From e4ea9d1198dbbc73a2cbbe7f4c87a10706c6a09e Mon Sep 17 00:00:00 2001 From: Eliza Neights Date: Wed, 28 Feb 2024 19:47:01 -0500 Subject: [PATCH 1/3] Fix link to grb notebook --- docs/tutorials/index.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index cff497e8..8e0d15e6 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -32,7 +32,7 @@ List of tutorials and contents, as a link to the corresponding Python notebook i - Meaning of the TS map and how to compute confidence contours - Computing a TS map, getting the best location and estimating the error -5. Fitting the spectrum of a GRB `(ipynb) `_ +5. Fitting the spectrum of a GRB `(ipynb) `_ - Introduction to 3ML and astromodels - Likelihood analysis. @@ -69,7 +69,7 @@ List of tutorials and contents, as a link to the corresponding Python notebook i Spacecraft orientation and location Detector response and signal expectation TS Map: localizing a GRB - Fitting the spectrum of a GRB + Fitting the spectrum of a GRB Fitting the spectrum of the Crab Extended source model fitting Image deconvolution From 936dea6de3197c160276ad0835104ae68d3085c4 Mon Sep 17 00:00:00 2001 From: Eliza Neights Date: Fri, 1 Mar 2024 12:09:00 -0500 Subject: [PATCH 2/3] Update COSILike to only print log-likelihood warning once per fit --- cosipy/threeml/COSILike.py | 10 +- .../continuum_fit/crab/SpectralFit_Crab.ipynb | 12594 +--------------- .../continuum_fit/grb/SpectralFit_GRB.ipynb | 6648 +------- 3 files changed, 364 insertions(+), 18888 deletions(-) diff --git a/cosipy/threeml/COSILike.py b/cosipy/threeml/COSILike.py index 3fa27e6a..639de5df 100644 --- a/cosipy/threeml/COSILike.py +++ b/cosipy/threeml/COSILike.py @@ -33,6 +33,8 @@ import logging logger = logging.getLogger(__name__) +import inspect + class COSILike(PluginPrototype): """ COSI 3ML plugin. @@ -127,6 +129,10 @@ def set_model(self, model): model : astromodels.core.model.Model Any model supported by astromodels """ + + # Temporary fix to only print log-likelihood warning once max per fit + if inspect.stack()[1][3] == '_assign_model_to_data': + self._printed_warning = False # Get point sources and extended sources from model: point_sources = model.point_sources @@ -294,7 +300,9 @@ def get_log_like(self): expectation = self._signal + self._bkg.contents expectation += 1e-12 # to avoid -infinite log-likelihood (occurs when expected counts = 0 but data != 0) - logger.warning("Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.") + if not self._printed_warning: + logger.warning("Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.") + self._printed_warning = True # This 1e-12 should be defined as a parameter in the near future (HY) # Convert data into an arrary: diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb index 7f294173..f4a2f72a 100644 --- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb +++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb @@ -71,12 +71,12 @@ { "data": { "text/html": [ - "
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Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=982551;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=171931;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -305,7 +305,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=891845;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=99596;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=958192;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=870302;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -315,11 +315,11 @@ { "data": { "text/html": [ - "
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        "                  performances in 3ML                                                                              \n",
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        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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+       "\u001b[38;5;46m12:04:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=444891;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=999475;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
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      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
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-      "\n",
-      "\n",
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-      "There are bins in which the total expected counts = 0 but data != 0, making log-likelihood = -inf. Masking these bins.\n",
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-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n"
+      "... Calculating point source responses ...\n"
      ]
     },
-    {
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      "text": [
       "\n",
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-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
+      "WARNING ErfaWarning: ERFA function \"utctai\" yielded 7979956 of \"dubious year (Note 3)\"\n",
       "\n"
      ]
     },
     {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "There are bins in which the total expected counts = 0 but data != 0, making log-likelihood = -inf. Masking these bins.\n",
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-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n"
+      "--> done (source name : source)\n",
+      "--> all done\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "l = 184.56\n",
+    "b = -5.78\n",
+    "\n",
+    "alpha = -1.99\n",
+    "beta = -2.32\n",
+    "E0 = 531. * (alpha - beta) * u.keV\n",
+    "xp = E0 * (alpha + 2) / (alpha - beta)\n",
+    "piv = 500. * u.keV\n",
+    "K = 3.07e-5 / u.cm / u.cm / u.s / u.keV\n",
+    "\n",
+    "spectrum = Band()\n",
+    "\n",
+    "spectrum.alpha.min_value = -2.14\n",
+    "spectrum.alpha.max_value = 3.0\n",
+    "spectrum.beta.min_value = -5.0\n",
+    "spectrum.beta.max_value = -2.15\n",
+    "spectrum.xp.min_value = 1.0\n",
+    "\n",
+    "spectrum.alpha.value = alpha\n",
+    "spectrum.beta.value = beta\n",
+    "spectrum.xp.value = xp.value\n",
+    "spectrum.K.value = K.value\n",
+    "spectrum.piv.value = piv.value\n",
+    "\n",
+    "spectrum.xp.unit = xp.unit\n",
+    "spectrum.K.unit = K.unit\n",
+    "spectrum.piv.unit = piv.unit\n",
+    "\n",
+    "spectrum.alpha.delta = 0.01\n",
+    "spectrum.beta.delta = 0.01\n",
+    "\n",
+    "source = PointSource(\"source\",                     # Name of source (arbitrary, but needs to be unique)\n",
+    "                     l = l,                        # Longitude (deg)\n",
+    "                     b = b,                        # Latitude (deg)\n",
+    "                     spectral_shape = spectrum)    # Spectral model\n",
+    "\n",
+    "# Optional: free the position parameters\n",
+    "#source.position.l.free = True\n",
+    "#source.position.b.free = True\n",
+    "\n",
+    "model = Model(source)  # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n",
+    "\n",
+    "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n",
+    "# 3ML does this internally during the fit though\n",
+    "cosi.set_model(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e955d7a4-a40f-4fa2-af9a-c277fce3dc26",
+   "metadata": {},
+   "source": [
+    "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f83dea31-2284-4f87-910a-be1003d970c8",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
     {
      "data": {
       "text/html": [
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+       "
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        "
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       ],
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@@ -12829,36 +760,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
       "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "There are bins in which the total expected counts = 0 but data != 0, making log-likelihood = -inf. Masking these bins.\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
+      "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
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19:07:01 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
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        "                  measurements such as AIC or BIC are unreliable                                                   \n",
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        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
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+       "\u001b[38;5;46m12:05:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=562228;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=53136;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -13049,11 +967,11 @@
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        "      \n",
-       "      \n",
+       "      \n",
        "    \n",
        "    \n",
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-       "      \n",
+       "      \n",
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        "
1.000.42-0.61-0.120.05
0.421.000.450.08-0.03
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@@ -13061,8 +979,8 @@
       ],
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        "       -log(likelihood)\n",
-       "cosi      -2.612137e+08\n",
-       "total     -2.612137e+08"
+       "cosi      -2.612135e+08\n",
+       "total     -2.612135e+08"
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      "metadata": {},
@@ -13112,11 +1030,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      -5.224273e+08\n",
+       "      -5.224270e+08\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      -5.224273e+08\n",
+       "      -5.224270e+08\n",
        "    \n",
        "  \n",
        "\n",
@@ -13124,8 +1042,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC         -5.224273e+08\n",
-       "BIC         -5.224273e+08"
+       "AIC         -5.224270e+08\n",
+       "BIC         -5.224270e+08"
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      "metadata": {},
@@ -13135,24 +1053,24 @@
      "data": {
       "text/plain": [
        "(                                    value  negative_error  positive_error  \\\n",
-       " source.spectrum.main.Band.K      0.000029   -2.325305e-07    2.284348e-07   \n",
-       " source.spectrum.main.Band.alpha -1.988617   -3.716644e-04    3.609998e-04   \n",
-       " source.spectrum.main.Band.xp     4.473464   -1.700670e-01    1.798891e-01   \n",
-       " source.spectrum.main.Band.beta  -2.196416   -1.562358e-03    1.599737e-03   \n",
-       " background_cosi                  0.991932   -2.039369e-04    1.939033e-04   \n",
+       " source.spectrum.main.Band.K      0.000029   -2.272835e-07    2.278935e-07   \n",
+       " source.spectrum.main.Band.alpha -1.988617   -3.560944e-04    3.585775e-04   \n",
+       " source.spectrum.main.Band.xp     4.473464   -1.691279e-01    1.784522e-01   \n",
+       " source.spectrum.main.Band.beta  -2.196416   -1.564268e-03    1.565798e-03   \n",
+       " background_cosi                  0.991932   -1.915462e-04    1.937451e-04   \n",
        " \n",
        "                                         error             unit  \n",
-       " source.spectrum.main.Band.K      2.304827e-07  1 / (cm2 keV s)  \n",
-       " source.spectrum.main.Band.alpha  3.663321e-04                   \n",
-       " source.spectrum.main.Band.xp     1.749780e-01              keV  \n",
-       " source.spectrum.main.Band.beta   1.581048e-03                   \n",
-       " background_cosi                  1.989201e-04                   ,\n",
+       " source.spectrum.main.Band.K      2.275885e-07  1 / (cm2 keV s)  \n",
+       " source.spectrum.main.Band.alpha  3.573359e-04                   \n",
+       " source.spectrum.main.Band.xp     1.737900e-01              keV  \n",
+       " source.spectrum.main.Band.beta   1.565033e-03                   \n",
+       " background_cosi                  1.926456e-04                   ,\n",
        "        -log(likelihood)\n",
-       " cosi      -2.612137e+08\n",
-       " total     -2.612137e+08)"
+       " cosi      -2.612135e+08\n",
+       " total     -2.612135e+08)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13183,18 +1101,18 @@
   },
   {
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-   "execution_count": 12,
+   "execution_count": 11,
    "id": "1758942e-55e6-47ea-ab06-779e8a0fc622",
    "metadata": {},
    "outputs": [
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      "data": {
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@@ -13238,7 +1156,7 @@
   },
   {
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-   "execution_count": 13,
+   "execution_count": 12,
    "id": "c3851355-28fa-4ff4-9c5b-522733bb110e",
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@@ -13353,7 +1271,7 @@
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@@ -13428,8 +1346,8 @@
       ],
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        "       -log(likelihood)\n",
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+       "cosi      -2.612135e+08\n",
+       "total     -2.612135e+08"
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@@ -13479,11 +1397,11 @@
        "  \n",
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        "      AIC\n",
-       "      -5.224273e+08\n",
+       "      -5.224270e+08\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      -5.224273e+08\n",
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@@ -13491,8 +1409,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC         -5.224273e+08\n",
-       "BIC         -5.224273e+08"
+       "AIC         -5.224270e+08\n",
+       "BIC         -5.224270e+08"
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@@ -13508,11 +1426,11 @@
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       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=89741;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=341856;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=521892;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=431406;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -13538,7 +1456,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=157516;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=299965;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=402878;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=82481;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -13569,28 +1487,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 2.8565586193551912e-05\n",
+      "            * value: 2.8565585663971596e-05\n",
       "            * desc: Differential flux at the pivot energy\n",
       "            * min_value: 1.0e-99\n",
       "            * max_value: null\n",
       "            * unit: keV-1 s-1 cm-2\n",
       "            * is_normalization: true\n",
       "          * alpha:\n",
-      "            * value: -1.9886166180646347\n",
+      "            * value: -1.9886166208617622\n",
       "            * desc: low-energy photon index\n",
       "            * min_value: -2.14\n",
       "            * max_value: 3.0\n",
       "            * unit: ''\n",
       "            * is_normalization: false\n",
       "          * xp:\n",
-      "            * value: 4.4734637141566\n",
+      "            * value: 4.473463779563324\n",
       "            * desc: peak in the x * x * N (nuFnu if x is a energy)\n",
       "            * min_value: 1.0\n",
       "            * max_value: null\n",
       "            * unit: keV\n",
       "            * is_normalization: false\n",
       "          * beta:\n",
-      "            * value: -2.1964164746917305\n",
+      "            * value: -2.196416422107725\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -5.0\n",
       "            * max_value: -2.15\n",
@@ -13632,20 +1550,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "804e78ca-2ccb-421b-aff2-ad41b70ed24f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING ErfaWarning: ERFA function \"utctai\" yielded 7979956 of \"dubious year (Note 3)\"\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n",
     "\n",
