{ "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/v0.12?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.spectrum.models import PowerLaw\n", "from gammapy.image.models import SkyGaussian\n", "from gammapy.cube.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.utils.fitting 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", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\tversion : 0.12 \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": [ "filename = (\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")\n", "irfs = load_cta_irfs(filename)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to simulate the data\n", "spatial_model = SkyGaussian(lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\")\n", "spectral_model = PowerLaw(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "sky_model = SkyModel(\n", " spatial_model=spatial_model, spectral_model=spectral_model\n", ")\n", "print(sky_model)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define map geometry\n", "axis = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0), binsz=0.02, width=(5, 4), coordsys=\"GAL\", axes=[axis]\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define some observation parameters\n", "# We read in the pointing info from one of the 1dc event list files as an example\n", "pointing = FixedPointingInfo.read(\n", " \"$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", 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" ] }, "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": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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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N/aj2V6byUzHuq35ulDC8yuWu1bO5QmFK+FUD1xKkLaGYhF/qlx9T80lY4ZSC/45jFdR3QZWERupLEnq6AuzCTPFKfWgJrjljn/qSylbWTWuM04JT1ZfEq3yWzmmn+iuaVHS6zPOEb6sfU7SfOzevMr/nlLtRwvBxWIZTAz/3U1lCVZstTVut5s1h5tZkneqbuolTAtUFWbICqrhTEnCavxIulVD2PqpAq/rU6tdcAeuW/pz4cRqzKjyQxl7b0k8ab6VzxYdVrHhKOFa0acWHE34+rlP8O4WDj0Mr341aTX7L/i/Df34WqDenVs8cWvm4kVY37Hpe/tfNoRvsN9uy/g3GG2N1oy5hifEGXZbRfLqhOW2GJtyTNM93jiE9z0O51Gdvg3UrbsT3HEM6ap70m/UpzRaWN6UTlEbEwTd/e55z+608UW2g9/57fm3z3NKZRjyWANbHdTsJl3shL/uqG6oT7SDp/HaaOA7pmf73fk+NL9N1rGHpmv88lEn9aMFzM/I8c6DEcUKmUxZezomb6vFnyhTKvKzr3PIofi5ItJyfGtDfqY86SaoB1rLejtenwkBPSng5nZiJ8RI9FU8VfKfyO+Gh46T9BWqanRfPlxgKhha/8LmfmvHxdAHizwnOb847zOOg+ficJ14cqvZT3nN75nR1nHT8qv670K3m0TI8T3yqeXzeVHj4nK7oOyUQ35bCMBGCvz0fMCbC1BEeZS4XCj7ZkuBQnFoTJ+EGjJlWLZI0Wfmc9fK4GNs8tXoVRz0O5hMVIX/CXdt9cLF/5oKN7WhZQuqT4+g01efVxKqOIHqffFwqPJbyXSnWZHUpLjrJVVGoYE9KjODl1VrVvnh9hNT/NEcqPm0JvCS8FhgrHa8n0TApqlYd2qY+17QXQx0P4WnHAK9jAWUxEY+oymjZqtwi/J4T1/LFgWrFTT8ef1uEtlL+KoZT9a3qk9ftNGrlh6V7PqeLpvnWE48zVqc9Em1a14Utw2/Fc2rxYhH+e1oVG6v6kPjR8UgLPlWMVOvxnQbe/2qPJcumOnX8lhiPXRqf1Jep/FW/5sxp/dyoBRQKw2rrREXoy34qovukmXMDcBrwqcGdm5b6TiZqCYJWWWXCJcaCoZrcifatSa1CIdG8muQVvV3pJIHr46H5T0PZ1kKEC5TW82rsUrpvVaoEiZZ3hTHVVouXSIe0LWxheVJ/q7GaWvSZOwcr3p2T98YJwzkTYo7QqP63BmaucKkYcO4gzimT0lvWTKvPLDeHmZP1OYWnC84pfHTbjW79cUGd2ltaO259envar4W0WW18ToKu1Z80zl6/CmOEul2oJ6WRhF3Cs1IqPq5OIxe6c5Rtxa+Oo+NzlTk6J9+NWk0+wAEOcIBrgadt6V3VTdZPFY+r3Bh3p4CsKVtazdMrKyFp8su477RSHNdkwbWsiKV9WvglOlWWo+KX+o1QP4/BJWugcqW9Tz7uaeyT9ZhoVVnAadxSXk9jbM7jaX6LtPch7d2sYoGwdhQ/rS8d/Uv4p/qTZZl4ZIH9a2GVti1rNY17Naeq/ldzMnku/Nw4NzkJI2euloCpTPCWQEwuRMv9rFyzakJWz32ytfrmOPlxrylatILZrefaZz8/WwnxKZcsCZMkYHlKwwUJ//tRNQqkSghUQhKh7sqt9vpbfOB5k5BwZbAs6q3GqjVmSdglHk31p2dVPyphlgToXH6s5lFrnt+om66Pjo76N3Y3XQP1loqFlfNtBKmM1omQzmcLDLcznGO7KfbsYr8dwLezpP1pqW5gvM+MbSi+Cyun+/eYZ87WD99HmLZd8Hm1tUHpdWrPUl/TdijHc1Hk0XzplmivK9FJ91Q6KO08fTGRR9vw/ix3afcwpivT+X+KzuSPpdXn/SZd0gbstK0m8b2Pv7bpeb2ONA5pnyHHgnUncLpU23sSv2v6i42brm+UMOR/FR7AcB9XS0AA+wFnmgozh8RUFRMgPHP8nZFgz3wPWWKOqv5TjE+baJvVJK6Ym890cqR6/fp7wj3sBYHi778TDq4YCGvZX+lKy+t1QdBSop5flRQw5jUtx7xLyZv29Tn9Ez+29hwmZattwv4vMaSjQ8to0LFuKXhXytUzTZuqK8FlnrWE4VN3ex9HzHDq0zKfq2ctN6AqW32q2EkVs9Q8zOd1YaJOTXdXU/MmF6nqvz5PK62VC89y1Y0zWuep5fe2FsgrvR4Ppfuc3C6m6zuWW9tIUnhkGf4vMXSVnaaL4nlyB9N3Gp8qHJP4z/tV8bBvW3M8E99V4+rpaZ4lPNMcatGnmmve3o2LGXpnlwUh06Br3McnmQ92xUTVpF9aHg9otxjQmaca0NYEnZoAaeJUMckWU3rsqpU/TeAFhrG7JTLOaVySAE2CMgkaDfI7Pi6cTjGOA84VbtU4Kj7st26EToK7xQcJx4rHWrRL/On/09sUE79p/5w/W/ztdGoJxMSjVZkbLQz11poUUK8GdE4ZZ+okAHRw04brioldY6lQTnhUFl5q25kxTcIKF58cXgfv3Ut35jFfqw8qNBIOLlSTtVoJiCWytZloPdc6998VD6Vrz5SO2o/WnZZVuy7Aqvye7jT3vZaVcE191rFN80PHL/XNL9pNY9kSelVfW4KvlXeJG7aAwleFLsKzKhbn/7XsnHidwwLbc7jri3FcxGMtGqjXNA0UL+xbYympzmWBmwfsW5dWaPzR611gvyjEGNxSyrVibek/cfExcVhM5EmxJ21zfTy80ID9SOOt/XE6wMr4YhnLe91anz7TPrX4baoej9m1Yn4+/pp+D8NLOYAxnzq/pYtGFKdWu8qvml8XTtJCoy/ueN7E41Vsn/W1YoZPdNN113Xf13Xd3+u67q9J2pd0XffDXdf9UNd175qqg1d4bcKHQKIskQPRzJOEkNa9sLz6XyedB8sV9Hqq9fGYiVUAuWDSOtPkVGBdzkhM8wUK77v2g0xHQaj9UFxcYDsjuoJY2jd/LzEGrc9/O77EZ3WxX6RZHY/7x/8qTHQ8NI/yVVKkSQjwU42P0k8/qS7Fb4NM+6XkB4bj6tfL8bkKlIWUr94ZnowKQuJNV8hMS30gjhWuWvZcygB5N4DPv4q3SniSLi6AfxnA1wD4a5L2nwP4SgD/JoB/b07MEMH8bZndbv7rx69xX6A2+4Gxq7awfIvwSWUcn/RM8yQ3z+NjC8uX8GvRR/uYNlJr/5MrmtrQNN0TmOqoYriVGz5F0/S8FZ+cE9dqjX1q00Mrmq7uttIWGIYsEq2cZxMPVGOcnqUxrnhlqo6K56s6K9pOuctzYodOi2cqZgjgyzAUhv8FgC8H8NsB/PtzheHUBG8NbhI0rYlTEdcHKAWZqwnqTJPyevzM+9JiHFcQGvd0Zq3whNXv7S0xxtGFW3qW6FoJkHR5gtLbhU2akB6XnBoXLTN1s4wK98Rr2m7iHV31dkE2R8FXQqoloFvCguOW6JF4pTJEphRXmp9TZZICm9OuflrC8Dk8fbgH4BUAvwDgzlPG5QAHOMDnKjxty3BmmVcAfAbAZ55//vlJTQ3TKtV7K4CxpkmaubIYXXMmnPRZ67iWluf+t6QNK+tGLZmq75VFnLaQuFWldfgKpeKr9advt4q9Pd1bWFnNqZ3Wfw8n6BlhtcqqW1iIR9oG42PYsnyTFTXX+kur5o6/07Rqp/KUKj7xdPcKkgVejceURTv1SThVNExtHR8f95Qlu88rz4ybfNnP++wKrxYRklCbQ7Q0gHMYhb8rt0knS4oBVjhVbqKXT3faJaZMuPlkVcFBIeAXRvgE8fb0QtDkbvpkgtXltEzj5wLqMjHUVF+Fj/Znit9cMDgPer5KQFR9ZhqVkCoSF0yVgEr9drqxzsvOpar+RJer1lUZKi3ZsMAzHjO87OeFsM8wCZUpi2qKoZf2XQmQqcFzzVVNSI83JiG6QH4vsuerJpoLJk2fq12d7n4DTWL2yjqtNkenMagEGwWgX8zQGlt/7rHHJACqMZzDT2pJe36lld9C4+NdCQu/fKLa1+i0dqGSFuQSntXccNq0FGAa68Q7Vf1z45Te9jMjDAH8DwB+DtsdMn8HwLc+ijCshFFi1Ip5E4P6RJjSXC2t5OlJEHkdqV/JLWtN+Lkfd639mQseF0o6AZNCqYRu1Vaif4tOafIlIa0TXutJG82T8EwufeUWa77knp5ib3Gnfiaec2GlfatCCtWL6ivaJR6tBH/F6y13Ps2xufyaxqOyvKuyCzxDwvBxfOa+N3mOQGzlmRI6ysiVoG39d4GcBjFZvinulCZ9qqPCqXWEzdOSwJsjSBK9qlMciebeH8dF070/cyZgNSmTANcTJh760PFMW4VcYGjZ1ntGnAZzwyw6Timtepb41ctV8yiNc+V5OG2T0k+hhJZX2PrcKGE4dVHDlICr8rimSUzQmigtN7OyenTCuCCZivPoZHRBlpi+dbFCS+jqJJ86mqXM69tqvF0/J6yT0n+3JmolSFP9STBoX1uCxC030oP5VACnMfCJ3aIh0N6mVfVDn6fQhOOdhJGXSYtKzjsu5JPwqubZ3LlWLW61hGBq40Ydx/MrvBQWGB+vWuye+QkCfs+9tsrrWSKfEmB55tc2WH7ZeOY4VlcZpXTipceV/P48ADi7Dazu18fEFgBOdic49EosfusphlOMae318bmOi+Ot5YiDvjM5jZMeKdMyms9x0pMLaZwVdwL7qnU5fn4KKfFOBRWujh/b8jFNeOnzNB8WlkZe0foq3kttp6Nz692RVX/u9a3s6jXnHfbBr4NL9zS28AZu2H2Gfja5NWBJSFVnFzVvOk9cQQsPyLOlfPsETpNRJ2A6apgEj4Iyj7bD/9WdgIm5W/10SLRO/fR++FlonVgnu0lFulBQP7jY34uY+qq09j6ogFCBy0lWCbCEe6U8VHgptOrWvuiEr4SfluPv6uhe6/haEkJJIOt4ITyv+uN8xnT99nkAjHmz+t+a21pPSxg+lxKfZXgLNTO5QKm0cho0JaTeOqyCg2l+UFwtvcq60HOwPqCtA/cKqR1/nm68Zvs6YZJQTVbwMjx3HE4xxqfKqzTwyb6+GB/aX2J7g/hAuIm1wTq0z/rNSUbwM64Urme7Ol0QumWr6bSmVlKe6Syj40BcXXi7Zat0YpkkBNxirS4tqAwC5Rct73ldmS4xvugBGPOA82vyiJQXN/ZfoSX4qotPpgSlw42wDOdYLUqQyvJzM5+/kyXl386YPqGqWz/SzTIVM3o9KR+Q3WO3VIivWmPMxxt51HVS/JxOSaC3hLULDK3fJ5fS92SHq1pywPD2EwogfQ2DTvaWtepuqOJdCcmWtaL9df5RxcR8K7GAFwDWt4GT++PxZblT+e8urv5OF0ewDuKp/z2P8zjst7cLyeO3oLfGO9XjfZ4j1CpYAHjPTXKTGTNsEVIH2S21FGvQ8s7gSetoPr8FxAVIwk3xapn32o8Uz/EJ5dpVy6n1lmJxzqw+aVM+F9LJevJy/ixNjqRIklvLvjOf11nFlRQvgrtzU5aU0lpdd1qxXkbrSsrYhfet420YIFlLFd97v1y4kx9aLrWnaxvaflLOboUzb4q5Ko9q2EjL+/ytFM8cY4j5b6QwrAivoEzijOQDU6UBtRZsET9p22RRpgUIdyGSIE34JMsK2C+YIDxLmrilgR0fnYBra8cFf7KUVNi65dEqp7gqTqxH8Up8Ugn2tFDCbxUqHAOf6FOW/BwrLD13BZCErbrhaS4kfJNVnyzhCr85ISLFoTIC/JnX42Ps5aq563U9M/cZHuAABzjAMwtz9vYB+FIAv3X3+wsB/BNPc5/hYsa+pKn0quzC8vCb+6MQ9jx5/mWjTv0/J/8ypHkfvPzScEzPtdwS+31t3PibNnKnfrYugphKZ3k985wuffAz0ToWC4z39c251MDLVhdEtMbH6QirS+tO9NM8C6s3tem/07ntJcY4L6193wCvdPPLRFIbiqOWbfFu6oPTJJ19b83bRLcKB34eadM1gN8P4McB/M3d/w8C+EtPSxi+zy53dYIoERLhnGCe5hMgEbg1QNX9dy0m9wmdJkA1MSp8E14+OXwSOS1UWCU6Or30ZEZr0utz36ycJg2Pr3mbfvyuwoEfbsRuCdJE1yT4fPK6oPXffiwu0Uk539XGAAAgAElEQVTb19MoLQGSBL63UfGnC8KKtxPPOX19fL3uNHcq/Ft8XglF5+lKHjzSpuuu634CwG0AP9b3/Vfv0n6q7/t/rlnwmsAXUBYYxgs83kZICyPMrytzuq2GzxNoQJ/teDBf2/YYJOteYhwvW9i3x3gWAS+uPLJvvkVD21W8+L2w/FVsizR0ei6RY0LVwgtQx4001kh8dP9fCrKnGJb3k21Ufdf0xDtOy7R1x9slvopniv8tpE6tL22TqsaAeAE5rqbviPG4qeOtZbVuj5XqswpP/+31pXY9tsnn+l/HI9WP8OxRY4b/qO/7X+Gfruuew1baPhV4fvetzHwuH33mgwH5fQ/jlUZfxGgRN22VUYGgn9Xx/jnrXmDLnD7xNZ/irQLW8V5if6LEcT85HgqAzS7NF3P4zhDdp6h9XN/eP/d+M5374fjxVWDm1+eanyuyS3vmcIq98iL9Tq1u4pTSfJGDY8TxU3Ahq7RsKV0toxPbBYDi4EqPCxkVHbT/PsZsV9tbmyB0QeTtJNq5kGUbThfvh9PJ50oSuqk/ilda7PJPUo4VzLEM/wSANwD8HgAvAfgPAPyffd9/9yXaeWyQjuO5tmdaJRQrLeUraIlpklWjg6WrmbA6kxDVelWwVGnplIbjqQzo22gIfL46zi+3SoyUNLW2rzixLT36RyWkzD5Vn6YTHwrN9cUwj/fTLXQVSNW+0YSXtqF05k4ApbG3pbilfagtq2zK6vR6+Hsl23SYR3HQtCTQnRZM0zlCS1M3m+szFVxpVb/idfeOvL+Kn/d/jjf39xuW4ZyY4a/DNm74AwD+x93v7mkuoCDEEoC8aLAo8iI899/68TxVoLcKfKeAtrfl5ar2NT7osSyP5WhQ3POne/Y03cunvMTt7PY4nsd8Hu9L8TzWtwx1MDZXXS2lZf3CU+83+6T5Ev08VppipGn8vT8LS3ecWjzifJX6Xl1V1uLzhK/j7n1P8ceq36ldfvuCmdMrzaeqXce9NZ/4ubEXNaglBmSN4LENoL2RumUN8ndrn9NUXASWL2l2/k4xpGQxaR/cNVX81CpRi1VddQXvK9M031rO5GobQNbyajnoBmDtk/aD9bDu9R1g/eo+b2pHrRFgzB9u2SmtqrYdP6fZwvLz2cnx+JhhwjVdVrC2EylVG8SrsiK1vWSlat9S/gXG/dZwiuIMjC1IYGhFM09lFSced74jaGzbrV9gSEPgihc1dF33U0AdG+z7/quqZ9cJc9xkF24uNIHaHGda5bJpmrtvfJZMemfec+zdDBUA3j4wFG7pecqTnulkUjemWjTyfquQ4bMTYzYFnQAL7N03F9oETVMm1+c6AYmThytUkSTXsFI2eqwvHW8juDLw/iu9SR9t289ks4zHOJPQ9T47XhqLczq5knA6kOZuKLTaTnMv0aLFW8qDlXHhgt/rnzIYmNY6gfJcStzBv7H7/vbd9yu7738HwGcb5a4VnpffC/sGxoTRyZBWVVXrupZdoD4EDowZLOGj7ejEW2Ko9RNzk1FbxwEVX21b++1MpH3y2I6249aK92cDABfj9gh6YuMc2+NlPF9MwbMSy1LjmGdmEd063ralk/nkeHiJg8bKbh0Dt3b5ziW2qH3xxZRbx8C9iyG9fbIp3RcYxi2ZpnnVYqYg1Do4viq4lUf43wV0JSyTxaft6HgQR58flYGgdRASrl6H8r7zrM8nLeNXgDk+qjhSHT6vNgBeDH0jzFlA+at93/9LU2lPCtQyrMz8Rfitpr27iMB4RVfP9CYG1Qkxx7ID8pYEwlRfKuuUec4beT2/W9Anx1kwJ8vQ2wW2QmR1MWReCjk9q+sCwK28hG/VT23fBcM9bHHh2V4dl5UIY59gmk/HrLL0CewrsLcC06JB6q/3CRi7327lMV2FuguHpTz3m3KW2I85MKaD4qBzIlmVK1nMUoufbaWbeipI87ayPFtut9KFz/j7UbfWfFHXdb+Ff7qu+80AvmhGuWuHxGzA2M1SU73a7kHgFhH9zzw62E5wClpgv93A4xk6wPrcY1cEbr1JluP6eFgHLI8zkuZza3At1otPSrd4ucWGCkPLaz3ri71LrNtWtK5bx8N2te1T+abVRxqnycktRKSBC+GFpAP7bSJuSbAO3Xq1sOf8zT6xr2cmCL0c6/OxVGuRE1z5ymnkV24RNtiOz0LqYPn1HRPgF8P/Oj5KR3W7F/ab9axEqLIvHubwfvC5zxXtS1LGSkv9r7Q4D//nwHMz8nwrgO/ruu49u/9vAPi9M+s/wAEOcIC3BcxeTe667t27/L9wvSi14f1HR/17didQKomvGsVdHFqFab8VMI6LpFiF1l25le5W++00Ws7bSO4+6/K2UznVzK04UCpLqNrWkEBaVXSLgnHBdLegXoqqF6ySTrzKihYX3V91xf22a3WXE400zqh0Tiv3wDCsouOWrErFnXXeQ47RQvLrb3dvdS+fu31p/J3fFCctp6v5FY87fyoPMz3NB6+jCl85KF8B4xi2jlFyqb3eNJafeJSbrruu+yP2HwDQ9/1/OlX2uqCKwyhBPI6SmBUYLhIAQ0HoxGd5j3/4UTFnkiS4nRFTXApFWgrqax7PiyKfu5C6AduVhjP12W1gfX87WRcL4NZmP2m9j4zR6QZsAFgugbPdNhnGHM8uhoIXJrgeiHv3QDYWc3WeoJuBlfaMI2KHD48wpsUjYL8tJuXRcfBwigoeH3uvh98b+7B952FYGeVXguPk7aqQ9no1lOTCjb+Ti56+vW+eHxjH/yqB6YpF651aVWb5R11A+U75+wXYrjL/dN/3T8VV/sDRUf+zsrUmCQ5NdyGGkO7PU8C9sqxcANGi0TrSVhBlYBegqvXVqkqxSsW9CsRXW0w8qH5u9aRJovRei2XGOhQn7atq/DSxAWB1Gzi7vxNsO0F1dhvYbIZnar0dXWnVZ7QmNUbKMslaUiWqEzIpmerkjlvLLlASLZKl5cooWXBKA8XTFWF6P4vypdOVdZztxqOyhCvloMJUcWQ+bTMZNk5rBcfdYWqh5rG+EKrrus8H8Of7vv9tlyr4mKD1djwgT3qFSqipJnTNk+pMpnlltruFqThUbqzW0bL8PF1BBXMSIt7ulAYHxgKU5W8dD62otKEaGFpwZxcZD8/74GK3tQbb34QTszQTXVO/deuO9wkYXlLrNK88hOQWuqBNefi/FUbxPrlF50I8LRCyXlUcrCt5PwqXXQmunvv/JOir+Zrw83mmfQSGfE/6tPYZXuVy13cC+GeuUO6xwPMYMxdBmcLTHVyTUqPpXixgSGRnNmC82gYMt1pQuDrzuxvjZVIMTvvCVVbfUuDMUwml1fGw/yzjbjHrPLXyxJEMp9tYOAa0SJzxVxf1Cvb6jrz97s7eKuZq7Qa7PYQ791z7ztXth5arXC6hfOFnmlXYLbF1/x/SSepQXlFwJafWu44RV6i1LlUorIsXabiAYF2umF0RJsFF/NxSvmfP1TJN4QGvT/EG9pdokD+BIf8pPaq4dlJubl37vFS+J51diVWGEGGOm6wnUd4B4H0A/mjf9y9P1H0twBdCATl25+6jCz3+9u0JlUnujFZpXn2e6vB8nndtCwjAuH/JcmM+nVzM67RIeHEy+r6/RA8uXqigWy6Bk1fHSsLbAoDX7gIfvDtMd+tAJ8gCe2F0dn8cMyLoQgtxp/WnCzMJkkJQnLVPSdF6P9T68knL+nVckyBNcTynlQsy/U78VoVA2L7yinpJ+txDMWluwMo5bVZi4TuvMQ6tvJTiq96e4528Jv5+pHegdF33pfL3VwH83b7vf7VZ6BohvRBKBVtiKh3YBD6JnQmUeTQekpicebxuxWEtwflKQ6ZJpP8To+s5WI2haYzK8XMGU3x9cUlXKHUysm3N60INGC5KsE+6N3R1e2vtrV8d0sJPrbDuW1bfQ8vvDnDr1bHi84k/NXm0fWD8Zj4dIw0PuIDwFelkaflKr8cutX8VD0LyJ9dW29f4oIZxFB+25ZCsYH/plD73mLXW43PYPSYH7V+agwtkGjDPowrDV/q+/6aptCcFXEBpCaFEeGrTKsbjhONzP3ubGNUFbjo/mwSk4uEa1+vVdN1eAnnmk8ItgNQHx88Zy/HTyb+x8i6s1UJT67Ny3SFlaemt7wCbzd5iWN/eu7GK48t3gZfu7nFdSppaILQilX6ufNIb9apz1VVYxj0HfpJ16+0nXlibhat5T+1/sgyTEtC2dOxcmWq+yjomTpX1qLwHw9uFOzDGKVmasDTlV4LP1UdaQOm67tN933+N/H8OwE/2ff8VzYLXBH4czy0kB9WALJMspUpruwZMAlfdR7c+KoHGOpLb631LrkzCUfOqsNLLAhTc7dqgxqe6cbmavEkwJgtcrTtXQNxHqHie3N6X58rz6njrrp+fby3L1f066E9LT1/DmQL4yQVT61ct7qofwFjYOB8BQzpWi3UJJ83jK8FuVWo7KrRceGkd3n+ljZfzhTUXytX8Sn1JNAKGdPD+JV5UmCMMn6sQ67ruuwD8RwC+sOu6X2QygF8B8L1VuScBaeLpM58Im5CXz0+lnNftadqGDoiuTLoG1EFLTJfaA8YXdKrASgKe6XRR1Bo6vxgH6qmZ+VsFQ9K0Kaao7VHAOCP7ZFG6LQGsxFSgi/9g970WvFc7i1BXj4nHS+sFXvroZlv3feDszjaOyXadTnR31dpRHBVvSHntn1tz1YUWmk/ppuOutF/dBjb24vg08bU/KVZbKVPFY2PPvP50lnqJoWXtYYNzDF1mtypTP3x8fA5pnkQTNyo0jOIeQAvmWIZ/vO/775qo54mBxgyBWtt4sLiyrpxAiamSC8pnVXwn5U2MoC6Jp/E/99xVuKZ07bcKUGDolmh5xc/LKA1WEugGtoJbV4eZXyeqClW6fLq4oXWe7e4sPNnFEAFgs9l+fPsO3V5gaE1qf5JXoMqANFlIulrV3EtJeqqQ8EC/0kBp5/+VTk4j1sV9fprmdWo9furEheZqt2XILbuK51Mev5bNIeFV8Zr3PVnE1Zxpue1eRv9f1TL8UN/3fwPAD3Rd9zX+vO/7T1dlD3CAAxzg7Qaty12/t+/7b+u67kfC477v+6+9XtQyvP/oqH/xjTeasQdgHENQrVu5vcmKa13h5e4Pn+t/BdWEbm16XY6juiOuNdNiBtNTv1O8Si0BWhmal/nUXfaYnoJbJW6dcyHE6z/H/ojfRoi4uthvvTix+NwCsu9ws40frl7dl324b1HO+LqLqRagnwdOOwKS+83fybVUmgB5W4xb8epaVtueFLdTy++LNB6qaOHtMUJ3ud0q0xBDyu/9T56av72vArUmW3RfhPRHXUD5gr7v/+FU2pOCo6Oj/r1vvBEFhgustGXF8zLdBRWQ72JrBbi9Xm8ztVO50akshZG36zHSNMGcKfmMbmG1uqngLr1etKCTBNi6uGtx7T1+Q5xfv7v9/5G7wwn22scWeHm1eSjgFgvgIyf7FWO2wf2HANDf3a8eP8Qj7DNkn33hbH1774o76DilCyIqJehCzpWzCoUUo9NyhDM5IZPwdAWugjD1ieBhEZ9PZ6LAyDP6il3nLxeOm10dwL6eNJ+A8XllF3wucBX/pIxI90c9gfKjM9OeCLwTtQWlg0ACVEJC05QhlUFopWiaTnplahVMS3m2kf+sn9+r4/29hCjqI1OeHA8ZQ9sYTJQ7e9xdEGr+liBM/UyCmws8nq7/b+3w1sUH1r++vRWCFIQ6oV5ebWthzPDk/jbfye3hXYOcYAsAL6+37b1+V3BwjYnhfkV9vLq/3z6jeLoltb7Y8wawX2k+kbGsgvpnt4fjovX7SaVqLM7uD/FxPJP1d8/qUr4EhmOXPAtgf6nFQ+VxPJxjKrh9UY6fk/vbj1u7Swxx0hMkPi+qPijdnN8R8jm03OQvBvABAB8DcAfblWQAeDeA7+n7/kMTdV8L0DIkaAfdpfE8rl1YBlIOIc+AIexWaK+X5ZOWTauxLeZ3K5V771LgWy2UpNUVkoWouHqdCT/F86GQsDO9DNhzBRLYurC09E5eHdb92se2q8K3ZIGFbni10stnJzs3+eT+kPaaXy3GgbVyB/jgre3pGC+r559TyAUhjfhUIYQEyYrTNrUfc3hN83tenx/eB+cft2qVdgmUd6t6H/KMeQxz+a9lHSbjhX1ovSq0JQy/GcC3APiNAD4lj34JwH/X9/3HY8FrBr21xgfWCaQajFsfaAmtJY7UWlUDxpPI91ghPFO3KQ2atpeOmCWNzYmi2ruig6Y7JKGi8STtn7oeaQN1NflIdz2z/ODOPpa33v1mGReATjMeyzsR91DHcLWrj0KR7q5ORuLlq9G0vH3VWOlCAafKSPmNNNU9px6qWWB8qkdXtvVIoVtnOt4u1NySRUhjPRyLpEw1v/IBQS2tB7sVf/KtxvsUD9JjdTy8Pm1jtHCB7zhWc01p41Z4suof9QTK7+j7/gebmZ4g6Nlk1aLAeHBJiNXtvcXgmtpfx8iyHuDWo2RJe1VWYhKChLkD7tZrshrVNXIrSl0nbce1PK1Xv7VH2wb2W2nUKnGcgK3Fdb5DhFtiHk6S3WRa3Rn2+fx89+IoWSzh/4+s9pYjsJ9cK9mCAuzLvvbJ5R4BbN1s0lVpdWIxOO134immV4LKlbGXdR5wZehjkASrb/R3ZZbamYobpkWzZCC0LENX2kxLFiwsvTIcHJdqLSDNPS1zD4/hCq+u6347gK/E9j5DAE/vctejo6P+o7vVZGeuSjOgka7Mxv9uafmA+cR3i5T7AiF1KbMm8IFb2d7Cqi19pv10rZgmZkUHt6aAYYCfjKXxGn2BFnHSSfzQMtr1y+9C9ItZX/vkEh/58PnWlZJ79XQsTnYLHrziS79Xu43X6qKr1XUmLjzxX1j93o9qkqqF45agwgbjiayWp4Y5WLcqTBdWvoCl+EK+eXImubrKF7oYohYrrBxxSZavWsDpFbJOk+TpOH7aZzUIUgiisniJ9yO9EKrruu8B8LsBvIRt3PB3AfjSZqFrhPe+Y2y1JcHEdHX1XDhqHTqxzy2vC4+0aKBMfFas9DH/AsOXRxE3Xn20wfAaKWDrlqhgbfXjHPsXA6mwIv4anIaUW9/Z4+K0UgtZb+lWRnY6re5s89863uOj/dI+P7jYv1gJAF4+Ocdrn1wOziIrHoSHQmi5F3rn2LrMq+PdivPFcEV5fTy84dqto3SF1oM7+/aXuzoejtXFWLBoHxVvVz5OCxX4ntcXjlZypFD5HJLmVpNercWxIz/qYogKt3tWhriQl+5JGeK9Es/BeZ3AehLNVEiqgk4Wos7dZAC0jBCFOW7yT/Z9/1Xy/S4AH+/7/l+fUf9jB7rJFfOlhRCCE5NplRb36/yBMXPpoLlGJiRznt9+soFtu+Z24aX940RobY9xi8dxYv1rs9zc3XYhoYyn22c0vqjxPH2PMG+XAYYxObbHOCXd5LP7wwsZ9J0oa9nKQ9ebCzWMU24EL/1Nt/MsWDJKO7fUdPwXllcFiFpQTv/EYz5ObrG2Fv2Ynnia+VwJtiwsxYnjt7ZxBDLPaj3u0Xhe7WN6jakrE6dby1Pi/NigvYAyZ2vNL+++P9t13ZcAeAvAr59R7lqBndNXRwJDy0QnanINCYtQBhgyneZVl9fjNKvjoUarBhMYx4UcB8dXyzquimcSXF7fUmiylLJcOdVtM2pRKh1UqJ9jL/y0/MntbdhuRQuNFuLOjT27vU8D9hukbx1vY47cKM2jei/d3V/uSovy9btbwbe6vaP7+TYeudnsjvXttr28/snl4Lw3hd8phteAnd0ZCwL23yefurhJqOjYKU/42AFDHuFWKvKb8m/ieR1D533th4+begzKf9o269BXwjq4Z8WyvERY++d95W/S0bc3MR/LqbD1PmseYOjmp/FRmGMZ/scAXgbwdQD+FLYXvf7pvu//SLPgNYHfZ+iWnms6X3SoLEFInqTJCGrBpf15Xn+l7ZK1qUzrlhssT8Jd+6Dt+Eqe4uUhh6oNPcfr9xjqTS1q8TxcYd3F53jm+Nbulhlgv77x0tkSr63OH1pzwM4S/OQ+Xc8lE24dAy/9fI+PdB022ArG1862ZU/MxWbfdHP1BsPFiJXFEp1mwPAsdmVJuhDStMoaU0vTLTK/cMCtKS/j9btA8gWVZJnqXNCywJDHtE23YlX5VhYoQflRFTXrUXe5EoZVbJf1tVaTn0uJCn3f/9Hdzx/suu5/wXYR5UNT5Q5wgAMc4O0El34hFAB0Xfd/933/T18DPpPg9xmqpUZNkLSeW32QMpXmZprW5Wa9u7LJ4vRySQM7TtRydD980SLFi5J16PEbx6m6p1DbAfKdiOoO8b++y4WWHBd06OrSTf7g+iV88L3bt0e8/rEFPvLRzcO2H74/ZbFfVdcVV2C/L49ba9JLo4gXsLUaGW9c7eKVvvJOumjIQceX4PE7Pn9wZ/8aBOenFENMoQ/FIbWtLjDjpukNjsm1XIZyvnLs74l21z5Ze26lJn5ynFI+t3od/M2TrFPx0pvk+Zy4PtJqcgGxslkFu27Zdd3/1XXdpuu6P7xL+8qu6z7Rdd33d13XxOmdu293ezWGxzSPrRCUyKdWBhi7v2qeezrrS/EYH9CFfViWH49rLCzNXfFqknob7rIo8/meQq1by6wuhv+56ADD5cHF9qPbWBbYuqUnx3tBuHoVeOm9Lz+MdX1wJwi5+PGwnvvbM8dL7Nze2/uY4XK532TNLTVc9FE8CR+8u3+2enWoUIB9vFfHWWNqBNItrVL6+2BSHEtjjM6bLjiosFxZbrAfOx51dJ7S3QlqKNw6loUnUV5cGdY7PpOr6YJNcWd6cldJF6UNedNfKZHmG/EnXjo3FK/1xXBbD3la+5rgiVqGXde9A8ADAP8agL8D4McBfCOA7wTwhwF8A4DX+74vY53vPzrq37OzDCsLywdCVwrV6mPZFBvRtGTVQcrrALmVCoyZPmlZBeb1vVoLK+ta2Q/oO55uQSYNq3m1P7pQ5Dj4xGddGtfh5unVLm748t3h8bcNti+Menm9/f/Savv9+gMM7jJkfQDw8j94CR/ZWZYUpOfn+7FOdNJvClC3XIG9hXtmsTq3mqoXabngSKeMNJ/zmfNw5XH42KkhkOKG6ZlbaGnXgeOb8FK+8xtoFtjzs+LiCsJx8X6ynaldI56P9V7pBErXdf8z9m/FGzwC8LV9339RLNiAruv+RQB3+c7l3W3aAPDPAvhD2O5n/Ft9379W1UFhmJgdkuYWUMoHZMKqtUhIgWZgqNGSee+mvwvPjaURKouvtWJZMUiaMGrN+qo3aUBhwb6pW+TCVRdLgP2JnwX2gk/3DPIEyonkd83NY3WcVCs5agfst9R85CSfKyZUl7+SJj5pNthaowDQ3R3fXqTt+OJGNalVwemKtvKHu61nsvHeLUYPmaQFBeULVwpJUDrPpXPD+g6ZNKd8XlY8nRQuFWjqQ3rBPHFRoeuX/Tp9rnQCpeu6fyU+2EHf93+59byo83cCWPZ9//t2/78JwL8A4L8F8D0AXgfwzX3f/+Oqjjn3GabVMK5ouvZTTema2AUq5H9ilClrTxkk1Vtpf2UIt2IJnEzKAK7B00qsx1W07766l5TDBvurnTRd6+D+Qb4qlC7v6v7eAlss9sfvXvr5l/HyP/nSQ8H30mpfTs+VswytwodC8v5+6w5xJ25L7K1TCmLiric1XNBrDFFP26hlTKgsF52Y1Wqrr7q2LLZk+bq1rn1wy1jbU8s14ebCVXHQfiveTKv4OkHLykv1aH9dsQNj/B75ON7jgq7rfheA32bC8Hbf9y9NlHsFwNcDwPPPP//Ot956KzKJWzeJsC6E3EJTYaPfvIcNGN7MomVhZRTSYCr+GsB2fFifxy6rNqp2Kgsy0QLYWkd8z7Ezpb8cSS86IKgLtJKD/WeysZtbbE5eHW+L4TYbfR3A+fnQ1TrZ1cFzzrrBOoFaYbTQOJH0hhoFp7ta+ZVycotE+W5lSqh6lammpX74pGdeP8rp1urK+u4WGsdIlYRupk+vhHU6UHGlOeX4ej9ZNvG68qgbHy7skyV9cXyMi4uLzwrpPs43fT5pYRjd5L7v//jcOtxNBtoayU3zE3NTmK71uDZJTOfWAJ9VK10KPrD+u1rhbGlhzeeWhgtDBc/n13B52RRCWIVLUfVl4RqbUzdVL2ng/kN9tnp1KKyA4dvxPI54ju3G6tfX51i9OlaWuu+Qt+Dom/R0cnEcfQGCdXn8T3nIV1nd0nO3Ngm/JHzTONJK1ph4ik1q+5UyVUhKUvvjVikwdmVb7SSLU3Gs5qLPExXUPt+T4H3Uy10fJ/w4gA92Xffru677PGwXTP78ZSp4C0NmSYzhVpq6FzwzqZD+64Rw5lQmcKtwfTxtMWpdOtGVwZKVt8GQebgyp2Wp4RU/1nEqH5Z110wtI217if3FpLD61/eH1gKwO7Zl54aX2AvHk9vD+ww39mwtghDYWYrA6FUApB/PP798co71rl7d5gMMzyOv7g83ZHMlOVkd7oYl2rjwch7iivAGtbAir6rAVetqET5cOU24sq5T+e/CXk+IuGBKgkr7TG+G/ORnmD02rsCLYbXvTE/K3T0Z9mVlfddxUZq7rEjwRC1DAOi67gTAnwTwDgDf1/f9H7tMeb3PsLomXa23tPCRLMbzotyZrTJqG5U5XgWnF5aPv93q0/zuyqjAq26o1v8qYF3TJzyVeX1Pl69EJnoRmP7axxb44Ec3Dy1CvqtkLdanlllg767Svf7gre0qs5ZjWdanoDfcaBtctGE7/jJ74uwnV7Qe9ywqN87jlD6ZvU1IfmC/mAIM+RPYu6pJuSYeTx5GxVuKkytenwNJ6fqrEyojQEMNGoP1I49OK/+djKJkaTL9UW+t+Ytd170g/4+6rvsLU+Uq6Pv+rO/7W33f/4bLCkIAUGffz1VWAhbrcIYAACAASURBVEVvzXAhAewHVgeK+U/uD9vw+t0y1bJLy18JD5vL8dYUMk5iLl3RVitFrROfcMyvVgOBOFYvFGLfVHgxbb3bC8jJ9tJOEC6Xu1jdxf7l79rvl+/uzhAzVojdKwPOt/cQnl1shZ7SRt3q9f1tvevd5mu1HgirV/evIVAgjXjDzur+kKdev7unzwOLM6riOMVwfNSKdk9B6cr2SZOHt/gcjxX1Blta+B5ad6Uhz513WvnYvgt70h1Sj/IC61+Jl9Bqi/Uob3ts1nl4dTysi6/kcFy9DsW5BXPOJv8ffd9/9VTakwK/9r+laWnJJK3hwkMF3kPXTPYnMr/vn/IAtAvIZJq7YPRVPBWobuKnmJAPeKUY9L8LBG1bX/yTNDOZ90QWQoDx2WHSQrfUuIXOvvAdwdxMzUtelR5+xnq9W0HebLZnm1/68PnDdom7xig1JOCWkG+f4bMzOVECKwMMV/BJx8pqUbprfl3wSFaOj6lbd7qzQYUUIXk9SeAouIBSAegWJNvwAwxq+Xm7lXDaYLi45dDf3W53Upx0wQXynQyOR40Z/lrXdQ83WHdd96XI+w8PcIADHOBtC3OE4XcD+N+7rntlt8XlrwD4roky1wbP7741SExQjUCtoBdPah5IuloSC+yvgtKrhNbHe42lLoVqVX8Dmbal7usCe7eDLh2sjLtcBL/2q3KF1GrlMTh396t+8HJa3U6kfaC7QqvwbHcMThchSFfeUs32Njv6qhW+xD4u++Biv7CiluXrd8cnS269unONL4BuZxWqi0yXUuHlTy4fuoK6+DRYUT/e43Vrd0msehAL7AP96Q7ExBPnGHsAhIfxUXHh/XJe5yW1LNcXQ7orDmq9KS+5V0Oa6OKaxu883OJ9YJ1u/ao1mMJGzAcMX63B/Pwwf3d3TI/05j+fg1MuMjD/2v/3AvgwtqdPPtH3/T+YUfe1gLrJyUWuLn1UFwEYLmDod8sl0XruhTS6Cr6qpt/6gnKfRCzDXfUao6vce2AcU1Kh6C43Qd0sd6khfeAWlLP7Qxz0ynmGE06CS7oJ+ZVpOQH44nde0PDSarjncHV/v+2Hp1w2m32d3GTttNIVZRVcpJOPc7U1imML7F8poDTTCf3AQyuyf1HHDxiOicYi023uBC+7xP7SChdyDs4zmkbwkADzsy3nIy+XlLjyMvOkcA6snM9Lx8FxSbhpuau+He9Dfd//ja7rviY97/v+07HgNYO/EEo1nxI4CZpEZK0HlsbvtOqqz9J/Z6Q00B7H8ZghMBbumscnbKp7Yb8Vh/RfJ5EfNWSaxoVak8MF64lo/rXEyZiXe8a4iqzCkSvRwFCoqpDl7TXOBz4+umJ8IsJSr+/fWBmmnclb/ZQOBB8fehr+wilIPqaleGYq4wpZaaBtV8JIv51PgCHtqrEF8pyp5oT2ERjfW6g4JlDaqLJXfNNCiuK5wNWF4ff2ff9tXdf9SHjc933/tQXe1wpfdnTUPz+x6fqhG2U7/QlptQwYbx1RcGZUZqHQYCA8ldPB1P9JAFJwpMA83Ti9TkuZNglLnxhOOz/5wjIulFPwXPPz5AIwXDBRWvt7pwfCcCfM7mEbKH/tbHwixAUFF3zUcnWLmHByG/jIeonuw+cD19FPOzjOcxYHFL+VWPbOm85betaXkLyKtNDmVqArVhdC2k9deEkCjOACU+v1kzTKe/7WycpKrXizUvCeh3OPbabXlQJ7Hv/EI74q9Av6vv+HU2lPCvQdKEkz+SQlVO6JM8bKGDNpabc2mJbci6TJkhvkOKq2S4PrgqjqL7B/ExzrqVxjdwmTu+/auxLudJ2dsfV9ybpHkBc7qLXHcfH9iWrN6fnk1Dd9r0p1Y7Suxnr/VICRJnrSQhUX87vlpuOY+It1c1I7v8D+u7vpeJ6Z1etbXbQeV0qsk/j7qRK/5dv538fbQ1NOW68jWaxOK+1LMhCSZ0V4pLPJXdd9uu/7r5lKe1KgwhCoXUPIc3cFEmO5IDjHcOCZ37cJVBZZ0mhusag2rU4l6GUA51YmubcLy5O0bLKq1UJMzFgJW1oLbF+vvtJ6lKYeOyRo7JHnjdUVBIbnnxdSH9t2i1n77YJNaaYC6yTgqG6vWmGsl1BtPdnYM+eX5CVUlr2OTctFJ5ztLrLVW3G8bykOpxYlMJ4nzv/JwnNc09ys+pcEZTIQHB9/pnDVK7y+GMAHAHwMwB3sL3R9N4Dv6fv+Q7HgNQPPJqtAUuapLDlYHmWc5Br7appDy1pMg5jw8kFdmyBJbSULyPuXrA9ndhUkTkN+bzDGY4PdsbzN0A3S8WBet2B4ESs3XWt7ahXoIgTz0W32Eyjs02t39y+JZzoXdFJbBBegVb+T4EmhFtapK98ubKZiY/dCPhfqbrl53mT9u/fC53xLoQpqD+kA43ngwjDtf3VlvLYFuWQ8eFn+Jw4ugJPSg+UjTa4qDL8ZwLcA+I3YnilmBb8I4Pv7vv94LHjNwON4UwICyEKPBNJgfhr0xMTMowsCvilbmYz/72GMl9aXtDzz69GrxNCa360LZ7K1uE1qsfnkS4zlsSkX/GkCEn8umKzuD+l5Yu4pV9H9gtwTEY5av96E466uCn6liSo5LuK41co4JDBc+CA9koUNS/N86gYrpIth02UY+lz5nfnTDopklWobip/zib903hd3Wgqebetdg8BwXJKSVR5z5epzJlmmlWBlG0s8eszwd/R9/4PNTE8QWm/HU+2YLoNkHhcoSnwVDn4/IPOnoDnkOTDU0p4vTQDixrIqrIB6uxDxVLfU23PLRl3EJFiJS2pL61Ztr3dFsjzjcX5VljL8LRHQD3bCDdgftdP3nOg7VIC9kLwVrDs9quegEzItRrg7q6ERhcoy57j5gotbjAS3OFXhuFCFpXtdycrXsh7bZfsuKKutPSoInT80j5+fdnCrUfFwnIFxPT7/lcdTuIFlH/UdKC+Gs8n/2Yxy1w4qoJQQZGBlcOaDlSGoQORzf6G5E1ddCdaRzH3C2e3avWJ6Ct5TSAPDW3EIeqBf+0XG1TZIG48/arlkweh/3TS9AR4ugrA9rdMnvzIwBd0ZrZDzfX99oePhDdmyEfxMhCXxX93ebqzm7UQUtsD+XPJrd4cb0b2fDyezbILWCQdJW2J4Tpj5XHltkG+49jFyt494QmjreCt/J95yy0zrSOEAF8T6X3lyYKXLRvUNhhYh63Y6qVJSPJKQVxoprjomKtTd2HGaJLjq2eSntoDygaOj/uvfeGO0Ugdkt8IZp2UZAmNmrLQz0LbIHBfW4y6p46M4utatYiS68qv0AIaMUlmznuZ1s3xiqg2GG8nZtq/Oq3vVcmdSIN4FEb/JA04vjaFVFtgphrfTnJiLzFMz7Df7o252WnRwpeqrnN4/h8SXzk+kiVv4rtAU3GpKlp0LnIFiKM7ja/1n9sZB1usWYEpXqxkYW+LuQns8Pwk9nwvAo1uG7+i67vP5p+u6LwTw+Y38BzjAAQ7wtoM5wvBjAP5S13Xf2nXd7wXwFwF8//WiVcNnkc1hYKzt/GorYBhHdLfONZ1C0r50T08tX7IKz9E+pqcmP9tKLjN/L+Wj+dx1J6gL7e4x8aAbT1dUXaIlhhdvsg29d48xQlqKS+wv09UFBKe9jgGk/tXtfb4FttbbOeRVoUa/c2wXVZQv2KbGLEkzPa2yEuuVG7/ViqdldM/qYBvaJ8WJ9bob6HQkqCvpfHRyPNxjWfEOcVarUeeMXscFDC94hdDr4XOLvWq7PM8MbI8ochzcinSLVK/MI52S5ao4ubWpfWfa1OtAWzD3bPJHAHwdtivK/2vf91e+z/BRwa/9dwFWxQd0QPT9HVUcwV1jdyfc5T0RV8KD046TTwCW1427KhS9j+526OS6dTxc9KlcxuTqAHuXSNvzNpzhHTfm18nAyaXXg3FDtQP7oBeFuntKUDzW4dib9pGuMRd7UtigCgVoXt+8rfm9TR8HxRsYClWW1/HVfFWduvDngtr7tGnUxT7ogpiOeXrHttOAfV/bHNNQA9O0Tf7X1/q6m6shg/Rc8wFj2m3wDL0Q6nEAL2pIE9JjSoSpJXeP5yykXMrvAkBXHNV6UFzOLa+2nYRlSkvvogXGzA4MLT9fuX5gb45zgema3fuu8TUtly5v0MlHfNMN3UoLZeLE8ErTdJyNbbFdWlN6IkaFtQumJAg13QWBj6nTlGWYN42vt6W8lN65nO5f1PK+Iuztu6fE9ohHokPruJsr8IU987nnPMtv3QWSVrUhz1Sgu5DUNnQcHvUEyocBvAzgywF8HrbX9X+m7/t3NwteE/imawXVZNXiBDAmLJBXeZXA7t5qG858qnk92OvffoZT21V81b30QLwKY83nfVQLyfvqLrivNrtl44yfFrF8AnIztYYLkiVJUOFMvF++u7/GyXFTHHUfKYF59JSMvx50UZRP1pXSms/9uJr+T1a686zSBPK/5QGxrCqellBIFrG2r2e9kxBzq0/7wXac59xa8/4pDtqmlktC1eeT1+dK71GF4aewfXHTD2C7Afv3AFj0ff/dzYLXBLQM/ZQFMK2RKuGmDOkukA+mp1eD45aZuxxqnSRcE37ucjqjeD/d2lrac9btt9OwrrPiqip3cRL+Cn4EjxPqzKwMv8TBhfJDQSWXQWg/tP8VXtW4u4DS/H48T78rxVPRwy1dzZesXK2TG8H9MhDF1fsFDMcsKRCC7mklbknpe6xUf2v/eZelCnmlyZmdfHG6J+WYhGMygrQM+3aOK55AeZih6z7V9/1v7LruJ/u+/6pd2o/2ff+bmwWvCY6OjvqP7rbW6HYHYLwNgqAmt2tygluRyWLUfJB63E1SSBOzsgA1X1VWLU236rQPvuncwRmNLplraT8NohbeueVz+qmQ1frVgqn6n+jJvvv7fXkVmOKlFtlKhKdbTT4WGk9mm8QnxaxOpB3Yc7cgtQ/aV+e1lrVDcKu7Evie163ZJMj5W/vrYSRXyK7YkyCu2koWrNfpdPStOC1eUpo+qmX4VwD8VgB/BsD/C+DnAHxL3/f/fLPgNcHR0VH/4TfeaGpyd/mS1eAEd6arrBx97lrKJwHs/2t39y9lT4yY6k4MQUhXb/kETIJfhaWnpwnAZ46rTmyPa1VKwye5uqMq3F0oa72az91S7Z8LVL1kteUBOM21H0pXt/pZj4cwILik69cUNtjGdG+9um9TFSDTfDw0hug01nzKGykc4jgloay/U+zRQ1TJRXa6erstTyPh4opE8VPj4Ur3GT7MsH3nyd/D9sb97wDwHgD/dd/3Fa7XCn4czxnPCUJGAeoVPIJuEAbGAo5pBDXPWb9rXNdMbmm0YiHJMlDLRp9xFVgtQoJbytonF4oJD4UUd9KJqoyvL2NivqkYpAoYbY/90AB+oonGT91iSmPiVk6irVq+PtF9Iiu9dHW7oqsrCa/Pv4mnP9O+OQ3TGGp9Vbw39W1R5Pd8aRy0f/xOhyf0ub4KQIUt5Hf1Mi/vy5RlOLnPsO/7v933/S/3ff+Lfd//J33f/8GnJQgVnHE2GL5vQgeAR/Mg/72eDcbH2lygqiXmcQmd3PruEB1gfQk7n6mQJLjw1j4RWIbMxnCBW46LXb+4l4xWEcFftwrs6ar7J13LbqSsv4GQ+dcX40npv8/lA3nOvi0glyZY3FEnmL/TxL/1Q0iLGR4bTh6ICl6feKwX2Mf2dKzWx9v+uBLVfiXlkuJgS/mw/DmGRxAdL407q3D3fp8Wv8k7LsBd6XifgOEYOD+zTrU2ydfkbVWYqtB03qfxcAOlgtatNT+FxlvwGD980sAFFCcwMNQSlcZ2hleiunumZn4VBF7Yb7c4XTO621Atgmjf/OiRT+IkZNzqU2ugsjY8LpQsK157f47hMTw+d9c0WVbA2IJNMU6WdYsQ9lzpqtal5lPaAMP9lDqhk5ZPlk5lXSqf6eT3eLXng/XF8yXl7/2vLDuvU3k6WblT1l/Kl15PkbyYpdXjrrO2o98+7/wiDJZPY6hK8kq31uzc4xL6vv/brefXBdxaAwxjHgSPtU0xoBPPN4b6YLINLUfB46twwJ7hVFBUVy5ByreYMV3S6Qyu9bj7zz7oJGWaC159JzIwPqfqfU1KSK/Gql6roNbWSdj0q/3zcfQ+6wu1tC8+sZLFByvjedWycr6A5dGYKtNVYQP7TehJ8VaKif+5ad2F49oWF7RvKX6nY6j5E98nhaD16Dh6PFL74AuaxEnzOv1Yzud8ilezPufTVszwuZQIPD1hd4ADHOAATwPelpuuee1/sgyBvWZKJnPSsgTXzP7c06jJ1A10a4v1EtKChV/mmQLE7qZ7/R5LSZe2Kv4LK6f0UjdHwS21ZMkkq4+46gWvaRVW++f1pbrdovFQw6JIq6wtd/u03rUd43OrtHLLq9CN5/HV1VZ4w0MKPpY6ppC6knWdwHlJ60p7ELXPOnbqDSHkS7+T9Z/wI6T+JJ4gXVr7DOdc1HAPwDcCeB3AFwL4fdgKx6cGKjRWx+NnHITkgqrp7qtywJioG/swvwopbS8JwiQYFxji6gydcPZB3liZlQW3tT9kTrbjTKJMnu6d4311Ltg3Vjc/GtwnflxQUCGtZVSh+MIBA/daN9N9bCC/na6cvB6rcnfq3PIQd1dGU0LF22IZFzYqVFwQOo+74KuEGy+bcKNAx0Rx8nwe0ybPEwddfNEFL+3zqX1rex560PnjPArLz/9Tgl3rSoJVoXSTFfq+33Rd946+7/8xgD/bdd2Pzil3nUBCeAA/aWslmk5yYDy5md+fedxDBWFLiyULS9t35oGltwSrg1/nnywwbd8twJOwZ0/rZn9gz0lftwK03bQ4kJRQoucCeU8gMNxyoWOa8PT2lhif+PCLKjSulRSWWmvezyQ0k9WtdFFLknzMcW29sAtWr/L/BvtYuCvBxLNpnFifK3tCslSr2Ke3o5AWN/nbLVWlZeo3LO8UzLEMP9t13ecB+Imu6/5E13XfAeCLZpS7VlDG9wnk/5Vp1nZLs4JqUkgZEpjuErCdRG41KsMS1BJ1TbYJz9Okc4uQloL2wa2yFNhO+VVjUggoDbilSIVa6oPjkASTC2QKbDLrPemb1kXw7R+r49yOKgEqBld07AuvGiOt+V8tHRUOr9/NC2gKSjvnJd+GpLSi8Eu7IZLFq3Vof5Xe/Kwuxu2SV9wqVrw22C7ynBd53YIm+FgRr2XxjDymQlXHrjIC0iLNVJ4K5m66/rvYxguf+qbr9x8d9b97dwIFGGoPIB8LS5abE1cHQAcaGFsdyjSqyXULiFtEuhLH9GpVTnEkTvomOyC74xo/UQu5Gii3RLT/KebqwlzbTdYZwW+7Zl0+Nm7NLKwOf4XAwvAGxquR3getR8fSx8CFpls3fl2Yx9TcM/HxcOXN3xuM23KBqm3qRE/t6X+C1ud8z/767ginGzC8sMFxSu/5UTc6HY1N/OC8W/GgPnd6ab2PdDb5WYO0tYagRPH34laaNQk/yPM0oapJ3FpAoJZTAQvsGU6v1XJN533QHffAWAC6UJv7dr7ElK0yKnw9kF5Z5t5WEqx87pOqwo31KB6OLzCMgQHjiyFa+DHNQw/JSttYfrX8iI8KBcVTFV9SVEDeOlMt4igtKro7XVR4pev1ndeAIS0VkgAH9mfMPXZ7ywSsv3ai2p/qgpy/tT8LXHEBpeu6f6vrum+X/z/Wdd3f2n1+Z1XuuuEtDJlLhZnfOF1NHEgZmu1KUGUMdRmBcX0uaL3+lM6Jroxw69UsOLScu9q8+Vj7vT4eC1OdmOr68blb0gpqrRAHplfxIy+bTqgAQzpD8um4UljoOLMfeqqG5WD9U2XmE0ct7TT2/O+W6MbyOx9B+qDjrDTXcIPzykpiezqWBMb+COQJFWLqNhNYRsMLxP3EFmkYTlJlqYLPlf4qWO38Vt5iv5VWZ3f2fViLZ5Xe8eJzQucnv33hSEMxLWhtuv6rAL6h7/v/Z/f/J7C97fqLAPzZvu+/bqLuawG6yekC1WQuVxocUkaf6517C6sHqF/C48K0urTA21ZcFXf9riaGl/HgOKRMWnXzSY5GnnNMnwRR8BMnmtddOmBoWailnep2q9nprf1yy0PLLexZOtPcshJZl1uG2gbsuQsWr8cFEL/deksLUpUC1vxsq1rUIJAevi3Iwz0uiJKF7Va75oOluZej+Ggf+FzdeSo795i0f1d9ifyP933/m+T/vb7vT3e/P9n3/YdjwWsGdZN14gDZGnJiuKXizKguCpAFE1BfkJoEsx9b0wmc8OcgK4OptVQBn3vsjXWo5aB98cnq/dPy3obSMwkRj5G5cqlA81UT1wV9Eore71aw361Lpik+XrdPOBUaagElGiOkez/ZL+/3AturyXTLj+KpwsSFoOPgStYFCi3H9KpT/Z/GKCkX/Xb6Vfl9vrT4yZWT8tGVbq3pum7T932ce13X/c2+739DLHjNwFtrlEG4Avngoi0E0EgjcOD9yFklVFxAJUslbZpm3qTNXeu75Zm0bEpbYvuCpNWrw+eKP61CZTJnZmDMbEnAJKHn7blV5paEWoRugae+u2VQCQJgP66+0KWTBcjCQMffY41JULpCcz5yYae0T8cnvS+VYlQhk/rD8fbYW+Jr/m7xR1IayQJNgnlhZVqxba1XeSSdhXfe3GC7Ir66f3XL8M8B+N/6vv/Tlv7vAvhX+77/xljwmkHvMwSGA+fCJblomg+N/5VmryyVKr+7f8n9SUzdEiCsS+8CbGllvcdP61acVoYn0F5Q0nr0d7KMtb+so5pYms5zu4prutHFg+7+UiHvrwsKtq0voEoT0QVx6hvrTHRASNfFKH9niVtrjo9bkh5CcUHnlvHS0j1/S/kCw/rSfCDeat1qG65ovC+VwaJ5/XflHRCPljB8rtHedwD4n7quuwPg07u0F7F9Z/K/3Sh3gAMc4ABvO5izz/BrAXzl7u9f7/v+h68dqwZ84Oiof+cuZqi3HANtre1WR7IM3WIjVLdi6ImNKTeWWs5fAJVwd42n2tJPRDBfso6TS+b4uXWWYoVab3IbKysvWavVM97A4iuNjkeFG1BbfVN903ecJEu95f4nC0bzp1evuvXuNGW5BxfbG3xO7merTtvyBTy19JjmfACMcdf8t+z1Dpo/eQLquqZx9z56n9wy1RukKkswufmtZzfqVaH+3mRgOFE8DpHM/+RO+ATQic+6tS11U4Cxy6H1+BXzWjaVc5e6mqC+B0tdEdbXWtWtXMjEyAoeCnAXyPFkHk4Uj18RKKxcgDi+Ot5+nRnbdGXDOnTR59zSNZ+3q3zk/U3KweuqhIC7kwkoGFxgbzCmibvPqa40vr5gt8EwxsodFt5fx7/qV7UK7b91jjqeKaRSLfwwvyqGKy+gPKvAF0KlVcFKE7ngqrSIDhrLO5O6VZUszEpz8b9bMvpc6/HyHhvxuKlbVRUuinsV2E4TSRmwsiyr/C50gWxNL0Ka9s8XJfxkhAsmH3dIWZ2YFJC+wq/1JCswKc1E21b9wLDfrphcwG4sfYp3HG+WU2tc8/viSmX9atxaz65znBSfcwxxU1yYL1l/bugk+qug9X6yLPG6UZbhlx0d9c+bZUhwrZeYyyExUku7kql18mkAvwr8TrkElRBgXn7WtsVB69d6fUJ5X4Ehk/hCTbL2qsmndfv/yoJ2vH3xyfPQ9ZoSxKRj5aolazbhnJSE16E4+KR1HqoEqvfXPQIgX2iqbSsN04mVap644FHeIS7aP7+Iwxe4vH+VNcz/jovSIrn6zlOOa8WL2s6NOo73gaOj/qt29xlWDK8nDZSxdDIp4TQtCVAdSNWmnl+FpL/IBsir0JC0lWhZnQDJQvVJwL4sLE3brcBdjZbFkSwPPw8O7F3dZBUm3F0Q6vgkYcFnJ7LXjmlaXxK8afK7AOMN334eGhjHipV+7JPj7ZPax6llfSalrH2qhEgV4vGx9Hb9NnMvy3b1v+LsMXaW9/pc6FdWIfvg/Lcyy1YVobartLhRbvIHjo76n90toFRaOxGTUFk2revEE7OzLbcANsjChekM1nu5ZAkCYwHq7hPboDB2dyFpVhXcFY4uyFhe92CyLrd8mO6B9/T6zspi1Lo3GF6P76DWrI9XK7bpQj95BEoTYB87c4ta8yeLqBLEbNfx9TFXoeECJdUDS9cxZV2tst4Ht3J9Hq0kLq78kLwxbU+/nXe1z07npHCY75YYIo7bjbIM33901L8o7012TeGxCwLz+vlUhSRU02qgaziPi7lF4K6dDqgOcrLq5jB7EjRuGeiE9ssJtP717sIIF1apvmrRgf9VS7t1R3CLmxPIX82p9FM8XFi4YHGoFp2qCewrmt5XF276XmRXaonv2Bf1clovxwKG7Xkc0j0K9lXniOOXjrEpbVwBuQDSflTtJX5byQJNdcxT+dDnfBVySEYSoSUM0ff9E/kA+BCATwD4RwD+kD37Bmz3Mv6BqXpeeOGFfrl9a1+/kO+FpQF4mO9U0vl7fbz9sMxSni1DfYtQ90J+n0qepeR7cGffntbN71Orn2lev9bruEDKMZ/icBryoqhL8dTnSkOt23E7NTzWx+38PnZO9zQWS0s/tbJsk32p2l5iSHflh+rjdDy7Pc7D9rWM06D6JDwSTp5Xx1nb8n4qb3i/Uv3OL6cY87Lm4zOnleLmc8DnoOPu/Ox9dLr7GCk+L774Yl/JlueihLweuACwQt6w/Q0AfhOAP9d13bv6vn+zquR55DgQMIz3+BVH1CjUwLrVBRhbEZXmS5bOiViPrjVv7a7lemiyhxeLuwb2eEsVeHZ8NZ9q5+p2GQ8N6PaNBcYWUIo1eX/VklxgHxJgXRqTBIZtuEtJt1c1vY73AuOTMwuIJR+u5wK2Y7C5P160WV+M8y7tt1psGwBndjZYzwv7YgbL675G0kT7dGaWqPed9FtKX9mWHknUvlTjtrAP8UmWHGlxL1i+rNIbZQAAIABJREFUBD3mSDwrXknx1nRburZ/Ytt8NA/DAG7hersVPHE3ueu6uwDe7Pv+v5S0HwLw9QA+BuDb+r7/par8B9971OPn9zHDaoVYB8vjbLr073EtZwKdAPrfGY3PfOvEwvIoXtqOCwgyXuUmJgGnky5tD0puXpr8vsCk377CqbhoH1SIrHcCQl1IQgotVBMtCXTdWuN0cdq34sXpslGCxyKVp1ohjqpf7t45f94yV9bBeUdftKWr7smF9JBAMizc3dZQgeKQQkP6v3K7IXl8TDRN+elMXsil9bsRwGfEUXnhHM/Y1ppCGH4zgD8A4GN93/9XrfK8qAHYWwIEJUzF2G5dkGAeb+CzpGX1u2IwFZC+wKGCJAkDt3CTFcX6gLHA9Po8f7UCq30828UOk4WkdSpT+uQiDq23w2neFFPT9HSjEOvX9pf2nOALGeyXC9CUT38nZZv222mblWXitEvKluA4Ku0XyF6L4uvGg9PaFS75lotXvrCW5pfH2H2u+e9kwSZPSMslK7mqz+fmo74d79qh7/vv7/v+qytB2HXdK13Xfabrus985jOfedLoHeAAB7gh8DM/8zOgLNl9XuGza7UMdzdl//7d35O+7382WYaXAX1vcjLNEf4zTTWagrp+6ciRQ7rhxd1TN/1Vm9NaoDZvXWXuWz8U31Z/3dJ0q/cc4z2Tehwu3dyiv92KW0pZoL4iy39reSBfyaTWgo+f15vcfs3rlnhrj6RCshxbfONhjzk4aczUxy6NReLPUwzPM2s7C4yv52ddq+PxnZtq+fu4pFBN5RGl8IZawcSbnl16f0rrfk22B4zni4eInnk3+TLATdcpruDg5r7GbFywqHvigXVgT0xdZNhYfqalSaltKG7VgJI5q3PNwHgS8bnTgG3QTW6dUNAJo0C3fX0x3LfFtv0FSR6vUTqlzfJJoLEeDytUgkLpp7id7d5ouLJ3H2uf07iwrlMAL98FurtDAa/l0ns3TrGN/6lrn5Ss4+39T7/XJtQSD1SCQumWLuaFpLOcH2RICs0VDX+n8JHjl4yapNA1Pc2tVP8zJwy7rvtiAJ8C8G4AvwbgTQBf0ff9L16mHh7H80FF+E1QgvqepmpAtI4W0VX7UJAkDatvEnPc3WJNwg4Y91dB6/IVubR5XGNO/O+Ch7eWsG1fyUsMqP2uLBpXHIo/+8AYXFocWdkz4u2WSvIc0sKH/q5W3lV5eoxLBRFx1bxuoSQc3WoHhvgpKL4OzjvAeC+iW3eah+2mPrngq4R28gjmLCzxd+qPK9TkLaR54bz2TAjDxwVcQKlcBR8IYHw8SBkpWYZuKbj1ScZYhxUuH5RK2zlDexuJ2VruFrV3EnrpDXMEF9LOcJo3rZDrRHHtrRPPj+fx/dNcyPFAuS8QpLHxRY4k9Dzdx6Oa1GpFuytdrbhrfa4k9Zm7e5VlmMZc++H4UFGkTeKJp/0q/5b7ruPr9WpbKvx0DJIXpPRIQrBVP9vXcUiLXm4ctIThM7GA8iiwkI8T4xR7YpxirH2VOdLE4flU12rMo24hywBj5lHm1biaDmb17g7tH+sm8O147Cvf3cx21O1PsMR+H6Cvfipu3pcF5l2xzzRth/06ub8/5K/9VguV+LB/C/us5OXmwJjWMLxJd6XhUvq7wN6l1jp8bNKRwtXxvn5tl31wfBRSbO1cvhMPKR3U6nxgIZVFqIdvx+Nb+PSGGcVDw0kqhEgz57NEc8WD4+rtufXmtEuryzo+bGNjzzivFJcWvO0swy87Our/m1tv4OT+2HLxJf9koldbUFwbu+WTtLa7r/q8CgC7ZZNM/JYVWLkEypxq1TBdJ6qGCdT9jBNH4m1kZqWtb+fwtik8GONyq2oK0oUVVFLshx/5cmtDF2d8nGgdOV21jIZWfGzcZdO6gSHfJD7x+tzSJi4utDQckyysKR5SpZ+2wzjebuVxXFUR+dsFkwCq5pnynvJIsprJZ6k9lwda9hTAJ26Sm6xnkx2SO6guROV2JGDcigsGerFmdSFCEo4pnpgGzhcl/GZtZZL0Mp802fjcNTehuvnZGVv7pxMv0VvrcAbWNhxcy2sdaQLB8HHrVcGFDfOpwGNd6kloGe9fhbOmJ8Hk4IpX62iVcQtVeT0tJLlwhaT5c39hlhsC5GVflXZllBZuUjxV++s3AhGSEeP8wXxpRwfrfybOJj+uzwsvvBDPp8LOIqLxbGFpwPB8qz5PbbCcnite2m+va4nh+d4Fxmehl42yS2uDOJ+i7utS2lli3Cctk86Oen3AuM+Jnt6XU2vbv5fYn/FNbRN/nidfYEi/NPZp7JwvFH/HQevQT+qvng/2cT27vX++DO36uWp/pnTw8/Rp7J2+c3nc54fztP+vzhD7+WjnB+XZU6tHzxY7Lt4PbUvPgnsfvXzrbPLbzjLUK7yAYVwDGB8NS9pVNbCfHwXGmrayIlTTwdpzDXiOoSUDS3P82Ae1StKKqQJPjSh+PAqndfnxKndZCKq507VVLOvWNzC2NtwCWNhzbTPhwbK+QOC4pPPA2k9g7xam/WwLyQPsFxgW1rbX6VaI0iT1W++71D4ka9rBx9/7rW0qPs573PpDWqQ9nimkk2LEG4z5imX07L661WmFuVp1b/GoW9bJYicNbtR9hkdHR/175SXyTjAgxzwI6Zwly2xQD7S7FjrZ3EWp3pnsboTi5/mSC5hWxbXfzsTuxmofKvezqjPlcSHo8ZqWoHOX9h72509b+LryqXB3HtD63N1SWnlfXKixzAL7PYQ+ni3F4PRNcS32N8V+tQ1g3kuTgL1SZBvJlXR6K+7arrvjkHIuiJw2biRAyhBnvxsRoYy3kRSr47LEDYsZ6tlkgmobF1ZMB8YMCUlHKKO/q/gSIQk8ltX4SDWBHR/fsJus1WTJKj6aN1kaXof+d8bbYCusqoWrpES0DZ9ICVwwKE5aTvNR+TiuOuF1ZZq4sJwKAC3riknzaLrzlOPD+pIlnepXgeD3aFZtu8B2q9wtaKad2UkVf548kaR4gemxZd7W/NoUz9muziF9Xp0c03Fh2ZYwfNttrXl+9+3WiS7xJ0JC8rvrlKwhfuiGu5DloPCTNKJbQvcwnByVQGTdK1shJY6JsV0LuyB02ijD8DsxslqlnDhL7Le6aF+Uob1+tw4WUo9vo2E+TdOJfw975q9uznZBx8/6eJtXlRrHb317/4ywxNbNSyvgblW5MJhSyirIdAw32C8inN3e94Pbd9SKVcWs+FULFEr7s/tjwb/BkOc9PfWd//nhljY1UtyKJPgYMG+y0nW+uODX544j009uF0js4G0nDA9wgAMc4Drgbecm86IGDf4mt1c1TLLakrnvwXTVZnplvluKLKtbXtJZ1cq6c23n8SNaK634iFuCWq9rdWC4yOA0cdpoGyl+pdZUshgUF411MXBPfNQicVeILh3zs0y6Hsxpq7i0LHKEdMVZLTJgaJ2lGHXyOgjKH+5tPLQO7Z3QaYtL2m7i1q0+U1x0flQ0Y5602KS4QJ65a6pz4l545i4yn3tdzt/Odz52bpkDN+w43geOjvp37o7jpZVNAomQgsXVYOpbwfxmGmcQYBzb0pVjx8P/uztQCWOfAO6SArUQdNc4MZHilYRVypOEsebxPuiZZt6NpwKe5Tjp1I2sBJa3x29/YZXuEdXJoePq7lRa5UwKKylVXUDzFVhdHEk8lAQ1eTgt+izlt+LhCxzeJ46J3w3ptHa+8kUPj+PqfYce59ZwC+vyuG2KUzs/6VidiELdhPJpvt+4F0K954034jVLBJ/YU0zMQVJGrVajvaw/T9qIq6Q+8dViO8fwZIVPgNS/ypLzvrul65PVJ+HC0hVS/GZ9ext/csb2K8J8gSfVXQXhdYL7eCSLIbWpSsAnIC0xP9fr4GPLupwHNJanJ3BUkGk7G0tTWnheV2ouANwKT89SP3yOqJVJIavt+UIQJN35pPI22E9XUnyWVrXdmNC01F8Vuq2tNW+7mOHz2Flu4j4wCK8DCOTJsT7OgWAuhFAoUoNpENjrduZV0AAyLaGT4/FEVY324GL7WR+Pg9ren7SnLC0kITwn6EQD9jT0Nl2gOmNuNnsho+3re2gq7U3woD3broL5iebsm7rc3t9k5RB8j5zSZimfBcb8oDgngaRCluOrY+ynikgvtqd1Q/KeyrONPSecYr9w5Pi5lcz01fGYl1xw6bMl9os92i8VZklon2K/QMU0LjTC8nsfdQ6ksaoMoAredpbh0dFR/2E5jld10gWBuxQuMNUiVAGTmN2fpfbdTdTrplI7WvfC8m1CWhIoOvn8yGAVTiCOTK+sCtZfWcLeb7UeWmdImU/fkwKMrX61ktSVd0tOY1zJCtS+LLGPOwP5olfW7a6f4uU0cOsvnYnWur0+7U/iMRcKrEMFauIPLesCUNsGcjxU87nnwD77PYjarrbhcURXXmr9aX4X5O5JJM9Cy9xINxkYT3K3apL7wnxJENG10yutgOFvZXTV5MrEWi65dokRtQ1lEh1IfQeICxSfgGxbn6U+J0XRctG1vOKsVq7XrZPPLRe3dp0Obg1NLd4kyzKd83ZrtxXnrTYp83+L7hsMX1qVFFFaRPB+JkhK0a1oL69CRmPcTmdgf5Gx15kUchqbh3wrG74TLjpvqz2e7i7TwvaTVK70nbdv1BVeb8nvZBW6m0FmrMxugroz6+PhquXGPpyALlSVmelicDDVvfCJqK5OcgccVAAtd/1bYOhee6ypEuzqmlSWqtahdblwqyzGBYZMybyan+2f2V4wt+Z1Ank/FEibZCW4q5ussPXx+Ao3H2+1ZDREobwDjF8Rofjxt/LWWviV6UqzRAPYc1ckKgSdpr5oRbwowL3/zuve/tquQTu7Px4f9htWv1t9vKYuKWx/NQXptrF6HM8K3naWYTqB4i6Ia1rdouDmvTMNULu1wH6LB6yNKn7oAkTbq1zyyiVjfa99comPfPi8HGTVmjphW8JOL19lHtf0Klj8OUFdQ23DxybFNVe7leZELz5T/IEhLWFlXAkozm7tqLvmbfj4sl2Py3n/PL/SQPun+dVSZLnqvToEteSAvTVXWbNucTlfKKTYm/Imn+lOjNSO45rmYuJ7NzYWGFrajpt6Aok3bpRlyBMoqt0VdMA4GHrO0TUusLccHw5scUEmsF8MYR2Q324NJUGY8rI/agVovYrfBsDLJ+dRiA8Ei105dnZ7aE24Nar5mZ4sDe+ba+FzjC0JMvnJ8XCByoP0J/f3ZfVy2PPds6Tl1SJNQlRxXFugnvxBwZGUj17Ee3K8Hw8uhulkTLhMWVNqBSXl0noNgVuAq4u9QKpudmd+d3O1nlMM+3UP29iqChYV1gvsFa/emwmpIy3G+PzRuavpKhg3u7bSIs0G9aKK/0/wtrMM+RJ5ZwbI7xS/4nO9p46gE0Q31rpZDowXAxzcNdOXXy8wthpSwF/rSQF6tz6SFQQMGXyD4VX72p5akeyX9pdt6Pc5xgsYynAuFJK17BZXsjqqdyXrb7cofHM0QQP8yQLh7xRD9DS3+HxiJ4Wpws5dRO+HgtOwsnY1zfnJ457JYoWkpZixL4Yl69QFUBK66bfTy18a731k3R4jrAQ889yoBRR1k5NZnBgxBVUh//U6JS1HcIbRieyWUbp4FVbWcareLOdCQyeM95Ptkh6LUE/Cw12J5CZqP9JESfW79k/4a3tarzO9T7pEUxVyBO9L4hVd/WRet4KToJujLDW/fvtiC9tRSILG+TpNfP1dhSySIFf66yJGEjpJMDPfyhQr7Dnzp9CA51FaJf7UMXC+guVnnhu1z/AABzjAAa4D3nbC8J0YBnVVQwBjDcs87qZq2vqivfVBrRyWZbB6ibEWTpaGWxRqOZwUq23JWmA53WTulm6yZirXfoP9e04WGN/QojhXoGEFXSV17b1AvedO3daz23v6Kp4b++1WidLi7PbYgqWbrNao1uuuMelBOLszDBOcW1nW7yu1ybr1sXArli+78sUUtaySBa104e0+PoZu2TJNx05x1TY0b8q3DvNIrXCCjuWt42GatwcMV5OBfbxZ5xv5XD9KwxYPA29TN/m9sprsA5rcESWGu2JTbprm4bMkbFiHp/lFAkv7r6twOtE8Dpa+HdLkaK2Cswy3MKzMvUn9cfdb8XW8KreI9ehzF0yenl6B6ThqrNjpBcvHsnrTs8eKtQ3idrY7euihCkIKTfB/UgQae3XeVXxWEkpJil1xncOfpIO2uWnkT3T0ueeuNseiWsH3sUtxSlcYSkfW4SvZrTDCjXoHyvteeCG+14Kfpb1PQr8XGL9f4ez2sKz+XwD9gzvD90kwfRnaW1jbS4zfw+B4aNvaxv/f3rnHanpVZfy3qKWlFkpLGyhlaksOpZZGEEstBaUKIUMTIyBYguFSBYIyDDRBUf5ACDHBoKLDiGC4tNykGApobY4gUFCKlFJbhpuTIwPS0AhaKTeltt3+8b6r5/mes97vzAwz58Z+ki/fe9mXtfZlXfZ+995+bsiC5JtnPviZGFW+TPw0nNLl9FbpebjqnfPkPDu/UzR4vKyzrCelb3tBg6eJha/Karv9qnLUNJ1PPcvDz2nxs0O8fXp72lHkp7R72Wwv8qj6iNJX8aZhNKzy4+3b6yLD5bk1Xq7Ou6frz71OK7q8rzi/W+oMlFyBooPhbgGqa1G5VHomg684WbI08j9dyV3XrrQaUiO65k2oNahh3EWpJoJ01xXVvBpeB+81rqIajM50nNclVk7qKF2ZnvKzyEr+NF7G8f9qMgGWVz8obTrrrfHVAvG6V1SWtVs3zpdaTE6Hpqtlu1DcaxoXWtm6BaO0KjxNt7oqS1vz0DDuGfnQSGXhYs+rNp/htW6ULw/rvDoNFf1+r1bldovjkzRbbjb5vG99a6aRVsIAZivTG4IL0GqMo6oYL3if/fLPOrxRL7Dy84R57qWP9/l1hnc6fZ2tCs8Mq/+ajobxRqpxqobpvFbupq94qGjSclrNfVVlonXpdCJhVaDM49VpqwSDP9c60nssDMDOp8POd80OBXi7mCeQ8n5KASbt2V4WWD5OomrTnn4lnN2N12faNrMOqzy0DDT+vP0JPW/svZdxdTjYljoQKs9NnldJlUUA8y0Gt4zc4vHwbkVW9x4eZitZhfQuswDPMCsoaamgYbwjTgmEiraMX53zkvTkvVuhPibk15r31KD8ThvX3GnfaDqtVX27pbmrWLkyJQSqDUx9QsTrPq+nytfbxwLLZwNrPpVCmer0eV8Jjnnt1r0pbX+edtKmm/Dqhgr5Xjc9rqxNzUvTdovOlXNlWc5TDlW+VXnMsww33WzycUcM/175CS/c1czuBVaejDZlvlfaPS0dJJyu9lDLRdNw4aGzcIusdAczjczXtWp+K+kaPv+3y0/58gbnFlPOSO6dWNrn6Wi56nsXJllmea7HIoPg0wbta1q9Dn2rs4ynwitXtagbqOWQ21vtffpsOlp/3tbyemcxY+3wCQ6Y/XKhEiwqYCsrdYmVB91XykJ50KEapcuFrwq7BZaFtgrenMVdHHm5SoYMMm3tW96+3M1XWrXudkt459+hfOW6cq0//560wqazDE87/vh2pFiGvg28d+7KHa20SmXJeFiN451eK9EtDteAKhTdDcTeu1BNePqu5XWG0gWg8z/lalS712xn1kp0YTRlFVQWkNeVW0duCXgH8obu44Ze1kj4amhhqoy0w847PzrDeLpOr4abZ8Vo+fqO3fpftaEpjyYtvV32FUPlzbhVq3xl2J1mGVbW8pRx4dvMKR2V8TLVbt2KdOjzLbU2+VZmG1xqqd3MFqZrOK30Clmgep9wbathqs62KM/T6qmEYNKzo0gvLRaEZk3XB4r1Whu6u43u0l917kqLKa+rowEWGaxEpSctALektDOntZzx1ApQflQAVm5SCjKtR72fCpP0w/zNQ6s2k+l62VXKFblXfvNbOk1LFUaWh37XqDxdJYJLy8ItcFc0Ljh3jS7vXhGsSxZeBU21o5LmpeuhVQhWCn578UzLxBVbZbR42er7ypjQ5x7Hseksw1yONyXcKgvLrZGpQf3qO7CqEqY6gGpLP6jeLQ1PF6YPkVqaiOcWx3Zmvxd0azjjukZOaEfPrepV8E1ZylNa2cu7mlxasnfOh2LqQKYl+8+0fSea1Qbwta2o4KzKWXlzy8npc4veXUelR91fFQi+1rgSys6L7mJTtQMfQ3da9EwTV0beNqcEjVttXk5T1ntlGXqaCbfSFT5OuqUmUPRAqKlCq1xI7zBVg3WB6Y0jw1fCRPN1AbDE7KC5QyctEu4+uRCpNKzSq5+mVPlV1oC781q2rlS8cWq6mg8Mn5LkGSmZdsZzQVMJlkrL5/ukt70CHvSKWR6q4Qo/ZdDLVLdv03zcYtL05p36Vk2ALFCXs5ejKyenR+tPBbQfipX8TZWJWnQwO5nn7XiJaWXvdafPpnYRr+DC2p/n9RT/1RBN5t/XJnd0dHSsgk1nGeZ3hrBSg1TaOZehuTUypZnd9fFJhMq1ynTUYqqsthwHqz6WnqcFfSawchOm3JRqOMGtZLcWMm8/LtTzV6vFLXR3wXyyZcpF93KrrKKpYZBqKMDprlx9r/PKwvXymdoKzMc+vU2kZ1HNkKr34XEo3vsOMfP4Q+Jmn0g+K0tP+XZrd96msQsWL59resrvVNtdzb2uhiBgZb46tLLIFjs3OQ+Rh/mzpMizyq2oOkg17jDVwBK7bJavosVNd3XVvBN6npVbOm9owPdr1Amaqckjp2Oe6wb1HoO7zoWlpZWdamfhdiaqelBMCVAvYy+b6ls6LI5eV/sfev15XWgnvcq+ZVxi5SSWu8uVgNMzR1TB+Hih7tzu499eZspD1Y4qfvO+GhPddUL9aZCjEoiah/cbzXcHy2vGfbxXlYQbI55WNWSyZYXhPI2S73Psr/qgNsO4pnFrzrW2dzJv2FVH0sbjzzD6lK8pS8Lz0bz0/bxxvnl8VIImw1SCubLsXJDOo0etJhU8VWdweitl5WH8nbaJ1SztambWrZYdFtfrMnlyRaL06LhX1dG9XXhnV0WgZVJZ7VWdJao2UXklLuC8rqo+V/VNtzzdM9M0HfOUXeajvG4pYZi71rhWhJUFoRueniGugLoFrmHyWZWehvd89Ov8agZTB6rnuVCZngtLzXte43bBrI1O06kakA83uFCsXBZ951aJ5zGl1T19V0oa3q2sq8blbMhztcoqvjVdnbVW11/z0HTz+8rVdjyv8vN42HONk/xU7UPjJb26a7nyA7P0T32rWKXrdejDNRVcIfokhvNQeSX+PtPScnSLFWb5g5Xlt8QWW5uclmGlqapZRO2oWsFqkVUzdVU6Ce+knr+HT+wsOl7lenkalfDwPFxgKK3VDKJbq5XVNrUDdyUs5jVm73g+FoXdTwn8KSvFO+g8a6FyLf15Kk9Y+T2e8+bKxi1EbSvzLFlY/pSpqhtFzhjrRhZuec+DC1ZvX24lZx/ZVbiumVdlYFQWciXolU9V4lPWuL9TK3KJlQJXFcSWsgxPOf749nVxkyvt7w3DBYG7X965poSTp+GNXdOoOkHGRcImdprluveW+S6PfgPoY5DKm1qZbvFOWXvZsCoh42Gm0lbBmQ3UhbumvSjhtWzdoqysJRUG1VCF0j/VAb3NeCe88AC9CRfq1SRN8g0racnwrqym+Jh657xX1rtbbVVdOqrjEjyvSlFWlm5VD96vptpXXk95Uz52ueUsw+NkAsUPUUp4Y85r137e2Kasu3kaTTuHz8BW4z/a2Cua3cpTmn05WNLkdE5ZMRq+siC8fNRayo0TMi0XllV9TI1DLtozz7sSCC6oqnHEKeGu6boFUp2BraiEtqenYSsBVildmKXHxwEr4QUr225OvqjXscBKoVcJp6qcXGDlhIYqaz+q08vMhRusrO+E9zMf1nF4Gblg1TJ3z29LLceD5QL2Lc0XqSdJ1DLRTu6N7y730A6R9/BInt75qkXrU5aTdnZ3Ex3ZaRP+EWvSncgOke8yL+VBO5W6IEqXYte1s2Wv0CM1NQ0dK/VO7PHnCaSl4gez6WQe3rkWWJlflvluufd0F4SvLI88NiDjezlV1snOc6frwqHje5UyVtrzWr0EP9UQVlrLyYdbatslbD5Li/GqW5bLVmeT85B35d15ybRcyTvfWibez6YUUt7rVxSLEkbDep92bDrLMN1k13SJ1VyNRMbRLcOrLfh1bEbjVdpWMaUNVYupq+ED+Qm3oCoeXCjvYFj1kdthTbm87p4oP5WwgdmG7GXi43GZRuafq0SUZh+ucMWR/FRDF5q+06zWp8MtLJ8wyjzz3YK9UzpTSbnA8fxd8W63cF4P2kb0vWNqzNDbpFvjXq6Vx5PxXIFPWW1ucWof1DAJ7Z8paKuhoYROYFWWoSvZqg1vqS28vs9ywemidpittCzYqkAy/gKz38X5EYdLrFwf62m5m6GNyBuUu4pZ8YuWj1uL7u7A0DBSky/Zu0UGQZjWhVrFahWq1ZZ07Wa2U3k4vYZZbbsoae2ybcx2sLxczgWJbrIBK4Xskjx32vK98u/KUOnzdDOcprXrhFm6kHiVleLuoFtuF4qn4XlreV8oVmiVr1qiboU5bd4u5imbykKtLHEXdguszNf7oHtkWLikVa10f5/87b1leeOTfJZlqO1S6akOoZrCprMM89OaZNqtpqoQs8HnIPhqGsQPH6rGtqbGixLauVzLe6PSNLEwmt6ipUERXhulDx4vWPh85taJWzvKn6+EWWD28xbkHRYu+fAdrJ0OLxftcFOTOr5lWWWtT8VZ7cNwt0S8w00Jpio9Vz6ahn4KNm/crJokmmr31b0rCQ2T/1Wbwq69jLNu/QD4qiwqy3OqjjXcvDKdx0/y0dcmd3R0dKyCTWcZ6qc1bgG4tnWNodaka3os3JRVNaXtUitW6fmYlrqKSxbfofTOatGQAAAMGUlEQVS6JeKa3OM5vRWUDh981zg5nqq0eFn4zLe+n7JmdXNQfz9vG37nNfPQyQ2no6LRP56uLGwvu6lnUO/44mlVHkF6LV5vGkYnV3LZXvLs38pOjdWp+1odbJWoJjgqC9Et+GrIwMfAFdp28jp50nF0tcQ1japv+PCJ5rUhxgwj4tci4rPj75qIeKi8e1pEXB8RL14tnf+T6+zA7nb4WAfyLs1wTSOfe8dxQejPkfDeaTXNShAofNxG4ya9GcYbWj7XxqIdTt1TTT+hM9B+6LiWja7o2GFpZDpKh87SVuN2Sc/eW5ZnQhXuKvpYJczWdTWWNm/wvtp0t1IY3sG3M38L+Z23LM+wervydqPlq2XgZe/1mi79lCCE6baCxEvh6+O42qcWmVUyyseFJ6zMQ+sdu9ZnOSuvs/86frrE7JcZ3s+UT61DpVMFtMaZwlq6yfuAx7TWfgp4FfCX8u5pwCOA8yLi2HmJHGP31Y4mfjbEzhNmKzvfLbHcKbTTugbUwtVdlNWa8sY7Txhr/v6Zi+ap6Xp8hU4YqHb1db5ulcCy5q0+O9CGlUIz0/LPGXylRtWRM03t7Po5hQq1jKOWnloRq81SZnr6CYkOvvuGEhmu+qTjQpkMWmT45k7DKU0prNzi8nrbIXm6cq4U7NTEifKrgmWnTS66JQ/DWG81NukztdnmdULOJ/3mKeMFlgWnTlZlWm4xV+0061s/OVJ+5302417GFNbFTY6I44HPtdZOGe8/ADwZeAfwvNbad6bi6rnJUJvd2nHc1coGX30cXDWYysz3tP0TAe+klfuxyGyndWT6/tmGp6c0Lkh4dbsqHtQKXmQ4EGlxcXkSYur8E++MbglXCkXp1nBuifvRjgsWv1IElStaWTFMhNPJHA2zZNfzJh2mrMR8r+WiNCQdCbcgNV+tQ1Vs2v68vbjidYXpZeQWtZedxvfTG7WOp+puaoPj5GNq0+SEehXafrwc3JjRPrvhluNFxEuAM1trzxnvnwW8GHhHa+2P58W93/3u1556yilcA2wDvjb+M/5vA66R+2sk7vl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" ] }, "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 -1.800e+02 1.800e+02 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 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 = SkyGaussian(lon_0=\"0.1 deg\", lat_0=\"0.1 deg\", sigma=\"0.5 deg\")\n", "spectral_model = PowerLaw(\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": [ { "data": { "text/html": [ "
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       "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 15.4 s, sys: 331 ms, total: 15.7 s\n", "Wall time: 7.89 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 -1.800e+02 1.800e+02 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 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 -1.800e+02 1.800e+02 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 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=9\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namelon_0lat_0sigmaindexamplitudereferencenormtilt
str9float64float64float64float64float64float64float64float64
lon_07.777e-053.525e-07-9.334e-08-4.088e-064.998e-170.000e+000.000e+000.000e+00
lat_03.525e-077.652e-051.826e-063.123e-064.978e-170.000e+000.000e+000.000e+00
sigma-9.334e-081.826e-063.720e-05-5.988e-077.925e-160.000e+000.000e+000.000e+00
index-4.088e-063.123e-06-5.988e-079.347e-04-1.197e-140.000e+000.000e+000.000e+00
amplitude4.998e-174.978e-177.925e-16-1.197e-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+00
norm0.000e+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+00
reference0.000e+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 3.525e-07 -9.334e-08 ... 0.000e+00 0.000e+00 0.000e+00\n", " lat_0 3.525e-07 7.652e-05 1.826e-06 ... 0.000e+00 0.000e+00 0.000e+00\n", " sigma -9.334e-08 1.826e-06 3.720e-05 ... 0.000e+00 0.000e+00 0.000e+00\n", " index -4.088e-06 3.123e-06 -5.988e-07 ... 0.000e+00 0.000e+00 0.000e+00\n", "amplitude 4.998e-17 4.978e-17 7.925e-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": [ "3.0555770550063204 0.03057263305196295\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": "3.7.1" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }