diff --git a/docs/source/examples/index.md b/docs/source/examples/index.md index 5cd18d9d..bc9eee1a 100644 --- a/docs/source/examples/index.md +++ b/docs/source/examples/index.md @@ -11,4 +11,5 @@ lateral-fill-idealized.ipynb lateral-fill-model-grid.ipynb pop_div_curl_xr_xgcm_metrics_compare.ipynb CloseHeatBudget_POP2.ipynb +xoak-example.ipynb ``` diff --git a/docs/source/examples/xoak-example.ipynb b/docs/source/examples/xoak-example.ipynb new file mode 100644 index 00000000..6fc1fd88 --- /dev/null +++ b/docs/source/examples/xoak-example.ipynb @@ -0,0 +1,2496 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "buried-texas", + "metadata": {}, + "source": [ + "# Nearest-neighbour indexing using xoak\n", + "\n", + "This notebook experiments subsetting datasets using `TLONG, TLAT, ULONG, ULAT` by making use of the [xoak](https://xoak.readthedocs.io/en/latest/) package" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "hungarian-switzerland", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xoak\n", + "\n", + "import pop_tools" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "hybrid-senator", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:            (bnds: 2, nlat: 305, nlon: 1301, time: 1, z_t: 62, z_t_150m: 15, z_w: 62, z_w_bot: 62, z_w_top: 62)\n",
+       "Coordinates:\n",
+       "  * time               (time) object 0036-12-07 00:00:00\n",
+       "    TLONG              (nlat, nlon) float64 ...\n",
+       "    ULAT               (nlat, nlon) float64 ...\n",
+       "  * z_t                (z_t) float32 500.0 1.5e+03 ... 5.625e+05 5.875e+05\n",
+       "    TLAT               (nlat, nlon) float64 ...\n",
+       "    ULONG              (nlat, nlon) float64 ...\n",
+       "  * z_t_150m           (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
+       "  * z_w                (z_w) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
+       "  * z_w_bot            (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.75e+05 6e+05\n",
+       "  * z_w_top            (z_w_top) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n",
+       "Dimensions without coordinates: bnds, nlat, nlon\n",
+       "Data variables: (12/32)\n",
+       "    time_bnds          (time, bnds) object 0036-12-02 00:00:00 0036-12-07 00:...\n",
+       "    UAREA              (nlat, nlon) float64 ...\n",
+       "    TAREA              (nlat, nlon) float64 ...\n",
+       "    DXU                (nlat, nlon) float64 ...\n",
+       "    DYU                (nlat, nlon) float64 ...\n",
+       "    DXT                (nlat, nlon) float64 ...\n",
+       "    ...                 ...\n",
+       "    dzw                (z_w) float32 500.0 1e+03 1e+03 ... 2.5e+04 2.5e+04\n",
+       "    grav               float64 980.6\n",
+       "    nsurface_t         float64 5.413e+06\n",
+       "    nsurface_u         float64 5.372e+06\n",
+       "    omega              float64 7.292e-05\n",
+       "    radius             float64 6.371e+08\n",
+       "Attributes: (12/13)\n",
+       "    CDI:               Climate Data Interface version 1.9.2 (http://mpimet.mp...\n",
+       "    history:           Tue Mar 24 11:51:32 2020: cdo selvar,UVEL,VVEL,DXT,DXU...\n",
+       "    source:            CCSM POP2, the CCSM Ocean Component\n",
+       "    Conventions:       CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-cu...\n",
+       "    title:             g.e20.G.TL319_t13.control.001_hfreq\n",
+       "    time_period_freq:  day_5\n",
+       "    ...                ...\n",
+       "    contents:          Diagnostic and Prognostic Variables\n",
+       "    revision:          $Id: tavg.F90 89091 2018-04-30 15:58:32Z altuntas@ucar...\n",
+       "    calendar:          All years have exactly  365 days.\n",
+       "    start_time:        This dataset was created on 2018-12-14 at 16:05:58.8\n",
+       "    cell_methods:      cell_methods = time: mean ==> the variable values are ...\n",
+       "    CDO:               Climate Data Operators version 1.9.2 (http://mpimet.mp...
" + ], + "text/plain": [ + "\n", + "Dimensions: (bnds: 2, nlat: 305, nlon: 1301, time: 1, z_t: 62, z_t_150m: 15, z_w: 62, z_w_bot: 62, z_w_top: 62)\n", + "Coordinates:\n", + " * time (time) object 0036-12-07 00:00:00\n", + " TLONG (nlat, nlon) float64 ...\n", + " ULAT (nlat, nlon) float64 ...\n", + " * z_t (z_t) float32 500.0 1.5e+03 ... 5.625e+05 5.875e+05\n", + " TLAT (nlat, nlon) float64 ...\n", + " ULONG (nlat, nlon) float64 ...\n", + " * z_t_150m (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n", + " * z_w (z_w) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n", + " * z_w_bot (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.75e+05 6e+05\n", + " * z_w_top (z_w_top) float32 0.0 1e+03 2e+03 ... 5.5e+05 5.75e+05\n", + "Dimensions without coordinates: bnds, nlat, nlon\n", + "Data variables: (12/32)\n", + " time_bnds (time, bnds) object ...\n", + " UAREA (nlat, nlon) float64 ...\n", + " TAREA (nlat, nlon) float64 ...\n", + " DXU (nlat, nlon) float64 ...\n", + " DYU (nlat, nlon) float64 ...\n", + " DXT (nlat, nlon) float64 ...\n", + " ... ...\n", + " dzw (z_w) float32 ...\n", + " grav float64 ...\n", + " nsurface_t float64 ...\n", + " nsurface_u float64 ...\n", + " omega float64 ...\n", + " radius float64 ...\n", + "Attributes: (12/13)\n", + " CDI: Climate Data Interface version 1.9.2 (http://mpimet.mp...\n", + " history: Tue Mar 24 11:51:32 2020: cdo selvar,UVEL,VVEL,DXT,DXU...\n", + " source: CCSM POP2, the CCSM Ocean Component\n", + " Conventions: CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-cu...\n", + " title: g.e20.G.TL319_t13.control.001_hfreq\n", + " time_period_freq: day_5\n", + " ... ...\n", + " contents: Diagnostic and Prognostic Variables\n", + " revision: $Id: tavg.F90 89091 2018-04-30 15:58:32Z altuntas@ucar...\n", + " calendar: All years have exactly 365 days.\n", + " start_time: This dataset was created on 2018-12-14 at 16:05:58.8\n", + " cell_methods: cell_methods = time: mean ==> the variable values are ...\n", + " CDO: Climate Data Operators version 1.9.2 (http://mpimet.mp..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# open sample data\n", + "filepath = pop_tools.DATASETS.fetch('Pac_POP0.1_JRA_IAF_1993-12-6-test.nc')\n", + "ds = xr.open_dataset(filepath)\n", + "\n", + "# get DZU and DZT, needed for operations later on\n", + "filepath_g = pop_tools.DATASETS.fetch('Pac_grid_pbc_1301x305x62.tx01_62l.2013-07-13.nc')\n", + "ds_g = xr.open_dataset(filepath_g)\n", + "ds.update(ds_g)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "republican-action", + "metadata": {}, + "outputs": [], + "source": [ + "grid, xds = pop_tools.to_xgcm_grid_dataset(ds)" + ] + }, + { + "cell_type": "markdown", + "id": "terminal-north", + "metadata": {}, + "source": [ + "## Set the \"index\"\n", + "\n", + "This is what allows the indexing magic." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "ceramic-royalty", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (x: 2, time: 13)>\n",
+       "array([[ 1.,  2., nan,  3.,  4.,  5., nan, nan, nan,  4.,  6., nan,  2.],\n",
+       "       [nan,  2., nan, nan, nan,  5., nan, nan, nan,  4., nan, nan, nan]])\n",
+       "Dimensions without coordinates: x, time
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<xarray.DataArray (x: 2, time: 13)>\n",
+       "array([[ 1.,  3.,  0.,  3.,  7., 12.,  0.,  0.,  0.,  4., 10.,  0.,  2.],\n",
+       "       [ 0.,  2.,  0.,  0.,  0.,  5.,  0.,  0.,  0.,  4.,  0.,  0.,  0.]])\n",
+       "Dimensions without coordinates: x, time
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<xarray.DataArray (x: 2, time: 13)>\n",
+       "array([[ 1.,  3.,  0.,  3.,  7., 12.,  0.,  0.,  0.,  4., 10.,  0.,  2.],\n",
+       "       [ 0.,  2.,  0.,  0.,  0.,  5.,  0.,  0.,  0.,  4.,  0.,  0.,  0.]])\n",
+       "Dimensions without coordinates: x, time