@@ -13681,7 +1589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "3402ff8a-d50e-4bad-a27d-f3b7bc4f4f54",
    "metadata": {
     "tags": []
@@ -13690,16 +1598,16 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -13734,17 +1642,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "e20787dd-42ce-4255-9994-2280912165c8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -13786,23 +1694,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "29823cda-ca7b-4c5c-ac11-681bfaf12ba8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
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diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
index b012e708..8f0a2579 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
@@ -72,12 +72,12 @@
     {
      "data": {
       "text/html": [
-       "
11:40:23 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
+       "
12:04:24 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
        "                  available                                                                                        \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m11:40:23\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=632053;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=922042;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m12:04:24\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=258198;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=478015;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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@@ -92,7 +92,7 @@
        "
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       ],
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-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=975981;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734970;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=322055;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=156430;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mwill not be available.                                                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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@@ -102,11 +102,11 @@
     {
      "data": {
       "text/html": [
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         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
12:04:25 WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
        "
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       ],
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-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=146941;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=118241;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n"
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"\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=460997;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=759566;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n" + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=559824;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=851427;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, @@ -236,7 +236,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=652000;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80994;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n" + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=915470;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=32249;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, @@ -249,7 +249,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=148575;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=672957;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n" + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=809874;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=490059;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, @@ -262,7 +262,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=396066;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=125;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n" + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=817192;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=443308;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, @@ -276,7 +276,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=562480;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=820025;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=844985;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=163953;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -291,7 +291,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=439066;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=946376;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=871918;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=23540;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -306,7 +306,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=963385;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=305974;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=354714;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=655302;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -320,7 +320,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=414310;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=315394;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=194521;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=717765;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, @@ -334,7 +334,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=147242;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=728056;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=22466;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=967970;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -349,7 +349,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=998970;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=33780;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=971686;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=989174;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -364,7 +364,7 @@ "\n" ], "text/plain": [ - "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=617970;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=608769;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=980480;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=239627;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "ed2c03a0-63e3-4044-9e16-50f0f17996af", "metadata": {}, "outputs": [], @@ -553,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "id": "3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26", "metadata": {}, "outputs": [], @@ -573,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "id": "620159d2-f01a-453e-9e4c-075c99740086", "metadata": {}, "outputs": [], @@ -593,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "acccab93-7f9c-4167-a8f9-eedcf74b8a05", "metadata": {}, "outputs": [], @@ -621,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "id": "a29ec8c4-edea-40bf-8a3e-8038ba47bf8e", "metadata": {}, "outputs": [], @@ -642,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "id": "a9f21e74-5f62-4030-9815-6c77ebaab16f", "metadata": {}, "outputs": [], @@ -672,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "id": "98b2d026-c24d-4cfe-8b7b-41415fce5d16", "metadata": {}, "outputs": [ @@ -680,8 +680,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "... Calculating point source responses ...\n", "Now converting to the Spacecraft frame...\n", - "Conversion completed!\n" + "Conversion completed!\n", + "--> done (source name : source)\n", + "--> all done\n" ] } ], @@ -735,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "id": "d56d3ad6-7226-437a-a037-57fbcd80d196", "metadata": { "scrolled": true, @@ -745,11 +748,11 @@ { "data": { "text/html": [ - "
12:42:48 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
+       "
12:04:56 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:42:48\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=98083;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=731551;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1042\u001b\\\u001b[2m1042\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m12:04:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=963889;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=362192;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1042\u001b\\\u001b[2m1042\u001b[0m\u001b]8;;\u001b\\\n"
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-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: divide by zero encountered in log\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
-      "\n",
-      "WARNING RuntimeWarning: invalid value encountered in multiply\n",
-      "\n",
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12:43:03 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
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-       " background_cosi                    0.164986       -0.012501        0.012824   \n",
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-       " background_cosi                  0.012663                   ,\n",
+       " source.spectrum.main.Band.K      0.002027  1 / (cm2 keV s)  \n",
+       " source.spectrum.main.Band.alpha  0.050010                   \n",
+       " source.spectrum.main.Band.xp     4.884611              keV  \n",
+       " source.spectrum.main.Band.beta   1.208137                   \n",
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        "  \n",
        "    \n",
        "      AIC\n",
-       "      85838.098697\n",
+       "      85838.098672\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      85840.098697\n",
+       "      85840.098672\n",
        "    \n",
        "  \n",
        "\n",
@@ -7806,8 +1375,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098697\n",
-       "BIC          85840.098697"
+       "AIC          85838.098672\n",
+       "BIC          85840.098672"
       ]
      },
      "metadata": {},
@@ -7839,28 +1408,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 0.030991288726256453\n",
+      "            * value: 0.030994516909178687\n",
       "            * desc: Differential flux at the pivot energy\n",
       "            * min_value: 1.0e-99\n",
       "            * max_value: null\n",
       "            * unit: keV-1 s-1 cm-2\n",
       "            * is_normalization: true\n",
       "          * alpha:\n",
-      "            * value: -0.27668340897900645\n",
+      "            * value: -0.27663221293105034\n",
       "            * desc: low-energy photon index\n",
       "            * min_value: -1.5\n",
       "            * max_value: 3.0\n",
       "            * unit: ''\n",
       "            * is_normalization: false\n",
       "          * xp:\n",
-      "            * value: 474.671132780481\n",
+      "            * value: 474.6507320770641\n",
       "            * desc: peak in the x * x * N (nuFnu if x is a energy)\n",
       "            * min_value: 10.0\n",
       "            * max_value: null\n",
       "            * unit: keV\n",
       "            * is_normalization: false\n",
       "          * beta:\n",
-      "            * value: -6.753586360838797\n",
+      "            * value: -6.756965748051311\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -15.0\n",
       "            * max_value: -1.6\n",
@@ -7902,19 +1471,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 14,
    "id": "cc7d6f50-06cd-450a-83d9-115b67d83b30",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Now converting to the Spacecraft frame...\n",
-      "Conversion completed!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n",
     "\n",
@@ -7950,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 15,
    "id": "f8dbd36f-4b16-4bec-8835-8f6f876ab169",
    "metadata": {
     "tags": []
@@ -7959,16 +1519,16 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 32,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -8003,23 +1563,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 16,
    "id": "7d1dd8d1-f86d-4e63-8286-db1d5bc14b04",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 33,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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enrz++uvn7dmZNm0ab7zxBvv27ePLL78ETu9AMWTIEJYtW8aTTz4JwJEjR6iurqZr166UlpaydetWhg4dyscff3ze9+zi4sIVV1zBkiVLeOONN+jVqxcJCQnk5uZa5mad/fmVlJRYZRL3772Hli1b4ujoyNVXX81VV13Fli1bSEhIoFevXpw6dYp3332X22+/nf/85z9kZ2fTq1cvWrduzd///neys7Px8/PjtddeY9SoURdcRyk5OZnOnTtzxx130L9/f6688krg9J/N5cuXM2DAANzd3SkpKSEtLY0ePXo06H2fTUWTFWhxy9oKfi3gH93+QUVJBW/yJq1pzUQmUkIJT/M0k5hEd7qzi12sYx0LWQjAu7yLI45MZjKVVLKEJdzADfSmN3vYwyd8whM8gQsufMiHVFGFH370ox/LWc44xtGPfiSSyPu8z6M8igcefMZnHOc4d3O6y/4pnmI4w7mSK0kllbd5m9nMxgUX/Bz9uPbNa4noEcHg5ME1eppuTr/Z0tN07dFra/Q0DUsdVqOn6fq06y09TRNyJlh6mg4lHiJlaQpT/ziVK668grLyMsYdHGfpaboh74YaPU0jD42s0dN0VcpVlp6miScmcseOO0icl0hJbomKJrGKm266CVdXVyoqKujUqZNlgvXtt99OXl4ew4cPx8HBgcrKSu6++24uu+wyHn/8cQ4cOICLiwvu7u68+uqrANx77708/PDDPPPMMzz11FOMHTu2zjl+77VOTk78+9//5oEHHmDx4sU4ODgwc+ZM7rvvPt59913uu+8+XnjhBRwcHFi1ahVBQUF1uuZdd93FY489Zil4AMaMGcOePXsYOHAgDg4O9OrVi1deeeWcr58wYQJ//OMfiYiIqLGkzbvvvsuf//xnoqKiAGjVqhWvvfYagYGBvPfee9x///2WzeWDg4PPm+9MUTJo0CAcHByIjo4mNTXVMnT4W+3atWPKlClERUXh4eHBzp076/QZnM/53kNVVRXTp0+noqKC6upqrrzySsaMGcPhw4fx9vYmJSWFK664gqKiIv7v//4PDw8PevTowd/+9jfLDVlBQUG8/vrrF8zw0Ucf8e677+Li4oJhGJY/m3PnzmXRokVcccUVlsJrzpw5Vi+aHAxDu0Ray5k5TStXrrT8ZNZcffPeN4y7dRzvLH2Hjt062qynKSMlg0/f/ZQHH36Qk8bJBvc0tXRpiUMrB/y6+Jn58V2U7z//nttvuJ23P3ubQdcPuvALpNEoKysjLS2N0NDQGsMRIk3BoUOH6NevH7m5uWZHsWjod049TWITfr5+jGIU3fp3IyomqsZzQf3/9xOfP/70pOf/Hp81nyOgX0CNc7vTvca5AxjAzX++udb1/fGnK13P2+5vH/vjTxfsdx0YJ0cnPPDAyVFfZxERW7LrdZqk8fJp58MABuDTThsa21pwQDA3cRPBAefv0hcRudQ6derUqHqZrEE/mlqB5jTVVlhUSAopFBYV4k/juRuoKaqqqqKMMqqqqsyOIiLSpKmnyQq0YW9taRlpvMM7pGWkmR2lydt3YB/LWMa+A/vMjiIi0qSpaBKb6BrelYd4iK7hXS98sjRIcEAwN3OzhudERGxMRZPYhIuzC1544eLsYnaUJq9N6zb0oAdtWrcxO4qISJOmoklsIjMrk3WsIzMr0+woTd7x/OP8xE8czz9udhRpAj799FOio6Pp06cP3bp1Y9SoUVRXV5sdy1QLFy7k1KlTZseQRkATwa1AE8FrKy0rJYssSstKzY7S5GVmZfJv/s0fs/5ID6y7kJs0L9nZ2cyYMYP//ve/ln2/4uPjL7hK88WwxurUl9qiRYt45JFHcHFRz3lzp54mK9BE8NoiQiO4l3uJCI0wO0qTF9U1iid5kqiuURc+WeR3ZGVl4eTkhLe3t+VY3759LUXTzp07GThwIL169aJ///58//33wOlFDH18/re8SFFRUa19xZ577jmGDx/O448/TkFBAffccw9RUVH07t3bspddRUUFc+fOpX///vTp04fJkyeTn59/zqwbNmzg8ssvp3fv3vTp04cff/wRgI0bN9K3b1969erFsGHDSEhIAGDz5s3069fP8vq9e/fSqVOnGvnnz59PdHQ04eHhfPHFFwDMmDEDgCuvvJI+ffpw7NgxVq1aRffu3enTpw9RUVGWa0vTZ1/lvojU4uDgQBll/LTlJ8tGmHsS9xDoH0i7Nu3IP5nPr4d/tayk/uvhX6msqrTs2bcncQ8dO3TEu603JwtPcijzkGUl9cysTMrKyywrqe87sI/23u3x9falqLiIg78epEvnLri2dOVw9mGKS4rp0vn0QqH7k/fTrk07Ovh2oKS0hJRDKZYtaLKOZeHi5ULPgT3P+76aq6ysLHJzcy1bVSQkJODp6UlQUBBlZWUkJCQQERGBp6cnR48eJTs7m969ewOndyVwdXUlJCSEiooK9uzZQ1hYGF5eXuTk5FBZWYm///mXAOnduzcDBw4kODiYYcOGceWVV3LrrbcSEBDAqVOnuPHGG1m5ciWjR4/mu+++46abbiIlJaVO76u8vJzNmzcDp7cqadWqFb/88gstWrQgJycHgGeeeYZWrVrxn//8B4C//vWvLFiwgBdffLFGWwcOHODuu+9m69atdOnShYqKCkpKSjh27Bi33XYbcXFxREVF8e677zJp0iT27t17wXx5eXlER0ezePFiNm7cyOzZsxk7diyvvfYa//znP/nhhx9o1aoVAA8//DD79++nY8eOVFRUUF5eXqfPQOyfiiaxiYTkBJ7maQYnD25Uu7Y3RRUuFaxzXMdnD3/GbdxGFVX8lb8ygQlcxmXsYx8f8RFzmYsrrnzMxxRTzJ3cCcBf+SujGU1/+nOAA/wf/8fDPIwnnvybf5NDDn/gD+xkJ7vZTT/6MYhBpJHGGtYwi1l4480XfMEhDnE/9wPwHM/Rl76MYASHOcxKVjKDGfjhxwY2kOqQyo7/7iAiWr2Rv/XPf/6TVatWkZl5ej7g5MmTGT58OC+99BKZmZlER0cTFxfH8OHD+de//sXf/vY3jh8/PZ9t6tSp9OjRg1WrVpGbm0t0dDTr169n3LhxfPjhh+Tk5LBw4cLzXrtFixZ88sknJCYmsmXLFr788kuWLl3Kzp07KS0txcXFhdGjRwMwePBg2rdvz+7du3+3EDvjTG8SwPr169m1axctWpwe7PD19QXg888/5+TJk3z88ccAnDp1irCwsFptffPNN4wdO5YuXU4X6M7Oznh5ebFu3TpL7w/AlClTmDlzJllZWRfM5+HhwYQJEwAYOHAgqamp5z135MiR3HHHHVx33XWMGTPGkkOaPhVNYhM+bX0YyEB82mpFcFsL7hnMR9s+ovBYIZ2COmEYBoMSB9Xoabr78N2WnqZrDl9To6dpYOLAGj1Nd2TeYelpGps1lrLyMpzKnDj83GHuufkeekX3svQ03fLrLZaepnHZ42r0NA1JHlKjp2nioYmWnqYrv7+Sbx78hlYOrcz86Bql++67j4kTJ1oev//++3h6egIQGBjIrl27iIg4XWjecccdlg1PAd566y3Lflo+Pj7s2rXLUnRMmjSJysrKOmXo2rUrXbt25b777uOaa65h7dq1xMTEnHNuk4ODA05OTjUWVy0rK6t13plemt9jGAavvPIKI0eOrFPOc72+vhl/uw+Zo6Pj7y4W++mnn7Jr1y42b97M2LFjWbJkCZMnT65XZrEzhlhNYmKiMWTIECMxMdHsKKY7suuIsZCFxpFdR8yOIo2Q/nycVlpaaiQkJBilpaVmRzEMwzAyMzON7777zvL4+PHjRpcuXYzPP//cKC8vN4KCgoxNmzYZhmEY33//veHn52cUFRUZFRUVRqtWrSx/9z333HPGb/95AYzCwkLL42nTphkPPPCAUVVVZRiGYRw7dswwDMNYvHixMW7cOKO4uNgwDMMoLi429u7dWytncnKy4efnZyQlJRmGYRinTp0y8vPzjWPHjhk+Pj5GQkKCYRiG8d577xk9evSwvDcvLy8jNzfXMAzDePDBB42QkBDDMAwjLS3N8Pb2trRfWFhYI7+np6eRmZlpGIZhVFRUGMnJyZbn5syZYzz00EN1/ITFbA39zqmnSWyiuKSYdNIpLik2O4o0QkmpSaxgBcNSh2n4thGprKxk8eLFpKWl4e7uTmVlJXfeeadl2OqTTz7hwQcfpLi4GFdXVz766CM8PDwAeOmllxgzZgyBgYGMGTPmd6/z/PPP89BDD9GzZ09cXFy4/PLLWblyJXPnzmXRokVcccUVlh6jOXPm0KNHzbtCw8PDWb16NbfccgsVFRU4Ojryz3/+k/79+/P2228zZcoUqqqqaNOmDR9++CEAAQEBPPLII/Tr149OnToxdOjQOn8uDz/8MCNHjsTNzY2vvvqKu+66ixMnTuDk5ISvry9vvvlmndsS++ZgGIZhdgh799slB3bv3s3KlSuJjIw0O5apvnr3K6657Ro2vrOR0VNGmx1HGpldX+7iwbEP8tIXLxE9JtrsOKYpKysjLS2N0NDQGsNDImIbDf3OqafJCmJiYoiJiSEpKYnp06ebHadR6NK5Cw/wgGV+i8hvdezQkau5mo4dOpodRUSkzrROk9iEa0tXfPDBtaV+epbaSstKOcYxLX4qInZFRZPYxJGjR9jIRo4cPWJ2FGmEktOSeYVXSE5LNjuKiEidqWgSmygqLiKVVIqKtbWM1BYWEsbd3E1YSO01eJojTS0VuTQa+l3TnCaxiS6duzCTmZrTJOfk4e5BEEF4uHuYHcVUzs7OODg4kJOTg6+vr1X3eBORmgzDICcnBwcHB5ydnevVhoomEbnkjuYcZQtbmJAzAX+a75IDjo6OBAYGkpmZyaFDh8yOI9LkOTg4EBgYiKOjY71er6LJThT8WsD+n/dTbVQTGhQKwO79uwnwC8C7rbdlf7HuEd1xcnLi18O/UlFZYRn+2Ju0Fz9fP3za+VBYVEhaRhpdw7vi4uxCZlYmpWWlls11E5IT8GnrQ3uf9hSXFJOanmpZ9fnI0SMUFRdZepASUxJp49UGP18/y/5i4Z3C+Xnbz7zIiwxNGap1eKSWvPw8/st/ycvPMzuK6Vq1akVERAQVFRVmRxFp8pydnetdMIGKJrtQ8GsB/+j2D94ueZsqqpjCFAAWspDruI5ookkggQ/5kMd4DHfc+YRPOMlJ7uIuAJaylFGMYgADSCGFd3iHh3gIL7xYxzpyyaUTnehHP17lVQYykCEMIZ103uRNHuABfPBhIxtJJZWZzATgeZ6nF70YxSiOcITXeZ17uRcvvOjt1BvfIF/TPjdpvLpHdOcRHqF7RHezozQKjo6ODfqLXEQuDRVNdqDoWBGlJaU88+wztAtvZ+lpGrB/QI2epmmHp1l6mq45fE2NnqYrk66s0dN0W8Ztlp6msVljSd2fyr/f+zcTH57IGMcxNXqa/pD+B0tP07VHr63R0zQ0ZWiNnqYbD91IeKdw3N3ccWrthG+4iiYREWkatCK4Fdh6RXCtri1NzdZPtvKHm/7ABx9/wNCJdd/OQkTETOppsgJbrwge1DGIG7iBoI5BVm9bxAxurm4EEYSbq5vZUURE6kzrNNmBtl5t6U1v2nq1NTuKiFUE+gcyjnEE+geaHUVEpM5UNNmBEwUn2MMeThScMDuKiFWUnyrnBCcoP1VudhQRkTpT0WQHMo5k8AmfkHEkw+woIlaRlJrEi7xIUmqS2VFEROpMRZMd6NGlB0/wBD269DA7iohVhAaFcju3W+4EFRGxByqa7ICjoyMuuGgdF2kyPFt5EkYYnq08zY4iIlJnKprsQHpmOh/yIemZ6WZHEbGKnLwcfuAHcvJyzI4iIlJnWnLADlRVV1FOOVXVVWZHEbGKo7lH2cxmEv+bSEFhAS1dWhLUMYiKigr2p+wnJDAEL08v8k7kcTj7ML269QLg4K8HcWzhSEhgCFVVVew7sI+gjkG09WrLiYITZBzJoGdkT1q0aEF6ZrrVtx1q498Gr2Av0z43ETGXiqbfWLhwIT/99BPl5eX4+flx7733cuWVV5odi87Bnbmd2+kc3NnsKCJW0X9Qfxa4L2D/X/bzCI/QjnbcwA0UU8wzPMNkJtOVruxkJxvYwAIWAPA2b9OSlkxiEqc4xVM8xUQmEkUUv/ALn/EZ85iHE058xEdUUcU4xuGJp1W2HfJx92Hm/pkqnESaKRVNv3HnnXfyl7/8BWdnZxISEnj44Yd5//338fLSX5Ai1uQV7MXM/TMpyS1h5KGRNXqaRqWMsvQ0TTwxkQeyH7D0NMX8GlOjp2nogaE1epruO3KfpacpamsUb696m4lPTKRT104N3naoIruChY8s5Ko9V3F58OVmfnwiYhIVTb8RGvq/O3kcHR2pqKggNzfX9KJp9/7dLGIRA/YPwL+vv6lZRKzFK9gLr2CvWn+mg68Itvy3P/70pOf/Hp91buDlgTXO7c7/NgC+se+N3PinG8/5Wn/86Ua387Z79rld6MLWT7aSRRalZaV1fo8i0rTYbdFUUlLCmjVrSE5OJjk5mYKCAqZOncq0adPOee6qVauIi4ujsLCQ4OBgpkyZwqhRo2qdu3jxYrZu3cqpU6cYMGAAnTubPyQW4BfAOMYR4BdgdhSRZisiNIJ7uZeI0Aizo4iISey2aCooKGDdunWEhYUxZMgQ1q9ff95z582bR2JiIvfddx9BQUHExsayaNEiqqurueqqq2qcO3/+fCorK4mPjyc9PR0HBwdbv5UL8m7rTT/64d3W2+woIiIizZbdFk1+fn5s2LABBwcH8vPzz1s0bd++nZ07dzJ//nxiYmIA6Nu3L9nZ2bz66quMHDmy1vpHTk5O9O/fn08++YTAwEAGDhxo8/fzewoKC0jk9F1G/mh4TsQMCckJPM3TDE4erGFykWbKbtdpcnBwqFMv0LZt23Bzc2P48OE1jo8dO5bc3FwSEhLO+9rq6moOHz583udzc3NJSkqy/EpPt806SumZ6bzP+1qnScREPm19GMhAfNr6mB1FRExitz1NdZWWlkZISAhOTjXfalhYmOX5qKgo8vLy2LNnD1dccQXOzs5s3bqVn376ifvuu++8ba9du5a33nrLlvEB6BbejUd5lG7h3S58sojYRHuf9gxhCO192psdRURM0uSLpoKCAjp27FjruKfn6e0bTp48aTn28ccf8/e//x0HBwcCAwNZuHAh4eHh5217/PjxDBo0yPI4PT2dJUuWWDH9ac7OznjggbOzs9XbFpG6KS4pJp10ikuKzY4iIiZp8kUTUKdhPG9vb15++eWLatfHxwcfHx9iY2OJjY2lqKiovhF/V8aRDD7jM8YcGaO5FCImSU1P5U3e5A/pfyB88Pl/mBKRpqvJF01eXl4UFBTUOl5YWAhA69atG3yNmJgYYmJiSEpKYvr06Q1u72zlp8o5znHKT5VbvW0RqZsunbvwAA/QpXMXs6OIiEnsdiJ4XXXu3Jn09HQqKytrHD948CBQc0HLxiq8Uzh3czfhnfTTrYhZXFu64oMPri1dzY4iIiZp8kXTkCFDKC0tZcuWLTWOb9y4ER8fH7p3736eV9ZdbGwsc+fOZcWKFQ1uS0QapyNHj7CRjRw5esTsKCJiErsentuxYwdlZWWUlJQApydib968GYABAwbg6urKgAED6NevH8uXL6ekpISAgAA2bdrEjz/+yLx582qt0VQfth6e25u0l6d4iiuTrtScJhGTFBUXkUoqRcW2mbsoIo2fXRdNy5cvJzs72/I4Li6OuLg4AD744AP8/U8XGEuWLGHlypWsXr3aso3KggULzrmNSmPUwacDwxlOB58OZkcRaba6dO7CTGZqTpNIM2bXRdOHH35Yp/Pc3d2ZPXs2s2fPtkkOW9895+vty5Vcia+3r03aFxERkQtr8nOaLoWYmBiWLVvGrFmzbNJ+YVEhqaRSWFRok/ZF5MISUxJ5nudJTEk0O4qImERFkx1Iy0jjbd4mLSPN7CgizVYbrzb0ohdtvNqYHUVETKKiyQ5EhkUym9lEhkWaHUWk2fLz9WMUo/Dz9TM7ioiYxK7nNDUWtp7T1NKlJW1pS0uXljZpX0QurKS0hCMcoaS0xOwoImIS9TRZga3nNGVmZbKBDWRmZdqkfRG5sJRDKbzO66QcSjE7ioiYREWTHSgtKyWDDErLSs2OItJshXcK517u1cr8Is2YiiY7EBEawQxmEBEaYXYUkWbL3c2djnTE3c3d7CgiYhLNabICW89pEhHzZedks4lNjM8Zjz9amV+kOVJPkxXYek5TQnICz/IsCckJNmlfRC4svyCf3ewmvyDf7CgiYhIVTXbAu403l3M53m28zY4i0mx1De/KQzxE1/CuZkcREZOoaLIDHXw7MIxhdPDV3nMiIiJmUdFkB4pLiskgg+KSYrOjiDRbBw4e4B/8gwMHD5gdRURMoongVmDrieCp6amsZjU3p99M+GDd7ixihlYerQgjjFYercyOIiImUdFkBTExMcTExJCUlMT06dOt3n5EaAT3c7+WHBAxUccOHbmGa+jYoaPZUUTEJBqeswNurm60pz1urm5mRxFptsrKy8gll7LyMrOjiIhJVDTZgSNHj/A1X3Pk6BGzo4g0WwcOHuBlXtacJpFmTEWTHSgsKiSJJAqLCs2OItJshYWEcRd3ERYSZnYUETGJiiY7EBkWySxmERkWaXYUkWbLw92DEELwcPcwO4qImERFk4hIHRzLPcbXfM2x3GMAJCYm8uuvvwJw6tQp4uPjOXny5Olzjx3j559/trz2wIEDpKWlAVBZWUl8fDwnTpwAIDc3l/j4eACysrJ48MEH2bFjBwDV1dXEx8dz/PhxAI4fP058fDxVVVUAHDx4kOTkZMt14uPjycnJASA/P5/4+HgqKips8nmINEcqmqwgNjaWuXPnsmLFCpu0n5SaxIu8SFJqkk3aF5ELO1Vxip/5GUdHRwBuv/12li5dCpwukqKjo9m+fTsA77//PoMGDbK89t577+XJJ58E4OTJk0RHRxMXFwfA559/TnR0NAApu1NYsWIFTz58+tyKigqio6P54osvAPjqq6+Ijo6mrOz0ZPQ5c+bwwAMPWK7Tv39/Pv30UwC2bt1KdHQ0+fn5Nvk8RJojB8MwDLNDNBVnlhxYuXIlkZHWG0qL3xjP7DGzefHLF+l7TV+rtSsidZcVn8Wz0c9y5zt34tvNl+S0ZNxc3Qj0D+RUxSkSUxIJDQrFs5Unucdzyc7JpmdkT+D0WmvOTs4EBwRTWVlJQnICwQHBtGndhrwTeRzOPkyvbr3Y+sVWJj85mZf//DI3TrmR6upq9ibtJahjEG292nKi4AQZRzLo0aUHjo6OpGemU1VdRefgzgDs3r+bAL8AvNt6U1BYQHpmOtEDovEJ8zHzoxNpMrROkx3wb+9PDDH4t9fO6iJmcfdxx9vdm89u++y85+xgR43HP/BDnc/dwQ4KKWQYw0hbnsbry1//3XPr4gQneNzxcd7a8ha9BvWq02tE5PxUNNmB0rJSssmmtKzU7CgizZZXsBcz98+kJLfEZtcoySkh4dMEut/YHXdf9wa3999N/+XTxz6l4GiBFdKJiIomO5CclsxrvMb1adfT+crOZscRaba8gr3wCvay6TXCRltvSYPLuZy7uZvwTtp+ScQaNBHcDoR3Cmc60/UXn4iIiIlUNNkBdzd3AgjA3a3h3fUi0nzsTdrLUzzF3qS9ZkcRaRJUNNmBozlHiSOOozlHzY4iInakg08HhjOcDj4dzI4i0iSoaLIDx/OPE088x/OPmx1FROyIr7cvV3Ilvt6+ZkcRaRI0EdwKYmNjiY2NpaioyCbtd4voxsM8TLeIbjZpX0SapsKiQlJJpbCoEH+0ZIlIQ6mnyQpiYmJYtmwZs2bNMjuKiIhFWkYab/M2aRlpZkcRaRJUNNmBAwcP8AqvcODgAbOjiIgdiQyLZDaztdm3iJWoaLIDHu4edKKTdlcXkYvS0qUlbWlLS5eWZkcRaRJUNNmBAL8AxjKWAL8As6OIiB3JzMpkAxvIzMo0O4pIk6CiyQ6UlZeRRx5l5WVmRxERO1JaVkoGGdqCScRKVDTZgQMHD7CCFZrTJCIXJSI0ghnMICI0wuwoIk2CiiY70Dm4M3dyJ52Dte+ciIiIWVQ02YFWHq0IJZRWHq3MjiIidiQhOYFneZaE5ASzo4g0CSqa7EBOXg7f8z05eTlmRxERO+LdxpvLuRzvNt5mRxFpErQi+P936tQpnnvuOXbu3ElxcTGdOnVi5syZREVFmR2NY3nH2MY2juUdMzuKiNiRDr4dGMYwOvhq7zkRa1BP0/9XVVWFv78/r7zyCl988QUTJkzgiSeeoKzM/DvWenTpwVzm0qNLD7OjiIgdKS4pJoMMikuKzY4i0iSoaPr/3NzcmDp1Kh06dKBFixaMGTOG6upqMjO1vomI2KfU9FRWs5rU9FSzo4g0CXY7PFdSUsKaNWtITk4mOTmZgoICpk6dyrRp08557qpVq4iLi6OwsJDg4GCmTJnCqFGjztt+eno65eXldOzY0ZZvo05SDqWwilWMPDQS/77adFNE6iYiNIL7uV9LDohYid32NBUUFLBu3ToqKioYMmTI7547b948Nm7cyNSpU3n66afp2rUrixYt4ptvvjnn+WVlZSxdupQ77rgDd3d3W8S/KK4tXfHFF9eWrmZHERE74ubqRnva4+bqZnYUkSbBbnua/Pz82LBhAw4ODuTn57N+/fpznrd9+3Z27tzJ/PnziYmJAaBv375kZ2fz6quvMnLkSBwdHS3nV1ZWsmDBAkJCQrj99tsvyXu5kED/QCYwgUD/QLOjiIgdOXL0CF/zNdcevRZ/1Est0lB229Pk4OCAg4PDBc/btm0bbm5uDB8+vMbxsWPHkpubS0LC/9Yvqa6uZunSpbRo0YI5c+ZcsP3c3FySkpIsv9LT0+v1Xi6koqKCQgqpqKiwSfsi0jQVFhWSRBKFRYVmRxFpEuy2p6mu0tLSCAkJwcmp5lsNCwuzPH9mWYFnn32WvLw8nnnmmVrnn8vatWt56623rJ75bPtT9vMcz3FVylUEXxFs8+uJSNMQGRbJLGYRGRZpdhSRJqHJF00FBQXnnMzt6ekJwMmTJwHIzs5m/fr1uLi4MH78eMt5Tz/9NL179z5n2+PHj2fQoEGWx+np6SxZssSa8QHoFNiJW7mVToGdrN62iIiI1E2TL5qAOg3j+fn5sXXr1otq18fHBx8fH2JjY4mNjaWoqKi+EX9Xa8/WdKELrT1b26R9EWmaklKTeJEXGZY6THfeiliB3c5pqisvLy8KCgpqHS8sPD3G37p1wwuRmJgYli1bxqxZsxrc1rnkncjjP/yHvBN5NmlfRJqm1p6t6UEP/cAlYiVNvmjq3Lkz6enpVFZW1jh+8OBBAEJDQ82IdVGOHD3CV3zFkaNHzI4iInbEv70/McTg3169TCLW0OSH54YMGcK6devYsmVLjcUsN27ciI+PD927d2/wNWw9PBfVNYoneZKorubvgyci9qO0rJRssiktKzU7ikiTYNdF044dOygrK6OkpAQ4PRF78+bNAAwYMABXV1cGDBhAv379WL58OSUlJQQEBLBp0yZ+/PFH5s2bV2ONpvqKiYkhJiaGpKQkpk+f3uD2RESsITktmdd4jevTrqfzlZ3NjiNi9+y6aFq+fDnZ2dmWx3FxccTFxQHwwQcf4O9/ukt6yZIlrFy5ktWrV1u2UVmwYMHvbqPSmBz89SBrWEPMrzGazCkidRbeKZzpTCe8U7jZUUSaBLsumj788MM6nefu7s7s2bOZPXu2TXLYenjOydEJDzxwcrTr/10icom5u7kTQADubuZvByXSFDT5ieCXgq3vngsOCOYmbiI4QAtbikjdHc05ShxxHM05anYUkSZBRZMdqKqqoowyqqqqzI4iInbkeP5x4onneP5xs6OINAka77ECWw/P7Tuwj2UsY/iB4QRerk17RaRuukV042EepltEN7OjiDQJKpqswNZ3zwUHBHMzN2t4TkRExEQanrMDbVq3oQc9aNO6jdlRRMSOHDh4gFd4hQMHD5gdRaRJUNFkB47nH+cnftK8BBG5KB7uHnSiEx7uHmZHEWkSVDTZgcysTP7Nv8nMyjQ7iojYkQC/AMYylgC/ALOjiDQJmtNkBdpGRUQao7LyMvLIo6y8zOwoIk2CepqswNbrNDk4OOCIIw4ODjZpX0SapgMHD7CCFZrTJGIlKprswKGMQ/wf/8ehjENmRxERO9I5uDN3ciedg7XvnIg1qGgSEWmiWnm0IpRQWnm0MjuKSJOgoskOdArqxK3cSqegTmZHERE7kpOXw/d8T05ejtlRRJoETQS3AltPBDcMgyqqMAzDJu2LSNN0LO8Y29jGsbxjZkcRaRLU02QFtp4IvidxD3/lr+xJ3GOT9kWkaerRpQdzmUuPLj3MjiLSJKhosgOB/oFMYAKB/tp3TkRExCwqmuxAuzbtuIzLaNemndlRRMSOpBxKYRWrSDmUYnYUkSZBRZMdyD+Zzz72kX8y3+woImJHXFu64osvri1dzY4i0iSoaLIDvx7+lY/4iF8P/2p2FBGxIxraF7EuFU12QJM5RaQ+KioqKKSQiooKm12jMKuQzQs3U5hVaLNriDQWWnLACmy95ICjoyOuuOLo6GiT9kWkadqfsp/neI6rUq4i+Ipgm1yjKKuILYu2EDk+Ek9/T5tcQ6SxUE+TFdh6yYFfD//Kx3ys4TkRuSidAv//wriBncyOItIkqKfJDlRWVVJMMZVVlWZHERE70tqzNV3oQnlmOVnxWTa5Ru7+3Bq/W4O7jztewV5Wa0/EWlQ02QFtuiki9VHiUMIu510U31aMBx42uUYhhexkJ4W3FeKJdYbnnN2dmbl/pgonaXRUNImINFEnOclXDl/xwLsPENU1yibX2PrFVp578jn++Nc/MnTs0Aa3l7M/h89u+4yS3BIVTdLoqGiyA2e2URmYOBD/vv5mxxERO3HZZZdRXl5u02u02d/m9O+hbfT3kzR5mghuBzp26MhoRtOxQ0ezo4iIiDRbKprsgHdbb/rTH++23mZHERE7kpyczIgRI0hOTqaqqor4+HhOnDgBQF5eHvHx8RiGAUBqaiqpqakAGIZBfHw8eXl5AJw4cYL4+HiqqqoASEtLIzk5uca1CgtPr9NUUFBAfHy8ZW2o9PR0kpKSLOf98ssvHD161PKa+Ph4ysrKAMjIyODAwQM2+SxErEFFkx04WXiSAxzgZOFJs6OIiB1p2bIlfn5+tGnThuLiYqKjo4mNjQVg3bp1REdHWwqhhx56iIceegiAqqoqoqOjWbduHXB6Lbro6GiKi4sBeOKJJ/jjH/8InL7TDeA/e/4DwHfffUd0dDS5uafvpvvrX//K1KlTLZlGjBjBv/71LwB27dpFdHQ0mZmZADzzzDP88fE/2uzzEGkozWmyAlsvbnko8xD/x/9xR+YdRBJpk2uISNMTHBzM66+/jqenJ1VVVezatYvQ0FAArrvuOnbt2mVZNPf555+3vM7R0ZFdu3YREhICnF6LbteuXXh4nL4D76mnnqKy8vQSKB6+p4+NvnE0AIMHD2bXrl34+PgA8OSTT1p6kgDi4uLw8/MDIDo6ml27dhEYeHqbl0cffZSJQyey+ebNNvk8RBrKwTjTNysNlpSUxPTp01m5ciWRkdYrbn798VdeGvASD+540Gar+oqI1Ed8fLyl+Onbt2+D2/v2g2+5fvL1fP7+54z8w0grJBSxHg3P2QFnZ2c88cTZ2dnsKCIiNtWuTTv60pd2bdqZHUWkFhVNdiAzK5N/828yszLNjiIiYlMdfDswghF08O1gdhSRWupdNKWlpbFx40bLxECA8vJynnvuOW688UZuueUWyyRCaZiy8jJyyKGsvOzCJ4uI2LGS0hIOc5iS0hKzo4jUUu+i6e233+af//wn7u7ulmOvv/46a9eupaSkhGPHjvHcc8+xa9cuqwRtzsI7hXMP9xDeKdzsKCIiNfj7+7NgwQL8/a2zsGXKoRRWspKUQylWaU/EmupdNO3fv5/LLrsMBwcHACorK/niiy/o1q0b//73v/nggw9o06YNH374odXCiohI4+Lv78/ChQutVjRFhEYwgxlEhEZYpT0Ra6p30XT8+HE6dPjfmHNCQgIlJSVMmDCBli1b4uPjw6BBg0hJ0U8LDbXvwD6WsYx9B/aZHUVExKbcXN3www83Vzezo4jUUu+iydHR0bLiK8Du3btxcHDgsssusxzz8vKioKCgYQmF9t7tGcIQ2nu3NzuKiIhNZR3LIpZYso5lmR1FpJZ6F01+fn789NNPlsebN2/G39/fsmgZQE5ODl5e2qW6oXy9fRnEIHy9fc2OIiJiUycLT7KPfdoBQRqlehdNV199NSkpKcyYMYMHHniAlJQURo0aVeOcAwcOWFZ6tQeff/45d999NyNGjOCNN94wO45FUXERaaRRVGybFcdFRBqLyLBIZjObyDDtfiCNT72LphtvvJHhw4eTmJjInj17uPzyy7n99tstz+/fv59Dhw5ZZYXYS8Xb25tp06YxZMgQs6PUcPDXg6xhDQd/PWh2FBERkWar3nvPubi4sGjRIoqLi3FwcKix9ACcvqNi9erVNYbrGrszxdJ3331ncpKaunTuwixm0aVzF7OjiIjYVFJqEitYwbDUYfj3tc4deSLWUu+i6eeff8bf37/GHXS/1aZNG06dOkVKSgp9+vSp72XOq6SkhDVr1pCcnExycjIFBQVMnTqVadOmnfPcVatWERcXR2FhIcHBwUyZMqXWcGJj5drSFW+8cW3panYUERGb8mzlSSSReLbyNDuKSC31Hp7705/+xJdffvm753zzzTf86U9/qu8lfldBQQHr1q2joqLigsNp8+bNY+PGjUydOpWnn36arl27smjRIr755hubZLO2w9mH+YIvOJx92OwoIiI21bFDR67majp26Gh2FJFa6t3TZBhGnc45s/iltfn5+bFhwwYcHBzIz89n/fr15zxv+/bt7Ny5k/nz5xMTEwNA3759yc7O5tVXX2XkyJE4OjrWK0Nubi55eXmWx+np6fVq50KKS4o5xCGKS4ovfLKIiB0rLSvlGMcoLSs1O4pILfUumuoiMzMTDw8Pm7Rd12Js27ZtuLm5MXz48BrHx44dy+LFi0lISCAqKqpeGdauXctbb71Vr9dejC6du3A/92tOk4g0eclpybzCK4xPG0/nKzubHUekhosqmpYtW1bj8bZt28jOzq51XlVVFTk5Ofzyyy9cccUVDUvYQGlpaYSEhODkVPOthoWFWZ4/UzRVVlZSVVVFdXU1VVVVlJeX4+TkdN6eqPHjxzNo0CDL4/T0dJYsWWKjdyIi0vSFhYRxN3cTFhJmdhSRWi6qaPrtHCYHBwdSUlLOu02Kg4MDXbt25YEHHmhYwgYqKCigY8faY+OenqcnGZ48+b8F1P71r3/V6Dl6++23efzxxxkzZsw52/bx8cHHx8e6gc9hf/J+nuM5hiQP0d0kItKkebh7EEQQHu62GaUQaYiLKpo++OAD4PRcpcmTJ3PzzTdz00031TqvRYsWeHp64ubWOPYOqutQ3rRp0855992FxMbGEhsbS1GRbRafbNemHX3pS7s27WzSvohIY3E05yhb2MKEnAn4ox8SpXG5qKLpt2suzZ07ly5dujT6dZjOt/9dYWEhAK1bt27wNWJiYoiJiSEpKYnp06c3uL2zdfDtwAhG0MH33Ms7iIg0FXn5efyX/5KXn3fhk0UusXpPBD/fkFVj07lzZ2JjY6msrKwxr+ngwdOra4eGhjb4GrbuaSopLeEwhykpLbFJ+yIijUX3iO48wiN0j+hudhSRWhp891xCQgKJiYkUFRVRXV1d63kHBwfuvPPOhl6m3oYMGcK6devYsmVLjcUsN27ciI+PD927N/yLaeueppRDKaxkJRMPTSRskCZHioiImKHeRdPJkyd54okn2Lt37++u2WTLomnHjh2UlZVRUnK6ByY9PZ3NmzcDMGDAAFxdXRkwYAD9+vVj+fLllJSUEBAQwKZNm/jxxx+ZN29evddoupQiQiOYwQwiQiPMjiIiYlPJacm8xmuMSBuhG1+k0al30fTyyy+zZ88e+vTpwzXXXEP79u0veQGyfPnyGksexMXFERcXB5yetO7vf/oLt2TJElauXMnq1ast26gsWLDAbrZRcXN1ww8/3Fwbx8R6ERFbcXN1I4gg/X0njVK9i6bt27fTrVs3XnjhBZut+n0hH374YZ3Oc3d3Z/bs2cyePdsmOWw9pynrWBaxxHLdset0N4mINGmB/oGMYxyB/oFmRxGppd57z506dYrevXubVjA1JjExMSxbtoxZs2bZpP2ThSfZxz5OFp688MkiInas/FQ5JzhB+alym10jKyuLhQsXkpWVZbNrSNNU76IpIiLinKuBi/VFhkUym9lEhkWaHUVExKaSUpN4kRdJSk2y2TWysrJYtGiRiia5aPUenrvrrruYM2cO+/bto0ePHtbMZHdsPTwnItJchAaFcju3ExrU8OVgRKyt3kVTTk4OAwcO5MEHH+Sqq64iIiLivJvzXnPNNfUOaA9sveRAUmoSK1jBsNRhuptERJo0z1aehBGGZytPs6OI1FLvoulvf/sbDg4OGIbBl19+yZdffllrfpNhGDg4ODT5osnWPFt5Ekmk/hIRkSYvJy+HH/iBG/Ju0I0v0ujUu2iaO3euNXPI7+jYoSNXczUdO9TeeFhEpCk5mnuUzWzmaO5Rs6OI1NLkt1G5FGw9p6m0rJRjHKO0rNQm7YuINBY9I3vyBE/QM7Knza5RnFNc43eRuqr33XPyP7ZeciA5LZlXeIXktGSbtC8i0pyU5JbU+F2krurd03T0aN27Tjt06FDfywgQFhLG3dxNWIj2nRORpi3lUAqrWc3IQyN144s0OvUumiZNmlSnhS0dHBwsW5tI/Xi4exBEEB7u5747UUSkqWjp0pJ2tKOlS0uzo4jUUu+iafTo0ecsmoqKikhNTSUrK4s+ffrg5+fXoIACR3OOsoUtTMiZoLtJRKRJC+oYxA3cQFDHILOjiNRS76LpiSeeOO9zhmHw/vvv89577zFnzpz6XkL+v7z8PP7Lf8nLzzM7ioiITVVUVFBMMRUVFWZHEanFJhPBHRwcuOWWWwgNDeWVV16xxSUaldjYWObOncuKFSts0n73iO48wiN0j+huk/ZFRBqL/Sn7eYZn2J+y3+woIrXY9O65yMhI4uPjbXmJRsHWd8+JiDQXIYEhTGYyIYEhZkcRqcWmRdPhw4epqqqy5SWaheS0ZF7jNS05ICJNnpenF13pipenl9lRRGqxetFUXV3N0aNHWbNmDd9//32z38zXGtxc3QgiCDdXN7OjiIjYVN6JPHayk7wTmsMpjU+9J4IPGzbsd5ccMAyDVq1acf/999f3EvL/BfoHMo5xBPoHmh1FRMSmDmcfZgMbeCD7AXpiu1XBReqj3kVT7969z1k0OTg44OnpSWRkJGPHjqVdu3YNCihQfqqcE5yg/FS52VFERGyqV7deLGABvbr1MjuKSC31Lppeeukla+awa7beey4pNYkXeZExqWPoNKCTTa4hIiIiv097z1mBre+eCw0K5XZuJzQo1Cbti4g0Fgd/PcjbvM3BXw+aHUWklnr3NP3Wnj17SElJobi4GHd3dyIiIoiKirJG0wJ4tvIkjDA8W3maHUVExKYcWzjSkpY4tnA0O4pILQ0qmhISEnjqqafIzMwETk/+PjPPKTAwkLlz59KzpybyNVROXg4/8AM35N2gbVREpEkLCQxhEpO0TpM0SvUumg4dOsRDDz1EWVkZ/fv3p0+fPrRr144TJ07w008/8Z///IdHHnmE1157jU6dOlkxcvNzNPcom9nM0dyjZkcREbGpqqoqTnFKa/xJo1Tvoumtt96isrKSZ599lssvv7zGc7feeis7d+5kzpw5vPXWWyxcuLChOZu1npE9eYIn6BmpXjsRadr2HdjHUzzF0ANDCbxcy6xI41LvieA//fQTw4YNq1UwndGvXz+GDRvGTz/9VO9wIiLSvAR1DGIiEwnqGGR2FJFa6l00FRcX4+//+/Nr/P39KS4uru8l5P9LOZTCalaTcijF7CgiIjbV1qstUUTR1qut2VFEaql30eTt7c2+fft+95yEhAS8vb3rewn5/1q6tKQd7Wjp0tLsKCIiNnWi4AS/8AsnCk6YHUWklnoXTYMHD+bnn39m1apVlJfXXKm6vLycN954g59++onBgwc3OGRzF9QxiBu4Qd3VItLkZRzJ4DM+I+NIBgcPHiQl5X897PHx8eTm5gJw4sQJ4uPjqaysBCAtLY0DBw5Yzv355585duwYACdPniQ+Pp5Tp04BWNo4Y/fu3WRnZwNQVFREfHw8ZWVlAGRmZpKQkGA5d+/evRw5cgSAkpIS4uPjKSkpsepncC6FWYVsXriZwqxCm19Lzq/eRdOdd96Jv78/77zzDjfffDNz5sxh2bJlzJkzh0mTJrFmzRr8/f258847rZm3UYqNjWXu3LmsWLHCJu1XVFRQTDEVFRU2aV9EpLHoGdmTecyjZ2RPHnnkEWbPnm15Ljo6ms8//xyAuLg4oqOjOXnyJABPPvkk9957r+XcQYMG8f777wOwfft2oqOjLUXU2n+vrXHNUaNG8eabbwKni63o6GjS09MBWL58OTfffLPl3GuvvZZXXnkFgMTERKKjo0lMTLTmR3BORVlFbFm0haIs2+w8IXVT77vnWrduzT//+U9eeeUVvv32W3bs2GF5zsXFhTFjxjBjxgxat25tlaCNWUxMDDExMSQlJTF9+nSrt78/ZT/P8AyjUkYRfEWw1dsXEWksWrRogRNOtGjRgmeffZbq6mrLc7t27SI4+PTfgSNGjGDXrl2Wf2P++te/1vjB8vvvv6djx44ADBw4kF27dtG+fXsAxk8Yz4dxH1rO3bRpk+W5Pn36sGvXLkJCTq8T9ec//5l77rnHcu769este6p27drVkmHevHncf//9lmtK09SgxS1bt27N3LlzeeSRR0hPT6ekpAR3d3dCQkJwcrLKYuPC6cXeJjNZi72JSLPSuXPnGo/79u1r+e+2bdvStu3/JouHhtbcZqpPnz6W/27dunWN1/r4+NQ4t1ev/20O3KpVqxrnBgbWXPbgtws2u7u707dvX/bu3cs777zD5MmTbVY0Hc05ShxxTMiZYDeLHCf/nMzSPy3lLy/8hYg+EWbHsYqLrmz+9a9/UVZWxrRp0yyFkZOTE2FhYZZzKioqWLlyJW5ubtx2223WS9tMeXl60ZWueHl6mR1FRETO0rNnTw4dOmTTaxzNPcoWttjVIscH9x1kzZY13LLvliZTNF3UnKadO3fyxhtv0Lp169/tSXJ2dqZ169asWrWKXbt2NThkc5d3Io+d7CTvRJ7ZUURERJqtiyqavvrqKzw9PbnxxhsveO4NN9yAp6cnX375Zb3DyWmHsw+zgQ0czj5sdhQRETlLQkICPXr0qHGXnTRNF1U07d27l+joaFxcXC54rouLC/369WPv3r31Dien9erWiwUsoFe3Xhc+WURELqnWrVszevToZnHjU3N3UUVTbm7uRU1y8/f3Jy9PQ0oiItJ0BQYGsnz58lqTxqXpuaiJ4C1atLAsJFYXlZWVtGhR76Wg5P87+OtB3uZtYn6Nwb+vfdw1ISLSEDn7c2zWdn5avuX3rPisBrdXVl5GXlkePQf2xNXVtcHtSeN1UUWTt7c3aWlpdT4/LS2t1q2djVl+fj5PPfUUP//8Mz4+Pjz00EPn3ZD4UnJs4UhLWuLYwtHsKCIiNuXu446zuzOf3faZza5xhNMren/75LckPtnwhSmPcITXeZ3N6zczbNywBrcnjddFFU29evXim2++ISsr64Kb9WZlZREfH8/o0aMbFPBSev7552nXrh1r165l586dLFiwgPfeew8vL3Nv9Q8JDGESk7ROk4g0eV7BXszcP5OSXNttTbL/h/0kzUriDyv+QLcruzW4vfSf0qm4p4IArwArpJPG7KKKphtuuIEvv/yS+fPn88wzz9CmTZtznldQUMCCBQuoqqpiwoQJ1shpcyUlJWzbto333nsPV1dXBg8eTHh4ON999x3jxo0zNVtVVRWnOEVVVZWpOURELgWvYC+8gm37w+oIRtDtym5Wm/IQQgge7h5WaUsar4sqmiIjI7n55pv56KOPuOOOO5gwYQKXXXYZvr6+wOmJ4rt27WLdunXk5+czadIkIiMjbRK8pKSENWvWkJycTHJyMgUFBUydOpVp06ad89xVq1YRFxdHYWEhwcHBTJkyhVGjRlnOyczMxM3NjQ4dOliOde7c+aKGI21l34F9PMVTDD0wlMDLNdFQRKQxOZZ7jG1s4/rc6+1mtW6pn4teEXzmzJm4uLjw3nvv8fbbb/P222/XeN4wDFq0aMFtt91WY78eaysoKGDdunWEhYUxZMgQ1q9ff95z582bR2JiIvfddx9BQUHExsayaNEiqqurueqqqwAoLS3Fw6PmTwkeHh7k5+fb7D3UVVDHICYykaCOQWZHERGRs+SeyGU728k9kWt2FLGxiy6aHBwcuPfeexk3bhxffPEFe/fu5fjx4wC0a9eOqKgoxowZQ0CAbcd2/fz82LBhAw4ODuTn55+3aNq+fTs7d+5k/vz5xMTEAKf3L8rOzubVV19l5MiRODo64ubmRnFxcY3XFhcX4+bmZtP3URdtvdoSRRRtvdpe+GQREbmkukd05zEeo3tEd7OjiI3Ve1fdgIAApk+fbs0sF8XBwaFO523btg03NzeGDx9e4/jYsWNZvHgxCQkJREVFERgYSGlpKceOHbPsdp2WlsbVV1993rZzc3NrrEOVnp5+8W+kDk4UnOAXfuFEwQl1/YqIiJikyS+ilJaWRkhISK298s5sMHxmzpK7uzuDBw/mzTffpLy8nB9++IHk5GQGDRp03rbXrl3L9OnTLb+WLFlik/eQcSSDz/iMjCMZNmlfRKQ5aeXfimELhtHKv5VV2ktOS+Z1Xic5Ldkq7UnjVe+eJntRUFBwzlXMPT09ATh58qTl2J///GeWLl3Ktddei4+PDwsXLjzvHYIA48ePr1FUpaen26Rw6hnZk3nMo2dkT6u3LSLS3Hj6ezJ84XCrtefm6oY//ri5mj+dQ2yryRdNUPehvDZt2vDMM8/UuV0fHx98fHyIjY0lNjaWoqKi+kb8XS1atMAJJ62uLiLSCAX6B3Id1xHor7ubm7om/6+wl5cXBQUFtY4XFhYCWGWDxZiYGJYtW8asWbMa3Na5pGem8z7vk55pmzlTIiJSf6cqTlFAAacqTpkdRWysyRdNnTt3Jj09vdaeeQcPHgQgNDTUjFgXpdqopooqqo1qs6OIiMhZElMSeZ7nSUxp+JYs0rg1+eG5IUOGsG7dOrZs2VJjMcuNGzfi4+ND9+4Nv0XU1sNzoUGhTGEKoUGNv8ATEWluQoNCuY3b9Hd0M2DXRdOOHTsoKyujpOT0HkXp6els3rwZgAEDBuDq6sqAAQPo168fy5cvp6SkhICAADZt2sSPP/7IvHnzcHRs+Ca4MTExxMTEkJSUZOoyDCIicul5tvIknHA8W3maHUVszK6LpuXLl5OdnW15HBcXR1xcHAAffPCBZVPhJUuWsHLlSlavXm3ZRmXBggU1ep4as937d7OQhQzYP8Bq+ySJiIh15B7PZQc7uPH4jVpLr4mz66Lpww8/rNN57u7uzJ49m9mzZ9skh62H5wL8AriO6wjw0w7aIiKNTXZONpvYRHZONlFEmR1HbMiui6bGwtbDc95tvYkmGu+23lZvW0REGqZnZE/+wl+0ll4z0OTvnmsK8k/mk0AC+SfzzY4iIiLSbKlosgO/Hv6VD/mQXw//anYUERE5S2p6Km/yJqnpqWZHERvT8JwV2HpOk3bQFhFpvJydnGlNa5ydnM2OIjamoskKbD2nycnJCXfca206LCIi5gsOCGYiEwkOCDY7itiYhufswK+Hf+UTPtHwnIhII1RZWUkJJbV2npCmR0WTHaiorOAkJ6morDA7ioiInCUhOYGneZqE5ASzo4iNabzHCmw9pyksJIy7uIuwkDCbtC8iIvUXHBDMJCZpeK4ZUNFkBdpGRUSk+WrTug3d6U6b1m3MjiI2puE5O7A3aS9LWcrepL1mRxERkbPknchjF7vIO5FndhSxMRVNdsDP149RjMLP18/sKCIicpbD2YdZxzoOZx82O4rYmIomO+DTzocBDMCnnY/ZUURE5Cy9uvViIQvp1a2X2VHExjSnyQpsPRG8sKiQFFIoLCrUDtoiIiImUU+TFcTExLBs2TJmzZplk/bTMtJ4h3dIy0izSfsiIlJ/aRlpvMu7+ju6GVDRZAe6hnflIR6ia3hXs6OIiMhZWji0wBFHWjjon9SmTv+H7YCLswteeOHi7GJ2FBEROUtIYAiTmUxIYIjZUcTGVDTZgcysTNaxjsysTLOjiIjIWaqrq6mkkurqarOjiI2paLIDpWWlZJFFaVmp2VFEROQse5P2soQlWkuvGVDRZAciQiO4l3uJCI0wO4qIiJwlqGMQN3ADQR2DzI4iNqYlB6zA1ksOiIhI49XWqy296U1br7ZmRxEbU0+TFdh6yQHtoC0i0nidKDjBHvZwouCE2VHExlQ02QGftj4MZCA+bbUiuIhIY5NxJINP+ISMIxlmRxEbU9FkB9r7tGcIQ2jv097sKCIicpYeXXrwBE/Qo0sPs6OIjalosgPFJcWkk05xSbHZUURE5CyOjo644IKjo6PZUcTGVDTZgdT0VN7kTVLTU82OIiIiZ0nPTOdDPiQ9M93sKGJjKprsQJfOXXiAB+jSuYvZUURE5CxV1VWUU05VdZXZUcTGVDTZAdeWrvjgg2tLV7OjiIjIWToHd+Z2bqdzcGezo4iNqWiyA0eOHmEjGzly9IjZUURERJotFU12oKi4iFRSKSrW4pkiIo3N7v27WcQidu/fbXYUsTGtCG4Ftl4RvEvnLsxkpuY0iYg0QgF+AYxjHAF+AWZHERtT0WQFMTExxMTEkJSUxPTp082OIyIil5B3W2/60Q/vtt5mRxEb0/CcHUhMSeR5nicxJdHsKCIicpaCwgISSaSgsMDsKGJjKprsQBuvNvSiF2282pgdRUREzpKemc77vK91mpoBFU12wM/Xj1GMws/Xz+woIiJylm7h3XiUR+kW3s3sKGJjKprsQElpCUc4QklpidlRRETkLM7OznjggbOzs9lRxMZUNNmBlEMpvM7rpBxKMTuKiIicJeNIBp/xGRlHMsyOIjamoskOhHcK517uJbxTuNlRRETkLOWnyjnOccpPlZsdRWxMRZMdcHdzpyMdcXdzNzuKiIicJbxTOHdzt36wbQZUNJ3l888/5+6772bEiBG88cYbZscBIDsnm01sIjsn2+woIiIizZaKprN4e3szbdo0hgwZYnYUi/yCfHazm/yCfLOjiIjIWfYm7eUpnmJv0l6zo4iNaUXws5wplr777juTk/xP1/CuPMRDdA3vanYUERE5SwefDgxnOB18OpgdRWysURZNJSUlrFmzhuTkZJKTkykoKGDq1KlMmzbtnOeuWrWKuLg4CgsLCQ4OZsqUKYwaNcqE5CIi0tz4evtyJVfi6+1rdhSxsUY5PFdQUMC6deuoqKi44DDZvHnz2LhxI1OnTuXpp5+ma9euLFq0iG+++eYSpbW9AwcP8A/+wYGDB8yOIiIiZyksKiSVVAqLCs2OIjbWKHua/Pz82LBhAw4ODuTn57N+/fpznrd9+3Z27tzJ/PnziYmJAaBv375kZ2fz6quvMnLkSBwdHQH405/+xN695x5vnjx5Mvfcc49t3owVtPJoRRhhtPJoZXYUERE5S1pGGm/zNlMyptCFLmbHERtqlEWTg4NDnc7btm0bbm5uDB8+vMbxsWPHsnjxYhISEoiKigLghRdesHJKyM3NJS8vz/I4Pd02+w517NCRa7iGjh062qR9ERGpv8iwSGYzm8iwSLOjiI01yqKprtLS0ggJCcHJqebbCAsLszx/pmiqq8rKSqqqqqiurqaqqory8nKcnJwsPVa/tXbtWt56661656+rsvIycsmlrLzM5tcSEZGL09KlJW1pS0uXlmZHERuz66KpoKCAjh1r9754enoCcPLkyYtu81//+leNQujtt9/m8ccfZ8yYMbXOHT9+PIMGDbI8Tk9PZ8mSJRd9zQs5cPAAL/My1x68ltCBoVZvX0RE6i8zK5MNbGBs1lj88Tc7jtiQXRdNUPehvLqaNm3aOe/SOxcfHx98fHyIjY0lNjaWoqIiq2Y5IywkjLu4i7CQMJu0LyIi9VdaVkoGGZSWlZodRWzMrosmLy8vCgoKah0vLDx9B0Pr1q0vSY6YmBhiYmJISkpi+vTpVm/fw92DEELwcPewetsiItIwEaERzGAGEaERZkcRG2uUSw7UVefOnUlPT6eysrLG8YMHDwIQGto0hrKO5R5jG9s4lnvM7CgiIiLNll0XTUOGDKG0tJQtW7bUOL5x40Z8fHzo3r37JckRGxvL3LlzWbFihU3azz2Ry3a2k3si1ybti4hI/SUkJ/Asz5KQnGB2FLGxRjs8t2PHDsrKyigpKQFOT7LevHkzAAMGDMDV1ZUBAwbQr18/li9fTklJCQEBAWzatIkff/yRefPmnfOON1uw9fBc94juPMZjdI+4NEWgiIjUnXcbby7ncsiBrPgsm1wjPy3f8ru1ruHu445XsJdV2mouGm3RtHz5crKzsy2P4+LiiIuLA+CDDz7A3//0HQpLlixh5cqVrF692rKNyoIFC7SNioiIXBKh3UKJcY/hu1nf8R222bf0CEcA+PbJb0l8MtEqbTq7OzNz/0wVTheh0RZNH374YZ3Oc3d3Z/bs2cyePdvGic7P1nfPJacl8zqvMyJtBP59dTuriEhj4tjOkcvfuZxQ31Cb3bCze/9uXr/tdW5850Z6devV4PZy9ufw2W2fUZJboqLpIjTaosme2Hp4zs3VDX/8cXN1s3rbIiLSMAcOHODqG69m165dhPcNt8k1sjg9JOfbzVc/PJtIRZMdCPQP5DquI9A/0OwoIiJylm7durF37146d+5sdhSxMRVNduBUxSkKKOBUxSmzo4iIyFnc3Nzo0aOH2THkErDrJQcaC1svOZCYksjzPE9iinUm/4mIiPVkZmby6KOPkpmZaXYUsTEVTVYQExPDsmXLmDVrlk3aDw0K5TZuIzSoaSzWKSLSlBQUFLB27dpz7lAhTYuG5+yAZytPwgnHs5Wn2VFEROQsPXr0ICkpyewYcgmop8kO5B7PZQc7yD2uFcFFRETMoqLJCmw9pyk7J5tNbCI7J/vCJ4uIyCW1b98+wsPD2bdvn9lRxMY0PGcFtl6nqWdkT/7CX+gZ2dPqbYuISMO0adOGm2++mTZt2pgdRWxMRZOIiEgDBAQE8Le//c3sGHIJaHjODqSmp/Imb5Kanmp2FBEROUtpaSm7d++mtLTU7ChiYyqa7ICzkzOtaY2zk7PZUURE5Cz79++nd+/e7N+/3+woYmManrMCW2/YGxwQzEQmEhwQbJP2RUSk/iIjI/nPf/5DZGSk2VHExlQ0WYGtJ4JXVlZSQgmVlZVWb1tERBrGw8ODyy+/3OwYcgloeM4OJCQn8DRPk5CcYHYUERE5S1ZWFgsXLiQrK8vsKGJjKprsQHBAMJOYpOE5EZFGKDc3l1WrVpGbqwWImzoNz9mBNq3b0J3utGndxuwoIiJylqioKG3W20yop8kO5J3IYxe7yDuRZ3YUERGRZktFkx04nH2YdazjcPZhs6OIiMhZEhIS6NWrFwkJmnfa1Gl4zgpsveRAr269WMhCenXrZZP2RUSk/jw9PRk+fDienp5mRxEbU9FkBbZeckBERBqvoKAgXnrpJbNjyCWg4Tk7kJaRxru8S1pGmtlRRETkLGVlZaSkpFBWVmZ2FLExFU12oIVDCxxxpIWD/neJiDQ2CQkJREREaE5TM6B/he1ASGAIk5lMSGCI2VFEROQsERERxMXFERERYXYUsTHNabID1dXVVFJJdXW12VFEROQsZyaCS9OnniY7sDdpL0tYwt6kvWZHERGRsxw9epRnnnmGo0ePmh1FbExFkx0I6hjEDdxAUMcgs6OIiMhZsrOz+dvf/kZ2drbZUcTGNDxnB9p6taU3vWnr1dbsKCIicpbevXtz/Phxs2PIJaCiyQpsvbjliYIT7GEPJwpO4I+/Ta4hIiIiv0/Dc1YQExPDsmXLmDVrlk3azziSwSd8QsaRDJu0LyIi9ZeUlMTAgQNJSkoyO4rYmIomO9CjSw+e4Al6dOlhdhQRETmLq6srPXr0wNXV1ewoYmManrMDjo6OuOCCo6Oj2VFEROQsISEhrFq1yuwYcgmop8kOpGem8yEfkp6ZbnYUERE5S0VFBVlZWVRUVJgdRWxMRZMdqKquopxyqqqrzI4iIiJn2bNnDx07dmTPnj1mRxEbU9FkBzoHd+Z2bqdzcGezo4iIyFnCwsJYv349YWFhZkcRG9OcJhERkQbw8vJi3LhxZseQS0A9TXZg9/7dLGIRu/fvNjuKiIicJScnh3/84x/k5OSYHUVsTEWTHQjwC2Ac4wjwCzA7ioiInCUzM5M///nPZGZmmh1FbEzDc79x6tQpnnvuOXbu3ElxcTGdOnVi5syZREVFmZrLu603/eiHd1tvU3OIiEhtl112GeXl5WbHkEtAPU2/UVVVhb+/P6+88gpffPEFEyZM4IknnqCsrMzUXAWFBSSSSEFhgak5REREmjMVTb/h5ubG1KlT6dChAy1atGDMmDFUV1eb3uWanpnO+7yvdZpERBqh5ORkYmJiSE5ONjuK2FijHJ4rKSlhzZo1JCcnk5ycTEFBAVOnTmXatGnnPHfVqlXExcVRWFhIcHAwU6ZMYdSoUQ3OkZ6eTnl5OR07dmxwWw3RLbwbj/Io3cK7mZpDRERqc3JywtfXFyenRvlPqlhRo/w/XFBQwLp16wgLC2PIkCGsX7/+vOfOmzePxMRE7rvvPoKCgoiNjWXRokVUV1dz1VVX1TtDWVkZS5cu5Y477sDd3b3e7ViDs7MzHnjg7Oxsag4REaktNDSU9957z+wYcgk0yqLJz8+PDRs24ODgQH5+/nmLpu3bt7Nz507mz59PTEwMAH379iU7O5tXX32VkSNHWvZr+9Of/sTevXvP2c7kyZO55557LI8rKytZsGABISEh3H777efNmZubS15enuVxerpths8yjmTwGZ8x5sgY/Pv62+QaIiJSP1VVVRQXF+Ph4aE9Qpu4Rlk0OTg41Om8bdu24ebmxvDhw2scHzt2LIsXLyYhIcFy59sLL7xQpzarq6tZunQpLVq0YM6cOb+bZe3atbz11lt1archyk+Vc5zjlJ/S3RkiIo3NL7/8QnR0NLt27aJv375mxxEbapRFU12lpaUREhJSaxz5zFL2aWlpF71cwLPPPkteXh7PPPPMBcenx48fz6BBgyyP09PTWbJkyUVdry7CO4VzN3cT3inc6m2LiEjDhIaG8uGHHxIaGmp2FLExuy6aCgoKzjlJ29PTE4CTJ09eVHvZ2dmsX78eFxcXxo8fbzn+9NNP07t371rn+/j44OPjc5GpRUSkKWnbti0333yz2THkErDrognqPpRXF35+fmzduvWiXxcbG0tsbCxFRUVWy/Jbe5P28hRPcWXSlZrTJCLSyOTl5bFu3Tquu+46vL21CHFTZtfrNHl5eVFQUHvBx8LCQgBat259SXLExMSwbNkyZs2aZZP2O/h0YDjD6eDTwSbti4hI/aWnp3PXXXfZ7GYgaTzsuqepc+fOxMbGUllZWWP+0cGDBwGazPiyr7cvV3Ilvt6+ZkcREZGzXHbZZVRUVOjOuWbArnuahgwZQmlpKVu2bKlxfOPGjfj4+NC9e/dLkiM2Npa5c+eyYsUKm7RfWFRIKqkUFhXapH0REak/BwcHnJycrDpdRBqnRtvTtGPHDsrKyigpKQFOd39u3rwZgAEDBuDq6sqAAQPo168fy5cvp6SkhICAADZt2sSPP/7IvHnzLlnVHxMTQ0xMDElJSUyfPt3q7adlpPE2bzMlYwpd6GL19kVEpP5SU1N56KGHeP755y13b0vT1GiLpuXLl5OdnW15HBcXR1xcHAAffPAB/v6nJ0QvWbKElStXsnr1ass2KgsWLLDKNiqNRWRYJLOZTWRYpNlRREREmq1GWzR9+OGHdTrP3d2d2bNnM3v2bBsnOj9b3z3X0qUlbWlLS5eWNmlfRETqLywsjLVr15odQy4Bu57T1FjY+u65zKxMNrCBzKxMm7QvIiL1ZxgGlZWVGIZhdhSxMRVNdqC0rJQMMigtKzU7ioiInOWnn37C2dmZn376yewoYmONdnjOnth6eC4iNIIZzCAiNMIm7YuISP2FhITw5ptvEhISYnYUsTEVTVZg67vnRESk8fL29mbq1Klmx5BLQMNzdiAhOYFneZaE5ASzo4iIyFlOnDjBRx99xIkTJ8yOIjamoskOeLfx5nIux7uN9jQSEWls0tLSmDRpEmlpaWZHERvT8Jwd6ODbgWEMo4Ov9p4TEWlsevfuTUFBAR4eHmZHERtT0WQFtp4IXlxSTAYZFJcU26R9ERGpP0dHx0u2QbyYS8NzVmDrdZpS01NZzWpS01Nt0r6IiNRfWloat9xyi4bnmgEVTXYgIjSC+7lfSw6IiDRClZWV5OTkUFlZaXYUsTENz9kBN1c32tMeN1c3s6OIiMhZIiIiiI2NNTuGXALqabIDR44e4Wu+5sjRI2ZHERERabbU02QFtp4IXlhUSBJJFBYV2qR9ERGpv59++okBAwawY8cOLrvsMrPjiA2paLICW68IHhkWySxmERkWafW2RUSkYQIDA1m+fDmBgYFmRxEbU9EkIiLSAL6+vsycOdPsGPWSsz/HZm3np+Vbfs+Kz7JKm+4+7ngFe1mlrfpQ0WQHklKTeJEXGZY6DP++/mbHERGR3ygoKOC7775j8ODBeHmZ9w/6xXD3ccfZ3ZnPbvvMZtc4wul5uN8++S2JTyZapU1nd2dm7p9pWuGkoskOtPZsTQ960NpTi6eJiDQ2qampXHvttezatYu+ffuaHadOvIK9mLl/JiW5JTa7xu79u3n9tte58Z0b6dWtV4Pby9mfw2e3fUZJbomKJjk///b+xBCDf3v1MomINDZRUVEcOXIEHx8fs6NcFK9gL5sWH1mcHpLz7ebbZEZJVDTZgdKyUrLJprSs1OwoIiJyFmdnZ/z9m0ZRIL9P6zRZQWxsLHPnzmXFihU2aT85LZnXeI3ktGSbtC8iIvWXnp7OPffcQ3p6utlRxMbU02QFtl5yILxTONOZTnincKu3LSIiDVNWVsa+ffsoKyszO4rYmIomO+Du5k4AAbi7uZsdRUREzhIZGcn27dvNjiGXgIbn7MDRnKPEEcfRnKNmRxEREWm2VDTZgeP5x4knnuP5x82OIiIiZ/nll19o164dv/zyi9lRxMZUNNmBbhHdeJiH6RbRzewoIiJyFj8/Px5//HH8/PzMjiI2pjlNIiIiDdChQwceffRRs2PIJaCeJjtw4OABXuEVDhw8YHYUERE5S2FhIZs3b6awsNDsKGJjKprsgIe7B53ohIe7h9lRRETkLMnJyYwYMYLkZK2l19RpeM4KYmNjiY2NpaioyCbtB/gFMJaxBPgF2KR9ERGpv+7du5OcnExgYKDZUcTGVDRZga0XtywrLyOPPMrKtXCaiEhj4+rqSni4Fh9uDjQ8ZwcOHDzAClZoTpOISCOUkZHBgw8+SEZGhtlRxMbU02RF5eXlAFbff8jR0ZHJHpNxdHQkKSnJqm2LiEjDpKWlsW3bNsaOHUtJSYlNrpGRkYGHh4fld3tg7cy5Gbmc9DjJwYyDnPQ4aYWEtYWEhODq6nre5x0MwzBscuVm6Ouvv2bJkiVmxxAREZF6WLlyJZGRked9Xj1NVtS/f3/Cw8N56KGHcHFxqdNrVqxYwaxZs373nPT0dJYsWcK8efMICQmxRlS7V5fPzUyXOp+trmetdhvSTn1eezGvqeu5+h7W1pi/h2Zks8U1m8N3sK7nX4rv4IXaVdFkRW3atKF9+/ZERUXV+TWtWrX63ar2t0JCQup8blN3MZ+bGS51Pltdz1rtNqSd+rz2Yl5zse3re/g/jfl7aEY2W1yzOXwHL/Z8M7+DmghuZTExMTY9X05r7J/bpc5nq+tZq92GtFOf117Maxr7n6XGrDF/dmZks8U1m8N3sL7XMIPmNNmBM0sZXGisVURsR99DEXM1hu+geprsgLe3N1OnTsXb29vsKCLNlr6HIuZqDN9B9TSJiIiI1IF6mkRERETqQEWTiIiISB2oaBIRERGpAxVNIiIiInWgoqmJOHXqFH/729+YOHEi11xzDTNmzGDPnj1mxxJpVhYuXMiECRO45pprmDp1Kj/88IPZkUSapb179zJs2DDWrFlj1XZ191wTUVpaygcffMCYMWPw9fXlq6++4pVXXuGjjz763c0HRcR60tLSCAwMxNnZmYSEBB5++GHef/99vLy8zI4m0mxUV1fzxz/+EQcHBwYOHMidd95ptbbV09REuLm5MXXqVDp06ECLFi0YM2YM1dXVZGZmmh1NpNkIDQ3F2dkZAEdHRyoqKsjNzTU5lUjzsnbtWqKioggODrZ629p7ziQlJSWsWbOG5ORkkpOTKSgoYOrUqUybNu2c565atYq4uDgKCwsJDg5mypQpjBo16rztp6enU15eTseOHW35NkTslq2+g4sXL2br1q2cOnWKAQMG0Llz50vxdkTsji2+gwUFBXz88ce89tprvPTSS1bPrKLJJAUFBaxbt46wsDCGDBnC+vXrz3vuvHnzSExM5L777iMoKIjY2FgWLVpEdXU1V111Va3zy8rKWLp0KXfccQfu7u62fBsidstW38H58+dTWVlJfHw86enpODg42PqtiNglW3wHX3/9dSZNmkSrVq1skllFk0n8/PzYsGEDDg4O5Ofnn/cPy/bt29m5cyfz58+3bGjYt29fsrOzefXVVxk5ciSOjo6W8ysrK1mwYAEhISHcfvvtl+S9iNgjW30HAZycnOjfvz+ffPIJgYGBDBw40ObvR8TeWPs7mJSUxIEDB3j44Ydtlllzmkzi4OBQp59At23bhpubG8OHD69xfOzYseTm5pKQkGA5Vl1dzdKlS2nRogVz5szRT7giv8MW38GzVVdXc/jw4YZGFWmSrP0d/OWXXzh06BATJkzguuuu49tvv+Xdd99l6dKlVsusnqZGLi0tjZCQEJycav6vCgsLszwfFRUFwLPPPkteXh7PPPNMrfNFpH7q+h3My8tjz549XHHFFTg7O7N161Z++ukn7rvvPjNiizQZdf0OXnvttTUKq3/84x/4+flx6623Wi2L/mVt5AoKCs45mdvT0xOAkydPApCdnc369etxcXFh/PjxlvOefvppevfufWnCijRBdf0OAnz88cf8/e9/x8HBgcDAQBYuXEh4ePglyyrSFNX1O+ju7l5jHm/Lli1xd3e36pIfKprsQF26L/38/Ni6deslSCPS/NTlO+jt7c3LL798CdKIND/1mW7yxBNPWD2H5jQ1cl5eXhQUFNQ6XlhYCEDr1q0vdSSRZkXfQRFzNabvoIqmRq5z586kp6dTWVlZ4/jBgweB04vpiYjt6DsoYq7G9B1U0dTIDRkyhNLSUrZs2VLj+MaNG/Hx8aF79+4mJRNpHvQdFDFXY/oOak6TiXbs2EFZWRklJSXA6VW8N2/eDMCAAQNwdXVlwIAB9OvXj+XLl1NSUkJAQACbNm3ixx9/ZN68ebXWhxGRutN3UMRc9vYd1Ia9Jpo0aRLZ2dnnfO6DDz7A398fOL18/MqVK2ssH3/bbbf97jYqInJh+g6KmMvevoMqmkRERETqQHOaREREROpARZOIiIhIHahoEhEREakDFU0iIiIidaCiSURERKQOVDSJiIiI1IGKJhEREZE6UNEkIiIiUgcqmkRELoHnn3+e6667zrJdBMAbb7zB0KFD+emnn0xM9j9Lly7l5ptvpry83OwoIo2S9p4TkYuWlZXFH/7wh989Jzw8nDfeeOMSJWrcMjIyWLt2Lffeey/u7u42vda///1vnnvuOcaPH88jjzzyu+fec889HDhwgJUrVxIZGcmdd95JbGwsH330EbfddptNc4rYIxVNIlJvAQEBXHXVVed8ztvb+xKnabzefPNNXFxcmDBhgs2vFRMTwz/+8Q++/fZbZs2aRcuWLc95XmpqKgcOHCAiIoLIyEgAAgMDGTx4MP/3f//HxIkTcXNzs3leEXuioklE6i0gIIBp06aZHaNRy8/PZ+vWrQwfPtzmvUwAHh4eDBs2jK+++ootW7Zw9dVXn/O89evXAzB27Ngax6+++mq2bNnCpk2buPbaa22eV8SeaE6TiFwSQ4cO5cEHHyQ/P59ly5Yxfvx4YmJimDFjxnnn9JSUlPDGG29wxx13EBMTw9ixY3nkkUfYvXt3rXMffPBBhg4dyqlTp1i9ejW33HILI0aMqDFEuGXLFqZPn05MTAwTJkzg6aefprCwkEmTJjFp0iTLeUuXLmXo0KHs37//nLleffVVhg4dytatWy/4vjdt2sSpU6cYPnz4Bc89IzU1lRtuuIHrrruOhIQEy/EjR47w97//nZtuuolRo0Zx/fXX89RTT9XaJX7cuHEAfPnll+dsv6KigtjYWFxcXGoVVQMGDMDNzY0vvviiznlFmgsVTSJyyRQVFXH//feTmprKVVddxdChQ0lKSuKRRx7h4MGDNc49efIkf/zjH3nrrbdo3bo1119/veX82bNns23btnNeY968eXzxxRf07t2bm2++mY4dOwKwYcMGnnzySQ4fPszo0aO55ppr2LdvH3/+85+prKys0cb48eOB//XG/FZlZSVfffUV7dq148orr7zge961axcAPXr0uPAHBPzyyy/MmjULR0dHXn75Zbp37w5AQkIC99xzDxs3biQyMpKbbrqJ3r17880333Dfffdx5MgRSxt9+vQhMDCQ+Ph4srKyal3j+++/p6CggKFDh+Lp6VnjOWdnZ7p06cL+/fspLS2tU2aR5kLDcyJSb4cPHz7vZO8ePXpwxRVX1DiWkpLC9ddfz5/+9CdatDj9M1vfvn15+umn+fTTT2tMXH7hhRdIS0tj7ty5NYaQjh8/zr333sszzzxD//79a83ZycvL480336R169aWY4WFhbz00ku4u7uzatUqSyE1ffp0HnvsMZKSkvDz87OcHxUVRWhoKJs2beKBBx6oMbfnhx9+4Pjx49x66604OV34r9C9e/fi6+tL27ZtL3jutm3bWLRoER07duTZZ5+lffv2wOlCbeHChVRXV7Ny5UrCw8Mtr9m9ezezZ8/mpZdeYtmyZZbjY8eO5fXXX2fjxo3cddddNa6zYcMG4H89UmeLjIzkl19+Yf/+/fTt2/eCuUWaC/U0iUi9HT58mLfeeuucv3788cda57u5uTFjxgxLwQRwzTXX4OjoSGJiouVYfn4+cXFxREdH15pz065dO2655Rby8/MtvTi/ddddd9UomAC+++47SktLufbaay0FE4CTkxN33333Od/b+PHjKSkp4dtvv61xfP369Tg4ONRpvk9FRQX5+fl1KpjWr1/P/PnziYiI4OWXX7YUTHC6UMvOzuaWW26pUTAB9OrVi0GDBrFjxw6Ki4stx898rl9++SWGYViO5+bmsnPnTvz8/M5bEJ3Jm5OTc8HcIs2JeppEpN769+/Ps88+W+fzAwMDa02GdnJyol27dhQVFVmOJSYmUlVVxalTp87Zk5WZmQlAenp6rSGybt261To/NTUVgJ49e9Z6rlu3bjg6OtY6fvXVV/Paa6+xfv16S49MTk4O//3vfy3DXxdSUFAAUGsI7Gwffvgh33//PQMGDGDx4sW4urrWeH7fvn0A/Prrr+f8PI4fP051dTUZGRl07doVAB8fH6644gp++OEH4uPjiY6OBk7Pc6qqqmLs2LE4ODicM8+ZovNMfhE5TUWTiFwyHh4e5zzu6OhIdXW15fHJkycB2LNnD3v27Dlve2VlZbWOtWvXrtaxMz0wbdq0qfVcixYt8PLyqnXc09OTESNGsHHjRg4dOkSnTp344osvqKqqqvNdZWeGDi+0WOSZie1XXHFFrYIJTg8vAnzzzTe/287Zn8e4ceP44Ycf+PLLL2sUTS1atGDMmDHnbedM3vMtVyDSXKloEpFG50xx9Yc//IGZM2de1GvP1Xtypr38/Pxaz1VXV1NQUICvr2+t58aPH8/GjRtZv349M2fO5Msvv6R169YMHTq0Tlk8PT1xcnKyFIHnM2fOHP71r3/x0ksv0aJFC2644YYaz5/pnVu2bFmdJp+fMXDgQNq1a8eWLVt46KGHSElJITMzk/79+9OhQ4fzvu5M3nMVmSLNmeY0iUij07VrVxwcHCzDUg0VFhYGnJ6Ufbb9+/dTVVV1ztf17NmTzp0789VXX7Fjxw6OHDnCVVdddVE9MKGhoWRnZ9e6Q++3PD09ef755+nSpQvPP/88n376aY3nz9xBd7Gfh5OTE6NHj6a8vJxNmzZZlhE43wTwMzIyMgDo3LnzRV1PpKlT0SQijY63tzcjRoxg7969vPfeezUmMp+RkJBwzuG5cxk8eDBubm6sX7++xq35lZWVrF69+ndfe91111FQUMAzzzwDcNELPvbp04dTp05Z5lWdj6enJ8uXL6dr16688MILfPLJJzXyd+jQgQ8++ICff/651msrKyvPuXYV/K9A+vzzz9m8eTNeXl4MHjz4d7MkJCTg7e1NUFDQBd6dSPOi4TkRqbffW3IAaNBq4X/+85/JyMjg1Vdf5auvvqJHjx60atWKY8eOkZSURGZmJp999tk55wCdzdPTkwceeIBnnnmGe+65h5EjR+Lh4cGOHTtwcXHBx8fnvJOiR48ezT//+U9yc3Pp3r27pdeqroYMGcJHH33Erl27LNuV/F7O5557jocffpgXX3wRwzC46aabcHFxYfHixTz22GM8+OCDREdHExoaCsDRo0fZvXs3Xl5evPPOO7XaDA4OJioqyjI37Nprr8XZ2fm8GQ4fPkxWVhbXX3/9Rb1PkeZARZOI1NuZJQfOpyFFU+vWrXnllVf49NNP+fbbb4mNjaW6upp27doRHh7OnXfeec4J3Odz3XXX4enpydtvv83GjRvx8PBg0KBBzJgxg5tvvpmAgIBzvq5Vq1YMHjyY2NjYem0r0qdPH4KDg/n666+59dZbL3j+mR6nhx9+mJdeegnDMLj55pvp1q0bb7zxBu+99x47duxgz549ODs74+Pjw5AhQxg1atR52xw3bpylaDp7CYezff3118D/FvgUkf9xMM7V7y0i0kxkZmZy6623MmLECBYtWnTOc+644w6OHj3KZ599Vq/949auXcuzzz7La6+9Zpmf1BhVVlYyZcoU/Pz8ePHFF82OI9LoaE6TiDQLhYWFnDp1qsax8vJyXn75ZeD0MNq5bN++nUOHDjF69Oh6b7g7btw4QkJCePPNN+v1+kvl66+/Jjs7m/vvv9/sKCKNkobnRKRZ+Pnnn/n73//O5ZdfTvv27SkoKCA+Pp7s7Gz69u3LyJEja5z/+eefc+zYMdatW0fLli255ZZb6n1tR0dHHn/8cX788UdKSkrqXXzZmoODA48++ugF516JNFcanhORZiEjI4PVq1ezd+9ey3pNAQEBjBw5ksmTJ9daRmDSpEnk5OQQFBTEjBkzLmp9JBFpmlQ0iYiIiNSB5jSJiIiI1IGKJhEREZE6UNEkIiIiUgcqmkRERETqQEWTiIiISB2oaBIRERGpAxVNIiIiInWgoklERESkDlQ0iYiIiNTB/wNLKs7I5BdmSQAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -8078,17 +1638,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "id": "06df3b27-d2ed-4214-bda7-d4fda667e145",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 35,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },

From fc983a80a3c79ddd15734cbf6048e7a609a68b5b Mon Sep 17 00:00:00 2001
From: Eliza Neights 
Date: Mon, 4 Mar 2024 13:21:34 -0500
Subject: [PATCH 3/3] Update to Chris's version of extended source notebook

---
 .../diffuse_511_spectral_fit.ipynb            | 7775 +++--------------
 1 file changed, 1400 insertions(+), 6375 deletions(-)

diff --git a/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb b/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
index 1d5c822b..fca5e2f4 100644
--- a/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
+++ b/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
@@ -39,256 +39,10 @@
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-       "                  performances in 3ML                                                                              \n",
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-       "                  performances in 3ML                                                                              \n",
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-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=948124;file:///home/ckarwin/.local/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=992004;file:///home/ckarwin/.local/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# imports:\n",
     "from cosipy import COSILike, test_data, BinnedData\n",
@@ -305,7 +59,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
-    "from threeML import PointSource, Model, JointLikelihood, DataList\n",
+    "from threeML import PointSource, Model, JointLikelihood, DataList, update_logging_level\n",
     "from astromodels import Parameter\n",
     "from astromodels import *\n",
     "from mhealpy import HealpixMap, HealpixBase\n",
@@ -556,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7006c9c0-d8ea-49d2-9ab7-66ecbcf3edea",
    "metadata": {},
    "outputs": [],
@@ -590,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "f459d4ec-8949-4e30-98e8-09d68cf3e4b9",
    "metadata": {
     "tags": []
@@ -1009,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5a3508cb-12a5-4174-8171-422960718cde",
    "metadata": {},
    "outputs": [],
@@ -1030,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "1b2ac4e3-fe0f-4ca0-b65a-1cfcf9a143e0",
    "metadata": {},
    "outputs": [
@@ -1040,8 +794,8 @@
      "text": [
       "... loading the pre-computed image response ...\n",
       "--> done\n",
-      "CPU times: user 1min 52s, sys: 36.9 s, total: 2min 29s\n",
-      "Wall time: 2min 38s\n"
+      "CPU times: user 1min 55s, sys: 37.4 s, total: 2min 32s\n",
+      "Wall time: 2min 49s\n"
      ]
     }
    ],
@@ -1084,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "371f159b-dfa8-4475-9aec-679dc6aa91cb",
    "metadata": {
     "scrolled": true,
@@ -1094,11 +848,11 @@
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-       "
13:47:53 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
+       "
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        "
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       "text/plain": [
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@@ -1108,82 +862,21 @@
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
       "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
       "\n",
       "WARNING RuntimeWarning: invalid value encountered in log\n",
-      "\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n",
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13:49:26 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
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        "                  measurements such as AIC or BIC are unreliable                                                   \n",
        "
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        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
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@@ -1280,7 +973,7 @@
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@@ -1423,8 +1116,8 @@
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-      "Wall time: 1min 33s\n"
+      "CPU times: user 6min, sys: 3min 20s, total: 9min 21s\n",
+      "Wall time: 1min 36s\n"
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@@ -1432,17 +1125,17 @@
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        "(                                      value  negative_error  positive_error  \\\n",
        " gaussian.spectrum.main.Gaussian.F  0.046951       -0.000025        0.000025   \n",
-       " background_cosi                    0.932137       -0.004748        0.004831   \n",
+       " background_cosi                    0.932137       -0.004667        0.004841   \n",
        " \n",
        "                                       error         unit  \n",
        " gaussian.spectrum.main.Gaussian.F  0.000025  1 / (cm2 s)  \n",
-       " background_cosi                    0.004789               ,\n",
+       " background_cosi                    0.004754               ,\n",
        "        -log(likelihood)\n",
        " cosi      -1.527559e+07\n",
        " total     -1.527559e+07)"
       ]
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-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
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@@ -1473,7 +1166,7 @@
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+   "execution_count": 7,
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",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1833,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "a2a754b4-5aef-48cb-bf0f-43cad8029e5d",
    "metadata": {
     "tags": []
@@ -1891,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "18997c67-c643-428a-beb6-57872daeb3ac",
    "metadata": {},
    "outputs": [
@@ -1901,7 +1594,7 @@
        "Text(0.5, 1.0, 'injected counts')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1972,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "ec211c66-d974-48e2-86a8-2c0f63b34fc2",
    "metadata": {},
    "outputs": [
@@ -2063,7 +1756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "5bbdd2c6-39b0-4c69-bcaf-5a5b8becefdb",
    "metadata": {},
    "outputs": [
@@ -2331,7 +2024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "id": "79816fcd-525e-4892-b179-b4cc8b502743",
    "metadata": {},
    "outputs": [
@@ -2341,8 +2034,8 @@
      "text": [
       "... loading the pre-computed image response ...\n",
       "--> done\n",
-      "CPU times: user 2min 2s, sys: 46.8 s, total: 2min 49s\n",
-      "Wall time: 2min 56s\n"
+      "CPU times: user 2min 10s, sys: 41.5 s, total: 2min 52s\n",
+      "Wall time: 3min 10s\n"
      ]
     }
    ],
@@ -2380,7 +2073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "a9423539-928d-41f6-9aab-04a18c1ea0b7",
    "metadata": {},
    "outputs": [
@@ -2516,6 +2209,26 @@
     "model.display()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e57b7109-e602-4a83-b575-abf8d602b579",
+   "metadata": {},
+   "source": [
+    "Before we perform the fit, let's first change the 3ML console logging level, in order to mimimize the amount of console output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c83d4333-90e1-4f55-b3f9-e5da1fd40598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is a simple workaround for now to prevent a lot of output. \n",
+    "from threeML import update_logging_level\n",
+    "update_logging_level(\"CRITICAL\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "17f87aa8-eade-410e-a793-c15ad4604703",
@@ -2526,50 +2239,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "id": "85eae192-0970-406c-bbd3-fa9a133d32dc",
    "metadata": {
     "scrolled": true,
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "... Calculating point source responses ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING ErfaWarning: ERFA function \"utctai\" yielded 7979956 of \"dubious year (Note 3)\"\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--> done (source name : point_source)\n",
-      "--> all done\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
14:00:56 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m14:00:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=152244;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=307979;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1042\u001b\\\u001b[2m1042\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -2580,6091 +2256,1455 @@
     {
      "data": {
       "text/html": [
-       "
14:00:58 INFO      trial values: 0.04,0.01,1 -> logL = 14908358.728                        joint_likelihood.py:1010\n",
+       "
Best fit values:\n",
+       "\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:00:58\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14908358.728\u001b[0m\u001b[1;38;5;251m                       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=543925;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=335378;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:00:59 INFO      trial values: 0.040804,0.01,1 -> logL = 14911295.359                    joint_likelihood.py:1010\n",
-       "
\n"
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
resultunit
parameter
gaussian.spectrum.main.Gaussian.F(4.6951 +/- 0.0025) x 10^-21 / (cm2 s)
point_source.spectrum.main.Gaussian.F(0.0 +/- 1.3) x 10^-91 / (cm2 s)
background_cosi(9.32 +/- 0.05) x 10^-1
\n",
+       "
"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:00:59\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.040804\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14911295.359\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=875816;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=935034;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "                                                            result  \\\n",
+       "parameter                                                            \n",
+       "gaussian.spectrum.main.Gaussian.F      (4.6951 +/- 0.0025) x 10^-2   \n",
+       "point_source.spectrum.main.Gaussian.F        (0.0 +/- 1.3) x 10^-9   \n",
+       "background_cosi                            (9.32 +/- 0.05) x 10^-1   \n",
+       "\n",
+       "                                              unit  \n",
+       "parameter                                           \n",
+       "gaussian.spectrum.main.Gaussian.F      1 / (cm2 s)  \n",
+       "point_source.spectrum.main.Gaussian.F  1 / (cm2 s)  \n",
+       "background_cosi                                     "
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:01:01 INFO      trial values: 0.039204,0.01,1 -> logL = 14904015.715                    joint_likelihood.py:1010\n",
+       "
\n",
+       "Correlation matrix:\n",
+       "\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.039204\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14904015.715\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=613799;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=178246;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "\n",
+       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:01:02 INFO      trial values: 0.04008,0.01,1 -> logL = 14908715.495                     joint_likelihood.py:1010\n",
-       "
\n"
+       "
\n",
+       "\n",
+       "\n",
+       "\n",
+       "
1.00-0.01-0.40
-0.011.00-0.03
-0.40-0.031.00
"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:02\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04008\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14908715.495\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=616322;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=74861;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       " 1.00 -0.01 -0.40\n",
+       "-0.01  1.00 -0.03\n",
+       "-0.40 -0.03  1.00"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:01:04 INFO      trial values: 0.03992,0.01,1 -> logL = 14907987.897                     joint_likelihood.py:1010\n",
+       "
\n",
+       "Values of -log(likelihood) at the minimum:\n",
+       "\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.03992\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14907987.897\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=707362;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=775270;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "\n",
+       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:01:05 INFO      trial values: 0.04,0.010201,1 -> logL = 14898734.111                    joint_likelihood.py:1010\n",
-       "
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+       "
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+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
-log(likelihood)
cosi-1.527559e+07
total-1.527559e+07
\n",
+       "
"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.010201\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14898734.111\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=882788;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=77148;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "       -log(likelihood)\n",
+       "cosi      -1.527559e+07\n",
+       "total     -1.527559e+07"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
14:01:07 INFO      trial values: 0.04,0.009801,1 -> logL = 14917820.671                    joint_likelihood.py:1010\n",
+       "
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+       "Values of statistical measures:\n",
+       "\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.009801\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14917820.671\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=267614;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=526736;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "\n",
+       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "
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-      ],
-      "text/plain": [
-       "\u001b[38;5;46m14:01:08\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.010042\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14906360.208\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=376195;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=215643;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "
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-      ],
-      "text/plain": [
-       "\u001b[38;5;46m14:01:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0099582\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14910350.138\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=513538;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=236698;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
statistical measures
AIC-3.055119e+07
BIC-3.055119e+07
\n",
+       "
"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0201\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14907769.198\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=35870;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=693168;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "     statistical measures\n",
+       "AIC         -3.055119e+07\n",
+       "BIC         -3.055119e+07"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
+      "CPU times: user 7min 24s, sys: 3min 55s, total: 11min 20s\n",
+      "Wall time: 1min 46s\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "
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-       "
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-      ],
       "text/plain": [
-       "\u001b[38;5;46m14:01:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.01\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98007\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m14908928.814\u001b[0m\u001b[1;38;5;251m                 \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=603180;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=490320;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
+       "(                                              value  negative_error  \\\n",
+       " gaussian.spectrum.main.Gaussian.F      4.695126e-02   -2.403110e-05   \n",
+       " point_source.spectrum.main.Gaussian.F  5.975791e-13    2.623492e-10   \n",
+       " background_cosi                        9.320815e-01   -4.914467e-03   \n",
+       " \n",
+       "                                        positive_error         error  \\\n",
+       " gaussian.spectrum.main.Gaussian.F        2.433950e-05  2.418530e-05   \n",
+       " point_source.spectrum.main.Gaussian.F    1.929678e-09  1.096013e-09   \n",
+       " background_cosi                          4.582905e-03  4.748686e-03   \n",
+       " \n",
+       "                                               unit  \n",
+       " gaussian.spectrum.main.Gaussian.F      1 / (cm2 s)  \n",
+       " point_source.spectrum.main.Gaussian.F  1 / (cm2 s)  \n",
+       " background_cosi                                     ,\n",
+       "        -log(likelihood)\n",
+       " cosi      -1.527559e+07\n",
+       " total     -1.527559e+07)"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n",
+    "\n",
+    "like = JointLikelihood(model, plugins, verbose = True)\n",
+    "\n",
+    "like.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c045f61-bd7a-44e3-933f-a4f1541b7aa3",
+   "metadata": {},
+   "source": [
+    "We see that the normalization of the point source has gone to zero, and we essentially get the same results as the first fit. This is not entirely surprising, considering that the two components have a high degree of degeneracy, and the point source is subdominant. \n",
+    "\n",
+    "Note (CK): The injected model may not be exactly the same as the astromodel, because MEGAlib uses a cutoff of the Gaussian spectral distribution at 3 sigma.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e47eea2",
+   "metadata": {},
+   "source": [
+    "## *****************************************\n",
+    "## Example 3: Working With a Realistic Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672fa8bd",
+   "metadata": {},
+   "source": [
+    "## Read in the binned data\n",
+    "We will start with the binned data, since we already learned how to bin data: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9bce7e04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# background:\n",
+    "bg_tot = BinnedData(\"Gal_511.yaml\")\n",
+    "bg_tot.load_binned_data_from_hdf5(binned_data=\"cosmic_photons_binned_data.hdf5\")\n",
+    "\n",
+    "# combined data:\n",
+    "data_combined_thin_disk = BinnedData(\"Gal_511.yaml\")\n",
+    "data_combined_thin_disk.load_binned_data_from_hdf5(binned_data=\"combined_binned_data_thin_disk.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3466ee97",
+   "metadata": {},
+   "source": [
+    "## Define source\n",
+    "This defines a multi-component source with a disk and gaussian component. The disk and bulge components have different spectral characteristics. Spatially, the bulge component is the sum of three different spatial models, with majority of the flux  \"narrow bulge\" with "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ca51102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Spectral Definitions...\n",
+    "\n",
+    "models = [\"centralPoint\",\"narrowBulge\",\"broadBulge\",\"disk\"]\n",
+    "\n",
+    "# several lists of parameters for, in order, CentralPoint, NarrowBulge, BroadBulge, and Disk sources\n",
+    "mu         = [511.,511.,511., 511.]*u.keV\n",
+    "sigma      = [0.85,0.85,0.85, 1.27]*u.keV\n",
+    "F          = [0.00012, 0.00028, 0.00073, 1.7e-3]/u.cm/u.cm/u.s\n",
+    "K          = [0.00046, 0.0011, 0.0027, 4.5e-3]/u.cm/u.cm/u.s/u.keV\n",
+    "\n",
+    "SpecLine   = [Gaussian(),Gaussian(),Gaussian(),Gaussian()]\n",
+    "SpecOPs    = [SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\")]\n",
+    "\n",
+    "# Set units and fitting parameters; different definition for each spectral model with different norms\n",
+    "for i in range(4):\n",
+    "    SpecLine[i].F.unit = F[i].unit\n",
+    "    SpecLine[i].F.value = F[i].value\n",
+    "    SpecLine[i].F.min_value =0\n",
+    "    SpecLine[i].F.max_value=1\n",
+    "    SpecLine[i].mu.value = mu[i].value\n",
+    "    SpecLine[i].mu.unit = mu[i].unit\n",
+    "    SpecLine[i].sigma.unit = sigma[i].unit\n",
+    "    SpecLine[i].sigma.value = sigma[i].value\n",
+    "\n",
+    "    SpecOPs[i].K.value = K[i].value\n",
+    "    SpecOPs[i].K.unit = K[i].unit\n",
+    "    \n",
+    "    SpecLine[i].sigma.free = False\n",
+    "    SpecLine[i].mu.free = False\n",
+    "    SpecLine[i].F.free = False#True\n",
+    "    SpecOPs[i].K.free = False # not fitting the amplitude of the OPs component for now, since we are only using the 511 response! \n",
+    "\n",
+    "SpecLine[-1].F.free = True# actually do fit the flux of the disk component\n",
+    "\n",
+    "# Generate Composite Spectra\n",
+    "SpecCentralPoint= SpecLine[0] + SpecOPs[0]\n",
+    "SpecNarrowBulge = SpecLine[1] + SpecOPs[1]\n",
+    "SpecBroadBulge  = SpecLine[2] + SpecOPs[2]\n",
+    "SpecDisk        = SpecLine[3] + SpecOPs[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "008ec971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Spatial Model Components\n",
+    "MapNarrowBulge = Gaussian_on_sphere(lon0=359.75,lat0=-1.25, sigma = 2.5)\n",
+    "MapBroadBulge = Gaussian_on_sphere(lon0 = 0, lat0 = 0, sigma = 8.7)\n",
+    "MapDisk = Wide_Asymm_Gaussian_on_sphere(lon0 = 0, lat0 = 0, a=90, e = 0.99944429,theta=0)\n",
+    "\n",
+    "# Fix fitting parameters (same for all models)\n",
+    "for map in [MapNarrowBulge,MapBroadBulge]:\n",
+    "    map.lon0.free=False\n",
+    "    map.lat0.free=False\n",
+    "    map.sigma.free=False\n",
+    "    \n",
+    "MapDisk.lon0.free=False\n",
+    "MapDisk.lat0.free=False\n",
+    "MapDisk.a.free=False\n",
+    "MapDisk.e.free=True#False\n",
+    "MapDisk.theta.free=False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4dc7eca-6881-45cb-801a-3e796a13dbfc",
+   "metadata": {},
+   "source": [
+    "For the Wide_Asymm_Gaussian_on_sphere model, note that e is the eccentricity of the Gaussian ellipse, defined such that the scale height b of the disk is given by $b = a \\sqrt{(1-e^2)}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "924aec1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Spatio-spectral models\n",
+    "\n",
+    "# Bulge\n",
+    "c = SkyCoord(l=0*u.deg, b=0*u.deg, frame='galactic')\n",
+    "c_icrs = c.transform_to('icrs')\n",
+    "ModelCentralPoint = PointSource('centralPoint', ra = c_icrs.ra.deg, dec = c_icrs.dec.deg, spectral_shape=SpecCentralPoint)\n",
+    "ModelNarrowBulge = ExtendedSource('narrowBulge',spectral_shape=SpecNarrowBulge,spatial_shape=MapNarrowBulge)\n",
+    "ModelBroadBulge = ExtendedSource('broadBulge',spectral_shape=SpecBroadBulge,spatial_shape=MapBroadBulge)\n",
+    "\n",
+    "# Disk\n",
+    "ModelDisk = ExtendedSource('disk',spectral_shape=SpecDisk,spatial_shape=MapDisk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b5784f63-3712-496b-a724-e64c2b66b180",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
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-     ]
-    },
-    {
-     "data": {
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "name": "stderr",
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-     "metadata": {},
-     "output_type": "display_data"
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-       "\u001b[38;5;46m14:02:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046797\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m6.4575e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.90926\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275455.595\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=260331;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=436496;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046676\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m6.4575e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.90926\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275406.324\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=72521;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=206222;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:12\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046737\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m7.3438e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.90926\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275412.007\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=514618;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=517132;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046737\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m5.6281e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.90926\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275453.370\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=683855;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=283265;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:15\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046737\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m6.4575e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.92081\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275445.583\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=387887;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=738689;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-    },
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-       "\u001b[38;5;46m14:02:16\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046737\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m6.4575e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.89777\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275416.592\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=864586;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=881872;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-    },
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-      "text/html": [
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-       "\u001b[38;5;46m14:02:18\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046895\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m3.706e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.92235\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275542.470\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=347986;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=991527;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-    },
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-      "text/html": [
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-       "\u001b[38;5;46m14:02:19\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046993\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m2.3897e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93044\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275563.633\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=781517;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=779368;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:21\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.047054\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m2.3897e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93044\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275552.836\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=442972;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=771384;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:22\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046932\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m2.3897e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93044\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275567.167\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=980658;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734442;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:24\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046993\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m3.1596e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93044\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275549.674\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=630583;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=205211;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
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-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "output_type": "display_data"
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-       "\u001b[38;5;46m14:02:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046917\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.102e-05\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93199\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275583.513\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=698034;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=192222;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:45\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046917\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m9.2826e-07\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93199\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275592.049\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=174181;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=727363;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046917\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3878e-06\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.94375\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275589.607\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=814069;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=950168;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:49\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046947\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m3.4605e-07\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93288\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.536\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=544958;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=363375;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:50\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.04695\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m2.6921e-07\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93298\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.589\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=557112;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=909772;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:52\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046954\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.9481e-07\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9331\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.609\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=672268;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=308346;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:02:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.047015\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.9481e-07\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9331\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275589.344\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=868400;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=310506;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
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-      ]
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-     "output_type": "display_data"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-       "\u001b[38;5;46m14:03:17\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93212\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.788\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=135278;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=458301;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:03:19\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.047012\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93212\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275590.152\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=449236;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=158210;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     "output_type": "display_data"
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-     "text": [
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-       "\u001b[38;5;46m14:03:23\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m4.8133e-06\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93212\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275590.119\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=86908;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=653906;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:03:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.92043\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275590.147\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=350087;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=823697;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
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-     "output_type": "display_data"
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-       "\u001b[38;5;46m14:03:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.92978\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.641\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=279500;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=643224;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:03:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93259\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.783\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=981468;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=811784;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
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-       "\u001b[38;5;46m14:03:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.047012\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.94387\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275583.576\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=343232;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=493942;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:03:59\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m4.803e-06\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.94387\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275585.723\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=907132;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=163225;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m14:04:00\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.046951\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.3798e-12\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.93212\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m15275593.788\u001b[0m\u001b[1;38;5;251m       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=982049;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=462569;file:///discover/nobackup/ckarwin/Software/COSI/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1010\u001b\\\u001b[2m1010\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     },
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-     "output_type": "display_data"
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-      ]
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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gaussian.spectrum.main.Gaussian.F(4.6951 +/- 0.0025) x 10^-21 / (cm2 s)
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-       "                                                            result  \\\n",
-       "parameter                                                            \n",
-       "gaussian.spectrum.main.Gaussian.F      (4.6951 +/- 0.0025) x 10^-2   \n",
-       "point_source.spectrum.main.Gaussian.F        (0.0 +/- 2.0) x 10^-9   \n",
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-       "                                              unit  \n",
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-       "gaussian.spectrum.main.Gaussian.F      1 / (cm2 s)  \n",
-       "point_source.spectrum.main.Gaussian.F  1 / (cm2 s)  \n",
-       "background_cosi                                     "
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-       " 1.00 0.01 -0.40\n",
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-       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
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-       "       -log(likelihood)\n",
-       "cosi      -1.527559e+07\n",
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statistical measures
AIC-3.055119e+07
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-       "     statistical measures\n",
-       "AIC         -3.055119e+07\n",
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 17min 15s, sys: 7min 56s, total: 25min 12s\n",
-      "Wall time: 6min 32s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(                                              value  negative_error  \\\n",
-       " gaussian.spectrum.main.Gaussian.F      4.695105e-02   -2.396938e-05   \n",
-       " point_source.spectrum.main.Gaussian.F  1.379828e-12    3.959404e-10   \n",
-       " background_cosi                        9.321207e-01   -4.310858e-03   \n",
-       " \n",
-       "                                        positive_error         error  \\\n",
-       " gaussian.spectrum.main.Gaussian.F        2.558651e-05  2.477795e-05   \n",
-       " point_source.spectrum.main.Gaussian.F    2.689562e-09  1.542751e-09   \n",
-       " background_cosi                          4.756121e-03  4.533489e-03   \n",
-       " \n",
-       "                                               unit  \n",
-       " gaussian.spectrum.main.Gaussian.F      1 / (cm2 s)  \n",
-       " point_source.spectrum.main.Gaussian.F  1 / (cm2 s)  \n",
-       " background_cosi                                     ,\n",
-       "        -log(likelihood)\n",
-       " cosi      -1.527559e+07\n",
-       " total     -1.527559e+07)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n",
-    "\n",
-    "like = JointLikelihood(model, plugins, verbose = True)\n",
-    "\n",
-    "like.fit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c045f61-bd7a-44e3-933f-a4f1541b7aa3",
-   "metadata": {},
-   "source": [
-    "We see that the normalization of the point source has gone to zero, and we essentially get the same results as the first fit. This is not entirely surprising, considering that the two components have a high degree of degeneracy, and the point source is subdominant. \n",
-    "\n",
-    "Note (CK): The injected model may not be exactly the same as the astromodel, because MEGAlib uses a cutoff of the Gaussian spectral distribution at 3 sigma.   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e47eea2",
-   "metadata": {},
-   "source": [
-    "## *****************************************\n",
-    "## Example 3: Working With a Realistic Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "672fa8bd",
-   "metadata": {},
-   "source": [
-    "## Read in the binned data\n",
-    "We will start with the binned data, since we already learned how to bin data: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "9bce7e04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# background:\n",
-    "bg_tot = BinnedData(\"Gal_511.yaml\")\n",
-    "bg_tot.load_binned_data_from_hdf5(binned_data=\"cosmic_photons_binned_data.hdf5\")\n",
-    "\n",
-    "# combined data:\n",
-    "data_combined_thin_disk = BinnedData(\"Gal_511.yaml\")\n",
-    "data_combined_thin_disk.load_binned_data_from_hdf5(binned_data=\"combined_binned_data_thin_disk.hdf5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3466ee97",
-   "metadata": {},
-   "source": [
-    "## Define source\n",
-    "This defines a multi-component source with a disk and gaussian component. The disk and bulge components have different spectral characteristics. Spatially, the bulge component is the sum of three different spatial models, with majority of the flux  \"narrow bulge\" with "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "3ca51102",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Spectral Definitions...\n",
-    "\n",
-    "models = [\"centralPoint\",\"narrowBulge\",\"broadBulge\",\"disk\"]\n",
-    "\n",
-    "# several lists of parameters for, in order, CentralPoint, NarrowBulge, BroadBulge, and Disk sources\n",
-    "mu         = [511.,511.,511., 511.]*u.keV\n",
-    "sigma      = [0.85,0.85,0.85, 1.27]*u.keV\n",
-    "F          = [0.00012, 0.00028, 0.00073, 1.7e-3]/u.cm/u.cm/u.s\n",
-    "K          = [0.00046, 0.0011, 0.0027, 4.5e-3]/u.cm/u.cm/u.s/u.keV\n",
-    "\n",
-    "SpecLine   = [Gaussian(),Gaussian(),Gaussian(),Gaussian()]\n",
-    "SpecOPs    = [SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\"),SpecFromDat(dat=\"OPsSpectrum.dat\")]\n",
-    "\n",
-    "# Set units and fitting parameters; different definition for each spectral model with different norms\n",
-    "for i in range(4):\n",
-    "    SpecLine[i].F.unit = F[i].unit\n",
-    "    SpecLine[i].F.value = F[i].value\n",
-    "    SpecLine[i].F.min_value =0\n",
-    "    SpecLine[i].F.max_value=1\n",
-    "    SpecLine[i].mu.value = mu[i].value\n",
-    "    SpecLine[i].mu.unit = mu[i].unit\n",
-    "    SpecLine[i].sigma.unit = sigma[i].unit\n",
-    "    SpecLine[i].sigma.value = sigma[i].value\n",
-    "\n",
-    "    SpecOPs[i].K.value = K[i].value\n",
-    "    SpecOPs[i].K.unit = K[i].unit\n",
-    "    \n",
-    "    SpecLine[i].sigma.free = False\n",
-    "    SpecLine[i].mu.free = False\n",
-    "    SpecLine[i].F.free = False#True\n",
-    "    SpecOPs[i].K.free = False # not fitting the amplitude of the OPs component for now, since we are only using the 511 response! \n",
-    "\n",
-    "SpecLine[-1].F.free = True# actually do fit the flux of the disk component\n",
-    "\n",
-    "# Generate Composite Spectra\n",
-    "SpecCentralPoint= SpecLine[0] + SpecOPs[0]\n",
-    "SpecNarrowBulge = SpecLine[1] + SpecOPs[1]\n",
-    "SpecBroadBulge  = SpecLine[2] + SpecOPs[2]\n",
-    "SpecDisk        = SpecLine[3] + SpecOPs[3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "008ec971",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define Spatial Model Components\n",
-    "MapNarrowBulge = Gaussian_on_sphere(lon0=359.75,lat0=-1.25, sigma = 2.5)\n",
-    "MapBroadBulge = Gaussian_on_sphere(lon0 = 0, lat0 = 0, sigma = 8.7)\n",
-    "MapDisk = Wide_Asymm_Gaussian_on_sphere(lon0 = 0, lat0 = 0, a=90, e = 0.99944429,theta=0)\n",
-    "\n",
-    "# Fix fitting parameters (same for all models)\n",
-    "for map in [MapNarrowBulge,MapBroadBulge]:\n",
-    "    map.lon0.free=False\n",
-    "    map.lat0.free=False\n",
-    "    map.sigma.free=False\n",
-    "    \n",
-    "MapDisk.lon0.free=False\n",
-    "MapDisk.lat0.free=False\n",
-    "MapDisk.a.free=False\n",
-    "MapDisk.e.free=True#False\n",
-    "MapDisk.theta.free=False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4dc7eca-6881-45cb-801a-3e796a13dbfc",
-   "metadata": {},
-   "source": [
-    "For the Wide_Asymm_Gaussian_on_sphere model, note that e is the eccentricity of the Gaussian ellipse, defined such that the scale height b of the disk is given by $b = a \\sqrt{(1-e^2)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "924aec1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define Spatio-spectral models\n",
-    "\n",
-    "# Bulge\n",
-    "c = SkyCoord(l=0*u.deg, b=0*u.deg, frame='galactic')\n",
-    "c_icrs = c.transform_to('icrs')\n",
-    "ModelCentralPoint = PointSource('centralPoint', ra = c_icrs.ra.deg, dec = c_icrs.dec.deg, spectral_shape=SpecCentralPoint)\n",
-    "ModelNarrowBulge = ExtendedSource('narrowBulge',spectral_shape=SpecNarrowBulge,spatial_shape=MapNarrowBulge)\n",
-    "ModelBroadBulge = ExtendedSource('broadBulge',spectral_shape=SpecBroadBulge,spatial_shape=MapBroadBulge)\n",
-    "\n",
-    "# Disk\n",
-    "ModelDisk = ExtendedSource('disk',spectral_shape=SpecDisk,spatial_shape=MapDisk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "b5784f63-3712-496b-a724-e64c2b66b180",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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        • value: 0.0
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        • desc: Longitude of the center of the source
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        • min_value: 0.0
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        • max_value: 360.0
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        • min_value: -90.0
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          • \n",
-       "\n",
-       "
        \n",
-       "\n",
-       "
        • \n",
-       "\n",
-       "
      • a: \n",
-       "
          \n",
-       "\n",
-       "
        • value: 90.0
          • \n",
-       "\n",
-       "
        • desc: Standard deviation of the Gaussian distribution (major axis)
          • \n",
-       "\n",
-       "
        • min_value: 0.0
          • \n",
-       "\n",
-       "
        • max_value: 90.0
          • \n",
-       "\n",
-       "
        • unit: deg
          • \n",
-       "\n",
-       "
        • is_normalization: False
          • \n",
-       "\n",
-       "
        \n",
-       "\n",
-       "
        • \n",
-       "\n",
-       "
      • e: \n",
-       "
          \n",
-       "\n",
-       "
        • value: 0.99944429
          • \n",
-       "\n",
-       "
        • desc: Excentricity of Gaussian ellipse, e^2 = 1 - (b/a)^2, where b is the standard deviation of the Gaussian distribution (minor axis)
          • \n",
-       "\n",
-       "
        • min_value: 0.0
          • \n",
-       "\n",
-       "
        • max_value: 1.0
          • \n",
-       "\n",
-       "
        • unit: 
          • \n",
-       "\n",
-       "
        • is_normalization: False
          • \n",
-       "\n",
-       "
        \n",
-       "\n",
-       "
        • \n",
-       "\n",
-       "
      • theta: \n",
-       "
          \n",
-       "\n",
-       "
        • value: 0.0
          • \n",
-       "\n",
-       "
        • desc: inclination of major axis to a line of constant latitude
          • \n",
-       "\n",
-       "
        • min_value: -90.0
          • \n",
-       "\n",
-       "
        • max_value: 90.0
          • \n",
-       "\n",
-       "
        • unit: deg
          • \n",
-       "\n",
-       "
        • is_normalization: False
          • \n",
-       "\n",
-       "
        \n",
-       "\n",
-       "
        • \n",
-       "\n",
-       "
      \n",
-       "\n",
-       "
      • \n",
-       "\n",
-       "
    • spectrum: \n",
-       "
        \n",
-       "\n",
-       "
      • main: \n",
-       "
          \n",
-       "\n",
-       "
        • composite: \n",
-       "
            \n",
-       "\n",
-       "
          • F_1: \n",
-       "
              \n",
-       "\n",
-       "
            • value: 0.0017
              • \n",
-       "\n",
-       "
            • desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution
              • \n",
-       "\n",
-       "
            • min_value: 0.0
              • \n",
-       "\n",
-       "
            • max_value: 1.0
              • \n",
-       "\n",
-       "
            • unit: s-1 cm-2
              • \n",
-       "\n",
-       "
            • is_normalization: False
              • \n",
-       "\n",
-       "
            \n",
-       "\n",
-       "
            • \n",
-       "\n",
-       "
          • mu_1: \n",
-       "
              \n",
-       "\n",
-       "
            • value: 511.0
              • \n",
-       "\n",
-       "
            • desc: Central value
              • \n",
-       "\n",
-       "
            • min_value: None
              • \n",
-       "\n",
-       "
            • max_value: None
              • \n",
-       "\n",
-       "
            • unit: keV
              • \n",
-       "\n",
-       "
            • is_normalization: False
              • \n",
-       "\n",
-       "
            \n",
-       "\n",
-       "
            • \n",
-       "\n",
-       "
          • sigma_1: \n",
-       "
              \n",
-       "\n",
-       "
            • value: 1.27
              • \n",
-       "\n",
-       "
            • desc: standard deviation
              • \n",
-       "\n",
-       "
            • min_value: 1e-12
              • \n",
-       "\n",
-       "
            • max_value: None
              • \n",
-       "\n",
-       "
            • unit: keV
              • \n",
-       "\n",
-       "
            • is_normalization: False
              • \n",
-       "\n",
-       "
            \n",
-       "\n",
-       "
            • \n",
-       "\n",
-       "
          • K_2: \n",
-       "
              \n",
-       "\n",
-       "
            • value: 0.004499999999999998
              • \n",
-       "\n",
-       "
            • desc: Normalization
              • \n",
-       "\n",
-       "
            • min_value: 1e-30
              • \n",
-       "\n",
-       "
            • max_value: 1000.0
              • \n",
-       "\n",
-       "
            • unit: keV-1 s-1 cm-2
              • \n",
-       "\n",
-       "
            • is_normalization: True
              • \n",
-       "\n",
-       "
            \n",
-       "\n",
-       "
            • \n",
-       "\n",
-       "
          • dat_2: OPsSpectrum.dat
            • \n",
-       "\n",
-       "
          \n",
-       "\n",
-       "
          • \n",
-       "\n",
-       "
        \n",
-       "\n",
-       "
        • \n",
-       "\n",
-       "
      \n",
-       "\n",
-       "
      • \n",
-       "\n",
-       "
    \n",
-       "\n",
-       "
    • \n",
-       "\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "  * disk (extended source):\n",
-       "    * shape:\n",
-       "      * lon0:\n",
-       "        * value: 0.0\n",
-       "        * desc: Longitude of the center of the source\n",
-       "        * min_value: 0.0\n",
-       "        * max_value: 360.0\n",
-       "        * unit: deg\n",
-       "        * is_normalization: false\n",
-       "      * lat0:\n",
-       "        * value: 0.0\n",
-       "        * desc: Latitude of the center of the source\n",
-       "        * min_value: -90.0\n",
-       "        * max_value: 90.0\n",
-       "        * unit: deg\n",
-       "        * is_normalization: false\n",
-       "      * a:\n",
-       "        * value: 90.0\n",
-       "        * desc: Standard deviation of the Gaussian distribution (major axis)\n",
-       "        * min_value: 0.0\n",
-       "        * max_value: 90.0\n",
-       "        * unit: deg\n",
-       "        * is_normalization: false\n",
-       "      * e:\n",
-       "        * value: 0.99944429\n",
-       "        * desc: Excentricity of Gaussian ellipse, e^2 = 1 - (b/a)^2, where b is the standard\n",
-       "          * deviation of the Gaussian distribution (minor axis)\n",
-       "        * min_value: 0.0\n",
-       "        * max_value: 1.0\n",
-       "        * unit: ''\n",
-       "        * is_normalization: false\n",
-       "      * theta:\n",
-       "        * value: 0.0\n",
-       "        * desc: inclination of major axis to a line of constant latitude\n",
-       "        * min_value: -90.0\n",
-       "        * max_value: 90.0\n",
-       "        * unit: deg\n",
-       "        * is_normalization: false\n",
-       "    * spectrum:\n",
-       "      * main:\n",
-       "        * composite:\n",
-       "          * F_1:\n",
-       "            * value: 0.0017\n",
-       "            * desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution\n",
-       "            * min_value: 0.0\n",
-       "            * max_value: 1.0\n",
-       "            * unit: s-1 cm-2\n",
-       "            * is_normalization: false\n",
-       "          * mu_1:\n",
-       "            * value: 511.0\n",
-       "            * desc: Central value\n",
-       "            * min_value: null\n",
-       "            * max_value: null\n",
-       "            * unit: keV\n",
-       "            * is_normalization: false\n",
-       "          * sigma_1:\n",
-       "            * value: 1.27\n",
-       "            * desc: standard deviation\n",
-       "            * min_value: 1.0e-12\n",
-       "            * max_value: null\n",
-       "            * unit: keV\n",
-       "            * is_normalization: false\n",
-       "          * K_2:\n",
-       "            * value: 0.004499999999999998\n",
-       "            * desc: Normalization\n",
-       "            * min_value: 1.0e-30\n",
-       "            * max_value: 1000.0\n",
-       "            * unit: keV-1 s-1 cm-2\n",
-       "            * is_normalization: true\n",
-       "          * dat_2:\n",
-       "            * value: OPsSpectrum.dat\n",
-       "        * polarization: {}"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ModelDisk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed7ac3ec",
-   "metadata": {},
-   "source": [
-    "Make some plots to look at these new extended sources:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "73d61cb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot spectra at 511 keV\n",
-    "energy = np.linspace(500.,520.,10001)*u.keV\n",
-    "fig, axs = plt.subplots()\n",
-    "for label,m in zip(models,\n",
-    "                   [ModelCentralPoint,ModelNarrowBulge,ModelBroadBulge,ModelDisk]):\n",
-    "    dnde = m.spectrum.main.composite(energy)\n",
-    "    axs.plot(energy, dnde,label=label)\n",
-    "\n",
-    "axs.legend()\n",
-    "axs.set_ylabel(\"dN/dE [$\\mathrm{ph \\ cm^{-2} \\ s^{-1} \\ keV^{-1}}$]\", fontsize=14)\n",
-    "axs.set_xlabel(\"Energy [keV]\", fontsize=14);\n",
-    "plt.ylim(0,);\n",
-    "#axs[0].set_yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db4cfb6e-e812-4f16-9c4c-95176bcc0dee",
-   "metadata": {},
-   "source": [
-    "The orthopositronium spectral component appears as the low-energy tail of the 511 keV line."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "8b588f46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define healpix map matching the detector response:\n",
-    "nside_model = 2**4\n",
-    "scheme='ring'\n",
-    "is_nested = (scheme == 'nested')\n",
-    "coordsys='G'\n",
-    "\n",
-    "mBroadBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
-    "mNarrowBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
-    "mPointBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
-    "mDisk = HealpixMap(nside = nside_model, scheme=scheme, dtype = float,coordsys=coordsys)\n",
-    "\n",
-    "coords = mDisk.pix2skycoord(range(mDisk.npix)) # common among all the galactic maps...\n",
-    "\n",
-    "pix_area = mBroadBulge.pixarea().value # common among all the galactic maps with the same pixelization\n",
-    "\n",
-    "# Fill skymap with values from extended source: \n",
-    "mNarrowBulge[:] = ModelNarrowBulge.spatial_shape(coords.l.deg, coords.b.deg)\n",
-    "mBroadBulge[:] = ModelBroadBulge.spatial_shape(coords.l.deg, coords.b.deg)\n",
-    "mBulge = mBroadBulge + mNarrowBulge\n",
-    "mDisk[:] = ModelDisk.spatial_shape(coords.l.deg, coords.b.deg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "b80ae9d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "List_of_Maps = [mDisk,mNarrowBulge,mBroadBulge]\n",
-    "List_of_Names = [\"Disk\",\"Narrow Bulge\",\"Broad Bulge\", ]\n",
-    "\n",
-    "for n, m in zip(List_of_Names,List_of_Maps):\n",
-    "    plot,ax = m.plot(ax_kw={\"coord\":\"G\"})\n",
-    "    ax.grid();\n",
-    "    lon = ax.coords['glon']\n",
-    "    lat = ax.coords['glat']\n",
-    "    lon.set_axislabel('Galactic Longitude',color='white',fontsize=5)\n",
-    "    lat.set_axislabel('Galactic Latitude',fontsize=5)\n",
-    "    lon.display_minor_ticks(True)\n",
-    "    lat.display_minor_ticks(True)\n",
-    "    lon.set_ticks_visible(True)\n",
-    "    lon.set_ticklabel_visible(True)\n",
-    "    lon.set_ticks(color='white',alpha=0.6)\n",
-    "    lat.set_ticks(color='white',alpha=0.6)\n",
-    "    lon.set_ticklabel(color='white',fontsize=4)\n",
-    "    lat.set_ticklabel(fontsize=4)\n",
-    "    lat.set_ticks_visible(True)\n",
-    "    lat.set_ticklabel_visible(True)\n",
-    "    ax.set_title(n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "915bc5ee",
-   "metadata": {},
-   "source": [
-    "## Instantiate the COSI 3ML plugin and perform the likelihood fit\n",
-    "The following two cells should be run only if not already run in previous examples..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "5b3abf0b-7631-419c-b5b7-a31dbfe1b65c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# if not previously loaded in example 1, load the response, ori, and psr: \n",
-    "response_file = \"SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\"\n",
-    "response = FullDetectorResponse.open(response_file)\n",
-    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month.ori\")\n",
-    "psr_file = \"psr_gal_511_DC2.h5\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "522db694-3a1d-4d0d-a3d9-0e028bb5cbcc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set background parameter, which is used to fit the amplitude of the background:\n",
-    "bkg_par = Parameter(\"background_cosi\",                                        # background parameter\n",
-    "                    1,                                                        # initial value of parameter\n",
-    "                    min_value=0,                                              # minimum value of parameter\n",
-    "                    max_value=5,                                              # maximum value of parameter\n",
-    "                    delta=0.05,                                               # initial step used by fitting engine\n",
-    "                    desc=\"Background parameter for cosi\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34287711-a61b-4496-bc3e-b5f2f9e02298",
-   "metadata": {},
-   "source": [
-    "We should re-run the following cell every time we set up a new fit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "5ca19bc5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "... loading the pre-computed image response ...\n",
-      "--> done\n",
-      "CPU times: user 1min 54s, sys: 4min 32s, total: 6min 27s\n",
-      "Wall time: 6min 26s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time \n",
-    "\n",
-    "# Instantiate the COSI 3ML plugin, using combined data for the thin disk\n",
-    "cosi = COSILike(\"cosi\",                                                       # COSI 3ML plugin\n",
-    "                dr = response_file,                                           # detector response\n",
-    "                data = data_combined_thin_disk.binned_data.project('Em', 'Phi', 'PsiChi'),# data (source+background)\n",
-    "                bkg = bg_tot.binned_data.project('Em', 'Phi', 'PsiChi'),       # background model \n",
-    "                sc_orientation = ori,                                          # spacecraft orientation\n",
-    "                nuisance_param = bkg_par,                                      # background parameter\n",
-    "                precomputed_psr_file = psr_file)                               # full path to precomputed psr file in galactic coordinates (optional)\n",
-    "plugins = DataList(cosi)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "774aba03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Model summary:

