Photovoltaic_Fault_Detector/GPS_Panel/ClassifierPanel_KML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from matplotlib import path\n",
    "import matplotlib.patches as patches\n",
    "from skimage import draw\n",
    "import scipy.ndimage as ndimage\n",
    "import Utils\n",
    "import georasters as gr\n",
    "import cv2\n",
    "from Utils import doubleMADsfromMedian\n",
    "from skimage.transform import resize\n",
    "import pickle\n",
    "import simplekml\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import time\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def classifier(base_model, recognizer, labels, image_input):\n",
    "    weight, height, dim = base_model.input.shape[1], base_model.input.shape[2], base_model.input.shape[3]\n",
    "    Im_resize = cv2.resize(image_input, (weight, height), interpolation = cv2.INTER_AREA)\n",
    "    vec = base_model.predict(tf.keras.backend.expand_dims(Im_resize,0)).flatten()\n",
    "    prob = recognizer.predict_proba([vec])[0]\n",
    "    return labels.classes_[np.argmax(prob)], prob[np.argmax(prob)]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_T = \"Pampa/THERMAL_01.tif\"\n",
    "path_String = \"Pampa/BP-A-1.tif\"\n",
    "ZonaPV = 'BP-A-1'\n",
    "path_kml_panel = 'Pampa/KML/Paneles_' + ZonaPV +'_classifier.kml'\n",
    "path_dict = 'Pampa/KML/Mesa_' + ZonaPV + '.pickle'\n",
    "path_new_dict = 'Pampa/KML/Mesa_' + ZonaPV + '_classifier.pickle'\n",
    "\n",
    "GR_String = gr.from_file(path_String)\n",
    "GR_T = gr.from_file(path_T)\n",
    "geot_T = GR_T.geot\n",
    "## Load List in coordinate latitud and longitude ###\n",
    "with open(path_dict, \"rb\") as fp:\n",
    "    L_strings_coord = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_dataset = './Classifier/Data_set_2/Data_prueba_0/' \n",
    "output_recognizer = path_dataset + \"model_SVM/recognizer.pickle\"\n",
    "output_label = path_dataset + \"model_SVM/le.pickle\"\n",
    "\n",
    "\n",
    "img_width, img_height = 224, 224\n",
    "base_model = tf.keras.applications.Xception(input_shape=(img_height, img_width, 3), weights='imagenet', include_top=False)\n",
    "\n",
    "x = base_model.output\n",
    "x  = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "base_model = tf.keras.models.Model(inputs=base_model.input, outputs=x)\n",
    "recognizer_SVM = pickle.loads(open(output_recognizer, \"rb\").read())\n",
    "le = pickle.loads(open(output_label, \"rb\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier each panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2df4b97090>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "epsilon = -2\n",
    "matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
    "\n",
    "for string_key in ['10']:# L_strings_coord.keys():\n",
    "    print(string_key)\n",
    "    string = L_strings_coord[string_key]\n",
    "    for panel_key in string['panels'].keys():\n",
    "        panel = string['panels'][panel_key]\n",
    "        Points =  Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
    "        \n",
    "        if not GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].size == 0:\n",
    "            Im = np.zeros((img_height, img_width, 3))\n",
    "            Im[:,:,0] = cv2.resize(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]], (img_width, img_height))\n",
    "            Im[:,:,1] = Im[:,:,0].copy()\n",
    "            Im[:,:,2] = Im[:,:,0].copy()\n",
    "            panel['status'], panel['prob'] = classifier(base_model, recognizer_SVM, le, Im)\n",
    "        else:\n",
    "            print('problem with coords panel: ', string_key, '_', panel_key)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(Im.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2df758e190>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lR8bI2JzqPsXbu42DUzCLFhyj/G9Wf22dkXi0maVAIv3TdvKzLqLqQmWpfk/38fcMBLLorNS3rAwnYNn5pTK2kdcB7CpZHzOM8QjAMw4aefq8uY75Ym4mRwPufhOAA5kzpSyspmhO0dmYiofLaig8ljFKBxFvlxuRluV1jQF4b/82si9Ht61E41ormqv2cDVi+xlADv3mulFzro+r/1YKuwns/lCyjHF7+erkhkL42vkvnefjug3gHYOMbUekF9n8ex0RuOta4T7behTg3rZtl2fUrWo0TN3uqAwoUi4HVM+TqYcE4vyjOgVdNNNUCv/7s0t4jQp3QWhelGWop94G7NiuMUz8dbL2WimBqormeCPHmV2fgbjqjOfva8QX2UspHR5n+mW5XG48d4R3WPtjFXg82s7pY6MPR8sAKNpkUCMRy9Tx83SVLyaOYfS1OnEIUXv1Ng0BrufanZjWgvrYSP8owB24B1TekJIJBynLXWXG5Nd4Ckd3Keg1bI+zosgQfHubOiAtzx0Mrxmbg9+HHAt7AvoLwlHqIIqEVGrGbiglUWJfpbJUuDPGz3Wd47n6Qgd9doi2JyqHosDtkYrqLEV1vcQ0o/EdSlGxbgd4HtdzvZ4aR/7Q4uknJwJD4+Hpl2jXlkb6TtQck74qcu5RGKuKo1vRXLLFtlqjZb0EZDWQmon1mxwycNJ6IoM8hBwba48AJIrA9Lj/7mV4pDZ2IT0q0+uOvvO3KN2WOSy9Ro9FZdQwRNVRvVbL8M/Z+GV9HaufHgW96ZKl+VxKeDO006wE4ENOhXKU4K6igEevpvtGI/DXz/zzXRJDe4iBOFT1crPtY9HklBZKttmx4OXUnDfWwKIx3ZfUOrWMWWbHVT88TTKGmZe2PEbtjdJBns7LRPVJHULkFLK26w4ZXh8BgaYXfBwZOUUS9b1WZ3fRu2OSSF/9PyXCCRUngfxN64quYdlDAH9U4K4KQ0XVhSFfJOU1OoCaX6Rh0QnwWTQMhVlGtPjkg0dD0wlRz1t6toxOmO+JVwWOmNfrlhrWGIWTQ5Kx8THXs4zS1kXdYgj013S2GWfPeevvmlLxuVeQ1uhTATkaa00BRhGI9lt3h7Hv6lxUovyxts11tvQ5cnLulNzZuRP062ujlYcQxZHomJ9HKUWN1B/dOReVmZVdI0cF7sC90kWpDSpNdk5JfHFMc98UNdoIpFWylADL8AcFZd7dy9x2YeuQMhTGP1T9kTi4ZZGWl7XPPkR648DngKdzrcRByQrBRF/0UGq3Arbn4dV+/Hwfo7F6GIG71pGB1baRak17srRaKUVaU67+z+oYGjOdjyjqjJymOm19iXpJjgbcIwOMWEIWluo1egOITkT0EKbM8JVZ+HXa3gjcM2A/lDJrO6JIpnT+rhIZzJiys5SMz0sGIBF7js7bVoZSOJnjjtqbtc+ZaqZbEcuLGHn0zBuNGDMHVDNvHtHW9n3bzQLbkonMtv0cyhiAr0mJZI6YYxFFbKXIdxuCchTgriFnZCx3d3fdrdTT6XTjEa5UnLu7u+59hc+fP8discB0Ou284dXVVbfl0nPwanQKOHyKnxqHel03ft/tMCbEin6rzQ+7wmkISWXyN/foNVk9Q6mWXVi9gpiCXARY2bi6gerY6mMXtC7vK69x/Vuv1xuvg4vGzo/5K9R0+yx1iX1Stu5sW/uu7F7br4CfbTDgcR/TobliPdl2YZeMjbsD07KjOvQ6rXOsRPpYsruonkxXtHx3pIy6vG+qA2dnZ905mkoeWu9wmy3J0YD79fU1JpNJD3TZeM1bae5UX5vHY+ox+Zo9fp/P573buZkn17yov/OQ4M72lHZeRIrjW6EiRddr/VitZ9c6qDC1jmGfso1BZk4tO1YqX+dwuVwC6C+wqsH7PRAEPE39sa7JZNKt02jdzqKj7bTsh0aVjCydyat+qSEzCs2AUNvpwM/6HXxrHLdHFWPF9dkZfOS4XdyhvU7JwJyiERrHm85ZCRePa+otkigCqB2HowB3oH7BQBV0uVx2i6SUs7MzTKdTXF5edr+t12vM5/PeThtlRqvVCovFovdWcgWJKBymKAhFuxt0cgi4UT62ZgyyMNIZcHZ8G9kldN1WIuPxtZWIsWsEpw+qAvqP7VV2pU971HUcbYPqAveIa9To4jtR9HrVk2z3lJavjwFW3ddNBiWw4VhlEVG0ZpVFJVG0FEnNea7zJQfv7TgGYaTnEUg2F+yjYoHqQHZ9RPao50PO+WjAXcNSIGeAqqgMm/X8k5MTTKfTHiDc3t7i9vYWp6envRCZf6vVCjc3N91La2nAapTZLgVtkw58tEYQ7ajJACuSGgOIAH5f4D6mrF0MMWt3FtFETJXzR8ar5xLk+ZRHPrlP58d1gECru6wyZ+/h+nK57DF2ZXbapmiNSaMOrTsSv5dDwSFbpFNGGYX+0djXyBBR0cXv0rkKjscE7DrH2Z9uxWX04ekXOljfiVPCl9q1u6MBdz6aFYjfNBIZKJkXjzHlwkGgkk4mE1xeXvbK5bmr1ap7Dd/NzQ0A4Pz8PKzTw15vH8uLRNMl+5CxjNqdZ035zg7GhOelc7PrI9YZjbnvLtHz/FnrADrn7o8JPjs7615gzEf6Zuxezz0/Pw/z23qN9pOPFri6usJisei1QXPlqh+ejuNjCCIwdED3dqnjiIB7n6A59n6N2oh927TMvp1CFJUokJME8DHRuuaipMJxJUrvUKIIjwShJEcB7m3bYj6fd8891l0uGv4y70nA8XwmAZt5cr4lnKAb7XrRNvB/BNBuiHqdvy9SPTtDeR4rKX4WDteM30OKA/cY0K8VVXYdwyjq0UVJT6dpNMXPPEYd4bzysavaDwd3fT58tCuFxq36Qn1eLBa9SKJ23rSs1WoVviTEz+VY+PrSUD0cT5Vdt+e686m9xtertpV9AryDbnQvAf8/f/4c77//Pp48eYLVaoWLiwtMp1NMp9OeznpfNa2nRIFzTnwacqJHAe7r9boDYgK9CkG9bVucn5+jbdvew7j0Gdj8TramwO/g7kah7fH6NZz3PJobq/+PbqyqScGMZVi7htK1EhlLrQFtY2QR29R6FbQ571xDUTbsUYinLaKUi5IMZdXUP++P5vBVNBpwBxX1zR0nf4serBcxdwfTbQB6V/1xRxkx1IyRj3UGQ7IvgPfUlaZK9L4F4tjV1RWurq5wcnKC5XLZveM3wxiWDfTf5+tkJyOpKluDe9M0Xw/gpwB8CsAawLtt2/5XTdP8pwD+KIAvvzr1x9u2/dlSWWQ1yrhVmFvny6iZP2eIwzIU7LWc9XqNxWLRgb8arA6cOhFnDRrua7u0D6WIQHOe0bkZKx2bfqkBjl1EjWSswQydWzpeM76r1aozKG6ZPT8/7+ZY59DTIcp4/Td/IxOZvAMT9Wk2m/VezH12dtYZI/W3FuB9/t1BlYiK/zakEx41jtUdjya8Hdn8Ds370DmvSwjkTL0wWuLvi8UC8/kcNzc3mEwmWCwWOD8/x3Q63bBxB3f+abpHyQHJTEl2Ye4rAH+8bdu/1zTNWwD+96Zpfu7VsT/Ttu2fHFMY2TVzo8C9MvM3pmSm02mXwnGGRGBfLBZYLpcb7yzMRHdTAOiF6Mx5Krh7COZljZEM2F2iLWE1RuiLvMcmYxg/DYH91i2y8/m8A3emUABgOp32gBy4X1dRg1Id0PkmqNNhUO80olNH4PdicF3n5OQEs9kMbXv/iGv2qbRQ5mE/kOe2FXDYLwWdGj3bt6hTUtAakrGR60OI6lBEwFQuLy/x9ttv45133sGTJ096eqJ3mfrcaz81ReyR2sGYe9u27wF479Xn503T/AqAr92mLPfymrP0TpC9aw5WQ0+9hgbJEImiuyB4HVkew233pMrEsjTMrsrn5dYwGp7njLombNu37Cv0daEusF8RC9VdCDp+EfPR9kaLsgROzbfreo/uMHG2xc8aqUURXpaOUNFrS3MZjfvQ+dEOMJdtyYBHM/vQi33Y2DbtqK03ivK4TjObzfDkyZNemZ6mK0U8quvUvZp+7CXn3jTNNwL4lwH8rwC+B8APN03zhwD8Il6y+w+Caz4L4LMA8IlPfGJjb7CzZjVqXX3Wjno+EkCXo2eKJgpNCd5UemWHHp67s/FyfFtZttJd43kziRxA1iY9p0Z2NR4vIzKoqI5S+snDfZapOxF4XO8O9adCRqxLy/Q/XzDjdQrQ7jh8TYbii5zRHCnY6jlDqRR37P5bNKZqN5HubAPsDxkNPKQMAbzOvf4x4mO0p3rsDNxJquu7Rt+eg89kZ3BvmuYpgP8OwH/Qtu2zpmn+HID/DED76v+fAvBH/Lq2bd8F8C4AfMu3fEvLEFpZT9fIV+DuhsHUjDJ9sitlVnd3d73cPNBnFPSyul1M2tlj7nzhhi7i8hof7CjUVhbq3rrGoNwQ3bCjelT5hmRXZhOBsB6vYUDZ7xHTBu5vDuNdqZwTbllUgB8aA91GyXlnVKfpGDVcB3bqGudfo0FPx0R99mhE+63g645IwSJbP+D1vtOsNtKLnJJfH+mE3gxYKzUA9lCSkRRPzanoHGiaTu/Cb9t4d57rOjMVUeYgk53AvWmaM7wE9r/Utu1//6pRvynH/zyAv1FTlm/58c/sqBqXPp+BhhAZ79AgqIGwDAcr3ymjnlrF86L+iAOXCJj1+qid0fkRw9wlNRNFN/sIid1IvH0Z4Hn9CijRnnc3vGjXgYuyfc6dzrEzdf+s4TjbyPSH3vk8BlB5bcbitS/a30icDJTWYrIyonM1ihojr2MNqBQh7qNsT8FlEmHc0HW1BImyy26ZBsB/A+BX2rb90/L7p9uX+XgA+H0AfrmirN5t3RRl4DSULPdN5qLl6WB7WE5xQ1PW422M/hxUvWwNtSgeIUSAFpVZUpYotB4jmdPw44cA+JprVBwUM8BWwI0cccbE/JEE/jlLxbjDV330h385E9f+su1DQFEaQwVyB3Nl+JnuRmUP6VUUpUX29hDpm5o+eZQZnTOmvtJc+p87Zb/Od+EMkaJIdmHu3wPgDwL4v5qm+T9f/fbjAH6gaZrvxMu0zK8D+GM1hUVG4XluhsAMcRzUKREbdlDOxI01K1PbGC16ZekJb09p0sYYwBhH4BFIbX3a1m0BWgF+6JrSsSEGpqDs4K5OXEXB2a9TgHcdUV3QenThlfVGAK+i12RsWJlyNo/qrBXcNWc7BLRjwcTb587F+3lI8fbWkIoxOq3XZOPoUX7pHoio7U52tR8147fLbpn/GUA0EsU97Zn4IgIHxO8anEzunwkSpSZ0chgKRzsD1NAozvwzT19i+Hrc2bA/FU4XBCPZRtmi/mWKUJODz8oG8oVTHZ8SI8qMwc9xI3FHoXl14D5vzhQJdSgrF+i/KN33wutOqojN8/ooJeL1+t2zQJwecfIQsWKPGj1Hr2lGjRTYhiiq1PHRcjMHSvG21+jtQzB4rQso62HT9J97P6Zs3/ABvNSl8/Pznu5QnyiRbUbbVlWip5NGchR3qDZN0z1vIWLbajhcxNIbRJxlsdPcN8+BdyX1uqK9t1m4xUkcYswaYmXlReITm0Un2XXZ77ukVbycMYYQLVaPbaPqgwKes1M/V+c2YvzaBwd87nTwxVSPBjIAjERBWSM4tl+jgsgenARl40jREF8B3lNVLC8iPaU0QiZDOvo6cu5jZGx06ratJICY5WPJdDRFCYJHBWNt+GjAPTI+YPPWfw+HnWmpkemCFsuOwE/Lj8SBNnJCGahy4nRb3bZ7l4ck8/TaFk8ljJVt0zHexm3qVzDUqIBC8IqeuFhingqaOj7UL32eTJR/1zKG+qWOyc9X0uDHos/R2Gb2o864dH6kO0NkpIasRJHzrkSjJJFNjmXjtfqbpWacmGb1aworaq/vjHqjwB24fzNS5M11R4wqvbMoTeEwLVDDmBXcM+X2ydP91CyDdfqOGQVzf+pfKTJwBRoT6maRBduqEm1ji84bKxmQD/WFxzNnpczX++vPTFfxOh2YFVQ99x4RCCcENexd9dHbFYXiDvQO8lpONE7uhBjSaz1KPFinR0NRCkZ/G+sUdiEYNbIrgdnmGtVDbYNjlotihOqktkXt+Y0CdzIkB2Q/x3OKfsMKjznD1nOBfu2BjMoAACAASURBVH5VgTRbwFI26Kyb1+giFf8rmAPY2OtbAt+h3O0+QTiTQ7OrUuhfMjCNgDjGBEF9hIXuUMmMSutXEkGyEDF1jRg1LZPprudQnUT4AqvrYA1zjvTNd/2oXejjg+nYfEyGoiy3hVI7o/WE3yniOpHpots054UYo5tLahzQUYB7SdTzeUqmBHh6/WQy6TkCZ/76SrqIITkTjhigh0001shpZMYwFEZm/SulhbT9mnfNzjuE4UX9ygAsqn8INBTcuVhY8+Q8deo67wrwzpT1OH9zY1XQ0ygiCttr+hkxejXyCNi9zV6WM35l6btKFpFuW05J9hkBDDmzmmtVHE+itDIlswcFdv6mn0tyFOAeGRAlMjDPq9PAeX6W09ZBadv79xdmi7k62UMhftQntrVUbimsrpUhcFeJwkf9Pcv7DZWZnTfksMZKFK6yL1F6oyR0eFEdkZOnlPLHDrjAZs7U++JpwczBRc7Ay3V9KrFqjzJLKZ+SeJpHidA+QL4kh4hgS3Owj7pqCWrkbLLsQiRHAe6U9fr+bfOZR/ZwuG3v38CkDNr3Ebfty2fBN03Thezn5+fdgisfI6wMyI2PbFBDXSB+uW3E6BghqBFHxqmfS0qkDi4CUXd0vsc622vtRl6SsWxfo4iSRCmapmm6tRm+VcmjMWDzfanaTh1zTad5+/yztitiXEzVAOieI+/18fwoHcTPzr51jnhu5DQ8MiAh0RfK83dd1GU//Z2smf1FuXe2y9OOXm603pDJGBA7RMSZOVMK2TRlqH5NC+urQZWN+3la9jaO5WjAnROvj+lVEGWH+OJjKqk+ITJbPKWSExyUTavy6WvYtBzfFaH5dQfNCLSH3u5eklKo6McylnSIVMu24qBXI5GT1BdO8xzdQ7xer3uPiWbdPKY5ek/FAOieAcPHSyupUMet86xvyNF+6gvYCe6+i2W9vn+hjLZlDMhpHz3CVQeg5/m1ma5oTr7UHl+D+p0sxCrXVdUZ1RFgcx1D59NJxZBeHAW4uwdzhQQ2t5p5SOqhoe/pJUjTQHWLYtSOKD0RsUldBPOcvDO2sWNSaxwZSI5h4A8pJRDJpMT0o3RGtAAK3M+DL065Q2ZZfAEIHyyn2yJdH3X+o9RhpssUjSZq0hklRhc5kJJ+62+luSnpcXR9tN5Uk645ZCpnjGTj5e3PorwoC6G45ruhauc9apPLUYA70Dc6/13Zs4eSUf7KGXW0bZHn1YKMsrto8SNTWmWWY8LMfYWaHoKrvA7A13kaW78biTJoXbzOngPD8yJWmY23vh9XdSBrl7aDoKpgre3hZ7K2DDCyMD2LgKJ+ezl63bZ64GPmn7N5ftPAPRufobnyeVYC4ddExLEkNXhyFODetm0XHvtt/5puUWVZr9e4vr7u3UGoT4n0FWUfMJ8MD7V1Z4kyL91eB6BLC1GiFMzQkyFrRQ3Vf9fforwux4RrDBl7fijAj0CpVph60fQYJQI1/o9ARj8TmEkcWJ7eNs7PQ2Gx6qrebs4nRkY7J7Q/bAudi57nABI5G9/SqIRH26fkaRuJSJNGUscUMY4Vj+iihXTPGlB8/U8l0pvI0fp5SmRqGP7RgDuFb7rn75pfVYazWq1wfX3dM76LiwvMZrPe80SYe412U3gOkkYRGZMas0YNvqjEtvF8tk0lA2k97oYxZjHK+5jV4XW5E90He8oMvCaVFB3TvegE4OVy2f3GlAlz2MvlsgNSOn/VJRVN3TRN/8XafKeAAn+W5oj0gp/58hjN7/O/bumMgKQ0LtF2zDHgWmLx0SJqqYxaqQGoqOw3xWFQz3Ttx1OIGeGIxlmjwBo5GnDXhS2yFQKzvkOV5yhoc9B4Llm8sxOvM1MYB3p1BG4EkdPw87dJzfjkDyl01L/s2OuWqD0RECkTjMbD5zdyWOqYGVXpwnnEcPW7R2s6355DV1EioFEgdcoX9lXX9U7rLK1SM67KKCNH730dC5pDTrsWvGvq3jWFtE+paa/qSU2bM1B3jBoT/R8FuK/Xa1xdXXWAqsa4XC6xXC67TpGVTyYTnJ+f91jyYrHo3rFKcI9SEGowHl7zmKYwKBqGsZ36dD0FAwV5Z8dRfUPMbBelVgMfc/6hZCxrj1IGOnf6smHOj96VyeOMAL1cDXf5XedFSYPrA7C5TqTt03a2bduldJjmYdtYB18HqX0oheElJ157bEg0zTdEULSdQ3UcA1CPlTFORnfz+RqQj02WqvE59J16JTkKcL+9vcUXv/jFXiisgEgm/uTJEzx9+rR7m70rvufDXTT85W3q/OzX+OMMSnc86qKbp3koYxSdgFAKh6OdF6U69Jj2w6ONCKj2KVHEpMrq4+CpKQV0zp2COwF8sVjg6uoKNzc3vfrp/FWHPDLQh4VdXFzg4uICy+WyY9zRuhANT3fhUGcuLi7w4sUL3Nzc9Mo/OTnpyAn7slwuu/stlM1lr6kr5WV18djz+Tr2NXOmjs+dobbF7cDlTc/Du6j9RSQyij6BmKFHYE498rIA9IhKJEcB7uv1GvP5vGu0Ps53Npt1e43Pz8+7XCsZsb/HUkHYAYx1rVarXkSQMTqKGqr/zv/uVJQ5lkLjsbILq1YGWWKCD2l8ERPSNijoc+yYoovmZLVa4erqCh9++GFPT6grwL2h+ZZGZcvUN11M5XtUp9Np94xuT9EAm8Tg6dOnePHiBW5vb7uIk9dE8+AO10lLafEzIwHutH2ex8x5aUFQI5ZMjhXgVdeylJPqpeuOX7NYLHBzc4Obm5sOu/w5P8B9uiUaQ8WmWiJHOQpwp1GR9RCwT09PMZvNeg+8145FYY/mRjUsVkPRZ49kNxhlAFgTDulzm6MJi3Jx/N2VaygUz8ZTz4tYpjo7d0Al9jwkNedGufVSpBOxWP3TMqJzeNydvd6UpGmzpml6+sbv+jedTjd2SrFc7ohhvZp+8blVo6bxax9Udyke1Q1JthhfO7c1+XBPxWQLutuC+kM7g2hs1J7cXlx3V6sVbm5u8Pz5c3zlK1/ppeIuLi4A9Hd2KbmMHDvrVccwNCZHAe5nZ2f41Kc+BeD+tl52RENYzaMT+AGEBqPAze9k66vVqsup0zuquGOg86GHdaegO3YyIM1y+xT35vpdjSdSuOxY5OW9Hfo9YgRDipSFo5lEbfSQVedEWY2Cte5CAO7nYDqd4unTp3j77bfx9ttvd7tcWMZisdhg9CocC6ZzWCaZFw2XAKuPA1bmx2vPzs66VBAJiN5qrsyaO2l4jOywNlrTfLjufIqcpqfChoA+W/fgfyc9mrrhHJ+fn3fjGjnlkrzuaFJ/5zGuyen5XPt7/vw5PvjgA1xcXGAymXRk4PT0FE+ePOk+E9M4/r624ekwdQRDKdSjAHeCJ9B/dowbDzvPAV0sFj2W7OE1B41g7EbIBVl3Ds7smZMnQDgY8booBZOlQaLPbHdp3SATjomHxG48+hhbZ66ZRIwliiwUMLL6o3O9TI6DKryvBxA0tDxPg+nr8Vgfc+fAvXF4zt316OzsDJeXl5jNZuECmW931cdh8LM+ekDPI3lhGsjHTAlFpHMR6NbqThRBevlaZu1uL5YXpTeVED00G6+VIXKi/fOoj8Tz7Oys2xDCuec58/l8g/E7Xml6UHf+aRQ3FL0dDbjrjR7Kchx8ImZM8XN9cYnXO2A4ACs7150v3mYXT3V4uRkDiwBxaLyyskpMjX2NFhG9PSWD34dRemirv0dt4m963He16O4qXSRXRkQmD6AzmuhPgZtrPxcXF70FVR1HN3Rl9nQqrFcXOnktAUHHN9K5LF2l4xM5Ky9vSN88CmG7a0TtQMtSQIsitmORIf0H4gjU/z99+hQf+9jH8M477/R0TYmLlqvY446C5+ruvDcC3IG+53JwB+q8aabcZOd+Ppme77qJGLez8gxE+TlS7Ojc6PuQlMCdx6PfFJAopXA7K2dfouw9AzQfS73W58CdcrQrREE1yrnrn4bOXEzldsjIQTqQ0jhV/9Tx83oyeIK79jtKH5V0yfvrnyM9zoCmViInHaVsVK/U4R6j7EJiOK9MD37sYx/r0npKLJUIaApZd8fow/AAbOh1SY4C3Jum6ULSu7u7Xuir4ikY3XrGclw8pRDl4rmDxm9W0TSMOgBvu4evY2QIpGtTHFlaBkC3w2gb8TnYN7iXGJIuQKuzjfLJNAQueM5ms26nVaRLBN6oDbrWQ2CfTqdd3tSBne2N+qaPw4h2rADo6uIcuR46eKruuiiAaFu0TWOAPEv9ULKo18dkrF2MkYdyEo49tW2iXqpeaZk8PyIIus2X4o8kz+RowF1z4bq6HzELXjPUOU/Z8DcFeP754zn1FnM10ghss8W5oTa9LlEG77ljffxDFnpG5WUKn81baayiyEHBw52YRlJcuJtOp92jKBSIVQ+cHPC4pmO0LG6NjNIJ6jw5jnQE/I37131hXYlKFLlkTD3SJV2biMBCmWMNQ8/sLxLvm0rN2k6tjClj7Lnefo8oNfJ1O9Fz+J/rPlxYVSzRc29vbzcyAoweo3U0vTaTowB3incMyBlRdC2QL9S4kkd/0RY6Gh3L0IF2UFNnRPEwOFoY0/bXStaX2mt9sVqBRfuj15TAnRIpoR7PHIGew7BUxzmKSJwZRjuSojWbbJwcaP3PU4UqzpQzp+G6oQDsf0PGW+pLiRC57tSQpOw8tqGkf5EeaBu31f2a82p0lm0BNnet1Yim2aL0U6Q/rpeqt3rMIyJN570x4K4haiZR6AJsLiRFSq8DEYW0OoDcXkdQYe5VF8R4PDJYrYe/6SRF7Y8+lyRyIrWiAKZApsbmSup1ZAar52Xg4nPsihrNn6ZodMz5f71e916OnaWneC7Fn5LJ8pXB1+wqapr7LWqZrugWyAgEojb42JcAnZIBbWQ7Q+DuhGXIYWQyRk/2IaV2lZxUpPsRqfDzPBLytUJdhI9IB7d1q56rznDbr77MaGjcdgL3pml+HcBzAHcAVm3b/itN03wcwE8D+EYAvw7gD7Rt+0GpHPV8nh/VtIjn3AFsGKK0rWfgBIToEQWessmk5BRYp+6H1/3Uel4EmiXjqZXMqKM2U3S8s8giKpNpDWfXXm/GQjUy0jYylHUn2DT9x9/qdao7zpZ1/SYab5bNudMb6PiYAt2jHJEK/sa0C1/fqHXpSz54r4U+mTKbO6Z43FkxtaJjresu2r4MCDLm52MT2SOPsRz9rG3yW+f1HJ+vfUs2z5k+qy5H6zr63cFbr6MuEZf8BS/O4IG+HWR3y/u4HxTcX8m/1rbtb8n3HwXwt9u2/YmmaX701fcfGSokymnp3lDtkDMPdQ5aljMkT2G499XyS+xIy/NzI/bl16k4MxuTXhkrpRROdqykQGPWGjScVOVVUCdgRQ48iujorDkHenen/vnjAKLoQfsb6Vcp6orYNZ2db6ltmqbbFqmszx+b4VFZiXR4FKO2pPqZ6V+mb9m8OttXduoP0ssciEdE3pcx7czaWNtuHnNi49GT/vd2Zzur9H0Tjg0R6dH0nc4bz+dNcUMRF3CYtMz3Afhdrz7/RQB/ByPAveYcV3ztqBqiL7plIB4ZTo3C1YS/CiI1O1ZK4F4KNTOhArFuXUzW5+R7zrqWHXhdpd98rlQUOPVpnBoRRHn6zOmzPr9GDSWLkrIxVkfBsnS8MhKhDF3TflqOlheBu35XfXUQ0H4o6Ylsx51SNBZjJCNSUdn+p5ItamZtzSTqQ0RqvO2uBxFbVxCmnfvNl55b93y7i0eXWWqvNLYqu4J7C+BvNU3TAviv27Z9F8An27Z971UD32ua5hODhbSbz1FQ0dSG31EahS3KgnSw9JZuskSWqefrLessj6LXlUSVgeAaveQhM9pSmZlRRou9OkYKHprD0+flR9fXSgbukXEOGZ4yQH3EgPZFU2zeVzUoT/Xp7y5ZOJ6JRw9suz6e4vb2trtTVu+QjcrSdxm4ZMye17Ld2q5o3GgD7iTGiM+VkgUe98Vt/cwUF0HRU5ZZ/2rbWgvsEahHnyM98rUYHWNNkWkEyV1YWZsjkurfa6OYXcH9e9q2/eIrAP+5pmn+79oLm6b5LIDPAsAnP/nJ3j5gb3hkuH5cPSlfZkwGqHlZBUe9Pbj0uOCM/ZckOndsaLmLZMZQA1jKVsakXmrKHSM1Y+WRyPX1dXeXahQplZhT1N599Nvr17ui3XhVlHBoyimbW50rXSCPoguvf9c+0b68Lm8fIxd/sqeDu0o0LkMSAW4pItZreH60IYL3TeiceKTuLH5Ij0rRZLQeWGuTO4F727ZffPX/S03T/AyA7wbwm03TfPoVa/80gC8l174L4F0A+NZv/dZWfgeQr8oDfbbuOx0UkPRmAZbNCfd8aPZ0SL22Ftz93IhVeX/3LV6Hj1EUQg+Vtw2T13nbNtQvlc1zOIf6cLisDSWDi37PyMUubNd/Lx2L+rCN3jiYu9PflhHzdwXTjCQ5wJdIVXT9WMkigUiieXWn5TcQqfNtmmYjmvS/rI2qlxm59ccWDNnT1rd2NU1z2TTNW/wM4F8H8MsA/jqAH3x12g8C+Gs15WnYkSm7DoCvOHsY5QMahV9eV2kSFLCHjDH6ixYK/Zp9SdbOrN6h+ndp25Bi11zvnyOQJWAwzeQpCQWfUnsyYNe+ROf5cUqNIy+Nb8QqS/WXZCgNs0uUUjPHGi04uB9KF3exLY1ylAzyrVlMZ/K/P6K5Rt+AfuYhIqsqJRxx2YW5fxLAz7yq4BTAX27b9n9omuYXAHyuaZofAvCPAfz+oYKcUbgSa6ejkFo9Xo2C+S6aKJerE+tt1f++Q8fr8nN1cg7N2msMOMrjZjscthGtr8TqStf5Q+WAzdcgsv00Poa6un1W2zBUd7R4G52TsSyKEhZnqB76a0rDHVi02O36XnI8temX7NqSzkbpl6jv3m+9I1zr38XJaB1Zf7Ytr23b7jWIQP8OVd6RzMdIRI80jnSoae4fMkd9ytb1oi2xJdka3Nu2/YcA/qXg998G8L1blNdreObFMlYN9F9i4Itl6gB0W1rTNL3XmrEsXdBjHVoPr/eBjsBSc2WHAnTKEEMYo+wKHtsai5ZRAphSHjxiP86KOK9ZWduklSJR4I2iQa1PdYELahFDjZwqf9N97lF+vnb9wNu2jZQcmQM4pRSJ6ziUIiaPrI9FSrnvLJvAz/qbrjVmi+bR2A8RlaO5Q5WiIc22Shh1nF7Rd+Sol4x2m3Cni7cnY8eeiol+r2F7teLtdafjbd7FOLYB+NprhpySSimSi9J0OiZD7cmAt1Z8nlWnxpSpfcvaswvYbdMvb9++HOaYel+XZMA8RDz9mO+bV/IU6YyeNybyBY4Q3CkZQFKyMNvTOPq7l6kGQ2aUKWwUlkZAHQH40DqCXz9WovC8pCxjyvOydwH4oeuyY1k4G627MDTmboWo/VHf/HM0z6UxjM53PYsAoiSl80pRDlC+Kzn6XpLIJjI7GZP+qYnGX4cMgbSDbc3v+j06lpFCoO5mtEiOGtwJSmTOvvXI9yrrfmegz9b1Gd8R8N7d3WGxWAAAzs/PN1i81svPvM5Fr3VgL4WY+9iWFkmkOA6QbIuPobd5bL28ziOKbRifpmGYe9ebsE5OTnBxcYHLy8vuxRqZEbHvQ846kyh8Zj1c11Fh/b6vOwqto/lS0C6F67VbeWsdbqS3QxLtoff1hKHIieUcQkrg7UJ7YMpX9YjXZf3JiOaYdmouvm3bDaAvydGAuwOBirJqnhPdVECj4kRwQHRFOxoU95Ylps36vd3Z95pjY1lU6TdX0ihPW9OvQ0jG9mqM2He8rNfr3q3dnEO+Dk+fvc66vS1RqkHXbfQcz5ln/Sv9povAQ3pI4qDRpBOLqP1ZpDXGce0ipY0PLkPHM33Zh5SihYzcRMRHy/N0YUmvSzarzF/Bnbqg2FaSowF3GtXd3V3vtVI8pkoebYFkZ3Wni/42n883nvHBa/meVL8tXKU2NNfFUyB2Vtm1lOiabULXkgJ5ZDE2VbCLuCLXlunX6S4DPvTr6dOnePLkSXezCdBn61pONH4RcJaAVEVJBa9T5h2NMeeF565Wq3C9SIWRrLP0rF8PBezaBv/sMtSejEnvQ7ZxKh55RmUOpZq8PG+LOucsW1DTfspRgLsOGN8Ur7krPY/pGWdXNIz5fN5tsQLu2djNzU13c0vbtt2bn4CXt4ff3Nz0jEZfqqx1+JZNn/DoBoYxkgF7Bu6artLxUE8PxE8RjB6H4PUMfc/Et/L559KjnaP2uvgWO77t5hOf+ATefvttXF5edkybc65PdVRw1Hn0sfMdLw7S6iS1XTxGwkCdvb297emu6jkJhr7EnORE00gkQX7jXQTuGbBr26Mysu+ZaESV6ZK+8MZflrNtvZl4GyKQBNBjwT5uUZuy34bYtLanlrwp+98GT44C3Jum6RbBptNpz9CUnfnjW6OBjPJiALrwHeg/K4YgR8OiUfkeak8N8XcPpUuAVGKMtVJilZlyOdA/hJS2iW1TFkVBWfszm83wkY98pAP11WrVOWwFZZ+D0s4bFSUQdBY67m7cbKfWr6SEbWGZvCmGuukvUAHun+FCXSzletkuradGDpkKGaqXsq/6fT4iwpiJOisvTx2Ub7/OUsAeNetfRnRYhzpN/lYjRwHubdt2b6s/Pz/fmBQ+bOfs7Cx8pjvP4351ZT3A5mNlHQydlTujcQan7dbfMgNyQCmFd9sA8NA1JWDfF+BnDGxsGbw+2zNNQON8sW+np6fda/UAdKk46hWA3gObqD/UJ83P8/vZ2Rmm02n36sFI97Qc11veMatjE0VQ+qwVze37oq33+aEkSlHsKkPpuEM6mcyGo4gji5hL/znP+owZpg19F5eSCnU+GWbURAmUowF3vmHEX7ihg8TBceNiJ7kLgdfoThgaqIaDvgc5Am89pvW50ZUkYuxaTjQeY8au5vx91FVTB1DPvLK6neXwP0E9A/eLi4vunalte7+QToalLwHRrZNqhKx/Mnn5Ltbb29vuudzK8jU9yHLUAAF0bBzor8Fo38ju/TkrEQnxPtcu2G0jtQCXSWYTzqQzGatLY8UBmZ+H0kN6TlQGxTd68DzHHU8V+QaAyMlHWBXJUYD7er3GixcvOiNSANenrLlBRdvRyIzcw+lgR57a37Lj7cvCKg2RxoBV9H2fWyEjpSyVX+skhmSMUWbtcQfvQhDU3SQEY+6UoS6RuSszVpbkz+BmnU3TYLFY9JiXnsfrlZX525r0tWiaH/dFfwX2aCxdz3T8hgA+Iis1c7NrBBZJ9nrBQ0mNA/F1p6GIQsvUdB7L0LIXiwWur6/x4sULAPfrLp5h0MhRHQfbp48kjwhnJkcB7jqwDIPVY6kB6SKD58YVhPV6ZTuRM9DwG+gvXkTe3MMiz+Fr2MXyvK/e/23HreacfTqNfUkpb6h5ZyAeU33wlM7lbDbrtkNSP/j4X81dqt747isaLFM93FYZ5W3d0F3/qJPK0j21QtLCvftq5JpSdHFdyvRsX/qm9WQAGOlbBkqHBvaaOnwMS2tFnCeeF+3YY7S4WCzw7NkzvP/++5hOp3jx4kVv7RDABrhH7F0XzjNnnclRgLsCKQdH2ZimZniOK4t6umzx03dYECyY09dcpxqTG7PuVfZB5/URcy6FU2MNrhQS7iKRM3td5em1vrAV3ZRGQ9Nor23bHing/JA9Af39xMrG+Lo05t9LTM1z8RmrzggGwV3ZYwTePBY9EsPreWiJdNudnsqhmTvryKSG/XqUMRSdR8xfcUkdAXc96XyyDG1fth74xoA7O3pzc9PlKXnHoeZDdTXajUdzkg4GvlXN6+aNMASNyPO7MmbRgNbL63ySdpVDGe8+yx0D7CU25wuQdPAa4WXlOTvWMjQPrqkWOobz8/NeGlAdgJbNMtSwNc/vxur90jUlGn3WFwUAz83vQxR8xgBvBjaRzTyUZM6jRLL0OD9HaVWPQJxY6PtU+RYunTuWG5EF1ytdhPUt3iU5GnBnY1+8eNEp+NnZWbfrYb1edzci6Ts/3eA0H0tRzxlNKAcvCv+BTeZORqhs0NMMkfJE9Y9lLplSZivo6jh5famcfQjLrt125tdS3FGrUeouGAr1SEPZ0uOblVW6kbnx6oK+Ar7XD6BHPCI2xrlSQOAuMe6Ld2enKR0e1za7bqk+KGB7W0vzEEUmNaJtisTtM6p7H4ze+6/CsYvSMB4tZkCvogvrTPN+9KMfxcc+9jG88847mM1mG7qt12h7tR0eDfqGkJIcBbgD6JRb89yTyQSLxQIffvhhzxvqTR4Ml6lQeqfp0GJnFD5pjnTofAV6oJ8fZVlDYLovRR4SNfB9s3OWv8++aDsdoNVZA/2FKl3cpH6wXczBO7A4yOtvvliqeVKVqN+ql+yTOiBex3Zxn3Q0Fr6AzH77edoeB/V9AabWUXsu8LCs3dsRgTc/R2MVlaHz5/rIm9P0BR76CGrdhksnrhgWbQ7htSS+mk14Y9IywL23j/Lb3LXAPcA87/T0tMvJR5OnTMonMZIMfFmWe3AFdzojV/6hELAkWVtLuT8VZez87tfuI++5D4CPQl/9rADnjtN3rXj+nOVnKQxl7gruCuxck9FydW7V6JURKov1yNAXVRXoszn2lMFQ7n3fUusohvbiZ5Fmbf01bajRwyFcGPpdQZ7z4EDtuqVl6E7ACCtKjueNAXeV6EmP3LnAXRCa09K0jrIt3d/etu0G41IjiSRSPg/VNbxzVukG7LKNcg4xjOh8VRB3ggpYvltojOF5+B+1fRfg51jyyZ0KsAyDqQ+a59Y+azpAx4Nz6Hn38/NzTKdTzGYzPHnypCvPUyFKOqJ+cscO28oNAzymToqRqG4MoP7rOGgKh6LPZBqau12jrBpgdnCjaA45kqxdUZ0ZI+f3TA/dqWbn63dts9uHR39KBjhnvq1Wy/fvDvSql2/UHapsuOfPAXRATUPjA6H0SgSKugAAIABJREFUiYDq9VzhNdep9fB8PnogYlq8jv+jBVFXXGdWvqtmH6Ex+7VLimVf7Ti06I4CN0SOu+6OUbZNI1PxW/pZlgO7pgAzJ0un7jffAeh2VOked10E1nUBT9956sgBQOfeF5vVAVCcbQ+RmpLsqndjpVSXHoucBe3PJUoP7cseNALTxXndbZXdZe9tj5wQMSTrm8pRgDuA0KsBLzupOU8ys+iuQs+xRwuJNMrophEe1zL4X43J2bsbjy+e6a6MKOUQsY8sfZAdGyOuUFn4fyziqQ/9vRTyKpuiRCDgDsOZvF+vv+niupej+VgHc70xRfvo92S4bvgioOpUpFs6TmPD+rGAlzFf/XPbHitZWqbE6P2cMRFDZKNjxfXCiYDePAfk78vluXTeQ206CnBXT6e3cVOU0ftjCFRZ/A4432XBujKl8JBIz1Fgj47xegWcbKta9F0VoKR8NUo8JNk1Q2mV1yU+R+qUo2hLj7mOKOhm4iCv0aSDlTsYr1N10Zm7t8OBPZoHB/hsnIZIRHSe1zNGMv31lEfJ/obaVrKbEhnya2rPjeoYGpeh8fTrNRrzsXE99/KHnONRgDuAjVDFQw4ahBqTiuY/s05rvtXTQMpueJOUGloE7GyX/66szs/zdM8usg8AjrbQHZNonlxvXFIdKIW3u4gSCmdbPk5RqK26zEcR3N7edlGm5tV9vUZ1KtJr6qw6K48CVHZloLVjrJGqX38Iyfq1z8iT46k44Ws6Oj6cR5WIZPhx/kURo7Yl2r4ZyVGAuzMfB0UA3WIan9URMSpf2KIRacis4TH3l/JcBV/WrYYVhUs1z9SuYYtjxsrLHyNqDMcG5CXRnTIu1Bm9m5QRIPVB0zrOqvU3Tff53vaSeKom+o36yFSOk4aMJHgkys/anyj1ty2gO3DsmpJgGrQEWkOyT7DeRe913pyMat+IRbotl/rInX/A5k4t4phuGGB5Y+UowB3ohyfZAiVwD85cZNPwVEPpEsvWgdWtbX5uVH/02Y2opLxDIWmNEm8L8FGY5+Vqn44pNQOUdw8pw85APCvTATgy1mxuIwD1RVslIzXRn8+vfncCUqNzpbksjal+9zbUiOpTTTsz2QbYDxXNAf01IK8v0h/VUS0jG5OaOamRowB3HQwHd6D/QDDubnFQ9q1EkXh46+DuwJ2Fx5lkbYgAIFPYbQxnrAy171iBnX/Obn0x3sFIby6jeBlujG6Uaog+Tj5eul1Nw/hsTBXco3Mi/XNwyBx+ib3XAL6D5JCj5HFdazok0LL8Q0g0HxHp8zHO7mLWNbht5kWP19j/UYA7gN7dfw6gGhIzZ0lF5/M/MhbMcD7bG6og7nlQT8mMAVPNu0Vsp2aCanNr+5Q3JVXjBuLOPktlRJFYNgc+91GUF+Vj9YXdPEfn0ffaq57ydw3vo77r/31KxDJZV6bD2XG91vsRLSA+lETpr23b4VGXbnfMsgLRNlW2o7RmEa3hlWRrcG+a5l8A8NPy0z8L4D8G8DaAPwrgy69+//G2bX92oKzeoLBz9HSuaEzJRC9fiCQbCPe+Q380cq8nMnivY2gispBcxyg6f1eJmEjN+VGb9nF+jShwOzscMtQo5aGhtOpbBHCRTjp487jeleptr2XDKkMEYx86odEIZWjeap3lNjo2JDWRek35tTZaSuN6lKj3XXjKz9dMXHdLDqh2DW9rcG/b9lcBfOerik8A/BMAPwPgDwP4M23b/skx5XlqppT+0I77IOm1+voyDY+jgVGG74slvkWN7dW2Z+3bVbS+6LPWnymDKoU/ez4L6UuOUmXIuLY9NzquIOrz4P3Vc0rjFl0ftcmNL5p/NVLNsUf9c2Kh+qWhe7ZLKxujfUlUlo+fEp2a+fOIeFcZ0qfatNNQWX6t9zkicBlwq3OI/rI6onYPtXlfaZnvBfD5tm3/0TbsTI1PJz3LvTuQ8zfumABepm9ubm4wn887cNfnwPjOi9Vq1T11kl6X5fr++QjgvT++Gu59HRqLqLyIMbhSaNt0QVEVSHcJ6Y1gvrNoSEoGFPXFnWat0CkR+DgnbL+/xYafOe/8zvo9hGb/nWUrC4vYFbCZEuL1bBOfjcSyGJnqzivugV8ulz3yMZQS9DH0nVx+bGjHT3Zt5FS9Tr2e5/E/25/duDVWtsEYFV9DqW1DxqydtQ+1kfhCiR5kx3I9rahlD6VQ9wXu3w/gr8j3H26a5g8B+EUAf7xt2w9KF08mk+4ZMQA2HoFJo9RbeGngejMTt7+t1+uuDDJ3Dbn1eh5fLpdYLpcbBs72aXscLP2GklJYOkbck6tyuQJELE/bWoqG9hlp7EuivpPR+g4pAJjP53j27Bmurq4wnU57D5Qb2jVDoNU35PBxF9EdgVomr+ciqr/Kj+OvzzViH3yNR9eG/Ho6ZX0ksPaHgJFFCpFopKC/RXMRkRs9Hv3uLP9YZAy58HPd1oE+SXCMUdF5V7vUMjJno+W7k8xk59WzpmnOAfxeAP/tq5/+HIB/Di9TNu8B+FPJdZ9tmuYXm6b5xQ8++KBnxPqcEFVuNVICcnTTh+6caNv+jSGaC9OydIuls99osjxsjozV0zlZ+OZlZpMWKUv0WUUX9d40KYWtnvvUCETnWHUBuDcwfSeAp0f4oK/FYtHphtYbLZZpJKHnaARIXYz0NkrDRNGaA7H2YQi0IsfPz7WSRZYlFkvZlXHvQ6LIaxvh/EePQYlA3n9zx6xSgwM1DmofzP3fBPD32rb9zVcN+01pxJ8H8Deii9q2fRfAuwDwHd/xHa0PuLIWYPO9kuw8316jg0EH4UxPj2tZNGgNld2r6m6IyCiGJiM67uGrti8Zs+L37PwhZTkGKSlqZJDO6JUdl8DQAbTErlarVZf2iepy3YqcDrD5lFN1uNomtstvxOO5Pk7bzGmmu172NhLpWUZkXpd4G4bYclaG3qgWOWAnIxk51ON6zNvgzrtmLPcB7j8ASck0TfPptm3fe/X19wH45ZpCVKHd47moAaoxac5YWRPPjTwo0L+tnW1Rb0wGRUP23KUzeBmLQdDi9TWyLTiXFuUiJndsEulDZDCau/brAXQpDa7NqENwvdE7Xv0cftdIkd+Xy2XvSZQ+/x5FaMTI9w5oCofnORPW8J71DOVgtbzst0hfIycYnaNOim10vfJU2aGkhB/RuZmUxkXtl9ijc8Vro7UQJQaeg1fciurP7tR22Qncm6Z5AuB3A/hj8vN/0TTNdwJoAfy6HasSVWQyc30076u6e6mVaJuigrnvEuH/aJeMlknx8F5Fn/bHa2s8awlw27b/omT2R8/Rftay+EMb1aEkcpQafS2XS8zncywWCywWi2789EXYvKZt7x8NrXqhj2bVF3QA92s3UXuapsF0Ou1+82hSX+hOnaSueAqITkeJheum9h3og8gxsGNKRnoeUrKNDSrRsShtpRG29yfaiKCOzuuKFsv1mB6n0yAO1hKxncC9bdtrAO/Yb39wbDlZWMLBJLDTAICXgzmdTjvA1zSMMj2G1Z6aYT3RIHFwnVmz/Oj8iElG/YyYcmmyNG1TGiP9Tevj/1LbjomxR7tVgHtQdDbEz55y0es4nxHIRmkUZc93d3e9hdaonU3TbLztS41RHxqmEaKmfyIgiIyf+s3v+5BDOwSfj4dyQGozY1NPQ+d5ugToP2ailJqJ9C1LX0VpGNWxkhzNHao6EHpnpwI7HxxG41PQJbO+vb3twmng5dtpdK975JG9DQruek2kmM4MMnalyhABe2miSpGAX+ffda3AFU2vOQaAj3KXZC7RjhcFeg+FVUcIoDc3Nx0Ik53rg8F0EZxbGPn2eoIq26HsXFOEdAiaotCogm9h4nEu2LIMFWWIrl9sS+QQxkotkPFzqY7MPmrr2pdENrNPgHdb0oeERYSB5xKnfFdUFpVmZQDDDxM7CnCnAbgRqxIRrHWP9t3dHebzeW8gyeZPTk46RqTA7zsUODEMx5UZafsiMCRwcJCz9QGty49luwrGMpwaT/6mCecG6EcwPKbgTgeu+WvO5Xw+x83NDZ49e9ZLwTm4ez6fBnt2dobLy8vu3Mlk0nvEgG+1vbi46NrNMm9ubrBYLDonwDay/U44Iofr0QrPG7POUzPmkR4p8Rm6PnI6xyIcr1J/SkRIAVojatWNyeT+Ec/z+RxXV1cA0HvpkL4ukvqkoJ3Nv+70Gnrd3lGA+2q1wpe//OUeo9LwhZ/Pzs4wm816zMfZ9Xq97t5ArgbvzNl3xdDjckB1G6Zf11oeVc+LnNMY0I0MMzJYbauHnhGbL6WgjlnUyDwtFjldis4pmbfuD47C4LZtO0fPvfIE+Ol02muXGjXP51jyOgrvn1DH4gv4es9G1H/WqdGFOgUti+dmUV0J/LPUXo1kjmHfkeFYBl4qxwlDqXxPuzkJI9njPTPPnz/Hhx9+iNlshsVi0QE436MLbO6RVzzztiru1IzpUYA70Gci2kllb8DmOxyzVAkHWd9+k7EiAoGuQkdA4mkAz+OPVbZscmrCwei3TDlL6SL97dAh85g6ImeWLU5RItCmwRG4KdndmnwvL1+MrWxLx1XZmzqQtm0xnU43Quebm5teO/W/L+RGu2W0rJKUCEUpvVcjY9IxJXE93aU9Wfoiq1M/K1DX1sPvSiyiutfrl++fuLm5wfX1dbdYTsLh6TYlshHAK7jXylGA+8nJCd5+++1eWgbobzFiCLVYLLrBXK1WmE6nnfHRI/JRAlzEYmqGZUbs3be36YQxzNLrlAEOAbwqgUcA/L3GODztMwSAml7QNJJGK/6Y3H3tplFmU7sAqOMQpcaG6nNGw3Tfixcvuh00y+Wy0xl9qJPuhlK2zpy7RpP62bfLNk2Di4uL7jcSjaurq425YF/Pz8+77ZOeinJd0XH0/mbpELcpEqgacf12cdCL0jIOYH5+phfez5JkZfjCd9Rv75+21zdW8Fx9pIlfp4+eYGpmPp93u6+4AUT77g8WczzxlEyNrR4FuE8mEzx58gTA5nMWzs/Pe9+VhSurIuPShSZ9pAENlyGTPnqAk0TDc+/oxgWMX5RRI91niFqSrB53UL7bhO0+NJMfEl3LiJTZUyNnZ2dYr9e4ubnpSAENgtfPZrPeQq3OB68B+iF4BE4OVNFdqxqBslytm4YeAXhJPJpViQw+WhPSvPMYyVI9Wm6J6DyU7tdKBJTebnWIQ5ERZTKZ4J133sGnPvUpfOITn+iRAq4Jqk5lqSFtp6fzhubuKMBdlTULn5yZ0bBp1GRXnKS7u7vOMShzp2QhvMvY1El0/hgGsm2KpyS+iOzpppr2UA4J+FnagPoRLShqigRA70Fdns7TdIcyb/3M8/RPj6lBqk75OaxDd/tEuXXdaVOai4hZ8vpszCKA2EdkNqSjtTr+kKJOOmvD0NhEji2zK+Clw2fkR3DnddQB6phHrO7slbXXEsSjAHfgPgdaCpu8Yxwc5rLoGZXV+wszMvBVw6pZ7c8Ue9cFyl3zokNll9j8MbAq9j8K61UPOEf6eAAaAF/oAtwzag2XKQ7cDup+3MEdiB/zqylADe2jdRwF/TFpKx8XZ38lBrgrwGt6Yoyuvi79isYpS8WoZFGKp5YygNc0HtDHONUt3z4ZRXKcN8WzN2IrJICNznmaAEBnoDrImprRvDm/A/e5V95E4rkyzaGNCY2jSeZnFz12aCCtYYE6tmNSAoeILKI6MjZKg2HqTFk911sIqmRNPE93RPEcBXXPu+veZXUSqqsRsHv6wxk+f+f/yIi1z6xLb3GPxobjNjQ3+wJ41lsS1cVjIA9Abp+udzV5bb2+xKr1sdSTyaTLv2s6mHVqO9xZ6OcsNdfVWdXyAws7q6FmBOwRw/YFQTU2oH8jlOYe1chY79C+Ua1zyHNGk/wQyl1KL9WmhdTB7iOM35coyAF9Zdd8JNkQF0SBzcU+joOnUSiqRxodRAAP9BfEvM2ub2xPKYry9pTOIeA7YYnG73VLZNuHFgVLB+DIsUapYf/uAKyESd89oDc2UUd0my2ADTzxtSDWq33geSU5CnAH4kHTgSZIZ9dlk8BrWR6/az214XApx1gD3JkX3kU8FC8BxiEM6pBppEwIlt6fyCA85aH/o5SK7qCJdtI4gYjSMxHAs30ZuDvgZGmPUmpRnfKQ1DD80rVjzs0Y8EPoTQTipSi75FhLabGxRE4X2HmuOmdP05X0KpOjAXeKes8IoGkgmZG60dFAzs/Pu0cX6B51D5mjvdT6P5KxjLxU1jYKXwLYsSmgsfU/RJrGJQMLZU8l5qp5cE+paCqGobOfEwGWMz79LVr01HnxBe8M2HldaUyivu8bWMeC+z7K2VZ0PPW3IXCP2pcBey05zCTaGqlt8cyCk9VMjgrcGcoooDob0cHPFrt8wULDYr+z1EOhLF+t4a8/YbKkLFE5QznTfYiHdseS79yHkJHrd84LHzXAtAzfpgT0jcF3K/ijCHxrreqT5zpZThQdNE0zuFga5dHVgVBPdU51XtVJlNIe+0jLRPqrRErtRxcAX6cMpb8ygjLWHjlfvj5zenraS8P4Oo+XofPJ8zXtVkuojgLcVYlL6QUduMwYaGgK6CVABTajgpIyOBPQKGAXyRjGUMQQGXlNXSpZmmNfso3Tyq7RVIs+rbFtW9ze3nb3MXDxXHOdQH8B0yM8BVA+KkDb4Mw9MjJn9B5ZZswwOq76Ft3YpOIkw/OyQ8zR2xGNO+uPfq+VzL5KALsP2UfqNDuWkUV1ttG4RsxfI71s7oYWUilHAe7AJttRiULVDAg1f0UmMcQgoomPvjv4R86glB6JFGJISl7a+18rUX9rwstIhq6pGZsh8VRI09zvmuLv3AbJp4fqdkg1FJ27yLiicVEj0/P5P8q3R59LTiEq34VtydYaIuc8dmdM5rAycI+ur9XzaBeNXl8TBe8r0q0R12V3vp5ViJy5RotR+pnnRQQWuH8OUY0cDbhTIi+ngxSBhSo22dr5+XnvzkSysCwy4IDRePxtJ3xGjd69GqU9SiA8xEoyxc76HNVTYrzRuTwWGXCN4ZSAyOvzuyKHdiboOTQKzqcDFtn2ixcvcHJy0j2Zka/K035m/V6v192uGL25JDM2N86ob03T9B4SxZdca3keTXi/sjEauqGFv/PGKXdwmWTjk50bgXlmR96u2mixRP52lWwOI9H+6o10nAedD+oQRTMP1DOWo7anJAbo35UftSOTowH3jF1EBkXRznHgfJP/9fV1t5AK9BVqMpl0t6xrCEWgcHbnk8rIoCSlqGCfSjomLXMsEm09owyNqwM853u5XOL6+hrX19e4ubnpntui2810Xn3NxkG9xOwVmNTxAPmir5aROSmXaI3HhUBKcUfjzkmJwBCYaT9LOqZE6lASkZJ9lRmRQK+DQOyESx/lrC8/53EXnx+2o9RGdSY1cnTgHnUwY/EANvKqaji6x9kXo9y4eV6Uu2ya/H2G0Xfth/4/BKh7fS6qoG54UeTx0FICeCDeSufzpuDDO1T1hiZeA2xGNxkrZtnRnao87tdGmwH8s4sD7lBO2tnhkPi6QlTvvkTH+tB1adn7tqchO40i7Ki/7hwyp+G6SYmuG5O2OgpwH/LApV0CDDn9MQNN03Qr1NFAZuAesRsanS88DfWpBji9nJJzq1G67HjGQI+B8XMrGFDH2H3egPuIzJ/jD+SLXzUSsXn+nhltdJx1ZiDtuhZFe5luZMavC8lj+ryLZO3M0i+7tOlQZCmaOz2WXUPJcGIsuEfX8byaPh8FuAPDzFPBXEXz4JpKadsWl5eXmE6nvRynG7cagntFT9VoOiYK2cb2Nbs+UtqM4WZpnxLYZG0aYtEPLVmb9Y4/htHUg8lk0j1giZJFTc7iVaJrnJ3zt+gJf9k4OlmIXt6h52a7YPQcbQPLZU6XbfCdGyUnOnbLZMbS3dZ898e2gO/juo/HKUS2ks2hjp+Ov26r9d16eh31g+JRqN+Fr20aWmdROUpwd+OJztFjnm5Zr9fdIpYaki5o8Dd9PjIHv4ahZ3nFGkbMMqN86xg2UhueZaIO61ikZKhZ9MHPfH+up1DcMY8J5TWXrdfqQplGHrXlRiH4mPlUnef56mR862T07KSIoAyRAI1IS+3heJe2Fm/T70OIA3u2i0XPjaI2XbdRHdT54DVA/6Y6/y1j6bouMCRHAe6Rt69pvBusgrvv8+WbUPibTk6WjtEB53caSWlxw40ukhJjL4m2y/uQtaOmzNcpDualZ2d4ZMXPXp4yqKyM6Lcx4fIukrUh6kvE3B1EtYwoutP/WVpgH1KaF/Zn1/oOlY7x8ofqyOZLr9OxdkeQle+/RQ64hqAcBbgDsSHp4EUA7NsXtbM8xlDp5uZmY2+plu3bjdyTs0yeS0biC7lRW72f+1JuLXMX8DkEeJXEmUg2HnpTUQRYkRHoU0L12TBR3fzu+9RLEYJfG7WrdiyHro2cDJlbRiCiaDDaDJCN+b6BvlT+rjo3JgIbUyalVHaWilKJdGtsO4acS0mOBtyBOB+qn73Dy+Wyy5Xf3t5iMnn55nmmZabTKS4vL7FerztwdwfAvyxE1vqy3RBROzMliZjkvsB1SBlrHdAhhW2svcuu9oYdfTECHx2g76z0RXOW4XcDRszT5zy6DXwo3aP18TzNs/r2OR8vnqf99vyrOkJn6zyejXEGPj4GY3XmdenZGNk2Ehi6bhtQj66n+O6woXE9CnAnG462Q2ZgSSPjohoBni/oWC6XnaHPZrPuphYame6qiG6/VmNkFEBj8nY6qI/ZVXMIiaKdYzewIfEI6vT0dGOcfVFKjYBz5yk7nTNdi/Hz9VyNGCl6Y0qtI3UnQ4BXncke41sqj2Pg4+dtLpVVe47XMwRA+n0oH18j7syA8Ta3L1YdkQ4938+rWVtyvR5jz4Pg3jTNTwL4PQC+1Lbtt7/67eMAfhrANwL4dQB/oG3bD14d+zEAPwTgDsC/37bt3xyqgzcb6cs1MiUF7tMw3M/sjIjgHb10GLhffNOdCv76M2dMvE5Zl06mMpsoTbINyJdY4DYKrJ+j6OR1OKIhqbnBg+K7VigRQdAXhVOoO+4M/DV46mT0OkYITlQyZszrqK+R02A7M4LjEjkWH0v/fYyuZlFBdq6X5dHHvmQXcC+VGeW6tb6oLrevjGzpQnxUp5cTpelKUsPc/wKAPwvgp+S3HwXwt9u2/YmmaX701fcfaZrmXwTw/QC+DcDXAPgfm6b559u2Ld6ytlqt8OUvf3mDeamHi/KhahDs9IsXL7pzyN45YNxBo7tpyPIXiwUuLi56r2zTcp2V60T5ZES7K/Q8ypCSe0iW5U410lDxNjobiMBGb3Q6BNhnjililnTK2j+9pVv1RfvG9+kSGDU6m06nnX7d3t6ibdvwOTWqf0ogoif56a4H4GW68OzsDG3bdg8xY8THxw+4HlOX+VYplkU9zdicO64sSvA2q92QqPhc+PyorpUAXiPcCIjozDydFZGiGtmG7AyVF0kE1kBMQPTeCHXQ1FXOtdapGYzMPvm/ZqwGwb1t27/bNM032s/fB+B3vfr8FwH8HQA/8ur3v9q27QLA/9s0za8B+G4A/0upjtVqhd/+7d8GcK/MFBq4d7ht225fM4BwQHjbOX9Xg6VyAf2JoNAQeY3m5WnsyuIj8cmQMU0nppQX9e9u8NmER8z1WMXDVfbTnbzvKWf0NZlMsFwucXV1hffff7+bN3XKOt+6P1m3rU2nU8xms25Rlvl7dS6uL9S3+XzePdaCxOErX/lKt1WTT7PUqI9lOcBnDJ3t0H5p3r5EHDLnVCsR6aiVY9TDjOzwmNqYOiI9rmk7Ouv5fI7FYtGLyBTwOWcqxCaWDaCHPTynZhy3zbl/sm3b9wCgbdv3mqb5xKvfvxbAz8t5X3j1W1Emk0n3hvDZbLbB3qJwRMGNv/vWNwK073ZxRq2vUXMGo6yGQKJhe6TcQ4xdz8uOj2EjrnxZXUPK8FBGF0U92l/NcfsxFf/N2397e4v5fN47T58dAty/JNv3iM/nc8zn894ibfQoaQp1mMycW2bZBmXmvq856gcdWqYD7gS1T0NgO5TvjT4/lGzL3g8h2hZ3soo9nkYjQN/e3mKxWOD6+npDx0gy9LHVQPwIcb/BzdeUMtn3gmqkDeFMNU3zWQCfBYBPfepTuLy87IXhvjAD9BegNHWgg+zgrkxLPaADu77AtpTOiAzRldFBaQio/bg7hzFSYu/R56jNDyGlbY7ergiM3HGqHmjqRFMp1CXVA5bNp0fqVkOWq3qRtdUfTKbsXstScjG0SyUCDu3/PnLXEWD574eQkp4dE8CrOPFzcqk6xXTvzc0Nrq6uwgeS8SUejjseHfhOqmxnlcu24P6bTdN8+hVr/zSAL736/QsAvl7O+zoAX4wKaNv2XQDvAsC3f/u3t0+fPuXvG+Cqu1XIgJqmwWw22yhXgV5flUajpUHRY7Zt2+XlGSbpy7SB+5QPw2A6AnUy0q/0+0MxoVL4yDZFyvHQITPHONsW6QuYlIjt0lCapsGTJ09weXmJp0+fYjab9ebf72dQ5q5pl9PTU1xcXPS2VjqBcALAa7lDC0AvLbRcLrt+aeqE9Q2lU7Q8Ro1OCnS8asE/c5y7SA05OQYA59xFhC6yjxpyR3n27FmIUQA6PSE+6UPu/A5Xbv7g3OvjgkuyLbj/dQA/COAnXv3/a/L7X26a5k/j5YLqNwP432oKVDCKDJeKr3uMo0nR/xQHc02nqCNwwFNPzHK4n9qBXZmYt2FbY4kYfVRmpFwa9vuY6TlRXvd1hONjxJm8PiObi5mz2QyXl5e9/DbnXddpFMw1/z6bzTCdTns5dzU4nWf9nbl3df40XHW6qmssH0DvfQEUd9C8VhdmFYzYPs0D73uHirZF68mEbdDF6qHyDwX+rutRijBqQwTswL1uuc0paVCSyWtUZy4uLnrzpp+5IUDTMzVErGYr5F9zeIzDAAAgAElEQVTBy8XTf6ppmi8A+E/wEtQ/1zTNDwH4xwB+/6sK/37TNJ8D8A8ArAD8e+3AThkOmuYxs0b78zIo2eKTGx1Zvy88OSh7iO0pC1/YjYwnC7WHpJQ+8e81KZ9Se2qOvS7x8XR26akPXagC7tlwtngYPbM9So3pNdG5USqD80Ld8pypp+2on05AvHwlJJ6uUj2ICFKJzW+bAhwjrrsZwDvAHkpqo4rI5rLzHZin0ykuLi66CFDBnUSDOqy7wnS+WafPdY3Drtkt8wPJoe9Nzv8TAP7EULkuaqi+eMDfgf62LWU+ykJ5zJlp9DhYVTItj6mfKOfP9ui+/CiUcweQSYmFZ9dpbi46VlNvVN4xS7QI6GCr2x7d4asx8VqgnwqkEZEEMASOnItey7pUJ1iG7n6IgFuvAzZ3R2j7I5YclafXad3RGDpzfSjJdO6QrH2MZEQrszmOPR315eUl3nrrLTx9+rS3pqe64tsho50wDvZ7A/eHEs0zRYxZwUy/A5uLqs64WD73LCuTivLmUfiVDTr3U1MiZ+FlPYQxaXujUDhTjtJOikOJ7/lV0V0GFO0Xw13gngjodsO2bbs0SZSr1NSMpmjOzs4wnU4xnU57rCtixQ7+ysZ8K67PReQ4lJn779njFHhc7UPbqkQg00ut+xijuUOJ4oevtW1Thq7R6Txp1JXNu0ZnFLeP+XxelWY7CnBnp3yrkB6nwvqCmIe4OnC6E4Y5TT2XZfO3iDVHf9oubUN0jcshGUlWb22eM5JoL/brkszxqKFw66JubVVgj3bCqJ7w7/z8vHtkhffdDdn3yJ+enmKxWHRjrjem8BydJzVo1/9o3rT+TGf9M78/pNQyzEh2Sc/UsGweixy1f4/K8K3Y0Xk6v9SrKLJyMPeMgxJeAD0sK8lRgDuAHsNxcTaiCpMpsC9OKCjozheGR3rz1BDI6++Z8mSOoJSC0ePbsHs1dv2ctccVqlRf1p5DsrwozTbEeKfTaY/lqJFwrnUBLAJq3Rrri6jaJgV+vZbGzPSfOqUMLCLGVqMrGaBE8pDg7o4nckQR0Okxyth219hOdk6NLvNa37Yd6STPY4SpEYLqTolAaNTHSLRGjgLcucMgEw8xeY2mVlw8zPdQmTeq8PNyueychr97U1MbrI/l6e6GEqPice9XdE5kBF5HJBF4lQzDy1GnF0lkEIcEfe1rlEf3HDqA7n4JXs/tkUydeRqNbVVSwDpWq1XH4n0BNkpx6aIl3wBG/fDxKM0hz2d7dH3Iz9UcvN/7oeI3vewz9ebEgWtW+n5j3bjAtg3pG+VQ5MHnjXUN6TLHejabbZAPT8cQ25S1uzOgDuuaSvR4iLa9f/ptCQcoRwHuQHk1PwrRFMh0F40e18EkG9PH/OqDw5bLZTegvqClK9maGorayYnw1FLWZzcM77+HkNsoOuuJoh5lBM4cIymlpdx4d5VSZKROmAZEIzo7O+vSM7yHQcUfH+Dsnn/c2x7NY7TbRgkEJXKEOu+ZjlCXSgRGy/EtiVFqzr+PBfio3FIfXrdo20p6qVkBBV4/Z6xe63qO6phHlhoBuK1q/cS6iOxGchTgrqBYmoCoQ6qgUepBmZiCm4bMurOB4J2Fa34DStaXaOtbqf96Xm3IOMTK/c/ryD6X6vT6nblsk04aEg/dncm2bduxRc6N6osar5aj7MojgsiZ+ni6waq+sVwadgaKqk8RUGZG7DrmedlDS6YvY8vInMM+9CfCk2h8XY+H2qBOIKsX6C+ou35RPPWnOOgYovj1xoA7UM75Robp1wGbbx2Pwmi9sSTaUeL73rUON8ioDTp5NQDvzG6MsUTnRtGL90Hr8jrHgHupXfsOpT3/7o/h5Y4Czu3Qky1LQA1srgFFznKoDAX36OYkH/+h+Y8cjT6BNDP4fQN+bXtry8rAfVfHUfO762oNuAN5WkuJX7Rmw99Vr3xR3rd/A/2NDVl04XI04J5NsjOUaKcBv/u5GhHord98DKtKxHIpyggJHBrGRQ6Hv2sUsIuyej1DBjZkeCWWty/2vWs56mSzMniOPlb35uame1iXr+VkrDwSZeLOviKnrClCXs8FVo8IM2KRMXx31GR2UepmKOLbh2wD7GPasc0Om4cSJ3vAJtj79lqN7nQzB7/rDXeKW55W0q3b2fZhytGAu0oUPkce1UNv3UfK4/oby4nYuT/HXdM1mt9Vo9TwKQrh+X/ICMYaSe35UXspziT09zGGdYg0jJcfRSMR+wHun89PsNcFZgK9tjkLl7U+vQElOjdjfpPJpMv7k1y4bkYAH7Uhq8+dwpBDV9nnompJsrF9yDTSthI5ymi+nXx5OkbTdtF6DSWzP2KX5+tLcjTgHoWc2XGgz2giZhMpjd4Rpk5BQzMPeci4tE4/Hn2O+lFKo2i5Q+mWaExqHUiUZvC2bhsSHyIdk4mCrKds+LhVZ/5KElRnsgVVXqf1lY676EK+1+2gHqX7tK+R1KYe9i1j9C5q+z7SOUOS6XBJPyNbGLKbrB+RbvrxUgSZ/ZalbSJ5Y8AdyPNhbsAZ++HNLczNRuUC/ccPRIy81LbMKZWU2Y9l5Q85vKF6eM0+GNtQrvLQQK9sSP8I7voE0JKhl1IuGWtzIsDPfj7BXR/rCmy+V0ABfkjHh9JxmZTWibaRGr0u1eX9OISu1IKmH8v65jhDyQhfSZ/0WOYA/BrfdPJGgHvJg1GisDMKhTT9ouWfnZ11D/Dh0wNZnj/YyR8ulgGiGqimaLLwnr+rUe8qHDf15qVys9Cf7aqRUiqmxlFtI3zOCxC/dUvTaYvFortvwR2QP+clamsEtH6upvyyPjItM5vNcHZ21rWJ1/qWXJ1DdZBR3Vmqym+aqpGhcx2sdDx0l84ubHyfjkfLGSpLz9HtpFH79Jjbsn/nOU4edDfVEHvP2vrGLaiOAfZIsZVN6YInz2GuS59TErF8gr2CNs+loQ2Bo9c9lHIZEl5TAhMtm/2qNRLtZ8SkMsNzMCqVv43BqiFoOdnisi4w8lx9XLQaYNZO/inoej3RdUA/h8/69XHC2jYFcycImUT6XztupbWUSK8yh7Jr1Kf2c+jUTImEbHONLpjrWl42LzW2WitRFDckRwPuqtiZIkaDmAGPnktDil7Dp15bz+dvGSNxwPYBHxuuDp0/xGzGhMm+Eh85n1qAz9oxVmoNMeqf6gpBPJrnCMCikNr1q2S8pfHib0zLROwuS/14mUORntpOFpEMLZbXOOFo/MfosV6jEYqXtS0RcqmJJH2cM5vW87kVV8vXLbhal++iyhy5O87IpqMxy+RowF33hmbA7uySEoWMzg5ub2+728gpJycnvf3uyvhZhy94eXv9sZ16LJMxjsCBJto14dcPhclsb7RH2tuetXUI4GtDfW1ztBMgMgy/XlnwZDLpXrTRtm33CAFeo1tpSzrE36gXLMP7qWCk13Cr7cnJSe+9wK4rXp9GGk5Q/MXJHp6r4WeRWMm+tnHaWmeU14/qo7PlS8RLDmPfAB/pc62Oa1TGl8IA93crX11dhU8ejezT2+NkRM91MlCybZWjAXcanYsbYolZAf33C/KBPefn55hOpwCw8Wqr1WrV2zrHsnyRTbdCAnF64tBSSn+MbUMWGg/lkSPhuOkc+mJm5JhqmGJWn6+REEgyw9AoTV9WzXZm+VbdupY5LB0vTeewTm7PnM/nvTZOJpPubUpRm9lXPab11TB5HbMISGquV+fP7xEj5hjtA5AfUrIoXImFM+bz8/Pu/2Ty8t4Kdex8s5um9qhLJAo6nn6TWzR/1I03Luc+pBA6uDr4rmi6X1gf+6oGR3DXhSx9WBjr03rZRp0obUPUXv+c9TELFQ8lJSfKzyVDLpW7DdMHcsBR0KCQSXMu9KYOnltyUBFDb5r7F2v7HmXvi/fL69PzdJGT+kN2rndKEwhYpkefWd1R37JzovmJxmnoezQe2m497hHIMQK/j0EG9sA9e9cFceqA3oykGQGvR8sjDhHwfRefks2xROgowF3D2CjEjG7E0ZSJit8YooNFlkRw19y6vsHH7yCLWJIuqGQShfg15+q4DF0XXbOPc7ep26+NPgM5KA0BTwmoqEORc8kiv4x9Rm2MIpwoGona6WE4wb1pmu65765/rMMlAvmSDBGP6HtNmccK1IcWJXUK7vpCGL1xbqgsJRUe6Q6tjwzJUYD77e0tvvzlL4cdBfIcsj/iVJ3Ber3uwmDuNSZTOjk5wXQ67e0oUeOilLwl25MxRPe6Y0WBVcPpmjZlErEoZZU8Z5cdEV5ODbCrRHV7uiWqjyk23ZGij9x1o9GdNa5Heo8D9UfTNpoecYYN9MGZekgAILjrWDBls16vey9K1nGM+j3G+CNALkU3mUREa6juYxdPuVDUxv13EkXeLPf06VPc3t522OLYQPJKndDH/DJFrN+1zsVi0ekyUz4183Y04P7ee+8B2FygLOXwyJw0LOLvHFz+PpvNNhhStICqLJ0r4t6Wsfnt38lMx4XjN/RcDJVoHcBZMkFzOp32lJ+GQSD3PDwN8vT0tGP+k8kEi8UC0+kU19fXGw8Ri1IywL0TIjPnOVdXV10b6FApq9UKNzc3HYM/Pz/vWKAzOaZ4dCxVovShnquOF4ifTeKR81gZqnsb+6mRKLVSa29unx7BqaPn2lzTvHwxDOdjsVjg9va2A/3ZbNa7aVJ3TGn6j3NAnaF+6FxR//UxGGx3SY4C3IHNJygqc9HfgViJNZXCc/QGgrZtu2e264uLFTiihYqIzTt7G5JjAHXt35suuiCpzFYXyOfzOV68eNEtnOrLVdyIeVxfhs3FeH18gM+1vzxbHxSlcz6fz3vf9YmWqo/eLpZNfcsYpkoEamOJReRIh9I7Ct7RDpno+n3axTaOKJNoHjw9q6k1AF1GgJEf2f3t7W13w6Te6wBg4+XprNvvs1GiyvJr0j5HAe5N03Srz8DmvuTIswL9xQd+Vs/InTFq/DQkf1F2FvpGedV9KlKtuJKxfWOuj6QG8GtTKmMkCne3FV0UJ7jf3Nzg2bNnXWqOOuFPA9Un9SmIE6T5kmxPDUVMSt/BqsbH6IDhN8E6MmItn+3TiNLP1WsyAK3VE9cvPxa1z+sqOZ9dnc6hJGtDZHNKCDivBGxueaUOXl9f4+rqqnsrlzJ3oP8mKr4xzHfYKLDzfEZ5QxHQUYD72dkZPvnJT4beiKGov/0oMhBd0NA38QAvwyZl7PzM+mezWVeuGqAqsa6Ue4SwLTvyCYqAO1IwSs0kZ3VHbLQE9kOAHIX0+rlU9hDQ63jo9kUKt7y+/fbb3d9HP/rRTh+UYWm7NK3HhXY1JKZJfH88wd37tl6/fKQ0IwqWR6a3XC57dTOVBNxv6dSxZvu0bt15w3Op/7uy9KH5yOpgG33xWEXttYZ5PrR4tAJgAwf07VxMwVBPmBJs2xbPnj3Db/zGb2C1WuH6+rr3Tl513Fx/oV6enZ1hOp327slhukfTPjVyFOAO9IGzJJlC6MD5IHoEoNe48elERjsh9Np9iZe9L6WPwuuIWSmARNftUn+NjBlLX2D0OT09PcVsNsNsNsOTJ096+WuCq9arIMtzdVsbUzMKapoKdDBmG1k+wUCZt94QxfYy3HbwZLmZM/axcYnIgqYG1FkNCccw0o9jYeHbSCli0fQr54jnPXnypBuP8/Pz7kZJH3NGlVqP6jAJCK+Zz+fdIjzrZln6xrEhOQpwV/arHeF/DoTeUcnr9M932uiE6DY5HSganW5fKjGZTLnHSASiDgz6m3+O2rVN/fwcAYAf26aOsdc6yLujHooaNC1Cg/GdB75ThXrji1yapuExB0JN1fiYegjO6/wZM2wTddAB3OvkmPirICO70WO7iDqDoTlgO6K8O8t6KBnT72zMdEx1jYfz4Fuv27btEQON/FRvXMe0Hb77yvEsam8kRwHuQH/l3kNQGha9lu5ycXAH7hmZ3hhwdXW1sVuB2yO5vUh30vggatitSlzDuiMPXHISGmZ7GX5taZKdTem4an98R5ACj4NjjXgfh0TboG1R0NXfHAA596enp7i4uOgMxm8Kcvar3xlm66K6OlwnDWxrFB3ymDoY7rKYzWZd2o/6Roav5UTOXscpAtsolejjTHGQVSDxc7xOFeqLEjQFQV1nUF2KdkDtU7YBdh+DyL6UeSsOzefz3lu/Li4u8PTpU7z11ltdepAO3/80tRqBuDuP2ghpENybpvlJAL8HwJfatv32V7/9lwD+LQBLAJ8H8Ifbtv2waZpvBPArAH711eU/37btv1vTkEjZqBDaOVUiaeMGa9C7FZX5a65dDZY5LzeOaJD1d2VuWS4xmwwHVjceNQA91wHbxyEywprc/OsMrZWxuC5ke71pGHT6wMsxuLy8xNOnT3F5edlFbgrQPC8CeWXumqbRtzhFwOoLrpra8W2Q1FPX1ajvzuSoX9F48Lx9bzXcdl3HHeWhgHxX8XZptKj2wHlyfFDAV9HHn+gc66OreR3XbyJgpxNxRzAUBdUw978A4M8C+Cn57ecA/Fjbtqumaf5zAD8G4EdeHft827bfWVFuJ2TZmbei6EKaKrozdx9UThB3U/jLk7PQvxQC7UtZI8YUneOK5qA+hsEDw3c/Hlpq+5sBO7DJXkkGNBwGNl9y4G2g7lAPPU3jOVElERQ11Ihg6M4urZ+GHfXFwbo0P1E6r3T+Q4Ht0Dy/LjLh7aC4/Wc2p+dmUbTqjpNNjxojYFdSp7bAc4buFRkE97Zt/+4rRq6//S35+vMA/u2hckqiwKwSsVUHOmXzyr50gczvGIxC1xqDoJTY9lgZSl3o8UwJ/fya3/TYsRgZpSad4+f43HP+PeRWkFVmqWFyloMH+kCrhqbMnefWMFY1cu+fL3rWzNGQo9fzan7bRhT0+D/q37HonLZlzM11QJxGoSioO7hn0YETXE+f8vcoUnDZR879jwD4afn+TU3T/B8AngH4j9q2/Z9qCvFBdfam5/A39WS+hY2MSBkUr1GvyJVsvTEgAhdnRjy+Tcg6VnSinelHEc42ZR+T+AKTSsbmJ5PJxt2dnvLQlAiv8bBawdz/gM1UTJTa0fIz1h6V4X2Nrs2iMHdi2tZDMniPrKOyjzkl41JrCzre0TqI/ilRcBKh12rdGrVpRAj0n4Jakp3AvWma/xDACsBfevXTewD+mfb/b+9tQ23bzvOwZ+xz9lp773POxY2uZCtXamUHudjujyvb+I+pK5rSpKGgppBE/pE6YFAMNm0ghcihYBMwuKV2KQQCN8jEhliOQPnwr6ZOaXEDcRw5VWvLqhvJFuqVL7q9uh/n3LP3Xvtjzf7Y+5n7mc963zHnXB97r3PPemGx1pof4/Mdz/u87xhzzKb5VinlBwD841LK9zVN8zi491MAPgUAH/rQhxYUkQ3DgayxRl1ZoJWnEAC4/Gg+n7eP/2qYhtfok6t07VWYhw+gjFENZU+ryBgwV8XIjFFm1DYtasTH5K118MFD1j6dTkOAU/AFupNk0eBTcNL5GwX0KKSn/1VPMu/KdV/12ve/UXESlOXRx9bXBcI6LsaGC+9CvH89dOJMWWPebPvIk9SltOoNFgn/ZSFDYLEdnXyuHJapNMiP4Wqi9U831z3UNM0MwOz69++UUr4K4LsBfCEo+CsAXgGAj33sY50edoXLFMBBnWDvwgkx7zAN06iiqfvj+Slz1HujsvUN4kiGhF5q94wRVRhNZ9MAH7noY6934FSjxbg7Y+66qkGNu4cNInalA13LE8VOMyMVDc4I/KL27wvp+O8awN+F4fb8I2Ny1wAfeYcRkfPzqj96nsCun0hf9F4lGp6PLgDxpZg1WQrcSyl/FlcTqP9B0zTHcvz9AN5smuaylPJdAD4K4A8HppkqtytFBuLRPWwEsrgopOKsSUWPK/irYYjuy9LKFLlvkK9bXIH19ybzzdhhNnE6hE3yPENrBHFd4cJ6RXrm7rMPxAjcne17Wl43BXgH+aH1zOqt92VLeTctQ/KJxvK2SOSNR6CvvyPgp07ovkT+JLMDuX5HEQgnYWsD91LKZwF8HMCLpZRXAfwMrlbHTAH8xnXBuOTxRwD8rVLKBYBLAD/RNM2bfXlIXgsd726RsjOPR/oA1ZgWJ9e4/UAppQPOvEcHYVS26BywGiCvOgBrXkLNu9h2iXRBQZXn1T09OTnBu+++2y6H5M58pZTORnGaR8QoPTyjfe/5a/m03BELY19EK7f4IJ2DdcYaa+vOb7O/MyDsI2DbKLqdSda/FF+rzz7USX2dVHXdVaDv08sIG/vacMhqmR8NDn8mufbzAD7fl6YLK+pK4hVza6UTTprGdVk6ykWl50NL+kSqlL9jKXmMbpMCvOZlbbBQt0zuym0eM/CXCRNF90dezCr1VWWnTty/fx+z2QxPnjzBwcEBHj582NkC2NeYu1HkXI4beA5afyo0KhONQdQOamipixTWwb1SD5vxN3e75HFeq9cNZdOZDF1P7ZLlrW2/TLqbkgxIWV6PBAAIdUgXdyhbp1552gru7sG5fusCEt37vSZb84QqJRr0au2cDURW1t0Y4OahEo1hqUfAJwg1Tf245VzHCpkM3N2CD71/aF4Z4GYewBAZwioyQxH97lNcZe1qeM/Pz9v90ZWpq2fmaWRl17R9Ujpqz0wXvQ38mBMHGhInPE5Y3DBFRiCq3ypeZiQKeJksG3Kr5bcuyYhGjTVH552ZZ+V0Q6Lf6hFGbabhy5WZ+22JKogri1tSZUEUV2iep2vEGBgNhFrlvb09HB8fL8RGnVnpfX1up5ZrqDJ63LkP4PqAmHVTV1PL5CxWPaEo/9ogyFzKbOC7Uvu2ArUBpQ+r6YAiKPLFCQDat9RrPgqEmocOSt33w+OkWagkIgGRZ8hy8OnViCnqOdV3XcqrfRT1c9SONZ3JQL+m5zousjy9/jrBPVaWNQh9ktXdgVSvc33REF6kIxGZcALKdF0ig/BMMHeyabKWCFQiBqOvrlLRJUrAlQt7cnLSvjRBt9nkEsizs7MOyKlLxfx0kAOxoq0axhgj2aqeIcL7oo3/OVizSc51SNQ2GgbIPDgVXqOg5lsG6Ha9zIMfHUg1TyMy5DqwM2MWeY7RZKozfqYzmUwwn99sIexl83KpgdiUeL/UgF/r6QsQdM9ylU2B91DxceBhE+CmjBpz5xYTusUJ+1yXQWoEQYmDh8A0DxUnQDXZCnAHctfOV7OoonBgU6IBQquqMS9lfkzfZ6kjBY7AbowiZoNuE27msvctw6jW7eZrun1p8hpnj35NNDjdBe7zNvR+TceNhDM3BXZO6HNQ6743WViF6UZzShGLqxmqTZERL3v0f0iIymXTxkrzifo1Eupcrb+i404Y9X+WT1+aNdkKcI8YT8SAMiXx66L0/RVqnOSgcdCJtCgdD+X0KYCWq698LGN079ABt8wgiEILy8qYe/uYh3pKfe1Sa6MasGjfRSw8qxd1SFlbVB8HfupcFnbrG+CR4fFlj0O9rJrxqumb6rK3UabnTs7GyG0Ce+07u56S6ZavVFOj4KuuojbV30NxRGUrwN0LrAodgS4bIYolA4vu1N7eXvumJb3//v377QoIAr02um4DzA5wlj8kdMDjvhJiSLtEaXp+6r4N7Xh3mTclWvZsosm3Fc7qEPWvrpTRkB7rdXFx0YbhFAzV4GcAHRkG6soYo6srJ7j9rXqgmpe3hxo5/iYhyd5vwHZZV78OTavWb5kh6WvHLDyxKamVh4TQ52CiJbr0snyFlHqavL/2pKnqB++/le0H1ik6cH2gudWie+vxYgIvr9P41+npaci2GLv3gVJbK6xutLNLVw4FG2ec2XLKqF3Gusy+pLMvjzEMadky9aUZMV8VXVKm7F7XCnsf87wPoqH19UGr/edroiMm5vM/FC7JjV7YzXtJbNyN17pryFDzqy02GNsG2hbLXDcExPvkNpZNuvH31x5SMmav403j6Qr4Tjoz7zSTMdduFbjrRxtVldNXDTijj1jYxcUF3nnnnc6gAdCCOt9Qrp2gs/osh5c3cqdqLi/P672anqfheY2Vdbi0DuRedme6y5bT27A2BxKJelfAzeBSNq9GoJYvvyOWFjFJZctabt9umnnWWGzNNdfr/Ldfr8f6jHcGVkMlu6+W3lhQuw0ZWh7Vm6j9HcOy5atjy0Zc6ltqSdkacI9YEAershNlTEC80sDl/Pwcb7/9dsv8tNG5cdjJyUm7soLvU1XW5BKBWwTKXsY+YM/S2pQM8RzciGWGrXZ/7ZzH1x3oanm7d6LgrqsU3HhGRILn1DB4CCYqm9/rD9bxOuquntP03KN0wqMgHS1B9PJovhl5WJVV+/zDUFkHm1+31MpT02Eg7y9KNuHv5LQvfyWnzwS48zV4HKT6uip3PRmKUaCnlHKz21rTXL1olssgHz9+vDBQNeZ5cnKywBiBxYmrqEHHKGrkoruwwzfliqo7OKTcy4ZhhoI/gBDgo3I7U9K3wWtIzvVDf/OcrpZiW7BtNB0aIA0DRROFXP7mb/riChl6itRT9wC0bMr43XNVgHd9jlh65nVmEo2DVXSxz1u5a6mVo89Tqnnq3m9MI/Mgo7kNJ4h67JkB9zfffLMdBPpwigoHWzYxoTuw3bt3r93fezabAbgxFBcXF+0TjDyu6251gKq40mfs0svsHV9TCL+3L31NM0tPlUGBKyvPtgw6F2e2KtFyw9ls1k6MN03TviSdOqQxVX5fXl52XvKhm4+pAfB3+RL0GeJjmThpr/F1rUP0ykfvR10+qXWP/jtrdGY4VJ+0fTWfDOQ1DwchrYffo9felc5FwKni46FGQHhcDa+TQ+qg9oV6kW5A/Bri1jMD7k+fPm0HYI1VOpPWiSRlWnRdGHbxziMYAN0JUg/D1Cy3lkm/o3ORaHghui8aMH0Mo5afhzT0nj5Do/f3Hasdj8oNxG9FAoa5s/P5vAVRKv/Z2RmOj4/bNGcsYBAAACAASURBVHlcDbwaa7YFCcJsNsNkMmk3HmMZdXAqAN6/f799qI5p6wosJQ/a5rrO3Z/S1fZhPbO28zmH6LpVpDYxH4UWonIsw/6X9RrXJRExi4iRjie9j5ji+ORzNm6Ao3TUmxyyUGIrwL1pmpbtUOFrIOqWTQFbN4eaTCaYzWYtS6crzO0ILi8vcXx8jLOzs5b1cbc+5qPx2hrIRANvCMup1TOqX9TxmmcG1tmg16WmtTJ5uYceGzI4/bV2OmDoiXm9NcQC3ExYEkhPT0/x1ltv4ezsrLP74v7+fsuiFRzZX5x34Yf/ycLd8FDI9pumWdi6gCuy1LiyHvQwgCsDwZd68zpdceF95wwyAvhlQNENs9bT2XttYYOK1yHyjP1+JVqrgHsf047KkY2fyHtRQkJd4hJq6iOfNgYW607P0tN0nVfm/syA+/379/GBD3yg/e/sJVrPzj1EtOP0QRHG3vnoNh9FV0vKwTuZTNqBpE+z8loyN5UhbFdjaiyjfjPPocx5XbLuvCLPRb0qKr/H1Sk0yDVPxtPXp4wBtG+9efDgAY6OjjCdTjsxdTXcakTUcOhWraWUdisDSuZF+Jt2gK7BYDiGoZyoXjzPHUt5nG3je9OzjbW9mZefi9qvT2pe2tjVH9FcCZC/pD1Kc5lJWy/3qqLGNgqf6NzK2dkZHj9+jMlkgvPzc+zv7y8saQVuCIyOkYgourEd0u5bAe6lXO2jUTuvbI3HHHip3PomHm4Ipq/l0+s1LqpL35Qtu3i8LJIhIYwas90E2DvDUulj41Fa0T2riq7f7ov1qrHUeukql2iOhIMz2nhMmbuz+IxFk/EzXZ88U0ZNXdTyaBtGoRs1fCpZm48Jz0SMUc8tC6ZDdKy2Eu2uwjCUIflH5dQQ3Lvvvtt6isrO3UCQHDBNn/+LSOGQNtoacPe9qf18xBrIslToDk8mk46Lr6CtA07ZuoYAam5PNKu9ijt8m4oc1SsCmbESsapa2Kl2XPta50WAbttrvJzMnP+1X9n3/tKEDNz1DTokCcrEa+AOoFOOUhYfhtF0HLjdQ6W+RsY/A/s+fRpz3zqB1nXstj3WPunzHCkZuHr9zs/PMZvNOvNBABawRtvBVzxlxs/zi2QrwB1YdOmjeJMObMY2HVTZcGRdk8mks60AsPj0pjMvXqOi5VF2qcwquq9P+jyAdUs0qGqrLGqTYFr3SNGic5p+ZlDoRfUpr7uwOueirCjzviLAJ7hzIpUrrpTxe19Tf7zuTL9pmpad0duk3tKIZKuzPETo4sCg90ai7exjTkFjCKBnq8pq5a2V/67EvelMqEcZAWUUgJ+joyO88MILODo6avWAOqlbX7heRYZcVwkODVFtBbiXUjCdTtvfKgqiHusjW+N5XdVA4cQpJ62oTL5eWMHErWYENOwU7+jaXjhDlHgdyr7JQRXVyb0hDUGoAmdl8JUEZLtcbaLbBkR1I8N3xk2A9kHp/ZqVUQHaGbsaPn6rnugkLJdXnp+fL3gjunyXadVAJBNlhix7JG6cMmOr4p6up+fiRndoOOm2pEZEXL+ifV8ijNLwHXXx277t2/C+970Ph4eHnbCMz+sxDffu+J99qxO0Q2RrwF0tWXQ+YnoKrj7YyHqi5WXOWjUGqiBTi2lGv6P0x8gyrupQF21d+dXScuWMWGJfe+l1TMfXgqsHp2zHJzQ5yCg6cDUur6DlK3f048xKy6qen4Z9ItBW8bT7wpNRG+nvoWGFsVID9dpYicJNns4mwT7Dk77rI6On3+6V+rForkf1QvvZ0/J9rjwdSs2rBrYM3IH4JbT8jjZ+8jANQd5jrlkna+yT6fQpWx8TV4WNjNIQl3mMZGk6C3EAVm9o1QHmrDhyLfvKSuakE6k6eDQcRokMrfa/biXhYYdomaUvg1QX2sHbwd0Ht//WMA3vc6/RCYuKA+EqBl3TrzH2mrih7cvTdU7T2RTARx53H7hnoStPMyOM6gVoZMHzdv0AFt+Dq7+HGG6VrQB3oMu2XaJZ9aa5Wu85nU47CkM3fjqdYjKZ4OzsrF0OyRihsnPGS9VYlFIWHvvW0MFQQIw6RwfvMoNzrLgR1OMZ6GVL/u5C/LV/+qSmC3WCcyyMmQM3+wg503eDQDCfTqc4PDxcWDLpYRP3ApmGll9DJjqxHzE5phv1l7vjHq7sAy4vrzNGPc70XbzuGgvmcbZT7YnU25CobjWDFQG7j1WfA+F48R1q3dArkfBls77M1d9SFc0Rapky2QpwJ/OJADxSCHVBOZBU+RlzPTg4aPeM0XTcknKii2nTEESDzCWypjUF9rj0umTsRK7LqgYn84wiNgsstoO2R7Yenuey8lMHdA9+X4Lm+8nwuJZRVzNoTNxXt+hgVg+RAODPZ+g5BQstI4mFx/dddxVcl9WjrI2XlZrhiIhOJJmB6jNcfddnv/2erFw8rn2qIbcIoyaTCQ4ODjCdTtv3STheKNnQsaCiz0co8XpmwjIcWJlLmt2jjaOD2N1m4KZjdHDyvK4YYRruSqlrNAT4azLExRo78MYwI3fzXGGXAXtPr2b0/Dq2J/suWhGgRjdzt50Z6UBQFs66uvGJgN7j8yyf1snj68DN1henp6ftZKq2Ncud6b2Gp3RSMwL6Wl9Fbb1OyRhv1P8O8vwdpRfpyzJlG/o7yy8jnfzt3gvvpz6pHqpR95BgVC4nIW5UarIV4A50K63WyS29K79W3h9C4guxeV43CpvPr148rEvn6NKzAdX94ePjPOcTen31Am52DRwKnH3nfQBEBsvXh2s7qtusbHYIYERl0XI7WHs5PfbMvCNGT+ljmqWUliXxCVW9j2EdB3MHdA2v+CB0psX01fPkKp+LiwscHx+3201rGbIXREftriuwKDoXxXZyw+zpRP3g1ywrOl41dMm2KKVUwzQOfmMNUI3x9wnbTbdj9rJoOdmOOqb8Xc7sMwd9DcVQz/waNxy8j/OOUT9HsjXgTokmWyhDLHxk8cnA9K03zqI0bqYD2ZWWHelx1KhcwGLM2GVZZR4qHGh9XsY6Bjiw+uodgsD9+/fbciubB9ABbYovQ/OHj5h+NFD525mU5u/iHoASAd0aQdNiGXz1j4aK+sKBGVMc47U9T5L1n1+TiY9x9/Qiz43iK69UVC9oDFTPM++ApGGI59+7TVsp5ZdKKa+XUn5Pjv1sKeUbpZQvXn/+nJz76VLKV0opf1BK+TN96XcKI+6GrnjwR78jlhUNYjaiLz3K4pQ6+COAZ8O7AYjK4UYpSjdo6zHNVZUaQACrx+jHlqV2rK8vtKzKwqM+0vXuDvZ6zBl51G9aDmBxNY4SB/328wA6gJzpc003IvF73HhlbXtbouWJwjG3XY5lztcwodaHwGJoJrue1zI/SnZsqJ4MYe5/D8DfBvArdvx/aJrmv9cDpZTvBfBJAN8H4E8C+GellO9umqZ31b1aPXcl+XG3TsMOLjoglVkpuGfuq+br53WwesfU7s/y8bKuqvhD0rgLYFdF1TJG5eWxvvaL+iwiBRov9xUN0XdNPI6vx2ofL6emES3xrEkGCpnX1AcYm5JorN2FsRnCcodI1K+1OaAMyDUt/515Y1HfrgzuTdP8ZinlI33XXcsnAPxa0zQzAH9USvkKgB8C8C/6bvRJgoiFAN2YV7b0iPfrcQ50jatrmnotcBMfjzqE8VjNx0Xj69m67Nr9y4gCxtCn2JSRrHvgqdJHK2DGGLVoAGWD1veT8SWPfZKRC2f3EfvS31wrz/KQEKgXqbFZtlNf2RQ01F0fKmP7eiw4rrKC564lwx/HlogguL65Eff/mmcE6t6GqifMryarxNx/qpTyXwD4AoC/3jTNWwBeAvBbcs2r18dWElX4oe4dz2mcdig75/0eJ/UldLW8+alNgKziqg69Z2i6Q9y8TYgrtYJWZAzY/t6v7C+dD1HWzmszr00B3NmWhlwyJq5lA27ewsQXfnC1DCfFIolA18He5xl0b+8MFJh2ls4Q0XYfcm8GTsB2gX9ENiKJyuzETaU2T8PfGfb4RnM6FvS6vn5Y9sWIfwfAnwLwMoDXAPwCyxhcG7ZYKeVTpZQvlFK+8MYbb/BY6vpE8fbovMffo+sjEGPHaYNRQSPXMvuoEfBY3VCplXOd92xqgI01VpnbrgNKH+TIXNf5/GbPdl32qtf2tVN0PLunxtwBtMydO5RSB2tGPjoe5ameSTTII91cVZwwjb3vrmWZcmjfejtm6UX6EpEJz6N2fiiOqSwF7k3TfLNpmsumaeYA/i6uQi/AFVP/sFz6IQB/nKTxStM0P9g0zQ++//3vr1bIB4wuUfPJ14h11SY9pDxRGUNA0RUoOnj8eJbuEBkL1usA9nUy91UGtIO6H9ewmhtevizBVxSwf5ZpV35nAK+/3d2eTqftZK6CewTkNfCIzjvBqcm6Aba2jFN/rzoO1iVDDFzUxtrnQ+qQGfsx9a+RiDHgvlRYppTywaZpXrv+++cBcCXNrwP41VLKL+JqQvWjAH57SJoOkJlbow3HNbR+v4ZEeI+DgIZKFCx8oGioQEWZocdNh9bVPQGvq35nxkevyWRIuZjGmHj9KuIDKDvn/7m9RFZ3vlbRX2vnbaBelcqyBlWNB5dyUv8ODw9xenravmmJMp9fvX6P3p6GlZQkMA9dh816+btZo/brk5oe9YUqhhx7FiQagzpPQokYeWY43PCqHrpHr32Xbf9MHPN7MukF91LKZwF8HMCLpZRXAfwMgI+XUl7GVcjlawD+6nXBvlRK+RyA3wdwAeAnmwErZZrm5r2A/lCGN6avKnBl8hgkcPP2eGfamv91XTv/9Zg+Iq4doI+T68qHjPUPlTHGYh2SKehQA1JLd5n7s/u0XaPYNVm7Gl+moaQhYpSZ662fyNV2gNdrSymdZZiMp9a8O9ebqDxa1k2x4iHpbroMm5asT4H4tYruLfn1GUnL8iZ59PJEeunp9bX5kNUyPxoc/kzl+p8D8HN96bp4TJUV8hi6K33kRumkhV4bxW198DIP3k9xpk8gr73oN/u/rvDHEHbv5zOGwfMRw2U7rlruGluPru0LN2j9GZYjUVDdYZ1qE9tZGypLrpVd7/frdXdJZdoRwYjKEJWRwJOVd4z0MfYx6dWurRnGWrhrVcnSGmqQIuaunm7Ud34s8069D/vGZ0YIItmKJ1QjgHWGEt0TMVtuvQosvjRAj6mnQJCuKbQ+Aq4MkFsajBVXGK+btwOw+CxANpGmaXroQJd4RsYhi6WuE9g1z1r6EVDpoOJ3KaV9UEmN92Qyac8r0DvDilxtPU6mrXvTAN0nWNWLUF0hc+dnPp+322K4ftaWNHo/RoZaPUeXdTFrT9vLW9Nr96IcKFU09BbpTjRWvd9qZaqlXQt7OGNXIqmkMFpUoeLGutY/6qUpTq3M3G9DWOhSrnZSc4Bx1s6B4e+q1AbWtJumaWOdyvL4jsOTk5MW9JQBRmVw6Xv4REM0/j8zXFqXPmXTewhkQ2Pm2fKudbKmVYR6kT26rcbo9PQUFxcXuH//Ps7OztqJVTeUGov39IAb0GEojxP4QP2NODQk+mwD+6P2XgHWUfuiBngRWQG6oDsknOckwCUKR2TXZSx0VcnS6vOMvXw6jvrKVzM61EOd2Ndxx99cIXV5eYmzszPcu3ev/aaOaLk1vxomaD7PDHOfz+d48uQJgEUr5pXW0IluAuWV5gAupSxsmO8hltlshvl83u6rrHk5U+R5ZW+Ze7xuGcJI9LqakXJXMGMYmxDtRy3P0Lz9uvn85nV1+s7TiBFF+qXngJtJWxoL326VaeseMTp4CfwcyP7gnObp80GUiDT01UXv1bZhnn2hgixfZadReZbpw5qsy0tknZcpn+uojikaDDXWOoZ0i169V4HdDUiUViTqOTwTzP3y8hJPnjwJ3VJtEKDL4s/PzxeWBmkjuyseTdZqiEYHp4KdKol2FMsXxVC1rOuUWghDfw9h+8Di/uCbBnc3vmNBXZkxjZcOPB18XDWj+7vr1rtMU/sTyN8GprqmIRWuZ9dlt2oIorX3rIODu67wiuK5/tE2jUT101l2dG2t7fV7nQzd89nUuGH6Q6+NdHPo/X1Y4GM0M5ya3tg23wpwv7i4wOuvv75wnAquuwAqY9YnuSLF9IlY3RWS6fM8rS0HblQO/VYL3bd8cNkBkQ22SOm0zCyPhi1q+S+7omdVGTPgeL0ypswwnJ6e4smTJ3jrrbc6hh1AG7JR0QeBVOcODg4WnpPwSVoaFb75i96fbi99eXmJd999tw0J6ZJHho8c3Jm2644OdI/XZ56mirebG7a+e/V3NFa2UVTPfE7CQ599afCtbvT0qSv+cnad+2uaptUH1RslC779to5JDQeNWaa8FeB+//59vPjii+1/Z1b8VuaRgVGkmJeXlzg+Pm4HBAfR3t5e2xkeI81imy4ZQ1ajMlbU86hdE4UUojzV6mua+laZZZjBJsTd1GjbiEgIlPfv32/nUZ48edIuP2Rc++zsrDPYHbT54ZbDOonq5VGPAegSDgX38/NznJ2dtfM+XNuuAM/+pB72zeWsIutmxpEX4WGH2rVDyqSg2HftMvWLxlxEoHycqS6oJ3ZycoLT01OcnJy0YK2RBScMPpadPFD4rMQzE5bZ29trX0MF1JcRRUqk/6PO0Xio3scY7cHBQduI2uhR43n+ukIhG4x9nZCdz+qoMsQriAZUZiTHsul1yjIuuQIzcPPGpXv37mEymeDw8LAFV4K/DjC9X1k72RefLK29OYd5RS/3YLloZCLi4vWpnXP91rpEk6vrlBqhWCYtlTHeW5+M0aGINGaGiXM7ZNqqNzx/cXHRgjo9x4ODg5bl8zqgu8GdT5YqvtTeyVuTrQD3Ukr7ImNgkYX6BEJk8SNQUld7Npt1lqjt7e21M9r6zkp1kyLrGIG7AoW7y1GdomPZ+SGDaSjr7rvOz98FwI8RBXQdjHzB9dHREV544QXMZjOcnp7i9PS0ZeRAN94egfvBwUFnb3gd9BreILjzt84FMN3ZbLbwAFPE2qIwSXROQUDTi/o3m0PIxEN8QKyL2dhg+TJZ1UscQpYyT9ol8iwUU/w8wT3SBc4D8tWKb7/9Ni4uLnB4eLgQpgEW31uREQedC9S+7hufWwXunARb5v6MObPhyNDpEvO+6XTaXqeD3h8DV/Ewhuetk7LuyvkAzAwBDYa7i2rEtiWUsmnxQaauqraLhkL29/dxdHSER48etewbWNyLhukpyBPcaSQI3NHkPY9za1/eS+E93H5AX+vYNE0nLKj1oURL87TvNZyTTexrWtnxzKDUzjEvj/1vWmoeLMsQeVlAv1fD+kQRAH2bG/vRXwZzcHCAw8NDHB4e4sGDB3j06FH72kc3lKp3/l/LS+bOPj45OXl2mDtwA9AEWxcFSrVeahF5nTbe/v4+zs/P8e6774Zpav4KvLriwT2HyGLyGgXzvrDOGGCOJkYzy63nV2HfEXMZkv9Y0ZUvykD1m1KLwfuAJNBma4wjBuQDLWL2EWC4i6510xU77m7z46CoeyaxPi4K9tFvrWef9IG4liU7FzH5seTD71mVvERhliHMX+PpwOL7ezWtzFs/ODjAgwcPcHBwgKOjozQNJ5YRLvG68/NzHB4eDqr7VoC7MrFsNlgZig56ukkUrn0H0MZNlQUzP3ZIFAPVOLqzdC83y6bf0WqbsQo71I30gezpO0CpW5+lPQS0Ixd8WbCP6pCFGPQ6Hyh6j7IthlUYhtN0o5Ui/qo+ZU/RnIyzfo9/68N2WnZ6BsDNhDBQf49w1n5e/2VkGYD3fDM9Zx0UxGrpDj0XibdzX3pDCIyTOy66oC44bpHNTyaTdmdQffeAG2NNO8MP3zSvT7YC3AF0HiBy0dCEV1yZHBkg09F3aCo72tvbCycmlJl5fkOsvYYGMgWqsXo97oCZgemQQaJv/onYZeQiDpEIYJcRV3hlt15HfXBI2a8OEoZhOPjI4Esp7eoFYDF+yvbRfdg5UcrQTOY+N03TeZpVY6j6pCtXVHjo5+zsDLPZDGdnZ22aCh6aT0Y0OGm7KtutLQ6IRNtPx40bP8/DjcJQ/Rli7Iak2Rfe0es0LS595RzO3t7VQ2ys4+XlZTtfQx3VdHViVgllZqBJQpUAPzNhGe18nRmmaEU8vqdK48vUnLlr3JV56qCsAa+HZjLRbYjH3BfVd1UmVkt/Xek644gMzrLi8xiZMePKBV7na9vVM1T2Hr0OTVfa8EOg9/xVbz1O6oDnW/O6IeMx36NGQ5GZ8F71amtteVcyhCBtWiJPPSJOKm7M3Shl7U2iQGPgeuZ6oKHIiHRqqOiZAXcgf8QZ6FbEY7POntQSsmE13kqg5zXKEjUE425Y1Jh9DZwx0iEyhsmMSdMfkPAJ39q9wPCQzdiyZ9drOC7TEe9DXUPuj/0r+9HBpd8aX1c9cnHmz/v1k72Zi32hehbF7SOXX+udhUm0Xe5SfCz58U2KG+Ohel7zbP0pY2f1ABYwJ5qT6Xt+w9NWozQ0NLMV4N7H9hzAgcVHwnlMQV8fR1dw58Aaa40jya6LmNnQNDfF2LU8Wkf/3Qe0femv0yhFRhyoGwOGPvitbanhmii+7ODsuqn65obAr9GB6WQBWHyjl6bndVKAd12N6pCduyu567LUxmmtbG5oFT+8TxVT1PA7uJNkZJ6o4lOGRc8MuKtEg1YHjj4A4OeivUbY0L6kKHPzFRjGxtu1vL60rU+B+tzqVUUV0plkdN06wytDJQJxGmn1yHygAN3tnRU0fcsJTWNs/TzckhmCLF09z/Kpd1FK6Txk5R6rvkeAkrH3uwbTSG6zTFk/OPnw/hwrztwZGSD+6LyfGu8ac/fFHNFzGUMI6NaBO1Bfi6px80gyF5kTb8DN25p8cEVr24fEPDPxe7KJKi2vdtgqLFgBIlqr79cOlQj4mY8yVv5ft3jfskx6Xo27DgJlyMDi8jYAnQGpk/F8wlDBWcM2WZswTfYBB6brlZbb+0zrw8lY9Uqcxd+2Ud6kaHuvIj6umDZwE87LjAFFSWEUDlZi2DRNZ5dSTuhrVKFWTp9v4rExpGtrwF2Bry8WRYlcGj+nA0yXLwFdhtfHZmv//Zy7bJH0udZRfcaAvZYjSzNq7z72OSTfTYJLje04m44m56N2d0Kgx9y1dsPgIRqWJWLz7lnynmjpW+YNRCu4PKS2jax9VZ1Ytl59+uj9wWPZtRT3nnQ+T5fEKluPxpWO66gsKlF4uiZbAe5RhWuSMZQopslG84kNnndwz8oXWf5IdODVYmVD3Cpe54xwHeBZq+uq6a+rjEONvB7TUFzEjtSzqOXl7TAEAByAM5B219pZWrSqJvof6WRWrmgS7jZW0EQGatl0gPUuSvBQata2fozfOl/nRlz3JIp0RwHdcUv1Q8PQfWTQZSvAXWXIGls/XwNSBW02toZcoiVqCv7a6WMAOesIX8qp+W56sLknsImwybokYtNA/KBNFMagEffwh66oyVxj5uN955P3zFv7WgmEpuEhGN13nueB7k6dy8x9aHmyydnbkk16cH2igNlX52guRdPI0lVRXWUoL2LamQHRa/Uax8O9vfr24ipbA+6Zi0rxWPsQK6Zhlsi1jpiTgh47uzbROQTwvWNrSqP/s8Gx6dCH50WJyr9sOYbep3ut1+7LQmG6Bp7gq2kDi6tbMsPn4REvi66W0MEeGZ9IB52JR2GXSDIAz4BoJzfiLFqPRaIPJPnSXJ3Ej97E5IQl8miisa2eoOpKH+5sDbgDdTcxYmx9TFcHTs3aOciPZUp99/RdU+ukaOBmIYIhRiZikllefWktcx+vd1YeKX60osnB08sUxUI9b6C7YiYLfQxhuVn/qE7dv38fp6en1VUPNU8v6ivPW8u8LFuveXJD20Tb1+dBKH1h0CHpL3vtGMLUR7Do/UU6xbxcjyNA7wsH8RotzxCPZCvAnQ2l/1Xh9WUIfk9t8FIuLy9xenrauY8D3wcij/tSyCFsu49B+XVDlTtTUi275hmVV9mjgoWvIBmiNJ6u3pcNhgjEfeWBn8uWLEb/eR3BdG9vr33sPwJVj4lHdc4AqNbvTFcfoppOp3j69GknNMR8o3erqp7rev2ozl7GSAeH9GcG7O411cDHPW+93lcuZe+UrclYYHcC4fVRGQLuOrbYL9PptPP0M/eRiSZW2UY06kxT0/d8XEhyh4RwtwLcXVG9chwwlOicn/e9s7ld5/n5eftaMwUQNy76W0Ge6emWn9F9rBewuZcnDJHawFagX0eox4Fe5yzWkb4z7ahuuhQN6D7sxrJlK1Q0D07KLuOR8Z2pPr9CHfN4v5ZNX8atuufbWmi5M9D1ceL51SRa4THU6Cv4KYnQOt3GZO5Qyfo4Io4e1tN9jKJ7dcGGkwoAnf5xQsnvqK2G9OFWgLsqg4KNno9i5RGDc6DX62ezWfvSZCB/IAaoh3xqzI2Gwo+twtK1oyPF6GPbEYvX3woCfWDWB9BROkPu6zufhVA0/Yzhe79GbCn7XzvneaouEby5xplbD2vsVNtKPQ/Pz0MdPOeMPepX1rkmChRqTKJ8h4izUi1HNL63QSLdUb3KjCjrw109dUKcWz3zZUA1TzTz8j0vN/g16QX3UsovAfhPAbzeNM2/d33sHwD4d68v+TYAbzdN83Ip5SMAvgzgD67P/VbTND/Rl4cWPLJUPB6tkgjKu3CeaSpz57X68eVH2WDOwiR6TVbHmkQKVHO59bgDQTbQs3PrHGwK5mNdb8oyLriHEDyNbKXBuuquOkR9nc/nHaCM+kG9hSg+q+nzvr5+G1KnsaA9RqKybRtjz8TH05C+qEUdeD7zgPqMtOYzRoYw978H4G8D+BXJ8C9JYX4BwDty/Vebpnl5VCmADnNRZhOtTwfiN4FngDCfX70wgaydbrHmyVht0zSdlyhTNHbLcmnoQevhnTlk6VLNKPTVj/mybKyz18HbYfJrBAAAIABJREFUkNfpfXfJqFg/3w9Iz9WMhYOHAv5Qr0xFwbdPmKbG2tmnfKkx3+OqsVjeq+vz6clqnanvDiSrSlQ35s9zQ8OKkcf8rEnG2KlLuie7hnb1aWaG86InnNnHmaFT722IEa9JL7g3TfOb14w8KkgB8BcB/Iejczbx9b38HQGxlK3zWztGj19eXrbATtC9d+9eO9joPinA6iSu7hHv0ueq+zkVDdc4K9C6appZ+KGv89UjWWUOYEiIZd3i7mcUzgCw8LILBSdvwyx+rQbFPUlnX25wOHD1hTE85ttPO5tnGqwrY+yllLZeLEONAa+zb9RgLZNuH2G5bT0aIkO862h+sJTui4NquBBFKcYAuS9EyGTVmPu/D+CbTdP8Gzn2naWU/wPAYwD/TdM0/3tfIk3TdF5DFrk2eq0Cv4pu0KPXE9jVIgNoGRXfbwksrl3NNnKiZGCrLm8ECPyv54ek2zdgaiGYSGoGqxZeus1B6/mpwdX83IMCgPPz844nwJBclC6PRSTj4uJiYfASqDUt3qNvX6K3qEbCH3JR0YHriwn62snbJPLexsqY0FpGQlYJL2xahtTP61ID4yFLPVW/HPeifJcZU6uC+48C+Kz8fw3Av900zbdKKT8A4B+XUr6vaZrHfmMp5VMAPgUAL730UvvS16jR3D3ltzIaoDt7zXvm8znOzs46k1nK9HiO4B+96R6IQyt+TRbaqLG9GrD3gbumEQH6kIHU542MVao+47OMOJgD3fATPTEyYvW61Cvj8khe5+zXl00CaK9nnpGHxfZXhqu6Sf3KvE4XNVbRBFwkyiA1DdZrSJ6RJzxWMn1aNcSwaRkzHrUO/ntIekD3oTsPszEkFuGAevR97bg0uJdS7gP4zwH8gFRoBmB2/ft3SilfBfDdAL7g9zdN8wqAVwDge77ne5qvf/3rYaGdxbiCqEtMF1iX39GKzmazNj2myXDN8fFxy664VlVZmndQFAvmb42L6vER7RoCZNbZes+yg6a29pfpj02HbTzE28ikFkbyZZZ7e3uYTCadpaqz2awDuAzFMUzCtLWMLDc/fAuTvk9V71PRnSS1/+ktqOGhrvB3RGA0VKPegEofWEaeSdS+alDGLpvMvASXbZ9QHarvJBP6TX30XUW1z90AU+hR+lJI6ojPNQ41kqsw9/8IwP/dNM2rUun3A3izaZrLUsp3AfgogD8ckhjDHyw8Fd4fPwcWX3IAdF9srGBH5k4GpsCuQOxx0owlM07urG2IKzZGonT8WBZWWEVWYd3qGbnnNYYJaj9EEm2CRUDnkrTZbIbT09NOWvv7++05zUtB3X/zHac6UablcK+Mr1TT+h4fH+P09LQN7WibeJ1ZB60Xy68hQgLKGLZYE01H27cG7sssj7wLidrIZYhHDdx4ZEosgJsXnHM55OXlJc7OznBycgKg+/pQXVHVNE3ndYpA/FwPRRec9LX/kKWQnwXwcQAvllJeBfAzTdN8BsAn0Q3JAMCPAPhbpZQLAJcAfqJpmjcH5NFONrlb7AxJlZrXu8XUjiKA379/v/OAiLM1puWvRcvKG5WrJlFaQxjCkHR4XNtOjZCm5Qwxy7fPTR0izN+/szyBHEyUwUSeC0UHzsXFRcvQWR6duPf+c4+MuqST7Xpe3Wceywal6kzGvKLQhV6j5ETroNcO1ako76gO2fWuO27k/FqCWHTvJiRrh2XGYXQ/vUW2F/WMod7T01McHx9jf3+/DSsqcdW0HOwzr98JwcrMvWmaH02O/5Xg2OcBfL4vTZe9vT0cHh6mbps/qqwMipNTfJN8dC+fTn333Xfb9HXZkj9lmgG7DigFI39pQnZfxNRqMlRB3eC54aIoENXqyGvHlDUTNxKRcnv5lJlqmEDZcZYGX2RNIH/w4EGnLroKhQxVHwrRNuMKF+rJZDLprHRRg6Dkgh6C9jtZHXDzFK0OdhKO2moY6rc/DKXkpMa0M8OTXZsZqxopqOks22/dEqXZZ8T0mppkdVJwp7cGXIH78fExHj9+jLfeegvz+Rynp6ftfE+0OET/64Q/haFm1jN6Oj6SrXhClZaPzNlBiN+ucLo+Xd+0pPdzolQZl7IoDsaDg4PW9Y06k8d9YlUHIgc584kmYWusJap3nwxxzzRtD2nx/gh0VwF1F3U13cPIyh9NeqoHptcp8HPe5MGDBzg8PAyZvf5W95jlKqW0b9HRb4YJ3WAw5s/1zNRF5kVGp+53xty9P9h3Dg7qBWg7eHpad21/B8UIzD29bY6Zj5XIy9A2UK+eGOLLED2Mpm91e/r0aQvqDKkBizudqk7yOsc6LafOK9Vka8Ddl35pxdzaZu60NqCeU0BXkCEYTyaTTudF5asxToq7thlw9bmGEateB9BycNb20dmkRHk5WGk9o7CHGgY38hcXFy1z1wdIVHSyk14Xy+MeD/UjCvtF4Rtl8Z6nXuP3e5to/iynXuNldWOl12lda/1bY92rSC3NTZCIVSTyVlS0vLqCikKdm06nmE6nePjwIR49eoSjo6POxmnUEfW21PsCus9oaJ+SzA4JwW0FuAPxAyeuvJEb7xaV10UW2DuEA5PsXc+7AXHxTsjCLn3hD7+2z4CsayBsGsjH5K8hCQ8HZcoP3IRReJwfBVrvc+9j1a8I3PXD4xoWcgIRMS497/ko23aW5uCubI3pUv81hOXAHrW55uO/1yl3BdzL6HfGlD3NzDti6I7g/uDBAzx69AiHh4edvtHVM6qvPOb94h6deqo12Spw90opM4vAYH9/P9z219NV5sX7dX0042XAzQCKvAeWI1ql4Oey1TORq8v61tzesbHCMeIexjrjojV2lrVhBvDAzRyKpxeBO1m3sn3qQM2l9fujj16n17IsPO/GQhcAAN34uZdRQzvu/ms5tM7RjpS8phabfl4l86Jr7L0G7tPpFIeHhzg8PMSDBw/w8OFDHB0dAbgJ4XDeR0O6jnc1/IvmFiPZCnCnQmcKSIailXS3NRtwbER3f71h1bIC6LjhFL/Hj+tqGx5Xa+8GwydXhkjkIfQNUA1HReEAlT7PYUj5aunX7nPl1nS0rS4vLxcU3Bm2Ai4NJ/XMw2/KolV3dG27s3nXM79XJ71ms1knjf39/ZZ9qd45k9f6ErQj73VZ2SRh6JNnNXbP8ef9wLHPvlKSF73SUceh4p6H1VbxsLcC3IGbwekVcivm4K6xW3ed9aUNFG3wDKwptWNuLDTtiN3r/VlIJstziAxVgiiMsEq+WVkUUKO6eQiqFtbK7tNjwOIKHyUNHqrT6xSIvW085Ofpu2GI0tD6ReXwsJC3SbR6RtvB23hMX24C4N+r3oC2cxaiUe/R9Qi4mUxVjNBx4t67jouxgL8V4K6uqE/4KfOiZANeB45Ofjn7poXtA3dvVL3OQdkNUtQJPnjXPQiGdLornQLQqqx9SNkyYM7CVX4PjbgOitpqG32C0A0b0yultG9tytqn5umol+fxeQdoDztldeZ/1SWPqbsMXYUU9fMQcqEg5fmtSk42LdFYjK7pqz+vq40RD+GpbgDoEA5tN8WjCE/GevpbAe5AdxlXBoy+R0bUETqx5CsTgJvNwrielB2qcVDtRF1NwfR5zRBFjoxH3306mNcp2ha1ttuUKCDzv+YNLG79nLFZKntWXjXy3leqS8qs9FoNy+i2Atp+ep/H6J2xa3hQgb9P1CBwOS63yohCA33hDm/PKCSgBCbyMCge89c8avW5K+kzQJmn7e3B/lQvbX9/v2PoDw8PO6DuE+ceBnacYnnY9xH+9OnP1oA7sFhBd1n0OLD48oWIRbtoWMbz5ff5+fmCC+Yf3pc1cs29igzDKmw5cg2z6zgQszppeg7Cm2ZlGfjX+tVXiQDdSXRfF+/sTMHWvT3uKaMg76xM9+t2YxJNzLsnqfXKwNQN8nw+bx9z9z7R/xF733RfjgkbbItom9QYvvZHBPhZ3d2gK1hH1/pv37GWffrMPMSkv1l5DRf0sXmgC2zeKd4BGov3CTIHZXWp9RNZ22XqHbmDYw1ATbn0eGSwoutXHfzef1FZVCI2r/f4dRFrz5hnjVW6YVeQVwDXY2oM/L/mp3rsIKD1UCOQrWhx1qhsO+orBRKd4NcyjZWavkfhBJdtBv2MQDgp82MUxQw3Em7c9ZvpRV4qRftwTL9tBbgD/ZNCGSNVK0qw1sbyRmEek8lkYadI7m+j6WRgFLmiWcPXgEvPZ/8p2f01tqSAQlees/mbdJGdDdXce6C7ra+zd+1PvyeKAQPxro2ZRGk70GtYRsE767vIqJRy85QpQZZhwr6JWz0/n887YUVe58RDz0dtNoacaJ2j40w7O6ekiun0EZp16mdtfGpZa16ih0d8Li/CHd7nq6Cit8hlJEj7XdPta5+tAHcOoj6rzwpqR3i8kOITpzpAlbED6DAwnbyIJjBoRT0+X5N1sPxlxZXVGZYbBvWcblu0jBHrjoTX+mSUxvC1v3SdfNPcPBDCawiiXK5I1q5PmdbKHwGcewUst76Szxm53s8yUTJDoOCeEaGobJFEbHMV0eXC/L8t4t6g6r7ijRNF/a/e/ZD8VCf8ONAdlxqW6QvFqGwFuEdxQK+oDnhlJzqgtdGjVTEah53P5+0EFeOlGrvVdJ05crab5XQre1eK62EjSm0GXsXb/y4lCh1oeVUnKA4gFA+VZExdr9ffGmOveWvZBK/e7yFCXTqXscUIZDNgjry4rG6ZrBPYtTy1lT7bKhE21cofjf1onPkEaxRXj5h8zbNw2QpwB7AAoH0MyX87E3VwZ5pq/XTyTC129u0gwWNkXXpNBky3AZpR++j/SDGGsOTblhrA+zmdWKVEIZIM3PskYlqetpbFyxvN5+i1UZ/UBrCSEb+2T9f66r5OPVgGlO5KhrRLrR56rOalu3cckaqoLNHKpppsDbi7uLJGjFTP6T26GsaZnMa/GEMlyOvysigfoN74TL9Wp9sE0AzIt3mAZVIrsz6xqitY9AljN9Dq9emgrcXqM1da48jRZLzfq09OayyauqguvoaXXLd8yWUUx70LYV37di3cJskMvuqLRhIy8Ql2D+W5Dnm+Gp3QNHWFjEcLMtkKcNcB4kIFz4DdJxjU7VVRps0BxAlUpkVw17AL7x3iUnr4KCpH5LbdNmvuM1zrzmcII/JYozIUZ+OZcmv4Q1e3ZH1GI6/nte98gzDv1zH9pvWKQkK6KsfDGGqINEbvE3x3GRIcKr5kdRvKG0UMtK99MltDtVE6vvrOjUOEd3ovEE+oDg0NUbYC3IF4RYkz9ohVR+t49Vo2WPROVA+p6ADUiTa9NmrUaDIlsvQZ2GVhkiGSKVnkaURp1kJc63bPvT28P6JrKNE1lIgYZLqT1anm4dRAfKw3lhnWLB8tgy/H5XGg/o7dseVetf/dEGo62lfb4kG6oc7KH7Fsxxk3CH1zNQrgmbhRGGoQb3/5RiK+SRcV2BlzpOCR1MDdG9S3ItAOUcbv5VLXObO0ESvNgGSIq1WrK8XzzlZ5uMGMjOe6pc9Ye3mGlIV180lw/UQGuK8f1LCrcY/KHQ3OGlj7/0xX/FoP13heQwG+1rZ9bTREauXIDPhdSmTQIrac1cvb3sE9ui66PxP3CIYY3q1h7joAKYydRwOO4k+aUrRR+bQh09DlkU3TtG9K0YZjR7LT/SlHH+QcdL7fdjZ73sfYVpWIhWyLeNtSIk/HJ5DIYDOm48ZfQX0oo3VwYzrMO2JyWh+PhbNc/v5WX0ETufFa5iGhQTVEQ+tWu1/HwrKigKeexraIG2slCxr6qoFq3zLIzHvJypMtDBgjWwHuBFj+9sEZsT29V5UvU1ZgMXySASy3H3AldyvOfB3YNW2N149pj3WxGi23eyGeJ7DZ9cfL1MnBlN/60f53VzganKoPEYhFa9JZDm1DBS1NT9fVR/WJdMrTU2/L1/D3tWOkp/yt3mmf8N6+RQJDQM3HQW3J4F0QkczQOalw/cnSqXlo+j12TIyZqN4qcO8Dc/3tyq5rhXWQkp37S7Z1MOvr+XQg+eBVxpmVa52uZsZsM1ffFYYDSgHLWVSN2a/K2LL6DGEitTZ1d1cNQLT1roJT9LxEpGt+3lmcnqe++Jr1CGQz/dDjvh6eaWs6kUTeonsP2X19EtVniESMPfKMs3vHGLJVZYx3x7yje7yPqDuRp5eBvIdyagYjk60B99PTU8zn84W3f0e7+vEeurrRsjFurMRwzNnZ2QIL4ROBk8kET58+XZjJJ3u7d+9e+9h45IazPPqtorH9se2i4kDsxozHPDTkbRftiaL5ucLVvKJMsklOvydLQ5XZ+z8yutpvfFE1l7pq3+ij/tkEJe9RvWKb6tatwM3AZXq6ksX37lbS4B5kVgZ+qIO1Aa1ehe8aWQP2oQA5Flyi+9kmtQe3linbstKXfkakouui5xui/9E4ZuTCCRfbKPL+nwlwn81m+OpXvwrghsXrW02cIQFXijydTjuN4Fu06gCnKGCfnJzg7OwMZ2dnOD4+BnDzMmNeS2FMnvt+66CvKaHHjJnuupTWlWDoPSoZaK/TC4nEQbmPoUf3s6z7+/vt68yePn2Kd955B2+++WYnzcvLS8xmsxb4oklJ3W5A34np3oCXs5TS6ht15Pz8HE3T4OTkpNUT7iB5eXmJs7Ozhbf2qNep6etWBVofbccoJu9paZpqPGoTwn26FbVlTXxuTfPbtGR5DB2TxAE19pzTm06nmEwmmEwmePDgQYs9PpdCjCPOzefzzoIPFSch6sH1tflWgPvl5WULrqy4KzvQVdRSSoeFqTvqik5Fvri4wN7eXrvdAL2F2WyG8/Pz8K3iCnSav7rpyiR9cPlA028/rjLERYyYbZ+764M4K5/WK5Kxx7WcmRcW3e+eiacXzSNcXl7i5OQE77zzDo6Pjztg4vFrFQ3p6Cv2ALR7zfC8X88VWe5pNk2D2WyG09NTNE3TbkGsAzuqb2RwXc/1epc+L2sIa47+u75EpCsDnSFkYdMA797o2OOqR5FBpH6dn593jLZiA1m+klclDZHHpV6ekt+abAW4R6Cpk1Jq1TJXPApB6CBjQ3Pg8tx8Pm+taAQU0SDxmWz/6H363Qd8nueyil4Lozi4ZnkNLesy5YokM0o6INSI+tN/Hio5Pz/H8fFxy5A1fMJrXBSsdWlu5BUqmKsh4HHNh4MRuHkfqsbno/o6QRgKjLWwQXb9Mv2pOlYDdJVNe4K1vKJ2XuaYphvhDsF3Npu1H6AbktV5QN00jv3heXh56H0+E8x9f38f3/Ed34H5fI6zs7POOW84jT3R9XVh6GU6nbZPoZ6cnLShGn+xAgecTqwyP4q7zuomOWhGAK+GaN2iXoN6O9Fgz9jz0Hz6ZFXD5GkpKBNIp9NpZ+kq0F1FQFbEwXVycoKLi4u27zNgZbnVcNDddi+JRt7ncDjgqGOaLv/rW5m4iZ2uzMmYWxbKcPBxbze6NzKamo62zxBiEgH8mFVi6x4XkUdau6bvOo0AcF5Hwy66IeHZ2RkeP36MN954A7PZDJPJpEMcta5sW5075HVKZlT8uZxMesG9lPJhAL8C4DsAzAG80jTN/1hK+RMA/gGAjwD4GoC/2DTNW9f3/DSAHwdwCeC/bJrmn9by2Nvbw+HhIQDg8PAwVESfOFXGoOCpzI7xL+DGWuokmzdeNoDYkL5Fq7NzddmjfWpWUeCh9yqoZ8qxTLqbFAUiH2AMo7Ev+YlCExrHBq506YUXXugYYRpx7kXj+fK3gvvBwUGHsfOchgQ1psoQTtNcTeDOZjOcnZ21xoeuuD5/kc31aP2iMFx2PmpjJy6aTjTn4WUA8onZaHLaPeksrh9JzQu5S7m4uGj1A0DrFXK+ZTqd4u2338Y3vvENPHnyBNPpFIeHh62xp04BN/1GPXADoLpG0X5YB3O/APDXm6b516WURwB+p5TyGwD+CoD/pWmany+lfBrApwH8jVLK9wL4JIDvA/AnAfyzUsp3N02TBohKKS070glUbQBlBjoh5kqpDROxEp2E4rUEDwJ/ZFzYmKrArsxZCGcVJV0lBBKxgOzabM1ulF5ULgeWWrjHPRw/7mXW/gSw4M4CN5Ov3K+doHxwcNDpA/W0orbxgXVwcIDpdNoJ52lZHOSjdwOoN6oEgswd6K5gcvGJe80zWqER6VsN3J3lZ5Kdy7xdzTvSA/1et4xNNxsffmwymXSiAsDNqjtOlpM8cEKeH/YZDbqS1P39/RDcfUGBep19dewF96ZpXgPw2vXvJ6WULwN4CcAnAHz8+rJfBvC/Afgb18d/rWmaGYA/KqV8BcAPAfgXWR5kOzoAPewBdF+uQffH2UAEBhSdDGkb4LoDuLOgKrh+O1vXNGuDah2yCsDXBlFtclX/R/eqqx657VTarFxePjXGek49DwKcxqx1+SuBUle5TKfThTBMBJZaDgXq/f39DrhHesVjOqGaGVYHVfUCsgef2Ja8xglPFP7rkz7GPIT91+6v5ZcZsb6yjZExaWTkJzpGoFbvjPjF1TJHR0c4PDzEo0ePcHR0hKOjo86KK2IdoxGOVxmpAW50dwhpHBVzL6V8BMDHAPxLAN9+Dfxomua1UsoHri97CcBvyW2vXh/ztD4F4FMA8NJLL7VPhU6n007sUvdkVwZNV8gBRAcnreh1GTtAoLHRBw8edAYll7B5ujU3SEMDeowdFcXTVhUHLJdISZQ11hh9n2EAuuuqo2szNzxioO5pqZD9llJaN1jbUkFRJ0R1UpNxbV6fgbSCqMbeFdyz3SKZrhsO1eeLiwtMJpPOcxdqwJyV1cJryv4U4IcM/CgPPx/9jvrUPevIm/a24v+aN7iKjEkr60+tq6Z3fn7eIZaqSwTvhw8f4n3ve18L8AzFkASo1+9Gvc9D9j7PZDC4l1IeAvg8gL/WNM3jSuNFJxZK0TTNKwBeAYCXX365mU6nbeHVFVG2wkHSN6HAjqGbxJgmJ9P8OmdEDjD+lvkoHOPlWYZN9YkOBu3gjFn7vTW25iwwYtHRfXrer6vdo6Cmywcd6IBuGEbrRsak7jCAdvVT0zSdeRemxfwj8fCK7g2f7RGvdYnIBsMyWh/eH4FypIOsL/uJe8JrvylYU1bVP++vKGSpc1gcp248In3U40O8glXrUDuf6bKTFmIPn2UgwXQdPjw8xIMHD/Dw4UMcHBx0DJ4/Pa1g7+FfFf730HUmg8C9lLKPK2D/+03T/MPrw98spXzwmrV/EMDr18dfBfBhuf1DAP64Lw8FV+90KrBbUmU+Vt5OjNQZRY29+jcb2l+u4ANTJbpG01xVPN2hAA902SlBkZM9Hs/rY+5ZnrV6Kgjwv+epK0SAxckjzcMBTb0AHUyeVw3c1ehQj/SBuIiZOvujqA6qCx4tfdMyRfql46APAGoyVA8jHWAZtWzR+HOj4PXydJYt49B6eLn0eBQSoQ5oH6kBjsgIj/uSWUYRtB/VGPj9rhfabrwfWMMLsstVrT8D4MtN0/yinPp1AD8G4Oevv/+JHP/VUsov4mpC9aMAfrsnj05DK7OJrmXldBkZj52fn7cWdTKZ4Pz8vGV9bDR3W9WQ8L82sDJB7+RINgXsyxgNN2ZkooxHc3WSPjE3pAz8rfk7668NXAKklk+lFmZylsy+USav6ftgjIAqAislCUyXhkIfF+c1Xhcv43w+byfk6IG6LjnJ0bZVo6IruDKSkbV9BMa1ts6u03wjMhbdw4d7dGVQxuz9/6oeiBrhyAC5/uryVG3vUkonhk7jrS/70fsALDwpr6EYjQxExMPHAtuXOpjJEOb+wwD+MoDfLaV88frY38QVqH+ulPLjAL4O4C8AQNM0XyqlfA7A7+Nqpc1PNpWVMi7Orv2/irMXB9z5/OphFgI8sOgJZIo+JK7lYM/7XBz8xkjEdDy01AfuUfvxWI0B9jEDvTaSjB2z3H3ld0CO7iETVwPPQedbB5RSFgaEsyD1ajQE01c+DU2ogVTy4dfpPI96pto3Kgoy3j/ujWaEpQbW2fFaP2b3qwfF9tdVJPQYfQ+cmkTjdUhZ+J3d654ir9XjTdO0K7GoRzx/7949HB0dtW3seuhzQDwXlTMzdBGp65Mhq2X+OeI4OgD86eSenwPwc4NLgdhSawMroxkiOmijl2TznF/P3wqKDoCahqbdV7aM+fj/sUbA3V5++4fWng9tXVxctJND0TzGKuUAFgFnHaJp1TwnXqtPIxNQvR+drevkbq0cnqfqlT7voGlmfc7fTk4iwz6mnyIPNapH5n0tI+p1KMj7E8BZaCYaB2PHhhs3NXpOFDKvwb0KXcCgafEcr2MYhoYs0ic9FhnpWr8poajJVjyhCsTKBXS3rY1YiQ9G/+8sSNON8gfQ7v+hDMnB3e/hf79G61STmuHKvAttgyw9Lbu6lgrwus9JVP4h0uc9rBPco7SdZQGLL5WOdCvTqaHg5gClQEa25+WtSUQotI6Zp1UzHBlQrKtPMtHxqO0SjdmofLWy94m2W81wRWM2Y9HsT51w1zQ8/Kkx98hrUobv4sSzFhHIZCvAnZ2uMS09lwEyG55MgAOJFlPT8YmQKI7F7/v372M2m3XWU2tYR1mIu8h9bCSSPmD39vDztXT1N+txfn7eAXaGrbQOQ5hBVsa+415GZadj7o1E4+8a9nBQqxlmlWgFjHsAWg/WRXd+9Lh4X5/pk9Cq+5lr7uA4lOUqqGX6uooB8LHhZYvqEl0zpC5R3ryvbwzxWATujhNcJXN4eNhh5U6mnLFn+u19l/V3tBikFjIEtgTcXbzSjEeSUfMaX0WjDxW4O8bzwM0AUrY1xs2lJ9E0N5MnzGPsnu1D6q/H9VuPD3HN1TBFaSsTWQerqwGEg5x6Z5H0hUp8xYM+gKS772l/RaCYhRA0lBCRA6YRzW34DoG6Xt7bXA3CfD5vn4L0PHzexa8Zy3IjkPFwxFCdUA+QZVGDRULB50mysg4xypnodX0kRVm+9gc9YnexAAAOM0lEQVSw+D4JLmtVMKe+6DM5igNZmdVrdA+y1r+1NFW2DtyjSRNlwxrrBnIm6665Lx/L2I+7xTynljO6N3OdMqmxr9o963SltS2GMsvbkL78oz6PjumKKaAbC3fReiurqn14n6ZBUR3ijqPMW1mZzwGoh+UA4vqbhVgcqFeRZfVBx5GvNsnqEElkuIZK1EdRmln66rXxnslk0vH+OfZ9LkHP1TyFjLBFTD0irjXZKnDvY6zeSdE+MbpqRQdTpCSRddQJWDUQZFW8VyfKPA3+zlzdrH5DZF3gmwH7qmAwRPo8jWXAnaKTkP7gGlc7eD2jsujAzEA/uk+ZFz8Ed3qeviQueulG5AHoNa6/7p1F9y4jq+paNHeVbeXRl/cY8qRgOEac0Dl26IZzjj8K7u619oVlauw+mncE8OyEZTI2RMkmQLlLmx6jAnG5FXDTEbq22NOm8vHtTBpzV2X0ydmIhWwSJCM3euh9bBs1gnouSm/ZAV5zoyOmkrHiyK3OBodPanFDutls1hr8DEDHlD+qg+qHvzGJjO/09DR83FzbX/P1VV8a2vD7ea+SnMzVz0iJ/mc5xi6F9PboO7YJcXCtia+e83CUiraHRwf0aWadTNVtLBSY9b9jin5zvJNQ6pYYNdkKcHcFylgLr2WFfcJCraxObLg74xKxWI+dOSBoOWosZKj7uQ4Zm89QJpS5/2NkrHcy5j5l2frQkS5Ho1H3eRoHgSj0EoVzMsaVGQ4+ps63h7mhpY4qC/R0MxJRI0MeZqgZbg83aP37DJ3n5flEY5p5DdXZVb2Isfdrv/A/vT+dMFXjoIsSFNzdA/CVWlHfKMYB3TlDDfNlshXgDvQvB1JRJdQKqqVlY2njauPdu9d90UeNfbsV1Q7fNmCvuedjwx3rKpcDyCppRPf7wFGDz2+GP5zZ+j1KFGpSq0c04algEO0logOd96hu67UOOppPBt7Z9VndhvZTNA583KyTwd8GudBx7sejnT+BLouPCEKUfx/hVODvm2CNZGvA3SsaVcqBy91sFV3a2DRNZzYbuFkuqSEXHWjMR5c6aShD2Vck6wb1vvCAh45WAfYxg7smGaCMdfO932v3+yDjb66eqfWJe3/eh94uWg4djP6hy87rGaLRSX43OlxOqeEXnw/S8tTq5UZjyPVDxa+teYO+bLDPo16XDPU4+sbY3t7VS4Xu3buHi4uLhZUzGp7R41meUf21LK7LxLuhHvfWgHsmTdN0nqp018cf9eZxfisT4/W6iiF6F2G081/fwyiRgaFEnddXZyrL8yi1QeZtwnYnq+Je2w58/BAsNWyhjFjvU6PAAatLaH2fEuZBZs5ljUoEaGDm85utMbgfUrSFgocu9EliDxlkDHmTnqOHKVlG1qHvvmdN9JmQKPSkgEzJwmZOJDKJiOsQ73KrwF0BUgetTiAoe/Z9QtQtzGKUPO+DxCUDZC0PyxFJzSJHZd603FY+y0rmadSYj16nehH1l7Jj9eA8Te1P9Qr58IqXyUNCCvxqdJiOPrnq8zoMG/mWCbVyjmW9t6EHWbnduxzCmLdNlAwAi3qq/aaxdu9Pj9d7iMd12I/pvFEmWwPurIDuUa3nyH5cEaLQjQ4eZz414Hel8+OR0tYYyBCrfJuyLeWIZEgYqRafBPLVIPyvuuHperhA0/V2U5c5ukYNCYB2xZZ6YxpiYXloQGqhJxqnCCCHhh82KX1l8LH6LAE7sLi/jIqHAjWM6PH3Wh9HehiR1D7vZ6vAHYjBOlq54gNa//O8vpRYmZLHLl1qM/hjQyy3JZmHsk1lzCQCpuh/H4BFg0YBXbdcIIvWe/wtWupi68MrQHdg+WoXrmvnNYeHh52dSVWnm6Zpz2WuegYSvuon8gozA7UpedbAeqz48khf5cc+jVYYZZ6KT5RTPyJw9znCmmwFuF9cXOCNN94AsMjCtKJeMd+HRvdt11AOBxzXrut6YVV6HXCepwN+DTjd0vZ1QpaWr6IYGtLJOj4Dx7vyMBywawDuwKfs2bfUjeLmwE076/78WViF+hU9RwFgwRDM5/M21n96etrR1f39/XYLAqbNmLvmoSEcrV/UXhmIRjH4Tfdr1H5jWeazIkOXhWYSGWoSDYbymqZZWCiimMJXNPb161aA+/n5Ob75zW+m572C/A3ET5+WUtr1qLzXmbtbRc2Lg17DOmOkFvLJrh+a7hilGlruCEhuWyLgUsOmk0g8Rubq7i1j5Ofn5y3QEkTPzs7aSUxl70BXFzRMw3udpbnusb118zLghnQoaCu5UB2lZKy7BvBD9W0TUtPL9wqwR6JgHa1nV0/S+44frr5RBg/Ec4hj2nIrwP3y8hJvv/02gG5cEqgDj64oiKyhM4qMeUchHQUWlbFAuO4Bd9cgvCmJQIvArSAeDQ4uawW6L2g5PT3F06dPW7BX703TVIPhZaKO+ROFwM2qKg5qgjq9Q17LzcuoVxoa1IGrYRvWxZfcuuFTqRn/LCSwbhmT/rMYwnFsch3RJ1NVPITmuKBvjIu8HmfxPF6TrQD3/f19fPCDHwSwaK08Tq6DQUHYLaSvOT0/P6/GJJVFKcg7awJuVkIMCbcA48Myq7h9z4p43DLqRx00vCcaXAT3yWTSguzTp0/xrW99CycnJ5jNZu1LSU5PT9v2jiZdXfRJV2X5fEqRSyX1vzMwN07Uaa8Pr+U5XxCg3qqCvg78yJMFFtn/WFGyM1Sf+657FnVcl6tqiEbHLLFqNpvh+PgYFxcX7UvaVWfVs9O17C7u3fkW3ZlsBbiXUtrKO5uOXBP+V1fZK6qv9eJ9+sSfT24xDR+YvCYD3OwhqtuQzHWPxAECWNxTY9nBtiwrHBI7zs6z7NFrzNRI7O3tYTqdtvvMHBwcdModtYfKZDLBZDLpvD81IhXK2HWdO49Np9PWCHgZ/cEUZezK1jLdzEKMy0jG/ocSlSHjoLZg4S5lqB6r8VJPjARiNpvh5OQEb7/9NmazWbsdBu9lKIb/dc4lahftV4YSFbcy2Rpw90kvl8iVAbrbDGgclGmyIbL1phSfsfZ89b4+xfRr+u7pA7hMsjS1bRw0+q7n/5qLn6VRu97B2vshCrcMYdaa/r1791oQ5V7uBwcHnRd/a8gn6mvPn2nRK1AmzfQUiDUNDStFfaXX6BgAFtftR56kpzVEx2p9FPWhhzZr967DuGyDaDu4nrq3pNtK+NwOvUR/0hhA5ylWfdl6X7nU43wmwB1YfKKrj5G6lWVj+6utdH28g4nmxY6Jwi3uDkc7S6pkBmQswA85Fg3IGgOhB1MD8rHAHokDcQTeEZhrSIaA5ZOeXkat7+HhISaTCQ4ODvDw4UM8ePAAJycn7VOgOtmpbIpMiOf8Rc7T6XThZS8UHldXmYNeJ+YjYTm8njynsmz4buh1tftqwK2GLfICtV/dMK5qDDYd2nH9dA+YYTnt67Ozs5bJax0Vl7LwS5Q/81VySbyqln0bLG0p5f8D8BTAG3ddli2TF7FrE5VdeyzKrk268ry1x7/TNM37oxNbAe4AUEr5QtM0P3jX5dgm2bVJV3btsSi7NunKrj1u5PncmWonO9nJTt7jsgP3nexkJzt5D8o2gfsrd12ALZRdm3Rl1x6LsmuTruza41q2Jua+k53sZCc7WZ9sE3PfyU52spOdrEnuHNxLKX+2lPIHpZSvlFI+fdfluSsppXytlPK7pZQvllK+cH3sT5RSfqOU8m+uv/+tuy7nJqWU8kullNdLKb8nx9I2KKX89LXe/EEp5c/cTak3J0l7/Gwp5RvXevLFUsqfk3Pv6fYAgFLKh0sp/2sp5cullC+VUv6r6+PPrZ6k4k+/3eYHwD0AXwXwXQAmAP5PAN97l2W6w7b4GoAX7dh/B+DT178/DeC/vetybrgNfgTA9wP4vb42APC91/oyBfCd13p0767rcAvt8bMA/uvg2vd8e1zX84MAvv/69yMA/8913Z9bPck+d83cfwjAV5qm+cOmac4A/BqAT9xxmbZJPgHgl69//zKA/+wOy7JxaZrmNwG8aYezNvgEgF9rmmbWNM0fAfgKrvTpPSNJe2Tynm8PAGia5rWmaf719e8nAL4M4CU8x3qSyV2D+0sA/l/5/+r1sedRGgD/cynld0opn7o+9u1N07wGXCk1gA/cWenuTrI2eJ5156dKKf/XddiG4Yfnrj1KKR8B8DEA/xI7PVmQuwb3aGOI53X5zg83TfP9AP4TAD9ZSvmRuy7Qlsvzqjt/B8CfAvAygNcA/ML18eeqPUopDwF8HsBfa5rmce3S4Nh7tl1U7hrcXwXwYfn/IQB/fEdluVNpmuaPr79fB/CPcOU6frOU8kEAuP5+/e5KeGeStcFzqTtN03yzaZrLpmnmAP4ubkIMz017lFL2cQXsf79pmn94fXinJyZ3De7/CsBHSynfWUqZAPgkgF+/4zLdupRSHpRSHvE3gP8YwO/hqi1+7PqyHwPwT+6mhHcqWRv8OoBPllKmpZTvBPBRAL99B+W7VSGAXcufx5WeAM9Je5SrbSA/A+DLTdP8opza6YnJnW752zTNRSnlpwD8U1ytnPmlpmm+dJdluiP5dgD/6Hr70vsAfrVpmv+plPKvAHyulPLjAL4O4C/cYRk3LqWUzwL4OIAXSymvAvgZAD+PoA2apvlSKeVzAH4fwAWAn2yapr4H6jMmSXt8vJTyMq5CC18D8FeB56M9ruWHAfxlAL9bSvni9bG/iedYTzLZPaG6k53sZCfvQbnrsMxOdrKTnexkA7ID953sZCc7eQ/KDtx3spOd7OQ9KDtw38lOdrKT96DswH0nO9nJTt6DsgP3nexkJzt5D8oO3Heyk53s5D0oO3DfyU52spP3oPz/iVRJf6hbfPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epsilon = 100\n",
    "matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
    "\n",
    "\n",
    "panel = string['panels']['29']\n",
    "Points =  Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]],cmap = 'gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'Mesa_010',\n",
       " 'points': array([[-70.1137603 , -18.88374416],\n",
       "        [-70.1137603 , -18.8841109 ],\n",
       "        [-70.11373707, -18.8841109 ],\n",
       "        [-70.11373707, -18.88374416]]),\n",
       " 'panels': {'1': {'id': 1,\n",
       "   'points': array([[-70.1137603 , -18.88374416],\n",
       "          [-70.1137603 , -18.88375319],\n",
       "          [-70.11373852, -18.88375319],\n",
       "          [-70.11373852, -18.88374416]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999974582974,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '2': {'id': 2,\n",
       "   'points': array([[-70.1137603 , -18.88375319],\n",
       "          [-70.1137603 , -18.88376222],\n",
       "          [-70.11373852, -18.88376222],\n",
       "          [-70.11373852, -18.88375319]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999994424,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '3': {'id': 3,\n",
       "   'points': array([[-70.1137603 , -18.88376222],\n",
       "          [-70.1137603 , -18.88377156],\n",
       "          [-70.11373852, -18.88377156],\n",
       "          [-70.11373852, -18.88376222]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999997602535461,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '4': {'id': 4,\n",
       "   'points': array([[-70.1137603 , -18.88377156],\n",
       "          [-70.1137603 , -18.88378059],\n",
       "          [-70.11373852, -18.88378059],\n",
       "          [-70.11373852, -18.88377156]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999980936,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '5': {'id': 5,\n",
       "   'points': array([[-70.1137603 , -18.88378059],\n",
       "          [-70.1137603 , -18.88378993],\n",
       "          [-70.11373852, -18.88378993],\n",
       "          [-70.11373852, -18.88378059]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999779162957,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '6': {'id': 6,\n",
       "   'points': array([[-70.1137603 , -18.88378993],\n",
       "          [-70.1137603 , -18.88379895],\n",
       "          [-70.11373852, -18.88379895],\n",
       "          [-70.11373852, -18.88378993]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999999447,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '7': {'id': 7,\n",
       "   'points': array([[-70.1137603 , -18.88379895],\n",
       "          [-70.1137603 , -18.88380829],\n",
       "          [-70.11373852, -18.88380829],\n",
       "          [-70.11373852, -18.88379895]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.999999999128643,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '8': {'id': 8,\n",
       "   'points': array([[-70.1137603 , -18.88380829],\n",
       "          [-70.1137603 , -18.88381732],\n",
       "          [-70.11373852, -18.88381732],\n",
       "          [-70.11373852, -18.88380829]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999976773,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '9': {'id': 9,\n",
       "   'points': array([[-70.1137603 , -18.88381732],\n",
       "          [-70.1137603 , -18.88382666],\n",
       "          [-70.11373852, -18.88382666],\n",
       "          [-70.11373852, -18.88381732]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999865502004,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '10': {'id': 10,\n",
       "   'points': array([[-70.1137603 , -18.88382666],\n",
       "          [-70.1137603 , -18.88383569],\n",
       "          [-70.11373852, -18.88383569],\n",
       "          [-70.11373852, -18.88382666]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.6644867214270673,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '11': {'id': 11,\n",
       "   'points': array([[-70.1137603 , -18.88383569],\n",
       "          [-70.1137603 , -18.88384472],\n",
       "          [-70.11373852, -18.88384472],\n",
       "          [-70.11373852, -18.88383569]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999943922003554,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '12': {'id': 12,\n",
       "   'points': array([[-70.1137603 , -18.88384472],\n",
       "          [-70.1137603 , -18.88385406],\n",
       "          [-70.11373852, -18.88385406],\n",
       "          [-70.11373852, -18.88384472]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999930979888187,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '13': {'id': 13,\n",
       "   'points': array([[-70.1137603 , -18.88385406],\n",
       "          [-70.1137603 , -18.88386309],\n",
       "          [-70.11373852, -18.88386309],\n",
       "          [-70.11373852, -18.88385406]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9745824938551096,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '14': {'id': 14,\n",
       "   'points': array([[-70.1137603 , -18.88386309],\n",
       "          [-70.1137603 , -18.88387243],\n",
       "          [-70.11373852, -18.88387243],\n",
       "          [-70.11373852, -18.88386309]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9898600718156667,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '15': {'id': 15,\n",
       "   'points': array([[-70.1137603 , -18.88387243],\n",
       "          [-70.1137603 , -18.88388145],\n",
       "          [-70.11373852, -18.88388145],\n",
       "          [-70.11373852, -18.88387243]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9806793434421213,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '16': {'id': 16,\n",
       "   'points': array([[-70.1137603 , -18.88388145],\n",
       "          [-70.1137603 , -18.88389079],\n",
       "          [-70.11373852, -18.88389079],\n",
       "          [-70.11373852, -18.88388145]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.999999837090797,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '17': {'id': 17,\n",
       "   'points': array([[-70.1137603 , -18.88389079],\n",
       "          [-70.1137603 , -18.88389982],\n",
       "          [-70.11373852, -18.88389982],\n",
       "          [-70.11373852, -18.88389079]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9924301909160113,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '18': {'id': 18,\n",
       "   'points': array([[-70.1137603 , -18.88389982],\n",
       "          [-70.1137603 , -18.88390916],\n",
       "          [-70.11373852, -18.88390916],\n",
       "          [-70.11373852, -18.88389982]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999942012,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '19': {'id': 19,\n",
       "   'points': array([[-70.1137603 , -18.88390916],\n",
       "          [-70.1137603 , -18.88391819],\n",
       "          [-70.11373852, -18.88391819],\n",
       "          [-70.11373852, -18.88390916]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999919790152,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '20': {'id': 20,\n",
       "   'points': array([[-70.1137603 , -18.88391819],\n",
       "          [-70.1137603 , -18.88392722],\n",
       "          [-70.11373852, -18.88392722],\n",
       "          [-70.11373852, -18.88391819]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999904970847652,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '21': {'id': 21,\n",
       "   'points': array([[-70.1137603 , -18.88392722],\n",
       "          [-70.1137603 , -18.88393656],\n",
       "          [-70.11373852, -18.88393656],\n",
       "          [-70.11373852, -18.88392722]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999959942511066,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '22': {'id': 22,\n",
       "   'points': array([[-70.1137603 , -18.88393656],\n",
       "          [-70.1137603 , -18.88394559],\n",
       "          [-70.11373852, -18.88394559],\n",
       "          [-70.11373852, -18.88393656]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9882801857680308,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '23': {'id': 23,\n",
       "   'points': array([[-70.1137603 , -18.88394559],\n",
       "          [-70.1137603 , -18.88395493],\n",
       "          [-70.11373852, -18.88395493],\n",
       "          [-70.11373852, -18.88394559]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.8638580569876031,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '24': {'id': 24,\n",
       "   'points': array([[-70.1137603 , -18.88395493],\n",
       "          [-70.1137603 , -18.88396395],\n",
       "          [-70.11373852, -18.88396395],\n",
       "          [-70.11373852, -18.88395493]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999954068,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '25': {'id': 25,\n",
       "   'points': array([[-70.1137603 , -18.88396395],\n",
       "          [-70.1137603 , -18.88397329],\n",
       "          [-70.11373852, -18.88397329],\n",
       "          [-70.11373852, -18.88396395]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999993415,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '26': {'id': 26,\n",
       "   'points': array([[-70.1137603 , -18.88397329],\n",
       "          [-70.1137603 , -18.88398232],\n",
       "          [-70.11373852, -18.88398232],\n",
       "          [-70.11373852, -18.88397329]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.995600030187818,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '27': {'id': 27,\n",
       "   'points': array([[-70.1137603 , -18.88398232],\n",
       "          [-70.1137603 , -18.88399166],\n",
       "          [-70.11373852, -18.88399166],\n",
       "          [-70.11373852, -18.88398232]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999993947400787,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '28': {'id': 28,\n",
       "   'points': array([[-70.1137603 , -18.88399166],\n",
       "          [-70.1137603 , -18.88400069],\n",
       "          [-70.11373852, -18.88400069],\n",
       "          [-70.11373852, -18.88399166]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9905119422535897,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '29': {'id': 29,\n",
       "   'points': array([[-70.1137603 , -18.88400069],\n",
       "          [-70.1137603 , -18.88401003],\n",
       "          [-70.11373852, -18.88401003],\n",
       "          [-70.11373852, -18.88400069]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.7757526418449818,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '30': {'id': 30,\n",
       "   'points': array([[-70.1137603 , -18.88401003],\n",
       "          [-70.1137603 , -18.88401906],\n",
       "          [-70.11373852, -18.88401906],\n",
       "          [-70.11373852, -18.88401003]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9860332917154839,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '31': {'id': 31,\n",
       "   'points': array([[-70.1137603 , -18.88401906],\n",
       "          [-70.1137603 , -18.88402809],\n",
       "          [-70.11373852, -18.88402809],\n",
       "          [-70.11373852, -18.88401906]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999561655515,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '32': {'id': 32,\n",
       "   'points': array([[-70.1137603 , -18.88402809],\n",
       "          [-70.1137603 , -18.88403742],\n",
       "          [-70.11373852, -18.88403742],\n",
       "          [-70.11373852, -18.88402809]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999999699,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '33': {'id': 33,\n",
       "   'points': array([[-70.1137603 , -18.88403742],\n",
       "          [-70.1137603 , -18.88404645],\n",
       "          [-70.11373852, -18.88404645],\n",
       "          [-70.11373852, -18.88403742]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999907283,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '34': {'id': 34,\n",
       "   'points': array([[-70.1137603 , -18.88404645],\n",
       "          [-70.1137603 , -18.88405579],\n",
       "          [-70.11373852, -18.88405579],\n",
       "          [-70.11373852, -18.88404645]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999999699,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '35': {'id': 35,\n",
       "   'points': array([[-70.1137603 , -18.88405579],\n",
       "          [-70.1137603 , -18.88406482],\n",
       "          [-70.11373852, -18.88406482],\n",
       "          [-70.11373852, -18.88405579]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.999999992402474,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '36': {'id': 36,\n",
       "   'points': array([[-70.1137603 , -18.88406482],\n",
       "          [-70.1137603 , -18.88407416],\n",
       "          [-70.11373852, -18.88407416],\n",
       "          [-70.11373852, -18.88406482]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.90917031183596,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '37': {'id': 37,\n",
       "   'points': array([[-70.1137603 , -18.88407416],\n",
       "          [-70.1137603 , -18.88408319],\n",
       "          [-70.11373852, -18.88408319],\n",
       "          [-70.11373852, -18.88407416]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.8419277241921781,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '38': {'id': 38,\n",
       "   'points': array([[-70.1137603 , -18.88408319],\n",
       "          [-70.1137603 , -18.88409253],\n",
       "          [-70.11373852, -18.88409253],\n",
       "          [-70.11373852, -18.88408319]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.8482129635733254,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '39': {'id': 39,\n",
       "   'points': array([[-70.1137603 , -18.88409253],\n",
       "          [-70.1137603 , -18.88410156],\n",
       "          [-70.11373852, -18.88410156],\n",
       "          [-70.11373852, -18.88409253]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.9999999999999699,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'},\n",
       "  '40': {'id': 40,\n",
       "   'points': array([[-70.1137603 , -18.88410156],\n",
       "          [-70.1137603 , -18.88411027],\n",
       "          [-70.11373852, -18.88411027],\n",
       "          [-70.11373852, -18.88410156]]),\n",
       "   'status': '1-Falla',\n",
       "   'prob': 0.999999999978633,\n",
       "   'T': 0,\n",
       "   'severidad': 'default'}},\n",
       " 'status': 'default',\n",
       " 'T': 0}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string = L_strings_coord['10']\n",
    "string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save KML Panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "kml=simplekml.Kml()\n",
    "\n",
    "\n",
    "for string_key in L_strings_coord.keys():\n",
    "    \n",
    "    string = L_strings_coord[string_key]\n",
    "    points = string['points']\n",
    "    \n",
    "    for panel_key in string['panels'].keys():\n",
    "        panel = string['panels'][panel_key]\n",
    "        points = panel['points']\n",
    "        \n",
    "        pmt = kml.newpolygon(outerboundaryis = points)\n",
    "        pmt.extendeddata.newdata(name= 'Id integer', value= str(string_key).zfill(3) + '_' + str(panel['id']).zfill(3))\n",
    "        pmt.extendeddata.newdata(name= 'Id panel', value= str(panel['id']).zfill(3))\n",
    "        pmt.extendeddata.newdata(name='Zona PV', value= ZonaPV)\n",
    "        pmt.extendeddata.newdata(name='Cód. Fall', value= 0)\n",
    "        pmt.extendeddata.newdata(name= 'Tipo falla', value= panel['status'])\n",
    "        pmt.extendeddata.newdata(name= 'Mesa', value= string['id'])\n",
    "        \n",
    "kml.save(path_kml_panel)\n",
    "## Save List in coordinate latitud and longitude ###\n",
    "with open(path_new_dict, 'wb') as handle:\n",
    "    pickle.dump(L_strings_coord, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "print('Listo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2df8b29190>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].astype(int), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}