{ "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/v0.11?urlpath=lab/tree/image_fitting_with_sherpa.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", "[image_fitting_with_sherpa.ipynb](../_static/notebooks/image_fitting_with_sherpa.ipynb) |\n", "[image_fitting_with_sherpa.py](../_static/notebooks/image_fitting_with_sherpa.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting 2D images with Sherpa\n", "\n", "### Introduction\n", "\n", "Sherpa is the X-ray satellite Chandra modeling and fitting application. It enables the user to construct complex models from simple definitions and fit those models to data, using a variety of statistics and optimization methods. \n", "The issues of constraining the source position and morphology are common in X- and Gamma-ray astronomy. \n", "This notebook will show you how to apply Sherpa to CTA data.\n", "\n", "Here we will set up Sherpa to fit the counts map and loading the ancillary images for subsequent use. A relevant test statistic for data with Poisson fluctuations is the one proposed by Cash (1979). The simplex (or Nelder-Mead) fitting algorithm is a good compromise between efficiency and robustness. The source fit is best performed in pixel coordinates.\n", "\n", "This tutorial has 2 important parts\n", "1. Generating the Maps\n", "2. The actual fitting with sherpa.\n", "\n", "Since sherpa deals only with 2-dim images, the first part of this tutorial shows how to prepare gammapy maps to make classical images." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Necessary imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import numpy as np\n", "from astropy.io import fits\n", "import astropy.units as u\n", "from astropy.wcs import WCS\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.data import DataStore\n", "from gammapy.irf import EnergyDispersion, make_mean_psf\n", "from gammapy.maps import WcsGeom, MapAxis, Map\n", "from gammapy.cube import MapMaker, PSFKernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate the required Maps\n", "\n", "We first generate the required maps using 3 simulated runs on the Galactic center, exactly as in the [analysis_3d](analysis_3d.ipynb) tutorial.\n", "\n", "It is always advisable to make the maps on fine energy bins, and then sum them over to get an image." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define which data to use\n", "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps/\")\n", "obs_ids = [110380, 111140, 111159]\n", "observations = data_store.get_observations(obs_ids)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "energy_axis = MapAxis.from_edges(\n", " np.logspace(-1, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0),\n", " binsz=0.02,\n", " width=(10, 8),\n", " coordsys=\"GAL\",\n", " proj=\"CAR\",\n", " axes=[energy_axis],\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 17.8 s, sys: 1.85 s, total: 19.6 s\n", "Wall time: 10.4 s\n" ] } ], "source": [ "%%time\n", "maker = MapMaker(geom, offset_max=4.0 * u.deg)\n", "maps = maker.run(observations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making a PSF Map\n", "\n", "Make a PSF map and weigh it with the exposure at the source position to get a 2D PSF " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# mean PSF\n", "src_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "table_psf = make_mean_psf(observations, src_pos)\n", "\n", "# PSF kernel used for the model convolution\n", "psf_kernel = PSFKernel.from_table_psf(table_psf, geom, max_radius=\"0.3 deg\")\n", "\n", "# get the exposure at the source position\n", "exposure_at_pos = maps[\"exposure\"].get_by_coord(\n", " {\n", " \"lon\": src_pos.l.value,\n", " \"lat\": src_pos.b.value,\n", " \"energy\": energy_axis.center,\n", " }\n", ")\n", "\n", "# now compute the 2D PSF\n", "psf2D = psf_kernel.make_image(exposures=exposure_at_pos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make 2D images from 3D ones\n", "\n", "Since sherpa image fitting works only with 2-dim images,\n", "we convert the generated maps to 2D images using `run_images()` and save them as fits files. The exposure is weighed with the spectrum before averaging (assumed to be a power law by default).\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "maps = maker.run_images()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Path(\"analysis_3d\").mkdir(exist_ok=True)\n", "\n", "maps[\"counts\"].write(\"analysis_3d/counts_2D.fits\", overwrite=True)\n", "maps[\"background\"].write(\"analysis_3d/background_2D.fits\", overwrite=True)\n", "maps[\"exposure\"].write(\"analysis_3d/exposure_2D.fits\", overwrite=True)\n", "fits.writeto(\"analysis_3d/psf_2D.fits\", psf2D.data, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read the maps and store them in a sherpa model\n", "\n", "We now have the prepared files which sherpa can read. \n", "This part of the notebook shows how to do image analysis using sherpa" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: imaging routines will not be available, \n", "failed to import sherpa.image.ds9_backend due to \n", "'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n", "WARNING: failed to import sherpa.astro.xspec; XSPEC models will not be available\n" ] } ], "source": [ "import sherpa.astro.ui as sh\n", "\n", "sh.set_stat(\"cash\")\n", "sh.set_method(\"simplex\")\n", "\n", "sh.load_image(\"analysis_3d/counts_2D.fits\")\n", "sh.set_coord(\"logical\")\n", "\n", "sh.load_table_model(\"expo\", \"analysis_3d/exposure_2D.fits\")\n", "sh.load_table_model(\"bkg\", \"analysis_3d/background_2D.fits\")\n", "sh.load_psf(\"psf\", \"analysis_3d/psf_2D.fits\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In principle one might first want to fit the background amplitude. However the background estimation method already yields the correct normalization, so we freeze the background amplitude to unity instead of adjusting it. The (smoothed) residuals from this background model are then computed and shown." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sh.set_full_model(bkg)\n", "bkg.ampl = 1\n", "sh.freeze(bkg)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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F2xQ5toi5wuUTfTOwB6SBo3WaseIWWVB29rJL1qOOWbh9Mscd9W0eQ9n44TSRM2nZvX7ZrkEvJRGTG+NRezpQT/peRpkxDsXWMp2tfNRAidaBW2w/qtOMcWX12WKNXgfHj9h0VGYVFtk8acSNnAynz6SXbfv4kS09elrsNbqWOR4lLbY+BoBbDLxVz6sH9C1Ad4tqnBaz5fOMNUd61fkYgPZxMxapviUYMZcWg+Tj7DNRPdNtVR4LY5au9Nu519dihdlsgjtwxPz401Kgc7WEwIzb26Ps5rzHTukz4FoF+YjyyOzgsk6hQUPpzUiIr9Mxzj8CZ39tPWx7dQBdStkB8ACAP621vjaLeyYBGjg6LVLSAmevyx+3WF828NR0NLIjAg0FFNF7eXtYVPQRWc5bsdGeKbE/Zkdg+fMrTbN3IrcGJq8N82CPptmHONq+3h5ls48TOQ3Os+WgfXl8etWnoz4WXVf5cLpodpQ5TPXv88zANiq3xVdt5uMzsGcsfT2y8iWOrwHwEIDbWxHPLECztBhxVL1qsEYMNRv8PpwHmGKAEVObijR+10I0A2iBtNLby3q8ZOX34ObBjr/BNyZ/DvNPPma2mP7WQG7NRiLGzeWJQFY5ax/u3xIH6L6D4DySDIhZsvqw8J569sctkM7SWlzuqxk4s6wHrFejtZRyN4DPBfBPAXx9K/6ZBOiIuXFYK60CZCV+8Nm58uysMwNnZau/HrFrTheBPMf3Px9XPYqt8ul1AJyfqgcrHz/AEgGTysvfWFU7M3qBN3KoHB8iPAIq5RiymR73L/Xaz6zNlZ0Z2eix38Izu7O0vt0zYSeWpeG4Ub9dvYzaxXFXKeUBd35frfU+d/5dAL4RwDN7lJ07gFaSgQsPJqWv13Nn4JwxENVJI92RnRkT4Xjqp+yKRNUD62CH5sMOnG08kFX5uX0M3NWQ8WnVTg9f1xFr5e8nKv1R/XOdqjg+nvqaTVSeSFrsVLUDPwSk+jiDswLrrC8o/SyrBNce21anPZRLtdZXqoBSymsBPFprfbCU8hk9ym4KgGaJBlMEphnI9nSKMc5DAW3vi5RYDzsJpa+HIam8WRgY1cBWoKwGM9usQNKzaAXU0Y3PaPZg19QuF8XWWoxYnZse/1MP0ERAljHmyAkoJ9VycNHso2fGEDnElv1KbxSvhyxl+sfLytagXwXg80opnwPgIoDbSyk/Umv94ijBap3MCYh6kpCBqmdq1RIFzBGbMumtTAYOxWijfb4ZQPpftp1O5R/ZoVhfjw5+nwUP2qydMmBVbZLpU47F1y/vqc7szPpUNkPy+UZ1vYy+KK5i9dn/GGmlifpFJj0EIUt3MiCmesOYHgLUWt9Qa7271vpCAK8H8MsZOAPnkEGPme7xoFfxVZ52rjqGYkuZZMyY4/Sw2MiJePasHo1Wun3ayAbPkPgRbGbOrbaauDQe6LlczMJZMkBiB6KAP9LdasvIGfpwtkOVsUUIlBNh6QXnjCErfVm8rH6i8cQMu8dZTcQ/3HnLlvGyfVDlWBJ10t6pkMX1/y3JwNmOI4Dh+BH4RkzId+aIOUc2Rcyc7WKgarEvrmseNH7XhXJEXBcWjz9oqto0AjI1+DPWqoRBle318ayc0ZqyBxClR4G0Shf1Ua7bnr6s2kK1ZdS+kR0qDxWPw1pt28pvDHMfJ6sF6Frr2wG8vRXvTAL0GOC1+IDuaFHHbwFYa7CpAd3quBlDzWzJGGrGIn2cFjvLZgysw4f54wgM2L6INSsGqvSww/Jp+NWj/p/3ifc6VRXO11ivHav3d7OebMbh9Ufsk+vd74tnezhPZU8v61YzCn/Mr42NxovqW5mMccB9sn0XxyjpZScsETirzhjlkTV+NqBbYp05e/l8NH3zLFV9+06xCtbbKhO/oyJikJwus5ttNAC1TskPjvi40buaTXibonJKyhlFIK1AjB2JKkPECP2MgrcbZuDU6gMsynnz/YnWuFH9p5fRK3KUOZRIT2STkrGzpOWtWL+cSYD2EjFBiOsc3gPCvVOsCBR7nIa30+8P7tHDHd+n4eNIsmkls03F/HoHusqPmW3moPw1rhfF9BV4gv65bIf0A4VnNnhw5nBm9F5PxqKjft2SzMkocFbjIgNRPyuI+ig7nDHliPpTC3gnONo+x5ftGvRo6elELL1x2curjm7/qqNnnUMxV34wgR/iUB2bAScqm2KJLcnYtGf6anCzzcopetvVe6NVfj2ONat75Vj8dcWgfZ7WRuorOBFAcxm8brY3s5vtyfqwYqyqj6p4Ss8Y4hERGx4bPf2w1e4qHx4TWwZ9SsINtYyXVp1dNWrWmSIAaQ0kBdKZzT0ANUZ6dGUDtmWvYvJ8nZlzll8GTuyoo7yVbtYftSfHZVBVfShzcmyDl8y5ZNLqP/5YgW5U3mXzzNKMLU8EwoCur9UCM7Bl0EtINgAyL5/FaTHsiEFneZmoAe3ZBN+CyAaVARug12IjmxXYZQylxaQ5XWuwMGuOnvTzdaI+EquYcsZEI0CK1mI5PjPpjKVFDn5sX1FpW/3Tp4/q1cIjXZG9UZl7AFKx9wx0o3gqj5ODze1Nwi4p6BsU/rpiX9EUTTFEHqB2Les8LSbnw/3NvWhg8es7M8mApgUUaiByPWbp1HnEnKN9yB6cI8YXsVfOOwKXFmBkIK1ssvPsy9lKf8vR9QIQO67MwQJxXXib1ccLWG/0IJLKr/dd5FH6rM2j8boa2TLopYWnuHDnKh4oDqdrsQufzl/Lpr0I4pjYY7cZIHpbVf69TDBKq2xUj0IrB+b1q/bImDjbZoP4AIs36hjYW49ys35QXGapnN4AxetRAKZIAJdLPb7NIJ0x1B7GmumInBwLt4Fi3HzdPzbOfUY5yh47fLwWwWHd64PRLUCPlowh9IgC2SwfBXA8KFoPgmTXe5hpC2QjcI7yjd794HVYeabi38flgaXqyeebOSIGAqvXfeSfvPL6xorPh8ujdnTwe6lNeGbASzTKRg7PALt13JLWTKIX0E18XPWgTmZnD7FRdvN1LsdxsWFRtgy6W6yqMqbLcpw4GUNnRhYxT4sf5RVN+3wZVScEjgKHyiNijV5Hj7Ma0+nVQOEyQByzfVb2ffdvx1G+rCMK93bZv9qB0ZsP98seVtciB1FYqy3U7ET1Xx8e9UHFyCMHwnnzOI32jqtjZU+PrIdJbwF6pdLLSLP0veE9bMeEB0uLtRhoMKNTZVOAqP4ZUID4Y6lqACr2pWyJQFrF42s+vmfMnjln4MvMlcuSsUTPotWMgNMrh8IPDCmAzHRkDkxdi/q7D1c6esGZ+5ASX5YsDduqxk+LfEWObT3g7HM8WVntTKAhpZQXlFJ+pZTyUCnlPaWUr5ldf14p5ZdLKT9VSrmtV18PiGbsIwKeXsaiOnE06FusWQ0UxcqjQd4aXKxL7T+O2EyPzp6O1HJeXiLn2lOPfD369djr6yt7bzPbrQA2c2jqegTSqgxj4IPrqCVR34jYNdvIfTrTCYrXEtX2fP34Yo969/xWKyfNoA8BfEOt9Z2llGcCeLCU8ksAvgTAPwTw0QC+GMD39So0UMwYRMQqW16aO54CdJWe1+EiRqQ6s7LBswq+caXsVU6BBwSzeP+vAF2VI2Ojqgw9YJ7pUqCkBj+nic4j4PR148vP9d8CWu6XWR81fdFThRG4M1vlOFFdZ+Dpj7M+5PUoG33dRTrZph5gjvrg+uQmWYOutX4AwAdmx0+WUh4C8HwAO5i3dWnpUd6XG60FYBGoRx1E5aHyZ9DlThkxPpaMESuQUp0/A/wJFu+8Z3lC/Ct7GfyZHWWPQEegxsBlesYwYQW8Xrc657iqrTk8+8KLYpAch22Y4KhNPeDbyz5b6RXbjZ6SjMBe7U7K+lJrPKr+oWxZvdwEAO2llPJCAC8D8A4A7wXwFgB/CeCL0nTQ4KjYbg/4mUTMrqVDgUmWzscbA9JsY8T6PMtjO/eTvLj8inFzfNavQGgMewaObjn0dTWh48guBXgKVDl9D7CpMkREweehyh8BsGLREThH9Zr1v95ysv6s/SJHy8RiTN5RGLevz399chMB9Gyd+a0AvrbW+gSAJwD8tST+WwC8DgAu7O3hGbPrY0E66iBZp/P/Ubg/b3VCS5PdwFIsb+rSZOCs0gH6M1eKcfAgVPkpB8l2LwPOPq1nyVHdcP4Qx77ufBqVJzNF1helz+oiqzsTtdVR9b2I2bJNLYLC8aNw1XatvsM2R+fKPgXkPWMpC3/44YdRSnnaXb6/1npvIynJTbLEAQCllD0M4Pyjtdb7e9LMKvReAHj2nXdWPP54OEjsuPfbbj0dQQmDo7/m40SAoTx+xlqjdGogtxh9BPjR9kAFcB7EIvDtBWYOj77SrVhdBsj+acTW61JbjNRL1G5ZfHY2WX/NnHwEmpFtLVF9jR1Vi8AA+jW3mWTArsoVjbFs5jABcM899+Cxxx67dYRpWqY3AUCXUgqAHwDwUK31O4+ji4FRsQsL89IDyIqZZ82TMWwGSwWwPaw+AvUeOxQrZp3ZYIicif2P3TvM6VssPWpfjjPF/AlE/ySi1690Kjuj8kZtFznWSFoEI4ofMVuVb2aP79fRLCF7Mx+3U6avl/yo8vT0RZXXSuG0VuDw5ngXx6swMOF3l1LeNbv2zbXWnxujRIGmApnWVMqnZVEgHcXt0d0CrozVtzpkK7yXyWaS1SOzOZ69qHQtxt8CLLvuO/AB6Yne5eFtWMYRoiOc8+J0mYOI0mbXxvSRHtIRSVQ/EZNdluWzjRlT9uFR/GNJBXD9JmDQtdZfR8cujVQH9E2UaMD5cPtXbEd1YNWpWoyF8+dtWdHgt+vRlzgiRmlh2YDxLFeBPgMs6/A37vy1XcwfvfbsydujWLM/jsBUORVuQ7U3+cDp4/cvM9tnsXTebnbUXqI+BRGX8+Fz7hcZw450tXRETrPHIUX5Ro4/crDRGI0cZXSd02XlPr5U3BRLHKsS9TmjKf17YQBqeWN/LRooGZv0Nlnc6NcjXK6MpUPEU05JAXTGWvcpbJd+StSg8Xn5euC14sh2lqg9uY69zcpGn7d3RqqupyKcv3WoJJtNsa09TLClw4tq+6jf+DQtgPRxo7Hn07bIhCpTj6welEnqFqC7RXUsBdIRYLEeBaYcB0G4vx6B5hhwZiCOBp3qkC3QVTb1CLPrCNBboOLBjIXBGi7uBHMHwe+/9i8s4httGeONyh59y5AdM4Ozn/lE70fxNttx1I4REcj6jTrP9GRgy+dqrPXYFdmZ5av6sbcjcgYcZ2VStwy6W3jDS4u1+HhesimR70SsPwLZqNPaNfVCorHS6tTsaJRtylEwAANH9ag4LbsihxGBBttn57Z/m+2zG4Em/HJ/YLFe+HuPvjxZ/1DnDM527h2GKpMqn3daPYy5p/9zHWfAbmEZGHC7RX0t089xx4yDHufqwXm1AI2b5ibh2iTyrBmTBMXzYVHn6+nw9s8DMcs7mtJ7Fs2AGulTA6gHfJUe1qHsiqb3mV6vX9mbHat6teNsT3H0XUFvaws02KGp9lIvps9AJXrPh8+rF5yVjlZf5fzYdq/TA0b2qlrOo9cGnx/32ax+OO1qZcugu0V9UQVoD5qooytvG3VqP73Lpnk9AzOzWU0jPfhEZcumy5GjiKaNPEg5jU/bM1CUXtbvWan6BJhnwv66YqtsM4erF/+zfZFwXKVf7SDhsgJHb7RGejOnzISEbVkWWnx6Zvje6fU4gcwG1X+yNJGzWpvcLLs41iVZ4xxnutNimL3pvSjgUiw/0hd1ymzqFzkSO+7JV52rmQqfZzapF9mzg1KAEwFZxLyBo7qYuXqdYyezGctv9QHV/gqAMwaujllP5ESj/qQcumqjHmlBW1YGzlvZp/rKamXLoEdJD4NufS0605ux5+PYGF3P7GwNeNW5ozgZWEZMMANAZtC+Dfz7m6P8vD7PKO08+urKMquBbNs+gIuYv/zfwu0hl6hOeDbi7bHj7Nt73NbqEfzM/kzYefl/1hWRD3/ON2vt2M6NWWc3RSOJiApLj74Tgc4tQPdLD0BHzMR3qN7BkJ0rHf4F+5nuaPqrQFGBbsagM8akgEAN6NY1BmsPsqoNLL49UMLigZ1vtvkpdYvBqXAYbgPLAAAgAElEQVTPmO2rLAzQmRNQwOyPrUzRk4vRMfdP/k1E2kyiGQQ7UJaIIPD+d3Z0CqSV7mys9ZCM6NqY8KWlYrO32ZVSPgrAx9Za/69Syi0AdmutT67XtFh6ABri3AZ+1qFWxZJbrNWLH1Stzh7ZEDFtNSgjcM7yYFBSaT34e/bLNxEZfNiZqDJFdnE9e4bH1/268777qbKofKI2VaCY9cFotuKPI+bdIhfR7ClzaC0d1p7qfRstB+JJgdI/xi6lW+lcvWzwo96llK8A8JUAngXgYwDcjeGF+n9jvaYF9iD3yJF4sFBTZzuOmATr4Y7HAMESDUoe1AfQgMDXsnz8IM6+j8h7fiO9qg6UTapsPs3YPNgxKMmAz1/zwM035LxTsa17EUCyvT0zHG4DTqOGvpqN+OsskQ1si0rXGk+RYzXdU/pnyYA06gusrwXiXIZlQV/Khu+D/ioAn4zhvc2otf5BKeU5a7WqQ6IBacLT70z8IGbAVsA6xutzB1YDs8dWP6VU9gBHbfd5eEbZAhUFRmrKD4qrBho/lt9ixVy/GXv3tiogZnvY6fEe6gPM159VeViPqm+uO2b0qs1b7dD6KknUZ6P25DQs3LY9hKMlrbr0eq1ue8Apw4CVgvQGA/TVWuvB8CI6oJSyi2FV5tQk8viKDfAAYIDKgEIxXGZePXZGjsP/ZzpYHwN65mDsn29keT2epambVuqrGGxjC5y9Xa2XGKljLk/WB9gmBdRmn3+829/g47b211T+Kg9QGtOVPfHIdkd6+djf3DRpOZooHgM7A3UP21Y29pz76z3OSB33su5uqdhogP7VUso3A7illPKZAP47AD+zXrPGSzRoGWT9AOF3BHsdKg5PuTImbdfVWqjXlQGeyqPFunuAsrW9TbG27H2/zHYtH8W8PTBGAKKcl2pH/lc36KL9uyAbGJwzezJmzsJlUA93tJwN56n6hbebdfGNPpZIdwuYuT0yp6vqtQWkHJ455ijtaqRu9D7obwLw5QDeDeDvA/g5AG9ap1FjhDumGlyq87aYc09c7ng9bEcxsN6Bw+myAazsbs0aIokGmLJVsfhoIGcMNAMDdY31t+q0ZQ9LxkyjsnP81ozB62jVB8ePJGO0Lbbbo28ZyQhFL7CeKEjXClzf0JuEtdYpgO+f/TZC1EDNpl8KILy0Bphd58Hv72xHzDpjWxELZ3bNcRQIcjmU3aD/zA4FeC3nx2Hq3NvD69MTitO708bH9/qz7Y7AYh7M6HsYsbclqz9OxzMUXwbP6KNZlCIGiu1y3tmrVqNysC4lHM790ROL1kwg09fj6KI6O7asaImjlHIRwK8BuIChSX6i1vqPo/ghQJdS3o1krbnW+gnHsPPYEoF0xmDsOGJunNZ/F0+l950umhYqaXWmrAzRtFOBaOa0IpssfgTkykZ2kKp+M2bNgMVgq+q29XVp1Rd49pE5az62NJFEQMPp1CtavYPhz3T5skaOPOvPqi05Drdv71hCEK8HGKPxEo21yNG1HM5KZLW7OK4CeHWt9anZ5/9+vZTy87XW31KRMwb92tn/V83+3zL7/zsALq/E1GOI8shelAdWOqLrXv+h++e0ChQjAFV5qAHoASqyP+rQXk8GMBFrUXkaG82Egc/yyBwWA2ak0+vyD5ZkIByxNbbV/2dO1uvN2Cvnz7rtvRu8/1o96ML5KeekbPBifYnjZcLtEjl6NQYzwFb/ytmqOuD8o/HfU76lZEUAXWutAJ6ane7NfiERDsddrfWPAaCU8qpa66tc0DeVUv49gG87vrnLSeTdTbhz9QK516+Yhr9R1tvZOa9sYHEHVo8Mq/Q8gH28iClGbDYbDK16zACPpcV8+CalAbN3Zio/n9YDAD9Cnjk99a/q3+ehzrm+1FdoAD1bUMw3elVqlLf9KwYdOXGf1sJ7Zg48VnpAOmvPqN96+1plWClQr3AXRyllB8CDAF4E4Htrre+I4vbcJLy1lPLps89VoZTyaQCO/5XcJYXfZteqtsjL9lS3H/AKKMeCs9cbda7IoShGw+nV4FNsRD2NFjFZX96WU8wcCYJwHrA+rnotqM8v+kyWT++B3a63HEfEkDPg4fzV8liUr3LUDFq+TK04LYBTtlvarGzqmmo37mNclypvZX8m3C+jPrISqaN2cdxVSnnAnd9Xa71vUV29DuCvllLuAPB/lFI+vtb6/yhlPQD95QB+sJTyYbPzxwH83V5r1yE9AKsajdnFMvlmHaGHZWbgoPLzxxnr8+fMPv2giTq/Gji+LAw0/l9tSfSi2BwP1oiJcd5cDgYAX3YGaE7j07EDVPFUHbCoB4J4q2a07c3bqfKM9quD4vo68rq982j1wRYQZ47Vv0elB5QtXQuc1RhYOVNWMu6r3pdqra/sU1sfL6W8HcBrACwH0LXWBwF8YinldgCl1vqXvZauS5YB2IypRgMvArIsjmKJWYfL8mJbe4Tt8+/24MfcM3syiQCklSbKMwJoBk77jwCIjyOG1+NoW3WROSwFbgqAonw8kPJ9j6iulF3sDHvSR6LKxMdj0rOouolmc6ciK3pZUinl2QCuzcD5FgD/BYBvj+L3vIvjW+gcAFBr/bbjmbqc2CevWg3Y6hAIwhVry3QyyLemZ63Bv2w3YDs8MHiA8DZ64O4FpAwUmDX5OlKOKwMMVQ9mc/ZACty1jO35FzgpppsBTwucIzCztAcinYV5Fm3hduzXr/nGGtunZhZRfWdOg+tR3aBkO1VYJNwno3YHxWs5p5Wz6tWtQX8EgDfP1qEnAP73Wuvbosg9SxxPu+OLGHZ3PHQsE48pGQiOZcM+LOskEThz3hPob9+peOq66Y86O59H7x424am9ByMFKoqxRqCjQJsBUDHiFpNT3xXk8vW0s0+n7PXXeF1e7azxOll6yQLbxsJgqN4KyDfWVH1aWp8mWtrg9eKovVugyE4z6+etsclhUb9U6dX4OZascJtdrfX3ALysN37PEsd3+PNSyr8E8NPjTVuNeAYNaOCKgDMSxYCzAZaBBYOBykulY/1Kr9LBealBwvlnjDXKWzEwZYv9Z0yrBc7ZdwWjvDhuazjxPmTTpQCvx4ErO5nFKsYa7feGS8d6QOFWHtaz68J4J4uJf/2uaiPOR5WBbVV2QoS1HBTH4fJFslJwvqF0LVqb0vU+aJJnAPjoVRsyRiImlHXm405/fProJUCZjf64BfgWp7dLqDyMJbON3q5o0DHIRwPXM7WWPT5d9ApUZvisi23KnIXXybrU/wQ5AGTtbGmjJyPZTournmLkh1g4/4g8eEBWjiFyyv6mZDR7YDu4Xr0tLYfUGoe+j/BHG1qyNgjd5NeN0hOFOwCeDeCfrNOoligQZnAGhbckGrwZU1Z5qIGdDVT7z1h7xDQyhjaBXqv1xz3MMxvsfkDz1FP91JvyIP6VRDOB3gdosi+DQByzDv5Fzs0vK3j7VLsD8wdUvJ1KL8fnsvN59opSdnRct7yEwja0ZhIqvZdsNpT1aR9XpY/0H1sqgGsb+i4OzJ8oBIb+8+e11tOxdiaKETALiAZbBtjcyaOOGOURsQ6v2+v3g5GBVKVXNrB+Bnb1jgnPUjguD9gIbNUAjBzJBIsPZ4wFZ28jOxjlyHjAe6eh2HsPW+xxor491XrxhOJxXmwzhxnjPUC8R1yBm9Lj4zBj5nHl+xGXX9U59zMF9Nk4ifoRgjAlqwXp02PQGV6Z/E+11j+e/f601npYSnlLO9n6pAWyWXirmiMgWtYm1XGjKWSUV8uOzF4FLt5Z+F9WZgaBqFzqZlQ0aL2NmWRsLaszpSPLq1XPGXBwHsrJ8UNWSk9PXQBHd7NEZVP9gY+j/pDdWGS7+bglq4S7FklYidRp32/F0sOgX+pPZi/sf8XKLRkhapCbTIPrHCfSx8zQM55sACib/DXu8L5DGTPi6XNvGRQ79Lo4LrM7xdjU+xsU6E7FdZ8/cBT8gcWHWyJQy+o7WqtVolhbj3NsOQUFqlYmv4sCGMDZx1GA6vVlTJjz6nGonM7nEYHxlOL2OvBeidq71RdaDnTlYL2Ja9CllDcAsBf1P2GXMcyw7ovSrVsUC1ENkzG0KCzq1BlDi4CLj/lOuoWzrVGDRMzH0njAjTo7A/m+i+NfnarSKHu4rLxLIJqq+7AIqBikOFyBvbeZy6+cCdsEHG0zfu1nJJ7ZAvNlCP+NyYKjjonzBI7Wacvm6BrrytL4MhZ3LWoHdiaqnfk/s4HtV23pdWT9kuOuRDYNoGutbwTwxlLKG2utbzhBm1YiETAoZtbqSBFAK+bYYpyZ9IAAD2o/iD2DtnLynf0W2PANP7sWPRihyuuBk+1m4IwAMmsD5Yi8eNAYw7x8nAiks2HKTocdlqXnB2J2Kb2/rmZeUR0yqEVOyRyHpeHysYPNxodqx6i+uP9E4Kza3tohqn+2baXgPO5R75VKxqBfUmv9jwB+vJTycg6vtb5zrZYlwh2EOySQN2RroKq0LRanAFoxSp9XxPgiZqCmm2r92NvoAVcBX8SYlR6VNmJ2aoAysKhtWmqgRXUPiqskc8g9DhPQTJpt4Hc5c1uZPmbOvn3UstIujr79ztdf9n6VrEzcblFb+Lwyx8D9NyMBynmqONyH1JZO1qnG5EpkAz959fUAvhLAd4iwCuDVa7FopEQAcRxR07qezuSPo4HA6fyxGthqEHA+PBiifDguM75IrwLmSCJGHA12bx+wuC/XzjOA9jtPFFDwoI2cbwb6BpL+OjvgQ8xZqX+3sy1xGKP27QsX5m1j58szmAwsM72cB18/gBbVT6J4SlQ/VzbYNTWGeMdTTz4rA+hNXIOutX7l7PCza61XfNjssy2nJtGg6wVT/5+JYnQ8uFpswSRiGL7TH4j4wOLg8IPX/yvQ7f2Mky+fX5/2L8c3yRii0umn734Pr4pj+jlMtbOvP17XVR9W8GVjHXzOZYr2FPu6947C17uB9NTFYYmcr7px55n6ARZZNDt1b0/W9grIe2xUx73inSeCfx5zvTOftcimAbST3wDASxzq2qmJAmb2tD0dagzgZjp6hNlBD8vzx5GtzHYUY8tsYnDYx/ymUcVREOK8WZd1ML4JyXH8gPXHHGai1tsnWHwRUVQ2Dm+F2b9fjojAU7WHfymVEtVP2Q4Pot5JqveWZMAWMeFIh52rsaXapVUOTt+bhtNm4WuRNWyh65FsDfq5AJ6PYRfHyzAfp7djeNz71KQHeFsdR13vAeeeDqD0REzNC7O/KK0BJ4O6pc+2rnF+rNvWOy9iaGQ7t/jXMAD1VRx9s6AHgl2R1pikX8awuNa5ruMoI7T03uZdDI+17szSXIeegShnFrWxD/Pb4nYoPjsPC/PLEcyifTq2UTk21bbWvp6RR7Chrvv81PhgkFbCTtGvC0csN+p3EYOOGH3mENYqm7jEAeCzAHwpgLsBfKe7/iSG7XenIgYKJswSAD0IekBXDQzWA4qjdLVAgNdVmW0qdqNAQLEcD5RZnTAbtf+Ls/9nzI4vYg6EwBwUDCSuYQBH+/dltA+uWfvsYBHUudym38Df2+vtNvDfwyJA+3o4xGJd8402y8vXjzkKA2TuB+qGa48D8I43AqAovW9PtXdcpWXJ+gkDQA8MRczXn0djMJsdqjRKh0rLfWSlsom7OGqtb8bw3tIvqLW+9QRtagoP8KxRWo2lPLjKq2UPsHjTSA0WHkg8QH1jqA5ncRQ4cx5mT1Q3Su8+5gBta9A7mH8f3my+Pot3bfbzN8JMr6UzwMNM33XMwdeHYRZmNh26uB5QPKgbQFt+Bl6+HfxHWq08mOn1LywqTp9izGxf5nzVVkVmr8p5ZuDMbNzH7+kTyhZ/rBxHC3hZlypDFFfVAdeRsiUb59EMZSWygQwaAFBrfWsp5XMxPFF40V3/tnUalgmz5sgzcxqWVqNHHTmTiIXzIFSMl1m0AtGo8/N+54hJK3s8OBtA72IAJA/y/kvU/kYVl8kAcweLrNTE2K6/Zoy34iij5jKYWD7+3C//+GMrm9nib7aZGIDvUT42OzDgzvqaB03PnKPZzITSZGAa5ePLaaKIhZWXx04L1CKwVa+G5THVclCtma/vzz0OYi1SsbkAXUr5Pgwz3r8O4E0AvhDAb6/ZrlHSM63JBpZPr6ZWy0z7ejq6YhHMnpg1KvD3abnzq50NFqZAmh+IMLZrAGDAy+DGg96AsMXugDkzjeqabS1YXCO26zvOzinmYGtsfl/oYQfk1/Z9eSoWl1K4LBkA8/EkOfblzfb8enA2p+pnBxEQZqzY56vsg4vn18KV01djUpESJQrUW45xvfA56qOxK5WeXRyfVmv9hFLK79Vav7WU8h0A7l+3YZlwh25N7fy5AmB/rIA+m1ZloJsx8wicLY2aynJ+0QzgUPxz2VXdAHMQskFuLNpA1JYt+OaQBwcD5z3MgdGvP/v1amC+VGLXDiiPCeY3J20pYg/zx/592fawWP/M5M1GYJF9+2UQv/PEyu9vSirwU+vCXnhbJD/EZHoiEOM+4J3qBcyXoaztmN2ybZYv75LpITvevkO6bm3Gy3UtYO4ZM2NkpXC6yQwawIdm/5dLKc8D8BiAe9ZnUp9E4OSlp7P5uGP1c9wo32xqxuxQsZCewcP5tabhLJ6tq/MdzEGTAZHzNPDYA3ArgAu7wN7u8ErdOgU+NB2AxByAt6uSPvVeCLPBbkD6MtlShF+bNvu94wGlNQD2QO51Wn7mSKKpu6WLmKq/yevLZTqybXORrj137GcixenI+rdi8JGoGQOHK6fDwuVedpx6feuTunnb7Jy8rZRyB4B/AeCdGPr496/Vqoa0mHMUP5MMMDNWndmgWA/Hi+IwM/QsWIG/AmhmLWrm4dP5m2CWxkB5FzM2PAHKBNiZDDe296dHQWuCgdnddhG44w7g9tuB/X3g4GD4PfEE8NRTw/FVzEG1Yg6kBt5+h4jp5z3IzIxB4VYHB5hPy72d9m/M3MrvnYWB/p6zRb3uU/ULZrxqKcn0qRuMXo8HeH/fwHbbGHu2evN2qgd4or7JztoL918Vzg/HcB/kfFogruxQ8VrkZCmp2LxdHCa1Vvt6yltLKW/D0BdeslarEvFvs2s1hGIHLC1g92wnS8Mdjnd0qB0anJfSyaxKAbpfCgAWgYPTsg5eH2abPQDsA7h4EZhMgN1ZgQ4Ph5+x4+kU2NkFbn0GcNddwHOfCzznOUO6K1eAy5eBRx8dfk89NVwzoLYbhPySJbXe7deX/VIF16cBvYG8Z8d+jdyWYwqObuW0ZRW/Dm0gnYEzhzHz9eLTe5BW78jwP1t3t/bxNz+vuH/lqP1ySzSTAxb7NtvRIxxPzTqy+JyWx0LmTFYjm7kP+ojUWq8CuFpK+XEAH7lMhqWU1wD4bgx960211n9WSnkphhuQ7wXwZbW25xNjGDOzGaWL4+zSz64rZsodLcpfMWB/3dvD5+olO8oR+PgeqCeUFjjKxPxugIIBsG7BsExxC4CL+wPQlglwYX8A6ul0AOiDg3kf3t0dmPNznwu88IXA3XcDt9w2wdXLUzz11BzkL12ag/TOAXD9cBGQeD3VxAD1lgnwjGcAFy4OjH4yGRzF9Zk95jwOpwNImRigGyibI7I6uobFNjJmavVzDYtb8TwAexuZtUZ9R7Wt6lsqveVlAG3O6Ar0bhUPzv7ci++nEZvneHw9AvCM3LQkAnQL63UYS8lZAGgnfBO9L1EpOwC+F8BnAngEwO+UUn4aw4uZPg/A6wH8TQC/MFZ3NLXkaz3ip8ctpqAGjBqEPbbwAOTpqWLC3uYeZuJ/Nt22we1ZpN3cewYGcH7G7gCs+/sDAO/uDky5zgB6d3feh/f3h2WNu+4a2PMtz7sDuP3DcOHy07jwxBM4ODi4Aei7uwOrvnx5Dqherk8H0PXl2Z8B8x13DPrvumuwbXd3APvHHwc++MFhKcX07l4GdqZzhn59Vl4DZv9ubGDxZqfVl3/a0dh09JSgr3NuFwbt6IawDzcdfufELo7elLUBvUc6IgIROYAe4XjRsolKF+UVLVH0AHNkz7GlbvYuDiW1HUXKJwP4w1rr+wCglPJjAD4fizf5U/Dn6SeDVfSvpMf72wDhTpB15iml81NIPy1TP9bNv4iVKBuBo/WQrWHaOuYehV2YLVkYCE4mAzjvBKPw4sUBQC9eHH7Y3wf294DpLcB0imfe/kHcccfAcnd25zqvHgDXZs9rT2Z6Dw+BvcNFdn7LDJxfcDfw4hcDH/HiZw4e4bZbgccfx4f+6FG8973A+x8BHrs05LO7C1w8AG65MjBqANidzJ0NMORxcDCMRRsYvl32MX+c3Pohf/SV0/hr1u4Rq+a+x9Xr70MYQE9ntvgB6W+SqiUsttfOoz3nCtC5z/aAc48DaDHhbHbcc31p2TQGXUr5GWggLgD+ypL5PR/A+935IwA+BcOSx88C+AMA39erLOoU0ZJGNF1TA4LZh9KjdKg8Ww8HcNwImD3Isg7F4r09/mfp/fqlX2f2U+a9XeCWi8De/hxIJ5OBOS/swJjM/y0eMADehcsfAiY7wOE14OAa9naBW28D7pihW53OQHEXuDIZjndm/5af/S5eHLD4uc8FXvIS4MM/9YXAp/9nwHM/HLj1LuDJR3HLAw/iE2/7vzGZDLXwocuD7oMD4Lbb5mRoj8D5ymwd5PDKvONfx+IDOrvuurVrwdE1aYhzLz4OAzYfs0O3dre9yFcxnxVM3bHdr9kVab196jhjvxyf7W6l47JBHCvpAd21LHPUOqybnYJkDPpfLhmWiWLHtdb6uxiAerRE4Oz/ezqS6iicLmOoLYkYsJrSRezEjlvTx0g/6/HTdw/eN36TAcQmQWZ2p2A6HX4epIEBCA8PAVx+GjcWrA+uDjcSHXvdnW3Dq9PFvHYmA9vd3R3Wvfd25+z8ttuAZz0LwN0vAD7mxRheGfPhwDP/HHjR48D73oc7/uj9uHRpvgxjJpiYXmC2/j2Lo/qQrwK/t9p2oFg83r0QSQRY0UzI9Cvnbev0/j3UfgeK6i+e0Wd2ZUyY4/WCtNerGPhGyqYx6Frrr64hv0cAvMCd3w3gz1qJZl8Rfx0A7O/t4Q7ohlwGpKMpZcR+FMBm7D1i01mHVsw5mqpmLFrZwYBsDNB+dgNuf7LImHdcRgbImUynw3LFE08Mll186i8BzNaUZ0iyuztb/difr2Hv7g5AOZ2xagPQW2bLK7fdNl/f3nveXcCLXgTgZQA+GsALAXwAeMHTwIv/CM/7o/fjgx+cs+QD97o7vxPF2PPBwdxB+N0eXP9+3zGwuMf6Ko464gx8suUCH6768AGGm4GXZ/ZenYXV2XX/8ioF+tlj9D6fqM+3bFTChKEXkKO6U3F8WR9++GGUUp520e6vtd7bme0gFajTZVd1jyfLrkEvK78D4GNLKfcA+FMMNwW/qJVoVqH3AsBdd95Z8fjjMl40NVOsg4U7cBQ36lwMekDciTl9ZJPX7QGhl70oUB4jfh3YgNozZeDouV0z0HviifkasI93eDisOdtNQb+MYeK37QHz9eod0yWp/QTABeDiLdi5uIv9/cMbTsDE9nEDs2UUoUUNR1/H/qEd+7+GxYdDIjas+lR0s1EdT12aAwBPzc4/hPk2QdteZy/1V6y31e8sTyY00TjjMvk03vYsbYsQcZyMgU8A3HPPPXjsscduza1tyykR6JMF6FrrYSnlqwH8Igbi8YO11vccV29P3UVMpcViW3n6jhGtDbO+XrD0wMoAzYyF84wchnIg1f1uvCBpOuyeAOZgVghAGVCtE9vNtsuXBwC0tV2/BGJxrlwBPnRlzppNbHnEwH5vF5hY/MvDTo3n/dmj2Hnve4GPehDAn2O4vfEY8ORDwJ/9Ka4+dXhjj7ZfhqnT+Y1C2zli2/Km03n9XpvZwi91mmK+fm/1aOvQ9qBLxeJ7m71EYM2MlttJsWjbPsgzM8vXb7fkrXbA0X7J4vuSiV/TbrHwMcTAj5WxZMLrUE7xOFIrMOWXsJyQnDSDRq315wD83Kr0RdO/ngbOwNnLmGmYmk4qNs4Mg/VkHT4TxdK9Tf4GoV/asLTGvq5i2D0BuKnyZL7feNf1HL/kMZnMAdlA+EOil5kDuHpl/oSh7am+djgsgxwcDMc7M0C3ZRAD0t1d4ON2//2wxv2sZwHP+fDBK7zvfcDvvwePPDJst7NtfN4BWP7XZvl+6Mpgy9WDeVsZi2Zgm2BxS5tn0NZ2dpNu6nQwq75hC/rARPWfKeYsmRm2//Ej5FE/9eFqHLXOs7CecmYg3UuY1kF2T2mFow3QpZRfAvC3a62Pz87vBPBjtdbPWrdxLfHgHE0lI2+qGlHpG8sAPINR00qVv2IpHG/aEU+ViUHawNn//NvPrjo9C3uBD+ds04DSmPB0OgCb3TQskzno7u9rpm2s9eBgMe2RsFmaC7MtecbM7anEJ54Anvcn77px4/DaIfAXHxwegrl0aWDaVw8Wb8JbHlcPBlC+waCnR1/kxOIdnImxZv+otb2G1e/uUA8O+XbiY8+IgbiNFRFoAbSfkQFH9R6HxZrt9t+yP5NeEuWvHcduJbUOG5BOQ3oY9F0GzgBQa/2LUspz1mjTUnJcr6mYOLNPJd4B+EHQw4CjDhUNzAiklSPgAR8tb9j7N665cH6nxXXMgdhu3u1izmQNTO0m3GQCXN0dANC2sjFAW/wrV+aPiVvYFEffcHcdg5O4NgPWK1eAp2dLHX/yJ/PdIH7p5MqVo44DGM4t/PJ0cRnAlitsl4avB3UfAHR+DfM95sDiMoN33kqUMzXd0SPfPv9egJ7g6KyAbWrN4saCLRMWn3eUbytu1uejNMvKJq9BT0spH1lr/RMAKKV8FJZ/UGVlEnn4nmma0tHTmFmcbHuVYhNsh4WrOIqRj53KqenidbpubHUXi3to9+BuzM3k8HAA6uuHc7Cz/bc7GHaBGINWNwhvgLoDSGDx8W7/YpE3TSAAACAASURBVH9j81MA09nj3FcPgCefmOu2QXR9Or/BeMOhTeZ2XDsELh8ON9UuY+4IJjj60QJff3u7R7cdmtM6OFjcF22OzfREzl7N2PhexmGS9oYdOMrSDylMjY0eiZyRsiXqu3zM484TpGgM9NjWGzZGasXmLnEA+B8A/Hopxbbd/TUAX7k+k8ZJ1KA+TF1veecx+atj08udzndI7qyeHXOZ2OZo2thyRvbPX0vx66eHONoxbO0ZmD+kcv1wcSnCALXCKQKO7PS4AdDTYSnAM2V777OfLfBb5W4Az+H8jW3A0Wm8r9+d6bBMYvHsfRVXXP6eNft3Tfs68E8eWtls6adQB9ghPa3ZWCRRn/DlVOCsmKhP02PLGDDO9LfAMgLniB2fNKHdWAZda/2FUsrLAXwqhv72dbXWS2u3LBHlbVVD9ixPtOIoxtoCfQZa1VG5E6qGaM0QFDBnDNyH+0Fmuw8M+HZxdErs15GnU+BwBsyXD+bbuXy59zEw2b0DYHJAabEIzP51osam/SemdrDY1v6l/FPSZdf9OrI9vm7vp7AtaE9jvl/Y9F5waXapAYyF28uerDxXZuvYC58Vd3r8l1tA/9xnFvLDYnsygHlmzDs1lONWIMd9laWFS4qUqHJyHCUROEd2tFj1qpY4TvF9/emj3i+ptf7HGTgD8wdKPnK25PHO9ZunJfPMUUfnuB7QIu/MQNbLODLGkrEam95mevmxcUvn00bMe+risSPzug0kPTu9oW+KG7seLh/OQc6/4c0A1R7gmDijDFD9uq8HZgNbEwNVY/j2Sk2Lc32Wv18W8Qz8utPjP4dlIO2/IL5L6XhQ+p0qtq/aL3HsCICGqxere24T/2Mgzxw9152/Biz2cS88HrJZVy8uqTGWAXXkELKlkmyJR8nKMHWF2+xKKS8A8G8BPBeDiffVWr87ip8x6K/HsJTxHSKsAnj1MexcqWTsOVpmaDEHlrHTU+6onukrdgPMmWvkgPwHWtmujCHxoFXx/M1CW0YwdnsI3JjKY3Z8bbaG+yTmAOl3OBgD9q/utHxMr9/GxgBta87XnM6dWTjfrLP4ft36Oo6+nN+v7Vo67wws/wMsvn8DmO9k8aCtnpOZ4ugNGs/szRZOY+H+5h3Pdji+Z86ePfs8IpBW/TOyKYqTCTshtis693kpgOe+r9KuWmoFDle3D/oQwDfUWt9ZSnkmgAdLKb9Ua/19FTl71NvWmT+71upfp4tSykWR5MQlaoxWI9p1D1ZZ58uWKqJ8VAe3f79jwOvnjsnT1QyM2ZYoPzVzsDADKwM7WzY4ADA5APam85tvfv3WLy8YEPpdIf4FLMx0rZwHTpcnov7NbKaLX9TPD0tYma9iEYD9DTvvLKw++NFpe/Wqvax/dzp3VAtLNrYFEYtLK8BRRq9kQuF8U9BmNQzEh5SuBcqZeB2rBDmlKxt7ikixPpVWxVulrIpB11o/AOADs+MnSykPYXiJ3DiAdvIbAF7ece1EJQJINXXnNFFHVh2jxVAj4UEHLA4wZnaeFfGAVADtlylU54yYOpfFnh608B2nw56Gs/Pdw/mxLQ/YcgWwyL7h9BrATt11A2ez0X/9wzsU3gFhjsQDtmeslgeD/RSLAG3XPGOdzmy94uJYfdjatH8lKTA4LLtReoij7Nnno2Yx3B6KLU6cjp6ljBZZ8NKK2wL5iBErwFe6xjiRlh2Ksa9C6rgPqtxVSnnAnd9Xa71PRSylvBDDi2TeESnL1qCfiwHZbymlvAxzInQ7hve4n6q0Gh5Y7ORZ/UbeOosXMeRe4c7EDw3woPb5sxNSjigaaAwGptOWFDwDtm13Bpz+y91XMWfQrGeKxd0Ylm9URwamfonBl9UvadgykOq4Xr9fNlHOnNmo1ckVzBm65XHjC+ITzPeDH8yPrx7MX/kJzG8KAosPtPj+aOLL4d9GZ4ye+7DvFwzi/t/XAzt5FS9y5iwcR5WJSYcKz2aBfmxEfUaN2VUD80J+/dvsLtVaX9mKVEq5DcBbAXxtrfWJKF7GoD8LwJdieOPcd2AO0E8A+OZea9cpkff2skwDqngMzpH+zItnIKVAlfNmYOZw1hOBtGLcflDZFjx757EaZLZ8YOvJXpdaZ1b5AfN3WKgZg4/PzLI1iHv0qPDq/k34u4f+nSPT6bDd0Dsj27dt6/oRIHF/UCwwcrxcH5lwGgbWjKC0Zo2sq1XGHjtVPqcpq97FUUrZwwDOP1prvT+Lm61BvxnAm0spX1BrfevqzFu9ZMDFojp0C8QVKKh0fJ2nslMs7j1WYGHX1DIIT/nZRjV4s/Kqm0r+/RFTLC4RKCbnt8TxXmr/eSbO2/I0nbye6uP6euCyZvt9GZRYJ99E9Labo7HHuIH5k4geoO1zXKbHv+XOnI/NCliU4+VyeHu5jiLMaPUN9epaflKR9SiHyI4nm6VF9kYOiPNkeyJ9a5EVPupdSikAfgDAQ7XW72zF71mDfkUp5d/Ruzi+odb6Px7P1NXIGHD2YYo99IAz644YmVpjZD1+UHjA8ec8zecGU3arAaEAStkLLDJgO1dMmJcPuP4ZoFsOzP53KY4vtwfRaAnDi28/r5f3DZstDIJ+jd5ukHrZmcyZsl+/N53XMOwIuYDFfmZ2RUswvqzA0fbiMvp0LJNGnMzBcZuy0/d9lImEhXM/j/LlsGgJRUlvvGWkAqt8kvBVGF6d/O5Syrtm17559hK5I9ID0J9da72xpDF7F8fnADh1gO5tuFbczMNn4O3Te9AA9KPC/j/rqHzMnZ8ZUWS71xUxd16D9QxrgqMDLBuwKkwxY2BxQDFQ+3P1DgxbE/c3KFmPyqsFcL485qRshnAwizCdPXTjnyacTIYdHrzNz2yyB2VsB4uJeqgkK4Mvp/1n4O71cZ369u1dhmgtjWT1zNdaQK1sa9VLz1hfSsbdJMxV1frrGPHR7R6A3imlXKi1XgWAUsotmN/UPjVpNVYU1mo8jtdqFwY7nip6RuhvdFkYMxJOy3kxA2w5D58P71hggPWgyu+eZqZk4kGGgUM5Ei4Pp+VdG7w1zsDZfr69ouWf1szKt7Xf8nfgwqeza3sYHhvfOxieNCxOuW01tJmDX4efYH4TNnKsbDu3m+rDTAIYoJUwQJtubg/W5x29F98/2GFmfSADbZ9+DOCug0VXYKPfB/0jAP5dKeWHMNj6dwG8ea1WLSEZSC3TaJE+NQCyzmcDzHc2Dz4RcClwPqTjCPiiQc4PwvCg9LrUE4uchwKZHmHnx3bwC4Z8HANQD9B+TdXPFJSd/rpyQl6/Ac4COGP+VOLudP7+DduJ8jTcwz1Y/KhrpbwiVsx1pPoDM+MeYFbpLA+fF8dVOji+cshKvxfFnLkMLaK0DkA+Iitk0GOl510c/7yU8m4AfwMDCfgntdZfXLtliSjwUscZk2LpYd+ZnmzaaToYIDhuZINiUREDi8Q7Co7LwKx0RXXey96jOuMBzvXoHYUBXrQfOHJYXp9qA18mBmjbqeLB2f/burN3Grb90D+UwztV2LasfqLZonf0CtSULp+fcoAQ8Xx+Ppz1WpjqQwp8Od9WXA7nOC0wP45s8tvsUGv9eQA/v2ZbTlys062qQZklZ3n6NGyL6mgZMGf2cN7ezixOD6hyGqVPpWmVr/WL8mGbJsE/61COwoB6F/Ntc7vi326k2k4NWxu3PeT+hf1ZOSKnPkaius/YN6eN2t3CGFBbxwrAe1nvmLhq1rgqqXX+moOTliZAl1I+FcC/BvBxGGZ2OwCerrXevmbbjiXRlNEkAudo6aKHqfplC8VsxoIqA4qaPqr0EZtn8OV40VTT59Xqp1Pom6OtNBGT4pmD3/Xg67qnvj0bz57ENHC2PDyDtgdYOL0BtO0P79kSp8obXWs5s6gv+z7JdWlxOI1ygj2zFLZbyViW20MGshntKmTTv0n4PRi+vv3jAF4J4EsAvGidRo2VqFNlDQjoDq3iZ52E09pxBhgMsNzpIpDOyhjZwwOixUqyAdgaVAz6GavxTFVtl8uYmtfv6zl6utDy8Wk8SKty8dKKfSnFfv42vN0M9E9DKmCN2oTfs9EC7gnF5+sZk/Z5KibN+So7mCSotmoBpJptZs7Mp4vOb+Yljj8spezUWq8D+KFSym+s2a7R0gKqdenPWAt3/gj8egDf/yugVtID3mp6r8A8YmmRc/HOaQL9QIkCaItr/6osHiDUL8rH9PhOn7F9/1IiSxcBtJXBr5F7G5R93jb/nzlJBcLeAUV5guJHsynQ9UyU07DjyPbWOe+ZHqtrHeAMYHNvEgK4XErZB/CuUso/x/AmplvXa9ZqpMWgAe35I2at0vG/H8gZO4jYIg8qPl6mn3D6jDll5eoFaB83AkzFoAENGArUVJ4cxnmpMkX1kbFWY98+ni/HAfraiW2LGDT3Y1XWMf3Cl8Wn7wHprA9yn1Ukg+NF+bXyiNKvQzZ9ieNeDEtvXw3g6wC8AMAXrNOoVciYKVYPSCtRg9j/1FN/Pbq4g6sX8XtbozBmsV63v6ZATL19LWNiPuxQxFfgnLFFBge2lcvUyofLE7Wd1xExeLYvKlPGTnuF+xTr8boie5WzY8fJfT5y4D5sDMPluojaLhqPLf3rlAqs8n3Qo6Rnm90fzw4/BOBb12vO6iQCr6zjRYwvYppqoGXslNObRDeU1EDIAA1YHAi291kNDDtWTJ5BzZxNBLrKFkvfAjFviz9WbCwDVWa4vj78EsIEiw6E22vi4qqnFFugnTlsziMrtwk/5m75qDL7MGWfAtWedKyDj7P4Gak4M7KJ+6Bne5/DpfFa6yesxaIVStQZoumc/8/iq6llDyj35tVKGzkfvs7HEXNW+Sqn0Rq4XEcK/KO0Spdydh6sW/lG5WMHwq8wjViwZ9YRg4zYHjP0nr4SOXvV9pmOlkREo1dnRnh6RI0n08v9vUfHOmTjABrAa0/MijVKL+BlaaNpoQKJiOkqphOxHwW6vMzRw4T8e0EUqHug4pt1zKB7wJlByDNolS9wVLfXox6Jn7rr/qadYmoRK5xicaeF12MsmZdEWI+6xmXg8kR16Gc7EQP3tvv/nhlNT3gP083OW7MJ1hn1dSURyTop2cg1aLe0caZlFVMsBmcvPBCzfP2xAZiKq9LwcoGyx19nR6Echp/6M0D7KX42EO2aB1Rlvz/313md3OtisAfF3cXRD8HuYnEZwufDdWE7LkxXtNbv9xEr9qqWMPi/tw5VvGwmwvkqAuDTchgThF5m3yut2Q7onK+16u2kZGO32Z3VB1UADQjLSMZ81CDsYQURu/PhPYwnm+7aNd62xsyZwdr/+/icpwJWzjv690DM79LgulRgw8w0Wj5gkOdyK318HrFB+3E5FJvk86xP+TQKZKN2YMlmMWMlIihj+revTxVvVeN11bLqF/aPkXPxoEqPjJk+qbDWgFJ5qU4aASt33gzk1YCLBiuDtF1joIp+EOfA0Y6TgYTX4cUD2i5d43yzH7NqX16uX+V4uAyenUei6sjn4+sdFIfzUvo4voqjZi0qnRLVT7KxkLF4H676sHJ+mY2t8BOXFb6wf6ycmwdVxgh3ph6Q5jjRuRr8WYdrMfAofnQcsdgWC4vyzQAx6jwZwFv6jJH5n1obt+uZ+LbNmDKwaKdiwIqpK/sV88+eKjSAnQRxInbJ13vSW55sJ+vm6zyDiPSr/u/DWuPMx+NjtuekpVZs7hIHzvCDKpm0QFoBXm/nGDNQlERgrcCF40d5qjzsPwJwXy8t1pkx7gg8IqD1YRFr5jxV+VtMT61XZ0sdHkyjOJaHrW+3AHrfHavywYUBR8ujrkV14vNlvZw26z9RvUYzu6n4B4Wr8x4nclKyyUsc92KokzP1oMpYyRp+TIfomZ6NaeseBq+my1FHV+f+umJZygGMkVZaA2MPUq3HnplNt2YgUZ33zJ7smOtE/SvbVDnUDUnlhEDh9r+MQ87KGjHzLA/uX616j8ZGz5hhnScJ0hu9Bu12c1zBGXtQxUs2pWZg42PW08o367At8csHPCA9k1NpWmwkAuqI8fv4y3ytpMepMGMzUIt2kXh7W7tMMsCzemQ23pqlZP3I7LHXjmaPfXunwp/zyl6AFIF0BnJRmXpYck8e0ViJWHVko0/TGr8nCtKbuM2ulPL5AO6utX7v7PwdAJ49C/7GWutPnIB9KxHVyaPGVdNKr8f+xzKYiH1kuhlcIhbEnVoBuz9XeWYAPRE/ZQ87hMjuKD8G5x7nEl1jcFb1PQnCVDzW65nxBIs7ZPzXXg4DPZmdLUenbGw5ZlUOlT/n6ftQRD4i+8Y4702XTVyD/kYMuzdMLgD4JAzrzz8E4MwANHC007YkmopFbESlyRiH6uCs3+KoV3FGefO/iscgaxLtA+Z4bKtPE80eMpCeiuOIwbHOXfpnGTOLUe2gdEXLFuqGprdz7DtVFMPmf8X8s77LZVH2RNIaQ1F9MYFgsF9GThLg64bu4tivtb7fnf96rfUxAI+VUs7kTcIx3r/H46vB0AOkGUtisPGdO3plZ5SvGpARg1bXxoCbz0cxf9bvy8XhiqXz+yg4zMfhB4AyZ8XidUYfV2WGz2WJ7Pc3GaNdKJzW94Vob/oYdurtjAAzA/NMb3ZtFeB8Wqy7buK7OADc6U9qrV/tTp+NDZcMYFrTZQa7DMR54KoBEzEftjVinT5MgQPboXTwoPfh3gYWBqFMuC74uspDOQkWftqQp++cvtdZsR0MprzurhhyNqvycfhRcvV4um9vdj6KZfcwaI7Ddads30Wuz9sb9Q+u72Ud/qbIJi5xvKOU8hW11u/3F0spfx/Ab6/XrNVIT6fgAZ55+qizc1iWZ8se//6Mlqg8I+fATLSlU03T4Y59PEBP+5lZtoAR9K+AmG3NQItZobKJbVHg7PUpJxyxfF8/XEaV3p9n5ebychnYqbPDVM6a8+9h0aqvRUxatUUkmwbkm7qL4+sA/GQp5YsAvHN27RUY1qL/q3UbtirpYdJqoEZp1MBQzFiBe6/YgMoap1dfBNgqXuZgGKg5Hpc9y7c1BVfsMpLIjqzNFZu0fwanCPgZcDKQHTMLiVioihfl6e32x/5p0ihd5EiWYcDKKZ5J2cQljlrrowA+rZTyagAvnV3+2VrrL5+IZSsU1TlaA0XFYXDOpnscbueZHRHz5PRZX1Hx7djfbIoYaQbUDFq8d7kFzEA8UBkk7CGSqE5bzF6Br6VTIJU5VH7DX49kzJ3bxSR72VTWJh5Uzbn5+vMOxbP9aPZix2wz2+JtVPXL0gLpTQXwTb1JCACYAfKZA2WWHkA2iRikGmA9eUWgpWxS7xrmKXMGnl5nD4AiuJ4NMl8GxZojB2XXMmBsgZhisFxOttVP+3v6Aecd1R/PJiI7fLnMXqVLMU1vR89Tj/yv6ovfe6LYOOfv9Ubt0gvMioBkfWYTZBPXoG8aGdMpePD2AFkLPNgOBegKCDluNNAy1tcC5siZZExZzSYyYQBRAB2VP9LnbVF2ZbOjyCFEZeT3bvu0Kh/Wx/WVOdGsb5gu++e+wG81NJvVckfmYNR5JhGgR/E2TSqweQ+q3KzS00m894/YC+vsBZUIuCNG6W1S091If5S/xYmcTwQ6XncveGd2RQxdAQenbZ1H0gOMqmz+fdFK2PFk+WZ9JXMemUTLb4pdR+Eq/4gZA3kZovQbK5u4Br2VQdRg9+DcAuYenSq8h1m39Pn1xilda01HewZPBsRZmmipKKtDFTcqy1g7etNGjsXAuWcwKbbKxz12+HwtfTYra0k2a1DgbhI53N48e53nacsWoDdIegG0Z1As266rWpvj9VcFasfpezxV57x70vNxyzl5x9PzBCGDce+UO4rLNqp8WssOkWPKmLiyCzj6siivU9Wpmp1x/aj99r32MPBGy0wq7iZKBU5tDfrE6qaU8ndKKb83+/1GKeUTXdjrSynvLKV87UnZE0lWIYqFGlAch8n1DH6WjAnz1NWDGQNaxJY4DwXq/qfeodHSoXRF516f0st7l7NZSFZmtsv+GWS43aP2t7phR2YfBPC/Xbqu9Gd58afLopuKXD62Nfo2paordZ61n9mR6dg0sV0cPb9Vy0ky6IcB/Oe11r8opXw2gPsAfMos7PUY3vPxo6WU22qtT52gXRshLSbR60lZjwfpsbp68spArlWerMyKiUZ61SzG7IvOI9av0nnWp2ZOkeOJdPG/P84YtGe6rTbkuMxcIxt7nVevnAWG3JSbYQ261uq/wvJbAO5258WiueONlKydWtPBbJBEYmwrG7T+OBvAvazS28pTZT7m9K1lDn/uP+QayRiQHsvMVHjWdhEoWxuZ8GPu9vNfV7e06olFS6cYsNeT2R61f4/TGtNHe/t0tuxxFuRm22b35QB+3p3fD+ABAD9Sa33ydEwaJxFgRcdZR1Yd1w9iBkoGrR6Qbk3DFRBy/q0prgfpbErOLDICfIvj9Wa2eJ1KP793Ocrb0io7GVgtnAcSv6s6coI9/xnwjgHTlpNl4T6obIhmImcNgDOpK3wfdCnlBwG8FsCjtdaPb8U/cYAupfx1DAD96Xat1vpmAG9O0rwFwOsAYH9vD89at5HonwL3DJAInLOpuhr0Nrj9+mLEcr3ObAkgA46W/azHnytRzsdfZ1uYQfZOlxnIVX3aJ6n4S+esJwIpDueZQaQTLoxtVeWI0o7tU5ETj9JmBCGTTQPqhx9+GKWUp92l+2ut947Vs8Iljh/G8CHuf9sTea0AXUr5KgBfMTv9HAB3AXgTgM+evbq0S2YVei8A3HXnnRWPP75qU29Ixix62QuvKXLabGApEPNpmflFAyBisDwwGRQzBsrSA0Csi+3jawbMisl6PQo4/VIBA5+/SWpxDwJbvS7OP5oheD3RzEn1Af9muyiuv84ziKh/ZrMTr4d1RjMensUo3S0wbvXZdcg999yDxx577FivR17ly5Jqrb9WSnlhb/y1AvTsayz2RZaPxLCUcW+t9b3rzHddMqZDRoDYiuvz8gMm0jsWoL14ptpicdEyRATuy4jSqZYDovKwzVxvDNA+X95pod5VEdmsbAcdR2kUa83AOdKrHDznp+JH11R61ZezMZE579Nm1mOl9x0sAO4qpTzgzu+rtd63bL4nucTxLQD+CoB/U0oBgMNa6ytPMP+VStbBezpfNhXkgc4PQUSMmI/VgPcMXL3bWOXBEoEolyOacTCoc1m9baxLMVpfNv76SATWwLCVzeJOsMjce4TLE73cqaXTv2WObc6WM3xcO2452Siflg5LP6Z+MrvPEkhXjCr3pVXi2knu4vh7AP7eSeW3TmmxDzvvHVReFGi1WHg0AL1OPs4A1v96OyY7jZZEQBYBXY8uQL+wqNUWDOaqvqJ8LX3rK+RZ2sguZQO3FTvksZKBMS+lREy8NZNb0erAqcpplWH7JOExJAJqxW7HMk0elP7LGgwonNaHKSC0f/5ytv34wZsWYPl0Km+1nKBsboGBz8ske/sfp4t2Viib+VjZqtp3DMvkvKJZTzZTaYFyL2hHfYnz5nqOwDma2Z1FGcmgVypbgF6hREDLjdu7XqjePsZxWgNQ2ZAxZ/99PwU+LAwqmT2Rs2K9yjYGRGZ3SrgcvJ/cOwvFuDOAzkQBUtTGfK7qkuuNl3t66iHKrxUv6j+KhGR5nHWQXpXtpZT/FcBnYFirfgTAP661/kAUfwvQJMfpSB5EeplNtDSimFpraSUa0Blj9WkZGNg5qPiclkXllTFCf67SqrAxgMPsWZUpA2oOM33+oZsee8aCtLff5xnpYD1RnEha8XpAOpNllmNOSypG3STMddX634yJvwXoEcJgmQFxxDYjnRYeMUvOy4NJBNIGzsy8fNyeMvSyZ5VW6WwxcrbPp+X6Z2cWpfW6Ob5yNkpvNhNSbLbFLCP9vbOSqO39ubrGdrSWKCI5z6DMclrsfwvQI4Wn2l7U9WyqyzqXydPrjPJnNh7Z44FFDeRMR6RH6VB5R3o4XIFaBIQ2A+CZgE/PIMsPmXhHx/XrmaxyjNksIDvPnKEvWzYL4RkaO7cI2DPAjxyhiqPCzqps16DPmLTAOQK3CHw4/tiOzQAeAWSk11/nLV+KETLrb9nWyjMLy9hjBlCcd1QHLbZq4G7r14o1q3TZh28jh9xjrw+PnAwft0Cc40SSObpI53kB6S1Ab5Ass6YWsc5MDwNrxm5busbE6ZWWrhYYROzWp7V4CvBbSy+ZXRHYZc5F5a2WVXqXIJQOla+X3vbj8rX6jgpjm3j25ONGjD/qo1z2sy5bgD4Dki1htBi0YqUcx8drrQsqcIyEdWZLCz0sO9IVLTu0ypjpZvC35QNm0EqnurHDWwh9HtHMyNKpLYlc/5kOO1b1wTdjewCup14z5xTZ3QLVnuWY8wLMwHaJ41yIYtBjOygDugKniIlFA5+3lil2NHFxlc5owLWcTu+0ObqubFIfBPDxuQ0ih8Zg1rMM0GL3kYOKZgvqWsRgI4kAmNNH+Xp7W7rGsPtW3zlLsspdHGNlC9CBtBiVCuuZqmfrki0d0ZJBi/XyNwk5TsYGLV+1phoBMjuTrC6ZvbaclL/uXzIEHLWf81XxIqBVcVrtyOye00R9p9XXekW1Y9Rn1FJUFi9yOL1y1kF6y6DPmGSDNRv8LVEDJ2MyDG4Ze2NR4ByxOwZa+/c3zlQnjl69qcBSOYBoOu2BPapvVR4OV8ctaS0HKJsjZ+HjKPYc1U/myDhciWL7vQ7Y55vpPsuAzLIF6A2UbFCZRMwgArtMIvBTAyS6pthbzzReDXDWw4NaffHDRD0u3hrgERCpsqqvk4DitQBa2cDXff1zf2gx0+i6slfVTWS/AmcOj/oN59lbhsg+FS8D7rMI2ts16A2W3gHe24DZwPB6IvBR+So2xPHsOFsSaIGPz8O/CY+XA/wSBNvJ9kZAyHZE7FvZF+lSSyU+rGcpQB1HtvB1VYZlBn6LufJ5BtCZcKDKdwAAFH9JREFUDT1LL1y+45Rrk2UL0FuRwoADHI+F9KTNlhbUNcXYs+m50qOAOss/Y83ZYOJ4rSUdu9ZyMGxX72xJxe1ZlumZ2Y21Y0zYWWTCy8qWQZ8ziZiSGuA9enjKHk1ro2WJXlD2ejjflv2eMe9ieNcy2xABb4sRe2Fmzrb7eD3TbWWb0uffU630+DwB3T6skyViptESGou1Tc9adNYPlwHf88aYWba7OM6p9E6Rgb61uxY7VWCnAFfZEA1aNYANKKfumH+cNwO80p+xbp+PWuJRM42M/feyR8tDbfHj+s4YtCq715WdR7pVHfXMQHrCetj6GGA+q6x7y6DPifROs7M40ZTbwhSDjPR7HQoceAD2sHvPOr1dnjX7d1dz/gpQezo/M8qsjlt10yqjYqGqDhWoLjuQx6xhRzMGJauwp+U8zjt7BrYAfS5kLGNrDUoGCl7a6FmbZnBhsGcblQ3KPuU0/Av7Peu0L2j7eoiWF7gcGTBnrDxa045mCL1r5b1OOLJTxcvOVf5ZWTM7uL1bbczpM7syOavM2WTLoM+4RCAZTUWzDqvAiYGadfaAi5qaq2WILJ9sqYUH9BRzYD4QNgCLna8HpCPQ8y/dV7a0lh2iZQHlDH351DJLJD0g1cP81SyE03J5MwCP7MiWyjjulkGvT7YAfUzpabgWcxrT+GrQZWuOCvjUV1OyvPxxBtIWbqDJ7Hkq4jOT93lxXB/u82HbVJpIepYWxkhrCcTijGHBPUyc4/NsI1qOiVg3/0eOzMc7r0C9fdT7jEpPZ13FFHAMWDCotqbWGQNSywM8WNXgZKaX2bAMEEa6MxY+RrJlpMymHn3ZEklWH7029DL1DHAtzlg5z2x6y6DPkPSuJa6yUXsHn8X1/yZqWUKBrUrLOjMHFE3DIxuifDJ2rpY3VLoI9BSTb0kveCoGyvGiZSVV3iivrHy9SzbA0fQ9zpvjZo7nrMt2DfoMSQuc19lBW2uKik1GtmTLIOq6YtPA0Rf8sx0KQPhaq87YkTBIR3a3pt7REg3rUmAWgWIEzpGNyl5v3xhnF0nkhFuybD/OwP+syhagz4gwmPCAUXGAvg4bxTnuFLs3zdjpbgtUVd1kgJqxQw9WUbpeNhyBnM+HxX8UtgXOKsz+2YnyMtGYNpkgfmhHHWfAqWZQ0cyjx7mfF2AGtgz6zMmy63OZ9HT6KA0PfMsvAjOejraAlvWp/9Yygk/PL9yPGLZK2wu+ERvtYZJj2H2rraY42j6RowHyT455mzw4Z0sc0fLLeVuGWLdsAfqcScRqOIylFzyWAfQsfrZ00wIMju/PFdAy2EdLA1E9KOBW4ZF9UbmicFtS8XF7xS/HKFs4LtsSgXPP8kykX9mg6uI4+s6TbHdxnBMZu8anpJfZLSstxqzy62GtDIo9ddEL9BlAqTit/Hucp1qiGeMUo9lNVKcR41VxozpQdo6128drEYns2nkD69Ni0OetHjdGWmtzGUNUaZZtqBYg9KbrYbMZcHCerWUDtSyg8mV7W/m07FK6M/3+vLWUoXT1ts+y/UZJ5gw4XrYcc7OIrUH3/FYtWwa9YuHOrFiFYmZj10az67zGrFhUFKbsjMDK/qMXGKllgQh01fTe8lDgyIw0YnQRiCt90TcZVXx1zEs39q8GLw+8rPzqeitf7k+qzlt9TqW/WeW0yr8F6DVI1pjRUkAPmGTXLYwHVc8yRQvAMzv8U4nR9Dpb08yuKdBUaTI23ZqmK4caOTWvL3MICpy5LVifkhZwtpiwqjffJt6WbMll7OztPE7LtwB9DiRjWzz1H8OcWedxp7hRXAXSfMzg53cS9C4DKFHAHsVngAEdj11CsGP/ibHWtxYRhEeArxhuxHyVvUo396dMMgc5hknfjLK9SXgORHX+43bqHsBT13oGexQeTYWVngn9WiwjAt0eBshgGIHwpBG3ZZMNRPWekhaTZzsjFm7gz04tysfSZvWknKhiydlsRukcG34egXy7D/ocS8ZKetL1rL/atV5wVeHKth6AU2kyMOmZlqsfh7N9GeAhuW5h3u5Dd53TZiCt8o9A2o6V7T1sf0w5VXsoO/3xzc6aWbYAfY6kh7H16mkN1hYAAIsDrjf/3gGq8lPLFZED8XEyUewwAu4x7I7r5hDDoPAs1+vvqT+1DNRiwJFz4XSt2UwmrTrJZiwtOe9gvgXoMy49jGZMZ28NWsV4IvEAFA3SaL00YlNTd53Bq4dBW76qPNlMgG3mNMoh+DScjwIiA2c/OKLlg5Z9HpyVTbyGz/WoJGPbLCrflvPtcUStWdh5ku0Sx00oPUCVrSlGEg2unmsKnFvSYtA9omz2tmRT7gzcI8eS2aCcWaseeuopcgw9yzOsowXOvQDLNmQ6fTpl03mXLUCfU+kFRpZs/TMCKzUwexm8Ynk8/Z9QXE7fkw8vQ2TM2++k8F9/yWYDbAPbHTFslW/EOtUSDpJwrjcGR8+il3VyPr9lhe0/ri3nRU5zF8fN4gA3Rno7ew/IqWs24P03ApVuP/gO6RcNTE6nlkQisPVAxP89Szpmp7eXbciWVaYdabPyZXm02K8qY1Tu4w7IzMaWvVH5t5L3hzF1VUp5TSnl/y2l/GEp5Zta8bcM+oRkGWYbDbRsmj42bwtXHUwN5B6mrJYZPBNm5uzz8+xVgbYC/shWtilyJCpO1D4qb389Clf2tECyJTzTifKM+kxvf4nkZmF3q1qDLqXsAPheAJ8J4BEAv1NK+ela6+9HabYAvWbpadhsaqsGcjb9Ps6gVyyrZ23U8lB5MthGNprwUgbHVyDKdvt8ueytY9UW/j+ySYFgBP4cv+ejB1n7ebtUmSLwXhVrvxlkRTOJTwbwh7XW9wFAKeXHAHw+gC1An4aMYboZMGQ3hzLw6Mk/GrzZu4YjicCB17AzxqcAXelv2dCafmazkyjfjK1nM4ve/Fu29Urk7FlXpJOdXKbjZpCRDPquUsoD7vy+Wut9s+PnA3i/C3sEwKdkyrYAvUaJpvE9kg2MVmfJwCJiUXxua8SZTZ6lZvlma+YMfhHb9ksfrDubfYwBGAbnVYBQtiST5R3FUZI50mzpg68f1zGcZxkB0Jdqra8Mwoq4VjNlW4Bes2RsNpomKwBiAIt0ell2LXEsc/Z5qTwVi850KLsix7SiqeeRPBRbboGd0tNq93UAYLTckuW7yno8b7LCXRyPAHiBO78bwJ9lCbYAfULSw1xb4JbF8/H9sWKREcBHrK7HEUSAFDHVnms+fZYmylOtqWf5Ruw9S2Pn3D4KpHvX9DN2H7HxiAn39iuVditzWVG9/A6Ajy2l3APgTwG8HsAXZQm2AH2Kkg2qSMYAYDS99udRx+sBXNYfrZ2zHg+6Uxz9JFQGOJleFae1fq8cgAJSXs/O2iiaLbSAOWPoSmfmSNSMC3RtC8Z9sqpdHLXWw1LKVwP4RQA7AH6w1vqeLM2JLzGVUj6plHK9lPKF7trXlVLeWUr5r0/ank0RBQqT4LqXiDFn669RuEqf6VE2eLt79EZ7kv11nwfXSbQ8o9glX4v2YVsYf4jAl4Xt5vyj17AqYT2Rs4rAWeWROa7IqY9p85tNsj48ps5qrT9Xa31xrfVjaq3/tBX/RBn0bB/gt2PwIHbtNgCfhGELylsB/G8nadNpSsTEMmY0drmhpauX1UU67HpLVzTN9oDn9bHdPXb0zC5ayxeA3sGilizUkog/V2Xj9Eqy+mwt2/TUQRRXhZ84g9tQOS2HddJLHP8QAwh/krtmdzbTu5nnTfw0UzGj6NynV+dq2aG11hiBVuvGSDSNZlBVyyDeVmWzf6Ncy05vgwJJS8PHUV1nsxnFlCIW7uNaXbbAmW3pAWoWpZtnAVvpl5vihf2llOcD+FsAXg0H0LXWJ0sp7wbwAIB/0dJz4cIFvPTlL1+bnScpEUPjcPZg2XRKrZf1sKwCzf6uz853cBS0vB3V2el1eHu4PNcx7/wcz9vj9yb5MMvDbKwuTHl9n87i+PApFvXbv+VxbfZ/HYsOYcf92D6L78vo29GLpdmhfyVcl1bWyIGweDsyUfvCzpJ8cAU6bpa32X0XgP++1nq9lMVmr7W+EcAbo4SllLcAeB0wAPQH7757nXbekIcffhj33HPPieR1knIey3UeywScz3KdZJne/e53o5TytLt0f6313rF6TgugS63rW1kopXwVgK+YnX4Y5g75LgCXAXxlrfUn12bAMaWU8nSt9dbTtmPVch7LdR7LBJzPcp21MpVSfgEDZvXIpVrra1aW9zoBOsy0lB8G8LZa60+ceOYj5Kx1pF45j+U6j2UCzme5zmOZ1iXbm7Rb2cpWtrKhcioPqtRav/Q08l1C7j9tA9Yk57Fc57FMwPks13ks01rkVJY4trKVrWxlK23ZLnFsZStb2cqGyhagt7KVrWxlQ2UL0CSllJ1Syu+WUt42O39pKeU3SylvLqWcufoqpbyglPIrpZSHSinvKaV8zez680opv1xK+anZ4/ZnStS33c5KW5VSLpZSfruU8h9mbfKts+s/XEp5uJTyrtnvr86uf1gp5Wdc/C9zujbiPTZjyzQL+4zZtfeUUn7VXX/9rExfexpl2SiptW5/7gfg6wH8Lxi2AQLADwB4NobH1F9z2vYtUZ6PAPDy2fEzAbwXwH8C4J8BeCmA/xLAPzhtO0eWaQfA/wfgowHsA/gPszKdibbC8DzAbbPjPQDvAPCpAH4YwBeK+N8M4Ntnx8/G8IDcPoDbZn11F8BPnbEy3YHhU08fOTt/jgv7yVkb/5jpvFl/G8syTkNKKXcD+FwAb3KXdzB/2vPMPflaa/1ArfWds+MnATyE4dM7O5g/GXzWynXj22611gMMA/nzcUbaqg7y1Ox0b/bL7tZXAM8swyO4t2EA6ENs0HtslijTF2F4qu9PZukfdWG+XBvbjichW4BelO8C8I1YfLLzuwH8LID/FMD/eRpGrUpKKS8E8DIM7OZ7APzPAP4BgB85PauWEvVtt+fjDLXVbCntXQAeBfBLtdZ3zIL+aSnl90op/6qUcmF27XsAfByGr2+8G8DX1FqnM4dr77E59bdAjizTiwHcWUp5eynlwVLKlzhV92Mo0wOzMt68ctoUflN+AF4L4N/Mjj8DsyWO8/LDwLweBPC607ZlBWX52wDe5M7vBfCvT9uuJctyB4BfAfDxGJajCoALAN4M4Ftmcb4QwL+ahb0IwMMAbj9t249Zpu8B8FsAbsXwGPUfAHjxadu+ab8tg57LqwB8XinljzBMmV9dSjlrzFJKKWUPw2tef7TWeh4eEhj9bbdNlVrr4wDejmHN/AN1kKsAfgjDUg4AfBmG5YBaa/1DDAD9klMxuEM6y/QIgF+otT5da70E4NcAfOKpGLzBsgXomdRa31BrvbvW+kIM3wr75VrrF5+yWceW2brlDwB4qNb6nf9/e/cWYlUVx3H8+5siVOjeIBMVUwSGEYkpk5dI8kK+KVZCEWlUEEYgREUvJb1IBFJZTiSlDwXZPSTELpRWdhFRx0x8cDQCKaUeops4/ntY6xz3mc45c2ZynE3z+8Aw++yz9t7rzAz/2Wfts39rpPtzilTndpN0Fun39f4I96llktolnZeXxwJzgH2SOvI6AQuAPXmTH4DZ+bnxwATgwOnudzNDeE3vATdIOlPSOKCLdH3ECjwn4f/fDNIQQE8eHwR4LCI+GME+/ScxhLndSqYDWJ9nGGoDNkTExvyxx3bSkMBO0vUBgCeBdTk3XaTY3qMj0fEmBvWaIuL7nBK3m3TNZ21E7Gmw71HLt3qbmZWUhzjMzErKBdrMrKRcoM3MSsoF2syspFygzcxKygXazKykXKCthqTxkl6TdCBnJGyTtHCAbTolDekzrJKWSLq48HitpIktbjurEgs7XCR9mb93Srp9CNsvkbT61PfMRgMXaKvKd3u9C2yJiCsi4jrSXXqXDONhlwDVAh0R90TE3mE83qBExPS82ElKYDM7bVygregm4FhEdFdWRMShiHgOqmeRW3OY+g5J0/vvoFkbSQ9L6smh7isl3QJMAV7Nwe1jc7rZlNz+5ryPXZI+bvVFSJqtNOlCj6SXKwlqkg5KWpH32SPpqry+XdKHef2Lkg5Juig/V4nQXEm6NXmnUkh+zZmxpI2SZuXlpZL25xD6GYU27ZLekvRt/qo+Z1aPC7QVXQ3saPL8z8DciJgMLAaebbWNpPmkLIauiLgWeCoi3iTFSt4REZMi4s/KTvLtwS8Bi3L7W1t5AZLGkELiF0fENaQ4g/sLTY7mvq0BHsrrHidlr0wG3gEuq7PrR4GtuZ+rmhy/A1hBKsxzSRMJVDwDrIqIqcAianPHzf7FWRzWkKTngZmks+qppBD21UrTFvWRMn37a9RmDvBKRPwBEBG/DHD460lDLb0ttq+YAPRGxP78eD2wjJT1DSlrGHL0al6eCSzMx9kk6dcWj1VPF/BpRBwBkPQ6tT+DiWkkCYBzJJ0doz3z2Bpygbai70hndgBExLL8Vn97XrUc+IkUC9kG/FVnH43aiMHN/DHY9sXtmvk7f+/j5N//UGbtOE7tO9AxheVG/W4DphXfKZg14yEOK/oEGCOpOCQwrrB8LnA4Ik6QEvLOqLOPRm02A3fnaEkkXZDX/0aaK7G/bcCNki7v134g+4BOSVfmx3cCnzVpD/A5cFs+zjzg/Dpt+vfzIDBJUpukSzmZc/w1MEvShUo53MWhmc3AA5UHKkygalaPC7RVRYo2XEAqjL2SviENETySm7wA3CXpK9Lb9t/r7KZum4jYRMps3p5jTyvjv+uA7spFwkJfjgD3AW9L2kXjKZ1mS/qx8kWa0msp8EaO5zwBdDfYtmIFME/SDmA+cJhUkIt2A8fzBcvlwBek4Pwe4Gny2H1EHAaeIP2D+YjaMf0HgSlK0z/t5WScqFldjhu1US9/yqMv50xPA9ZEhM9ubcR5DNosfWpjg6Q24Bhw7wj3xwzwGbSZWWl5DNrMrKRcoM3MSsoF2syspFygzcxKygXazKy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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid = Map.read(\"analysis_3d/counts_2D.fits\")\n", "resid.data = sh.get_data_image().y - sh.get_model_image().y\n", "resid_smooth = resid.smooth(width=4)\n", "resid_smooth.plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find and fit the brightest source\n", "We then find the position of the maximum in the (smoothed) residuals map, and fit a (symmetrical) Gaussian source with that initial position:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "yp, xp = np.unravel_index(\n", " np.nanargmax(resid_smooth.data), resid_smooth.data.shape\n", ")\n", "ampl = resid_smooth.get_by_pix((xp, yp))[0]\n", "\n", "# creates g0 as a gauss2d instance\n", "sh.set_full_model(bkg + psf(sh.gauss2d.g0) * expo)\n", "g0.xpos, g0.ypos = xp, yp\n", "sh.freeze(g0.xpos, g0.ypos) # fix the position in the initial fitting step\n", "\n", "# fix exposure amplitude so that typical exposure is of order unity\n", "expo.ampl = 1e-9\n", "sh.freeze(expo)\n", "sh.thaw(g0.fwhm, g0.ampl) # in case frozen in a previous iteration\n", "\n", "g0.fwhm = 10 # give some reasonable initial values\n", "g0.ampl = ampl" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 291433\n", "Final fit statistic = 289598 at function evaluation 230\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 1834.36\n", " g0.fwhm 113.287 \n", " g0.ampl 0.439958 \n", "CPU times: user 11.1 s, sys: 150 ms, total: 11.2 s\n", "Wall time: 11.2 s\n" ] } ], "source": [ "%%time\n", "sh.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit all parameters of this Gaussian component, fix them and re-compute the residuals map." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 289598\n", "Final fit statistic = 289503 at function evaluation 383\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 95.2247\n", " g0.fwhm 106.247 \n", " g0.xpos 234.649 \n", " g0.ypos 195.635 \n", " g0.ampl 0.478146 \n" ] } ], "source": [ "sh.thaw(g0.xpos, g0.ypos)\n", "sh.fit()\n", "sh.freeze(g0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid.data = sh.get_data_image().y - sh.get_model_image().y\n", "resid_smooth = resid.smooth(width=3)\n", "resid_smooth.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iteratively find and fit additional sources\n", "Instantiate additional Gaussian components, and use them to iteratively fit sources, repeating the steps performed above for component g0. (The residuals map is shown after each additional source included in the model.) This takes some time..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# initialize components with fixed, zero amplitude\n", "for i in range(1, 10):\n", " model = sh.create_model_component(\"gauss2d\", \"g\" + str(i))\n", " model.ampl = 0\n", " sh.freeze(model)\n", "\n", "gs = [g0, g1, g2]\n", "sh.set_full_model(bkg + psf(g0 + g1 + g2) * expo)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 289067\n", "Final fit statistic = 288814 at function evaluation 214\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 253.351\n", " g1.fwhm 3.76114 \n", " g1.ampl 16.7742 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288814\n", "Final fit statistic = 288735 at function evaluation 428\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 79.3409\n", " g1.fwhm 2.13994 \n", " g1.xpos 253.123 \n", " g1.ypos 197.983 \n", " g1.ampl 50.184 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288525\n", "Final fit statistic = 288460 at function evaluation 223\n", "Data points = 200000\n", "Degrees of freedom = 199998\n", "Change in statistic = 64.6649\n", " g2.fwhm 20.0902 \n", " g2.ampl 0.919413 \n", "Dataset = 1\n", "Method = neldermead\n", "Statistic = cash\n", "Initial fit statistic = 288460\n", "Final fit statistic = 288348 at function evaluation 333\n", "Data points = 200000\n", "Degrees of freedom = 199996\n", "Change in statistic = 112.159\n", " g2.fwhm 35.2131 \n", " g2.xpos 185.688 \n", " g2.ypos 197.289 \n", " g2.ampl 0.587144 \n", "CPU times: user 1min 2s, sys: 526 ms, total: 1min 2s\n", "Wall time: 1min 3s\n" ] } ], "source": [ "%%time\n", "for i in range(1, len(gs)):\n", " yp, xp = np.unravel_index(\n", " np.nanargmax(resid_smooth.data), resid_smooth.data.shape\n", " )\n", " ampl = resid_smooth.get_by_pix((xp, yp))[0]\n", " gs[i].xpos, gs[i].ypos = xp, yp\n", " gs[i].fwhm = 10\n", " gs[i].ampl = ampl\n", "\n", " sh.thaw(gs[i].fwhm)\n", " sh.thaw(gs[i].ampl)\n", " sh.fit()\n", "\n", " sh.thaw(gs[i].xpos)\n", " sh.thaw(gs[i].ypos)\n", " sh.fit()\n", " sh.freeze(gs[i])\n", "\n", " resid.data = sh.get_data_image().y - sh.get_model_image().y\n", " resid_smooth = resid.smooth(width=6)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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rRzmI/AUAP55S+sEQwr8O4J0hhM9N6XJLz48c0AP12d0D+drmol3pW/ax6/5tZZDUBoNNz1sU1cHM+mn5islCuoEHjDqhqIayakuAImCoF4hd6NV0LVjxXmuP9wbfyjwnK3yeNsyvij1xyQ56C5Y1UxBfG5k8vKBdnNAJntoeagJj/mra0fws4LKsbIs5c4amYwOS0dxF06A9lUz7Voy9h9Im33vewTWXaR/SiYO/zRKnpr7Xonaf99zts/Xu0X62X4+cC9noX5yJR/8CgNfL59dhapr5VgBvBoCU0i+FEI4APAfgQ4uL68ijB/TBZ3I6mDw278mDqO9WRSSQeK5u1kXQAo2WV80onp15iRZSu8ZqDcxzqTqsG668dRCbv7cDl5vR1GXVMkPPJECh2WhuY5zGWmc51NWUYQtsm2h7qCbmCcHVhjbQ7xXIum70jbfp6ATXoOyzunHPKwP/DgwpUM1ga663YK/mMYpqPmpaUzZvzwRoUCc5tUlOr/X67ZBe/1n7jmXzOt6Aq3K73Eui7wHwxhDCGwB8EMA3Avgmc80HAHwZgB8PIXw28ml8f3jZjB89oMc4EGo4xU7t2YCXMDvAj52u5gRlEHrGaZFeJR/LzMmm7WlLFjhpClliT9V6AHXNRNk6MIKDBXjP9MJonroQbt1NyXS9SW5usvG+9ybTpsEAiMrudfeqmpFsGTRGvDLjOW1u6H8GwLydrVp2L+SwrXORX4vBm8trO9UgXDJRyc/TJraNaCiOi7BOYPYUMxV9LhbkrdT6sTs2UWp1NRPc1YD8frxuUkpdCOHbAfw88nr8j6WUfi2E8DYAz6eU3gXgOwH8aAjhb/QZf0vawzGAjxzQq41eY7d75pK5WNkUr2N4kwO/KxZCIyYLZ3r/nL3Qqu8ew62ptzVNZFDHm9Fffk7sxNV15efzrjS91DQSj0nZNvTqYlmt1qmm0Xg22aG9hn9TmQtENzfZWKBnfrqICUxJhQU6/r4L/Lwy2M/eRrehDRcCp1dPL88aG9+Vx6489XfP3q59c3jFNFje9cl+Mut94t9tvvteef8+AF+yl8xEHjmgB0qAqam2umGKYsFiaZRABT+PuVqg5z2Mc5LM4NRy8jouiKomYYNmzUWfvChDttoC48FTyEA18qXVaoAprip4zgY5wzhg1SzjBVWzbWwXNpVBMw21pfMau0DrgSgZ6+F6OvFq/jWtUoGX+yvIlL3QGlYKArDjOasmWFwj96k5qzZh18qk7VEw+qbsA3Nask3XjhNl4rVxNJnMUSc7nAguulN3Xm5i3Vy/hJKxe4OtBjB8XQryOkjmAF7TVaHrJVCqnupGSbuup4Lbsnh5ajvUQifUxNNC9Ddr49bfdk0otk4KDJ3RFFIcQV/Vc7WBq5ahJpyVgHzTAGvjWWJda71nryaXg/V40Pp6Pe1DdrLYmjS89tDJSNvR21HKtqu1sU48BHl97oN9H+PEx7Wi2L/aZ+lpaXzfGndKq7lY059Na04r5bPYGkJT09i8+22ewL5BfsjxKhK9Nnn0gF6kMOGI1GyrtkNYm6+CSoORZVpVcY691GyWLJeWnXZhMjM7MLTjz5mI+D7GkdHUBoE3QLy6eGsPdtCpWUKFZij659P2rRoBUC7uqobA7f/K6IaYLEazqAVAK8BdFh5rz7BpRpA/OhqBXuupYNa2WduxG/UKNg8UMXIKE4u01dDmGMG+BvJsb52cbN1V09XyFZqbgL1tl1q+loSQfNhJm+JFWtV8vHg9+lyUaHl7KbR8nOSuRm6Cmr3iZBfI2w7mdfChQ1ZAu8aAKGRReq12dqsCe6qtFwpXxabHe1uTxgrzwa88+7oHhrX8vbg86q6pJhBPA9IdjpqPF+OlNsFaYLcA6E1sntunMvn1OgM/MAXLbSzXM3a54Npn7rUj33saB8ug108mM0Motk1ZPq3/VsyJNQ21pg0zJhFQEgHPpNg0KBBGf6uNQdsnB+1kx5jzNIfa83gwuQH6hy67AjbNgbztaNYm6KWzK5+LyMBOY7mz1JpNvEHYGICxZo7BpxqYHBVXm/DmxOYzt+5gJzK9hjuJucPUO/Bl7lAPBXP1PPFiC3mMknU/N5uErGZl6+bZ2C8KJvZ6O1HrNdoPvfuW5DXbT01/sCRAJx7rSqr3nUv/tOsnQOnZBJQaco1I2PUbbwLi59pv+5UboH+osnQmt9fMseUleerrXCdUsRMMmQuBXNNTv28v3LDH6L22sCxW07UD0yv33MQ3V1dgfuGPrFPtyxr/Ryc4zYN/B+vRHfVg7ft4W08mZb9cUO2MKciWn/nqhjgbQdTeazd41QiC7UdW66lN4vb+XVqOl7ctv/echjWmvo9oOAJtxznt2JsgPPH6ridLAF+/24/cLMY+VNkFQpbJA/PeMjZdvre/PQi7Yjn1kBAggx3tphr6uLajszZwa5Ob7lD1jhT0QOUi9arVlRPIGUpwUNGF1E52Zep2fJ0EQzOagg7a0ZbugbnWiezf+vxT7DNRjYh7BZb0m6I9nTZR8YBYf5v77KVnr9VF6DkA9bRaFTVR0Ry09HxbO4HWJh5vHNuQCNbDzk54S8jIg8uNjf4VKbuYwUUZPMXrpDU25YFb7Bkr465zEPFvi3LgDZuY+jwuGuApxpK5UwWvuWjaOtr67Mpz7jOPHrTsLhkQ2GzG37aVMur1BHFt610Hg1jXS5bDtteQD+ogP7f4XvtujmlfRB5U05pLr/ZZ+3Pblnb6peOpRlZsPXgdJ1g131yteWZOboD+FSFeJ9LBuGSTxZxazfcWTJb2O8tutl2Zlg1KpTZq5ssFNJ1kLAjbicJGkCSr1/vtVndbb362bWdNFHZhd7MZzSnnbcmqC9CQeijIKovmtWwvGzKiFivHanUraXP7PJmEMnxtBztJqmkpNaOvvvUQ2TVZqaaxauq7O2uTSA389sF0LSGokRgPvO39nqasWgc1O5qLPG1hbpxfrdwA/UMTT930mJbnwqXigZz3vnbIgk1nVxm96xXotA4WFDWQFlCq1sxH2TvB/fQUONuMZhzNd85ve1f97DGGnklIN4Q1DQAJ89A0KA5UIfBp+zEtL66QytzJQ961qSnbk2sXwwTWlXW2B617Qd7syWZaDxXbp/R6TkpAPVa9Sq0tuh3P1CuTfleLKxSjH3ivBvA1sW2QYhlDiLu7i2eyQ64G9G9MN9cvyQfEuS3u9j3Fs5HajmsPWCiu78T9yymqxyiXnGBfDLY+T40XQ/dNa8rRPCybV5CvqdAPyvysuYMLvkC54LmN5S5OjS2jG33m2sMDW5pXdgFBiigelN1gxvQJLLTPU9h+59K2FNbDTsRWLImwC8Zsx6YZ29D2I1vm2nOjtuS2g5OG7UvWrRRAYXpUZj8JEudM+l67eJvGmKaduC3YXy+rvwH6axcFvF0HJM9JDeT1vXURtGYTigf4lmXbjj9XP5aB7EnTok9xF6f36d+ZLL5WA681dZu2vU7FhkuwZeBzUXNT04zhdxsDDHOx4ouJT8CWpiGWT997dfLYtmeKKHbfSt7DhNWVk5nWg0y0ZtbQ7+Z2D9u+XAN6z6Smeda0TG9x05pWqqYiU6aaVmL7vZZN816ysOtJbRztH/RvvG6uXRLKgbiLNexi7N41FBsvx9oTLfvJF/Ud1jB5r6xeubzye8BEsaqyToLnmxGQLIsfWFNbMmtPaqYrCv30NW0bSppsv23zBBRj1lJ0Y49OEpqvrefZptyaT/dKfS5tmwul7WfZs/p1d91YrtizeduntC3VrRAYF3djRHFMoAf4Cu4K9nQvVdnVx+eYve07es9gvqu47SqRsfsw7LVeQD4tLzWUWvROXTux5VaNgkRBQzzYel2d3DD665VUboO3g6AWxwSYgpwCWJHFjmdq1Vy3mJXOuot9eeaUXVEtlTXRnZKDmYCk+ejBI5q+XmNlcsCFAsICEOJnu9Bqt/DrhOQdGkNGTe+cAYyaETBo5rLl18lNMw4ADroOQJwshOszs6GO7fP0nq/X9zygZ9nOnTUXO4HXRAHT9h1rgmLaqSmfpZa7Vpfa4r3+2bbYxnmzFuvFQ3T0mZM02DGnn68W5G9s9NcuCT7IW88aZSPAVC2l7CMAkgdU9uQjjV/usWubjg7OJWAPTE1ZXjmZro2VUgN4vb5WZgKjtbHaNuF7fWaNU1Yvhr5nuohxBMYhv3Z+Qm20gO1Bn9gWaBo0fWGshuFNWBcxM1hNR8tyEX/3JQdreKdiWU3MnrW7S2rrJxRPa/HIy9wZEjY9jmFvgd2eJ7ukXJeXG6C/XklTxuUFqyo6nNhuH0Q89r4kra108pXsMPQGw9wpUMr+hrRFza8BIYVmAbveMHeSkhU7KdjBrc9EY697Jge9lp4Wal9XVs54M+rXTtCjSeAM46BfA+iaUZOyzy4Q6NuDyYNoms71+PHCMejkPTepaFtZkGdda89AzZO7JvEhX2ASSI2mFSUhtkxWlIzEOJ085siBxozntZS5w0P0HAXbdnY9ypMHGaPL5IbRX7uQ0QPjAKjZMYcH3aEA+11ml4uwfDvQlbU3zVRdVQbrMVf7VzvpSM8GpVAVVxnMNCgBnvnbAWy9HLz62klHfcdpyvF2+fJ375VpA9PwwCpqqy1MQc4gbzsMkSuHxeGYTTVk8UOifUHUY0lDUGj+trzaLk0zfZ41H39ORnrouxd7qPZ9TRosv9aLOzNJzyn/3PixIO+NNwK6FfYbbyKZy9MjT/uVG6B/KMJOWXOrVAAmMNAtTDtf7ZCCqkvlwnKpWg6UwOiZbuyfx561HJYdU5omA5SNWmknDZse01TGvHTC8+yqFMbJr8XU8eqtZVMA0voE87sF4PMuAz0XT/V5HDRC0fnadZONZTbombab3RTF76wnjeedpG6Ta4x9cC70hW2LJaLaQBF2Y0censwBqKe11cqrGsJFzF9WS6iVzdM4Ly83XjcPTdhhCDLebwoWQMl0LNgDji9wM03Tvp/r5DYNC/Y1RmhNNXPshr91Tif3BpktkxUtl9UWtK5LmD9Fwd62h+5w5YIv4AM5bbxWE7ETVIyjC2SzAVJb+rzHCLRtN9xDzeD0FLh/ml8J9kzT07oYr5711UNLeK6qLZclGLksKMpiNTXv0BmvnVXsYSO6qc1u+OJ33jNlW9e0YJZ1cPntyv5XGx+63hCkvy2JKa+