\n",
-       "\n",
-       "\n",
-       "  \n",
-       "    \n",
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-       "    \n",
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-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "
N
Point sources1
Extended sources3
Particle sources0
\n",
-       "


Free parameters (2):

\n",
-       "\n",
-       "\n",
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-       "    \n",
-       "  \n",
-       "
valuemin_valuemax_valueunit
disk.Wide_Asymm_Gaussian_on_sphere.e0.9994440.01.0
disk.spectrum.main.composite.F_10.00170.01.0s-1 cm-2
\n",
-       "


Fixed parameters (27):

\n",
-       "\n",
-       "\n",
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-       "
valuemin_valuemax_valueunit
disk.Wide_Asymm_Gaussian_on_sphere.lon00.00.0360.0deg
disk.Wide_Asymm_Gaussian_on_sphere.lat00.0-90.090.0deg
disk.Wide_Asymm_Gaussian_on_sphere.a90.00.090.0deg
disk.Wide_Asymm_Gaussian_on_sphere.theta0.0-90.090.0deg
disk.spectrum.main.composite.mu_1511.0NoneNonekeV
disk.spectrum.main.composite.sigma_11.270.0NonekeV
disk.spectrum.main.composite.K_20.00450.01000.0keV-1 s-1 cm-2
broadBulge.Gaussian_on_sphere.lon00.00.0360.0deg
broadBulge.Gaussian_on_sphere.lat00.0-90.090.0deg
broadBulge.Gaussian_on_sphere.sigma8.70.020.0deg
broadBulge.spectrum.main.composite.F_10.000730.01.0s-1 cm-2
broadBulge.spectrum.main.composite.mu_1511.0NoneNonekeV
broadBulge.spectrum.main.composite.sigma_10.850.0NonekeV
broadBulge.spectrum.main.composite.K_20.00270.01000.0keV-1 s-1 cm-2
narrowBulge.Gaussian_on_sphere.lon0359.750.0360.0deg
narrowBulge.Gaussian_on_sphere.lat0-1.25-90.090.0deg
narrowBulge.Gaussian_on_sphere.sigma2.50.020.0deg
narrowBulge.spectrum.main.composite.F_10.000280.01.0s-1 cm-2
narrowBulge.spectrum.main.composite.mu_1511.0NoneNonekeV
narrowBulge.spectrum.main.composite.sigma_10.850.0NonekeV
narrowBulge.spectrum.main.composite.K_20.00110.01000.0keV-1 s-1 cm-2
centralPoint.position.ra266.4049880.0360.0deg
centralPoint.position.dec-28.936178-90.090.0deg
centralPoint.spectrum.main.composite.F_10.000120.01.0s-1 cm-2
centralPoint.spectrum.main.composite.mu_1511.0NoneNonekeV
centralPoint.spectrum.main.composite.sigma_10.850.0NonekeV
centralPoint.spectrum.main.composite.K_20.000460.01000.0keV-1 s-1 cm-2
\n",
-       "


Properties (4):

\n",
-       "\n",
-       "\n",
-       "  \n",
-       "    \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
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-       "      \n",
-       "    \n",
-       "  \n",
-       "
valueallowed values
disk.spectrum.main.composite.dat_2OPsSpectrum.datNone
broadBulge.spectrum.main.composite.dat_2OPsSpectrum.datNone
narrowBulge.spectrum.main.composite.dat_2OPsSpectrum.datNone
centralPoint.spectrum.main.composite.dat_2OPsSpectrum.datNone
\n",
-       "


Linked parameters (0):

(none)

Independent variables:

(none)

Linked functions (0):

(none)
"
-      ],
-      "text/plain": [
-       "Model summary:\n",
-       "==============\n",
-       "\n",
-       "                  N\n",
-       "Point sources     1\n",
-       "Extended sources  3\n",
-       "Particle sources  0\n",
-       "\n",
-       "Free parameters (2):\n",
-       "--------------------\n",
-       "\n",
-       "                                         value min_value max_value      unit\n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.e  0.999444       0.0       1.0          \n",
-       "disk.spectrum.main.composite.F_1        0.0017       0.0       1.0  s-1 cm-2\n",
-       "\n",
-       "Fixed parameters (27):\n",
-       "---------------------\n",
-       "\n",
-       "                                               value min_value max_value  \\\n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.lon0          0.0       0.0     360.0   \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.lat0          0.0     -90.0      90.0   \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.a            90.0       0.0      90.0   \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.theta         0.0     -90.0      90.0   \n",
-       "disk.spectrum.main.composite.mu_1              511.0      None      None   \n",
-       "disk.spectrum.main.composite.sigma_1            1.27       0.0      None   \n",
-       "disk.spectrum.main.composite.K_2              0.0045       0.0    1000.0   \n",
-       "broadBulge.Gaussian_on_sphere.lon0               0.0       0.0     360.0   \n",
-       "broadBulge.Gaussian_on_sphere.lat0               0.0     -90.0      90.0   \n",
-       "broadBulge.Gaussian_on_sphere.sigma              8.7       0.0      20.0   \n",
-       "broadBulge.spectrum.main.composite.F_1       0.00073       0.0       1.0   \n",
-       "broadBulge.spectrum.main.composite.mu_1        511.0      None      None   \n",
-       "broadBulge...sigma_1                            0.85       0.0      None   \n",
-       "broadBulge.spectrum.main.composite.K_2        0.0027       0.0    1000.0   \n",
-       "narrowBulge.Gaussian_on_sphere.lon0           359.75       0.0     360.0   \n",
-       "narrowBulge.Gaussian_on_sphere.lat0            -1.25     -90.0      90.0   \n",
-       "narrowBulge.Gaussian_on_sphere.sigma             2.5       0.0      20.0   \n",
-       "narrowBulge.spectrum.main.composite.F_1      0.00028       0.0       1.0   \n",
-       "narrowBulge.spectrum.main.composite.mu_1       511.0      None      None   \n",
-       "narrowBulge...sigma_1                           0.85       0.0      None   \n",
-       "narrowBulge.spectrum.main.composite.K_2       0.0011       0.0    1000.0   \n",
-       "centralPoint.position.ra                  266.404988       0.0     360.0   \n",
-       "centralPoint.position.dec                 -28.936178     -90.0      90.0   \n",
-       "centralPoint.spectrum.main.composite.F_1     0.00012       0.0       1.0   \n",
-       "centralPoint...mu_1                            511.0      None      None   \n",
-       "centralPoint...sigma_1                          0.85       0.0      None   \n",
-       "centralPoint.spectrum.main.composite.K_2     0.00046       0.0    1000.0   \n",
-       "\n",
-       "                                                    unit  \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.lon0              deg  \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.lat0              deg  \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.a                 deg  \n",
-       "disk.Wide_Asymm_Gaussian_on_sphere.theta             deg  \n",
-       "disk.spectrum.main.composite.mu_1                    keV  \n",
-       "disk.spectrum.main.composite.sigma_1                 keV  \n",
-       "disk.spectrum.main.composite.K_2          keV-1 s-1 cm-2  \n",
-       "broadBulge.Gaussian_on_sphere.lon0                   deg  \n",
-       "broadBulge.Gaussian_on_sphere.lat0                   deg  \n",
-       "broadBulge.Gaussian_on_sphere.sigma                  deg  \n",
-       "broadBulge.spectrum.main.composite.F_1          s-1 cm-2  \n",
-       "broadBulge.spectrum.main.composite.mu_1              keV  \n",
-       "broadBulge...sigma_1                                 keV  \n",
-       "broadBulge.spectrum.main.composite.K_2    keV-1 s-1 cm-2  \n",
-       "narrowBulge.Gaussian_on_sphere.lon0                  deg  \n",
-       "narrowBulge.Gaussian_on_sphere.lat0                  deg  \n",
-       "narrowBulge.Gaussian_on_sphere.sigma                 deg  \n",
-       "narrowBulge.spectrum.main.composite.F_1         s-1 cm-2  \n",
-       "narrowBulge.spectrum.main.composite.mu_1             keV  \n",
-       "narrowBulge...sigma_1                                keV  \n",
-       "narrowBulge.spectrum.main.composite.K_2   keV-1 s-1 cm-2  \n",
-       "centralPoint.position.ra                             deg  \n",
-       "centralPoint.position.dec                            deg  \n",
-       "centralPoint.spectrum.main.composite.F_1        s-1 cm-2  \n",
-       "centralPoint...mu_1                                  keV  \n",
-       "centralPoint...sigma_1                               keV  \n",
-       "centralPoint.spectrum.main.composite.K_2  keV-1 s-1 cm-2  \n",
-       "\n",
-       "Properties (4):\n",
-       "--------------------\n",
-       "\n",
-       "                                                    value allowed values\n",
-       "disk.spectrum.main.composite.dat_2        OPsSpectrum.dat           None\n",
-       "broadBulge.spectrum.main.composite.dat_2  OPsSpectrum.dat           None\n",
-       "narrowBulge...dat_2                       OPsSpectrum.dat           None\n",
-       "centralPoint...dat_2                      OPsSpectrum.dat           None\n",
-       "\n",
-       "Linked parameters (0):\n",
-       "----------------------\n",
-       "\n",
-       "(none)\n",
-       "\n",
-       "Independent variables:\n",
-       "----------------------\n",
-       "\n",
-       "(none)\n",
-       "\n",
-       "Linked functions (0):\n",
-       "----------------------\n",
-       "\n",
-       "(none)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# add sources to thin disk and thick disk models \n",
-    "totalModel =  Model(ModelDisk, ModelBroadBulge,ModelNarrowBulge,ModelCentralPoint)\n",
-    "totalModel.display(complete=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "c424a2e2-9bf9-457d-a54b-23d8ea30fd56",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "... Calculating point source responses ...\n",
-      "--> done (source name : centralPoint)\n",
-      "--> all done\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
08:21:04 INFO      set the minimizer to minuit                                             joint_likelihood.py:1045\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m08:21:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=89984;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=185921;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\u001b\\\u001b[2m1045\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
08:21:11 INFO      trial values: 0.99944,0.0017,1 -> logL = 166712.805                     joint_likelihood.py:1013\n",
-       "
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99944\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0017\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99437\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166727.639\u001b[0m\u001b[1;38;5;251m              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=339112;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=804375;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:16\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99944\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0017\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0007\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166710.575\u001b[0m\u001b[1;38;5;251m               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=702237;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=280544;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:21\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99944\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0017\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99929\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166714.956\u001b[0m\u001b[1;38;5;251m              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=736526;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=529079;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.9998\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016312\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98042\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166758.820\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=747700;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=687159;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:31\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99971\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016514\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98618\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166768.518\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=860002;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=321147;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:36\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99973\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016514\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98618\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166769.136\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=327564;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=599044;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99969\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016514\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98618\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166767.828\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=429101;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=286154;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99971\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016537\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98618\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166768.608\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=389327;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=196401;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99971\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.001649\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98618\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166768.349\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=303676;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=286561;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:22:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99971\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016514\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98689\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166768.821\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=264237;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=556493;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:23:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99971\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016514\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98547\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166768.134\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=635417;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=930569;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:23:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016522\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98929\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.416\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=162609;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=884446;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:23:40\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016522\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98788\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.311\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=136596;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=377725;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016409\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99017\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.667\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=1395;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=326268;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:15\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99088\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.748\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=155058;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=612908;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:20\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98946\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.648\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=259019;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=188817;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:25\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=483129;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=165712;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:30\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99987\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.701\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=277687;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=92090;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99984\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.719\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=198976;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=134359;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:40\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016456\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.715\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=152512;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=937366;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016408\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.713\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=638804;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=373583;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99129\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.717\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=559924;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=922018;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:24:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98987\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.711\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=918715;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=738229;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:25:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99061\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=708240;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=963685;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "output_type": "display_data"
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-     "output_type": "display_data"
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-     "output_type": "stream",
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-       "\u001b[38;5;46m08:27:06\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99987\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99131\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.659\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=783617;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=244490;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016456\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99131\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.628\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=626723;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=800154;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:16\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=379119;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=729302;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:21\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=511505;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=703434;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=92190;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=297286;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:31\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016437\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.753\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=917380;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=204633;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:36\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016427\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.752\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=806007;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=918998;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-       "\u001b[38;5;46m08:27:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99075\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.753\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=56101;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=466665;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
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-      "text/html": [
-       "
08:28:07 INFO      trial values: 0.99985,0.0016431,0.9906 -> logL = 166772.754             joint_likelihood.py:1013\n",
-       "
\n"
+       "
    \n",
+       "\n",
+       "
  • disk (extended source): \n",
+       "
      \n",
+       "\n",
+       "
    • shape: \n",
+       "
        \n",
+       "\n",
+       "
      • lon0: \n",
+       "
          \n",
+       "\n",
+       "
        • value: 0.0
          • \n",
+       "\n",
+       "
        • desc: Longitude of the center of the source
          • \n",
+       "\n",
+       "
        • min_value: 0.0
          • \n",
+       "\n",
+       "
        • max_value: 360.0
          • \n",
+       "\n",
+       "
        • unit: deg
          • \n",
+       "\n",
+       "
        • is_normalization: False
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      • lat0: \n",
+       "
          \n",
+       "\n",
+       "
        • value: 0.0
          • \n",
+       "\n",
+       "
        • desc: Latitude of the center of the source
          • \n",
+       "\n",
+       "
        • min_value: -90.0
          • \n",
+       "\n",
+       "
        • max_value: 90.0
          • \n",
+       "\n",
+       "
        • unit: deg
          • \n",
+       "\n",
+       "
        • is_normalization: False
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      • a: \n",
+       "
          \n",
+       "\n",
+       "
        • value: 90.0
          • \n",
+       "\n",
+       "
        • desc: Standard deviation of the Gaussian distribution (major axis)
          • \n",
+       "\n",
+       "
        • min_value: 0.0
          • \n",
+       "\n",
+       "
        • max_value: 90.0
          • \n",
+       "\n",
+       "
        • unit: deg
          • \n",
+       "\n",
+       "
        • is_normalization: False
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      • e: \n",
+       "
          \n",
+       "\n",
+       "
        • value: 0.99944429
          • \n",
+       "\n",
+       "
        • desc: Excentricity of Gaussian ellipse, e^2 = 1 - (b/a)^2, where b is the standard deviation of the Gaussian distribution (minor axis)
          • \n",
+       "\n",
+       "
        • min_value: 0.0
          • \n",
+       "\n",
+       "
        • max_value: 1.0
          • \n",
+       "\n",
+       "
        • unit: 
          • \n",
+       "\n",
+       "
        • is_normalization: False
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      • theta: \n",
+       "
          \n",
+       "\n",
+       "
        • value: 0.0
          • \n",
+       "\n",
+       "
        • desc: inclination of major axis to a line of constant latitude
          • \n",
+       "\n",
+       "
        • min_value: -90.0
          • \n",
+       "\n",
+       "
        • max_value: 90.0
          • \n",
+       "\n",
+       "
        • unit: deg
          • \n",
+       "\n",
+       "
        • is_normalization: False
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      \n",
+       "\n",
+       "
      • \n",
+       "\n",
+       "
    • spectrum: \n",
+       "
        \n",
+       "\n",
+       "
      • main: \n",
+       "
          \n",
+       "\n",
+       "
        • composite: \n",
+       "
            \n",
+       "\n",
+       "
          • F_1: \n",
+       "
              \n",
+       "\n",
+       "
            • value: 0.0017
              • \n",
+       "\n",
+       "
            • desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution
              • \n",
+       "\n",
+       "
            • min_value: 0.0
              • \n",
+       "\n",
+       "
            • max_value: 1.0
              • \n",
+       "\n",
+       "
            • unit: s-1 cm-2
              • \n",
+       "\n",
+       "
            • is_normalization: False
              • \n",
+       "\n",
+       "
            \n",
+       "\n",
+       "
            • \n",
+       "\n",
+       "
          • mu_1: \n",
+       "
              \n",
+       "\n",
+       "
            • value: 511.0
              • \n",
+       "\n",
+       "
            • desc: Central value
              • \n",
+       "\n",
+       "
            • min_value: None
              • \n",
+       "\n",
+       "
            • max_value: None
              • \n",
+       "\n",
+       "
            • unit: keV
              • \n",
+       "\n",
+       "
            • is_normalization: False
              • \n",
+       "\n",
+       "
            \n",
+       "\n",
+       "
            • \n",
+       "\n",
+       "
          • sigma_1: \n",
+       "
              \n",
+       "\n",
+       "
            • value: 1.27
              • \n",
+       "\n",
+       "
            • desc: standard deviation
              • \n",
+       "\n",
+       "
            • min_value: 1e-12
              • \n",
+       "\n",
+       "
            • max_value: None
              • \n",
+       "\n",
+       "
            • unit: keV
              • \n",
+       "\n",
+       "
            • is_normalization: False
              • \n",
+       "\n",
+       "
            \n",
+       "\n",
+       "
            • \n",
+       "\n",
+       "
          • K_2: \n",
+       "
              \n",
+       "\n",
+       "
            • value: 0.004499999999999998
              • \n",
+       "\n",
+       "
            • desc: Normalization
              • \n",
+       "\n",
+       "
            • min_value: 1e-30
              • \n",
+       "\n",
+       "
            • max_value: 1000.0
              • \n",
+       "\n",
+       "
            • unit: keV-1 s-1 cm-2
              • \n",
+       "\n",
+       "
            • is_normalization: True
              • \n",
+       "\n",
+       "
            \n",
+       "\n",
+       "
            • \n",
+       "\n",
+       "
          • dat_2: OPsSpectrum.dat
            • \n",
+       "\n",
+       "
          \n",
+       "\n",
+       "
          • \n",
+       "\n",
+       "
        \n",
+       "\n",
+       "
        • \n",
+       "\n",
+       "
      \n",
+       "\n",
+       "
      • \n",
+       "\n",
+       "
    \n",
+       "\n",
+       "
    • \n",
+       "\n",
+       "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m08:28:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016431\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=951908;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=335304;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "  * disk (extended source):\n",
+       "    * shape:\n",
+       "      * lon0:\n",
+       "        * value: 0.0\n",
+       "        * desc: Longitude of the center of the source\n",
+       "        * min_value: 0.0\n",
+       "        * max_value: 360.0\n",
+       "        * unit: deg\n",
+       "        * is_normalization: false\n",
+       "      * lat0:\n",
+       "        * value: 0.0\n",
+       "        * desc: Latitude of the center of the source\n",
+       "        * min_value: -90.0\n",
+       "        * max_value: 90.0\n",
+       "        * unit: deg\n",
+       "        * is_normalization: false\n",
+       "      * a:\n",
+       "        * value: 90.0\n",
+       "        * desc: Standard deviation of the Gaussian distribution (major axis)\n",
+       "        * min_value: 0.0\n",
+       "        * max_value: 90.0\n",
+       "        * unit: deg\n",
+       "        * is_normalization: false\n",
+       "      * e:\n",
+       "        * value: 0.99944429\n",
+       "        * desc: Excentricity of Gaussian ellipse, e^2 = 1 - (b/a)^2, where b is the standard\n",
+       "          * deviation of the Gaussian distribution (minor axis)\n",
+       "        * min_value: 0.0\n",
+       "        * max_value: 1.0\n",
+       "        * unit: ''\n",
+       "        * is_normalization: false\n",
+       "      * theta:\n",
+       "        * value: 0.0\n",
+       "        * desc: inclination of major axis to a line of constant latitude\n",
+       "        * min_value: -90.0\n",
+       "        * max_value: 90.0\n",
+       "        * unit: deg\n",
+       "        * is_normalization: false\n",
+       "    * spectrum:\n",
+       "      * main:\n",
+       "        * composite:\n",
+       "          * F_1:\n",
+       "            * value: 0.0017\n",
+       "            * desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution\n",
+       "            * min_value: 0.0\n",
+       "            * max_value: 1.0\n",
+       "            * unit: s-1 cm-2\n",
+       "            * is_normalization: false\n",
+       "          * mu_1:\n",
+       "            * value: 511.0\n",
+       "            * desc: Central value\n",
+       "            * min_value: null\n",
+       "            * max_value: null\n",
+       "            * unit: keV\n",
+       "            * is_normalization: false\n",
+       "          * sigma_1:\n",
+       "            * value: 1.27\n",
+       "            * desc: standard deviation\n",
+       "            * min_value: 1.0e-12\n",
+       "            * max_value: null\n",
+       "            * unit: keV\n",
+       "            * is_normalization: false\n",
+       "          * K_2:\n",
+       "            * value: 0.004499999999999998\n",
+       "            * desc: Normalization\n",
+       "            * min_value: 1.0e-30\n",
+       "            * max_value: 1000.0\n",
+       "            * unit: keV-1 s-1 cm-2\n",
+       "            * is_normalization: true\n",
+       "          * dat_2:\n",
+       "            * value: OPsSpectrum.dat\n",
+       "        * polarization: {}"
       ]
      },
+     "execution_count": 9,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ModelDisk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed7ac3ec",
+   "metadata": {},
+   "source": [
+    "Make some plots to look at these new extended sources:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "73d61cb7",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "
08:28:12 INFO      trial values: 0.99985,0.0016432,0.99063 -> logL = 166772.754            joint_likelihood.py:1013\n",
-       "
\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "\u001b[38;5;46m08:28:12\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99063\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=256181;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=520728;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "
"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "# Plot spectra at 511 keV\n",
+    "energy = np.linspace(500.,520.,10001)*u.keV\n",
+    "fig, axs = plt.subplots()\n",
+    "for label,m in zip(models,\n",
+    "                   [ModelCentralPoint,ModelNarrowBulge,ModelBroadBulge,ModelDisk]):\n",
+    "    dnde = m.spectrum.main.composite(energy)\n",
+    "    axs.plot(energy, dnde,label=label)\n",
+    "\n",
+    "axs.legend()\n",
+    "axs.set_ylabel(\"dN/dE [$\\mathrm{ph \\ cm^{-2} \\ s^{-1} \\ keV^{-1}}$]\", fontsize=14)\n",
+    "axs.set_xlabel(\"Energy [keV]\", fontsize=14);\n",
+    "plt.ylim(0,);\n",
+    "#axs[0].set_yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db4cfb6e-e812-4f16-9c4c-95176bcc0dee",
+   "metadata": {},
+   "source": [
+    "The orthopositronium spectral component appears as the low-energy tail of the 511 keV line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8b588f46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define healpix map matching the detector response:\n",
+    "nside_model = 2**4\n",
+    "scheme='ring'\n",
+    "is_nested = (scheme == 'nested')\n",
+    "coordsys='G'\n",
+    "\n",
+    "mBroadBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
+    "mNarrowBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
+    "mPointBulge = HealpixMap(nside = nside_model, scheme = scheme, dtype = float,coordsys=coordsys)\n",
+    "mDisk = HealpixMap(nside = nside_model, scheme=scheme, dtype = float,coordsys=coordsys)\n",
+    "\n",
+    "coords = mDisk.pix2skycoord(range(mDisk.npix)) # common among all the galactic maps...\n",
+    "\n",
+    "pix_area = mBroadBulge.pixarea().value # common among all the galactic maps with the same pixelization\n",
+    "\n",
+    "# Fill skymap with values from extended source: \n",
+    "mNarrowBulge[:] = ModelNarrowBulge.spatial_shape(coords.l.deg, coords.b.deg)\n",
+    "mBroadBulge[:] = ModelBroadBulge.spatial_shape(coords.l.deg, coords.b.deg)\n",
+    "mBulge = mBroadBulge + mNarrowBulge\n",
+    "mDisk[:] = ModelDisk.spatial_shape(coords.l.deg, coords.b.deg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b80ae9d2",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "
08:28:17 INFO      trial values: 0.99985,0.0016432,0.99058 -> logL = 166772.754            joint_likelihood.py:1013\n",
-       "
\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "\u001b[38;5;46m08:28:17\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.754\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=110502;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=689540;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "
"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
-      "text/html": [
-       "
08:28:22 INFO      trial values: 0.99985,0.0016437,0.9906 -> logL = 166772.752             joint_likelihood.py:1013\n",
-       "
\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "\u001b[38;5;46m08:28:22\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016437\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9906\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.752\u001b[0m\u001b[1;38;5;251m            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=516287;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=550790;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "
"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
     {
      "data": {
-      "text/html": [
-       "
08:28:27 INFO      trial values: 0.99985,0.0016432,0.99075 -> logL = 166772.752            joint_likelihood.py:1013\n",
-       "
\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "\u001b[38;5;46m08:28:27\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016432\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99075\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.752\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=773396;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=213207;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "
"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "List_of_Maps = [mDisk,mNarrowBulge,mBroadBulge]\n",
+    "List_of_Names = [\"Disk\",\"Narrow Bulge\",\"Broad Bulge\", ]\n",
+    "\n",
+    "for n, m in zip(List_of_Names,List_of_Maps):\n",
+    "    plot,ax = m.plot(ax_kw={\"coord\":\"G\"})\n",
+    "    ax.grid();\n",
+    "    lon = ax.coords['glon']\n",
+    "    lat = ax.coords['glat']\n",
+    "    lon.set_axislabel('Galactic Longitude',color='white',fontsize=5)\n",
+    "    lat.set_axislabel('Galactic Latitude',fontsize=5)\n",
+    "    lon.display_minor_ticks(True)\n",
+    "    lat.display_minor_ticks(True)\n",
+    "    lon.set_ticks_visible(True)\n",
+    "    lon.set_ticklabel_visible(True)\n",
+    "    lon.set_ticks(color='white',alpha=0.6)\n",
+    "    lat.set_ticks(color='white',alpha=0.6)\n",
+    "    lon.set_ticklabel(color='white',fontsize=4)\n",
+    "    lat.set_ticklabel(fontsize=4)\n",
+    "    lat.set_ticks_visible(True)\n",
+    "    lat.set_ticklabel_visible(True)\n",
+    "    ax.set_title(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915bc5ee",
+   "metadata": {},
+   "source": [
+    "## Instantiate the COSI 3ML plugin and perform the likelihood fit\n",
+    "The following two cells should be run only if not already run in previous examples..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5b3abf0b-7631-419c-b5b7-a31dbfe1b65c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if not previously loaded in example 1, load the response, ori, and psr: \n",
+    "response_file = \"SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\"\n",
+    "response = FullDetectorResponse.open(response_file)\n",
+    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month.ori\")\n",
+    "psr_file = \"psr_gal_511_DC2.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "522db694-3a1d-4d0d-a3d9-0e028bb5cbcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set background parameter, which is used to fit the amplitude of the background:\n",
+    "bkg_par = Parameter(\"background_cosi\",                                        # background parameter\n",
+    "                    1,                                                        # initial value of parameter\n",
+    "                    min_value=0,                                              # minimum value of parameter\n",
+    "                    max_value=5,                                              # maximum value of parameter\n",
+    "                    delta=0.05,                                               # initial step used by fitting engine\n",
+    "                    desc=\"Background parameter for cosi\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34287711-a61b-4496-bc3e-b5f2f9e02298",
+   "metadata": {},
+   "source": [
+    "We should re-run the following cell every time we set up a new fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5ca19bc5",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
+      "... loading the pre-computed image response ...\n",
+      "--> done\n",
+      "CPU times: user 1min 56s, sys: 37 s, total: 2min 33s\n",
+      "Wall time: 2min 51s\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%time \n",
+    "\n",
+    "# Instantiate the COSI 3ML plugin, using combined data for the thin disk\n",
+    "cosi = COSILike(\"cosi\",                                                       # COSI 3ML plugin\n",
+    "                dr = response_file,                                           # detector response\n",
+    "                data = data_combined_thin_disk.binned_data.project('Em', 'Phi', 'PsiChi'),# data (source+background)\n",
+    "                bkg = bg_tot.binned_data.project('Em', 'Phi', 'PsiChi'),       # background model \n",
+    "                sc_orientation = ori,                                          # spacecraft orientation\n",
+    "                nuisance_param = bkg_par,                                      # background parameter\n",
+    "                precomputed_psr_file = psr_file)                               # full path to precomputed psr file in galactic coordinates (optional)\n",
+    "plugins = DataList(cosi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "774aba03",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/html": [
-       "
08:28:32 INFO      trial values: 0.99985,0.0016437,0.99075 -> logL = 166772.749            joint_likelihood.py:1013\n",
-       "
\n"
+       "Model summary:

