{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from DataContainer.ReadData import read_hdf5_file\n", "from Analyser.ImagingAnalyser import ImageAnalyser\n", "from Analyser.FitAnalyser import FitAnalyser\n", "from ToolFunction.ToolFunction import *\n", "\n", "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n", "xr.plot.dataarray_plot.errorbar = errorbar\n", "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n", "\n", "imageAnalyser = ImageAnalyser()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Start a client for parallel computing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Program Files\\Python\\Python38\\Lib\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n", "Perhaps you already have a cluster running?\n", "Hosting the HTTP server on port 55915 instead\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "
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Client

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Client-b786dde8-ed1c-11ed-b3ac-9c7bef43b4fb

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Connection method: Cluster objectCluster type: distributed.LocalCluster
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Cluster Info

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LocalCluster

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7243c492

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Status: runningUsing processes: True
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Scheduler Info

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Scheduler

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Scheduler-73d23a11-ab74-4d22-b9a2-387f2aed7f10

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Workers

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Worker: 0

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Worker: 1

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Worker: 2

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Worker: 3

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Worker: 4

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Worker: 5

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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set global path for experiment" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "groupList = [\n", " \"images/MOT_3D_Camera/in_situ_absorption\",\n", " # \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n", "]\n", "\n", "dskey = {\n", " \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n", " # \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n", "}\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "SequenceName = \"Evaporative_Cooling\" + \"/\"\n", "folderPath = SequenceName + get_date()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# An example for one experimental run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:     (runs: 3, x: 1200, y: 1920)\n",
       "Coordinates:\n",
       "  * runs        (runs) float64 0.0 1.0 2.0\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    atoms       (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
       "    background  (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
       "    dark        (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
       "    shotNum     (runs) int64 0 1 2\n",
       "    OD          (runs, x, y) float64 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
       "Attributes: (12/96)\n",
       "    TOF_free:                          0.02\n",
       "    abs_img_freq:                      110.858\n",
       "    absorption_imaging_flag:           True\n",
       "    backup_data:                       True\n",
       "    blink_off_time:                    nan\n",
       "    blink_on_time:                     nan\n",
       "    ...                                ...\n",
       "    y_offset_img:                      0\n",
       "    z_offset:                          0.189\n",
       "    z_offset_img:                      0.189\n",
       "    runs:                              [0. 1. 2.]\n",
       "    scanAxis:                          ['runs']\n",
       "    scanAxisLength:                    [3.]
" ], "text/plain": [ "\n", "Dimensions: (runs: 3, x: 1200, y: 1920)\n", "Coordinates:\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Dimensions without coordinates: x, y\n", "Data variables:\n", " atoms (runs, x, y) uint16 dask.array\n", " background (runs, x, y) uint16 dask.array\n", " dark (runs, x, y) uint16 dask.array\n", " shotNum (runs) int64 0 1 2\n", " OD (runs, x, y) float64 dask.array\n", "Attributes: (12/96)\n", " TOF_free: 0.02\n", " abs_img_freq: 110.858\n", " absorption_imaging_flag: True\n", " backup_data: True\n", " blink_off_time: nan\n", " blink_on_time: nan\n", " ... ...\n", " y_offset_img: 0\n", " z_offset: 0.189\n", " z_offset_img: 0.189\n", " runs: [0. 1. 2.]\n", " scanAxis: ['runs']\n", " scanAxisLength: [3.]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shotNum = \"0000\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "filepath = r\"./testData/0002/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filepath, groupList[i])\n", " for i in range(len(groupList))\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "dataSet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate an plot OD images" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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BBbuW0JeQCFm7kcSmkxLhMVnM17OP4AD1mEKvnrwOal/iPfNiJXZ5Dms727YehT9JKpMH83sQe65D8BcJaw7gTWBe3EJlqpbrieq1Dfv+gEmzHHDzoZ7k1osT8MP12Elay/fUNfp7kBBf6hlo1bP622N4vPL5PV39dQD+hgRoZxhsP3hx7RSWidc0agdsN1lDTh3a1L7LgcekajgaZegsrI20erOrerkrPp2qDNn2g8SMbMMCEqPp9eUg5jQePPMOtr+G1/L63JoZtI1VpmNfagy/BXNvFrm5hTI+MOlVqEAok0544X66oPOZRobost1men6nmuqoF723sbTf5zGF/drDRhvw5lxP1Uxovp76fDpel5xnm4cda5McruZIgrXGFmEOCwoKCgoKCgq2Ptokh4U5XDaGUe8SVM3VpRZVVUDXDljfGzPPNOizFyjUevXpiRzsdvUA1XiLbUcZAbkKbgi517GnDtNI99xteU4NnhRJjZ4tdDfpqTE99V2XdI7fx7bVnaGeAOJ5LXrmAEAtgeVOuMvbsV+9PFrxVEosazXmw54Kif3bFS9Pn3uq9Da1qIX3rCtYbJeDiUpRrLpIx4WHfmrEtrJUHabv8Z5nftIlNdX29aSPVoKqDhfWvKArbh6QB1DWtqU2oc3By/YR8/ccm8aRaEO1JJ5zmA26vhqoCl9PI2lTCfeTvGvbW6mRagc8+rVzxjh8rYVFP09Y65V/GKnv+M1qXmDpZ9Qp/yLJ19Z7GOlb6WU9I+nU9InwVLb2mSeV9UwPFDbmqI7FLml/1/hTiaDSL2nEzu/6W+d/r7+7gvZvDLbBd0jZTAzZZqrLADiMnFg15APRz3vKC5rLe566WgdZWwBgb4E+gKZNoi3LC6LsLTQWHlNywKlbW2R+oHmiwsXxuhc5w9ovwDHzPS9eu2z6gJypnkeaVFnftkmkjXnxgqN7C+7+jvrqJKJe3pzoycwuwrdFXS68UDZdk5duNLyJ3jLuuvjwm+clD9uWntqIedt62HSqFrf06k2BWk9PhTRIuBLPc31CnnuMo1emPf3iAHwbLIL2wZ+SZ3bhV1ry8uj6PjJ4yuwRugjaek9Ieo5hPdLTm8/UM3gt/SQ1jIl6fneZQxAeU91l9zYhz7qYTFW/euPMfr+OJ2vjq3XzQjbZPhqGH7bMHpW6Dzlde3XVOdYyahqM2zP58caA5yHeRaNcLw7G6xL6tw3/t2FlvIgZajOvamrCbt66+t1jjDcebZLDE+tdkQ5sMeawoKCgoKCgoGDros3msDCHy8YiamnFEHLVRJtqwHpSAu0x3haQnxu8gDxYNdAu3fBUvV79PImA9w0qYeO3qiedjSM2j+SpxZ1llzGx5qs70TYvQz02TuHlYaGxxdTIn5hpJm9EgdLvtLHmvNhmXmwvpQGvHZhGnVl4tQbfeq/LiL4fGOewDZ7qpm0HvGTqB5N2UBWP1/fe0ZRtY+4Q8phwC0hqsOl41QDlKsG030ea8VT+s+j2lraG7P1iGlq1GOBLyL1jMLtUsZ7pQ5fKjuXsc+558fUOmKvmr+V7dfIkLoNEFRgEfJ9SphnkfUC6uA+55Mk7/lL7S6X7XfW1R+XNwp+7mR/nGOa7H/lcq2pz7XvrrMi2bNOEsAzP7Id97jklLcoz+y1tQc5t/gq7HqmJEjGONN7old+mwub/XQ4gtkzVMOiBCp6Wz9ZN52ErWZ5H0xRlc+C0YWBkW37/eAXg6LpXx8UWYQ5PQZoYPNWi53U7PUC+nt2QetXawa7u+WTEFuWZx5Ba9XBXsGgvHMWw1MkLvqyTv118vIXBs7dRGw+rirAqFQudzPV/zbfrLGZl+rQPqC5SexHLqCmT6N1bjq3bEPIJX5naC+P1Nnmny1ZzOfAWZFW7E3ZB7McAerBqK11cB1G/dNG2nhKkNHqPSQ/4TCyhqnGgnbGz9D6JNO49pt7CW9zUc1cZTHuaxaAe6tZmCkhzh0YpsLZaqqJXe2iPsbH327zBWY7HgBKroWWdpwkyEl44LdLFLnnHOxe567SmLkZb3+lnb2xpSRmOQcLy6BoyKG14+ZEOWA/Waxea65tegW57YYVNpwHbCWW+NEwT+1LtiYFmAHv225KkU6GFbV/dFHT1pbaVZVvabK6JlUSTOLk4dRgYcSxuCnNYUFBQUFBQUPAMxNA2YNjhvoY3kbvyFmEOTyAZ8Nodo8Y+1F2y3QGPIEn2vN2phYrFddfNnYlVhQL5WZPz6N61tHkeAqn+E+ivnkCsqw2mrMbPXeoiNWC3cQhtcF6F7pY9Rx5PhWyDle6Xd/Q4NO+M1zaJ6CTyttFA2p4Uwkp0ppCribXvKDHUWF3Wa3ilsH2iTh9qxkAV3c3mWT+Jn9KZpfl+R5N10Y13nJ+nSrI01Q+eN21XfEwv7mKXJMX7Js9xRuMF8rdGImjLX6EONGxjSgy1/2z7K+11xVHsUu0BvgpOj4YD+h8HtxbootFDyGlJ53qdY6yaVaVYy1Ub8l3vXGR1KvGiA3Cu8jRH9ui7fWiqSoH2GKJt64XSQL+j8rpg083A13ixP/bIvTZnNtZFMSLPrVYOyCWNbZJiLy5iV4xEOwZWQhfrgGHUXikWm8jocIswh/Ts9EJEKOPTNUAWkIjfU83YoNmHkXuTaeBoO+m22fl0icpVhQQ0z0BWlewVJr3nWbsfvt0kMWzSH0D6Fj6bgX86RVv9l5Dbc6g9jk5+djLYK3l4J694sEwZ20XbXvtlzjxfQH7CjVXf9MOkk3atPD1Vjep5EJIpZJ2VoelSd3aNiy67IG8xBrrVkja/BfgLgEVXkGR9VzeFdmOi/eudOUt46nuPvvUdtoPte89TvC0v26eeqmy5tKRmH11qeA+zLb/XEt7Z89xoqT1gmwe84jI0zTqAZriurjZUOuhKZ73hlaY0vaUDNanoClruqUDVg9fm620AB+0rfbeN+Z+Aby/ubcDZNp69u21THQPqrWxDmXnw7HS7mD0NfUbQLGnzqZQB1B4ppzv3C3NYUFBQUFBQUPAMRJEcrhXUs5Mie+7CVXWrAVc9WHE/d7WHkCQD3LWox9aQXG3gaE8FprDpdAfGfD0HA92d3Rd/W+km0NyBcbfkHVem6g/CCxhtVcFdR3hpbC+FFwi1Ta3kxR3zpEgaTNm2m7fz9ZyM9F2iS+Vg4xAC9U7W7uZXMoxORU0Lo0iSJJUEdjkueTRnzS12oSmdZT3t93j9xzzG4EsC29RLanaxXFXOHLo95QmlC0pL+Z5nTtIlZfG8MhX6fW2e6Sr18uIcEiPo7lMrqR9U+kdTg7YjOK3EqC1YtNUQ2EMBBgHNf/T7VSp2n/cSahrsMlsgPXjjXOlXndz4njV3GUF3lAHrbKH9qOpXrj/8vsNI85FV03rjzkOb6ritjkvw6cRTV9t8qDW6B75a2WrIPMm7p5nidQd8k4ZZk86bm1VK33UcJpxnnqnAJkQbc3h8vSvSji3CHKoXnA2U7A2AtlM6bJgCVRfYc1p10HkDe9DFr23CaMvXC2Rt7XFm0QxczffaVKM6OenE4qnXrZrNs5kiPC/DIXQfSm/b4TDys161HFVHW9Up89DAr6q+semVLrzQC7Y/2iZ0z551uTiGZCbh0QbR9cwDaWUa/jm5dsjrmLAMijchq6rHtqXXR0o3GhqK5Sq9tzFyap+l4D1lgttUux6DeagjPeDPAXbRVvr1bGzh3LNMz040mQyWw3GkKjLbvsoU9lONd6Wz4aHagpcPAjW/0ba25fJ/VYnrZtTO4V32lR5dAu0n2yg0vJgtU+dEtSm3du56LrJVmWrECYUNi6bzU9f60sVYafgXhRf0nuB3KW3bse8JEAjPzEnnRC27y17Qqu+9iBOeHbLHuLJfdF5ZyTx9krAN/jQR1rsi7dgizGFBQUFBQUFBwdMARXK4VqiQqwJ0l87dCncZs8jj86knpe5mEdPa3VY/L8AuaZeC72gcLftOv6O1bMy2EeRxHMeRdqx2d+YF4G1T86oxMOCro4kZtEvigNQvKgVhPXQ3ab9ZJaXazlYi2aVCaFN5E54ktcsjVqU91qNxJcczjSBJLPhdnoH3IBJD3bl7dSa8oLnWsx3oVuF0STfPQDpSi9CxqKYVXpBdC41R6Emt95h0SkvWdOSAc0+PEyQ8BxOVYFgJiScRY8B+SF5KI5ZevDOcNe6bSuXbaFrNM7Q+GgMSqPuvi16t+c5KYWPytUU7APwA6B68I9YIz9NWpWhdat19SMciUhKrUk0LLbsrVmQ/EwF7X9tg1Fw97/xJ5CZJngZEPYetyY9KK701zItz2+W13DYXe79tObbNx9DNorC+VyDRK+cEbw7bRMfoDcN3SFkr38Y1wBZhDgmP8IfRTQga3sUSri6ebQGW4ZQJdKspmK9nJ6eeY1Ysrt/HBXcCyUOva6JTld5Z8UobHbXp6Qq86nkvapgZT/1gF0Ha0QFp4lSPcqsW88LceAuperpxkfAmuC4MI1fpqfqsa3LUekybNCtRVzyF3BNPv8H2sefJSHj94JkBeHZA6vHrqdQGMZ9gP4