" + ], + "text/plain": [ + "\n", + "array([[ 1., 3., 0., 3., 7., 12., 0., 0., 0., 4., 10., 0., 2.],\n", + " [ 0., 2., 0., 0., 0., 5., 0., 0., 0., 4., 0., 0., 0.]])\n", + "Dimensions without coordinates: x, time" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the last fillna(0) is really just for the beginning of the array\n", + "# pad with nans instead. this may be more efficient with dask,\n", + "# since we don't touch every element at the end\n", + "padded = arr.pad(time=(1, 0))\n", + "cumsum = padded.cumsum(\"time\")\n", + "(cumsum - cumsum.where(padded.isnull()).ffill(\"time\")).isel(time=slice(1, None))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "northern-horse", + "metadata": {}, + "outputs": [], + "source": [ + "xds.xoak.set_index(['TLAT', 'TLONG'], 'scipy_kdtree')" + ] + }, + { + "cell_type": "markdown", + "id": "seventh-pottery", + "metadata": {}, + "source": [ + "## Extracting sections" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "fatty-seventh", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 278, + "width": 403 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# have to know that 0.1 is a reasonable choice\n", + "lons = xr.Variable(\"points\", np.arange(150, 270, 0.1))\n", + "lats = xr.zeros_like(lons)\n", + "\n", + "eqsection = xds.xoak.sel(TLONG=lons, TLAT=lats)\n", + "\n", + "# plot\n", + "eqsection.TEMP.sel(z_t=slice(50000)).plot(y=\"z_t\", cmap=mpl.cm.Spectral_r, yincrease=False)" + ] + }, + { + "cell_type": "markdown", + "id": "sublime-petersburg", + "metadata": {}, + "source": [ + "## Extracting multiple points" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "moral-remark", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ma4ElBDOe7AX0JRg0eFmbfwERERERaVf05GAX5+5/JVhg5wWgG3AUMJpgppAlMUW3A38kmH1kAsFMLEXAgwSzj0xvs6BFREREpF3SbEUiIiIiIgLoyYGIiIiIiEQpORAREREREUDJgYiIiIiIRCk5aCNm9i8z+1fDJUVERERE4kNTmbadUePGjRsHfCvegYiIiIhIh2fNOUlPDkREREREBFByICIiIiIiUUoOREREREQEUHIgIiIiIiJRSg5ERERERARQciAiIiIiIlFKDkREREREBFByICIiIiIiUUoOREREREQEUHIgIiIiIiJRSg5ERERERARQciAiIiIiIlFKDkREREREBFByUCsz+56ZzTOzYjNbaWa3mFlavOMSEREREWlNSg5qMLMbgH8AQ4B3oruvA541M/1eIiIiItJh6WI3hpmNBH4FrAP2dvdjgd2BN4HjgHPiGJ6IiIiISKtSclDd+UAicKu7rwZw91Lgx9HjF8UrMBERERGR1qbkoLrJ0df/xe509zlADjBJYw9EREREWknBZigviXcUoVmbWxTvEJpMyUF1e0Rfl9dybCXBU4Xh9VVgZh/XtgGjwg1VREREpANZ9DLcOxGm3xLvSELx5MerOeK26bw0b128Q2mSpHgHsIvpChS7e20p65aYMiIiIiLSUhXlsPojePdOWPJqsG/Nx8H+xPZ7mTpt0QZ++tQ8KiLO+vzieIfTJO33V99Fufv42vZHnx6Ma+NwREREROKjvASK86A4H4pzIXcFbF0OW6OvuSsgdxV4RVA+KR2m/AomXgoJ7bNzS1lFhKmzV/ObFxdQEXGuOGp3vnfosHiH1SRKDqrLA3qZWWotTw+6x5QRERER6TgiESgv3rGVFcW8j9lXkh+92M+Lvs+LeZ8fsy8fKho5dqDLINj/WzDxEsjs1brfM2R5RWUs31TAZ2vyeGfxRmZ+tZltJeUA/N+EwVxzzMg4R9h0Sg6qWwL0AkYCn1XujK5vsDtQASyNT2giIiLSqVWUQeEWKNwMhZuC16KtOy7eqy7qS6A8+hr7ObZc7EV/eTFUlIYfb0ISpHWF1C7Ba9dB0H3ojq3bEOg2GJLTw287RPnFQQKwbFMByzcVsmJzAcs2F7BicyFbCnb+3Xbvk8WVU/bglDED4hBtyyk5qO4d4CDgeGKSA+AAgicH77t7++o4JiIiIvHlHlyglxZAWQGUFlZ/X1YQfK58X5wXzNpTuLl6IlDcyp0XktJ2bMlptXxOh7QuOy72d3rftfr+5HQwa92YQxKJOKu2FvLF+m0s3rCNpZsKWL6pgOV1JACV0pMTGdIzg937ZHHI7r04fGRvBnbbtZOdhig5qO5hgjUNrjazf7v7ajNLAW6OHv973CITERGRcFVetFfdVa/rNaabTW2vZdGL/dKC6PtaLvg90vJ4LQHSewRdbzJ6QkYPSO8OyRk7X8QnpQYX50mpDXyuTABS282FfHNVDg5es7WINbmFrN5SxMothSzesI3FG7ZTVFZR63lpyQkM7ZnJ0J6ZDOmVwbCemQztlcmwXpn0yU7FOtjvpuQghrsvMrObCVZJ/tzMPgBGA4OBN4B/xjM+ERGRTqOiPLioLivacdFd9T56QR57IV5WFH0fuzXioh9vm++TmBJcxKdkQUpGLe8zgy05I7jznhFNAKoSgZ6Q1q3dDtRtDeUVEfKKysgtKiO3sIzcwtLgtaiMvMLSqv0b8otZk1vE+rxiyiN1/737ZKeyZ79sRvbNZkTvLIb2ymBYr0z6ZqeRkNCxEoD6KDmowd2vN7NVwA8JFkXbCPwBuNHda08pRUREpGGRCshdCZu/hE1LYPOS4H3R1uiFfdGOrjaRsraJKTEluJNe2Y2m8m56tdfUmDI1X9NiLvIzg9eUzJ3ft+NpOWuqiDhlFRFKKyKUlkeC91WvTmnFjn2xZXbs8x37KsvsVJfXWn9RWQW5hWXkFZZVDfxtit7ZqQzsls6g7ukM7J7OoO4ZjOyTxci+2XTPTGmFX6v96Tj/UkPk7n9HXYhERER25h7cca+cpabalhu8FuXWfixvTeNnsLGEHRfYyenB++T0HRfhVe9j7rwnp1d/X+cFfcyFfUJiK/5Yrc89uIguLotQXFZBcVkFRWUVFJVWVO0ritlfta+0ln01zi8pj1BSVlF1kR5c9DsV9dx9b0tm0DU9mW7pyXTLSKFbxo73XdOT6ZaRTPeMFHpmpTCwWzoDuqWTlty+/95tQcmBiIjIrqpkW9D9paIkmE2moizoI19RFv1cYyuP/VzWjPMqj8eUrVlneXHL7upnD4Beu0PP3aHnHtBrD8jsvfPFfWJKh+8D3xSVScDWgjI+Wr6FmV9t5oOlm1mxuYC2vlY3g5TEhGBLSiA5MYHkJCMlMXifWrkv5niwz6rtS0lKqDonJXp8530JpCQZKYmJJCcaacmJ0SQghey0pE7V3aetKDkQERGJh0gENi6CvNWQvwby10a3mPel2+IdZe0SU4L+72ldd97Sa9sf3ZfdH1Kz4hx8uNydkvIIRaXRO+613JUvKquguLSC4vKKauVKyqqfV1zjDn/1eirqTAKSE420pETSUhJJT04kLTkh+hps6cmJpKcE+ys/V72mJJKWlBAcT6ost6OO1OTEGomAkZSocQ8dmZIDERGRtlJWFO1ykwuv3wBLXq2/fFJ6cBc9KRUSk4OL8mpbdF+147Hvk6PHUuo4ngJJKXUfr6vddjKzTc0L98KYi+3C0uoX8VXHalzk17x4r3leUVkF3kZ37lMSE8hITWTfgV05aERPDhrek70HdCUlSRfrEh4lByIiIk1RUR5c3BdtDRakKtq6YyvO3XHxX9trbf3tdzsIeo4IVontMgC6DIy+DgjutreDi/DmKK+IUFy+o598bJ/52D7wNS/Ki8p2fi0sLaeoLEJRaflOF/Vt0eUmJSm4y1511z4lifTkHXfjK+/oV7urX21f9E5+LXf+q+7kJyXojr20CSUHIiIikQrIWxXMnLNlGRRsgqItNRKA6GtLFqKq7I6T3i143fvrcNBlIX2JlimriFBSy8V6SXns+0j1C/nymhf1FRTFfC6p7FITWzbavaasom1ut6ckJlRddNf5Wvk+9oI9JZGMahfswfuMGp910S4djZIDERHp2MpLgov9go3BSrMF0RVn89fClqWw+SvYuiwYcNsoFlzcp3cPFqRK7x7dukUv/GPf13htxoqxlQNRC0sqKCgtp7C0goKSGq+l5VXHa7uor7qgLw8u2Kteqy7cI20+A40ZO+6aJyVE+75X3nmvfB9sGSl1X9hnpNRycR9zka8Ld5GmUXIgIiLtizuU5MO2DbC9cssJXgs2RROATTsSgcYO6s3uH8yg02MYZPXbsfpseo+Y992Drj6NmP6ypLyC/KJythWXsS2vnPwNBWwrzgs+F5eTX1RGQWnQJaagpPrr9hoX//Ut3BSWBKPqYjwtKRi4mhrt4pIa/Vx58Z6WnBjsi7mIT4/u37FVH/waO8A1LTkY4NrRVpYV6QiUHIiISPxVlAXddwo3QeHmHVvB5hoJwPrgtby48XUnJAWrzWbGrjjbC7L6BH39e4yAHsNrnUWntDxCbmEpWwpL2ZJbytY1JWwpXM3WglLyi4KL/G0lZTuSgOJy8ovLyC8up7Q8EtrPk5RgZKYmkZmSSEbla0pSsC81+j7a5aXygj01OSHm7ntC1WtqUuzrjmPJusMuIig5EBGR1lBWHHTjKcgJ7uJvz4le8G+KJgGVF//RzyVN7MefkhVc3Gf1jdl6B/PlVyUCwaundqG43NlWXFZ14V555z5/exm5OWVsKVjJ1oIgCdjxWsb2ZqzAWikpwchOS6JLejLZaUlkpybTJT2J7LTo57RkslOTyEhNJDMliYyURDJTg9es1KRqSYBmoxGRtqLkQEREGq9kG+Qsgm3rggv/7Rt3TgIKNgbdfprCEoLuO5V39zN6RF97QlZfStN7k5/Ug63WnS3WndyKlKoL/Mq79dtyonfwq+7kryK/eBnbisuaPfg1McHonpFCj8xgpdXuGSl0z0yhe0YyXdOTyU6rfsHfJS2JLmnB/rRkdZsRkfZHyYGIiOzMHXJXwPr5sGE+rP8seN26vHHnJyQFd/Eze0Fmn6r3kfSeFCd3Y1tiV/KsC1s8m42RLHLK0sktKmdrYSlbC8vI3VbK1vVlVV16issiQEF0W93kr5OSlBBz4V79bn73zB0X/z0yg4v/HtEkIDtVK7CKSOei5EBEpDNzD1bkzVkUrNa7cWH0/Re1D+RNTIHee0LX3YIL/6zgwt8ze1OY0oONkS6sK89mTXEqG7aVsiG/mPV5xWxYV8KGvGI2bi+JmRWnFNgc3eqXnGh0ywju2HdLT6FL+s4X+pV37Cvv5HdJ23FHPy254QHEIiKi5EBEpPPJWQSz/g7r5gZJQF1dgDJ7E+mzN4U9RrMle0/Wp+3BioQBbCp0Nm0vYdP2EtavKyZnWwnr84opKqu8s7+u3uazU5PoFr1TX3nBH7yv/lr1PjOFzJREddEREWkDSg5ERDqLdfNgxu34gucxdvTBL07uxqb0YaxJHsIy241FFQOYV9KfJQXpbFsYOyA3L7rVLj05kX5d0+jbJZV+XdLoG7P165pK3y5p9M5OJTVJd/FFRHZVSg5ERDqY0vII6/KKWLMpj5Kl75G98k122zyDPqVBX/1ST+KJiiN4OTKRxZFBbC7uCrUuBVBOYoLRMzOFXlmp9MxKoXf0tVdWKr2yUqMX/6n07ZpGdmqS7u6LiLRzSg5ERNohd2dtXjGfr8ljwbp8VmwuZO2WbaRs+YLdCj/noITPOTxhHl2sqOqcXM/k6YrD+HvF17AuA+jbNY2xman0zo5e/Gem0Cs7lZ7RfT0zU+manqwBuSIinYiSAxGRXVxFxFm2aTufr82PbnksWJtPpHArYxOWMC5hCWfYEvZP+IosK4bkHeeuSx3G6l6HsW23KaQNn8QxPbtwTtc0LXglIiK1UnIgIrILqYg4SzduZ97qPD5bk8e81bksWpdHj/Ic9rSV7GmrOSthFXvbcnZPW7vT+eVdBpMw+EAShhwEexxD/+5D6R+H7yEiIu2TkgMRkThxd1ZuKWTu6jw+W53L3NV5rF2zisHly9nTVrGnreKUhFWMTFhNVmrxzucnpmIDx8GgCTD4QBh0IEnZfePwTUREpKNQciAi0gbKKyIs3VTAgrX5LFiXzxdrNlK8ZiGDSpcyKmElk20FFyesondCPqTUUkFmH+gzGvruDX32gr57YX33haTaCouIiDSPkgMRkZAVlJSzaH0+C9bm8/maPNavWU7Sps/ZPbKCUQkrOcNWMsLWkmSRnRKBSHImCX33ChKAaBJAn72CBcdERERamZIDEZEWKC2P8PnaPGYv38rclZvZvnYh3fMWsrctY7St5KSEFfSw7ZBIsEU5Rnn3EST23xfru0/wRKDvXiR03Q0SNFhYRETiQ8mBiEgT5BeX8cmKrXyyNIf1X80hKWcee0aWMi5hOd+xFaRbabXZggDKU7pA331I6r9vkAT02wfrPZqklIz4fAkREZE6KDkQEanHurwiZn21nrULZ1G6+lN6b1/I3racy20VqVYOCQRbVGn2YJIG7k/CgDHQd1/otw9JXQaCFgcTEZF2QMmBiEiM4rIKZn21gS/nvIMvm8EehXM4JmFx8EQAqnUNKsgeRtLA/UkdPBb6j4F++5GS0SM+gYuIiIRAyYGIdGruzlfrc1n4yTsULX6bAbmzGM8XHGYlQYFoMrAlYxiR/mPpOvwAkgeNhb77kJnWJX6Bi4iItAIlByLS6eQXlfDZ7PfIX/Aa3TZ8wL4VC9ndousIRHv/bE4bSvluh9Bz36NJGnYYPbJ6xy9gERGRNqLkQEQ6vPKKCJ9/uZS1H79M+srp7F00m0Msb0cBg5yUwWzvfxC99plCl1FH0lOLiYmISCek5EBEOqRVm/JZOHsaZV+8zpCtM9nXlzLGPDhosCmhFxt6H0z26CkM2v8Y+nQbSJ/4hiwiIhJ3Sg5EpEPYXlLO7PkLyZ3zAt3XvsPY8rkca4VVx0stiZXZY6kYPoVBB3yNXoP2oZdmEBIREalGyYGItFsrNhfwzucr2D7nGfbe9CqH2TwSY54ObEgeRO6AyfQYcwK99zmK3VMy4xuwiIjILk7JgYi0G2UVEWYt38L0hevI/fwNJm5/g9MTZpFpJZAAZSSxovskkkYfz4BxX6Nvr2Fo5ICIiEjjKTkQkV3a5u0lTP9iI9MWbmDDktkcVzGd7yW+T1/LrZpmdHP3/UkdfzZZ485iuNYZEBERaTYlByKyy9m4rYTn5qzhpc/WsXrVCk5LmMEViTMYlbCq6r9axdlDSB57Non7f5OePYbHN2AREZEOQsmBiOwSissqeGPhBp7+ZA3vLl7P4XzKpYnTOTJlDslWAUBFWncS9z0D9vs/0gYdABpQLCIiEiolByISN+7OJyu38uTHa3hx3lr6lKzgG4nv8PvkGfSx3KCMJcLIE2H/b5O4x7GQlBLfoEVERDowJQci0uZWbSnkmU/X8PQnq9m4eTMnJX7AQ4lvc0Dq4h2Feo2Esd/B9vs/0IJkIiIibULJgYi0iYKScl76bB1PfbyaD5dtYW9bxmWJr3Fy2gekUxIUSsmCfU6HsefAoAnqNiQiItLGlByISKtxdz5atoWpH6/m5c/WUVxaxjEJHzM19X9MsIU7Cg45BMZ+B/Y6FbQWgYiISNwoORCR0K3JLeKpj1fz5MerWbmlkGwKOTvxLS7OeIO+kQ1BoZRsGPddmPA96DkivgGLiIgIoORAREJSXFbBq5+vZ+rs1bz31SbcYait4w8Zb3Ia00mJFEIE6D4MJn4f9v8WpHWJd9giIiISQ8mBiDSbu/Ppqlye/Hg1L8xdy7bicsCZnLyAH3d5i30KZmIRDwoPOxwmXQZ7HAsJiXGNW0RERGqn5EBEmmxbcRnPfLqGxz5YweIN26v2n913FT/yR+mV/zkUAImpsN83YOKl0G+f+AUsIiIijaLkQEQabcHafB77cAXPfrqGwtJgYbJeWSmct5dx7vYHyV76UlAwsw8ceBGMPx+yescxYhEREWkKJQciUq+S8gpe+Ww9j32wgtkrtlbtnzS8B+eP78XRmx8j8cN7oaIUktLh0Kvg4Cs065CIiEg7pORARGq1akshj3+0kidmrWJzQSkA2alJnDF+EN+eMJA91j0Pb34XCnKCE/b7P5hyPXQdGMeoRUREpCWUHIhIlYqI887ijTz2wQqmfZGDR8cSj+7fhXMmDeHU/QeQuXYmPHcJrP8sODjoQDj+9zBofPwCFxERkVCEmhyY2ZXAV+7+Uh3HU4G/Aovc/dYw2xaR5isuq+CxD1bwyMzlrNpSBEBKYgIn7def70wawrjdumGbv4RnzoVFLwYndRkEx9wE+5yhlYxFREQ6iLCfHNwJPAvUmhy4e4mZjQXGA0oOROKstDzCf2ev4p5pS9iQXwLAoO7pfHviEM46YBA9s1IhdxU8/0uY8zh4BJIz4NBr4OAfQHJ6nL+BiIiIhCke3YpWA5Pj0K6IRFVEnOfmrOGONxZXPSnYe0AXrj56JEeO6kNigsH2jfDKH2H2A8FgY0uE8efB5J9ClwHx/QIiIiLSKto0OYh2K9oHKGvLdkUk4O68+vkG/vjaFyzJCdYnGNE7kx8duyfH792PhASDolyYeQ/MvBfKCoIT9zkTjvw59BwRv+BFRESk1bUoOTCzEcC3a+weZWbX11I8GzgJGEzQ9UhE2oi7M2PJJm5/7Qvmrc4DYGC3dK4+ZiSn7T+ApMQEKC2Ej/4G794JxbnBiSOPh6N+Cf32jVvsIiIi0nZa+uQgAbgRcKByROKo6L66zAR+2MJ2RaSRZi/fwm2vfsGHy7YA0CsrlSun7M43JwwmNSkRykvho0fgndtg+4bgpCGHBtOS7jYxjpGLiIhIW2tpcvAVcH4jyjlQQDBL0ectbFNEGmHhunxue/ULpi0K1iHomp7M9yeP4NyDh5CRkgSRCpjzb5h+C+SuCE4aMDZICoYfqRmIREREOqEWJQfuHgEeCSkWEQlBQUk5d7y+mIfeX05FxMlISeTCQ4fxvcOG0zU9GSIR+OxJmP572LwkOKnXnkH3odEnKykQERHpxLQImkgH8urn67nx+c9Zl1dMgsG5Bw3hiil70CsrNUgKPn82SAo2LgxO6DYEjrgO9vsmJCTGNXYRERGJv1ZJDszsGIK1DAYAWbUUcXf/Xmu0LdIZrckt4obnPueNhcGYgX0HduXmr+/LvoO6gjssegneugU2RFc17joYDv8J7P8tSEyOY+QiIiKyKwl7heRs4EXgUHYMUK6NA0oORFqorCLCQ+8t447Xl1BUVkFWahI/PnYk5xw0lEQDFr8Gb/0O1s0JTsgeAIf/GMaeA0kp8QxdREREdkFhPzm4ATgMWADcRzBguSjkNkQE+GTlVn7+9GcsWr8NgJP27c+vvrYX/bqkwlfT4K2bYc3soHBWXzjsRzDuXEhOi2PUIiIisisLOzk4A8gFjnD3TSHXLSJAXmEZt766iH9/tBJ3GNQ9nd+cug9HjuoDy96Bp26GlTODwhm94NCrYcL3IDk9voGLiIjILi/s5GAA8K4SA5HwuTvPz13Lb15cwKbtpSQlGBdPHs4VR+1B+roP4eELYPmMoHB6Dzjkh3DgRZCSGd/ARUREpN0IOzmoAPJDrlOk08vJL+a6pz+rWrNgwtDu/O7r+zLSVsPUs2HJq0HBtG5w8BUw8RJIzY5fwCIiItIuhZ0c5ABDzGxf6h+QjLvPC7ltkQ7p+blr+dWz88krKiM7LYlfnjSab4xMJuHtn8Onj4FHICUbDrocDroM0rrGO2QRERFpp8JODhYDRwNzGijnrdC2SIeypaCUXz03n5fmrQPg8JG9+cPJw+g3/+9wz91QVggJSXDA92DyTyGrd5wjFhERkfYu7Av0ZwBNmi7SQm8s2MB1T3/Gpu0lZKQk8qsT9uD/kqZjD38bCoKuRYw+GabcCL12j2usIiIi0nGEmhy4+30EU5iKSDPkF5fx6xcW8OTHqwE4cEh3/nLAenp/8E3YvCQoNOhAOPY3sNukOEYqIiIiHZG69ojsIt77chM/mTqXtXnFpCQlcNukUk7J+TX2UnRa0h7D4egbYfQpYPUO6RERERFplmYnB2aWCYwGPnX3ivBCEulcCkvL+f0ri3h05goAju1fyB97PEf27BeCAhk9gzEF48/XqsYiIiLSqlry5OAjYBTwNPANADNrbJLg7q6nFtLpfbxiCz96Yi7LNxfSO2Ebfx86nTHrn8S2lkFSGky6DA69SjMQiYiISJtoyQX6JoLpSmMXPFtFMBORiNSjpLyCO15fwv3vfAUe4SfdZvD9isdJXLsNMNj/23DkL6DrwHiHKiIiIp1IS5KDI4Be7r6xcoe7D21pQCId3fw1efzoibl8sWEbeyas5sGe/2Tg9s+CgyOOgmN+Df32jW+QIiIi0ik1Ozlwdwc2NlhQRAAor4hw7/Sv+PObS0iIlHJT9sucU/E0CdvLIKsfnHR7MD2piIiISJy0Wr9/M9sfmAR0B7YCH7r7p63Vnsiu7MucbfzoibnMXZ3HAbaI+7o+Qu+SYAAy488PZiFK7xbPEEVERETCTw7MbA/gEWBi5S6i4xDM7EPgPHdfHHa7IruiSMR58L1l3PbqF6SUb+dPGVM5PfIqlAA994BT/gxDDo53mCIiIiJAyMmBmQ0CZgB9gIXA68A6oD9wNMGThLfN7AB3XxNm2yK7mlVbCvnR1Ll8tGwLxyTM5vasR+lavgkSkuDQq+GwH0NyWrzDFBEREakS9pODGwgSg18Av4+OSwDAzAz4KXAzcD1wSchttwozuwb4I3C+uz8c53CkHXB3/jNrFb99cQEZpZt4IP2fTPEPoBwYeEDwtKDv3vEOU0RERGQnYScHJwIL3f2WmgeiicLvzewc4Gshtxu66CJvfwAui3cs0n5syC/mp0/NY/oXOXwzcTo3ZDxORqQAkjPh6BtgwoWQkBjvMEVERERqFXZy0BOY00CZ5cBRIbcbGjNLBq4CrgV6AeuBfvGMSXZ97s7zc9dy/XOf06N4JVPTHmACCyAC7HEsnPQn6DY43mGKiIiI1Cvs5GAFMNbMUty9tOZBM0sBxkbL7aoiwJUE07ReDIwh6C4lUqstBaX84pnPeH3+ai5OfImrU58mmTLI6AUn3Ar7nAFm8Q5TREREpEFhJwf/JhhP8LiZXenuaysPmFl/4M9AX+BvIbcbGnevMLNDgZXu7mY2Jt4xya5r/po8Lvnnx0RyVzM19W7GWnQirv2/Dcf+FjJ6xDdAERERkSYIOzm4lWDcwenASWa2gB3dcvYCUoHZBH35d1nu3uwnG2b2cR2HRjW3Ttk1vThvLT+eOpdJFZ/w5/S/0sXzIXsAnHYvjDgy3uGJiIiINFmoyYG7F5nZZOBG4CKCLkSV8gieHNzk7kVhtivSliIR50+vL+a+t77gR0lTuSzl+WAlj92Phq//DTJ7xTtEERERkWYJfRG06IX/T83sZ8Ce7Fgh+Qt3j4Td3q7G3cfXtj/6RGFcG4cjIdtWXMbV/53DZwsX8e+UezgwYRFuCdhRv4RDroaEhHiHKCIiItJsoScHlaKJwMLWql+krS3fVMBFj86m/6b3eCXtXnqwDbL6YWc+CEMPiXd4IiIiIi3WKsmBmWUAZwIHETw5yAc+Aaa6++bWaFOkNb27ZBNX/msW55f/h8tTniMBh+FHwul/h6ze8Q5PREREJBShJwdmdhTBrEW9gNj5Gx24zcx+4O6PhN2uSGtwdx58bzn3v/Qe9ybfw6SkhbglwBG/gMN+pG5EIiIi0qGEmhyY2R7Ac0Am8ALwPLCaYHG0Y4CzgQfMbI27vxFm2yJhKymv4BfPzGfdp6/wYspf6G35eFZf7IwHYNhh8Q5PREREJHRhPzn4GUFi8BN3/2ONY4+b2bPA09FySg5kl5WTX8yl//yIw9c9xB+SnyHBHIZNxs74B2T1iXd4IiIiIq0i7OTgGGB1LYkBAO7+nJnNBiaE3K5IaOauyuVnj77BL4r/xCFJn+MYHPFzOPzHkJAY7/BEREREWk3YyUFfGn4isBkoDrndVuPuNxKs2yCdwLOfruGpp//NIwl/pndiHpGM3iSc+QAMnxzv0ERERERaXdjJQSnVByFXY2YJBAujzQu5XZEW+8eMpcx75R88lHwfSRYhMuSwIDHI7hvv0ERERETaRNjJwVpgHzM7h9qThDFAH+BNM/tu7AF3fzTkWEQa7S9vfcnqN+7lzuQHg/EFB19JwtE3qhuRiIiIdCphJwfzgNOBh+s4XpkwfItg5qLKfQ4oOZA25+7c8cYSiqbfwS3Jjwc7j74RDr06rnGJiIiIxEPYycE9wGch1ynSKtydW19ZRPr7f+AXyU8HO0+8HQ68KL6BiYiIiMRJqMmBu08Hpre0HjN7sMXB7ODu/r0Q65MOwN35zQsLGPDRb7gw6RWcBOy0e2H/sxs+WURERKSDCn2F5JCcR9DVqM7BzU3ggJIDqRKJODc8N5e9Pr6Rs5PeImLJJHzjAdjr1HiHJiIiIhJXzU4OzOwwmn/xvtTdVzdQ5nXgkmbWX+l+4OgW1iEdSEXE+cVTn3DIvJ9zctIHVCSmkXj2v2B3/TMRERERacmTg38D/Zt57kPAhQ2UKXT3Fc2sHwAzK2zJ+dKxlFdEuO6/H3H8wus4OvFTypOzSPrOVBhycLxDExEREdkltCQ5uAfYs8a+3YFDgBnA0jrOOwM4oIG6VwI5LYit0sZoXdLJlVVEuPbx9zlz8bUckvg5ZSndSD73GRg4Lt6hiYiIiOwymp0cuPvva+4zsyeA7cBJ7r69tvPMbCjBQmj11T20KbGYWQ+gq7svq1HPxU2pRzqmkvIKfvLoO5y3/MeMS/ySsvTeJJ/3PPTdK96hiYiIiOxSEkKu70Dgw7oSg6htQGZTKzazM8zsIjNLidmXYWYvEDwh+NLM3jezbk2tWzqu4rIKfvTQG3x/+Q8Zl/AlpVmDSL7wVSUGIiIiIrUIOznoAzS0pOwIIK8ZdV8H/NrdS2P23QCcBKwj6D40EfhRM+qWDqiwtJwf/+Mlrl71Q/ZKWEFJ1+GkXPQq9BwR79BEREREdklhJweLgYPMbO/aDprZ8cBo4INm1D0KWBJTVzZwKTCfYKzDXgTjFM5oRt3SwZSUV3DNP17muvVXMyJhHSU99yL1oleh66B4hyYiIiKyywo7ObgPSAVeN7PLzWyUmfU2s/3N7FfAEwTrDvyxmfUXxLz/FkH3pBvcvdjdi4A5gK7+hD+8OI9L1t/EINtEcd+xpF74MmT1iXdYIiIiIru0sFdI/lv0qcEPgD/XOGxABPixu7/VjOqXApPMrBdBgnEdsMLdn4kNAUhuRt3Sgbz6+XqGz/4NY5O+pDRzIGnffQrSu8c7LBEREZFdXugrJLv7lWb2H4JVjscCXYFc4CPgb+7+WTOrfgC4E1hOkGikATVnIxoBrGlm/dIBrN5ayHtT7+DXSW9SnpBCyrf/BZk94x2WiIiISLsQenIA4O7vA++HXO09wDCCpKMQ+Ie7P1B50MwGAQOBf4XcrrQTZRUR7nz0v/zOHwCDxK/9EQbUO2uuiIiIiMRoleSgNbh7BLg6utV2fLWZdQHS2zQw2WX85aUPuWrLb0m1MkrGnEvquO/GOyQRERGRdqXZyYGZXQKcDNzr7i9H913fyNPd3X/TxPb+DBS5+0/rqTRC9UHL0km8tWAd42f9mEGJm9jea3+yTr4t3iGJiIiItDsteXJwK9AF6Am8HN13I8GgYGvgXAealBwA3wYWNvEc6QTW5RWx7InruCBxPoXJ3ck653FISo13WCIiIiLtTkuSg3OA44FHYvad37Jw6pUBbGnF+qUdKq+I8K+H7uHHPEsFCaSd/Sh0HRjvsERERETapWYnB+7+AvBCjX2P1FE8DOuBfq1Yv7RD/3zhdb6/9XYwKJ58PZnDD493SCIiIiLtVrsZkAysBCaa2bQGyrm7T2mLgCS+Pli4nMM+uYqshGI2DTmRXkdcFe+QRERERNq1VkkOzGwiMBroRdAdqKYmD0gG3gYOA45ooJw3sV5ph3Lyiyh44hImJaxlU8Zwen3r72ANDXURERERkfqEmhyYWW/geeDAyl11FG3OgORbCBZCk06uIuK8+cAvONs/oNAy6H7+E5CaFe+wRERERNq9sJ8c3ApMBL4CngaWAsVhVOzuRcCKMOqS9u2FZx7nrNwHwaD05HvJ6L1HvEMSERER6RDCTg5OBDYC+0Uv5luFmSUBg4FMd5/fWu3IrmfO559z2LyfkmjOir0vY8i4r8c7JBEREZEOIyHk+roCn7ZWYmBmY8zsJWA78CUwJ+ZYqpmdYmYH1nW+tG9lFREKnrmanraNZV0nMuSM38Y7JBEREZEOJewnB8uA7iHXCYCZjSMYlJwJrI6+dospUgY8BHwEnNAaMUh8vfXcIxxb/iEFpDPgvAchITHeIYmIiIh0KGEnB88DPzKzA4BsoA9Q61K17v5oE+v+DUFCcLW732VmzwCnxNQXMbN5wKhmRS67tI1btrLX3N+BwZqxVzOy+6B4hyQiIiLS4YSdHNwJ/Aj4sJ4yRjBbUVOTg4OBee5+Vz1lCtFCaR3SvMd/xRTbyMrkEYz82o/iHY6IiIhIhxR2cnAukEgwJuANYDEQ1viDFCCvgTK7AQUhtSe7iAXzZnHYxsfBIPmUOyCxPa3dJyIiItJ+hH2VdRnBBfxe7r4u5LrnAuPNbLC7r6p50MwmAXsBb4bcrsRRpCJC+QvXkGIVzO1zKmP2nRzvkEREREQ6rLBnKxoAzG6FxADgjwSrLb9uZmcQjD/AzEaa2RXAC9Fyf2mFtiVOPnrhfvYrm0cu2ex+9u3xDkdERESkQwv7yUEpQb//0Ln7U2Z2PXAT8ETMoYXRVwNucffnWqN9aXt5uZvZfc4tACwbey1ju/eJc0QiIiIiHVvYycE6YE8zO6Whgu7+fFMrd/ffmtkLwCXAAQRTmeYDHwMPufsHTa1Tdl1f/OtaDiSXRcl7sf/Jl8c7HBEREZEOL+zkYBHBKsnPNKJssyapd/e5BGMbpANbOf89xuc8RTkJJJ1yB6Y1DURERERaXdjJwSPAxpDrBMDM/gR85u4P1XE8FXgZmOvu17RGDNI2vKKcsueuItGcd3ufxaH7Top3SCIiIiKdQqjJgbtPBaaGWWeMq4BnCVZBrq3tEjPLIlgdWclBO/b5i3ezT9liNtCDvb/1+3iHIyIiItJphD1bUbxtBobEOwhpvqKt69nt09sAWLT/z+nevUecIxIRERHpPDpMcmBm3YBxwLY4hyItsOzxa+hCAbOSxnPoyd+LdzgiIiIincouu9Ssme0F/LjG7nFm9mAtxbOBI4HuBOMepB3KmfcGe218iRJPJuXk20lM7DC5q4iIiEi7sMsmBwTrJZxXY99uteyrVA78B7i69UKSVhOpoPzFHwEwrfe3OWHMuDgHJCIiItL57LLJgbsvN7MjG1MUKAC+cvfc1o1KWsvGWU8yoHQ5q70X+599Y7zDEREREemUWjU5MLN0goXKct29qKnnu/vboQclux53yt7+EwCzBpzD13t2j3NAIiIiIp1Tq3TqNrOLzWwOweDg1cB2M5tjZheHVH+SmQ0zs33CqE/ia/vCNxlQuIhN3oW9T9L6diIiIiLxEmpyYGaJZvYkcB+wH0FysBjIj36+z8yeNrNmtWtmY8zsJWA78CUwJ+ZYqpmdYmYHtvBrSBvLfe1WAKZ1O4ORg/rEORoRERGRzivsJweXA6cDs4GD3b27u4929+7AwcAs4NRouSYxs3HAuwSLnOUAuYDFFCkjWCDtppZ8AWlbZStnMyj3I7Z5OgOOuSLe4YiIiIh0amEnB98DtgLHuvsHsQein48H8qLlmuo3QCZwtbvvBrxTo/4IMA8Y1Yy6JU42vhKsgPxy6vEcsvfwOEcjIiIi0rmFnRzsAXzo7nm1HYzOJvQhMLIZdR8MzHP3u+opUwj0a0bdEge+8Qv6rXuDEk8i7bArMLOGTxIRERGRVhN2crAdaKjTeO9ouaZKIXjqUJ/dCKY1lXZg46u3kYDzYsKRHDdp/3iHIyIiItLphZ0cTAfGmtk3ajsY3T8OeKsZdc8FxpvZ4DrqngTsBXzSjLqlreWtoceXz1DhRv64S0lLTox3RCIiIiKdXtjJwY1AEfAfM3vBzK4ws29EX18gWMG4kOYNGv4jkAG8bmZnEIw/wMxGmtkVwAvRcn9p6ZeQ1pc77U6SKOdVn8jJRx4a73BEREREhJAXQXP3BWZ2IvAYcBJwYsxhI1jz4LvuvqAZdT9lZtcTJBZPxBxaGFP/Le7+XLOCl7ZTuIX0z/4JwJcjL+LErNQ4ByQiIiIi0AorJLv7O2Y2AvgaMAnoTjCD0YfAi+5e2oK6fxt9AnEJcADB6sv5wMfAQzVnSJJdU9F7fyU9UsTbFftxwjHHxTscEREREYkKPTkAcPcy4JnoVo2ZpQBZ7r6lmXXPBbSMbntVWgAf/hWAmQO+y3V9s+MckIiIiIhUCnuF5E/M7PZ6jqcC64H7w2xX2o/y2Y+QXp7Hp5HdOXTKafEOR0RERERihP3kYH9geV0H3b3EzOYSdAlqsmhycRowGuhFMEC5lma8OYusSWurKKP0nbtIAp7L/iY37NEr3hGJiIiISIxW6VbUgHKasVCZmQ0D3gSGEAw+rovTvBWYpZX5vCfIKF7PkshA9j7im1r0TERERGQX06bJgZkdDhwCrG3G6bcBQ4F3gX8DS4Hi0IKT1uVO0fQ/kQE8lvR1fj52ULwjEhEREZEaWpQcmNlEggv1Sg4ca2ZLaymeDfQguOv/YDOaO5JgKtQj3D3SjPMlnlbPJiPvS9Z7d3pO+hapSVr0TERERGRX09InB0sJ7ubHyqhlH0ABwerF9wP/aEZbacCHSgzap9J5T5ICvFgxia8fMCze4YiIiIhILVqUHLj7RsJfZbkui4H+bdSWhCkSofyzZ0gBlvY9jsE9ahtHLiIiIiLxFo8Byc01FfiNmZ1J0EWpD1Db0rru7r9p08ikfqs+JKN4A6u9F/seeFS8oxERERGROrSn5ODvwE3Af6Of65rqxgElB7uQgk+nkgm84gdx1n4D4h2OiIiIiNShPSUH1wKJwErgaYJuRkVxjUgaFqmABc8BsHnIiXRNT45zQCIiIiJSl/aUHHwH2ATs7e4F8Q5GGmnlTDJLN7Ei0odxE4+MdzQiIiIiUo+2Gkwchl7Ax0oM2pctH/0HgDcTDuaIUX3jHI2IiIiI1Kc9JQfFQFm8g5AmqCgndfGLABSPPJWUpPb0z01ERESk8wm9W5GZGXAmcAIwAshw9wk1yiS5e3kTq14D7GdmVzZU0N3/3MS6pRVULHuXzPKtLI30Y+LBR8Q7HBERERFpQKjJgZllAS8Bh7JjNiGPOZ4JLAOeAy5qYvULgNOAO+oLIdqekoNdQM4H/6Y/8F7qYXxnSPd4hyMiIiIiDQj7ycFNwGHAe9H3PwWqJrZ39wIzWwkc3Iy67wHmhhGktIGKMrKXvRK83+d0ggdKIiIiIrIrCzs5OAPYABwfTQQur6XMOoIEokncfRowrYXxSRspXjKdrIo8vowM4LCDD493OCIiIiLSCGGPEO0PzG9gRqGetK8pVKUZNrz/OAAfZx3B0N5ZcY5GRERERBoj7Iv01cAoM0tw90jNg2bWHdgbWFpfJWY2jqA70lPuviy6r9G3n939nSZFLeEqL6Xn6tcBSB97ZpyDEREREZHGCjs5eIJgnMHdZnZV7AEz6wU8CGQBTzVQz6tAD+BUdnRBmk7M4OYGJDaynLSC3M9fo1tkG4t9EIcd3OQeZCIiIiISJ2EnB78jmML0UuCsyp1mNhMYA6QBnwO3N1DPP4BTgEdj9j1K45ODZjOz04HLgXEE8X4ZbfuOZky/2inlfPAfugELehzNaZkp8Q5HRERERBop1OTA3beb2SHAb4HzgS7RQxOBQuBvwM8bWuXY3X8G/KzGvvPCjLU2ZnYvQWJTCHwCRAhmVvoDMBq4oLVjaPfKSxiwPhg33n3CWQ0UFhEREZFdSegDg6MX/leb2Y+BPYFuQD6wqLXvvJvZUCDJ3b9sZhWvEvwmP3X3rdE69yeYmvV8M7vJ3VeEEWtHte7TV+jvBSxiCBMnTIp3OCIiIiLSBKHOVmRmPcwsAcDdK9x9gbu/7+7z3b3czBLMbKKZ7deMureY2aP1HE8FPgXuam787v6cu19cmRhE980B3op+HN3cujuLrXNeAGBFryNJS9bQDxEREZH2JOypTB8E1ppZXU8knGB15OZcwHcjGMxce8XuJcAsgrENYatsd2Mr1N1xuNNn3dsAZO5zUpyDEREREZGmCrtb0URgaV3dh9zdzWwR0OQnB42UDPQKs8LoU45DCRKPTxpR/uM6Do0KM65d0baV8+gV2chG78p+EyfHOxwRERERaaKwnxx0B7Y1UKaQep4ANIeZZZrZdwku4peFWG8PYCpQBnzf3Vt9tqT2bOWHzwKwIPNAuqSnxjcYEREREWmysJ8cbAD2NjOr7ULazBKBfYH1DVVkZocCb8fscuBUM6uo7zTgjqaFXGf7PYE3gN2B/3P3Bp8aALj7+Drq+5hgetQOK3XZGwCUDT8mzpGIiIiISHOEnRy8BFxCMJXpL2o5/ntgAME6Bg2ZD6yi4bUNHCgAFgH3u/vrjY62DtHE4E2CROZ8d5/a0jo7uvLtWxhaNJ8yT2T3g06JdzgiIiIi0gxhJwe/Br4OXGdmpxDceV8L9AWOA/YCcgkWS6uXu+cCQ0OOr0Fmlga8SDAu4jx3r3OGJNlh+UcvsjsRPk3ah7ED+8c7HBERERFphrAXQVtvZocDDxEsHrY3wZ19ixZZAHzX3VeG2W7I/gRMAq5VYtB4RQteBmBz/yPiG4iIiIiINFtrLIK2BDg0unjYJKAHwSJon7j7+2G1Ex0sXOvA5uYmH2Y2ErgIeMPdb2tBeJ1LpILBm98DoOfYr8U5GBERERFprtCTg0rRxcPmhFmnmR0D3EgwsDelrqZp/vf6ZvTcfmb2bC3H57v7L5tZd4e1fuFM+nk+q+nNPmMOjHc4IiIiItJMrZYchC2aGLwMJAKbgKVAUcjNDIm+7hPdauoWcnsdwvrZz9EP+KrrIQxK0qrIIiIiIu1V6MmBmX0b+AEwkrovpqe6+/81seqfE6zLcLm739f8COvm7hcCF7ZG3R1Z19XTAEgefXycIxERERGRlgg1OYgmBo8SDEDeBmwlGHOwHiiJFutPsHZAU40DPm+txECap2DzaoaVfUmRpzBq0onxDkdEREREWiDsFZKvJujzf767dwX+Hv08zt2Hufsw4C1gcDPqTifoSiS7kKXvPwvAgtT96dGta3yDEREREZEWCTs5GA184O6PRD8Pj77mxJQppnl997cDqc0PTVrF4lcB2D7kqDgHIiIiIiItFfaYgwqCaUsr5UZfexAMIoYgMShuRt0rgElm9nQD5dzdz2hG/dJEkbIShm2bBcCgA0+LbzAiIiIi0mJhJwfrgD4xnxdGXy8Efm9mQ4GJwOJm1P0ZMAY4rYFy3oy6pRmWfvIGu1PEUhvM8N1HxTscEREREWmhsJODT6g+BehjwM3A78zs+0Bvgq5BzVl5+JfAAy2OUEKTO+dFANb0PpzhZg2UFhEREZFdXajJgbufXePzJjP7FvA3YDegDLgbuLMZda8EmrXysbSOPhveASBrX81SJCIiItIRNDs5MLMe7r6loXLu/qyZPU/Q3SjX3Zsz3qC29hOADHffHkZ90jT5OavZLbKa7Z7O6AOPiXc4IiIiIhKClsxWNM3MPovdYWbTzOzXNQu6e8Td17c0MTCz/mb2FzNbSvAUIjfmWKKZ7WdmzVlDQZpo7eLZAKxIHk5aqiaREhEREekIWtKtaCjwYY19RxBzwR4mMxsBvA/0IpgVqRRIiSmSBLwHvA6c3hoxyA7bV80FIC97jzhHIiIiIiJhacmTgxQgK6xAGuFmggHNdwFdgP/FHnT3EuBTYL82jKnTspxFAHif0XGORERERETC0pLk4AvgADObHFYwDTgKWOLu19TTPSkP6N9G8XRq3bYtASB7t/3jG4iIiIiIhKYl3YruBB4iGHuwANgY3X+omU1r4Fx39ylNbC8LmNdAmUEE3Y2kFXlFOQPLlwMwYOTY+AYjIiIiIqFpdnLg7o+YWTFwPTAa2Dt6qBfB2IN6T29Gk4uAcWbW3d231jxoZqOiMXzQjLqlCXJWLqIvZaynJ/169413OCIiIiISkhatc+Du/wX+a2bJQD9a1k2pIfcBfwVeNLMrAQMws3TgaOCPQCLwYCvGIEDOl5/QF1ifOpx+8Q5GREREREITyiJo7l4GrAqjrnrauN/MDgQuAD6KOZRPkJQY8Li7P9yacQgUrZkPQEG3kXGORERERETCFOqdfjOb2YjxBs3m7hcCXwdeBTYTTGmaB7wBnO3u32mttmWHlM0LAUjst3cDJUVERESkPQnlyUGMfWnlPv/u/hzwXGu2IfXrWfAVAN2GjolzJCIiIiISprDHCESAkpDrBMDMJpnZ+HqOJ5nZtWb2f63RvgTKigsYULGWck9g8Mj94x2OiIiIiIQo7ORgHcHKya3hD8CrZlZrzO5eDlwEXNtK7QuwZslcEs1ZkzCAzMy2XANPRERERFpb2MnBEmCUmVU0sJU3o+69gHnuHqmnzFJgj2ZFLo2yZdkcADZmjIhvICIiIiISurDHHDzLjvUOwpZJMFVpfRIaUUZaoHxdMFNRSY894xyJiIiIiIQt1OTA3f8B/CPMOmN8BYwxsy7unl/zoJn1BA4keHogrSQj9wsAUgfsE+dIRERERCRsrbloWdgeA7oQLLo2IPaAmQ0E/g1kAU/EIbZOo29RkHv13n1snCMRERERkbCF3a2oNd0BnAwcB3xlZouAtUBfgq5MqcA84Pa4RdjBbc/dSG+2UOQpDBw6Ot7hiIiIiEjIwl4EbUsjt1uaWre7lwBHAbcAhcAY4ARgHMFiaH8FJrt7YYhfSWKs+eJjAFYlDSEpOTnO0YiIiIhI2MJ+ctCtkeUOa07l0QThF2b2S2BPoAeQDyyKTmUqrSh/xVwAtmbtHudIRERERKQ1hD0gucEnEWY2neDCvr4yDwKfuPs9dbTjwKJGtHUFMNbdL2iorDRCzgIAKnqpS5GIiIhIRxSPAclbge4NlDmPoAtRSx0FnBtCPQKkbVsBQOZAJQciIiIiHVE8koM+gMehXWmhbiXrAOgzWOvMiYiIiHREbTpbkZntBYwFljWi+LFm1tI1C/q28HyJyi8qoY9vAlNyICIiItJRhZocmNl36ziUCewFnE0w5ejDDVS1kuDpgrUwpJwWni9RK1YsY18rJ9e60i0tK97hiIiIiEgrCPvJwcPU3WXIosf+DtxWXyXuPjTUqKTFNq1aAkBuSv9GT0klIiIiIu1L2MnBr6k9OdgGrAE+dPflIbcpbWDb+qCHV2nWwDhHIiIiIiKtJeypTG8Msz7ZdVRsWQ5AQvch8Q1ERERERFpNm81WZGY9zGxYW7Un4UratgaArL76E4qIiIh0VKEmB2Z2hpldZGYpMfsyzOwFYCPwpZm9b2bdwmxXWldpeYSupcE0pj0GaqYiERERkY4q7CcH1wG/dvfSmH03ACcB6whmIZoI/CjkdqUVrdhcwEA2ApDSU92KRERERDqqsJODUcCSyg9mlg1cCswHdieYzjQHOKOpFZvZ0WY2LqQ4pQm+3LCNgbYp+NBtcHyDEREREZFW0xqLoBXEvP8WwRoHN7h7MYCZzQEOaUa99wPFBAmGtKF1a1eQZmUUJnUlIzU73uGIiIiISCsJ+8nBUmCSmfUys54E3YxWuPszMWUcSG5G3YOAr0KIUZoob13wsxdnDIhzJCIiIiLSmsJODh4AugLLCcYX7Ab8rkaZEQRrHjTVVoKnENLGyjavAMA0jamIiIhIhxZ2t6J7gGHAeUAh8A93f6DyoJkNAgYC/2pG3WuBfczsHILVluvk7o82o36pRSTiJOavhgRI761pTEVEREQ6srAXQYsAV0e32o6vNrMuQHozqv8YGAM8XE8ZI+i2pOQgJOvyi+kb2QAJkKbkQERERKRDa40ByfWKJhAFDRbc2V+AVSGHIw34Kmc7gypnKuqqmYpEREREOrI2Tw6ay90/BT6NdxydzZc52zmsahrT3eIbjIiIiIi0qrAHJEsH81XONgZZsACa1jgQERER6djazZMDADMz4EzgBIJZjzLcfUKNMknuXh6P+DqinPVrSLdSylK6kpzWNd7hiIiIiEgrajfJgZllAS8Bh7JjtiKPOZ4JLAOeAy5q8wA7qNLNywFwjTcQERER6fDaU7eim4DDgPeBY4E3Yw+6ewHB2goHt31oHVNBSTlZRWsBSO6hNQ5EREREOrrQnxyY2X7ApcBooBeQUUsxd/cRTaz6DGADcLy7F5jZ5bWUWUeQQEgI1ucXM9hyALDuQ+MbjIiIiIi0ulCTAzM7CngZSKaBhcqaoT/wTvQJQV160o66Su3q1ucVM7hyMLJWRxYRERHp8MLuVvRrIAW4FdgbSHf3hNq2ZtS9GhhlZrWea2bdo20ubW7wUl2QHARPDuim5EBERESkows7ORgDfO7uP3f3he5eEmLdTwADgbvNLDn2gJn1Ah4BsoCnQmyzU1ufX7xjGlM9ORARERHp8MLuglNOMCi4NfyOYArTS4GzKnea2UyCpCQN+By4vZXa73Q25BYyUAugiYiIiHQaYT85mAPsFXKdALj7duAQ4C6CMQ09CcY1TAQiwN+AwxsYkyBNULx1DalWTklqD0jJjHc4IiIiItLKwn5y8BDwsJndA2QDfYDUWsq5u09pauXRC/+rzezHwJ5ANyAfWKSFz8JnucFDoPLswbX+EUVERESkYwk7OZgGVBB0/alvtiKv51itzGyMu88FcPcKYEGzIpRGSy1YBUBCj6HxDURERERE2kTYycFdQCLwNPAosBgoCqnuT80sh2Dxs9eBN9x9dUh1Sw1lFRG6layDJEjpNSze4YiIiIhIGwg7OTgaWOruZ4ZcL8CrwCTgbOD/AMzsC+AF4Cl3/6gV2uy0craVMJhgGtNEPTkQERER6RTCHpCcTjBjUOjc/QSgB7A/cAUwFegL/ASYaWYrzOy3ZrZHa7Tf2azPK2ZwgqYxFREREelMwk4OtlP7AORQeGCeu9/r7v9HMOD5SOCPQAHwM2CRmb1lZse2Vhydwfq8mDUOtACaiIiISKcQdreiFcBEM3u6gXLu7me0tDF3rzCzecA4gkTHgDJgFPCKmd0HXOHuTR4A3dltyN1GfzYTwUjoOjje4YiIiIhIGwg7OfiMYEGy0xoo16KLdTPLJFgQ7VvR1xSgELgT+BOwEbgZuAr4MrpfmqB40woSzdmW2pfspJR4hyMiIiIibSDs5OCXwAMh1wmAmY0FjgCOjb6mEDwpWAj8FXjE3fNjTvmRmU0GLkbJQZNFtiwHoDhrMNnxDUVERERE2kioyYG7rwRWhllnjI8JnjgYsA54Evi3u39QzzmfAt9upXg6tOT8YI0D77pbnCMRERERkbYS9pODaswsnWAV41x3b+l6B7OAVwimNP2gkeMIBgAbWthup5RRGCwhkdRTaxyIiIiIdBatkhyY2cXAZcA+RFdKNrPPgHvd/f7m1OnuE5tx2tcIpjuVJnB3upeugwTI7Dc83uGIiIiISBsJNTkws0Tgv8DXCZKCPGA90A/YD7jPzI4HznT3SDPqTwKmACMJnkhYLcXmuPvzEEyJFG1fmmBrYRkDowugpfZSciAiIiLSWYT95OBy4HSCLkA/jB0PYGaTCAYGnxotd3dTKjaz/sAbBNOUVjsU896Bt4Dnmxq47LAur0hrHIiIiIh0QmEvgvY9YCtwbM2BwtHPxxM8TfheM+q+BRgNzAeuBZ6K7v85cH50WwzoaraFNm7ZSm/Lo5wkyO4X73BEREREpI2E/eRgD2C6u+fVdtDdc83sQ2ByM+o+jqCL0KHuvs3MKqdMvatysLOZnQYc3Yy6Jcb2nOUA5Kb0pVdCYnyDEREREZE2E/aTg+1AnwbK9I6Wa6oewCfuvi36OT36WhpTJglIbUbdEqN0czCNaWGanhqIiIiIdCZhJwfTgbFm9o3aDkb3jyMYF9BUm6Haelyro6/jYvaNRFOXtpjnBT9tWdaAOEciIiIiIm0p7G5FNwInAv8xs+8Cr7FjtqJjo8cKgZuaUfcKIHZFrv8BPwb+YWZ3AAcCu7NjLII0U/L2NQB4l0FxjkRERERE2lLYKyQvMLMTgceAkwiSgUpGcLf/u+6+oBnV/xroGdPWNDN7m2D8wgPR+nOB3zYvejCzbgQzKZ1GMPg5gWCQ8z8JxjaUN7fu9iSjKJj9NaXH4DhHIiIiIiJtKfRF0Nz9HTMbQbAA2SSgO8EMRh8CL7p7aX3n11PvK7XsPg64CNgX2Ag86O7LmhV44DsEycWXwDtAF2AicDtBsnBhC+puN7JLg55ZGb018ZOIiIhIZxL2Img93X2zu5cBz0S3VhNNNP4S035ti6I1xd+AD919VkydewGzgfPN7KfuvrmFbezSKiJOr8gmMOjab1i8wxERERGRNhT2gOS3zGxhyHU2xctmNr+5J7t7WWxiEN23AFhA8Ft1a1l4u77N24sZwCYAkrurW5GIiIhIZ9Ia6xy83ZIKzKwnQb//Jp0GDCUY9PxZS9qvJZ4TgTHRelvSZald2LxxA32slO1kkpXWJd7hiIiIiEgbCjs5KAa8hXUUANcTXPA3tZtQCc2bCakaMzsZ+D4wAtiTYPzBd9w90ohzP67j0KiWxtUWtuUE+c/W5N5kxTkWEREREWlbYScH64A9zGwwDVzYu/vKOvYXm9m32LHIWWNtBT529zVNPK82I6g+01Ia0BdYFULdu7TiTcGfZXtq3zhHIiIiIiJtLezkYCHwdWB5A+W8vrbd/YkQY2oyd7/TzP5OkCR8C/gJ8LaZjXH3Lxs4d3xt+6NPFMbVdmxXUrE1yH9KMrUAmoiIiEhnE3Zy8C+gR8h1xoW7FwDzgHlmtgW4FbgGuCyugbWyhG3RBdCyB8Y5EhERERFpa2EvgvY08HSYde4iXiBIDvaLdyCtLa0wWAAtUTMViYiIiHQ6zZ7K1MzuNLPf19j3XTM7oqVB7YIqVwMriGsUbSC7JEgO0nrtFudIRERERKSttWSdg28Ch9TY9zBwZQvqjCszG2VmqTX29QJuiX58qe2jalvdy3MA6NJveJwjEREREZG21pJuRT2AwrACiWVmPwGK3P2emH2HA5uii5K1lvuB/c1sLpBLsOjZAQSzFU0jWEG5w6ooL6e3bwGDHn2HNHyCiIiIiHQoLXlysAEYY2aZYQUT40rgOzX2TQd+0wptxfoX8D7BYm7HA/sAs4FLgePcvaSV24+rrTkrSbIIm+hGSlpTZ5IVERERkfauJU8OXiC4aH7XzP4JbIru383MvtvQye7+aD2HewPza9nf1EXRmsTd/0YHfzpQn7z1y+gFbE7sTa94ByMiIiIiba4lycHPgOHAccAYdqyMPA54qJ7zLFq2vuQgF9jbzJLdvawFMUoTFG1aAUB+ihZAExEREemMmp0cuHs+cIKZHUCQHAygZd2UYr1OsPjY89HFyCqfSvSKjj1oKLZ3QoqjUynbshqAovT+cY5EREREROKhxescuPtsgn75YfopsD/BU4ljY/YfCrzViPMTQ46nU7C8IDmoyNbqyCIiIiKdUdgrJIfC3dea2RjgdMJ/KiF1SCoI1jhI6KbVkUVEREQ6o10yOQBw9wjwZHSTNpBavBGAzB5KDkREREQ6I