\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
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+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
N
Point sources1
Extended sources3
Particle sources0
\n",
+       "


Free parameters (2):

\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
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+       "    \n",
+       "  \n",
+       "
valuemin_valuemax_valueunit
disk.Wide_Asymm_Gaussian_on_sphere.e0.9994440.01.0
disk.spectrum.main.composite.F_10.00170.01.0s-1 cm-2
\n",
+       "


Fixed parameters (27):

\n",
+       "\n",
+       "\n",
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+       "    \n",
+       "  \n",
+       "
valuemin_valuemax_valueunit
disk.Wide_Asymm_Gaussian_on_sphere.lon00.00.0360.0deg
disk.Wide_Asymm_Gaussian_on_sphere.lat00.0-90.090.0deg
disk.Wide_Asymm_Gaussian_on_sphere.a90.00.090.0deg
disk.Wide_Asymm_Gaussian_on_sphere.theta0.0-90.090.0deg
disk.spectrum.main.composite.mu_1511.0NoneNonekeV
disk.spectrum.main.composite.sigma_11.270.0NonekeV
disk.spectrum.main.composite.K_20.00450.01000.0keV-1 s-1 cm-2
broadBulge.Gaussian_on_sphere.lon00.00.0360.0deg
broadBulge.Gaussian_on_sphere.lat00.0-90.090.0deg
broadBulge.Gaussian_on_sphere.sigma8.70.020.0deg
broadBulge.spectrum.main.composite.F_10.000730.01.0s-1 cm-2
broadBulge.spectrum.main.composite.mu_1511.0NoneNonekeV
broadBulge.spectrum.main.composite.sigma_10.850.0NonekeV
broadBulge.spectrum.main.composite.K_20.00270.01000.0keV-1 s-1 cm-2
narrowBulge.Gaussian_on_sphere.lon0359.750.0360.0deg
narrowBulge.Gaussian_on_sphere.lat0-1.25-90.090.0deg
narrowBulge.Gaussian_on_sphere.sigma2.50.020.0deg
narrowBulge.spectrum.main.composite.F_10.000280.01.0s-1 cm-2
narrowBulge.spectrum.main.composite.mu_1511.0NoneNonekeV
narrowBulge.spectrum.main.composite.sigma_10.850.0NonekeV
narrowBulge.spectrum.main.composite.K_20.00110.01000.0keV-1 s-1 cm-2
centralPoint.position.ra266.4049880.0360.0deg
centralPoint.position.dec-28.936178-90.090.0deg
centralPoint.spectrum.main.composite.F_10.000120.01.0s-1 cm-2
centralPoint.spectrum.main.composite.mu_1511.0NoneNonekeV
centralPoint.spectrum.main.composite.sigma_10.850.0NonekeV
centralPoint.spectrum.main.composite.K_20.000460.01000.0keV-1 s-1 cm-2
\n",
+       "


Properties (4):

\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
valueallowed values
disk.spectrum.main.composite.dat_2OPsSpectrum.datNone
broadBulge.spectrum.main.composite.dat_2OPsSpectrum.datNone
narrowBulge.spectrum.main.composite.dat_2OPsSpectrum.datNone
centralPoint.spectrum.main.composite.dat_2OPsSpectrum.datNone
\n",
+       "


Linked parameters (0):

(none)

Independent variables:

(none)

Linked functions (0):

(none)
"
       ],
       "text/plain": [
-       "\u001b[38;5;46m08:28:32\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99985\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.0016437\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.99075\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m166772.749\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=701615;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=993964;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1013\u001b\\\u001b[2m1013\u001b[0m\u001b]8;;\u001b\\\n"
+       "Model summary:\n",
+       "==============\n",
+       "\n",
+       "                  N\n",
+       "Point sources     1\n",
+       "Extended sources  3\n",
+       "Particle sources  0\n",
+       "\n",
+       "Free parameters (2):\n",
+       "--------------------\n",
+       "\n",
+       "                                         value min_value max_value      unit\n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.e  0.999444       0.0       1.0          \n",
+       "disk.spectrum.main.composite.F_1        0.0017       0.0       1.0  s-1 cm-2\n",
+       "\n",
+       "Fixed parameters (27):\n",
+       "---------------------\n",
+       "\n",
+       "                                               value min_value max_value  \\\n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.lon0          0.0       0.0     360.0   \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.lat0          0.0     -90.0      90.0   \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.a            90.0       0.0      90.0   \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.theta         0.0     -90.0      90.0   \n",
+       "disk.spectrum.main.composite.mu_1              511.0      None      None   \n",
+       "disk.spectrum.main.composite.sigma_1            1.27       0.0      None   \n",
+       "disk.spectrum.main.composite.K_2              0.0045       0.0    1000.0   \n",
+       "broadBulge.Gaussian_on_sphere.lon0               0.0       0.0     360.0   \n",
+       "broadBulge.Gaussian_on_sphere.lat0               0.0     -90.0      90.0   \n",
+       "broadBulge.Gaussian_on_sphere.sigma              8.7       0.0      20.0   \n",
+       "broadBulge.spectrum.main.composite.F_1       0.00073       0.0       1.0   \n",
+       "broadBulge.spectrum.main.composite.mu_1        511.0      None      None   \n",
+       "broadBulge...sigma_1                            0.85       0.0      None   \n",
+       "broadBulge.spectrum.main.composite.K_2        0.0027       0.0    1000.0   \n",
+       "narrowBulge.Gaussian_on_sphere.lon0           359.75       0.0     360.0   \n",
+       "narrowBulge.Gaussian_on_sphere.lat0            -1.25     -90.0      90.0   \n",
+       "narrowBulge.Gaussian_on_sphere.sigma             2.5       0.0      20.0   \n",
+       "narrowBulge.spectrum.main.composite.F_1      0.00028       0.0       1.0   \n",
+       "narrowBulge.spectrum.main.composite.mu_1       511.0      None      None   \n",
+       "narrowBulge...sigma_1                           0.85       0.0      None   \n",
+       "narrowBulge.spectrum.main.composite.K_2       0.0011       0.0    1000.0   \n",
+       "centralPoint.position.ra                  266.404988       0.0     360.0   \n",
+       "centralPoint.position.dec                 -28.936178     -90.0      90.0   \n",
+       "centralPoint.spectrum.main.composite.F_1     0.00012       0.0       1.0   \n",
+       "centralPoint...mu_1                            511.0      None      None   \n",
+       "centralPoint...sigma_1                          0.85       0.0      None   \n",
+       "centralPoint.spectrum.main.composite.K_2     0.00046       0.0    1000.0   \n",
+       "\n",
+       "                                                    unit  \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.lon0              deg  \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.lat0              deg  \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.a                 deg  \n",
+       "disk.Wide_Asymm_Gaussian_on_sphere.theta             deg  \n",
+       "disk.spectrum.main.composite.mu_1                    keV  \n",
+       "disk.spectrum.main.composite.sigma_1                 keV  \n",
+       "disk.spectrum.main.composite.K_2          keV-1 s-1 cm-2  \n",
+       "broadBulge.Gaussian_on_sphere.lon0                   deg  \n",
+       "broadBulge.Gaussian_on_sphere.lat0                   deg  \n",
+       "broadBulge.Gaussian_on_sphere.sigma                  deg  \n",
+       "broadBulge.spectrum.main.composite.F_1          s-1 cm-2  \n",
+       "broadBulge.spectrum.main.composite.mu_1              keV  \n",
+       "broadBulge...sigma_1                                 keV  \n",
+       "broadBulge.spectrum.main.composite.K_2    keV-1 s-1 cm-2  \n",
+       "narrowBulge.Gaussian_on_sphere.lon0                  deg  \n",
+       "narrowBulge.Gaussian_on_sphere.lat0                  deg  \n",
+       "narrowBulge.Gaussian_on_sphere.sigma                 deg  \n",
+       "narrowBulge.spectrum.main.composite.F_1         s-1 cm-2  \n",
+       "narrowBulge.spectrum.main.composite.mu_1             keV  \n",
+       "narrowBulge...sigma_1                                keV  \n",
+       "narrowBulge.spectrum.main.composite.K_2   keV-1 s-1 cm-2  \n",
+       "centralPoint.position.ra                             deg  \n",
+       "centralPoint.position.dec                            deg  \n",
+       "centralPoint.spectrum.main.composite.F_1        s-1 cm-2  \n",
+       "centralPoint...mu_1                                  keV  \n",
+       "centralPoint...sigma_1                               keV  \n",
+       "centralPoint.spectrum.main.composite.K_2  keV-1 s-1 cm-2  \n",
+       "\n",
+       "Properties (4):\n",
+       "--------------------\n",
+       "\n",
+       "                                                    value allowed values\n",
+       "disk.spectrum.main.composite.dat_2        OPsSpectrum.dat           None\n",
+       "broadBulge.spectrum.main.composite.dat_2  OPsSpectrum.dat           None\n",
+       "narrowBulge...dat_2                       OPsSpectrum.dat           None\n",
+       "centralPoint...dat_2                      OPsSpectrum.dat           None\n",
+       "\n",
+       "Linked parameters (0):\n",
+       "----------------------\n",
+       "\n",
+       "(none)\n",
+       "\n",
+       "Independent variables:\n",
+       "----------------------\n",
+       "\n",
+       "(none)\n",
+       "\n",
+       "Linked functions (0):\n",
+       "----------------------\n",
+       "\n",
+       "(none)"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "# add sources to thin disk and thick disk models \n",
+    "totalModel =  Model(ModelDisk, ModelBroadBulge,ModelNarrowBulge,ModelCentralPoint)\n",
+    "totalModel.display(complete=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5de3240f-7d7e-4cb4-9f23-6f976525cdf1",
+   "metadata": {},
+   "source": [
+    "Before we perform the fit, let's first change the 3ML console logging level, in order to mimimize the amount of console output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a9d24b46-70a6-4b3c-be9a-df701d9f26e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is a simple workaround for now to prevent a lot of output. \n",
+    "from threeML import update_logging_level\n",
+    "update_logging_level(\"CRITICAL\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c424a2e2-9bf9-457d-a54b-23d8ea30fd56",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
@@ -8672,21 +3712,6 @@
       "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
      ]
     },
-    {
-     "data": {
-      "text/html": [
-       "
08:28:37 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:130\n",
-       "                  measurements such as AIC or BIC are unreliable                                                   \n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m08:28:37\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=707237;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=487416;file:///home/field/COSItools_dev/python-env/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -8741,7 +3766,7 @@
        "    \n",
        "      disk.spectrum.main.composite.F_1\n",
        "      (1.643 +/- 0.011) x 10^-3\n",
-       "      1 / (s cm2)\n",
+       "      1 / (cm2 s)\n",
        "    \n",
        "    \n",
        "      background_cosi\n",
@@ -8756,7 +3781,7 @@
        "                                                           result         unit\n",
        "parameter                                                                     \n",
        "disk.Wide_Asymm_Gaussian_on_sphere.e  (9.9985 +/- 0.0005) x 10^-1             \n",
-       "disk.spectrum.main.composite.F_1        (1.643 +/- 0.011) x 10^-3  1 / (s cm2)\n",
+       "disk.spectrum.main.composite.F_1        (1.643 +/- 0.011) x 10^-3  1 / (cm2 s)\n",
        "background_cosi                         (9.906 +/- 0.032) x 10^-1             "
       ]
      },
@@ -8783,7 +3808,7 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
\n",
        "\n",
        "\n",
        "\n",
@@ -8842,11 +3867,11 @@
        "  \n",
        "    \n",
        "      \n",
-       "      \n",
+       "      \n",
        "    \n",
        "    \n",
        "      \n",
-       "      \n",
+       "      \n",
        "    \n",
        "  \n",
        "
1.00-0.330.09
-0.331.00-0.60
0.09-0.601.00
cosi-166772.754013-166772.754018
total-166772.754013-166772.754018
\n",
@@ -8854,8 +3879,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi     -166772.754013\n",
-       "total    -166772.754013"
+       "cosi     -166772.754018\n",
+       "total    -166772.754018"
       ]
      },
      "metadata": {},
@@ -8905,11 +3930,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      -333547.508027\n",
+       "      -333547.508036\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      -333545.508027\n",
+       "      -333545.508036\n",
        "    \n",
        "  \n",
        "\n",
@@ -8917,8 +3942,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC        -333547.508027\n",
-       "BIC        -333545.508027"
+       "AIC        -333547.508036\n",
+       "BIC        -333545.508036"
       ]
      },
      "metadata": {},
@@ -8928,28 +3953,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 15min 28s, sys: 23min 36s, total: 39min 4s\n",
-      "Wall time: 11min 5s\n"
+      "CPU times: user 30min 29s, sys: 15min 41s, total: 46min 11s\n",
+      "Wall time: 8min 12s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "(                                         value  negative_error  \\\n",
-       " disk.Wide_Asymm_Gaussian_on_sphere.e  0.999853       -0.000046   \n",
+       " disk.Wide_Asymm_Gaussian_on_sphere.e  0.999853       -0.000045   \n",
        " disk.spectrum.main.composite.F_1      0.001643       -0.000011   \n",
-       " background_cosi                       0.990604       -0.003256   \n",
+       " background_cosi                       0.990610       -0.003091   \n",
        " \n",
        "                                       positive_error     error         unit  \n",
-       " disk.Wide_Asymm_Gaussian_on_sphere.e        0.000044  0.000045               \n",
-       " disk.spectrum.main.composite.F_1            0.000011  0.000011  1 / (s cm2)  \n",
-       " background_cosi                             0.003140  0.003198               ,\n",
+       " disk.Wide_Asymm_Gaussian_on_sphere.e        0.000045  0.000045               \n",
+       " disk.spectrum.main.composite.F_1            0.000011  0.000011  1 / (cm2 s)  \n",
+       " background_cosi                             0.003209  0.003150               ,\n",
        "        -log(likelihood)\n",
-       " cosi     -166772.754013\n",
-       " total    -166772.754013)"
+       " cosi     -166772.754018\n",
+       " total    -166772.754018)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8971,7 +3996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 23,
    "id": "dd097a0a",
    "metadata": {
     "scrolled": true
@@ -9031,7 +4056,7 @@
        "    \n",
        "      disk.spectrum.main.composite.F_1\n",
        "      (1.643 +/- 0.011) x 10^-3\n",
-       "      1 / (s cm2)\n",
+       "      1 / (cm2 s)\n",
        "    \n",
        "    \n",
        "      background_cosi\n",
@@ -9046,7 +4071,7 @@
        "                                                           result         unit\n",
        "parameter                                                                     \n",
        "disk.Wide_Asymm_Gaussian_on_sphere.e  (9.9985 +/- 0.0005) x 10^-1             \n",
-       "disk.spectrum.main.composite.F_1        (1.643 +/- 0.011) x 10^-3  1 / (s cm2)\n",
+       "disk.spectrum.main.composite.F_1        (1.643 +/- 0.011) x 10^-3  1 / (cm2 s)\n",
        "background_cosi                         (9.906 +/- 0.032) x 10^-1             "
       ]
      },
@@ -9073,7 +4098,7 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
\n",
        "\n",
        "\n",
        "\n",
@@ -9132,11 +4157,11 @@
        "  \n",
        "    \n",
        "      \n",
-       "      \n",
+       "      \n",
        "    \n",
        "    \n",
        "      \n",
-       "      \n",
+       "      \n",
        "    \n",
        "  \n",
        "
1.00-0.330.09
-0.331.00-0.60
0.09-0.601.00
cosi-166772.754013-166772.754018
total-166772.754013-166772.754018
\n",
@@ -9144,8 +4169,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi     -166772.754013\n",
-       "total    -166772.754013"
+       "cosi     -166772.754018\n",
+       "total    -166772.754018"
       ]
      },
      "metadata": {},
@@ -9195,11 +4220,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      -333547.508027\n",
+       "      -333547.508036\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      -333545.508027\n",
+       "      -333545.508036\n",
        "    \n",
        "  \n",
        "\n",
@@ -9207,8 +4232,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC        -333547.508027\n",
-       "BIC        -333545.508027"
+       "AIC        -333547.508036\n",
+       "BIC        -333545.508036"
       ]
      },
      "metadata": {},
@@ -9223,13 +4248,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 24,
    "id": "5b6c0a71",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "
"
       ]
@@ -9274,9 +4299,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:COSI]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-COSI-py"
   },
   "language_info": {
    "codemirror_mode": {