{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.14?urlpath=lab/tree/simulate_3d.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[simulate_3d.ipynb](../_static/notebooks/simulate_3d.ipynb) |\n", "[simulate_3d.py](../_static/notebooks/simulate_3d.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D simulation and fitting\n", "\n", "This tutorial shows how to do a 3D map-based simulation and fit.\n", "\n", "For a tutorial on how to do a 3D map analyse of existing data, see the `analysis_3d` tutorial.\n", "\n", "This can be useful to do a performance / sensitivity study, or to evaluate the capabilities of Gammapy or a given analysis method. Note that is is a binned simulation as is e.g. done also in Sherpa for Chandra, not an event sampling and anbinned analysis as is done e.g. in the Fermi ST or ctools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and versions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from astropy.coordinates import Angle\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.maps import WcsGeom, MapAxis, WcsNDMap\n", "from gammapy.modeling.models import PowerLawSpectralModel\n", "from gammapy.modeling.models import GaussianSpatialModel\n", "from gammapy.modeling.models import SkyModel, BackgroundModel\n", "from gammapy.cube import MapDataset, PSFKernel\n", "from gammapy.cube import make_map_exposure_true_energy, make_map_background_irf\n", "from gammapy.modeling import Fit\n", "from gammapy.data import FixedPointingInfo" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Gammapy package:\r\n", "\r\n", "\tversion : 0.14 \r\n", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\r\n" ] } ], "source": [ "!gammapy info --no-envvar --no-dependencies --no-system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "irfs = load_cta_irfs(\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg nan nan False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t e 0.000e+00 nan 0.000e+00 1.000e+00 True\n", "\t phi 0.000e+00 nan deg nan nan True\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to simulate the data\n", "spatial_model = GaussianSpatialModel(\n", " lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLawSpectralModel(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "sky_model = SkyModel(\n", " spatial_model=spatial_model, spectral_model=spectral_model\n", ")\n", "print(sky_model)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define map geometry\n", "axis = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0), binsz=0.02, width=(5, 4), coordsys=\"GAL\", axes=[axis]\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define some observation parameters\n", "# We read in the pointing info from one of the 1dc event list files as an example\n", "pointing = FixedPointingInfo.read(\n", " \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n", ")\n", "\n", "livetime = 1 * u.hour\n", "offset_max = 2 * u.deg\n", "offset = Angle(\"2 deg\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exposure = make_map_exposure_true_energy(\n", " pointing=pointing.radec, livetime=livetime, aeff=irfs[\"aeff\"], geom=geom\n", ")\n", "exposure.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level [astropy.coordinates.builtin_frames.utils]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "background = make_map_background_irf(\n", " pointing=pointing, ontime=livetime, bkg=irfs[\"bkg\"], geom=geom\n", ")\n", "background.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "psf = irfs[\"psf\"].to_energy_dependent_table_psf(theta=offset)\n", "psf_kernel = PSFKernel.from_table_psf(psf, geom, max_radius=0.3 * u.deg)\n", "psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\");" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/github/adonath/gammapy/gammapy/utils/interpolation.py:159: Warning: Interpolated values reached float32 precision limit\n", " \"Interpolated values reached float32 precision limit\", Warning\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy = axis.edges\n", "edisp = irfs[\"edisp\"].to_energy_dispersion(\n", " offset, e_reco=energy, e_true=energy\n", ")\n", "edisp.plot_matrix();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to compute `npred` maps, i.e. \"predicted counts per pixel\" given the model and the observation infos: exposure, background, PSF and EDISP. For this we use the `MapDataset` object:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "background_model = BackgroundModel(background)\n", "dataset = MapDataset(\n", " model=sky_model,\n", " exposure=exposure,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "npred = dataset.npred()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "npred.sum_over_axes().plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# This one line is the core of how to simulate data when\n", "# using binned simulation / analysis: you Poisson fluctuate\n", "# npred to obtain simulated observed counts.\n", "# Compute counts as a Poisson fluctuation\n", "rng = np.random.RandomState(seed=42)\n", "counts = rng.poisson(npred.data)\n", "counts_map = WcsNDMap(geom, counts)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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6Ue2vTOWnYtxX/dwoYXiVy12rZ3OFwpTwqwauJUhbQjEJv9QvP6bmk7DCKQX/HccqqO+CKgmN1Jck9HQF2IWZ4pX60BJcc8Y+9SWVrayb1hinBaeqL4lX+Syd0071VzSp6HSZ5wnfVj+maD93bl5lfs8pd6OE4eOwDKcGfu6nsoSqNluatlrNm8PMrck61Td1E6cEqguyZAVUcack4DR/JVwqoex9VIFW9anVr7kC1i39OfHjNGZVeCCNvbalnzTeSueKD6tY8ZRwrGjTig8n/Hxcp/h3Cgcfh1a+G7Wa/Fn7vwz/+Vmg3pxaPXNo5eNGWt2w63n5XzeHbrDfbMv6NxhvjNWNuoQlxht0WUbz6YbmtBmacE/SPN85hvQ8D+VSn70N1q24Ed9zDOmoedJv1qc0W1jelE5QGhEH3/ztec7tt/JEtYHe++/5tc1zS2ca8VgCWB/X7SRc7oW87KtuqE60g6Tz22niOKRn+t/7PTW+TNexhqVr/vNQJvWjBc/NyPPMgRLHCZlOWXg5J26qx58pUyjzsq5zy6P4uSDRcn5qQH+nPuokqQZYy3o7Xp8KAz0p4eV0YibGS/RUPFXwncrvhIeOk/YXqGl2XjxfYigYWvzC535qxsfTBYg/Jzi/Oe8wj4Pm43OeeHGo2k95z+2Z09Vx0vGr+u9Ct5pHy/A88anm8XlT4eFzuqLvlEB8RwrDRAj+9nzAmAhTR3iUuVwo+GRLgkNxak2chBswZlq1SNJk5XPWy+NibPPU6lUc9TiYT1SE/Al3bffBxf6ZCza2o2UJqU+Oo9NUn1cTqzqC6H3ycanwWMp3pViT1aW46CRXRaGCPSkxgpdXa1X74vURUv/THKn4tCXwkvBaYKx0vJ5Ew6SoWnVom/pc014KdTyEpx0DvI4FlMVEPKIqo2Wrcovwe05cyxcHqhU3/Xj8bRHaSvmrGE7Vt6pPXrfTqJUflu75nC6a5ltPPM5YnfZItGldF7YMvxXPqcWLRfjvaVVsrOpD4kfHIy34VDFSrcd3Gnj/qz2WLJvq1PFbYjx2aXxSX6byV/2aM6f1c6MWUCgMq60TFaEv+6mI7pNmzg3AacCnBnduWuo7maglCFpllQmXGAuGanIn2rcmtQqFRPNqklf0dqWTBK6Ph+Y/DWVbCxEuUFrPq7FL6b5VqRIkWt4VxlRbLV4iHdK2sIXlSf2txmpq0WfuHKx4d07eGycM50yIOUKj+t8amLnCpWLAuYM4p0xKb1kzrT6z3BxmTtbnFJ4uOKfw0W03uvXHBXVqb2ntuPXp7Wm/FtJmtfE5CbpWf9I4e/0qjBHqdqGelEYSdgnPSqn4uDqNXOjOUbYVvzqOjs9V5uicfDdqNfkABzjAAa4Fnrald1U3WT9VPK5yY9ydArKmbGk1T6+shKTJL+O+00pxXJMF17IilvZp4ZfoVFmOil/qN0L9PAaXrIHKlfY++binsU/WY6JVZQGncUt5PY2xOY+n+S3S3oe0d7OKBcLaUfy0vnT0L+Gf6k+WZeKRBfavhVXatqzVNO7VnKr6X83J5Lnwc+Pc5CSMnLlaAqYywVsCMbkQLfezcs2qCVk998nW6pvj5Me9pmjRCma3nmuf/fxsJcSnXLIkTJKA5SkNFyT870fVKJAqIVAJSYS6K7fa62/xgedNQsKVwbKotxqr1pglYZd4NNWfnlX9qIRZEqBz+bGaR615fqNuuj46Ourf2t10DdRbKhZWzrcRpDJaJ0I6ny0w3M5wju2m2LOL/XYA386S9qeluoHxPjO2ofgurJzu32OeOVs/fB9h2nbB59XWBqXXqT1LfU3boRzPRZFH86Vbor2uRCfdU+mgtPP0xUQebcP7s9yl3cOYrkzn/yk6kz+WVp/3m3RJG7DTtprE9z7+2qbn9TrSOKR9hhwL1p3A6VJt70n8rukvNW66vlHCkP9VeADDfVwtAQHsB5xpKswcElNVTIDwzPF3RoI98z1kiTmq+k8xPm2ibVaTuGJuPtPJker16+8J97AXBIq//044uGIgrGV/pSstr9cFQUuJen5VUsCY17Qc8y4lb9rX5/RP/Njac5iUrbYJ+7/EkI4OLaNBx7ql4F0pV880baquBJd51hKGT93tfRwxw6lPy3yunrXcgKps9aliJ1XMUvMwn9eFiTo13V1NzZtcpKr/+jyttFYuPMtVN85onaeW39taIK/0ejyU7nNyu5iu71hubSNJ4ZFl+L/E0FV2mi6K58kdTN9pfKpwTOI/71fFw75tzfFMfFeNq6eneZbwTHOoRZ9qrnl7Ny5m6J1dFoRMg65xH59kPtgVE1WTfml5PKDdYkBnnmpAWxN0agKkiVPFJFtM6bGrVv40gRcYxu6WyDincUkCNAnKJGg0yO/4uHA6xTgOOFe4VeOo+LDfuhE6Ce4WHyQcKx5r0S7xp/9Pb1NM/Kb9c/5s8bfTqSUQE49WZW60MNRba1JAvRrQOWWcqZMA0MFNG64rJnaNpUI54VFZeKltZ8Y0CStcfHJ4Hbx3L92Zx3ytPqjQSDi4UE3WaiUglsjWZqL1XOvcf1c8lK49UzpqP1p3WlbtugCr8nu609z3WlbCNfVZxzbNDx2/1De/aDeNZUvoVX1tCb5W3iVu2AIKXxW6CM+qWJz/17Jz4nUOC2zP4a4vxnERj7VooF7TNFC8sG+NpaQ6lwVuHrBvXVqh8Uevd4H9ohBjcEsp14q1pf/ExcfEYTGRJ8WetM318fBCA/Yjjbf2x+kAK+OLZSzvdWt9+kz71OK3qXo8ZteK+fn4a/o9DC/lAMZ86vyWLhpRnFrtKr9qfl04SQuNvrjjeROPV7F91teKGT7RTddd131f13V/v+u6vyFpX9x13Q93XfdDXde9Z6oOXuG1CR8CibJEDkQzTxJCWvfC8up/nXQeLFfQ66nWx2MmVgHkgknrTJNTgXU5IzHNFyi879oPMh0FofZDcXGB7YzoCmJp3/y9xBi0Pv/t+BKf1cV+kWZ1PO4f/6sw0fHQPMpXSZEmIcBPNT5KP/2kuhS/DTLtl5IfGI6rXy/H5ypQFlK+emd4MioIiTddITMt9YE4Vrhq2XMpA+TdAD7/Kt4q4Um6uAD+RQBfDeBvSNp/DuArAPzrAP6dOTFDBPO3ZXa7+a8fv8Z9gdrsB8au2sLyLcInlXF80jPNk9w8j48tLF/Cr0Uf7WPaSK39T65oakPTdE9gqqOK4VZu+BRN0/NWfHJOXKs19qlND61ourrbSltgGLJItHKeTTxQjXF6lsa44pWpOiqer+qsaDvlLs+JHTotnqmYIYAvxVAY/ikAXwbgdwH4d+cKw6kJ3hrcJGhaE6cirg9QCjJXE9SZJuX1+Jn3pcU4riA07unMWuEJq9/bW2KMowu39CzRtRIg6fIEpbcLmzQhPS45NS5aZupmGRXuide03cQ7uurtgmyOgq+EVEtAt4QFxy3RI/FKZYhMKa40P6fKJAU2p139tIThc3j6cA/AawB+EcCdp4zLAQ5wgM9VeNqW4cwyrwH4NIBPP//885OaGqZVqvdWAGNNkzRzZTG65kw46bPWcS0tz/1vSRtW1o1aMlXfK4s4bSFxq0rr8BVKxVfrT99uFXt7urewsppTO63/Hk7QM8JqlVW3sBCPtA3Gx7Bl+SYraq71l1bNHX+nadVO5SlVfOLp7hUkC7wajymLduqTcKpomNo6Pj7uKUt2n9eeGTf5sp8P2BVeLSIkoTaHaGkA5zAKf1duk06WFAOscKrcRC+f7rRLTJlw88mqgoNCwC+M8Ani7emFoMnd9MkEq8tpmcbPBdRlYqipvgof7c8Uv7lgcB70fJWAqPrMNCohVSQumCoBlfrtdGOdl51LVf2JLletqzJUWrJhgWc8ZnjZzwthn2ESKlMW1RRDL+27EiBTg+eaq5qQHm9MQnSB/F5kz1dNNBdMmj5Xuzrd/QaaxOyVdVptjk5jUAk2CkC/mKE1tv7cY49JAFRjOIef1JL2/Eorv4XGx7sSFn75RLWv0WntQiUtyCU8q7nhtGkpwDTWiXeq+ufGKb3tZ0YYAvjvAPw8tjtk/i6Ab3sUYVgJo8SoFfMmBvWJMKW5WlrJ05Mg8jpSv5Jb1prwcz/uWvszFzwulHQCJoVSCd2qrUT/Fp3S5EtCWie81pM2mifhmVz6yi3WfMk9PcXe4k79TDznwkr7VoUUqhfVV7RLPFoJ/orXW+58mmNz+TWNR2V5V2UXeIaE4eP4zH1v8hyB2MozJXSUkStB2/rvAjkNYrJ8U9wpTfpUR4VT6wibpyWBN0eQJHpVpzgSzb0/joume3/mTMBqUiYBridMPPSh45m2CrnA0LKt94w4DeaGWXScUlr1LPGrl6vmURrnyvNw2ialn0IJLa+w9blRwnDqooYpAVflcU2TmKA1UVpuZmX16IRxQTIV59HJ6IIsMX3rYoWW0NVJPnU0S5nXt9V4u35OWCel/25N1EqQpvqTYNC+tgSJW26kB/OpAE5j4BO7RUOgvU2r6oc+T6EJxzsJIy+TFpWcd1zIJ+FVzbO5c61a3GoJwdTGjTqO51d4KSwwPl612D3zEwT8nnttldezRD4lwPLMr22w/LLxzHGsrjJK6cRLjyv5/XkAcHYbWN2vj4ktAJzsTnDolVj81lMMpxjT2uvjcx0Xx1vLEQd9Z3IaJz1SpmU0n+OkJxfSOCvuBPZV63L8/BRS4p0KKlwdP7blY5rw0udpPiwsjbyi9VW8l9pOR+fWuyOr/tzrW9nVa8477INfB5fuaWzhDdyw+wz9bHJrwJKQqs4uat50nriCFh6QZ0v59gmcJqNOwHTUMAkeBWUebYf/qzsBE3O3+umQaJ366f3ws9A6sU52k4p0oaB+cLG/FzH1VWntfVABoQKXk6wSYAn3Snmo8FJo1a190QlfCT8tx9/V0b3W8bUkhJJA1vFCeF71x/mM6frt8wAY82b1vzW3tZ6WMHwuJT7L8FnUzOQCpdLKadCUkHrrsAoOpvlBcbX0KutCz8H6gLYO3Cukdvx5uvGa7euESUI1WcHL8NxxOMUYnyqv0sAn+/pifGh/ie0N4gPhJtYG69A+6zcnGcHPuFK4nu3qdEHolq2m05paSXmms4yOA3F14e2WrdKJZZIQcIu1urSgMgiUX7S853VlusT4ogdgzAPOr8kjUl7c2H+FluCrLj6ZEpQON8IynGO1KEEqy8/NfP5OlpR/O2P6hKpu/Ug3y1TM6PWkfEB2j91SIb5qjTEfb+RR10nxczolgd4S1i4wtH6fXErfkx2uaskBw9tPKID0NQw62VvWqruhinclJFvWivbX+UcVE/OtxAJeAFjfBk7uj8eX5U7lv7u4+jtdHME6iKf+9zzO47Df3i4kj9+C3hrvVI/3eY5Qq2AB4H03yU1mzLBFSB1kt9RSrEHLO4MnraP5/BYQFyAJN8WrZd5rP1I8xyeUa1ctp9ZbisU5s/qkTflcSCfrycv5szQ5kiJJbi37znxeZxVXUrwI7s5NWVJKa3XdacV6Ga0rKWMX3reOt2GAZC1VfO/9cuFOfmi51J6ubWj7STm7Fc68KeaqPKphIy3v87dSPHOMIea/kcKwIryCMokzkg9MlQbUWrBF/KRtk0WZFiDchUiCNOGTLCtgv2CC8Cxp4pYGdnx0Aq6tHRf8yVJSYeuWR6uc4qo4sR7FK/FJJdjTQgm/VahwDHyiT1nyc6yw9NwVQBK26oanuZDwTVZ9soQr/OaEiBSHygjwZ16Pj7GXq+au1/XM3Gd4gAMc4ADPLMzZ2wfgSwD8jt3vLwDwTzzNfYaLGfuSptKrsgvLw2/uj0LY8+T5l4069f+c/MuQ5n3w8kvDMT3Xckvs97Vx42/ayJ362boIYiqd5fXMc7r0wc9E61gsMN7XN+dSAy9bXRDRGh+nI6wurTvRT/MsrN7Upv9O57aXGOO8tPZ9A7zSzS8TSW0ojlq2xbupD06TdPa9NW8T3Soc+HmkTdcA/iCAnwDwt3f/XwTwV56WMPyAXe7qBFEiJMI5wTzNJ0AicGuAqvvvWkzuEzpNgGpiVPgmvHxy+CRyWqiwSnR0eunJjNak1+e+WTlNGh5f8zb9+F2FAz/ciN0SpImuSfD55HVB67/9WFyik9/28ocAACAASURBVLavp1FaAiQJfG+j4k8XhBVvJ55z+vr4et1p7lT4t/i8EorO05U8eKRN113X/SSA2wB+vO/7r9ql/XTf9/9Ms+A1gS+gLDCMF3i8jZAWRphfV+Z0Ww2fJ9CAPtvxYL627TFI1r3EOF62sG+P8SwCXlx5ZN98i4a2q3jxe2H5q9gWaej0XCLHhKqFF6COG2mskfjo/r8UZE8xLO8n26j6rumJd5yWaeuOt0t8Fc8U/1tInVpf2iZVjQHxAnJcTd8R43FTx1vLat0eK9VnFZ7+2+tL7Xpsk8/1v45Hqh/h2aPGDP9x3/e/yj9d1z2HrbR9KvD87luZ+Vw++swHA/L7HsYrjb6I0SJu2iqjAkE/q+P9c9a9wJY5feJrPsVbBazjvcT+RInjfnI8FACbXZov5vCdIbpPUfu4vr1/7v1mOvfD8eOrwMyvzzU/V2SX9szhFHvlRfqdWt3EKaX5IgfHiOOn4EJWadlSulpGJ7YLAMXBlR4XMio6aP99jNmutrc2QeiCyNtJtHMhyzacLt4Pp5PPlSR0U38Ur7TY5Z+kHCuYYxn+SQBvAfhmAK8A+PcA/O9933/3Jdp5bJCO47m2Z1olFCst5StoiWmSVaODpauZsDqTENV6VbBUaemUhuOpDOjbaAh8vjrOL7dKjJQ0tbavOLEtPfpHJaTMPlWfphMfCs31xTCP99MtdBVI1b7RhJe2oXTmTgClsbeluKV9qC2rbMrq9Hr4eyXbdJhHcdC0JNCdFkzTOUJLUzeb6zMVXGlVv+J19468v4qf93+ON/cPGpbhnJjhb8A2bvgDAP773e/uaS6gIMQSgLxosCjyIjz33/rxPFWgtwp8p4C2t+XlqvY1PuixLI/laFDc86d79jTdy6e8xO3s9jiex3we70vxPNa3DHUwNlddLaVl/cJT7zf7pPkS/TxWmmKkafy9PwtLd5xaPOJ8lfpeXVXW4vOEr+PufU/xx6rfqV1++4KZ0yvNp6pdx701n/i5sRc1qCUGZI3gsQ2gvZG6ZQ3yd2uf01RcBJYvaXb+TjGkZDFpH9w1VfzUKlGLVV11Be8r0zTfWs7kahtA1vJqOegGYO2T9oP1sO71HWD9+j5vaketEWDMH27ZKa2qth0/p9nC8vPZyfH4mGHCNV1WsLYTKVUbxKuyIrW9ZKVq31L+Bcb91nCK4gyMLUhgaEUzT2UVJx53viNobNutX2BIQ+CKFzV0XffTQB0b7Pv+K6tn1wlz3GQXbi40gdocZ1rlsmmau298lkx6Z95z7N0MFQDePjAUbul5ypOe6WRSN6ZaNPJ+q5DhsxNjNgWdAAvs3TcX2gRNUybX5zoBiZOHK1SRJNewUjZ6rC8dbyO4MvD+K71JH23bz2SzjMc4k9D1PjteGotzOrmScDqQ5m4otNpOcy/RosVbyoOVceGC3+ufMhiY1jqB8lxK3MG/tvv+jt33a7vvfwvAZxrlrhWel98L+wbGhNHJkFZVVeu6ll2gPgQOjBks4aPt6MRbYqj1E3OTUVvHARVfbVv77UykffLYjrbj1or3ZwMAF+P2CHpi4xzb42U8X0zBsxLLUuOYZ2YR3TretqWT+eR4eImDxspuHQO3dvnOJbaoffHFlFvHwL2LIb19sindFxjGLZmmedVipiDUOji+KriVR/jfBXQlLJPFp+3oeBBHnx+VgaB1EBKuXofyvvOszyct41eAOT6qOFIdPq82AF4KfSPMWUD5633f/wtTaU8K1DKszPxF+K2mvbuIwHhFV8/0JgbVCTHHsgPylgTCVF8q65R5zht5Pb9b0CfHWTAny9DbBbZCZHUxZF4KOT2r6wLArbyEb9VPbd8Fwz1sceHZXh2XlQhjn2CaT8essvQJ7CuwtwLTokHqr/cJGLvfbuUxXYW6C4elPPebcpbYjzkwpoPioHMiWZUrWcxSi59tpZt6KkjztrI8W2630oXP+PtRt9Z8Ydd1v51/uq77bQC+cEa5a4fEbMDYzVJTvdruQeAWEf3PPDrYTnAKWmC/3cDjGTrA+txjVwRuvUmW4/p4WAcsjzOS5nNrcC3Wi09Kt3i5xYYKQ8trPeuLvUus21a0rlvHw3a17VP5ptVHGqfJyS1EpIEL4YWkA/ttIm5JsA7derWw5/zNPrGvZyYIvRzr87FUa5ETXPnKaeRXbhE22I7PQupg+fUdE+AXw/86PkpHdbsX9pv1rESosi8e5vB+8LnPFe1LUsZKS/2vtDgP/+fAczPyfBuA7+u67n27/28B+P0z6z/AAQ5wgHcEzF5N7rruvbv8v3i9KLXhg0dH/ft2J1Aqia8axV0cWoVpvxUwjoukWIXWXbmV7lb77TRazttI7j7r8rZTOdXMrThQKkuo2taQQFpVdIuCccF0t6BeiqoXrJJOvMqKFhfdX3XF/bZrdZcTjTTOqHROK/fAMKyi45asSsWddd5DjtFC8utvd291L5+7fWn8nd8UJy2nq/kVjzt/Kg8zPc0Hr6MKXzkoXwHjGLaOUXKpvd40lj/2KDddd133R+0/AKDv+/90qux1QRWHUYJ4HCUxKzBcJACGgtCJz/Ie//CjYs4kSXA7I6a4FIq0FNTXPJ4XRT53IXUDtisNZ+qz28D6/nayLhbArc1+0nofGaPTDdgAsFwCZ7ttMow5nl0MBS9McD0Q9+6BbCzm6jxBNwMr7RlHxA4fHmFMi0fAfltMyqPj4OEUFTw+9l4Pvzf2YfvOw7Ayyq8Ex8nbVSHt9WooyYUbfycXPX173zw/MI7/VQLTFYvWO7WqzPKPuoDyR+Tv52O7yvwzfd8/FVf5Q0dH/c/J1pokODTdhRhCuj9PAffKsnIBRItG60hbQZSBXYCq1lerKsUqFfcqEF9tMfGg+rnVkyaJ0nstlhnrUJy0r6rx08QGgNVt4Oz+TrDtBNXZbWCzGZ6p9XZ0pVWf0ZrUGCnLJGtJlahOyKRkqpM7bi27QEm0SJaWK6NkwSkNFE9XhOn9LMqXTlfWcbYbj8oSrpSDClPFkfm0zWTYOK0VHHeHqYWax/pCqK7rPg/AX+z7/ndequBjgtbb8YA86RUqoaaa0DVPqjOZ5pXZ7ham4lC5sVpHy/LzdAUVzEmIeLtTGhwYC1CWv3U8tKLShmpgaMGdXWQ8PO+Di93WGmx/E07M0kx0Tf3WrTveJ2B4Sa3TvPIQklvogjbl4f9WGMX75BadC/G0QMh6VXGwruT9KFx2Jbh67v+ToK/ma8LP55n2ERjyPenT2md4lctd3w3gn75CuccCz2PMXARlCk93cE1KjaZ7sYAhkZ3ZgPFqGzDcakHh6szvboyXSTE47QtXWX1LgTNPJZRWx8P+s4y7xazz1MoTRzKcbmPhGNAiccZfXdQr2Os78va7O3urmKu1G+z2EO7cc+07V7cfWq5yuYTyhZ9pVmG3xNb9f0gnqUN5RcGVnFrvOkZcoda6VKGwLl6k4QKCdblidkWYBBfxc0v5nj1XyzSFB7w+xRvYX6JB/gSG/Kf0qOLaSbm5de3zUvmedHYlVhlChDlusp5EeReADwD4Y33fvzpR97UAXwgF5Nidu48u9PjbtydUJrkzWqV59Xmqw/N53rUtIADj/iXLjfl0cjGv0yLhxcno+/4SPbh4oYJuuQROXh8rCW8LAN64C7x4d5ju1oFOkAX2wujs/jhmRNCFFuJO608XZhIkhaA4a5+SovV+qPXlk5b167gmQZrieE4rF2T6nfitCoGwfeUV9ZL0uYdi0tyAlXParMTCd15jHFp5KcVXvT3HO3lN/P1I70Dpuu5L5O+vAfh7fd//WrPQNUJ6IZQKtsRUOrAJfBI7EyjzaDwkMTnzeN2Kw1qC85WGTJNI/ydG13OwGkPTGJXj5wym+Prikq5Q6mRk25rXhRowXJRgn3Rv6Or21tpbvz6khZ9aYd23rL6Hlt8d4NbrY8XnE39q8mj7wPjNfDpGGh5wAeEr0snS8pVej11q/yoehORPrq22r/FBDeMoPmzLIVnB/tIpfe4xa63H57B7TA7avzQHF8g0YJ5HFYav9X3/TVNpTwq4gNISQonw1KZVjMcJx+d+9jYxqgvcdH42CUjFwzWu16vpur0E8swnhVsAqQ+OnzOW46eTf2PlXVirhabWZ+W6Q8rS0lvfATabvcWwvr13YxXHV+8Cr9zd47qUNLVAaEUq/Vz5pDfqVeeqq7CMew78JOvW20+8sDYLV/Oe2v9kGSYloG3p2Lky1XyVdUycKutReQ+Gtwt3YIxTsjRhacqvBJ+rj7SA0nXdp/q+/2r5/xyAn+r7/subBa8J/DieW0gOqgFZJllKldZ2DZgErrqPbn1UAo11JLfX+5ZcmYSj5lVhpZcFKLjbtUGNT3XjcjV5k2BMFrhad66AuI9Q8Ty5vS/PlefV8dZdPz/fWpar+3XQn5aevoYzBfCTC6bWr1rcVT+AsbBxPgKGdKwW6xJOmsdXgt2q1HZUaLnw0jq8/0obL+cLay6Uq/mV+pJoBAzp4P1LvKgwRxg+VyHWdd13AfiPAHxB13W/xGQAvwrge6tyTwLSxNNnPhE2IS+fn0o5r9vTtA0dEF2ZdA2og5aYLrUHjC/oVIGVBDzT6aKoNXR+MQ7UUzPztwqGpGlTTFHbo4BxRvbJonRbAliJqUAX/8Huey14r3YWoa4eE49X1gu88tHNtu77wNmdbRyT7Tqd6O6qtaM4Kt6Q8to/t+aqCy00n9JNx11pv7oNbOzF8Wnia39SrLZSporHxp55/eks9RJDy9rDBucYusxuVaZ++Pj4HNI8iSZuVGgYxT2AFsyxDP9E3/ffNVHPEwONGQK1tvFgcWVdOYESUyUXlM+q+E7KmxhBXRJP43/uuatwTenabxWgwNAt0fKKn5dRGqwk0A1sBbeuDjO/TlQVqnT5dHFD6zzb3Vl4soshAsBms/349h26vcDQmtT+JK9AlQFpspB0taq5l5L0VCHhgX6lgdLO/yudnEasi/v8NM3r1Hr81IkLzdVuy5BbdhXPpzx+LZtDwqviNe97soirOdNy272M/r+qZfjhvu//FoAf6Lruq/153/efqsoe4AAHOMA7DVqXu35v3/ff3nXdj4THfd/3X3u9qGX44NFR/9JbbzVjD8A4hqBat3J7kxXXusLL3R8+1/8Kqgnd2vS6HEd1R1xrpsUMpqd+p3iVWgK0MjQv86m77DE9BbdK3DrnQojXf479Eb+NEHF1sd96cWLxuQVk3+FmGz9cvb4v+3DfopzxdRdTLUA/D5x2BCT3m7+Ta6k0AfK2GLfi1bWstj0pbqeW3xdpPFTRwttjhO5yu1WmIYaU3/ufPDV/e18Fak226L4I6Y+6gPL5fd//o6m0JwVHR0f9+996KwoMF1hpy4rnZboLKiDfxdYKcHu93mZqp3KjU1kKI2/XY6RpgjlT8hndwmp1U8Fder1oQScJsHVx1+Lae/yGOL95d/v/5bvDCfbGxxZ4dbV5KOAWC+Dlk/2KMdvg/kMA6O/uV48f4hH2GbLPvnC2vr13xR10nNIFEZUSdCHnylmFQorRaTnCmZyQSXi6AldBmPpE8LCIz6czUWDkGX3FrvOXC8fNrg5gX0+aT8D4vLILPhe4in9SRqT7o55A+dGZaU8E3o3agtJBIAEqIaFpypDKILRSNE0nvTK1CqalPNvIf9bP79Xx/l5CFPWRKU+Oh4yhbQwmyp097i4INX9LEKZ+JsHNBR5P1/+3dnjr4gPrX9/eCkEKQp1Qr662tTBmeHJ/m+/k9vCuQU6wBYBX19v23rwrOLjGxHC/oj5e3d9vn1E83ZJaX+x5A9ivNJ/IWFZB/bPbw3HR+v2kUjUWZ/eH+Dieyfq7Z3UpXwLDsUueBbC/1OKh8jgezjEV3L4ox8/J/e3Hrd0lhjjpCRKfF1UflG7O7wj5HFpu8hcB+BCAjwG4g+1KMgC8F8D39H3/4Ym6rwVoGRK0g+7SeB7XLiwDKYeQZ8AQdiu018vyScum1dgW87uVyr13KfCtFkrS6grJQlRcvc6En+L5UEjYmV4G7LkCCWxdWFp6J68P637jY9tV4VuywEI3vFrp5bOTnZt8cn9Ie82vFuPAWrkDvHhrezrGy+r55xRyQUgjPlUIIUGy4rRN7cccXtP8ntfnh/fB+cetWqVdAuXdqt6HPGMew1z+a1mHyXhhH1qvCm0Jw28B8K0AvgbAJ+XRLwP4b/q+/3gseM2gt9b4wDqBVINx6wMtobXEkVqrasB4EvkeK4Rn6jalQdP20hGzpLE5UVR7V3TQdIckVDSepP1T1yNtoK4mH+muZ5Yf3NnH8ta73yzjAtBpxmN5J+Ie6hiudvVRKNLd1clIvHw1mpa3rxorXSjgVBkpv5GmuufUQzULjE/16Mq2Hil060zH24WaW7IIaayHY5GUqeZXPiCopfVgt+JPvtV4n+JBeqyOh9enbYwWLvAdx2quKW3cCk9W/aOeQPndfd//YDPTEwQ9m6xaFBgPLgmxur23GFxT++sYWdYD3HqULGmvykpMQpAwd8Ddek1Wo7pGbkWp66TtuJan9eq39mjbwH4rjVoljhOwtbjOd4hwS8zDSbKbTKs7wz6fn+9eHCWLJfz/8mpvOQL7ybWSLSjAvuwbn1juEcDWzSZdlVYnFoPTfieeYnolqFwZe1nnAVeGPgZJsPpGf1dmqZ2puGFaNEsGQssydKXNtGTBwtIrw8FxqdYC0tzTMvfwGK7w6rrudwH4CmzvMwTw9C53PTo66j+6W0125qo0Axrpymz875aWD5hPfLdIuS8QUpcyawIfuJXtLaza0mfaT9eKaWJWdHBrChgG+MlYGq/RF2gRJ53EDy2jXb/8LkS/mPWNTyzx8kfOt66U3KunY3GyW/DgFV/6vdptvFYXXa2uM3Hhif/C6vd+VJNULRy3BBU2GE9ktTw1zMG6VWG6sPIFLMUX8s2TM8nVVb7QxRC1WGHliEuyfNUCTq+QdZokT8fx0z6rQZBCEJXFS7wf6YVQXdd9D4DfB+AVbOOGvxfAlzQLXSO8/11jqy0JJqarq+fCUevQiX1ueV14pEUDZeKzYqWP+RcYvjyKuPHqow2G10gBW7dEBWurH+fYvxhIhRXx1+A0pNz6zh4Xp5VayHpLtzKy02l1Z5v/1vEeH+2X9vnBxf7FSgDw6sk53vjEcnAWWfEgPBRCy73QO8fWZV4d71acL4Yryuvj4Q3Xbh2lK7Qe3Nm3v9zV8XCsLsaCRfuoeLvycVqowPe8vnC0kiOFyueQNLea9Gotjh35URdDVLjdszLEhbx0T8oQ75V4Ds7rBNaTaKZCUhV0shB17iYDoGWEKMxxk3+q7/uvlO/3APh43/f/6oz6HzvQTa6YLy2EEJyYTKu0uF/nD4yZSwfNNTIhmfP89pMNbNs1twsv7R8nQmt7jFs8jhPrX5vl5u62CwllPN0+o/FFjefpe4R5uwwwjMmxPcYp6Saf3R9eyKDvRFnLVh663lyoYZxyI3jpb7qdZ8GSUdq5pabjv7C8KkDUgnL6Jx7zcXKLtbXox/TE08znSrBlYSlOHL+1jSOQeVbrcY/G82of02tMXZk43VqeEufHBu0FlDlba35l9/2Zruu+GMBnAfzmGeWuFdg5fXUkMLRMdKIm15CwCGWAIdNpXnV5PU6zOh5qtGowgXFcyHFwfLWs46p4JsHl9S2FJkspy5VT3TajFqXSQYX6OfbCT8uf3N6G7Va00Ggh7tzYs9v7NGC/QfrW8TbmyI3SPKr3yt395a60KN+8uxV8q9s7up9v45Gbze5Y327by5ufWA7Oe1P4nWJ4DdjZnbEgYP998qmLm4SKjp3yhI8dMOQRbqUivyn/Jp7XMXTe1374uKnHoPynbbMOfSWsg3tWLMtLhLV/3lf+Jh19exPzsZwKW++z5gGGbn4aH4U5luF/DOBVAF8H4L/C9qLXP9v3/R9tFrwm8PsM3dJzTeeLDpUlCMmTNBlBLbi0P8/rr7RdsjaVad1yg+VJuGsftB1fyVO8PORQtaHneP0eQ72pRS2ehyusu/gczxzf2t0yA+zXN145W+KN1flDaw7YWYKf2KfruWTCrWPglV/o8XLXYYOtYHzjbFv2xFxs9k03V28wXIxYWSzRaQYMz2JXlqQLIU2rrDG1NN0i8wsH3JryMl6/CyRfUEmWqc4FLQsMeUzbdCtWlW9lgRKUH1VRsx51lythWMV2WV9rNfm5lKjQ9/0f2/38wa7r/idsF1E+PFXuAAc4wAHeSXDpF0IBQNd1/1ff97/pGvCZBL/PUC01aoKk9dzqg5SpNDfTtC43692VTRanl0sa2HGilqP74YsWKV6UrEOP3zhO1T2F2g6Q70RUd4j/9V0utOS4oENXl27yi+tX8OL7t2+PePNjC7z80c3Dth++P2WxX1XXFVdgvy+PW2vSS6OIF7C1GhlvXO3ilb7yTrpoyEHHl+DxOz5/cGf/GgTnpxRDTKEPxSG1rS4w46bpDY7JtVyGcr5y7O+Jdtc+WXtupSZ+cpxSPrd6HfzNk6xT8dKb5PmcuD7SanIBsbLJQl237Lru/+i6btN13X+4S/virut+uOu6H9otzjTh3btvd3s1hsc0j60QlMinVgYYu79qnns660vxGB/QhX1Ylh+PaywszV3xapJ6G+6yKPP5nkKtW8usLob/uegAw+XBxfaj21gW2LqlJ8d7Qbh6HXjl/a8+jHW9uBOEXPx4WM/97ZnjJXZu7+19zHC53G+y5pYaLvoonoQX7+6frV4fKhRgH+/VcdaYGoF0S6uU/j6YFMfSGKPzpgsOKixXlhvsx45HHZ2ndHeCGgq3jmXhSZQXV4b1js/karpgU9yZntxV0kVpQ970V0qk+Ub8iZfODcVrfTHc1kOe1r4meGKWYdd17wLwAMC/AuDvAvgJAN8I4JsBvIbtG/c+1Pf997Tq+eDRUf++nWVYWVg+ELpSqFYfy6bYiKYlqw5SXgfIrVRgzPRJyyowr+/VWlhZ18p+QN/xdAsyaVjNq/3RhSLHwSc+69K4DjdPr3Zxw1fvDo+/bbB9YdSr6+3/V1bb7zcfYHCXIesDgFf/4St4eWdZUpCen+/HOtFJvylA3XIF9hbumcXq3GqqXqTlgiOdMtJ8zmfOw5XH4WOnhkCKG6ZnbqGlXQeOb8JL+c5voFlgz8+KiysIx8X7yXamdo14PtZ7pRMoXdf9j9i/FW/wCMDX9n3/hbFgAV3X/fMA7vJ9y7ubtAHgGMD3YSsMf1Pf93+mVQ+FYWJ2SJpbQCkfkAmr1iIhBZqBoUZL5r2b/i48N5ZGqCy+1oplxSBpwqg166vepAGFBfumbpELV10sAfYnfhbYCz7dM8gTKCeS3zU3j9VxUq3kqB2w31Lz8kk+V0yoLn8lTXzSbLC1RgGguzu+vUjb8cWNalKrgtMVbeUPd1vPZOO9W4weMkkLCsoXrhSSoHSeS+eG9R0yaU75vKx4OilcKtDUh/SCeeKiQtcv+3X6XOkEStd1/1J8sIO+7/9q63mo7/cAWPZ9/wd2/78JwD8H4E9haxn+IoA7fd//cqueOfcZptUwrmi69lNN6ZrYBSrkf2KUKWtPGSTVW2l/ZQi3YgmcTMoArsHTSqzHVbTvvrqXlMMG+6udNF3r4P5BviqULu/q/t4CWyz2x+9e+YVX8eo/+cpDwffKal9Oz5WzDK3Ch0Ly/n7rDnEnbkvsrVMKYuKuJzVc0GsMUU/bqGVMqCwXnZjVaquvurYstmT5urWufXDLWNtTyzXh5sJVcdB+K95Mq/g6QcvKS/Vof12xA2P8Hvk43uOArut+L4DfacLwdt/3r8wo+xqArweA559//t2f/exnI5O4dZMI60LILTQVNvrNe9iA4c0sWhZWRiENpuKvAWzHh/V57LJqo2qnsiATLYCtdcT3HDtT+suR9KIDgrpAKznYfyYbu7nF5uT18bYYbrPR1wGcnw9drZNdHTznrBusE6gVRguNE0lvqFFwuquVXyknt0iU71amhKpXmWpa6odPeub1o5xura6s726hcYxUSehm+vRKWKcDFVeaU46v95NlE68rj7rx4cI+WdIXx8e4uLj4jJDu43zT55MUhtFN7vv+T1ymHneTgbZGctP8xNwUpms9rk0S07k1wGfVSpeCD6z/rlY4W1pY87ml4cJQwfP5NVxeNoUQVuFSVH1ZuMbm1E3VSxq4/1CfrV4fCitg+HY8jyOeY7ux+s31OVavj5Wl7jvkLTj6Jj2dXBxHX4BgXR7/Ux7yVVa39NytTcIvCd80jrSSNSaeYpPafqVMFZKS1P64VQqMXdlWO8niVByruejzRAW1z/ckeB/1ctfHBT8B4MWu635z13W/EcA3APiLl63ksxgyS2IMt9LUveCZSYX0XyeEM6cygVuF6+Npi1Hr0omuDJasvA2GzMOVOS1LDa/4sY5T+bCsu2ZqGWnbS+wvJoXVv74/tBaA3bEtOze8xF44ntwe3me4sWdrEYTAzlIERq8CIP14/vnVk3Osd/XqNh9geB55dX+4IZsrycnqcDcs0caFl/MQV4Q3qIUVeVUFrlpXi/DhymnClXWdyn8X9npCxAVTElTaZ3oz5Cc/w+yxcQVeDKt9Z3pS7u7JsC8r67uOi9LcZUWCJ2YZAkDXdScA/jSAdwH4vr7v//hl69D7DKtr0tV6SwsfyWI8L8qd2SqjtlGZ41VwemH5+NutPs3vrowKvOqGav2vAtY1fcJTmdf3dPlKZKIXgelvfGyBFz+6eWgR8l0la7E+tcwCe3eV7vWLt7arzFqOZVmfgt5wo21w0Ybt+MvsibOfXNF63LOo3DiPU/pk9jYh+YH9Ygow5E9g76om5Zp4PHkYFW8pTq54fQ4kpeuvTqiMAA01aAzWjzw6rfx3MoqSpcn0R7215i93XfeC/D/quu4vTZVL0Pf9Wd/3t/q+/y1XEYQAoM6+n6usBIre8gXvMgAAIABJREFUmuFCAtgPrA4U85/cH7bh9btlqmWXlr8SHjaX460pZJzEXLqirVaKWic+4ZhfrQYCcaxeKMS+qfBi2nq3F5CT7ZWdIFwud7G6i/3L37Xfr97dnSFmrBC7Vwacb+8hPLvYCj2ljbrV6/vbete7zddqPRBWr+9fQ6BAGvGGndX9IU+9eXdPnwcWZ1TFcYrh+KgV7Z6C0pXtkyYPb/E5HivqDba08D207kpDnjvvtPKxfRf2pDukHuUF1r8SL6HVFutR3vbYrPPw6nhYF1/J4bh6HYpzC+acTf7f+r7/qqm0JwV+7X9L09KSSVrDhYcKvIeumexPZH7fP+UBaBeQyTR3weireCpQ3cRPMSEf8Eox6H8XCNq2vvgnaWYy74kshADjs8OkhW6pcQudfeE7grmZmpe8Kj38jPV6t4K82WzPNr/ykfOH7RJ3jVFqSMAtId8+w2dncqIEVgYYruCTjpXVonTX/LrgkawcH1O37nRngwopQvJ6ksBRcAGlAtAtSLbhBxjU8vN2K+G0wXBxy6G/u93upDjpggvkOxkcjxoz/PWu6x5usO667kuQ9x8e4AAHOMA7FuYIw+8G8L92XffabovLXwPwXRNlrg2e331rkJigGoFaQS+e1DyQdLUkFthfBaVXCa2P9xpLXQrVqv4GMm1L3dcF9m4HXTpYGXe5CH7tV+UKqdXKY3Du7lf94OW0up1I+0B3hVbh2e4YnC5CkK68pZrtbXb0VSt8iX1c9sHFfmFFLcs3745Pltx6fecaXwDdzipUF5kupcKrn1g+dAV18Wmwon68x+vW7pJY9SAW2Af60x2IiSfOMfYACA/jo+LC++W8zktqWa4vhnRXHNR6U15yr4Y00cU1jd95uMX7wDrd+lVrMIWNmA8YvlqD+flh/u7umB7pzX8+B6dcZGD+tf/vB/ARbE+f/Fjf9/9wRt3XAuomJxe5uvRRXQRguICh3y2XROu5F9LoKviqmn7rC8p9ErEMd9VrjK5y74FxTEmForvcBHWz3KWG9IFbUM7uD3HQK+cZTjgJLukm5Fem5QTgi995QcMrq+Gew9X9/bYfnnLZbPZ1cpO100pXlFVwkU4+ztXWKI4tsH+lgNJMJ/QDD63I/kUdP2A4JhqLTLe5E7zsEvtLK1zIOTjPaBrBQwLMz7acj7xcUuLKy8yTwjmwcj4vHQfHJeGm5a76drwP933/t7qu++r0vO/7T8WC1wz+QijVfErgJGgSkbUeWBq/06qrPkv/nZHSQHscx2OGwFi4ax6fsKnuhf1WHNJ/nUR+1JBpGhdqTQ4XrCei+dcSJ2Ne7hnjKrIKR65EA0OhqkKWt9c4H/j46IrxiQhLvb5/Y2WYdiZv9VM6EHx86Gn4C6cg+ZiW4pmpjCtkpYG2XQkj/XY+AYa0q8YWyHOmmhPaR2B8b6HimEBpo8pe8U0LKYrnAlcXht/b9/23d133I+Fx3/f91xZ4Xyt86dFR//zEpuuHbpTt9Cek1TJgvHVEwZlRmYVCg4HwVE4HU/8nAUjBkQLzdOP0Oi1l2iQsfWI47fzkC8u4UE7Bc83PkwvAcMFEae3vnR4Iw50wu4dtoPyNs/GJEBcUXPBRy9UtYsLJbeDl9RLdR84HrqOfdnCc5ywOKH4rseydN5239KwvIXkVaaHNrUBXrC6EtJ+68JIEGMEFptbrJ2mU9/ytk5WVWvFmpeA9D+ce20yvKwX2PP5jj/iq0M/v+/4fTaU9KdB3oCTN5JOUULknzhgrY8ykpd3aYFpyL5ImS26Q46jaLg2uC6Kqv8D+TXCsp3KN3SVM7r5r70q403V2xtb3JeseQV7soNYex8X3J6o1p+eTU9/0vSrVjdG6Guv9UwFGmuhJC1VczO+Wm45j4i/WzUnt/AL77+6m43lmVq9vddF6XCmxTuLvp0r8lm/nfx9vD005bb2OZLE6rbQvyUBInhXhkc4md133qb7vv3oq7UmBCkOgdg0hz90VSIzlguAcw4Fnft8mUFlkSaO5xaLatDqVoJcBnFuZ5N4uLE/SssmqVgsxMWMlbGktsH29+krrUZp67JCgsUeeN1ZXEBief15IfWzbLWbttws2pZkKrJOAo7q9aoWxXkK19WRjz5xfkpdQWfY6Ni0XnXC2u8hWb8XxvqU4nFqUwHieOP8nC89xTXOz6l8SlMlAcHz8mcJVr/D6IgAfAvAxAHewv9D1vQC+p+/7D8eC1ww8m6wCSZmnsuRgeZRxkmvsq2kOLWsxDWLCywd1bYIktZUsIO9fsj6c2VWQOA35vcEYjw12x/I2QzdIx4N53YLhRazcdK3tqVWgixDMR7fZT6CwT2/c3b8knulc0EltEVyAVv1OgieFWlinrny7sJmKjd0L+Vyou+XmeZP1794Ln/MthSqoPaQDjOeBC8O0/9WV8doW5JLx4GX5nzi4AE5KD5aPNLmqMPwWAN8K4GuwPVfMCn4JwPf3ff/xWPCagcfxpgQEkIUeCaTB/DToiYmZRxcEfFO2Mhn/38MYL60vaXnm16NXiaE1v1sXzmRrcZvUYvPJlxjLY1Mu+NMEJP5cMFndH9LzxNxTrqL7BbknIhy1fr0Jx11dFfxKE1VyXMRxq5VxSGC48EF6JAsblub51A1WSBfDpssw9LnyO/OnHRTJKtU2FD/nE3/pvC/utBQ829a7BoHhuCQlqzzmytXnTLJMK8HKNpZ49Jjh7+77/gebmZ4gtN6Op9oxXQbJPC5QlPgqHPx+QOZPQXPIc2CopT1fmgDEjWVVWAH1diHiqW6pt+eWjbqISbASl9SW1q3aXu+KZHnG4/yqLGX4WyKgH+yEG7A/aqfvOdF3qAB7IXkrWHd6VM9BJ2RajHB3VkMjCpVlznHzBRe3GAlucarCcaEKS/e6kpWvZT22y/ZdUFZbe1QQOn9oHj8/7eBWo+LhOAPjenz+K4+ncAPLPuo7UF4KZ5P/sxnlrh1UQCkhyMDK4MwHK0NQgcjn/kJzJ666EqwjmfuEs9u1e8X0FLynkAaGt+IQ9EC/9ouMq22QNh5/1HLJgtH/uml6AzxcBGF7WqdPfmVgCrozWiHn+/76QsfDG7JlI/iZCEviv7q93VjN24kobIH9ueQ37g43ons/H05m2QStEw6StsTwnDDzufLaIN9w7WPkbh/xhNDW8Vb+TrzllpnWkcIBLoj1v/LkwEqXjeobDC1C1u10UqWkeCQhrzRSXHVMVKi7seM0SXDVs8lPbQHlQ0dH/de/9dZopQ7IboUzTssyBMbMWGlnoG2ROS6sx11Sx0dxdK1bxUh05VfpAQwZpbJmPc3rZvnEVBsMN5KzbV+dV/eq5c6kQLwLIn6TB5xeGkOrLLBTDG+nOTEXmadm2G/2R93stOjgStVXOb1/DokvnZ9IE7fwXaEpuNWULDsXOAPFUJzH1/rP7I2DrNctwJSuVjMwtsTdhfZ4fhJ6PheAR7cM39V13efxT9d1XwDg8xr5D3CAAxzgHQdzhOHHAPyVruu+reu63w/gLwP4/utFq4bPIJvDwFjb+dVWwDCO6G6dazqFpH3pnp5avmQVnqN9TE9NfraVXGb+XspH87nrTlAX2t1j4kE3nq6oukRLDC/eZBt67x5jhLQUl9hfpqsLCE57HQNI/avb+3wLbK23c8irQo1+59guqihfsE2NWZJmelplJdYrN36rFU/L6J7VwTa0T4oT63U30OlIUFfS+ejkeLjHsuId4qxWo84ZvY4LGF7wCqHXw+cWe9V2eZ4Z2B5R5Di4FekWqV6ZRzoly1VxcmtT+860qdeBtmDu2eSXAXwdtivK/3Pf91e6z/BxgF/77wKsig/ogOj7O6o4grvG7k64y3siroQHpx0nnwAsrxt3VSh6H93t0Ml163i46FO5jMnVAfYukbbnbTjDO27Mr5OBk0uvB+OGagf2QS8KdfeUoHisw7E37SNdYy72pLBBFQrQvL55W/N7mz4OijcwFKosr+Or+ao6deHPBbX3adOoi33QBTEd8/SObacB+762OaahBqZpm/yvr/V1N1dDBum55gPGtNvgGXkh1OMCXtSQJqTHlAhTS+4ez1lIuZTfBYCuOKr1oLicW15tOwnLlJbeRQuMmR0YWn6+cv3A3hznAtM1u/dd42taLl3eoJOP+KYbupUWysSJ4ZWm6Tgb22K7tKb0RIwKaxdMSRBqugsCH1OnKcswbxpfb0t5Kb1zOd2/qOV9Rdjbd0+J7RGPRIfWcTdX4At75nPPeZbfugskrWpDnqlAdyGpbeg4POoJlI8AeBXAlwH4jdhe2f/pvu/f2yx4TeCbrhVUk1WLE8CYsEBe5VUCu3urbTjzqeb1YK9/+xlObVfxVffSA/EqjDWf91EtJO+ru+C+2uyWjTN+WsTyCcjN1BouSJYkQYUz8X717v4aJ8dNcdR9pATm0VMy/nrQRVE+WVdKaz7342r6P1npzrNKE8j/lgfEsqp4WkIhWcTavp71TkLMrT7tB9txnnNrzfunOGibWi4JVZ9PXp8rvUcVhp/E9uVNP4DtBuxvBrDo+/67mwWvCWgZ+ikLYFojVcJNGdJdIB9MT68Gxy0zdznUOkm4Jvzc5XRG8X66tbW056zbb6dhXWfFVVXu4iT8FfwIHifUmVkZfomDC+WHgkoug9B+aP8rvKpxdwGl+f14nn5Xiqeih1u6mi9ZuVonN4L7ZSCKq/cLGI5ZUiAE3dNK3JLS91ip/tb+8y5LFfJKkzM7+eJ0T8oxCcdkBGkZ9u0cVzyB8jBD132y7/uv6brup/q+/8pd2o/2ff/bmgWvCY6OjvqP7rbW6HYHYLwNgqAmt2tygluRyWLUfJB63E1SSBOzsgA1X1VWLU236rQPvuncwRmNLplraT8NohbeueVz+qmQ1frVgqn6n+jJvvv7fXkVmOKlFtlKhKdbTT4WGk9mm8QnxaxOpB3Yc7cgtQ/aV+e1lrVDcKu7Evie163ZJMj5W/vrYSRXyK7YkyCu2koWrNfpdPStOC1eUpo+qmX41wD8DgB/DsD/A+DnAXxr3/f/bLPgNcHR0VH/kbfeampyd/mS1eAEd6arrBx97lrKJwHs/xt39y9lT4yY6k4MQUhXb/kETIJfhaWnpwnAZ46rTmyPa1VKwye5uqMq3F0oa72az91S7Z8LVL1kteUBOM21H0pXt/pZj4cwILik69cUNtjGdG+9vm9TFSDTfDw0hug01nzKGykc4jgloay/U+zRQ1TJRXa6erstTyPh4opE8VPj4Ur3GT7MsH3nyd/H9sb97wTwPgD/dd/3Fa7XCn4czxnPCUJGAeoVPIJuEAbGAo5pBDXPWb9rXNdMbmm0YiHJMlDLRp9xFVgtQoJbytonF4oJD4UUd9KJqoyvL2NivqkYpAoYbY/90AB+oonGT91iSmPiVk6irVq+PtF9Iiu9dHW7oqsrCa/Pv4mnP9O+OQ3TGGp9Vbw39W1R5Pd8aRy0f/xOhyf0ub4KQIUt5Hf1Mi/vy5RlOLnPsO/7v9P3/a/0ff9Lfd//J33f/+GnJQgVnHE2GL5vQgeAR/Mg/72eDcbH2lygqiXmcQmd3PruEB1gfQk7n6mQJLjw1j4RWIbMxnCBW46LXb+4l4xWEcFftwrs6ar7J13LbqSsv4GQ+dcX40npv8/lA3nOvi0glyZY3FEnmL/TxL/1Q0iLGR4bTh6ICl6feKwX2Mf2dKzWx9v+uBLVfiXlkuJgS/mw/DmGRxAdL407q3D3fp8Wv8k7LsBd6XifgOEYOD+zTrU2ydfkbVWYqtB03qfxcAOlgtatNT+NxlvwGD980sAFFCcwMNQSlcZ2hleiunumZn4VBF7Yb7c4XTO621Atgmjf/OiRT+IkZNzqU2ugsjY8LpQsK157f47hMTw+d9c0WVbA2IJNMU6WdYsQ9lzpqtal5lPaAMP9lDqhk5ZPlk5lXSqf6eT3eLXng/XF8yXl7/2vLDuvU3k6WblT1l/Kl15PkbyYpdXjrrO2o98+7/wiDJZPY6hK8kq31uzc4xL6vv87refXBdxaAwxjHgSPtU0xoBPPN4b6YLINLUfB46twwJ7hVFBUVy5ByreYMV3S6Qyu9bj7zz7oJGWaC159JzIwPqfqfU1KSK/Gql6roNbWSdj0q/3zcfQ+6wu1tC8+sZLFByvjedWycr6A5dGYKtNVYQP7TehJ8VaKif+5ad2F49oWF7RvKX6nY6j5E98nhaD16Dh6PFL74AuaxEnzOv1Yzud8ilezPufTVszwuZQIPD1hd4ADHOAATwPekZuuee1/sgyBvWZKJnPSsgTXzP7c06jJ1A10a4v1EtKChV/mmQLE7qZ7/R5LSZe2Kv4LK6f0UjdHwS21ZMkkq4+46gWvaRVW++f1pbrdovFQw6JIq6wtd/u03rUd43OrtHLLq9CN5/HV1VZ4w0MKPpY6ppC6knWdwHlJ60p7ELXPOnbqDSHkS7+T9Z/wI6T+JJ4gXVr7DOdc1HAPwDcCeBPAFwD4A9gKx6cGKjRWx+NnHITkgqrp7qtywJioG/swvwopbS8JwiQYFxji6gydcPZB3liZlQW3tT9kTrbjTKJMnu6d4311Ltg3Vjc/GtwnflxQUCGtZVSh+MIBA/daN9N9bCC/na6cvB6rcnfq3PIQd1dGU0LF22IZFzYqVFwQOo+74KuEGy+bcKNAx0Rx8nwe0ybPEwddfNEFL+3zqX1rex560PnjPArLz/9Tgl3rSoJVoXSTFfq+33Rd966+7/8/AH++67ofnVPuOoGE8AB+0tZKNJ3kwHhyM78/87iHCsKWFksWlrbvzANLbwlWB7/OP1lg2r5bgCdhz57Wzf7AnpO+bgVou2lxICmhRM8F8p5AYLjlQsc04entLTE+8eEXVWhcKykstda8n0loJqtb6aKWJPmY49p6YResXuX/DfaxcFeCiWfTOLE+V/aEZKlWsU9vRyEtbvK3W6pKy9RvWN4pmGMZfqbrut8I4Ce7rvuTXdd9J4AvnFHuWkEZ3yeQ/1emWdstzQqqSSFlSGC6S8B2ErnVqAxLUEvUNdkmPE+Tzi1CWgraB7fKUmA75VeNSSGgNOCWIhVqqQ+OQxJMLpApsMms96RvWhfBt3+sjnM7qgSoGFzRsS+8aoy05n+1dFQ4vHk3L6ApKO2cl3wbktKKwi/thkgWr9ah/VV687O6GLdLXnGrWPHaYLvIc17kdQua4GNFvJbFM/KYClUdu8oISIs0U3kqmLvp+u9hGy986puuP3h01P++3QkUYKg9gHwsLFluTlwdAB1oYGx1KNOoJtctIG4R6Uoc06tVOcWROOmb7IDsjmv8RC3kaqDcEtH+p5irC3NtN1lnBL/tmnX52Lg1s7A6/BUCC8MbGK9Geh+0Hh1LHwMXmm7d+HVhHlNzz8THw5U3f28wbssFqrapEz21p/8JWp/zPfvruyOcbsDwwgbHKb3nR93odDQ28YPzbsWD+tzppfU+0tnkZw3S1hqCEsXfi1tp1iT8IM/ThKomcWsBgVpOBSywZzi9Vss1nfdBd9wDYwHoQm3u2/kSU7bKqPD1QHplmXtbSbDyuU+qCjfWo3g4vsAwBgaML4Zo4cc0Dz0kK21j+dXyIz4qFBRPVXxJUQF560y1iKO0qOjudFHhla7Xd14DhrRUSAIc2J8x99jtLROw/tqJan+qC3L+1v4scMUFlK7r/o2u675D/v9413X/5+7ze6py1w2fxZC5VJj5jdPVxIGUodmuBFXGUJcRGNfngtbrT+mc6MoIt17PgkPLuavNm4+13+vjsTDViamuH5+7Ja2g1gpxYHoVP/Ky6YQKMKQzJJ+OK4WFjjP7oadqWA7WP1VmPnHU0k5jz/9uiW4sv/MRpA86zkpzDTc4r6wktqdjSWDsj0CeUCGmbjOBZTS8QNxPbJGG4SRVlir4XOmvgtXOb+Ut9ltpdXZn34e1eFbpHS8+J3R+8tsXjjQU04LWpuu/DuAb+r7/v3f/fxLb266/EMCf7/v+6ybqvhagm5wuUE3mcqXBIWX0ud65t7B6gPolPC5Mq0sLvG3FVXHX72pieBkPjkPKpFU3n+Ro5DnH9EkQBT9xonndpQOGloVa2qlut5qd3tovtzy03MKepTPNLSuRdbllqG3Anrtg8XpcAPHbrbe0IFUpYM3PtqpFDQLp4duCPNzjgihZ2G61az5Ymns5io/2gc/Vnaeyc49J+3fVl8j/RN/3v1X+3+v7/nT3+xN9338kFrxmUDdZJw6QrSEnhlsqzozqogBZMAH1BalJMPuxNZ3ACX8OsjKYWksV8LnH3liHWg7aF5+s3j8t720oPZMQ8RiZK5cKNF81cV3QJ6Ho/W4F+926ZJri43X7hFOhoRZQojFCuveT/fJ+L7C9mky3/CieKkxcCDoOrmRdoNByTK861f9pjJJy0W+nX5Xf50uLn1w5KR9d6daarus2fd/Hudd13d/u+/63xILXDLy1RhmEK5APLtpCAI00Agfej5xVQsUFVLJU0qZp5k3a3LW+W55Jy6a0JbYvSFq9Pnyu+NMqVCZzZgbGzJYETBJ63p5bZW5JqEXoFnjqu1sGlSAA9uPqC106WYAsDHT8PdaYBKUrNOcjF3ZK+3R80vtSKUYVMqk/HG+PvSW+5u8WfySlkSzQJJgXVqYV29Z6lUfSWXjnzQ22K+Kr+1e3DP8CgP+l7/s/a+n/NoB/ue/7b4wFrxn0PkNgOHAuXJKLpvnQ+F9p9spSqfK7+5fcn8TULQHCuvQuwJZW1nv8tG7FaWV4Au0FJa1HfyfLWPvLOqqJpek8t6u4phtdPOjuLxXy/rqgYNv6Aqo0EV0Qp76xzkQHhHRdjPJ3lri15vi4JekhFBd0bhkvLd3zt5QvMKwvzQfirdattuGKxvtSGSya139X3gHxaAnD5xrtfSeA/6HrujsAPrVLewnbdyb/m41yBzjAAQ7wjoM5+wy/FsBX7P7+zb7vf/jasWrAh46O+nfvYoZ6yzHQ1tpudSTL0C02QnUrhp7YmHJjqeX8BVAJd9d4qi39RATzJes4uWSOn1tnKVao9Sa3sbLykrVaPeMNLL7S6HhUuAG11TfVN33HSbLUW+5/smA0f3r1qlvvTlOWe3CxvcHn5H626rQtX8BTS49pzgfAGHfNf8te76D5kyegrmsad++j98ktU71BqrIEk5vfenajXhXq700GhhPF4xDJ/E/uhE8AnfisW9tSNwUYuxxaj18xr2VTOXepqwnqe7DUFWF9rVXdyoVMjKzgoQB3gRxP5uFE8fgVgcLKBYjjq+Pt15mxTVc2rEMXfc4tXfN5u8pH3t+kHLyuSgi4O5mAgsEF9gZjmrj7nOpK4+sLdhsMY6zcYeH9dfyrflWr0P5b56jjmUIq1cIP86tiuPICyrMKfCFUWhWsNJELrkqL6KCxvDOpW1XJwqw0F/+7JaPPtR4v77ERj5u6VVXhorhXge00kZQBK8uyyu9CF8jW9CKkaf98UcJPRrhg8nGHlNWJSQHpK/xaT7ICk9JMtG3VDwz77YrJBezG0qd4x/FmObXGNb8vrlTWr8at9ew6x0nxOccQN8WF+ZL154ZOor8KWu8nyxKvG2UZfunRUf+8WYYE13qJuRwSI7W0K5laJ58G8KvA75RLUAkB5uVnbVsctH6t1yeU9xUYMokv1CRrr5p8Wrf/ryxox9sXnzwPXa8pQUw6Vq5asmYTzklJeB2Kg09a56FKoHp/3SMA8oWm2rbSMJ1YqeaJCx7lHeKi/fOLOHyBy/tXWcP877goLZKr7zzluFa8qO3cqON4Hzo66r9yd59hxfB60kAZSyeTEk7TkgDVgVRt6vlVSPqLbIC8Cg1JW4mW1QmQLFSfBOzLwtK03Qrc1WhZHMny8PPgwN7VTVZhwt0FoY5PEhZ8diJ77Zim9SXBmya/CzDe8O3noYFxrFjpxz453j6pfZxa1mdSytqnSohUIR4fS2/XbzP3smxX/yvOHmNnea/PhX5lFbIPzn8rs2xVEWq7Sosb5SZ/6Oio/7ndAkqltRMxCZVl07pOPDE723ILYIMsXJjOYL2XS5YgMBag7j6xDQpjdxeSZlXBXeHogozldQ8m63LLh+keeE+v76wsRq17g+H1+A5qzfp4tWKbLvSTR6A0AfaxM7eoNX+yiCpBzHYdXx9zFRouUFI9sHQdU9bVKut9cCvX59FK4uLKD8kb0/b023lX++x0TgqH+W6JIeK43SjL8INHR/1L8t5k1xQeuyAwr59PVUhCNa0GuobzuJhbBO7a6YDqICerbg6zJ0HjloFOaL+cQOtf7y6McGGV6qsWHfhftbRbdwS3uDmB/NWcSj/Fw4WFCxaHatGpmsC+oul9deGm70V2pZb4jn1RL6f1cixg2J7HId2jYF91jjh+6Rib0sYVkAsg7UfVXuK3lSzQVMc8lQ99zlchh2QkEVrCEH3fP5EPgA8D+DEA/xjAf2DPvgHbvYx/aKqeF154oV9u39rXL+R7YWkAHuY7lXT+Xh9vPyyzlGfLUN8i1L2Q36eSZyn5HtzZt6d18/vU6mea16/1Oi6QcsynOJyGvCjqUjz1udJQ63bcTg2P9XE7v4+d0z2NxdLST60s22RfqraXGNJd+aH6OB3Pbo/zsH0t4zSoPgmPhJPn1XHWtryfyhver1S/88spxrys+fjMaaW4+RzwOei4Oz97H53uPkaKz0svvdRXsuW5KCGvBy4ArJA3bH8DgN8K4C90Xfeevu/frip5HjkOBAzjPX7FETUKNbBudQHGVkSl+ZKlcyLWo2vNW7truR6a7OHF4q6BPd5SBZ4dX82n2rm6XcZDA7p9Y4GxBZRiTd5ftSQX2IcEWJfGJIFhG+5S0u1VTa/jvcD45MwCYsmH67mA7Rhs7o8XbdYX47xL+60W2wbAmZ0N1vPCvpjB8rqvkTTRPp2ZJep9J/2W0le2pUcStS/VuC3sQ3ySJUda3AuWL0FtHymaAAAgAElEQVSPORLPildSvDXdlq7tn9g2H83DMIBbuN5uBU/cTe667i6At/u+/y8k7YcAfD2AjwH49r7vf7kq/+L7j3r8wj5mWK0Q62B5nE2X/j2u5UygE0D/O6PxmW+dWFgexUvbcQFBxqvcxCTgdNKl7UHJzUuT3xeY9NtXOBUX7YMKkfVOQKgLSUihhWqiJYGuW2ucLk77Vrw4XTZK8Fik8lQrxFH1y907589b5so6OO/oi7Z01T25kB4SSIaFu9saKlAcUmhI/1duNySPj4mmKT+dyQu5tH43AviMOCovnOMZ21pTCMNvAfCHAHys7/v/slWeFzUAe0uAoISpGNutCxLM4w18lrSsflcMpgLSFzhUkCRh4BZusqJYHzAWmF6f569WYLWPZ7vYYbKQtE5lSp9cxKH1djjNm2Jqmp5uFGL92v7SnhN8IYP9cgGa8unvpGzTfjtts7JMnHZJ2RIcR6X9AtlrUXzdeHBau8Il33LxyhfW0vzyGLvPNf+dLNjkCWm5ZCVX9fncfNS341079H3//X3ff1UlCLuue63ruk93XffpT3/6008avQMc4AA3BH72Z38WlCW7z2t8dq2W4e6m7D+4+3vS9/3PJcvwMqDvTU6mOcJ/pqlGU1DXLx05ckg3vLh76qa/anNaC9TmravMfeuH4tvqr1uabvWeY7xnUo/DpZtb9LdbcUspC9RXZPlvLQ/kK5nUWvDx83qT26953RJv7ZFUSJZji2887DEHJ42Z+tilsUj8eYrheWZtZ4Hx9fysa3U8vnNTLX8flxSqqTyiFN5QK5h407NL709p3a/J9oDxfPEQ0TPvJl8GuOk6xRUc3NzXmI0LFnVPPLAO7Impiwwby8+0NCm1DcWtGlAyZ3WuGRhPIj53GrANusmtEwo6YRTotq8vhvu22La/IMnjNUqntFk+CTTW42GFSlAo/RS3s90bDVf27mPtcxoX1nUK4NW7QHd3KOC1XHrvxim28T917ZOSdby9/+n32oRa4oFKUCjd0sW8kHSW84MMSaG5ouHvFD5y/JJRkxS6pqe5lep/5oRh13VfBOCTAN4L4NcBvA3gy/u+/6XL1MPjeD6oCL8JSlDf01QNiNbRIrpqHwqSpGH1TWKOu1usSdgB4/4qaF2+Ipc2j2vMif9d8PDWErbtK3mJAbXflUXjikPxZx8Yg0uLIyt7RrzdUkmeQ1r40N/VyrsqT49xqSAirprXLZSEo1vtwBA/BcXXwXkHGO9FdOtO87Dd1CcXfJXQTh7BnIUl/k79cYWavIU0L5zXnglh+LiACyiVq+ADAYyPBykjJcvQLQW3PskY67DC5YNSaTtnaG8jMVvL3aL2TkIvvWGO4ELaGU7zphVynSiuvXXi+fE8vn+aCzkeKPcFgjQ2vsiRhJ6n+3hUk1qtaHelqxV3rc+VpD5zd6+yDNOYaz8cHyqKtEk88bRf5d9y33V8vV5tS4WfjkHygpQeSQi26mf7Og5p0cuNg5YwfCYWUB4FFvJxYpxiT4xTjLWvMkeaODyf6lqNedQtZBlgzDzKvBpX08Gs3t2h/WPdBL4dj33lu5vZjrr9CZbY7wP01U/FzfuywLwr9pmm7bBfJ/f3h/y132qhEh/2b2GflbzcHBjTGoY36a40XEp/F9i71FqHj006Urg63tev7bIPjo9Ciq2dy3fiIaWDWp0PLKSyCPXw7Xh8C5/eMKN4aDhJhRBp5nyWaK54cFy9PbfenHZpdVnHh21s7BnnleLSgnecZfilR0f9n7n1Fk7ujy0XX/JPJnq1BcW1sVs+SWu7+6rPqwCwWzbJxG9ZgZVLoMypVg3TdaJqmEDdzzhxJN5GZlba+nYOb5vCgzEut6qmIF1YQSXFfviRL7c2dHHGx4nWkdNVy2hoxcfGXTatGxjyTeITr88tbeLiQkvDMcnCmuIhVfppO4zj7VYex1UVkb9dMAmgap4p7ymPJKuZfJbac3mgZU8B/NhNcpP1bLJDcgfVhajcjgSMW3HBQC/WrC5ESMIxxRPTwPmihN+srUySXuaTJhufu+YmVDc/O2Nr/3TiJXprHc7A2oaDa3mtI00gGD5uvSq4sGE+FXisSz0JLeP9q3DW9CSYHFzxah2tMm6hKq+nhSQXrpA0f+4vzHJDgLzsq9KujNLCTYqnan/9RiBCMmKcP5gv7ehg/c/E2eTH9XnhhRfi+VTYWUQ0ni0sDRieb9XnqQ2W03PFS/vtdS0xPN+7wPgs9LJRdmltEOdT1H1dSjtLjPukZdLZUa8PGPc50dP7cmpt+/cS+zO+qW3iz/PkCwzpl8Y+jZ3zheLvOGgd+kn91fPBPq5nt/fPl6FdP1ftz5QOfp4+jb3Tdy6P+/xwnvb/1RliPx/t/KA8e2r16Nlix8X7oW3pWXDvo5dvnU1+x1mGeoUXMIxrAOOjYUm7qgb286PAWNNWVoRqOlh7rgHPMbRkYGmOH/ugVklaMVXgqRHFj0fhtC4/XuUuC0E1d7q2imXd+gbG1oZbAAt7rm0mPFjWFwgcl3QeWPsJ7N3CtJ9tIXmA/QLDwtr2Ot0KUZqkfut9l9qHZE07+Ph7v7VNxcd5j1t/SIu0xzOFdFKMeIMxX7GMnt1XtzqtMFer7i0edcs6WeykwY26z/Do6Kh/v7xE3gkG5JgHIZ2zZJkN6oF210Inm7so1TuT3Y1Q/DxfcgHTqrj225nY3VjtQ+V+VnWmPC4EPV7TEnTu0t7D/vxpC19XPhXuzgNan7tbSivviws1lllgv4fQx7OlGJy+Ka7F/qbYr7YBzHtpErBXimwjuZJOb8Vd23V3HFLOBZHTxo0ESBni7HcjIpTxNpJidVyWuGExQz2bTFBt48KK6cCYISHpCGX0dxVfIiSBx7IaH6kmsOPjG3aTtZosWcVH8yZLw+vQ/854G2yFVbVwlZSItuETKYELBsVJy2k+Kh/HVSe8rkwTF5ZTAaBlXTFpHk13nnJ8WF+ypFP9KhD8Hs2qbRfYbpW7Bc20Mzup4s+TJ5IULzA9tszbml+b4jnb1Tmkz6uTYzouLNsShu+4rTXP777dOtEl/kRISH53nZI1xA/dcBeyHBR+kkZ0S+gehpOjEoise2UrpMQxMbZrYReEThtlGH4nRlarlBNnif1WF+2LMrTX79bBQurxbTTMp2k68e9hz/zVzdku6PhZH2/zqlLj+K1v758Rlti6eWkF3K0qFwZTSlkFmY7hBvtFhLPb+35w+45asaqYFb9qgUJpf3Z/LPg3GPK8p6e+8z8/3NKmRopbkQQfA+ZNVrrOFxf8+txxZPrJ7QKJHbzjhOEBDnCAA1wHvOPcZF7UoMHf5PaqhklWWzL3PZiu2kyvzHdLkWV1y0s6q1pZd67tPH5Ea6UVH3FLUOt1rQ4MFxmcJk4bbSPFr9SaShaD4qKxLgbuiY9aJO4K0aVjfpZJ14M5bRWXlkWOkK44q0UGDK2zFKNOXgdB+cO9jYfWob0TOm1xSdtN3LrVZ4qLzo+KZsyTFpsUF8gzd011TtwLz9xF5nOvy/nb+c7Hzi1z4IYdx/vQ0VH/7t1xvLSySSARUrC4Gkx9K5jfTOMMAoxjW7py7Hj4f3cHKmHsE8BdUqAWgu4aJyZSvJKwSnmSMNY83gc908y78VTAsxwnnbqRlcDy9vjtL6zSPaI6OXRc3Z1Kq5xJYSWlqgtovgKriyOJh5KgJg+nRZ+l/FY8fIHD+8Qx8bshndbOV77o4XFcve/Q49wabmFdHrdNcWrnJx2rE1Gom1A+zfcb90Ko9731VrxmieATe4qJOUjKqNVqtJf150kbcZXUJ75abOcYnqzwCZD6V1ly3ne3dH2y+iRcWLpCit+sb2/jT87YfkWYL/CkuqsgvE5wH49kMaQ2VQn4BKQl5ud6HXxsWZfzgMby9ASOCjJtZ2NpSgvP60rNBYBb4elZ6ofPEbUyKWS1PV8IgqQ7n1TeBvvpSorP0qq2GxOalvqrQre1teYdFzN8HjvLTdwHBuF1AIE8OdbHORDMhRAKRWowDQJ73c68ChpApiV0cjyeqKrRHlxsP+vjcVDb+5P2lKWFJITnBJ1owJ6G3qYLVGfMzWYvZLR9fQ9Npb0JHrRn21UwP9GcfVOX2/ubrByC75FT2izls8CYHxTnJJBUyHJ8dYz9VBHpxfa0bkjeU3m2seeEU+wXjhw/t5KZvjoe85ILLn22xH6xR/ulwiwJ7VPsF6iYxoVGWH7vo86BNFaVAVTBO84yPDo66j8ix/GqTrogcJfCBaZahCpgErP7s9S+u4l63VRqR+teWL5NSEsCRSefHxmswgnEkemVVcH6K0vY+63WQ+sMKfPpe1KAsdWvVpK68m7JaYwrWYHalyX2cWcgX/TKut31U7ycBm79pTPRWrfXp/1JPOZCgXWoQE38oWVdAGrbQI6Haj73HNhnvwdR29U2PI7oykutP83vgtw9ieRZaJkb6SYD40nuVk1yX5gvCSK6dnqlFTD8rYyumlyZWMsl1y4xorahTKIDqe8AcYHiE5Bt67PU56QoWi66llec1cr1unXyueXi1q7Twa2hqcWbZFmmc95u7bbivNUmZf5v0X2D4UurkiJKiwjezwRJKboV7eVVyGiM2+kM7C8y9jqTQk5j85BvZcN3wkXnbbXH091lWth+ksqVvvP2jbrC67PyO1mF7maQGSuzm6DuzPp4uGq5sQ8noAtVZWa6GBxMdS98Iqqrk9wBBxVAy13/Fhi61x5rqgS7uiaVpap1aF0u3CqLcYEhUzKv5mf7Z7YXzK15nUDeDwXSJlkJ7uomK2x9PL7CzcdbLRkNUSjvAONXRCh+/K28tRZ+ZbrSLNEA9twViQpBp6kvWhEvCnDvv/O6t7+2a9DO7o/Hh/2G1e9WH6+pSwrbX01Bum2sHsezgnecZZhOoLgL4ppWtyi4ee9MA9RuLbDf4gFro4ofugDR9iqXvHLJWN8bn1ji5Y+cl4OsWlMnbEvY6eWrzOOaXgWLPyeoa6ht+NikuOZqt9Kc6MVnij8wpCWsjCsBxdmtHXXXvA0fX7brcTnvn+dXGmj/NL9aiixXvVeHoJYcsLfmKmvWLS7nC4UUe1Pe5DPdiZHacVzTXEx878bGAkNL23FTTyDxxo2yDHkCRbW7gg4YB0PPObrGBfaW48OBLS7IBPaLIawD8tutoSQIU172R60ArVfx2wB49eQ8CvGBYLErx85uD60Jt0Y1P9OTpeF9cy18jrElQSY/OR4uUHmQ/uT+vqxeDnu+e5a0vFqkSYgqjmsL1JM/KDiS8tGLeE+O9+PBxTCdjAmXKWtKraCkXFqvIXALcHWxF0jVze7M726u1nOKYb/uYRtbVcGiwnqBveLVezMhdaTFGJ8/Onc1XQXjZtdWWqTZoF5U8f8J3nGWIV8i78wA+Z3iV3yu99QRdILoxlo3y4HxYoCDu2b68usFxlZDCvhrPSlA79ZHsoKAIYNvMLxqX9tTK5L90v6yDf0+x3gBQxnOhUKylt3iSlZH9a5k/e0WhW+OJmiAP1kg/J1iiJ7mFp9P7KQwVdi5i+j9UHAaVtaupjk/edwzWayQtBQz9sWwZJ26AEpCN/12evlL472PrNtjhJWAZ54btYCibnIyixMjpqAq5L9ep6TlCM4wOpHdMkoXr8LKOk7Vm+VcaOiE8X6yXdJjEepJeLgrkdxE7UeaKKl+1/4Jf21P63Wm90mXaKpCjuB9Sbyiq5/M61ZwEnRzlKXm129fbGE7CknQOF+nia+/q5BFEuRKf13ESEInCWbmW5lihT1n/hQa8DxKq8SfOgbOV7D8zHOj9hke4AAHOMB1wDtOGL4bw6CuaghgrGGZx91UTVtftLc+qJXDsgxWLzHWwsnScItCLYeTYrUtWQssp5vM3dJN1kzl2m+wf8/JAuMbWhTnCjSsoKukrr0XqPfcqdt6dntPX8VzY7/dKlFanN0eW7B0k9Ua1XrdNSY9CGd3hmGCcyvL+n2lNlm3PhZuxfJlV76YopZVsqCVLrzdx8fQLVum6dgprtqG5k351mEeqRVO0LG8dTxM8/aA4WoysI8363wjn+tHadjiYeAd6ia/X1aTfUCTO6LEcFdsyk3TPHyWhA3r8DS/SGBp/3UVTieax8HSt0OaHK1VcJbhFoaVuTepP+5+K76OV+UWsR597oLJ09MrMB1HjRU7vWD5WFZvevZYsbZB3M52Rw89VEFIoQn+T4pAY6/Ou4rPSkIpSbErrnP4k3TQNjeN/ImOPvfc1eZYVCv4PnYpTukKQ+nIOnwluxVGuFHvQPnACy/E91rws7T3Sej3AuP3K5zdHpbV/wugf3Bn+D4Jpi9Dewtre4nxexgcD21b2/D3hiykXb7zwd+Jkdr9/9s791hNr6qM/1ZLaZmCpaVFShkyJYdKKhGEUktbpVzEYRKDoFhSUykEEGEYaYIX/ENLiAkRUDMOCApF5GKLgmKa5gCCBaRIKZUyQHE8MkUKDaC1lYtaWrZ/vO/qeb7nrPc708ucM+ewn+TL9172Za19WfvZe797byZ+6k7lcnmr8Nxd9c51cp1d3ykZ3F/mWeaTyre9kMHDxNxXabXdflU6apiup57l4ee0+NkhXj69PO0s4lPZPW22F3FUdUTlq3RTN+pW9fHy7XmR7vLcGk9X193D9eeep5VcXldc3011BkquQNHBcGeA2rWoulR6JoOvOFmyMPI/u5K7r1rJGrJF9JY3oWxQ3XgXpZoI0l1XtOVV9zp4r34V1WB0huO6LrFyUkflyvBUn0VW6qf+0o//V5MJsLz6QWXTWW/1rwzE815RMWtnN66XMiaXQ8PVtF0o7jWMHZa2zmBUVoWH6ayrYtoah7rxnpEPjVQMF3telfl0r3mjerlb19VlqOT3e2WV282PT9JsutnkM26+eaaQVsYAZjPTC4Ib0GqMo8oYT3if/fLPOrxQL7Dy84R53Usf7/PrdO9y+jpbNZ7pVv81HHXjhVT9VAXTda26m77ioZJJ02m17qs2JpqXLifiVg3KPF1dtsow+HPNI73H3ADsOg92vWt2KMDLxTyDlPdTDWDKnuVlgeXjJKoy7eFXxtm78fpMy2bmYRWHpoH6n7c/oceNvfc0rg4H21QHQuW5yfMyqWIEMJ8xODNyxuPunUVW9+4eZjNZjfRuY4CnGAtKWSqoG6+IUwahki39V+e8pDx57yzUx4T8WuOeGpTfZeOau+wbTZe1ym9nmruLlStTRqDawNQnRDzv83oqfb18LLB8NrDGUzUoU5U+7yvDMa/cem9Ky5+HnbLpJry6oUK+102PK7apcWnYzui8ca6Y5bzGoYq3So95zHDDzSYfc/jw75mf8MRdjXYvsPJktCn6XrXuyXQQd7raQ5mLhuHGQ2fhFlnZHcwwMl5vVfNbSW/h83+7/FQvL3DOmHJGct/E0j4PR9NV37sxyTTLcz0WGQyfFmhf0+p56FudpT81XrmqRbuBmg65vdW+82bD0fzzspbXu4oZa4dPcMDslwuVYVEDW7HUJVYedF81FqqDDtWoXG581dgtsGy01fDmLO7iqMvlMmSQYWvd8vLl3XyVVfNuj7h3/R2qV64r1/zz70krbDhmuO3YY9sRwgx9G3iv3FV3tGpVKibjbtWPV3rNRGcc3gKqUfRuIPbejWrCw/dWXmco3QC6/lNdjWr3mu3MskQ3RlOsoGJAnlfOjpwJeAXygu7jhp7WiPtqaGEqjbTCzjs/Ot14uC6vupvHYjR9fcdu/a/K0FSPJpnebvuKoerNOKtVvdLtLmOGFVueIhe+zZzKUZGXqXLrLNKhzzfV2uRbmC1w2UrtYTYxvYXTTK+QCar3CW9t1U1V2RblebKeygimPDuL8JKxIDJruD5QrNda0L3b6F36y09fyZjyujoaYJGBJao8yQCcSWllTrac/pQFqD5qAKtuUhoyzUe9n3KT8sP8zUOrMpPhetpVjStyr/rmt3QaljYYmR76XaPqdLkYLk0LZ+De0Ljh3D12efeJYV0y92poqh2VNC5dD61GsGrgtxfPNE28YatIi6etvq/IhD53P44NxwxzOd6UcasYlrORqUH96juwKhOmKoC2ln5QvTMNDxemD5FamvDnjGM7s98LOhtOv94iJ7Si51b1avimmPJUq+zpXU0uLdk710MxdSDTkv1n2L4TzWoD+FpW1HBW6ay6OXNy+ZzRe9dR5dHurxoEX2tcGWXXRXexqcqBj6G7LHqmiTdGXjanDI2zNk+nKfZeMUMPM+EsXeHjpJtqAkUPhJpKtKoL6RWmKrBuML1wpPvKmGi8bgCWmB00d+ikRcK7T25EqhZW5dVPU6r4Kjbg3XlNW29UvHBquBoPDJ+S5BkpGXb6c0NTGZaqlc/3KW+7CB5+0awO1XCFnzLoaarbt2k8zpg0vHmnvlUTIAvU6ezp6I2Ty6P5pwbaD8VK/abSRBkdzE7meTleYrqx97zTZ1O7iFdwY+3P83pK/2qIJuPva5M7Ojo6VsGGY4b5nSGsbEGq1jmXoTkbmWqZvevjkwhV1yrDUcZUsbYcB6s+lp7XCvpMYNVNmOqmVMMJzpKdLWTcflyox6+sxRm6d8F8smWqi+7pVrGiqWGQaijA5a66+p7nFcP19JnaCszHPr1MZM+imiHV3of7oXjvO8TM0w/xm3Ui9ayYnurtbHfeprEL5i+fa3iq71TZXa17XQ1BwMp4dWhlkU12bnIeIg/zZ0mRZ1W3oqog1bjDVAFL7LZZvkoWp+7aVfNK6HFW3dJ5QwO+X6NO0ExNHrkc87puUO8xuPt0WFpaWal2Fd3ORJUPiikD6mnsaVN9S4f50etq/0PPP88LraSX27eMS6ycxPLucmXg9MwRbWB8vFB3bvfxb08z1aEqR5W+eV+Nie4+rv40yFEZRI3D643Gu5PlNeM+3quNhJMRD6saMtm0xnBei5Lvc+yv+qA23XhL42zOW22vZF6wq4qkhcefYfKpXlNMwuPRuPT9vHG+eXpUhibdVIa5YnZuSOfJo6xJDU9VGVzeqrFyN/5Oy8RqTLuamXXWstP8el6mTt6QqDw67lVVdC8XXtm1IdA0qVh7lWeJqkxUvRI3cJ5XVZ2r6qYzT++ZaZiOeY1dxqO6bipjmLvWeKsIKxNCNzw9RboC2i3wFiafVeGpe49Hv86vZjB1oHpeFyrDc2Opcc8r3G6YtdBpOFUB8uEGN4pVl0XfOSvxOKZadQ/fGyV17yzr8nE5G/JcWVmlt4ars9ba9dc4NNz8vnK1Hc+r+Nwf9lz9pD5V+VB/Ka/uWq76wKz8U98qVuF6HvpwTQVvEH0Sw3WoeiX+PsPSdHTGCrP6wcr0W2KTrU1OZli1VNUsolZUzWBlZNVMXRVOwiupx+/uE7uKild1vTyMynh4HG4wVNZqBtHZasXapnbgrozFvMLsFc/HorD7KYM/xVK8gs5jC1XX0p9n4wkrv8dz3byxcYaoZWUek4XlT5mqvFHkjLFuZOHMex7csHr5cpacdWR30XXNuCqCUTHkytCrntqIT7Fxf6cscomVBlcbiE3FDE869tj2NekmV62/Fww3BN798so1ZZw8DC/sGkZVCdIv4jaxy5jrvpvmd3n0G0Afg1TdlGU6451ie1mwKiPjbqbCVsOZBdSNu4a9KO41bZ1RVmxJjUE1VKHyT1VALzNeCXfcyd6EG/Vqkib1hpWypHtvrKb0mHrnulfs3VlblZeO6rgEj6tqKCumW+WD16up8pXXU70pH7vcdMzwGJlA8UOUEl6Y89pbPy9sU+xuXoumlcNnYKvxHy3slczO8lRmXw6WMrmcUyxG3VcMwtNH2VJunJBhubGs8mNqHHLRnnnclUFwQ1WNI04Zdw3XGUh1BraiMtoenrqtDFjV6MKsPD4OWBkvWFl2c/JFex0LrDR6lXGq0skNVk5oaGPtR3V6mrlxg5X5nfB65sM6Dk8jN6ya5t7z21TL8WA5gX1L80XqSRJlJlrJvfDd0T20Q+TdPRKnV75q0foUc9LK7t1ER1bahH/EmnInskLku4xLddBKpV0QlUux+6rZtFfokZoaho6VeiV2//MM0lLxg9lwMg6vXAusjC/TfI/ce7gLolemRx4bkP49nSp2suv06bxw6Phe1Rir7HmtvQQ/1RBWsuXUw5nadnGbz5IxXn7TctrqbHIe8q66uy4ZljfyrremidezqQYp7/UrikVxo269Tjs2HDPMbrK3dInVuhqJ9KNbhldb8OvYjPqrWlvFVGuorZh2NXwgP+EMqtLBjfJOhlUfuR3WVJfXuyeqT2VsYLYge5r4eFyGkfHnKhGV2YcrvOFIfaqhCw3fZVb26XCG5RNGGWe+W7B3Kmc2Um5wPH5veLebO88HLSP63jE1Zuhl0tm4p2vV40l/3oBPsTZnnFoH1U1C62ca2mpoKKETWBUz9Ea2KsObaguv77KccLqoHWYzLRO2SpD0v8Dsd3F+xOESK9fHeljezdBC5AXKu4qZ8YsWj7NF7+7AUDCyJV+yd4sMhjDZhbJiZYXK2lKuPcxWKnen1zDb2i5KWLttG7OdLC+Xc0Oim2zASiO7JM9dtnyv+ntjqPJ5uOlOw9p93KxciL+KpXh30JnbDulpeNya3juEhVbxKhN1FuayebmY19hUDLVi4m7sFlgZr9dB75Fh7lJWZen+PvXbd9Pyxif5LNNQy6XKUx1CNYUNxwzz05pU2llTlYhZ4HMQfLUWxA8fqsa2psaLElq5vJX3QqVhYm40vEULg8K9FkofPF4w9/nM2YmzHdXPV8IsMPt5C/IOc5d6+A7WLoeni1a4qUkd37KsYutTflb7MNyZiFe4KcNUheeNj4ahn4LNGzerJommyn11742Eusn/qkxh157Gmbd+AHyVFhXznMpjdTcvTefpk3r0tckdHR0dq2DDMUP9tMYZgLe23mIom/SWHnM3xaqmWrtsFavwfExLu4pL5t+h8sbp1MsAAAwRSURBVDoT8Zbc/bm8FVQOH3xXPzmeqrJ4WvjMt76fYrO6Oai/n7cNv+uacejkhstRyegfT1cM29Nu6hnUO754WFWPIHstnm/qRidXctle6uzfyk6N1Wn3tTrYKlFNcFQM0Rl8NWTgY+AKLTt5nTrpOLoycQ2jqhs+fKJxHRJjhhHxSxHx2fF3ZUQ8St49OyKuiYiXrRbO9+Q6K7B3O3ysA3mXNFzDyOdecdwQ+nPEvVdaDbMyBAoft1G/KW+68YKWz7WwaIXT7qmGn9AZaD90XNNGV3TstDAyHJVDZ2mrcbuUZ99NyzOhCu8q+lglzOZ1NZY2b/C+2nS3ajC8gm9n/hbyu25anmH1cuXlRtNX08DT3vM1u/RThhCmywriL42vj+NqnVpktpFRPXYctzIOzXfsWp/lrLzO/uv46RKzX2Z4PVM9NQ9VTjXQ6mcKa9lN3g88obX2Y8CrgD+Vd88GHgecERH3nRfIFruvdjTxsyF2HTeb2fluieVKoZXWW0BNXN1FWdmUF955xljj989cNE4N1/0rdMJAW1df5+usBJZb3uqzAy1YaTQzLP+cwVdqVBU5w9TKrp9TqFFLP8r0lEWsNkuZ4eknJDr47htKpLvqk44dMhm0yPDNnbpTmdJYOePyfNspcXrjXDWwUxMnqq8all02uehMHoax3mps0mdqs8zrhJxP+s1rjBdYNpw6WZVhOWOuymnmt35ypPrO+2zGexlTWJduckQcC3yutXbSeP8+4JnAO4AXtta+NeVXz02GmnZrxfGuVhb46uPgqsBUNN/D9k8EvJJW3Y9FZiutI8P3zzY8PJVxQdxrt6vSQVnwIsOBSIuLy5MQU+efeGV0Jlw1KCq3unMm7kc7Lpj/qiGouqIVi2HCnU7mqJslu5436TDFEvO9povKkHIknEFqvJqH2rBp+fPy4g2vN5ieRs6oPe3Uv5/eqHk8lXdTGxynHlObJie0V6Hlx9PByYzW2UNuOV5EvBx4RGvt+eP9c4CXAe9orb1unt8HPehB7VknncSVwFbgK+M/4/9W4Eq5v1L8nmn36ebMMZz8nTsnjHy3Vdz/+jFwyS3DtcbF6FbDr96l/JdKnDAbnuqWMlfut7IS/jzvn30MfPyW4dlZx8BrbplNyysLfyl/yn6W6H4mszK7e8TNVlbqh4QLs3ngbhLpVtP3XIa0SR0vuWXZTcqyVe41L70MTeWplj1N38yXqlxpWno+aVnyfHD/mjcuQ1UGqnT2tNgq7y41d5meVb58xa7PBR466uCYynMPQ/Mm4zp3fDZV7zOcTEMYyraXoRsiDh1jGBFPBN4AnN1a+88D9PN2BubIkUceueWRj3zkQZRwFvv37+fkk09es/jWCl2vjYfNqtta6rV3715uvfXW78qj97bWzoeDbAwj4iXAC8bbHcDxwN8AT2ut7TtoEd+DiIjvtNaOXm857ml0vTYeNqtuh4pea8YMI+KhwIeBX26teW/1kMWhklH3NLpeGw+bVbdDRa97rWFcvwM8AHhDRADc1lo7bQ3j7+jo6JjEmhnDcbLk+WsV3z2I9663AAcJXa+Nh82q2yGh14ZbgdLR0dFxMNDXJnd0dHTQjWFHR0cH0I3hDCJia0T8Q0RcFxGfj4hfG58/OCI+HBHvW2254KGIiLg4Ir4REZ+TZxtap0REbI+If4mIpYj4rfHZhtAtIo6KiKsi4tqxvL1yfH5RRHw1Ij4z/naMz+8dEW+NiL2jn3MkrHMi4uqI+P11UmcGd0G3IyLibaNu10XEKySstdGttdZ/4w84EXjMeH0/YB9wKvBq4EeBnwVetN5y3gW9fgp4DMMSyHy2oXUadTgc+DfgYcC9gWs3Un4BAdx3vD4C+CRwBnAR8PLC/UuAt47XDwQ+DRw23l8K3Ad4HcPqro2m23nAJeP1FuB6YNta6taZoaC1dmNr7Zrx+lvAdcBJDJXu++OvXMpzKKO19lHAV4VuaJ1GnA4stda+1Fq7FbgEeDobRLc24Nvj7RHjb96M5qnAh0a/3wBuBvLztMNGv4eEzndBtwYcHRH3YjB8twL/Pb5bE926MZxARGwDfpyhRdsDvAl4EcNmEpsBm0Gnk5hd7nrD+GzD6BYRh0fEZ4BvAB9srX1yfLVz3O7u4nFjExiY79Mj4l4RcTLwWJaX3b6ZYSnuYa2169ZShyncSd3+GvgOcCPw78BrW2vZgK+Jbv3TmgLjONNHgN9rrR0S30DdXYzG/bLW2tot7D7IiIhnAT/Tljf8OB84vbX20vWV7M4jIu7PsFT1pcA3gf9gYEOvAk5srT1vZE2vAZ4IfJmBbb2ptfa+9ZH6wHCAup0FvBi4ADgW+BjDst0vrZWcnRkaIuII4D3AOzeLIdzEuIHZTVoeAnxtnWS5W2it3QxcAWxvrX29tXZ7a+37wJ8xDAfQWruttXZha+3RrbWnA/cH/nXdhD5AHIhuDGOGi621741DAB9neQhgTdCNoSCGdYJvAa5rrf3BesvTsSo+BTw8Ik6OiHszbBL8d+ss0wEjIk4YWRMRcR/gKcAXI+JEcfYM4HOjmy0RcfR4/dMMS1q/sMZiHxDurG4MXeMnxYCjGSZbvriWMq/l2uSNgLOA84G941gHwG+31i5fR5nuNiLiL4FzgOMj4gbgd1trb1lfqe4+Wmu3RcRO4P0MkyYXt9Y+v85i3RmcCLwtIg5nICbvbq1dFhFvj4hHM3Qlrwd+ZXT/QOD9EfF94KsMZfVQxZ3V7fXAWxmMYzDMmn92LQXuY4YdHR0d9G5yR0dHB9CNYUdHRwfQjWFHR0cH0I1hR0dHB9CNYUdHRwfQjWFHR0cH0I1hxwQi4ocj4l0R8aWI+HREfCIinrGKn226TdidjO+CiHiw3L85Ik49QL/nRMRldyXeA0VEXDn+b4uI8+6C/wsiYs89L1nHPYVuDDtWYFyJ87fAR1trD2utPZZhdcdDDmK0FwB3GMPW2vMPpdUVrbU8Q30bw9Kxjk2Gbgw7KjwJuLW19sZ80Fr7cmvtj+EOdvSxiLhm/J3pAcxzExG/IRuUvjoifoFhHeo7xw0/7xMRV0TEaaP77WMY10bEhw5UiYh4ckT88xjXxRFx5Pj8+oh45Rjm3oh4xPj8hIj44Pj8TRHx5Yg4fnyX21G9GvjJUc4LnfFFxGUxbroaEc+NiH0R8RGG1U1IPO+JiE+Nvzvedawj1nsTyP479H7ALuAP57zfAhw1Xj8cuHq83sa4gewcN09j2I5py3h/3Ph/BXCaxHEFg4E8gWGbrpPVvclzDsOOPPrsqNHfKeP9XwAvG6+vB146Xr8YePN4vQd4xXi9nWHJ2PHj/beruBgY7R65v2x0cyLDetsTGDae/Xi6A94FnD1eP5RhLfy65/sP+q+vTe5YFRHxeuBsBrb4OIato/aMa0xvB04pvE25eQrDutPvArTlPeumcAZDd33/AbpP/Aiwv7W2b7x/G8NO0X803ueORJ8Gnjlen82weQCttcWI+K8DjKvCTwBXtNa+CRARlzKbBqcOoxEA/FBE3K8NGwp3rBO6Meyo8Hng5/OmtfaSsbt49fjoQuDrwKMYhlr+twhjyk0wf8djx511r/7m4f/G/9tZrgd3ZRfl25gdbjpKrqfkPgx4fGvtf+5CfB0HCX3MsKPCh4GjIuJX5dkWuT4GuLENe9Kdz7BjjGPKzQeA50XEFoCIOG58/i2Gc2ccnwCeEMPOzup+NXwR2BYRC+P9+Qwb9s7DPwK/OMbzVIZNRh0u5/XAoyPisIjYyvL+fJ8EzomIB8SwR+azxM8HgJ15M7LnjnVGN4YdK9CGwayfYzBC+yPiKoZu5m+OTt4APCci/omh6/edIpjSTWttkWHPwavHbdJePrr/c+CNOYEisnwTeCHw3oi4luFwoApPjogb8sdwZMNzgb+KiL0M52e8ccJv4pXAUyPiGoaxzRsZjJ/is8Bt42TOhQxjgfuBvcBrgTxD50aGw48+Afx9Ph+xCzgthq3vv8BwPEHHOqNv4dXRMWKcbb69DfskPh74k9ZaZ20/IOhjhh0dy3go8O6IOIzhdLYXrLM8HWuIzgw7Ojo66GOGHR0dHUA3hh0dHR1AN4YdHR0dQDeGHR0dHUA3hh0dHR0A/D8IENiiPOw67QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts_map.sum_over_axes().plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "\n", "Now let's analyse the simulated data.\n", "Here we just fit it again with the same model we had before, but you could do any analysis you like here, e.g. fit a different model, or do a region-based analysis, ..." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 1.000e-01 nan deg nan nan False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 5.000e-01 nan deg 0.000e+00 nan False\n", "\t e 0.000e+00 nan 0.000e+00 1.000e+00 True\n", "\t phi 0.000e+00 nan deg nan nan True\n", "\t index 2.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to fit the data\n", "spatial_model = GaussianSpatialModel(\n", " lon_0=\"0.1 deg\", lat_0=\"0.1 deg\", sigma=\"0.5 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLawSpectralModel(\n", " index=2, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)\n", "print(model)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BackgroundModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- ---- --------- --- ------\n", "\t norm 1.000e+00 nan 0.000e+00 nan True\n", "\t tilt 0.000e+00 nan nan nan True\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "background_model.parameters[\"norm\"].value = 1.0\n", "background_model.parameters[\"norm\"].frozen = True\n", "background_model.parameters[\"tilt\"].frozen = True\n", "\n", "print(background_model)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "dataset = MapDataset(\n", " model=model,\n", " exposure=exposure,\n", " counts=counts_map,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 2.34E+05 | Ncalls=214 (214 total) |\n", "| EDM = 4.94E-06 (Goal: 1E-05) | up = 1.0 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", "| True | True | False | False |\n", "------------------------------------------------------------------\n", "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", "CPU times: user 8.61 s, sys: 205 ms, total: 8.82 s\n", "Wall time: 8.82 s\n" ] } ], "source": [ "%%time\n", "fit = Fit(dataset)\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "True model:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg nan nan False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t e 0.000e+00 nan 0.000e+00 1.000e+00 True\n", "\t phi 0.000e+00 nan deg nan nan True\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "print(sky_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Best-fit model:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 1.859e-01 nan deg nan nan False\n", "\t lat_0 8.768e-02 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 2.963e-01 nan deg 0.000e+00 nan False\n", "\t e 0.000e+00 nan 0.000e+00 1.000e+00 True\n", "\t phi 0.000e+00 nan deg nan nan True\n", "\t index 3.056e+00 nan nan nan False\n", "\tamplitude 9.060e-12 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the errors on the model, we can check the covariance table:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=11\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namelon_0lat_0sigmaephiindexamplitudereferencenormtilt
str9float64float64float64float64float64float64float64float64float64float64
lon_07.777e-053.542e-07-9.078e-080.000e+000.000e+00-4.063e-064.966e-170.000e+000.000e+000.000e+00
lat_03.542e-077.651e-051.832e-060.000e+000.000e+003.122e-064.989e-170.000e+000.000e+000.000e+00
sigma-9.078e-081.832e-063.719e-050.000e+000.000e+00-5.590e-077.918e-160.000e+000.000e+000.000e+00
e0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
phi0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
index-4.063e-063.122e-06-5.590e-070.000e+000.000e+009.348e-04-1.197e-140.000e+000.000e+000.000e+00
amplitude4.966e-174.989e-177.918e-160.000e+000.000e+00-1.197e-142.129e-250.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
norm0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
tilt0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
" ], "text/plain": [ "\n", " name lon_0 lat_0 sigma ... reference norm tilt \n", " str9 float64 float64 float64 ... float64 float64 float64 \n", "--------- ---------- --------- ---------- ... --------- --------- ---------\n", " lon_0 7.777e-05 3.542e-07 -9.078e-08 ... 0.000e+00 0.000e+00 0.000e+00\n", " lat_0 3.542e-07 7.651e-05 1.832e-06 ... 0.000e+00 0.000e+00 0.000e+00\n", " sigma -9.078e-08 1.832e-06 3.719e-05 ... 0.000e+00 0.000e+00 0.000e+00\n", " e 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " phi 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " index -4.063e-06 3.122e-06 -5.590e-07 ... 0.000e+00 0.000e+00 0.000e+00\n", "amplitude 4.966e-17 4.989e-17 7.918e-16 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " norm 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " tilt 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.covariance_to_table()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.055577332085187 0.03057392216430327\n" ] } ], "source": [ "# Or, to see the value of and error on an individual parameter, say index:\n", "print(result.parameters[\"index\"].value, result.parameters.error(\"index\"))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# TODO: show e.g. how to make a residual image" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 4 }