/Cbwtrx6ae8qyPx4zoGNRgvIAfbN1Ty3m2vrppspsfbxFU2rMbSy1f3/PGqPXs977BY4AGDVlkmXC1qVSVqF2Evb5f7UJqv38ROS3pBp+qc2V2BrHr9tpGx6qdf6eQn328hMQU2jKvQm7PqXkQGn5qpXa3Co+htIyamoJ4Y4DQOdaqWL121jzU9MGOLS96hneqmGeiYNfZeeTe/IOG12qzZSXspnAToE1y6JyaslHYYsxhQUFBQUFBQcEWRlsom2eK5DCE8AsAfhy1XngPgB8BcA6A96IWb90N4PVVVR3rzokOKZ7YfRi+QbgnKufnWtG6qnq7pFF6nJoXb5Gwns9A84g4K8HUY8Ws16l66HXt9Bfhn7PKunUdb6e7SSvV0DQ7zL1JJEkO780hV5Nq3c+LV/1OG9uRdbbgdzG+oSchgNyzeaiK00owPe90NSS3AcYhz1YiIRhC8nRlnSg92Y98d9yvDOtQ5R3/dQDtY8DLv59phcWw5KeSAZV0EB6NWsmXShz2NJO76uqLkZ/72uX1O4emdBCo2806MXjqZ6JffMa2yAIW/JZ+ark2zCLXOvQzdfGw0niLCp2fPDMWzwmoK2IDv2faqZe3JqiEy3phax90aYRY5p3I28RzZPJMCRSedHk58Ob8XUjf450drfFzbX3V3MLWxXPoaXN0AXyNU9tzwko8F5AfUanf4r2r/3exMsyjy1xmnXEKfCnhM+GElBDCJICfA3BJVVULIYT3A/gBAK8C8Laqqt4bQvhDAD8G4A/65IZ6odCJ2FOH8XN2OPeWkBZfO6AAX9xucQhJvdXlmegxVMoMWTsvtZ3ju6yjt2iq3ZvHnFoswfcgtgtjF4OgDKZ65dl3dyIPGOwxnVQrLiFXV3ghRtRm1Ia0aKu3p3Ji3dgf1taoLV8Ng7QWw2YROWPW5cmuwdD5/crgHjD3gNy0Yl7ytd6pS+i2kRok1MhuAJ/qyEO9p+1CqvlbxnU3cuZQN4C6EVTPXsU4mhsYoGmLpehiKO0mwdtsKa1C7tl0nmpPYdvXY0R1HNmTn7xF2dtI6f9t7bcceMyF5mfnM50DPExLOgvOx3ud517/eraPWifWg2WqOYJ3oownFPCiBNj+1c2b0rudT0ckja55rCPnBHuajC3fmm0MyvxzrGheVP+qWYx3drZlAEfQbtql7ejRXr8zzW2bD2qKsUFokxyu1tR3DXHKSc5/CMBICGEItbX6IwBeDuAD8fm7AHzfSa5DQUFBQUFBQcHmAG0Ovb9NgpMmOayq6lAI4bcBfBX1tuCvUKuRj1RVxS3FQTRFHS0YRi3VUM8wT8LHbA8498ade10OCONoqhyZ3kowCHUm0d2N9RJbRO7t6tVXd5re7oc7ukHVE7a9RpGkBCrV884WZb26jrlj/gckXdcZmarCtVIkbQ/dSQ8i6fTQFQNM1eLWUH8Eecy/td6Jqic3oXHXFDZAL6/XIBlle44SnjrFM8U47Dxrk2QAuROXF6RY4cUjU1hJIOGNuTHk9LiEnCbU6/OAuaftotIQT/IDc8+bOj2vTE3XFdjYk4jYdvDe88YR6Wk3cqmbmjGohmM1JhIETSWUlrrUgkrPXvlW+qNSPNZdacPSlCetVXMEzyyF8x81N9ovcy2/gfpbrCOKN3d57eEFkSe8aBGs4z6k71fJnY1V60G/q83UBPDHqfVEB/x50auHOs605e9pMLS97Xqh2pyd5plndrAJ0CY5PNniumXgZKqVzwTwGgAXADgC4H8AeOUy3n8TgDfV/z0babG0Xq8LyCdW9exkeo3O3mUbokFYvWDAXpBfpu+Cut13LaL9xOfEcpiUIeTMoWeTtwifKQTqgWWZQs1D8/LaQlWb+r+nalCsdLFSpkv72QaV1kWDEwvrcwXys1zbyuqPnKZZrsU+5ENzCSnYs1XR74c/8Xnerm2MdZta2WMKbd3Ui97Lf4eTrkuF7TGu3kbGquomkdv1eQznmHNPmXGr2/FMV7pUgYB/Jrnt6+vi9TbkfdWm8m6DhinSoOSWgV+U+nr5L0+vldO07Rfmp96uXlgyjst9kt6ePe/1OTGK5jnLhGVQvL5Ukxm+q/bVzLdNMADUHsxd6nyWr+GybPt32ebpfKa06s2/dq4fh78hsmX0ozcb4LrfGtRl0+mFsrLhuMaRaEX7zJo+6Ca2y65wE6mY27yVN1EQ7JPJp14H4MGqqv6hqqpFAB9ELeLYHtXMQO2d4HIGVVXdWFXVlVVVXVlrpAsKtjYKTRc83VBouqBgBTgFSdhv//oghHB6COHOEMLfhRC+GEL4tZNRxZPprfxVAN8WQjgDNWv/nQDuAnALgBtQeyy/AcBfLC9b6+wwivwzPLXnPJoqY8A/l5OSh4uRJDUK67ji7bRpJLwXTScL3muTXO5EtwTSk1DY+G8ediH3BPM87lS9Y8/K9AzZF5BLcg8glwq1OZgAPgnq7p7qvkNybxD1cls7kjZsnL0Rp46e1HAHUpssV7XvQVVlWoa3i7cSD36jpu+K++ZB21D7C2gGle7n8NQF+y1e0HkvDqGaInh7SHuv65xoNRPpZ1zvOWxYibM69Ni2aZP6Wa0A67GIXMKlRv5dzndse8+L3jNv0TIIzddK+FcCleTMd9xTcH7i3Hkf/O+wUM2NddhQByzIPcKLaWnTqUpY1w87V2gw9C5tkmeWMIj2SZ/1c7agZE21YDpXAP3pU+NCAvW6yLWR+atZgJVQH0Y+Zrri0Y4iPy7S0yqp45Wae1lHR1V9D6rdW0esTnJ4FMDLq6r6eghhGMBnQgh/WVXV59awhifV5vCOEMIHUOtQlwB8HsCNAP4ngPeGEP5DvPf2wXPVidgbIF1EoN6K9lxMtZNTz2EbHsSDDgoOOE9t7OXh2drZ0160nt53eUyh7VYNCaK2Gx6TZdVmHGRt6iZPnG8nnjYbQqDb9gxIE4t+gzIvbVCG1GOgbVsuoMmIsm7eomEX0NUEV/X6VO3Cumxm+MyzEWpjMK13oMf0ebTaFV5C6biLibSLhULVwfbb2+ySBjn9gvWZQ/7tWhf26bDUxcuXtKce0jZ4r9cGypx6Xvd2XOiGjlDaY3qlVc9kwrMh9pjfQdRyy4HXBpYB9Whf20QZRcA3R/Dy6MfYet7SbSdw6W8N4t61MWVeXd+pYbLUw7aLebLP2u4pM0ZMmns2agPQXBtY90nnGceHMvt6yAOfsR5eBAXbj0p3nlmAB8/28bBzzwp0NgHabA4HYA6rqqoAfD3+Oxz/1jwIzkmNc1hV1VsBvNXc/gpS/JCCgoKCgoKCgmcO2iSHNc4JIdwl/99YVdWNmiCEsA21g+8uAL9fVdUda13FLXZCygTSbkElSVbNpkdEdcXtskGPgaa0xe74+h3T5ons7U5fJS9WMqGONLoztbt59YymtGBOylIVGdCUmHmSToVVr3P3Podc4uNJIYDcG1LL6TJ4129mHpfFq6p4vdiOnhSWeXDXq9/MstQz1Upq1HtQpcxsGw3Ou1J4jkGAH9TbSl4U3lniXlld6mfCM3tQSSPf9RwAKEU7IOk5LlSlb71ZPc9HvjeOnKbbpKs7TDrSyhhySeMuNGMear1ZT+bB8q1zzU74nuUWGtvRQsc94UlXVRpupSCexGjJyadNympV+auVslgaHYJPL12wGpiuuIV6lJyWSXR51qozhEf71ozE669JNGMjAk0nShs1YxS55KtLAj7s/PbiKE4hmUPpfOF5X/MZ89EymB/jlqpDkWceYI/I03lCy7TSba+PVDOk6ybQjOSga7ulVxsTFthUQQS7mcNHqqp6adfrVVUdB3B5CGE7gA+FEC6tquoLa1nFLcIcMpSNqp66qq5ibrVPsAG0u9Rcbfm2oU3FaRlM9RzjM/W8tqqUcaQFiYNCJxFOuNNSv2GTru0UB6tiUIaC9zTgMgW+h8zVwmMKd5pn3oLqla9MYVf4gyWTRhcSDdLMe/YkDw/aZlyUtS1JNxdh5RjUXrHL67WNSSeUiSLNdy0SGuzcekYzHw87nHy98CNql+p9v+3nfcgZcc82EMhDR1nbUs1/Dv64Zd2sjSuQt72eE63qR9LGNfH6gORnVb3T6A4KrmURdv6bR745VQv3KbmnjDth7SFXYnMYkBghb2Nt21pNWLwwSrZOy7EtBprChC54QdkVnJM5F+l6YfsSyCNrTMgzz6N5kAgVs85vFVaQ8dmL9A2arzVJYVvuljyelPytbesBDH5gAGGZySH4wg/Wleu70oLt333wfQZs+Z5pVz/b6HXENvgszDKrWFXVkRDCLagjwawpc7iJouoUFBQUFBQUFDzNsYog2CGE50SJIUIIIwC+C4OpL5aFLSI55E70GiQVaVegTi+un2doPS2/uUPxnBe8Y/YG9YCyOySNt0iolx1VAvpNtu6e80ubmpj52nhRbWk9cTxQ79K6AgD3aw9+g5XUeZ7MnvrXk44xL5UQeCoS1ncP8v7wpENd8NSDKxmXp6BdpaygVFK9iS3UkUhVptyJa7tRNWaN8jWu56DbV6uKVDWQdZiwZXY5E9l+vgpJWsHxOQNf+mvVyaR7PYJPYx96EiN7tKBKPwdpG407yXG0KO9Om/Renv1MWKxTlEpZVMXGPlLHm0GPLFsuKrS3j0aLsI5GSgt63KiNJasOiVayNYz8WNJD8L1XLUaRq4KJSXTHpdX5zh6pp6ZNNg+d47wxMogmwCtT50n1rrbaJ6u9svVgW3I9WEBam2wdvfnai7voOfl4ZXPuHnLKsnWGpAVy1XSXGdcGos0hZTCcA+Bd0e7wFADvr6rqo2tUsx62CHO4DbWqZho5YXh2em1qNiu+V2LxIu6Pm+u0POtiClVdzd92YdL66EJqB1KbN6tVl3URfpsHohdsuG0inESaMNTWwy7CQFJh2YkcyBfetnAPXYGFrWr4MHKm05u0NQSFDR2i53Fre6hNIjFoENgunEA+QXr0qwuoXfx0kbD32kwlbGBh75nCLj7eSTLaL11e0LqAaLsDfrgay2ABzfFpbR/3I/fs1z61C8gO5Myk1snzpGQeLHNS6qQ02EUb3li1myvv1BANo2TH9CLyQNKe3aCGZtHvtNEalhOAmxhCbsKic26XOYId05636biTh6rhvQ2qbSdlunWe5pxmba7bmGY7f2i5VoXreaV7NrNdm0W1tVMzGU+YYD2H55DPs7reeSGTCFUhe+sF0B4InliUZ96pN0Dd3l74IxsdoE0IYk2pvNOP1ly4tnK02Rye6P9qVVX3AXjhGtcowxZhDgsKCgoKCgoKngYYhi85XPOANCvHFmEOKWXxdvddRqkK9Q7zRNVdxs4Xx+sM2s/v9IL47kB+9qWqV+yudKeTv3q6qYGzBvflszbVrubRVnfWxwb39gLRMv8p5BKaEaTdLFUSn0Le5qqq7/LMJbxg0Qq7mxxxylSpSb/3Ad+wXvPTslaDrl23SuI8JwS+3xUX0ZPYdAWMVskA6YA0sojcIFylkTZfj/YWMJgz2JSksXnocWLqGETpgJX+HUKS9tk4dEDqZ1VhK9iWrBPHtUpgtf8G+T6NNGAl6h49dknzPM9krQ/V8ZpG25Rt0mU60g/eeLHe4LZeth5Ke7bPVaPA+qoznTVpUDpjvkqfqmr/FHx4DlULTt2GpC5W5a1H8KlTpZVgqmSNfc3vVEcMtvE0cun9BBKN0oFG62rz9+baefjxdm2/6Xiy3zIj9/SZpVHtDyu51EgdXUeAesHhlS6sg9AmwLb4593fJNhErdUFTwXXT+1hF8Z55KpYFZ+PoQklWvWYbfNqOwR/QaCHFxmmHWgPG+Gd4TzjlKleXwrLpGgA0y41l8dgt4UE0XvKpHrhcqbjVQev5yHNhUttjKynG5Da4ep4Zb949lkLyBlcVSPqxKJ1BrrVEF7ohUHtFdvgvW+97ZVWvUCyfK7ek7YNNayMF0DahnpQL1KOD+8kGeIQmmpioEl7uriRfnVjomE+gERTyvTppsiGyFE6nopX0so9SN9HhuWw/Gaf34lcXT2DZjBtIPWPjkNVi02jCY9uvJBMah7QNWbtnNCmkmSdyJQswN9EehvF5cKzo/W+wdKg5y28hME28Uq/dqwMoendDvNc1a4ewwE0z6L2PJN1DveYPeZv7UHV3lXTWTtLTa+BqDUNkOj9TqRvZhiw+yQfjmed4+yhAgtSD2W2bNt7G+euzdCs1NnawGtZvHcR0lzTFTJNn3ne/l2b7w1Cm+RwALXyemGLMIcFBQUFBQUFBU8DtNkcHl/virRjCzOHXvwyhd1pjDpp51t+E+ogANSShBknHfP3gmxSRaW7detpqFI1T2JmpZQ8MQfwHQWIfmeS2t2s5uFJZq0qW6Ux1ghb8/B2bKzbNcjPMFZJgqcOYjB4u9O04E7fGrsrvHe7jhBTw3cNirvW8FSxNpCufo91YAHyNhxFkoYNYtStjgqeBNOTQNlguPPwIwC0mVYoaJZwEE11FdBUqREjAC4091QCYulXpWhqIG8D7U8hSWO6aKMrgoL+39UeniRFJd+ewwbQLhWxEiMvdpymW43Xsmp4PGcLzhVWha7zjhfnsMuRQNvIzgcjzj37m+/a9tTxZKXEnhOkFxFAzVmYjiZKe5C+mTS9JOnYR6+M149L/qQfDfztxSHVeZW07B3F6h0GQXTF3ewHK7FTyaeX37Xxyvl9H9K3qnq9Kw8bfL1fNJENQpu3cpEcLheeukJDo3hesYQ3eViiUi/ALjWeEp6ntu4q3/N2tf9PSB5d4vlxND3AWL7nWcv87aSjnmBcUPcit0nRNreeY/qMeTwg97x37eJzG/JJRO2NulQHCqsO8zYDyhB7AbWtp60XGHpW7tnwEMsBA7t7KjgNN+L1gwYPBpoLr6ei1Py7VJVsw2nJ1zIdXSEldsJnMiwtt0UTsCYH9PJUeiCNe+8vIZlv0JxDVWu23ktIizXb7xCagX+BeoGy39rl3axq8OvidS9Sm7COHBPDSN6yXNBV7egFlYd5NgrfVMDa/Gl4EDWLsO3pncLTD6Rpj8485pXfrGpdnQvYhioIUNtXoMkc8V3vBBEPg4Qjm0S+iZ6W5/qdfH6h+f9mqS83BDsBnBF/K52znaj2ZVmjyOdwjb6gtqJ23CszS3pgO6rqVvuc9WC9R7A8D/Y2hp+wXv8jqNvJYo88B5oCFN2I8Js5nslgarpNxO60SQ43UbSdTdRaBQUFBQUFBQVPc2yDz31tIo5sE1WlC10x4Q6j+zNUFdd2fBOQdvUaBNrunofhO0OwPp4E09sKWIkW39uP3KvQi8+mbaHSrjbVknqd2veApvdxWzsfQO4Jqjs27n6vQB4rcRLpm62Eb9L81mdA0wvNU6WzHlYiqSoapld1tZXGjCKP6adqEA2Svt+kWwkW0U6PKp0k9LzwQXbwnmcg0O5so+cXE8MYzClC1dF2DGjw6X5SfO0nWzdC+5mqZpVuTMXflN4cjNer0VTf8X2q6zypBvPfi9xrW4Ot2/44LOkowbhQ6u7FYmPbqxTSqvQ8b/AuSXGbp7g+b8NK1HGnom7/O5GPPdV2WOc0nTs9MwCVlFlHCe1Lq+r1oG3ijV87Pg6hKaGy76mpAuumBxMwDSVaPKLuEJr9yrpBnmtZS0i0zTVKJW1ef3HcqCmUjZO5C3nbA6kdNCKBdbjk941JOt5T2mb+2kf8ds6x/ejNzvkKnaPY9p7pzSYSyxXJ4cmAqmKAZpgLnTDs5KT2U1b9ogSkjI1HsJbxYT3GpCwyeHulHjohWQLXaPV7zDNVGarnoY1I76mOOcCXkCYItX3ht5wXr3PIQ3Tsk/+pBmJeFyJvj3ukDJ0oWBcvzI61H51EbrezhDzIr04YNohvmw1p1yRjoSOV7bEHzfAlbWUNit1IqqOufJZbxn74KnnLyKj3oFWPevSvah3LvKhKlpiW3+pZaxeRISmXix/Vr6r2JK1o3bnwfgT52cCxnDACVDbo/CKAX46/b43XWSnXC7e0zzzz7Iw9u2IdF55dXpfqeBD7TI/RWWr5zT5ie3hBvlcSnukbqBlDVasTXn6eOYZ6BneZ1igtwaTVMW5pWpm4Uecd1smLPMG5XseAzl3WllTblfOZCiHU1AGomb+29WUSaSzx3oiUz7l5HGnD441f0ivNLqblmW62LT3pJs9ukHRT6NEZ7+1HfliBCje6OKNBTo4Bchtp3QzYsjcQbTaHhTksKCgoKCgoKHjm4fgwsORIDpe8veYGYYswhwFpB9AlflbPMW9nP4gaUFVl1olC451ZKeQU0i5TpX/cZanxsc1Dvfc8Y3GVdADN71NDZNbdnps5htwL+iCSNI8q8knkUhvPs5K4A0lqw7IOoWncT7Q5DR1ALq1Q9a/uPrlj7pJwqaEz7780XjXQrY11qWp5b/um0gYvPt1KMY28b/Qc3i6v/K7dtKdW1sDRVjLinXEN5Ibji8jVySoxZzvZ4Ogsg89sWROSDyUqLFPHgEqXWRa/ibQK1F7wAM6JdXxEHBZGopRlYQb5kZAzaB7HR3AcW0P9WaRxdEDS2Flepc2sN8u8GHlUA5W0qfNFm/f4JJpSftYNJp2qq9WpzzrkrUYa7kn8PC/pLk3PfuSSJ0813bWELUiZpA0NcK7SXY4H5neGpLGBycfR7ZznmW7YNrkDqf9Zx08hH+8aN5TpVeLJ9lCTCTs/av/aIym9eJILAF4bf39I6midzbQc71AKL5qAmmOgI62FF02A0LnL0pjmu5IjIU8OFocDjp4WsvtHFytslmNStghzSLR5OfIzOHjU0++Qk67LO1YXQatCmpM8LBGql64ypp56pc17SoM/a4gYWzePQV5CpkprhHCx6nidzNSGkNht0k3Kc050U0jtOyXp+A4ZSlVJWPWSnmmsEziZ1Ol4XUQe0FZV6l5QVU74bacf2PQePE+7NrvM5YAbnrYAv9ZjVssZxMvSY1yV6bSLVVuIFtvWqjK15c8gDxLt2RQCaQHlIjzj5Od5YGtIG6veH0FGI49Gej97PJEvTyHYPy55c+4A8iDYWi9+u6qePZs0thfz8ALRc+woI9TlBa3vEF4IIy80is45dlM6j/ZN70owjHyxn0VOk220AbSfoUt49fTGKtuHTKEGwldmaDr+tuY03qZp1vl9FVK7d9Xb29CRzncgZxhJP7uRNvFTkp75cL7cg7Rh5/UeyYffovaOdrPneQ57kQvUs3qQcENd9rFAmv+72tEzrVABg2eysflw4pRtOD50Snb/+FAF31Rl/bHFmMOCgoKCgoKCgq2LxW1DOLYtZ7+ObSvM4TIRkGJonRXvqeGuJw1RdSvTtAX0nEDakbBjxuQ5pSFdom9P1asSJd3JWK9clQjZALEaRHha0llVGp9B8lBpIduG9bgaaZc15ZRPWEcWoKnytgGhZ+S5HoPGdF6QUtaD3zAC4HF5TliDYm0ru0McQn6EmaJfEHXC7pZVRU+sRHJYwXek4v/WK149zhfM1YMe+6UqffvdnrRFYU0VAF9tCdT9YdtCj93S/qKkQceFbQfPE9Q7F1mDVavTFoDnTaVi2G3nxOv2YeBLw6l4AHW7W9X4S5Hay3o8j0j5KsWhJoGSID0ekPBUx+rooMeIAXX72blF1bCUqs7K1cbL1N8aV9J6iq8kziHh0dKs1M964nrQ+noSVAt1SmMfqeqSdL+ENBeS3nResfPBATQjWABN1SbLnJZ3vMXdxmAE0pyp0kLem4pXOo7sk3Ss74VoalaA+jtJmzTPmJJ8bRDsCTSPdgXqb+46PpUgfZ6FbomzdSTUsrRPPdMlwjtmU8eCpaXNKTEkFjGMozg1u38MFWrHro3HFmEOGcpmDGmA6sRqPVSHzG/CerTyfyVavqdhPDxGwqqol5APQE2vKt82teR+pEGgi5z13NVv4sC+GLkqwpvoOTmpTdqCPLMTLIMIz8k9TlhLSEycx2TxGzzGRgMuW5seVf0Qauvn9aPHsHl2e54XItBUfdtyIfl7ao3VqODUpEEXfDKF3qRr7THVU504jJ7dXS+cyhLawyhdhNQmql6ytqKe7Zi27yB2mEvIbVt3oXnmsV6BtJhMSb2t6nYGPc/7odhu0/HRdyNFtXlWvH49JcdT8fqgMhks8x40F1NI2eqhzbFyUPLQME12k8k8d0o6MoIL8GmNsP29gHwDCHQHzu5aQFdjT+st1kCaPyzzNIo8cLWOC7X1a5s7Z+GHI7OmMMwbaNp8crx5czfXHLUttXbgc5KfJ0Sw9dbdCsMuTSHRHMcFafs69NrvhbGczwM5HexB+n5dQy4299hWag6l9MY2V1Vv27nbt8lvq6IGmuei242itwFUdJmHEf0821fjgX9ysIQhHHOZwxMbUBsfW4Q5LCgoKCgoKCjY+miTHB59pjCHIYTtAP4YwKWo9Wg/CuBLAN6HdGDp91dV9cRgOWpQYu5APEncMHIvWt01cJehai7rqKHSEe4idYdvjZpVvdTv/NW28x/bdk9Mr8FSKfKYjlfdOVE6xp3sA5IHpX57kLx4uUudQtrdWwP5XcglDgtSDzj3KKpR6QXrwfpq/ETiTuQqZFWvs75ee6tk0J6tPCTlWgniHvjG+550RYO/Au3nbQ8CVeUrTXd5YluaVpMJlcrojr4fPM/yBeReiKNoBi8Gmipqz+mL7aSxxyiNUU9y9q+nyrKOF3uQjqbT2HgjzWRXSha/FK8fjdftSOENH2VWE8CCjZ13WOppJUxqFqHfzjFwn6RrwwH4WgyiS6XmnWlO7JB0Xc4fHlYiOTwdSQLsvW+PIdW6dUkx+Y1z8DUKFqr+tY4281LuIbnyufUS1rlLvdEJdYri+mQdmryxBSSa4vyhzofEdH058zLgCZUYEjae5vWo430CaX68UJ5r2wBNUxCdQ6yUsJ92xM6dnvR0Eb5nNMvpOiChywxoGmk+t9qHISRtQ5d0cX1xAqfguMN+nXimMIcAfg/Ax6uquiGEcCpq18RfBfDJqqp+I4TwFgBvAfBvurM5BfnZjsqoqGcv73XZL9g8NADnqHOPmEXzNAig2x7mIiRvTIrvh9FkcoHmBOMFbbUHo08hLToqvudAszY3U8hF6kNIA14ZPDJqY+Z/DaOhHpgsS+0Xp01Zqg4iY6cBeDlRcIDbvgaadlyWuVYVo8Lah+k5qXaiUIZMGcedJt0B5MzcauxbJpB/6wJyJmsezdAuQHPxsV56kDy0v9gW/EadnHlvOl6V/nVBtxO7TtxKo8zLO9WCdK4hXOxC5DFFHOtvAM6MtPmELFwh0vwr4//PlSs/59vi9SHUW1cA+Nl4fRBI7cVvuBmJXqg6pqpeTTHUPIPPdZPKuusZzEDNELbZEgLdDKDSHuvBeqtddpdtqao4V4OnkI8rLbfNpEODLivdW0/c29A0JQGaqnmmV/tnGzJlFjkTMi75dM3nrOMuNBk65sV3u8I4cS6/Qp4z/dXyO9JZiPTzLKRufSheRwAsMVSTtsf1pvyDUi5pW0147IZEzxLXsGht6ln1die8M6lH0G0qYaFpdQNqVdJqUmFZGs/GdOOxhOFNr1bOfanXCCGEZwN4CYC3A0BVVceqqjoC4DUA3hWTvQvA952sOhQUFBQUFBQUbCbUauXTnL+cYdwonExW+gIA/wDgT0II/xjA3QB+HsBEVVWPxDSPItcpAgBCCG8C8Kb6v2cj31lqXCMa3nfFs1N4Z2TyHqujcclUVO5JSwi76zyEfJc166TrUuHorlYD1aoKEmiK7BdM+knkavNrUxFsyuMAKj4fMVcgnQvqieeZ/zTydhtCbiRN9Zw6xlgHGcVe5O02KlcbH3II+XF/XbvWw/CN160aQw3liS7JTkJO00At4bZeuipxUMmh9cDX4WvVNSoJVaN9tqG3m7aSRk9K6H2rdajR/LUvKTlSz2Q1fKdEhxIvbXs1bwCAQ8AT1ttefn5LvL44Xo8AeIH5hN9GCudGId5ZAO5i+VRz/yDS3GJVhkCSvKtJBiWGnpMI20lj6WkEAMLS4why6ZSC9zSSgdcP/Fg+81SAg3krN2l6HInuPC/3NqmiSummJC2fa9uzrjZYuBcPVLU/+v12Huh3UII9CGAG+XGK+q6NI6n1mzRpgB5NhymgIg3FftZ4yI/F61S8noOkiCGdf3pcBLQ067kM+fqp87yNJKFriTowsm78djVRsu2mktRB4j7yHbS8p44rVmq7iHx989ZnHbMbi8VWh5TjG1AbHyeTOSRH8K+rqrojhPB7qFXIPVRVVYUQ3HDgVVXdCOBGAAjhXEmjKjKgHvyDMoWEp0KZ73imk421e1OoKhhoLq7MQz3zIOksOEBm0DwFgXXk9/PdKTTVHkBzkSdzRu/iRWB3rMf2eOs0AMOxnn/rVOlIXASXqCr/a+T9MYJ8EO5HHthUww/ZEBzq1aaTmVU36mJjbYZUTakhK1SdojiMPBi3Z5qgC+nyVHFNmp6schWsnXxZL8D3zvds8nTBtR6Vmh+h38OFTqcFO+kvINGv9SQcR75BWUAeWHgXmoF/gfrbqWIm86LqP072ulGLdPPCeOspJD3E2fEah+nITz2BhXvPrP85/2h9fctpOX90BMBoHCvzNGOYR2L21EQBqOmeY4pp9iL1hzLtupgpFpA8VtVOVJlppmuz/dLNEPtjD5obKOZlT1LxbGYH88DP52lLr1PxqieTeAGKrS0y0O1las9AVsZRmTT7HZ4KXVXYHnNo5wE1+9DwU8yD858y4bpRB+o5xjC61c3ozdMj4ylbALgBqXuZ1WMA7oq/6YF/PoCjrCc3LXcgD5vGOu6VOqmJyVT8zW/X+dIGgh+Cz4zZdU7Nizw/AcJjJq33P9C0WbUbHM82cTVRJdYWixhqCWWzeZjDk6ZWRm3ocLCqKhrffAA15c+EEM4BgHjdPGfaFBQUFBQUFBScRJzANhzHkPO3rf/L64STJjmsqurREMJDIYQXVFX1JQDfCeD++PcGAL8Rr3+xvJytM0dbTC3PI9ju9jxVjzoleCoZu4tUUbyN1cV8gOaZpdZrT2MATsXf0/E6gaZXHd+zu1/9Phug9aXIpD2jw3ns6f+MZKBPNQVxs/7DfF8nL+jOj+2mzhx8bs84VY/KRbna80l1x8+Kq2SJdMH2XpR06ghg1SVdcQvVE57bdpVstMX9GgSnxvcPYDAvOqVBK1FRSZzumD1TjCVzj9Iu76hHT4Wu9GihQZet4TvQlBJaVfQYmsGetf5A6ntKQ65I71JqchFS98frOS95EABwPh7CndtfAgC46tx6v3rXzLfixFPfVCekyg6QUzgpzVPJy1S8quaC30qpn8bNU+ktM7b74cPOvUmkuYZ9dQVSBADmq4GLreRQx4wnfdN+tFJmT9W8HKgUivlbT1adjy2t7pJ7XdEfpk2eLAvI1wrAl/YfRh7oWvNSswyWZVWx40iSNPYD8xqVewvybCr+pnPhdfV5D5qMDvlLAP4u/v6BeJ1G8sAnWZyOWvoNAA+xLSeR5ipO5OwXHc9WMwM0HYA4Z7JdWcnD8oz06DmKqlmLpYU20ypvjrWS5zYewOYxmPnPeqA9zuHJlNctDyfbfedfA3hP9FT+CoAfQS2tfH8I4cdQj/rvX16WVlQ9ilzkfBWaB4sT1g6IUG+rnZLG8xy2gZj7iaq9hZ+2GmSQNJQAf+tEr4evA/UCaQfqhfKbkxRtMafRm4jOjAvZRUiqNzKCTyF5eXKx/JwUebpkB0QvUQ48XvciP5R+BE0PQqC5MHASGZb3bLtNSH7ewqVq6jaMtvwGmipna/sC9D/DdblYRLvQ/Arki5SC30qmxFPF9VN/a0gYoPmtyrhNmnsevWt7WE/NUeTqfR1HGvR9j/wG/FMfOO4OoqeK/XS89TKkRTLS9vyTsZ/PAJ5/Wb0I3/nw1QCAZ5/9BL5G5pC0fQ2A/8nvYvlql2UZACDRNhmMWbR75vJ7AF/Fxt9zyPvyceSnH2noFY4tXVC98ExWNa6BvFdD0zwvfFjysZETWFetu1eublS65toJeeaZonjjwYOqSIGmzTXf1Q3rlHl/TupJetCThNgOU3KPNHJtvD4APDfSNEMwMcl29JjCkUvryG8Lj54JMAgcs90G52CoM+QmTxhT+2Xr5f6kPFdbPtKy3eTQ1hRI4/5iNANsE9asx9s8KhNuTR5GnN8aBNsbd7pZ2hygQ4rFsWeC5BAAqqq6F81IY8R3nsxyCwoKCgoKCgo2I9pD2TxzJIcnAVbCt4BcMrEHTS8roN5JUDLBdzXe2Ky5p+pOYtb5rUF8LXYgl+4MoWmcDTR3mNa7UQOBEnPIDb7vQZJ0MBbbrfH6xpQdtXLT8iqbaAq9Y8RO/cl693vsv42l9/5A3gWA0XFgni8z8OoVSLs97lJnkDulM5MRqTcr5MXeWkTuTauSJXtW8CJyaYEG0p426RWeF6XCegsP5tnZxAnUtKtOHOzne5CrTrzj/bpUdh661IwLSDt3DcjcpdKzKp9dyAODq7E/MSHp1LnGSg44hg8jl/rvRk9MeEEk6otSUd/+6ltiqlpaeDeuxO7YfjvOnYm5TuBrV8ZAiNMx27sAfBPryTbV+rO+fOEqJAmJSjk85zRKpzhm1FSB30zplMZ2ZF7DyB1+NI0XYcBKDlU7ofOKjTU6iKmDBaVQ88hpQ8v1zEIIT6LUJc30TBw8GvfUzl6+/H6VrnJuV0nVA/IbqOmZbXZ1vNJR6RDSvOM4ywzR0e/COr4HkB/1+K+AHVd8FQBw+OFIN69cAh6L9Ph1yZ4WDefHMfaQmthw7SPdPIB8LphHrgbXMWAl1Opgxf6YQxq/GkGC7erNrdbcStXVnvpZnZiG5Hcb1iKW59qgTXJ4tDCHK4VnA3UY+SQzjESQymR4YQ+AmqA81YHnVWfR5Smtp6F0TUg62DhoqBL+CPLD0tV7ku8uIAU/5b3vlmfGPmsISa3MT38MOOV13wAAHPtMnFgujc/uRfJq5r1Psy5Amkz2IPf2vQp56BQNsso8OJkqVN3JicK25TzyAM7D8G2wPI9gwqozPXoDEvN9m/NsuZhDvglRe1dCPU9t3b1h7NXd88rUUDnexG3zVvWgVb8vIrW/9i/L0BNQmI4L6QxylZ6eTGTVnsKQUxNzEMDl9c8jkVhvfPgnAQDXnnsLfgJ/CAB4J34EADCNC9In3xCvZ6OmdQC4N46BpWuQ1PwaAQBonmXLbzqA/Iz0HcgD4nNu2o1Ec17UBLXj9EximL9nq2ltcaelbt5CuhKmkNgW67gE396Pfeh5ZltGWDfF+j1Wnc72HUK+qdMNoo4dq37W6ArsI/a3tgfnEzWd4fMxpLHxuHl2kZTJssRWVYcpf/Os70gWz770UTz5ZF237zv3QwCAbTiOP9//Q3UCbvrPRNJSM+LEwWGgstEqSL/nybfqOuOp8rWtpXIYRx7pYBj9Ta4Uamqi6l/PBMFukDybQ2uzznSbA8dxiut8cvyZEAS7oKCgoKCgoKCgCTqk2L/FAYJghxDODyHcEkK4P4TwxRDCz7eke1kI4YMxzRdDCB8IIVw7aB23mOSQ3p1A0/vWU5F54H1rPKvpdafBbZymt5KLOZNWoepO3eVYI2n12OR3USI5hdzIf4e8y93smORLCU2s25kTKTnPkH0e0JNqU4L4LODqiVrl/eVXPw8AcPimb66f7QfwjZiO740CmJ+K/9Dj7qVI6jNVBXIXzW9V9Rl/q1SK99h+6mlsvbGH0rf2ytyDXOWknuLWi/xidDuBEKPIJYar2ZEq3XiqEVWzzTv3WP5U/G3jSWq+Ho2qMTq/S6UzKllkOitdUscKO/bmkTtzXIjUT/rudPxtJY1A6ieacYi0h2RzJXrNMBrrSxXyu/AGvA2/AAB4Vnz2c/hP+GX8p/oFxot7DHkYQhxEPlV6sfRUSsVxqbRBXSHrzjadQdNRAWh6yquHK2Fpri02HPObjtdh5OOiTUK+XHjH56nJDL/Xjp+LkJuReM40zEehKmr7XZ5Tlvet2rcM9K9etZRuk35HnfpeIflw7lZzExKVBsGO/RmmUjJa42+P19fGWnz9DJw/Xp+b97HZVwEAdo/vwfe/vD5s7P1f+eE64XRI2iHGO6y07lYiqOY6pKErkOZz+ovuRX5uMeH11RzSPKL9Ytc8tse0pOkyKdiB7piUhCcBX42z1dpilWrlJQC/WFXVPSGEUQB3hxD+uqqq+5kghPA9qOOP/DsAv4baW+wKAO8IIfxsVVUf61fIFmEOactyGGlRJ/pNamoX4XlgeWUxX8+b0A4uVXtaW5oDyAPfqpqNg4B5jaFpv2XLVrUbJ7F9cs/WI3pFPyFqAs5zu4FevE16JvPMWQBHj8UdDJnJ85BL6u8AcHbM9zGqWseRdBycYMaQn1GsoRQ8exJrM7oH+QKqgcBtQF9loD31hp081LNOJzq2OcveDz/Q83JBz06lX7WvsWo2hRcKpWvCtOpyIFfT3ifPyJB7AYP3w1frW3hTC2lkr+Sh3vnsE2WagKSGBJoek7G9rol5PIZeN03VhyT37Azfh9fhysgBvg+vizU8jusv+R8AgI98+F/UL56N5KF/frw+eB4S0VsvXF1I9ZtIm0+a9wCfibFBiheQbwaUUWFIEvUmt6YCqtpVD3drK6veyqthErchpxnPJtCqc/ehyUQC/kZjCO0mPoOGk1pCblepG0+WqXOYbevDSJMh1dDTkh/zZ/veKek12kU0HariuDx/OJGVMbE99oUxvOglt9e3xuv8j+FU3B7zOHV73S7HXjmGkcujN/O+GPT90wDOjGrlJ26NGattMMclNy86F/Lb9fxrzuWWQQaafeudLsV2tuvoEnyzGc51loFVjCI392B6zcsefLBxaA9l0x/xhLlH4u/5EMJe1MR5vyT7PwB8X1VVfyf37g0h3AXg/wHwdGEOCwoKCgoKCgq2PtolhwEAzolMHHFjPIkoQwhhCvUZUXeYR881jCEAoKqq+0II1jvUxRZhDpeQdgSeStFKgdQD1ttR2ntXIXkQc+fMeF1At1rD28moNM/b+dhdunqJWqnYGPI4aoekDKrZ9ks6Rk6NCONJ1UBsQzLCj+risy5P7TI0VIsVz3tT3bYH/2ZXauYj8foK1Ofe9OrEb7Hexxq0W2PHAbVH85x5Ng8/oPe0PFdoLD2VCljpq0oCVboLNFXaKmFjPZXGPInGclEhl9J48e40ULqqxmDeZ3rvuDZP2mKPzPK+2ctDYzCSbtVQ33OCIU3zvUnkUtcZ+Opyplk06WeTOo4S8LMBXLsUU9USnVvxMgC1JPFjqNVx10fP+tvxItx+4kWICQAAp75yDsf2RSkEPUAfBJKKWyXTQC1Zsc+07tPmfw+jSJKqg3LfOl+IKjJzUtuB3Dv0AHITGiD3pFYJtjWXWQ6OI5kRdDkjeBJ9lRj2ew/odvDTZ55jBccy2/AsecY+VBW+1Xpo3E06eKgnOdvO8wa/Nl7vBEJ0IOThsAtIgd+OxGt0/tv5kn2Yj/mfi4cBAC/C7fgj/AQA4NhjNc2eeukctsW5uyesvE6q+QSlhOQlLkai0Smptw0KPo88oofCzhOLyMfzEhKN2rOmdXxon+kxlYjv69rIZ20SZV3HN89hbPUJKblDyon63iNVVb00e2gQQngWgD8H8OaqquyA/YbzyiDPetgizCGh4uOu0B36WZYJgPPMhpZhvksmnQ52q5rRYKk60VkxuoZ0sAE+tR4qnh+X36yH2uwxf3oncyaIi/bpSB7GXEiH0Rszz/6OWnf8+EcnsfDq2q7l1FNqATfVcwef2pWqxKJvBnBB/P0gPaVvRfK0pm2RBsWdjle2ywNIg5bj4RByT0VvEdBJxHrSjSOP5K+LoKUHDaWiwbbtpKO2TZ66diXoCvKrahI9q1XLX0CazFUl5DGxnrc/Yt7WzAHIv3+v/D5srpNoBmUHarXcTnNvTvJR22FLB573In9PpVt0in8uMBLVa9siof8SfhsAsAMz+Lnj/09d2207emmOnLIdAHDLDdcCALafegQHtsVx1uMZFtAce4pdSP03Ha8jkk7Vpdbmmd87j6Sa1vlEGXKgXqj5XAPG88rNkBdcWk1erOqNJjuQZ6uxo1X6Wc0pQp5Xapv5hNKejkvve6w3/AE0N95a9jhyRuYKJOZK7fQWzT32x4zkJ8w91/ORSFvnIA3PfxWvF9U3rsTdmIl0882o5+hPyzFW5zy/nqfPwJP48sOxXAoEHkeyYey1Ec0TbkO+lk4gtYNuJOx8NxWv08hpe0He1dBxbZuGBeTrBpDWSKs2BpprpO1n/Sa1Q98cWGxVK1dO6hwhhGHUjOF7qqr6oJPkeSGEm7xXAfyjQcrYYsxhQUFBQUFBQcHWRfsJKf0RQggA3g5gb1VVv9OS7DXxOoJ6V16hliosAHHH3Ad9mcMQwiXqBRPvXVtV1a2DFLC20OCqngrHM5DX46WsmnjQ3ayXzqphvF2ReuGpcblNx7yukrpxt3OHvEvj4F3Id0pTAN4Tf0e18pnD6RERY/7ileipLr52sJYwfuurb8OPx7OSH4rW+H90olZbnHXdITz+t7HtXyB5/df4+0HuSFWlxJ3aS5H6i0cHflyejZv0Q8jjW6kqctQ8U1jvcL6LWK8J+Q3k56badwk13u+6NyjUIcXbTdvvX0KiJRvEHfA9rb0Yb9ZQu8u43KuPxjSzkgR1bKAk7AokKaE6EKkjCu/Z+Jj8X7349dzXCDqOTKGnUjs1umpORWneQzgfx7adGp/VU/CP/t2f4d5/fDkA4GvT9cD42tJz8zjpu0eAPfxnKl7paKKS1PPkN9vXk3hYSbHGaWOfjspzVR2PyTuKHchV2Avym/VR8wje0/iqxFrJDTjux5HHclQMok5W+rXzr+cR65ldtJVFdb7tG60r6fdWJC0Ny51GouUDcg+o+4xlUno+jl5fTsVbQ0gSPjoJRnqewY7e3PxDc+8HAPynsZ/Gk9F84kWonVU+jRdj6dxaXXlg+0Up/96QjYUtUPJ5kdSX/TON/HhAdaQ8ZNLvkgI4Bu6BHwidsGZDfMfi2ni91XmmNNtmZnARVjY/n1y0OaQsDiY5vAbA6wHsCSHcG+/9qvFAvh3ArwP4UQBfjffOB/BOAL86SCGDzADvDyH8KYDfQq2g/C3UlhHfPkgBawN6wY3AX8QGXeAscet9z2V+EBsFO4FrOYeRn4usbv+2+fdLeg5eDZ+jZTIP5kuHJaA3SdH7+CCSRnIqXp8FXHXF38RbtUriEuztqeOIHacc7qXZc3Vtr3LwV6Pa4ohUo1cPDX5KpuROqRsXU37np5DUL5x8PUZQ+8wyU54HuPafqpeZj2Ww1D50XNJbD3QtezWTzqlIi7JVS6uJgqeWs/ZZSr/jHel0s2JtW4F84dVQTMQh5PZFyjhyU/OReN2LJo0C9QLJsph+DkmdpCYYQL1oUNU8VV9GkExruR4NAa8+o9ak3Hj0fwMAfOy07wEAXC322n8Sg2C/dfw3e0wkTo9lPTKU7L4+HK8NbRSZQn77ZfKMi9soUpuw//bId9HDVYO6ax8B/nwC5PZyxAJ8ExpV+QNNOmKZXtiPtTpNoitiAKGhfzzmVedJy+x6+S43esAI8lOXPPtnpXfWU213adLBPuS3LyIRqQgwzjRZjCLN2ZEGn3/ulwAAu/DlXm1vGfsOAMAr8Al8IBqO74k7mhvwAfzU/X9SJ2QVt8vnsGqfn0IzEdC0PbThpKbRtMsGmhsZe27xEBKt2U0f0LTtZ5mWYZxH7im+3Dn3AFKb20gnG4c2yeEgBkpVVX0GtXShC7+F2rDggqqq5gEghDCGWmr42wDc2IiKQYLqXI2a47wddcz1h5GMAwoKCgoKCgoKCgbEiXhCSv63ZueSfC+AN5ExBIDotPJTQPTM64NBJIc8mHEEteTwwaqq1vmMlxNoqkiAppTDqrl2IakzuCPtMkbVnSZ3QCr1466yK7DmuNTJkxIy3yHkO1H7HtCMh2UdUrTb+GwePZVtiGXeGx9