## LO-REDUCTION 5 0.589360 0.505864 ## LO-REDUCTION 7 0.583160 0.505864 ## HI-REDUCTION 9 0.532805 0.505864 ## HI-REDUCTION 11 0.513600 0.505864 ## LO-REDUCTION 13 0.509560 0.502226 ## HI-REDUCTION 15 0.505864 0.502226 ## HI-REDUCTION 17 0.502842 0.502226 ## HI-REDUCTION 19 0.502747 0.502100 ## HI-REDUCTION 21 0.502226 0.501965 ## HI-REDUCTION 23 0.502100 0.501962 ## REFLECTION 25 0.501965 0.501888 ## HI-REDUCTION 27 0.501962 0.501878 ## HI-REDUCTION 29 0.501888 0.501862 ## HI-REDUCTION 31 0.501878 0.501859 ## REFLECTION 33 0.501862 0.501850 ## HI-REDUCTION 35 0.501859 0.501850 ## HI-REDUCTION 37 0.501850 0.501848 ## HI-REDUCTION 39 0.501850 0.501847 ## REFLECTION 41 0.501848 0.501846 ## LO-REDUCTION 43 0.501847 0.501846 ## HI-REDUCTION 45 0.501846 0.501846 ## HI-REDUCTION 47 0.501846 0.501846 ## HI-REDUCTION 49 0.501846 0.501846 ## LO-REDUCTION 51 0.501846 0.501846 ## HI-REDUCTION 53 0.501846 0.501846 ## LO-REDUCTION 55 0.501846 0.501846 ## HI-REDUCTION 57 0.501846 0.501846 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.413930 ## Scaled convergence tolerance is 6.16803e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.557263 0.413930 ## HI-REDUCTION 5 0.540686 0.413930 ## HI-REDUCTION 7 0.475171 0.413930 ## LO-REDUCTION 9 0.456539 0.413930 ## LO-REDUCTION 11 0.429062 0.409608 ## LO-REDUCTION 13 0.413930 0.409608 ## HI-REDUCTION 15 0.412507 0.409608 ## HI-REDUCTION 17 0.410374 0.409608 ## HI-REDUCTION 19 0.409774 0.409216 ## HI-REDUCTION 21 0.409608 0.408982 ## HI-REDUCTION 23 0.409216 0.408843 ## LO-REDUCTION 25 0.408982 0.408836 ## HI-REDUCTION 27 0.408843 0.408826 ## HI-REDUCTION 29 0.408836 0.408814 ## HI-REDUCTION 31 0.408826 0.408814 ## HI-REDUCTION 33 0.408814 0.408809 ## REFLECTION 35 0.408814 0.408807 ## HI-REDUCTION 37 0.408809 0.408807 ## HI-REDUCTION 39 0.408808 0.408807 ## HI-REDUCTION 41 0.408807 0.408807 ## HI-REDUCTION 43 0.408807 0.408807 ## HI-REDUCTION 45 0.408807 0.408807 ## LO-REDUCTION 47 0.408807 0.408806 ## HI-REDUCTION 49 0.408807 0.408806 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.662481 ## Scaled convergence tolerance is 9.87174e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.726804 0.662481 ## HI-REDUCTION 5 0.715502 0.662481 ## HI-REDUCTION 7 0.664010 0.651208 ## HI-REDUCTION 9 0.662481 0.650567 ## LO-REDUCTION 11 0.651660 0.650567 ## REFLECTION 13 0.651208 0.648906 ## HI-REDUCTION 15 0.650567 0.646197 ## HI-REDUCTION 17 0.648906 0.646197 ## LO-REDUCTION 19 0.647688 0.646197 ## HI-REDUCTION 21 0.646622 0.646197 ## LO-REDUCTION 23 0.646541 0.646150 ## HI-REDUCTION 25 0.646207 0.646150 ## HI-REDUCTION 27 0.646197 0.646130 ## HI-REDUCTION 29 0.646150 0.646119 ## HI-REDUCTION 31 0.646130 0.646119 ## LO-REDUCTION 33 0.646122 0.646114 ## HI-REDUCTION 35 0.646119 0.646113 ## HI-REDUCTION 37 0.646114 0.646113 ## HI-REDUCTION 39 0.646114 0.646113 ## HI-REDUCTION 41 0.646113 0.646113 ## HI-REDUCTION 43 0.646113 0.646113 ## LO-REDUCTION 45 0.646113 0.646113 ## LO-REDUCTION 47 0.646113 0.646113 ## HI-REDUCTION 49 0.646113 0.646113 ## LO-REDUCTION 51 0.646113 0.646113 ## REFLECTION 53 0.646113 0.646113 ## REFLECTION 55 0.646113 0.646113 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.556899 ## Scaled convergence tolerance is 8.29844e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.782408 0.556899 ## LO-REDUCTION 5 0.660493 0.556899 ## LO-REDUCTION 7 0.639391 0.550746 ## HI-REDUCTION 9 0.582261 0.550746 ## HI-REDUCTION 11 0.560411 0.550746 ## LO-REDUCTION 13 0.556899 0.547400 ## LO-REDUCTION 15 0.550746 0.547400 ## LO-REDUCTION 17 0.548108 0.545007 ## HI-REDUCTION 19 0.547400 0.544818 ## LO-REDUCTION 21 0.545007 0.544505 ## HI-REDUCTION 23 0.544818 0.544450 ## REFLECTION 25 0.544505 0.544176 ## HI-REDUCTION 27 0.544450 0.544176 ## REFLECTION 29 0.544305 0.544156 ## REFLECTION 31 0.544176 0.544101 ## HI-REDUCTION 33 0.544156 0.544101 ## HI-REDUCTION 35 0.544116 0.544101 ## LO-REDUCTION 37 0.544109 0.544098 ## HI-REDUCTION 39 0.544101 0.544096 ## HI-REDUCTION 41 0.544098 0.544096 ## REFLECTION 43 0.544096 0.544095 ## HI-REDUCTION 45 0.544096 0.544094 ## HI-REDUCTION 47 0.544095 0.544094 ## HI-REDUCTION 49 0.544095 0.544094 ## LO-REDUCTION 51 0.544094 0.544094 ## HI-REDUCTION 53 0.544094 0.544094 ## HI-REDUCTION 55 0.544094 0.544094 ## LO-REDUCTION 57 0.544094 0.544094 ## HI-REDUCTION 59 0.544094 0.544094 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.602798 ## Scaled convergence tolerance is 8.98239e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.754891 0.602798 ## HI-REDUCTION 5 0.691000 0.602798 ## HI-REDUCTION 7 0.638621 0.602798 ## HI-REDUCTION 9 0.635769 0.602798 ## LO-REDUCTION 11 0.620563 0.602798 ## LO-REDUCTION 13 0.605715 0.602798 ## HI-REDUCTION 15 0.603087 0.602488 ## HI-REDUCTION 17 0.602798 0.601819 ## HI-REDUCTION 19 0.602488 0.601819 ## HI-REDUCTION 21 0.601947 0.601819 ## HI-REDUCTION 23 0.601940 0.601795 ## LO-REDUCTION 25 0.601819 0.601795 ## HI-REDUCTION 27 0.601795 0.601764 ## HI-REDUCTION 29 0.601795 0.601752 ## LO-REDUCTION 31 0.601764 0.601752 ## HI-REDUCTION 33 0.601753 0.601752 ## HI-REDUCTION 35 0.601752 0.601751 ## HI-REDUCTION 37 0.601752 0.601750 ## HI-REDUCTION 39 0.601751 0.601750 ## HI-REDUCTION 41 0.601751 0.601750 ## HI-REDUCTION 43 0.601750 0.601750 ## HI-REDUCTION 45 0.601750 0.601750 ## LO-REDUCTION 47 0.601750 0.601750 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.558332 ## Scaled convergence tolerance is 8.3198e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.761450 0.558332 ## LO-REDUCTION 5 0.590417 0.558332 ## HI-REDUCTION 7 0.582868 0.558332 ## HI-REDUCTION 9 0.564589 0.556985 ## HI-REDUCTION 11 0.558332 0.550727 ## HI-REDUCTION 13 0.556985 0.543300 ## LO-REDUCTION 15 0.550727 0.543300 ## HI-REDUCTION 17 0.546025 0.543300 ## LO-REDUCTION 19 0.545146 0.543300 ## EXTENSION 21 0.544510 0.542726 ## REFLECTION 23 0.543300 0.541935 ## HI-REDUCTION 25 0.542726 0.541935 ## LO-REDUCTION 27 0.542406 0.541935 ## LO-REDUCTION 29 0.542112 0.541935 ## HI-REDUCTION 31 0.541981 0.541935 ## HI-REDUCTION 33 0.541978 0.541935 ## LO-REDUCTION 35 0.541948 0.541935 ## HI-REDUCTION 37 0.541943 0.541934 ## REFLECTION 39 0.541935 0.541932 ## HI-REDUCTION 41 0.541934 0.541931 ## HI-REDUCTION 43 0.541932 0.541931 ## HI-REDUCTION 45 0.541932 0.541931 ## HI-REDUCTION 47 0.541931 0.541931 ## HI-REDUCTION 49 0.541931 0.541931 ## LO-REDUCTION 51 0.541931 0.541931 ## HI-REDUCTION 53 0.541931 0.541931 ## HI-REDUCTION 55 0.541931 0.541931 ## LO-REDUCTION 57 0.541931 0.541931 ## HI-REDUCTION 59 0.541931 0.541931 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.595932 ## Scaled convergence tolerance is 8.88008e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.674920 0.595932 ## HI-REDUCTION 5 0.646873 0.592481 ## HI-REDUCTION 7 0.605194 0.592481 ## HI-REDUCTION 9 0.595932 0.591583 ## HI-REDUCTION 11 0.592481 0.588242 ## HI-REDUCTION 13 0.591583 0.588242 ## HI-REDUCTION 15 0.588810 0.588242 ## LO-REDUCTION 17 0.588363 0.587807 ## HI-REDUCTION 19 0.588242 0.587715 ## HI-REDUCTION 21 0.587807 0.587715 ## LO-REDUCTION 23 0.587732 0.587671 ## HI-REDUCTION 25 0.587715 0.587653 ## HI-REDUCTION 27 0.587671 0.587653 ## LO-REDUCTION 29 0.587656 0.587648 ## HI-REDUCTION 31 0.587653 0.587646 ## HI-REDUCTION 33 0.587648 0.587646 ## HI-REDUCTION 35 0.587646 0.587646 ## HI-REDUCTION 37 0.587646 0.587645 ## HI-REDUCTION 39 0.587646 0.587645 ## LO-REDUCTION 41 0.587645 0.587645 ## HI-REDUCTION 43 0.587645 0.587645 ## HI-REDUCTION 45 0.587645 0.587645 ## Exiting from Nelder Mead minimizer ## 47 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.509626 ## Scaled convergence tolerance is 7.59401e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.596233 0.509626 ## HI-REDUCTION 5 0.578223 0.509626 ## HI-REDUCTION 7 0.528795 0.509626 ## HI-REDUCTION 9 0.510375 0.509626 ## HI-REDUCTION 11 0.510266 0.502343 ## LO-REDUCTION 13 0.509626 0.502343 ## LO-REDUCTION 15 0.502422 0.501147 ## HI-REDUCTION 17 0.502343 0.500002 ## LO-REDUCTION 19 0.501147 0.499858 ## HI-REDUCTION 21 0.500176 0.499858 ## HI-REDUCTION 23 0.500002 0.499858 ## HI-REDUCTION 25 0.499929 0.499858 ## REFLECTION 27 0.499872 0.499856 ## HI-REDUCTION 29 0.499858 0.499835 ## HI-REDUCTION 31 0.499856 0.499835 ## HI-REDUCTION 33 0.499837 0.499835 ## HI-REDUCTION 35 0.499835 0.499832 ## HI-REDUCTION 37 0.499835 0.499832 ## HI-REDUCTION 39 0.499832 0.499832 ## HI-REDUCTION 41 0.499832 0.499832 ## HI-REDUCTION 43 0.499832 0.499831 ## HI-REDUCTION 45 0.499832 0.499831 ## LO-REDUCTION 47 0.499831 0.499831 ## HI-REDUCTION 49 0.499831 0.499831 ## HI-REDUCTION 51 0.499831 0.499831 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.490916 ## Scaled convergence tolerance is 7.31522e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.608547 0.490916 ## HI-REDUCTION 5 0.590807 0.490916 ## HI-REDUCTION 7 0.534275 0.490916 ## HI-REDUCTION 9 0.515659 0.490916 ## HI-REDUCTION 11 0.511434 0.490916 ## LO-REDUCTION 13 0.503283 0.490492 ## LO-REDUCTION 15 0.490966 0.490492 ## HI-REDUCTION 17 0.490916 0.489661 ## LO-REDUCTION 19 0.490492 0.489661 ## LO-REDUCTION 21 0.489789 0.489513 ## HI-REDUCTION 23 0.489661 0.489386 ## LO-REDUCTION 25 0.489513 0.489386 ## HI-REDUCTION 27 0.489405 0.489386 ## LO-REDUCTION 29 0.489398 0.489377 ## HI-REDUCTION 31 0.489386 0.489376 ## HI-REDUCTION 33 0.489377 0.489374 ## LO-REDUCTION 35 0.489376 0.489374 ## HI-REDUCTION 37 0.489374 0.489373 ## HI-REDUCTION 39 0.489374 0.489373 ## LO-REDUCTION 41 0.489373 0.489373 ## HI-REDUCTION 43 0.489373 0.489373 ## HI-REDUCTION 45 0.489373 0.489373 ## HI-REDUCTION 47 0.489373 0.489373 ## HI-REDUCTION 49 0.489373 0.489373 ## HI-REDUCTION 51 0.489373 0.489373 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.392680 ## Scaled convergence tolerance is 5.85138e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.514414 0.392680 ## HI-REDUCTION 5 0.480591 0.392680 ## HI-REDUCTION 7 0.435361 0.392680 ## LO-REDUCTION 9 0.410461 0.392680 ## HI-REDUCTION 11 0.394678 0.392680 ## REFLECTION 13 0.393386 0.391126 ## HI-REDUCTION 15 0.392680 0.386541 ## HI-REDUCTION 17 0.391126 0.386541 ## LO-REDUCTION 19 0.388645 0.386541 ## HI-REDUCTION 21 0.387160 0.386541 ## LO-REDUCTION 23 0.387032 0.386540 ## HI-REDUCTION 25 0.386569 0.386540 ## HI-REDUCTION 27 0.386541 0.386468 ## HI-REDUCTION 29 0.386540 0.386453 ## LO-REDUCTION 31 0.386468 0.386451 ## HI-REDUCTION 33 0.386453 0.386448 ## HI-REDUCTION 35 0.386451 0.386447 ## HI-REDUCTION 37 0.386448 0.386447 ## HI-REDUCTION 39 0.386447 0.386446 ## HI-REDUCTION 41 0.386447 0.386446 ## LO-REDUCTION 43 0.386446 0.386446 ## HI-REDUCTION 45 0.386446 0.386446 ## LO-REDUCTION 47 0.386446 0.386446 ## HI-REDUCTION 49 0.386446 0.386446 ## LO-REDUCTION 51 0.386446 0.386446 ## HI-REDUCTION 53 0.386446 0.386446 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.568157 ## Scaled convergence tolerance is 8.4662e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.683653 0.568157 ## HI-REDUCTION 5 0.653178 0.568157 ## HI-REDUCTION 7 0.598967 0.568157 ## HI-REDUCTION 9 0.584430 0.568157 ## HI-REDUCTION 11 0.578937 0.568157 ## LO-REDUCTION 13 0.573214 0.567749 ## HI-REDUCTION 15 0.568157 0.567749 ## HI-REDUCTION 17 0.567849 0.566894 ## HI-REDUCTION 19 0.567749 0.566894 ## LO-REDUCTION 21 0.567012 0.566860 ## HI-REDUCTION 23 0.566894 0.566818 ## HI-REDUCTION 25 0.566860 0.566791 ## HI-REDUCTION 27 0.566818 0.566791 ## HI-REDUCTION 29 0.566803 0.566791 ## HI-REDUCTION 31 0.566792 0.566789 ## HI-REDUCTION 33 0.566791 0.566785 ## HI-REDUCTION 35 0.566789 0.566785 ## LO-REDUCTION 37 0.566785 0.566784 ## HI-REDUCTION 39 0.566785 0.566784 ## HI-REDUCTION 41 0.566784 0.566784 ## HI-REDUCTION 43 0.566784 0.566784 ## HI-REDUCTION 45 0.566784 0.566784 ## HI-REDUCTION 47 0.566784 0.566784 ## HI-REDUCTION 49 0.566784 0.566784 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.536183 ## Scaled convergence tolerance is 7.98974e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.654355 0.536183 ## HI-REDUCTION 5 0.570541 0.536183 ## HI-REDUCTION 7 0.539877 0.535975 ## HI-REDUCTION 9 0.536183 0.528907 ## HI-REDUCTION 11 0.535975 0.527394 ## HI-REDUCTION 13 0.529032 0.527394 ## HI-REDUCTION 15 0.528907 0.527394 ## HI-REDUCTION 17 0.527511 0.527394 ## HI-REDUCTION 19 0.527431 0.527174 ## HI-REDUCTION 21 0.527394 0.527115 ## HI-REDUCTION 23 0.527174 0.527115 ## LO-REDUCTION 25 0.527117 0.527084 ## HI-REDUCTION 27 0.527115 0.527079 ## HI-REDUCTION 29 0.527084 0.527079 ## HI-REDUCTION 31 0.527080 0.527077 ## HI-REDUCTION 33 0.527079 0.527075 ## HI-REDUCTION 35 0.527077 0.527075 ## LO-REDUCTION 37 0.527076 0.527075 ## HI-REDUCTION 39 0.527076 0.527075 ## REFLECTION 41 0.527075 0.527075 ## HI-REDUCTION 43 0.527075 0.527075 ## HI-REDUCTION 45 0.527075 0.527075 ## LO-REDUCTION 47 0.527075 0.527075 ## HI-REDUCTION 49 0.527075 0.527075 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.478880 ## Scaled convergence tolerance is 7.13587e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.582598 0.478880 ## HI-REDUCTION 5 0.547802 0.478880 ## HI-REDUCTION 7 0.500623 0.478880 ## HI-REDUCTION 9 0.489762 0.478880 ## HI-REDUCTION 11 0.484249 0.478880 ## HI-REDUCTION 13 0.480427 0.478880 ## HI-REDUCTION 15 0.479060 0.477658 ## HI-REDUCTION 17 0.478880 0.477270 ## HI-REDUCTION 19 0.477658 0.476846 ## LO-REDUCTION 21 0.477270 0.476846 ## HI-REDUCTION 23 0.477037 0.476846 ## REFLECTION 25 0.476909 0.476751 ## LO-REDUCTION 27 0.476846 0.476751 ## LO-REDUCTION 29 0.476756 0.476728 ## HI-REDUCTION 31 0.476751 0.476721 ## LO-REDUCTION 33 0.476728 0.476717 ## HI-REDUCTION 35 0.476721 0.476717 ## HI-REDUCTION 37 0.476719 0.476717 ## HI-REDUCTION 39 0.476718 0.476717 ## LO-REDUCTION 41 0.476717 0.476716 ## HI-REDUCTION 43 0.476717 0.476716 ## HI-REDUCTION 45 0.476717 0.476716 ## HI-REDUCTION 47 0.476716 0.476716 ## HI-REDUCTION 49 0.476716 0.476716 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.632103 ## Scaled convergence tolerance is 9.41906e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.806430 0.632103 ## LO-REDUCTION 5 0.667493 0.632103 ## LO-REDUCTION 7 0.659661 0.625722 ## HI-REDUCTION 9 0.632103 0.625722 ## HI-REDUCTION 11 0.629033 0.620735 ## HI-REDUCTION 13 0.625722 0.620735 ## REFLECTION 15 0.623144 0.619777 ## LO-REDUCTION 17 0.620735 0.619777 ## HI-REDUCTION 19 0.619790 0.619494 ## HI-REDUCTION 21 0.619777 0.619267 ## HI-REDUCTION 23 0.619494 0.619246 ## LO-REDUCTION 25 0.619267 0.619246 ## HI-REDUCTION 27 0.619266 0.619202 ## LO-REDUCTION 29 0.619246 0.619202 ## LO-REDUCTION 31 0.619205 0.619202 ## HI-REDUCTION 33 0.619204 0.619196 ## LO-REDUCTION 35 0.619202 0.619196 ## LO-REDUCTION 37 0.619198 0.619196 ## HI-REDUCTION 39 0.619197 0.619196 ## LO-REDUCTION 41 0.619196 0.619196 ## HI-REDUCTION 43 0.619196 0.619196 ## HI-REDUCTION 45 0.619196 0.619196 ## HI-REDUCTION 47 0.619196 0.619196 ## HI-REDUCTION 49 0.619196 0.619196 ## HI-REDUCTION 51 0.619196 0.619196 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.617735 ## Scaled convergence tolerance is 9.20497e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.755738 0.617735 ## HI-REDUCTION 5 0.659942 0.617735 ## HI-REDUCTION 7 0.628864 0.617735 ## HI-REDUCTION 9 0.622427 0.615215 ## HI-REDUCTION 11 0.617735 0.613065 ## HI-REDUCTION 13 0.615215 0.612070 ## LO-REDUCTION 15 0.613065 0.611427 ## HI-REDUCTION 17 0.612070 0.611427 ## HI-REDUCTION 19 0.611582 0.611237 ## LO-REDUCTION 21 0.611427 0.611206 ## HI-REDUCTION 23 0.611237 0.611205 ## HI-REDUCTION 25 0.611206 0.611164 ## LO-REDUCTION 27 0.611205 0.611164 ## HI-REDUCTION 29 0.611176 0.611164 ## LO-REDUCTION 31 0.611173 0.611162 ## HI-REDUCTION 33 0.611164 0.611162 ## HI-REDUCTION 35 0.611162 0.611161 ## HI-REDUCTION 37 0.611162 0.611161 ## REFLECTION 39 0.611161 0.611160 ## HI-REDUCTION 41 0.611161 0.611160 ## HI-REDUCTION 43 0.611160 0.611160 ## HI-REDUCTION 45 0.611160 0.611160 ## Exiting from Nelder Mead minimizer ## 47 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.622215 ## Scaled convergence tolerance is 9.27172e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.810537 0.622215 ## LO-REDUCTION 5 0.708024 0.622215 ## LO-REDUCTION 7 0.701509 0.622215 ## HI-REDUCTION 9 0.647897 0.622215 ## HI-REDUCTION 11 0.628038 0.622215 ## LO-REDUCTION 13 0.624931 0.618775 ## HI-REDUCTION 15 0.622215 0.618134 ## HI-REDUCTION 17 0.618831 0.618134 ## HI-REDUCTION 19 0.618775 0.617998 ## HI-REDUCTION 21 0.618134 0.617916 ## HI-REDUCTION 23 0.617998 0.617692 ## LO-REDUCTION 25 0.617916 0.617692 ## HI-REDUCTION 27 0.617750 0.617692 ## HI-REDUCTION 29 0.617744 0.617692 ## REFLECTION 31 0.617714 0.617677 ## LO-REDUCTION 33 0.617692 0.617677 ## LO-REDUCTION 35 0.617688 0.617677 ## LO-REDUCTION 37 0.617682 0.617677 ## LO-REDUCTION 39 0.617678 0.617677 ## HI-REDUCTION 41 0.617678 0.617677 ## LO-REDUCTION 43 0.617678 0.617677 ## HI-REDUCTION 45 0.617677 0.617677 ## LO-REDUCTION 47 0.617677 0.617677 ## HI-REDUCTION 49 0.617677 0.617677 ## LO-REDUCTION 51 0.617677 0.617677 ## HI-REDUCTION 53 0.617677 0.617677 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.460247 ## Scaled convergence tolerance is 6.85821e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595625 0.460247 ## HI-REDUCTION 5 0.566459 0.460247 ## HI-REDUCTION 7 0.518363 0.460247 ## LO-REDUCTION 9 0.492034 0.456962 ## LO-REDUCTION 11 0.460247 0.449228 ## LO-REDUCTION 13 0.456962 0.449228 ## LO-REDUCTION 15 0.449274 0.447361 ## HI-REDUCTION 17 0.449228 0.445644 ## LO-REDUCTION 19 0.447361 0.445527 ## HI-REDUCTION 21 0.445886 0.445527 ## HI-REDUCTION 23 0.445644 0.445527 ## HI-REDUCTION 25 0.445548 0.445486 ## HI-REDUCTION 27 0.445527 0.445486 ## HI-REDUCTION 29 0.445486 0.445475 ## HI-REDUCTION 31 0.445486 0.445469 ## LO-REDUCTION 33 0.445475 0.445466 ## HI-REDUCTION 35 0.445469 0.445466 ## HI-REDUCTION 37 0.445468 0.445466 ## LO-REDUCTION 39 0.445466 0.445466 ## HI-REDUCTION 41 0.445466 0.445466 ## HI-REDUCTION 43 0.445466 0.445466 ## HI-REDUCTION 45 0.445466 0.445466 ## HI-REDUCTION 47 0.445466 0.445466 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.676910 ## Scaled convergence tolerance is 1.00867e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.785600 0.676910 ## HI-REDUCTION 5 0.776660 0.676910 ## HI-REDUCTION 7 0.717414 0.676910 ## HI-REDUCTION 9 0.695463 0.676910 ## HI-REDUCTION 11 0.693745 0.676910 ## LO-REDUCTION 13 0.684729 0.674914 ## HI-REDUCTION 15 0.677731 0.674914 ## HI-REDUCTION 17 0.676910 0.674914 ## HI-REDUCTION 19 0.675815 0.674914 ## LO-REDUCTION 21 0.675500 0.674914 ## LO-REDUCTION 23 0.674947 0.674914 ## HI-REDUCTION 25 0.674923 0.674858 ## HI-REDUCTION 27 0.674914 0.674842 ## LO-REDUCTION 29 0.674858 0.674842 ## HI-REDUCTION 31 0.674852 0.674842 ## LO-REDUCTION 33 0.674843 0.674840 ## HI-REDUCTION 35 0.674842 0.674840 ## HI-REDUCTION 37 0.674840 0.674840 ## HI-REDUCTION 39 0.674840 0.674840 ## HI-REDUCTION 41 0.674840 0.674839 ## HI-REDUCTION 43 0.674840 0.674839 ## HI-REDUCTION 45 0.674839 0.674839 ## HI-REDUCTION 47 0.674839 0.674839 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.509735 ## Scaled convergence tolerance is 7.59564e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.677654 0.509735 ## HI-REDUCTION 5 0.571044 0.509735 ## LO-REDUCTION 7 0.536958 0.481215 ## HI-REDUCTION 9 0.509735 0.481215 ## HI-REDUCTION 11 0.489270 0.475982 ## REFLECTION 13 0.481215 0.460989 ## HI-REDUCTION 15 0.475982 0.460989 ## LO-REDUCTION 17 0.467155 0.460989 ## LO-REDUCTION 19 0.462470 0.459736 ## HI-REDUCTION 21 0.460989 0.459736 ## HI-REDUCTION 23 0.460298 0.459736 ## HI-REDUCTION 25 0.460048 0.459736 ## LO-REDUCTION 27 0.459883 0.459736 ## HI-REDUCTION 29 0.459798 0.459736 ## REFLECTION 31 0.459750 0.459727 ## HI-REDUCTION 33 0.459736 0.459712 ## HI-REDUCTION 35 0.459727 0.459712 ## HI-REDUCTION 37 0.459713 0.459712 ## HI-REDUCTION 39 0.459712 0.459709 ## HI-REDUCTION 41 0.459712 0.459709 ## LO-REDUCTION 43 0.459709 0.459709 ## HI-REDUCTION 45 0.459709 0.459709 ## HI-REDUCTION 47 0.459709 0.459709 ## HI-REDUCTION 49 0.459709 0.459709 ## LO-REDUCTION 51 0.459709 0.459709 ## HI-REDUCTION 53 0.459709 0.459709 ## HI-REDUCTION 55 0.459709 0.459709 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.591495 ## Scaled convergence tolerance is 8.81396e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.694478 0.591495 ## HI-REDUCTION 5 0.689410 0.591495 ## HI-REDUCTION 7 0.630600 0.591495 ## HI-REDUCTION 9 0.608476 0.591495 ## HI-REDUCTION 11 0.607354 0.591495 ## LO-REDUCTION 13 0.598496 0.589559 ## HI-REDUCTION 15 0.592005 0.589559 ## HI-REDUCTION 17 0.591495 0.589559 ## HI-REDUCTION 19 0.590293 0.589559 ## LO-REDUCTION 21 0.590100 0.589559 ## LO-REDUCTION 23 0.589652 0.589559 ## HI-REDUCTION 25 0.589625 0.589550 ## HI-REDUCTION 27 0.589559 0.589531 ## HI-REDUCTION 29 0.589550 0.589530 ## REFLECTION 31 0.589531 0.589523 ## HI-REDUCTION 33 0.589530 0.589521 ## HI-REDUCTION 35 0.589523 0.589519 ## HI-REDUCTION 37 0.589521 0.589519 ## REFLECTION 39 0.589519 0.589518 ## HI-REDUCTION 41 0.589519 0.589518 ## HI-REDUCTION 43 0.589518 0.589518 ## HI-REDUCTION 45 0.589518 0.589518 ## HI-REDUCTION 47 0.589518 0.589518 ## HI-REDUCTION 49 0.589518 0.589518 ## HI-REDUCTION 51 0.589518 0.589518 ## LO-REDUCTION 53 0.589518 0.589518 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.685105 ## Scaled convergence tolerance is 1.02089e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.831025 0.685105 ## HI-REDUCTION 5 0.779278 0.685105 ## HI-REDUCTION 7 0.723822 0.685105 ## HI-REDUCTION 9 0.715486 0.685105 ## LO-REDUCTION 11 0.703496 0.685105 ## LO-REDUCTION 13 0.690733 0.685105 ## HI-REDUCTION 15 0.688502 0.685105 ## LO-REDUCTION 17 0.686844 0.685105 ## HI-REDUCTION 19 0.686292 0.685105 ## LO-REDUCTION 21 0.685741 0.685065 ## LO-REDUCTION 23 0.685126 0.685065 ## HI-REDUCTION 25 0.685105 0.685036 ## HI-REDUCTION 27 0.685065 0.685033 ## HI-REDUCTION 29 0.685036 0.685017 ## HI-REDUCTION 31 0.685033 0.685017 ## REFLECTION 33 0.685021 0.685017 ## HI-REDUCTION 35 0.685017 0.685013 ## LO-REDUCTION 37 0.685017 0.685013 ## HI-REDUCTION 39 0.685014 0.685013 ## HI-REDUCTION 41 0.685013 0.685013 ## HI-REDUCTION 43 0.685013 0.685013 ## HI-REDUCTION 45 0.685013 0.685012 ## LO-REDUCTION 47 0.685013 0.685012 ## HI-REDUCTION 49 0.685013 0.685012 ## REFLECTION 51 0.685012 0.685012 ## HI-REDUCTION 53 0.685012 0.685012 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.599833 ## Scaled convergence tolerance is 8.93822e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.679547 0.599833 ## HI-REDUCTION 5 0.669670 0.598766 ## HI-REDUCTION 7 0.619864 0.598766 ## HI-REDUCTION 9 0.601289 0.598766 ## HI-REDUCTION 11 0.599833 0.595275 ## HI-REDUCTION 13 0.598766 0.593197 ## HI-REDUCTION 15 0.595275 0.593197 ## REFLECTION 17 0.594349 0.593131 ## HI-REDUCTION 19 0.593197 0.593056 ## HI-REDUCTION 21 0.593131 0.592630 ## HI-REDUCTION 23 0.593056 0.592630 ## REFLECTION 25 0.592768 0.592625 ## HI-REDUCTION 27 0.592630 0.592601 ## HI-REDUCTION 29 0.592625 0.592570 ## HI-REDUCTION 31 0.592601 0.592570 ## HI-REDUCTION 33 0.592573 0.592570 ## HI-REDUCTION 35 0.592571 0.592566 ## HI-REDUCTION 37 0.592570 0.592564 ## HI-REDUCTION 39 0.592566 0.592564 ## REFLECTION 41 0.592565 0.592564 ## HI-REDUCTION 43 0.592564 0.592564 ## HI-REDUCTION 45 0.592564 0.592564 ## HI-REDUCTION 47 0.592564 0.592564 ## HI-REDUCTION 49 0.592564 0.592564 ## HI-REDUCTION 51 0.592564 0.592564 ## HI-REDUCTION 53 0.592564 0.592564 ## HI-REDUCTION 55 0.592564 0.592564 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.499725 ## Scaled convergence tolerance is 7.44649e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.640893 0.499725 ## HI-REDUCTION 5 0.608286 0.499725 ## HI-REDUCTION 7 0.549849 0.499725 ## HI-REDUCTION 9 0.536574 0.499725 ## LO-REDUCTION 11 0.526483 0.499725 ## LO-REDUCTION 13 0.509528 0.499725 ## LO-REDUCTION 15 0.501752 0.498303 ## LO-REDUCTION 17 0.499725 0.498303 ## LO-REDUCTION 19 0.498585 0.498141 ## HI-REDUCTION 21 0.498303 0.497898 ## HI-REDUCTION 23 0.498141 0.497898 ## LO-REDUCTION 25 0.497996 0.497876 ## HI-REDUCTION 27 0.497898 0.497875 ## HI-REDUCTION 29 0.497876 0.497866 ## HI-REDUCTION 31 0.497875 0.497863 ## HI-REDUCTION 33 0.497866 0.497861 ## HI-REDUCTION 35 0.497863 0.497861 ## REFLECTION 37 0.497861 0.497861 ## HI-REDUCTION 39 0.497861 0.497860 ## HI-REDUCTION 41 0.497861 0.497860 ## HI-REDUCTION 43 0.497860 0.497860 ## HI-REDUCTION 45 0.497860 0.497860 ## LO-REDUCTION 47 0.497860 0.497860 ## HI-REDUCTION 49 0.497860 0.497860 ## REFLECTION 51 0.497860 0.497860 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.582547 ## Scaled convergence tolerance is 8.68063e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.739057 0.582547 ## HI-REDUCTION 5 0.671122 0.582547 ## HI-REDUCTION 7 0.620619 0.582547 ## HI-REDUCTION 9 0.620032 0.582547 ## LO-REDUCTION 11 0.603066 0.582547 ## LO-REDUCTION 13 0.585978 0.581524 ## HI-REDUCTION 15 0.582547 0.581524 ## HI-REDUCTION 17 0.582306 0.581270 ## HI-REDUCTION 19 0.581524 0.581270 ## HI-REDUCTION 21 0.581296 0.580965 ## LO-REDUCTION 23 0.581270 0.580927 ## LO-REDUCTION 25 0.580976 0.580927 ## HI-REDUCTION 27 0.580965 0.580915 ## LO-REDUCTION 29 0.580927 0.580908 ## HI-REDUCTION 31 0.580915 0.580898 ## HI-REDUCTION 33 0.580908 0.580890 ## LO-REDUCTION 35 0.580898 0.580890 ## HI-REDUCTION 37 0.580891 0.580890 ## LO-REDUCTION 39 0.580891 0.580889 ## LO-REDUCTION 41 0.580890 0.580889 ## LO-REDUCTION 43 0.580889 0.580889 ## LO-REDUCTION 45 0.580889 0.580889 ## LO-REDUCTION 47 0.580889 0.580889 ## HI-REDUCTION 49 0.580889 0.580889 ## REFLECTION 51 0.580889 0.580889 ## HI-REDUCTION 53 0.580889 0.580889 ## EXTENSION 55 0.580889 0.580889 ## LO-REDUCTION 57 0.580889 0.580889 ## EXTENSION 59 0.580889 0.580889 ## HI-REDUCTION 61 0.580889 0.580889 ## EXTENSION 63 0.580889 0.580889 ## LO-REDUCTION 65 0.580889 0.580889 ## EXTENSION 67 0.580889 0.580889 ## EXTENSION 69 0.580889 0.580889 ## HI-REDUCTION 71 0.580889 0.580889 ## REFLECTION 73 0.580889 0.580889 ## HI-REDUCTION 75 0.580889 0.580889 ## Exiting from Nelder Mead minimizer ## 77 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.591532 ## Scaled convergence tolerance is 8.81451e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.760876 0.591532 ## HI-REDUCTION 5 0.687641 0.591532 ## HI-REDUCTION 7 0.667766 0.591532 ## REFLECTION 9 0.623998 0.571125 ## LO-REDUCTION 11 0.591532 0.556044 ## HI-REDUCTION 13 0.571125 0.556044 ## HI-REDUCTION 15 0.566313 0.556044 ## LO-REDUCTION 17 0.557740 0.553203 ## HI-REDUCTION 19 0.556044 0.553203 ## HI-REDUCTION 21 0.554297 0.553203 ## LO-REDUCTION 23 0.553805 0.553201 ## HI-REDUCTION 25 0.553215 0.553201 ## HI-REDUCTION 27 0.553203 0.553080 ## HI-REDUCTION 29 0.553201 0.553054 ## LO-REDUCTION 31 0.553080 0.553054 ## HI-REDUCTION 33 0.553073 0.553053 ## LO-REDUCTION 35 0.553054 0.553051 ## HI-REDUCTION 37 0.553053 0.553049 ## HI-REDUCTION 39 0.553051 0.553049 ## HI-REDUCTION 41 0.553049 0.553049 ## HI-REDUCTION 43 0.553049 0.553049 ## HI-REDUCTION 45 0.553049 0.553049 ## HI-REDUCTION 47 0.553049 0.553049 ## REFLECTION 49 0.553049 0.553049 ## HI-REDUCTION 51 0.553049 0.553049 ## HI-REDUCTION 53 0.553049 0.553049 ## HI-REDUCTION 55 0.553049 0.553049 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.560681 ## Scaled convergence tolerance is 8.3548e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.681649 0.560681 ## HI-REDUCTION 5 0.655570 0.560681 ## HI-REDUCTION 7 0.607463 0.560681 ## LO-REDUCTION 9 0.584576 0.560255 ## LO-REDUCTION 11 0.560681 0.555462 ## HI-REDUCTION 13 0.560255 0.554575 ## HI-REDUCTION 15 0.555462 0.553442 ## HI-REDUCTION 17 0.554575 0.553314 ## REFLECTION 19 0.553442 0.553182 ## HI-REDUCTION 21 0.553314 0.552740 ## HI-REDUCTION 23 0.553182 0.552589 ## HI-REDUCTION 25 0.552740 0.552589 ## LO-REDUCTION 27 0.552704 0.552556 ## HI-REDUCTION 29 0.552589 0.552556 ## HI-REDUCTION 31 0.552570 0.552548 ## HI-REDUCTION 33 0.552556 0.552548 ## HI-REDUCTION 35 0.552551 0.552548 ## HI-REDUCTION 37 0.552548 0.552546 ## HI-REDUCTION 39 0.552548 0.552545 ## LO-REDUCTION 41 0.552546 0.552545 ## HI-REDUCTION 43 0.552545 0.552545 ## HI-REDUCTION 45 0.552545 0.552545 ## HI-REDUCTION 47 0.552545 0.552545 ## HI-REDUCTION 49 0.552545 0.552545 ## HI-REDUCTION 51 0.552545 0.552545 ## HI-REDUCTION 53 0.552545 0.552545 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.691915 ## Scaled convergence tolerance is 1.03103e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.860556 0.691915 ## HI-REDUCTION 5 0.801679 0.691915 ## HI-REDUCTION 7 0.744326 0.691915 ## HI-REDUCTION 9 0.739412 0.691915 ## LO-REDUCTION 11 0.722397 0.691915 ## LO-REDUCTION 13 0.698927 0.688279 ## LO-REDUCTION 15 0.691915 0.687937 ## LO-REDUCTION 17 0.688279 0.687839 ## HI-REDUCTION 19 0.687937 0.687499 ## HI-REDUCTION 21 0.687839 0.687363 ## HI-REDUCTION 23 0.687499 0.687363 ## HI-REDUCTION 25 0.687458 0.687363 ## HI-REDUCTION 27 0.687383 0.687363 ## HI-REDUCTION 29 0.687365 0.687347 ## HI-REDUCTION 31 0.687363 0.687340 ## HI-REDUCTION 33 0.687347 0.687337 ## LO-REDUCTION 35 0.687340 0.687337 ## HI-REDUCTION 37 0.687337 0.687336 ## HI-REDUCTION 39 0.687337 0.687336 ## LO-REDUCTION 41 0.687336 0.687336 ## HI-REDUCTION 43 0.687336 0.687336 ## HI-REDUCTION 45 0.687336 0.687336 ## LO-REDUCTION 47 0.687336 0.687336 ## HI-REDUCTION 49 0.687336 0.687336 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.459809 ## Scaled convergence tolerance is 6.85168e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.601866 0.459809 ## HI-REDUCTION 5 0.551544 0.459809 ## HI-REDUCTION 7 0.501147 0.459809 ## HI-REDUCTION 9 0.494955 0.459809 ## LO-REDUCTION 11 0.482140 0.459809 ## LO-REDUCTION 13 0.466706 0.459809 ## LO-REDUCTION 15 0.460665 0.458807 ## HI-REDUCTION 17 0.459809 0.458807 ## HI-REDUCTION 19 0.458951 0.458712 ## LO-REDUCTION 21 0.458807 0.458595 ## HI-REDUCTION 23 0.458712 0.458531 ## HI-REDUCTION 25 0.458595 0.458527 ## LO-REDUCTION 27 0.458531 0.458499 ## HI-REDUCTION 29 0.458527 0.458497 ## HI-REDUCTION 31 0.458503 0.458497 ## HI-REDUCTION 33 0.458499 0.458497 ## HI-REDUCTION 35 0.458497 0.458496 ## HI-REDUCTION 37 0.458497 0.458495 ## LO-REDUCTION 39 0.458496 0.458495 ## HI-REDUCTION 41 0.458495 0.458495 ## HI-REDUCTION 43 0.458495 0.458495 ## HI-REDUCTION 45 0.458495 0.458495 ## HI-REDUCTION 47 0.458495 0.458495 ## HI-REDUCTION 49 0.458495 0.458495 ## HI-REDUCTION 51 0.458495 0.458495 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.546488 ## Scaled convergence tolerance is 8.1433e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.674401 0.546488 ## HI-REDUCTION 5 0.640320 0.546488 ## HI-REDUCTION 7 0.584994 0.546488 ## HI-REDUCTION 9 0.571998 0.546488 ## LO-REDUCTION 11 0.564160 0.546488 ## HI-REDUCTION 13 0.554473 0.546488 ## LO-REDUCTION 15 0.554300 0.546488 ## LO-REDUCTION 17 0.548616 0.546341 ## LO-REDUCTION 19 0.546697 0.546341 ## HI-REDUCTION 21 0.546488 0.546305 ## HI-REDUCTION 23 0.546341 0.546248 ## HI-REDUCTION 25 0.546305 0.546235 ## LO-REDUCTION 27 0.546248 0.546231 ## HI-REDUCTION 29 0.546235 0.546222 ## HI-REDUCTION 31 0.546231 0.546219 ## LO-REDUCTION 33 0.546222 0.546219 ## HI-REDUCTION 35 0.546219 0.546218 ## HI-REDUCTION 37 0.546219 0.546218 ## LO-REDUCTION 39 0.546218 0.546218 ## HI-REDUCTION 41 0.546218 0.546218 ## LO-REDUCTION 43 0.546218 0.546218 ## HI-REDUCTION 45 0.546218 0.546217 ## HI-REDUCTION 47 0.546218 0.546217 ## LO-REDUCTION 49 0.546217 0.546217 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.481520 ## Scaled convergence tolerance is 7.17521e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.599929 0.481520 ## HI-REDUCTION 5 0.587969 0.481520 ## HI-REDUCTION 7 0.528572 0.481520 ## HI-REDUCTION 9 0.508383 0.481520 ## LO-REDUCTION 11 0.504648 0.481520 ## LO-REDUCTION 13 0.492677 0.481520 ## LO-REDUCTION 15 0.484624 0.480430 ## LO-REDUCTION 17 0.481520 0.480065 ## LO-REDUCTION 19 0.480430 0.480013 ## HI-REDUCTION 21 0.480065 0.479871 ## HI-REDUCTION 23 0.480013 0.479719 ## LO-REDUCTION 25 0.479871 0.479719 ## HI-REDUCTION 27 0.479796 0.479719 ## REFLECTION 29 0.479764 0.479718 ## LO-REDUCTION 31 0.479719 0.479706 ## HI-REDUCTION 33 0.479718 0.479706 ## HI-REDUCTION 35 0.479706 0.479706 ## HI-REDUCTION 37 0.479706 0.479704 ## HI-REDUCTION 39 0.479706 0.479704 ## LO-REDUCTION 41 0.479704 0.479704 ## HI-REDUCTION 43 0.479704 0.479704 ## HI-REDUCTION 45 0.479704 0.479704 ## HI-REDUCTION 47 0.479704 0.479704 ## LO-REDUCTION 49 0.479704 0.479704 ## HI-REDUCTION 51 0.479704 0.479704 ## HI-REDUCTION 53 0.479704 0.479704 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.362732 ## Scaled convergence tolerance is 5.40512e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.454957 0.362732 ## HI-REDUCTION 5 0.440590 0.362732 ## HI-REDUCTION 7 0.389880 0.362732 ## HI-REDUCTION 9 0.374580 0.362732 ## HI-REDUCTION 11 0.371778 0.362732 ## REFLECTION 13 0.364796 0.360495 ## HI-REDUCTION 15 0.362732 0.359902 ## HI-REDUCTION 17 0.360495 0.359849 ## HI-REDUCTION 19 0.359902 0.359024 ## HI-REDUCTION 21 0.359849 0.359024 ## LO-REDUCTION 23 0.359201 0.358889 ## HI-REDUCTION 25 0.359024 0.358889 ## HI-REDUCTION 27 0.358948 0.358889 ## LO-REDUCTION 29 0.358904 0.358889 ## HI-REDUCTION 31 0.358891 0.358881 ## HI-REDUCTION 33 0.358889 0.358881 ## HI-REDUCTION 35 0.358881 0.358879 ## REFLECTION 37 0.358881 0.358878 ## HI-REDUCTION 39 0.358879 0.358878 ## HI-REDUCTION 41 0.358878 0.358877 ## HI-REDUCTION 43 0.358878 0.358877 ## LO-REDUCTION 45 0.358877 0.358877 ## HI-REDUCTION 47 0.358877 0.358877 ## LO-REDUCTION 49 0.358877 0.358877 ## HI-REDUCTION 51 0.358877 0.358877 ## HI-REDUCTION 53 0.358877 0.358877 ## LO-REDUCTION 55 0.358877 0.358877 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.533882 ## Scaled convergence tolerance is 7.95546e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.647255 0.533882 ## HI-REDUCTION 5 0.622845 0.533882 ## HI-REDUCTION 7 0.569033 0.533882 ## HI-REDUCTION 9 0.553738 0.533882 ## HI-REDUCTION 11 0.548768 0.533882 ## LO-REDUCTION 13 0.542760 0.533882 ## HI-REDUCTION 15 0.536437 0.533882 ## LO-REDUCTION 17 0.534650 0.533882 ## HI-REDUCTION 19 0.534093 0.533742 ## LO-REDUCTION 21 0.533882 0.533695 ## HI-REDUCTION 23 0.533742 0.533641 ## HI-REDUCTION 25 0.533695 0.533608 ## LO-REDUCTION 27 0.533641 0.533599 ## HI-REDUCTION 29 0.533608 0.533599 ## HI-REDUCTION 31 0.533602 0.533596 ## HI-REDUCTION 33 0.533599 0.533596 ## HI-REDUCTION 35 0.533596 0.533596 ## HI-REDUCTION 37 0.533596 0.533595 ## HI-REDUCTION 39 0.533596 0.533595 ## LO-REDUCTION 41 0.533595 0.533595 ## HI-REDUCTION 43 0.533595 0.533595 ## LO-REDUCTION 45 0.533595 0.533595 ## HI-REDUCTION 47 0.533595 0.533595 ## HI-REDUCTION 49 0.533595 0.533595 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.571933 ## Scaled convergence tolerance is 8.52246e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.668545 0.571933 ## HI-REDUCTION 5 0.667817 0.571933 ## HI-REDUCTION 7 0.600808 0.571933 ## HI-REDUCTION 9 0.588120 0.571933 ## HI-REDUCTION 11 0.581290 0.571933 ## LO-REDUCTION 13 0.574816 0.568417 ## HI-REDUCTION 15 0.571933 0.568417 ## HI-REDUCTION 17 0.569443 0.568417 ## HI-REDUCTION 19 0.569199 0.568417 ## LO-REDUCTION 21 0.568570 0.568341 ## HI-REDUCTION 23 0.568417 0.568327 ## HI-REDUCTION 25 0.568341 0.568297 ## HI-REDUCTION 27 0.568327 0.568296 ## HI-REDUCTION 29 0.568297 0.568293 ## HI-REDUCTION 31 0.568296 0.568286 ## HI-REDUCTION 33 0.568293 0.568286 ## LO-REDUCTION 35 0.568289 0.568286 ## HI-REDUCTION 37 0.568288 0.568286 ## LO-REDUCTION 39 0.568287 0.568286 ## HI-REDUCTION 41 0.568286 0.568286 ## HI-REDUCTION 43 0.568286 0.568286 ## LO-REDUCTION 45 0.568286 0.568286 ## HI-REDUCTION 47 0.568286 0.568286 ## HI-REDUCTION 49 0.568286 0.568286 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.594867 ## Scaled convergence tolerance is 8.86421e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.733016 0.594867 ## HI-REDUCTION 5 0.706121 0.594867 ## HI-REDUCTION 7 0.643515 0.594867 ## HI-REDUCTION 9 0.627810 0.594867 ## LO-REDUCTION 11 0.619071 0.594867 ## LO-REDUCTION 13 0.603980 0.594867 ## LO-REDUCTION 15 0.598576 0.594867 ## LO-REDUCTION 17 0.595741 0.594430 ## HI-REDUCTION 19 0.594867 0.594359 ## HI-REDUCTION 21 0.594430 0.594328 ## HI-REDUCTION 23 0.594359 0.594247 ## HI-REDUCTION 25 0.594328 0.594225 ## HI-REDUCTION 27 0.594247 0.594225 ## HI-REDUCTION 29 0.594242 0.594223 ## HI-REDUCTION 31 0.594225 0.594222 ## HI-REDUCTION 33 0.594223 0.594217 ## LO-REDUCTION 35 0.594222 0.594217 ## HI-REDUCTION 37 0.594219 0.594217 ## LO-REDUCTION 39 0.594218 0.594217 ## LO-REDUCTION 41 0.594217 0.594217 ## HI-REDUCTION 43 0.594217 0.594217 ## HI-REDUCTION 45 0.594217 0.594217 ## HI-REDUCTION 47 0.594217 0.594217 ## HI-REDUCTION 49 0.594217 0.594217 ## LO-REDUCTION 51 0.594217 0.594217 ## HI-REDUCTION 53 0.594217 0.594217 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.621944 ## Scaled convergence tolerance is 9.26768e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.764670 0.621944 ## HI-REDUCTION 5 0.709613 0.621944 ## HI-REDUCTION 7 0.657822 0.621944 ## HI-REDUCTION 9 0.652043 0.621944 ## LO-REDUCTION 11 0.639335 0.621944 ## LO-REDUCTION 13 0.626730 0.621944 ## HI-REDUCTION 15 0.624247 0.621944 ## HI-REDUCTION 17 0.623147 0.621944 ## LO-REDUCTION 19 0.622320 0.621872 ## HI-REDUCTION 21 0.621944 0.621753 ## HI-REDUCTION 23 0.621872 0.621746 ## LO-REDUCTION 25 0.621753 0.621716 ## HI-REDUCTION 27 0.621746 0.621701 ## HI-REDUCTION 29 0.621716 0.621699 ## LO-REDUCTION 31 0.621701 0.621699 ## HI-REDUCTION 33 0.621699 0.621695 ## LO-REDUCTION 35 0.621699 0.621695 ## LO-REDUCTION 37 0.621696 0.621695 ## HI-REDUCTION 39 0.621695 0.621695 ## LO-REDUCTION 41 0.621695 0.621695 ## HI-REDUCTION 43 0.621695 0.621695 ## HI-REDUCTION 45 0.621695 0.621695 ## HI-REDUCTION 47 0.621695 0.621695 ## HI-REDUCTION 49 0.621695 0.621695 ## HI-REDUCTION 51 0.621695 0.621695 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.568834 ## Scaled convergence tolerance is 8.47629e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.664628 0.568834 ## HI-REDUCTION 5 0.646727 0.568834 ## HI-REDUCTION 7 0.596693 0.568834 ## HI-REDUCTION 9 0.579577 0.568834 ## HI-REDUCTION 11 0.577361 0.568834 ## REFLECTION 13 0.569635 0.566510 ## HI-REDUCTION 15 0.568834 0.564766 ## LO-REDUCTION 17 0.566510 0.564754 ## HI-REDUCTION 19 0.564766 0.564351 ## HI-REDUCTION 21 0.564754 0.563966 ## LO-REDUCTION 23 0.564351 0.563966 ## HI-REDUCTION 25 0.564053 0.563966 ## LO-REDUCTION 27 0.564025 0.563956 ## HI-REDUCTION 29 0.563966 0.563943 ## HI-REDUCTION 31 0.563956 0.563938 ## REFLECTION 33 0.563943 0.563935 ## HI-REDUCTION 35 0.563938 0.563932 ## HI-REDUCTION 37 0.563935 0.563931 ## HI-REDUCTION 39 0.563932 0.563931 ## LO-REDUCTION 41 0.563931 0.563930 ## HI-REDUCTION 43 0.563931 0.563930 ## HI-REDUCTION 45 0.563930 0.563930 ## LO-REDUCTION 47 0.563930 0.563930 ## HI-REDUCTION 49 0.563930 0.563930 ## HI-REDUCTION 51 0.563930 0.563930 ## LO-REDUCTION 53 0.563930 0.563930 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.551090 ## Scaled convergence tolerance is 8.21188e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.739905 0.551090 ## LO-REDUCTION 5 0.667719 0.551090 ## REFLECTION 7 0.655222 0.540546 ## HI-REDUCTION 9 0.586407 0.540546 ## HI-REDUCTION 11 0.556422 0.540546 ## REFLECTION 13 0.551090 0.538485 ## HI-REDUCTION 15 0.540546 0.536176 ## HI-REDUCTION 17 0.538485 0.532491 ## HI-REDUCTION 19 0.536176 0.532491 ## HI-REDUCTION 21 0.533341 0.532491 ## HI-REDUCTION 23 0.533289 0.532388 ## LO-REDUCTION 25 0.532491 0.532241 ## HI-REDUCTION 27 0.532388 0.532118 ## HI-REDUCTION 29 0.532241 0.532041 ## LO-REDUCTION 31 0.532118 0.532041 ## HI-REDUCTION 33 0.532075 0.532041 ## REFLECTION 35 0.532048 0.532028 ## HI-REDUCTION 37 0.532041 0.532028 ## HI-REDUCTION 39 0.532030 0.532027 ## REFLECTION 41 0.532028 0.532025 ## HI-REDUCTION 43 0.532027 0.532024 ## HI-REDUCTION 45 0.532025 0.532023 ## HI-REDUCTION 47 0.532024 0.532023 ## HI-REDUCTION 49 0.532024 0.532023 ## HI-REDUCTION 51 0.532023 0.532023 ## HI-REDUCTION 53 0.532023 0.532023 ## LO-REDUCTION 55 0.532023 0.532023 ## HI-REDUCTION 57 0.532023 0.532023 ## HI-REDUCTION 59 0.532023 0.532023 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.533546 ## Scaled convergence tolerance is 7.95045e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.823126 0.533546 ## LO-REDUCTION 5 0.599163 0.533546 ## REFLECTION 7 0.569045 0.513700 ## HI-REDUCTION 9 0.533546 0.513700 ## LO-REDUCTION 11 0.516383 0.504567 ## HI-REDUCTION 13 0.513700 0.501713 ## HI-REDUCTION 15 0.504567 0.499515 ## LO-REDUCTION 17 0.501713 0.498896 ## HI-REDUCTION 19 0.499515 0.498896 ## HI-REDUCTION 21 0.498987 0.498456 ## HI-REDUCTION 23 0.498896 0.498456 ## LO-REDUCTION 25 0.498566 0.498456 ## HI-REDUCTION 27 0.498482 0.498426 ## LO-REDUCTION 29 0.498456 0.498418 ## HI-REDUCTION 31 0.498426 0.498413 ## HI-REDUCTION 33 0.498418 0.498406 ## LO-REDUCTION 35 0.498413 0.498406 ## HI-REDUCTION 37 0.498406 0.498406 ## HI-REDUCTION 39 0.498406 0.498405 ## HI-REDUCTION 41 0.498406 0.498405 ## HI-REDUCTION 43 0.498405 0.498405 ## REFLECTION 45 0.498405 0.498405 ## HI-REDUCTION 47 0.498405 0.498405 ## HI-REDUCTION 49 0.498405 0.498405 ## HI-REDUCTION 51 0.498405 0.498405 ## HI-REDUCTION 53 0.498405 0.498405 ## HI-REDUCTION 55 0.498405 0.498405 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.471266 ## Scaled convergence tolerance is 7.0224e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.614025 0.471266 ## HI-REDUCTION 5 0.573752 0.471266 ## HI-REDUCTION 7 0.515562 0.471266 ## HI-REDUCTION 9 0.505746 0.471266 ## LO-REDUCTION 11 0.493995 0.471266 ## LO-REDUCTION 13 0.476865 0.470594 ## HI-REDUCTION 15 0.472096 0.470594 ## LO-REDUCTION 17 0.471266 0.470100 ## HI-REDUCTION 19 0.470594 0.470041 ## HI-REDUCTION 21 0.470100 0.469945 ## LO-REDUCTION 23 0.470041 0.469929 ## HI-REDUCTION 25 0.469945 0.469895 ## HI-REDUCTION 27 0.469929 0.469878 ## REFLECTION 29 0.469895 0.469875 ## HI-REDUCTION 31 0.469878 0.469870 ## HI-REDUCTION 33 0.469875 0.469866 ## HI-REDUCTION 35 0.469870 0.469866 ## HI-REDUCTION 37 0.469867 0.469866 ## HI-REDUCTION 39 0.469866 0.469865 ## HI-REDUCTION 41 0.469866 0.469865 ## LO-REDUCTION 43 0.469865 0.469865 ## HI-REDUCTION 45 0.469865 0.469865 ## HI-REDUCTION 47 0.469865 0.469865 ## HI-REDUCTION 49 0.469865 0.469865 ## LO-REDUCTION 51 0.469865 0.469865 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.574463 ## Scaled convergence tolerance is 8.56016e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.663001 0.574463 ## HI-REDUCTION 5 0.653992 0.574463 ## HI-REDUCTION 7 0.592792 0.574463 ## HI-REDUCTION 9 0.578317 0.574427 ## HI-REDUCTION 11 0.574463 0.568349 ## HI-REDUCTION 13 0.574427 0.568349 ## LO-REDUCTION 15 0.568410 0.566687 ## HI-REDUCTION 17 0.568349 0.566687 ## HI-REDUCTION 19 0.567061 0.566687 ## HI-REDUCTION 21 0.566968 0.566687 ## HI-REDUCTION 23 0.566731 0.566684 ## HI-REDUCTION 25 0.566687 0.566631 ## HI-REDUCTION 27 0.566684 0.566600 ## LO-REDUCTION 29 0.566631 0.566600 ## HI-REDUCTION 31 0.566617 0.566600 ## LO-REDUCTION 33 0.566609 0.566600 ## LO-REDUCTION 35 0.566601 0.566599 ## HI-REDUCTION 37 0.566600 0.566599 ## LO-REDUCTION 39 0.566599 0.566598 ## HI-REDUCTION 41 0.566599 0.566598 ## HI-REDUCTION 43 0.566598 0.566598 ## HI-REDUCTION 45 0.566598 0.566598 ## HI-REDUCTION 47 0.566598 0.566598 ## HI-REDUCTION 49 0.566598 0.566598 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.577943 ## Scaled convergence tolerance is 8.61203e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.727232 0.577943 ## LO-REDUCTION 5 0.632311 0.577943 ## LO-REDUCTION 7 0.622652 0.577943 ## HI-REDUCTION 9 0.589416 0.577943 ## HI-REDUCTION 11 0.580794 0.577943 ## REFLECTION 13 0.578638 0.577525 ## HI-REDUCTION 15 0.577943 0.573829 ## LO-REDUCTION 17 0.577525 0.573829 ## LO-REDUCTION 19 0.574806 0.573829 ## LO-REDUCTION 21 0.574733 0.573829 ## LO-REDUCTION 23 0.574081 0.573696 ## HI-REDUCTION 25 0.573829 0.573696 ## LO-REDUCTION 27 0.573726 0.573691 ## LO-REDUCTION 29 0.573696 0.573680 ## HI-REDUCTION 31 0.573691 0.573671 ## HI-REDUCTION 33 0.573680 0.573667 ## LO-REDUCTION 35 0.573671 0.573666 ## HI-REDUCTION 37 0.573667 0.573666 ## HI-REDUCTION 39 0.573666 0.573665 ## HI-REDUCTION 41 0.573666 0.573665 ## HI-REDUCTION 43 0.573665 0.573665 ## LO-REDUCTION 45 0.573665 0.573665 ## HI-REDUCTION 47 0.573665 0.573665 ## LO-REDUCTION 49 0.573665 0.573665 ## HI-REDUCTION 51 0.573665 0.573665 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.593863 ## Scaled convergence tolerance is 8.84925e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.722320 0.593863 ## HI-REDUCTION 5 0.661061 0.593863 ## HI-REDUCTION 7 0.613907 0.593863 ## HI-REDUCTION 9 0.610663 0.593863 ## LO-REDUCTION 11 0.598769 0.593863 ## HI-REDUCTION 13 0.595552 0.592856 ## LO-REDUCTION 15 0.593863 0.591836 ## HI-REDUCTION 17 0.592856 0.591742 ## HI-REDUCTION 19 0.591836 0.591648 ## HI-REDUCTION 21 0.591742 0.591557 ## HI-REDUCTION 23 0.591648 0.591520 ## HI-REDUCTION 25 0.591557 0.591501 ## LO-REDUCTION 27 0.591520 0.591501 ## HI-REDUCTION 29 0.591501 0.591492 ## HI-REDUCTION 31 0.591501 0.591491 ## REFLECTION 33 0.591492 0.591491 ## HI-REDUCTION 35 0.591491 0.591488 ## HI-REDUCTION 37 0.591491 0.591488 ## HI-REDUCTION 39 0.591488 0.591488 ## HI-REDUCTION 41 0.591488 0.591488 ## HI-REDUCTION 43 0.591488 0.591488 ## HI-REDUCTION 45 0.591488 0.591487 ## LO-REDUCTION 47 0.591488 0.591487 ## Exiting from Nelder Mead minimizer ## 49 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.678440 ## Scaled convergence tolerance is 1.01095e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.871752 0.678440 ## LO-REDUCTION 5 0.775639 0.678440 ## LO-REDUCTION 7 0.761189 0.678273 ## HI-REDUCTION 9 0.707565 0.678273 ## LO-REDUCTION 11 0.678440 0.673744 ## LO-REDUCTION 13 0.678273 0.672030 ## HI-REDUCTION 15 0.673744 0.671650 ## HI-REDUCTION 17 0.672030 0.669533 ## LO-REDUCTION 19 0.671650 0.669533 ## HI-REDUCTION 21 0.670168 0.669533 ## HI-REDUCTION 23 0.670008 0.669533 ## REFLECTION 25 0.669798 0.669397 ## LO-REDUCTION 27 0.669533 0.669397 ## LO-REDUCTION 29 0.669496 0.669397 ## LO-REDUCTION 31 0.669417 0.669395 ## HI-REDUCTION 33 0.669397 0.669394 ## HI-REDUCTION 35 0.669395 0.669392 ## HI-REDUCTION 37 0.669394 0.669391 ## HI-REDUCTION 39 0.669392 0.669391 ## HI-REDUCTION 41 0.669391 0.669391 ## HI-REDUCTION 43 0.669391 0.669391 ## HI-REDUCTION 45 0.669391 0.669391 ## HI-REDUCTION 47 0.669391 0.669391 ## HI-REDUCTION 49 0.669391 0.669391 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.655526 ## Scaled convergence tolerance is 9.76809e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.824594 0.655526 ## LO-REDUCTION 5 0.708727 0.655526 ## LO-REDUCTION 7 0.704422 0.655526 ## HI-REDUCTION 9 0.667883 0.655526 ## HI-REDUCTION 11 0.660097 0.655526 ## REFLECTION 13 0.656252 0.652702 ## HI-REDUCTION 15 0.655526 0.650238 ## LO-REDUCTION 17 0.652702 0.649788 ## HI-REDUCTION 19 0.650238 0.649788 ## HI-REDUCTION 21 0.650017 0.649600 ## HI-REDUCTION 23 0.649788 0.649600 ## HI-REDUCTION 25 0.649649 0.649600 ## HI-REDUCTION 27 0.649608 0.649571 ## HI-REDUCTION 29 0.649600 0.649563 ## HI-REDUCTION 31 0.649571 0.649557 ## HI-REDUCTION 33 0.649563 0.649557 ## LO-REDUCTION 35 0.649558 0.649555 ## HI-REDUCTION 37 0.649557 0.649554 ## LO-REDUCTION 39 0.649555 0.649554 ## HI-REDUCTION 41 0.649554 0.649554 ## LO-REDUCTION 43 0.649554 0.649553 ## HI-REDUCTION 45 0.649554 0.649553 ## HI-REDUCTION 47 0.649554 0.649553 ## LO-REDUCTION 49 0.649553 0.649553 ## HI-REDUCTION 51 0.649553 0.649553 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.540986 ## Scaled convergence tolerance is 8.06132e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.730514 0.540986 ## HI-REDUCTION 5 0.660050 0.540986 ## HI-REDUCTION 7 0.602342 0.540986 ## HI-REDUCTION 9 0.600412 0.540986 ## LO-REDUCTION 11 0.579842 0.540986 ## REFLECTION 13 0.547923 0.534959 ## REFLECTION 15 0.540986 0.527758 ## HI-REDUCTION 17 0.534959 0.526600 ## HI-REDUCTION 19 0.527758 0.524574 ## HI-REDUCTION 21 0.526600 0.523747 ## HI-REDUCTION 23 0.524574 0.523747 ## HI-REDUCTION 25 0.524056 0.523434 ## LO-REDUCTION 27 0.523747 0.523434 ## HI-REDUCTION 29 0.523460 0.523366 ## HI-REDUCTION 31 0.523434 0.523309 ## HI-REDUCTION 33 0.523366 0.523282 ## HI-REDUCTION 35 0.523309 0.523282 ## LO-REDUCTION 37 0.523299 0.523273 ## HI-REDUCTION 39 0.523282 0.523273 ## HI-REDUCTION 41 0.523275 0.523273 ## HI-REDUCTION 43 0.523274 0.523272 ## HI-REDUCTION 45 0.523273 0.523272 ## LO-REDUCTION 47 0.523273 0.523272 ## HI-REDUCTION 49 0.523272 0.523272 ## LO-REDUCTION 51 0.523272 0.523272 ## HI-REDUCTION 53 0.523272 0.523272 ## HI-REDUCTION 55 0.523272 0.523272 ## LO-REDUCTION 57 0.523272 0.523272 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.542328 ## Scaled convergence tolerance is 8.08131e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.624966 0.542328 ## HI-REDUCTION 5 0.622533 0.542328 ## HI-REDUCTION 7 0.569658 0.542328 ## HI-REDUCTION 9 0.549146 0.542328 ## LO-REDUCTION 11 0.547839 0.538020 ## HI-REDUCTION 13 0.542328 0.538020 ## HI-REDUCTION 15 0.538800 0.537240 ## HI-REDUCTION 17 0.538020 0.537240 ## HI-REDUCTION 19 0.537351 0.537127 ## HI-REDUCTION 21 0.537240 0.536949 ## HI-REDUCTION 23 0.537127 0.536880 ## LO-REDUCTION 25 0.536949 0.536880 ## HI-REDUCTION 27 0.536883 0.536871 ## HI-REDUCTION 29 0.536880 0.536867 ## HI-REDUCTION 31 0.536871 0.536864 ## HI-REDUCTION 33 0.536867 0.536862 ## LO-REDUCTION 35 0.536864 0.536861 ## HI-REDUCTION 37 0.536862 0.536861 ## HI-REDUCTION 39 0.536861 0.536861 ## HI-REDUCTION 41 0.536861 0.536861 ## HI-REDUCTION 43 0.536861 0.536861 ## HI-REDUCTION 45 0.536861 0.536861 ## HI-REDUCTION 47 0.536861 0.536861 ## HI-REDUCTION 49 0.536861 0.536861 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.574089 ## Scaled convergence tolerance is 8.55459e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.678888 0.574089 ## HI-REDUCTION 5 0.629012 0.574089 ## HI-REDUCTION 7 0.588701 0.574089 ## HI-REDUCTION 9 0.583413 0.574089 ## HI-REDUCTION 11 0.575819 0.574089 ## HI-REDUCTION 13 0.574131 0.572233 ## HI-REDUCTION 15 0.574089 0.571700 ## HI-REDUCTION 17 0.572233 0.571333 ## HI-REDUCTION 19 0.571700 0.571333 ## LO-REDUCTION 21 0.571492 0.571267 ## HI-REDUCTION 23 0.571333 0.571209 ## HI-REDUCTION 25 0.571267 0.571209 ## REFLECTION 27 0.571226 0.571199 ## HI-REDUCTION 29 0.571209 0.571183 ## LO-REDUCTION 31 0.571199 0.571183 ## HI-REDUCTION 33 0.571185 0.571183 ## HI-REDUCTION 35 0.571184 0.571181 ## HI-REDUCTION 37 0.571183 0.571181 ## HI-REDUCTION 39 0.571181 0.571180 ## LO-REDUCTION 41 0.571181 0.571180 ## HI-REDUCTION 43 0.571180 0.571180 ## HI-REDUCTION 45 0.571180 0.571180 ## HI-REDUCTION 47 0.571180 0.571180 ## HI-REDUCTION 49 0.571180 0.571180 ## HI-REDUCTION 51 0.571180 0.571180 ## LO-REDUCTION 53 0.571180 0.571180 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.622694 ## Scaled convergence tolerance is 9.27887e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.778554 0.622694 ## LO-REDUCTION 5 0.675776 0.622694 ## LO-REDUCTION 7 0.667924 0.622694 ## HI-REDUCTION 9 0.632848 0.622694 ## HI-REDUCTION 11 0.624374 0.621744 ## REFLECTION 13 0.622694 0.620256 ## HI-REDUCTION 15 0.621744 0.617610 ## HI-REDUCTION 17 0.620256 0.617037 ## LO-REDUCTION 19 0.617831 0.617037 ## LO-REDUCTION 21 0.617610 0.616888 ## LO-REDUCTION 23 0.617037 0.616844 ## LO-REDUCTION 25 0.616888 0.616707 ## HI-REDUCTION 27 0.616844 0.616664 ## HI-REDUCTION 29 0.616707 0.616631 ## LO-REDUCTION 31 0.616664 0.616631 ## HI-REDUCTION 33 0.616634 0.616625 ## REFLECTION 35 0.616631 0.616615 ## HI-REDUCTION 37 0.616625 0.616615 ## LO-REDUCTION 39 0.616619 0.616615 ## REFLECTION 41 0.616616 0.616613 ## HI-REDUCTION 43 0.616615 0.616613 ## REFLECTION 45 0.616614 0.616612 ## LO-REDUCTION 47 0.616613 0.616612 ## LO-REDUCTION 49 0.616612 0.616612 ## HI-REDUCTION 51 0.616612 0.616612 ## LO-REDUCTION 53 0.616612 0.616612 ## LO-REDUCTION 55 0.616612 0.616612 ## HI-REDUCTION 57 0.616612 0.616612 ## HI-REDUCTION 59 0.616612 0.616612 ## Exiting from Nelder Mead minimizer ## 61 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.520485 ## Scaled convergence tolerance is 7.75583e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.685045 0.520485 ## HI-REDUCTION 5 0.664394 0.520485 ## HI-REDUCTION 7 0.595387 0.520485 ## LO-REDUCTION 9 0.578105 0.520485 ## REFLECTION 11 0.540794 0.508911 ## LO-REDUCTION 13 0.520485 0.508911 ## HI-REDUCTION 15 0.510062 0.508503 ## HI-REDUCTION 17 0.508911 0.506635 ## HI-REDUCTION 19 0.508503 0.505636 ## HI-REDUCTION 21 0.506635 0.505636 ## LO-REDUCTION 23 0.506336 0.505547 ## HI-REDUCTION 25 0.505687 0.505547 ## HI-REDUCTION 27 0.505636 0.505517 ## HI-REDUCTION 29 0.505547 0.505472 ## HI-REDUCTION 31 0.505517 0.505472 ## REFLECTION 33 0.505488 0.505453 ## HI-REDUCTION 35 0.505472 0.505453 ## LO-REDUCTION 37 0.505458 0.505450 ## HI-REDUCTION 39 0.505453 0.505450 ## HI-REDUCTION 41 0.505451 0.505450 ## LO-REDUCTION 43 0.505450 0.505449 ## HI-REDUCTION 45 0.505450 0.505449 ## HI-REDUCTION 47 0.505449 0.505449 ## HI-REDUCTION 49 0.505449 0.505449 ## HI-REDUCTION 51 0.505449 0.505449 ## HI-REDUCTION 53 0.505449 0.505449 ## LO-REDUCTION 55 0.505449 0.505449 ## HI-REDUCTION 57 0.505449 0.505449 ## Exiting from Nelder Mead minimizer ## 59 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.609325 ## Scaled convergence tolerance is 9.07966e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.809806 0.609325 ## LO-REDUCTION 5 0.696989 0.609325 ## LO-REDUCTION 7 0.683483 0.608640 ## HI-REDUCTION 9 0.634254 0.608640 ## LO-REDUCTION 11 0.609325 0.605687 ## HI-REDUCTION 13 0.608640 0.603439 ## HI-REDUCTION 15 0.605687 0.602172 ## HI-REDUCTION 17 0.603439 0.602172 ## REFLECTION 19 0.602502 0.601343 ## HI-REDUCTION 21 0.602172 0.601343 ## LO-REDUCTION 23 