TpwfyugN7f3LD6K9VeGasdjyrts+ZZnQAUPTaGpNXMLEgbwcmMAU/L705saDsTQyarpoJakDvSY1x2brY+Dy2bLuASE/AAkZQZwgDmmvU1KU+95pObW+DbV/rwcLvt+207AT2k5MM9AT7oSzNGFQNKCdhgorWxTJ+G3Ka5T4XTYCy67wEFdsvBoIhAG6vVzdRqzFN8opj3WviHXnIdDxyMFlriOXznTNT1TYwWpDf5TJ8Mbmx0T8UsQ9w8EF2OjZQgrSenzlJt/9XU/3swJvbsGU7oEpoUNjqa4OkVg9Nj54uChSDndOUa07z8PK06vV5NwYnqwGMl44tsw5KjSc/aCfdtM3s/Z5pSwe4nex0QVXf8xlqqAg13/A65tk15aQETE02usag4E5ffcuWd22aqtnRdwGZHRO819uHogHk9HvmwUBm1FRUPKKj6SyZuIZnVqmrtpc3uSvRsX1gP7IfoA8hvBnADyFbV9+RUvoB55p/F8D3Ic8w/09K6Zsum+8jCfTA9CHqaj0wHRCqMsNhrbs6he2g3gEmXn4qhWlIQLTBvGpcEwU4siKd0MiOPJZeG3C1/OwBEJ5cREvhIR8KkHqd1RIGpuqweQsAc5M0Q0KzTgRhDfh2bswamoYX30jLYZm8zd8GalNt1FvHuaho//fcDy2Y18ppwX6YfLsxRpDeQ/PQknGkZahdv0uT0bruF9A92Q+jDyGsAPwwgK8A8AKA94QQ3pVSep9c80YAbwXwJSmlPwohvObSGeMRBPoQSvV+woDEjbEmtUWghkzYYSweM6mBvL4O5fbYiJR7Ltyt7eyFOyJKd0bmXRsg1gPGhh6ohQ6w8V80dLKVOROTll9jyXvgS7uxbV9v0VPzUGanMqQjoE+Gzdg13BRVA3oL1Prnre0oW9Y4PXq/XlPbQFZr4yUgp+SEbWxF62XDOwAVqCO4AAAgAElEQVTTfRcxjruqh/ZA2Qdt2va5UpiGnTy07tq2tQ1+Vwv4e1mM/SIAv5lS+i0ACCH8JICvB/A+ueYvA/jhlNIfAUBK6UP7yPiRA3pgquICAv69K6UGVeI9lFokPLIg7/tdB4Jo2bSMHtu0WsdQ7pl8vXoMnR5lOTwWZ/90B60FUmsO4ase70ahf7OdIDzTi5psPM8UG4ytMztR9V5Gw6RNFqh7FFngVDCjZw0/a34Uu7OZ7qSWHdO7hSBOcD/vtYWtAL09Y4DrunMmq1q95r7zFlh3sXnbDtQ87OKxVy4NOW3zZx6WXNQY/lwfmpvolmgDF5MLMfrnQgjPy+e3p5Te3r9/LYDfkd9eAPDF5v7PBIAQwj9GNu98X0rp5y5cZCOPJNBTvIfc9ox+C6Dm8kfxOuHKv3T4XQe73m/DCdOWaT1nLNCzM7btdABagIlmENnOrvdfJFKmZZgsj7JUYOriVgBBLL0nVGoDlqJsshY7X9uWJ0upRuDls8tsocyddmut/9DmziTJyYinZMUINF35vLVOyuT12XTShrzHEpm53bNzwKjPxHMO8NpEY/job22LjHX9+FIvKp38aguzu8Srhwf8dgypUKuokY/LyeJKvZhSelPlt+B8l8znFsAbAXwpgNcB+EchhM9NKb20tACePJJAP2GP5ndrgqmx+mCu4/eaTo35UKxKSZA/MAtsliHbctsyUruogb0yYQv0c0CrPu3bprxH3Qh1kBVajpTJtrsuMnqTqC2nAoWCImPnq5nJMnmCvW2LAUDNAqSWg3l75hlqYLoPwV6TTNl5iIwXKkLz0k1YdkKvaYecbHfZv2uA703MVjR/TmJanrl8eY91hPDcG219gClB0knbY/q15x3Nc9+v7M3r5gUAr5fPrwPwu841/zSldA7g/SGE/w8Z+N9zmYwfPaCXOXEIb4oSdLQzKUsDds/ytYiVACbHz1kAJcgfiidJNeJjZQB6g9GaHoARaDhheaFebbwdglfTlPZvTox6v2fO4ffDIJT7FFAs4CuYMw8bCE0XRc82Y+RNskkL9OqtU7QrpiYKa0awu1/5nfXYUFOGB8567GHt1dbPcx2sxW634L2UKdt7a2SFf8m8V1EAtu0zjAspt2pgySn/kJ5MIvaPmqMlHLy+NobtBPUgmkVd9pLYewC8MYTwBgAfBPCNAKxHzf8K4C8A+PEQwnPIppzfumzGjx7Q96LgOTAeTE0Q1ha/ZBOFVQ2pxuvWbNsJlckru7dpeYOLQOAtcHoxVZQN84+DyoK7rU+Mo3dIzdSh6XreLAUYkQlKOmrLD810sGoadu2DdnkbO5/mC7sQy/JGyVMZtDf4LYBrezXN1D2y7cryDfk4Y39uYvfi7VuNRiffGlDpc9Jrd4H7kt+YLl+bpuzXfAZ6HetHjZCTs+37Hlu3AG/Dfc+NV29C5zjaZba8uFw+wZRSF0L4dgA/j8y7fiyl9GshhLcBeD6l9K7+t68MIbwPeVj95ymlD18272sF+hDC6wH8DwA+Abnl3p5S+qEQwicB+B8BvAzgL6aU7i5JzwKPep/MqZs2DY+FeenrodJ2QvFUS/u7ZZZ2gKs5SRcWdYLZxVYaTHcOeuVkvgdtBlY16Xh1sIPWEw01a01gc2l4QOfWTcpk79f8+d0c0NcWua1pQIGGdujaYmL+ML/BaM58Yq/xCILV/pb2812TBsWGs6iZIb3Q3mqy0/hHti762QN5r041T7m557o/Rr+/EAgppXcDeLf57nvlfQLwHf3f3uS6GX0H4DtTSr8cQngSwHtDCP8AwL8P4K8D+DQA3wzgR+YS8YJ8UVLl+5pYEPQYrL6SDVvTRo3JeaCk7nyq1ucLxgU+ra+ajXTQa9msy+VcPe2EoWYIoNdQKkBvJ4uhLJJmbQereiFZ0PI0HdUsbDvawU9PEbug621ssyDg7dod6tmO7dN1GBb75+zfS7qg1ze23TS8g538vImgJruYvce4iQq6mcvbcargCmCIc68aKPtlLS87abjlMWJDkmtZrGfcfuRmZ+yFJKX0ewB+r3//cgjh15FdjlbILRnhr0wPwhAINaCfY3ze9ZqOd6/eR8BZGlfbE/XY0OiIhSobxwy8shWDS7QNXl94SGA6eKx2wUFhzT7eYrKKN/EMg1uuUS8kLy1rmpoDsMKP3RnstLvrTlc7mXreUkA5wbYG0CYiYF9jq554k6Vl7qrxWVCb03aWiG1nPndOdDGOz87br2Cfua71aPl4zUpA3t6/pJ2seBOe1QY97efycgP0DyQhhE8F8PkA/hmAfwngnQA+iunixEQ8hskHu2owu2nKG5QeECrTaMxvNTVSFxntxhrLONSH27LzJR1UB3/TlG56mmeSJ2yDcHHiGhhXnP5uy6RtVfivG8asaZAJepuBbOwcbzHcis2Hr8MCq4D86enUhKNp65kBa8vKHVBW0Mo/Tst3WdF9HjUzhAXOOXLjsWn9nRqjNbUwto+uhwzPxzw7m6/mSVNW0de0PP1r7YhA77tO8qlpg/uVG6C/sIQQbgP4aQD/WUrpDoA7AP70zPXvBPANAPDM8cEsw6QffZLBOce4NK05G6EnhflGvlOGoQBIpqlgxOts3nbjiebnxSWhWcFu0CrKKCzdgpZnjvLqaYFOF88oljEzT2/xlIBiv1OQ9criPVdOHNSYTk9zkLIYyxgzwDTOTGimC8AqFlRr7TUHfPbaWl4x1ucP2za6Yay4TtPaAZxMx+5Wte7CvDbGMWAcULpL2noM/vXor+mmz3NoV5TpeBPtReX9738/Qgj35KufSSm95WKp3JhuLiwhhANkkP+fUko/s+Se/sG8BQD+xOtflWLMewfmWIouKtlBZjuQssiLgrxN24aHVfc9u+tTvT66WDIqa6f1Igt6zLZpxklO1W16v9jBUgN3bTerkmsZFcA1TQvals3b5+LJLtXeanTM+0zY/P1TDGfMKui1bWbzvIcTpXr9eBOMNe9ZDVCfWzVao0iNfXosfC5PbQPGfarFObJi29m6s3pmLPUG02dRbDRryvsskLskDFOw32XmsXXRa9/whjfgwx/+8BPL7p6R/asI1yrX7XUTAPz3AH49pfR3HyQNPTN2Tr2nOmpFO4LniliTOROCvm8wMhm9L8ZygfBsU24zn/NQ8PLjdd6EQ9Gt+t5uR2oTS8DelqFtS9ZnzRu1NL066SRoXStrmoWC3LYHEZaXwH7vBLh/MqYNjPfEWPqH04XS+tfb/FUr4DOznilaVqvhPajbn6fh1CZPPZJvV1A7fW81BRtZFHDWk2QiV5LjaWfBtGVtEmqGf/PX2TIzkuvSSWGxJDm/9BGV62b0X4LMzH81hPB/9999T+9ytFjOu93ArOIxo4uAvJcOxQMk+5sOdm/jzBLW5ZVF8/bMKzWpTQ5LGLdnVuFC62CCmtmV6k0IdtJhO+m1XrA5XUhVm62dNCb2+bYsQyN58Pq2Kw/c4O9ee+iEN9d+Cr4PIlp/66lykf7jaWoecbKag7fwbMvmbV605dVyFIzf0YYa8wxq0jSllrFXSfAf/iMk1+1184vY4VWzO41s39ZFNM+1cQhbbO5fcvKMB5S7zB56j7fzcxtHu7zGPqEs6ZwDsDbTAbVUbJlrMVB2lYEs72Atm8Q0fcfFzTMHcYep/WN7qtcGkBdM58wgap/nn5aDuzJ5kAq/58Sgbqa6Kc1rP7aDNXGoCYVaW9dNQ1p4r1772+8saM4BoBefyJtgbV52gxoAN37UZOJ30vTGJr+rkZWG+RnxJtyJZt8/4/1JujHdXLekNN163jRT4KfUdorWRNlDI98tETXfqG1eWaYNcGVlUEGbeVYNjL7q1n5vB6stH9/v8jeugY6CvQZuq4mXPvM934w29bMNcHY6+r4z7S0P+ojAph03dynTZNuqWyUPEeGz0OdvgcaGNIgxT2JeRExluRbsKcMOWvMMrTvtvkTroTIH8hraoWlKYF2ZOgL5VU/9spurVHYxcBu90xMP7L1JiaLeY3vH5f1vtb1WeeSAHj3Q29C0BBuu8AO7w9cCdRDiq8dCvVOlJoAqTBEoAd5z9avJLrAHlrF7ArsyPAs4qiZrfefYJq/jvbat5ti3eiARlE9Oxu/5XI+O8j16spOGVmDdlMUXjL7PU3c1a5RSrRfbSOulwKZhEvh+ZUw3NY3QBgyz5hL9A/zdp3zlLlROiJ5lwQsRoCC/Nf2wbXNfUtasYweYukfafu+ZYuwkpJjpsXabnp2EPMy1WsZegT7dMPprl4TSfqvbzZtG3LkMyM91TM8kUwMv69qo99g09a8We0XLpKYZD1Rrfc3G+tE0NX9bdws2tYlNPxPMzjug6Y/aU1XcxrmfY/Ndl1m8RqxU33drulFTSuzz4fddlycJ/nHiOEPuMwEAYtYKtN0HE5T0mRTHM1UtACcBdH1eNc+jwkQiZhx9njYPG35hDsRtpFAvns4ck7dlsW6jWj4+/23fjkxLPQ+9sWQBvrgmToFc8+X1Hsfwyum9v7QQdB5heeSAntGbC6BEOWiA0k9agZ6ioDZn3rDvva3XzNd+pwGWPHV9Yt+sgLsywaX9jYOj5rsMTFnb3GRAUabNelk2W4Cb04acDDabcd2CNvIz+QyUoHpggo2dcaLp2+beSXm49/0InEubrKTOPJhcPUvUJ90LubCEKUbzrIaFYact1QSmkU5rIGXDLsD5bMvi/dnfvOuT8/zUdVKdCSxIW2K0aGcv5tn9UrFaxH7khtFfv8hSrmVA+r136DIw7dg1hq/XLVlwt8xYO/kugOcgt2BvB5VdwN0Vcplgz+P/gKnZqWnKnYs2f2XoKm2bAbkWpVPFsk6CvD1gpOvKncNaVmvLpedV04zXWja/xbhBOvTvD6XN12vg+Lh0j2S9rdRMf8A4EasZSeMY0Uxi02I7zkXjnOSFZebiOZCvRdHU+6x3mJZNAV/rbdOsgbwlZbXyezLXNlfK6G+8bq5fvIdo7Zs1dVjZv5eG/W1OLfbuqYGEHUxaRsBnkPqeA8dqMnNREjXf2oDj77TPqsyZYTjIvVgorJu+Uuw29RoYaRkUUNoeRIExvANBqTiTFeMf22pot76cltFbsINzj1cn3sNXL/aLpqN9gX3TTpSeBkq5jIumJzYv70wCvc4bW3NMep9keC6tqyPdN4z+2oVBzYDpdn9Vg71dfcAIGla85+gtHlGsXVu/1+8InrqI5anvWg4NWKZs3i6eAT7YM32PbXkApPZX3WSmJgjv8A21cStYTr7DCE7KeJXN19R6G+mTdTjvxm35bDN67BDodeIK/R+Z+9ERcHg0MvrDddk+3kK1F14ixpKt6+Yvy2o9oLRxgDyyoXkNHiXNlGTuAn+moxuLagxZJ07tcxq9lX0mOnWk2HMi7CTnta3+Vpts5rSuK5EboL9mCaUayddil2M7ZZoUBUH9XGPdtcEz16k0rSJ0L/OqMNfBRt6Vbnk1G2+NrVvWbpmqlgWoeyUpyOvmI01XJ1hunCJTphDsFUS5EOu5mnoMU68Z6iC91wPnFcaF2IP+jwTg6Ai4dZRf7RoD6+i1kfY1e70tB8uiezfU22dXGGgVBXsFXuahm7G8iVzfMzSHXVehMA2+8toYc5kV7Pl63qG6K1XdgO3vNg59re7avjW5MixO+NhwrwwhfAqAN6aU/o8Qwi0AbUrp5astWqUsmO5A1D971BkHJkX9r22cojl2o9d4763YCUPZmLpDDoMWpYlizoxgy2YHQg0MgJKZDyDRjCF6vbyViVtm77FSnZToGqlARYZ+vqnvK1g1eW2BbXNuwHQbp3Z1rVOD3LnZNGsAR+YYwprZRsugbaL9yW7n1wlQyzG3uKpp15jr0FeB4SjIWl41M7KnNQJlYDKPSbNea8mniaWpiXsaDtqyLEXesczXq+NSJq9yfST7YyAEQgjhLwP4NgDPAvh05ANtfwTAl11t0WoFmrqyqRrMz/Y3+5mxcKLphMp4PXZkgVXT9d5rmkNaXQb7rcMEvTLX0q0BPDUAYLrG4DFObU+NUujlqfnxlVqFTixKgOj/riBvTTd2AtJyd3EEizPJW/O0dSXQD++bEuTVV15NXLb9bFvsAhydAO09FtBtG+q9ZMvWpBHjlLlr37HHG9aeYzGGDIZ5G+1qIK51tGPD+26ufWr37JIlk8Kl5GPEj/6vAfgi5LjxSCn9RgjhNVdaqhlRG31N7GDymK3nD68deu6kmhoL4mTjhVhQezdZr1fe2s4/xtn3zD52IpmbpBTI9ExXm59+ri0W2vbjztJo2tYCqd3QpHZ69fboIia29oNu6uLqmSyaJvt6W42D7D3F6cY762HEOp8LY/UmcQvS2kZeH/H6ZO0ZWPNOjBhCTfBZkzBY0ukBr4oSHpunJ0O5Me1bTM9Gja1NjlqeWgiTmtjdvktI0aXlYwDoz1JKmxx4EgghtBi82R+O1NQ7fse/884HHgLJhGljHDwKisow2SGtKxzLVDtFiYDBa70BNgcSVJuVvdk2mJukbH0UbLAu07HlP2hH+yoBz5bB5qPeMmSmurCqbpCDH70B+A4oPGeA3t4bgXia2bm2vdUGLMivxMzTdTmEse4dsBOFmidWTalxWS8l+7xqZMCTOY1A0wdKJj48U8P2YZ7Hrjz5bGy4bo+J2/ayYs8KtnWxIUmq2mtlbAOli6klcJreLvfjxcKQuXuQEMKbAfwQsiXuHSmlH6hc9+cB/BSAP5lSev6y+S4B+n8YQvgeALdCCF8B4D8G8Pcum/EDS/Ddy8g0GKyKg5KqvnbOcwE7uyhrPV2sTZrR8dRG7DFfb6cksMwzwv7xfgWimo+y1VQUQPlZQXpgio45TN/b4+aYlzU/Ffm2vr+7snmGa2Yy2/6vBvQtpNNKTBwtD69vm9LOzvecbLoOOKuwwaYZTU4UXcfQ6+yxj7tAfu65ehMmP9e0A9a96crfvedMsYzcWxTVA2G2cXqf3eltJyxbb89LzgN62wbeb/Qc8rSWJYELLyZpL370IYQVgB8G8BUAXgDwnhDCu1JK7zPXPQngP0FvRdmHLAH67wbwrQB+FcBfQT7B/B37KsA+xaqVyoL1Gi82O+B3Zu8zMFVfa2zcip0M7P2289dMBbV+5532U7PNW5kjLXpAOculp2DVwKqNGALOMX/ufj3rA46dA8MZPhEl2G/hq48N+smhK+ur1os5jW8u1DVNPLY/2Iiknllk17O3oscweqCmkTZrZhhOYnxVO30NfO1Eoml5dRiONxRGX/O392QO5AeTD0ryMqc5eKIgv1fTTUolo3lw+SIAv5lS+i0ACCH8JICvB/A+c91/BeBvAfiufWQKLAD6lFIE8KP93ytSbEew7oh2UFomMrcpytoDVbaxbEANsKadN2c69RayIF/bfs+8qM52nQC6MwisOsuye4xefaLtdV46CmgKKlZ70PS1DttOGH1uloG5szr8zoJ8AAqbfUTZ/pwogIFko5F2ZxvaNrD1O1hLXWVdhPd4i/78PAf0QOkVNpRbymQZcoxlG1oNUqNw2jRrO3ItqOp3Vmx/sObGuXbkq2cSsnsu7ARkn5GdCD2/fW8y2ZssN908F0JQU8vbU0pv79+/FsDvyG8vAPhivTmE8PkAXp9S+tkQwtUDfQjhVzFji08pfd6+CvEgoqobpca2AF9tVZCvmVRqRxJaqXXgoVwzU6rXMT311XpU1NLyzCx8b3fX0kedr6sW8GLj1DQIHZTc1KSTqw5WXne6ySB/Cp/NJ0yBPmD0i2dTdvJeJ4qmv7ZBr1HEUpNTV07rK8/n1vG96VPUGvU7trt1uwTK4GM09a3XJUjrWQXWNMJ82liCm5qi0OYJzfYhu1azVGsc6ibtZaNWWuH3dLe0Yvch2FO5VDPsutJsxPJYc6XmW9MY9iIX87p5MaX0pspv3lkcA8aGEBoA/w2Ab7lQ+RbIHKP/2v71r/Wv7+xf/yKAk30XZKkE+GFZrdkAQKHHeyaQXTtflUmlOGXk2rHUJc3Gk+f3yUwATMebGOyhFbV+ZtNSQGJanlaj9eJr1wGNHOqiayFeespC9cAQ9XnX80y7Lk9WBPkNxuiSwAjWBH+CPUFbWccWeQPUxmsTZL95Th66SYmhfT2vJJ4h2zTlhi9+R7GMXvvAgRlRCkgEef4xna2YkXQ3Ksupk7PnWszXIOXQ58O1EeZn95oApRapfYhjpOumfc0rg73XbsazIK+L5NaEZQmZ51lFWWL7v5QsB/o5eQHA6+Xz6wD8rnx+EsDnAviF3vnlEwC8K4TwdZddkK0CfUrptwEghPAlKaUvkZ++O4TwjwG87TIZX0b4AD2GW9iHe0am2+UtM98V25oArB3O61S1Q000Pz3xSu2JnvradXUtg6KmEzvYNA9PU7DBzcjadMOWZ/7h67kBeXrQxDjOrw2m9vMOGZwJ8rTFU8jiLbsno4/ymZEptcktyHtS87iiXb6mwXjgoRO1MlRLPCzQh/U4s6/aiDW6YePRuekLDaaTki1PwfBFYhTzTZz2j5oQsFNE2cBOviqFBlHRIjTgIHdT54vGOEYsA7VNDf9hy+KVba8gz8JcXt4D4I0hhDcA+CCAbwTwTfwxpfRRAM/xcwjhFwB813V53TwRQvg3+mMAEUL4UwAuf6r6HsTubrWzPT/bsz9tGrvAXtNr5bPa4pd2LoK7VWWt2xwwbisH/BAITK+R92oOYLobox4zD7vD0nppeH172IhlmPxmA2xiaW8HSjZ9jgzeZ8jXKaBbic57a0ekHsw86JET5Lu5R2Lbk+YdT2rs0V5TC47WNOMBJaHlhX0pu/Pi8BL2ha4rbeIee7dAauvH39j/9T6Wd8kC5oTYtP49JAueJsm8LOhre1E0TLKdmLVM1iR1JZL243WTUupCCN8O4OeRu+iPpZR+LYTwNgDPp5TedelMKrIE6L8VwI+FEJ7uP78E4C9dVYH2JQpojcNKgKkJyAO2morKQcN0avl7Yk/F0rQgRbUeFLZcqinwei8Oi8foNZ6Lvd5ea80+vE69ZzYxm2OsXZ3Ft7Z3y+Q98Wz3kO8ADPb4FXJnPoLfqa3ppcEUGJYCBRd1tV1qrrW6Ma0w6zgdxN5vF2GVBVN7CA2Arsxf+xc1NZs+7/V2QtuJQDea6WRm2bMC+3nnt5Gajex6hvVy4sTrERFeZ/tljaBcStL+QiCklN6N7Lmo331v5dov3UumWOZ1814AfyKE8BSA0KsXrwi5TJyhpR2ipi57IK8qKxamvy8W4nkb2EHI4FQcrJbNeqYaa4LQ3zT4FcGbYA5gsKsHlCA/Kbt8T3Af8sGYNlAu3jKPtv+9xzxElJPCyik/4AP9RTfZuBqWBTBUTHvSuNaLhOlYMsDNW0NZmwZNE930FeznwFuv56vH/i3I2+CCKvYAe+unX5PamPFMYTqxqKuvZfp7kcc9qFkI4XvNZwBASultV1SmWeEmtQfRpJY8/BqwaWe3GzIsqyD7qPpNO2Bg3TiX1tGWQ9VxL12Csx6MYfPV91ZVpuiiYRdLlk5Q5kIq5DNBmkDcYArsEdmGT1MPXyOm7pgNsk1eO3Lbf7dBXrBt1G6M3DYeqy920Zrn42k3CqR2R6hOsGruG0QaPnWxWHBve8+n9Xq8rG1zKGXa+VctBtQN6NC2cQBvrgPEXpM4hG8qZD+2nkdaN73egr2tEwGWWsSuQ7pp/+c1c2cVsC29ECFqnlXZK7Pfu5pwvbLEdHNP3h8he+P8+tUUZ5lY27KKzuYeoNprgXJLtU1f0xwYDEqWZsswdHQsY4ges1ZQniu/lpGvxUQkzMfagO2kpnnX4r7Yk6rOu9KUYn3fi0Od5bqAKcgnZFA/RwZzLtZuMIL7BmV+bf9+AFOMZhwCPgA0p/l1vS7B3k6Ulu1q8DPb1gw9oP7vXux5QPpBU3YarnXoYd28jAzexupZtVLY3s7fthscSHjo4+Oyf9Dc45mXpDiTSazG3peMq5pYJr5E1F4PjJvIhnJH2V+xb0z+WAhqllL6Qf0cQvg7AK5s0WCnzLT5PlQ2C3oqhb0TvhnCpmNV1znbpuatYRpqrNrWtVZvO3hZB4KCBhQjwNtdtHbg157BCuXuVLuAqkUcDqXAyNzPMbpe8o+eOmT0mjU7cNen1WBcjOX7Q/Q39WDftnlT1MoAGOtnD6zxtDy+56K33ewEjKaVCePoL9LYP7pBiGmsWCEvmabJIN9/GdoGh+uI7dE4OWgsIPv8dgG1ndjtQeVzJMRj+R7b1vFk23dXGW1a1NaW7DV5IHncgd6RYwCftu+CLBWNL6Sg43WKCZuqiO4y9UBMP9Pe2bbj9V4e0fw2d1A5ry/CC1fcyWzd7Hf6vXcwedOUwb3UT5r5ev7Xc14ZjfwBo01eJZjrKTTFKMifoAR7NddYNo/+mhYDjk/kvL8n9WB/dNS3w3pqzrB/QxrGPMN2a2P2ajrbAOvNCK65on3C68Ny1ujOgRgRuziYwGhKswx3NmZL3I6A37Y4WG9wJNqb9h0159WepRICBXlguiiqO6I9wqFjk7/XAN+WxZZjDmM1bfv93uRjgdGbHbIrAK9GjsXwUGUpkx3MM7F0s9RrBvbU20Y5SDymTVFvljnQteqvDritYXBzrmS1aJeWcQepby3GDT0t1v19BBkdjOfduPGpibltus4AGcpFN5pjdHFVhfZ6Xk8TTYfRY+dE/gj0HpNvzPvofM+8DrU8EQibcsLTZ0P7t3X70wmTz5Omm/Nu3Nm6EbBfAUB7ALSrUX2KsY/90KFpRlbvhSsIDYbgeUPx+7IEG6Oir8ThUYcUy+dkCUBBShSV24P8XdsCTT81x+xxErpzrGJEs+lcU58mp69872lD9r2KTjZL2P2SNB9YEspdgI+gLGH0XyvvOwB/kFJ6aLVmPPqLmGfISpeCve7Q8zqzJ95il9p6CSLKUhj6dlddVDNoMB3fHBAWnDoBJxtil+YhyqUAACAASURBVEClE6GWJcURPCPKNvHYWxtLtk3wJcDqoizTpCfOBiPQk9ET6C2bZ9racVWbAMpy0+6v17Zdjmu/Ns+V7UJbtppnbF+guQboQV5YPcF+1cYx4fagb/QuF0AaMcZRk2KbDmV1PFtopggxZlY/VK4B1muEGAeX0wJ5bScWbSBPRocl8AM9fT8HTs8y4MeIpovu2oUtpxXVWDiBqUeQimofDKbnBewb2kOq5xGzy8nHAKMH8F+nlN6iX4QQ3mm/u07xzCVLvZ/mwH780HdIx2/XK4sXtMn6Va8E9Nkp7W5Ur44ee7Znb6ppyBNvfAPlomwtGBQ9Z+zgcbUYlke+swCvnjdqtuGiq4K72uZt0+t3+kpb/aZ/Peu/P0PWYM6QPQq4BqJ1Asx+BgMennank4CavfjZ1l8beKkrp/dcikJZMC9sH1ICvufkYEF+vc5MXjsSmX07zkKNFyz+AuIdeFMbW/azPRfCyv5BvpfH3b0SwOfoh/7gkS+8muIskOAvEHGHq6cmNk0ZXMoLzcrX4X0cXc88e7ll0jUbLxf9Cpc4IKvBTSwCRh205W5Y5qO20fzltBw6iaipxotRop91AVBtxW0LxM0ku+Favg6DFaXpZrgWUx952uPJ5DX2jX1vTTbA6Glj5+JY+TuXcq2QTTnrbmTeHqDUwEdxlBNkjDmtts2HmVB7A4BbmzPg6HCa0AKx5iQNsTBMErVZXBZqCzMMAETp8AT5ox7o24OSAWzO8vvufChU03R5bBgtwxZnjt0PYY9RajLeOLObwTy5UsL9ONvoQwhvBcADR+7wa+Qx+PbafdchNnAUUIK9laLj9K9qylFRICxMHsZ0wQ5INd8ORg7QwS7PQ0uFVQVs0CJO7N5UcVemPrp2oKzcbn6hlqAmBnUl1DprACyms425qKrVWA1D1xUmWIPSl14BPiGzai683gNwH1NPG2X2Nm0Vy+ItyAMYDglvkf3qj5h+N06EXuA4V2upaIExZtMNTT8E+vW6w2p9hsEGHrdAtwViuUlK+x3XWto2LxyzHw1ulbZgVbDvgVwfXoxlKxa2xoOR4QNADEA86BtplQdBsyo2hSk4D5YpmSesFAfLI5sX2Tf1em2bVQOkVgiIo1XaiXmXOfTC8rgCfUrp+wF8fwjh+1NKb73GMs3K3JmxtYiWnjAJApLH6oGy03Lru2XxTZMH4eF6CvKrtVBu2mmBzKpizJtdYhwWBplXI2PSmpEUGOzkoixewdtOUoPINbqOwIHm7fxmWwyLvSgBPcp72uHpF0+gJ3M/Q927xntm+leUaazO5H5uqOImrHPmuxmDaHGy9eKc8yhF1h0onz2v5cLq6WlpknuquYcQt5k5s1FjRIwjM9fQ0kz76Cj/HRwJSVBE6713fLvHxGg0Vmww3ZDpSxoxZoAH+glpW7WH1OYba3evFdEDfJs+h466sdbKsHdwp+wxBMLDkjlG/1kppX8B4KdCCF9gf08p/fKVlqwmYcqk2PftQRj6CozXWVOm/q7vFSwVfLUDkn0R5A/WI6MrWDw9GYbe37OlpsEqnuJwncEH8PsUPW54Oz1DVLUHRrPJsGlLymqZkG0bBXogv9ejFFXD2cbc1jakMMxnzlFk8XxvQd6CM8G8lc9Ayc5t59U01K++wbgWwMmEZVTbu8cI7VqIxq3RhXZdnOSZuBpX/TieYrUeT0/fbuIwsVALYP4kCet1D/LHTwBrIQldN5phNmeDdlAAZdz2ZCJMKzJck+TBbnNaTYPiENquy78Nk8rWTc7b+KRjTduwwVSDRf+cdOJrJB/reTSnfV0J4O8hqNnDlDkb/XcA+DYAP+j8lgD82Ssp0QXFgreNaPkg6akN1LIIZdOePZ6AH9aiDqsKXdCclAfjOqKNm0nn13yt6WVuc8+2KYHK0/a9eqsmQoarXjt6kHeMpSlGY9xYc40y+4gyXAINCfrXyvdsEvt7K58tm18ivK4GGmwPoF9I751l2NesF5WmpZugKF0HHB3ldRlea81emu96DRweATi6lUGeHjFKm3vhRNwACKzZMHuZAWEZUAyj/X1gMaVrJbrzcXKRAntxfdT2bkH+IkLHA7sfINd9er2dmC8as2hWHmcbfUrp2/q3X51SKvaihBCOrrRUc5KmbFzB3kZj1GtUFMwtEHoqpP3sAr3a4webpy6KNaNKPdjqcyFDzDHJB+nKTq5RBgnwyiyZlGWnS/unZaYq9Ca5fwrcPxF/8t7soWBfM7sA001TNbbO3xS49ZoW/R4AjEBPzUExoDHXH2A043DnrD57faac8Ch6GAgX1+3C6LnscN1syrS33bhgqwDoaRIrTtx2AUYN4jGVZhWMWu14Tc/q2UrDYqxk2snN6o0DjOmT1YtKyEBqJBWDYwTq0sg1fJ46lr3AZ7y20XxaoOb4szR42oXlcQV6kX8CwJpuvO+uVWpgb0P7zplnAD8omH6vO2a967STDoOegF4AvPkuxjwRdOcDwoSuGzqz3Ryl+Vr//FqHnuubHOuqkbYygLSO3NADjJt6hnyVLGJk7ACKmPB6FCBj0VghsOvhTjWgV1avmgHkvV5DsGfeFugVSzU2PGUw48Fn9DTDeFoZQR+Yevl4jL66C3kAb3+WGO7xVDiCvM1UWb96Hmj6eq+I7d5Mdi7gn5cGMHVrVbFRNAExE8bp/V4al5bH1b0yhPAJyIfZ3uoPrCUhewo5DMJDkQTf71xFD+MACiIyvHq+7/x+ZTquagxFHo0zOAuqv5IVtgO5oYe9mCZIfdBs0GzikKcGctJF4IM1yp4fYw4Kb+psxcMINVPoZ63vZgOc9iB1eiogdjKGPODGJIYmBjKLjhiBfouRreviq/rS2/AHKgRsxp1XcGcoBNrn2/66NXKHPUY+MedW//7oKJtHDtejqeToCLgl3xckWhi9Xc/Q/hFj9sBhu/Hes82YpiUKK9PeQ1/ShxW3JbumNkgWqw9S+xulAPjtWOiOeZHRN5hOBiVX1/Hi1ce+WrHMvxZmGRjJiM0n6uyO6X17A/vH2XQD4KuQD6l9HYC/K9+/jOx2+XAkle6Ccw/TA2h9r53L66xeWnxfPaNysMuHksXrYmwxgIewW1KuDquuK0Ik6MAa/J21YN05VnGDxrhDelKzS6u7qPUoUbMO1y14hOBpxOTUY40g2fPFIl49/2iv54LsaX8PX3WhVsGbAN5iBA2GJu7k862+dZ9BZihPAbiNHN3x1jHwxHGeNI/797dv5z+6NeqEq9ElrUWFZJgkhHswGL8oxt57pCtDLAxt7WmhavQvTDY98NoHNYC7LPrrTKUxgSevc0YXI02DIAW2rN4bR0twcqKEGKKjedmJdS6dS8vj7HWTUvoJAD8RQvhzKaWfvsYyzYoy+tSUNjkV3UB1EZkNIiVpA6XaOYCw9nr6JXPwWfQsELcH+3YFbM574M6/D1vZhzSdhd1N7v1t2w22fZogzrsyOzXZ2KBlJITFxILRL5yD7vg4g/x6DbSnGdjJ3oGSTdOe7oE9mThDEp9gnCRO+vv0AHC1uTN9SDoHGO26a2QGfwTgaWSwf6IFnrydy390lF/X6wzuTz2V/273vxOQCfJcYOX2/Yk5IY4TwAD8YtLRjUHWrdeN6dJP4C6qNauRRKzp/y6bnTQNptN1mVhs4pS16/VWnEE0d+SiBfroZKf36yZGvU5deDV9S96uRR5jRg8ASCn9dAjha5B3yB7J92+7yoLVC2QePOoLQLUNVHMy50XlaQZFh7MXDH8C8mRd1p4EADgcB2t3kFV0FQK8quVm5XUVI9Z9LJKuA7b9IrFqB5YlecWvBW1j+FuaLtZrYH06Aizt8kfIIHu8HkGToqECeNbsPbm/aFKMkwQwgrwCPX9nGXjfETJ7PwLwJIBXHY8gTh91Aj1BnkB/6xjFKvd2E3H/dFxwZbt4pgpOsApY/E3dcflZ92AUJhzODsUp2zLJt6vslcNdretDFB4zBPjNJhMBphmT67lSyByaxjgUzYuOqm1j8VFJxnBP/9sWJQGxmmcNa2tz1t5EQ+Y+orIT6EMIP4Js0vw3AbwDwJ8H8M+vuFw7xYJ9zZ3qIgd/xDhOHHb3qIo993KXCWmiWvO74fdYumPwWnV7A8b7aQYCMNj6RQ7jPTQbYHvUh1QwbHHCkvp/NiCixhWx9Vv1oMR9A4cCHAcAbjcZNJ95JgPn4dHYpnTTPD3N/uanp8Dh3VxWagZN/0rzDZOnpnCMEeg5udDrp+mvo03+ECOA0yxDkFegf+aZ/Lq63V8w7EDbYnV6H080G5w2o92dQkBiO9GGPzy2/nEPE+O6DI1BgLehrIsolWqmIYM/utXPXLdKX/u+zLmR7/f95n4uTLEFuPWRtAbyohISsG1k1KFPNX4baehs/a1pMFlXMxxmsewfk/dzODgAhBDeDOCHkLvtO1JKP2B+/w4A/xFyl/9DAH8ppfTbl813idfNn0opfV4I4f9NKf2XIYQfBPAzl81432IZ6pIY9CrF4iRGsPe8cixwuokMN4XpzbyWo4Fb4/l9G0vUVbrkqRWIZRjcbo0DbLDa9PFz2t3toXHp1SzMbAhGbOdV0wN8v3dg241mlOOjDJqveQ3w6tcAH/fseOIRkMH97t38d+dOfj06yrh0cCdPGiuMtvhTjAu9+v0R8mRwwOboX4fJoDfJtO3I5A9lsZU2+fU6l/fpZwA89XT/pQHNdoXQnOCoj3qvB4Ww2enay52cSsTJ2jnJcB7RTXdVUeAli2/bcnY6vpW/5/Wn93NDNw2Ae5k0MDAZ7fsxYpgmY0TeYMU+t52WoX8lUVDNzIYT2aEQTLq1d7297qHJnhh9CGEF4IcBfAWAFwC8J4TwrpTS++SyXwHwppTSSQjhrwL4WwD+vcvmvQToeyqAkxDCJwH4MIA3XDbjy4pd6Kl1qiU2d03Hpu2p5vrnsnnbO73CWcpjt6w3nG5mCjhQoWYcvPK3auJiW6aGPCDQ07ZvYwvZeYdmHA724+MRg179XAb8p57K13Zdxp87d0rPnm3Mtv6uA9LdsmnorUO2TrONB/Rc2iZjJ9AryK/7yelgPS66Hh31Nx31Ab4KDasDukOg2yJ0HdquKwiex2a505XXcbf0WsC+aSREhj7TGo1Vs836IP8dHWaQv/1k/qz3dVug7Xe7Dvs3mmlfG2uCgldXFmgVgLWOW6d7etXw0huqaJrhQRj9/iXty73yiwD8ZkrptwAghPCTAL4ewAD0KaX/S67/pwC+eR8ZLwH6nw0hPAPgbwP4ZeT57Uf3kfkDiXEocC8hi9plUumlNlGotmzzdE+UiowR7lCRYZCaSIJzBRwYfbkxpgD4JokZB+NAbvxwsjQ3ebmyyHoQRoxl/BdtBwL88fHISg9a4NlngU/8JODTPw34xM94AnjdazPqNyscdOd4+qWX8PSH/hDP/f5H8aEP9Wz7I3lD1vFxngRunwBPngAn3RjpksIIlEcYY/0AIztWswxNM/zTa564DTz9VB9P5tkneyP9kyPdZqNszovndBBfRoxxYoLg/gddD+WjIsDT0yccSaRIz65u1Spt9KPD3mzTl/nZZ/Pr0a3x/vZOfj09mPYxtferDPnajiHapphttGgDDooSaskY+5I3RDxWr7/X1pWAcixehNgtFnqAXF5eC+B35PMLAL545vpvBfC/7SPjJYuxPE3qp0MIP4s8vj5rH5k/iHhBzfSz3Rnnmlh6mWMJGrTMnjhUSytGYEWTi/V+IMiruQWmV9v3Sq91ZyIPrqAtf7I1djsMyEkRhG1a8Q6BmJsACZgxjnbnwzXwCZ/Qg/znPgv8sc8BPuMzgOc+Lt+4OQdefBH4wAdw6wO/jU956gXc/v0OR0fZVv9Sz8JPTkb7vYZdAEqTh2oFOvEcH5feM1zsbJr8+dZRv3bwTE+vn3kmm204M6jGtdmUnk4x4jC+7LLNwWxjlC5ONIfrHuSPbqHYZ8GLNW40H4r7AFYZ8An2TzwD4BawisBBjn+Dk/tA0weejVsUsW20Q2hBgcxYIN87YoO87RJvHFptoAbS9uQ1m65uaLyAk+gF5EJ+9M+FEJ6Xz29PKTHar/VC7hOfSgjhmwG8CcCfWVzMGVnC6McSpXQG4CyE8FMAPvlBMvQWI0IIn4O80PsvAfyHKS3Xk8hQAR/ga0AfncEITEGEjNGmY9XKgZEUrN50u0lhmIDuWuS28+3oEnd6NrItpsOF2XUcd9cO93VIXSxYF7er29ghWpxObK40s7Bu3g5cBXtg9GZ5zWuAT/zkFvjMfwX4vM8DXv3ZyGSmAW5tgKd/N7PQfmX0444+iMOjl3H/JH91505m92enmHi62KasaRhPPpXdKGm60efYtj3Y0s3m6FZp5y7sB1txXywb4RZexvkm979z2RjVYgpKLEPLlVndZ2EbFUC2lastHT7K0WaffZwwbEk7Ousnp5WQhW6sk2e+UfNOxWwzdyh4bZ1Bq6DB3xTAmwZFyGKKeujsstc3zdRtc2+yHOhfTCm9qfLbCwBeL59fB+B37UUhhC8H8F8A+DM95l5aLgT0WpYHuqmyGIEcQO3rAHwjgK8E8HNL0iPIr8yAV2DyHrb61xdERu7jq8aSsbZHfa+vK+2Vdvu4O+v0g4rAvjkfXVMI+JtNud1bZxeq/zHlCIRdlxfIzILqqsWwMUfbTetFj5LNJu/k1IFo62zbjCz62WcBvObjs8nm1Z8G4LMBfCKyweUMwLPAq46Az2wGsLvdfhC3T05w63gzMHoNolbTnG1ZaLbhGuXhU72JhHb3lrbtntLTtn37tn+IN5CfwfqwB/yDAiQPNmdoTzfYtGWkT8/q4hZ+bqMSY8APnWsrs3EZe2Z0PO13J6yk4xb9ceuDlv5W/T1OkqPMhTCYEzt+GoyOEOpq6d2n5p6iKVC/74Ek7c3r5j0A3hhCeAOADyLj3TfpBX0Ugv8OwJtTSh/aR6bAgwO9H/xit9QWIxgGJWLhJMIH6u1Qpb3YM7fEXiv1jiTTTUM6SVj2f2Gp6pwoezoRYrPJgN1vnMJmMwVtOHEg4ra41gKNDYymZikKAYruj0oECWQsrmoMyqiPjgDcfiIjLZ4B8CyAj0cG+nPkx30GPH0CvO7+eIrR3bu43b6IW0f3BqDXRWEVAqqGGCDQcyE4PPtMfkOaf3SrL2Bv4z4+ziyeO40Hreh8GmogRuD0cDTrsNKbM4S793C4OcN60xUakVpitCusmHbjROjzyAEfDNCTgPO8yHra/202vX8rt6AlsXVoHUx91C3IkpNKR6/1/0mY5EvKLly9yGSyF3mggV9KSqkLIXw7gJ9HHgQ/llL6tRDC2wA8n1J6F/Ja6G3k8PAA8IGU0tddNu8q0IcQ/h58QA8APu4B86stRvwQgL8P4DcA/MjSxHSHqtpfvfNPLQsnY6iZe/RavnrYTFHgnGa8wnCykNh5y0JtRzbfnY+vco3iS2lzGf3tUxcHcLQgrGYYLpxygZJtuRWgX69HG/nA9MW8cy4Hd2hxxmPuNJ5lP8MOHu5PAXgOeO4jwN17GbCODoEYsWpbPHl0D9vTrmgm23Qs12YzPksCfXi29+989uPGLwnsR7fG19VhXy4A2ALb3vd8w52kshDJCUL3M5zez2z/9D7C6RkONmc42GxwdlrvO6uuG2O/Vz1gAD1YZPCp35zlPAHg5N7oo/pqDSh7ZuqQRvDWWPa6WK/grhqEfnZEF0lT/4jtuNhlbrG/e5OGS9hmZO9hircO23igpNK7AbzbfPe98v7L95KRkTlG/3ce8Lc5cRcjUkq/gvnV5/Fi89kDZwvaVr0DlmliGlsbqHeuogxzs4WXSMGkKn+oAOnC3l9MECjDG+sfAHSi7Zx3KEIo6EHYdqOMFjdFiL3lDONhgexuBP0VsFqProI0j6wPgE2L1Trv9LUNvUJOX8GTKR+sgdVR2+8YvZU1C9rgbz/Rg/0TPcDfwiSIwgoYAs8DGM5XZf4xjS6NXYepx1O+fh03syannUglDVrEm2fjDya+HtTPToFDThpbAflpXxpfnfIouNv1gQWsdqmLpcfIayxdd7jb8XxtzH4PjP5hShXoU0r/8AryW7QYYSWE8E4A3wAAT986qF7XND6bt/1VQb622j881x7sbYeyNu62lTj060MUZ3UCPSisxt5JmzpNBTHKAuwWhe22acY4OqzQRN3Pf5YBE8DVn3tYf9CDUtqc3kGMOfBWM4I2WTvdCfUQEv4GjJ9PToAn794FPvIR4FP/AFi9gNzVnkJG0lNk8O+DHqx7L5Rum19Pz4B1bVdwD2bdFgen90FfQDbrrSNkIH/qyWy2efbZ/PnZVwFPPo2sFd/q/7gHN/ZluZfTXguThz7Hw/HZtr29fnOWvVtO7+dyn9wD7h4gNHdxsMkh2bjWwXJuu970tkCsO2KLDuH0NPedOx/twx/0cZJuP9m3TVfuRju9P5oC2d9ULoLGRmxYcJvk3Hc8XMTm4rFxLyLt3MTA9N///vcjhHBPfv6ZlNJb/DsrkoBUCdX8qMiD2ugfVHYuRnjSP5i3AMAff92rUowvAVjuM6sDxQZKapq6S1at3zfNuGg02PN5bCAX+9p2VMsJ6IO6bgpeeEQY9sVrmV4N5GXwNk25S1PZj5puuGmo8CaJESt0aLpKNl0J9uqZw7NS794FPv4jHwF+//eAD3wAeANt889gDGr8MoCXgPN7o1cRTSW2HbwJrklA0wxrKQ36ybdFv9h6awwNcHzcg+Bt5MmGMS1bYDArydk6oTep0IZdxJc5GN0i1+veK4ogfwLcfXm04282OMAdALGIYhkjCl91i7MegPG3rgMOGCf67sF4VOXmbNx+HGMP9C/3YH9vnIxES7BpW2maWICud5g521/NN3MujimW9+YEl2nYHrOvCbHhDW94Az784Q8/sTv1eXnECf31An1tMeJiiZQfLS5y67m9hiq0549L0Nbrd5EZag98PwDC4G8tTDDGfmBvUfrSswKG2dteNUQm9I4jDBjiiTfjJqlh4VVAhINS3QwLu01R+Th8VIDamvbjey7eNk3Glo++2OHpF17IQAsAn3QPOHwa4z7WM+D+S5n1f+QjGZROekA6OQFO7w82+qYBVutubIemGRapN5txXcFuXSgeVn7TvyaMAZK7/vMJhtNszzcTjao/ACCbmg6PerNQ769+epaB9u494M7xyLBPz4CYcICXcyznvg/a8xSsllk7IWno4x3QxA1Cc3dEvNP7I+jHbc777st9rIl7wMm9wgV0Dlg1f+txRtFom2TmwAjEXvr2u2LdoqnfZ2UXyO9bUsKsc9SjINfN6N3FiIvKwHwioDvxAAyDaWXwUIMpWcLMPw17bIkkxXb4EUMs064sZNXoGhdjVUUcCqAx7RvRFMxpQ02OE86FVxu6wNY3tL52kLrSBKQyqNvStlz4PTnJrx/5CDcrfQgH7UGu10svZTt522IIxXxyL88KBPvTM+Cll7C9M3rd8Dms1xGh2eBwvRnCAJ9tsmma5iOS7EOaUE7u51cgs/wnOYGdIvOMiPFU2/vA9mxwT82Hwohf+bAftwXQAAcx/yECT54DqQfW41vjyjdPCI8RB/FlAGNcGAU06waoQfU8WzcnioPTXgvpulzXYjfv2VD/89M4eFB5yqL2DfQt0vTakWqCViwz38b6Imht05PtfkvEEo1JuTANTHhZecQtN4uiV/4DAP9OSuml/vOrAPxkSumrrrpwu4SdngOfnS5GILWYqJ2eZcSKdpKq1aBxOvSQcCOFuQDtKAq3y7daCsLNUvJHVl8bnEXcfCd/Ze6UyaKtsNIUszcOoxnytKb1Gnhd80GsNmfAS3/U714SRk7WeecOcOcO0ukGd+6Mu2LVm0ZDI7MMZ5tyMxW/f279MgJt100DHN8bN54dfXRk3FwP6brRdMRGYshfIt5g1z9EXsBVv4ItEO7lHVrrQwyHaq/XvW08//EAeM8/XDUuezaAiu2/bTzN3jgnJyOb71Wss9PRa+psM3Uc0fxsf2kaoI15k5264NprdMyp7zswncx2jT2Vmg98jNP3NQ18TwEn87kj57uveyXLEkb/HEEeAFJKfxRCeM0Vlmmn2I7Sdf4GIE/1XdLZCPb2cAmLjapCroat69sSjIuEDT2zhbQFYw+OPIDPK+wKQFcUMPQHN1d3CdYapuvc+DaH63Jdg7b5+0IoeS7q+WYEKWoUH3f6Im6/9FIGepaz64DTU6ST0yGS5WYDvHw3H0B+KkydlpODfheuPRBEgZ5awLPdizjYnGeTxvETfQCdJ8owmgzlS3BsD0af+9voN1kdIIP7IXJU+2OMgE8NoUM2/byUv372bLRjUbPYnOczgbs4AUQFKg1VPLjqxohGtNEhbED/HJpNh6bpBu2KmqsumHsbuPictqZfq1ZL5SS1035k0/PAXheha0zcdse5gHHWZFi7bo9k3k3/UZMlQB9DCJ+cUvoAAIQQPgUPvmHq0lKLGNpg3EJ9YBg+O+FFHlYtPLENtwCWpwOa2I3xw3mjJlITNdHY3Yl8VVe+VjyPCluyLCACCKhUeJhAynyUdYVmDG/A97pruOtGICeoAOVxgzxsZLMBnrrb4ejopRFjuzEe/d27I4NXNq8AzjhjesQfq6JE/GA9pvvM3Y/iVXc+OvrR3749hviNccrij49zvBsggzwzxyFyiIHbyAvKPM6ErP4cwN3+/Qa4fRe4fSc/m+MngOPR86XtTkdbO8pd2rR7D/saZJE8dB2amKORqhlkixJEaRqipkOPH3Xz1L6sppmByMTRjKlgXzPhaLo6xqypxhuD+hzt/d71dlioGeqqwDiljwHTDXLMhV8MIdDd8k8D+LarK9LFxGMGVpVb2gEsmGuaKhOQl/f5+ji8Vn3evYznJG6B2O427TA9M3Im7N6MkJr91Iu5pe07AEzM3LaJ48Ls/R60GSp/sxnb9lwOHiHYE+hP7dCthwAAIABJREFUe5MDmd3hujTR2eCSunP2sJuq/QfrU9weTDc9SyfQs8LrwzGT27fNyG4xBkRm3MxjjLyRbp69WSfQzkUPnT5sQrMawHxgv6iYKawK2UyjkVoN9bwbY+7YCKT6bJXN63dMc+g+6NdjPGK1gzzZOs2BvKbHicxKzWSj3+0aQpeRx57Rp5R+LoTwBQD+NWQK8zdSSi9eecl2iNfwZB/snLsevHZ4fq6ZgOxvthyqUlPKGDxxcs+Ydix31Hp0ZRiJB/WZzLO7Y8qsygEW3YGzarKDCZDrtV6PDD40cqYFMqDwEG8AwCnQ3h0JKd26NSjkxgH6s42YbeLgZY91M4I7A6ep3VgDPjJ42UsvjbF38glSHY6PPzqZJNgex8fA6pkn+w9PZB98VnZYiH0C2XzzDDLQ0z2Th7ufYJgI1ofAUb+9eH0wvnZrtHEz5O95ohTkYAg0NpIGjadj3V3VdKPajme2odg1rkJ67YFtpu66u8RORB4pqxGpzvTJGrP38ty3PAYnCc6GQPislNK/6EEeGDc2fXJvyvnlqy+eL3ONTqakomzBfm/NMl7snF0gX/MooPeClsGWn7bYvJEpViaE/H3ourxrk2xxqISeYiVb6mOsehrZMtlyDQ4+AqZNM3rY8GzTwU6O/BeQmeDJyQgOm03JwglMarq5e3fcQ3vSt13q0zuMwK2TzKeP75SRhFlu26YMC8xY+XpWrN7Ltn/iNvDq7mUcHh1mkD+9n234eXdTX6sDjGz+NlBYgu8Dw0GI7ehrzzDSBPvNOcI6osUY3kFZfez7UkD/ozxPGzCNC6y62Gq3Y8wtSOpEUFv41eev7btTAd0B8lZhURnMSl2Z1lK56PU75TF3r/wOZBPNDzq/JQB/9kpKdAGxdr2L2A+Vzdv4OPq7VS9tWgryFlDtfd7g4+Lbdl2G0tXrKW0LtLEbwyAT2HWUNk3RK1WtL6JfYj7GPicgpn3QRsQYh6MJ7WTKE6CADNghAuHuuGh7uC5BQ3fR3o15+xQt3Qr0NJocIXPlWwCOToDDk8HRcchXx+Kqv5deOjzwg5ME1xz4/plnctt//FPZA4i+/FgfALc05aZPmbnDfO7/tIMNh7n3zypuEWJE0402dyA/H57x27YMdzDuEGbfsbGGaLKx/a8W/ZG1oaVqyYTA6tQcH+bEA905oK/d62uky/K7jKSU5/xHWapAn1KiHf6rU0qn+lsI4ci55aHILtucx+L1feOweL1O2byXnpZDQdXK3Mk6anvWgE72cAcOzLaNOFiLewtQpWQK8kOeyHl4g9ut31JdHSPongO41wHnd4GD02mEzC5mb/ZTZJC/h2z+IdAzskKD8ehAAv4TGC3mWlwtYYOsDRxsgGaTNYJbzXik4KEc67de5+rlNdsNbnEn6UkfsOzWBuPUE+WvMZ8ldwI7XTTJ7NsV0GWvrNACbf+MNuwzMU+UMQJrdIMrbOpiBvVNyeQ1jPNSJqu7VwfO0H9Hd9bZMYX67lfel2YImNWUNV1vPNt66cRjpWbquaw8zoye8k8AfMGC716RUpsItKPNgbyXHl9ptrAg7x3OwN+5SKbsWjc2WZOE1mE6SUWEJuZeGJsyozgusOokMwzEOIK9ZfpDnrGPmIgSWBRchsPCIeCBDOBN/xpk4iNUbjDsQ8V9ZIAn0J9itPm3GIG+7V/v998RzGvxH5v+Nx4ofhyzA8zR3RHg1QzEQ09u3bmT/ftP7+fQxDgDhhDAsX9/1r/f9qXl770MwK5/NOf0sYy6Dk1/ri/ZMpCT4jPL83cstCCy9/OuBHn2Cz4/dXWcaJkY17LYL1kOBfyaKNh7QE5PLY/YWHMosMznfVeZ7Njcl6QLHTD1ypQ5G/0nIIcVvtUHw6cv2VPIRspXhMypgNa0w+9qotcXJiGUnX/wYxaQ97wcWD6mzT/r7gZk1ql1suVtmlLN5veraBLrP9twxS7Tcb5Tn2q9R10f6RmjoZBVFBLRt51yYQYfINjTLr/pXwn0wMjou/79BmMcTBpKKOygK/mtlfs6ZF5+hryj9lbvocJjEBkD7OMZOuDkfn9607mUmKVuMRqszvq/NJZgsMvzwBK12XdAzGssNOEMjySW/U8XU09lA5Q111A886Jd2LT9q8aWFcg90tE0GM7IteRID0dv4rSMc2GELTlR4FawV+JyVUx+KNNj7F75VQC+BTnC5A9iHEd3AHzP1RZrmcyZZShLH3wN5Ie0+1cunCnAk8lbdr/L4sFrbHrVmC21RIACERjCoOt8Nm9v9bQP+5kgY3da2muV1RPwrdGD3xMyCZ+d/PHaVr4jYEM+Uxp55R+DHOhn2vy3yJ49bZ/guWgp246Hg5yPjRi0VIyR08hn1hA5pyCbDoZDwI36KAutwQCWFbvI6mmNS+zlmsdqJk87GcyNDRua2L7nTvPabld7XOBFxLt+n2weeMy9blJKPwHgJ0IIfy6l9NPXWKadsmsxZu5cS3tPzczC6xQgbedXM4xnJ/VMQh6b4URRG6ihmf42lLnLJhyCjJZBT1/ytBqPMXkxVtQV8u7d0Ud+2HFpyktgPkMGeb4nFJIf8zqydL43RpAhzVZ+h3MNGXwr79f9a5D7tSkJ9OrqeXICPMnTm077qI+3OCXdRzYE0XCUTKllWhni66/794dTW0vbomk2WDW5XfQZWWY/Z5P3NFveFzgfobzWyoTZowwHotdM8hMkUXdlmgB5rwVh67HGsbALWOeIy97lYyQEwheGEP5PE