92NlyrZZcHQjq59Bsc5EhERERGJh132yUFdzKwH8F1gEtAd2Ap8CDzq7pvjGVu75k73yFYw6N1fyYGIiIhIZ9SukgMzOwN4EMii+poH3wBuMLML3V3dkJohP28zXayMAk+ja9fu8Q5HREREROKg3XQrMrNJwL+BVODvBIOVD4q+/h1IAx43s4lxC7Id27xuJQBbEnpg1qprzYmIiIjILir0Jwdm1h/4ATCeYJahrFqKubuPaGLVvyR4WnCcu79d49izZvZv4E3gF8ApTay708vNWQVAQUrPOEciIiIiIvESanJgZiOBd4GeVO/2E4aDgY9qSQwAcPe3zexDgrUQpIkKN68BoDitd5wjEREREZF4Cbtb0e+AXsB/gH2BDHdPqG1rRt2pwPYGyhQAKc2ou9Mry1sLgGf2iXMkIiIiIhIvYXcrOgJYBXzX3StCrvtz4GAz6+/u62oejHZnOgiYH3K7ncO2DQAkdukf50BEREREJF7CfnKQBSxohcQA4C9AJjDdzM4ws2wAM8uOzmL0FpAB3NsKbXd4yUU5AKR2HxDnSERERCRMpaWlbNiwId5hSDsRdnKwEugScp0AuPsjBBf+ewBPALlmVgDkRj+PBP7q7o+2RvsdXXpJsEREdu9BcY5EREQkHAsXLuTrX/86PXv2JC0tjaFDh/LPf/4z3mHV6Wc/+xm9evVi9uzZjSp/+umn061bt3rLzJw5k8zMTH7605+GEGH4IpEIkydPZq+99mLbtm1t3v4jjzzC0KFDeeSRR9q87V1Vs7sVmdlF7DzoOBcYY2ZX1HKsGnf/c1PbdPcfmNlLwKXARIJF0DYRLIJ2n7u/0tQ6BSIRp2vFZjDoodWRRUSkA9i2bRtHHHEEOTk5jBgxggkTJrB+/Xqys7NrLV9WVsYXX3xBt27dGDQoPjfKnnvuOTZv3sy0adM44IADGiz/ySefkJeXV2+ZkpISysvLwwoxdJs2beKdd94BgmTuwAMPBGDr1q3Mnz+fww47rFXbX7ZsGStWrMDdW7Wd9qQlYw6uJ5iqNFZlQnBnPecZ4ECTkwOAaAKgJCBEmwtK6U0uAGk9BsY3GBERkRD89a9/JScnh/POO48HHniAhIT6O0v87ne/46abbuKhhx7ivPPOa5sga7jqqqt45JFHOOOMM+LSfjz06dOHiy++mI0bNzJ27Niq/WPHjtVFe5y0JDn4FTAkrEAkftZt2sp+VkgZSSSna3VkERFp/+bOnQvAZZdd1mBisKu4+OKLufjii+MdRpv729/+Fu8QJEazkwN3fzjEOCSOtm4IVkfOT+xOT62OLCIiHcDatcEU3b17a/0ekaYINZU2sy5mll7P8UQzO8LMDgyzXWmZ/E2rAShK1X9ARUQ6mrPPPpuRI0eSnZ1NRkYG48eP57HHHtup3N///ndOOumkqrKZmZlMnDiRSCRCYWEh119/PYcffjhDhw4lLS2NHj168IMf/KDR7bzyyiuYGSeffPJObX/44YeYGaeeemq936W8vJw777yT/fbbj7S0NHr37s03v/lNFixYsFPZSCQCwLBhwzCzqm358uX1tnH++edXK//ss8/WWx5g6dKlnH/++QwYMIDk5GSys7PZd999Oeecc5g3b15Vua+++orLL7+cAw44gP79+5OcnMygQYOqBsOed955mBnTp0+vVv/27du57rrrGDp0KOnp6YwePZof/OAHFBYWNhhbfTZv3szVV1/NkCFDSE1NZejQoVxzzTVs2bJlp7KXX345Bx98MP369SM1NZX09HT2228/7r777lq7/kQiEf7xj39w4IEHkpmZSWpqKgMGDGDKlCnccsst1coOHToUq+PmZOzfInbw9RFHHFHr33P69OmYGaeddtpOdT355JMcdNBBZGZm0r9/f0466aQ6B3+7O/fffz9jx44lLS2NXr16cdZZZ/Hll1/uVPaf//wnEyZMoGvXrmRkZLDffvtxzz331FpvexD2OgdbgWeB+jrL/QdYABwVctvSTCVbg2UjytK1AJqISEfz2WefkZWVxSGHHEJ+fj4ffPAB55xzDu7OOeecU1Xud7/7HStWrGDUqFEceuihlJSUUFFRQUJCAjk5OfzmN78hOTmZ/fbbj3322YctW7aQlpbW6HaOOOII0tLSmDFjBpFIpFpXn3fffReAY445ps7vEYlEOOOMM3j++efp1q1b1WDjJ554gpdeeon//e9/HHrooTudd8wxx5CRkVH1OfZ9bcaOHctuu+1W9bl///rX/5k1axbHHHMMeXl5jBo1ivHjx1NQUMCnn37K/PnzmTJlCvvttx8AM2bM4N577yU7O5sxY8Ywfvx4Vq5cWecgaYDi4mKOOuooZs2aRY8ePZg8eTK5ubn89a9/paKi+TPHb9iwgUMOOYSvvvqKoUOHctRRR7Fw4ULuuOMOXnzxRd5991369NlxXXDvvfeSkJDAYYcdRrdu3Vi3bh1z5szhyiuvZMOGDfz2t7+tVv8555zD448/TkZGBpMmTSItLY1Vq1Yxbdo0vvrqK372s581Ks7YhDEzM7PZ3/dPf/oTP/rRj0hMTGTixIlkZWUxc+ZMtm7dWmv5iy66iAceeIAuXbpw0EEHsXr1aqZOncpbb73FnDlzGDgwGKN51113cdVVV5GWlsbBBx9MeXk5S5cuZc2aNc2ONe7cPbQNiABPN1DmZWB9mO22hw34eNy4cb4r+u89v3S/oYsvfeiieIciIiIhq6ioqPb5wQcfdMAPPPDAavuHDBniwWXBzpYtW+aAT548uUXtHHPMMQ74+++/X63saaed5oAvWrSozvrvvfdeB3zSpEm+devWqv2PPfaYA77bbrt5SUlJ1f7Jkyc74MuWLauzzlg33HCDA/7QQw81qry7e1lZmQ8fPtwBv/feexus76GHHnLAb7jhhlrrO/fccx3wt956q2rfb3/7Wwf8kEMO8S1btlTt//LLL7179+51/s0qvfXWWw74ueeeW23/WWed5YBfdtllXl5e7u7u5eXlfskllzjg3/zmN6uVB7xr167V9s2aNcvT09M9JSXF8/LyqvZX/u33228/X79+/U71DBkypNq+2v7t1ffv0b3uv2/l9z311FOr9i1btsyTk5M9MzPTp02bVrW/qKio6neI/Ts9++yzDvjEiRN948aN7u4eiUT8hz/8oQP+/e9/v6rsmDFjHPC5c+dWi6OwsLDO2NtQs65Z4zFCpxuttBaCNI8VBAujpHTTAmgiIh1NzcG45513HtnZ2SxevLjN26ns6vHCCy9UK/vee+8xePBg9txzzzrrv++++4DgTm1s95Jvf/vbHHvssaxcuZIXX3yxhd+iaV555RWWLl3K4YcfzqWXXtoqbVR2Obr33nvp3n3HpCEjRoygS5fmXU5t3LiRJ598kl69evGHP/yBxMREABITE7n99tvp2bMnU6dOZePGjfXWc8ABB3DiiSdSWlrK/Pnzq/ZXdqm588476du3b7NiDNPjjz9OWVkZF198MUceeWTV/rS0NEaPHr1T+coB0nfffTe9evUCgu5Nt9xyC0lJSbzyyo5JM9PTg970Nf/9V+5vj1rUrcjM0oCafVEyzGy3WopnE3Q3mgTMbUm7Eq604uB//Bk96n90KiIi7c/GjRt57LHHWLhwIbm5uQwfPpzk5ORa+5W3djvf+MY3uPLKK3n22We5+eabAVi0aBEbN27kggsuqLPugoIC5s+fT7du3armwY91/PHH89prr/HBBx9w+umnh/q9cnJydppBqE+fPtx///28//77AA2OlWiubdu2sWTJEnr37l3VNSkMs2bNIhKJcOihh+7UVaeya9jzzz/PrFmzOPHEE+uta8SIEcCOAeBFRUXMmTOH7Ozsahfi8fTJJ58AcPTRRzeq/KxZswD47W9/u9NYiMTERFasWEF5eTlJSUlcdNFFfPDBBxx55JFce+21nHfeee1+EHxLxxzsB8ysse8YYFkd5Y2g69FNLWxXQlJaHiG7fDMkaHVkEZGO5tlnn+Xss8+muLh4l2ind+/eTJkyhddee405c+aw//7789JLLwH1jzfIzc3F3enXr1+txyvHBdTVf7wlCgsLee6556rtGzIkmMl906ZNAK22aFp+fj4APXv2DLXeyt8pjN+z8g759u3bAdiyZQuRSCRuC8nVpnKhuMb+jrm5uQA8//zzdZYpLCykS5cuXHDBBXTr1o3rr7+ea6+9ll/96ld85zvf4dZbbw3979ZWWtqt6HNgBvBOdHs75v07Nfa/DPwR2Nvdn21huxKSnG3F9IkugJbURU8OREQ6ioqKCi655BKKi4v57W9/y4YNGygrK2PNmjV1XhS2RTuVTwgqZzJ68cUXSU5O5rjjjquzjcpuROvXr6/1+Lp1wcQasd1umqqu2XKGDh26U5/syhlysrKygB0X8WHr2rUrQOiDWyt/p9b4PcP6Ter6e1SqHMRdUFDQYF2V/34a+zt26dKFhIQEtm3bVme//NguXaeffjrz58/ntddeY/z48TzwwAMcffTRlJWVNaq9XU2LkgN3L3D3I9z9yAa2o9z9ZHf/ibt/EVbw0nI520roY9E7A9lKDkREOoqcnBxycnIYPHgwv/jFL+jTpw9JSUkMGDCA1NTUuLVz+umn07dvXx555BFycnJ49913Oeqoo+q9EM3MzGSfffYhNze3qstHrFdffRWAiRMnNvt7VHav2bZtW6PP2WeffQB4+eWXm91ufbKysthrr73Ytm0bb7/9dovqct8x3egBBxxAQkIC77777k7ToRYUFPDee++RkJDAhAkTmtxO165dGTRoEGvWrKlaiK45Gvp7VM4WVNs0tjVVdkWrOdalLuPHjycSifDmm282qnylY445hnfeeYfx48czZ86cFn3/eGofSwZKq9mYm08vyydCAmS27z5yIiKyQ7du3UhOTmbjxo0sW1ZXb9+2byc5OZkLL7yQTZs2cfHFF1NeXs6ZZ57Z4HmVA36vuuqqqm4iAP/+97959dVXGTx4cK1rKDTWqFGjgKCLVOUaCUC9XaVOO+00MjMzeeaZZ/jPf/5Ttb+srKzW+fCbo/J7f//732fJkiVNPr8y6YpdD6BPnz6cccYZbNq0ieuuu67q+1ZUVHDttdeyefNmzjzzzGb3nf/2t78NBKtTx445acyFfKXKv8dTTz1VtS/2b3HQQQcBcPvttzfY/em73/0umZmZPProo1UDvOtT+XTrRz/6EQsXLqx2rLi4mPLy8qrPq1atqnbczEhJSQGo9u+0XWnuNEf1bUAG8F3gPoJ1De4Hvg/0bI322sPGLjqV6ZNvvu9+QxfP/c3weIciIiIhu/LKKx3wtLQ0nzx5sp966ql+3HHHeUpKSpOmjmxoKtOmtOPuvn79ek9LS6s6J3Zq0rpUVFT4SSed5IB3797djzvuOB83bpwDnpGR4W+//Xa18k2dyrSsrMwHDx7sgI8cOdJPPPFEHzlypP/qV7+q97y///3vDjjg+++/v0+ePLlqilFCmMq0oqKiaqrXlJQUnzBhgp988sl+4IEHemJiYoNTmRYWFnqvXr2qpkPNyclxd/e1a9f6sGHDHPChQ4f6CSec4EOHDnXAR4wYUesUpDWnMnWvfcrW/Px832uvvRzwHj16+JQpU3yvvfbyhISERk9l+uqrrzrgiYmJPnnyZD/66KO9a9eu/tVXX7m7e0lJie+7777V2jj55JN977333mkqU3f3//73v56cnOyADx8+3E888USfMmWK9+jRo9YpbM8++2wHPCkpyceMGeMnnXSSH3rooZ6ZmemffvppVbk99tjDBw8e7Mcee6yfcMIJVd9lyJAhu8J0prvGVKZmdhTBgOSHgEuAs4ALgb8Ay83s3LDblOYr2hKsjlycpgXQREQ6mj/+8Y/cdttt7LHHHnz88ce89NJLfPzxx+y9996NulvfWu307duXc88NLge+/vWvV5uatC4JCQk8++yz/OlPf2LgwIFMnz6dFStWcNZZZ/HRRx9x+OGHt+g7JCUl8dJLL3H00Uezdu1aZsyYQWZmJkOHDq33vAsvvJAXX3yRQw89lMWLFzN79mzGjRvHscceCwRPSloiISGBp556ir/97W/sv//+LFiwgFdeeYWvvvqKCRMm8MMf/rDe89PT05k6dSoTJ05kwYIFVb91//79mTVrFj/84Q+JRCK88cYbRCIRrrrqKj788MMWTUGanZ3Nu+++y5VXXklGRgZvv/02BQUFXHXVVUDjfpNjjz2W++67jxEjRvD+++/z+eefM2nSpKq79ikpKUybNo1LLrmEtLQ0pk+fzmuvvUZ+fj5HH330Tiskn3XWWcycOZMzzzyT7du38+qrr/LBBx/Qp08fzj77bMaNG1et/GOPPcYDDzzAxIkTWbZsGW+++SZr1qxhypQp1RbSO+mkk8jIyOCdd97hrbfeIjExkcsvv5wZM2a02+lMzWP6oLW4MrM9gE+ATOAF4HlgNdCTYBajswlmSDre3d8IreF2wMw+Hjdu3LiPP/443qFU88gDf+bcVb9idZ8jGHTZcw2fICIiEoI77riDa665hjfffJOjjjoq3uGE7rzzzuORRx7psN+vOZYvX86wYcM47LDDeOedd+IdTmdQ/6juOrR0KtOafkaQGPzE3f9Y49jjZvYs8HS0XKdKDnZVCdujMxVoMLKIiLSRpUuXcvPNNzNx4sR2f+G8adOmqoWyKn300UdMnTqV7OzsWtdl6OgqBznH3mEvLi7mV7/6FQBTpkyJS1zSOGEnB8cAq2tJDABw9+fMbDbQ9OHv0iqSC3OCV62OLCIirejBBx9k+vTplJSU8PLLL5OUlMTDDz8c77Ba7OCDD6agoIBhw4bRpUsX1qxZw7x58wD485//XDW1Z2fy7LPPcv755zN69Gj69etHUVERn3/+OZs3b2b33XfnyiuvjHeIUo+wk4O+NPxEYDPQuquxSKNllgarI6f3GBjnSEREpCNbtmwZTz75JJmZmRx55JHccsstVTPStFfuznHHHceMGTNYtGgReXl5dO/enRNOOIErrriCE044Id4hxkW/fv04+uijmT9/Pl988QVmxrBhw/je977Hdddd16L1KKT1hT3mYDsww91r/V+DmSUAa4DP3b1xa1h3ELvimIPS8ggf3HQ4hyd+RsXZU0nc89h4hyQiIiIi4dglxhysBfYxs3OoPaAxQB/gTTP7buwBd3805FikAZu2l9A3ugBaYleNORARERHp7MJODuYBpwMP13G8MmH4FsHMRZX7HFBy0MZytpUwVKsji4iIiEhU2MnBPcBnIdcprWTj1lz2twLKSSIpvUe8wxERkVZQOX1krIceeojzzjuv1vLz5s3j7LPP5uCDD+bvf/97G0QorWHo0KGsWLGi6vO5557bIQaAS+sLNTlw9+nA9DDrlNazfdMaALYl96R7Qujr4YmIyC5k8ODBVQs97bbbbnWW27JlCwsWLGDCBE0s2B6sWbOGDRs27LSI17HHHktOTg6FhYW8/vrrcYpO2qOwnxxIO1ISXR25KK0PmjdARKRjO+qoo3TnuIOpfCo0efJkpk+fXu3Y/fffX62MSGOFnhyYmQFnAicAI4AMd59Qo0ySu5eH3bY0TUXeOgDKM/rEORIRERER2RWEmhyYWRbwEnAoOwYfe8zxTGAZ8BxwUZhtS9NZdHVk02BkEREREQHC7mh+E3AY8D5wLPBm7EF3LwBWAgeH3K40Q0rhBgCStDqyiEinNGfOHE455RS6d+9Ot27dOPjgg3n88cfrLD99+nSOOeYYsrOzycrKYvLkybzxRu1rny5dupTzzz+fAQMGkJycTHZ2Nvvuuy/nnHNO1QrCAF999RWXX345BxxwAP379yc5OZlBgwbxyCOPVJXZtm0bP/vZzxg+fDgpKSkMGTKEa665hm3btu3UbllZGbfeeiujR48mNTWV/v37873vfY8NGzZUK1dRUcGf//xn9ttvP7KyssjKyuKAAw5g6tSpTf0ZmxRjfn4+p5xyCsOHDycjI4OsrCwOO+ww/ve//+1U58yZMznhhBPo27cvKSkpDBs2jIsu2vne6ttvv42ZVW2nnXZas76DCITfregMYANwvLsXmNnltZRZR5BASJxllG4KXnsOinMkIiLS1t59912OPfZYioqK2GuvvRgyZAgLFy5k5syZtZZ/5JFHuOCCC0hMTGTcuHFEIhFmzJjB8ccfz/PPP8+JJ55YVXbWrFkcc8wx5OXlMWrUKMaPH09BQQGffvop8+fPZ8qUKey3334AzJgxg3vvvZfs7GzGjBnD+PHjWblyJdnZ2QBs376dww47jLlz5zJgwAAOO+ww5s+fzx133MGsWbOYPn06iYmJQHDB/7WvfY3XXnuNnj17cuihh7JkyRIefPBBZsyYwSeffEJWVhYAP/7xj7nzzjvp0qULhxxyCAUFBSxZsoQtW7Y0+bdsSoyZmZm8//77DBs2jFGjRrF+/XreffddTjrpJKZNm8bkyZMBmD17NpMnT6a8vJwJEyYwZswYVq5cyYIFC3Zqv/K7Vpo4cWKTv4NIFXcPbQNKgNdjPj8DVNQo8z6wPcx228MGfDxu3DjfVZSVV/h7v5rkfkMXL1/8RrzDERGRVrJs2TIH/Nxzz63aF