0.601385 0.601185 ## HI-REDUCTION 25 0.601343 0.601175 ## HI-REDUCTION 27 0.601185 0.601166 ## HI-REDUCTION 29 0.601175 0.601143 ## HI-REDUCTION 31 0.601166 0.601143 ## HI-REDUCTION 33 0.601147 0.601143 ## LO-REDUCTION 35 0.601144 0.601140 ## HI-REDUCTION 37 0.601143 0.601139 ## HI-REDUCTION 39 0.601140 0.601139 ## LO-REDUCTION 41 0.601140 0.601139 ## HI-REDUCTION 43 0.601139 0.601139 ## HI-REDUCTION 45 0.601139 0.601139 ## HI-REDUCTION 47 0.601139 0.601139 ## LO-REDUCTION 49 0.601139 0.601139 ## HI-REDUCTION 51 0.601139 0.601139 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.604200 ## Scaled convergence tolerance is 9.00328e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.780605 0.604200 ## LO-REDUCTION 5 0.646963 0.604200 ## LO-REDUCTION 7 0.646568 0.604200 ## HI-REDUCTION 9 0.611875 0.604200 ## HI-REDUCTION 11 0.606789 0.601630 ## REFLECTION 13 0.604200 0.601227 ## LO-REDUCTION 15 0.601630 0.597507 ## HI-REDUCTION 17 0.601227 0.597030 ## HI-REDUCTION 19 0.597507 0.596149 ## LO-REDUCTION 21 0.597030 0.596108 ## HI-REDUCTION 23 0.596149 0.596105 ## HI-REDUCTION 25 0.596108 0.595985 ## LO-REDUCTION 27 0.596105 0.595984 ## LO-REDUCTION 29 0.595985 0.595953 ## HI-REDUCTION 31 0.595984 0.595944 ## HI-REDUCTION 33 0.595953 0.595936 ## HI-REDUCTION 35 0.595944 0.595936 ## REFLECTION 37 0.595939 0.595931 ## HI-REDUCTION 39 0.595936 0.595931 ## REFLECTION 41 0.595931 0.595927 ## REFLECTION 43 0.595931 0.595927 ## REFLECTION 45 0.595927 0.595924 ## REFLECTION 47 0.595927 0.595924 ## REFLECTION 49 0.595924 0.595923 ## LO-REDUCTION 51 0.595924 0.595923 ## HI-REDUCTION 53 0.595923 0.595923 ## HI-REDUCTION 55 0.595923 0.595922 ## REFLECTION 57 0.595923 0.595922 ## HI-REDUCTION 59 0.595922 0.595922 ## HI-REDUCTION 61 0.595922 0.595922 ## HI-REDUCTION 63 0.595922 0.595922 ## LO-REDUCTION 65 0.595922 0.595922 ## Exiting from Nelder Mead minimizer ## 67 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.545196 ## Scaled convergence tolerance is 8.12405e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.645332 0.545196 ## HI-REDUCTION 5 0.624446 0.545196 ## HI-REDUCTION 7 0.577959 0.545196 ## LO-REDUCTION 9 0.558642 0.543442 ## HI-REDUCTION 11 0.545196 0.543442 ## HI-REDUCTION 13 0.544819 0.539722 ## LO-REDUCTION 15 0.543442 0.539188 ## LO-REDUCTION 17 0.539722 0.539188 ## LO-REDUCTION 19 0.539675 0.538899 ## HI-REDUCTION 21 0.539188 0.538778 ## HI-REDUCTION 23 0.538899 0.538778 ## REFLECTION 25 0.538806 0.538694 ## HI-REDUCTION 27 0.538778 0.538679 ## LO-REDUCTION 29 0.538694 0.538673 ## HI-REDUCTION 31 0.538679 0.538667 ## HI-REDUCTION 33 0.538673 0.538666 ## HI-REDUCTION 35 0.538667 0.538666 ## HI-REDUCTION 37 0.538666 0.538664 ## HI-REDUCTION 39 0.538666 0.538664 ## HI-REDUCTION 41 0.538664 0.538664 ## REFLECTION 43 0.538664 0.538664 ## HI-REDUCTION 45 0.538664 0.538664 ## HI-REDUCTION 47 0.538664 0.538664 ## HI-REDUCTION 49 0.538664 0.538664 ## HI-REDUCTION 51 0.538664 0.538664 ## HI-REDUCTION 53 0.538664 0.538664 ## HI-REDUCTION 55 0.538664 0.538664 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.833819 ## Scaled convergence tolerance is 1.24249e-08 ## Stepsize computed as 0.147026 ## BUILD 3 1.035586 0.833819 ## LO-REDUCTION 5 0.888919 0.833819 ## LO-REDUCTION 7 0.885229 0.830422 ## HI-REDUCTION 9 0.842238 0.830422 ## HI-REDUCTION 11 0.833819 0.828858 ## HI-REDUCTION 13 0.830422 0.825698 ## LO-REDUCTION 15 0.828858 0.825698 ## HI-REDUCTION 17 0.826280 0.825698 ## HI-REDUCTION 19 0.826177 0.825643 ## HI-REDUCTION 21 0.825698 0.825595 ## HI-REDUCTION 23 0.825643 0.825394 ## LO-REDUCTION 25 0.825595 0.825394 ## HI-REDUCTION 27 0.825467 0.825394 ## LO-REDUCTION 29 0.825467 0.825394 ## LO-REDUCTION 31 0.825432 0.825394 ## REFLECTION 33 0.825400 0.825377 ## LO-REDUCTION 35 0.825394 0.825377 ## REFLECTION 37 0.825379 0.825376 ## HI-REDUCTION 39 0.825377 0.825376 ## LO-REDUCTION 41 0.825376 0.825375 ## HI-REDUCTION 43 0.825376 0.825375 ## HI-REDUCTION 45 0.825375 0.825375 ## LO-REDUCTION 47 0.825375 0.825375 ## HI-REDUCTION 49 0.825375 0.825375 ## REFLECTION 51 0.825375 0.825375 ## HI-REDUCTION 53 0.825375 0.825375 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.562230 ## Scaled convergence tolerance is 8.37789e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.680158 0.562230 ## HI-REDUCTION 5 0.602472 0.562230 ## HI-REDUCTION 7 0.564518 0.562230 ## HI-REDUCTION 9 0.563541 0.554159 ## HI-REDUCTION 11 0.562230 0.554110 ## HI-REDUCTION 13 0.554159 0.553898 ## HI-REDUCTION 15 0.554110 0.552900 ## HI-REDUCTION 17 0.553898 0.552852 ## HI-REDUCTION 19 0.552902 0.552852 ## HI-REDUCTION 21 0.552900 0.552732 ## HI-REDUCTION 23 0.552852 0.552732 ## HI-REDUCTION 25 0.552750 0.552732 ## HI-REDUCTION 27 0.552739 0.552725 ## HI-REDUCTION 29 0.552732 0.552719 ## HI-REDUCTION 31 0.552725 0.552719 ## REFLECTION 33 0.552719 0.552718 ## HI-REDUCTION 35 0.552719 0.552716 ## HI-REDUCTION 37 0.552718 0.552716 ## HI-REDUCTION 39 0.552716 0.552716 ## HI-REDUCTION 41 0.552716 0.552716 ## HI-REDUCTION 43 0.552716 0.552716 ## HI-REDUCTION 45 0.552716 0.552716 ## HI-REDUCTION 47 0.552716 0.552716 ## HI-REDUCTION 49 0.552716 0.552716 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.567536 ## Scaled convergence tolerance is 8.45694e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.695738 0.567536 ## HI-REDUCTION 5 0.631433 0.567536 ## HI-REDUCTION 7 0.585193 0.567536 ## HI-REDUCTION 9 0.583188 0.567536 ## LO-REDUCTION 11 0.571104 0.567536 ## HI-REDUCTION 13 0.567575 0.565866 ## HI-REDUCTION 15 0.567536 0.565596 ## HI-REDUCTION 17 0.565866 0.565258 ## HI-REDUCTION 19 0.565596 0.565137 ## REFLECTION 21 0.565258 0.565093 ## HI-REDUCTION 23 0.565137 0.564990 ## HI-REDUCTION 25 0.565093 0.564960 ## HI-REDUCTION 27 0.564990 0.564960 ## HI-REDUCTION 29 0.564977 0.564956 ## HI-REDUCTION 31 0.564960 0.564952 ## HI-REDUCTION 33 0.564956 0.564947 ## LO-REDUCTION 35 0.564952 0.564947 ## HI-REDUCTION 37 0.564949 0.564947 ## LO-REDUCTION 39 0.564948 0.564947 ## HI-REDUCTION 41 0.564947 0.564947 ## HI-REDUCTION 43 0.564947 0.564947 ## HI-REDUCTION 45 0.564947 0.564947 ## HI-REDUCTION 47 0.564947 0.564947 ## HI-REDUCTION 49 0.564947 0.564947 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598115 ## Scaled convergence tolerance is 8.91261e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.685973 0.598115 ## HI-REDUCTION 5 0.656843 0.595745 ## HI-REDUCTION 7 0.609328 0.595745 ## HI-REDUCTION 9 0.598115 0.593421 ## HI-REDUCTION 11 0.595745 0.588876 ## HI-REDUCTION 13 0.593421 0.588876 ## LO-REDUCTION 15 0.590219 0.588876 ## HI-REDUCTION 17 0.589412 0.588871 ## HI-REDUCTION 19 0.588876 0.588710 ## HI-REDUCTION 21 0.588871 0.588520 ## HI-REDUCTION 23 0.588710 0.588520 ## LO-REDUCTION 25 0.588632 0.588520 ## HI-REDUCTION 27 0.588538 0.588520 ## HI-REDUCTION 29 0.588522 0.588516 ## HI-REDUCTION 31 0.588520 0.588512 ## HI-REDUCTION 33 0.588516 0.588510 ## LO-REDUCTION 35 0.588512 0.588510 ## HI-REDUCTION 37 0.588510 0.588510 ## LO-REDUCTION 39 0.588510 0.588510 ## HI-REDUCTION 41 0.588510 0.588510 ## LO-REDUCTION 43 0.588510 0.588510 ## HI-REDUCTION 45 0.588510 0.588510 ## HI-REDUCTION 47 0.588510 0.588510 ## LO-REDUCTION 49 0.588510 0.588510 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505212 ## Scaled convergence tolerance is 7.52824e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.622560 0.505212 ## HI-REDUCTION 5 0.593863 0.505212 ## HI-REDUCTION 7 0.542925 0.505212 ## HI-REDUCTION 9 0.530756 0.505212 ## LO-REDUCTION 11 0.523453 0.505212 ## LO-REDUCTION 13 0.512755 0.505212 ## LO-REDUCTION 15 0.508668 0.505212 ## LO-REDUCTION 17 0.506145 0.504948 ## HI-REDUCTION 19 0.505212 0.504948 ## HI-REDUCTION 21 0.504954 0.504835 ## HI-REDUCTION 23 0.504948 0.504802 ## HI-REDUCTION 25 0.504835 0.504802 ## HI-REDUCTION 27 0.504817 0.504791 ## HI-REDUCTION 29 0.504802 0.504791 ## HI-REDUCTION 31 0.504793 0.504787 ## LO-REDUCTION 33 0.504791 0.504786 ## HI-REDUCTION 35 0.504787 0.504786 ## HI-REDUCTION 37 0.504786 0.504785 ## LO-REDUCTION 39 0.504786 0.504785 ## HI-REDUCTION 41 0.504785 0.504785 ## REFLECTION 43 0.504785 0.504785 ## HI-REDUCTION 45 0.504785 0.504785 ## HI-REDUCTION 47 0.504785 0.504785 ## HI-REDUCTION 49 0.504785 0.504785 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.636106 ## Scaled convergence tolerance is 9.47872e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.809384 0.636106 ## LO-REDUCTION 5 0.676363 0.636106 ## LO-REDUCTION 7 0.672086 0.632637 ## HI-REDUCTION 9 0.637653 0.632637 ## HI-REDUCTION 11 0.636106 0.628258 ## HI-REDUCTION 13 0.632637 0.627448 ## REFLECTION 15 0.628258 0.624736 ## HI-REDUCTION 17 0.627448 0.624736 ## LO-REDUCTION 19 0.625787 0.624736 ## HI-REDUCTION 21 0.625041 0.624736 ## HI-REDUCTION 23 0.624947 0.624736 ## HI-REDUCTION 25 0.624770 0.624725 ## HI-REDUCTION 27 0.624736 0.624681 ## HI-REDUCTION 29 0.624725 0.624656 ## LO-REDUCTION 31 0.624681 0.624656 ## HI-REDUCTION 33 0.624665 0.624656 ## LO-REDUCTION 35 0.624661 0.624655 ## LO-REDUCTION 37 0.624656 0.624655 ## HI-REDUCTION 39 0.624655 0.624654 ## HI-REDUCTION 41 0.624655 0.624654 ## HI-REDUCTION 43 0.624654 0.624654 ## REFLECTION 45 0.624654 0.624654 ## HI-REDUCTION 47 0.624654 0.624654 ## REFLECTION 49 0.624654 0.624654 ## HI-REDUCTION 51 0.624654 0.624654 ## HI-REDUCTION 53 0.624654 0.624654 ## HI-REDUCTION 55 0.624654 0.624654 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598797 ## Scaled convergence tolerance is 8.92276e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.744383 0.598797 ## HI-REDUCTION 5 0.723447 0.598797 ## HI-REDUCTION 7 0.656835 0.598797 ## HI-REDUCTION 9 0.637572 0.598797 ## LO-REDUCTION 11 0.629464 0.598797 ## LO-REDUCTION 13 0.610929 0.598797 ## LO-REDUCTION 15 0.601248 0.596826 ## LO-REDUCTION 17 0.598797 0.596826 ## LO-REDUCTION 19 0.597047 0.596415 ## HI-REDUCTION 21 0.596826 0.596287 ## LO-REDUCTION 23 0.596415 0.596287 ## HI-REDUCTION 25 0.596298 0.596245 ## HI-REDUCTION 27 0.596287 0.596245 ## HI-REDUCTION 29 0.596245 0.596236 ## HI-REDUCTION 31 0.596245 0.596229 ## LO-REDUCTION 33 0.596236 0.596226 ## HI-REDUCTION 35 0.596229 0.596226 ## HI-REDUCTION 37 0.596228 0.596226 ## REFLECTION 39 0.596226 0.596225 ## HI-REDUCTION 41 0.596226 0.596225 ## HI-REDUCTION 43 0.596225 0.596225 ## HI-REDUCTION 45 0.596225 0.596225 ## LO-REDUCTION 47 0.596225 0.596225 ## HI-REDUCTION 49 0.596225 0.596225 ## HI-REDUCTION 51 0.596225 0.596225 ## LO-REDUCTION 53 0.596225 0.596225 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.537674 ## Scaled convergence tolerance is 8.01197e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.653704 0.537674 ## HI-REDUCTION 5 0.585139 0.537674 ## HI-REDUCTION 7 0.542221 0.537674 ## HI-REDUCTION 9 0.540913 0.530789 ## HI-REDUCTION 11 0.537674 0.530789 ## HI-REDUCTION 13 0.532024 0.530442 ## LO-REDUCTION 15 0.530789 0.529873 ## HI-REDUCTION 17 0.530442 0.529522 ## HI-REDUCTION 19 0.529873 0.529388 ## LO-REDUCTION 21 0.529522 0.529304 ## HI-REDUCTION 23 0.529388 0.529304 ## HI-REDUCTION 25 0.529308 0.529297 ## HI-REDUCTION 27 0.529304 0.529281 ## HI-REDUCTION 29 0.529297 0.529281 ## HI-REDUCTION 31 0.529286 0.529281 ## LO-REDUCTION 33 0.529284 0.529281 ## HI-REDUCTION 35 0.529281 0.529281 ## LO-REDUCTION 37 0.529281 0.529280 ## HI-REDUCTION 39 0.529281 0.529280 ## HI-REDUCTION 41 0.529280 0.529280 ## LO-REDUCTION 43 0.529280 0.529280 ## HI-REDUCTION 45 0.529280 0.529280 ## HI-REDUCTION 47 0.529280 0.529280 ## HI-REDUCTION 49 0.529280 0.529280 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.450901 ## Scaled convergence tolerance is 6.71894e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.572860 0.450901 ## HI-REDUCTION 5 0.534733 0.450901 ## HI-REDUCTION 7 0.496393 0.450901 ## LO-REDUCTION 9 0.469331 0.447074 ## HI-REDUCTION 11 0.452130 0.447074 ## LO-REDUCTION 13 0.450901 0.443557 ## HI-REDUCTION 15 0.447074 0.443557 ## HI-REDUCTION 17 0.444266 0.442732 ## REFLECTION 19 0.443557 0.442643 ## HI-REDUCTION 21 0.442732 0.442356 ## HI-REDUCTION 23 0.442643 0.442340 ## HI-REDUCTION 25 0.442356 0.442309 ## HI-REDUCTION 27 0.442340 0.442241 ## HI-REDUCTION 29 0.442309 0.442241 ## LO-REDUCTION 31 0.442272 0.442241 ## HI-REDUCTION 33 0.442255 0.442241 ## LO-REDUCTION 35 0.442247 0.442238 ## HI-REDUCTION 37 0.442241 0.442238 ## HI-REDUCTION 39 0.442240 0.442238 ## LO-REDUCTION 41 0.442238 0.442238 ## HI-REDUCTION 43 0.442238 0.442238 ## HI-REDUCTION 45 0.442238 0.442238 ## HI-REDUCTION 47 0.442238 0.442238 ## HI-REDUCTION 49 0.442238 0.442238 ## HI-REDUCTION 51 0.442238 0.442238 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.589111 ## Scaled convergence tolerance is 8.77843e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.717457 0.589111 ## HI-REDUCTION 5 0.684726 0.589111 ## HI-REDUCTION 7 0.631972 0.589111 ## HI-REDUCTION 9 0.618775 0.589111 ## LO-REDUCTION 11 0.610954 0.589111 ## LO-REDUCTION 13 0.599362 0.589111 ## LO-REDUCTION 15 0.593630 0.588706 ## REFLECTION 17 0.589111 0.588481 ## LO-REDUCTION 19 0.588706 0.587541 ## HI-REDUCTION 21 0.588481 0.587434 ## HI-REDUCTION 23 0.587541 0.587434 ## HI-REDUCTION 25 0.587527 0.587381 ## HI-REDUCTION 27 0.587434 0.587355 ## HI-REDUCTION 29 0.587381 0.587352 ## REFLECTION 31 0.587355 0.587343 ## HI-REDUCTION 33 0.587352 0.587334 ## HI-REDUCTION 35 0.587343 0.587334 ## HI-REDUCTION 37 0.587334 0.587334 ## HI-REDUCTION 39 0.587334 0.587332 ## HI-REDUCTION 41 0.587334 0.587332 ## LO-REDUCTION 43 0.587332 0.587332 ## HI-REDUCTION 45 0.587332 0.587332 ## HI-REDUCTION 47 0.587332 0.587332 ## HI-REDUCTION 49 0.587332 0.587332 ## LO-REDUCTION 51 0.587332 0.587332 ## HI-REDUCTION 53 0.587332 0.587332 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.546616 ## Scaled convergence tolerance is 8.14521e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.639771 0.546616 ## HI-REDUCTION 5 0.631414 0.546616 ## HI-REDUCTION 7 0.572790 0.546616 ## HI-REDUCTION 9 0.558815 0.546616 ## HI-REDUCTION 11 0.554291 0.546616 ## REFLECTION 13 0.547684 0.545760 ## HI-REDUCTION 15 0.546616 0.542904 ## HI-REDUCTION 17 0.545760 0.542904 ## HI-REDUCTION 19 0.543665 0.542904 ## LO-REDUCTION 21 0.543137 0.542534 ## HI-REDUCTION 23 0.542904 0.542482 ## HI-REDUCTION 25 0.542554 0.542482 ## HI-REDUCTION 27 0.542534 0.542471 ## HI-REDUCTION 29 0.542482 0.542464 ## HI-REDUCTION 31 0.542471 0.542445 ## LO-REDUCTION 33 0.542464 0.542445 ## HI-REDUCTION 35 0.542453 0.542445 ## LO-REDUCTION 37 0.542451 0.542445 ## LO-REDUCTION 39 0.542445 0.542445 ## REFLECTION 41 0.542445 0.542445 ## HI-REDUCTION 43 0.542445 0.542444 ## HI-REDUCTION 45 0.542445 0.542444 ## LO-REDUCTION 47 0.542444 0.542444 ## HI-REDUCTION 49 0.542444 0.542444 ## LO-REDUCTION 51 0.542444 0.542444 ## LO-REDUCTION 53 0.542444 0.542444 ## LO-REDUCTION 55 0.542444 0.542444 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.789032 ## Scaled convergence tolerance is 1.17575e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.927519 0.789032 ## HI-REDUCTION 5 0.900760 0.789032 ## HI-REDUCTION 7 0.837902 0.789032 ## HI-REDUCTION 9 0.820546 0.789032 ## LO-REDUCTION 11 0.812873 0.789032 ## LO-REDUCTION 13 0.799799 0.789032 ## LO-REDUCTION 15 0.795274 0.789032 ## REFLECTION 17 0.790478 0.788608 ## HI-REDUCTION 19 0.789032 0.788288 ## HI-REDUCTION 21 0.788608 0.788164 ## HI-REDUCTION 23 0.788288 0.787982 ## HI-REDUCTION 25 0.788164 0.787982 ## LO-REDUCTION 27 0.788009 0.787933 ## HI-REDUCTION 29 0.787982 0.787930 ## HI-REDUCTION 31 0.787935 0.787930 ## HI-REDUCTION 33 0.787933 0.787924 ## HI-REDUCTION 35 0.787930 0.787923 ## HI-REDUCTION 37 0.787924 0.787923 ## HI-REDUCTION 39 0.787923 0.787923 ## HI-REDUCTION 41 0.787923 0.787922 ## HI-REDUCTION 43 0.787923 0.787922 ## LO-REDUCTION 45 0.787922 0.787922 ## HI-REDUCTION 47 0.787922 0.787922 ## LO-REDUCTION 49 0.787922 0.787922 ## HI-REDUCTION 51 0.787922 0.787922 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.604981 ## Scaled convergence tolerance is 9.01491e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.706696 0.604981 ## HI-REDUCTION 5 0.661801 0.604981 ## HI-REDUCTION 7 0.618024 0.604981 ## HI-REDUCTION 9 0.608587 0.603795 ## HI-REDUCTION 11 0.604981 0.601194 ## HI-REDUCTION 13 0.603795 0.600142 ## HI-REDUCTION 15 0.601194 0.600142 ## HI-REDUCTION 17 0.600994 0.600142 ## LO-REDUCTION 19 0.600230 0.600041 ## HI-REDUCTION 21 0.600142 0.599902 ## HI-REDUCTION 23 0.600041 0.599902 ## LO-REDUCTION 25 0.599962 0.599902 ## HI-REDUCTION 27 0.599905 0.599899 ## LO-REDUCTION 29 0.599902 0.599892 ## HI-REDUCTION 31 0.599899 0.599890 ## HI-REDUCTION 33 0.599892 0.599890 ## REFLECTION 35 0.599890 0.599890 ## HI-REDUCTION 37 0.599890 0.599888 ## HI-REDUCTION 39 0.599890 0.599888 ## LO-REDUCTION 41 0.599888 0.599888 ## HI-REDUCTION 43 0.599888 0.599888 ## HI-REDUCTION 45 0.599888 0.599888 ## HI-REDUCTION 47 0.599888 0.599888 ## LO-REDUCTION 49 0.599888 0.599888 ## HI-REDUCTION 51 0.599888 0.599888 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.634935 ## Scaled convergence tolerance is 9.46126e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.738426 0.634935 ## HI-REDUCTION 5 0.682008 0.634935 ## HI-REDUCTION 7 0.643565 0.634935 ## HI-REDUCTION 9 0.639372 0.632130 ## HI-REDUCTION 11 0.634935 0.631442 ## HI-REDUCTION 13 0.632130 0.629993 ## HI-REDUCTION 15 0.631442 0.629993 ## HI-REDUCTION 17 0.630247 0.629993 ## HI-REDUCTION 19 0.630163 0.629831 ## HI-REDUCTION 21 0.629993 0.629831 ## HI-REDUCTION 23 0.629831 0.629746 ## HI-REDUCTION 25 0.629831 0.629746 ## REFLECTION 27 0.629764 0.629726 ## HI-REDUCTION 29 0.629746 0.629722 ## REFLECTION 31 0.629726 0.629719 ## HI-REDUCTION 33 0.629722 0.629714 ## HI-REDUCTION 35 0.629719 0.629713 ## HI-REDUCTION 37 0.629714 0.629713 ## HI-REDUCTION 39 0.629713 0.629712 ## HI-REDUCTION 41 0.629713 0.629712 ## HI-REDUCTION 43 0.629712 0.629712 ## HI-REDUCTION 45 0.629712 0.629712 ## HI-REDUCTION 47 0.629712 0.629712 ## HI-REDUCTION 49 0.629712 0.629712 ## LO-REDUCTION 51 0.629712 0.629712 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.636291 ## Scaled convergence tolerance is 9.48147e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.846800 0.636291 ## LO-REDUCTION 5 0.726408 0.636291 ## REFLECTION 7 0.711578 0.630505 ## HI-REDUCTION 9 0.651561 0.630505 ## HI-REDUCTION 11 0.636291 0.630148 ## HI-REDUCTION 13 0.630505 0.624840 ## HI-REDUCTION 15 0.630148 0.622782 ## LO-REDUCTION 17 0.624840 0.622782 ## HI-REDUCTION 19 0.623436 0.622782 ## HI-REDUCTION 21 0.622867 0.622517 ## HI-REDUCTION 23 0.622782 0.622396 ## HI-REDUCTION 25 0.622517 0.622349 ## HI-REDUCTION 27 0.622396 0.622349 ## HI-REDUCTION 29 0.622364 0.622334 ## HI-REDUCTION 31 0.622349 0.622324 ## HI-REDUCTION 33 0.622334 0.622316 ## LO-REDUCTION 35 0.622324 0.622316 ## HI-REDUCTION 37 0.622318 0.622316 ## REFLECTION 39 0.622316 0.622316 ## HI-REDUCTION 41 0.622316 0.622315 ## HI-REDUCTION 43 0.622316 0.622315 ## HI-REDUCTION 45 0.622315 0.622315 ## HI-REDUCTION 47 0.622315 0.622315 ## HI-REDUCTION 49 0.622315 0.622315 ## HI-REDUCTION 51 0.622315 0.622315 ## LO-REDUCTION 53 0.622315 0.622315 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.582153 ## Scaled convergence tolerance is 8.67476e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.824125 0.582153 ## LO-REDUCTION 5 0.696517 0.582153 ## REFLECTION 7 0.661103 0.558560 ## HI-REDUCTION 9 0.595303 0.558560 ## HI-REDUCTION 11 0.582153 0.558560 ## HI-REDUCTION 13 0.570357 0.558560 ## HI-REDUCTION 15 0.564325 0.558560 ## HI-REDUCTION 17 0.560689 0.558041 ## HI-REDUCTION 19 0.558560 0.556733 ## HI-REDUCTION 21 0.558041 0.555238 ## LO-REDUCTION 23 0.556733 0.555238 ## HI-REDUCTION 25 0.555650 0.555238 ## HI-REDUCTION 27 0.555468 0.555238 ## REFLECTION 29 0.555387 0.555224 ## LO-REDUCTION 31 0.555238 0.555192 ## HI-REDUCTION 33 0.555224 0.555192 ## HI-REDUCTION 35 0.555199 0.555192 ## LO-REDUCTION 37 0.555194 0.555188 ## HI-REDUCTION 39 0.555192 0.555188 ## HI-REDUCTION 41 0.555188 0.555188 ## HI-REDUCTION 43 0.555188 0.555187 ## HI-REDUCTION 45 0.555188 0.555187 ## HI-REDUCTION 47 0.555187 0.555187 ## LO-REDUCTION 49 0.555187 0.555187 ## HI-REDUCTION 51 0.555187 0.555187 ## HI-REDUCTION 53 0.555187 0.555187 ## LO-REDUCTION 55 0.555187 0.555187 ## Exiting from Nelder Mead minimizer ## 57 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.598240 ## Scaled convergence tolerance is 8.91447e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.753555 0.598240 ## HI-REDUCTION 5 0.685536 0.598240 ## HI-REDUCTION 7 0.633685 0.598240 ## HI-REDUCTION 9 0.631778 0.598240 ## LO-REDUCTION 11 0.615777 0.598240 ## LO-REDUCTION 13 0.601414 0.598240 ## HI-REDUCTION 15 0.599726 0.598240 ## HI-REDUCTION 17 0.598321 0.597880 ## HI-REDUCTION 19 0.598240 0.597695 ## HI-REDUCTION 21 0.597880 0.597650 ## HI-REDUCTION 23 0.597695 0.597635 ## HI-REDUCTION 25 0.597650 0.597609 ## HI-REDUCTION 27 0.597635 0.597591 ## HI-REDUCTION 29 0.597609 0.597591 ## LO-REDUCTION 31 0.597599 0.597590 ## HI-REDUCTION 33 0.597591 0.597589 ## HI-REDUCTION 35 0.597590 0.597588 ## LO-REDUCTION 37 0.597589 0.597588 ## HI-REDUCTION 39 0.597588 0.597588 ## HI-REDUCTION 41 0.597588 0.597588 ## HI-REDUCTION 43 0.597588 0.597588 ## HI-REDUCTION 45 0.597588 0.597588 ## HI-REDUCTION 47 0.597588 0.597588 ## LO-REDUCTION 49 0.597588 0.597588 ## HI-REDUCTION 51 0.597588 0.597588 ## HI-REDUCTION 53 0.597588 0.597588 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.586047 ## Scaled convergence tolerance is 8.73279e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.696613 0.586047 ## HI-REDUCTION 5 0.648203 0.586047 ## HI-REDUCTION 7 0.603319 0.586047 ## HI-REDUCTION 9 0.596396 0.586047 ## HI-REDUCTION 11 0.588705 0.586047 ## HI-REDUCTION 13 0.586515 0.584502 ## HI-REDUCTION 15 0.586047 0.583804 ## HI-REDUCTION 17 0.584502 0.583250 ## LO-REDUCTION 19 0.583804 0.583250 ## HI-REDUCTION 21 0.583486 0.583250 ## REFLECTION 23 0.583298 0.583194 ## HI-REDUCTION 25 0.583250 0.583166 ## HI-REDUCTION 27 0.583194 0.583154 ## HI-REDUCTION 29 0.583166 0.583151 ## HI-REDUCTION 31 0.583154 0.583147 ## HI-REDUCTION 33 0.583151 0.583144 ## HI-REDUCTION 35 0.583147 0.583144 ## LO-REDUCTION 37 0.583145 0.583144 ## HI-REDUCTION 39 0.583144 0.583143 ## HI-REDUCTION 41 0.583144 0.583143 ## HI-REDUCTION 43 0.583143 0.583143 ## HI-REDUCTION 45 0.583143 0.583143 ## REFLECTION 47 0.583143 0.583143 ## HI-REDUCTION 49 0.583143 0.583143 ## HI-REDUCTION 51 0.583143 0.583143 ## HI-REDUCTION 53 0.583143 0.583143 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.542604 ## Scaled convergence tolerance is 8.08543e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.708938 0.542604 ## HI-REDUCTION 5 0.641696 0.542604 ## HI-REDUCTION 7 0.587335 0.542604 ## HI-REDUCTION 9 0.585664 0.542604 ## LO-REDUCTION 11 0.567827 0.542604 ## LO-REDUCTION 13 0.546935 0.539218 ## LO-REDUCTION 15 0.542604 0.539218 ## HI-REDUCTION 17 0.540628 0.539218 ## LO-REDUCTION 19 0.540234 0.539218 ## HI-REDUCTION 21 0.539636 0.539218 ## REFLECTION 23 0.539455 0.539126 ## HI-REDUCTION 25 0.539218 0.539126 ## HI-REDUCTION 27 0.539168 0.539111 ## HI-REDUCTION 29 0.539126 0.539110 ## HI-REDUCTION 31 0.539111 0.539095 ## HI-REDUCTION 33 0.539110 0.539095 ## LO-REDUCTION 35 0.539097 0.539092 ## HI-REDUCTION 37 0.539095 0.539092 ## HI-REDUCTION 39 0.539093 0.539092 ## HI-REDUCTION 41 0.539093 0.539092 ## HI-REDUCTION 43 0.539092 0.539092 ## HI-REDUCTION 45 0.539092 0.539092 ## LO-REDUCTION 47 0.539092 0.539092 ## HI-REDUCTION 49 0.539092 0.539092 ## HI-REDUCTION 51 0.539092 0.539092 ## HI-REDUCTION 53 0.539092 0.539092 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.532740 ## Scaled convergence tolerance is 7.93845e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.686241 0.532740 ## HI-REDUCTION 5 0.613358 0.532740 ## HI-REDUCTION 7 0.562992 0.532740 ## HI-REDUCTION 9 0.562265 0.532740 ## LO-REDUCTION 11 0.546388 0.532740 ## HI-REDUCTION 13 0.536847 0.532740 ## LO-REDUCTION 15 0.534518 0.532453 ## HI-REDUCTION 17 0.532740 0.532333 ## HI-REDUCTION 19 0.532453 0.531994 ## HI-REDUCTION 21 0.532333 0.531994 ## HI-REDUCTION 23 0.532082 0.531994 ## REFLECTION 25 0.532027 0.531986 ## HI-REDUCTION 27 0.531994 0.531936 ## HI-REDUCTION 29 0.531986 0.531936 ## LO-REDUCTION 31 0.531949 0.531936 ## HI-REDUCTION 33 0.531940 0.531934 ## LO-REDUCTION 35 0.531936 0.531933 ## HI-REDUCTION 37 0.531934 0.531932 ## HI-REDUCTION 39 0.531933 0.531932 ## LO-REDUCTION 41 0.531932 0.531932 ## HI-REDUCTION 43 0.531932 0.531932 ## HI-REDUCTION 45 0.531932 0.531932 ## HI-REDUCTION 47 0.531932 0.531932 ## HI-REDUCTION 49 0.531932 0.531932 ## HI-REDUCTION 51 0.531932 0.531932 ## HI-REDUCTION 53 0.531932 0.531932 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.505690 ## Scaled convergence tolerance is 7.53537e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.630551 0.505690 ## HI-REDUCTION 5 0.620169 0.505690 ## HI-REDUCTION 7 0.561481 0.505690 ## LO-REDUCTION 9 0.541900 0.505690 ## REFLECTION 11 0.517142 0.503370 ## HI-REDUCTION 13 0.505690 0.502368 ## HI-REDUCTION 15 0.503370 0.499488 ## HI-REDUCTION 17 0.502368 0.497714 ## LO-REDUCTION 19 0.499488 0.497714 ## HI-REDUCTION 21 0.498375 0.497714 ## LO-REDUCTION 23 0.498112 0.497714 ## HI-REDUCTION 25 0.497776 0.497714 ## LO-REDUCTION 27 0.497733 0.497651 ## HI-REDUCTION 29 0.497714 0.497633 ## HI-REDUCTION 31 0.497651 0.497633 ## LO-REDUCTION 33 0.497649 0.497629 ## HI-REDUCTION 35 0.497633 0.497629 ## HI-REDUCTION 37 0.497631 0.497628 ## HI-REDUCTION 39 0.497629 0.497628 ## HI-REDUCTION 41 0.497628 0.497628 ## HI-REDUCTION 43 0.497628 0.497628 ## HI-REDUCTION 45 0.497628 0.497627 ## LO-REDUCTION 47 0.497628 0.497627 ## HI-REDUCTION 49 0.497627 0.497627 ## HI-REDUCTION 51 0.497627 0.497627 ## HI-REDUCTION 53 0.497627 0.497627 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.376971 ## Scaled convergence tolerance is 5.61731e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.494204 0.376971 ## HI-REDUCTION 5 0.445798 0.376971 ## HI-REDUCTION 7 0.419724 0.376971 ## LO-REDUCTION 9 0.391149 0.371340 ## LO-REDUCTION 11 0.376971 0.371262 ## HI-REDUCTION 13 0.371340 0.369929 ## HI-REDUCTION 15 0.371262 0.367303 ## HI-REDUCTION 17 0.369929 0.367303 ## REFLECTION 19 0.368709 0.367092 ## HI-REDUCTION 21 0.367310 0.367092 ## HI-REDUCTION 23 0.367303 0.367011 ## HI-REDUCTION 25 0.367092 0.367011 ## HI-REDUCTION 27 0.367011 0.366948 ## LO-REDUCTION 29 0.367011 0.366948 ## HI-REDUCTION 31 0.366963 0.366948 ## LO-REDUCTION 33 0.366959 0.366942 ## HI-REDUCTION 35 0.366948 0.366942 ## HI-REDUCTION 37 0.366943 0.366942 ## HI-REDUCTION 39 0.366942 0.366941 ## LO-REDUCTION 41 0.366942 0.366941 ## HI-REDUCTION 43 0.366941 0.366941 ## HI-REDUCTION 45 0.366941 0.366941 ## HI-REDUCTION 47 0.366941 0.366941 ## HI-REDUCTION 49 0.366941 0.366941 ## LO-REDUCTION 51 0.366941 0.366941 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.426218 ## Scaled convergence tolerance is 6.35114e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.587314 0.426218 ## HI-REDUCTION 5 0.524047 0.426218 ## HI-REDUCTION 7 0.469487 0.426218 ## HI-REDUCTION 9 0.465189 0.426218 ## LO-REDUCTION 11 0.449697 0.426218 ## LO-REDUCTION 13 0.431507 0.424190 ## LO-REDUCTION 15 0.426218 0.424190 ## HI-REDUCTION 17 0.424801 0.424190 ## HI-REDUCTION 19 0.424329 0.423941 ## HI-REDUCTION 21 0.424190 0.423857 ## HI-REDUCTION 23 0.423941 0.423785 ## HI-REDUCTION 25 0.423857 0.423785 ## LO-REDUCTION 27 0.423803 0.423768 ## HI-REDUCTION 29 0.423785 0.423757 ## HI-REDUCTION 31 0.423768 0.423757 ## LO-REDUCTION 33 0.423764 0.423756 ## HI-REDUCTION 35 0.423757 0.423756 ## HI-REDUCTION 37 0.423756 0.423755 ## HI-REDUCTION 39 0.423756 0.423755 ## LO-REDUCTION 41 0.423755 0.423755 ## HI-REDUCTION 43 0.423755 0.423755 ## HI-REDUCTION 45 0.423755 0.423755 ## HI-REDUCTION 47 0.423755 0.423755 ## HI-REDUCTION 49 0.423755 0.423755 ## HI-REDUCTION 51 0.423755 0.423755 ## HI-REDUCTION 53 0.423755 0.423755 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.668259 ## Scaled convergence tolerance is 9.95784e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.833265 0.668259 ## HI-REDUCTION 5 0.755189 0.668259 ## HI-REDUCTION 7 0.703265 0.668259 ## LO-REDUCTION 9 0.702614 0.668259 ## LO-REDUCTION 11 0.681122 0.667199 ## HI-REDUCTION 13 0.669508 0.667199 ## HI-REDUCTION 15 0.668259 0.666696 ## HI-REDUCTION 17 0.667199 0.666585 ## HI-REDUCTION 19 0.666696 0.666308 ## HI-REDUCTION 21 0.666585 0.666308 ## LO-REDUCTION 23 0.666311 0.666308 ## HI-REDUCTION 25 0.666311 0.666238 ## LO-REDUCTION 27 0.666308 0.666238 ## LO-REDUCTION 29 0.666258 0.666238 ## HI-REDUCTION 31 0.666238 0.666233 ## HI-REDUCTION 33 0.666238 0.666233 ## LO-REDUCTION 35 0.666233 0.666232 ## HI-REDUCTION 37 0.666233 0.666231 ## HI-REDUCTION 39 0.666232 0.666231 ## LO-REDUCTION 41 0.666231 0.666231 ## HI-REDUCTION 43 0.666231 0.666231 ## REFLECTION 45 0.666231 0.666231 ## HI-REDUCTION 47 0.666231 0.666231 ## HI-REDUCTION 49 0.666231 0.666231 ## HI-REDUCTION 51 0.666231 0.666231 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.507589 ## Scaled convergence tolerance is 7.56367e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.645362 0.507589 ## HI-REDUCTION 5 0.636714 0.507589 ## HI-REDUCTION 7 0.573306 0.507589 ## LO-REDUCTION 9 0.553533 0.507589 ## REFLECTION 11 0.522282 0.493601 ## LO-REDUCTION 13 0.507589 0.493601 ## HI-REDUCTION 15 0.499004 0.493601 ## LO-REDUCTION 17 0.496977 0.493096 ## HI-REDUCTION 19 0.493601 0.493096 ## HI-REDUCTION 21 0.493333 0.492416 ## HI-REDUCTION 23 0.493096 0.492416 ## LO-REDUCTION 25 0.492728 0.492416 ## HI-REDUCTION 27 0.492467 0.492416 ## HI-REDUCTION 29 0.492456 0.492410 ## HI-REDUCTION 31 0.492416 0.492408 ## HI-REDUCTION 33 0.492410 0.492394 ## HI-REDUCTION 35 0.492408 0.492394 ## LO-REDUCTION 37 0.492400 0.492393 ## HI-REDUCTION 39 0.492394 0.492393 ## HI-REDUCTION 41 0.492394 0.492393 ## HI-REDUCTION 43 0.492393 0.492393 ## LO-REDUCTION 45 0.492393 0.492393 ## LO-REDUCTION 47 0.492393 0.492393 ## HI-REDUCTION 49 0.492393 0.492393 ## LO-REDUCTION 51 0.492393 0.492393 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.572406 ## Scaled convergence tolerance is 8.52951e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.654488 0.572406 ## HI-REDUCTION 5 0.652801 0.572406 ## HI-REDUCTION 7 0.594692 0.572406 ## HI-REDUCTION 9 0.583929 0.572406 ## HI-REDUCTION 11 0.578655 0.572406 ## REFLECTION 13 0.573347 0.572264 ## HI-REDUCTION 15 0.572406 0.569228 ## HI-REDUCTION 17 0.572264 0.569228 ## HI-REDUCTION 19 0.569907 0.569228 ## LO-REDUCTION 21 0.569665 0.569094 ## HI-REDUCTION 23 0.569228 0.569018 ## HI-REDUCTION 25 0.569094 0.569005 ## REFLECTION 27 0.569018 0.568988 ## HI-REDUCTION 29 0.569005 0.568953 ## HI-REDUCTION 31 0.568988 0.568948 ## HI-REDUCTION 33 0.568953 0.568948 ## HI-REDUCTION 35 0.568953 0.568946 ## HI-REDUCTION 37 0.568948 0.568945 ## HI-REDUCTION 39 0.568946 0.568943 ## LO-REDUCTION 41 0.568945 0.568943 ## HI-REDUCTION 43 0.568944 0.568943 ## REFLECTION 45 0.568944 0.568943 ## HI-REDUCTION 47 0.568943 0.568943 ## HI-REDUCTION 49 0.568943 0.568943 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.571957 ## Scaled convergence tolerance is 8.52282e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.744999 0.571957 ## LO-REDUCTION 5 0.627196 0.571957 ## LO-REDUCTION 7 0.624647 0.569140 ## HI-REDUCTION 9 0.582690 0.569140 ## HI-REDUCTION 11 0.571957 0.569133 ## HI-REDUCTION 13 0.569140 0.565188 ## LO-REDUCTION 15 0.569133 0.565188 ## LO-REDUCTION 17 0.566382 0.565188 ## HI-REDUCTION 19 0.566173 0.565188 ## LO-REDUCTION 21 0.565529 0.565175 ## LO-REDUCTION 23 0.565188 0.565098 ## HI-REDUCTION 25 0.565175 0.565098 ## HI-REDUCTION 27 0.565102 0.565092 ## REFLECTION 29 0.565098 0.565082 ## HI-REDUCTION 31 0.565092 0.565078 ## HI-REDUCTION 33 0.565082 0.565078 ## HI-REDUCTION 35 0.565078 0.565077 ## HI-REDUCTION 37 0.565078 0.565076 ## HI-REDUCTION 39 0.565077 0.565076 ## LO-REDUCTION 41 0.565076 0.565076 ## HI-REDUCTION 43 0.565076 0.565076 ## HI-REDUCTION 45 0.565076 0.565076 ## LO-REDUCTION 47 0.565076 0.565076 ## HI-REDUCTION 49 0.565076 0.565076 ## LO-REDUCTION 51 0.565076 0.565076 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.481535 ## Scaled convergence tolerance is 7.17543e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.591242 0.481535 ## HI-REDUCTION 5 0.586433 0.481535 ## HI-REDUCTION 7 0.524878 0.481535 ## LO-REDUCTION 9 0.511136 0.481535 ## LO-REDUCTION 11 0.489333 0.476667 ## HI-REDUCTION 13 0.481535 0.476667 ## HI-REDUCTION 15 0.478756 0.476667 ## HI-REDUCTION 17 0.477855 0.476667 ## LO-REDUCTION 19 0.476896 0.476351 ## HI-REDUCTION 21 0.476667 0.476283 ## HI-REDUCTION 23 0.476351 0.476283 ## HI-REDUCTION 25 0.476329 0.476259 ## HI-REDUCTION 27 0.476283 0.476250 ## HI-REDUCTION 29 0.476259 0.476236 ## LO-REDUCTION 31 0.476250 0.476236 ## HI-REDUCTION 33 0.476240 0.476236 ## REFLECTION 35 0.476238 0.476235 ## HI-REDUCTION 37 0.476236 0.476234 ## HI-REDUCTION 39 0.476235 0.476234 ## HI-REDUCTION 41 0.476234 0.476234 ## HI-REDUCTION 43 0.476234 0.476234 ## HI-REDUCTION 45 0.476234 0.476234 ## HI-REDUCTION 47 0.476234 0.476234 ## HI-REDUCTION 49 0.476234 0.476234 ## HI-REDUCTION 51 0.476234 0.476234 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.534200 ## Scaled convergence tolerance is 7.9602e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.625720 0.534200 ## HI-REDUCTION 5 0.551147 0.534200 ## HI-REDUCTION 7 0.550163 0.522786 ## HI-REDUCTION 9 0.534200 0.513641 ## HI-REDUCTION 11 0.522786 0.513641 ## REFLECTION 13 0.517723 0.512005 ## HI-REDUCTION 15 0.513641 0.511127 ## HI-REDUCTION 17 0.512005 0.511099 ## HI-REDUCTION 19 0.511127 0.510393 ## HI-REDUCTION 21 0.511099 0.510375 ## REFLECTION 23 0.510393 0.510316 ## HI-REDUCTION 25 0.510375 0.510159 ## HI-REDUCTION 27 0.510316 0.510159 ## HI-REDUCTION 29 0.510176 0.510159 ## HI-REDUCTION 31 0.510164 0.510144 ## HI-REDUCTION 33 0.510159 0.510139 ## HI-REDUCTION 35 0.510144 0.510139 ## HI-REDUCTION 37 0.510140 0.510138 ## HI-REDUCTION 39 0.510139 0.510137 ## HI-REDUCTION 41 0.510138 0.510137 ## REFLECTION 43 0.510137 0.510137 ## HI-REDUCTION 45 0.510137 0.510137 ## HI-REDUCTION 47 0.510137 0.510137 ## HI-REDUCTION 49 0.510137 0.510137 ## HI-REDUCTION 51 0.510137 0.510136 ## HI-REDUCTION 53 0.510137 0.510136 ## Exiting from Nelder Mead minimizer ## 55 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.549906 ## Scaled convergence tolerance is 8.19424e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.666903 0.549906 ## HI-REDUCTION 5 0.603833 0.549906 ## HI-REDUCTION 7 0.562408 0.549906 ## HI-REDUCTION 9 0.559067 0.549906 ## HI-REDUCTION 11 0.550021 0.549679 ## HI-REDUCTION 13 0.549906 0.547204 ## HI-REDUCTION 15 0.549679 0.547006 ## LO-REDUCTION 17 0.547204 0.546728 ## HI-REDUCTION 19 0.547006 0.546674 ## HI-REDUCTION 21 0.546728 0.546671 ## HI-REDUCTION 23 0.546674 0.546611 ## HI-REDUCTION 25 0.546671 0.546610 ## LO-REDUCTION 27 0.546613 0.546610 ## HI-REDUCTION 29 0.546611 0.546602 ## HI-REDUCTION 31 0.546610 0.546600 ## HI-REDUCTION 33 0.546602 0.546600 ## LO-REDUCTION 35 0.546601 0.546600 ## HI-REDUCTION 37 0.546600 0.546600 ## HI-REDUCTION 39 0.546600 0.546599 ## LO-REDUCTION 41 0.546600 0.546599 ## HI-REDUCTION 43 0.546599 0.546599 ## REFLECTION 45 0.546599 0.546599 ## HI-REDUCTION 47 0.546599 0.546599 ## HI-REDUCTION 49 0.546599 0.546599 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.581946 ## Scaled convergence tolerance is 8.67167e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.704669 0.581946 ## HI-REDUCTION 5 0.629002 0.581946 ## HI-REDUCTION 7 0.591742 0.581946 ## HI-REDUCTION 9 0.590439 0.580275 ## HI-REDUCTION 11 0.581946 0.579959 ## HI-REDUCTION 13 0.580275 0.577405 ## LO-REDUCTION 15 0.579959 0.577405 ## LO-REDUCTION 17 0.578203 0.577405 ## HI-REDUCTION 19 0.577739 0.577379 ## LO-REDUCTION 21 0.577405 0.577237 ## HI-REDUCTION 23 0.577379 0.577232 ## HI-REDUCTION 25 0.577250 0.577232 ## HI-REDUCTION 27 0.577237 0.577223 ## HI-REDUCTION 29 0.577232 0.577220 ## HI-REDUCTION 31 0.577223 0.577219 ## HI-REDUCTION 33 0.577220 0.577219 ## HI-REDUCTION 35 0.577219 0.577218 ## HI-REDUCTION 37 0.577219 0.577218 ## LO-REDUCTION 39 0.577218 0.577218 ## HI-REDUCTION 41 0.577218 0.577218 ## LO-REDUCTION 43 0.577218 0.577218 ## HI-REDUCTION 45 0.577218 0.577218 ## HI-REDUCTION 47 0.577218 0.577218 ## HI-REDUCTION 49 0.577218 0.577218 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.492827 ## Scaled convergence tolerance is 7.3437e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.617351 0.492827 ## HI-REDUCTION 5 0.613171 0.492827 ## HI-REDUCTION 7 0.543053 0.492827 ## LO-REDUCTION 9 0.527563 0.492827 ## LO-REDUCTION 11 0.501574 0.486890 ## HI-REDUCTION 13 0.492827 0.486890 ## HI-REDUCTION 15 0.489578 0.486890 ## HI-REDUCTION 17 0.488561 0.486890 ## LO-REDUCTION 19 0.487394 0.486523 ## HI-REDUCTION 21 0.486890 0.486523 ## HI-REDUCTION 23 0.486573 0.486523 ## HI-REDUCTION 25 0.486535 0.486465 ## HI-REDUCTION 27 0.486523 0.486465 ## HI-REDUCTION 29 0.486476 0.486465 ## HI-REDUCTION 31 0.486472 0.486463 ## HI-REDUCTION 33 0.486465 0.486461 ## HI-REDUCTION 35 0.486463 0.486458 ## LO-REDUCTION 37 0.486461 0.486458 ## HI-REDUCTION 39 0.486458 0.486458 ## HI-REDUCTION 41 0.486458 0.486458 ## HI-REDUCTION 43 0.486458 0.486458 ## LO-REDUCTION 45 0.486458 0.486458 ## HI-REDUCTION 47 0.486458 0.486458 ## HI-REDUCTION 49 0.486458 0.486458 ## HI-REDUCTION 51 0.486458 0.486458 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.489471 ## Scaled convergence tolerance is 7.29369e-09 ## Stepsize computed as 0.147026 ## BUILD 3 0.595300 0.489471 ## HI-REDUCTION 5 0.582442 0.489471 ## HI-REDUCTION 7 0.526612 0.489471 ## HI-REDUCTION 9 0.507384 0.489471 ## HI-REDUCTION 11 0.504877 0.489471 ## LO-REDUCTION 13 0.497241 0.488735 ## HI-REDUCTION 15 0.490921 0.488735 ## HI-REDUCTION 17 0.489471 0.488735 ## HI-REDUCTION 19 0.489121 0.488623 ## LO-REDUCTION 21 0.488735 0.488463 ## HI-REDUCTION 23 0.488623 0.488463 ## HI-REDUCTION 25 0.488477 0.488450 ## HI-REDUCTION 27 0.488463 0.488439 ## HI-REDUCTION 29 0.488450 0.488432 ## HI-REDUCTION 31 0.488439 0.488430 ## LO-REDUCTION 33 0.488432 0.488427 ## HI-REDUCTION 35 0.488430 0.488427 ## HI-REDUCTION 37 0.488427 0.488427 ## HI-REDUCTION 39 0.488427 0.488427 ## HI-REDUCTION 41 0.488427 0.488427 ## HI-REDUCTION 43 0.488427 0.488427 ## HI-REDUCTION 45 0.488427 0.488427 ## HI-REDUCTION 47 0.488427 0.488427 ## HI-REDUCTION 49 0.488427 0.488427 ## LO-REDUCTION 51 0.488427 0.488427 ## Exiting from Nelder Mead minimizer ## 53 function evaluations used ## Nelder-Mead direct search function minimizer ## function value for initial parameters = 0.688720 ## Scaled convergence tolerance is 1.02627e-08 ## Stepsize computed as 0.147026 ## BUILD 3 0.821253 0.688720 ## HI-REDUCTION 5 0.747123 0.688720 ## HI-REDUCTION 7 0.707236 0.688720 ## HI-REDUCTION 9 0.705874 0.688720 ## HI-REDUCTION 11 0.693771 0.688720 ## LO-REDUCTION 13 0.693004 0.687222 ## HI-REDUCTION 15 0.688720 0.687222 ## HI-REDUCTION 17 0.687861 0.686848 ## HI-REDUCTION 19 0.687222 0.686848 ## LO-REDUCTION 21 0.687014 0.686782 ## HI-REDUCTION 23 0.686848 0.686772 ## HI-REDUCTION 25 0.686782 0.686748 ## LO-REDUCTION 27 0.686772 0.686748 ## HI-REDUCTION 29 0.686748 0.686743 ## HI-REDUCTION 31 0.686748 0.686737 ## LO-REDUCTION 33 0.686743 0.686737 ## HI-REDUCTION 35 0.686738 0.686737 ## HI-REDUCTION 37 0.686737 0.686736 ## HI-REDUCTION 39 0.686737 0.686736 ## HI-REDUCTION 41 0.686736 0.686736 ## REFLECTION 43 0.686736 0.686736 ## HI-REDUCTION 45 0.686736 0.686736 ## HI-REDUCTION 47 0.686736 0.686736 ## HI-REDUCTION 49 0.686736 0.686736 ## Exiting from Nelder Mead minimizer ## 51 function evaluations used #### We used only 101 iterations in that example to limit the calculation time but #### in practice you should take at least 1001 bootstrap iterations # Calculation of the quantile of interest (here the 5 percent hazard concentration) (HC5 <- quantile(bootsample, probs = 0.05)) ## (original) estimated quantiles for each specified probability (censored data) ## p=0.05 ## estimate 1.12 ## Median of bootstrap estimates ## p=0.05 ## estimate 1.12 ## ## two-sided 95 % CI of each quantile ## p=0.05 ## 2.5 % 1.05 ## 97.5 % 1.20 # visualizing pointwise confidence intervals on other quantiles par(mfrow=c(1,1), mar=c(4,4,2,1)) CIcdfplot(bootsample, CI.output = \"quantile\", CI.fill = \"pink\", xlim = c(0.5,2), main = \"\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-can-we-compute-confidence-intervals-on-any-function-of-the-parameters-of-the-fitted-distribution","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.3. How can we compute confidence intervals on any function of the parameters of the fitted distribution ?","title":"Frequently Asked Questions","text":"bootstrap sample parameter estimates can used calculate bootstrap sample variable defined function parameters fitted distribution. bootstrap sample can easily compute conidence interval using percentiles. example uses bootstrap sample parameters previous example (FAQ 4.2) calculate 95 percent confidence interval Potentially Affected Portion (PAF) species given exposure salinity (fixed 1.2 log10 example). complex calculations especially tranfer uncertainty within quantitative risk assessment, recommend use package mc2d aims making calculations easy gives extensive examples use bootstrap samples parameters estimated using functions package fitdistrplus.","code":"exposure <- 1.2 # Bootstrap sample of the PAF at this exposure PAF <- pnorm(exposure, mean = bootsample$estim$mean, sd = bootsample$estim$sd) # confidence interval from 2.5 and 97.5 percentiles quantile(PAF, probs = c(0.025, 0.975)) ## 2.5% 97.5% ## 0.0487 0.1470"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-we-choose-the-bootstrap-number","dir":"Articles","previous_headings":"4. Questions regarding uncertainty","what":"4.4. How do we choose the bootstrap number?","title":"Frequently Asked Questions","text":"Generally, need choose number bootstrap values high original sample size. search number mean standard values become stable. log-normal example , enough 100 bootstrap values.","code":"f.ln.MME <- fitdist(rlnorm(1000), \"lnorm\", method = \"mme\", order = 1:2) # Bootstrap b.ln.50 <- bootdist(f.ln.MME, niter = 50) b.ln.100 <- bootdist(f.ln.MME, niter = 100) b.ln.200 <- bootdist(f.ln.MME, niter = 200) b.ln.500 <- bootdist(f.ln.MME, niter = 500) d1 <- density(b.ln.50, b.ln.100, b.ln.200, b.ln.500) plot(d1)"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"can-i-personalize-the-default-plot-given-for-an-object-of-class-fitdist-or-fitdistcens","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.1. Can I personalize the default plot given for an object of class fitdist or fitdistcens?","title":"Frequently Asked Questions","text":"default plot given using plot() function object class fitdist fitdistcens hard personalize. Indeed plot designed give quick overview fit, used graph manuscript formal presentation. personalize () goodness--fit plots, rather use specific graphical functions, denscomp, cdfcomp, ppcomp, qqcomp cdfcompcens (see following paragraphs).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-personalize-goodness-of-fit-plots","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.2. How to personalize goodness-of-fit plots ?","title":"Frequently Asked Questions","text":"default plot object class fitdist can easily reproduced personalized using denscomp, cdfcomp, ppcomp qqcomp. similar way, default plot object class fitdistcens can easily personalized using cdfcompcens.","code":"data(groundbeef) serving <- groundbeef$serving fit <- fitdist(serving, \"gamma\") par(mfrow = c(2,2), mar = c(4, 4, 1, 1)) denscomp(fit, addlegend = FALSE, main = \"\", xlab = \"serving sizes (g)\", fitcol = \"orange\") qqcomp(fit, addlegend = FALSE, main = \"\", fitpch = 16, fitcol = \"grey\", line01lty = 2) cdfcomp(fit, addlegend = FALSE, main = \"\", xlab = \"serving sizes (g)\", fitcol = \"orange\", lines01 = TRUE) ppcomp(fit, addlegend = FALSE, main = \"\", fitpch = 16, fitcol = \"grey\", line01lty = 2)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-obtain-ggplot2-plots","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.3. Is it possible to obtain ggplot2 plots ?","title":"Frequently Asked Questions","text":"argument plotstyle added functions denscomp, cdfcomp, ppcomp, qqcompand cdfcompcens, ppcompcens, qqcompcens enable generation plots using ggplot2 package. argument default fixed graphics must simply fixed ggplot purpose, following example. latter case graphical functions return graphic object can personalized using ggplot2 functions.","code":"require(\"ggplot2\") ## Loading required package: ggplot2 fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") dcomp <- denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), xlab = \"serving sizes (g)\", xlim = c(0, 250), fitcol = c(\"red\", \"green\", \"orange\"), fitlty = 1, fitlwd = 1:3, xlegend = \"topright\", plotstyle = \"ggplot\", addlegend = FALSE) dcomp + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Ground beef fits\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"is-it-possible-to-add-the-names-of-the-observations-in-a-goodness-of-fit-plot-e-g--the-names-of-the-species-in-the-plot-of-the-species-sensitivity-distribution-ssd-classically-used-in-ecotoxicology","dir":"Articles","previous_headings":"5. How to personalize plots","what":"5.4. Is it possible to add the names of the observations in a goodness-of-fit plot, e.g. the names of the species in the plot of the Species Sensitivity Distribution (SSD) classically used in ecotoxicology ?","title":"Frequently Asked Questions","text":"argument named name.points can used functions cdfcomp CIcdfcomp pass label vector observed points add names points left point. option available ECDF goodness--fit plots non censored data. option can used , example, name species classical plot Species Sensitivity Distributions (SSD) ecotoxicology.","code":"data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV taxaATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa f <- fitdist(ATV, \"lnorm\") cdfcomp(f, xlogscale = TRUE, main = \"Species Sensitivty Distribution\", xlim = c(1, 100000), name.points = taxaATV, addlegend = FALSE, plotstyle = \"ggplot\")"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-code-censored-data-in-fitdistrplus","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.1. How to code censored data in fitdistrplus ?","title":"Frequently Asked Questions","text":"Censored data must rpresented package dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. type representation corresponds coding names \"interval2\" function Surv package survival. way represent censored data fitdistrplus function Surv2fitdistcens() can used help format data use fitdistcens() one format used survival package (see help page Surv2fitdistcens()). toy example .","code":"dtoy <- data.frame(left = c(NA, 2, 4, 6, 9.7, 10), right = c(1, 3, 7, 8, 9.7, NA)) dtoy ## left right ## 1 NA 1.0 ## 2 2.0 3.0 ## 3 4.0 7.0 ## 4 6.0 8.0 ## 5 9.7 9.7 ## 6 10.0 NA"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-do-i-prepare-the-input-of-fitdistcens-with-surv2fitdistcens","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.2. How do I prepare the input of fitdistcens() with Surv2fitdistcens()?","title":"Frequently Asked Questions","text":"Let us consider classical right-censored dataset human life: twenty values randomly chosen canlifins dataset CASdatasets package. refer help Surv2fitdistcens() censoring types. performing survival analysis, common use Surv() function package survival handle different types censoring. order ease use fitdistcens(), dedicated function Surv2fitdistcens() implemented arguments similar ones Surv(). Let us now fit two simple distributions.","code":"exitage <- c(81.1,78.9,72.6,67.9,60.1,78.3,83.4,66.9,74.8,80.5,75.6,67.1, 75.3,82.8,70.1,85.4,74,70,71.6,76.5) death <- c(0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0) svdata <- Surv2fitdistcens(exitage, event=death) flnormc <- fitdistcens(svdata, \"lnorm\") fweic <- fitdistcens(svdata, \"weibull\") par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcompcens(list(fweic, flnormc), xlim=range(exitage), xlegend = \"topleft\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-represent-an-empirical-distribution-from-censored-data","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.3. How to represent an empirical distribution from censored data ?","title":"Frequently Asked Questions","text":"representation empirical distribution censored data trivial problem. One can simply represent observation interval y-value defined rank observation done using function plotdistcens. representation can interesting visualize raw data, remains difficult correctly order observations case (see example right using data smokedfish). Many authors worked development algorithms non parametric maximum likelihood estimation (NPMLE) empirical cumulative distribution function (ECDF) interval censored data (including left right censored data can considered interval censored data one bound infinity). old versions fitdistrplus used Turnbull algorithm using calls functions package survival. Even Turnbull algorithm still available package, default plot now uses function npsurv package npsurv. package provides performant algorithms developped Yong Wang (see references cited help page plotdistcens). Due lack maintenance package forced rewrite main functions package, using another optimization function. ECDF plot also implemented using Turnbull algorithm survival (see ). can see example, new implementation NPMLE provides different type plot ECDF, representing filled rectangles zones non-uniqueness NPMLE ECDF. Indeed NPMLE algorithm generally proceeds two steps. first step aims identifying equivalence classes (also named litterture Turnbull intervals maximal intersection intervals innermost intervals maximal cliques data). Equivalences classess points/intervals NPMLE ECDF may change. Equivalence classes shown correspond regions left bound interval (named L following plot previous toy example) immediately followed right bound interval (named R following plot). equivalence class may null length (example non censored value). second step aims assigning probability mass equivalence class, may zero classes. NPMLE unique equivalence classes non uniqueness NPMLE ECDF represented filled rectangles. Various NPMLE algorithms implemented packages Icens, interval npsurv. less performant enable handling data survival data, especially left censored observations.","code":"par(mfrow = c(1,2), mar = c(3, 4, 3, 0.5)) plotdistcens(dtoy, NPMLE = FALSE) data(smokedfish) dsmo <- log10(smokedfish) plotdistcens(dsmo, NPMLE = FALSE) par(mfrow = c(2, 2), mar = c(3, 4, 3, 0.5)) # Turnbull algorithm with representation of middle points of equivalence classes plotdistcens(dsmo, NPMLE.method = \"Turnbull.middlepoints\", xlim = c(-1.8, 2.4)) # Turnbull algorithm with representation of equivalence classes as intervals plotdistcens(dsmo, NPMLE.method = \"Turnbull.intervals\") # Wang algorithm with representation of equivalence classes as intervals plotdistcens(dsmo, NPMLE.method = \"Wang\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/FAQ.html","id":"how-to-assess-the-goodness-of-fit-of-a-distribution-fitted-on-censored-data","dir":"Articles","previous_headings":"6. Questions regarding (left, right and/or interval) censored data","what":"6.4. How to assess the goodness-of-fit of a distribution fitted on censored data ?","title":"Frequently Asked Questions","text":"available method fitdistrplus fit distributions censored data maximum likelihood estimation (MLE). distribution fitted using fitdistcens, AIC BIC values can found summary object class fitdistcens returned function. values can used compare fit various distributions dataset. Function gofstat yet proposed package fits censored data plan develop future calculation goodness--fit statistics censored data. Considering goodness--fit plots, generic plot function object class fitdistcensprovides three plots, one CDF using NPMLE ECDF plot (default using Wang prepresentation, see previous part details), Q-Q plot P-P plot simply derived Wang plot ECDF, filled rectangles indicating non uniqueness NPMLE ECDF. Functions cdfcompcens(), qqcompens() ppcompcens() can used individualize personnalize CDF, Q-Q P-P goodness--fit plots /compare fit various distributions dataset. Considering Q-Q plots P-P plots, may easier compare various fits splitting plots done automatically using plotstyle ggplot qqcompens() ppcompcens() can also done manually plotstyle graphics.","code":"fnorm <- fitdistcens(dsmo,\"norm\") flogis <- fitdistcens(dsmo,\"logis\") # comparison of AIC values summary(fnorm)$aic ## [1] 178 summary(flogis)$aic ## [1] 177 par(mar = c(2, 4, 3, 0.5)) plot(fnorm) par(mfrow=c(1,1), mar=c(4,4,2,1)) cdfcompcens(list(fnorm, flogis), fitlty = 1) qqcompcens(list(fnorm, flogis)) ppcompcens(list(fnorm, flogis)) qqcompcens(list(fnorm, flogis), lwd = 2, plotstyle = \"ggplot\", fitcol = c(\"red\", \"green\"), fillrect = c(\"pink\", \"lightgreen\"), legendtext = c(\"normal distribution\", \"logistic distribution\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"quick-overview-of-main-optimization-methods","dir":"Articles","previous_headings":"","what":"1. Quick overview of main optimization methods","title":"Which optimization algorithm to choose?","text":"present quickly main optimization methods. Please refer Numerical Optimization (Nocedal & Wright, 2006) Numerical Optimization: theoretical practical aspects (Bonnans, Gilbert, Lemarechal & Sagastizabal, 2006) good introduction. consider following problem minxf(x)\\min_x f(x) x∈ℝnx\\\\mathbb{R}^n.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"derivative-free-optimization-methods","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods","what":"1.1. Derivative-free optimization methods","title":"Which optimization algorithm to choose?","text":"Nelder-Mead method one well known derivative-free methods use values ff search minimum. consists building simplex n+1n+1 points moving/shrinking simplex good direction. set initial points x1,…,xn+1x_1, \\dots, x_{n+1}. order points f(x1)≤f(x2)≤…≤f(xn+1)f(x_1)\\leq f(x_2)\\leq\\dots\\leq f(x_{n+1}). compute xox_o centroid x1,…,xnx_1, \\dots, x_{n}. compute reflected point xr=xo+α(xo−xn+1)x_r = x_o + \\alpha(x_o-x_{n+1}). f(x1)≤f(xr) 1.2. Hessian-free optimization methods","what":"1.2.1. Computing the direction dkd_k","title":"Which optimization algorithm to choose?","text":"desirable property dkd_k dkd_k ensures descent f(xk+1)1k>1, initiated d1=−g(x1)d_1 = -g(x_1). βk\\beta_k updated according scheme: βk=gkTgkgk−1Tgk−1\\beta_k = \\frac{ g_k^T g_k}{g_{k-1}^T g_{k-1} }: Fletcher-Reeves update, βk=gkT(gk−gk−1)gk−1Tgk−1\\beta_k = \\frac{ g_k^T (g_k-g_{k-1} )}{g_{k-1}^T g_{k-1}}: Polak-Ribiere update. exists also three-term formula computing direction dk=−g(xk)+βkdk−1+γkdtd_k = -g(x_k) + \\beta_k d_{k-1}+\\gamma_{k} d_t tt+1k>t+1 otherwise γk=0\\gamma_k=0 k=tk=t. See Yuan (2006) well-known schemes Hestenses-Stiefel, Dixon Conjugate-Descent. three updates (Fletcher-Reeves, Polak-Ribiere, Beale-Sorenson) (non-linear) conjugate gradient available optim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"computing-the-stepsize-t_k","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods > 1.2. Hessian-free optimization methods","what":"1.2.2. Computing the stepsize tkt_k","title":"Which optimization algorithm to choose?","text":"Let ϕk(t)=f(xk+tdk)\\phi_k(t) = f(x_k + t d_k) given direction/iterate (dk,xk)(d_k, x_k). need find conditions find satisfactory stepsize tkt_k. literature, consider descent condition: ϕk′(0)<0\\phi_k'(0) < 0 Armijo condition: ϕk(t)≤ϕk(0)+tc1ϕk′(0)\\phi_k(t) \\leq \\phi_k(0) + t c_1 \\phi_k'(0) ensures decrease ff. Nocedal & Wright (2006) presents backtracking (geometric) approach satisfying Armijo condition minimal condition, .e. Goldstein Price condition. set tk,0t_{k,0} e.g. 1, 0<α<10 < \\alpha < 1, tk,+1=α×tk,it_{k,+1} = \\alpha \\times t_{k,}. end Repeat backtracking linesearch available optim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"benchmark","dir":"Articles","previous_headings":"1. Quick overview of main optimization methods","what":"1.3. Benchmark","title":"Which optimization algorithm to choose?","text":"simplify benchmark optimization methods, create fitbench function computes desired estimation method optimization methods. function currently exported package.","code":"fitbench <- function(data, distr, method, grad = NULL, control = list(trace = 0, REPORT = 1, maxit = 1000), lower = -Inf, upper = +Inf, ...)"},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"theoretical-value","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution > 2.1. Log-likelihood function and its gradient for beta distribution","what":"2.1.1. Theoretical value","title":"Which optimization algorithm to choose?","text":"density beta distribution given f(x;δ1,δ2)=xδ1−1(1−x)δ2−1β(δ1,δ2), f(x; \\delta_1,\\delta_2) = \\frac{x^{\\delta_1-1}(1-x)^{\\delta_2-1}}{\\beta(\\delta_1,\\delta_2)}, β\\beta denotes beta function, see NIST Handbook mathematical functions https://dlmf.nist.gov/. recall β(,b)=Γ()Γ(b)/Γ(+b)\\beta(,b)=\\Gamma()\\Gamma(b)/\\Gamma(+b). log-likelihood set observations (x1,…,xn)(x_1,\\dots,x_n) logL(δ1,δ2)=(δ1−1)∑=1nlog(xi)+(δ2−1)∑=1nlog(1−xi)+nlog(β(δ1,δ2)) \\log L(\\delta_1,\\delta_2) = (\\delta_1-1)\\sum_{=1}^n\\log(x_i)+ (\\delta_2-1)\\sum_{=1}^n\\log(1-x_i)+ n \\log(\\beta(\\delta_1,\\delta_2)) gradient respect aa bb ∇logL(δ1,δ2)=(∑=1nln(xi)−nψ(δ1)+nψ(δ1+δ2)∑=1nln(1−xi)−nψ(δ2)+nψ(δ1+δ2)), \\nabla \\log L(\\delta_1,\\delta_2) = \\left(\\begin{matrix} \\sum\\limits_{=1}^n\\ln(x_i) - n\\psi(\\delta_1)+n\\psi( \\delta_1+\\delta_2) \\\\ \\sum\\limits_{=1}^n\\ln(1-x_i)- n\\psi(\\delta_2)+n\\psi( \\delta_1+\\delta_2) \\end{matrix}\\right), ψ(x)=Γ′(x)/Γ(x)\\psi(x)=\\Gamma'(x)/\\Gamma(x) digamma function, see NIST Handbook mathematical functions https://dlmf.nist.gov/.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"r-implementation","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution > 2.1. Log-likelihood function and its gradient for beta distribution","what":"2.1.2. R implementation","title":"Which optimization algorithm to choose?","text":"fitdistrplus package, minimize opposite log-likelihood: implement opposite gradient grlnL. log-likelihood gradient exported.","code":"lnL <- function(par, fix.arg, obs, ddistnam) fitdistrplus:::loglikelihood(par, fix.arg, obs, ddistnam) grlnlbeta <- fitdistrplus:::grlnlbeta"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"random-generation-of-a-sample","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.2. Random generation of a sample","title":"Which optimization algorithm to choose?","text":"","code":"#(1) beta distribution n <- 200 x <- rbeta(n, 3, 3/4) grlnlbeta(c(3, 4), x) #test ## [1] -133 317 hist(x, prob=TRUE, xlim=0:1) lines(density(x), col=\"red\") curve(dbeta(x, 3, 3/4), col=\"green\", add=TRUE) legend(\"topleft\", lty=1, col=c(\"red\",\"green\"), legend=c(\"empirical\", \"theoretical\"), bty=\"n\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"fit-beta-distribution","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.3 Fit Beta distribution","title":"Which optimization algorithm to choose?","text":"Define control parameters. Call mledist default optimization function (optim implemented stats package) without gradient different optimization methods. case constrained optimization, mledist permits direct use constrOptim function (still implemented stats package) allow linear inequality constraints using logarithmic barrier. Use exp/log transformation shape parameters δ1\\delta_1 δ2\\delta_2 ensure shape parameters strictly positive. extract values fitted parameters, value corresponding log-likelihood number counts function minimize gradient (whether theoretical gradient numerically approximated one).","code":"ctr <- list(trace=0, REPORT=1, maxit=1000) unconstropt <- fitbench(x, \"beta\", \"mle\", grad=grlnlbeta, lower=0) ## BFGS NM CGFR CGPR CGBS L-BFGS-B NM-B G-BFGS ## 14 14 14 14 14 14 14 14 ## G-CGFR G-CGPR G-CGBS G-BFGS-B G-NM-B G-CGFR-B G-CGPR-B G-CGBS-B ## 14 14 14 14 14 14 14 14 dbeta2 <- function(x, shape1, shape2, log) dbeta(x, exp(shape1), exp(shape2), log=log) #take the log of the starting values startarg <- lapply(fitdistrplus:::startargdefault(x, \"beta\"), log) #redefine the gradient for the new parametrization grbetaexp <- function(par, obs, ...) grlnlbeta(exp(par), obs) * exp(par) expopt <- fitbench(x, distr=\"beta2\", method=\"mle\", grad=grbetaexp, start=startarg) ## BFGS NM CGFR CGPR CGBS G-BFGS G-CGFR G-CGPR G-CGBS ## 14 14 14 14 14 14 14 14 14 #get back to original parametrization expopt[c(\"fitted shape1\", \"fitted shape2\"), ] <- exp(expopt[c(\"fitted shape1\", \"fitted shape2\"), ])"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"results-of-the-numerical-investigation","dir":"Articles","previous_headings":"2. Numerical illustration with the beta distribution","what":"2.4. Results of the numerical investigation","title":"Which optimization algorithm to choose?","text":"Results displayed following tables: (1) original parametrization without specifying gradient (-B stands bounded version), (2) original parametrization (true) gradient (-B stands bounded version -G gradient), (3) log-transformed parametrization without specifying gradient, (4) log-transformed parametrization (true) gradient (-G stands gradient). Unconstrained optimization approximated gradient Unconstrained optimization true gradient Exponential trick optimization approximated gradient Exponential trick optimization true gradient Using llsurface, plot log-likehood surface around true value (green) fitted parameters (red). can simulate bootstrap replicates using bootdist function.","code":"llsurface(min.arg=c(0.1, 0.1), max.arg=c(7, 3), xlim=c(.1,7), plot.arg=c(\"shape1\", \"shape2\"), nlev=25, lseq=50, data=x, distr=\"beta\", back.col = FALSE) points(unconstropt[1,\"BFGS\"], unconstropt[2,\"BFGS\"], pch=\"+\", col=\"red\") points(3, 3/4, pch=\"x\", col=\"green\") b1 <- bootdist(fitdist(x, \"beta\", method = \"mle\", optim.method = \"BFGS\"), niter = 100, parallel = \"snow\", ncpus = 2) summary(b1) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## shape1 2.73 2.272 3.283 ## shape2 0.75 0.652 0.888 plot(b1, trueval = c(3, 3/4))"},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"theoretical-value-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution > 3.1. Log-likelihood function and its gradient for negative binomial distribution","what":"3.1.1. Theoretical value","title":"Which optimization algorithm to choose?","text":"p.m.f. Negative binomial distribution given f(x;m,p)=Γ(x+m)Γ(m)x!pm(1−p)x, f(x; m,p) = \\frac{\\Gamma(x+m)}{\\Gamma(m)x!} p^m (1-p)^x, Γ\\Gamma denotes beta function, see NIST Handbook mathematical functions https://dlmf.nist.gov/. exists alternative representation μ=m(1−p)/p\\mu=m (1-p)/p equivalently p=m/(m+μ)p=m/(m+\\mu). Thus, log-likelihood set observations (x1,…,xn)(x_1,\\dots,x_n) logL(m,p)=∑=1nlogΓ(xi+m)−nlogΓ(m)−∑=1nlog(xi!)+mnlog(p)+∑=1nxilog(1−p) \\log L(m,p) = \\sum_{=1}^{n} \\log\\Gamma(x_i+m) -n\\log\\Gamma(m) -\\sum_{=1}^{n} \\log(x_i!) + mn\\log(p) +\\sum_{=1}^{n} {x_i}\\log(1-p) gradient respect mm pp ∇logL(m,p)=(∑=1nψ(xi+m)−nψ(m)+nlog(p)mn/p−∑=1nxi/(1−p)), \\nabla \\log L(m,p) = \\left(\\begin{matrix} \\sum_{=1}^{n} \\psi(x_i+m) -n \\psi(m) + n\\log(p) \\\\ mn/p -\\sum_{=1}^{n} {x_i}/(1-p) \\end{matrix}\\right), ψ(x)=Γ′(x)/Γ(x)\\psi(x)=\\Gamma'(x)/\\Gamma(x) digamma function, see NIST Handbook mathematical functions https://dlmf.nist.gov/.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"r-implementation-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution > 3.1. Log-likelihood function and its gradient for negative binomial distribution","what":"3.1.2. R implementation","title":"Which optimization algorithm to choose?","text":"fitdistrplus package, minimize opposite log-likelihood: implement opposite gradient grlnL.","code":"grlnlNB <- function(x, obs, ...) { m <- x[1] p <- x[2] n <- length(obs) c(sum(psigamma(obs+m)) - n*psigamma(m) + n*log(p), m*n/p - sum(obs)/(1-p)) }"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"random-generation-of-a-sample-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.2. Random generation of a sample","title":"Which optimization algorithm to choose?","text":"","code":"#(2) negative binomial distribution n <- 200 trueval <- c(\"size\"=10, \"prob\"=3/4, \"mu\"=10/3) x <- rnbinom(n, trueval[\"size\"], trueval[\"prob\"]) hist(x, prob=TRUE, ylim=c(0, .3), xlim=c(0, 10)) lines(density(x), col=\"red\") points(min(x):max(x), dnbinom(min(x):max(x), trueval[\"size\"], trueval[\"prob\"]), col = \"green\") legend(\"topright\", lty = 1, col = c(\"red\", \"green\"), legend = c(\"empirical\", \"theoretical\"), bty=\"n\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"fit-a-negative-binomial-distribution","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.3. Fit a negative binomial distribution","title":"Which optimization algorithm to choose?","text":"Define control parameters make benchmark. case constrained optimization, mledist permits direct use constrOptim function (still implemented stats package) allow linear inequality constraints using logarithmic barrier. Use exp/log transformation shape parameters δ1\\delta_1 δ2\\delta_2 ensure shape parameters strictly positive. extract values fitted parameters, value corresponding log-likelihood number counts function minimize gradient (whether theoretical gradient numerically approximated one).","code":"ctr <- list(trace = 0, REPORT = 1, maxit = 1000) unconstropt <- fitbench(x, \"nbinom\", \"mle\", grad = grlnlNB, lower = 0) ## BFGS NM CGFR CGPR CGBS L-BFGS-B NM-B G-BFGS ## 14 14 14 14 14 14 14 14 ## G-CGFR G-CGPR G-CGBS G-BFGS-B G-NM-B G-CGFR-B G-CGPR-B G-CGBS-B ## 14 14 14 14 14 14 14 14 unconstropt <- rbind(unconstropt, \"fitted prob\" = unconstropt[\"fitted mu\", ] / (1 + unconstropt[\"fitted mu\", ])) dnbinom2 <- function(x, size, prob, log) dnbinom(x, exp(size), 1 / (1 + exp(-prob)), log = log) # transform starting values startarg <- fitdistrplus:::startargdefault(x, \"nbinom\") startarg$mu <- startarg$size / (startarg$size + startarg$mu) startarg <- list(size = log(startarg[[1]]), prob = log(startarg[[2]] / (1 - startarg[[2]]))) # redefine the gradient for the new parametrization Trans <- function(x) c(exp(x[1]), plogis(x[2])) grNBexp <- function(par, obs, ...) grlnlNB(Trans(par), obs) * c(exp(par[1]), plogis(x[2])*(1-plogis(x[2]))) expopt <- fitbench(x, distr=\"nbinom2\", method=\"mle\", grad=grNBexp, start=startarg) ## BFGS NM CGFR CGPR CGBS G-BFGS G-CGFR G-CGPR G-CGBS ## 14 14 14 14 14 14 14 14 14 # get back to original parametrization expopt[c(\"fitted size\", \"fitted prob\"), ] <- apply(expopt[c(\"fitted size\", \"fitted prob\"), ], 2, Trans)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"results-of-the-numerical-investigation-1","dir":"Articles","previous_headings":"3. Numerical illustration with the negative binomial distribution","what":"3.4. Results of the numerical investigation","title":"Which optimization algorithm to choose?","text":"Results displayed following tables: (1) original parametrization without specifying gradient (-B stands bounded version), (2) original parametrization (true) gradient (-B stands bounded version -G gradient), (3) log-transformed parametrization without specifying gradient, (4) log-transformed parametrization (true) gradient (-G stands gradient). Unconstrained optimization approximated gradient Unconstrained optimization true gradient Exponential trick optimization approximated gradient Exponential trick optimization true gradient Using llsurface, plot log-likehood surface around true value (green) fitted parameters (red). can simulate bootstrap replicates using bootdist function.","code":"llsurface(min.arg = c(5, 0.3), max.arg = c(15, 1), xlim=c(5, 15), plot.arg = c(\"size\", \"prob\"), nlev = 25, lseq = 50, data = x, distr = \"nbinom\", back.col = FALSE) points(unconstropt[\"fitted size\", \"BFGS\"], unconstropt[\"fitted prob\", \"BFGS\"], pch = \"+\", col = \"red\") points(trueval[\"size\"], trueval[\"prob\"], pch = \"x\", col = \"green\") b1 <- bootdist(fitdist(x, \"nbinom\", method = \"mle\", optim.method = \"BFGS\"), niter = 100, parallel = \"snow\", ncpus = 2) summary(b1) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## size 57.33 57.33 57.33 ## mu 3.46 3.24 3.72 plot(b1, trueval=trueval[c(\"size\", \"mu\")])"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/Optimalgo.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"4. Conclusion","title":"Which optimization algorithm to choose?","text":"Based two previous examples, observe methods converge point. reassuring. However, number function evaluations (gradient evaluations) different method another. Furthermore, specifying true gradient log-likelihood help fitting procedure generally slows convergence. Generally, best method standard BFGS method BFGS method exponential transformation parameters. Since exponential function differentiable, asymptotic properties still preserved (Delta method) finite-sample may produce small bias.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Introduction","dir":"Articles","previous_headings":"","what":"1. Introduction","title":"Overview of the fitdistrplus package","text":"Fitting distributions data common task statistics consists choosing probability distribution modelling random variable, well finding parameter estimates distribution. requires judgment expertise generally needs iterative process distribution choice, parameter estimation, quality fit assessment. R (R Development Core Team 2013) package MASS (Venables Ripley 2010), maximum likelihood estimation available via fitdistr function; steps fitting process can done using R functions (Ricci 2005). paper, present R package fitdistrplus (Delignette-Muller et al. 2014) implementing several methods fitting univariate parametric distribution. first objective developing package provide R users set functions dedicated help overall process. fitdistr function estimates distribution parameters maximizing likelihood function using optim function. distinction parameters different roles (e.g., main parameter nuisance parameter) made, paper focuses parameter estimation general point--view. cases, estimation methods prefered, maximum goodness--fit estimation (also called minimum distance estimation), proposed R package actuar three different goodness--fit distances (Dutang, Goulet, Pigeon 2008). developping fitdistrplus package, second objective consider various estimation methods addition maximum likelihood estimation (MLE). Functions developped enable moment matching estimation (MME), quantile matching estimation (QME), maximum goodness--fit estimation (MGE) using eight different distances. Moreover, fitdistrplus package offers possibility specify user-supplied function optimization, useful cases classical optimization techniques, included optim, adequate. applied statistics, frequent fit distributions censored data Commeau et al. (2012). MASS fitdistr function enable maximum likelihood estimation type data. packages can used work censored data, especially survival data Jordan (2005), packages generally focus specific models, enabling fit restricted set distributions. third objective thus provide R users function estimate univariate distribution parameters right-, left- interval-censored data. packages CRAN provide estimation procedures user-supplied parametric distribution support different types data. distrMod package (Kohl Ruckdeschel 2010) provides object-oriented (S4) implementation probability models includes distribution fitting procedures given minimization criterion. criterion user-supplied function sufficiently flexible handle censored data, yet trivial way, see Example M4 distrMod vignette. fitting functions MLEstimator MDEstimator return S4 class coercion method class mle provided respective functionalities (e.g., confint logLik) package stats4 available, . fitdistrplus, chose use standard S3 class system understanding R users. designing fitdistrplus package, forget implement generic functions also available S3 classes. Finally, various packages provide functions estimate mode, moments L-moments distribution, see reference manuals modeest, lmomco Lmoments packages. package available Comprehensive R Archive Network . paper organized follows: Section 2 presents tools fitting continuous distributions classic non-censored data. Section 3 deals estimation methods types data, Section 4 concludes.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Choice","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.1. Choice of candidate distributions","title":"Overview of the fitdistrplus package","text":"illustrating use various functions fitdistrplus package continuous non-censored data, first use data set named groundbeef included package. data set contains pointwise values serving sizes grams, collected French survey, ground beef patties consumed children 5 years old. used quantitative risk assessment published Delignette-Muller Cornu (2008). fitting one distributions data set, generally necessary choose good candidates among predefined set distributions. choice may guided knowledge stochastic processes governing modeled variable, , absence knowledge regarding underlying process, observation empirical distribution. help user choice, developed functions plot characterize empirical distribution. First , common start plots empirical distribution function histogram (density plot), can obtained plotdist function fitdistrplus package. function provides two plots (see Figure @ref(fig:figgroundbeef)): left-hand plot default histogram density scale (density plot , according values arguments histo demp) right-hand plot empirical cumulative distribution function (CDF). Histogram CDF plots empirical distribution continuous variable (serving size groundbeef data set) provided plotdist function. addition empirical plots, descriptive statistics may help choose candidates describe distribution among set parametric distributions. Especially skewness kurtosis, linked third fourth moments, useful purpose. non-zero skewness reveals lack symmetry empirical distribution, kurtosis value quantifies weight tails comparison normal distribution kurtosis equals 3. skewness kurtosis corresponding unbiased estimator (Casella Berger 2002) sample (Xi)∼..d.X(X_i)_i \\stackrel{\\text{..d.}}{\\sim} X observations (xi)(x_i)_i given : sk(X)=E[(X−E(X))3]Var(X)32,sk̂=n(n−1)n−2×m3m232,(#eq:eq1)\\begin{equation} sk(X) = \\frac{E[(X-E(X))^3]}{Var(X)^{\\frac{3}{2}}}~,~\\widehat{sk}=\\frac{\\sqrt{n(n-1)}}{n-2}\\times\\frac{m_{3}}{m_{2}^{\\frac{3}{2}}},(\\#eq:eq1) \\end{equation} kr(X)=E[(X−E(X))4]Var(X)2,kr̂=n−1(n−2)(n−3)((n+1)×m4m22−3(n−1))+3,(#eq:eq2)\\begin{equation} kr(X) = \\frac{E[(X-E(X))^4]}{Var(X)^{2}}~,~\\widehat{kr}=\\frac{n-1}{(n-2)(n-3)}((n+1) \\times \\frac{m_{4}}{m_{2}^{2}}-3(n-1)) + 3,(\\#eq:eq2) \\end{equation} m2m_{2}, m3m_{3}, m4m_{4} denote empirical moments defined mk=1n∑=1n(xi−x¯)km_{k}=\\frac{1}{n}\\sum_{=1}^n(x_{}-\\overline{x})^{k}, xix_{} nn observations variable xx x¯\\overline{x} mean value. descdist function provides classical descriptive statistics (minimum, maximum, median, mean, standard deviation), skewness kurtosis. default, unbiased estimations three last statistics provided. Nevertheless, argument method can changed \"unbiased\" (default) \"sample\" obtain without correction bias. skewness-kurtosis plot one proposed Cullen Frey (1999) provided descdist function empirical distribution (see Figure @ref(fig:descgroundbeefplot) groundbeef data set). plot, values common distributions displayed order help choice distributions fit data. distributions (normal, uniform, logistic, exponential), one possible value skewness kurtosis. Thus, distribution represented single point plot. distributions, areas possible values represented, consisting lines (gamma lognormal distributions), larger areas (beta distribution). Skewness kurtosis known robust. order take account uncertainty estimated values kurtosis skewness data, nonparametric bootstrap procedure (Efron Tibshirani 1994) can performed using argument boot. Values skewness kurtosis computed bootstrap samples (constructed random sampling replacement original data set) reported skewness-kurtosis plot. Nevertheless, user needs know skewness kurtosis, like higher moments, high variance. problem completely solved use bootstrap. skewness-kurtosis plot regarded indicative . properties random variable considered, notably expected value range, complement use plotdist descdist functions. call descdist function describe distribution serving size groundbeef data set draw corresponding skewness-kurtosis plot (see Figure @ref(fig:descgroundbeefplot)). Looking results example positive skewness kurtosis far 3, fit three common right-skewed distributions considered, Weibull, gamma lognormal distributions. Skewness-kurtosis plot continuous variable (serving size groundbeef data set) provided descdist function.","code":"require(\"fitdistrplus\") ## Loading required package: fitdistrplus ## Loading required package: MASS ## Loading required package: survival data(\"groundbeef\") str(groundbeef) ## 'data.frame': 254 obs. of 1 variable: ## $ serving: num 30 10 20 24 20 24 40 20 50 30 ... plotdist(groundbeef$serving, histo = TRUE, demp = TRUE) descdist(groundbeef$serving, boot = 1000) ## summary statistics ## ------ ## min: 10 max: 200 ## median: 79 ## mean: 73.65 ## estimated sd: 35.88 ## estimated skewness: 0.7353 ## estimated kurtosis: 3.551"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"FIT","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.2. Fit of distributions by maximum likelihood estimation","title":"Overview of the fitdistrplus package","text":"selected, one parametric distributions f(.|θ)f(.\\vert \\theta) (parameter θ∈ℝd\\theta\\\\mathbb{R}^d) may fitted data set, one time, using fitdist function. ..d. sample assumption, distribution parameters θ\\theta default estimated maximizing likelihood function defined : L(θ)=∏=1nf(xi|θ)(#eq:eq3)\\begin{equation} L(\\theta)=\\prod_{=1}^n f(x_{}\\vert \\theta)(\\#eq:eq3) \\end{equation} xix_{} nn observations variable XX f(.|θ)f(.\\vert \\theta) density function parametric distribution. proposed estimation methods described Section 3.1.. fitdist function returns S3 object class fitdist print, summary plot functions provided. fit distribution using fitdist assumes corresponding d, p, q functions (standing respectively density, distribution quantile functions) defined. Classical distributions already defined way stats package, e.g., dnorm, pnorm qnorm normal distribution (see ?Distributions). Others may found various packages (see CRAN task view: Probability Distributions ). Distributions found package must implemented user d, p, q functions. call fitdist, distribution specified via argument dist either character string corresponding common root name used names d, p, q functions (e.g., \"norm\" normal distribution) density function , root name extracted (e.g., dnorm normal distribution). Numerical results returned fitdist function (1) parameter estimates, (2) estimated standard errors (computed estimate Hessian matrix maximum likelihood solution), (3) loglikelihood, (4) Akaike Bayesian information criteria (-called AIC BIC), (5) correlation matrix parameter estimates. call fitdist function fit Weibull distribution serving size groundbeef data set. plot object class fitdist provides four classical goodness--fit plots (Cullen Frey 1999) presented Figure @ref(fig:groundbeefcomp): density plot representing density function fitted distribution along histogram empirical distribution, CDF plot empirical distribution fitted distribution, Q-Q plot representing empirical quantiles (y-axis) theoretical quantiles (x-axis), P-P plot representing empirical distribution function evaluated data point (y-axis) fitted distribution function (x-axis). CDF, Q-Q P-P plots, probability plotting position defined default using Hazen’s rule, probability points empirical distribution calculated (1:n - 0.5)/n, recommended Blom (1959). plotting position can easily changed (see reference manual details (Delignette-Muller et al. 2014)). Unlike generic plot function, denscomp, cdfcomp, qqcomp ppcomp functions enable draw separately four plots, order compare empirical distribution multiple parametric distributions fitted data set. functions must called first argument corresponding list objects class fitdist, optionally arguments customize plot (see reference manual lists arguments may specific plot (Delignette-Muller et al. 2014)). following example, compare fit Weibull, lognormal gamma distributions groundbeef data set (Figure @ref(fig:groundbeefcomp)). Four Goodness--fit plots various distributions fitted continuous data (Weibull, gamma lognormal distributions fitted serving sizes groundbeef data set) provided functions denscomp, qqcomp, cdfcomp ppcomp. density plot CDF plot may considered basic classical goodness--fit plots. two plots complementary can informative cases. Q-Q plot emphasizes lack--fit distribution tails P-P plot emphasizes lack--fit distribution center. present example (Figure @ref(fig:groundbeefcomp)), none three fitted distributions correctly describes center distribution, Weibull gamma distributions prefered better description right tail empirical distribution, especially tail important use fitted distribution, context food risk assessment. data set named endosulfan now used illustrate features fitdistrplus package. data set contains acute toxicity values organochlorine pesticide endosulfan (geometric mean LC50 ou EC50 values μg.L−1\\mu g.L^{-1}), tested Australian non-Australian laboratory-species (Hose Van den Brink 2004). ecotoxicology, lognormal loglogistic distribution often fitted data set order characterize species sensitivity distribution (SSD) pollutant. low percentile fitted distribution, generally 5% percentile, calculated named hazardous concentration 5% (HC5). interpreted value pollutant concentration protecting 95% species (Posthuma, Suter, Traas 2010). fit lognormal loglogistic distribution whole endosulfan data set rather bad (Figure @ref(fig:fitendo)), especially due minority high values. two-parameter Pareto distribution three-parameter Burr distribution (extension loglogistic Pareto distributions) fitted. Pareto Burr distributions provided package actuar. , define starting values (optimization process) reasonable starting values implicity defined within fitdist function distributions defined R (see ?fitdist details). distributions like Pareto Burr distribution, initial values distribution parameters supplied argument start, named list initial values parameter (appear d, p, q functions). defined reasonable starting values1 various distributions can fitted graphically compared. example, function cdfcomp can used report CDF values logscale emphasize discrepancies tail interest defining HC5 value (Figure @ref(fig:fitendo)). CDF plot compare fit four distributions acute toxicity values various organisms organochlorine pesticide endosulfan (endosulfan data set) provided cdfcomp function, CDF values logscale emphasize discrepancies left tail. None fitted distribution correctly describes right tail observed data set, shown Figure @ref(fig:fitendo), left-tail seems better described Burr distribution. use considered estimate HC5 value 5% quantile distribution. can easily done using quantile generic function defined object class fitdist. calculation together calculation empirical quantile comparison. addition ecotoxicology context, quantile generic function also attractive actuarial-financial context. fact, value--risk VARαVAR_\\alpha defined 1−α1-\\alpha-quantile loss distribution can computed quantile fitdist object. computation different goodness--fit statistics proposed fitdistrplus package order compare fitted distributions. purpose goodness--fit statistics aims measure distance fitted parametric distribution empirical distribution: e.g., distance fitted cumulative distribution function FF empirical distribution function FnF_{n}. fitting continuous distributions, three goodness--fit statistics classicaly considered: Cramer-von Mises, Kolmogorov-Smirnov Anderson-Darling statistics (D’Agostino Stephens 1986). Naming xix_{} nn observations continuous variable XX arranged ascending order, Table @ref(tab:tabKSCvMAD) gives definition empirical estimate three considered goodness--fit statistics. can computed using function gofstat defined Stephens (D’Agostino Stephens 1986). (#tab:tabKSCvMAD) Goodness--fit statistics defined Stephens (D’Agostino Stephens 1986). Fi=△F(xi)F_i\\stackrel{\\triangle}{=} F(x_i) giving weight distribution tails, Anderson-Darling statistic special interest matters equally emphasize tails well main body distribution. often case risk assessment Vose (2010). reason, statistics often used select best distribution among fitted. Nevertheless, statistics used cautiously comparing fits various distributions. Keeping mind weighting CDF quadratic difference depends parametric distribution definition (see Table @ref(tab:tabKSCvMAD)), Anderson-Darling statistics computed several distributions fitted data set theoretically difficult compare. Moreover, statistic, Cramer-von Mises Kolmogorov-Smirnov ones, take account complexity model (.e., parameter number). problem compared distributions characterized number parameters, systematically promote selection complex distributions case. Looking classical penalized criteria based loglikehood (AIC, BIC) seems thus also interesting, especially discourage overfitting. previous example, goodness--fit statistics based CDF distance favor Burr distribution, one characterized three parameters, AIC BIC values respectively give preference Burr distribution Pareto distribution. choice two distributions seems thus less obvious discussed. Even specifically recommended discrete distributions, Chi-squared statistic may also used continuous distributions (see Section 3.3. reference manual examples (Delignette-Muller et al. 2014)).","code":"fw <- fitdist(groundbeef$serving, \"weibull\") summary(fw) ## Fitting of the distribution ' weibull ' by maximum likelihood ## Parameters : ## estimate Std. Error ## shape 2.186 1.667 ## scale 83.348 40.272 ## Loglikelihood: -1255 AIC: 2514 BIC: 2522 ## Correlation matrix: ## shape scale ## shape 1.0000 0.3218 ## scale 0.3218 1.0000 par(mfrow = c(2, 2), mar = c(4, 4, 2, 1)) fg <- fitdist(groundbeef$serving, \"gamma\") fln <- fitdist(groundbeef$serving, \"lnorm\") plot.legend <- c(\"Weibull\", \"lognormal\", \"gamma\") denscomp(list(fw, fln, fg), legendtext = plot.legend) qqcomp(list(fw, fln, fg), legendtext = plot.legend) cdfcomp(list(fw, fln, fg), legendtext = plot.legend) ppcomp(list(fw, fln, fg), legendtext = plot.legend) require(\"actuar\") ## Loading required package: actuar ## ## Attaching package: 'actuar' ## The following objects are masked from 'package:stats': ## ## sd, var ## The following object is masked from 'package:grDevices': ## ## cm data(\"endosulfan\") ATV <- endosulfan$ATV fendo.ln <- fitdist(ATV, \"lnorm\") fendo.ll <- fitdist(ATV, \"llogis\", start = list(shape = 1, scale = 500)) fendo.P <- fitdist(ATV, \"pareto\", start = list(shape = 1, scale = 500)) fendo.B <- fitdist(ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) cdfcomp(list(fendo.ln, fendo.ll, fendo.P, fendo.B), xlogscale = TRUE, ylogscale = TRUE, legendtext = c(\"lognormal\", \"loglogistic\", \"Pareto\", \"Burr\")) quantile(fendo.B, probs = 0.05) ## Estimated quantiles for each specified probability (non-censored data) ## p=0.05 ## estimate 0.2939 quantile(ATV, probs = 0.05) ## 5% ## 0.2 gofstat(list(fendo.ln, fendo.ll, fendo.P, fendo.B), fitnames = c(\"lnorm\", \"llogis\", \"Pareto\", \"Burr\")) ## Goodness-of-fit statistics ## lnorm llogis Pareto Burr ## Kolmogorov-Smirnov statistic 0.1672 0.1196 0.08488 0.06155 ## Cramer-von Mises statistic 0.6374 0.3827 0.13926 0.06803 ## Anderson-Darling statistic 3.4721 2.8316 0.89206 0.52393 ## ## Goodness-of-fit criteria ## lnorm llogis Pareto Burr ## Akaike's Information Criterion 1069 1069 1048 1046 ## Bayesian Information Criterion 1074 1075 1053 1054"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Uncertainty","dir":"Articles","previous_headings":"2. Fitting distributions to continuous non-censored data","what":"2.3. Uncertainty in parameter estimates","title":"Overview of the fitdistrplus package","text":"uncertainty parameters fitted distribution can estimated parametric nonparametric bootstraps using boodist function non-censored data (Efron Tibshirani 1994). function returns bootstrapped values parameters S3 class object can plotted visualize bootstrap region. medians 95% confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations (due lack convergence optimization algorithm bootstrapped data sets), number iterations estimation converges also printed summary. plot object class bootdist consists scatterplot matrix scatterplots bootstrapped values parameters providing representation joint uncertainty distribution fitted parameters. example use bootdist function previous fit Burr distribution endosulfan data set (Figure @ref(fig:bootstrap)). Bootstrappped values parameters fit Burr distribution characterized three parameters (example endosulfan data set) provided plot object class bootdist. Bootstrap samples parameter estimates useful especially calculate confidence intervals parameter fitted distribution marginal distribution bootstraped values. also interesting look joint distribution bootstraped values scatterplot (matrix scatterplots number parameters exceeds two) order understand potential structural correlation parameters (see Figure @ref(fig:bootstrap)). use whole bootstrap sample also interest risk assessment field. use enables characterization uncertainty distribution parameters. can directly used within second-order Monte Carlo simulation framework, especially within package mc2d (Pouillot, Delignette-Muller, Denis 2011). One refer Pouillot Delignette-Muller (2010) introduction use mc2d fitdistrplus packages context quantitative risk assessment. bootstrap method can also used calculate confidence intervals quantiles fitted distribution. purpose, generic quantile function provided class bootdist. default, 95% percentiles bootstrap confidence intervals quantiles provided. Going back previous example ecotoxicolgy, function can used estimate uncertainty associated HC5 estimation, example previously fitted Burr distribution endosulfan data set.","code":"bendo.B <- bootdist(fendo.B, niter = 1001) summary(bendo.B) ## Parametric bootstrap medians and 95% percentile CI ## Median 2.5% 97.5% ## shape1 0.1983 0.09283 0.3606 ## shape2 1.5863 1.05306 3.0629 ## rate 1.4907 0.70828 2.7775 plot(bendo.B) quantile(bendo.B, probs = 0.05) ## (original) estimated quantiles for each specified probability (non-censored data) ## p=0.05 ## estimate 0.2939 ## Median of bootstrap estimates ## p=0.05 ## estimate 0.2994 ## ## two-sided 95 % CI of each quantile ## p=0.05 ## 2.5 % 0.1792 ## 97.5 % 0.4999"},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Alternatives","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.1. Alternative methods for parameter estimation","title":"Overview of the fitdistrplus package","text":"subsection focuses alternative estimation methods. One alternative continuous distributions maximum goodness--fit estimation method also called minimum distance estimation method Dutang, Goulet, Pigeon (2008). package method proposed eight different distances: three classical distances defined Table @ref(tab:tabKSCvMAD), one variants Anderson-Darling distance proposed Luceno (2006) defined Table @ref(tab:modifiedAD). right-tail AD gives weight right-tail, left-tail AD gives weight left tail. Either tails, , can receive even larger weights using second order Anderson-Darling Statistics. (#tab:modifiedAD) Modified Anderson-Darling statistics defined Luceno (2006). Fi=△F(xi)F_i\\stackrel{\\triangle}{=} F(x_{}) F¯=△1−F(xi)\\overline F_i\\stackrel{\\triangle}{=}1-F(x_{}) fit distribution maximum goodness--fit estimation, one needs fix argument method mge call fitdist specify argument gof coding chosen goodness--fit distance. function intended used continuous non-censored data. Maximum goodness--fit estimation may useful give weight data one tail distribution. previous example ecotoxicology, used non classical distribution (Burr distribution) correctly fit empirical distribution especially left tail. order correctly estimate 5%\\% percentile, also consider fit classical lognormal distribution, minimizing goodness--fit distance giving weight left tail empirical distribution. follows, left tail Anderson-Darling distances first second order used fit lognormal endosulfan data set (see Figure @ref(fig:plotfitMGE)). Comparison lognormal distribution fitted MLE MGE using two different goodness--fit distances: left-tail Anderson-Darling left-tail Anderson Darling second order (example endosulfan data set) provided cdfcomp function, CDF values logscale emphasize discrepancies left tail. Comparing 5% percentiles (HC5) calculated using three fits one calculated MLE fit Burr distribution, can observe, example, fitting lognormal distribution maximizing left tail Anderson-Darling distances first second order enables approach value obtained fitting Burr distribution MLE. moment matching estimation (MME) another method commonly used fit parametric distributions (Vose 2010). MME consists finding value parameter θ\\theta equalizes first theoretical raw moments parametric distribution corresponding empirical raw moments Equation @ref(eq:eq4): E(Xk|θ)=1n∑=1nxik,(#eq:eq4)\\begin{equation} E(X^{k}|\\theta)=\\frac{1}{n}\\sum_{=1}^{n}x_{}^{k},(\\#eq:eq4) \\end{equation} k=1,…,dk=1,\\ldots,d, dd number parameters estimate xix_{} nn observations variable XX. moments order greater equal 2, may also relevant match centered moments. Therefore, match moments given Equation @ref(eq:eq5): E(X|θ)=x¯,E((X−E(X))k|θ)=mk, k=2,…,d,(#eq:eq5)\\begin{equation} E(X\\vert \\theta) = \\overline{x} ~,~E\\left((X-E(X))^{k}|\\theta\\right)=m_k, \\text{ } k=2,\\ldots,d,(\\#eq:eq5) \\end{equation} mkm_k denotes empirical centered moments. method can performed setting argument method \"mme\" call fitdist. estimate computed closed-form formula following distributions: normal, lognormal, exponential, Poisson, gamma, logistic, negative binomial, geometric, beta uniform distributions. case, distributions characterized one parameter (geometric, Poisson exponential), parameter simply estimated matching theoretical observed means, distributions characterized two parameters, parameters estimated matching theoretical observed means variances (Vose 2010). distributions, equation moments solved numerically using optim function minimizing sum squared differences observed theoretical moments (see fitdistrplus reference manual technical details (Delignette-Muller et al. 2014)). classical data set Danish insurance industry published McNeil (1997) used illustrate method. fitdistrplus, data set stored danishuni univariate version contains loss amounts collected Copenhagen Reinsurance 1980 1990. actuarial science, standard consider positive heavy-tailed distributions special focus right-tail distributions. numerical experiment, choose classic actuarial distributions loss modelling: lognormal distribution Pareto type II distribution (Klugman, Panjer, Willmot 2009). lognormal distribution fitted danishuni data set matching moments implemented closed-form formula. left-hand graph Figure @ref(fig:danishmme), fitted distribution functions obtained using moment matching estimation (MME) maximum likelihood estimation (MLE) methods compared. MME method provides cautious estimation insurance risk MME-fitted distribution function (resp. MLE-fitted) underestimates (overestimates) empirical distribution function large values claim amounts. Comparison MME MLE fitting lognormal Pareto distribution loss data danishuni data set. second time, Pareto distribution, gives weight right-tail distribution, fitted. lognormal distribution, Pareto two parameters, allows fair comparison. use implementation actuar package providing raw centered moments distribution (addition d, p, q r functions (Goulet 2012). Fitting heavy-tailed distribution first second moments exist certain values shape parameter requires cautiousness. carried providing, optimization process, lower upper bound parameter. code calls L-BFGS-B optimization method optim, since quasi-Newton allows box constraints 2. choose match moments defined Equation @ref(eq:eq4), function computing empirical raw moment (called memp example) passed fitdist. two-parameter distributions (.e., d=2d=2), Equations @ref(eq:eq4) @ref(eq:eq5) equivalent. shown Figure @ref(fig:danishmme), MME MLE fits far less distant (looking right-tail) Pareto distribution lognormal distribution data set. Furthermore, two distributions, MME method better fits right-tail distribution visual point view. seems logical since empirical moments influenced large observed values. previous traces, gave values goodness--fit statistics. Whatever statistic considered, MLE-fitted lognormal always provides best fit observed data. Maximum likelihood moment matching estimations certainly commonly used method fitting distributions (Cullen Frey 1999). Keeping mind two methods may produce different results, user aware great sensitivity outliers choosing moment matching estimation. may seen advantage example objective better describe right tail distribution, may seen drawback objective different. Fitting parametric distribution may also done matching theoretical quantiles parametric distributions (specified probabilities) empirical quantiles (Tse 2009). equality theoretical empirical quantiles expressed Equation @ref(eq:eq6) , similar Equations @ref(eq:eq4) @ref(eq:eq5): F−1(pk|θ)=Qn,pk(#eq:eq6)\\begin{equation} F^{-1}(p_{k}|\\theta)=Q_{n,p_{k}}(\\#eq:eq6) \\end{equation} k=1,…,dk=1,\\ldots,d, dd number parameters estimate (dimension θ\\theta fixed parameters) Qn,pkQ_{n,p_{k}} empirical quantiles calculated data specified probabilities pkp_{k}. Quantile matching estimation (QME) performed setting argument method \"qme\" call fitdist adding argument probs defining probabilities quantile matching performed (see Figure @ref(fig:danishqme)). length vector must equal number parameters estimate (vector moment orders MME). Empirical quantiles computed using quantile function stats package using type=7 default (see ?quantile Hyndman Fan (1996)). type quantile can easily changed using qty argument call qme function. quantile matching carried numerically, minimizing sum squared differences observed theoretical quantiles. Comparison QME MLE fitting lognormal distribution loss data danishuni data set. example fitting lognormal distribution `danishuni} data set matching probabilities (p1=1/3,p2=2/3)(p_1= 1/3, p_2=2/3) (p1=8/10,p2=9/10)(p_1= 8/10, p_2=9/10). expected, second QME fit gives weight right-tail distribution. Compared maximum likelihood estimation, second QME fit best suits right-tail distribution, whereas first QME fit best models body distribution. quantile matching estimation particular interest need focus around particular quantiles, e.g., p=99.5%p=99.5\\% Solvency II insurance context p=5%p=5\\% HC5 estimation ecotoxicology context.","code":"fendo.ln.ADL <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"ADL\") fendo.ln.AD2L <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD2L\") cdfcomp(list(fendo.ln, fendo.ln.ADL, fendo.ln.AD2L), xlogscale = TRUE, ylogscale = TRUE, main = \"Fitting a lognormal distribution\", xlegend = \"bottomright\", legendtext = c(\"MLE\", \"Left-tail AD\", \"Left-tail AD 2nd order\")) (HC5.estimates <- c( empirical = as.numeric(quantile(ATV, probs = 0.05)), Burr = as.numeric(quantile(fendo.B, probs = 0.05)$quantiles), lognormal_MLE = as.numeric(quantile(fendo.ln, probs = 0.05)$quantiles), lognormal_AD2 = as.numeric(quantile(fendo.ln.ADL, probs = 0.05)$quantiles), lognormal_AD2L = as.numeric(quantile(fendo.ln.AD2L, probs = 0.05)$quantiles))) ## empirical Burr lognormal_MLE lognormal_AD2 lognormal_AD2L ## 0.20000 0.29393 0.07259 0.19591 0.25877 data(\"danishuni\") str(danishuni) ## 'data.frame': 2167 obs. of 2 variables: ## $ Date: Date, format: \"1980-01-03\" \"1980-01-04\" ... ## $ Loss: num 1.68 2.09 1.73 1.78 4.61 ... fdanish.ln.MLE <- fitdist(danishuni$Loss, \"lnorm\") fdanish.ln.MME <- fitdist(danishuni$Loss, \"lnorm\", method = \"mme\", order = 1:2) require(\"actuar\") fdanish.P.MLE <- fitdist(danishuni$Loss, \"pareto\", start = list(shape = 10, scale = 10), lower = 2+1e-6, upper = Inf) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious ## Warning in sqrt(diag(varcovar)): NaNs produced memp <- function(x, order) mean(x^order) fdanish.P.MME <- fitdist(danishuni$Loss, \"pareto\", method = \"mme\", order = 1:2, memp = \"memp\", start = list(shape = 10, scale = 10), lower = c(2+1e-6, 2+1e-6), upper = c(Inf, Inf)) ## Warning in cov2cor(varcovar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious par(mfrow = c(1, 2)) cdfcomp(list(fdanish.ln.MLE, fdanish.ln.MME), legend = c(\"lognormal MLE\", \"lognormal MME\"), main = \"Fitting a lognormal distribution\", xlogscale = TRUE, datapch = 20) cdfcomp(list(fdanish.P.MLE, fdanish.P.MME), legend = c(\"Pareto MLE\", \"Pareto MME\"), main = \"Fitting a Pareto distribution\", xlogscale = TRUE, datapch = 20) gofstat(list(fdanish.ln.MLE, fdanish.P.MLE, fdanish.ln.MME, fdanish.P.MME), fitnames = c(\"lnorm.mle\", \"Pareto.mle\", \"lnorm.mme\", \"Pareto.mme\")) ## Goodness-of-fit statistics ## lnorm.mle Pareto.mle lnorm.mme Pareto.mme ## Kolmogorov-Smirnov statistic 0.1375 0.3124 0.4368 0.37 ## Cramer-von Mises statistic 14.7911 37.7166 88.9503 55.43 ## Anderson-Darling statistic 87.1933 208.3143 416.2567 281.58 ## ## Goodness-of-fit criteria ## lnorm.mle Pareto.mle lnorm.mme Pareto.mme ## Akaike's Information Criterion 8120 9250 9792 9409 ## Bayesian Information Criterion 8131 9261 9803 9420 fdanish.ln.QME1 <- fitdist(danishuni$Loss, \"lnorm\", method = \"qme\", probs = c(1/3, 2/3)) fdanish.ln.QME2 <- fitdist(danishuni$Loss, \"lnorm\", method = \"qme\", probs = c(8/10, 9/10)) cdfcomp(list(fdanish.ln.MLE, fdanish.ln.QME1, fdanish.ln.QME2), legend = c(\"MLE\", \"QME(1/3, 2/3)\", \"QME(8/10, 9/10)\"), main = \"Fitting a lognormal distribution\", xlogscale = TRUE, datapch = 20)"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"Customization","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.2. Customization of the optimization algorithm","title":"Overview of the fitdistrplus package","text":"time numerical minimization carried fitdistrplus package, optim function stats package used default Nelder-Mead method distributions characterized one parameter BFGS method distributions characterized one parameter. Sometimes default algorithm fails converge. interesting change options optim function use another optimization function optim minimize objective function. argument optim.method can used call fitdist fitdistcens. internally passed mledist, mmedist, mgedist qmedist, optim (see ?optim details different algorithms available). Even error raised computing optimization, changing algorithm particular interest enforce bounds parameters. instance, volatility parameter σ\\sigma strictly positive σ>0\\sigma>0 probability parameter pp lies p∈[0,1]p\\[0,1]. possible using arguments lower /upper, use automatically forces optim.method=\"L-BFGS-B\". examples fits gamma distribution 𝒢(α,λ)\\mathcal{G}(\\alpha, \\lambda) groundbeef data set various algorithms. Note conjugate gradient algorithm (CG) needs far iterations converge (around 2500 iterations) compared algorithms (converging less 100 iterations). also possible use another function optim minimize objective function specifying argument custom.optim call fitdist. may necessary customize optimization function meet following requirements. (1) custom.optim function must following arguments: fn function optimized par initialized parameters. (2) custom.optim carry MINIMIZATION must return following components: par estimate, convergence convergence code, value=fn(par) hessian. example code written wrap genoud function rgenoud package order respect optimization ``template’’. rgenoud package implements genetic (stochastic) algorithm. customized optimization function can passed argument custom.optim call fitdist fitdistcens. following code can example used fit gamma distribution groundbeef data set. Note example various arguments also passed fitdist genoud: nvars, Domains, boundary.enforcement, print.level hessian. code compares parameter estimates (α̂\\hat\\alpha, λ̂\\hat\\lambda) different algorithms: shape α\\alpha rate λ\\lambda parameters relatively similar example, roughly 4.00 0.05, respectively.","code":"data(\"groundbeef\") fNM <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"Nelder-Mead\") fBFGS <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"BFGS\") fSANN <- fitdist(groundbeef$serving, \"gamma\", optim.method = \"SANN\") fCG <- try(fitdist(groundbeef$serving, \"gamma\", optim.method = \"CG\", control = list(maxit = 10000))) if(inherits(fCG, \"try-error\")) {fCG <- list(estimate = NA)} mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values = par, ...) standardres <- c(res, convergence = 0) return(standardres) } fgenoud <- mledist(groundbeef$serving, \"gamma\", custom.optim = mygenoud, nvars = 2, max.generations = 10, Domains = cbind(c(0, 0), c(10, 10)), boundary.enforcement = 1, hessian = TRUE, print.level = 0, P9 = 10) ## Loading required package: rgenoud ## ## rgenoud (Version 5.9-0.11, Build Date: 2024-10-03) ## ## See http://sekhon.berkeley.edu/rgenoud for additional documentation. ## ## Please cite software as: ## ## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011. ## ## ``Genetic Optimization Using Derivatives: The rgenoud package for R.'' ## ## Journal of Statistical Software, 42(11): 1-26. ## ## cbind(NM = fNM$estimate, BFGS = fBFGS$estimate, SANN = fSANN$estimate, CG = fCG$estimate, fgenoud = fgenoud$estimate) ## NM BFGS SANN CG fgenoud ## shape 4.00956 4.21184 3.93636 4.03958 4.00834 ## rate 0.05444 0.05719 0.05366 0.05486 0.05443"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"otherdata","dir":"Articles","previous_headings":"3. Advanced topics","what":"3.3. Fitting distributions to other types of data","title":"Overview of the fitdistrplus package","text":"section modified since publication vignette Journal Statistical Software order include new goodness--fit plots censored discrete data. Analytical methods often lead semi-quantitative results referred censored data. Observations known limit detection left-censored data. Observations known limit quantification right-censored data. Results known lie two bounds interval-censored data. two bounds may correspond limit detection limit quantification, generally uncertainty bounds around observation. Right-censored data also commonly encountered survival data (Klein Moeschberger 2003). data set may thus contain right-, left-, interval-censored data, may mixture categories, possibly different upper lower bounds. Censored data sometimes excluded data analysis replaced fixed value, cases may lead biased results. recommended approach correctly model data based upon maximum likelihood Helsel (2005). Censored data may thus contain left-censored, right-censored interval-censored values, several lower upper bounds. use package fitdistrplus, data must coded dataframe two columns, respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. illustrate use package fitdistrplus fit distributions censored continous data, use another data set ecotoxicology, included package named salinity. data set contains acute salinity tolerance (LC50 values electrical conductivity, mSmS.cm−1cm^{-1}) riverine macro-invertebrates taxa southern Murray-Darling Basin Central Victoria, Australia (Kefford et al. 2007). Using censored data coded salinity} data set, empirical distribution can plotted using theplotdistcens} function. older versions package, default function used Expectation-Maximization approach Turnbull (1974) compute overall empirical cdf curve optional confidence intervals, calls survfit plot.survfit functions survival package. Even representation always available (fixing argument NPMLE.method \"Turnbull.middlepoints\"), now default plot empirical cumulative distribution function (ECDF) explicitly represents regions non uniqueness NPMLE ECDF. default computation regions non uniqueness associated masses uses non parametric maximum likelihood estimation (NPMLE) approach developped Wang Wang Fani (2018). Figure @ref(fig:cdfcompcens) shows top left new plot data together two fitted distributions. Grey filled rectangles plot represent regions non uniqueness NPMLE ECDF. less rigorous sometimes illustrative plot can obtained fixing argument NPMLE FALSE call plotdistcens (see Figure @ref(fig:plotsalinity2) example help page Function plotdistcens details). plot enables see real nature censored data, points intervals, difficulty building plot define relevant ordering observations. Simple plot censored raw data (72-hour acute salinity tolerance riverine macro-invertebrates salinity data set) ordered points intervals. non censored data, one parametric distributions can fitted censored data set, one time, using case fitdistcens function. function estimates vector distribution parameters θ\\theta maximizing likelihood censored data defined : $$\\begin{equation} L(\\theta) = \\prod_{=1}^{N_{nonC}} f(x_{}|\\theta)\\times \\prod_{j=1}^{N_{leftC}} F(x^{upper}_{j}|\\theta) \\\\ \\times \\prod_{k=1}^{N_{rightC}} (1- F(x^{lower}_{k}|\\theta))\\times \\prod_{m=1}^{N_{intC}} (F(x^{upper}_{m}|\\theta)- F(x^{lower}_{j}|\\theta))(\\#eq:eq7) \\end{equation}$$ xix_{} NnonCN_{nonC} non-censored observations, xjupperx^{upper}_{j} upper values defining NleftCN_{leftC} left-censored observations, xklowerx^{lower}_{k} lower values defining NrightCN_{rightC} right-censored observations, [xmlower;xmupper][x^{lower}_{m} ; x^{upper}_{m}] intervals defining NintCN_{intC} interval-censored observations, F cumulative distribution function parametric distribution Helsel (2005). fitdist, fitdistcens returns results fit parametric distribution data set S3 class object can easily printed, summarized plotted. salinity data set, lognormal distribution loglogistic can fitted commonly done ecotoxicology data. fitdist, distributions (see Delignette-Muller et al. (2014) details), necessary specify initial values distribution parameters argument start. plotdistcens function can help find correct initial values distribution parameters non trivial cases, manual iterative use necessary. Computations goodness--fit statistics yet developed fits using censored data quality fit can judged using Akaike Schwarz’s Bayesian information criteria (AIC BIC) goodness--fit CDF plot, respectively provided summarizing plotting object class fitdistcens. Functions cdfcompcens, qqcompcens ppcompcens can also used compare fit various distributions censored data set. calls similar ones cdfcomp, qqcomp ppcomp. examples use functions two fitted distributions salinity data set (see Figure @ref(fig:cdfcompcens)). qqcompcens ppcompcens used one fitted distribution, non uniqueness rectangles filled small noise added y-axis order help visualization various fits. rather recommend use plotstyle ggplot qqcompcens ppcompcens compare fits various distributions provides clearer plot splitted facets (see ?graphcompcens). goodness--fit plots fits lognormal loglogistic distribution censored data: LC50 values salinity data set. Function bootdistcens equivalent bootdist censored data, except proposes nonparametric bootstrap. Indeed, obvious simulate censoring within parametric bootstrap resampling procedure. generic function quantile can also applied object class fitdistcens bootdistcens, continuous non-censored data. addition fit distributions censored non censored continuous data, package can also accomodate discrete variables, count numbers, using functions developped continuous non-censored data. functions provide somewhat different graphs statistics, taking account discrete nature modeled variable. discrete nature variable automatically recognized classical distribution fitted data (binomial, negative binomial, geometric, hypergeometric Poisson distributions) must indicated fixing argument discrete TRUE call functions cases. toxocara data set included package corresponds observation discrete variable. Numbers Toxocara cati parasites present digestive tract reported random sampling feral cats living Kerguelen island (Fromont et al. 2001). use illustrate case discrete data. fit discrete distribution discrete data maximum likelihood estimation requires procedure continuous non-censored data. example, using toxocara data set, Poisson negative binomial distributions can easily fitted. discrete distributions, plot object class fitdist simply provides two goodness--fit plots comparing empirical theoretical distributions density CDF. Functions cdfcomp denscomp can also used compare several plots data set, follows previous fits (Figure @ref(fig:fittoxocarapoisnbinom)). Comparison fits negative binomial Poisson distribution numbers Toxocara cati parasites toxocara data set. fitting discrete distributions, Chi-squared statistic computed gofstat function using cells defined argument chisqbreaks cells automatically defined data order reach roughly number observations per cell. number roughly equal argument meancount, sligthly greater ties. choice define cells empirical distribution (data), theoretical distribution, done enable comparison Chi-squared values obtained different distributions fitted data set. arguments chisqbreaks meancount omitted, meancount fixed order obtain roughly (4n)2/5(4n)^{2/5} cells, nn length data set (Vose 2010). Using default option two previous fits compared follows, giving preference negative binomial distribution, Chi-squared statistics information criteria:","code":"data(\"salinity\") str(salinity) ## 'data.frame': 108 obs. of 2 variables: ## $ left : num 20 20 20 20 20 21.5 15 20 23.7 25 ... ## $ right: num NA NA NA NA NA 21.5 30 25 23.7 NA ... plotdistcens(salinity, NPMLE = FALSE) fsal.ln <- fitdistcens(salinity, \"lnorm\") fsal.ll <- fitdistcens(salinity, \"llogis\", start = list(shape = 5, scale = 40)) summary(fsal.ln) ## Fitting of the distribution ' lnorm ' By maximum likelihood on censored data ## Parameters ## estimate Std. Error ## meanlog 3.3854 0.6741 ## sdlog 0.4961 0.5669 ## Loglikelihood: -139.1 AIC: 282.1 BIC: 287.5 ## Correlation matrix: ## meanlog sdlog ## meanlog 1.0000 0.2938 ## sdlog 0.2938 1.0000 summary(fsal.ll) ## Fitting of the distribution ' llogis ' By maximum likelihood on censored data ## Parameters ## estimate Std. Error ## shape 3.421 4.321 ## scale 29.930 20.210 ## Loglikelihood: -140.1 AIC: 284.1 BIC: 289.5 ## Correlation matrix: ## shape scale ## shape 1.0000 -0.2022 ## scale -0.2022 1.0000 par(mfrow = c(2, 2)) cdfcompcens(list(fsal.ln, fsal.ll), legendtext = c(\"lognormal\", \"loglogistic \")) qqcompcens(fsal.ln, legendtext = \"lognormal\") ppcompcens(fsal.ln, legendtext = \"lognormal\") qqcompcens(list(fsal.ln, fsal.ll), legendtext = c(\"lognormal\", \"loglogistic \"), main = \"Q-Q plot with 2 dist.\") data(\"toxocara\") str(toxocara) ## 'data.frame': 53 obs. of 1 variable: ## $ number: int 0 0 0 0 0 0 0 0 0 0 ... (ftoxo.P <- fitdist(toxocara$number, \"pois\")) ## Fitting of the distribution ' pois ' by maximum likelihood ## Parameters: ## estimate Std. Error ## lambda 8.679 2.946 (ftoxo.nb <- fitdist(toxocara$number, \"nbinom\")) ## Fitting of the distribution ' nbinom ' by maximum likelihood ## Parameters: ## estimate Std. Error ## size 0.3971 0.6035 ## mu 8.6803 14.0871 par(mfrow = c(1, 2)) denscomp(list(ftoxo.P, ftoxo.nb), legendtext = c(\"Poisson\", \"negative binomial\"), fitlty = 1) cdfcomp(list(ftoxo.P, ftoxo.nb), legendtext = c(\"Poisson\", \"negative binomial\"), fitlty = 1) gofstat(list(ftoxo.P, ftoxo.nb), fitnames = c(\"Poisson\", \"negative binomial\")) ## Chi-squared statistic: 31257 7.486 ## Degree of freedom of the Chi-squared distribution: 5 4 ## Chi-squared p-value: 0 0.1123 ## the p-value may be wrong with some theoretical counts < 5 ## Chi-squared table: ## obscounts theo Poisson theo negative binomial ## <= 0 14 0.009014 15.295 ## <= 1 8 0.078237 5.809 ## <= 3 6 1.321767 6.845 ## <= 4 6 2.131298 2.408 ## <= 9 6 29.827829 7.835 ## <= 21 6 19.626223 8.271 ## > 21 7 0.005631 6.537 ## ## Goodness-of-fit criteria ## Poisson negative binomial ## Akaike's Information Criterion 1017 322.7 ## Bayesian Information Criterion 1019 326.6"},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"ccl","dir":"Articles","previous_headings":"","what":"4. Conclusion","title":"Overview of the fitdistrplus package","text":"R package fitdistrplus allows easily fit distributions. main objective developing package provide tools helping R users fit distributions data. encouraged pursue work feedbacks users package various areas food environmental risk assessment, epidemiology, ecology, molecular biology, genomics, bioinformatics, hydraulics, mechanics, financial actuarial mathematics operations research. Indeed, package already used lot practionners academics simple MLE fits Voigt et al. (2014), MLE fits goodness--fit statistics Vaninsky (2013), MLE fits bootstrap Rigaux et al. (2014), MLE fits, bootstrap goodness--fit statistics (Larras, Montuelle, Bouchez 2013), MME fit Sato et al. (2013), censored MLE bootstrap Contreras, Huerta, Arnold (2013), graphic analysing (Anand, Yeturu, Chandra 2012), grouped-data fitting methods (Fu, Steiner, Costafreda 2012) generally Drake, Chalabi, Coker (2014). fitdistrplus package complementary distrMod package (Kohl Ruckdeschel 2010). distrMod provides even flexible way estimate distribution parameters use requires greater initial investment learn manipulate S4 classes methods developed distr-family packages. Many extensions fitdistrplus package planned future: target extend censored data methods moment available non-censored data, especially concerning goodness--fit evaluation fitting methods. also enlarge choice fitting methods non-censored data, proposing new goodness--fit distances (e.g., distances based quantiles) maximum goodness--fit estimation new types moments (e.g., limited expected values) moment matching estimation. last, consider case multivariate distribution fitting.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/fitdistrplus_vignette.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Overview of the fitdistrplus package","text":"package stage without stimulating contribution Régis Pouillot Jean-Baptiste Denis, especially conceptualization. also want thank Régis Pouillot valuable comments first version paper. authors gratefully acknowledges two anonymous referees Editor useful constructive comments. remaining errors, course, attributed authors alone.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"geometric-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.1. Geometric distribution","title":"Starting values used in fitdistrplus","text":"MME used p̂=1/(1+m1)\\hat p=1/(1+m_1).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"negative-binomial-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.2. Negative binomial distribution","title":"Starting values used in fitdistrplus","text":"MME used n̂=m12/(μ2−m1)\\hat n = m_1^2/(\\mu_2-m_1).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"poisson-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.3. Poisson distribution","title":"Starting values used in fitdistrplus","text":"MME MLE λ̂=m1\\hat \\lambda = m_1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"binomial-distribution","dir":"Articles","previous_headings":"1. Discrete distributions > 1.1. Base R distribution","what":"1.1.4. Binomial distribution","title":"Starting values used in fitdistrplus","text":"MME used Var[X]/E[X]=1−p⇒p̂=1−μ2/m1. Var[X]/E[X] = 1-p \\Rightarrow \\hat p = 1- \\mu_2/m_1. size parameter n̂=⌈max(maxixi,m1/p̂)⌉. \\hat n = \\lceil\\max(\\max_i x_i, m_1/\\hat p)\\rceil.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"logarithmic-distribution","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.2. logarithmic distribution","title":"Starting values used in fitdistrplus","text":"expectation simplifies small values ppE[X]=−1log(1−p)p1−p≈−1−pp1−p=11−p. E[X] = -\\frac{1}{\\log(1-p)}\\frac{p}{1-p} \\approx -\\frac{1}{-p}\\frac{p}{1-p} =\\frac{1}{1-p}. initial estimate p̂=1−1/m1. \\hat p = 1-1/m_1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"zero-truncated-distributions","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.3. Zero truncated distributions","title":"Starting values used in fitdistrplus","text":"distribution distribution X|X>0X\\vert X>0 XX follows particular discrete distributions. Hence initial estimate one used base R sample x−1x-1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"zero-modified-distributions","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.4. Zero modified distributions","title":"Starting values used in fitdistrplus","text":"MLE probability parameter empirical mass 0 p̂0=1n∑i1xi=0\\hat p_0=\\frac1n \\sum_i 1_{x_i=0}. estimators use classical estimator probability parameter 1−p̂01-\\hat p_0.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"poisson-inverse-gaussian-distribution","dir":"Articles","previous_headings":"1. Discrete distributions","what":"1.5. Poisson inverse Gaussian distribution","title":"Starting values used in fitdistrplus","text":"first two moments E[X]=μ,Var[X]=μ+ϕμ3. E[X]=\\mu, Var[X] = \\mu+\\phi\\mu^3. initial estimate μ̂=m1,ϕ̂=(μ2−m1)/m13. \\hat\\mu=m_1, \\hat\\phi = (\\mu_2 - m_1)/m_1^3.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"normal-distribution","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.1. Normal distribution","title":"Starting values used in fitdistrplus","text":"MLE MME use empirical mean variance.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"lognormal-distribution","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.2. Lognormal distribution","title":"Starting values used in fitdistrplus","text":"log sample follows normal distribution, normal log sample.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"beta-distribution-of-the-first-kind","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.3. Beta distribution (of the first kind)","title":"Starting values used in fitdistrplus","text":"density function beta ℬe(,b)\\mathcal (,b) fX(x)=Γ()Γ(b)Γ(+b)xa−1(1−x)b−1. f_X(x) = \\frac{\\Gamma()\\Gamma(b)}{\\Gamma(+b)} x^{-1}(1-x)^{b-1}. initial estimate MME ̂=m1δ,b̂=(1−m1)δ,δ=m1(1−m1)μ2−1,(#eq:betaguessestimator)\\begin{equation} \\hat = m_1 \\delta, \\hat b = (1-m_1)\\delta, \\delta = \\frac{m_1(1-m_1)}{\\mu_2}-1, (\\#eq:betaguessestimator) \\end{equation}","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"log-gamma","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.1. Log-gamma","title":"Starting values used in fitdistrplus","text":"Use gamma initial values sample log(x)\\log(x)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"gumbel","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.2. Gumbel","title":"Starting values used in fitdistrplus","text":"distribution function F(x)=exp(−exp(−x−αθ)). F(x) = \\exp(-\\exp(-\\frac{x-\\alpha}{\\theta})). Let q1q_1 q3q_3 first third quartiles. $$ \\left\\{\\begin{array} -\\theta\\log(-\\log(p_1)) = q_1-\\alpha \\\\ -\\theta\\log(-\\log(p_3)) = q_3-\\alpha \\end{array}\\right. \\Leftrightarrow \\left\\{\\begin{array} -\\theta\\log(-\\log(p_1))+\\theta\\log(-\\log(p_3)) = q_1-q_3 \\\\ \\alpha= \\theta\\log(-\\log(p_3)) + q_3 \\end{array}\\right. \\Leftrightarrow \\left\\{\\begin{array} \\theta= \\frac{q_1-q_3}{\\log(-\\log(p_3)) - \\log(-\\log(p_1))} \\\\ \\alpha= \\theta\\log(-\\log(p_3)) + q_3 \\end{array}\\right.. $$ Using median location parameter α\\alpha yields initial estimate θ̂=q1−q3log(log(4/3))−log(log(4)),α̂=θ̂log(log(2))+q2. \\hat\\theta= \\frac{q_1-q_3}{\\log(\\log(4/3)) - \\log(\\log(4))}, \\hat\\alpha = \\hat\\theta\\log(\\log(2)) + q_2.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-gaussian-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.3. Inverse Gaussian distribution","title":"Starting values used in fitdistrplus","text":"moments distribution E[X]=μ,Var[X]=μ3ϕ. E[X] = \\mu, Var[X] = \\mu^3\\phi. Hence initial estimate μ̂=m1\\hat\\mu=m_1, ϕ̂=μ2/m13\\hat\\phi=\\mu_2/m_1^3.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"generalized-beta","dir":"Articles","previous_headings":"2. Continuous distributions > 2.4. Other continuous distribution in actuar","what":"2.4.4. Generalized beta","title":"Starting values used in fitdistrplus","text":"distribution θX1/τ\\theta X^{1/\\tau} XX beta distributed ℬe(,b)\\mathcal (,b) moments E[X]=θβ(+1/τ,b)/β(,b)=θΓ(+1/τ)Γ()Γ(+b)Γ(+b+1/τ), E[X] = \\theta \\beta(+1/\\tau, b)/\\beta(,b) = \\theta \\frac{\\Gamma(+1/\\tau)}{\\Gamma()}\\frac{\\Gamma(+b)}{\\Gamma(+b+1/\\tau)}, E[X2]=θ2Γ(+2/τ)Γ()Γ(+b)Γ(+b+2/τ). E[X^2] = \\theta^2 \\frac{\\Gamma(+2/\\tau)}{\\Gamma()}\\frac{\\Gamma(+b)}{\\Gamma(+b+2/\\tau)}. Hence large value τ\\tau, E[X2]/E[X]=θΓ(+2/τ)Γ(+b+2/τ)Γ(+b+1/τ)Γ(+1/τ)≈θ. E[X^2] /E[X] = \\theta \\frac{\\Gamma(+2/\\tau)}{\\Gamma(+b+2/\\tau)} \\frac{\\Gamma(+b+1/\\tau)}{\\Gamma(+1/\\tau)} \\approx \\theta. Note MLE θ\\theta maximum use τ̂=3,θ̂=m2m1maxixi1m2>m1+m1m2maxixi1m2≥m1. \\hat\\tau=3, \\hat\\theta = \\frac{m_2}{m_1}\\max_i x_i 1_{m_2>m_1} +\\frac{m_1}{m_2}\\max_i x_i 1_{m_2\\geq m_1}. use beta initial estimate sample (xiθ̂)τ̂(\\frac{x_i}{\\hat\\theta})^{\\hat\\tau}.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"feller-pareto-family","dir":"Articles","previous_headings":"2. Continuous distributions","what":"2.5. Feller-Pareto family","title":"Starting values used in fitdistrplus","text":"Feller-Pareto distribution distribution X=μ+θ(1/B−1)1/γX=\\mu+\\theta(1/B-1)^{1/\\gamma} BB follows beta distribution shape parameters α\\alpha τ\\tau. See details https://doi.org/10.18637/jss.v103.i06 Hence let Y=(X−μ)/θY = (X-\\mu)/\\theta, Y1+Y=X−μθ+X−μ=(1−B)1/γ. \\frac{Y}{1+Y} = \\frac{X-\\mu}{\\theta+X-\\mu} = (1-B)^{1/\\gamma}. γ\\gamma close 1, Y1+Y\\frac{Y}{1+Y} approximately beta distributed τ\\tau α\\alpha. log-likelihood ℒ(μ,θ,α,γ,τ)=(τγ−1)∑ilog(xi−μθ)−(α+τ)∑ilog(1+(xi−μθ)γ)+nlog(γ)−nlog(θ)−nlog(β(α,τ)).(#eq:fellerparetologlik).\\begin{equation} \\mathcal L(\\mu, \\theta, \\alpha, \\gamma, \\tau) = (\\tau \\gamma - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - (\\alpha+\\tau)\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) + n\\log(\\gamma) - n\\log(\\theta) -n \\log(\\beta(\\alpha,\\tau)). (\\#eq:fellerparetologlik). \\end{equation} MLE μ\\mu minimum. gradient respect θ,α,γ,τ\\theta, \\alpha, \\gamma, \\tau ∇ℒ(μ,θ,α,γ,τ)=(−(τγ−1)∑ixiθ(xi−μ)+(α+τ)∑ixiγ(xi−μθ)γ−1θ2(1+(xi−μθ)γ)−n/θ−∑ilog(1+(xi−μθ)γ)−n(ψ(τ)−ψ(α+τ))(τ−1)∑ilog(xi−μθ)−(α+τ)∑(xi−μθ)γ1+(xi−μθ)γlog(xi−μθ)+n/γ(γ−1)∑ilog(xi−μθ)−∑ilog(1+(xi−μθ)γ)−n(ψ(τ)−ψ(α+τ))).(#eq:fellerparetogradient)\\begin{equation} \\nabla \\mathcal L(\\mu, \\theta, \\alpha, \\gamma, \\tau) = \\begin{pmatrix} -(\\tau \\gamma - 1) \\sum_{} \\frac{x_i}{\\theta(x_i-\\mu)} + (\\alpha+\\tau)\\sum_i \\frac{x_i\\gamma(\\frac{x_i-\\mu}\\theta)^{\\gamma-1}}{\\theta^2(1+(\\frac{x_i-\\mu}\\theta)^\\gamma)} - n/\\theta \\\\ - \\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) -n(\\psi(\\tau) - \\psi(\\alpha+\\tau)) \\\\ (\\tau - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - (\\alpha+\\tau)\\sum_i \\frac{(\\frac{x_i-\\mu}\\theta)^\\gamma}{ 1+(\\frac{x_i-\\mu}\\theta)^\\gamma}\\log(\\frac{x_i-\\mu}\\theta) + n/\\gamma \\\\ (\\gamma - 1) \\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - \\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) -n (\\psi(\\tau) - \\psi(\\alpha+\\tau)) \\end{pmatrix}. (\\#eq:fellerparetogradient) \\end{equation} Cancelling first component score γ=α=2\\gamma=\\alpha=2, get −(2τ−1)∑ixiθ(xi−μ)+(2+τ)∑ixi2(xi−μ)θ3(1+(xi−μθ)2)=nθ⇔−(2τ−1)θ21n∑ixixi−μ+(2+τ)1n∑ixi2(xi−μ)(1+(xi−μθ)2)=θ2 -(2\\tau - 1) \\sum_{} \\frac{x_i}{\\theta(x_i-\\mu)} + (2+\\tau)\\sum_i \\frac{x_i 2(x_i-\\mu)}{\\theta^3(1+(\\frac{x_i-\\mu}\\theta)^2)} = \\frac{n}{\\theta} \\Leftrightarrow -(2\\tau - 1)\\theta^2\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} + (2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{(1+(\\frac{x_i-\\mu}\\theta)^2)} = \\theta^2 ⇔(2+τ)1n∑ixi2(xi−μ)1+(xi−μθ)2=(2τ−1)θ2(1n∑ixixi−μ−1)⇔(2+τ)1n∑ixi2(xi−μ)1+(xi−μθ)2(2τ−1)(1n∑ixixi−μ−1)=θ. \\Leftrightarrow (2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{1+(\\frac{x_i-\\mu}\\theta)^2} = (2\\tau - 1)\\theta^2\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} -1\\right) \\Leftrightarrow \\sqrt{ \\frac{(2+\\tau) \\frac1n\\sum_i \\frac{x_i 2(x_i-\\mu)}{1+(\\frac{x_i-\\mu}\\theta)^2} }{(2\\tau - 1)\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\mu} -1\\right)} } = \\theta. Neglecting unknown value τ\\tau denominator θ\\theta, get μ̂\\hat\\mu set (@ref(eq:pareto4muinit)) θ̂=1n∑ixi2(xi−μ̂)1+(xi−μ̂)2(1n∑ixixi−μ̂−1).(#eq:fellerparetothetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac{ \\frac1n\\sum_i \\frac{x_i 2(x_i-\\hat\\mu)}{1+(x_i-\\hat\\mu)^2} }{\\left(\\frac1n \\sum_{} \\frac{x_i}{x_i-\\hat\\mu} -1\\right)} }. (\\#eq:fellerparetothetahat) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=(xi−μ̂)/θ̂, z_i = y_i/(1+y_i), y_i = (x_i - \\hat\\mu)/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)). Cancelling last component gradient leads (γ−1)1n∑ilog(xi−μθ)−1n∑ilog(1+(xi−μθ)γ)=ψ(τ)−ψ(α+τ)⇔(γ−1)1n∑ilog(xi−μθ)=ψ(τ)−ψ(α+τ)+1n∑ilog(1+(xi−μθ)γ). (\\gamma - 1) \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) - \\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) = \\psi(\\tau) - \\psi(\\alpha+\\tau) \\Leftrightarrow (\\gamma - 1) \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) = \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)^\\gamma) . Neglecting value γ\\gamma right-hand side obtain γ̂=1+ψ(τ)−ψ(α+τ)+1n∑ilog(1+(xi−μθ))1n∑ilog(xi−μθ).(#eq:fellerparetogammahat)\\begin{equation} \\hat\\gamma = 1+ \\frac{ \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+(\\frac{x_i-\\mu}\\theta)) }{ \\frac1n\\sum_{} \\log(\\frac{x_i-\\mu}\\theta) }. (\\#eq:fellerparetogammahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"transformed-beta","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.1. Transformed beta","title":"Starting values used in fitdistrplus","text":"Feller-Pareto μ=0\\mu=0. first component @ref(eq:fellerparetogradient) simplifies γ=α=2\\gamma=\\alpha=2−(2τ−1)∑ixiθ(xi)+(2+τ)∑i2xi2θ3(1+(xiθ)2)=nθ⇔−(2τ−1)θ2+(2+τ)1n∑i2xi21+(xiθ)2=θ2 -(2\\tau - 1) \\sum_{} \\frac{x_i}{\\theta(x_i)} + (2+\\tau)\\sum_i \\frac{2x_i^2}{\\theta^3(1+(\\frac{x_i}\\theta)^2)} = \\frac{n}{\\theta} \\Leftrightarrow -(2\\tau - 1) \\theta^2 + (2+\\tau)\\frac1n\\sum_i \\frac{2x_i^2}{1+(\\frac{x_i}\\theta)^2} = \\theta^2 θ2=2+τ2τ1n∑i2xi21+(xiθ)2. \\theta^2=\\frac{2+\\tau}{2\\tau}\\frac1n\\sum_i \\frac{2x_i^2}{1+(\\frac{x_i}\\theta)^2}. Neglecting unknown value τ\\tau denominator θ\\theta, get θ̂=1n∑i2xi21+xi2.(#eq:trbetathetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac1n\\sum_i \\frac{2x_i^2}{1+x_i^2} }. (\\#eq:trbetathetahat) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=xi/θ̂, z_i = y_i/(1+y_i), y_i = x_i/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)). Similar Feller-Pareto, set γ̂=1+ψ(τ)−ψ(α+τ)+1n∑ilog(1+xiθ)1n∑ilog(xiθ).(#eq:fellerparetogammahat)\\begin{equation} \\hat\\gamma = 1+ \\frac{ \\psi(\\tau) - \\psi(\\alpha+\\tau) +\\frac1n\\sum_i \\log(1+\\frac{x_i}\\theta) }{ \\frac1n\\sum_{} \\log(\\frac{x_i}\\theta) }. (\\#eq:fellerparetogammahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"generalized-pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.2. Generalized Pareto","title":"Starting values used in fitdistrplus","text":"Feller-Pareto μ=0\\mu=0γ=1\\gamma=1. first component @ref(eq:fellerparetogradient) simplifies γ=2\\gamma=2−(τ−1)nθ+(2+τ)∑ixiθ2(1+xiθ=n/θ⇔−(τ−1)θ+(2+τ)1n∑ixi(1+xiθ=θ. -(\\tau - 1) \\frac{n}{\\theta} + (2+\\tau)\\sum_i \\frac{x_i}{\\theta^2(1+\\frac{x_i}\\theta} = n/\\theta \\Leftrightarrow -(\\tau - 1) \\theta + (2+\\tau)\\frac1n\\sum_i \\frac{x_i}{(1+\\frac{x_i}\\theta} = \\theta. Neglecting unknown value τ\\tau leads θ̂=1n∑ixi1+xi(#eq:generalizedparetotheta)\\begin{equation} \\hat\\theta = \\frac1n\\sum_i \\frac{x_i}{1+x_i} (\\#eq:generalizedparetotheta) \\end{equation} Initial value τ,α\\tau,\\alpha obtained sample (zi)(z_i)_izi=yi/(1+yi),yi=xi/θ̂, z_i = y_i/(1+y_i), y_i = x_i/\\hat\\theta, initial values beta distribution based MME (@ref(eq:betaguessestimator)).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"burr","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.3. Burr","title":"Starting values used in fitdistrplus","text":"Burr Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1. survival function 1−F(x)=(1+(x/θ)γ)−α. 1-F(x) = (1+(x/\\theta)^\\gamma)^{-\\alpha}. Using median q2q_2, log(1/2)=−αlog(1+(q2/θ)γ). \\log(1/2) = - \\alpha \\log(1+(q_2/\\theta)^\\gamma). initial value α=log(2)log(1+(q2/θ)γ),(#eq:burralpharelation)\\begin{equation} \\alpha = \\frac{\\log(2)}{\\log(1+(q_2/\\theta)^\\gamma)}, (\\#eq:burralpharelation) \\end{equation} first component @ref(eq:fellerparetogradient) simplifies γ=α=2\\gamma=\\alpha=2, τ=1\\tau=1, μ=0\\mu=0. −n/θ+3∑i2xi(xiθ)θ2(1+(xiθ)2)=n/θ⇔θ21n∑i2xi(xiθ)(1+(xiθ)2)=2/3. - n/\\theta + 3\\sum_i \\frac{2x_i(\\frac{x_i}\\theta)}{\\theta^2(1+(\\frac{x_i}\\theta)^2)} = n/\\theta \\Leftrightarrow \\theta^2\\frac1n\\sum_i \\frac{2x_i(\\frac{x_i}\\theta)}{(1+(\\frac{x_i}\\theta)^2)} = 2/3. Neglecting unknown value denominator θ\\theta, get θ̂=231n∑i2xi21+(xi)2.(#eq:trbetathetahat)\\begin{equation} \\hat\\theta = \\sqrt{ \\frac{2}{3 \\frac1n\\sum_i \\frac{2x_i^2}{1+(x_i)^2} } }. (\\#eq:trbetathetahat) \\end{equation} use γ̂\\hat\\gamma @ref(eq:fellerparetogammahat) τ=1\\tau=1 α=2\\alpha=2 previous θ̂\\hat\\theta.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"loglogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.4. Loglogistic","title":"Starting values used in fitdistrplus","text":"Loglogistic Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1, α=1\\alpha=1. survival function 1−F(x)=(1+(x/θ)γ)−1. 1-F(x) = (1+(x/\\theta)^\\gamma)^{-1}. 11−F(x)−1=(x/θ)γ⇔log(F(x)1−F(x))=γlog(x/θ). \\frac1{1-F(x)}-1 = (x/\\theta)^\\gamma \\Leftrightarrow \\log(\\frac{F(x)}{1-F(x)}) = \\gamma\\log(x/\\theta). Let q1q_1 q3q_3 first third quartile. log(1/32/3)=γlog(q1/θ),log(2/31/3)=γlog(q3/θ)⇔−log(2)=γlog(q1/θ),log(2)=γlog(q3/θ). \\log(\\frac{1/3}{2/3})= \\gamma\\log(q_1/\\theta), \\log(\\frac{2/3}{1/3})= \\gamma\\log(q_3/\\theta) \\Leftrightarrow -\\log(2)= \\gamma\\log(q_1/\\theta), \\log(2)= \\gamma\\log(q_3/\\theta). difference previous equations simplifies γ̂=2log(2)log(q3/q1). \\hat\\gamma=\\frac{2\\log(2)}{\\log(q_3/q_1)}. sum previous equations 0=γlog(q1)+γlog(q3)−2γlog(θ). 0 = \\gamma\\log(q_1)+\\gamma\\log(q_3) - 2\\gamma\\log(\\theta). θ̂=12elog(q1q3).(#eq:llogisthetahat)\\begin{equation} \\hat\\theta = \\frac12 e^{\\log(q_1q_3)}. (\\#eq:llogisthetahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"paralogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.5. Paralogistic","title":"Starting values used in fitdistrplus","text":"Paralogistic Feller-Pareto distribution μ=0\\mu=0, τ=1\\tau=1, α=γ\\alpha=\\gamma. survival function 1−F(x)=(1+(x/θ)α)−α. 1-F(x) = (1+(x/\\theta)^\\alpha)^{-\\alpha}. log(1−F(x))=−αlog(1+(x/θ)α). \\log(1-F(x)) = -\\alpha \\log(1+(x/\\theta)^\\alpha). log-likelihood ℒ(θ,α)=(α−1)∑ilog(xiθ)−(α+1)∑ilog(1+(xiθ)α)+2nlog(α)−nlog(θ).(#eq:paralogisloglik)\\begin{equation} \\mathcal L(\\theta, \\alpha) = ( \\alpha - 1) \\sum_{} \\log(\\frac{x_i}\\theta) - (\\alpha+1)\\sum_i \\log(1+(\\frac{x_i}\\theta)^\\alpha) + 2n\\log(\\alpha) - n\\log(\\theta). (\\#eq:paralogisloglik) \\end{equation} gradient respect θ\\theta, α\\alpha ((α−1)−nθ−(α+1)∑−xiα(xi/θ)α−11+(xiθ)α−n/θ∑ilog(xiθ1+(xiθ)α)−(α+1)∑(xiθ)αlog(xi/θ)1+(xiθ)α+2n/α). \\begin{pmatrix} ( \\alpha - 1)\\frac{-n}{\\theta} - (\\alpha+1)\\sum_i \\frac{-x_i\\alpha(x_i/\\theta)^{\\alpha-1}}{1+(\\frac{x_i}\\theta)^\\alpha} - n/\\theta \\\\ \\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+(\\frac{x_i}\\theta)^\\alpha }) - (\\alpha+1)\\sum_i \\frac{(\\frac{x_i}\\theta)^\\alpha \\log(x_i/\\theta)}{1+(\\frac{x_i}\\theta)^\\alpha} + 2n/\\alpha \\\\ \\end{pmatrix}. first component cancels −(α+1)∑−xiα(xi/θ)α−11+(xiθ)α=αn/θ⇔(α+1)1n∑(xi)α+11+(xiθ)α=θα. - (\\alpha+1)\\sum_i \\frac{-x_i\\alpha(x_i/\\theta)^{\\alpha-1}}{1+(\\frac{x_i}\\theta)^\\alpha} = \\alpha n/\\theta \\Leftrightarrow (\\alpha+1)\\frac1n\\sum_i \\frac{ (x_i)^{\\alpha+1}}{1+(\\frac{x_i}\\theta)^\\alpha} = \\theta^\\alpha. second component cancels 1n∑ilog(xiθ1+(xiθ)α)=−2/α+(α+1)1n∑(xiθ)αlog(xi/θ)1+(xiθ)α. \\frac1n\\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+(\\frac{x_i}\\theta)^\\alpha }) = -2/\\alpha +(\\alpha+1)\\frac1n\\sum_i \\frac{(\\frac{x_i}\\theta)^\\alpha \\log(x_i/\\theta)}{1+(\\frac{x_i}\\theta)^\\alpha}. Choosing θ=1\\theta=1, α=2\\alpha=2 sums leads 1n∑ilog(xiθ1+xi2)−1n∑ixi2log(xi)1+xi2=−2/α+(α)1n∑ixi2log(xi)1+xi2. \\frac1n\\sum_{} \\log(\\frac{ \\frac{x_i}\\theta}{1+x_i^2 }) - \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} = -2/\\alpha +(\\alpha)\\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2}. Initial estimators α̂=1n∑ilog(xi1+xi2)−1n∑ixi2log(xi)1+xi21n∑ixi2log(xi)1+xi2−2,(#eq:paralogisalphahat)\\begin{equation} \\hat\\alpha = \\frac{ \\frac1n\\sum_{} \\log(\\frac{ x_i}{1+x_i^2 }) - \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} }{ \\frac1n\\sum_i \\frac{x_i^2\\log(x_i)}{1+x_i^2} - 2 }, (\\#eq:paralogisalphahat) \\end{equation}θ̂=(α̂+1)1n∑(xi)α̂+11+(xi)α̂.(#eq:paralogisthetahat)\\begin{equation} \\hat\\theta = (\\hat\\alpha+1)\\frac1n\\sum_i \\frac{ (x_i)^{\\hat\\alpha+1}}{1+(x_i)^{\\hat\\alpha}}. (\\#eq:paralogisthetahat) \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-burr","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.6. Inverse Burr","title":"Starting values used in fitdistrplus","text":"Use Burr estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-paralogistic","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.7. Inverse paralogistic","title":"Starting values used in fitdistrplus","text":"Use paralogistic estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.8. Inverse pareto","title":"Starting values used in fitdistrplus","text":"Use pareto estimate sample 1/x1/x","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-iv","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.9. Pareto IV","title":"Starting values used in fitdistrplus","text":"survival function 1−F(x)=(1+(x−μθ)γ)−α, 1-F(x) = \\left(1+ \\left(\\frac{x-\\mu}{\\theta}\\right)^{\\gamma} \\right)^{-\\alpha}, see ?Pareto4 actuar. first third quartiles q1q_1 q3q_3 verify ((34)−1/α−1)1/γ=q1−μθ,((14)−1/α−1)1/γ=q3−μθ. ((\\frac34)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_1-\\mu}{\\theta}, ((\\frac14)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_3-\\mu}{\\theta}. Hence get two useful relations γ=log((43)1/α−1(4)1/α−1)log(q1−μq3−μ),(#eq:pareto4gammarelation)\\begin{equation} \\gamma = \\frac{ \\log\\left( \\frac{ (\\frac43)^{1/\\alpha}-1 }{ (4)^{1/\\alpha}-1 } \\right) }{ \\log\\left(\\frac{q_1-\\mu}{q_3-\\mu}\\right) }, (\\#eq:pareto4gammarelation) \\end{equation}θ=q1−q3((43)1/α−1)1/γ−((4)1/α−1)1/γ.(#eq:pareto4thetarelation)\\begin{equation} \\theta = \\frac{q_1- q_3 }{ ((\\frac43)^{1/\\alpha}-1)^{1/\\gamma} - ((4)^{1/\\alpha}-1)^{1/\\gamma} }. (\\#eq:pareto4thetarelation) \\end{equation} log-likelihood Pareto 4 sample (see Equation (5.2.94) Arnold (2015) updated Goulet et al. notation) ℒ(μ,θ,γ,α)=(γ−1)∑ilog(xi−μθ)−(α+1)∑ilog(1+(xi−μθ)γ)+nlog(γ)−nlog(θ)+nlog(α). \\mathcal L(\\mu,\\theta,\\gamma,\\alpha) = (\\gamma -1) \\sum_i \\log(\\frac{x_i-\\mu}{\\theta}) -(\\alpha+1)\\sum_i \\log(1+ (\\frac{x_i-\\mu}{\\theta})^{\\gamma}) +n\\log(\\gamma) -n\\log(\\theta)+n\\log(\\alpha). Cancelling derivate ℒ(μ,θ,γ,α)\\mathcal L(\\mu,\\theta,\\gamma,\\alpha) respect α\\alpha leads α=n/∑ilog(1+(xi−μθ)γ).(#eq:pareto4alpharelation)\\begin{equation} \\alpha =n/\\sum_i \\log(1+ (\\frac{x_i-\\mu}{\\theta})^{\\gamma}). (\\#eq:pareto4alpharelation) \\end{equation} MLE threshold parameter μ\\mu minimum. initial estimate slightly minimum order observations strictly μ̂={(1−ϵ)minixiif minixi<0(1+ϵ)minixiif minixi≥0.(#eq:pareto4muinit)\\begin{equation} \\hat\\mu = \\left\\{ \\begin{array}{ll} (1-\\epsilon) \\min_i x_i & \\text{} \\min_i x_i <0 \\\\ (1+\\epsilon)\\min_i x_i & \\text{} \\min_i x_i \\geq 0 \\\\ \\end{array} \\right. . (\\#eq:pareto4muinit) \\end{equation} ϵ=0.05\\epsilon=0.05. Initial parameter estimation μ̂\\hat\\mu, α⋆=2\\alpha^\\star = 2 , γ̂\\hat\\gamma @ref(eq:pareto4gammarelation) α⋆\\alpha^\\star, θ̂\\hat\\theta @ref(eq:pareto4thetarelation) α⋆\\alpha^\\star γ̂\\hat\\gamma, α̂\\hat\\alpha @ref(eq:pareto4alpharelation) μ̂\\hat\\mu, θ̂\\hat\\theta γ̂\\hat\\gamma.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-iii","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.10. Pareto III","title":"Starting values used in fitdistrplus","text":"Pareto III corresponds Pareto IV α=1\\alpha=1. γ=log(43−14−1)log(q1−μq3−μ),\\begin{equation} \\gamma = \\frac{ \\log\\left( \\frac{ \\frac43-1 }{ 4-1 } \\right) }{ \\log\\left(\\frac{q_1-\\mu}{q_3-\\mu}\\right) }, \\label{eq:pareto3:gamma:relation} \\end{equation} θ=(13)1/γ−(3)1/γq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac13)^{1/\\gamma} - (3)^{1/\\gamma} }{q_1- q_3 }. \\label{eq:pareto3:theta:relation} \\end{equation} Initial parameter estimation μ̂\\hat\\mu, γ̂\\hat\\gamma , θ̂\\hat\\theta γ̂\\hat\\gamma.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-ii","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.11. Pareto II","title":"Starting values used in fitdistrplus","text":"Pareto II corresponds Pareto IV γ=1\\gamma=1. θ=(43)1/α−41/αq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac43)^{1/\\alpha} - 4^{1/\\alpha} }{q_1- q_3 }. \\label{eq:pareto2:theta:relation} \\end{equation} Initial parameter estimation μ̂\\hat\\mu, α⋆=2\\alpha^\\star = 2 , θ̂\\hat\\theta α⋆\\alpha^\\star γ=1\\gamma=1, α̂\\hat\\alpha μ̂\\hat\\mu, θ̂\\hat\\theta γ=1\\gamma=1,","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto-i","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.12. Pareto I","title":"Starting values used in fitdistrplus","text":"Pareto corresponds Pareto IV γ=1\\gamma=1, μ=θ\\mu=\\theta. MLE μ̂=miniXi,α̂=(1n∑=1nlog(Xi/μ̂))−1.\\begin{equation} \\hat\\mu = \\min_i X_i, \\hat\\alpha = \\left(\\frac1n \\sum_{=1}^n \\log(X_i/\\hat\\mu) \\right)^{-1}. \\label{eq:pareto1:alpha:mu:relation} \\end{equation} can rewritten geometric mean sample Gn=(∏=1nXi)1/nG_n = (\\prod_{=1}^n X_i)^{1/n} α̂=log(Gn/μ̂). \\hat\\alpha = \\log(G_n/\\hat\\mu). Initial parameter estimation μ̂\\hat\\mu, α̂\\hat\\alpha .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"pareto","dir":"Articles","previous_headings":"2. Continuous distributions > 2.5. Feller-Pareto family","what":"2.5.13. Pareto","title":"Starting values used in fitdistrplus","text":"Pareto corresponds Pareto IV γ=1\\gamma=1, μ=0\\mu=0. θ=(43)1/α−41/αq1−q3.\\begin{equation} \\theta = \\frac{ (\\frac43)^{1/\\alpha} - 4^{1/\\alpha} }{q_1- q_3 }. \\label{eq:pareto:theta:relation} \\end{equation} Initial parameter estimation α⋆=max(2,2(m2−m12)/(m2−2m12)), \\alpha^\\star = \\max(2, 2(m_2-m_1^2)/(m_2-2m_1^2)), mim_i empirical raw moment order ii, θ̂\\hat\\theta α⋆\\alpha^\\star γ=1\\gamma=1, α̂\\hat\\alpha μ=0\\mu=0, θ̂\\hat\\theta γ=1\\gamma=1.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"transformed-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.1. Transformed gamma distribution","title":"Starting values used in fitdistrplus","text":"log-likelihood given ℒ(α,τ,θ)=nlog(τ)+ατ∑ilog(xi/θ)−∑(xi/θ)τ−∑ilog(xi)−nlog(Gamma(α)). \\mathcal L(\\alpha,\\tau,\\theta) = n\\log(\\tau) + \\alpha\\tau\\sum_i \\log(x_i/\\theta) -\\sum_i (x_i/\\theta)^\\tau - \\sum_i\\log(x_i) - n\\log(Gamma(\\alpha)). gradient respect α,τ,θ\\alpha,\\tau,\\theta given (τ−nψ(α))n/τ+α∑ilog(xi/θ)−∑(xi/θ)τlog(xi/θ)−ατ/θ+∑iτxiθ2(xi/θ)τ−1). \\begin{pmatrix} \\tau- n\\psi(\\alpha)) \\\\ n/\\tau + \\alpha\\sum_i \\log(x_i/\\theta) -\\sum_i (x_i/\\theta)^{\\tau} \\log(x_i/\\theta) \\\\ -\\alpha\\tau /\\theta +\\sum_i \\tau \\frac{x_i}{\\theta^2}(x_i/\\theta)^{\\tau-1} \\end{pmatrix}. compute moment-estimator gamma α̂=m22/μ2,θ̂=μ2/m1. \\hat\\alpha = m_2^2/\\mu_2, \\hat\\theta= \\mu_2/m_1. cancelling first component gradient set τ̂=ψ(α̂)1n∑ilog(xi/θ̂). \\hat\\tau = \\frac{\\psi(\\hat\\alpha)}{\\frac1n\\sum_i \\log(x_i/\\hat\\theta) }.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.2. gamma distribution","title":"Starting values used in fitdistrplus","text":"Transformed gamma τ=1\\tau=1 compute moment-estimator given α̂=m22/μ2,θ̂=μ2/m1.\\begin{equation} \\hat\\alpha = m_2^2/\\mu_2, \\hat\\theta= \\mu_2/m_1. \\label{eq:gamma:relation} \\end{equation}","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"weibull-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.3. Weibull distribution","title":"Starting values used in fitdistrplus","text":"Transformed gamma α=1\\alpha=1 Let m̃=1n∑ilog(xi)\\tilde m=\\frac1n\\sum_i \\log(x_i) ṽ=1n∑(log(xi)−m̃)2\\tilde v=\\frac1n\\sum_i (\\log(x_i) - \\tilde m)^2. use approximate MME τ̂=1.2/sqrt(ṽ),θ̂=exp(m̃+0.572/τ̂). \\hat\\tau = 1.2/sqrt(\\tilde v), \\hat\\theta = exp(\\tilde m + 0.572/\\hat \\tau). Alternatively, can use distribution function F(x)=1−e−(x/σ)τ⇒log(−log(1−F(x)))=τlog(x)−τlog(θ), F(x) = 1 - e^{-(x/\\sigma)^\\tau} \\Rightarrow \\log(-\\log(1-F(x))) = \\tau\\log(x) - \\tau\\log(\\theta), Hence QME Weibull τ̃=log(−log(1−p1))−log(−log(1−p2))log(x1)−log(x2),τ̃=x3/(−log(1−p3))1/τ̃ \\tilde\\tau = \\frac{ \\log(-\\log(1-p_1)) - \\log(-\\log(1-p_2)) }{ \\log(x_1) - \\log(x_2) }, \\tilde\\tau = x_3/(-\\log(1-p_3))^{1/\\tilde\\tau} p1=1/4p_1=1/4, p2=3/4p_2=3/4, p3=1/2p_3=1/2, xix_i corresponding empirical quantiles. Initial parameters τ̃\\tilde\\tau θ̃\\tilde\\theta unless empirical quantiles x1=x2x_1=x_2, case use τ̂\\hat\\tau, θ̂\\hat\\theta.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"exponential-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.6. Transformed gamma family","what":"2.6.4. Exponential distribution","title":"Starting values used in fitdistrplus","text":"MLE MME λ̂=1/m1.\\hat\\lambda = 1/m_1.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-transformed-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.1. Inverse transformed gamma distribution","title":"Starting values used in fitdistrplus","text":"transformed gamma distribution (1/xi)(1/x_i)_i.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-gamma-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.2. Inverse gamma distribution","title":"Starting values used in fitdistrplus","text":"compute moment-estimator α̂=(2m2−m12)/(m2−m12),θ̂=m1m2/(m2−m12). \\hat\\alpha = (2m_2-m_1^2)/(m_2-m_1^2), \\hat\\theta= m_1m_2/(m_2-m_1^2).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-weibull-distribution","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.3. Inverse Weibull distribution","title":"Starting values used in fitdistrplus","text":"use QME.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"inverse-exponential","dir":"Articles","previous_headings":"2. Continuous distributions > 2.7. Inverse transformed gamma family","what":"2.7.4. Inverse exponential","title":"Starting values used in fitdistrplus","text":"transformed gamma distribution (1/xi)(1/x_i)_i.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"general-books","dir":"Articles","previous_headings":"3. Bibliography","what":"3.1. General books","title":"Starting values used in fitdistrplus","text":"N. L. Johnson, S. Kotz, N. Balakrishnan (1994). Continuous univariate distributions, Volume 1, Wiley. N. L. Johnson, S. Kotz, N. Balakrishnan (1995). Continuous univariate distributions, Volume 2, Wiley. N. L. Johnson, . W. Kemp, S. Kotz (2008). Univariate discrete distributions, Wiley. G. Wimmer (1999), Thesaurus univariate discrete probability distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"books-dedicated-to-a-distribution-family","dir":"Articles","previous_headings":"3. Bibliography","what":"3.2. Books dedicated to a distribution family","title":"Starting values used in fitdistrplus","text":"M. Ahsanullah, B.M. Golam Kibria, M. Shakil (2014). Normal Student’s t Distributions Applications, Springer. B. C. Arnold (2010). Pareto Distributions, Chapman Hall. . Azzalini (2013). Skew-Normal Related Families. N. Balakrishnan (2014). Handbook Logistic Distribution, CRC Press.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/articles/starting-values.html","id":"books-with-applications","dir":"Articles","previous_headings":"3. Bibliography","what":"3.3. Books with applications","title":"Starting values used in fitdistrplus","text":"C. Forbes, M. Evans, N. Hastings, B. Peacock (2011). Statistical Distributions, Wiley. Z. . Karian, E. J. Dudewicz, K. Shimizu (2010). Handbook Fitting Statistical Distributions R, CRC Press. K. Krishnamoorthy (2015). Handbook Statistical Distributions Applications, Chapman Hall. Klugman, S., Panjer, H. & Willmot, G. (2019). Loss Models: Data Decisions, 5th ed., John Wiley & Sons.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marie-Laure Delignette-Muller. Author. Christophe Dutang. Author. Regis Pouillot. Contributor. Jean-Baptiste Denis. Contributor. Aurélie Siberchicot. Author, maintainer.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Marie Laure Delignette-Muller, Christophe Dutang (2015). fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34. DOI 10.18637/jss.v064.i04.","code":"@Article{, title = {{fitdistrplus}: An {R} Package for Fitting Distributions}, author = {Marie Laure Delignette-Muller and Christophe Dutang}, journal = {Journal of Statistical Software}, year = {2015}, volume = {64}, number = {4}, pages = {1--34}, doi = {10.18637/jss.v064.i04}, }"},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"help-to-fit-of-a-parametric-distribution-to-non-censored-or-censored-data","dir":"","previous_headings":"","what":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Please note! Since January 2024, repository belonged lbbe-software organization. avoid confusion, strongly recommend updating existing local clones point new repository URL. can using git remote command line: git remote set-url origin git@github.com:lbbe-software/fitdistrplus.git git remote set-url origin https://github.com/lbbe-software/fitdistrplus.git fitdistrplus extends fitdistr() function (MASS package) several functions help fit parametric distribution non-censored censored data. Censored data may contain left censored, right censored interval censored values, several lower upper bounds. addition maximum likelihood estimation (MLE), package provides moment matching (MME), quantile matching (QME) maximum goodness--fit estimation (MGE) methods (available non-censored data). Weighted versions MLE, MME QME available. fitdistrplus allows fit probability distribution provided user restricted base R distributions (see ?Distributions). strongly encourage users visit CRAN task view Distributions proposed Dutang, Kiener & Swihart (2024).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"the-package","dir":"","previous_headings":"","what":"The package","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"stable version fitdistrplus can installed CRAN using: development version fitdistrplus can installed GitHub (remotes needed): Finally load package current R session following R command:","code":"install.packages(\"fitdistrplus\") if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\") remotes::install_github(\"lbbe-software/fitdistrplus\") require(\"fitdistrplus\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Four vignettes attached fitdistrplus package. Two beginners Overview fitdistrplus package Frequently Asked Questions last two vignettes deal advanced topics optimization algorithm choose? Starting values used fitdistrplus","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"authors--contacts","dir":"","previous_headings":"","what":"Authors & Contacts","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"Please read FAQ contacting authors Marie-Laure Delignette-Muller: marielaure.delignettemuller<<@))vetagro-sup.fr Christophe Dutang: dutangc<<@))gmail.com Aurélie Siberchicot: aurelie.siberchicot<<@))univ-lyon1.fr Issues can reported fitdistrplus-issues.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Help to Fit of a Parametric Distribution to Non-Censored or Censored Data","text":"use fitdistrplus, cite: Marie Laure Delignette-Muller, Christophe Dutang (2015). fitdistrplus: R Package Fitting Distributions. Journal Statistical Software. https://www.jstatsoft.org/article/view/v064i04 DOI 10.18637/jss.v064.i04.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"cdfband plots empirical cumulative distribution function bootstraped pointwise confidence intervals probabilities quantiles.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"","code":"CIcdfplot(b, CI.output, CI.type = \"two.sided\", CI.level = 0.95, CI.col = \"red\", CI.lty = 2, CI.fill = NULL, CI.only = FALSE, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datapch, datacol, fitlty, fitcol, fitlwd, horizontals = TRUE, verticals = FALSE, do.points = TRUE, use.ppoints = TRUE, a.ppoints = 0.5, name.points = NULL, lines01 = FALSE, plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"b One \"bootdist\" object. CI.output quantity (bootstraped) bootstraped confidence intervals computed: either \"probability\" \"quantile\"). CI.type Type confidence intervals : either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. CI.col color confidence intervals. CI.lty line type confidence intervals. CI.fill color fill confidence area. Default NULL corresponding filling. CI.logical whether plot empirical fitted distribution functions confidence intervals. Default FALSE. xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot, see also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datapch integer specifying symbol used plotting data points, see also points (non censored data). datacol specification color used plotting data points. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. fitlty (vector ) line type(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. horizontals TRUE, draws horizontal lines step empirical cdf function (non censored data). See also plot.stepfun. verticals TRUE, draws also vertical lines empirical cdf function. taken account horizontals=TRUE (non censored data). .points logical; TRUE, also draw points x-locations. Default TRUE (non censored data). use.ppoints TRUE, probability points empirical distribution defined using function ppoints (1:n - .ppoints)/(n - 2a.ppoints + 1) (non censored data). FALSE, probability points simply defined (1:n)/n. argument ignored discrete data. .ppoints use.ppoints=TRUE, passed function ppoints (non censored data). name.points Label vector points drawn .e. .points = TRUE (non censored data). lines01 logical plot two horizontal lines h=0 h=1 cdfcomp. plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). ... graphical arguments passed matlines polygon, respectively CI.fill=FALSE CI.fill=TRUE.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"CIcdfplot provides plot empirical distribution using cdfcomp cdfcompcens, bootstraped pointwise confidence intervals probabilities (y values) quantiles (x values). interval computed evaluating quantity interest (probability associated x value quantile associated y value) using bootstraped values parameters get bootstraped sample quantity interest calculating percentiles sample get confidence interval (classically 2.5 97.5 percentiles 95 percent confidence level). CI.fill != NULL, whole confidence area filled color CI.fill thanks function polygon, otherwise borders drawn thanks function matline. graphical arguments can passed functions using three dots arguments ....","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/CIcdfplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empirical cumulative distribution function with pointwise confidence intervals on probabilities or on quantiles — CIcdfplot","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. if (requireNamespace (\"ggplot2\", quietly = TRUE)) {ggplotEx <- TRUE} # (1) Fit of an exponential distribution # set.seed(123) s1 <- rexp(50, 1) f1 <- fitdist(s1, \"exp\") b1 <- bootdist(f1, niter= 11) #voluntarily low to decrease computation time # plot 95 percent bilateral confidence intervals on y values (probabilities) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", plotstyle = \"ggplot\") # \\donttest{ # plot of the previous intervals as a band CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.fill = \"pink\", CI.col = \"red\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.fill = \"pink\", CI.col = \"red\", plotstyle = \"ggplot\") # plot of the previous intervals as a band without empirical and fitted dist. functions CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"red\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"red\", plotstyle = \"ggplot\") # same plot without contours CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"pink\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"probability\", CI.only = TRUE, CI.fill = \"pink\", CI.col = \"pink\", plotstyle = \"ggplot\") # plot 95 percent bilateral confidence intervals on x values (quantiles) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quantile\") if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quantile\", plotstyle = \"ggplot\") # plot 95 percent unilateral confidence intervals on quantiles CIcdfplot(b1, CI.level = 95/100, CI.output = \"quant\", CI.type = \"less\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1) if (ggplotEx) CIcdfplot(b1, CI.level = 95/100, CI.output = \"quant\", CI.type = \"less\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1, plotstyle = \"ggplot\") CIcdfplot(b1, CI.level= 95/100, CI.output = \"quant\", CI.type = \"greater\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1) if (ggplotEx) CIcdfplot(b1, CI.level= 95/100, CI.output = \"quant\", CI.type = \"greater\", CI.fill = \"grey80\", CI.col = \"black\", CI.lty = 1, plotstyle = \"ggplot\") # (2) Fit of a normal distribution on acute toxicity log-transformed values of # endosulfan for nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, with their # confidence intervals, from a small number of bootstrap # iterations to satisfy CRAN running times constraint and plot of the band # representing pointwise confidence intervals on any quantiles (any HCx values) # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(endosulfan) log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) namesATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa fln <- fitdist(log10ATV, \"norm\") bln <- bootdist(fln, bootmethod =\"param\", niter=101) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.844443 2.190122 2.565053 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.334340 1.697255 2.099378 #> 97.5 % 2.531564 2.770455 3.053706 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlim = c(0,5), name.points=namesATV) if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlim = c(0,5), name.points=namesATV, plotstyle = \"ggplot\") # (3) Same type of example as example (2) from ecotoxicology # with censored data # data(salinity) log10LC50 <-log10(salinity) fln <- fitdistcens(log10LC50,\"norm\") bln <- bootdistcens(fln, niter=101) (HC5ln <- quantile(bln,probs = 0.05)) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 #> estimate 1.11584 #> Median of bootstrap estimates #> p=0.05 #> estimate 1.120901 #> #> two-sided 95 % CI of each quantile #> p=0.05 #> 2.5 % 1.045539 #> 97.5 % 1.191979 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\",xlim=c(0.5,2),lines01 = TRUE) if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\",xlim=c(0.5,2),lines01 = TRUE, plotstyle = \"ggplot\") # zoom around the HC5 CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1)) abline(h = 0.05, lty = 2) # line corresponding to a CDF of 5 percent if (ggplotEx) CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"lightblue\", CI.col = \"blue\", xlab = \"log10(LC50)\", lines01 = TRUE, xlim = c(0.8, 1.5), ylim = c(0, 0.1), plotstyle = \"ggplot\") + ggplot2::geom_hline(yintercept = 0.05, lty = 2) # line corresponding to a CDF of 5 percent # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Provide function prepare data frame needed fitdistcens() data classically coded using Surv() function survival package","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"","code":"Surv2fitdistcens(time, time2, event, type = c('right', 'left', 'interval', 'interval2'))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"time right censored data, follow time. interval data, first argument starting time interval. event status indicator, normally 0=alive, 1=dead. choices TRUE/FALSE (TRUE = death) 1/2 (2=death). interval censored data, status indicator 0=right censored, 1=event time, 2=left censored, 3=interval censored. factor data, assume two levels second level coding death. time2 ending time interval interval censored. Intervals assumed open left closed right, (start, end]. type character string specifying type censoring. Possible values \"right\", \"left\", \"interval\", \"interval2\".","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Surv2fitdistcens makes data.frame two columns respectively named left right, describing observed value interval required fitdistcens(): left column contains either NA left-censored observations, left bound interval interval-censored observations, observed value non-censored observations. right column contains either NA right-censored observations, right bound interval interval censored observations, observed value non-censored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Surv2fitdistcens returns data.frame two columns respectively named left right.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/Surv2fitdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Handling of data formated as in the survival package for use in fitdistcens() — Surv2fitdistcens","text":"","code":"# (1) randomized fictive survival data - right-censored # origdata <- data.frame(rbind( c( 43.01, 55.00, 0), c( 36.37, 47.17, 0), c( 33.10, 34.51, 0), c( 71.00, 81.15, 1), c( 80.89, 81.91, 1), c( 67.81, 78.48, 1), c( 73.98, 76.92, 1), c( 53.19, 54.80, 1))) colnames(origdata) <- c(\"AgeIn\", \"AgeOut\", \"Death\") # add of follow-up time (for type = \"right\" in Surv()) origdata$followuptime <- origdata$AgeOut - origdata$AgeIn origdata #> AgeIn AgeOut Death followuptime #> 1 43.01 55.00 0 11.99 #> 2 36.37 47.17 0 10.80 #> 3 33.10 34.51 0 1.41 #> 4 71.00 81.15 1 10.15 #> 5 80.89 81.91 1 1.02 #> 6 67.81 78.48 1 10.67 #> 7 73.98 76.92 1 2.94 #> 8 53.19 54.80 1 1.61 ### use of default survival type \"right\" # in Surv() survival::Surv(time = origdata$followuptime, event = origdata$Death, type = \"right\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 # for fitdistcens() Surv2fitdistcens(origdata$followuptime, event = origdata$Death, type = \"right\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # use of survival type \"interval\" # in Surv() survival::Surv(time = origdata$followuptime, time2 = origdata$followuptime, event = origdata$Death, type = \"interval\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 # for fitdistcens() Surv2fitdistcens(time = origdata$followuptime, time2 = origdata$followuptime, event = origdata$Death, type = \"interval\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # use of survival type \"interval2\" origdata$survivalt1 <- origdata$followuptime origdata$survivalt2 <- origdata$survivalt1 origdata$survivalt2[1:3] <- Inf origdata #> AgeIn AgeOut Death followuptime survivalt1 survivalt2 #> 1 43.01 55.00 0 11.99 11.99 Inf #> 2 36.37 47.17 0 10.80 10.80 Inf #> 3 33.10 34.51 0 1.41 1.41 Inf #> 4 71.00 81.15 1 10.15 10.15 10.15 #> 5 80.89 81.91 1 1.02 1.02 1.02 #> 6 67.81 78.48 1 10.67 10.67 10.67 #> 7 73.98 76.92 1 2.94 2.94 2.94 #> 8 53.19 54.80 1 1.61 1.61 1.61 survival::Surv(time = origdata$survivalt1, time2 = origdata$survivalt2, type = \"interval2\") #> [1] 11.99+ 10.80+ 1.41+ 10.15 1.02 10.67 2.94 1.61 Surv2fitdistcens(origdata$survivalt1, time2 = origdata$survivalt2, type = \"interval2\") #> left right #> 1 11.99 NA #> 2 10.80 NA #> 3 1.41 NA #> 4 10.15 10.15 #> 5 1.02 1.02 #> 6 10.67 10.67 #> 7 2.94 2.94 #> 8 1.61 1.61 # (2) Other examples with various left, right and interval censored values # # with left censored data (d1 <- data.frame(time = c(2, 5, 3, 7), ind = c(0, 1, 1, 1))) #> time ind #> 1 2 0 #> 2 5 1 #> 3 3 1 #> 4 7 1 survival::Surv(time = d1$time, event = d1$ind, type = \"left\") #> [1] 2- 5 3 7 Surv2fitdistcens(time = d1$time, event = d1$ind, type = \"left\") #> left right #> 1 NA 2 #> 2 5 5 #> 3 3 3 #> 4 7 7 (d1bis <- data.frame(t1 = c(2, 5, 3, 7), t2 = c(2, 5, 3, 7), censtype = c(2, 1, 1, 1))) #> t1 t2 censtype #> 1 2 2 2 #> 2 5 5 1 #> 3 3 3 1 #> 4 7 7 1 survival::Surv(time = d1bis$t1, time2 = d1bis$t2, event = d1bis$censtype, type = \"interval\") #> [1] 2- 5 3 7 Surv2fitdistcens(time = d1bis$t1, time2 = d1bis$t2, event = d1bis$censtype, type = \"interval\") #> left right #> 1 NA 2 #> 2 5 5 #> 3 3 3 #> 4 7 7 # with interval, left and right censored data (d2 <- data.frame(t1 = c(-Inf, 2, 3, 4, 3, 7), t2 = c(2, 5, 3, 7, 8, Inf))) #> t1 t2 #> 1 -Inf 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 Inf survival::Surv(time = d2$t1, time2 = d2$t2, type = \"interval2\") #> [1] 2- [2, 5] 3 [4, 7] [3, 8] 7+ Surv2fitdistcens(time = d2$t1, time2 = d2$t2, type = \"interval2\") #> left right #> 1 NA 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 NA (d2bis <- data.frame(t1 = c(2, 2, 3, 4, 3, 7), t2 = c(2, 5, 3, 7, 8, 7), censtype = c(2,3,1,3,3,0))) #> t1 t2 censtype #> 1 2 2 2 #> 2 2 5 3 #> 3 3 3 1 #> 4 4 7 3 #> 5 3 8 3 #> 6 7 7 0 survival::Surv(time = d2bis$t1, time2 = d2bis$t2, event = d2bis$censtype, type = \"interval\") #> [1] 2- [2, 5] 3 [4, 7] [3, 8] 7+ Surv2fitdistcens(time = d2bis$t1, time2 = d2bis$t2, event = d2bis$censtype, type = \"interval\") #> left right #> 1 NA 2 #> 2 2 5 #> 3 3 3 #> 4 4 7 #> 5 3 8 #> 6 7 NA"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap simulation of uncertainty for non-censored data — bootdist","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Uses parametric nonparametric bootstrap resampling order simulate uncertainty parameters distribution fitted non-censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"","code":"bootdist(f, bootmethod = \"param\", niter = 1001, silent = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bootdist' print(x, ...) # S3 method for class 'bootdist' plot(x, main = \"Bootstrapped values of parameters\", enhance = FALSE, trueval = NULL, rampcol = NULL, nbgrid = 100, nbcol = 100, ...) # S3 method for class 'bootdist' summary(object, ...) # S3 method for class 'bootdist' density(..., bw = nrd0, adjust = 1, kernel = \"gaussian\") # S3 method for class 'density.bootdist' plot(x, mar=c(4,4,2,1), lty=NULL, col=NULL, lwd=NULL, ...) # S3 method for class 'density.bootdist' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"f object class \"fitdist\", output fitdist function. bootmethod character string coding type resampling : \"param\" parametric resampling \"nonparam\" nonparametric resampling data. niter number samples drawn bootstrap. silent logical remove show warnings errors bootstraping. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bootdist\" \"density.bootdist\". object object class \"bootdist\". main overall title plot: see title, default \"Bootstrapped values parameters\". enhance logical get enhanced plot. trueval relevant, numeric vector true value parameters (backfitting purposes). rampcol colors interpolate; must valid argument colorRampPalette(). nbgrid Number grid points direction. Can scalar length-2 integer vector. nbcol integer argument, required number colors ... arguments passed generic methods \"bootdist\" objects density. bw, adjust, kernel resp. smoothing bandwidth, scaling factor, kernel used, see density. mar numerical vector form c(bottom, left, top, right), see par. lty, col, lwd resp. line type, color, line width, see par.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Samples drawn parametric bootstrap (resampling distribution fitted fitdist) nonparametric bootstrap (resampling replacement data set). bootstrap sample function mledist (mmedist, qmedist, mgedist according component f$method object class \"fitdist\") used estimate bootstrapped values parameters. function fails converge, NA values returned. Medians 2.5 97.5 percentiles computed removing NA values. medians 95 percent confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations, number iterations function converges also printed summary. default (enhance=FALSE), plot object class \"bootdist\" consists scatterplot matrix scatterplots bootstrapped values parameters. uses function stripchart fitted distribution characterized one parameter, function plot two paramters function pairs cases. last cases, provides representation joint uncertainty distribution fitted parameters. enhance=TRUE, personalized plot version pairs used upper graphs scatterplots lower graphs heatmap image using image based kernel based estimator 2D density function (using kde2d MASS package). Arguments rampcol, nbgrid, nbcol can used customize plots. Defautls values rampcol=c(\"green\", \"yellow\", \"orange\", \"red\"), nbcol=100 (see colorRampPalette()), nbgrid=100 (see kde2d). addition, fitting parameters simulated datasets backtesting purposes, additional argument trueval can used plot cross true value. possible accelerate bootstrap using parallelization. recommend use parallel = \"multicore\", parallel = \"snow\" work Windows, fix ncpus number available processors. density computes empirical density bootdist objects using density function (Gaussian kernel default). returns object class density.bootdist print plot methods provided.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"bootdist returns object class \"bootdist\", list 6 components, estim data frame containing bootstrapped values parameters. converg vector containing codes convergence obtained iterative method used estimate parameters bootstraped data set (0 closed formula used). method character string coding type resampling : \"param\" parametric resampling \"nonparam\" nonparametric resampling. nbboot number samples drawn bootstrap. CI bootstrap medians 95 percent confidence percentile intervals parameters. fitpart object class \"fitdist\" bootstrap procedure applied. Generic functions: print print \"bootdist\" object shows bootstrap parameter estimates. inferior whole number bootstrap iterations, number iterations estimation converges also printed. summary summary provides median 2.5 97.5 percentiles parameter. inferior whole number bootstrap iterations, number iterations estimation converges also printed summary. plot plot shows bootstrap estimates stripchart function univariate parameters plot function multivariate parameters. density density computes empirical densities return object class density.bootdist.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 181-241. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap simulation of uncertainty for non-censored data — bootdist","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # (1) Fit of a gamma distribution to serving size data # using default method (maximum likelihood estimation) # followed by parametric bootstrap # data(groundbeef) x1 <- groundbeef$serving f1 <- fitdist(x1, \"gamma\") b1 <- bootdist(f1, niter=51) print(b1) #> Parameter values obtained with parametric bootstrap #> shape rate #> 1 4.015562 0.05365499 #> 2 4.214437 0.05762101 #> 3 4.176366 0.05807901 #> 4 4.119164 0.05944029 #> 5 5.013486 0.07194809 #> 6 4.461409 0.05807600 plot(b1) plot(b1, enhance=TRUE) summary(b1) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.12112272 3.32325118 5.11745944 #> rate 0.05518452 0.04684843 0.07170367 quantile(b1) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.16733 42.32692 50.91831 59.15298 67.62801 76.88308 87.67764 #> p=0.8 p=0.9 #> estimate 101.5208 122.9543 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> estimate 32.71222 42.80078 50.98942 59.25093 67.5939 76.42124 87.17521 100.8405 #> p=0.9 #> estimate 121.5466 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> 2.5 % 27.77396 37.42586 45.73489 53.96687 62.26638 71.30894 81.64618 93.9737 #> 97.5 % 35.67197 45.22459 53.97730 62.58326 71.31751 81.30652 92.96508 107.7329 #> p=0.9 #> 2.5 % 113.9634 #> 97.5 % 130.6715 CIcdfplot(b1, CI.output = \"quantile\") density(b1) #> #> Bootstrap values for: gamma for 1 object(s) with 51 bootstrap values (original sample size 254). plot(density(b1)) # (2) non parametric bootstrap on the same fit # b1b <- bootdist(f1, bootmethod=\"nonparam\", niter=51) summary(b1b) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.08546931 3.47931694 4.71280030 #> rate 0.05561944 0.04797494 0.06302539 quantile(b1b) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.16733 42.32692 50.91831 59.15298 67.62801 76.88308 87.67764 #> p=0.8 p=0.9 #> estimate 101.5208 122.9543 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 32.25183 42.25577 51.01738 59.05788 67.47548 76.95389 87.65113 #> p=0.8 p=0.9 #> estimate 100.8612 121.7738 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> 2.5 % 28.77577 38.76800 47.17181 55.16178 63.29618 72.15077 82.21068 95.20268 #> 97.5 % 36.49366 46.74605 55.27953 63.37110 71.62773 80.58611 91.32593 105.92939 #> p=0.9 #> 2.5 % 115.0083 #> 97.5 % 128.1651 # (3) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for # nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution, what is called the 5 percent hazardous concentration (HC5) # in ecotoxicology, with its two-sided 95 percent confidence interval calculated by # parametric bootstrap # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") bln <- bootdist(fln, bootmethod = \"param\", niter=51) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.811067 2.156258 2.529461 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.187935 1.634263 2.095273 #> 97.5 % 2.276507 2.563692 2.917189 # (4) comparison of sequential and parallel versions of bootstrap # to be tried with a greater number of iterations (1001 or more) # # \\donttest{ niter <- 1001 data(groundbeef) x1 <- groundbeef$serving f1 <- fitdist(x1, \"gamma\") # sequential version ptm <- proc.time() summary(bootdist(f1, niter = niter)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.02609408 3.46463055 4.71706986 #> rate 0.05458836 0.04622389 0.06476728 proc.time() - ptm #> user system elapsed #> 3.981 0.096 3.969 # parallel version using snow require(\"parallel\") #> Loading required package: parallel ptm <- proc.time() summary(bootdist(f1, niter = niter, parallel = \"snow\", ncpus = 2)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.02321963 3.45598967 4.80078519 #> rate 0.05450354 0.04632331 0.06524721 proc.time() - ptm #> user system elapsed #> 0.041 0.003 3.763 # parallel version using multicore (not available on Windows) ptm <- proc.time() summary(bootdist(f1, niter = niter, parallel = \"multicore\", ncpus = 2)) #> Parametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> shape 4.04947721 3.47970416 4.71828189 #> rate 0.05496497 0.04672265 0.06498123 proc.time() - ptm #> user system elapsed #> 0.032 0.012 2.136 # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap simulation of uncertainty for censored data — bootdistcens","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Uses nonparametric bootstrap resampling order simulate uncertainty parameters distribution fitted censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"","code":"bootdistcens(f, niter = 1001, silent = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bootdistcens' print(x, ...) # S3 method for class 'bootdistcens' plot(x, ...) # S3 method for class 'bootdistcens' summary(object, ...) # S3 method for class 'bootdistcens' density(..., bw = nrd0, adjust = 1, kernel = \"gaussian\") # S3 method for class 'density.bootdistcens' plot(x, mar=c(4,4,2,1), lty=NULL, col=NULL, lwd=NULL, ...) # S3 method for class 'density.bootdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"f object class \"fitdistcens\", output fitdistcens function. niter number samples drawn bootstrap. silent logical remove show warnings errors bootstraping. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bootdistcens\". object object class \"bootdistcens\". ... arguments passed generic methods \"bootdistcens\" objects density. bw, adjust, kernel resp. smoothing bandwidth, scaling factor, kernel used, see density. mar numerical vector form c(bottom, left, top, right), see par. lty, col, lwd resp. line type, color, line width, see par.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Samples drawn nonparametric bootstrap (resampling replacement data set). bootstrap sample function mledist used estimate bootstrapped values parameters. mledist fails converge, NA values returned. Medians 2.5 97.5 percentiles computed removing NA values. medians 95 percent confidence intervals parameters (2.5 97.5 percentiles) printed summary. inferior whole number iterations, number iterations mledist converges also printed summary. plot object class \"bootdistcens\" consists scatterplot matrix scatterplots bootstrapped values parameters. uses function stripchart fitted distribution characterized one parameter, function plot cases. last cases, provides representation joint uncertainty distribution fitted parameters. possible accelerate bootstrap using parallelization. recommend use parallel = \"multicore\", parallel = \"snow\" work Windows, fix ncpus number available processors. density computes empirical density bootdistcens objects using density function (Gaussian kernel default). returns object class density.bootdistcens print plot methods provided.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"bootdistcens returns object class \"bootdistcens\", list 6 components, estim data frame containing bootstrapped values parameters. converg vector containing codes convergence iterative method used estimate parameters bootstraped data set. method character string coding type resampling : case \"nonparam\" available method censored data. nbboot number samples drawn bootstrap. CI bootstrap medians 95 percent confidence percentile intervals parameters. fitpart object class \"fitdistcens\" bootstrap procedure applied. Generic functions: print print \"bootdistcens\" object shows bootstrap parameter estimates. inferior whole number bootstrap iterations, number iterations estimation converges also printed. summary summary provides median 2.5 97.5 percentiles parameter. inferior whole number bootstrap iterations, number iterations estimation converges also printed summary. plot plot shows bootstrap estimates stripchart function univariate parameters plot function multivariate parameters. density density computes empirical densities return object class density.bootdistcens.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 181-241. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/bootdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap simulation of uncertainty for censored data — bootdistcens","text":"","code":"# We choose a low number of bootstrap replicates in order to satisfy CRAN running times # constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # (1) Fit of a normal distribution to fluazinam data in log10 # followed by nonparametric bootstrap and calculation of quantiles # with 95 percent confidence intervals # data(fluazinam) (d1 <-log10(fluazinam)) #> left right #> 1 0.5797836 0.5797836 #> 2 1.5263393 1.5263393 #> 3 1.9395193 1.9395193 #> 4 3.2304489 NA #> 5 2.8061800 2.8061800 #> 6 3.0625820 NA #> 7 2.0530784 2.0530784 #> 8 2.1105897 2.1105897 #> 9 2.7678976 2.7678976 #> 10 3.2685780 NA #> 11 0.2041200 0.2041200 #> 12 0.6812412 0.6812412 #> 13 1.9138139 1.9138139 #> 14 2.1903317 2.1903317 f1 <- fitdistcens(d1, \"norm\") b1 <- bootdistcens(f1, niter = 51) b1 #> Parameter values obtained with nonparametric bootstrap #> mean sd #> 1 2.148176 1.2301856 #> 2 2.359487 1.1144722 #> 3 1.886811 0.7960468 #> 4 1.983487 0.9941790 #> 5 1.912052 0.9906398 #> 6 2.189226 0.9088450 #> 7 2.287131 1.2049569 #> 8 2.288832 0.7645444 #> 9 1.787691 1.0077846 #> 10 2.893830 1.2229467 #> 11 2.569893 0.9597859 #> 12 2.343772 1.2402711 #> 13 2.645568 1.2934746 #> 14 1.942141 0.5982854 #> 15 1.932680 1.0077309 #> 16 1.824771 1.0653955 #> 17 2.983895 1.8018944 #> 18 2.347785 1.3994097 #> 19 1.845464 0.8555560 #> 20 2.427059 1.5893095 #> 21 1.948223 0.8705864 #> 22 1.692356 1.0223265 #> 23 2.275639 0.8147514 #> 24 2.148972 1.0345423 #> 25 2.348520 1.1739100 #> 26 1.893396 1.1106869 #> 27 1.911591 1.1574565 #> 28 2.610027 1.0803468 #> 29 2.080525 1.3340362 #> 30 1.985938 0.9870137 #> 31 1.742953 1.0956522 #> 32 2.549440 1.0330325 #> 33 2.268481 0.4832085 #> 34 2.144250 1.3228431 #> 35 2.184267 1.2698264 #> 36 1.821893 1.5316162 #> 37 2.085662 1.1654912 #> 38 1.868720 1.0912928 #> 39 2.138497 1.1356628 #> 40 2.119477 0.9868753 #> 41 2.153767 1.1818298 #> 42 1.933517 0.5773863 #> 43 2.074073 0.7280150 #> 44 2.421981 1.1254148 #> 45 2.486787 0.6096348 #> 46 2.030623 1.0934793 #> 47 1.938514 1.0258803 #> 48 1.678181 1.2224439 #> 49 2.339840 1.3061770 #> 50 2.278660 0.7921537 #> 51 2.195027 1.1382020 summary(b1) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.144250 1.7050054 2.831765 #> sd 1.091293 0.5826111 1.574886 plot(b1) quantile(b1) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.6655064 1.179033 1.549321 1.86572 2.161449 2.457179 2.773577 #> p=0.8 p=0.9 #> estimate 3.143865 3.657392 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.7210295 1.215519 1.593624 1.854354 2.14425 2.418499 2.691487 #> p=0.8 p=0.9 #> estimate 2.961351 3.394931 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> 2.5 % 0.1683713 0.6922166 1.066910 1.433480 1.705005 1.996046 2.241195 #> 97.5 % 1.5718878 1.8638800 2.141966 2.479624 2.831765 3.146062 3.482325 #> p=0.8 p=0.9 #> 2.5 % 2.472445 2.753590 #> 97.5 % 3.883480 4.463155 CIcdfplot(b1, CI.output = \"quantile\") plot(density(b1)) #> List of 1 #> $ :List of 6 #> ..$ estim :'data.frame':\t51 obs. of 2 variables: #> .. ..$ mean: num [1:51] 2.15 2.36 1.89 1.98 1.91 ... #> .. ..$ sd : num [1:51] 1.23 1.114 0.796 0.994 0.991 ... #> ..$ converg: num [1:51] 0 0 0 0 0 0 0 0 0 0 ... #> ..$ method : chr \"nonparam\" #> ..$ nbboot : num 51 #> ..$ CI : num [1:2, 1:3] 2.144 1.091 1.705 0.583 2.832 ... #> .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. ..$ : chr [1:3] \"Median\" \"2.5%\" \"97.5%\" #> ..$ fitpart:List of 17 #> .. ..$ estimate : Named num [1:2] 2.16 1.17 #> .. .. ..- attr(*, \"names\")= chr [1:2] \"mean\" \"sd\" #> .. ..$ method : chr \"mle\" #> .. ..$ sd : Named num [1:2] 1.206 0.984 #> .. .. ..- attr(*, \"names\")= chr [1:2] \"mean\" \"sd\" #> .. ..$ cor : num [1:2, 1:2] 1 0.135 0.135 1 #> .. .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. ..$ vcov : num [1:2, 1:2] 1.455 0.16 0.16 0.969 #> .. .. ..- attr(*, \"dimnames\")=List of 2 #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. .. .. ..$ : chr [1:2] \"mean\" \"sd\" #> .. ..$ loglik : num -20.4 #> .. ..$ aic : num 44.8 #> .. ..$ bic : num 46.1 #> .. ..$ n : int 14 #> .. ..$ censdata :'data.frame':\t14 obs. of 2 variables: #> .. .. ..$ left : num [1:14] 0.58 1.53 1.94 3.23 2.81 ... #> .. .. ..$ right: num [1:14] 0.58 1.53 1.94 NA 2.81 ... #> .. ..$ distname : chr \"norm\" #> .. ..$ fix.arg : NULL #> .. ..$ fix.arg.fun: NULL #> .. ..$ dots : NULL #> .. ..$ convergence: int 0 #> .. ..$ discrete : logi FALSE #> .. ..$ weights : NULL #> .. ..- attr(*, \"class\")= chr \"fitdistcens\" #> ..- attr(*, \"class\")= chr \"bootdistcens\" #> NULL # (2) Estimation of the mean of the normal distribution # by maximum likelihood with the standard deviation fixed at 1 # using the argument fix.arg # followed by nonparametric bootstrap # and calculation of quantiles with 95 percent confidence intervals # f1b <- fitdistcens(d1, \"norm\", start = list(mean = 1),fix.arg = list(sd = 1)) b1b <- bootdistcens(f1b, niter = 51) summary(b1b) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> 2.175510 1.729164 2.788799 plot(b1b) quantile(b1b) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> estimate 0.8527461 1.292676 1.609897 1.880951 2.134298 2.387645 2.658698 #> p=0.8 p=0.9 #> estimate 2.975919 3.415849 #> Median of bootstrap estimates #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 #> estimate 0.8939584 1.333889 1.651109 1.922163 2.17551 2.428857 2.69991 3.017131 #> p=0.9 #> estimate 3.457062 #> #> two-sided 95 % CI of each quantile #> p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 #> 2.5 % 0.4476121 0.8875425 1.204763 1.475817 1.729164 1.982511 2.253564 #> 97.5 % 1.5072477 1.9471780 2.264399 2.535452 2.788799 3.042146 3.313200 #> p=0.8 p=0.9 #> 2.5 % 2.570785 3.010715 #> 97.5 % 3.630420 4.070351 # (3) comparison of sequential and parallel versions of bootstrap # to be tried with a greater number of iterations (1001 or more) # # \\donttest{ niter <- 1001 data(fluazinam) d1 <-log10(fluazinam) f1 <- fitdistcens(d1, \"norm\") # sequential version ptm <- proc.time() summary(bootdistcens(f1, niter = niter)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.146743 1.5792689 2.877993 #> sd 1.129426 0.6853478 1.709083 proc.time() - ptm #> user system elapsed #> 4.593 0.091 4.575 # parallel version using snow require(\"parallel\") ptm <- proc.time() summary(bootdistcens(f1, niter = niter, parallel = \"snow\", ncpus = 2)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.144793 1.5914352 2.899763 #> sd 1.108123 0.6912424 1.673702 proc.time() - ptm #> user system elapsed #> 0.005 0.004 3.346 # parallel version using multicore (not available on Windows) ptm <- proc.time() summary(bootdistcens(f1, niter = niter, parallel = \"multicore\", ncpus = 2)) #> Nonparametric bootstrap medians and 95% percentile CI #> Median 2.5% 97.5% #> mean 2.163302 1.5524788 2.874380 #> sd 1.119044 0.7072572 1.656059 proc.time() - ptm #> user system elapsed #> 0.006 0.016 2.422 # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":null,"dir":"Reference","previous_headings":"","what":"Danish reinsurance claim dataset — danish","title":"Danish reinsurance claim dataset — danish","text":"univariate dataset collected Copenhagen Reinsurance comprise 2167 fire losses period 1980 1990. adjusted inflation reflect 1985 values expressed millions Danish Krone. multivariate data set data total claim divided building loss, loss contents loss profits.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Danish reinsurance claim dataset — danish","text":"","code":"data(danishuni) data(danishmulti)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Danish reinsurance claim dataset — danish","text":"danishuni contains two columns: Date day claim occurence. Loss total loss amount millions Danish Krone (DKK). danishmulti contains five columns: Date day claim occurence. Building loss amount (mDKK) building coverage. Contents loss amount (mDKK) contents coverage. Profits loss amount (mDKK) profit coverage. Total total loss amount (mDKK). columns numeric except Date columns class Date.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Danish reinsurance claim dataset — danish","text":"Embrechts, P., Kluppelberg, C. Mikosch, T. (1997) Modelling Extremal Events Insurance Finance. Berlin: Springer.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Danish reinsurance claim dataset — danish","text":"Dataset used McNeil (1996), Estimating Tails Loss Severity Distributions using Extreme Value Theory, ASTIN Bull. Davison, . C. (2003) Statistical Models. Cambridge University Press. Page 278.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/danish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Danish reinsurance claim dataset — danish","text":"","code":"# (1) load of data # data(danishuni) # (2) plot and description of data # plotdist(danishuni$Loss) # (3) load of data # data(danishmulti) # (4) plot and description of data # idx <- sample(1:NROW(danishmulti), 10) barplot(danishmulti$Building[idx], col = \"grey25\", ylim = c(0, max(danishmulti$Total[idx])), main = \"Some claims of danish data set\") barplot(danishmulti$Content[idx], add = TRUE, col = \"grey50\", axes = FALSE) barplot(danishmulti$Profits[idx], add = TRUE, col = \"grey75\", axes = FALSE) legend(\"topleft\", legend = c(\"Building\", \"Content\", \"Profits\"), fill = c(\"grey25\", \"grey50\", \"grey75\"))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":null,"dir":"Reference","previous_headings":"","what":"Datasets for the FAQ — dataFAQ","title":"Datasets for the FAQ — dataFAQ","text":"Datasets used FAQ vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Datasets for the FAQ — dataFAQ","text":"","code":"data(dataFAQlog1) data(dataFAQscale1) data(dataFAQscale2)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Datasets for the FAQ — dataFAQ","text":"dataFAQlog1 dataFAQscale1 dataFAQscale2 vectors numeric data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/dataFAQ.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Datasets for the FAQ — dataFAQ","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of an empirical distribution for non-censored data — descdist","title":"Description of an empirical distribution for non-censored data — descdist","text":"Computes descriptive parameters empirical distribution non-censored data provides skewness-kurtosis plot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of an empirical distribution for non-censored data — descdist","text":"","code":"descdist(data, discrete = FALSE, boot = NULL, method = \"unbiased\", graph = TRUE, print = TRUE, obs.col = \"red\", obs.pch = 16, boot.col = \"orange\") # S3 method for class 'descdist' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of an empirical distribution for non-censored data — descdist","text":"data numeric vector. discrete TRUE, distribution considered discrete. boot NULL, boot values skewness kurtosis plotted bootstrap samples data. boot must fixed case integer 10. method \"unbiased\" unbiased estimated values statistics \"sample\" sample values. graph FALSE, skewness-kurtosis graph plotted. print FALSE, descriptive parameters computed printed. obs.col Color used observed point skewness-kurtosis graph. obs.pch plotting character used observed point skewness-kurtosis graph. boot.col Color used bootstrap sample points skewness-kurtosis graph. x object class \"descdist\". ... arguments passed generic functions","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Description of an empirical distribution for non-censored data — descdist","text":"Minimum, maximum, median, mean, sample sd, sample (method==\"sample\") default unbiased estimations skewness Pearsons's kurtosis values printed (Sokal Rohlf, 1995). skewness-kurtosis plot one proposed Cullen Frey (1999) given empirical distribution. plot, values common distributions also displayed tools help choice distributions fit data. distributions (normal, uniform, logistic, exponential example), one possible value skewness kurtosis (normal distribution example, skewness = 0 kurtosis = 3), distribution thus represented point plot. distributions, areas possible values represented, consisting lines (gamma lognormal distributions example), larger areas (beta distribution example). Weibull distribution represented graph indicated legend shapes close lognormal gamma distributions may obtained distribution. order take account uncertainty estimated values kurtosis skewness data, data set may bootstraped fixing argument boot integer 10. boot values skewness kurtosis corresponding boot bootstrap samples computed reported blue color skewness-kurtosis plot. discrete TRUE, represented distributions Poisson, negative binomial distributions, normal distribution previous discrete distributions may converge. discrete FALSE, uniform, normal, logistic, lognormal, beta gamma distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of an empirical distribution for non-censored data — descdist","text":"descdist returns list 7 components, min minimum value max maximum value median median value mean mean value sd standard deviation sample estimated value skewness skewness sample estimated value kurtosis kurtosis sample estimated value method method specified input (\"unbiased\" unbiased estimated values statistics \"sample\" sample values.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Description of an empirical distribution for non-censored data — descdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-159. Evans M, Hastings N Peacock B (2000), Statistical distributions. John Wiley Sons Inc, doi:10.1002/9780470627242 . Sokal RR Rohlf FJ (1995), Biometry. W.H. Freeman Company, USA, pp. 111-115. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Description of an empirical distribution for non-censored data — descdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/descdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of an empirical distribution for non-censored data — descdist","text":"","code":"# (1) Description of a sample from a normal distribution # with and without uncertainty on skewness and kurtosis estimated by bootstrap # set.seed(1234) x1 <- rnorm(100) descdist(x1) #> summary statistics #> ------ #> min: -2.345698 max: 2.548991 #> median: -0.384628 #> mean: -0.1567617 #> estimated sd: 1.004405 #> estimated skewness: 0.6052442 #> estimated kurtosis: 3.102441 descdist(x1,boot=11) #> summary statistics #> ------ #> min: -2.345698 max: 2.548991 #> median: -0.384628 #> mean: -0.1567617 #> estimated sd: 1.004405 #> estimated skewness: 0.6052442 #> estimated kurtosis: 3.102441 # (2) Description of a sample from a beta distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # with changing of default colors and plotting character for observed point # descdist(rbeta(100,shape1=0.05,shape2=1),boot=11, obs.col=\"blue\", obs.pch = 15, boot.col=\"darkgreen\") #> summary statistics #> ------ #> min: 3.937372e-36 max: 0.8890347 #> median: 5.660314e-06 #> mean: 0.04094397 #> estimated sd: 0.1281058 #> estimated skewness: 4.368522 #> estimated kurtosis: 25.02241 # (3) Description of a sample from a gamma distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # without plotting # descdist(rgamma(100,shape=2,rate=1),boot=11,graph=FALSE) #> summary statistics #> ------ #> min: 0.0753002 max: 8.631328 #> median: 1.627968 #> mean: 1.989657 #> estimated sd: 1.443636 #> estimated skewness: 1.509842 #> estimated kurtosis: 6.691933 # (4) Description of a sample from a Poisson distribution # with uncertainty on skewness and kurtosis estimated by bootstrap # descdist(rpois(100,lambda=2),discrete=TRUE,boot=11) #> summary statistics #> ------ #> min: 0 max: 6 #> median: 2 #> mean: 1.98 #> estimated sd: 1.377892 #> estimated skewness: 0.5802731 #> estimated kurtosis: 3.037067 # (5) Description of serving size data # with uncertainty on skewness and kurtosis estimated by bootstrap # data(groundbeef) serving <- groundbeef$serving descdist(serving, boot=11) #> summary statistics #> ------ #> min: 10 max: 200 #> median: 79 #> mean: 73.64567 #> estimated sd: 35.88487 #> estimated skewness: 0.7352745 #> estimated kurtosis: 3.551384"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect bounds for density function — detectbound","title":"Detect bounds for density function — detectbound","text":"Manual detection bounds parameter density function/","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect bounds for density function — detectbound","text":"","code":"detectbound(distname, vstart, obs, fix.arg=NULL, echo=FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect bounds for density function — detectbound","text":"distname character string \"name\" naming distribution corresponding density function dname must classically defined. vstart named vector giving initial values parameters named distribution. obs numeric vector non censored data. fix.arg optional named vector giving values fixed parameters named distribution. Default NULL. echo logical show traces.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detect bounds for density function — detectbound","text":"function manually tests following bounds : -1, 0, 1.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect bounds for density function — detectbound","text":"detectbound returns 2-row matrix lower bounds first row upper bounds second row.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Detect bounds for density function — detectbound","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detect bounds for density function — detectbound","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/detectbound.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect bounds for density function — detectbound","text":"","code":"# case where the density returns a Not-an-Numeric value. detectbound(\"exp\", c(rate=3), 1:10) #> rate #> lowb 0 #> uppb Inf detectbound(\"binom\", c(size=3, prob=1/2), 1:10) #> size prob #> lowb -Inf 0 #> uppb Inf 1 detectbound(\"nbinom\", c(size=3, prob=1/2), 1:10) #> size prob mu #> lowb 0 0 -Inf #> uppb Inf 1 Inf"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":null,"dir":"Reference","previous_headings":"","what":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"Summary 48- 96-hour acute toxicity values (LC50 EC50 values) exposure Australian Non-Australian taxa endosulfan.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"","code":"data(endosulfan)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"endosulfan data frame 4 columns, named ATV Acute Toxicity Value (geometric mean LC50 ou EC50 values micrograms per liter), Australian (coding Australian another origin), group (arthropods, fish non-arthropod invertebrates) taxa.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"Hose, G.C., Van den Brink, P.J. 2004. Confirming Species-Sensitivity Distribution Concept Endosulfan Using Laboratory, Mesocosms, Field Data. Archives Environmental Contamination Toxicology, 47, 511-520.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/endosulfan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species Sensitivity Distribution (SSD) for endosulfan — endosulfan","text":"","code":"# (1) load of data # data(endosulfan) # (2) plot and description of data for non Australian fish in decimal logarithm # log10ATV <-log10(subset(endosulfan,(Australian == \"no\") & (group == \"Fish\"))$ATV) plotdist(log10ATV) descdist(log10ATV,boot=11) #> summary statistics #> ------ #> min: -0.69897 max: 3.60206 #> median: 0.4911356 #> mean: 0.5657595 #> estimated sd: 0.7034928 #> estimated skewness: 1.764601 #> estimated kurtosis: 9.759505 # (3) fit of a normal and a logistic distribution to data in log10 # (classical distributions used for SSD) # and visual comparison of the fits # fln <- fitdist(log10ATV,\"norm\") summary(fln) #> Fitting of the distribution ' norm ' by maximum likelihood #> Parameters : #> estimate Std. Error #> mean 0.5657595 0.6958041 #> sd 0.6958041 0.4920032 #> Loglikelihood: -48.58757 AIC: 101.1751 BIC: 104.8324 #> Correlation matrix: #> mean sd #> mean 1 0 #> sd 0 1 #> fll <- fitdist(log10ATV,\"logis\") summary(fll) #> Fitting of the distribution ' logis ' by maximum likelihood #> Parameters : #> estimate Std. Error #> location 0.5082818 0.5901708 #> scale 0.3457256 0.2917097 #> Loglikelihood: -44.31825 AIC: 92.6365 BIC: 96.29378 #> Correlation matrix: #> location scale #> location 1.00000000 0.04028287 #> scale 0.04028287 1.00000000 #> cdfcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\"), xlab=\"log10ATV\") denscomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\"), xlab=\"log10ATV\") qqcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\")) ppcomp(list(fln,fll),legendtext=c(\"normal\",\"logistic\")) gofstat(list(fln,fll), fitnames = c(\"lognormal\", \"loglogistic\")) #> Goodness-of-fit statistics #> lognormal loglogistic #> Kolmogorov-Smirnov statistic 0.1267649 0.08457997 #> Cramer-von Mises statistic 0.1555576 0.04058514 #> Anderson-Darling statistic 1.0408045 0.37407465 #> #> Goodness-of-fit criteria #> lognormal loglogistic #> Akaike's Information Criterion 101.1751 92.63650 #> Bayesian Information Criterion 104.8324 96.29378 # (4) estimation of the 5 percent quantile value of # logistic fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # parametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(ATV) bll <- bootdist(fll,niter=51) HC5ll <- quantile(bll,probs = 0.05) # in ATV 10^(HC5ll$quantiles) #> p=0.05 #> estimate 0.309253 10^(HC5ll$quantCI) #> p=0.05 #> 2.5 % 0.1891451 #> 97.5 % 0.5457214 # (5) estimation of the 5 percent quantile value of # the fitted logistic distribution (5 percent hazardous concentration : HC5) # with its one-sided 95 percent confidence interval (type \"greater\") # calculated by # nonparametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(ATV) bllnonpar <- bootdist(fll,niter=51,bootmethod = \"nonparam\") HC5llgreater <- quantile(bllnonpar,probs = 0.05, CI.type=\"greater\") # in ATV 10^(HC5llgreater$quantiles) #> p=0.05 #> estimate 0.309253 10^(HC5llgreater$quantCI) #> p=0.05 #> 5 % 0.1860103 # (6) fit of a logistic distribution # by minimizing the modified Anderson-Darling AD2L distance # cf. ?mgedist for definition of this distance # fllAD2L <- fitdist(log10ATV,\"logis\",method=\"mge\",gof=\"AD2L\") summary(fllAD2L) #> Fitting of the distribution ' logis ' by maximum goodness-of-fit #> Parameters : #> estimate #> location 0.4965288 #> scale 0.3013154 #> Loglikelihood: -44.96884 AIC: 93.93767 BIC: 97.59496 plot(fllAD2L)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit of univariate distributions to non-censored data — fitdist","title":"Fit of univariate distributions to non-censored data — fitdist","text":"Fit univariate distributions non-censored data maximum likelihood (mle), moment matching (mme), quantile matching (qme) maximizing goodness--fit estimation (mge). latter also known minimizing distance estimation. Generic methods print, plot, summary, quantile, logLik, AIC, BIC, vcov coef.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit of univariate distributions to non-censored data — fitdist","text":"","code":"fitdist(data, distr, method = c(\"mle\", \"mme\", \"qme\", \"mge\", \"mse\"), start=NULL, fix.arg=NULL, discrete, keepdata = TRUE, keepdata.nb=100, calcvcov=TRUE, ...) # S3 method for class 'fitdist' print(x, ...) # S3 method for class 'fitdist' plot(x, breaks=\"default\", ...) # S3 method for class 'fitdist' summary(object, ...) # S3 method for class 'fitdist' logLik(object, ...) # S3 method for class 'fitdist' AIC(object, ..., k = 2) # S3 method for class 'fitdist' BIC(object, ...) # S3 method for class 'fitdist' vcov(object, ...) # S3 method for class 'fitdist' coef(object, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit of univariate distributions to non-censored data — fitdist","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. method character string coding fitting method: \"mle\" 'maximum likelihood estimation', \"mme\" 'moment matching estimation', \"qme\" 'quantile matching estimation', \"mge\" 'maximum goodness--fit estimation' \"mse\" 'maximum spacing estimation'. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). may account closed-form formulas. fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. use argument possible method=\"mme\" closed-form formula used. keepdata logical. TRUE, dataset returned, otherwise sample subset returned. keepdata.nb keepdata=FALSE, length (>1) subset returned. calcvcov logical indicating (asymptotic) covariance matrix required. discrete TRUE, distribution considered discrete. discrete missing, \t discrete automaticaly set TRUE distr belongs \t \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\" FALSE cases. thus recommended enter argument using another discrete distribution. argument directly affect results fit passed functions gofstat, plotdist cdfcomp. x object class \"fitdist\". object object class \"fitdist\". breaks \"default\" histogram plotted function hist default breaks definition. Else breaks passed function hist. argument taken account discrete distributions: \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\". k penalty per parameter passed AIC generic function (2 default). ... arguments passed generic functions, one functions \"mledist\", \"mmedist\", \"qmedist\" \"mgedist\" depending chosen method. See mledist, mmedist, qmedist, mgedist details parameter estimation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit of univariate distributions to non-censored data — fitdist","text":"assumed distr argument specifies distribution probability density function, cumulative distribution function quantile function (d, p, q). four possible fitting methods described : method=\"mle\" Maximum likelihood estimation consists maximizing log-likelihood. numerical optimization carried mledist via optim find best values (see mledist details). method=\"mme\" Moment matching estimation consists equalizing theoretical empirical moments. Estimated values distribution parameters computed closed-form formula following distributions : \"norm\", \"lnorm\", \"pois\", \"exp\", \"gamma\", \"nbinom\", \"geom\", \"beta\", \"unif\" \"logis\". Otherwise theoretical empirical moments matched numerically, minimization sum squared differences observed theoretical moments. last case, arguments needed call fitdist: order memp (see mmedist details). Since Version 1.2-0, mmedist automatically computes asymptotic covariance matrix, hence theoretical moments mdist defined order equals twice maximal order given order. method = \"qme\" Quantile matching estimation consists equalizing theoretical empirical quantile. numerical optimization carried qmedist via optim minimize sum squared differences observed theoretical quantiles. use method requires additional argument probs, defined numeric vector probabilities quantile(s) () matched (see qmedist details). method = \"mge\" Maximum goodness--fit estimation consists maximizing goodness--fit statistics. numerical optimization carried mgedist via optim minimize goodness--fit distance. use method requires additional argument gof coding goodness--fit distance chosen. One can use classical Cramer-von Mises distance (\"CvM\"), classical Kolmogorov-Smirnov distance (\"KS\"), classical Anderson-Darling distance (\"AD\") gives weight tails distribution, one variants last distance proposed Luceno (2006) (see mgedist details). method suitable discrete distributions. method = \"mse\" Maximum goodness--fit estimation consists maximizing average log spacing. numerical optimization carried msedist via optim. default, direct optimization log-likelihood (criteria depending chosen method) performed using optim, \"Nelder-Mead\" method distributions characterized one parameter \"BFGS\" method distributions characterized one parameter. optimization algorithm used optim can chosen another optimization function can specified using ... argument (see mledist details). start may omitted (.e. NULL) classic distributions (see 'details' section mledist). Note errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1) ... argument. parameter(s) () estimated, fitdist computes log-likelihood every estimation method maximum likelihood estimation standard errors estimates calculated Hessian solution found optim user-supplied function passed mledist. default (keepdata = TRUE), object returned fitdist contains data vector given input. dealing large datasets, can remove original dataset output setting keepdata = FALSE. case, keepdata.nb points () kept random subsampling keepdata.nb-2 points dataset adding minimum maximum. combined bootdist, use non-parametric bootstrap aware bootstrap performed subset randomly selected fitdist. Currently, graphical comparisons multiple fits available framework. Weighted version estimation process available method = \"mle\", \"mme\", \"qme\" using weights=.... See corresponding man page details. Weighted maximum GOF estimation (method = \"mge\") allowed. yet possible take account weighths functions plotdist, plot.fitdist, cdfcomp, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). parameter(s) () estimated, gofstat allows compute goodness--fit statistics. NB: data values particularly small large, scaling may needed optimization process. See example (14) man page examples (14,15) test file package. Please also take look Rmpfr package available CRAN numerical accuracy issues.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit of univariate distributions to non-censored data — fitdist","text":"fitdist returns object class \"fitdist\", list following components: estimate parameter estimates. method character string coding fitting method : \"mle\" 'maximum likelihood estimation', \"mme\" 'matching moment estimation', \"qme\" 'matching quantile estimation' \"mge\" 'maximum goodness--fit estimation' \"mse\" 'maximum spacing estimation'. sd estimated standard errors, NA numerically computable NULL available. cor estimated correlation matrix, NA numerically computable NULL available. vcov estimated variance-covariance matrix, NULL available estimation method considered. loglik log-likelihood. aic Akaike information criterion. bic -called BIC SBC (Schwarz Bayesian criterion). n length data set. data data set. distname name distribution. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. dots list arguments passed ... used bootdist iterative calls mledist, mmedist, qmedist, mgedist NULL arguments. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. discrete input argument automatic definition function passed functions gofstat, plotdist cdfcomp. weights vector weigths used estimation process NULL. Generic functions: print print \"fitdist\" object shows traces fitting method fitted distribution. summary summary provides parameter estimates fitted distribution, log-likelihood, AIC BIC statistics maximum likelihood used, standard errors parameter estimates correlation matrix parameter estimates. plot plot object class \"fitdist\" returned fitdist uses function plotdist. object class \"fitdist\" list objects class \"fitdist\" corresponding various fits using data set may also plotted using cdf plot (function cdfcomp), density plot(function denscomp), density Q-Q plot (function qqcomp), P-P plot (function ppcomp). logLik Extracts estimated log-likelihood \"fitdist\" object. AIC Extracts AIC \"fitdist\" object. BIC Extracts estimated BIC \"fitdist\" object. vcov Extracts estimated var-covariance matrix \"fitdist\" object (available method = \"mle\"). coef Extracts fitted coefficients \"fitdist\" object.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit of univariate distributions to non-censored data — fitdist","text":". Ibragimov R. 'minskii (1981), Statistical Estimation - Asymptotic Theory, Springer-Verlag, doi:10.1007/978-1-4899-0027-2 Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fit of univariate distributions to non-censored data — fitdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit of univariate distributions to non-censored data — fitdist","text":"","code":"# (1) fit of a gamma distribution by maximum likelihood estimation # data(groundbeef) serving <- groundbeef$serving fitg <- fitdist(serving, \"gamma\") summary(fitg) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> plot(fitg) plot(fitg, demp = TRUE) plot(fitg, histo = FALSE, demp = TRUE) cdfcomp(fitg, addlegend=FALSE) denscomp(fitg, addlegend=FALSE) ppcomp(fitg, addlegend=FALSE) qqcomp(fitg, addlegend=FALSE) # (2) use the moment matching estimation (using a closed formula) # fitgmme <- fitdist(serving, \"gamma\", method=\"mme\") summary(fitgmme) #> Fitting of the distribution ' gamma ' by matching moments #> Parameters : #> estimate Std. Error #> shape 4.22848617 6.64959843 #> rate 0.05741663 0.09451052 #> Loglikelihood: -1253.825 AIC: 2511.65 BIC: 2518.724 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9553622 #> rate 0.9553622 1.0000000 #> # (3) Comparison of various fits # fitW <- fitdist(serving, \"weibull\") fitg <- fitdist(serving, \"gamma\") fitln <- fitdist(serving, \"lnorm\") summary(fitW) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> summary(fitg) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> summary(fitln) #> Fitting of the distribution ' lnorm ' by maximum likelihood #> Parameters : #> estimate Std. Error #> meanlog 4.1693701 0.5366095 #> sdlog 0.5366095 0.3794343 #> Loglikelihood: -1261.319 AIC: 2526.639 BIC: 2533.713 #> Correlation matrix: #> meanlog sdlog #> meanlog 1 0 #> sdlog 0 1 #> cdfcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) denscomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) qqcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) ppcomp(list(fitW, fitg, fitln), legendtext=c(\"Weibull\", \"gamma\", \"lognormal\")) gofstat(list(fitW, fitg, fitln), fitnames=c(\"Weibull\", \"gamma\", \"lognormal\")) #> Goodness-of-fit statistics #> Weibull gamma lognormal #> Kolmogorov-Smirnov statistic 0.1396646 0.1281486 0.1493090 #> Cramer-von Mises statistic 0.6840994 0.6936274 0.8277358 #> Anderson-Darling statistic 3.5736460 3.5672625 4.5436542 #> #> Goodness-of-fit criteria #> Weibull gamma lognormal #> Akaike's Information Criterion 2514.449 2511.250 2526.639 #> Bayesian Information Criterion 2521.524 2518.325 2533.713 # (4) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view # dedicated to probability distributions # dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q, a, b) exp(-exp((a-q)/b)) qgumbel <- function(p, a, b) a-b*log(-log(p)) fitgumbel <- fitdist(serving, \"gumbel\", start=list(a=10, b=10)) #> Error in fitdist(serving, \"gumbel\", start = list(a = 10, b = 10)): The dgumbel function must be defined summary(fitgumbel) #> Error: object 'fitgumbel' not found plot(fitgumbel) #> Error: object 'fitgumbel' not found # (5) fit discrete distributions (Poisson and negative binomial) # data(toxocara) number <- toxocara$number fitp <- fitdist(number,\"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) fitnb <- fitdist(number,\"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb) cdfcomp(list(fitp,fitnb)) gofstat(list(fitp,fitnb)) #> Chi-squared statistic: 31256.96 7.48606 #> Degree of freedom of the Chi-squared distribution: 5 4 #> Chi-squared p-value: 0 0.1123255 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo 1-mle-pois theo 2-mle-nbinom #> <= 0 14 0.009014207 15.295027 #> <= 1 8 0.078236515 5.808596 #> <= 3 6 1.321767253 6.845015 #> <= 4 6 2.131297825 2.407815 #> <= 9 6 29.827829425 7.835196 #> <= 21 6 19.626223437 8.271110 #> > 21 7 0.005631338 6.537242 #> #> Goodness-of-fit criteria #> 1-mle-pois 2-mle-nbinom #> Akaike's Information Criterion 1017.067 322.6882 #> Bayesian Information Criterion 1019.037 326.6288 # (6) how to change the optimisation method? # data(groundbeef) serving <- groundbeef$serving fitdist(serving, \"gamma\", optim.method=\"Nelder-Mead\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 fitdist(serving, \"gamma\", optim.method=\"BFGS\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.21183650 5.72702767 #> rate 0.05719298 0.08257022 fitdist(serving, \"gamma\", optim.method=\"SANN\") #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 3.85618448 5.225475 #> rate 0.05179296 0.074932 # (7) custom optimization function # # \\donttest{ #create the sample set.seed(1234) mysample <- rexp(100, 5) mystart <- list(rate=8) res1 <- fitdist(mysample, dexp, start= mystart, optim.method=\"Nelder-Mead\") #show the result summary(res1) #> Fitting of the distribution ' exp ' by maximum likelihood #> Parameters : #> estimate Std. Error #> rate 5.120312 5.120312 #> Loglikelihood: 63.32596 AIC: -124.6519 BIC: -122.0467 #the warning tell us to use optimise, because the Nelder-Mead is not adequate. #to meet the standard 'fn' argument and specific name arguments, we wrap optimize, myoptimize <- function(fn, par, ...) { res <- optimize(f=fn, ..., maximum=FALSE) #assume the optimization function minimize standardres <- c(res, convergence=0, value=res$objective, par=res$minimum, hessian=NA) return(standardres) } #call fitdist with a 'custom' optimization function res2 <- fitdist(mysample, \"exp\", start=mystart, custom.optim=myoptimize, interval=c(0, 100)) #show the result summary(res2) #> Fitting of the distribution ' exp ' by maximum likelihood #> Parameters : #> estimate #> rate 5.120531 #> Loglikelihood: 63.32596 AIC: -124.6519 BIC: -122.0467 # } # (8) custom optimization function - another example with the genetic algorithm # # \\donttest{ #set a sample fit1 <- fitdist(serving, \"gamma\") summary(fit1) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384578 #> rate 0.9384578 1.0000000 #> #wrap genoud function rgenoud package mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values=par, ...) standardres <- c(res, convergence=0) return(standardres) } #call fitdist with a 'custom' optimization function fit2 <- fitdist(serving, \"gamma\", custom.optim=mygenoud, nvars=2, Domains=cbind(c(0, 0), c(10, 10)), boundary.enforcement=1, print.level=1, hessian=TRUE) #> Loading required package: rgenoud #> ## rgenoud (Version 5.9-0.11, Build Date: 2024-10-03) #> ## See http://sekhon.berkeley.edu/rgenoud for additional documentation. #> ## Please cite software as: #> ## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011. #> ## ``Genetic Optimization Using Derivatives: The rgenoud package for R.'' #> ## Journal of Statistical Software, 42(11): 1-26. #> ## #> #> #> Mon Dec 2 13:33:35 2024 #> Domains: #> 0.000000e+00 <= X1 <= 1.000000e+01 #> 0.000000e+00 <= X2 <= 1.000000e+01 #> #> Data Type: Floating Point #> Operators (code number, name, population) #> \t(1) Cloning........................... \t122 #> \t(2) Uniform Mutation.................. \t125 #> \t(3) Boundary Mutation................. \t125 #> \t(4) Non-Uniform Mutation.............. \t125 #> \t(5) Polytope Crossover................ \t125 #> \t(6) Simple Crossover.................. \t126 #> \t(7) Whole Non-Uniform Mutation........ \t125 #> \t(8) Heuristic Crossover............... \t126 #> \t(9) Local-Minimum Crossover........... \t0 #> #> HARD Maximum Number of Generations: 100 #> Maximum Nonchanging Generations: 10 #> Population size : 1000 #> Convergence Tolerance: 1.000000e-03 #> #> Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation. #> Checking Gradients before Stopping. #> Not Using Out of Bounds Individuals But Allowing Trespassing. #> #> Minimization Problem. #> #> #> Generation#\t Solution Value #> #> 0 \t4.936206e+00 #> #> 'wait.generations' limit reached. #> No significant improvement in 10 generations. #> #> Solution Fitness Value: 4.935532e+00 #> #> Parameters at the Solution (parameter, gradient): #> #> X[ 1] :\t4.008339e+00\tG[ 1] :\t2.759167e-10 #> X[ 2] :\t5.442735e-02\tG[ 2] :\t-3.841725e-07 #> #> Solution Found Generation 1 #> Number of Generations Run 11 #> #> Mon Dec 2 13:33:36 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit2) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 4.00833889 5.44012540 #> rate 0.05442735 0.07867032 #> Loglikelihood: -1253.625 AIC: 2511.25 BIC: 2518.325 #> Correlation matrix: #> shape rate #> shape 1.0000000 0.9384394 #> rate 0.9384394 1.0000000 #> # } # (9) estimation of the standard deviation of a gamma distribution # by maximum likelihood with the shape fixed at 4 using the argument fix.arg # data(groundbeef) serving <- groundbeef$serving f1c <- fitdist(serving,\"gamma\",start=list(rate=0.1),fix.arg=list(shape=4)) summary(f1c) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters : #> estimate Std. Error #> rate 0.05431619 0.02714888 #> Fixed parameters: #> value #> shape 4 #> Loglikelihood: -1253.625 AIC: 2509.251 BIC: 2512.788 plot(f1c) # (10) fit of a Weibull distribution to serving size data # by maximum likelihood estimation # or by quantile matching estimation (in this example # matching first and third quartiles) # data(groundbeef) serving <- groundbeef$serving fWmle <- fitdist(serving, \"weibull\") summary(fWmle) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> plot(fWmle) gofstat(fWmle) #> Goodness-of-fit statistics #> 1-mle-weibull #> Kolmogorov-Smirnov statistic 0.1396646 #> Cramer-von Mises statistic 0.6840994 #> Anderson-Darling statistic 3.5736460 #> #> Goodness-of-fit criteria #> 1-mle-weibull #> Akaike's Information Criterion 2514.449 #> Bayesian Information Criterion 2521.524 fWqme <- fitdist(serving, \"weibull\", method=\"qme\", probs=c(0.25, 0.75)) summary(fWqme) #> Fitting of the distribution ' weibull ' by matching quantiles #> Parameters : #> estimate #> shape 2.268699 #> scale 86.590853 #> Loglikelihood: -1256.129 AIC: 2516.258 BIC: 2523.332 plot(fWqme) gofstat(fWqme) #> Goodness-of-fit statistics #> 1-qme-weibull #> Kolmogorov-Smirnov statistic 0.1692858 #> Cramer-von Mises statistic 0.9664709 #> Anderson-Darling statistic 4.8479858 #> #> Goodness-of-fit criteria #> 1-qme-weibull #> Akaike's Information Criterion 2516.258 #> Bayesian Information Criterion 2523.332 # (11) Fit of a Pareto distribution by numerical moment matching estimation # # \\donttest{ require(\"actuar\") #> Loading required package: actuar #> #> Attaching package: ‘actuar’ #> The following objects are masked from ‘package:stats’: #> #> sd, var #> The following object is masked from ‘package:grDevices’: #> #> cm #simulate a sample x4 <- rpareto(1000, 6, 2) #empirical raw moment memp <- function(x, order) mean(x^order) #fit fP <- fitdist(x4, \"pareto\", method=\"mme\", order=c(1, 2), memp=\"memp\", start=list(shape=10, scale=10), lower=1, upper=Inf) #> Error in mmedist(data, distname, start = arg_startfix$start.arg, fix.arg = arg_startfix$fix.arg, checkstartfix = TRUE, calcvcov = calcvcov, ...): the empirical moment must be defined as a function summary(fP) #> Error: object 'fP' not found plot(fP) #> Error: object 'fP' not found # } # (12) Fit of a Weibull distribution to serving size data by maximum # goodness-of-fit estimation using all the distances available # # \\donttest{ data(groundbeef) serving <- groundbeef$serving (f1 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"CvM\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.093204 #> scale 82.660014 (f2 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"KS\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.065634 #> scale 81.450487 (f3 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.125425 #> scale 82.890502 (f4 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"ADR\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.072035 #> scale 82.762593 (f5 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"ADL\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.197498 #> scale 82.016005 (f6 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2R\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 1.90328 #> scale 81.33464 (f7 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2L\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.483836 #> scale 78.252113 (f8 <- fitdist(serving, \"weibull\", method=\"mge\", gof=\"AD2\")) #> Fitting of the distribution ' weibull ' by maximum goodness-of-fit #> Parameters: #> estimate #> shape 2.081168 #> scale 85.281194 cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8)) cdfcomp(list(f1, f2, f3, f4, f5, f6, f7, f8), xlogscale=TRUE, xlim=c(8, 250), verticals=TRUE) denscomp(list(f1, f2, f3, f4, f5, f6, f7, f8)) # } # (13) Fit of a uniform distribution using maximum likelihood # (a closed formula is used in this special case where the loglikelihood is not defined), # or maximum goodness-of-fit with Cramer-von Mises or Kolmogorov-Smirnov distance # set.seed(1234) u <- runif(50, min=5, max=10) fumle <- fitdist(u, \"unif\", method=\"mle\") summary(fumle) #> Fitting of the distribution ' unif ' by maximum likelihood #> Parameters : #> estimate #> min 5.047479 #> max 9.960752 #> Loglikelihood: -79.59702 AIC: 163.194 BIC: 167.0181 plot(fumle) gofstat(fumle) #> Goodness-of-fit statistics #> 1-mle-unif #> Kolmogorov-Smirnov statistic 0.1340723 #> Cramer-von Mises statistic 0.1566892 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mle-unif #> Akaike's Information Criterion 163.1940 #> Bayesian Information Criterion 167.0181 fuCvM <- fitdist(u, \"unif\", method=\"mge\", gof=\"CvM\") summary(fuCvM) #> Fitting of the distribution ' unif ' by maximum goodness-of-fit #> Parameters : #> estimate #> min 5.110497 #> max 9.552878 #> Loglikelihood: -Inf AIC: Inf BIC: Inf plot(fuCvM) gofstat(fuCvM) #> Goodness-of-fit statistics #> 1-mge-unif #> Kolmogorov-Smirnov statistic 0.11370966 #> Cramer-von Mises statistic 0.07791651 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mge-unif #> Akaike's Information Criterion Inf #> Bayesian Information Criterion Inf fuKS <- fitdist(u, \"unif\", method=\"mge\", gof=\"KS\") summary(fuKS) #> Fitting of the distribution ' unif ' by maximum goodness-of-fit #> Parameters : #> estimate #> min 5.092357 #> max 9.323818 #> Loglikelihood: -Inf AIC: Inf BIC: Inf plot(fuKS) gofstat(fuKS) #> Goodness-of-fit statistics #> 1-mge-unif #> Kolmogorov-Smirnov statistic 0.09216159 #> Cramer-von Mises statistic 0.12241830 #> Anderson-Darling statistic Inf #> #> Goodness-of-fit criteria #> 1-mge-unif #> Akaike's Information Criterion Inf #> Bayesian Information Criterion Inf # (14) scaling problem # the simulated dataset (below) has particularly small values, hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 0:6) cat(i, try(fitdist(x2*10^i, \"cauchy\", method=\"mle\")$estimate, silent=TRUE), \"\\n\") #> 0 1.876032e-05 0.000110131 #> 1 0.0001876032 0.00110131 #> 2 0.001870693 0.01100646 #> 3 0.01871473 0.1100713 #> 4 0.1870693 1.100646 #> 5 1.876032 11.0131 #> 6 18.76032 110.131 # (15) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for # nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution (which is called the 5 percent hazardous concentration, HC5, # in ecotoxicology) and estimation of other quantiles. # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") quantile(fln, probs = 0.05) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 #> estimate 1.744227 quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 # (16) Fit of a triangular distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # # \\donttest{ set.seed(1234) require(\"mc2d\") #> Loading required package: mc2d #> Loading required package: mvtnorm #> #> Attaching package: ‘mc2d’ #> The following objects are masked from ‘package:base’: #> #> pmax, pmin t <- rtriang(100, min=5, mode=6, max=10) fCvM <- fitdist(t, \"triang\", method=\"mge\", start = list(min=4, mode=6,max=9), gof=\"CvM\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. fKS <- fitdist(t, \"triang\", method=\"mge\", start = list(min=4, mode=6,max=9), gof=\"KS\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. cdfcomp(list(fCvM,fKS)) # } # (17) fit a non classical discrete distribution (the zero inflated Poisson distribution) # # \\donttest{ require(\"gamlss.dist\") #> Loading required package: gamlss.dist set.seed(1234) x <- rZIP(n = 30, mu = 5, sigma = 0.2) plotdist(x, discrete = TRUE) fitzip <- fitdist(x, \"ZIP\", start = list(mu = 4, sigma = 0.15), discrete = TRUE, optim.method = \"L-BFGS-B\", lower = c(0, 0), upper = c(Inf, 1)) #> Warning: The dZIP function should return a zero-length vector when input has length zero #> Warning: The pZIP function should return a zero-length vector when input has length zero summary(fitzip) #> Fitting of the distribution ' ZIP ' by maximum likelihood #> Parameters : #> estimate Std. Error #> mu 4.3166098 2.3777816 #> sigma 0.1891794 0.4062398 #> Loglikelihood: -67.13886 AIC: 138.2777 BIC: 141.0801 #> Correlation matrix: #> mu sigma #> mu 1.00000000 0.06418931 #> sigma 0.06418931 1.00000000 #> plot(fitzip) fitp <- fitdist(x, \"pois\") cdfcomp(list(fitzip, fitp)) gofstat(list(fitzip, fitp)) #> Chi-squared statistic: 3.579708 35.91516 #> Degree of freedom of the Chi-squared distribution: 3 4 #> Chi-squared p-value: 0.3105704 3.012341e-07 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo 1-mle-ZIP theo 2-mle-pois #> <= 0 6 5.999996 0.9059215 #> <= 2 7 4.425507 8.7194943 #> <= 4 5 9.047522 12.1379326 #> <= 5 5 4.054142 3.9650580 #> <= 7 5 4.715294 3.4694258 #> > 7 2 1.757539 0.8021677 #> #> Goodness-of-fit criteria #> 1-mle-ZIP 2-mle-pois #> Akaike's Information Criterion 138.2777 153.7397 #> Bayesian Information Criterion 141.0801 155.1409 # } # (18) examples with distributions in actuar (predefined starting values) # # \\donttest{ require(\"actuar\") x <- c(2.3,0.1,2.7,2.2,0.4,2.6,0.2,1.,7.3,3.2,0.8,1.2,33.7,14., 21.4,7.7,1.,1.9,0.7,12.6,3.2,7.3,4.9,4000.,2.5,6.7,3.,63., 6.,1.6,10.1,1.2,1.5,1.2,30.,3.2,3.5,1.2,0.2,1.9,0.7,17., 2.8,4.8,1.3,3.7,0.2,1.8,2.6,5.9,2.6,6.3,1.4,0.8) #log logistic ft_llogis <- fitdist(x,'llogis') x <- c(0.3837053, 0.8576858, 0.3552237, 0.6226119, 0.4783756, 0.3139799, 0.4051403, 0.4537631, 0.4711057, 0.5647414, 0.6479617, 0.7134207, 0.5259464, 0.5949068, 0.3509200, 0.3783077, 0.5226465, 1.0241043, 0.4384580, 1.3341520) #inverse weibull ft_iw <- fitdist(x,'invweibull') # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting of univariate distributions to censored data — fitdistcens","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Fits univariate distribution censored data maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"","code":"fitdistcens(censdata, distr, start=NULL, fix.arg=NULL, keepdata = TRUE, keepdata.nb=100, calcvcov=TRUE, ...) # S3 method for class 'fitdistcens' print(x, ...) # S3 method for class 'fitdistcens' plot(x, ...) # S3 method for class 'fitdistcens' summary(object, ...) # S3 method for class 'fitdistcens' logLik(object, ...) # S3 method for class 'fitdistcens' AIC(object, ..., k = 2) # S3 method for class 'fitdistcens' BIC(object, ...) # S3 method for class 'fitdistcens' vcov(object, ...) # S3 method for class 'fitdistcens' coef(object, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"censdata dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution, corresponding density function dname corresponding distribution function pname must defined, directly density function. start named list giving initial values parameters named distribution. argument may omitted distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood. x object class \"fitdistcens\". object object class \"fitdistcens\". keepdata logical. TRUE, dataset returned, otherwise sample subset returned. keepdata.nb keepdata=FALSE, length subset returned. calcvcov logical indicating (asymptotic) covariance matrix required. k penalty per parameter passed AIC generic function (2 default). ... arguments passed generic functions, function plotdistcens order control type ecdf-plot used censored data, function mledist order control optimization method.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Maximum likelihood estimations distribution parameters computed using function mledist. default direct optimization log-likelihood performed using optim, \"Nelder-Mead\" method distributions characterized one parameter \"BFGS\" method distributions characterized one parameter. algorithm used optim can chosen another optimization function can specified using ... argument (see mledist details). start may omitted (.e. NULL) classic distributions (see 'details' section mledist). Note errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1) ... argument. function able fit uniform distribution. parameter estimates, function returns log-likelihood standard errors estimates calculated Hessian solution found optim user-supplied function passed mledist. default (keepdata = TRUE), object returned fitdist contains data vector given input. dealing large datasets, can remove original dataset output setting keepdata = FALSE. case, keepdata.nb points () kept random subsampling keepdata.nb-4 points dataset adding component-wise minimum maximum. combined bootdistcens, aware bootstrap performed subset randomly selected fitdistcens. Currently, graphical comparisons multiple fits available framework. Weighted version estimation process available method = \"mle\" using weights=.... See corresponding man page details. yet possible take account weighths functions plotdistcens, plot.fitdistcens cdfcompcens (developments planned future). parameter(s) () estimated, gofstat allows compute goodness--fit statistics.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"fitdistcens returns object class \"fitdistcens\", list following components: estimate parameter estimates. method character string coding fitting method : \"mle\" 'maximum likelihood estimation'. sd estimated standard errors. cor estimated correlation matrix, NA numerically computable NULL available. vcov estimated variance-covariance matrix, NULL available. loglik log-likelihood. aic Akaike information criterion. bic -called BIC SBC (Schwarz Bayesian criterion). censdata censored data set. distname name distribution. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. dots list arguments passed ... used bootdistcens control optimization method used iterative calls mledist NULL arguments. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. discrete always FALSE. weights vector weigths used estimation process NULL. Generic functions: print print \"fitdist\" object shows traces fitting method fitted distribution. summary summary provides parameter estimates fitted distribution, log-likelihood, AIC BIC statistics, standard errors parameter estimates correlation matrix parameter estimates. plot plot object class \"fitdistcens\" returned fitdistcens uses function plotdistcens. logLik Extracts estimated log-likelihood \"fitdistcens\" object. AIC Extracts AIC \"fitdistcens\" object. BIC Extracts BIC \"fitdistcens\" object. vcov Extracts estimated var-covariance matrix \"fitdistcens\" object (available method = \"mle\"). coef Extracts fitted coefficients \"fitdistcens\" object.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting of univariate distributions to censored data — fitdistcens","text":"","code":"# (1) Fit of a lognormal distribution to bacterial contamination data # data(smokedfish) fitsf <- fitdistcens(smokedfish,\"lnorm\") summary(fitsf) #> Fitting of the distribution ' lnorm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> meanlog -3.627606 4.706165 #> sdlog 3.544570 4.949219 #> Loglikelihood: -90.65154 AIC: 185.3031 BIC: 190.5725 #> Correlation matrix: #> meanlog sdlog #> meanlog 1.0000000 -0.4325873 #> sdlog -0.4325873 1.0000000 #> # default plot using the Wang technique (see ?plotdiscens for details) plot(fitsf) # plot using the Turnbull algorithm (see ?plotdiscens for details) # with confidence intervals for the empirical distribution plot(fitsf, NPMLE = TRUE, NPMLE.method = \"Turnbull\", Turnbull.confint = TRUE) #> Warning: Turnbull is now a deprecated option for NPMLE.method. You should use Turnbull.middlepoints #> of Turnbull.intervals. It was here fixed as Turnbull.middlepoints, equivalent to former Turnbull. #> Warning: Q-Q plot and P-P plot are available only #> with the arguments NPMLE.method at Wang (default value) or Turnbull.intervals. # basic plot using intervals and points (see ?plotdiscens for details) plot(fitsf, NPMLE = FALSE) #> Warning: When NPMLE is FALSE the nonparametric maximum likelihood estimation #> of the cumulative distribution function is not computed. #> Q-Q plot and P-P plot are available only with the arguments NPMLE.method at Wang #> (default value) or Turnbull.intervals. # plot of the same fit using the Turnbull algorithm in logscale cdfcompcens(fitsf,main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, xlim = c(1e-2,1e2)) # zoom on large values of F cdfcompcens(fitsf,main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", addlegend = FALSE,lines01 = TRUE, xlogscale = TRUE, xlim = c(1e-2,1e2),ylim=c(0.4,1)) # (2) Fit of a normal distribution on acute toxicity values # of fluazinam (in decimal logarithm) for # macroinvertebrates and zooplancton, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology # data(fluazinam) log10EC50 <-log10(fluazinam) fln <- fitdistcens(log10EC50,\"norm\") fln #> Fitting of the distribution ' norm ' on censored data by maximum likelihood #> Parameters: #> estimate #> mean 2.161449 #> sd 1.167290 summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 2.161449 1.2060732 #> sd 1.167290 0.9842019 #> Loglikelihood: -20.41212 AIC: 44.82424 BIC: 46.10235 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.1350237 #> sd 0.1350237 1.0000000 #> plot(fln) # (3) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view dedicated to # probability distributions # dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) fg <- fitdistcens(log10EC50,\"gumbel\",start=list(a=1,b=1)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. summary(fg) #> Error: object 'fg' not found plot(fg) #> Error: object 'fg' not found # (4) comparison of fits of various distributions # fll <- fitdistcens(log10EC50,\"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1518291 1.2058724 #> scale 0.6910423 0.6530058 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05097494 #> scale 0.05097494 1.00000000 #> cdfcompcens(list(fln,fll,fg),legendtext=c(\"normal\",\"logistic\",\"gumbel\"), xlab = \"log10(EC50)\") #> Error: object 'fg' not found # (5) how to change the optimisation method? # fitdistcens(log10EC50,\"logis\",optim.method=\"Nelder-Mead\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1518291 #> scale 0.6910423 fitdistcens(log10EC50,\"logis\",optim.method=\"BFGS\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1519961 #> scale 0.6910665 fitdistcens(log10EC50,\"logis\",optim.method=\"SANN\") #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.0238931 #> scale 0.5261822 # (6) custom optimisation function - example with the genetic algorithm # # \\donttest{ #wrap genoud function rgenoud package mygenoud <- function(fn, par, ...) { require(\"rgenoud\") res <- genoud(fn, starting.values=par, ...) standardres <- c(res, convergence=0) return(standardres) } # call fitdistcens with a 'custom' optimization function fit.with.genoud <- fitdistcens(log10EC50,\"logis\", custom.optim=mygenoud, nvars=2, Domains=cbind(c(0,0), c(5, 5)), boundary.enforcement=1, print.level=1, hessian=TRUE) #> #> #> Mon Dec 2 13:33:42 2024 #> Domains: #> 0.000000e+00 <= X1 <= 5.000000e+00 #> 0.000000e+00 <= X2 <= 5.000000e+00 #> #> Data Type: Floating Point #> Operators (code number, name, population) #> \t(1) Cloning........................... \t122 #> \t(2) Uniform Mutation.................. \t125 #> \t(3) Boundary Mutation................. \t125 #> \t(4) Non-Uniform Mutation.............. \t125 #> \t(5) Polytope Crossover................ \t125 #> \t(6) Simple Crossover.................. \t126 #> \t(7) Whole Non-Uniform Mutation........ \t125 #> \t(8) Heuristic Crossover............... \t126 #> \t(9) Local-Minimum Crossover........... \t0 #> #> HARD Maximum Number of Generations: 100 #> Maximum Nonchanging Generations: 10 #> Population size : 1000 #> Convergence Tolerance: 1.000000e-03 #> #> Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation. #> Checking Gradients before Stopping. #> Not Using Out of Bounds Individuals But Allowing Trespassing. #> #> Minimization Problem. #> #> #> Generation#\t Solution Value #> #> 0 \t1.475558e+00 #> 1 \t1.468136e+00 #> #> 'wait.generations' limit reached. #> No significant improvement in 10 generations. #> #> Solution Fitness Value: 1.468136e+00 #> #> Parameters at the Solution (parameter, gradient): #> #> X[ 1] :\t2.151910e+00\tG[ 1] :\t-1.894464e-08 #> X[ 2] :\t6.909672e-01\tG[ 2] :\t2.323783e-06 #> #> Solution Found Generation 1 #> Number of Generations Run 12 #> #> Mon Dec 2 13:33:43 2024 #> Total run time : 0 hours 0 minutes and 1 seconds summary(fit.with.genoud) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1519105 1.2057751 #> scale 0.6909672 0.6528594 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05106547 #> scale 0.05106547 1.00000000 #> # } # (7) estimation of the mean of a normal distribution # by maximum likelihood with the standard deviation fixed at 1 using the argument fix.arg # flnb <- fitdistcens(log10EC50, \"norm\", start = list(mean = 1),fix.arg = list(sd = 1)) # (8) Fit of a lognormal distribution on acute toxicity values of fluazinam for # macroinvertebrates and zooplancton, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of # the fitted distribution (which is called the 5 percent hazardous concentration, HC5, # in ecotoxicology) and estimation of other quantiles. data(fluazinam) log10EC50 <-log10(fluazinam) fln <- fitdistcens(log10EC50,\"norm\") quantile(fln, probs = 0.05) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 #> estimate 0.2414275 quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 0.2414275 0.6655064 1.179033 # (9) Fit of a lognormal distribution on 72-hour acute salinity tolerance (LC50 values) # of riverine macro-invertebrates using maximum likelihood estimation data(salinity) log10LC50 <-log10(salinity) fln <- fitdistcens(log10LC50,\"norm\") plot(fln)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistrplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Overview of the fitdistrplus package — fitdistrplus-package","title":"Overview of the fitdistrplus package — fitdistrplus-package","text":"idea package emerged 2008 collaboration JB Denis, R Pouillot ML Delignette time worked area quantitative risk assessment. implementation package part general project named \"Risk assessment R\" gathering different packages hosted R-forge. fitdistrplus package first written ML Delignette-Muller made available CRAN 2009 presented 2009 useR conference Rennes. months , C Dutang joined project starting participate implementation fitdistrplus package. package also presented 2011 useR conference 2eme rencontres R 2013 (https://r2013-lyon.sciencesconf.org/). Four vignettes available within package: general overview package published Journal Statistical Software (doi:10.18637/jss.v064.i04 ), document answering Frequently Asked Questions, document presenting benchmark optimization algorithms finding parameters, document starting values. fitdistrplus package general package aims helping fit univariate parametric distributions censored non-censored data. two main functions fitdist fit non-censored data fitdistcens fit censored data. choice candidate distributions fit may helped using functions descdist plotdist non-censored data plotdistcens censored data). Using functions fitdist fitdistcens, different methods can used estimate distribution parameters: maximum likelihood estimation default (mledist), moment matching estimation (mmedist), quantile matching estimation (qmedist), maximum goodness--fit estimation (mgedist). classical distributions initial values automatically calculated provided user. Graphical functions plotdist plotdistcens can used help manual calibration initial values parameters non-classical distributions. Function prefit proposed help definition good starting values special case constrained parameters. case maximum likelihood chosen estimation method, function llplot enables visualize loglikelihood contours. goodness--fit fitted distributions (single fit multiple fits) can explored using different graphical functions (cdfcomp, denscomp, qqcomp ppcomp non-censored data cdfcompcens censored data). Goodness--fit statistics also provided non-censored data using function gofstat. Bootstrap proposed quantify uncertainty parameter estimates (functions bootdist bootdistcens) also quantify uncertainty CDF quantiles estimated fitted distribution (quantile CIcdfplot).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fitdistrplus.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Overview of the fitdistrplus package — fitdistrplus-package","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":null,"dir":"Reference","previous_headings":"","what":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"48-hour acute toxicity values (EC50 values) exposure macroinvertebrates zooplancton fluazinam.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"","code":"data(fluazinam)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"fluazinam data frame 2 columns named left right, describing observed EC50 value (micrograms per liter) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value noncensored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"Hose, G.C., Van den Brink, P.J. 2004. species sensitivity distribution approach compared microcosm study: case study fungicide fluazinam. Ecotoxicology Environmental Safety, 73, 109-122.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fluazinam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species-Sensitivity Distribution (SSD) for Fluazinam — fluazinam","text":"","code":"# (1) load of data # data(fluazinam) # (2) plot of data using Turnbull cdf plot # log10EC50 <- log10(fluazinam) plotdistcens(log10EC50) # (3) fit of a lognormal and a logistic distribution to data # (classical distributions used for species sensitivity # distributions, SSD, in ecotoxicology) # and visual comparison of the fits using Turnbull cdf plot # fln <- fitdistcens(log10EC50, \"norm\") summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 2.161449 1.2060732 #> sd 1.167290 0.9842019 #> Loglikelihood: -20.41212 AIC: 44.82424 BIC: 46.10235 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.1350237 #> sd 0.1350237 1.0000000 #> fll <- fitdistcens(log10EC50, \"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 2.1518291 1.2058724 #> scale 0.6910423 0.6530058 #> Loglikelihood: -20.55391 AIC: 45.10781 BIC: 46.38593 #> Correlation matrix: #> location scale #> location 1.00000000 0.05097494 #> scale 0.05097494 1.00000000 #> cdfcompcens(list(fln,fll), legendtext = c(\"normal\", \"logistic\"), xlab = \"log10(EC50)\") # (4) estimation of the 5 percent quantile value of # the normal fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # non parametric bootstrap # with a small number of iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(EC50) bln <- bootdistcens(fln, niter = 101) HC5ln <- quantile(bln, probs = 0.05) # in EC50 10^(HC5ln$quantiles) #> p=0.05 #> estimate 1.743522 10^(HC5ln$quantCI) #> p=0.05 #> 2.5 % 0.3021309 #> 97.5 % 13.5760675 # (5) estimation of the HC5 value # with its one-sided 95 percent confidence interval (type \"greater\") # # in log10(EC50) HC5lnb <- quantile(bln, probs = 0.05, CI.type = \"greater\") # in LC50 10^(HC5lnb$quantiles) #> p=0.05 #> estimate 1.743522 10^(HC5lnb$quantCI) #> p=0.05 #> 5 % 0.4182488"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":null,"dir":"Reference","previous_headings":"","what":"Fictive survival dataset of a french Male population — fremale","title":"Fictive survival dataset of a french Male population — fremale","text":"100 male individuals randomly taken frefictivetable CASdatasets package","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fictive survival dataset of a french Male population — fremale","text":"","code":"data(fremale)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fictive survival dataset of a french Male population — fremale","text":"fremale data frame 3 columns names AgeIn, AgeOut respectively entry age exit age; Death binary dummy: 1 indicating death individual; 0 censored observation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fictive survival dataset of a french Male population — fremale","text":"See full dataset frefictivetable CASdatasets http://dutangc.perso.math.cnrs.fr/RRepository/","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/fremale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fictive survival dataset of a french Male population — fremale","text":"","code":"# (1) load of data # data(fremale) summary(fremale) #> AgeIn AgeOut Death #> Min. :23.87 Min. :30.20 Min. :0.0 #> 1st Qu.:47.29 1st Qu.:53.82 1st Qu.:1.0 #> Median :63.95 Median :69.49 Median :1.0 #> Mean :60.34 Mean :67.00 Mean :0.8 #> 3rd Qu.:72.00 3rd Qu.:80.23 3rd Qu.:1.0 #> Max. :89.17 Max. :97.11 Max. :1.0"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":null,"dir":"Reference","previous_headings":"","what":"Goodness-of-fit statistics — gofstat","title":"Goodness-of-fit statistics — gofstat","text":"Computes goodness--fit statistics parametric distributions fitted censored non-censored data set.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Goodness-of-fit statistics — gofstat","text":"","code":"gofstat(f, chisqbreaks, meancount, discrete, fitnames=NULL) # S3 method for class 'gofstat.fitdist' print(x, ...) # S3 method for class 'gofstat.fitdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Goodness-of-fit statistics — gofstat","text":"f object class \"fitdist\" (\"fitdistcens\" ), output function fitdist() (resp. \"fitdist()\"), \tlist \"fitdist\" objects, list \"fitdistcens\" objects. chisqbreaks usable non censored data, numeric vector defining breaks cells used compute chi-squared statistic. omitted, breaks automatically computed data order reach roughly number observations per cell, roughly equal argument meancount, sligthly ties. meancount usable non censored data, mean number observations per cell expected definition breaks cells used compute chi-squared statistic. argument taken account breaks directly defined argument chisqbreaks. chisqbreaks meancount omitted, meancount fixed order obtain roughly \\((4n)^{2/5}\\) cells \\(n\\) length dataset. discrete TRUE, Chi-squared statistic information criteria computed. \tmissing, discrete passed first object class \"fitdist\" list f. \tcensored data argument ignored, censored data considered continuous. fitnames vector defining names fits. x object class \"gofstat.fitdist\" \"gofstat.fitdistcens\". ... arguments passed generic functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Goodness-of-fit statistics — gofstat","text":"type data (censored ), information criteria calculated. non censored data, added Goodness--fit statistics computed described . Chi-squared statistic computed using cells defined argument chisqbreaks cells automatically defined data, order reach roughly number observations per cell, roughly equal argument meancount, sligthly ties. choice define cells empirical distribution (data), theoretical distribution, done enable comparison Chi-squared values obtained different distributions fitted data set. chisqbreaks meancount omitted, meancount fixed order obtain roughly \\((4n)^{2/5}\\) cells, \\(n\\) length data set (Vose, 2000). Chi-squared statistic computed program fails define enough cells due small dataset. Chi-squared statistic computed, degree freedom (nb cells - nb parameters - 1) corresponding distribution strictly positive, p-value Chi-squared test returned. continuous distributions, Kolmogorov-Smirnov, Cramer-von Mises \tAnderson-Darling statistics also computed, defined Stephens (1986). approximate Kolmogorov-Smirnov test performed assuming distribution parameters known. critical value defined Stephens (1986) completely specified distribution used reject distribution significance level 0.05. approximation, result test (decision rejection distribution ) returned data sets 30 observations. Note approximate test may conservative. data sets 5 observations distributions test described Stephens (1986) maximum likelihood estimations (\"exp\", \"cauchy\", \"gamma\" \"weibull\"), Cramer-von Mises Anderson-darling tests performed described Stephens (1986). tests take account fact parameters known estimated data maximum likelihood. result decision reject distribution significance level 0.05. tests available maximum likelihood estimations. recommended statistics automatically printed, .e. Cramer-von Mises, Anderson-Darling Kolmogorov statistics continuous distributions Chi-squared statistics discrete ones ( \"binom\", \"nbinom\", \"geom\", \"hyper\" \"pois\" ). Results tests printed stored output function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Goodness-of-fit statistics — gofstat","text":"gofstat() returns object class \"gofstat.fitdist\" \"gofstat.fitdistcens\" following components sublist (aic, bic nbfit censored data) , chisq named vector Chi-squared statistics NULL computed chisqbreaks common breaks used define cells Chi-squared statistic chisqpvalue named vector p-values Chi-squared statistic NULL computed chisqdf named vector degrees freedom Chi-squared distribution NULL computed chisqtable table observed theoretical counts used Chi-squared calculations cvm named vector Cramer-von Mises statistics \"computed\" computed cvmtest named vector decisions Cramer-von Mises test \"computed\" computed ad named vector Anderson-Darling statistics \"computed\" computed adtest named vector decisions Anderson-Darling test \"computed\" computed ks named vector Kolmogorov-Smirnov statistic \"computed\" computed kstest named vector decisions Kolmogorov-Smirnov test \"computed\" computed aic named vector values Akaike's Information Criterion. bic named vector values Bayesian Information Criterion. discrete input argument automatic definition function first object class \"fitdist\" list input. nbfit Number fits argument.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Goodness-of-fit statistics — gofstat","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Stephens MA (1986), Tests based edf statistics. Goodness--fit techniques (D'Agostino RB Stephens MA, eds), Marcel Dekker, New York, pp. 97-194. Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Goodness-of-fit statistics — gofstat","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/gofstat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Goodness-of-fit statistics — gofstat","text":"","code":"# (1) fit of two distributions to the serving size data # by maximum likelihood estimation # and comparison of goodness-of-fit statistics # data(groundbeef) serving <- groundbeef$serving (fitg <- fitdist(serving, \"gamma\")) #> Fitting of the distribution ' gamma ' by maximum likelihood #> Parameters: #> estimate Std. Error #> shape 4.00955898 5.44184369 #> rate 0.05443907 0.07868664 gofstat(fitg) #> Goodness-of-fit statistics #> 1-mle-gamma #> Kolmogorov-Smirnov statistic 0.1281486 #> Cramer-von Mises statistic 0.6936274 #> Anderson-Darling statistic 3.5672625 #> #> Goodness-of-fit criteria #> 1-mle-gamma #> Akaike's Information Criterion 2511.250 #> Bayesian Information Criterion 2518.325 (fitln <- fitdist(serving, \"lnorm\")) #> Fitting of the distribution ' lnorm ' by maximum likelihood #> Parameters: #> estimate Std. Error #> meanlog 4.1693701 0.5366095 #> sdlog 0.5366095 0.3794343 gofstat(fitln) #> Goodness-of-fit statistics #> 1-mle-lnorm #> Kolmogorov-Smirnov statistic 0.1493090 #> Cramer-von Mises statistic 0.8277358 #> Anderson-Darling statistic 4.5436542 #> #> Goodness-of-fit criteria #> 1-mle-lnorm #> Akaike's Information Criterion 2526.639 #> Bayesian Information Criterion 2533.713 gofstat(list(fitg, fitln)) #> Goodness-of-fit statistics #> 1-mle-gamma 2-mle-lnorm #> Kolmogorov-Smirnov statistic 0.1281486 0.1493090 #> Cramer-von Mises statistic 0.6936274 0.8277358 #> Anderson-Darling statistic 3.5672625 4.5436542 #> #> Goodness-of-fit criteria #> 1-mle-gamma 2-mle-lnorm #> Akaike's Information Criterion 2511.250 2526.639 #> Bayesian Information Criterion 2518.325 2533.713 # (2) fit of two discrete distributions to toxocara data # and comparison of goodness-of-fit statistics # data(toxocara) number <- toxocara$number fitp <- fitdist(number,\"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) fitnb <- fitdist(number,\"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb) gofstat(list(fitp, fitnb),fitnames = c(\"Poisson\",\"negbin\")) #> Chi-squared statistic: 31256.96 7.48606 #> Degree of freedom of the Chi-squared distribution: 5 4 #> Chi-squared p-value: 0 0.1123255 #> the p-value may be wrong with some theoretical counts < 5 #> Chi-squared table: #> obscounts theo Poisson theo negbin #> <= 0 14 0.009014207 15.295027 #> <= 1 8 0.078236515 5.808596 #> <= 3 6 1.321767253 6.845015 #> <= 4 6 2.131297825 2.407815 #> <= 9 6 29.827829425 7.835196 #> <= 21 6 19.626223437 8.271110 #> > 21 7 0.005631338 6.537242 #> #> Goodness-of-fit criteria #> Poisson negbin #> Akaike's Information Criterion 1017.067 322.6882 #> Bayesian Information Criterion 1019.037 326.6288 # (3) Get Chi-squared results in addition to # recommended statistics for continuous distributions # set.seed(1234) x4 <- rweibull(n=1000,shape=2,scale=1) # fit of the good distribution f4 <- fitdist(x4,\"weibull\") plot(f4) # fit of a bad distribution f4b <- fitdist(x4,\"cauchy\") plot(f4b) (g <- gofstat(list(f4,f4b),fitnames=c(\"Weibull\", \"Cauchy\"))) #> Goodness-of-fit statistics #> Weibull Cauchy #> Kolmogorov-Smirnov statistic 0.02129364 0.114565 #> Cramer-von Mises statistic 0.06261917 1.854791 #> Anderson-Darling statistic 0.43120643 17.929123 #> #> Goodness-of-fit criteria #> Weibull Cauchy #> Akaike's Information Criterion 1225.734 1679.028 #> Bayesian Information Criterion 1235.549 1688.843 g$chisq #> Weibull Cauchy #> 35.76927 306.99824 g$chisqdf #> Weibull Cauchy #> 25 25 g$chisqpvalue #> Weibull Cauchy #> 7.517453e-02 2.364550e-50 g$chisqtable #> obscounts theo Weibull theo Cauchy #> <= 0.1547 36 27.86449 131.86592 #> <= 0.2381 36 34.87234 16.94381 #> <= 0.2952 36 30.58611 14.10775 #> <= 0.3745 36 50.14472 24.12899 #> <= 0.4323 36 41.16340 21.90706 #> <= 0.4764 36 33.55410 19.88887 #> <= 0.5263 36 39.57636 26.45041 #> <= 0.5771 36 41.67095 32.12597 #> <= 0.6276 36 42.36588 37.99145 #> <= 0.669 36 35.03524 35.92961 #> <= 0.7046 36 30.15737 34.26649 #> <= 0.7447 36 33.82481 41.80511 #> <= 0.7779 36 27.74805 36.41317 #> <= 0.8215 36 35.88169 48.69182 #> <= 0.8582 36 29.58833 40.27626 #> <= 0.9194 36 47.80044 62.45332 #> <= 0.9662 36 35.04387 42.03891 #> <= 1.017 36 36.19084 39.23047 #> <= 1.08 36 42.46698 40.45810 #> <= 1.119 36 24.49715 20.76625 #> <= 1.169 36 29.68482 22.91028 #> <= 1.237 36 36.49226 25.22891 #> <= 1.294 36 27.94301 17.49247 #> <= 1.418 36 51.25543 29.00440 #> <= 1.5 36 27.82405 14.64740 #> <= 1.65 36 38.72011 20.11799 #> <= 1.892 36 37.73807 21.69844 #> > 1.892 28 30.30916 81.16036 # and by defining the breaks (g <- gofstat(list(f4,f4b), chisqbreaks = seq(from = min(x4), to = max(x4), length.out = 10), fitnames=c(\"Weibull\", \"Cauchy\"))) #> Goodness-of-fit statistics #> Weibull Cauchy #> Kolmogorov-Smirnov statistic 0.02129364 0.114565 #> Cramer-von Mises statistic 0.06261917 1.854791 #> Anderson-Darling statistic 0.43120643 17.929123 #> #> Goodness-of-fit criteria #> Weibull Cauchy #> Akaike's Information Criterion 1225.734 1679.028 #> Bayesian Information Criterion 1235.549 1688.843 g$chisq #> Weibull Cauchy #> 6.532102 303.031817 g$chisqdf #> Weibull Cauchy #> 8 8 g$chisqpvalue #> Weibull Cauchy #> 5.878491e-01 9.318101e-61 g$chisqtable #> obscounts theo Weibull theo Cauchy #> <= 0.0264 1 0.9414531 111.941831 #> <= 0.3374 123 118.0587149 63.070591 #> <= 0.6483 222 240.3305518 167.852511 #> <= 0.9593 261 252.4491129 318.542341 #> <= 1.27 204 191.1128355 165.083876 #> <= 1.581 111 112.9380271 62.221846 #> <= 1.892 49 53.8525607 30.121634 #> <= 2.203 19 21.0847217 17.463676 #> <= 2.514 6 6.8505892 11.335604 #> <= 2.825 4 1.8602036 7.933114 #> > 2.825 0 0.5212296 44.432977 # (4) fit of two distributions on acute toxicity values # of fluazinam (in decimal logarithm) for # macroinvertebrates and zooplancton # and comparison of goodness-of-fit statistics # data(fluazinam) log10EC50 <-log10(fluazinam) (fln <- fitdistcens(log10EC50,\"norm\")) #> Fitting of the distribution ' norm ' on censored data by maximum likelihood #> Parameters: #> estimate #> mean 2.161449 #> sd 1.167290 plot(fln) gofstat(fln) #> #> Goodness-of-fit criteria #> 1-mle-norm #> Akaike's Information Criterion 44.82424 #> Bayesian Information Criterion 46.10235 (fll <- fitdistcens(log10EC50,\"logis\")) #> Fitting of the distribution ' logis ' on censored data by maximum likelihood #> Parameters: #> estimate #> location 2.1518291 #> scale 0.6910423 plot(fll) gofstat(fll) #> #> Goodness-of-fit criteria #> 1-mle-logis #> Akaike's Information Criterion 45.10781 #> Bayesian Information Criterion 46.38593 gofstat(list(fll, fln), fitnames = c(\"loglogistic\", \"lognormal\")) #> #> Goodness-of-fit criteria #> loglogistic lognormal #> Akaike's Information Criterion 45.10781 44.82424 #> Bayesian Information Criterion 46.38593 46.10235"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"cdfcomp plots empirical cumulative distribution fitted distribution functions, denscomp plots histogram fitted density functions, qqcomp plots theoretical quantiles empirical ones, ppcomp plots theoretical probabilities empirical ones. cdfcomp able plot fits discrete distribution.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"","code":"cdfcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datapch, datacol, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, horizontals = TRUE, verticals = FALSE, do.points = TRUE, use.ppoints = TRUE, a.ppoints = 0.5, name.points = NULL, lines01 = FALSE, discrete, add = FALSE, plotstyle = \"graphics\", fitnbpts = 101, ...) denscomp(ft, xlim, ylim, probability = TRUE, main, xlab, ylab, datacol, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"topright\", ylegend = NULL, demp = FALSE, dempcol = \"black\", plotstyle = \"graphics\", discrete, fitnbpts = 101, fittype=\"l\", ...) qqcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, plotstyle = \"graphics\", ...) ppcomp(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fitpch, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, use.ppoints = TRUE, a.ppoints = 0.5, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"ft One \"fitdist\" object list objects class \"fitdist\". xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot. See also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datapch integer specifying symbol used plotting data points. See also par. datacol specification color used plotting data points. See also par. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. See also par. fitlty (vector ) line type(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions/densities. fewer values fits recycled standard fashion. See also par. fitpch (vector ) line type(s) plot fitted quantiles/probabilities. fewer values fits recycled standard fashion. See also par. fittype type plot fitted probabilities case discrete distributions: possible types \"p\" points, \"l\" lines \"o\" overplotted (plot.default). fittype used non-discrete distributions. fitnbpts numeric number points compute fitted probabilities cumulative probabilities. Default 101. addlegend TRUE, legend added plot. legendtext character expression vector length \\(\\ge 1\\) appear legend. See also legend. xlegend, ylegend \\(x\\) \\(y\\) coordinates used position legend. can specified keyword. plotstyle = \"graphics\", see xy.coords legend. plotstyle = \"ggplot\", xlegend keyword must one top, bottom, left, right. See also guide_legend ggplot2 horizontals TRUE, draws horizontal lines step empirical cumulative distribution function (ecdf). See also plot.stepfun. verticals TRUE, draws vertical lines empirical cumulative distribution function (ecdf). taken account horizontals=TRUE. .points TRUE (default), draws points x-locations. large dataset (n > 1e4), .points ignored point drawn. use.ppoints TRUE, probability points empirical distribution defined using function ppoints (1:n - .ppoints)/(n - 2a.ppoints + 1). FALSE, probability points simply defined (1:n)/n. argument ignored discrete data. .ppoints use.ppoints=TRUE, passed ppoints function. name.points Label vector points drawn .e. .points = TRUE (non censored data). lines01 logical plot two horizontal lines h=0 h=1 cdfcomp. line01 logical plot horizontal line \\(y=x\\) qqcomp ppcomp. line01col, line01lty Color line type line01. See also par. demp logical add empirical density plot, using density function. dempcol color empirical density case added plot (demp=TRUE). ynoise logical add small noise plotting empirical quantiles/probabilities qqcomp ppcomp. probability logical use probability scale denscomp. See also hist. discrete TRUE, distributions considered discrete. missing, discrete set TRUE least one object list ft discrete. add TRUE, adds already existing plot. FALSE, starts new plot. parameter available plotstyle = \"ggplot\". plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). ... graphical arguments passed graphical functions used cdfcomp, denscomp, ppcomp qqcomp plotstyle = \"graphics\". plotstyle = \"ggplot\", arguments used histogram plot (hist) denscomp function. plotstyle = \"ggplot\", graphical output can customized relevant ggplot2 functions store output.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"cdfcomp provides plot empirical distribution fitted distribution cdf, default using Hazen's rule empirical distribution, probability points defined (1:n - 0.5)/n. discrete TRUE, probability points always defined (1:n)/n. large dataset (n > 1e4), point drawn line ecdf drawn instead. Note horizontals, verticals .points FALSE, empirical point drawn, fitted cdf shown. denscomp provides density plot fitted distribution histogram data conyinuous data. discrete=TRUE, distributions considered discrete, histogram plotted demp forced TRUE fitted empirical probabilities plotted either vertical lines fittype=\"l\", single points fittype=\"p\" lines points fittype=\"o\". ppcomp provides plot probabilities fitted distribution (\\(x\\)-axis) empirical probabilities (\\(y\\)-axis) default defined (1:n - 0.5)/n (data assumed continuous). large dataset (n > 1e4), lines drawn instead pointss customized fitpch parameter. qqcomp provides plot quantiles theoretical distribution (\\(x\\)-axis) empirical quantiles data (\\(y\\)-axis), default defining probability points (1:n - 0.5)/n theoretical quantile calculation (data assumed continuous). large dataset (n > 1e4), lines drawn instead points customized fitpch parameter. default legend added plots. Many graphical arguments optional, dedicated personalize plots, fixed default values omitted.