+vmO1NKf/Nqi7ZbLICqMHDU3CIqv1O10MpcJEEdcGq6Ge5t86Kf3q9s3cYeUVbvTS5aT4/9aOA2YKrW2+utz7UVBRgC4b2TzH5pvlGg1yTIc9XYQUOHsnYFdzaHmmRa+YO5304Ieu0aox0fGCeW8/57TjgdgKYr63l6Cjx5cq//0AP+rfvIS8bHGHUCicQ52OwF7Fc9m1/L36a313fn4GaPVYshLLRnalRfeTtxaz9REwrv5Wsy/eiinjMqOrYK0iTmRS/dGEvTp3VJ1nWfJez+ulh2Ah5r0w3lq1NKg6mmj3XzbwF4aEDvMfr/v71vj/bnqur77Pl+7/0lGAKBBIwEVmLBB9RKIYD4qJGHC6xtfPDIwsYEAYtCa+mykGqXlbpcBm2L1uAjBkkEXYIYIUUEJYiPCkhEMCIWkURMzSISAnn+cu/3zu4fM3vmM3v2OTPf+77fO5+17v3O48w5+8zjc/bZZ599et3Ket/CIkTXctoUokVH7FrWmo0o7HgzKSeYaNIMvhV9TTqyt/bqiapekWbOH0hqoBXo9kJs2rwPsmX1Y6I3042R/FZJtlzE3hglqo+FyT0y01gebH/nbUtnjcdJoEf0NnBrebH2Hz1mCyFgDbXVrem+rNfG+wffCchpaGPcbKKdKGV9lvvQIX1uyTtmnI2e+aYo2pAR/rnydspMwstDNvedtN7OmJOzIEXwjQHL1dy7sh0v8hq2Nz/6d5LrxYqJD4NtWCYSpY+3s2Os8mAsYSYiJ1T1fgAQEYvodCDgmIFDXbeGVJF2B4swxuzD3UxbhcnIgsvmvOzDmaGvnafqxDJFJhgmgCFNKBoYjibaWBo2F3iC3yTtq8kflY5r0d5n9GcTqcK6sYzoEjx7q5doyZ3t+gbT4M3MkyJ3g8lmMM+ie+8FNu/ewNqdd1Y3zQKyPdQcOheonDztMzDJ7kLjLLp5T2v26ajiwZtYVOENeA2BIWJhoualIf17ZaYbXh2Rr48WFslp+5YHP/cFafiWZqtsG52I5KM/9mLzdV2WaHczRLEifmxHCWOI/k0ArheRN6Cq8/cAuGZPpRqBXHczeikKxCP9KZNF6jyX67uZHdMNex3QNfbye00pV46lYQ+M1EczFixvMwGH5Odp9d427Al+hpbcjZxNi+c/0G8jB1pt3apseUTaPNDvESBIAzrO2+yR0/zRc7i/bszuvhs44/Ofrw6edlpldgGAh26h8g1i7b5EpcXfC+ALwH31pKuNjaqRMA8eW8d14QLX1URfFG38m1mBJmJlzumg58Hpvmi7PvLOSSkbPo0/Hw2eFkVlutkq6+Upa9L2PWjfQ+HeZO5dHiL7Zc1PS2EXNXoReRaAn0H1Kl6lqpe78ycA/AqAJwK4HcDzVfXmnZY7SPSq+pMiciOAp6NSzH5MVd+904J3gqGBpFS6aJmz0JSD7gs5hLEvgbf383quudD1JrefhWtlp0g+d598Xt5dz0ifCZ7D3Vo+UhOSoNLmS8QrwAvaoUs+b+RsdNmrA6XzvyWliRqHwuXh822uI9v21qLS6O+8Ezj9c1+ovG9OOdGy5cn7gAffBXzRA1ERvc0g2ATuu6tazcnWa22m/t7VDux2Fu2u59bTw+Nxo+jZ8mpONqt6Pm/DOHuiN63da/RcVjQukOtZ8rb/ToqijY0T9TD8b0TyKURkH5k39wK7YaMXkRmA1wF4Jqr1sz8kItep6l9RshcBuENVHy0iFwF4DYDn77TsUY58qvo7AH5np4XtFXbS2uY0eiZFoOt9kIK9bENdR375o6XYPHiW7FA8k5zbHBBrZD2/ZTLnsN9+VF5TJ5YXVZ3Mbl6iOxhrbpND32YZbFvD4HsAObK367ihF8QapzVq950ETpvfU5P3XXXkSxpssYVNyrJe0enelujvvqtdRcuTfLMAfJ/ZmPzGwIi0G0CvPd+JjuGKs3R+4e1lvVn8+aYnm7lu2R5ohP0iedVWsdohngzgk6r6KQAQkV8HcCEAJvoLAfxovf1WAFeIiKjqjpqaQaIXka8B8LMAvhLVNzsDcI+qnr6Tgvca0YPPTdJIIVoxx8/m65A7aevzefcD9GYcu5Zf+EhGT8JMwJ4QhgaeU/FPvPnJm2p8N9uO+XysSFuDaYYuIfP3YqTvyZjP2QvKg7Z2m2uHpobYT6n/zOMmmlTF3u5Af2a0kfy991ZWl80N4Iz5P1aJNzYrE8ydd9bmHE/097Ua/L33oVlFyxY3YU1+sWi0eZvcFnnWeG9MQ4rYU2RvdQMSikGRb8AjMo+08LFy8r6Z/HweQ73U/ST6JWz0Z4rIDbR/papeWW8/AsDf07lbADzFXd+kqdfY/gKAhwLYUcTgMRr9FQAuAvAbAM4H8N0AHr2TQncbniz5xfKDTqlY8kPggapUjBj2H+aAWc2KQYkubIp8GZ7k2XTDMqTA52xtz8IdZ03em2oaDxsiombWrSvLbPZm2GDteoF2ZqwRuZ2LSD7ymjGS91q8J3keyK2NK52eBd8bbiAXtflm/U6zMy/woPIzFZmfvK/S2Dl2QlnWI9X3tfb4k/fVxzcRrh9c30QmeW8ai3pvQL+3yBq0fx884ft3zm+Pgfea4V+7HWUtj59dmyJ9I/ux36TPJ2WC3S0sYbr5rKqenzgXWTV9zmPSLI2xpptPishMVbcAvEFE/mSnBe82Im0h5zo2BH5Z1G37uOC+PB8ZkYkkIvdIg4m6z95UM9aMxA1hUbRdapuklTJf8CCtD3dsM0h5QJQRvVgm7gm0xFu4vyh9p05oidzKWafjXpP32vwm/fHXw5qnuVna2t4mzemLz0FMqz/lRED0dUNQL5HlZyXze2DvlCd5Wyc3slvnBmUt8qUhstX79yVSLIZ6vZGN3BP40HcXnS+K7qTB1LudI3gv025il/K7BcAjaf8ctOHffZpbRGQO4EEAPrfTgscQ/b0isg7gIyLykwBuReVbdmjhu3SRNs8YsmV7+KiSXGYbW77iAQ7cxHmm3Mx8ft4zZ8iNskDrtWHXR2Tu62LHI2+bSE6bKNXTjt22vWDNIiKoGoYZnZ9THj6vTpceXc2fyd1r+TbwC3e99SS83Axzl+0SfbV9+uIurNk6tPNZyyqLrVrjP4n7T3aD21kPL/J08b00+7PzjBSJNg0Buu69XGZE8uE7R6QtRWCaK/oztZdVsvy1JsfavHvP/D1INSZDjcNOsaTpJocPAXiMiJwH4P+hspS8wKW5DsAlAN4P4DkA3rtT+zwwjugvRvVdvhzAK1C1Nt+504L3ClFLz5EabUA19XJ4co+0Ht8D5zLXAm3eX7e16H7gnsD5QwP6swlTRNDcA6C3ylZEMJHJwpM8E73dn4Yk0CVTy4rNNUBrqzeTToHKQ2cdlWZtAQXMDg93fXP/6DzQnwG77q4p4cIK0J9p9VsLoJy3A852D+7faO9Jc32d5tSNBU49+QXM1ummLhadEMJ8Hff0PLg35xt87glwPv5avt7SaKZM79HjewxNg9S/tDVBztEJbeBlGwrtnWqweKnFCPtll2cosCvx6Gub+8sBvBvV5/DLqvoxEflvAG5Q1esAvB7AG0Xkk6g0+Yt2XvI498q/qzfvA/Dq3Sh0P2Avn/1GZG+IVq33eeWOmfbkF3fu2SGJJHlGKhM/I2d/TMnUaI9Imw1YrNyydCdJM03Bhwnu2ORrDdEcEAt0Sd/iy1uwMSBN9EzSdt6I3vJiTd7IvKDrrAG0BspCIGxsAOsbVXQCbgx5CT1/nzc3gLX1EjNyLYk08px2C/TPsQbOPYDI7MPacGQrj/L09eC/qEGK3sF5TfI5U2NUT9+j6ZkURxL3ULpd1+x30Y9eVd8J4J3u2I/Q9kkAz92d0lokib72nU92GVT1n+22MLuNHNkD/anSkSfKGDRmm8wLGGluQ6tTpbTxIdlY+x7TZfMmmU5PY9GSJMsEOsbokHxwjmfLWsQYzqtTFh2z6lpbaH776/XvzKW3fDfRzW/m0iyAZmLS5qLyN2dCskZ8o17+dWOjS4S8XGREuGPAnj+GlPnR0gNppYTlMU+vlFxRDyLS0L1M0X6EVF5Rup2Q6V6ZbfYr/71Gjge+dd+kWBK5l8drNJGm0/S2t0nsuW5lyhberK1KA5ysyfsZglyHoTEGD55NuWANkeTyg4D3b/RNNhsb1XxPoKt1cygHIB1PB6hJv2y1bdPETQM3cw5r6kDX9cC0cPPWsXSWT0qbt3GBKH9LYyEVinvbuvAzWiM798mT7f2zNWmjcAHb1VIj+zcjWqTDNzKsnZvmvbnoXhvFemclyGLJR7b0ZcwwQyTveyM5pMoYc2yn2EUb/YEhSfRksjmUGPMS8a8hZ/P016bSRJpNGDSM8vEfovdiyc0QNJk5wNqYOQFG6L6+Vi7HsdlMkLz3cY4GFaNymx4C1cNIl1+6iOA78WfqP1txymzuzZqwaP3pizrNwv35hU8KtNq8hUcuAZT14Ov6erXc4KkloOstaXZm0NK9tXNDMWIs7kxuqb5Zgc4yfIzIDTdFlGwKLAp0olf6d6wZG0qcT5laGL6RG0rPafl3KMS4pRtL/LuFlY9eeVQnTOW6qpFJJMKYgZ9ooW3WZrzJxttxUzKW7mPude0DGaI8yrL1xGCbLg+6RpN1FmV/clGjJRZ9j6LIm2hjoxsiwbRw0K8R+xx5krdGwojbzDUn0B2EZW3efkHbXJ8S7SKBVt7mRrXkINDWbX29q+U3ZL9o74Vp+V7xi8jSN5T8vLfK/rWNsP5YUJY/v1ikI0mmymOf/IjAo7r59zUnq08b1d0wRhlLYbeIf6UXHiEc2glTQy176iXypJzT8CMNmk0oQ3Z0v+3DCXAeWwDMvS3Xpc2ZdLzpqDQVl+C9fvyknF4vqP7jwFlmurAyO1q8azS0AGZlO+DDGri5V87mfdPUYlGtAtXs179z+rNJUnxppM37Y0bIdr62ymAG4P4FsLboPiu/nq4RaFnSIHfdEHJcIkvf3Msibe7xPT1/bQR/PTe43vNlqAfW5Bm8o7wdKSI+Py9j9K367yO6djvYdVI+JguPHOoJU/4FSnV5fVjVVBc6Im6fdoymH73EXo6wa1tPB+WFlVketuV697Vsd3bRJwUDewtZufMC2CxbTduI3oJn2eLafuBvK1FOSWviGozgo2BcjdmBjnGPgBuJedGG6i3LqlGZL1rtnnsC5mnDjUBTJh33990CiDFB27vnl4IsilZ+nnBm99ry4h6Sbyj52aXmc3hZgG7jUqDvZsuwejXLP5KSYaYpfi5eGRgzozalhKTgG4VcjzzCXmjeqlh90w2OwISpXLcyOmYfJ3ezIw0f6C7mEHW7ozKij4E/wJ4GxY2LyZhIN5v3G51IUxq6F3zt2hzYqhfINi29LAGcbK81Df5EPT8gIvpF7ZNuvvdbZau9A7VWHxBeRPJWdwtb0JOd/pg0TZZyXptZasK3cAqz+teW9y7rvwJthM1Znac1QE3cImen5/vLNvuy7K9TYGm4zvY8ebEQbhRSWm5HKXAy+Ot8LymlFKW0+igN9wJzKIpKqeLnwsi9l14B8nIMKXe7jf0oYy8xdsJUgSMyYWoMUt1IIE3gnuS3MxiUM8dwOaaZMnFYeb7L739ZK0vVleszIxJbm7dEb9ock9Ns3iUpG4AE2gbKBhytZ8C+6GwSsPI9cXYiZiL27zVS7mj7TrtdLNqBTWsphK5dQzcEgx1r5gG4Z+21+QhWN47z7+PXbBWtSagoqoYwCqsx6l1xygrLMKT5ppAz2dj22BAcQCWDn7syVi5P+Dlz0V7iWNjoyfvmJI7QhKkI/mVJIfVCMrlFS6ylZqz66eY5jZ5liDQhTzRjGhwmf77ea9KmSdrx+byt01qtzXvTjaU1gi8cYQFd4mdEWlmjyS9aMwu7VNoltmrVvOhq3VKgWcHLYIOLRdlq9ex6CbSa/CkATllvezD25+Or90hn0Xq2GMFbjH/r4TQNxwLYqhtOe86+cR7Sdptfq8tIs0jq3YnKbO6be8+j99nn1ykX/QaJy+Vr2cvr0JDrKrtXisiFAM5R1dfV+x8EcFZ9+pWq+tZ9kG/bSJF6ihgj043v+pptfG3e/2BK08oCkvdl8Udi1zKJ+AkxY7S7oYaE7a3e7GO2WoN51MxPtkRvA7CnntJun3JK3EjNy+5AbUTyXm6z7wNk7qJ05ldvGnfjcVMTsfVI+LnP7R5TGY1JApU2zd/vGtpGzBqyiORZ9qguPkjZJtVrxvfDzaBN5Zs6Z8+UJ/75HqAd8+/WULtQljS5LHh2Oc0/MrfkyP4oYJVt9K9EN87CCQBPQmWffwOqoPiHHqwpAPHknpTZA0By8YTI9MMDW/5c9OfzMlJMaXgpX3veT8WoNyJnUwkTP9dnox44nRUt0UvR2uc9udp51viMhKLAWHxfgNZLx7r3i0V1HwXtgCvQkv0aKk3eGhtPxiyHlcNmMKB/n+weWZ72Z+MWnrxSjS+TvU8TmX6iVc8YQ+Mvlod/n+1d5gF79tJalN0GIGoYvAzLati+gfJkP8aEkzPf7Bd0xb1u1lWVg+T/sareDuB2ETlUg7EpRNoNv2je6yal7fPHzVqOf/G8F4J3pWxILPGysvdL1K02Dw44sudIiVGPoSgq2zDX0/8ZURshsh3Zfs1Ob9dwz2ZGsm8unBYf3OfOPZ0DBQ3gNuncgJ/JaVqxNTjWs7CBak+y0bOy45y35fmAB7Qkf2I9fhZRQ83b3Jia+YMJNGnmSOTly/TPBugqGgXyk+o47II3rdm95FnBqXuYkj9VP2vYxs7yPgzmG93FWDcHhRzRn8E7qvpy2j0LRwz2kqZ84D0iAi/L1rywNu8SSRTiIPK08VEqzSuhrI+zTDzQyTNcPcnzsn8LZ/4AWmI0cEPC5LNFHjBr82rfymMTjyeslMbl76/XFLkuZn4oija4GBO2z4P9+dfW+3VkYhxa1s7uCZuk2MOIG0HfGPH9YDNcU27RvX9An4Aj193oXlm9GjMN1dHe62YCW3CdudjyuWaMJaMIdPKgNCnyS31XkYlqjFZ/GLDKppsPishLVPWX+KCI/FsAf7q3Yu0evFkmOpd6mQ2mhTRku+h2+5l4jQxS8eb9QFZE3g25OTn4eo586RsXboCA1tPDyuNGg++FJ/CIbFKmhC1Xr+gepzRaI0cjfEvnZxAb2MyS8m2fzSsbvdeGI2LhxoMHYC0tP3+us5Hm3OXDsiyC8lIYuke5HkmqbiC5uZ6c3ru4DhF8NBYV1SUlE2v1y2jKB6VVr7rXzSsAvE1EXgDgw/WxJ6Ky1X/bXgu22xjSNqN0ne5xfT6aRGK/rLX780Dslmbp57Tvtdio256LjRNdg9rH3TcC0b0Jg5MltPEx3fkor9S+uXSu07mUFp3KKyL11Lbtcy8h8pVPLtRB2zzQySQa3Z/tEgcHIPOInk8UltsPzPvr/DoIxYC8Kc18WW19GbfNfcUqm25U9TYAXysiTwPwuPrwb6vqe/dFsj3AkHYagcnMogBGXVD7jVaA8to8X1MUXS09pUl5n2wf3jgahOWyzXa+HsiQQs7G25BvrS1ynXPx61P5zwp0XPlME27uS6C9pp6fmZh4cYyoPH8Nu2gWrnzfoDbatyubF4j3PTTDslqs7xEC9W/QqEXvDw+os4krFYit0wsO6s15e6VoGURjVct4mu0XVn0wFgBQE/uRJfcUIg0v9WLZS2xdTtOSPMZMCY8+ejYvsI3dh5b1gcj4I/MDcb7MaDunhaV+O3LX98PHZ+G8UuYGu59A3zPHSGc2Ryd0g5ffg8lm7kg+VxdvXoo04FSvgkNVcEA2W3w9qruB10WI0nmS92MW1hP0PUAGr0gG9M14XI/IZGNl5p5ldD5F+s3zj08fKoJnrLKNfmWRsjPn4F9QP9g1K7oDYYW7bgjs422aPYerZTu8JwD7yBrCKLr5WZqhmZ1cl6Kg0A9EyL17EpDSGHTSjdR2U+e8bZxt9ZyGf/22gRemsfJ8L62oewv2rMzLaMhV0vLMKRRRWm7oO2RadtMU7j76JQO5zrZOAXtRRSZJqz+T+phV2Ibeg6F7sGx+ewkFVnfC1IRxXVFvL2bi9b7bHp6kU118JhT2sPEaVMfWXV+3uag0TR9jJSI+CQiTewqseXo5I20+hdxH7M0UQ+Q+n/e3OU9fvyGY/duHke7db9pXetbc0ER1ZflSQek8wfsI1XRfAAAgAElEQVSGnfP1q1PlFhjx98Uap0gzt3duseivneD/op5sNEFvW0rAYcAq2+gnpJGya/IHbiEB7NwQ2ObqGwdPfClijbreLJ95lbA/fASv4XFs8tTYROqjT2nQ0bV8LpqD4OWz2DvmIRMR/Rj4wHO+gfG9pxQico+OpRpavsZMM5EMdl1Z1g1/nce87M6XMLKO7i8TuYW64He4LLsLnfuQ1h5271jxjWI1Rdfmzh0Wgt0POUTkIQDeDOBcADcDeJ6q3uHSPB7AzwM4HdXt/nFVffNQ3seS6Ic+2KEuv+37Pztu+c+K6sPbzkuSs0unbOueiP02z2iNYvWw/GZ7TtXfk3C0+lHKFp6qlx1LxXK3PKxncsLFoimCOg31HiIMaeNFEa/z6vOItGivGFheQF+7bnpTZf994HQbG90JbpEcnYlodMzIm+vHjYBfKMdr9t4JwFZW46ivQxh6DgcNBfbLRn8ZgOtV9XIRuazef5VLcy+A71bVvxGRLwHwZyLyblX9fC7jfSN6EfkutELfDeD7VPWj9bmLUIVc+BVV/en9kGeI7HPwiznkNGlONwZRfqlY7bk8gK6bHLsO+lg9JrOW3euNVFO9iMjEkJKHwzAYbB5AlH9EcJYXkz2HS+bFS1LeT6yxRmY13+j5soFuDH1vPvL5+LIsPTe2nfDKZTcIWkPCZfzcG3kXXbdLvteRacWeN88NiIh+c9FPl3oXOvcK3TV3ubEbg8NC8sC+et1cCOCCevsaAO+DI3pV/QRt/4OI3IZqAuvhIHoANwH4RlW9Q0SeDeBKAE+pz12EKo7Or4rIaap6934ItMzLl2oYtttYDCFFcH7x6FQd/BKHXnvk61OmIPMc8eYCvi4i1Ejm5DqptWcN52+k4tdO5XpE4ZI9qdigeETofO/GwNeF48n7xtk3KHzMEHm7eE250zNEWlvvyIm27ilwOdHz52O+l7EsAfv3M3d96jkdOJaz0Z8pIjfQ/pWqeuXIax+uqrcCgKreKiIPyyUWkSejmnLyt0MZ7xvRqyqvSvUBAOfQvsWrUtreN+QIPyL4reBDTuXJ8P7ZnC+bAvyknbKs7dELoNiobLIRwXoy9ysZRas5eWJhuY1gzIbr65TTyL3JhzXXyAzBpoH5vA2sZsd8PhyywDR6i5hp+Xr7NJuXUiEj/DOx+8D3l8vmfXYT7fROgp6SXcehnrmuFuRtY4MawCJtc7dfXwbDL4fI9fdgc892CD7Kb0gpOpQkX2MJ081nVfX81EkReQ+ALw5O/fAy8ojI2QDeCOAS1eGZKwdlo38RgN+h/WsB3ADgTap618GIlH4ZfTeftdyUZ83QS+tNO6zhCZEH57dYVGS9SX70m8FkIqCryTPJRyRr3jwp7ZO1SU7jJ21FDWZZAutO6/Xrw3KIYpsr4OEbM66X3StvuvE9EH8vO2aLsg0n7O+fL5cXaeF9znuMWYh7aFxOdIwJ2XsENXUo+iYYfhZm2jEC9yQf9RT9cT5nx7078V71cg8Kuovx6FX1GalzIvIZETm71ubPBnBbIt3pAH4bwH9R1Q+MKXffiV5EvgkV0X+9HVPVa1DZpFLXvBHAdwDAg05d21P5Ui+4J3vzqomm43sN0s41Kx4h/iDs4+Yl7KyMrbLS6Oek6bEdNzWZx5M8y8gkn5rwVBSALarBpOJ9tFkDjO6fN7H4Hov9RSRv0/DZbGImEyZkbsiKshunPWpYNjZaLZw1Z667N3v58Y5ofYLI28rPZLV6R/dqzTWETP5cB7/o+xiwHZ/vO7/PLA+TuAfLb/fR9ygPGjfddBNE5B46dK2qXrxsPvvU27gOwCUALq9/3+4T1Mu6/haq8czfGJvxnhK9iLwMwEvq3W8BcCaAqwA8uw55PAr1g7kYAL7qEWfowLjDrsF/iJFmzzNlvTZl10Vg0o00OG+/nQPtOqj1Md6OTDiNBhpoZBHJ+64/y+qJxrt6Msl7bZrh/fmNIPxMX9aMOVJo03i5++PJ1cq3F5zvB/eOzCwlRXsvInL39zS3GIk3qTWuh3XPwcqfz6syLbRyr5fnvk5blrHJN7jHPg+gv5CNycL3OrqO75f3rumZ+Mp4wP2gtfvzzjsPt99++47Cqu9jULPLAbxFRF4E4NMAngsAInI+gJeq6osBPA/AvwDwUBG5tL7uUlX9SC7jPSX6enUqW6HqUahMNBfzyPFRQUT6AEYNftk1DWHScd8ryAUUY+Kwj/N+ks83LqZxMng6v2m1EclzmdZYjO29eq0whWjxFw+WxepkRMtg27ivh13HvQibzXqyoPEB9xw8wfs8rTze9j7n3IAV6DZgDPbCiUw83NhGCkJE1v7ZD8UuikJ4G3K9Nb42eo4HTfa7gW10npZGrfw+PTh+A4AX19tvAvCmZfPeT9PNjwB4KICfExEAWOQGLQ4L/AfkyTRnk7SVfPyxaPGTyKyylSAvXijCPrKhlaWAbmhjJiIeLGSN1TD0oXry89ovozPoG7jscYiHqHxPYEycuYls4uoVPT8z3/hyo3pYXfhX6Z7evxF7I22RvGwCSzVgkSZpsljgNl7HABhvOonq6us7RpP192YViJ2hwFBU5kOP/fS6eTHqVumowA/kjX2Bx34kOS3ITAAW7zwaVPTXRho3m5WiafG5sLfW+0gRXSSD7XuC9XWLNGFv9kjVNRUEbGuB5o32ZrYZ+pgFcvqwwql6lyU6qzh5Tx4fhbGgdCmtvgjeh9S94HTRc4/cWce8j6l30e+n8lo1kjdMRH+MkNPegfGDT7kJV0x0Rgb3J/JmYipLNN43DfHVv342YzSdvXB5NqskZeqR0vxZ6/Y9iqKo6mghn01O73WT8uTxJGg9ErNzs908pZGzNm3n1ubD8f1DOEI3eXw+fD3PRC1LoFgAm0U6bLDl4ye0NXUbIF1v2uF7mmrEvdypuRKp8qK8jiomjX6FkXs5x2h8EcZ2cc2jwj7OYtHv0vt87GP2Hyj7uXuCZ20zpYVn17dF12SSqhNr1kw4m+ge93Lmei92jZlbNtGVnydP2e/6en9AkRs7Hj/xMvPvWEQ9O9vmGPFA3x0WyJtx+JlHBM6/XpbITBP1vCx95GEVyZPCMmagw4ojLDqAieiXRqTVWzfeL4+Wi0+f0jgtL4sXAnS9MlI2ZC8P0DcnRBEI7Xr2WokGBT2JmCdM1Cvh9J7IIzl9nBxugFK9qMj0YfIVRashc9AunpLP5du2LRoSjXf4tDn7NtvOI0T3wjdUPjxCzszjnw3fB1+uJ/xcQ+21+ZTsqbxXBYr9GYzdS0xEn0Ck0fnjvWvQNXUMLT+XQpPePrQi1vx8fiwba2E2MJjSlPn6MaF8La0RERMGMBxaNyL2KP+ofjzhJwqkBvT93Yui0uYXC2Bz3s3b31c7bprskIydRpLIk72biuAe8LZvdPnecuPUUwgymnsqtpDdL5/Ov1MpMo+2/TWpxucoY9LojxmGup/bfa+9FtQrpz6/KPtd+tTHxLbdnDmkCEgh0uZ8eUZG3kV0c4HeOqNGepanryPXIdXYsE2fZYvk06Ib6tg0ez95rHG5DHolZl6xGbPe1GV+7hylkc+zaSjlh86NIU/usuvnQRk5M4zduyhwnR/D4bxSv1EvjGXNIacUHSVMNvpjjNQLnCPslDnEn0t1ue3Xz7hNyeY1ag9vdkmZgPw1hmhmqJlAFq6ekTmEtUpDbuFvlika8PTpiqL1KLJ087KdeBR52OQ0W9bMfYNRFH1ZTIbFAp3QwDmzEK9S5QdtczCZo8ldLIsvz18/BlFPyg/a58xuRxET0R9jpD4YJuZl8vEf4l59JCktcJnymFhSyDUyQBw0zH6jhsfy9CEfcuVXG4AtFM4aKc+aBW37ck2rtp6KyZxqJO2eFrTP8GEFondlyxGz34+2eUDVN0DLmlOG3l3fWHkXXa+cHHVMRH8M4D9E7mKPJfOcNh+lXQapjylFQrztY6snwwmPkMtr2wZPyqkQyl5mb2dmN8yybOMGjZGJST6qU1TuVln5328tgvwGiL4JLYx+TH8LoDYrALhB9jFrzkYNQ9RYpRrL6P6MsclHPRYum1eZ8iFBjjIm082Kw2tBTbe9SC/mYGlziAiHbbtRHmN6CKnzPuwCa4AcQC2K3cLT7H18eqA7e9f2vbbHsVBsPyJKlotj2XD9Fu6++EWxOW00RmAvfDRZikmetWE4G7y/j6lGFkgHBWvi91DeHDI56inleopl3ShF9zPVy/QmvqiOqaUDuY4RuA5HnfAnr5tjgBzZM3JatQeTIsM+jqLoT0Tij7JAq/lFWhdfx6sX+UiOdp7joud6BmZ7N3nKEkCgWbMcDem4uvpFQ3ycFTvnid4jRVI+uiP3ViLtfch8o3Ss8ywy5MlIvhuLNu9ltfKxBOplisZw+F1MzTXIKRIehyFy5W5i0uiPASJ7qCGnxQ+RvPc+KYqu/7y3sXbyRq0tBh+v16ojzZPJNLVKEkAmCEe4Y80gnN6n5cHc1PVeZp9Hirzsvth96PVMqPHKkSX3MnKkzo1fqiH3dbDr+Hlzz47rPyRnJFPqmCf43KxXTssNwph4OkdZi/eYiP4YISL5oZfZE6dt+0konF+BboiAlEmnCPJNkXwkUypWfSo9lxOFKGANnAlshr6bpp2PyvblsX8+TyBi2zfQJS1eVIR7SmMQmdVScwM6Jh70CXF47Z82H36ffK8jdQ3Da+Kp3p4n+Jx5JtVDGRuhdGyaw4zJRn/MEGljqf0IngRS2pd94GYSSZkXLC/f7R4yG6Q0/IgY/BKDTHS2cLQnomg1K/OUyRFfiuytjMZHfL3bG/J/vFJUFOc9B3Zv5Dz4jwPBjW1EovvHx1k231gu20jxO+TL8Rr8kFnGI/KQSsmRO3/UMBH9MUBKK44+0gipLrH/jbT/ouiTT0SCth112XOIBsyYjDjPrQRBsUxsDuHxATu/hWEtd4gcOFTBrEBnMQ87n7J7e0Tr/xbo3m9vujAvHMsuWrg7QtRT8PAa/TJgJcKXEU0yi0wyhjE2dk/2Y7+Ho4ZJoz8GGLLfDpFppMl5m6j/DT+c5l+M1Oo/0T6XERFdZLs1uX1+rHGur1f7fmA3tYaq5TXWZOSn6lv4ZiYqjk3P5h2fX6P5lm2YAZs9W5ZdecuyjajZifhp8gT1YM+sqKeU63XxuEAUmiFCc7yuv618lpqNy5p81NMyF8kISVNi8N6uCiavmwk9pAiNTSGptEPjAH6Qbig2TaoRScnbxJEJol56GXh7fb0leF6o2y8x2Ol1lGgmMqXqb3XMzfg0Td60+I7HUkCsPiZ8gXZMJDLJ8ZKLfC9Si4UYIfMM4UiL9nUsEE8i8wQ6ikgzjX2qQbfyPMaY/lYZk0Y/oYOU+SX1cQPpj4i3WUPlAbrOZCJ045Uva8IBYi2eY7VwPn5Q1IjeSN9kYFLdcvbggswuqfuRIvmh3pSZkMyP3ac3/3uOCsrB4wws/8ZG1z5f+PxILvuN5h54NM/REahvcOx3zKzgCGMVjCjQWSptroxVwlGv1kT0u4yI7FNd5KGPyHfh7RjbwRlGapG5KMo/inDorwXy7po28Lq+Xg2SckAuWzYvWkyE75Gf3h/dm6inYwRsg8K+vgUR6FbZt3xx+kYrD2RZOG2e5beJT7yCFZM90M498PWLnktErpHJha/bom1Ll3u3/HlTErg34T2ovCzHCful0YvIQwC8GcC5AG4G8DxVvSOR9nQAHwfwW6r68qG8j0nna/tY5sVmjc7/RYsr5/5m866nCV/jtfuU5sXyR6aXSGvzH3ek+ZkWzyQ/n8fhCjY2uvbtlJdH1ChFNmpLyw3I5ka3nFTvKTJzRQ1b9Pz8ffRpc77o0fPie8n3lM1B/tlEC4BY2SzTEHrvWtE+Pw6H4eUY+86tIsqRfzvEZQCuV9XHALi+3k/hxwD8wdiMJ41+BFIaUgqsrXnbsJ1PoeeqmNH0fFks75C5yBYY9+CZr5Y2ZSpgzY/lNpv2xka1zQSfmt6fgicVq1NkN895jqTCVuwEWqJRlXKmkKJoA6LZIigGe7dSDYGZlhaLdGMy9t0cSsflpmZJs9yWdtW1/H0MgXAhgAvq7WsAvA/Aq3wiEXkigIcDeBeA88dkfIza5J0hepmXsX0PwWvvs2J708gjO6wPaxyZgthMxGlz8vZMR2Xfzu9dPpeZaJOC5Wskb43JVv2nQb1Z7qiXlNNY/baXP7rPnD9Q1XtMlFD/HIZ6atG1YzCmribH2MVolpXhKGGfNPqHq+qtAFD/PswnEJECwP8A8J+WyXjS6JfAWE2KB+MM0SpIEXYjRkhkMmI7OBBryqa9R9P9h8rhgcmhsQFvFvDnbT8yLfHAqHd5jLRjs1/7++oHeC0cg51jH33Tqr027sPyzug+RnUCqgFffj8is5nwb3Bf+R5aw70dgk31EDlfDkinTu7jotUvaaM/U0RuoP0rVfVK2xGR9wD44uC6Hx6Z//cDeKeq/r2IjBZqIvo9wLIvPq+ctKxpg01EfkCSB1GHtDKT2T5sb8NNRWG0snkGKpOumYhStv5oQNin8aYwb6OPfNvNLOXNN5x3zibOPvoePjicwUw5Y0x9XB/f2HbyQ1cG3zBEC6D4Mizt0CCtbfvn4sl+CKtI/EtU57OqmjSnqOozUudE5DMicraq3ioiZwO4LUj2VADfICLfD+A0AOsicreq5uz5E9FvF5EGahh6yaOAUN7fnMGzVxmsWUUDdZ106F8b/TKYMHmFpEgGn4cdW5t3G7IUPAn5hilH9hzyYKvsLlzuQzQ0RB4MOPIxT8SdPJxmzXWIPIi44R0yAfKz4ufoe2RAfy2B6DlE+17uFEzpyIXkPg7YRz/66wBcAuDy+vftPVlUv8u2ReRSAOcPkTwwEf2eIPrgpOi7sG0H3kQQeX1YuVFIBEu7hT65GrwGz8HDfLjhyPRg29F4QcqsFOVh+5GG6MlnTM/F26K9Rs8DyxaILZIp2ZgWcawhq6ufRxDdHx+bJzKB8bqwLLcvM/odA2tsxJF8pM0fF8Lfp2peDuAtIvIiAJ8G8FwAEJHzAbxUVV+83Ywnot8HRAtvbAf2MbOJAMhryhHh2odsU/7teE/m+pejT0bEw939Th60HU3E4oHbomhnkRphRnKxfJu0nyL43ISrofRR+bzQeqoxWyz68Yf8vfO/KXOHVxas7tw4zegr1oxctp0z3/D5FLHnGo9VJP798rpR1dsBPD04fgOAHsmr6tUArh6T90T0e4DItuvtwz591EUeClOcgn3oPIDmyyuKdnaovzba5kbFE0Dk8+2va8waZWVXt54FhxMuiu5MVT/I6E1HPIbg6+DliYg+F/7X32824UTmnX7F23Mpk1p0n6L8+P2IGq1ohnRkPvJ5ch1TJjg+H8m7isQe4ahXcyL6PYb/SFIEYh/zWIwdtPUmI4OVl9LwInmbelCjxNrl0DhFKrJiRCJsjsrlGTUwvO8nXKUaI/tLzXBlUo3Wdc0ROA8WDz233PMYGyLYe+ukykj9ejmGnsGqY4p1M6FB7gOItOTURxp96EPmhlRZBjPBjLk+Kpf3rZ5zJ9fMvUkpk0UjU9knQaA/E9N75bDd3P78sot8PJrZ6eUA0AywsimJ68y9i9yz879RHTmmTmpt2Fw5nM56Zt5DJlIouL6MHNmPqedxwFGv6kT020RO80p9AGNMMCnTSWRyGLLN++0c4eWQyytaJcrIjc0yBm/jLcuu10wEncdeOEzwbFph8wYTPfuFA10TlMkw9vnkTDfezJWK/lkEvYOI+H2vkEk51RM0rd6TuCEKyeHl98ePMyaiP8bwZJ8jePtN2UVT1/jtCJGnR5SHJ7FltHzfOBhpRl4fHBOevxAeYPWzZw1MuKxF+0Fjk2d9va+BRz2NKK672bPLEk2oYsbcDXJy3VOISD4i+sZtFX3t3j+TnDZellVvhCeGsVYfkfzYuRVc9nHGPoZA2DNMRL9DLGuy4W3+AD3xL6t1D8kQEXWEoQaDXfu8xszpU2Tlf/3iHAX6Nnpv+vHkz2Vyj8cPXDLM68fyTbk1+utS93OIEP15nm3LZJ965ql5Fo2ciJ+pJ/kxGDLdHDdMNvoJg1hGM4/MIqlJVYaxMxU57xzJR116T3hDg4memFMkb3ktXJl+8peXh+XiY1Ec9YiYrT42sOrHEriB8SYik1mD/Hx9udwhwk3Ff09p4n5lsN0ysUTvayT3cWsEjnp1J6LfI6Q0QWC4F8Cash3jLvhOozDywtaGyG69hcoskCORiLw9YXL+Y+2/lgeTPhM60B2AtPyi3gWfS5UdjR346zkfcfthvYp0nByWdWjSk0fkReXReAeNfFeixvG4kXkOR/1WTES/y8gRfApeI/Pxyy2fyHMlij8SlelttZZkERC0gWfEsoxM2pEJgz1NIrOLJznL09fDNxY5pEw0Y+AHhVNEHz3blDZt5yxY2pZbN5cbjIjoUwO9wPgIo711YBP30RP8EI5bAzCZbiZsG2PJy34j8wB7kIzpMfjxAL/tCc406jldb8XkfMkj7d3XyzdmPHM4JzuQtkc3MmbO2S/3FuyYNSpK950XC4/q5Htb/pxBHTnbuSggGeeXMvnsRpRTlnEi+TyOerUnot9FDH0suY8kpxkO5d0sJUfX5MjeE1eK6OfzNq5OLz8jdScbD27m7OtrwZuXsgWnym96GK6MaHzAy8gLefjVr1K9KCZjs9N7Uk5hPq/93dFez2YbnodQICb5ZeLCR41tKhTHsqa544bJ62bCIHb6gQyZBvicTYzy8U6iXy9XZGIBYnMK/6Zk9uk4UBp76tzvrl04f3agDZ3gTUWNJozh2arcsJmZyEIcbzozk10X+eQzbOHvqDxfblF069CRv+iHMTCyj5DT5sdGmkwNWk+IcdTbuYnodxFDWt1YsKYZfYi+W9/TQE3TLfvBwSKCj44b/OIavJ3Ky8pmNCtmFf0YO4siNlV4c4YvhwnYk300uMxy2/2NliQsCrr3NE6RWrEqpYFHiEh+zFoBVqcUyZtZzQdT84gGrv07lnuuxxGTjX5CDzshe/6gLLKk12KH4NN518WovMjEkiozsnMbUhEni5rgOW9P1nYsWsGJyc0TvnmzWB7q0qZ6Mya7Dx1saZqGp3RyzWOyjXpQkcwRLGy0mVaGSNrKi56PBvXka/z1qWd93Mnd46jfjono9wCpj8R/TENeFkYuPnRuCpFGlgtwFZl/vMYZhexlomRSiurA2qjZ5e0YkzQ3apFd3fLtrZpFDaJfySpF9E0c/Mw4gi/X/mwWKs9AjZDrKUXPPWV+2mkPMVV+rjGf0MWk0U8YjYhM/XlPpN4GHXXdI9Jg5Fwvo96HNylEJM95e9t2RJzzeWyfBrrr0/pzUd06rp3ou4f6HoP/ZZMO1y3Sdr1NnsuNSDlKy2UCbcRQRlN/d52dSyFF2P5ZDOU39A6NMUmtOo569SeiP0BEpJvq9pv91ZP9kPY7pvzIJj6k6UXT8D2x7ERb7I07BLB7NqRVM6L1T1ONHZBeeFvLeA6Dh5ctaqxTz943gGMbgGUx9O4dd0xeNxOWRqS1jv3Q7NuOIh76tGxWiTTtlExDttrINOLt3izDWmYws0yYfiK57ByHTGjS1/dvUXbDRqR6UUycHKbY4E1lUS+hSJjEol6CPYtoTYDU9Tm5eZ8bJCBedCSXZyTLRPB9HPVbMhH9PiD1oaW055TmZuThIymm1k9dhuQjOYBYW05583hPFyn69u1F2SUl9naJiMnb7Mc0hmZDHzIHefm5zFSjl+u18LYfL0iZeobIfsh004uHhH5PJff8c72lCRUmG/02ICJPAvABAM9X1bfWx14B4GIAr1HVN++3TAeBnCaWSpsyITC8nZ+PRXlEdnqf9xh7dA4mS+OySHL5yUomYxQV085H/uvcK2Kyt/xyZqmoJ2EkCuQXUYny5gaJ4/ID6VnDqfGSaNvuge+58P1B5plzmRPG4ajfqn0lehGZAXgNgHfTsdMAPAnAkwH8JoBjQfRAtyufWt91rC12iOB9uqG8O9eW/YYpgp95yXl5s4cdu3+j9WP352fz2NuIJ1+lGqooGqQn8OZ4xmxjMlkjZQ2St7VHA7h+HMPqmLLblyXFqXfvxpj6cX45k+AQpgagj/24JSLyEFT8dy6AmwE8T1XvCNI9CsBVAB6JqsPxLap6cy7vkTSya/h3qMj8Njom9a/usyyHDj4iopGS/2N4Eh3689fyr0fUaGjZ7UV4LdncDpsJUi4v/rMwBKzV898WxYz38s+KeKA0JPJEY2F/FoZgPu/v2x/naaarLSeTyWPXrNWhDaLeid1H+1tk6hrJPWbg3PcycuaoMWa+4wobjB3zt0NcBuB6VX0MgOvr/Qi/AuCnVPUrUSnItyXSNdg3jV5EHgHg2wE8DZUGDwBQ1btE5EYANwD4qcF81k7gAf/kCXsm517Aa2eF1KQt6WtyUGoSG1u5tvtl2e4nZaKyjRwLJw/nYTJ35KjL2Vp0y4/QNGLS5r3YAsoFsFaTuh0vpNbmZ8B8BqAAZsG92jL5XG/B0luduH7FrG0kuB4s52zWvRdbW4BsAvMFMN8EFpskRAGsz4G1tSrv9bWuyWd9q6rnYrP+3erK22RDcs9m1f5s1n9PWHZ+JvYsrAFSbY9FSE1uWzl8ZudZ7KON/kIAF9Tb1wB4H4BXcQIReSyAuar+HgCo6t1jMt5P081PA3iVqm6JYzhV/QkAP5G6UETeCOA7AODEiRO45E/O2Us5G9x0000477zz9qWs/cQq1msV6wSsZr32s0433ngjROQeOnStql68bD5LEP2ZInID7V+pqleOvPbhqnorAKjqrSLysCDNlwH4vIhcC+A8AO8BcJmqZofSRHXvLCYi8jIAL6l3H4TWTHMmgHsBfK+qvm3PBNghROQeVf2ig5Zjt7GK9VrFOgGrWa+jVicReRcqzhqDz6rqszJ5vWpcEXkAAAi+SURBVAfAFwenfhjANar6YEp7h6qe4a5/DoDXA/jnAD6Nyqb/TlV9fU6oPdXoVfV1AF7nj4vI1QDecZhJfsKECRMAIEfc28jrGalzIvIZETm71ubPRmx7vwXAn6vqp+pr3gbga1CRfxL7PRg7YcKECRNiXAfgknr7EgBvD9J8CMAZInJWvf80AH81lPGBEL2qXmo+9Icc1x60AHuEVazXKtYJWM16rWKddgOXA3imiPwNgGfW+xCR80XkKgCobfE/COD62olFAPzSUMZ7aqOfMGHChAkHj8l0M2HChAkrjonoJ0yYMGHFMRG9g4jMROTPReQd9f7jROT9InKNyDLLMx8OiMgjReT3ReTjIvIxEfmB+viXiMh7ReTtdRiKIwUReZaI/F8R+aSIXFYfOxLPSkROEZE/FZGP1s/k1fXxq0XkJhH5SP33+Pr4g0Tkf1P6F1JerxCRD4vI8w+qPrUcS9WpPndBfexjIvIHdPyiuk7/4SDqspJQ1emP/gD8RwC/hsr9E6jcls5CFb7hWQct3zbqczaAJ9TbDwTwCQCPRTXQ8zgA/wrASw9aziXrNAPwtwC+FMA6gI/WdToSzwrVANpp9fYagA+icpG7GsBzgvQ/hCrgH+r6fa6u92n1uzoH8PYjVqcHo/IWeVS9/zA697b6Gf+65Tn97ezv0Go9BwEROQfAv0QVMMgwQzsLeptBCw4Oqnqrqn643r4LwMcBPAJVvUoczXo9GcAnVfVTqrqBihAuxBF5VlrBpq6v1X85rwgF8ECpppSfhoroFzhEcaK2UacXoJql+un6+lT8q0P7HI8SJqLv4qcBvBLdGc8/A+C3ATwVwO8ehFC7BRE5F9WMug8CuALALwJ4KYA3HZxU28IjAPw97d9SHzsyz6o2EX4E1aSY31PVD9anflxE/kJEXisiJ+pjVwD4SgD/AOBGAD+gqmXdcFucqAOP+rpknb4MlT/4+0Tkz0Tkuymra1HV6Ya6jhN2ioPuUhyWPwDfCuDn6u0LUJtuVuUPlSb4ZwC+46Bl2YW6PBfAVbR/MYCfPWi5tlmXBwP4fQD/FJWZTQCcQBXU6kfqNM8B8Nr63KMB3ATg9IOWfYd1ugLVuhRfhCq8wN8A+LKDln1V/yaNvsXXAfjXInIzKlPA00TkqGm6IURkDVV46F9V1VWYrHILqljchnNQabtHDqr6eVRRCp+llZlNVfV+AG9AZaICgBeiMnOoqn4SFdF/xYEIPAIj63QLgHep6j2q+lkAfwjgqw9E4GOAiehrqOp/VtVzVPVcABcBeK+q/psDFmvHqO26rwfwcVX9nwctzy7hQwAeIyLnicg6qud13QHLNBoicpaIPLjePhXAMwD8dR3fxJ7ZtwH4y/qSTwN4en3u4QC+HMCn9lvuHLZRp7cD+AYRmYvIAwA8BdX40YQ9wLRm7Orj61CZNm6s7acA8EOq+s4DlGlHUNWFiLwc1UplMwC/rKofO2CxlsHZAK6RasW1AsBbVPUdtbvrWahMHR9BNX4CAD8G4Gqa8v6qWgs+TFiqTqr68Toq5F+gGhO7SlX/MpH3hB1iCoEwYcKECSuOyXQzYcKECSuOiegnTJgwYcUxEf2ECRMmrDgmop8wYcKEFcdE9BMmTJiw4piIfsKECRNWHBPRT+hARB4uIr8mIp+qY5C8X0S+feCac0VkWz7QInKpiHwJ7V8lIo8dee0FFk56ryAif1L/nisiL9jG9ZeKyBW7L9mECeMxEf2EBvXsxbcB+ENV/VJVfSKqWafn7GGxlwJoiF5VX6yqg4sd7xdU9WvrzXNRRVycMOHIYSL6CYynAdhQ1V+wA6r6d6r6s0Cj1f5RvSjEh0Xka30GuTQi8koRubFenOJyEXkOgPMB/Gq9AMWpdTTD8+v0z6rz+KiIXD+2EiLydKkWj7lRRH7ZIiaKyM0i8uo6zxtF5Cvq42eJyO/Vx39RRP5ORM6sz1no3ctRTdn/iFSLfXQ0dRF5h4hcUG+/UEQ+US+m8XWU5iwR+U0R+VD915ybMGEvMRH9BMbjAHw4c/42AM9U1ScAeD6A/zU2jYg8G1Wsk6eo6lcD+ElVfSuqcLTfpaqPV9X7LJN62vwvAfjOOv1zx1RARE5BtdjF81X1q1CF+fg+SvLZWrafB/CD9bH/iiq20RMA/BaARwVZXwbgj2o5X5sp/2wAr0ZF8M9EtSCK4WcAvFZVnwTgO9Fd92DChD3DFOtmQhIi8joAX49Ky38SqsUkrpBqObgtVDHFPVJpngHgDap6LwCo6ucGiv8aVCakm0amN3w5gJtU9RP1/jUAXoZqrQGginUO1CGb6+2vB/DtdTnvEpE7RpYV4SkA3qeq/wgAIvJmdO/BYysLGQDgdBF5oE4x1yfsMSain8D4GCpNEwCgqi+rTRg31IdeAeAzqMLJFgBOBnmk0giWWwlp2fR8XQ73179baN//7axitEC3R3wKbafkLgA8lXsuEybsBybTzQTGewGcIiJs6ngAbT8IwK2qWqKKiDkL8kil+V0A31OHpIWIPKQ+fheqtWw93g/gG0XkPJd+CH8N4FwReXS9fzGAP8ikB4A/BvC8upxvBnBGkMbLeTOAx4tIISKPRBtn/YMALhCRh0q1DgCbnH4XwMttR2ih7AkT9hIT0U9ooFUo029DRbA3icifojJ9vKpO8nMALhGRD6AyR9wTZBOmUdV3oYoZf0MdLtns41cD+AUbjCVZ/hHA9wK4VkQ+ivRSeU8XkVvsD9VSiS8E8Bt1WN8SwC8krjW8GsA3i8iHATwbwK2oiJ3xFwAW9cDwKwD8H1QLgNwI4L+jHttQ1VsB/Ciqhuo96I55/HsA50u1rN5foQ1DPGHCnmIKUzzh2KP2ytmq49w/FcDPq+qkbU9YGUw2+gkTKi+bt4hIAWADwEsOWJ4JE3YVk0Y/YcKECSuOyUY/YcKECSuOiegnTJgwYcUxEf2ECRMmrDgmop8wYcKEFcdE9BMmTJiw4vj/RBV6sUA7CtYAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid_smooth.plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating output table and Test Statistics estimation\n", "When adding a new source, one needs to check the significance of this new source. A frequently used method is the Test Statistics (TS). This is done by comparing the change of statistics when the source is included compared to the null hypothesis (no source ; in practice here we fix the amplitude to zero).\n", "\n", "$TS = Cstat(source) - Cstat(no source)$\n", "\n", "The criterion for a significant source detection is typically that it should improve the test statistic by at least 25 or 30. We have added only 3 sources to save time, but you should keep doing this till del(stat) is less than the required number." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/sherpa/instrument.py:723: UserWarning: No PSF pixel size info available. Skipping check against data pixel size.\n", " warnings.warn(\"No PSF pixel size info available. Skipping check against data pixel size.\")\n" ] }, { "data": { "text/html": [ "Table length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "
delstatglonglatsigma
float64float64float64float64
1825.10.31702-0.0973090.90238
768.39359.95-0.0503390.018175
386.671.2962-0.064230.29907
" ], "text/plain": [ "\n", "delstat glon glat sigma \n", "float64 float64 float64 float64 \n", "------- ------- --------- --------\n", " 1825.1 0.31702 -0.097309 0.90238\n", " 768.39 359.95 -0.050339 0.018175\n", " 386.67 1.2962 -0.06423 0.29907" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from astropy.stats import gaussian_fwhm_to_sigma\n", "from astropy.table import Table\n", "\n", "rows = []\n", "for g in gs:\n", " ampl = g.ampl.val\n", " g.ampl = 0\n", " stati = sh.get_stat_info()[0].statval\n", " g.ampl = ampl\n", " statf = sh.get_stat_info()[0].statval\n", " delstat = stati - statf\n", "\n", " geom = resid.geom\n", " # sherpa uses 1 based indexing\n", " coord = geom.pix_to_coord((g.xpos.val - 1, g.ypos.val - 1))\n", " pix_scale = geom.pixel_scales.mean().deg\n", " sigma = g.fwhm.val * pix_scale * gaussian_fwhm_to_sigma\n", " rows.append(\n", " dict(delstat=delstat, glon=coord[0], glat=coord[1], sigma=sigma)\n", " )\n", "\n", "table = Table(rows=rows, names=rows[0])\n", "for name in table.colnames:\n", " table[name].format = \".5g\"\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "1. Keep adding sources till there are no more significat ones in the field. How many Gaussians do you need?\n", "2. Use other morphologies for the sources (eg: disk, shell) rather than only Gaussian.\n", "3. Compare the TS between different models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More about sherpa\n", "\n", "These are good resources to learn more about Sherpa:\n", "\n", "* https://python4astronomers.github.io/fitting/fitting.html\n", "* https://github.com/DougBurke/sherpa-standalone-notebooks\n", "\n", "You could read over the examples there, and try to apply a similar analysis to this dataset here to practice.\n", "\n", "If you want a deeper understanding of how Sherpa works, then these proceedings are good introductions:\n", "\n", "* http://conference.scipy.org/proceedings/scipy2009/paper_8/full_text.pdf\n", "* http://conference.scipy.org/proceedings/scipy2011/pdfs/brefsdal.pdf" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [] } ], "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": 2 }