4lEfOTIkQ74rbfeWq38HXfcsVP5devWeXp6uvfo0cPnzJlTtf+pp55ywEeNGlW1r6yszIcPH+6A33vvvdXqvuGGGxzwhx56qGrfQw895IDfcMMNtcZ/1VVXOeCXXHKJl5WVubt7fn6+H3LIIQ74f/7zn6qyd955pwN+yimn+Pbt293dvaSkxM8880wH/Pe//31V2a5du3p6erqvX7++2u9SVFRUxy9Zt6bE6O5eUVFR7fP111/vgJ911llV+374wx864HfddVe1sgUFBVXvK/+2kydPrjO22v7+0mk065o17G5Fq4FRZlZrvWbWHdgbWBpyu9JEm7aX0pfK1ZHVrUhEpDOZOXMmixcvZuzYsVx77bXVju2///47lX/00UcpKiriuuuuY8yYMVX7Tz/9dA4++GAWLVrE8uXLAXjllVdYunQphx9+OJdeemmL4iwvL+fBBx+kR48e3HXXXSQlBR0esrOzueGGG6raq/S3v/2NhIQE7r//fjIzMwFISUnh5ptv3qlsRkZGcCEUM5W3mZGWltaqMQLV2gS44oorAFi8eHHVvvT09FrLZmRkNCk+kaYKu1vRE8BPgbvN7KrYA2bWC3gQyAKeCrldaaIN+cUMt9zgQ3a/uMYiIiJt65NPPgHg6KOPblT5WbNmAfDss8/y3nvvVTtWudDWV199xdChQ3n//fcBOPXUU1sc5xdffEF+fj49evTgm9/8ZrVj27dvr2oXoKCggIULF5KWlsYll1xSrWwkEqlWFuDCCy/kN7/5DRMmTOC6667jW9/6Fl26dGnVGCutWLGCf/3rX3z55ZcUFhYycuRIAHJzc6vKfOc73+Guu+7immuuYfHixVx66aWMHj26yfGJNFXYycHvCKYwvRQ4q3Knmc0ExgBpwOfA7SG3K020ectmxlgRpZZCSlq3eIcjIiJtKC8vDwj6qjdG5UVr5YV/bQoKCgDYtCkYzzZoUMvHs1W2u2XLFp577rl6260sW1xc3GBZgJtuuonBgwfzu9/9jksvvZSf/OQnXHrppdxwww1VTx3CjhHg3nvv5corr6SiomKncu5VEzyy9957M3PmTK699lruuece7r77bqZMmcKdd97JPvvs0+j4RJoq1G5F7r4dOAS4C0gGehJMaToRiAB/Aw73YNYiiaNtlasjJ/UEswZKi4hIR9KtWzcgWF23MSrvqL/88st19lM+5ZRTAKoG/Obn57c4zsp2DzzwwDrbrXwKUlm2T58+dZaNHWxsZlx00UUsXbqUJ598kiFDhnDbbbdx1lln7RxISDFu2bKFq6++moSEBP72t7+xdetWSktLWbZsWa11jxkzhldffZXPP/+cc845h2nTpnHwwQezdOnSqu8gErawxxzg7gXufjVBYrAPwZoH+wHd3P1Sd98adpvSdMVbgwXQStJ6xTkSERFpawceeCAA//vf/ygvb3hN0vHjxwPw2muvNVi28q72yy+/3IIIA6NGjSIjI4O5c+eSk5NTb9ns7Gz22GMPcnJymDt3bqPbSEhI4IwzzuCjjz5iwIABvPzyy1VPP8KO8YsvvqC0tJRJkyZx8cUX061bN5KTkxk6dGi9540ePZpHH32UH/7wh2zbto2nn34aoOoJR21Tuoo0V+jJQSV3r3D3Be7+vrvPd62IvEspyw8WQNPqyCIinc+ECRM44IADWLJkCVdeeSWlpaX1lv/2t79NSkoK9957Ly+++GK1Y+5OYWFh1efTTjuNzMxMnnnmGf7zn/9U7S8rK+PLL79sUpzJycmcc845lJSUcMEFF+w0zWhln/5KF1xwAQAXX3wxq1atqrdszeNmRnJyMrCj21XYMfbu3RuAL7/8ssEpU9evX09ZWVm1fSkpKdXi69WrF7169WLevHnVftvi4uJGxy9SU7PHHJjZbi1od0u0C5LEy7ZgMZiELCUHIiKd0aOPPsqRRx7Jfffdx5NPPsmYMWNISkpi0aJFO5UdMmQId911F5dddhknn3wyu+++OyNGjKC8vJz58+dz3XXXcdVVVwHBOIY777yTiy66iLPPPptbb72Vrl27Mm/ePLZubXrngZtvvpkZM2bw0ksvMXToUPbee2969uzJunXrWLFiRbW7/Ndccw3/+9//ePvttxk5ciT77LMPffv2ZdOmTcyZM4d169bRvXt3KioqGDVqFAMHDmTEiBG4O3PmzGHDhg0ceOCBjBgxolVi3H333TnllFN4/vnnGTZsGBMmTCArK6vamIRK119/PVOnTmX//fcnMzOT1atXM3fuXJKTkznzzDOryl100UXccsstHHDAAUycOJGCggLWr1/f5ERMpFJLBiTPJug61Bx/BWpbIE3aSFJR8B+qpK6aqUhEpDMaPXo0n376KTfffDMvvvgib7/9NgkJCfTr148TTzyRk08+uVr573//+4wePZo//vGPzJw5k2nTptG7d2/23HNP9thjj2plL7zwQvr378/vf/97PvnkE8yMSZMmkZiYyGuvvVZ1h74xevTowQcffMDtt9/OU089xdy5c0lKSmLgwIGccsoplJaWVt1RT0lJ4bXXXuOee+7hX//6FwsXLmTBggUMHDiQ0047raoLVVFREd/61rd48803mTZtWtWqzBdccAE//vGPm/xbNiXGxx57jD/84Q9MnTqV999/n/Lycnr27MmECROqLV42ceJE5syZw+zZsykpKaFPnz6cfvrpXHvttdWmk73hhhsoKChg6tSpTJ8+nUGDBjFp0iTKysqa9DuLVLLYkfFNOtHsboIxBbH6AKOBxQQrIddmEjDf3Sc0q+F2ysw+Hjdu3LiPP/443qEA8OxNp3Oav0n+0bfR5dCL4x2OiIi0kuXLlzNs2DDOPfdcHn744bjGct555/HII4/w5ptvctRRR8U1ls5iV/r7S5tr1oj1Zj85cPcrdorA7CFgOHCYu2+s7TwzmwHs1dx2peXKKiJkl2+BRMjsoQXQREQkXJs2baJXr+oTXnz00UdMnTqV7OzsqgHRIrLrCXudg0OBD+tKDKK2ANkhtytNsGl7Cb0sGMyUmN03ztGIiEhbmDZtGqeddhoAV155ZaveuT/44IMpKChg2LBhdOnShTVr1jBv3jwA/vznP1dNd7qrysnJ4eKLG36q3qdPH+6///42iKjpLr74YnJycqoNFhdpjLCTg0HA0gbK7AZonYM42pBfQp/K1ZE1IFlEpFNYtWpV1Qw9lUlCa3B3jjvuOGbMmMGiRYvIy8uje/funHDCCVxxxRWccMIJrdZ2WAoLC+tc0CzWkCFD2iCa5nnttdeqVq8WaYqwk4PlwEFmNsDd19Y8aGYTCMYpzAi5XWmCDXlF7EV0mjYlByIiHdrQoUNp7vjC5jAz7r777jZrrzW09W/WGpYvXx7vEKSdCnudg0eALOB1MzvJzNIAzKybmZ0DPBtt8y8htytNkLs5hxSroCghC5LT4x2OiIiIiOwiwn5ycDtwEHAy8DyAmRUDadHjBtzl7k+F3K40QeGW4KFOUWoPlBqIiIiISKVQnxy4e7m7nwqcA7xJMPg4GdgMvAKc7O5Xh9mmNF1pXjDLbFla7zhHIiIiIiK7krCfHADg7v8C/tUadUvLRbblBG803kBEREREYoQ95kDagYSCIDlI7KLVkUVERERkByUHnVBqySYA0rorORARERGRHZQcdDKl5dHVkYGM7lodWURERER2UHLQyWzcXkKv6BoHCdl6ciAiIiIiOyg56GQ25BfT27QAmoiIiIjsTMlBJ5OTX0xvyw0+KDkQERERkRhKDjqZnLxCepAffMjUOgciIiIisoOSg04mf/N6Es0pTOoGicnxDkdERKTK008/zZQpU+jevTvp6ensu+++3HbbbZSXl9d5zowZM8jKyuK8886rt95JkyaRkZFB9+7dOfnkk5k9e3aj43r77bc58sgjyc7OpkuXLhx55JG8/vrrtZa98cYbMbNat9rafO6555g4cSIZGRn069ePSy+9lNzc3Frr/vzzz/na175Gt27d6Nq1K1/72tdYsGBBrWW3bt3K97//ffr160dGRgYTJ07k+eefr7VsRUUFf/jDH9hjjz1IS0tj5MiR3HbbbUQikVrLK+amxxyJRHjwwQc56KCD6NKlC5mZmUyYMIF//OMftdYbV+6urQ024ONx48Z5vN32yJPuN3TxrbfFPxYREZFKl156qQOekZHhhx56qB9++OGelJTkgJ9//vk7la+oqPC//vWvnpaW5oCfe+65tdb7l7/8xQFPSUnxo446yidOnOhm5qmpqT5t2rQG43r++ec9MTHRExISquJKTEx0M/PHHntsp/I33HCDAz5lyhQ/9dRTq21ffvlltbIPP/ywA56amupT/r+9O4+vqjr3P/55GEOYwhAGqYIiWCyTETF1Ag0REQdub2n114IjThXb66vg9YKK1daiv9bOUMGickuxrRP2KoIIVSzQW0QSFAVEQECZwyQzz/1j73M4Sc5JTsYTku/79TqvbdZee+8nZ2XLfvZea6+cHO/WrZsD3qdPH9+3b1+huvn5+d68eXMH/Pzzz/fs7GwHvEWLFv7BBx8Uqrt3717v1auXA96tWzfPycnxxo0bO+DTp08vFvONN97ogGdmZnpubq5nZmY64Lfeemuxuoq5fDEPGTLEAW/ZsqUPHDjQs7Oz3cwc8IcffrhYzJWkfNes5d1Qn5MzObj1qbf8+vt/6u/NfyHVoYiIiES9/PLLPmrUKN+5c2e0bNmyZZ6enu6Ar1u3Llr+4osv+te+9jUHvEOHDgmTgy1btnhaWpo3adLElyxZEi2fPn26A37mmWf68ePHE8Z06NAh79Chg5uZz5o1K1o+b948b9y4sbds2dL37t1baJsxY8Y44Bs3bizx992xY4e3bNnS09PTPS8vz92DhGfkyJEO+IQJEwrVv+iiixzwqVOnRsumTp3qgA8cOLBQ3fHjxzvgI0eO9GPHjrm7e15enqenp3urVq18165dhX4XwHv27OkFBQXu7l5QUOA9e/Z0wBcsWKCYKyHmX/3qV/7AAw/4/v37o2WzZ8/2evXqeZMmTfzAgQNeBZQc1ORPTUkOBj/5d+983988f2NBqkMREREp1dChQx3w119/PVp23XXXeZs2bfzxxx/3OXPmJEwO/vSnPzngo0aNKrauf//+DvjixYsTHnvRokUOeG5ubrF13/rWtxzwmTNnFiq//fbbHSj1Ym/y5MkO+A9+8INC5Vu3bvUGDRr4V77ylWji8tFHHzngffv2LbafPn36OOCrVq1yd/fjx4/7Kaec4g0bNvRt27YVqvv973/fAZ8yZUq07LrrrnPAX3755UJ1X3rpJQf8O9/5jmKuhJgTiSS5K1euLLVuOZTrmlVjDuqYLXsOAtCuReMURyIiIlK6ffv2AZCZeeIlGk8++SQbN25kzJgxNGyYePzchg0bADj99NOLrRswYAAAy5Ytq9Ttt23bRnp6OmlpaQn3C8E4BoArrriiUHlmZiZZWVls3LiRTz75pMS6AIMHDy5UZ/Xq1WzevJlzzz2Xtm3bxq27YMGCQnHUr1+f3NzcQnVzc3OpV69esbqKuXwxJ7Jv3z7MjDZt2pRat7ooOahDDh89zq4vj1DPoE1TJQciIlKz5eXlsXDhQs477zyysrKi5R06dCj14hvg1FNPBYIBpkVFko2dO3dW6vabNm0CYPTo0Tz66KPMnj077oDq1atXA9ClS5di60477TQAVq1aVaV19+/fz+eff05mZibp6emF6jZt2pTWrVuzadMmvvzyS8VcgbqJvPbaa6xfv55vfOMbhZLfVGuQ6gCk+mzbdwiAzOaNqV/PUhyNiIgko8t//k+qQ0jKup8OrdT97dy5k+HDh9OwYUMmT56MWdn/3Ro4cCCNGjVi5syZXHTRRQwfPpy0tDTWrFnDkiVLAGjcOPHNsnPOOYfMzEzeffddHn74Ye644w5atmzJunXrmD9/ftztmzZtSvv27ZkyZQqHDgX/7nbu3JkZM2ZwwQUXROvt3h1MSJqRkVHsuK1bty5UJxV1I/W3b9/O7t27SU9PV8zlrBvP2rVruemmm2jbti1PPvlkwnqpoCcHSTCzu8zMzWxBqmOpiGiXoual320RERFJlR07dpCTk8OaNWt47rnnCj01KIuOHTvym9/8BnfnzjvvpG3btjRr1oy+ffvywgsvABTrEhIrLS2NadOmkZaWxoQJE+jQoQNNmjShR48eTJo0Ke728+bNY+3atRw4cIDly5dzxx13sH79eoYMGcLmzZuj9TwYj5iUmlC3psRxMsZc1Keffspll13G3r17eemll6JPqGoKPTkohZkNBX6V6jgqw9Y9wR2M9hpvICJy0qjsO/I1XSQxyM/PZ9q0aQwfPrxC+xs1ahTZ2dnMmDGDTz/9lGbNmtGvXz8mTZpEXl4e55xzTonbDx06lA8//JBnn32WVatW0ahRI3r37s0bb7zBnDlzEm5vZvTu3TuaREyePJmnn36aBx54ADhxx7mgoID27dsX2nbXrl0AtGzZsljdoqqqbmn1FXPydWN9+umnDBw4kG3btvHKK69w0UUXxf09UklPDkpgZt2BmcDiVMdSGbbtDZ4cZOrJgYiI1EAHDx7kqquuIi8vj2nTpjFy5MhK2W+vXr147LHHmDlzJlOnTuXyyy/ngw8+oFOnTvTs2bPU7U8//XQmTJjAjBkzeOaZZ7j11lv55z//SXp6Opdcckmp21999dUAhSbS6tatGxC/X/rHH38MQPfu3au0btOmTenYsSNbt24tdpG7Y8cOtm/fTqdOnaJ9+xVz+erGxpebm8uWLVuYNWtWsQHVNYWSgwTMrBHwJ2An8G8pDqdSbAmfHLRrricHIiJS89x7770sXryYiRMnVlpiUNSRI0e4/fbbOXbsGPfeey/16pXtUsjdueeeeygoKOCWW25J2Pc91tatWwEKDaCNJBWzZ88uVHfLli3k5+fTqVMnunbtWqjuG2+8UWzfb775ZqE63bp1o2PHjixbtix63IjIrM6RNy1Ftjt+/HixGZ/nzZuHuxerq5jLFzPAiBEj+OSTT5g+fTqDBg0qdowao7zvQK3tH+Bx4ChwUfizAwuS2G5pgs/+VM9zMOYv73vn+/7mf1y8PqVxiIiIFPXxxx97gwYNfNCgQWXabv78+SXOkBxr0aJFfuGFF0YntDp69Gih9aNHj/ZLLrnE16+P/+/kihUr/Oqrr3bAe/ToUWwCtNWrV/vu3bsLla1cudLPOOMMB/z555+Plm/fvt1btGhRaHKu48ePRyfneuihhwrtJxJ3vMm5BgwYUKjuuHHjopNzRd7hH5mcKyMjo9BEc3Pnzi1xQrHYWaQVc/ljfuuttxzwW265xatR+a6By7thbf4AfcPE4LGYspM+ObjhD0u8831/87kffJHSOERERIr60Y9+FL3gu/baa4t9xo0bF3e70pKDRx991M8991zPzMz08N9yv/76633fvn2F6m3dujW6/oknnoiWT5061fv37++nnHJKdP2gQYN869atxY41YsQIb9SokWdlZfmgQYM8KyvL69ev74APHTo0OotuxNNPP+2AN27c2HNycrx79+4OeK9evYolHsuXL/dmzZo54NnZ2Z6dne2AN2/e3PPz8wvV3bNnT3Ryre7du3tOTo43btzYAX/mmWfixg14ZmamX3755dHv6qabbipWVzGXL+abb77ZAe/fv3/cv+9f/vKXxeKuBEoOKuMDGLAI+BRoElOeVHJQwn5TPkPyFb942zvf9zfP+6wgpXGIiIgUdcstt0QvvuN9it61jSgtOfj2t7/tbdq08aysLB89erQvXbo0br3jx497bm6ut2vXzt9///1o+dixY71Vq1bes2dPHzVqVKG70kW98MILPmzYMO/YsaM3aNDAW7Zs6RdeeKFPnjy52FOKiBdffNH79evnaWlp3q5dO7/tttt8x44dcevm5eX5lVde6c2bN/fmzZv7lVdeWeyCNWLHjh1+2223ebt27TwtLc379evnL730Uty6R48e9ccee8y7du3qjRo18q5du/pPf/pTxVyJMefk5JT4953Mk69yKNc1q7mX/1VMtZGZjQCeA77l7n+JKXfg7+4+sJz7XZqVlZW1dOnSygm0HPo9Opft+w6z5L9yaN9Cg5JFREREarFyTWqlAckxzKw+MAFYDvw1tdFUriPHjrNj/2HMoE3TRqkOR0RERERqICUHhWUDZwB9gOPhxGcePjUAGBD+fGPKIiyn7fsO4Q5tmzWmQX01u4iIiIgUp0nQCtsNvJJg3bXADmAhsKHaIqokW/UaUxEREREphZKDGO6+AhgWb1349GCFu8ddX9Nt3avkQERERERKpv4ldcSWPcHsyBqILCIiIiKJKDmoI/TkQERERERKo25FSXL3cr0OqqbYtjd4ctBOTw5EREREJAE9OagjtmhAsoiIiIiUQslBHbFVTw5EREREpBRKDuqIXfuPAJoATUREREQSU3JQR+w5GCQHLdIapjgSEREREamplBzUAcePO/sOHQWgWZrGoIuIiIhIfEoO6oC9B4/iDs3TGlC/3kn90iURERERqUJKDuoAdSkSERERkWQoOagDdh8Ik4MmSg5EREREJDElB3XAnkhyoPEGIiJSg7344ovk5OTQqlUrmjRpQq9evXjiiSc4evRooXrr1q3DzOJ+7r777mL7/eyzz7j++utp06YNzZo149JLL+Xdd99NOq5du3Zxxx130KFDB9LT0zn//POZNWtW3LoTJkxIGNu//vWvsn0hIimgq8U6INKtqKWeHIiISA111113MWnSJNLT08nKyqJevXr84x//YOzYsaxcuZI//OEPxbbp1q0bZ599dqGyPn36FPp58+bNZGdns3nzZvr06UNGRgZvv/02l156KXPmzGHgwIElxrVv3z4GDBhAfn4+3bp1o2fPnixcuJBrr72W6dOn893vfjfudjk5OTRr1qxQWatWrZL4JkRSS8lBHbDnQHDHRd2KRESkpho8eDBHjx5l4sSJ0Yvo999/nwsvvJBp06bx0EMP0blzZwC+/PJLAG644QbGjRtX4n7vu+8+Nm/ezPjx43nkkUcAmDt3LoMHD2bUqFGsXLmSBg0SXw5NnDiR/Px8Ro4cybRp06hXrx75+flkZ2dzzz33cNVVV5GRkRGtH4nt2WefpVOnTuX+PkRSRd2K6oDomAMNSBYRkRrq2muv5amnnip0d71v375ceumlAKxcuTJavmfPHgDatGlT4j737NnDn//8ZzIzM3nwwQej5bm5uVxzzTWsWbOGBQsWJNze3fnDH/5Aw4YN+dnPfka9esFlU69evRg1ahS7du3ir3/9a7FjJhObSE2l5KAOULciERE5We3btw+AzMzMaNm2bduA0i/AFy9ezOHDh8nJyaFhw8L/Bg4ePBigxORg9erVbN68mXPPPZe2bdsmtf22bdtIT08nLS2txNhEaip1K6oDogOSm6i5RUTk5JGXl8fChQs577zzyMrKipZv2rQJgOnTp7N06VJ69OjBkCFDaNeuXaHtV69eDUCXLl2K7fu0004DYNWqVQmPX57tI7GNHj2a9u3b069fPwYNGlRi1yWRmkR/qXXAnoPhmAN1KxIROflMaJnqCJIzYXel7m7nzp0MHz6chg0bMnnyZMxOTOJ59OhRunfvzsKFC3n11VcBaNiwIePGjeOhhx6K1tu9O4gpdkxAROvWrQvViac82zdt2pT27dszZcoUDh06BEDnzp2ZMWMGF1xwQWm/tkjKqVtRHRAZc6BuRSIicjLYsWMHOTk5rFmzhueee67QUwOAu+++m48//pidO3fyxRdf8NRTT5GRkcGECRP47W9/G63n7hWKozzbz5s3j7Vr13LgwAGWL1/OHXfcwfr16xkyZAibN2+uUDwi1UFPDuqAPZoETUTk5FXJd+RrukhikJ+fz7Rp0xg+fHiJ9du3b8+oUaPo3bs32dnZ/PznP+d73/secOKOf0FBQbHtdu3aBUDLlomfzFRkezOjd+/eTJo0CYDJkyfz9NNP88ADD5T4+4ikmp4c1AGRAckacyAiIjXZwYMHueqqq8jLy2PatGmMHDky6W3PP/98MjMzWbt2bbQ7T7du3YD44wo+/vhjALp3755wnxXdPuLqq68G4MMPPyy1rkiqKTmoA/QqUxERORnce++9LF68mIkTJ5YpMQA4dOgQe/bsoWHDhtE3E2VnZ9OoUSPeeustjhw5Uqj+3LlzARgwYEDCfXbr1o2OHTuybNkytm7dWubtIyLbpqenJ/8LiaSIkoM6IDIJmsYciIhITbVq1SqmTJnCoEGDGDNmTMJ6Bw4cKDTnAQQTj40ePZpDhw4xaNCg6HwELVq0YPjw4RQUFEQnQIPgwn727Nl07do1Oo8CwD333MOAAQPYsGEDEHQNuvnmm3F3xowZEx2DkJ+fz7PPPktGRgbf/OY3o9uvWbMmOs9BxEcffRQ9duT1pyI1mVV0sI4kx8yWZmVlZS1durRaj3v46HG6j3+d+vWMNT8eUuhtDyIiIjXFI488woMPPkjPnj3p2rVrsfU9e/bk0Ucf5a233iInJ4fTTz+dLl26cOzYMfLz89m1axetW7fmnXfe4eyzz45ut2nTJs477zw+//xz+vTpQ0ZGBu+88w716tVj9uzZ5OTkAMH8BJFXoT7xxBP88Ic/BGDv3r18/etf54MPPqB79+6ceuqpLFy4kEOHDvHMM89www03RI81cuRInn/+eXr27Enr1q3ZuXMny5cv59ixYwwdOpRZs2ZFExeRalCuiz51Qq/louMN0hooMRARkRpr/fr1AKxYsYIVK1YUWx8ZFNylSxfuuusuFixYwKJFi3B3OnfuzIgRIxg7diydOnUqtF2nTp1YvHgxY8eOZc6cOaxevZqLL76YRx55hIsvvjhar23btuTm5rJ8+XJyc3Oj5c2bN+ftt9/m/vvv5+WXX2bDhg306tWLcePGMWzYsELHGjZsGHv37mXJkiXk5eXRtGlTsrOzGTFiBLfeeqsSAzkp6MlBNUnVk4O12/Zx2c/+Tpc26SwYc2npG4iIiIhIbVCuu8JKYWu56ARoGm8gIiIiIqVQclDL6U1FIiIiIpIsjTmo5bpmNuWRYT3JbNYo1aGIiIiISA2n5KCW+0qrdEZkd051GCIiIiJyElC3IhERERERAZQciIiIiIhISMmBiIiIiIgASg5ERERERCSk5EBERERERAAlByIiIiIiElJyICIiIiIigJIDEREREREJKTkQERERERFAyYGIiIiIiISUHIiIiIiICKDkQEREREREQkoOREREREQEUHIgIiIiIiIhJQciIiIiIgIoORARERERkZC5e6pjqBPMbEeTJk1a9+jRI9WhiIiIiEgt9957781w9++UdTslB9XEzD4FWgDrElT5arj8qFoCklRRO9cdauu6Qe1cd6it64ba1M4fKTk4iZnZUgB3PzfVsUjVUTvXHWrrukHtXHeoresGtbPGHIiIiIiISEjJgYiIiIiIAEoOREREREQkpORAREREREQAJQciIiIiIhLS24pERERERATQkwMREREREQkpORAREREREUDJgYiIiIiIhJQciIiIiIgIoORARERERERCSg5ERERERARQciAiIiIiIiElByIiIiIiAig5EBERERGRkJKDFDOzW8wsz8wOmtkGM3vMzNJSHZdULjO70cw8weebqY5PKsbM7g3b8sYE6y8ys/lmts/MdpjZDDP7SjWHKZWgpLY2sy4lnOe/SUG4kiQz+4aZzTOzXWZ2wMzyzWyMmTWIU1fn80ks2bauy+dzsT96qT5m9hAwAdgDvA18FfhP4Bwzu9Ldj6cwPKkai4CtRco2pSIQqTgzawo8DtxVQp0cYDbBzZh3gFbA9cDFZnaeu39RHbFKxSTT1jFWAx8WKVte6UFJpTCz3wF3Al8C7wHHgQsI2rsHcHNMXZ3PJ7GytHWMOnc+KzlIETPrDjwAfA70d/eNZtYIeA0YDIwAnk1hiFK50sPlGHd/N6WRSIWZWUPgB8BYoC3wBdAhQb2ngPrAYHefG5Y/AowHHgNuqp6opTySbetQ5Dx/1t1/XPXRSSV5g+B66D533wVgZn2Bd4GbzOxhd1+v87lWSKqtw7p19nxWt6LUuYngfzAT3X0jgLsfBn4Yrh+VqsCkSrQIlztSGoVUluPAPcA24BvA7xPUuww4A3glciER+lG47XVm1qwqA5UKS7atQef5ScndX3H32yIXi2HZ+8D88Mce4VLn80muDG0Ndfh8VnKQOgPC5ezYwvCPdCuQrbEHtUpmuKxz/5Opjdz9GHAR8DV3f6mEqonO8yME/xilAdlVEqRUijK0Neg8r20iF/rbwqXO59qraFtDHT6flRykTrdwuS7Oug0ETxXOqLZopKp1CpcPm9nDZjbMzJqkNCKpEHdf7+5eSrXSznOA7pUWlFSJJNsaTpznI8zsp2Z2g5m1q8rYpGqYWW+CpPB/Cfqmg87nWilBW0MdPp815iB1WgIH3f1QnHU7Y+pI7XAY+AS4gRP9GLeb2a3u/krqwpIqFjmHC+Ks03le+zQAVhFcaFwdlh0xsx+7+8OpC0vKwsxaA38BjgB3xCSGOp9rmRLaGurw+awnByLVwN1HuvuZ7t4UOAv4CZAB/CUcDCW1k6U6AKk+7v4bdz/L3VsTDFq+jeBCcoKZfS+lwUlSzKwNMA84Exjp7rF3knU+1yKltHWdPp+VHKTObiDNzBrHWdcqpo7UMu6+yt3HEbzdoiEwOsUhSdUpCJcZcdbpPK/F3H2Lu0/hxB3He1MZj5Qu5mKxN3CTu/+lSJWCcJkRZ3OdzyeRJNq6kLp2Pis5SJ3V4bJQ/0Qzq0eQxR4D1lZ3UFKtXg2XZ6c0CqlKcc/z0FnhclU1xSIp4O5LCAY5npHgZpDUAOELQP7GiYvF5+JU0/lcCyTZ1nHVlfNZyUHqvB0uryhS3o/gDsQSdz9YvSFJNYsMbPoypVFIVYp7nofvSx8AHAKWVHdQUn3CC4gWBH2aj6Q4HEns5wRvGrqvhItFnc+1QzJtHVddOZ+VHKTOMwRPB/4jMu16OAnaT8L1U1IUl1QyM/uKmWUWLQMmhj++Uf1RSTWZR/BmkyvMLDem/EGCgYsz3X1vKgKTymVmTcysR5GydODXQGPgTc16XzOFk5KOImijJ0qoqvP5JJdsW9f181lvK0oRd//IzH5CMEvyB2a2mGDyjVOBN4HpqYxPKtVtwH+Z2YcEc1g0A/oS/A9mKfDb1IUmVcndj5jZ7QQzn79uZgsJ+iv3ATYD96cwPKlcXwfmmdmnBBeQ9YFeBE+Cd3Jigkupeb5NcD3UwcxejrN+hbuP1/lcKyTV1tTx81nJQQq5+4Nm9hnwfYJHktuAx4EJ4cQ7UjvMJ3g/9gXAVwkePa8geH3aL9V9rHZz9zlmdhnwCHAeQfs/D4xx989TGpxUpnXA74CBBBcWBqwnuNHzuLtvSllkUprO4bJn+CkqI/IfOp9Pesm29Trq8Plsyc3rIiIiIiIitZ3GHIiIiIiICKDkQEREREREQkoOREREREQEUHIgIiIiIiIhJQciIiIiIgIoORARERERkZCSAxERERERAZQciIiIiIhISMmBiIiIiIgASg5ERERERCSk5EBEpBYws9Zm1jTVcYiIyMlNyYGISIqZWWcz22hmfyzn9r8BdgDnVW5kcrIys4Fm5kU+A0vZ5nIzW2VmD1RPlMkzswVFfpcFqY5JpLZqkOoARESqi5m1Anq6+zvVfNz6QFfguLuviVPlfKAT8G/lPESz8sYmyTOzZsDXgHx3/zLV8STpY+Cj8L+3l1L3FKAbUL9KIyqfhUBB+N/XpjAOkVpPTw5EpMYws77hHfSj4d3BY2a228yWmdkvzOysCh5iGfB2ZcRaRiMILtK+m2D9m8A8YGJVBRDnzmuiz7qqiqEW+BuwGGiX6kDKYKa7Dws/K1IdTCJmdqmZPW9mm8zssJltN7MlZvaYmZ3m7uMjv0eqYxWp7fTkQERqkgyCO+jbgX8Cx4BWwNnA94Hvmdmt7v5syiKsAu6+ExhUxYeJvfMKcBpwDrAWyI8p31rFcYhEmVlDYDJwc1i0CsgD0oDeQP+wbFpKAhSpg5QciEhN9G7sHUIzawDcCvwW+L2ZveXun6UquJORu4+P/dnMbiS44HrV3X+QiphEgF8SJAYfASPd/X8jK8ysHnA58I8UxSZSJ6lbkYjUeO5+1N0nA38CGgNDIuvMrIGZ/cDM8szsoJltC7snnJ1of0W60RQUWXeGmU03s63h/vLN7O44+7jLzOaa2XozOxB2hVhnZj8zsxYJDv1QkWP/INxXl3iDLM3sRjNbGsZy2Mw2mNmvS9h/pTKzfzezf5jZl2ZWYGavmdm5Req0NLMpZva/YVeQw2a238zeNLMeZnZ1uN32cD/vmdm34xxrfNj16fNwH1+a2ftmNtbM0hLE19fMXjKzXWH9fyXY9ygz+59wsO3eML4l4cUnZnZf+Pezw8wOmdkaM3skvKsdz6dF2jEj3M8zFmfgb0z7vl+k/P7we/okjH+3mb1c1jYoj7Abz1tmtidsm7cILsQT1U86jrBd/hKei0fD+u+Z2VQzOzWmXj/gToKnVQNjEwMAdz/u7rPdfU9Ff18RSZ6eHIjIyWRjuGwN0TuLLwDXEHSZWUDQH/xbwFAzu8LdF8bZzysx/70/8h9m1jvcRytgJfAhwRuAfm1mndz9/pjtvgUMAJYD7wMtCLrp3Av0N7NL3N2LHDd2cCjAJ6X8vqeG+/0QOABkA3cTdLPKKWXbCjGz8cAj4XGXAi0JkrIBZnaRuy8Lq7YieKpzCHgX2EcwqDWHoLtSfYKLv38RtE1fYKaZ7XP3/4k55K1A5/BYS8P99iEYhzHczC5z970x8eUCs4BGBN1Q9hN0QZlpZhnu/vuYfY8L9/0RQfeqxkB9dz8erj8TaEjQjseBC4HxBAN0b4nz9cwFYgckH0nwNZbmJ4ATfE/zCQaWH4j5HZNtgzIxs+HATIIbhO8RtE9f4NIE9ZOOw8yuIjgnGxF8n4sJugueE37+G4g89bs9XD7h7lvK87uISBVwd3300UefGvEBBhJcLL0cZ50Bi8L13w7L7gx/XgRkxNT9Tli+HmgUU74u+N9ewuMvDbcbEVN2JsEYiKNAp5jyBWHdvjFl7YAPwvLBMeU3hmUTEhy3S7h+QZHyekV+PgX4PKzbI6b8mbBsYBm+60hMv4izri/BRfJqoEtM+X+E27weJ/b3i+zjybD8VaBBTPntYfkbReoXa5vw+5wf1p8UU54GbCZISHJiyrPDsl1AWhnavej3fDbBhfARoHmcNu+SYD9x26GE78iBdQn2lXQblHIuTShS3gzYSfD3fH1MeX3gZ0W3KePfQqvwuz8KXF3adxPu04GvJvt3G/O9LSjLNvroo0/yH3UrEpEay8wamln7sJvGiwQXf5uByB3nO8Pl9929ILKdu/8RmEMw6PaqJI/VH8gCXnP36TH7WgP8nuDiKbekfbj7VmBS+GOFu374iTvbkZ83E9yVBehe0f2X4DaCZOy/3H1dTPkvCJ7e5JhZo1L28Uy4zHD3ozHlMwgGmvcoLYjw+xxBcKF+o5k1CVddA3QEprr7vJj6i4G/Etypzi5t/zHbFf2ePyR4e1QD4Ixk91PJKqMN4rmG4CJ+lrv/KVLo7scoPDC9PHF8l+C7/6O7v5pELKeEy3UlVRKR6qVuRSJSE11rZkW75ACsAYa7+z4LZgPuCRS4+z/j1J1N0Ic6myCxKE1kArEzi/b7JrjzC8FcBaWJdBU6pcRaSbBgfoRrgEvC/RVw4mI1o6L7L0Hku7jNzL5TZF1Tgi44p1Jyt6hV4bLQaz/dfa+ZbQfaJhOIu280s/cIuvr0JXhKFInvgjhtFRlr0pXgTn+pwjENwwm6JbUn6GbTPlydkcw+qkBltEE8WeHyzSqI44Kw/BWSE7lBGe9cF5EUUXIgIjXRF8ASgjvM+4ANBP3Z34y5C51BcEfziwT7+DxctkrymBnhsjuJ78o3TWI/kT7jFZqYzMzaEnSp6ZmoSkX2X4qMcFnS61VL/C7c/YCZQdC/v6iDnLj4TkZkrEmkLSPx9Q0/ZY4vwszOBN4iuMCNWyWZ/VSBjHBZ7jZIoGW43FEFcUQSvo2JKhaxhWAsyGkEXYxEpAZQciAiNdESL32yo4Jw2SHB+o7hcldMWUl3KCNvRHnc3e8r5dhlVZ47o/9JkBi8DXyPYDBtE4IBuneWsF1liHwXZ7v7yio+VjLahMs9RZZ3ufukOPXL4qcEicFfgfsJurg0B54Fri5St7R2jAyYLs9Fe1FV1QYF4bJTFcSxL1wm+zatRQTJweUoORCpMTTmQEROSu6+H1gBZJjZeXGqDA6XS2LK9gOYWfM49ZeGy0EW3vKuRJE3IsU7biK9wuXD7r7Cg9e57qXkScoqK+7Id5Hw1ZbVJWyr/gSDXPPC4sqML/I93+fua8LveRfBoN2iSmvHTeEy4Wt0y6Cq2iDSBa9o4lMZcURmYL4yyX3/d7gca9X0el4RKZ2SAxE5mUXuGv/CzCLdJTCz6wmSg88I3pYTEXmN6L/H1I28Q38RwetLs4CHLZh4jZh6FekmFDnu0Nh39id6f39oW7jsn8T+I09HupQ9tLgis9E+aGZfj11hZvVLibtCYt+dH84z8DuCO9Ev+Yn33b9C0C1mmJndVjSZK2NbleV7jvf30yDmb2VRuLzDzL5Shhjiqao2mEWQxAw0s3GRuR4qKY4/ETxducvMBsTUSyd4QlCIB6+ynUvQrWiOmZ1VtI6Zfc3MKjx+R0SSp+RARE5mkwneXHQBwcRUs81sKcEbcb4Evuvuh2PqPxUup1ow4dZc4AszO8PdneD1nruBB4DPzGy+mb1uZqs4cZezzNx9BcGF41nAxxZMyLWc4C1IifwaOAw8ZsEEXa+a2SvATXHqvhvZxsx+XN44Y+JdRNDdpjXwrpmtCCe9WkBwMX1FRY9Rgn+Z2TIzew1YS/AGnE0E80dE4ttP8D0cJvgO11kwId0bZrYBeLQMx/t5uJxhwURur5jZq8S/+z2V4LWeD5nZIjN7PYztkjCuvwNvEAwaX2lmfw/b7C9liCfyO1ZJG7j7IeD/EXQBehTYEJ43bxDMu1DuOMK3PP2YYJzJPDNbbGYLCcYFDUwQ0vUEXefOJ/jOlofnx1wzW0/wNOLm8vyuIlI+Sg5E5KQVvoJyGMGF4yaCC5DOwJ+B/u7+dpH6cwj660ferPI1gkmaGoTr/0kwUdPTBBeeFwD9CPqSv0vFXAe8TDAgdCDBq1E/SlTZ3ZcQdOWYTTB4dwjBJFUHCe62roqp/gLwMMHFWlkG+ibkwYRv3yAYrHsKwaRmpxNMZpZoEHhleIzguxlI0C5PE7RloUGu4asy+xNM5tWQYEK6PgSvun0/2YO5+4sEv+c7BL/fUOBigrktXgv3F6n7EcFbjfII/k7OJWjD2AnRvgE8Hm5/AcHFcyeCC+A/JhtXeLwqaYPwvMjixOtmcwh+5y+BlyjylqeyxOHuDwA3ELRBH4JxM/M50b3vSJH6Owj+rr9L8PrhjgR/9/0Jnoj9jqAdRKSaWHCzTEREJHXMbB3Q2d1T9XagWiWcG2Q+wZiVCSkNBgifNAwAurr72gruy4G/u/vASghNRIrQkwMRERGpFGaWGafsGoInE59WNDEQkaqnV5mKiIjUXteZWd/wv8eH41+qRDhp3zoz20owN8kBgkHyZxGM1bg38dal7vtREs/5ISKVSMmBiIhI7XVW+AH4RRUfqxnBGJBsoHf48zaCMTH/390XV2DfFxF0SxKRKqYxByIiIiIiAmjMgYiIiIiIhJQciIiIiIgIoORARERERERCSg5ERERERARQciAiIiIiIiElByIiIiIiAig5EBERERGRkJIDEREREREBlByIiIiIiEhIyYGIiIiIiABKDkREREREJKTkQEREREREACUHIiIiIiIS+j8XpCdzaICw/QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 