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"*comp returns list drawn points /lines plotstyle == \"graphics\" object class \"ggplot\" plotstyle == \"ggplot\".","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"Christophe Dutang, Marie-Laure Delignette-Muller Aurelie Siberchicot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcomp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graphical comparison of multiple fitted distributions (for non-censored data) — graphcomp","text":"","code":"# (1) Plot various distributions fitted to serving size data # data(groundbeef) serving <- groundbeef$serving fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") cdfcomp(list(fitW, fitln, fitg), horizontals = FALSE) cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE) cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, verticals = TRUE, datacol = \"purple\") cdfcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", ylab = \"F\", xlim = c(0, 250), xlegend = \"center\", lines01 = TRUE) denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\") ppcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlegend = \"bottomright\", line01 = TRUE) qqcomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlegend = \"bottomright\", line01 = TRUE, xlim = c(0, 300), ylim = c(0, 300), fitpch = 16) # (2) Plot lognormal distributions fitted by # maximum goodness-of-fit estimation # using various distances (data plotted in log scale) # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV taxaATV <- subset(endosulfan, group == \"NonArthroInvert\")$taxa flnMGEKS <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"KS\") flnMGEAD <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD\") flnMGEADL <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"ADL\") flnMGEAD2L <- fitdist(ATV, \"lnorm\", method = \"mge\", gof = \"AD2L\") cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), xlogscale = TRUE, main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\"), verticals = TRUE, xlim = c(1, 100000), name.points=taxaATV) qqcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\"), xlogscale = TRUE, ylogscale = TRUE) ppcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), main = \"fits of a lognormal dist. using various GOF dist.\", legendtext = c(\"MGE KS\", \"MGE AD\", \"MGE ADL\", \"MGE AD2L\")) # (3) Plot normal and logistic distributions fitted by # maximum likelihood estimation # using various plotting positions in cdf plots # data(endosulfan) log10ATV <-log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") fll <- fitdist(log10ATV, \"logis\") # default plot using Hazen plotting position: (1:n - 0.5)/n cdfcomp(list(fln, fll), legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\") # plot using mean plotting position (named also Gumbel plotting position) # (1:n)/(n + 1) cdfcomp(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\", use.ppoints = TRUE, a.ppoints = 0) # plot using basic plotting position: (1:n)/n cdfcomp(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10ATV\", use.ppoints = FALSE) # (4) Comparison of fits of two distributions fitted to discrete data # data(toxocara) number <- toxocara$number fitp <- fitdist(number, \"pois\") fitnb <- fitdist(number, \"nbinom\") cdfcomp(list(fitp, fitnb), legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"l\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"p\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) denscomp(list(fitp, fitnb),demp = TRUE, fittype = \"o\", dempcol = \"black\", legendtext = c(\"Poisson\", \"negative binomial\")) # (5) Customizing of graphical output and use of ggplot2 # data(groundbeef) serving <- groundbeef$serving fitW <- fitdist(serving, \"weibull\") fitln <- fitdist(serving, \"lnorm\") fitg <- fitdist(serving, \"gamma\") if (requireNamespace (\"ggplot2\", quietly = TRUE)) { denscomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") cdfcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") qqcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") ppcomp(list(fitW, fitln, fitg), plotstyle = \"ggplot\") } # customizing graphical output with graphics denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), main = \"ground beef fits\", xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\", addlegend = FALSE) # customizing graphical output with ggplot2 if (requireNamespace (\"ggplot2\", quietly = TRUE)) { dcomp <- denscomp(list(fitW, fitln, fitg), legendtext = c(\"Weibull\", \"lognormal\", \"gamma\"), xlab = \"serving sizes (g)\", xlim = c(0, 250), xlegend = \"topright\", plotstyle = \"ggplot\", breaks = 20, addlegend = FALSE) dcomp + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Ground beef fits\") }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"cdfcompcens plots empirical cumulative distribution fitted distribution functions, qqcompcens plots theoretical quantiles empirical ones, ppcompcens plots theoretical probabilities empirical ones.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"","code":"cdfcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, datacol, fillrect, fitlty, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, lines01 = FALSE, Turnbull.confint = FALSE, NPMLE.method = \"Wang\", add = FALSE, plotstyle = \"graphics\", ...) qqcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, NPMLE.method = \"Wang\", plotstyle = \"graphics\", ...) ppcompcens(ft, xlim, ylim, xlogscale = FALSE, ylogscale = FALSE, main, xlab, ylab, fillrect, fitcol, fitlwd, addlegend = TRUE, legendtext, xlegend = \"bottomright\", ylegend = NULL, line01 = TRUE, line01col = \"black\", line01lty = 1, ynoise = TRUE, NPMLE.method = \"Wang\", plotstyle = \"graphics\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"ft One \"fitdistcens\" object list objects class \"fitdistcens\". xlim \\(x\\)-limits plot. ylim \\(y\\)-limits plot. xlogscale TRUE, uses logarithmic scale \\(x\\)-axis. ylogscale TRUE, uses logarithmic scale \\(y\\)-axis. main main title plot, see also title. xlab label \\(x\\)-axis, defaults description x. ylab label \\(y\\)-axis, defaults description y. datacol specification color used plotting data points. fillrect specification color used filling rectanges non uniqueness empirical cumulative distribution (used NPMLE.method equal \"Wang\" cdfcompcens). Fix NA want fill rectangles. fitcol (vector ) color(s) plot fitted distributions. fewer colors fits recycled standard fashion. fitlty (vector ) line type(s) plot fitted distributions. fewer values fits recycled standard fashion. See also par. fitlwd (vector ) line size(s) plot fitted distributions. fewer values fits recycled standard fashion. See also par. addlegend TRUE, legend added plot. legendtext character expression vector length \\(\\geq 1\\) appear legend, see also legend. xlegend, ylegend \\(x\\) \\(y\\) coordinates used position legend. can specified keyword. plotstyle = \"graphics\", see xy.coords legend. plotstyle = \"ggplot\", xlegend keyword must one top, bottom, left, right. See also guide_legend ggplot2 lines01 logical plot two horizontal lines h=0 h=1 cdfcompcens. Turnbull.confint TRUE confidence intervals added Turnbull plot. case NPMLE.method forced \"Turnbull\" NPMLE.method Three NPMLE techniques provided, \"Wang\", default one, rewritten package npsurv using function constrOptim package stats optimisation, \"Turnbull.middlepoints\", older one implemented package survival \"Turnbull.intervals\" uses Turnbull algorithm package survival associates interval equivalence class instead middlepoint interval (see details). \"Wang\" \"Turnbull.intervals\" enable derivation Q-Q plot P-P plot. add TRUE, adds already existing plot. FALSE, starts new plot. parameter available plotstyle = \"ggplot\". line01 logical plot horizontal line \\(y=x\\) qqcompcens ppcompcens. line01col, line01lty Color line type line01. See also par. ynoise logical add small noise plotting empirical quantiles/probabilities qqcompcens ppcompcens. ynoise used various fits plotted \"graphics\" plotstyle. Facets used instead \"ggplot\" plotstyle. plotstyle \"graphics\" \"ggplot\". \"graphics\", display built graphics functions. \"ggplot\", graphic object output created ggplot2 functions (ggplot2 package must installed). \"cdfcompcens\", \"ggplot\" graphics available \"Wang\" NPMLE technique. ... graphical arguments passed graphical functions used cdfcompcens, ppcompcens qqcompcens.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"See details plotdistcens detailed description provided goddness--fit plots.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"Turnbull BW (1974), Nonparametric estimation survivorship function doubly censored data. Journal American Statistical Association, 69, 169-173. Wang Y (2008), Dimension-reduced nonparametric maximum likelihood computation interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402. Wang Y Taylor SM (2013), Efficient computation nonparametric survival functions via hierarchical mixture formulation. Statistics Computing, 23, 713-725. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/graphcompcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graphical comparison of multiple fitted distributions for censored data — graphcompcens","text":"","code":"# (1) Plot various distributions fitted to bacterial contamination data # data(smokedfish) Clog10 <- log10(smokedfish) fitsfn <- fitdistcens(Clog10,\"norm\") summary(fitsfn) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean -1.575392 2.043857 #> sd 1.539446 2.149561 #> Loglikelihood: -87.10945 AIC: 178.2189 BIC: 183.4884 #> Correlation matrix: #> mean sd #> mean 1.0000000 -0.4325228 #> sd -0.4325228 1.0000000 #> fitsfl <- fitdistcens(Clog10,\"logis\") summary(fitsfl) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location -1.5394230 1.706269 #> scale 0.8121862 1.352708 #> Loglikelihood: -86.45499 AIC: 176.91 BIC: 182.1794 #> Correlation matrix: #> location scale #> location 1.0000000 -0.3189915 #> scale -0.3189915 1.0000000 #> dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) fitsfg<-fitdistcens(Clog10,\"gumbel\",start=list(a=-3,b=3)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. summary(fitsfg) #> Error: object 'fitsfg' not found # CDF plot cdfcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol=\"orange\",fillrect = NA, legendtext=c(\"normal\",\"logistic\",\"Gumbel\"), main=\"bacterial contamination fits\", xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", xlegend = \"bottom\",lines01 = TRUE) #> Error: object 'fitsfg' not found # alternative Turnbull plot for the empirical cumulative distribution # (default plot of the previous versions of the package) cdfcompcens(list(fitsfn,fitsfl,fitsfg), NPMLE.method = \"Turnbull.middlepoints\") #> Error: object 'fitsfg' not found # customizing graphical output with ggplot2 if (requireNamespace (\"ggplot2\", quietly = TRUE)) { cdfcompcens <- cdfcompcens(list(fitsfn,fitsfl,fitsfg),datacol=\"orange\",fillrect = NA, legendtext=c(\"normal\",\"logistic\",\"Gumbel\"), xlab=\"bacterial concentration (CFU/g)\",ylab=\"F\", xlegend = \"bottom\",lines01 = TRUE, plotstyle = \"ggplot\") cdfcompcens + ggplot2::theme_minimal() + ggplot2::ggtitle(\"Bacterial contamination fits\") } #> Error: object 'fitsfg' not found # PP plot ppcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found ppcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE) #> Error: object 'fitsfg' not found par(mfrow = c(2,2)) ppcompcens(fitsfn) ppcompcens(fitsfl) ppcompcens(fitsfg) #> Error: object 'fitsfg' not found par(mfrow = c(1,1)) if (requireNamespace (\"ggplot2\", quietly = TRUE)) { ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = \"ggplot\") ppcompcens(list(fitsfn,fitsfl,fitsfg), plotstyle = \"ggplot\", fillrect = c(\"lightpink\", \"lightblue\", \"lightgreen\"), fitcol = c(\"red\", \"blue\", \"green\")) } #> Error: object 'fitsfg' not found # QQ plot qqcompcens(list(fitsfn,fitsfl,fitsfg)) #> Error: object 'fitsfg' not found qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE) #> Error: object 'fitsfg' not found par(mfrow = c(2,2)) qqcompcens(fitsfn) qqcompcens(fitsfl) qqcompcens(fitsfg) #> Error: object 'fitsfg' not found par(mfrow = c(1,1)) if (requireNamespace (\"ggplot2\", quietly = TRUE)) { qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = \"ggplot\") qqcompcens(list(fitsfn,fitsfl,fitsfg), ynoise = FALSE, plotstyle = \"ggplot\", fillrect = c(\"lightpink\", \"lightblue\", \"lightgreen\"), fitcol = c(\"red\", \"blue\", \"green\")) } #> Error: object 'fitsfg' not found"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":null,"dir":"Reference","previous_headings":"","what":"Ground beef serving size data set — groundbeef","title":"Ground beef serving size data set — groundbeef","text":"Serving sizes collected French survey, ground beef patties consumed children 5 years old.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ground beef serving size data set — groundbeef","text":"","code":"data(groundbeef)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ground beef serving size data set — groundbeef","text":"groundbeef data frame 1 column (serving: serving sizes grams)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ground beef serving size data set — groundbeef","text":"Delignette-Muller, M.L., Cornu, M. 2008. Quantitative risk assessment Escherichia coli O157:H7 frozen ground beef patties consumed young children French households. International Journal Food Microbiology, 128, 158-164.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/groundbeef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ground beef serving size data set — groundbeef","text":"","code":"# (1) load of data # data(groundbeef) # (2) description and plot of data # serving <- groundbeef$serving descdist(serving) #> summary statistics #> ------ #> min: 10 max: 200 #> median: 79 #> mean: 73.64567 #> estimated sd: 35.88487 #> estimated skewness: 0.7352745 #> estimated kurtosis: 3.551384 plotdist(serving) # (3) fit of a Weibull distribution to data # fitW <- fitdist(serving, \"weibull\") summary(fitW) #> Fitting of the distribution ' weibull ' by maximum likelihood #> Parameters : #> estimate Std. Error #> shape 2.185885 1.66666 #> scale 83.347679 40.27156 #> Loglikelihood: -1255.225 AIC: 2514.449 BIC: 2521.524 #> Correlation matrix: #> shape scale #> shape 1.000000 0.321821 #> scale 0.321821 1.000000 #> plot(fitW) gofstat(fitW) #> Goodness-of-fit statistics #> 1-mle-weibull #> Kolmogorov-Smirnov statistic 0.1396646 #> Cramer-von Mises statistic 0.6840994 #> Anderson-Darling statistic 3.5736460 #> #> Goodness-of-fit criteria #> 1-mle-weibull #> Akaike's Information Criterion 2514.449 #> Bayesian Information Criterion 2521.524"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":null,"dir":"Reference","previous_headings":"","what":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"llplot plots (log)likelihood around estimation distributions fitted maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"","code":"llplot(mlefit, loglik = TRUE, expansion = 1, lseq = 50, back.col = TRUE, nlev = 10, pal.col = terrain.colors(100), fit.show = FALSE, fit.pch = 4, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"mlefit object class \"fitdist\" \"fitdistcens\" obtained maximum likelihood (method = \"mle\") loglik logical plot log-likelihood likelihood function. expansion expansion factor enlarge default range values explored parameter. lseq length sequences parameters. back.col logical (llsurface ). Contours plotted background gradient colors TRUE. nlev number contour levels plot. pal.col Palette colors. Colors used back (llsurface ). fit.show logical plot mle estimate. fit.pch type point used plot mle estimate. ... graphical arguments passed graphical functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"llplot plots (log)likelihood surface(s) (curve one estimated parameter) around maximum likelihood estimation. internally calls function llsurface llcurve. two estimated parameters, (log)likehood surface plotted combination two parameters, fixing ones estimated value. (log)likelihood surface, back.col image (2D-plot) used nlev > 0 contour (2D-plot) used add nlev contours. default range values explored estimated parameter 2 standard error around mle estimate range can expanded (contracted) using argument expansion.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Log)likelihood plot for a fit using maximum likelihood — logLikplot","text":"","code":"# (1) a distribution with one parameter # x <- rexp(50) fite <- fitdist(x, \"exp\") llplot(fite) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fite, col = \"red\", fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fite, col = \"red\", fit.show = TRUE, loglik = FALSE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # (2) a distribution with two parameters # data(groundbeef) serving <- groundbeef$serving fitg <- fitdist(serving, \"gamma\") llplot(fitg) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # \\donttest{ llplot(fitg, expansion = 2) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fitg, pal.col = heat.colors(100), fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fitg, back.col = FALSE, nlev = 25, fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # } # (3) a distribution with two parameters with one fixed # fitg2 <- fitdist(serving, \"gamma\", fix.arg = list(rate = 0.5)) llplot(fitg2, fit.show = TRUE) # (4) a distribution with three parameters # # \\donttest{ data(endosulfan) ATV <-endosulfan$ATV require(\"actuar\") fBurr <- fitdist(ATV, \"burr\", start = list(shape1 = 0.3, shape2 = 1, rate = 1)) llplot(fBurr) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fBurr, back.col = FALSE, fit.show = TRUE, fit.pch = 16) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fBurr, nlev = 0, pal.col = rainbow(100), lseq = 100) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced # } # (5) a distribution with two parameters fitted on censored data # data(salinity) fsal <- fitdistcens(salinity, \"lnorm\") llplot(fsal, fit.show = TRUE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced llplot(fsal, fit.show = TRUE, loglik = FALSE) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":null,"dir":"Reference","previous_headings":"","what":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"llsurface plots likelihood surface distributions two parameters, llcurve plots likelihood curve distributions one parameters.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"","code":"llsurface(data, distr, plot.arg, min.arg, max.arg, lseq = 50, fix.arg = NULL, loglik = TRUE, back.col = TRUE, nlev = 10, pal.col = terrain.colors(100), weights = NULL, ...) llcurve(data, distr, plot.arg, min.arg, max.arg, lseq = 50, fix.arg = NULL, loglik = TRUE, weights = NULL, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"data numeric vector non censored data dataframe two columns respectively named left right, describing observed value interval censored data. case left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution corresponding density function dname corresponding distribution function pname must classically defined. plot.arg two-element vector names two parameters vary llsurface, one element llcurve. min.arg two-element vector lower plotting bounds llsurface, one element llcurve. max.arg two-element vector upper plotting bounds llsurface, one element llcurve. lseq length sequences parameters. fix.arg named list fixed value parameters. loglik logical plot log-likelihood likelihood function. back.col logical (llsurface ). Contours plotted background gradient colors TRUE. nlev number contour levels plot (llsurface ). pal.col Palette colors. Colors used back (llsurface ). weights optional vector weights used fitting process. NULL numeric vector strictly positive values (classically number occurences observation). ... graphical arguments passed graphical functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"two function intended called directly internally called llplot. llsurface plots likelihood surface distributions two varying parameters parameters fixed. back.col, image (2D-plot) used. nlev > 0, contour (2D-plot) used add nlev contours. llcurve plots likelihood curve distributions one varying parameter parameters fixed.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/logLik-surface.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Log)likelihood surfaces or (log)likelihood curves — logLiksurface","text":"","code":"# (1) loglikelihood or likelihood curve # n <- 100 set.seed(1234) x <- rexp(n) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4, loglik = FALSE) llcurve(data = x, distr = \"exp\", plot.arg = \"rate\", min.arg = 0, max.arg = 4, main = \"log-likelihood for exponential distribution\", col = \"red\") abline(v = 1, lty = 2) # (2) loglikelihood surface # x <- rnorm(n, 0, 1) llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), back.col = FALSE, main=\"log-likelihood for normal distribution\") llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), main=\"log-likelihood for normal distribution\", nlev = 20, pal.col = heat.colors(100),) points(0, 1, pch=\"+\", col=\"red\") llsurface(data =x, distr=\"norm\", plot.arg=c(\"mean\", \"sd\"), min.arg=c(-1, 0.5), max.arg=c(1, 3/2), main=\"log-likelihood for normal distribution\", nlev = 0, back.col = TRUE, pal.col = rainbow(100, s = 0.5, end = 0.8)) points(0, 1, pch=\"+\", col=\"black\")"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Fit univariate continuous distribution maximizing goodness--fit (minimizing distance) non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"","code":"mgedist(data, distr, gof = \"CvM\", start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. gof character string coding name goodness--fit distance used : \"CvM\" Cramer-von Mises distance, \"KS\" Kolmogorov-Smirnov distance, \"AD\" Anderson-Darling distance, \"ADR\", \"ADL\", \"AD2R\", \"AD2L\" \"AD2\" variants Anderson-Darling distance described Luceno (2006). start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. silent logical remove show warnings bootstraping. gradient function return gradient gof distance \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"mgedist function numerically maximizes goodness--fit, minimizes goodness--fit distance coded argument gof. One may use one classical distances defined Stephens (1986), Cramer-von Mises distance (\"CvM\"), Kolmogorov-Smirnov distance (\"KS\") Anderson-Darling distance (\"AD\") gives weight tails distribution, one variants last distance proposed Luceno (2006). right-tail AD (\"ADR\") gives weight right tail, left-tail AD (\"ADL\") gives weight left tail. Either tails, , can receive even larger weights using second order Anderson-Darling Statistics (using \"AD2R\", \"AD2L\" \"AD2\"). optimization process mledist, see 'details' section function. function intended called directly internally called fitdist bootdist. function intended used continuous distributions weighted maximum goodness--fit estimation allowed. NB: data values particularly small large, scaling may needed optimization process. See example (4).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"mgedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. gof code goodness--fit distance maximized.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Luceno (2006), Fitting generalized Pareto distribution data using maximum goodness--fit estimators. Computational Statistics Data Analysis, 51, 904-917, doi:10.1016/j.csda.2005.09.011 . Stephens MA (1986), Tests based edf statistics. Goodness--fit techniques (D'Agostino RB Stephens MA, eds), Marcel Dekker, New York, pp. 97-194. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mgedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum goodness-of-fit fit of univariate continuous distributions — mgedist","text":"","code":"# (1) Fit of a Weibull distribution to serving size data by maximum # goodness-of-fit estimation using all the distances available # data(groundbeef) serving <- groundbeef$serving mgedist(serving, \"weibull\", gof=\"CvM\") #> $estimate #> shape scale #> 2.093204 82.660014 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.002581367 #> #> $hessian #> shape scale #> shape 0.0159565105 3.639558e-04 #> scale 0.0003639558 9.522745e-05 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 65 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.623 #> #> $gof #> [1] \"CvM\" #> mgedist(serving, \"weibull\", gof=\"KS\") #> $estimate #> shape scale #> 2.065634 81.450487 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.112861 #> #> $hessian #> shape scale #> shape 122.668263 6.509057 #> scale 6.509057 7.599584 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 127 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.975 #> #> $gof #> [1] \"KS\" #> mgedist(serving, \"weibull\", gof=\"AD\") #> $estimate #> shape scale #> 2.125425 82.890502 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0137836 #> #> $hessian #> shape scale #> shape 0.1158157367 0.0007180241 #> scale 0.0007180241 0.0005332051 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 67 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.393 #> #> $gof #> [1] \"AD\" #> mgedist(serving, \"weibull\", gof=\"ADR\") #> $estimate #> shape scale #> 2.072035 82.762593 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.006340469 #> #> $hessian #> shape scale #> shape 0.053243854 -0.0013083937 #> scale -0.001308394 0.0003140377 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.837 #> #> $gof #> [1] \"ADR\" #> mgedist(serving, \"weibull\", gof=\"ADL\") #> $estimate #> shape scale #> 2.197498 82.016005 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.007267475 #> #> $hessian #> shape scale #> shape 0.060343316 0.0021420124 #> scale 0.002142012 0.0002184993 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 65 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1255.415 #> #> $gof #> [1] \"ADL\" #> mgedist(serving, \"weibull\", gof=\"AD2R\") #> $estimate #> shape scale #> 1.90328 81.33464 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.04552816 #> #> $hessian #> shape scale #> shape 1.31736538 -0.041034447 #> scale -0.04103445 0.002056365 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1259.112 #> #> $gof #> [1] \"AD2R\" #> mgedist(serving, \"weibull\", gof=\"AD2L\") #> $estimate #> shape scale #> 2.483836 78.252113 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0385314 #> #> $hessian #> shape scale #> shape 0.44689737 0.0161843919 #> scale 0.01618439 0.0009217762 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1265.933 #> #> $gof #> [1] \"AD2L\" #> mgedist(serving, \"weibull\", gof=\"AD2\") #> $estimate #> shape scale #> 2.081168 85.281194 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.1061089 #> #> $hessian #> shape scale #> shape 2.10614403 -0.04170905 #> scale -0.04170905 0.00299467 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 69 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1256.313 #> #> $gof #> [1] \"AD2\" #> # (2) Fit of a uniform distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # set.seed(1234) u <- runif(100,min=5,max=10) mgedist(u,\"unif\",gof=\"CvM\") #> $estimate #> min max #> 4.788260 9.568912 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.001142423 #> #> $hessian #> min max #> min 0.02906956 0.01461523 #> max 0.01461523 0.02570923 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 59 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"CvM\" #> mgedist(u,\"unif\",gof=\"KS\") #> $estimate #> min max #> 4.664535 9.463995 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.08 #> #> $hessian #> min max #> min 43.06566 -33.35097 #> max -33.35097 -61.06933 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 29 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"KS\" #> # (3) Fit of a triangular distribution using Cramer-von Mises or # Kolmogorov-Smirnov distance # # \\donttest{ require(\"mc2d\") set.seed(1234) t <- rtriang(100,min=5,mode=6,max=10) mgedist(t,\"triang\",start = list(min=4, mode=6,max=9),gof=\"CvM\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. #> $estimate #> min mode max #> 5.051036 5.796428 9.391579 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.0006428299 #> #> $hessian #> min mode max #> min 0.03051858 0.03248860 0.01522501 #> mode 0.03248860 0.03821007 0.01800899 #> max 0.01522501 0.01800899 0.01593900 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 106 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"CvM\" #> mgedist(t,\"triang\",start = list(min=4, mode=6,max=9),gof=\"KS\") #> Warning: Some parameter names have no starting/fixed value but have a default value: mean. #> $estimate #> min mode max #> 4.939094 5.813200 9.248592 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.06191245 #> #> $hessian #> min mode max #> min 158.93759 158.9436 70.39038 #> mode 158.94358 199.0473 70.39510 #> max 70.39038 70.3951 106.08995 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 268 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -Inf #> #> $gof #> [1] \"KS\" #> # } # (4) scaling problem # the simulated dataset (below) has particularly small values, hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 6:0) cat(i, try(mgedist(x*10^i,\"cauchy\")$estimate, silent=TRUE), \"\\n\") #> 6 Error in eval(expr, envir) : object 'x' not found #> #> 5 Error in eval(expr, envir) : object 'x' not found #> #> 4 Error in eval(expr, envir) : object 'x' not found #> #> 3 Error in eval(expr, envir) : object 'x' not found #> #> 2 Error in eval(expr, envir) : object 'x' not found #> #> 1 Error in eval(expr, envir) : object 'x' not found #> #> 0 Error in eval(expr, envir) : object 'x' not found #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum likelihood fit of univariate distributions — mledist","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Fit univariate distributions using maximum likelihood censored non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum likelihood fit of univariate distributions — mledist","text":"","code":"mledist(data, distr, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum likelihood fit of univariate distributions — mledist","text":"data numeric vector non censored data dataframe two columns respectively named left right, describing observed value interval censored data. case left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution corresponding density function dname corresponding distribution function pname must classically defined. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see details). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. optim.method \"default\" (see details) optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying MLE optimisation (see details). weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MLE used, otherwise ordinary MLE. silent logical remove show warnings bootstraping. gradient function return gradient log-likelihood \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum likelihood fit of univariate distributions — mledist","text":"function intended called directly internally called fitdist bootdist used maximum likelihood method fitdistcens bootdistcens. assumed distr argument specifies distribution probability density function cumulative distribution function (d, p). quantile function random generator function (q, r) may needed function mmedist, qmedist, mgedist, fitdist,fitdistcens, bootdistcens bootdist. following named distributions, reasonable starting values computed start omitted (.e. NULL) : \"norm\", \"lnorm\", \"exp\" \"pois\", \"cauchy\", \"gamma\", \"logis\", \"nbinom\" (parametrized mu size), \"geom\", \"beta\", \"weibull\" stats package; \"invgamma\", \"llogis\", \"invweibull\", \"pareto1\", \"pareto\", \"lgamma\", \"trgamma\", \"invtrgamma\" actuar package. Note starting values may good enough fit poor. function uses closed-form formula fit uniform distribution. start list, named list names d,p,q,r functions chosen distribution. start function data, function return named list names d,p,q,r functions chosen distribution. mledist function allows user set fixed values parameters. start, fix.arg list, named list names d,p,q,r functions chosen distribution. fix.arg function data, function return named list names d,p,q,r functions chosen distribution. custom.optim=NULL (default), maximum likelihood estimations distribution parameters computed R base optim constrOptim. finite bounds (lower=-Inf upper=Inf) supplied, optim used method specified optim.method. Note optim.method=\"default\" means optim.method=\"Nelder-Mead\" distributions least two parameters optim.method=\"BFGS\" distributions one parameter. finite bounds supplied (among lower upper) gradient != NULL, constrOptim used. finite bounds supplied (among lower upper) gradient == NULL, constrOptim used optim.method=\"Nelder-Mead\"; optim used optim.method=\"L-BFGS-B\" \"Brent\"; case, error raised (behavior constrOptim). errors raised optim, good idea start adding traces optimization process adding control=list(trace=1, REPORT=1). custom.optim NULL, user-supplied function used instead R base optim. custom.optim must (least) following arguments fn function optimized, par initialized parameters. Internally function optimized also arguments, obs observations ddistname distribution name non censored data (Beware potential conflicts optional arguments custom.optim). assumed custom.optim carry MINIMIZATION. Finally, return least following components par estimate, convergence convergence code, value fn(par), hessian, counts number calls (function gradient) message (default NULL) error message custom.optim raises error, see returned value optim. See examples fitdist fitdistcens. Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary MLE carried , otherwise specified weights used balance log-likelihood contributions. yet possible take account weights functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat, descdist, bootdist, bootdistcens mgedist. (developments planned future). NB: data values particularly small large, scaling may needed optimization process. See Example (7).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum likelihood fit of univariate distributions — mledist","text":"mledist returns list following components, estimate parameter estimates. convergence integer code convergence optim/constrOptim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. used fitdist estimate standard errors. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. method \"closed formula\" appropriate otherwise NULL.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Venables WN Ripley BD (2002), Modern applied statistics S. Springer, New York, pp. 435-446, doi:10.1007/978-0-387-21706-2 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum likelihood fit of univariate distributions — mledist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mledist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum likelihood fit of univariate distributions — mledist","text":"","code":"# (1) basic fit of a normal distribution with maximum likelihood estimation # set.seed(1234) x1 <- rnorm(n=100) mledist(x1,\"norm\") #> $estimate #> mean sd #> -0.1567617 0.9993707 #> #> $convergence #> [1] 0 #> #> $value #> [1] 1.418309 #> #> $hessian #> mean sd #> mean 1.00126 0.000000 #> sd 0.00000 2.002538 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 43 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -141.8309 #> #> $vcov #> NULL #> # (2) defining your own distribution functions, here for the Gumbel distribution # for other distributions, see the CRAN task view dedicated to probability distributions dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) mledist(x1,\"gumbel\",start=list(a=10,b=5)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (3) fit of a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) mledist(x2,\"pois\") #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> [1] 1.539478 #> #> $hessian #> lambda #> lambda 0.5882357 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 4 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $vcov #> NULL #> # (4) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) mledist(x3,\"beta\") #> $estimate #> shape1 shape2 #> 4.859798 10.918841 #> #> $convergence #> [1] 0 #> #> $value #> [1] -0.7833052 #> #> $hessian #> shape1 shape2 #> shape1 0.16295311 -0.06542753 #> shape2 -0.06542753 0.03047900 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 47 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 78.33052 #> #> $vcov #> NULL #> # (5) fit frequency distributions on USArrests dataset. # x4 <- USArrests$Assault mledist(x4, \"pois\") #> $estimate #> lambda #> 170.76 #> #> $convergence #> [1] 0 #> #> $value #> [1] 24.2341 #> #> $hessian #> lambda #> lambda 0.005856175 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 2 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1211.705 #> #> $vcov #> NULL #> mledist(x4, \"nbinom\") #> $estimate #> size mu #> 3.822579 170.747853 #> #> $convergence #> [1] 0 #> #> $value #> [1] 5.806593 #> #> $hessian #> size mu #> size 3.518616e-02 -3.987921e-07 #> mu -3.987921e-07 1.282598e-04 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 47 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -290.3297 #> #> $vcov #> NULL #> # (6) fit a continuous distribution (Gumbel) to censored data. # data(fluazinam) log10EC50 <-log10(fluazinam) # definition of the Gumbel distribution dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) pgumbel <- function(q,a,b) exp(-exp((a-q)/b)) qgumbel <- function(p,a,b) a-b*log(-log(p)) mledist(log10EC50,\"gumbel\",start=list(a=0,b=2),optim.method=\"Nelder-Mead\") #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (7) scaling problem # the simulated dataset (below) has particularly small values, # hence without scaling (10^0), # the optimization raises an error. The for loop shows how scaling by 10^i # for i=1,...,6 makes the fitting procedure work correctly. set.seed(1234) x2 <- rnorm(100, 1e-4, 2e-4) for(i in 6:0) cat(i, try(mledist(x*10^i, \"cauchy\")$estimate, silent=TRUE), \"\\n\") #> 6 Error in eval(expr, envir) : object 'x' not found #> #> 5 Error in eval(expr, envir) : object 'x' not found #> #> 4 Error in eval(expr, envir) : object 'x' not found #> #> 3 Error in eval(expr, envir) : object 'x' not found #> #> 2 Error in eval(expr, envir) : object 'x' not found #> #> 1 Error in eval(expr, envir) : object 'x' not found #> #> 0 Error in eval(expr, envir) : object 'x' not found #> # (17) small example for the zero-modified geometric distribution # dzmgeom <- function(x, p1, p2) p1 * (x == 0) + (1-p1)*dgeom(x-1, p2) #pdf x2 <- c(2, 4, 0, 40, 4, 21, 0, 0, 0, 2, 5, 0, 0, 13, 2) #simulated dataset initp1 <- function(x) list(p1=mean(x == 0)) #init as MLE mledist(x2, \"zmgeom\", fix.arg=initp1, start=list(p2=1/2)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'."},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Matching moment fit of univariate distributions — mmedist","title":"Matching moment fit of univariate distributions — mmedist","text":"Fit univariate distributions matching moments (raw centered) non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matching moment fit of univariate distributions — mmedist","text":"","code":"mmedist(data, distr, order, memp, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matching moment fit of univariate distributions — mmedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution (see 'details'). order numeric vector moment order(s). length vector must equal number parameters estimate. memp function implementing empirical moments, raw centered consistent distr argument (weights argument). See details . start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization . weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MME used, otherwise ordinary MME. silent logical remove show warnings bootstraping. gradient function return gradient squared difference \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matching moment fit of univariate distributions — mmedist","text":"argument distr can one base R distributions: \"norm\", \"lnorm\", \"exp\" \"pois\", \"gamma\", \"logis\", \"nbinom\" , \"geom\", \"beta\" \"unif\". case, arguments data distr required, estimate computed closed-form formula. distributions characterized one parameter (\"geom\", \"pois\" \"exp\"), parameter simply estimated matching theoretical observed means, distributions characterized two parameters, parameters estimated matching theoretical observed means variances (Vose, 2000). Note closed-form formula, fix.arg used start ignored. argument distr can also distribution name long corresponding mdistr function exists, e.g. \"pareto\" \"mpareto\" exists. case arguments arguments order memp supplied order carry matching numerically, minimization sum squared differences observed theoretical moments. Optionnally arguments can supplied control optimization (see 'details' section mledist details arguments control optimization). case, fix.arg can used start taken account. non closed-form estimators, memp must provided compute empirical moments. weights=NULL, function must two arguments x, order: x numeric vector data order order moment. weights!=NULL, function must three arguments x, order, weights: x numeric vector data, order order moment, weights numeric vector weights. See examples . Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary MME carried , otherwise specified weights used compute (raw centered) weighted moments. closed-form estimators, weighted mean variance computed wtdmean wtdvar Hmisc package. numerical minimization used, weighted expected computed memp function. yet possible take account weighths functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). function intended called directly internally called fitdist bootdist used matching moments method. Since Version 1.2-0, mmedist automatically computes asymptotic covariance matrix using . Ibragimov R. 'minskii (1981), hence theoretical moments mdist defined order equals twice maximal order given order. instance, normal distribution, fit expectation variance need mnorm order \\(2\\times2=4\\).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matching moment fit of univariate distributions — mmedist","text":"mmedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function (appropriate) name optimization function used maximum likelihood. optim.method (appropriate) optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. method either \"closed formula\" name optimization method. order order moment(s) matched. memp empirical moment function.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matching moment fit of univariate distributions — mmedist","text":". Ibragimov R. 'minskii (1981), Statistical Estimation - Asymptotic Theory, Springer-Verlag, doi:10.1007/978-1-4899-0027-2 Evans M, Hastings N Peacock B (2000), Statistical distributions. John Wiley Sons Inc, doi:10.1002/9780470627242 . Vose D (2000), Risk analysis, quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matching moment fit of univariate distributions — mmedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/mmedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matching moment fit of univariate distributions — mmedist","text":"","code":"# (1) basic fit of a normal distribution with moment matching estimation # set.seed(1234) n <- 100 x1 <- rnorm(n=n) mmedist(x1, \"norm\") #> $estimate #> mean sd #> -0.1567617 0.9993707 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] -141.8309 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 2 #> #> $memp #> NULL #> #> $vcov #> NULL #> #weighted w <- c(rep(1, n/2), rep(10, n/2)) mmedist(x1, \"norm\", weights=w)$estimate #> Warning: weights are not taken into account in the default initial values #> mean sd #> 0.08565839 1.02915474 # (2) fit a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) mmedist(x2, \"pois\") #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 #> #> $memp #> NULL #> #> $vcov #> NULL #> # (3) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) mmedist(x3, \"beta\") #> $estimate #> shape1 shape2 #> 4.522734 10.219685 #> #> $convergence #> [1] 0 #> #> $value #> NULL #> #> $hessian #> NULL #> #> $optim.function #> NULL #> #> $opt.meth #> NULL #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> NULL #> #> $optim.message #> NULL #> #> $loglik #> [1] 78.19503 #> #> $method #> [1] \"closed formula\" #> #> $order #> [1] 1 2 #> #> $memp #> NULL #> #> $vcov #> NULL #> # (4) fit a Pareto distribution # # \\donttest{ require(\"actuar\") #simulate a sample x4 <- rpareto(1000, 6, 2) #empirical raw moment memp <- function(x, order) mean(x^order) memp2 <- function(x, order, weights) sum(x^order * weights)/sum(weights) #fit by MME mmedist(x4, \"pareto\", order=c(1, 2), memp=memp, start=list(shape=10, scale=10), lower=1, upper=Inf) #> $estimate #> shape scale #> 4.560420 1.464763 #> #> $convergence #> [1] 0 #> #> $value #> [1] 4.474863e-13 #> #> $hessian #> NULL #> #> $optim.function #> [1] \"constrOptim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 534 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -80.49091 #> #> $method #> [1] \"default\" #> #> $order #> [1] 1 2 #> #> $memp #> function (x, order) #> mean(x^order) #> #> #> $vcov #> NULL #> #fit by weighted MME w <- rep(1, length(x4)) w[x4 < 1] <- 2 mmedist(x4, \"pareto\", order=c(1, 2), memp=memp2, weights=w, start=list(shape=10, scale=10), lower=1, upper=Inf) #> Warning: weights are not taken into account in the default initial values #> $estimate #> shape scale #> 5.656722 1.630818 #> #> $convergence #> [1] 0 #> #> $value #> [1] 7.397593e-14 #> #> $hessian #> NULL #> #> $optim.function #> [1] \"constrOptim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> [1] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [38] 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [112] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 #> [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 #> [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 #> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 #> [260] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 #> [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [334] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 2 #> [371] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 #> [408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 #> [445] 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 #> [482] 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 #> [519] 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 #> [556] 2 2 2 2 1 2 2 1 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 #> [667] 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 #> [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [741] 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 #> [778] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [815] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [852] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2 #> [889] 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 #> [926] 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 #> [963] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 #> [1000] 2 #> #> $counts #> function gradient #> 999 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 119.7362 #> #> $method #> [1] \"default\" #> #> $order #> [1] 1 2 #> #> $memp #> function (x, order, weights) #> sum(x^order * weights)/sum(weights) #> #> #> $vcov #> NULL #> # }"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum spacing estimation of univariate distributions — msedist","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Fit univariate distribution maximizing (log) spacings non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum spacing estimation of univariate distributions — msedist","text":"","code":"msedist(data, distr, phidiv=\"KL\", power.phidiv=NULL, start = NULL, fix.arg = NULL, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights=NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum spacing estimation of univariate distributions — msedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. phidiv character string coding name phi-divergence used : \"KL\" Kullback-Leibler information (corresponds classic maximum spacing estimation), \"J\" Jeffreys' divergence, \"R\" Renyi's divergence, \"H\" Hellinger distance, \"V\" Vajda's measure information, see details. power.phidiv relevant, numeric power used phi-divergence : NULL phidiv=\"KL\" phidiv=\"J\" , positive different 1 phidiv=\"R\", greater equal 1 phidiv=\"H\" phidiv=\"V\", see details. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted MSE used, otherwise ordinary MSE. silent logical remove show warnings bootstraping. gradient function return gradient gof distance \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Maximum spacing estimation of univariate distributions — msedist","text":"msedist function numerically maximizes phi-divergence function spacings, spacings differences cumulative distribution function evaluated sorted dataset. classical maximum spacing estimation (MSE) introduced Cheng Amin (1986) Ranneby (1984) independently phi-diverence logarithm, see Anatolyev Kosenok (2005) link MSE maximum likelihood estimation. MSE generalized Ranneby Ekstrom (1997) allowing different phi-divergence function. Generalized MSE maximizes $$ S_n(\\theta)=\\frac{1}{n+1}\\sum_{=1}^{n+1} \\phi\\left(F(x_{()}; \\theta)-F(x_{(-1)}; \\theta) \\right), $$ \\(F(;\\theta)\\) parametric distribution function fitted, \\(\\phi\\) phi-divergence function, \\(x_{(1)}<\\dots0, \\alpha\\neq 1 $$ Hellinger distance (phidiv=\"H\" power.phidiv=p) $$\\phi(x)=-|1-x^{1/p}|^p \\textrm{ } p\\ge 1 $$ Vajda's measure information (phidiv=\"V\" power.phidiv=beta) $$\\phi(x)=-|1-x|^\\beta \\textrm{ } \\beta\\ge 1 $$ optimization process mledist, see 'details' section function. function intended called directly internally called fitdist bootdist. function intended used non-censored data. NB: data values particularly small large, scaling may needed optimization process, see mledist's examples.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Maximum spacing estimation of univariate distributions — msedist","text":"msedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. phidiv character string coding name phi-divergence used either \"KL\", \"J\", \"R\", \"H\" \"V\". power.phidiv Either NULL numeric power used phi-divergence.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Anatolyev, S., Kosenok, G. (2005). alternative maximum likelihood based spacings. Econometric Theory, 21(2), 472-476, doi:10.1017/S0266466605050255 . Cheng, R.C.H. N..K. Amin (1983) Estimating parameters continuous univariate distributions shifted origin. Journal Royal Statistical Society Series B 45, 394-403, doi:10.1111/j.2517-6161.1983.tb01268.x . Ranneby, B. (1984) maximum spacing method: estimation method related maximum likelihood method. Scandinavian Journal Statistics 11, 93-112. Ranneby, B. Ekstroem, M. (1997). Maximum spacing estimates based different metrics. Umea universitet.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Maximum spacing estimation of univariate distributions — msedist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/msedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum spacing estimation of univariate distributions — msedist","text":"","code":"# (1) Fit of a Weibull distribution to serving size data by maximum # spacing estimation # data(groundbeef) serving <- groundbeef$serving msedist(serving, \"weibull\") #> $estimate #> shape scale #> 1.423799 80.894950 #> #> $convergence #> [1] 0 #> #> $value #> [1] 3.789824 #> #> $hessian #> shape scale #> shape 0.792656647 -0.0043440632 #> scale -0.004344063 0.0002995895 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 59 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -1287.97 #> #> $phidiv #> [1] \"KL\" #> #> $power.phidiv #> NULL #> # (2) Fit of an exponential distribution # set.seed(123) x1 <- rexp(1e3) #the convergence is quick msedist(x1, \"exp\", control=list(trace=0, REPORT=1)) #> $estimate #> rate #> 0.967625 #> #> $convergence #> [1] 0 #> #> $value #> [1] 7.516802 #> #> $hessian #> rate #> rate 1.066843 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 12 2 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1029.544 #> #> $phidiv #> [1] \"KL\" #> #> $power.phidiv #> NULL #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of empirical and theoretical distributions for non-censored data — plotdist","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Plots empirical distribution (non-censored data) theoretical one specified.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"","code":"plotdist(data, distr, para, histo = TRUE, breaks = \"default\", demp = FALSE, discrete, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. argument may omitted para omitted. para named list giving parameters named distribution. argument may omitted distr omitted. histo logical plot histogram using hist function. breaks \"default\" histogram plotted function hist default breaks definition. Else breaks passed function hist. argument taken account discrete TRUE. demp logical plot empirical density first plot (alone superimposed histogram depending value argument histo) using density function. discrete TRUE, distribution considered discrete. \tdistr discrete missing, discrete set \tFALSE. discrete missing distr, \tdiscrete set TRUE distr belongs \t\"binom\", \"nbinom\",\"geom\", \"hyper\" \"pois\". ... graphical arguments passed graphical functions used plotdist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Empirical , specified, theoretical distributions plotted density cdf. plot density, user can use arguments histo demp specify wants histogram using function hist, density plot using function density, (least one two arguments must put \"TRUE\"). continuous distributions, function hist used default breaks definition breaks \"default\" passing breaks argument differs \"default\". continuous distribution theoretical distribution specified arguments distname para, Q-Q plot (plot quantiles theoretical fitted distribution (x-axis) empirical quantiles data) P-P plot (.e. value data set, plot cumulative density function fitted distribution (x-axis) empirical cumulative density function (y-axis)) also given (Cullen Frey, 1999). function ppoints (default parameter argument ) used Q-Q plot, generate set probabilities evaluate inverse distribution. NOTE VERSION 0.4-3, ppoints also used P-P plot cdf plot continuous data. personalize four plots proposed continuous data, example change plotting position, recommend use functions cdfcomp, denscomp, qqcomp ppcomp.