NoSdlOfWVtaRoaXEbrkctZfkIXg0AOB8P4fxo7PxuvD6+WksV78clOI1SFloz3IGkjfn2WObnLk7edz1J0YT8tupcdTJiX6kjiPYp25/bX7a9erB5baPx56bi7ztN+jbHJr5L+lGJZJcjST8cRfu5xV6gYiA/y9Rz1lG60fwAX7KuUkI9wxfwHRtUkrLDPNuH1Jeq/qTkgMGB9VgzNT6fileOXRLXS1N+KqA4Yj7hcvR24//Xab8OIKnbzsCTuCnS+Y+glqy85/x/ju+J4b7eeV4tTdz2zcdx+PZvTp/KMuf5PZQcEiqxJ62cIeloPqFHSFrHCe2rfXKP+bK9NQKFjdowi3w+WUIucfGkwVrWaoz2aSoB+Or0NmiMRs/DXqWFXRJNr2wbT7NN0ujN7YTGH2VepM2D8mzMpNO4h9PxN8Xdk8nEh76jz0HPiWTH82tNIOfj1+F9+DLqgwmORRr/Ep6P1+G9AIDToqnE7+On8e2X1OeKf/bKlzWrCKlGj5buQT7XzCDJf/jt+5FobVzuAfX4Zh6k7WuRpH6c+9WZj/mzcmPy25Ooc87TM+ZVlWylxZ6auauP1xdLGG5RK68Za1VVVZXpqKuqOh5CGEh3PQhz+LcA/gLAP0EdLOIPQwj/vKqqf7Gsqq4KDPsxizyAqRIQrxpSQycDqyZWmymmvzZeb5b89D0bXoJqsb3wJyCr7gTyCY6ecjOSL8sZRhpcOkkOmXuyCNPb8pvio+3o2bAw5MGzpx7tqSJ+GO8GANyFK/GyJ28FANx+Rh3i435cAgD4ffws/sVcHTC4Ecyec11PSD2H3Jt2L4B/Fn+/L145OS2i6WHL77OeitNIkxIXULXLI10oY8421PAKZAqtzdaS5D8Vr54NTFf0/tVC6dGb3CSMC4Cm/Z2d4JUG9azotgDwbYyqpTM98cSGadKwNXp+77RJB6T+V896jkEuMHQX/lTKbyHaeg0j2WWJhvXeeLjya1GfP7sr0uLDOBc/MvtnAIB/F7OdqEbxpWhA+/xT/h4A8NnbX5ZODKJ52Ce17soxAs2TdZSmyCio2pHtxvE85TzTPramDMoAWqZPbZm9Mll/r59nsbINjgXn6X6hbCx046/ewuqJDaTDEADf5Mfa2KqHuNK79Rb3oKGCLNN9BXJzojnk5z0rc2/nmwVgexyLVCVPpCKuRc3gPYxzAQCfwCvwtgd/BQDwOxf8NID6FKB78UIAybb2StyNd371x+tMroyb+btOS7aMPf5I1bU2BNJuAJ+X7wLq9rDe8xzDaq7Db05mHM0II+wbMoIaGWJWfhN2/TyM/CxzpRXb314g/41Hm1p5aUBv5QFwfwjhh6uqerfeDCH8K/S3vwMwGHP4Y1VVMSDjIwBeE0J4/fLqWVBQUFBQUFBQ0BbKZmntJIc/A+CDIYQfBXB3vHclam77tYNk0Jc5FMZQ7/3pMiq5BjgdSTSv3m+AH/dIvae4K7oYaceuwTOZhrsV7qgWkJ8XB6TdIcvSPK30RsXo6mVtd7j8Jj1uT9Wv/IYpqbf1utVzaE3229ELa3XWrro+j39hEhOXHY4l1Lush3Eubj+j9jN6VVS3/WD0gP4p/CFeMFYbR987Fbe6n0DaiUYNBu4dA55gBVjHi5Gkg5S08sVR+T5K/6ad7xtFMyYf0DSIZn6qorM7+QXkx8YRujNWFbWnbl6L+Ibe8XmeSQPL8Lyau4yzPccUhSeZ9M4cXzLp1HHHlqV11GDnvDcVr+oEoypAPqdUQY+7iqqkpZhmaRdwZaSbSFLfesVtvePylmKAWZ6xvB1HcOz0Ot1bY/Svbxx9N+4+rbb8n45l/9MX3YS/uqdWP/eG/ekAFuI3hFhYxW+aRqIpDQRqzSL2y/d5bco28VTHak5g1bWq0h427w2j6aB1skGa9saFmt1YKc8Icm2DRqaYk3ttEkmVEGmEiq7jJ1ULxbFn46CqExfrprEB48Ta0GxwjhuV9LzHbxoG/sEUeQg9J+iPzNU0+N6xH4hfchhLUeL9c3P/BQDwpbHn9WIf/gzqez+L/4w3f3MdqeR3/+wt9Qt/CQl7zLWG0rcn0TRNAuo2VnU5v4lONWw/ndMpaWUaNRdRDY8XUQSxDnau1Vi1HlQ62MbK6Py2eSL3tUsO14Y5rKrqEICrQwgvB/At8fbHqqr65KB5bJ7W6gQPdNcJxgsDQeLyVH+zyFXNVtSv6T3bHF3MNbwCr15ojx3y28KqTSaRq1fUZkuZSC4iulhoEFMkrdyl6E0OT/zDdgDAt192S48BvDme9v5reCsOx/reFd3lOPlci1t7Ni8PvnAKAPC1f/XceuIB6nCcQGQMOSlyorgJuS2c5/1L9dwwcnXYYaS2sSFdRpCHV9AD2lVF3aaW0tMk+tmmWA/mlXgtUwWnNOWdYqFQUwOgfz2tzaXnqarwyrTB3nUDY9XcS8hPVgDSIszyr0cKTbFL0nOx4ffRtugQ0mY3lhmG4+mi6J1kMoIneyGYHo+6YarlvvXoXTiNoWJfUV/+6LSf7J1S8Mj9dTT3R3AB8FFT/IIwZb2DCDTw9Q/F3++PV09Nu4C0YNp5Zzrl32tb3ZAqc2TVz+pdS/rRwOJWhTqJPPi+t0FZiSqONO3lp3XQ4NBAe5gZO648FaHOI9Y2U7+zywbNW0OsVzTQDITOOZkhmy523pmWOioHiLquM3F+fCfS9QuxltvrdvvEWE2sr8P7MBTFNPe9/PkAgA/gBtwQj6j6Lvw1gPr0H87nPZwPRNNF4MzYNk9QqDGKZAvM+k8jDxk1gqQqFgYXkPc1D2XMScsqLLHQ/lNPZm9zrlEBgLq/22xlVfDjrfcbAzqkWHj3VoOqqv4XgP+1kne3CHNYUFBQUFBQULD1sYghHHXUysdNKLmNxBZjDtUhRc9rJDxvZu5y9Exja4CrXsIqArfSK925dxlca53sEUKHkO+GWMeLkSQqqi6yqo4dyI8VmkrZ8XOOxOtjqIWvAE5M114q2yeewLaY7789/u/rnP70GPa/sd7N3hd3xpdEtflHcD1uOXYtAOBr+6Kb3ReQPKJj/ggAet7z3J2ehaRWofREDbqt1+ZhJLGnSluYznora3vq7h4mnablrlM94+xwUEmFd2axOgMsF6cgj6+pnu9W0jqLdqcRD570So9O87xJPUmo9Vy+Au3n+16H3DljAbmEaA5Jqsz8z0IuOWS+u+S3qF1ZRJSKfOYr1+G7/1HtiEInq7P/rhYrHr0QiL4qwGfqywtf+3nMxPyuuqSO+Xnnu1+SpCxRxYf/NgxU7AdKT1jHq5EkRKSDe9B0uAJqSRPTWamfF2h5J/IjJEfRVMXqVecr5qHSRM3fzj+jyCWSqzXitxJIjUXqSS5t3T2JkVcnTxIl0rmsPgvItRYLaD/XW7U/6mVuTSVGkJzd2IbXyP9PmvSzqXqXxuvN6B2bd+k31w4hO2J9Xjx7Jz748pogXztbq2s+PT6Dt+LXAAD/6/jLAQA/uu0dOEzaixJ1fAeA/1/83TP58aI7qKqcamWO02mpu42WAKTDDdgGKnn0ApWzTXWeY/7M4zDy9VbnTJV0tnmla/rN45hSeyvnauXCHC4bXEiHkFQByujpogo0iVAXVFVDAknc3cZgDiKG1kWQBK9icc871IZcYL1nkAfq3I+00Og3T5l8DyF5dMZbX4rXF6PHa41cXM8Ol2EP/hi1V9uHt9Uqu+e98cs9e5bpsVrNdjtqr+ULMI3RU+vZ5vzL/hYA8IVH/0kKFHwkXiutJ6HtqJMN0LQPZbsps6WTgzfp8z3rRd4Gq+b3mHwvL04syripynu5YHgmz5NRbba8Sc5uchbge/Hb/DyGWdN5Cy3Tsd0el2dsHy68qtrhZuAepLYjU38b8nNUZ+UeGUy15bNqVySv4m+P16dCz8aQi+b5/7jm9KYwjRdf+mkAwLFL6x37yx76LH7k/D+oaz4X1YQvQGrC6XjdDuAJLpZc9Bjk+zKk/tfwMnbcLyG3gdXze3lPzWZ4T20ULdOkNGrNZYaQb6B0gfROm1oLqH0w0S8EjTcOu8JMEV7AemUESbcenetmyNZP68MyuGG9AzlzNYHc7ts7cYS0fUUqMoavwXnofep8/Ib5+DAcB149VzOFPBPjqpff0dvcvHVbTe8vxt/g8uhp/Edv+kkAwMF/uyvZHPZ4dtZnEXkopgkkmpiWexwDbAfipchP5xmSwtT+mH1E+tB5wM5JekqQB++EFJve68eNR1som+OdtL6+2CLMYUFBQUFBQUHB1kebQ8qJZ5LkMISwDcBdAA5VVfW9IYQLALwX9fbjbgCvr6qqz3nTlLKo6k+dL7iLU8mHE1eqM5ab58yihuCA7/2mkj5KTrSONk7cJPIYibpztyJ7DRCr0hPWiZKcYfR2SWdP1VfGabsUPQHN+Vc/FHPdhofwzQCAX8Dv1q/hMQz9VZ3uyhtq62fGOTyC7Tg7Bpa79yvRQ+9ZSLHmGv4NE+bmYaS2ZH3VSUGPXyOs0TGQq4OsKgxoqoe8oK02xJP2jyddsVC6Wotd3lJLeZbOF5Gr6lRFbr21VX3Iezvk3n65B/jH4u1Eamsb05DPgUTPE0iSYaqIdyOPPaYeueo0sxdNkN7PQhYb8HQAj8Zb7NLvPdqj6dfFeJqUfL/lkd/FO875wVij2ongE+e/An/45E8BAL409gIAwGdnXpZCnf5uvD4BpCPvqFaml+qcfAP10PcgtSXViY8jN/JX5xM7X00idzjynORU+mVVcOPIaWsXcqm5SjVX49VMb+V+knQrOUXLO57pgwXz8LxaL4Mfq5TwVKoE5yf1JCdNT0rdOOeT7oEkYWO95YCCHmaA0YmUHVCfEhv5hddGlcyXYz1e/Zz346b93w8A+K8vryPJnYXHcFacgCkx34Uv4yfxh3UtPhi/4TEkf9UvsnzW/zbkEuRD8lyjg1AaPh2vqta1UnGN06lzLtv8snjlePKOQF2Qe56DkGrg2iJHjGJt5um1RZtDyok1dkhZDdZDcvjzqGd2UttvAnhbVVXvDSH8IYAfA/AHg2XleVgqY6Uifqsi04XRTjZLyL3ZvECuh510qkrjRKVpOKA4wejkPmOuml5tJu0AmUNarIhd6A2Qx2LdLo7p9yMteBG345qezeHDOAdAHXH/P95QL6Z/9PY3AwD+w4/9IoD6QPdncWA+Go91vFXynZZPWLIhNXSCHTZXVZ953oDaj/zNiVuZDi8UCNuQi+xVyE9G8SYdVW9YmptB3s8rUSv3g53Q2phImPucJMeQ2kRtq6zqzVOdE4eRq3p0EuYz9VDuCkmiAd7ZD6yj1pe08cp43Y9ED7H8hUXg0ZhuOj666zQ8/upa5XVnZN6OxANsf+ict+OnY7gPYg92491n1AvtZ98RYzFdhGRHeyReA4CKhVjbOP3NcTyFfLE6A377As3TaUiXSvee+YKaohAa9snWkVCGUxlBq+5biWcnvZX7gW3Tb9zY+nvMMf/X+Zr0sxc53S7BD1FmN55q72m9cw8g0TzP/P4Ucjv3qXgdQ1onmP8vJ/s/mv8cB6KzPf7LhXWg698a/z8A1Ceg/MSu3wWQwtYcwfae3RrPCP8QXovDs7FfuXkaQlIr0xTjIbXz1vkB8L28Z5Cb/Wi0DbtRvAg+LdgwcjqX813d8HhrPtG2nit0zF3cmmq90eaQcmJV4dHWFqeczMxDCOcB+B4Afxz/DwBeDkT/e+BdAL7vZNahoKCgoKCgoGCzYDE6pHh/mwUnW3L4uwB+GWkrexaAI1VVkeU/iFzXAgAIIbwJwJvq/57tpFAVipXmedz34baizLueYauq2+xORoOn2riFQNpRqROBGioDaReq0gLucmaR1BnfL+lYBiVle5DaJN7bH7/lbAB1LFU8NFdvTY+PbeupIj4Zz8A7Bw/3zvKMx9D2dqkvwad7Rv49yfdzkQQR87FdhtSrmLvDBeRBe9ke6ilJzCCXIg0hqfLYHirl9ZwpNLYb0OzTYfPMMztQyaGqXrj7Xd4ur52mLf16EkugPQadl77NwN8GIlbYGJQqFffqQ9pTEwCrjlL1qMbrJH3z2Zy8O2GeDSNXOU8kkwYa9F++hP0zdSzOJydqyTpNIc7HQ70YiPtjvM7ff+gXcd35dVDDnT9aS5EOfPCi2ghGUc0hjdH7zMPDUjca188gN3lQid2UpAOa07Cn0idUw0HaY1upZEf71qMZL9anBtq2z9rh03RbfDrCe+Y5n1i1skfT6ohl1aNtdWA+KkG0DkHaD6oBAer28zx2bSxD5rGI1Oacw+eAs+P3kX73ALih/vni8dp56u4Yb/YV+ATOjKJs0vEo5ntmPxPxm0/DUewer8ft3VORHt8qVewF5tC52XpXvx+5ZkEjBtg4pzq/sz88Uwn1trdmLUpv7L9dyE0rxk25hDVV8OjXjt2Nw9KJIRw7kUsOqxObR3J40pjDEML3AjhcVdXdIYRrl/t+VVU3Arixzutc58BBnSjawjsoPEIj1IZPJ3U7UVwk5dqQKePIF8sRdHvP2pAo2h20xZhE87xnxDypzqAN32Wp/DNj3R6Jjy5Fb95auKtWsz18JXDtWPP8TgD4CfwRAOCXnlOHtzk75n8E23F99NC8dTiq+6aRJraXxTa/FUg2N9ovVjWjnnF8tte5x4liFqldyViwnDn4qiLbvuPI7RCZ3jlhpkFjynwubwEl2ml6yFzbGEO1fdXyBw3X0C+Ug12ER5EHFNY89kg6oBk5wHpPA00VsmWMJpA2S1adeAXSwiXlM+RMDE2Di4Zwol5LcUfcSHAD9Dx8uXcKykI0yfiv578e26IB+IGvxvr+JdI5tNzEnz0GPHar1B1o2nvSnoz1H0IKDEy6HJNv4Nj21GJsjwPIN7Nqz6dByfm/FwbG63OPySJ9jzrP2uHTtNqn2fwBPyC1ZeQ0ZFJbGiDRl5jVdJ5qNII8DNo4cptdQu3vxs19xW6k8WDnmHmkvuH1rNTUPCnlOalKdzxZ0+8Lzqh1zq/Cx3onWb0PrwMAXIL7sSfOW7Q9vA+78fmZy+tMWJ3vQm3lDyTabmzWp+Nvtrd6Jmu7XRd/W/XvMJJtrY0mophEmm/bnmuZ+5Hb0c4i90CfRfucrHPj5rE9XDw2jOqpXEpYPfUMYA5Rz4SvDiG8CrX5+BiA3wOwPYQwFKWH52Flx0sUFBQUFBQUFGw5nDhxCo4v5c4nlXNvo3DSmMOqqn4FwK8AQJQc/lJVVT8UQvgfqIXn7wXwBgB/MVg1uTPu4v674s55sbzUiNaqkxeQdi3k5vcjV93oLlg9u3jPNvEURBdrrrr7VENnOp+oinM6/j5LnsWd33zc4UUpCu4C8MZ6U3/W5Q/3anI81u1js3X6z4xfg5twPYDaYQUAXhTdnB/C+fgQjzBjc3xBPosqvoaMlxK+ReRSKZVoUCLKHfokksMCJXoaD9BKFDRWIqHpWZZ60FlpbdvxS17MQ0tfy5O2NDECX7pj66l0b6VzI+g2yiaUFu048oz9F5BolfWZR2pXto2q77362rE1j9Sv6o2tZQDu2cqUogf0TCV6WtpnAWedV49Bxnp7JErF5zGKx+JYmUYdw3M/nodbZ2N+98a2OQpE/6wk/HpsFqmtefM98cqQAPqdQBq/rLd+i3X6WIAvTbOOAqqW4zP2wTz8udHS5ji6tRldDgCDQg8c0JivGq/RwjqIeV7GnjpXpUw2ryXkZybPoWnSAvixIq3ThULlGept76lI+b91XpuoRSZAkuZNoXeU3te3154jMy85AgC4GddhV/zGX8WvAwD+C36mZwZEr+a/xwtw4tA3Nas5KWU8QdWqar5UwskX7No3j9yJTNMTfE8dzGiSoeYWdn7bIWXp3OzNaxyDnubEYi3oee2xeHQYSwuOWvmp/N5GYSPiHP4bAO8NIfwHAJ8H8Pb+ryyhffFbiUrNmxSsCnkSuZpYm8uKf3ehqQJtw340VWlAGgxqN8iBuAvNwMOsh6caifkuxbqNxEF0OnDWVM0UPuuU1A5XRuOqF4/Xp0P8Jt7Sm4Auwf0AgHvxQgC1N9wLolvd3w/HMAQ/Gf+AZiSHLCzGYeSqANZbPcg4YU0jVzVfjRQw2aoalPFXdabtB88DXb3nbN3UDlEntb1OupXiYiT60pA+ltnVMuadex6sreWI+a15eAzDEhITx29WBoXv8H9VB+uCYc08diL3al5EGg8s07Mpjequ6sJ0mNC3xeuPV3j8C3W533NZfW74Lai9kLdhqccoXo+bAAB/hVfg+eM1TX/hsX9S53E2ks0hvT0bauJ/Fq9kCh+X31TFAWnzpswJ25DfR8ZRVaicG6aQK1VGkG9OufDeJul0Q2PnxK6wSawny1oNPG/7LljzBSC3l/SUTBOSRukFqOcdm6+OGfWstWWoXZtlXHch9R3XgRnkNs6ciyaQB3GfAY7ESZNM4r9Ezwrh+15U64FvfrLeuD98xjk9E4mLPlDX+3k37Mf74g7pjKjWvR4fwc1XfGf9df89ZnY2kud9j1ZJi0PIx+IsUnupfaGN0qDBwZX24/f12lTbpStItUUbzXhBzAnLoHeZk20cqsVhVEcd55Ojzwy1cg9VVd2KOJVXVfUV5EGfCgoKCgoKCgqe/ji2DXjKYb+8exuEzVOTgaBqK/Uus6qDNomKDfDKnY0a+xMz8M8AJWw8wj1SD93xcveo3oWUEljDe93l8L1ZJEN23e3Zo4lEjUqJoVT38c/VuyzKIC990d/2jPHfMvf/BwC8e+wHe8c1PRYDYs3ENnscZ2P+yVgmhVofQHJIOWK/A6it+4FaOsad3HS8TsXrbcjPoQXyc63vQ/pm71xOK03bJWVB0lupI+lIg5MrPINoi9UYOqsn42G5el6mVtrnqW7VKN9Kpj3vPpUWqCkD0JSSEvPwnX+ApkOKGufr8VlA3fd2rA7Lc/XyZBrrobgALMb098ZbtwY8/0djgGu8AkA6m/YjeDW+hBcAAN6IPwEA3IUrU6wxDrslJFr+PKUhZ8Efg0AtEZqOv9nOu9E8ixoxjR2zHCcqteb1kKRT+rBmDuyr+mubzzQ/1YxYla/S0WpMJBgEexdy6bIGI7YSPpVUEXoWNeEdQsDxqEuZprEOEp4KUunYjrEhJHr0HOY4xh5Han+RDgJommKIM8cj0/H3VH35DHok8eE7aongv776PwKo1cr/FrWTIIWQ07ig56X8ibma3j/5he/ByKUxgCJjG34Z6Vzxz1t1/C7kGgY1o+A9VfPb+W4vciectjOvrRSW8NZghb5npbxqsmDpY7ojzw3EIoCnnPtH17si7dgizOFp8G2igHqkWIaxDW3epW0LsJefneA4IaiNBVUI6qXmqe9YLrthv5Q5Ha+TSANTB7ENND2LXqiXhTiJLUaV7aVIKrKp+nIGFnBLtN967diHAADHcCrOjS7OH472hVQv345r8Gtn1DERfvHzv58+oReZQMPzsI1iYZhAbrtGjCBNRLQ91AWKauePy7tsQwqgvZArQ8i9DD1VM+t9AGnhhdzjc0+F0cYkDYJTUH97m62jN1HaiV1tUL3JuOt0CI/JJaPRz46N7U+6Zdt4TO0O5Mzpp5DGjXqlT8ffqq4C6g0ET1RgvaeBxyLdXBzHwhHgrLj9WYrxlsgkzuNZ+JHIFP5RtIU4C4/js3//suanDSF152hU+80/AODP4k0G5taQHVYFp6YjfDaBxFzYMDTemdeefaHaC5L2dFNEevdUaupp22Wb2M+jfRDMo2lLpnUCmudH2zqRCb8HOb0rfdm2GXKe6XequtxupJThsM+U0WV/3YmcidZNv45LCw1QHtM9OlVfT0c6q/7NdX0ZtuYMPNmj5S/90+cDAKYx1TsB6PKxewEAn516ERY+V0ek6NHxdvmdzR1zSH2ldPMR+Q3U7cE1h205IVeKHTifDSE/A143wvZAg36qX113bb5d5jVj8nsQu+x1wnH4w3DzOFRvFeawoKCgoKCgoOBpgGPwJYfevQ3CFmEOj6HeaehOWNUvXdCdoGXL9SgsK1pXb1dvZ8J3dUdjY49dhdwzGUi7fkI9PK3KZR5Jeqaxsrgjukrucecad980rL8QyWifamAAL4z6uJtixOu3fuE38TOX1irmy+MzBlk9Fw/jt/FL9YtslscgYnB+n0pGp+J1r/NdxCKSZwHjaGmwUjrm6C6c0gjdObId2B8HJR23zapSsg5Itu42vYeVSAyJE8ilz3qcmsZmBGq6ZJ/bOrWNAU/aSbBMdeay6m2Vnmt7tZU/jNSe7I8xpH6akXuUOjA+mnq4EtPxehlS4HO2wVSqx9dj/s9KR0ESr47OJzfh1fhI9MQnbe95cnfziDGgjp24Pf5+UE0POGatelslSKpp4Lt6tJ4JUt+TJCqdXSHPLF2oQ5UNtLwDufRlv9RP5zXPU93S20oM409D3S+qIva0NVYCpbjNuacSRE8qCNT19sqy8VI9tfoYmhENFKpRUEcI/qZEe0bSMQ9GmdiHRAfXp3oMxb4myX4cwBvrn/S6fzLmsR1HcC5qp0I6Cb4e78Z87F96Ld9/9sX42hPPRXypxochUSTYvuxfXftIl0B+fKnGiGX/afB7fRdol9hZybSu54M49mm/20DzQD7njWBltHyS0cYcFrXycsEzO3XyU2ZjyNxT2xS1VWjzlNKJRQe4nWx0ch7E9uxO5HaOO5CYCqsaGkFaHEjQaselA5WTDeuxG2mwxIX0nDhxPYrUdJfWdTwfD+HPTvwgAOC3TvllAMAPX3ojPh+NU34kxlTgYnvr7LWYGp8GADxC77oJpEDbDdtHjxngb9ZbVUV6SglQT6qPm3SjSO3GNtXQQWQiIfdsO5+FNDkOIr9v84S3KjjL8C4H6nlOLMC3ofHsqwi7kRlBbt+jk6gt0ws63DZhW5pWz3mOH6qXdNPEvNSeTBcRLjCHTfoF5GYUh9Cjm8/HW0/FE04AHBiqr9OvngIAPHLPBfjWK26LNasXvtNOP4qv05KB42O7VK0R2NeeZU7GbhjNjRxQb+ZuNfeAPEyLZVI03wXk7azzn11kdT7SDYXtPy900gH4qt7lokJT/dgGz+bQjinPBk2/xaojPcZwHPlpKbpeaLQD64GrGypLe6OSh2fryLxIP3p4gjCYS9P1z4ci3Z8/0hs2j//Hmrb/6rvr6/df9i58KJ40+3i0B9+D3T2b2ZvjxvrYU6chHieeSPB0JEak4lzLefgwcs9r/WaaUXwEaWDwO9Xr3tqIa9g5pXNLj0oDXtgwi13I1eBaht0Ib9Iwym02h0VyuFx4Noe6cHCiILHopMIFR+357ES0KPlxEHuxuryFUhdeK02YRz5p6f+eDZl1UvFCJIwhDWSxwbKSCTJu25HiEN5Vf9OfP/JDvWq+7ZI3AwCGcLx3esTH8CoAaef6reN347N/Fu2zKG35MkRIoIwawYVuPxITYA2dR5AmHWWC7WSjfWrjv/0zJHvFQyYNIEZkaIcnUQHShKVMYtfCvFxomVZCYWFtqvS+rfsC0nghfekYIi0xTw1F4bWTt8nSGG+8WtutISRaVWnFsPzWOmv5pJUZNGOlMf/IZI1GW9tPA3hxfBz3A498rI5p+PxX3Yf752op+MLXI6N35LT0Waza2wFU/Oc8ud4af9t2HkYewmoB6ahHfWZtAvm/Sjd0fvDooM02cFjq5tnyEW3SGZvOMqaDYBH5ptwbU55TmI2Z5327borJFNqwQArPAU3bhuNtCc1Nh2IBeaxKZXJ0E8S6T8s9Xu3YVYlk/KYhkTy/MF7jvP3h2df2jsVjSJtPz74Y3zp+NwDg4F/FsfIogM/Fd7mJf0LK6NVNmWHWjRts3ZhwM62bII5d5gXkWhRl7j1Jsdpesj5dTKHOjR4NWzrTNXUt7WnXCEWtXFBQUFBQUFBQ0MMSfG13cUhZLo6j5vp1N6I7Axuu4DIk1Y2qYqz3sUql+Iy7nLYTI6w3mxd+RXenNtK+7noomdDDzfl8StJR+qmSRpahwaSNp/OZ8b0hJCFeNEfBtx0Fvn4agHRSygLOwO64E96P5wEAvjxX70gXPnNmw14RALBNqnRmVGE/8R6pO20lh5F21RriBPG+ns0L+GEsLkZ+Ag7b9F1I7dwVSFrP9rThj1QNq2pNa0eqdGG95laLrp0zkO98VQ1jv98LZ6J06QVYJrq8j9Vj1lPlWZvOcaT24XUfcpsmrYP12NfxybFwM3rSxPk4Bm8dTsko3Y70/vf/38uA74j3dkXDng8AUUOXwn5Ui8CQDQX1AJIEkPQzKf8zBBDbaAa+7RxBe0xKatrMEqwdl5pWUJJK+1xvpRlB6j9V/XvzlMXyzg2vQTvaEfirXJvqWmmvSw3o1Ve9Y+38vtDyjrUfVbs7ls/2vQd5G+6UfNXj2UZHWJD0GuAfqCXLXKOuTY/ooE9Bfmyy81/+EO5+R7Ql/5b6cuqFc/js7VGbE6PX4DQk2qcUKgDYFstfojReNWVqx0vYk2j01Co7d3rnSqtGYFzSMQ/bL0vw7ZuJrrlRtT6e1mMQW8Z1xiJ8+8Jic7hcHEfTCQPodhJR2yld3LomQw4azxWfhLcDiWB5TxdoaxO2BP+oNRs3i+EbbkMeH3wauU3eIpone7As5huvT8Rn28ZS7LbnMN/TsOOnv1rX4qvx8PbzZvDhfXV8rcsvqXUTC9PRgOUgkk/A9ng9AmBpplkWrkNaSMmYaxwzMozsoyuQJkwuhtPI7eTuQb4x0Nha9uglVY2wD5RZtIbtal+nKiBr5K+nvbQZxw8CxoTzYhVquAa127HldB3pp85beiKQdbzyNjlq22nHmR4FZplOTct+nkauatwJXw3IMTgdr1Pyvy1jCJma63EAn423aOR/qWTB7PbVmyJ8HM31EKgZwyUydhzbH0cew1SnTtvObQyOmoA0CkVuOjIu+Xpz3UFJB/jM3E6kvlR68hZwHQdtZQ6KNgcAnUe1DJ2XB1UB2vHh2VJqOCBlwO2mUcMtWXtm5q3XA8jVxOrQNRWv0056tfljuki/VfrZi915eX358v3fAlwbvUqOBADAsY+PJUaQKuQHkUyISPufRrJv7H0nv+8MAHfE30orHL9Kg219swc5KzGB3MTHUwkrQzgm6Qi79qn5FuGFLNL6sIxNpFZehc1hCOEdAL4XwOGqqi7tl36lOOVkZVxQUFBQUFBQUGBAm0P7N5jk8J1InkInDVtEchhQb6n6Gf57u+dBdwtWkqEnNqiTioWK3dXAmu9Z7zCtD+9RCjCCJD27Ve5R/awev1ouy2K5lD7G/4/KI27q9wOH/+qbG1/y5PYzejuXex+OFtH083gWEuFyd7v/IJIeRHeHlApqWAwa6FMNxu+8RyqnO0eVPGn+QK6empP0GsKA/cX2mEZOD6qOOGTu8b6t21qoKUjTbVJH3ld1l1W/q6R6QtIBTYmnShi71HZWGqP0rt6hbWpJlYpzPI0glwgeQOoTx8EkOzf4GuRq2ikkYo40te0yxJjBSbrNJC9FHlXqWwD8efzNTz4OqRuJf0rKJR3w269CUpVRVT4q6dimXVJelXJbr1agKQG3UhA1AbDw+noIg501uxoP/CXkS4vSuYbmsdDQPF2qbTtuaHYE5ONDy9SQWDbKhabTNF492L8quWS70sxgWJ7xu5jXlDwnkT4APHph/XPaqcbZtcSwt1ycLr9pHnEUSUtExRTuQ34OPb3vF+U32+9qSWfXGaBpysSCPCkwoc47VmpMeraOmISegqLp2+CppDeh5PAY/GVkAOawqqq/CSFMrXGNMmwR5pDwQmvsQhpJK7Xm1EVNJwxPzWjVyTroPc+5qXh9QP5nfdWTGqgH4EfkN1APDor9mZeqMNSGjhOWEcV//YqeWUsP26XY+Gxh/5kpu89F1RvF3NNI6ooPM5MzkGYnnRTYbqpKvjX+JpM4LXVlmzL0wyISM8BvUrs+a+M3gdTm2gdD8lvrBeSe68qoqb2RNSNQrMYLTuMcEmovyPp5CzzbWpkuL50XGqLL3sz7RvUYtuUTVlWtWEC+0k2iab4BNO0KaROmmwy7kOr4jAvu0nnALfH7GM6D6rZdQDw9L6nsRpFscHsL6UHkC+Ih5KcwcWG8GXn4HiCPcHAR0uDahSamkYdFUtpgu+o8ZTedHgZdINV8wFPtrQSDzMVKL3b36jFk2oYWOyQ/bUtVRwKNEEiNuIXz5p4yI9buGEh9qMcXMh03C3qcnkdTpCGxW2Q1GGmCdt6fRvJg5pz8GfTsD3sxbb8BORWRa84sEoGzjtykX4Y0h3Peuxk5467CBzu/A3n/6QZBac8ydzofdTF+alLEucDaiGs6Qm3EV3M05BrjePzz7gPnhBDukrs3VlV143pUS7HFmMOCgoKCgoKCgi2MNpvDYwCAR6qqeqnzdF2xRZhDT8pCTKMpvufVUw+0GV3rjkY9sawkRINl2x3KDjRPPGD+95l0Kn206h3d1apRN3epqtJi3akCU3XfdLxyZypV4070sfQIH4jX85AMnG386EeQNoqn8cVF5J7UC0gnnbDQe6RuVNURKvFUKQi/WU830XI1jcawZJ9dgbQjZp9pMFre0/NwmYcNBg00T2Wx0g17JvNK0U9dzXZSr1jAPzXAM9AHBhvyKhFVBx/mayUCGpx9l7mnajk9i9rGsVS1la33JPJzl4ekTuybveg5dz0RpSYXXJgekRwvj9cvI40DOrA8cghpXOr4ZN1tjLdJ1EcQAf4ZyYRKvKw6U6MUEJ4UVttluZK9rigMqq5WadpK0RYztAvWccRzPJhFajuOBT0FydZZ20i1AVZC5UkjWf95+NEI7MlNSueeKQpB6fJO5DR9da4Z/3q8PoUU7J2qxyXk4R7fiTSHN7zdrQPaVLw+juaZ0UA9f3MO1LOurZRav4tzPud3lZZqYHNLe/wALwIHkMdS9QJf62++q/Xpirm5QTgKf7o/tt4VaccWYQ4JjY6ui5W1MfEWxTF0q2BsuIIh5OqfReSqOl3wtJ6859m46WkiQJOpVTs91od5s+xJ5IyB2p2ZAVUhHTjCAKnfgaReY4gPnYBYpW3xeo7c+wK/ZRhpgqNd5B3w1bjsI8s8AD4Tzu9SBtC2Pb9Xg0BrMFu+yz7Yh3ZbKO9EBjVj4DNdoFh+vxA0Hk6R/O1i4h0lN4Jc5aXMovXg9uzKRuFvmoCapvgdqpazfaN9am0D70FTBQo01UUaBsaqadU04HrzTEMhsf6zSPau0/F6FpIJRjRfeJAbhAXgysjE3R1vPQVgIXKM86SVcaQ21BOMLAOhY5cbQLUTnZHfQP29GspH8+gXXoabvD3w1Xes9yDB2Nto1TJnq1ErL3cR1rrrN+jYB5q06wWE76qzbvisbaCqhu0ReJ4qVOdp3RjxXY3SwPQcK2qOZO3trq69jSHJKV26XIo6Eq+7UG9wAOA98dpgOGiao0HnY8ixXntchjR+7MEKQDKHWpC62wD2h5FvqCeQz0Hz8L3MgabKV+1D24Kh23raUDY3O882EXPYFspmEzGHxVu5oKCgoKCgoGC9QIcU+zdYKJv/jjpo1wtCCAdDCD92Mqq4xSSHqjpViQ4/o8tBwNtleFITYgpp58MdrLfrVomCDa7ahraAnareUVg1uJ6FqpKDB0w6SvOuT4b3+8wjIBnt34KmVh1Im+qnkDaYDLiKx5F2dnpUEneglHiopyl3nSzoPPkW6v3G5BuY/26kPrQG5Tvg940nSaG0zRqqQ+ox7Dzz8lrNge40lVCpidKtpeFF5H2uUgvm0eWJquqxLs9kYlBpFCVnGm9S68/+UomKpWmtG+mFaWbQDKgO1N/OdByfenyb9cocS0b7DVD68Z6ULos6cAhN8w1ImovR9MxmPQjWZxfS4KMOcMZJx/lNVXAs0zOuV+ktpSV6zJsnkevCWnjiK7okotYUBGhqYrx3NGZnG/qpEUmPpB/1jLbHS6pphX6LVWHrvG092tUbne2rERb0KLlIj49FrcTjkd6PIPdtPAjgofibZhELWobGBSbo8EgavE3qwW96AHm/aYxfjhmmUQ0by9Z5SOdrOiSy7TXyhJUmTiGXHPZjWdTbHVhZMPd1wAl0OaR0oqqqf7nW1fGwRZjDU5FEzB6DQKLSBcYSh54hac+c9dR4SpQk9H62NMy3a4L11HKqomIeuyWdZYhVBcqBqhMp1SVso4PA3ljvC6bq6zQSg0hV83NRx/sF0ninfeE80kTUW9Tm0QzCzYx5j6oGTvhA6iNdUO2CN4e8nb1Auf3Csti6ATlT6C0kntewprcMVb/wCl3wGKUh5Ey0lkd4YW504+GFTIFJ5z0jZpx7ukjYd0aQGCOtmy4wiO9PyjtAUy1l7aIWkOyhqMKeRa4CHJF83x+vPxSvw0ge89fG633yDbQbBPLg0zuRn5+sdo52IVWziElJR9jTJLwwTRo4eV7uWS9ST43mbQIUXXPYajY8hAZg1/Zqo0fdVOxz0istDRIZoJ/60Aa87goDpB7S2k93oj+Y/xhSv6k5EBkkbjxmkeasuKmp4nsPIVlREI8gpX8k5hsulLPBlTas2QkneDVXUY9gGyViAe2M1ph8l0dvOk/PmXSkAW+OVqGCmhjYMaiMpfVgVtOg1YRnWmO0na28FsNvjbBFmMOCgoKCgoKCgqcB2pjDTWQWuUWYQ1WPWEPgEfjnydpdjnr4EqrSmoq/VSpGeBIabwdr859E2rF56j7rYKEGuNPxql2kUiQaFh+UeyzLxtm6GMlAn983kTyXvyle70Xy5KTq4hvx+shBqZt6F1unAD0GSaUg1qOXu2WVGFEqtB/NczuBpoTGxnrTs4XV3MCWsYR2qa6qUDWNTb+EXNK31qq4JeSmB96undtMlbKoV7V1lvK2pcx/HLmqzqPxw8idplTNbb1eh5CPXQ0Y7MEavKvU/wFJp0F4WeZsy7NJyZdjQOmGUoop5B7UKiH3YqvZmIZ6VKdKNa3Xqzf9qser1/5Wba9S6x0mbT+VmkqT2F+s42pWKa9vPWcS1Zx4GqEh+U1YCZjnse/BmqLYOhFWmqs0ohLzNscgLw+lB+t9CzRjENLJ7aZ4fWPMfg64y0Z1EKlYiJLvSr2K+c1Xoan9ApKKaBRJhcQ0O5AfCqHrrI340GYOReh7Nl+uC0vIndS0nT2aUu9mOycT6jU9qGnFOqAtlE1hDpeLCrknlRKDqn8A3x5lCL6HG//3TjAhdEJZjk2DTnBeKBbWU5koa/c2hdymSUN7qD2MFZ+zrtNIth5xMj1zIs1JvO5C8k7OxuJ5SJPIrXLfY4ys2H9B6mLDwChTpge7Mx2/fQfyU0B0sNv8Nb8ucDRqXt4kohO+/eaVDCN6K3vqX2UM9JlddLpO7NFnGhjcTuL8fm/MjCNn9rR8Dx492HHpLSQecz4t6T2VEBk6qoQPI2cipyT9nLmOSB4sc0Z+qzcpf5PpPOQ8W5SrPUtXw/cwnYbg8iIXeCo3Oz9pnt4YsNB8rV2v3lstujbR9p5uOJRB8L7DYxjtM7bhmKTr533t2T8i1sGzQfaYEc+UiWlZPttlEfl41nBar45X8ba3KmcAvTGyLc6NSwtIm3MajKu9soaVYV3tOqT9obF1vAgZ/D7mS/XvAvLIIlNoHvjAspjGW7/5rhcVROuhJldAc/70NgYbjC0gOTxp3sohhPNDCLeEEO4PIXwxhPDz8f54COGvQwgPxOuZ/fIqKCgoKCgoKHha4DiSgkj/BnBIWS+cTMnhEoBfrKrqnhDCKIC7Qwh/jVpO/smqqn4jhPAWAG8B8G+6s6pidipR8Rw7dMevcda0SraKBKViKnGxu8M2iQ7Tcqc7Lmm8GEsj5p63XbCSMyDtyvRoOAYpvQ1p52q9tyeQdqBx9/QE1dJA7/v2LwJDbd68Q+ndnqhRHQs0hiS/n9+1E3mAU+0f65wwgjwm5WHkqlNiCI0jqHp164K38/e8TQlPWrYajzh6K0/K+12Sb68OXV6ZKonj96i3qwbvJdi/njTVK9dKcj2HrSXk36PqIra/trlVCc8j70+VFqhpAutycTM55pAkH0w/jESrlD4+7tRNg9978Qs9aYwGZ2Z6fqN1lFJJqtKSmgiwPlat7TkNebEPPekJy/Xix65kaRhGMnHwApl3HY3XFU9TYfNVR4Vhk8bLo+3MZistVqmtJ3FifbV8GwTbevUCTdMcqwUDkmMR52GOsSeRTIk07m1Mt3Sr1JvRA7im3SNl2LlTpdakkTOQxoh+Z9f6SahqmpgyzwC/Tb1j7tiXXWYoI1IXr8+tt/kmQNvZys8E5rCqqkcQ/amqqpoPIexFTX2vQXIXfBdqHWUf5nAISW3qqSs8OwPPg7ltsdsB/1QMO1Go+JygndxeDMaYqFrHA+ut6mtOOmQUr5Lf6tHFd8bM9RCSikG9LIk4aMKEjHeq5dRrlpPOhLznBfQlONksID9Hmul3wFddWlOBYaT+Y158z1ND9EOXt7LaO3KC1zKs6n8lCDEfnfRYhhdCxlvUWL4uNEo/1iNQvV3JVCjjYcfWOHyPbxtKx94HmlOLXUTUO5XQ+npjkdAQIvabgfazgXch2cfqQsJ2YLtMSb6eXd8Bcx2CP+5t/2l9LKOmKjVllrs2Haq+s/l7KlL10LVewHuQvp9Mtdp2DgrWWe28PFqxY07r5J1P78GzRWtTLSrUPk7nCjtWCGVUOFa0XUk/KhywUSj2I+/zKaSTCXRuYTq2Dc+bH0Oaf0nH+i0auon1m4pXNTniu5z/NQyYF1JLT5AizU2jiX729zPOPQ0wD9TzjGXqPeZT10+loyFzT/uqrW83EG1BsJ8JzKEihDCF+tjwOwBMRMYRqA/6mWh5500A3lT/VzTPBVsfTZp+9obWpaBgLVBouqBgBTgKn/s6sd4VacdJZw5DCM8C8OcA3lxV1VwIofesqqoqhFB571VVdSOAG+s8zq3qHYBKVNQQm+J2StEmkIuhvSO71LvQU7t4Ow37rg083VYm4aksvZ2s1seqAu5EriZQKYPnKckdOSUrN8u9O+pLNYYkObCBXOeQ2pk7WK/uo3JPDf/bvNmuAfAhc28cuQe6qgS4651y8lMPZguVAni7Uzsc2qQXbZKzbuQ0bQ27u9S5XaqwRece4Es6+NuTrlgJgO66CU8qRKiKqkt6rvVhf80491RdbVXYXvyyWcnbSqDmkB9DqXkwnedtv+TUSeutklagGcBaJZ02FiWh0hvPaY55eP3ozSe2XTT9YSRtx0Hn+fI8Ops0fX7V7mXtOdh4nt/9JIa2Db02X3DSqxSYbeY5T1h45ksKT53prSVKy0Az0LTmbw+15/8aO5K0raYVrNsh5F75Wj9K5T2aomnFXqmbSt2s1qfLs33e+QYvnqU3Dyl9WNOrLs9kvadzvaWVTYA2yeEzhTkMIQyjZgzfU1XVB+PtmRDCOVVVPRJCOAfLMthq8yK9zdzz7BM8pk8XFTs5eOpf9eKyg9Lmx+ukSa9Bj70wJdYbbwi5TZpOwOr1tSjv8BuYnnVSmywuuMoIWq865vGXSB7PfO9TyNvDW5DGkE/6HLAfl3u6oHsez/ZdtemxHuAKJfM2pqXtbGXL7HghHexpCcuFxyxYFUubrZR9X9vQmzzJGHCRYLifNs9uW7cupk9V04TXrp7tsKqG7FicQFrolHmytKdjVs0mgKaHp9p9WS9ZtS8kdN6x6RWevZ6n7rXjU20UNXiv58FrbYG74AWTB3y1fZcqdiVQL1fAn7u1bmo7DbSr67s2JpYZ6Qq8DXTbsWkdvWDlXn4WqoZlfTnXjjvl6yZov6Tj1W66F5AzQ4eQ2vI+SWcZOjXlYT2VcbQ2qECiUc+cx2PY1N6WZXUxaHau15BehG5OWW8NoWXr07X53UDQIcViEzGHJ9NbOQB4O4C9VVX9jjy6CcAb4u83APiLk1WHgoKCgoKCgoJNBcY5tH+eNHGDcDIlh9cAeD2APSGEe+O9XwXwGwDeHw+LPgDg+wfPUnfO3GV4O3PPG9KDJ3HQXY7d6WsQZbubVOmc5t8lkRzkaCsVo6vqSY2deY87I6oHVEJgvd8WkOIV8tku5OfgTsfrMJIaSneMnveb9fw7hFzVYiVMgK9S8rzHvWcasNje89Svtvy2gMOWfkakXOuRuhxsQ9oF2z73JIRevENPijQdr6qGUtwjz4HBYkFaDCIVItpUcdZpQp1fSNtj8g5NGlj/ncjV8Fcg/75heW8Q9f08kvR1WuphY2tqXlYau8MpC8hpSfvR9rkGpla6t2rnNk91oEnvqmnwpJ7W4/pCJ00/0ANf0UUb+l1W+6POf9qW9nt1bFvV7XKiCLR5/nvzuqdV0vFm23dI8vfU957UlnTLOUZVxAfkmfWQnkTuROY5rE1LflZKqfOGtktXe3ats17syhFznZc8VEvj9Qvr6UlQmZ86C25Ch5Rj8J1PXCO7jcHJ9Fb+DGqXTA/fubJcF5zf80gLgRKLR0Aqhua7FnymYT8IJVZIOn0P8AnaY4ZYvhfgk9iBnKgXkNsEjiPZ4FGldp2kUa86plEVB1BPLGQWbMDTXcjDeMwit+OaR84A7kA+YXLwDnqYpOfBq6qSrnATduJgPb0y9D1VdSpD4alVlgsupJ7No6o2tXyPzpne8zS00HRdNrkeNMyPNWlQcPOhjKMXtsbanQH5wqzetLZuXtvrZsgyFpq/LsZkBNkeC0jjRzdShB3HC1KutwjpCTRtoYLUY9+afwCp3/dK3l1TdxdDpvl6oZPYbl0e421gyDHNh/DGr2UUFJ4ZidqgWZpqO4HFMl5tNpGWHpV5sYy4J3zwNmI6n3j9ZTdBqv7lVedXa2bgmSMoo0TMIm8HlqlBvr3wb3w2hZyJZP/peuSNcW0rz0yIaWyIN4+Zawtrpd8DNJlpu1ZuAhyDzx09E5jDgoKCgoKCgoICB5uIEfSwxZjDnUg7JN3t2h0NkH/aBNKOw3oXetINTzLh7WSsmgnwVT3cgWnMKabTXadVv2osK62jrcsBec7dGQOqjiJJFam6HUfeRncil9Cwjfai6fzCemj5QC0FtfmqiH/Qo4w8j1WCklav/VSa6El5bDrd3XbFFrR9oPmtxCHl1Piexh5Uj1LrtKRqlylJx/esd6G3S25z6AL69wd35Bq0276zA77azNKqFzPRq5tnZqD9xrqrZM2L8Qc0A+OrWYBKP2ydPG2AJ3WyEjjtP51rvHlB62Praz2pVZvh5WUlUf0kJWqmcrKN9b2lRj3QbTD9Nume9W735pN+TghW2+PRo0rCbHsOumySttqc3di/qga2axThxVlVVS+/SZ3/RuWZ9Z732tnTRHiSUU/9bKMgeFo+IKd11UJYjcigWELeN0pbm0hiuIWwRZhD2mcdQnv4DyARlw5GErLH7OkpHFZd49k4qOfwIFDvMz0dwbMrJKxKVpkCVf922TLab51HkykE/HOJJ5GfVuJ5sHnqNvU0ZTgclqnqHVU1E16beraJrLtVv2vd2JYT6GZ42myXAF/N79n3dZkD9IMGOSZzo+FU7CJ1GPk5sVr+IBOg5kt473WFl1C7W0+1p96VNv82u66Vgnmr3WSXrTHHYL+TbbrmAPvOVfDtNr3N20vjbwak14XaMgOe6vIA2r2Kdc7z+lS/ydpSz7ekWy644fGYau/kl0GZb4Ud08sLJ9U88US/0dKE5ms9bCeQh1TTNlcTGz6zdqzDSP3KzfNFyOcSZbasp7EKNTzbbH7fAaT27WK+vY2i931kCjn/HEI7U2thadpj7tkXYxjMo1w38TaSxWbFEnyTqkHNrE4+tghzWFBQUFBQUFDwdEDb+Xmbx2lmizCHNN6fQh4LTSUZlMDo7qtrB+E5A3g7Zm8nvtM8844cUucMq/IF8kCkbaJ4qyqbRE5EauxrjXOXkKvU9IhBvqe7tFfKuyybvxflaqVIKuJXqAQMLWk8eJ5rhCeBILxBpjtzT3pkd8lqAuA5AqzkTGWiQmpHSp7UC5jfzT7xPBM1jphtgzZVof1G77gyfc+qTLXfbKByfa6qds9TnWXo+cHWCUqdA+YlHdNY4/Zh5H2iY9FTYVt1mHqjK9r6WgPSa32t5yWQJIZ2DhtFbhZwGDkNDyHv036emJ4k1arS22IKLhfH4MeohLlvTTUW4Gt2rFe+qh7bVPGa3sNinzp5dbBzVb+DEuyzceQmEjqPeHFx7RgHuh01tE/tuNT+9RxBeK/LqU/HLL3zVQrpBRsnmP9OpwyvzMPmCjQdTL3Yq9YBVQNf2/iQmwFH4ffjU+tdkVZsEeaQXnBtImbvkHtPdaAel0DzQHD1ygVqQupa/Dnwdsk9T7Q+ZNLtge8lCDRtQ9TGzaoO9iA/KUDDYdh660DltysDoqERLFNCjMuzw056/WYNLQLUi36X+ke9dIH6W+wipd9EdcYZzjNCmXD10GXbe+8MwrAqPa3GlkU9O4muhbntTFigabNFBsubsJVxZ179TqSwJ9SMwA+QS1j1FeunZc4jZxgnkS/aag/qqc8ss+wFq+7XR9a7VvtAg52z/Kl41Xaz7TCP5pgjbH8MqkolppDUkkvmquFSdNNnwy4dhr8BXkubQ8+eztYBaLab9cQ/jHyh34/cjljHhGfvRuicuxxV9ErUfN73EV021+q17pmq8Pum4nUcacOhm3Mb6UA3V54pzCBjRRkwz5Pdi1Jg4W0A+jH06u3flq9nL9xme7xZUCSHBQUFBQUFBQUFPRSbwzWGJ+IH8p3wIfiG257EheBOoy0AaBs8aabGU+SOh5IGPUfVSnu8eGP6m0edTSPt0FT6YI2k+eyAlGGdRRTe0WEayJrwjv0jdiHt+DUocNfOj+3L/CbRX4rGdG3Yh5y8xyRfz6nCk2Z53ttWFTmB5YNOVoMacHve1MQkckmVp4LUILTWa7GfipzptA4sk6rpvcgdN9QcoGsszjr39Jkdg5738W4kuu7yPB/U2ULjXbLunsOY1gloRhPQdLY/+tXDi6Vny2U/avxJO5709zh8SetaSA6HkMaH1z5tNDaCJBHVuctrn7Y28wJTA77pg5VG6TPSMudLr86eGt5To3r/M19v7A8jl+yraZD1XqdmhvmxvtbcQrUIes4x0D3uFDq2PAziOAKpmzXx0P7WdZfrnBfRg/X2vsHT3m0mtEkOi1p5hWhTG2kwWaAeIFZ1owGNPfsar6M8Yh3ErkUJ2TIXCx3PhpAzffoOJxYNV6DgmZpkytTLjgyMhjng96nKhb+9we5Ftbcqck8d76kz1Y6IzzgReioXTWev3mkgegYn4TFXunjYsDl6So5ORHaCG3RiVBxH3X5dgX31t9KDDULrtdeo5KdqJi80hYXHyHueu56ayPO6te/ZvIGmutCzi/Lytel0TFjmpC1Ismd7avthBD6T0VaWrSfzGGQR5sZyv6TX/rBlLNdb11tI1TOa8NTi/aDjvCsIthduhOjqhyG0b27mkZilvfKex9xZEwLtm0H6SBk1YlB1YFeYIc9jV9Wjtk3vQR7E3TtPWjeKzI9CAj2ZpouO9/R5PoggxTObIvQ9tStmmeznMXlXA1131a0t0sBGos3msKiVCwoKCgoKCgqegSiSwzWG7op0N2l3oLoT5A7zAHLjc/Uy9OLe2d2QqhlXGg+sTQ0F1N+hEiqiLb4fkHaYFwK4z+SneXieWvwG7jqX0H0knK3vOJrOLEDdP3YX5wUZZ5oZ+Eep2Tp2HU847dzTdlbVm909sv47kehI49bZ+urOddqp76Dw1Mo6Wdg29Bx+PHSpMceQO1SoxM72gyeF0PJVykXYCU89xLvq3aa+s1DpkJWqamBsSw9tkQBU3co68l2Vils64NnDe9F9VB+lICqds+pwnX+mpWzrId6mam1DW/95ZiHqUQqszFTC5q/wjmTrB0tLXjxYdZSabnlP0WaaRHhxGT0M0g86FruC0xMr8ca2GqTxlnRWGzAtz7wjYD14ZipA01SqC4MeN6prtwZqB5qaQuKlSI45FusR4H0lKJLDNYZ6LSpj56lnCc+dn8Si7v92MWkLYWDDKwxqN9QVcJZos//wJhSrMtQAxF3hLTSNtVHqspvReui3WAbBs3XTvG2Ijx1IqhZlMjS0CNMrgwY0vRm7Jt1BVG+qVuY3qdpCmTQNOcPylwuqlT14QdyBbptDe154G/2y/XeY9AvI+3IX8kVqEqldps0zj3nZB99Lt6vNLK0oLdJkYidyxk8Xji571H7hqjyG3IZfecCpm6e+0ueW8fA2mGTKvIXUq3db5AOtq4XHeNh8VnJu+KB2tOwbz76b6DemvI2JZ8w/yCZe37Pe+f2CuHvex16feKYjdi3TjbK1i9Sx24VZ+J7fdnOjzHVXX+um39ITv3MlhwB4JgPD5l7bqTddwgGb/2ZkDIE6PN9ybGrXH1uMOSwoKCgoKCgo2Mp4CsCTLfc3B7YIc8gdqe5wdMfhqeD4adyNqDcfseT8VvG/Z+RqpTHqVeZx/awnnUVuQ/tufzmxnmxaPdfW1kOlPfrMqpIOwN+Rsw6eis6m8xwX1HnAi3XWZUCtbc8dvqcK1HoCdb97AVGt9NOrO79JnWCY/mL0jw04CALaJWhdklcPnmR0Drnh/xz8NgHqfrB9uYC8jb0gtN44GdRL15NWe+pEKy3wPEw9T+pB1TRdUnOP9ikx6ueNq+1n243/e6r3QVXIKim1ntTaRl3x9XaheQ4wkKTzy0GXNFyh4wuo5yZPkmSD+WvezEPfs+3lecD2i3bR5ZjizW1qZrTk3AOaanT9PluWOtbZ+c8LyK9xZjXfrvnJ0m2/MeM5cvFeP4nhcg6W6EfrGhHBzl1djnHj2FzBr4lic7hG0Emn62xgQgcqmZ195jnQX6zb5flk7Z1UTeDZjqlX2Hnx6hG1VzcbmsIjqhH43n9Ac2FS9YaGugFqJrEtztIEcuZBJyethxf+hrAemDpwPRsdb0JmO+i7Vo3gLa6jSHTDCVvrYW3HdALnPW/iXckw2oZ68ZtFmvg8T2KP8fU88W0a9ajsUqt3MU+LyOlBAzx3BZpVuugKY6QRBGxd9PvsgqDPvT7x6NiePrGA7oDehJoSEDNOOj3xyLP7taYSGpyd0Hp4Nr52s6KquK6g2l004JlzeN/XD10bHg9qxuF5z7OvvTAmdqPowZtD27zGLU13zWGjaAZIt2XxXTK3Xh5eG3lqdmVu7Tyjqt6uvtfnrIue/zyop7ZnYqJ5WnjfaAOV6/psVeq6DnCM6/pCGlD7XAvP3n0zYHXMYQjhlQB+D/VC8sdVVf3G2tWtxhZhDgsKCgoKCgoKng5oc0g52vfNEMI2AL8P4LsAHATwtyGEm6qqun8ta7hFmEMNrtoljvbUW7qjaFPnXiTveLugLqcA7nzm4UsrPAK4zbkHDB4wGMjVs56quat7x5GrP7zzXIlp5LtZ3YWr1NKTENm2V4nRVPytO2cvHph3FBjzsGoeb9eq/W53n1NoOtUwj67dsc1jOVAnGu6K9cg/e8yVqto9OrdOSFr3uQHSa4Bs9uU4fPMCT5Vly1TJjpWyKIYlXdsz7csux6Pd8APKs472aDJ1arESGKDpNGXr59Wtn+PTVLx6UjIbuFglgeqpacfgoMeDqUeqHRsqkfQ8rwfFKUhj0ba/zm0a/QGxbHqh69FsbCeldzvWdB7xtBiDxiNcTpzDCeQqVY0r6pmAcDyzXTyTjV1ITl5W47WEbi2NV29Pkk2sxOHIQiW/VksxAt/xyKqCPdU05/55dMeAhaQjOI6m4nUGuQPYZsBxrMIh5SoA+6uq+goAhBDeC+A1AJ6JzKHnjeoRS1cQWvtboQSqNms22PIoBpucPTsYL1+7yLfVz1PL2cVKT//wQuRw0HLwqNpIB6AN8KwqLbv4qVpj2ilLYe2hlLEjc6TBna36zLMLUsbNsyvxPP6mTH2YlzIxrJsGSSbU9qfLK7wfTkWyE2MbqtrcTpr9zB087zx+o9rrtAXh1W84ZK4Kr2xVfXX1vWKQ4NNeu+qYsRuOPWhu5ICmDa21ddMFUjdlXfadXRuCrgVsFGmMWEZUTQC0ffu1tf7fD/1CVHkbv+VCzX+s7fIichrVMdg13vmeZ9rC/tZNy6Dj0ttkdXl/Ex5z7zEyamvIdzwzGV1n+Jzjn0ylt3lSW26137S2jwo7n3Wd2W7r2BZo2lOHextLrZONJnCo5be3ifXs1ttsSjdT4Gvipf8n8HW0M7pTL+mTwSSAh+T/gwCuXqPK9XDKWmdYUFBQUFBQUFDg4VMX17F0K+fZ5wA8GyGEu+TvTetcQQBbRnKoamXucrpiLHln4u6Ue54kjrsR7va83YtKFbwdtnX60N2s5+lrnTO8Y5xGpJ66Y7IOHV591QjZk9QQGvOubaflGajrzlXjZ3ll8fvtrtPb9Wn/tUkQtE7q7algX2rsMquqItSjW6UXbTv21eIY6m8cQh6XrJ9Rvydd6fISVYeNQdSQmr+VpHR5xataWb3jPXW4jRuotOTBUzsyELVXN8+ZgVAps1XfMx/N15MqE57EysMIkiPaPc7zLqmNJ0lVlSGfqfczUNfffotnguF5oA8aqFrBqBLDyGNgeuO4yyN4HM1zhYFmX1qnnln4AdDbtDRt5Xc5exFXoRkon7BOQhwD2uY6LqxZzyLyeZ30ps50HlQzo6YlhI2Lq/DMkaxmzFtTPbQ5RRJdUk0PVoI9j1yqq2YfXfENveDs64+qqvaF8ELUsVKfL0/mAXwFwCPDVVV1LQCHAJwv/5+H5Z+j2RdbhDmskCYKG0hWoROGZQAPIPeUYhqN8K5hArwQK3YgcTB4dh2qlmReKk5nf3qnoSzK/15wXTtR6IBert2QDqw21UE/9ZVO6nyXjIWeC2rVcktIqt7peD2EphqeeVhv3nFJ4zH8LIvMV5cJwgL8iY391c8bcKWYwmChcbR/7cZEwzVo23uboa5+9BggOxF3qWn6LSDzTjq1S+pakLmojsv15o66WTMD3Xj168OuANRU0bPPhp17ZJCARI+H0d12g4T76FdHa1O5A00bMKDpoU14AeZXstacjpphn0aTNlnfru+xDGObuU6XyYEN06RQZteecnMPcsZSbf7sszbGcDlmPZ6pVBcDrd+7XNV7mzc44Z1JrbacQH/vdbvhGBRdQgtdb5m/ejB3MbxdZ7VvBtw7BfzDdN03VOD+DYAXo6re12+x/VsAF4YQLkDd4D8A4AfXuoYbolYOIbwyhPClEML+EMJbNqIOBQUFBQUFBQXrjaqqDtQCP/qQPAHgEQDv78uTRanizwL4BGqO/v1VVX1xreu47pLDlblhq6GzFZmrRKWf84l6HwJpx7Eo6b1zR7sY+Qfkt93R6Y7N21lx56Ox6azEQ+PveWJxVVtbz1ptI7u72oFckqIqbAtPdavxpbwdrj13GcglfPNSX409aD2TNSguoSolplO1DHe7NpiuQtuMkk6VnlpHGqUFL8jtoKBDSteOXqFSE9aB39PmBNLlCWy/Z0HSeapWNTS33qaUmA0qNdB0nmNUv3eA/oFtL4tXqnD7OaOpt7CVLHrnHCsdWymZBuz3pDxWVT/o+a9dsSA9aL9PxasGnNbn1vxlJXgK9Xyo3sqkQc9UQufhQaQ7nocv0a8NmV7nsXuc56Tpfc6zLgwjj0dLKO12SY8X0D92ILC8cQI05xi79o0jtYPOtTYI90ifcm0btZn6tL3n0Yf+r+uBOgz2y1fPZ14NbZ8M3PFc4KuP1gcr3ArgWlTVn3qGiBmqqvoYgI+dzNpthFp5lW7YnmrNc3e3YnkN2GwHqBLZmHOP8E5R6CJQVZV1TTCcQNXjzYOqi5dzgsES8knVi3zv2bVYlZmibaKz3pjeItQV/LjNFskyI8oIeiYAVh2vtj/8Tp1M1S4LqNUtj5s6eZuGlUw6J5AWA3sChE7OkGceQ4d4X9UufGbbUUNfdC2u+5x7hKe6VQbIMxuw8GyEhuGfhMH8bV/qYqygOtDa9Y07v/cjXyx3IGcQVIV7VbyqatEuyFpftrPSuRfmpStMETGojZ4H9tWF8G0eCTKug25aFKRp/b6ueW/QOVbRll8/5tqq3IFmW5MOrLmDMtNdZagq1qNL0gPzXUAyo9HN5SAbLC3H2ijuQs6UDaFmQIC8/XRz49mFq/26Vecq42jHez/GUDdSQJNmNFi8ndf1f4+RtmuPnvrVTzW+vqiqaiaEl6A2j5kD8N9OAf50g2uVsBHM4UBu2NFDJ3rpPHs96lVQcFLRpOkzN7QuBQVrgTJPFxSsBp8+EzjjCeDJ76iqaiCp4XohrHd9Qgg3AHhlVVU/Hv9/PYCrq6r62Y53/gHANwA8tj617MTZKPXYTHUANkc9dlZV9ZxBExeadlHqsbnqACyDrgtNuyj1aGIz1GNZc/UzFRshOVy2G3ZVVc8JIdxVVdWVJ7VmA6DUY3PVYTPVYzkoNF3qsdnrsFwUmi712Cr1KOiPjfBW7rlhhxBORe2GfdMG1KOgoKCgoKCgoMBg3SWHVVUthRDohr0NwDtOhht2QUFBQUFBQUHB8rEhQbBX6IZ948moywpQ6pGwGeoAbJ56LBebpd6lHk1shnpshjqsBJul3qUeTZR6FCwL6+6QUlBQUFBQUFBQsHmxISekFBQUFBQUFBQUbE4U5rCgoKCgoKCgoKCHTc8cbtQ5zCGE80MIt4QQ7g8hfDGE8PPx/ngI4a9DCA/E67pEMw4hbAshfD6E8NH4/wUhhDtiu7wven6f7DpsDyF8IISwL4SwN4Tw7RvRHiGEX4h98oUQwn8PIZy+Ee2xUhSa7tWn0HSqR6HplZVbaDqvQ6HpglVjUzOHIZ3D/N0ALgHwL0MIl6xT8UsAfrGqqksAfBuAn4llvwXAJ6uquhDAJ+P/64GfR33INvGbAN5WVdUu1Kd2/9g61OH3AHy8qqqLAPzjWJ91bY8QwiSAnwNwZVVVl6L2eP8BbEx7LBuFphsoNI1C06tEoekchaYLVo+qqjbtH4BvB/AJ+f9XAPzKBtXlLwB8F4AvATgn3jsHwJfWoezzUA/olwP4KICAOsr8kNdOJ6kOzwbwIKITk9xf1/ZAOn5xHLW3/UcBvGK922MV9S80XRWaNuUVml67uhSaLjRd/tbgb1NLDuGfwzzZkvakIYQwBeCFAO4AMFFV1SPx0aMAJtahCr8L4JdRn2wPAGcBOFJVFU85X492uQDAPwD4k6g2+eMQwjdhndujqqpDAH4bwFcBPALgawDuxvq3x0pRaLrG76LQNIBC02uFQtMACk0XrBE2O3O44QghPAvAnwN4c1VVc/qsqrc/JzUWUAjhewEcrqrq7pNZzgAYAnAFgD+oquqFqM9Qbagm1qk9zgTwGtST4LkAvgnAK09mmU83FJruodD00wSFpnsoNF2wJtjszOGyz2FeS4QQhlFPOO+pquqD8fZMCOGc+PwcAIdPcjWuAfDqEMI0gPeiVln8HoDtIQQGMV+PdjkI4GBVVXfE/z+AehJa7/a4DsCDVVX9Q1VViwA+iLqN1rs9VopC04WmLQpNrwKFphsoNF2wJtjszOGGncMcQggA3g5gb1VVvyOPbgLwhvj7DahtXE4aqqr6laqqzquqagr19/+vqqp+CMAtAG5Yx3o8CuChEMIL4q3vBHA/1rk9UKspvi2EcEbsI9ZjXdtjFSg0XWjaotD0ClFoOqtHoemCtcFGGz32+wPwKgB/D+DLAP7PdSz3O1CL3u8DcG/8exVqO5JPAngAwM0AxtexTtcC+Gj8/Y8A3AlgP4D/AeC0dSj/cgB3xTb5MIAzN6I9APwagH0AvgDgTwGcthHtsYr6F5pOdSo0XRWaXkW5habz8gtNl79V/5Xj8woKCgoKCgoKCnrY7GrlgoKCgoKCgoKCdURhDgsKCgoKCgoKCnoozGFBQUFBQUFBQUEPhTksKCgoKCgoKCjooTCHBQUFBQUFBQUFPRTmsKCgoKCgoKCgoIfCHBYUFBQUFBQUFPRQmMNnEEII/y6E8Gb5/9dDCD+/gVUqKFgVCk0XPN1QaLpgM6AEwX4GIYQwBeCDVVVdEUI4BXW0/Kuqqnp8Y2tWULAyFJoueLqh0HTBZsBQ/yQFTxdUVTUdQng8hPBCABMAPl8mnIKtjELTBU83FJou2AwozOEzD38M4I0AngvgHRtblYKCNUGh6YKnGwpNF2woilr5GYYQwqkA9gAYBnBhVVXHN7hKBQWrQqHpgqcbCk0XbDSK5PAZhqqqjoUQbgFwpEw4BU8HFJoueLqh0HTBRqMwh88wRAPnbwPwLza6LgUFa4FC0wVPNxSaLtholFA2zyCEEC4BsB/AJ6uqemCj61NQsFoUmi54uqHQdMFmQLE5LCgoKCgoKCgo6KFIDgsKCgoKCgoKCnoozGFBQUFBQUFBQUEPhTksKCgoKCgoKCjooTCHBQUFBQUFBQUFPRTmsKCgoKCgoKCgoIf/F8crmo0WNCYCAAAAAElFTkSuQmCC", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "imageAnalyser.center = (890, 950)\n", "imageAnalyser.span = (100,100)\n", "imageAnalyser.fraction = (0.1, 0.1)\n", "\n", "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n", "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n", "\n", "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do a 2D two-peak gaussian fit to the OD images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do the fit" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "fitAnalyser = FitAnalyser(\"Two Gaussian-2D\", fitDim=2)\n", "\n", "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get the result of the fit" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:      (runs: 3)\n",
       "Coordinates:\n",
       "  * runs         (runs) float64 0.0 1.0 2.0\n",
       "Data variables:\n",
       "    A_amplitude  (runs) float64 707.7 677.1 672.9\n",
       "    A_centerx    (runs) float64 53.79 54.95 53.33\n",
       "    A_centery    (runs) float64 41.15 45.33 42.93\n",
       "    A_sigmax     (runs) float64 4.623 4.042 4.348\n",
       "    A_sigmay     (runs) float64 11.14 11.08 10.93\n",
       "    B_amplitude  (runs) float64 226.6 255.8 236.7\n",
       "    B_centerx    (runs) float64 56.33 56.77 55.35\n",
       "    B_centery    (runs) float64 40.97 45.56 43.29\n",
       "    B_sigmax     (runs) float64 15.93 14.59 13.32\n",
       "    B_sigmay     (runs) float64 11.67 11.32 10.98\n",
       "    delta        (runs) float64 -11.31 -10.54 -8.974
" ], "text/plain": [ "\n", "Dimensions: (runs: 3)\n", "Coordinates:\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Data variables:\n", " A_amplitude (runs) float64 707.7 677.1 672.9\n", " A_centerx (runs) float64 53.79 54.95 53.33\n", " A_centery (runs) float64 41.15 45.33 42.93\n", " A_sigmax (runs) float64 4.623 4.042 4.348\n", " A_sigmay (runs) float64 11.14 11.08 10.93\n", " B_amplitude (runs) float64 226.6 255.8 236.7\n", " B_centerx (runs) float64 56.33 56.77 55.35\n", " B_centery (runs) float64 40.97 45.56 43.29\n", " B_sigmax (runs) float64 15.93 14.59 13.32\n", " B_sigmay (runs) float64 11.67 11.32 10.98\n", " delta (runs) float64 -11.31 -10.54 -8.974" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fitAnalyser.get_fit_value(fitResult)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:      (runs: 3)\n",
       "Coordinates:\n",
       "  * runs         (runs) float64 0.0 1.0 2.0\n",
       "Data variables:\n",
       "    A_amplitude  (runs) float64 7.958 7.015 9.184\n",
       "    A_centerx    (runs) float64 0.02158 0.0181 0.02139\n",
       "    A_centery    (runs) float64 0.06839 0.06528 0.07269\n",
       "    A_sigmax     (runs) float64 0.03128 0.02593 0.03292\n",
       "    A_sigmay     (runs) float64 0.06829 0.06524 0.07257\n",
       "    B_amplitude  (runs) float64 9.231 8.323 9.877\n",
       "    B_centerx    (runs) float64 0.4686 0.3437 0.3496\n",
       "    B_centery    (runs) float64 0.4251 0.3388 0.3642\n",
       "    B_sigmax     (runs) float64 0.6246 0.4683 0.4882\n",
       "    B_sigmay     (runs) float64 0.4245 0.3386 0.3637\n",
       "    delta        (runs) float64 0.6074 0.4543 0.4687
" ], "text/plain": [ "\n", "Dimensions: (runs: 3)\n", "Coordinates:\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Data variables:\n", " A_amplitude (runs) float64 7.958 7.015 9.184\n", " A_centerx (runs) float64 0.02158 0.0181 0.02139\n", " A_centery (runs) float64 0.06839 0.06528 0.07269\n", " A_sigmax (runs) float64 0.03128 0.02593 0.03292\n", " A_sigmay (runs) float64 0.06829 0.06524 0.07257\n", " B_amplitude (runs) float64 9.231 8.323 9.877\n", " B_centerx (runs) float64 0.4686 0.3437 0.3496\n", " B_centery (runs) float64 0.4251 0.3388 0.3642\n", " B_sigmax (runs) float64 0.6246 0.4683 0.4882\n", " B_sigmay (runs) float64 0.4245 0.3386 0.3637\n", " delta (runs) float64 0.6074 0.4543 0.4687" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fitAnalyser.get_fit_std(fitResult)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:      (runs: 3)\n",
       "Coordinates:\n",
       "  * runs         (runs) float64 0.0 1.0 2.0\n",
       "Data variables:\n",
       "    A_amplitude  (runs) object 708+/-8 677+/-7 673+/-9\n",
       "    A_centerx    (runs) object 53.788+/-0.022 54.955+/-0.018 53.330+/-0.021\n",
       "    A_centery    (runs) object 41.15+/-0.07 45.33+/-0.07 42.93+/-0.07\n",
       "    A_sigmax     (runs) object 4.623+/-0.031 4.042+/-0.026 4.348+/-0.033\n",
       "    A_sigmay     (runs) object 11.14+/-0.07 11.08+/-0.07 10.93+/-0.07\n",
       "    B_amplitude  (runs) object 227+/-9 256+/-8 237+/-10\n",
       "    B_centerx    (runs) object 56.3+/-0.5 56.77+/-0.34 55.35+/-0.35\n",
       "    B_centery    (runs) object 41.0+/-0.4 45.56+/-0.34 43.3+/-0.4\n",
       "    B_sigmax     (runs) object 15.9+/-0.6 14.6+/-0.5 13.3+/-0.5\n",
       "    B_sigmay     (runs) object 11.7+/-0.4 11.32+/-0.34 11.0+/-0.4\n",
       "    delta        (runs) object -11.3+/-0.6 -10.5+/-0.5 -9.0+/-0.5
" ], "text/plain": [ "\n", "Dimensions: (runs: 3)\n", "Coordinates:\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Data variables:\n", " A_amplitude (runs) object 708+/-8 677+/-7 673+/-9\n", " A_centerx (runs) object 53.788+/-0.022 54.955+/-0.018 53.330+/-0.021\n", " A_centery (runs) object 41.15+/-0.07 45.33+/-0.07 42.93+/-0.07\n", " A_sigmax (runs) object 4.623+/-0.031 4.042+/-0.026 4.348+/-0.033\n", " A_sigmay (runs) object 11.14+/-0.07 11.08+/-0.07 10.93+/-0.07\n", " B_amplitude (runs) object 227+/-9 256+/-8 237+/-10\n", " B_centerx (runs) object 56.3+/-0.5 56.77+/-0.34 55.35+/-0.35\n", " B_centery (runs) object 41.0+/-0.4 45.56+/-0.34 43.3+/-0.4\n", " B_sigmax (runs) object 15.9+/-0.6 14.6+/-0.5 13.3+/-0.5\n", " B_sigmay (runs) object 11.7+/-0.4 11.32+/-0.34 11.0+/-0.4\n", " delta (runs) object -11.3+/-0.6 -10.5+/-0.5 -9.0+/-0.5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fitAnalyser.get_fit_full_result(fitResult)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the Ncount" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do a 1D fit" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "\n", "Ncount.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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3tTEi8lj9AsaYccA4cNy8ljhpVXIhIrxY6kXqF6xPOvd0uLu6UCL/GT5r78aKXVkZuTyMP7ad5KPmJWhQIkd895NSKnHYOtBsjBlvjHnGGPMscAk4AJy90y1kfb8zU9pJHGcSdwRY61QalNU7K97u3gB8vPJjWk9vQHT6cfzYuQQ+nq50/3UrXX7ZwpHz2qWkVGKy++qj7Nb3vDjGEyYBc4BXrCavALOt5TlAJ+sqpCrAlQTdTCoNm/niTPpX6c+4beN4ZX4NejS8yKCmgQQfucRzw1czVLuUlEo0dt+n8IeIhAJzgV7GmMvAl0ADETkI1Ld+BpgPhANhwA9AT5uzqRTCx8OHoQ2HsvHVjWTzzkbb6W3InXMfy9+uRZPSOflueRj1h61i0Z4zepWSUk9JJ8RTKUpMXAy/7vqVzuU64yIuHLp4iIhLmfhoTij7z16jVlE/Pm5RkgLZfJwdValk60ET4mlRUCnW+cjzFP2uKOVzlWd04/+xbr8bI5YcICr2Nt2eLUCvOoXx9rDtWgqlUiydJVWlSlnSZeGLel8QfCqYCuPKclFmsKh/dZqVycXoFYdoMGw1C0NOa5eSUo9BzxRUinfy6kl6ze/F7P2zqZCrAitfWcneUzF8ODuEfWeuUbNINga3KElBv/TOjqpUsqBnCipV88/oz8wXZzK93XSqBVQjg2cGKhXIwtw3q/NR8xLsOHaZhiNW8/XCfURGxzo7rlLJmhYFlSqICG1LtOW7Jt8BsDdiL+XHlaWA/1GWDahF87K5GbPyEPWHrmLBbu1SUup+tCioVOlq1FVuxt6kzoQ6DFrZmw9b5GfG61XJ5O3BG79vo9NPmzkUcd3ZMZVKdrQoqFSpckBldr+xm3eqvcP47eMpMboEJ26tZu6b1fm4eQl2HL9MoxGr+XLBPm5EaZeSUndoUVCplre7N183+JrNr20mR/ocrD++HjdXFzpXL8Dyt2vToqw//1t1iPrDVvHXLu1SUgr06iOVRsTExRBn4vBy82LlkZWEXQzj1fKvsu3YJT6YtYfQ01epXjgrg1uUonB2vUpJpW569ZFK89xd3fFy8wJg4s6JdJvbjXoT65Epw0XmvFmdwS1KsuvEFRp/u5ovFuzVLiWVZmlRUGnOjy1+ZFyzcWw7vY3SY0szdMMQOlQJYMWA2rQq58/3q8KpN3QV83ad0i4lleZoUVBpjou40O2ZboT2CqVx4ca8t+w9poRMIVt6T4a0K8sfb1Qja3oP3py0nQ4/biLs3DVnR1YqyeiYgkrzlhxaQr2C9XARF4JPBVPCrwSerumYtOkoQxbtJzI6jq41CtC7XhHSe+pcSirl0zEFpR6gQaEGuIgL16Ov0+i3RpQZW4ZVR1fwr6r5WTGgNm0q+DNudTj1hq5kzk7tUlKpmxYFpSzpPdIzrd00AOpNrMdrc17DxTWSr9uW5c+e1fDL4Envydt5+YdNHDirXUoqddLuI6XucjPmJoNXDeab9d/g5+PHztd3kt0nO3G3DZM2H+ObRfu5ERVLl+r56VO/qHYpqRRHn6eg1BPYdnobs/bN4pM6nwBwK/YWXm5eXLgexZBF+5my5TjZM3jyftNAWpTNjYg4ObFSj0bHFJR6AhVyVYgvCKERoeQdnpdxW8fh6+POl8+XYWbPauTI6EWfKTtoP26jdimpVMHWoiAi/URkj4iEiMhkEfESkboiss1aN0FE3Ky2IiIjRSRMRHaJSAU7syn1OLzcvCiVvRQ95vWg7oS6HLhwgPJ5fZnVqzqftS7FvjPXaPztGj6dF8q1WzHOjqvUE7OtKIiIP9AbCDLGlAJcgZeBCUB7a91R4BVrl8ZAEeurOzDWrmxKPa6CvgVZ1mkZ41uMZ+fZnZQZW4av132Nq4vQoXI+VgyozQtBAYxfd5i6Q1cxa/tJvUpJpUh2dx+5AemsswFv4AYQbYw5YG1fAjxvLbcEJhqHjUBmEcllcz6lHpmI0LV8V0J7htK8WHMuRF6I35bFx4Mv2pRhZs/q5MrkRd+pO+jyyxbOXr3lxMRKPT7bioIx5iTwDXAMOA1cAaYBbiJyZ4CjLZDHWvYHjid4iRPWur8Rke4iEiwiwREREXbFV+q+cmXIxfR20/m83ucALAtfxtuL3uZG9A3K5cnMzJ6O6bk3hl/gueGrmbvzlJMTK/XoHloURKSOiPxpjQ3sEZEZIlL7EfbzxfHbfwEgN+ADdADaA8NFZDNwDYh7nMDGmHHGmCBjTJCfn9/j7KpUonJ1cQVg3fF1DNs4jNJjS7M0fCmuLkLn6gWY37smBbL58Nbk7bw5aRuXbkQ7ObFSD/fAoiAiTYGfgLk4xgM6APOBn0SkyUNeuz5w2BgTYYyJAf4EqhljNhhjahpjKgGrgTtdSSf5/7MGgABrnVLJ2oe1PmTlKytxc3Gjwa8N6Dq7KxdvXqSgX3pmvF6VAc8VZWHIGRqOWM2K/eecHVepB3rYmcI7QCtjzM/GmJ3GmB3GmJ+AVsC7D9n3GFBFRLzFcQF3PWCviGQHEBFP6zX+Z7WfA3SyrkKqAlwxxpx+sj+WUkmrVv5a7HpjFwNrDGTizonMCJ0BgJurC2/WLcKsXtXx9fagy89bGPjnbp2aWyVbD7x5TUT2GWOKP+62BG0GAy8CscB24DXgU6AZjoI01hgzwmorwCigERAJdDHGPPDONL15TSVHeyP2UixbMVzEhWXhyyiWrRgBGQO4FRPH8CUHGLcmnADfdAxtV45KBbI4O65Kg574jmZrx2ced1tS0aKgkrOYuBgKjizIlVtX+Kr+V/QI6oGLuLD58EUGTN/J8UuRdKtZkP4NiuLl7ursuCoNeZqicBlHv/8/NgE1jDG+iZLwCWlRUMld+KVweszrwdLwpdTIW4Mfmv9A8WzFuREVy2fz9zJp0zGK5kjPsBfKUco/k7PjqjTiaYpCLWsxHY6bygwQBtwEMMasStyoj0eLgkoJjDFM2DmB/ov6ExkTSWivUAr6FgRgxf5zvDtjFxdvRNOnXhHeqF0IN1edfUbZ62mKgjvwGdAVx8AxOK4Q+gX4j3VVkdNoUVApyZnrZ5i+ZzpvVX4LgLPXz5IjfQ4uR0bzwew9zN15irJ5MjPshbIU8kvv5LQqNXuaCfG+BnyBAsaYCsaYCkAhIBOOG9OUUo8oZ/qc8QUhNCKU/N/mp/+i/ri7xfDdS+X57qXyHL1wg6Yj1/DzusPcvq3TZKik97Ci0AzoboyJn/7RGHMVeAN42H0KSqn78M/gT5dyXRi+cTilxpZi8aHFNC+bm8V9n6VqwawMnhtKx/GbOHn5prOjqjTmYUXBmHv0Lxlj4nCMLyilnkAmr0yMaTqG1Z1X4+nqScPfGvLanNfwy+DJT50r8kWb0uw8fplGw1czY+sJnVxPJZmHFYVQEel090oR6QjssyeSUmlHzXw12fH6DgbVHESu9LkQEUSElyrlZUGfZwnMlZEB03fS/detnL8e5ey4Kg142ECzP47pKW4CW63VQTiuRmptTXrnNDrQrFKjZeHLGLFpBGOajCF3hgDGrw3nm0UHyODlxmetS9OoVE5nR1Qp3BMPNBtjThpjKgOfAEesr0+MMZWcXRCUSq1OXD3B8sPLKTmmJN9vHctrNQswr3cNcmby4vXfttJ/2g6u3NQH+Sh76DOalUqGDl86TI95PVgSvoRqearxY/MfKeRbjFHLDzJ65SGyZ/BkSNuy1CiSzdlRVQqkz2hWKoUp4FuARR0XMaHVBPad38eKIyvwcHOh/3PF+OONaqTzcKXj+E18NDuEm9GPNfu8Ug+kZwpKJXPnI8+TJV0WXMSF2ftmkyN9Dspmr8jXi/bx87ojFMzmw9AXylI+r1NnnVEpiJ4pKJWCZfPOhou4cNvc5sOVH1JtfDUGLn+btxvmY9JrlYmKvc3zY9fzzaL9RMfednZclcJpUVAqhXARF9Z0WUPPij35dtO3lBxTkqsEs6BvTdpUCGDUijBajV7H/jPXHv5iSt2HFgWlUpCMnhkZ1WQUa7usxdvdm8a/N+bQpRC+aVeWcf96hnPXbtH8u7V8v+oQcTpNhnoCWhSUSoGq563Ojh47mNZ2GuVzlQcgt995FvapSZ3ifnyxYB/tx23g6IUbTk6qUhotCkqlUJ5unrQr2Q6A/ef3U+H7CnSa04aBzbIx7IWy7Dt9jcbfruH3TUd1mgz1yLQoKJUKFM5SmCENhrD66GpKjS3FyZiZzO9TnQp5fXl/Zgidf97C2au3nB1TpQC2FgUR6Scie0QkREQmi4iXiNQTkW0iskNE1opIYautp4hMFZEwEdkkIvntzKZUauLq4kqfKn0I6RlCjbw16L2wNx1mN+Hnzs/wScuSbDp8geeGr2bOzlPOjqqSOduKgjVvUm8gyBhTCnAF2gNjgQ7GmHLAJGCQtcurwCVjTGFgOPCVXdmUSq3yZ87Pgg4L+K31bzQp0gR3Nzc6Vc3P3DerUdDPh96Tt9Nr0jYu3Yh2dlSVTNndfeQGpBMRN8AbOIVjyu2M1vZM1jqAlsAEa3kGUE9ExOZ8SqU6IkKHMh14r8Z7ACw5tITWf1Snf1N4p2ExFu85w3MjVrNi3zknJ1XJkW1FwZow7xscj/E8DVwxxiwGXgPmi8gJ4F/Al9Yu/sBxa99Y4AqQ9e7XFZHuIhIsIsERERF2xVcq1XB1ceVq1FVq/VKT/bdG8nu3MmTx9qDLL1sY+OcurkfFOjuiSkbs7D7yxfHbfwEgN+BjPYehH9DEGBMA/AwMe5zXNcaMM8YEGWOC/Pz8Eju2UqlO3QJ12dNzD29WepNRm0fRdmY13mpynR61CjJly3Eaf7uaTeEXnB1TJRN2dh/VBw4bYyKMMTE4nstQHShrjNlktZkKVLOWTwJ5AKzupkyAflKVSgQZPDMwsvFI1nVdR3qP9Jy4doyBjQOZ1qMqgtD+h4189lcot2J0cr20zs3G1z4GVBERbxwP6akHBAPtRKSoMeYA0ADYa7WfA7wCbADaAsvv9ShQpdSTq5qnKtt7bMfd1R2AsGuL6FQvivCjFfhhzWFW7o9g+IvlKOWfyclJlbPYOaawCceA8TZgt/Ve44BuwB8ishPHmMI71i7jgawiEgb0B96zK5tSaZmnmycu4vin/+uuX+k2rwvbb77Lly/k5OqtGFqNXsfIZQeJjdPJ9dIinTpbqTQs7nYcY7aMYeCygQAMqjmYE8drMW/XWcoGZGLoC+UonD29k1OqxKZTZyul7snVxZW3Kr9FaK9Qns33LAOXD6BdtRuMerk8Ry9G0nTkGn5ed5jbOrlemmHnmIJSKoXImykvf738F+uOr6NG3hoARLvuZvbmjAyeG8riPWcZ0q4MAb7eTk6q7KZnCkopwHHT252CcOLqCdr/2Zwtkd3oWieKXScu02jEGqYHH9fJ9VI5LQpKqX8IyBjAnJfmcCPmBh9vbEvFcrMoktOVd2bsotvErURci3J2RGUTLQpKqXtqVLgRe3ruoXfl3kzcPY6d0d35d6MCrD4YQcMRq1kYctrZEZUN9OojpdRDbTqxia2nt9KzYk8OnL1G76nr2Xcqljbl/fmoRUkypXN3dkT1GPTqI6XUU6kcUJmeFXsCcPT6RoJvdaBa6T3M2nmSRiNWs+agzkOWWmhRUEo9loCMARTLVozJYe+So8BQxO0c/xq/mQ9nhxAZrZPrpXRaFJRSjyXQL5A1XdYwusloQs5vYWfMa5QuupmJG47SdORath275OyI6iloUVBKPTYXcaFnxZ6E9gylboG6NCuTm0ndKhMde5u2Y9czZNE+omN1moyUSG9eU0o9sTyZ8jCn/RzAcZ9Dx3qHmbB5A6NWNGX5vgiGv1iW4jkzPuRVVHKiZwpKqaciItx5SOL+C7vZdH48t/3eIfzqZlp8t46xKw8Rp9NkpBhaFJRSiWZEoxEs7rgYN7c4DtwegHvW8XyxcCsvfr+BoxduODueegRaFJRSiapBoQaEvBFC/yr92Xd9Jp3rRLP/7DUaf7uG3zYe1Wkykjm9eU0pZZtDFw9RKEshTl2+yYu/DufIybzULVqUr54vQ85MXs6Ol2bpzWtKKacolKUQAOk8b7Hj+hdcyvAmi49MpsHwlczecVLPGpIhLQpKKdv5pvNlS7ctVMhdhjOuIzjrMYieUxfy5qTtXLwR7ex4KgEtCkqpJFE8W3FWdl7J2KZjuSUHuOjTlwWhYTQcsZrl+846O56y2FoURKSfiOwRkRARmSwiXiKyRkR2WF+nRGSW1VZEZKSIhInILhGpYGc2pVTScxEXXg96nb299jK+5ffM69WIrD4edJqwgPf+2MX1KJ0mw9lsKwoi4g/0BoKMMaUAV6C9MaamMaacMaYcsAH409qlMVDE+uoOjLUrm1LKufwz+vNy6ZcpkTsjbzWJ5HS6bvxvx6c0GL6EjeEXnB0vTbO7+8gNSCciboA3cOrOBhHJCNQFZlmrWgITjcNGILOI5LI5n1LKyarmqcQrZf/FFbfp7IzuTusff+DTeaHciolzdrQ0ybaiYIw5CXwDHANOA1eMMYsTNGkFLDPGXLV+9geOJ9h+wlr3NyLSXUSCRSQ4IkKn61UqpcuSLgs/tfyJJf9aQs5MHpzxHMiQjR/Q/Lu17D5xxdnx0hw7u498cfz2XwDIDfiISMcETV4CJj/u6xpjxhljgowxQX5+fokTVinldPUL1iek524GVB1A92rVuXorhlZj1vLt0oPExOnkeknFzu6j+sBhY0yEMSYGx9hBNQARyQZUAv5K0P4kkCfBzwHWOqVUGuHt7s2Q54YwpEl/FvetRYG8m/jP6s40HT2XsHPXnR0vTbCzKBwDqoiItzhmy6oH7LW2tQXmGWNuJWg/B+hkXYVUBUd3kz4EVqk0KpO3Oy3KZyPGfSvLL3WkxneD+HFNOLd1cj1b2TmmsAmYAWwDdlvvNc7a3J5/dh3NB8KBMOAHoKdd2ZRSKUPvyr0J6bmLSv7lOOv2LW8uaU3z/83gxKVIZ0dLtXTuI6VUsnfb3OaHrT/Qf9G/yRo1gIxSiQ+bl6DdMwHx03arR/eguY+0KCilUozLty5zLdKDAdN3svTYJGrmq8wPL7XDL4Ons6OlKDohnlIqVcjslZk8Wbz5qXNZyPAHs0+9QqlhrzBrR7izo6UaWhSUUimOj6c3YX320DawI+eYSruZNWj70w9cuRnj7GgpnhYFpVSK5JvOl+kvTmBRhyVk8nbnj2M9qTPsT9Yc1Jtan4YWBaVUivZc4foc67+XMY2m4OuVg3+N38yrk6YQGa2T6z0JLQpKqRTP292bN6o8z1+9a1K37Hl+OvgS+b6uw8K9oc6OluJoUVBKpRpe7q6Me7EDPct/yMW4zTSdWonnJ37CrRg9a3hUWhSUUqmKm4sbo1sMJrjbdvx9Avnz8Efk+7oOe09fffjOSouCUip1Kp+7BEcHbKZ/0DC846rRYtRaRq84yK0Yffzng2hRUEqlWiLC0Kb92Pr2f6kfmIMPl44k59elmBu61tnRki0tCkqpVC+LjwdjOlTg9RrPcCPmAi2m1aLJhB7ciL7h7GjJjhYFpVSaICJ81rgzu98IoXD65iw4Mo6cXxdjVuhSZ0dLVrQoKKXSlOI5cnHg7ZkMrDiZqFjoN20Ts7afJCXPA5eYtCgopdIcEeHzJu0JeSOE8tlr03fqDqqP7s/P2yan+eKgRUEplWYVzZGZ6a9X4+2GhdgWsZCuc1+mxo9NOHk17T70UYuCUipNc3UR3qpTnM3d1lHU6w02nFxBwW+L8+3GMdw2ae/Z0FoUlFIKKBOQhV1vf8c75ebgElOQfot6M2PnFmfHSnJaFJRSyuLp5spXrZ5jTdfllPMazbtTz/PfeaFM3zOTmLi0MS23rUVBRPqJyB4RCRGRySLiJQ6ficgBEdkrIr2ttiIiI0UkTER2iUgFO7MppdT9BOXPytp+r9Kxcj7GrFvCCzPaUGpMeYJPpf4nPdpWFETEH+gNBBljSgGuQHugM5AHKG6MCQSmWLs0BopYX92BsXZlU0qph/H2cOO/rUoxtcvLFHEdzKELZ6j0Q2X6L3o7Vd/0Znf3kRuQTkTcAG/gFPAG8IkxjhEcY8w5q21LYKJx2AhkFpFcNudTSqkHeraoH1v6D6R70Zn4xD7H8I3DqPxDjVQ7CG1bUTDGnAS+AY4Bp4ErxpjFQCHgRREJFpEFIlLE2sUfOJ7gJU5Y6/5GRLpb+wZHROgTlpRS9svk7c6Yl2sy7YWfKMw3XDjThJ/WHiE2Lo7Lty47O16isrP7yBfHb/8FgNyAj4h0BDyBW8aYIOAH4KfHeV1jzDhjTJAxJsjPzy+xYyul1H01Lp2Ldf170bRwKz79ay9Vv/sPRUYWZWrI1FRz05ud3Uf1gcPGmAhjTAzwJ1ANxxnAn1abmUAZa/kkjrGGOwKsdUoplWxkz+DFD52C+Pr5Mly6lJ/rkZlp/0d7Wk5pyYmrJ5wd76nZWRSOAVVExFtEBKgH7AVmAXWsNrWAA9byHKCTdRVSFRzdTadtzKeUUk9ERHihYh5W9HmFZjnH4xvzKvMPLiZwdAkm7Z7k7HhPxc2uFzbGbBKRGcA2IBbYDowD0gG/i0g/4DrwmrXLfKAJEAZEAl3syqaUUokhTxZvpnavwc/rA/h0YTUumNGEnxUo7exkT05Scj9YUFCQCQ5O/dcNK6WSv7Bz1+g3dQe7T16lVbncpMs6E28Pd96t8S4erh7Ojvc3IrLVGtf9B9vOFJRSKi0pnD0Df/aszugVYYxcfpCrXhu4YJYyLXQaPzb/kcoBlZ0d8ZHoNBdKKZVI3F1d6Fu/KLN6Vqdc+kH4RX3A0UsRVB1flX4L+3E9+rqzIz6UFgWllEpkZQIyM++tGvSu1h7fa9+Ry60Fo7eM4cjlI86O9lBaFJRSygZe7q4MalaCqd3qUsCtNzkjx/HXNjeiYuP4Pvh7zkeed3bEe9KBZqWUstn1qFg+nRfKlC3Hyet3lQ2Rr+DrlZlvG31L+1LtcVy1n3QeNNCsZwpKKWWz9J5ufPl8Gca/EkTkTT9yRY3A2zUXL//5Ms0mN+PYlWPOjhhPi4JSSiWReoE5WNzvWZoFVsac+y+lfXqz4vBKavxUI9k8r0EvSVVKqSSUxceD0S9XoOHOnHwwy5NccRVoUwbcXNy4bW4TfimcwlkKOy2fnikopVQSExFalvNncb9aVC8QyPR12ej002aGrRtLidElGLxyMNFx0U7JpkVBKaWcJGcmLyZ0qcinrUoRfOQS45dmo0quJny86mMqfF+BjSc2JnkmLQpKKeVEIkLHKvlY0KcmJXLk4VhYNxrnHMHlW1eoNr4aX6z5IknzaFFQSqlkIH82H6b1qMq7jYoTdqwofjdG0aJwF57J/QxAkj3pTYuCUkolE64uwhu1CzH7zerkyODLjt1tWLo9B9duxTBo+SA6/tnR9pvetCgopVQyE5grI3PerEGvOoWYsfUEjUas4fw1mLZnGoGjA/l91++2PelNi4JSSiVDHm4uvNOwONNfr4a7q7B4Y3W6F5tBQd9CvL/8fW7G3rTlfbUoKKVUMvZMPl/m96lJp6r5mLfNFZ/LnzG24Ry83b1teT8tCkoplcx5e7jxSctS/PpqJSKj4c1fTzB+7WFb3kuLglJKpRA1i/ixqN+ztCybmwLZUuCZgoj0E5E9IhIiIpNFxEtEfhGRwyKyw/oqZ7UVERkpImEisktEKtiZTSmlUqJM6dwZ9mI56hbPYcvr2zb3kYj4A72BEsaYmyIyDWhvbX7HGDPjrl0aA0Wsr8rAWOu7UkqpJGJ395EbkE5E3ABv4NQD2rYEJhqHjUBmEcllcz6llFIJ2FYUjDEngW+AY8Bp4IoxZrG1+TOri2i4iHha6/yB4wle4oS17m9EpLuIBItIcEREhF3xlVIqTbKtKIiIL47f/gsAuQEfEekIDASKAxWBLMC7j/O6xphxxpggY0yQn59fIqdWSqm0zc7uo/rAYWNMhDEmBvgTqGaMOW11EUUBPwOVrPYngTwJ9g+w1imllEoidhaFY0AVEfEWxwNI6wF774wTWOtaASFW+zlAJ+sqpCo4uptO25hPKaXUXWy7+sgYs0lEZgDbgFhgOzAOWCAifoAAO4DXrV3mA02AMCAS6GJXNqWUUvcmdk2qlBSCgoJMcHCws2MopVSKIiJbjTFB99yWkouCiEQAR59w92yAvXPQPpnkmguSbzbN9Xg01+NJjbnyGWPueaVOii4KT0NEgu9XKZ0pueaC5JtNcz0ezfV40lounftIKaVUPC0KSiml4qXlojDO2QHuI7nmguSbTXM9Hs31eNJUrjQ7pqCUUuqf0vKZglJKqbtoUVBKKRUvVRYFEWkkIvutB/a8d4/tniIy1dq+SUTyJ9g20Fq/X0QaJnGu/iISas0gu0xE8iXYFpfgwURzkjhXZxGJSPD+ryXY9oqIHLS+XkniXMMTZDogIpcTbLPzeP0kIudEJOQ+2+/7wCibj9fDcnWw8uwWkfUiUjbBtiPW+h0ikqh3hD5CrtoiciXB39eHCbY98DNgc653EmQKsT5TWaxtthwvEckjIius/wf2iEife7Sx9/NljElVX4ArcAgoCHgAO3E86Cdhm57A/6zl9sBUa7mE1d4Tx+yuhwDXJMxVB/C2lt+4k8v6+boTj1dnYNQ99s0ChFvffa1l36TKdVf7t4Cf7D5e1ms/C1QAQu6zvQmwAMdULlWATXYfr0fMVe3O++F4qNWmBNuOANmcdLxqA/Oe9jOQ2LnuatscWG738QJyARWs5QzAgXv8e7T185UazxQqAWHGmHBjTDQwBccU3gm1BCZYyzOAeiIi1vopxpgoY8xhHPMwVSJxPDSXMWaFMSbS+nEjjpli7fYox+t+GgJLjDEXjTGXgCVAIyflegmYnEjv/UDGmNXAxQc0ud8Do+w8Xg/NZYxZb70vJN3n61GO1/08zWczsXMlyefLOGaR3mYtXwP28s/nytj6+UqNReFRHtYT38YYEwtcAbI+4r525kroVRy/DdzhJY6HC20UkVaJlOlxcj1vnarOEJE7U5wni+NldbMVAJYnWG3X8XoU98tu5/F6XHd/vgywWES2ikh3J+SpKiI7RWSBiJS01iWL4yUi3jj+c/0jwWrbj5c4urXLA5vu2mTr58u2WVLVkxPHw4iCgFoJVuczxpwUkYLAchHZbYw5lESR5gKTjTFRItIDx1lW3SR670fRHphhjIlLsM6ZxytZE5E6OIpCjQSra1jHKzuwRET2Wb9JJ4VtOP6+rotIE2AWjme1JxfNgXXGmIRnFbYeLxFJj6MI9TXGXE2s130UqfFM4VEe1hPfRhzPj84EXHjEfe3MhYjUB94HWhjHg4iA+MebYowJB1bi+A0iSXIZYy4kyPIj8Myj7mtnrgTac9epvY3H61HcL7vTHyQlImVw/B22NMZcuLM+wfE6B8wk8bpNH8oYc9UYc91ang+4i0g2ksHxsjzo85Xox0tE3HEUhN+NMX/eo4m9n6/EHihx9heOs59wHN0JdwanSt7Vphd/H2ieZi2X5O8DzeEk3kDzo+Qqj2Ngrchd630BT2s5G3CQRBpwe8RcuRIstwY2mv8f2Dps5fO1lrMkVS6rXXEcg36SFMcrwXvk5/4Dp035+0DgZruP1yPmyotjnKzaXet9gAwJltcDjZIwV847f384/nM9Zh27R/oM2JXL2p4Jx7iDT1IcL+vPPREY8YA2tn6+Eu3gJqcvHKPzB3D8B/u+te4THL99A3gB061/IJuBggn2fd/abz/QOIlzLQXO4nj40A5gjrW+GrDb+kexG3g1iXN9Aeyx3n8FUDzBvl2t4xgGdEnKXNbPHwNf3rWf3cdrMnAaiMHRb/sqjodFvW5tF2C0lXs3EJREx+thuX4ELiX4fAVb6wtax2qn9ff8fhLnejPB52sjCYrWvT4DSZXLatMZx8UnCfez7Xjh6NIzwK4Ef09NkvLzpdNcKKWUipcaxxSUUko9IS0KSiml4mlRUEopFU+LglJKqXhaFJRSSsXToqCUUiqeFgWllFLxtCgolYhEpKI1caCXiPhYc+KXcnYupR6V3rymVCITkU9x3DWfDjhhjPnCyZGUemRaFJRKZCLiAWwBbuGYsiHuIbsolWxo95FSiS8rkB7Hk7O8nJxFqceiZwpKJTLrmdBTcMzumcsY86aTIyn1yPQhO0olIhHpBMQYYyaJiCuwXkTqGmOWP2xfpZIDPVNQSikVT8cUlFJKxdOioJRSKp4WBaWUUvG0KCillIqnRUEppVQ8LQpKKaXiaVFQSikV7/8AmoMqee5uVFcAAAAASUVORK5CYII=", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fitAnalyser = FitAnalyser(\"Linear\", fitDim=1)\n", "\n", "params = fitAnalyser.guess(Ncount, x=\"runs\", dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(Ncount, params, x=\"runs\").load()\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(3), dask=\"parallelized\").load()\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount.plot.errorbar(ax=ax)\n", "fitCurve.plot.errorbar(ax=ax, fmt='--g')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate the mean and standard deviation" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "Ncount_mean = Ncount.mean(dim='runs')\n", "Ncount_std = Ncount.std(dim='runs')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "env", "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": "3.8.10" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650" } } }, "nbformat": 4, "nbformat_minor": 2 }