278, + "width": 387 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "moorings = xds.xoak.sel(\n", + " TLONG=xr.Variable(\"moor\", [360 - 140, 360 - 110]),\n", + " TLAT=xr.Variable(\"moor\", [0, 0]),\n", + ")\n", + "\n", + "moorings.TEMP.plot(hue=\"TLONG\", y=\"z_t\", yincrease=False)" + ] + }, + { + "cell_type": "markdown", + "id": "retained-browser", + "metadata": {}, + "source": [ + "## Extracting single points" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "civilian-shift", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'int' object has no attribute 'chunks'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in 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indexers)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoords_to_point_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index_coords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m 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For a single point we create a 1D variable representing the coordinate location we want: in this case `TLONG=220, TLAT=0`. Seems like this could be fixed: https://github.com/xarray-contrib/xoak/issues/37" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "relevant-bottle", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'TEMP' (time: 1, z_t: 62)>\n",
+       "array([[25.794353  , 25.759989  , 25.728874  , 25.674746  , 25.541134  ,\n",
+       "        25.282745  , 25.026842  , 24.813889  , 24.512192  , 23.9899    ,\n",
+       "        22.513605  , 21.532747  , 21.387815  , 20.417183  , 19.48939   ,\n",
+       "        18.584412  , 17.558277  , 16.449156  , 15.601459  , 14.897679  ,\n",
+       "        14.326382  , 13.539371  , 12.851948  , 12.320872  , 11.637564  ,\n",
+       "        11.112046  , 10.732501  , 10.340948  ,  9.864892  ,  9.434968  ,\n",
+       "         9.031752  ,  8.564738  ,  8.070933  ,  7.620849  ,  7.1164446 ,\n",
+       "         6.5846534 ,  6.0993133 ,  5.625975  ,  5.127931  ,  4.5544333 ,\n",
+       "         4.004363  ,  3.477641  ,  3.0496979 ,  2.6729817 ,  2.360019  ,\n",
+       "         2.0861135 ,  1.8473006 ,  1.6999204 ,  1.5623759 ,  1.4673579 ,\n",
+       "         1.3774252 ,  1.2729893 ,  1.1820002 ,  1.0894325 ,  0.99818563,\n",
+       "         0.9394488 ,         nan,         nan,         nan,         nan,\n",
+       "                nan,         nan]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) object 0036-12-07 00:00:00\n",
+       "    TLONG    float64 220.0\n",
+       "  * z_t      (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.625e+05 5.875e+05\n",
+       "    TLAT     float64 -0.05\n",
+       "    nlon_t   float64 600.5\n",
+       "    nlat_t   float64 152.5\n",
+       "Attributes:\n",
+       "    long_name:     Potential Temperature\n",
+       "    units:         degC\n",
+       "    grid_loc:      3111\n",
+       "    cell_methods:  time: mean
" + ], + "text/plain": [ + "\n", + "array([[25.794353 , 25.759989 , 25.728874 , 25.674746 , 25.541134 ,\n", + " 25.282745 , 25.026842 , 24.813889 , 24.512192 , 23.9899 ,\n", + " 22.513605 , 21.532747 , 21.387815 , 20.417183 , 19.48939 ,\n", + " 18.584412 , 17.558277 , 16.449156 , 15.601459 , 14.897679 ,\n", + " 14.326382 , 13.539371 , 12.851948 , 12.320872 , 11.637564 ,\n", + " 11.112046 , 10.732501 , 10.340948 , 9.864892 , 9.434968 ,\n", + " 9.031752 , 8.564738 , 8.070933 , 7.620849 , 7.1164446 ,\n", + " 6.5846534 , 6.0993133 , 5.625975 , 5.127931 , 4.5544333 ,\n", + " 4.004363 , 3.477641 , 3.0496979 , 2.6729817 , 2.360019 ,\n", + " 2.0861135 , 1.8473006 , 1.6999204 , 1.5623759 , 1.4673579 ,\n", + " 1.3774252 , 1.2729893 , 1.1820002 , 1.0894325 , 0.99818563,\n", + " 0.9394488 , nan, nan, nan, nan,\n", + " nan, nan]], dtype=float32)\n", + "Coordinates:\n", + " * time (time) object 0036-12-07 00:00:00\n", + " TLONG float64 220.0\n", + " * z_t (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.625e+05 5.875e+05\n", + " TLAT float64 -0.05\n", + " nlon_t float64 600.5\n", + " nlat_t float64 152.5\n", + "Attributes:\n", + " long_name: Potential Temperature\n", + " units: degC\n", + " grid_loc: 3111\n", + " cell_methods: time: mean" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xds.xoak.sel(\n", + " TLONG=xr.Variable(\"points\", [360 - 140]), TLAT=xr.Variable(\"points\", [0])\n", + ").TEMP.squeeze(\"points\")" + ] + }, + { + "cell_type": "markdown", + "id": "reasonable-building", + "metadata": {}, + "source": [ + "## Limitations" + ] + }, + { + "cell_type": "markdown", + "id": "residential-soviet", + "metadata": {}, + "source": [ + "- [ ] cannot simply index by point\n", + "- [ ] Can only set one index at a time (so only, TLONG, TLAT or ULONG, ULAT)\n", + "- [ ] indexes are not propagated so `xds.TEMP.xoak` will only work after `xds.TEMP.set_index` is called" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "equal-microwave", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The index(es) has/have not been built yet. Call `.xoak.set_index()` first", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTEMP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxoak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTLONG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m220\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTLAT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xoak/accessor.py\u001b[0m in \u001b[0;36msel\u001b[0;34m(self, indexers, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 246\u001b[0m \"\"\"\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_index'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 249\u001b[0m \u001b[0;34m'The index(es) has/have not been built yet. Call `.xoak.set_index()` first'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 250\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: The index(es) has/have not been built yet. Call `.xoak.set_index()` first" + ] + } + ], + "source": [ + "xds.TEMP.xoak.sel(TLONG=220, TLAT=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "polished-ministry", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'int' object has no attribute 'chunks'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxoak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTLONG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m220\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTLAT\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xoak/accessor.py\u001b[0m in \u001b[0;36msel\u001b[0;34m(self, indexers, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'xoak.sel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xoak/accessor.py\u001b[0m in \u001b[0;36m_query\u001b[0;34m(self, indexers)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoords_to_point_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index_coords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXoakIndexWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xoak/accessor.py\u001b[0m in \u001b[0;36mcoords_to_point_array\u001b[0;34m(coords)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \"\"\"\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mc_chunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunks\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchunks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchunks\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc_chunks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xoak/accessor.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \"\"\"\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mc_chunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunks\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchunks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchunks\u001b[0m \u001b[0;32min\u001b[0m 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