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Cullen AC Frey HC (1999), Probabilistic techniques exposure assessment. Plenum Press, USA, pp. 81-155. Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of empirical and theoretical distributions for non-censored data — plotdist","text":"","code":"# (1) Plot of an empirical distribution with changing # of default line types for CDF and colors # and optionally adding a density line # set.seed(1234) x1 <- rnorm(n=30) plotdist(x1) plotdist(x1,demp = TRUE) plotdist(x1,histo = FALSE, demp = TRUE) #> Warning: arguments ‘freq’, ‘main’, ‘xlab’ are not made use of plotdist(x1, col=\"blue\", type=\"b\", pch=16) #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete plotdist(x1, type=\"s\") #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete # (2) Plot of a discrete distribution against data # set.seed(1234) x2 <- rpois(n=30, lambda = 2) plotdist(x2, discrete=TRUE) plotdist(x2, \"pois\", para=list(lambda = mean(x2))) plotdist(x2, \"pois\", para=list(lambda = mean(x2)), lwd=\"2\") # (3) Plot of a continuous distribution against data # xn <- rnorm(n=100, mean=10, sd=5) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn))) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), pch=16) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), demp = TRUE) plotdist(xn, \"norm\", para=list(mean=mean(xn), sd=sd(xn)), histo = FALSE, demp = TRUE) # (4) Plot of serving size data # data(groundbeef) plotdist(groundbeef$serving, type=\"s\") #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete #> Warning: graphical parameter \"type\" is obsolete # (5) Plot of numbers of parasites with a Poisson distribution data(toxocara) number <- toxocara$number plotdist(number, discrete = TRUE) plotdist(number,\"pois\",para=list(lambda=mean(number)))"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of empirical and theoretical distributions for censored data — plotdistcens","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Plots empirical distribution censored data theoretical one specified.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"","code":"plotdistcens(censdata, distr, para, leftNA = -Inf, rightNA = Inf, NPMLE = TRUE, Turnbull.confint = FALSE, NPMLE.method = \"Wang\", ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"censdata dataframe two columns respectively named left right, describing observed value interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations. distr character string \"name\" naming distribution, corresponding density function dname corresponding distribution function pname must defined, directly density function. para named list giving parameters named distribution. argument may omitted distr omitted. leftNA real value left bound left censored observations : -Inf finite value 0 positive data example. rightNA real value right bound right censored observations : Inf finite value realistic maximum value. NPMLE TRUE NPMLE (nonparametric maximum likelihood estimate) technique used estimate cdf curve censored data previous arguments leftNA rightNA used (see details) Turnbull.confint TRUE confidence intervals added Turnbull plot. case NPMLE.method forced \"Turnbull.middlepoints\" NPMLE.method Three NPMLE techniques provided, \"Wang\", default one, rewritten package npsurv using function constrOptim package stats optimisation, \"Turnbull.middlepoints\", older one implemented package survival \"Turnbull.intervals\" uses Turnbull algorithm package survival associates interval equivalence class instead middlepoint interval (see details). \"Wang\" \"Turnbull.intervals\" enable derivation Q-Q plot P-P plot. ... graphical arguments passed methods. title plot can modified using argument main CDF plot.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"NPMLE TRUE, NPMLE.method \"Wang\" , empirical distributions plotted cdf using either constrained Newton method (Wang, 2008) hierarchical constrained Newton method (Wang, 2013) compute overall empirical cdf curve. NPMLE TRUE, NPMLE.method \"Turnbull.intervals\" , empirical plotted cdf using EM approach Turnbull (Turnbull, 1974). two cases, grey rectangles represent areas empirical distribution function unique. cases theoretical distribution specified, two goodness--fit plots also provided, Q-Q plot (plot quantiles theoretical fitted distribution (x-axis) empirical quantiles data) P-P plot (.e. value data set, plot cumulative density function fitted distribution (x-axis) empirical cumulative density function (y-axis)). Grey rectangles Q-Q plot P-P plot also represent areas non uniqueness empirical quantiles probabilities, directly derived non uniqueness areas empirical cumulative distribution. NPMLE TRUE, NPMLE.method \"Turnbull.middlepoints\", empirical , specified, theoretical distributions plotted cdf using EM approach Turnbull (Turnbull, 1974) compute overall empirical cdf curve, confidence intervals Turnbull.confint TRUE, calls functions survfit plot.survfit survival package. NPMLE FALSE empirical , specified, theoretical distributions plotted cdf, data directly reported segments interval, left right censored data, points non-censored data. plotting, observations ordered rank r associated . Left censored observations ordered first, right bounds. Interval censored non censored observations ordered mid-points , last, right censored observations ordered left bounds. leftNA (resp. rightNA) finite, left censored (resp. right censored) observations considered interval censored observations ordered mid-points non-censored interval censored data. sometimes necessary fix rightNA leftNA realistic extreme value, even exactly known, obtain reasonable global ranking observations. ranking, n observations plotted point (one x-value) segment (interval possible x-values), y-value equal r/n, r rank observation global ordering previously described. second method may interesting certainly less rigorous methods prefered.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Turnbull BW (1974), Nonparametric estimation survivorship function doubly censored data. Journal American Statistical Association, 69, 169-173, doi:10.2307/2285518 . Wang Y (2008), Dimension-reduced nonparametric maximum likelihood computation interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402, doi:10.1016/j.csda.2007.10.018 . Wang Y Taylor SM (2013), Efficient computation nonparametric survival functions via hierarchical mixture formulation. Statistics Computing, 23, 713-725, doi:10.1007/s11222-012-9341-9 . Wang, Y., & Fani, S. (2018), Nonparametric maximum likelihood computation U-shaped hazard function. Statistics Computing, 28(1), 187-200, doi:10.1007/s11222-017-9724-z . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/plotdistcens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of empirical and theoretical distributions for censored data — plotdistcens","text":"","code":"# (1) Plot of an empirical censored distribution (censored data) as a CDF # using the default Wang method # data(smokedfish) d1 <- as.data.frame(log10(smokedfish)) plotdistcens(d1) # (2) Add the CDF of a normal distribution # plotdistcens(d1, \"norm\", para=list(mean = -1.6, sd = 1.5)) # (3) Various plots of the same empirical distribution # # default Wang plot with representation of equivalence classess plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Wang\") # same plot but using the Turnbull alorithm from the package survival plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Wang\") # Turnbull plot with middlepoints (as in the package survival) plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Turnbull.middlepoints\") # Turnbull plot with middlepoints and confidence intervals plotdistcens(d1, NPMLE = TRUE, NPMLE.method = \"Turnbull.middlepoints\", Turnbull.confint = TRUE) # with intervals and points plotdistcens(d1,rightNA=3, NPMLE = FALSE) # with intervals and points # defining a minimum value for left censored values plotdistcens(d1,leftNA=-3, NPMLE = FALSE)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-fitting procedure — prefit","title":"Pre-fitting procedure — prefit","text":"Search good starting values","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-fitting procedure — prefit","text":"","code":"prefit(data, distr, method = c(\"mle\", \"mme\", \"qme\", \"mge\"), feasible.par, memp=NULL, order=NULL, probs=NULL, qtype=7, gof=NULL, fix.arg=NULL, lower, upper, weights=NULL, silent=TRUE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-fitting procedure — prefit","text":"data numeric vector. distr character string \"name\" naming distribution corresponding density function dname, corresponding distribution function pname corresponding quantile function qname must defined, directly density function. method character string coding fitting method: \"mle\" 'maximum likelihood estimation', \"mme\" 'moment matching estimation', \"qme\" 'quantile matching estimation' \"mge\" 'maximum goodness--fit estimation'. feasible.par named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). may account closed-form formulas. order numeric vector moment order(s). length vector must equal number parameters estimate. memp function implementing empirical moments, raw centered consistent distr argument (weights argument). probs numeric vector probabilities quantile matching done. length vector must equal number parameters estimate. qtype quantile type used R quantile function compute empirical quantiles, (default 7 corresponds default quantile method R). gof character string coding name goodness--fit distance used : \"CvM\" Cramer-von Mises distance,\"KS\" Kolmogorov-Smirnov distance, \"AD\" Anderson-Darling distance, \"ADR\", \"ADL\", \"AD2R\", \"AD2L\" \"AD2\" variants Anderson-Darling distance described Luceno (2006). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated maximum likelihood procedure. use argument possible method=\"mme\" closed-form formula used. weights optional vector weights used fitting process. NULL numeric vector. non-NULL, weighted MLE used, otherwise ordinary MLE. silent logical remove show warnings. lower Lower bounds parameters. upper Upper bounds parameters. ... arguments passed generic functions, one functions \"mledist\", \"mmedist\", \"qmedist\" \"mgedist\" depending chosen method. See mledist, mmedist, qmedist, mgedist details parameter estimation.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-fitting procedure — prefit","text":"Searching good starting values achieved transforming parameters (constraint interval real line) probability distribution. Indeed, positive parameters \\((0,Inf)\\) transformed using logarithm (typically scale parameter sd normal distribution, see Normal), parameters \\((1,Inf)\\) transformed using function \\(log(x-1)\\), probability parameters \\((0,1)\\) transformed using logit function \\(log(x/(1-x))\\) (typically parameter prob geometric distribution, see Geometric), negative probability parameters \\((-1,0)\\) transformed using function \\(log(-x/(1+x))\\), real parameters course transformed , typically mean normal distribution, see Normal. parameters transformed, optimization carried quasi-Newton algorithm (typically BFGS) transform back original parameter value.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-fitting procedure — prefit","text":"named list.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pre-fitting procedure — prefit","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pre-fitting procedure — prefit","text":"Christophe Dutang Marie-Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/prefit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-fitting procedure — prefit","text":"","code":"# (1) fit of a gamma distribution by maximum likelihood estimation # x <- rgamma(1e3, 5/2, 7/2) prefit(x, \"gamma\", \"mle\", list(shape=3, scale=3), lower=-Inf, upper=Inf) #> $shape #> [1] 2.57829 #> #> $scale #> [1] 3.559245 #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile matching fit of univariate distributions — qmedist","title":"Quantile matching fit of univariate distributions — qmedist","text":"Fit univariate distribution matching quantiles non censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile matching fit of univariate distributions — qmedist","text":"","code":"qmedist(data, distr, probs, start = NULL, fix.arg = NULL, qtype = 7, optim.method = \"default\", lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL, checkstartfix=FALSE, calcvcov=FALSE, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile matching fit of univariate distributions — qmedist","text":"data numeric vector non censored data. distr character string \"name\" naming distribution corresponding quantile function qname corresponding density distribution dname must classically defined. probs numeric vector probabilities quantile matching done. length vector must equal number parameters estimate. start named list giving initial values parameters named distribution function data computing initial values returning named list. argument may omitted (default) distributions reasonable starting values computed (see 'details' section mledist). fix.arg optional named list giving values fixed parameters named distribution function data computing (fixed) parameter values returning named list. Parameters fixed value thus estimated. qtype quantile type used R quantile function compute empirical quantiles, (default 7 corresponds default quantile method R). optim.method \"default\" optimization method pass optim. lower Left bounds parameters \"L-BFGS-B\" method (see optim). upper Right bounds parameters \"L-BFGS-B\" method (see optim). custom.optim function carrying optimization. weights optional vector weights used fitting process. NULL numeric vector strictly positive integers (typically number occurences observation). non-NULL, weighted QME used, otherwise ordinary QME. silent logical remove show warnings bootstraping. gradient function return gradient squared difference \"BFGS\", \"CG\" \"L-BFGS-B\" methods. NULL, finite-difference approximation used, see details. checkstartfix logical test starting fixed values. change . calcvcov logical indicating (asymptotic) covariance matrix required. (currently ignored) ... arguments passed optim, constrOptim custom.optim function.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile matching fit of univariate distributions — qmedist","text":"qmedist function carries quantile matching numerically, minimization sum squared differences observed theoretical quantiles. Note discrete distribution, sum squared differences step function consequently, optimum unique, see FAQ. optimization process mledist, see 'details' section function. Optionally, vector weights can used fitting process. default (weigths=NULL), ordinary QME carried , otherwise specified weights used compute weighted quantiles used squared differences. Weigthed quantiles computed wtdquantile Hmisc package. yet possible take account weighths functions plotdist, plotdistcens, plot.fitdist, plot.fitdistcens, cdfcomp, cdfcompcens, denscomp, ppcomp, qqcomp, gofstat descdist (developments planned future). function intended called directly internally called fitdist bootdist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile matching fit of univariate distributions — qmedist","text":"qmedist returns list following components, estimate parameter estimates. convergence integer code convergence optim defined defined user user-supplied optimization function. 0 indicates successful convergence. 1 indicates iteration limit optim reached. 10 indicates degeneracy Nealder-Mead simplex. 100 indicates optim encountered internal error. value minimal value reached criterion minimize. hessian symmetric matrix computed optim estimate Hessian solution found computed user-supplied optimization function. optim.function name optimization function used maximum likelihood. optim.method optim used, name algorithm used, field method custom.optim function otherwise. fix.arg named list giving values parameters named distribution must kept fixed rather estimated maximum likelihood NULL parameters. fix.arg.fun function used set value fix.arg NULL. weights vector weigths used estimation process NULL. counts two-element integer vector giving number calls log-likelihood function gradient respectively. excludes calls needed compute Hessian, requested, calls log-likelihood function compute finite-difference approximation gradient. counts returned optim user-supplied function set NULL. optim.message character string giving additional information returned optimizer, NULL. understand exactly message, see source code. loglik log-likelihood value. probs probability vector quantiles matched.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile matching fit of univariate distributions — qmedist","text":"Klugman SA, Panjer HH Willmot GE (2012), Loss Models: Data Decissions, 4th edition. Wiley Series Statistics Finance, Business Economics, p. 253, doi:10.1198/tech.2006.s409 . Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile matching fit of univariate distributions — qmedist","text":"Christophe Dutang Marie Laure Delignette-Muller.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/qmedist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile matching fit of univariate distributions — qmedist","text":"","code":"# (1) basic fit of a normal distribution # set.seed(1234) x1 <- rnorm(n=100) qmedist(x1, \"norm\", probs=c(1/3, 2/3)) #> $estimate #> mean sd #> -0.3025734 0.8521385 #> #> $convergence #> [1] 0 #> #> $value #> [1] 2.427759e-10 #> #> $hessian #> mean sd #> mean 2.000000e+00 -2.784663e-14 #> sd -2.784663e-14 3.710520e-01 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 57 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -146.1278 #> #> $probs #> [1] 0.3333333 0.6666667 #> # (2) defining your own distribution functions, here for the Gumbel # distribution for other distributions, see the CRAN task view dedicated # to probability distributions dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b)) qgumbel <- function(p, a, b) a - b*log(-log(p)) qmedist(x1, \"gumbel\", probs=c(1/3, 2/3), start=list(a=10,b=5)) #> Error in checkparamlist(arg_startfix$start.arg, arg_startfix$fix.arg, argddistname, hasnodefaultval): 'start' must specify names which are arguments to 'distr'. # (3) fit a discrete distribution (Poisson) # set.seed(1234) x2 <- rpois(n=30,lambda = 2) qmedist(x2, \"pois\", probs=1/2) #> $estimate #> lambda #> 1.7 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.25 #> #> $hessian #> lambda #> lambda 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 1 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -46.18434 #> #> $probs #> [1] 0.5 #> # (4) fit a finite-support distribution (beta) # set.seed(1234) x3 <- rbeta(n=100,shape1=5, shape2=10) qmedist(x3, \"beta\", probs=c(1/3, 2/3)) #> $estimate #> shape1 shape2 #> 5.820826 14.053655 #> #> $convergence #> [1] 0 #> #> $value #> [1] 3.889731e-12 #> #> $hessian #> shape1 shape2 #> shape1 0.002714767 -0.0010963293 #> shape2 -0.001096329 0.0004477195 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 89 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] 76.02016 #> #> $probs #> [1] 0.3333333 0.6666667 #> # (5) fit frequency distributions on USArrests dataset. # x4 <- USArrests$Assault qmedist(x4, \"pois\", probs=1/2) #> $estimate #> lambda #> 170.76 #> #> $convergence #> [1] 0 #> #> $value #> [1] 144 #> #> $hessian #> lambda #> lambda 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"BFGS\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 1 1 #> #> $optim.message #> NULL #> #> $loglik #> [1] -1211.705 #> #> $probs #> [1] 0.5 #> qmedist(x4, \"nbinom\", probs=c(1/3, 2/3)) #> $estimate #> size mu #> 2.518966 182.313344 #> #> $convergence #> [1] 0 #> #> $value #> [1] 0.1111111 #> #> $hessian #> size mu #> size 0 0 #> mu 0 0 #> #> $optim.function #> [1] \"optim\" #> #> $optim.method #> [1] \"Nelder-Mead\" #> #> $fix.arg #> NULL #> #> $fix.arg.fun #> NULL #> #> $weights #> NULL #> #> $counts #> function gradient #> 37 NA #> #> $optim.message #> NULL #> #> $loglik #> [1] -292.5969 #> #> $probs #> [1] 0.3333333 0.6666667 #>"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile estimation from a fitted distribution — quantile","title":"Quantile estimation from a fitted distribution — quantile","text":"Quantile estimation fitted distribution, optionally confidence intervals calculated bootstrap result.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile estimation from a fitted distribution — quantile","text":"","code":"# S3 method for class 'fitdist' quantile(x, probs = seq(0.1, 0.9, by=0.1), ...) # S3 method for class 'fitdistcens' quantile(x, probs = seq(0.1, 0.9, by=0.1), ...) # S3 method for class 'bootdist' quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = \"two.sided\", CI.level = 0.95, ...) # S3 method for class 'bootdistcens' quantile(x, probs = seq(0.1, 0.9, by=0.1),CI.type = \"two.sided\", CI.level = 0.95, ...) # S3 method for class 'quantile.fitdist' print(x, ...) # S3 method for class 'quantile.fitdistcens' print(x, ...) # S3 method for class 'quantile.bootdist' print(x, ...) # S3 method for class 'quantile.bootdistcens' print(x, ...)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile estimation from a fitted distribution — quantile","text":"x object class \"fitdist\", \"fitdistcens\", \"bootdist\", \"bootdistcens\" \"quantile.fitdist\", \"quantile.fitdistcens\", \"quantile.bootdist\", \"quantile.bootdistcens\" print generic function. probs numeric vector probabilities values [0, 1] quantiles must calculated. CI.type Type confidence intervals : either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. ... arguments passed generic functions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quantile estimation from a fitted distribution — quantile","text":"Quantiles parametric distribution calculated probability specified probs, using estimated parameters. used object class \"bootdist\" \"bootdistcens\", percentile confidence intervals medians etimates also calculated bootstrap result. CI.type two.sided, CI.level two-sided confidence intervals quantiles calculated. CI.type less greater, CI.level one-sided confidence intervals quantiles calculated. print functions show estimated quantiles percentile confidence intervals median estimates bootstrap resampling done previously, number bootstrap iterations estimation converges inferior whole number bootstrap iterations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile estimation from a fitted distribution — quantile","text":"quantile returns list 2 components (first two described ) called object class \"fitdist\" \"fitdistcens\" 8 components (described ) called object class \"bootdist\" \"bootdistcens\" : quantiles dataframe containing estimated quantiles probability value specified argument probs (one row, many columns values probs). probs numeric vector probabilities quantiles calculated. bootquant data frame containing bootstraped values quantile (many rows, specified call bootdist argument niter, many columns values probs) quantCI CI.type two.sided, two bounds CI.level percent two.sided confidence interval quantile (two rows many columns values probs). CI.type less, right bound CI.level percent one.sided confidence interval quantile (one row). CI.type greater, left bound CI.level percent one.sided confidence interval quantile (one row). quantmedian Median bootstrap estimates (per probability). CI.type Type confidence interval: either \"two.sided\" one-sided intervals (\"less\" \"greater\"). CI.level confidence level. nbboot number samples drawn bootstrap. nbconverg number iterations optimization algorithm converges.","code":""},{"path":[]},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Quantile estimation from a fitted distribution — quantile","text":"Delignette-Muller ML Dutang C (2015), fitdistrplus: R Package Fitting Distributions. Journal Statistical Software, 64(4), 1-34, doi:10.18637/jss.v064.i04 .","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantile estimation from a fitted distribution — quantile","text":"Marie-Laure Delignette-Muller Christophe Dutang.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile estimation from a fitted distribution — quantile","text":"","code":"# (1) Fit of a normal distribution on acute toxicity log-transformed values of # endosulfan for nonarthropod invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, followed with calculations of their # confidence intervals with various definitions, from a small number of bootstrap # iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(endosulfan) ATV <- subset(endosulfan, group == \"NonArthroInvert\")$ATV log10ATV <- log10(subset(endosulfan, group == \"NonArthroInvert\")$ATV) fln <- fitdist(log10ATV, \"norm\") quantile(fln, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 bln <- bootdist(fln, bootmethod=\"param\", niter=101) quantile(bln, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.206058 1.615810 2.040136 #> 97.5 % 2.372660 2.617113 2.937556 quantile(bln, probs = c(0.05, 0.1, 0.2), CI.type = \"greater\") #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> left bound of one-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.299871 1.64396 2.126053 quantile(bln, probs = c(0.05, 0.1, 0.2), CI.level = 0.9) #> (original) estimated quantiles for each specified probability (non-censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.744227 2.080093 2.4868 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.831458 2.128334 2.515952 #> #> two-sided 90 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.299871 1.643960 2.126053 #> 95 % 2.297746 2.565286 2.894080 # (2) Draw of 95 percent confidence intervals on quantiles of the # previously fitted distribution # cdfcomp(fln) q1 <- quantile(bln, probs = seq(0,1,length=101)) points(q1$quantCI[1,],q1$probs,type=\"l\") points(q1$quantCI[2,],q1$probs,type=\"l\") # (2b) Draw of 95 percent confidence intervals on quantiles of the # previously fitted distribution # using the NEW function CIcdfplot # CIcdfplot(bln, CI.output = \"quantile\", CI.fill = \"pink\") # (3) Fit of a distribution on acute salinity log-transformed tolerance # for riverine macro-invertebrates, using maximum likelihood estimation # to estimate what is called a species sensitivity distribution # (SSD) in ecotoxicology, followed by estimation of the 5, 10 and 20 percent quantile # values of the fitted distribution, which are called the 5, 10, 20 percent hazardous # concentrations (HC5, HC10, HC20) in ecotoxicology, followed with calculations of # their confidence intervals with various definitions. # from a small number of bootstrap iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # data(salinity) log10LC50 <-log10(salinity) flncens <- fitdistcens(log10LC50,\"norm\") quantile(flncens, probs = c(0.05, 0.1, 0.2)) #> Estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 blncens <- bootdistcens(flncens, niter = 101) quantile(blncens, probs = c(0.05, 0.1, 0.2)) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> two-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 2.5 % 1.057448 1.138889 1.239646 #> 97.5 % 1.203538 1.270419 1.355852 quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.type = \"greater\") #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> left bound of one-sided 95 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.062249 1.145186 1.245616 quantile(blncens, probs = c(0.05, 0.1, 0.2), CI.level = 0.9) #> (original) estimated quantiles for each specified probability (censored data) #> p=0.05 p=0.1 p=0.2 #> estimate 1.11584 1.194121 1.288913 #> Median of bootstrap estimates #> p=0.05 p=0.1 p=0.2 #> estimate 1.127552 1.204485 1.299218 #> #> two-sided 90 % CI of each quantile #> p=0.05 p=0.1 p=0.2 #> 5 % 1.062249 1.145186 1.245616 #> 95 % 1.195896 1.266786 1.346183"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":null,"dir":"Reference","previous_headings":"","what":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"72-hour acute salinity tolerance (LC50 values) riverine macro-invertebrates.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"","code":"data(salinity)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"salinity data frame 2 columns named left right, describing observed LC50 value (electrical condutivity, millisiemens per centimeter) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value noncensored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"Kefford, B.J., Nugegoda, D., Metzeling, L., Fields, E. 2006. Validating species sensitivity distributions using salinity tolerance riverine macroinvertebrates southern Murray-darling Basin (Vitoria, Australia). Canadian Journal Fisheries Aquatic Science, 63, 1865-1877.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/salinity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species-Sensitivity Distribution (SSD) for salinity tolerance — salinity","text":"","code":"# (1) load of data # data(salinity) # (2) plot of data using Turnbull cdf plot # log10LC50 <- log10(salinity) plotdistcens(log10LC50) # (3) fit of a normal and a logistic distribution to data in log10 # (classical distributions used for species sensitivity # distributions, SSD, in ecotoxicology)) # and visual comparison of the fits using Turnbull cdf plot # fln <- fitdistcens(log10LC50, \"norm\") summary(fln) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean 1.4702582 0.2927558 #> sd 0.2154709 0.2461943 #> Loglikelihood: -61.79623 AIC: 127.5925 BIC: 132.9567 #> Correlation matrix: #> mean sd #> mean 1.0000000 0.2937484 #> sd 0.2937484 1.0000000 #> fll <- fitdistcens(log10LC50, \"logis\") summary(fll) #> Fitting of the distribution ' logis ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> location 1.4761562 0.2933442 #> scale 0.1269994 0.1604527 #> Loglikelihood: -62.81293 AIC: 129.6259 BIC: 134.9901 #> Correlation matrix: #> location scale #> location 1.0000000 0.2024688 #> scale 0.2024688 1.0000000 #> cdfcompcens(list(fln, fll),legendtext = c(\"normal\", \"logistic\"), xlab = \"log10(LC50)\", xlim = c(0.5, 2), lines01 = TRUE) # (4) estimation of the 5 percent quantile value of # the normal fitted distribution (5 percent hazardous concentration : HC5) # with its two-sided 95 percent confidence interval calculated by # non parametric bootstrap # from a small number of bootstrap iterations to satisfy CRAN running times constraint. # For practical applications, we recommend to use at least niter=501 or niter=1001. # # in log10(LC50) bln <- bootdistcens(fln, niter = 101) HC5ln <- quantile(bln, probs = 0.05) # in LC50 10^(HC5ln$quantiles) #> p=0.05 #> estimate 13.0569 10^(HC5ln$quantCI) #> p=0.05 #> 2.5 % 11.08712 #> 97.5 % 15.50325 # (5) estimation of the HC5 value # with its one-sided 95 percent confidence interval (type \"greater\") # # in log10(LC50) HC5lnb <- quantile(bln, probs = 0.05, CI.type = \"greater\") # in LC50 10^(HC5lnb$quantiles) #> p=0.05 #> estimate 13.0569 10^(HC5lnb$quantCI) #> p=0.05 #> 5 % 11.31157"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":null,"dir":"Reference","previous_headings":"","what":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"Contamination data Listeria monocytogenes smoked fish Belgian market period 2005 2007.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"","code":"data(smokedfish)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"smokedfish data frame 2 columns named left right, describing observed value Listeria monocytogenes concentration (CFU/g) interval. left column contains either NA left censored observations, left bound interval interval censored observations, observed value non-censored observations. right column contains either NA right censored observations, right bound interval interval censored observations, observed value non-censored observations.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"Busschaert, P., Geereard, .H., Uyttendaele, M., Van Impe, J.F., 2010. Estimating distributions qualitative (semi) quantitative microbiological contamination data use risk assessment. International Journal Food Microbiology. 138, 260-269.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/smokedfish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contamination data of Listeria monocytogenes in smoked fish — smokedfish","text":"","code":"# (1) load of data # data(smokedfish) # (2) plot of data in CFU/g # plotdistcens(smokedfish) # (3) plot of transformed data in log10[CFU/g] # Clog10 <- log10(smokedfish) plotdistcens(Clog10) # (4) Fit of a normal distribution to data in log10[CFU/g] # fitlog10 <- fitdistcens(Clog10, \"norm\") summary(fitlog10) #> Fitting of the distribution ' norm ' By maximum likelihood on censored data #> Parameters #> estimate Std. Error #> mean -1.575392 2.043857 #> sd 1.539446 2.149561 #> Loglikelihood: -87.10945 AIC: 178.2189 BIC: 183.4884 #> Correlation matrix: #> mean sd #> mean 1.0000000 -0.4325228 #> sd -0.4325228 1.0000000 #> plot(fitlog10)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":null,"dir":"Reference","previous_headings":"","what":"Parasite abundance in insular feral cats — toxocara","title":"Parasite abundance in insular feral cats — toxocara","text":"Toxocara cati abundance feral cats living Kerguelen island.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parasite abundance in insular feral cats — toxocara","text":"","code":"data(toxocara)"},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parasite abundance in insular feral cats — toxocara","text":"toxocara data frame 1 column (number: number parasites digestive tract)","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parasite abundance in insular feral cats — toxocara","text":"Fromont, E., Morvilliers, L., Artois, M., Pontier, D. 2001. Parasite richness abundance insular mainland feral cats. Parasitology, 123, 143-151.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/reference/toxocara.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parasite abundance in insular feral cats — toxocara","text":"","code":"# (1) load of data # data(toxocara) # (2) description and plot of data # number <- toxocara$number descdist(number, discrete = TRUE, boot = 11) #> summary statistics #> ------ #> min: 0 max: 75 #> median: 2 #> mean: 8.679245 #> estimated sd: 14.29332 #> estimated skewness: 2.630609 #> estimated kurtosis: 11.4078 plotdist(number, discrete = TRUE) # (3) fit of a Poisson distribution to data # fitp <- fitdist(number, \"pois\") summary(fitp) #> Fitting of the distribution ' pois ' by maximum likelihood #> Parameters : #> estimate Std. Error #> lambda 8.679245 2.946056 #> Loglikelihood: -507.5334 AIC: 1017.067 BIC: 1019.037 plot(fitp) # (4) fit of a negative binomial distribution to data # fitnb <- fitdist(number, \"nbinom\") summary(fitnb) #> Fitting of the distribution ' nbinom ' by maximum likelihood #> Parameters : #> estimate Std. Error #> size 0.3971457 0.6034502 #> mu 8.6802520 14.0870858 #> Loglikelihood: -159.3441 AIC: 322.6882 BIC: 326.6288 #> Correlation matrix: #> size mu #> size 1.000000000 -0.000103855 #> mu -0.000103855 1.000000000 #> plot(fitnb)"},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-12-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.2-2","title":"fitdistrplus 1.2-2","text":"NEW FEATURES website bringing together resources related fitdistrplus package now exists github.io following URL: https://lbbe-software.github.io/fitdistrplus/ BUG FIXES mgedist() may suffer numerical issue Anderson-Darling GoF metrics. GoF metrics now take care numerical issue, log(0) 1/0, properly scaled sample sized avoid large sample size issues. Thanks Ethan Chapman reporting bug. default starting value gamma distribution wrongly computed rate parameter. Thanks Wendy Martin reporting bug. mledist(), mmedist(), qmedist() may suffer scaling issue objective function properly scaled sample sized avoid large sample size issues. mledist() now takes care numerical issue, log(0).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-12-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.2-1","title":"fitdistrplus 1.2-1","text":"CRAN release: 2024-07-12 NEW FEATURES fitdistrplus git repo now belongs lbbe-software organization modify add initial value univariate distributions provided actuar. create new vignette regarding default initial values. add generic functions AIC() BIC() fitdist fitdistcens objects. make gofstat() work fitdistcens objects (giving AIC BIC values). add calculation hessian using optimHess within fitdist given optim. compute asymptotic covariance matrix MME : Now theoretical moments m defined order equals twice maximal order given order. add new argument calcvcov order (dis)able computation covariance matrix method. graphics function *comp() now return list drawn points /lines plotstyle == \"graphics\". add density function bootdist(cens) objects. add DOIs man pages. BUG FIXES scale parameter fixed, startarg function also set rate parameter. leads error calling density. add sanity check plotdistcens: following code plotdistcens(data.frame(right=smokedfish$right, left=smokedfish$left)) raised error via npsurv(), thanks R. Pouillot. bug fixed using breaks plotdist. solve extremely long time taking lines descdist. add defensive programming input data (check NA, NaN, Inf values). correct links man pages URL DOI. remove use plot.np vignettes.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-11","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-11","title":"fitdistrplus 1.1-11","text":"CRAN release: 2023-04-25 NEW FEATURES add print argument descdist function allow plot skewness-kurtosis graph, without printing computed parameters BUG FIX use deprecated ggplot2 functions updated use deprecated BibTeX entries updated bug fixed drawing CI lines CIcdfcplot ggplot2 called bug fixed drawing horizontal lines cdfcompcens","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-8","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-8","title":"fitdistrplus 1.1-8","text":"CRAN release: 2022-03-10 WARNING FIX update URL fitdistrplus.Rd replace (class(x) == XX) (inherits(x, XX)) replace dontrun tags donttest examples rd files BUG FIX fix error t-detectbound.R producing “failure: length > 1 coercion logical” reported Brian Ripley","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-6","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-6","title":"fitdistrplus 1.1-6","text":"CRAN release: 2021-09-28 NEW FEATURES new function Surv2fitdistcens() format data use fitdistcens() format used survival package new dataset fremale order illustrate Surv2fitdistcens() support use ggplot2 CIcdfplot add taxon names endosulfan dataset new argument name.points cdfcomp CIcdfplot add labels next points","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-5","title":"fitdistrplus 1.1-5","text":"CRAN release: 2021-05-28 WARNING FIX reduce testing times test files","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-3","title":"fitdistrplus 1.1-3","text":"CRAN release: 2020-12-05 NEW FEATURE take account fix.arg uniform distribution BUG FIXES add loglikelihood value uniform distribution (mledist()) correct usage triple dots argument llsurface() fix error ppcomp() qqcomp() raised large dataset","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-11-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.1-1","title":"fitdistrplus 1.1-1","text":"CRAN release: 2020-05-19 NEW FEATURES add internal functions cope problems lack maintenance package npsurv remove dependence package remove deprecated argument Turnbull plotdistcens()","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-14","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-14","title":"fitdistrplus 1.0-14","text":"CRAN release: 2019-01-23 NEW FEATURES add new estimation method called maximum spacing estimation via msedist()","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-13","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-13","title":"fitdistrplus 1.0-13","text":"BUG FIXES fix issues coming noLD (–disable-long-double) configuration R","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-12","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-12","title":"fitdistrplus 1.0-12","text":"BUG FIXES bug fixed qmedist() fitdistcens() raised error checkparamlist(). bug fixed testdpqfun() assumes first argument d,p,q,r functions exactly base R.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-11","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-11","title":"fitdistrplus 1.0-11","text":"CRAN release: 2018-09-10 NEW FEATURES update FAQ beta(,). improve graphics discrete distributions denscomp(). improve automatic naming legends xxxcomp(). harmonize outputs mledist(), qmedist(), mmedist(), mgedist(), fitdist() fitdistcens(). automatic test d, p, q functions fitdist() raise warnings. improve test starting fixed values. add new default starting values distributions actuar. change default CDF plot censored data, using Wang NPMLE algorithm provided package npsurv (plotdistcens() cdfcompcens()) add two new goodness--fit plots (QQ-plot PP-plot) censored data (cf. plotdistcens, qqcompcens ppcompcens). add part dedicated censored datain FAQ vignette. homogeneization xlim ylim default definition plotdistcens. Removing name first argument calls dpq functions order make package compatible distributions defined non classical name first argument (resp. x, q, p d, p, q functions). add possibility change title CDF plot plotdistcens() using argument main. support use ggplot2 cdfcompcens, qqcompcens, ppcompcens. BUG FIXES bug fixed concerning use gofstat chi squared df <=0 (error message blocking functions) bug fix mledist() bounds set (NULL) censored MLE enable correct use non-equidistant breaks denscomp histogram plotstyle = “ggplot”, prohibit use non-equidistant breaks probability = FALSE (adding stop case).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-9","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-9","title":"fitdistrplus 1.0-9","text":"CRAN release: 2017-03-24 update FAQ linear inequality constraints.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-8","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-8","title":"fitdistrplus 1.0-8","text":"CRAN release: 2017-02-01 NEW FEATURES support use ggplot2 cdfcomp, denscomp, qqcomp, ppcomp. BUG FIXES correct legend qqcomp ppomp large data. correct weights mmedist. correct name Akaike gofstat. correct use trueval plot.bootdist. correct vignette truncate (inflated) distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-7","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-7","title":"fitdistrplus 1.0-7","text":"CRAN release: 2016-07-02 NEW FEATURES keep JSS vignette pdf. start FAQ vignette add datasets (?dataFAQ) . provide likelihood plot/surface/curve: llplot, llcurve, llsurface. provide parallelization bootstrap bootdist bootdistcens. provide graphic (e)cdf bootstraped confidence interval/area: CIcdfplot. allow use constrOptim() mledist, mmedist, mgedist, qmedist functions. add possible pre-fitting procedure: prefit. BUG FIXES add invisible() graphical functions. bug fixed concerning use weights censored data.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-6","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-6","title":"fitdistrplus 1.0-6","text":"CRAN release: 2015-11-30 BUG FIXES automatic definition starting values distributions llogis invweibull now working.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-5","title":"fitdistrplus 1.0-5","text":"CRAN release: 2015-09-21 NEW FEATURES update starting/fixing values mledist, mmedist, mgedist, qmedist functions. update graphics bootstrap procedure. add argument .points cdfcomp. add argument weights mledist, qmedist, mmedist, fitdist, fitdistcens. add argument keepdata fitdist, fitdistcens. suppress warnings/errors fitdist(cens), bootdist(cens). BUG FIXES defensive programming plotdist, cdfcomp,… simplify plotting curves cdfcomp seq(xmin, xmax, =1) used.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-4","title":"fitdistrplus 1.0-4","text":"CRAN release: 2015-02-23 release JSS publication.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-3","title":"fitdistrplus 1.0-3","text":"CRAN release: 2014-12-13 NEW FEATURES new generic functions fitdist(cens): loglik, vcov coef. vignette updated version paper accepted Journal Statistical Software. add argument discrete fitdist order able take account non classical discrete distributions plotting fit plot.fitdist cdfcomp calculating goodness--fit statistics gofstat (add example : fit zero inflate Poisson distribution). add S3 class descdist print method. BUG FIXES fitdist can handle non invertible Hessian matrices.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-2","title":"fitdistrplus 1.0-2","text":"CRAN release: 2014-02-12 NEW FEATURES plotdist can plot empirical density histogram, density plot superimposed. strong warning added documentation function descdist problematic high variance skewness kurtosis. BUG FIXES bug fixed bootdistcens : argument fix.arg now correctly passed mle.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-1","title":"fitdistrplus 1.0-1","text":"CRAN release: 2013-04-10 NEW FEATURES gofstat can handle multiple fitdist objects. plotdist discrete data slightly enhanced.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-10-0","dir":"Changelog","previous_headings":"","what":"fitdistrplus 1.0-0","title":"fitdistrplus 1.0-0","text":"CRAN release: 2012-12-27 NEW FEATURES update cdfcomp add denscomp, ppcomp qqcomp functions. add argument Turnbull.confint functions plotdistcens cdfcompcens order draw confidence intervals empirical distribution requested. ppoints now used “fitdist” QQ plot, PP plot cdf plot continuous data (used QQ plot previous versions) enable Blom type plotting position (using default Hazen plotting position can chanfge using arguments use.ppoints .ppoints) many changes examples given reference manual. vignette removed, transformed paper soon submit journal. add four data sets : fluazinam, salinity, danishuni danishmulti. add functions calculate quantiles fitted distribution, 95 percent CI calculated bootstrap : quantile generic function available fitdist bootdist objects quantile generic function available fitdistcens bootdistcens objects. BUG FIXES correction formula CvM test Weibull distribution. elimination CvM AD tests normal, lognormal logistic distributions : formulas previously used (given Stephens 1986) use exactly MLE estimates thus results approximates. make arguments xlim ylim functional cdfcompcens. bug fix closed formula mmedist lognormal distributions.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-4","title":"fitdistrplus 0.3-4","text":"CRAN release: 2012-03-22 NEW FEATURES posibility fix xlegend keyword (e.g. bottomright) cdfcomp cdfcompdens. improvement new vignette. BUG FIXES correction NAMESPACE file order enable correct print summary fitdistcens object (correlation matrix, loglikelihood AIC BIC statistics).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-3","title":"fitdistrplus 0.3-3","text":"NEW FEATURES new function (cdfcompcens) plot cumulative distributions corresponding various fits using censored data set. add example scaling problem man pages.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-2","title":"fitdistrplus 0.3-2","text":"NEW FEATURES new plot empirical cdf curve plotdistcens, using Turnbull algorithm call function survfit{survival}. new arguments function cdfcomp : verticals, horizontals xlim.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-1","title":"fitdistrplus 0.3-1","text":"NEW FEATURES add draft new version vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-03-0","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.3-0","title":"fitdistrplus 0.3-0","text":"NEW FEATURES new function (cdfcomp) plot cumulative distributions corresponding various fits using non censored data set. add two data sets : endosulfan toxocara.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-02-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.2-2","title":"fitdistrplus 0.2-2","text":"CRAN release: 2011-04-27 BUG FIXES elimination NON-ASCII characters vignette.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-02-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.2-1","title":"fitdistrplus 0.2-1","text":"CRAN release: 2011-03-18 NEW FEATURES new fitting method implemented continuous distributions : maximum goodness--fit estimation (function mgedist) (moment available non censored data).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-5","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-5","title":"fitdistrplus 0.1-5","text":"NEW FEATURES new goodness--fit statistic added gofstat, corresponding test : Cramer-von Mises distance. new fitting method implemented : quantile matching estimation (function qmedist). moment, available non censored data. moment matching estimation extended (function mmedist) enable numerical matching closed formula available. BUG FIXES correction bug inserted adding argument fix.arg prevent print results goodness--fit tests.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-4","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-4","title":"fitdistrplus 0.1-4","text":"CRAN release: 2010-09-16 NEW FEATURES component named dots added list returned fitdist fitdistcens order pass optional arguments control optimization mledist bootdist bootdistcens. bootdist bootdistcens changed take account optional arguments defined call fitdist fitdistcens. argument added fitdist, fitdistcens mledist, named fix.arg, giving possibility fix distribution parameters maximizing likelihood. Functions bootdist, bootdistcens gofstat also changed order take new argument account. new data file bacterial contamination censored data extracted Busschaert et al. 2000 examples corresponding analysis dataset. BUG FIXES correction bug print plot bootstraped samples using bootdist bootdistcens one parameter estimated maximum likelihood.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-3","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-3","title":"fitdistrplus 0.1-3","text":"CRAN release: 2010-06-02 NEW FEATURES new data file groundbeef (groundbeef.rda groundbeef.Rd) new use dataset examples. new function gofstat. Goodness--fit statistics computed fitdist may computed printed use function gofstat. new function, whole results computed printed : results tests printed argument print.test==TRUE continuous distributions Anderson-Darling Kolomogorov-Smirnov statistics printed default (complete results returned gofstat). modifications descdist : three arguments added descdist 1/ method, choose unbiased estimations standard deviation, skewness kurtosis (default choice) sample values. 2/ obs.col choose color used plot observed point graph. 3/ boot.col choose color used plot bootstrap sample points graph. modifications plotfit : minor changes performed order facilitate use argument … personnalize plots (see examples plotdist.Rd) modication vignette BUG FIXES correction bug plotdist due redefinition previous version parameter “ylim” plot histogram theoretical density function (problem infinite values theoretical density function).","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-2","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-2","title":"fitdistrplus 0.1-2","text":"CRAN release: 2009-12-29 NEW FEATURES deletion mledistcens modification mledist order maximize likelihood censored non censored data. possibility choose optimization method used maximum likelihood estimation (MLE) distribution parameters using new argument “optim.method” mledist. possibility specify contraints distribution parameters using new arguments “lower” “upper” mledist. possibility use custom optimization function MLE using new argument “custom.optim”. moment matching estimation longer done argument method set “mom” set “mme” fitdist. renaming momdist mmedist. calculation AIC BIC criterion maximum likelihood estimation distribution parameters change default number iterations 999 1001 bootstrap order avoid interpolation using quantile function use argument “log” (resp. “log.p”) density (resp. distribution) available compute loglikelihood. BUG FIXES omitting name first argument calls density function maximization likelihood order enable use density function defined first parameter (vector quantiles) name differing “x” (classical name density distributions defined R), density function dexGAUS package gamlss.dist.","code":""},{"path":"https://lbbe-software.github.io/fitdistrplus/news/index.html","id":"fitdistrplus-01-1","dir":"Changelog","previous_headings":"","what":"fitdistrplus 0.1-1","title":"fitdistrplus 0.1-1","text":"CRAN release: 2009-02-16 Initial release.","code":""}]