{
   "cells": [
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Calculating user contributions\n",
            "==============================\n",
            "\n",
            "When gearing up for publication, you will find that due to the collaborative approach of CATMAID a great many people will have contributed to the data you would like to publish. In order to come up with a sensible author list, it is useful to quantify how much a user has contributed to the reconstruction of a set of neurons. CATMAID provides you a broad summary of contributions e.g. via the selection table. Pymaid let's you fetch that data and more.\n",
            "\n",
            "First, let's get a set of olfactory projection neurons to demonstrate user statistics on:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 1,
         "metadata": {},
         "outputs": [
            {
               "name": "stderr",
               "output_type": "stream",
               "text": [
                  "INFO  : Found 9 skeletons with matching annotation(s) (pymaid)\n"
               ]
            }
         ],
         "source": [
            "import pymaid\n",
            "\n",
            "# Initialize connection\n",
            "rm = pymaid.connect_catmaid()\n",
            "\n",
            "# Fetch some neurons\n",
            "nl = pymaid.get_neurons('annotation:glomerulus DA1 right')"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "First some basic stats:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 2,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
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                     "<style scoped>\n",
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                     "\n",
                     "    .dataframe tbody tr th {\n",
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                     "    }\n",
                     "\n",
                     "    .dataframe thead th {\n",
                     "        text-align: right;\n",
                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>skeleton_id</th>\n",
                     "      <th>n_nodes</th>\n",
                     "      <th>node_contributors</th>\n",
                     "      <th>n_presynapses</th>\n",
                     "      <th>pre_contributors</th>\n",
                     "      <th>n_postsynapses</th>\n",
                     "      <th>post_contributors</th>\n",
                     "      <th>review_contributors</th>\n",
                     "      <th>multiuser_review_minutes</th>\n",
                     "      <th>construction_minutes</th>\n",
                     "      <th>min_review_minutes</th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>0</th>\n",
                     "      <td>61221</td>\n",
                     "      <td>7875</td>\n",
                     "      <td>{'adamjohn': 2442, 'ranftp': 4109, 'ratliffj':...</td>\n",
                     "      <td>404</td>\n",
                     "      <td>{'hsuj': 1, 'adamjohn': 10, 'ranftp': 249, 'ha...</td>\n",
                     "      <td>128</td>\n",
                     "      <td>{'ranftp': 89, 'alij': 1, 'koppenhaverb': 5, '...</td>\n",
                     "      <td>{'ratliffj': 2834, 'masoodpanahn': 84, 'koppen...</td>\n",
                     "      <td>165</td>\n",
                     "      <td>416</td>\n",
                     "      <td>135</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>1</th>\n",
                     "      <td>27295</td>\n",
                     "      <td>9975</td>\n",
                     "      <td>{'koppenhaverb': 102, 'robertsr': 53, 'lovef':...</td>\n",
                     "      <td>412</td>\n",
                     "      <td>{'robertsr': 32, 'alij': 6, 'schlegelp': 205, ...</td>\n",
                     "      <td>59</td>\n",
                     "      <td>{'alij': 2, 'heatha': 2, 'hallouc': 2, 'schleg...</td>\n",
                     "      <td>{'schlegelp': 517, 'adesinaa': 2586, 'kmecoval...</td>\n",
                     "      <td>367</td>\n",
                     "      <td>502</td>\n",
                     "      <td>313</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>2</th>\n",
                     "      <td>57323</td>\n",
                     "      <td>4585</td>\n",
                     "      <td>{'kmecoval': 2194, 'robertsr': 159, 'koppenhav...</td>\n",
                     "      <td>361</td>\n",
                     "      <td>{'calles': 2, 'michaelLingelbach': 1, 'kmecova...</td>\n",
                     "      <td>76</td>\n",
                     "      <td>{'vallas': 1, 'ranftp': 1, 'edmondsona': 20, '...</td>\n",
                     "      <td>{'ratliffj': 2116, 'kmecoval': 350, 'masoodpan...</td>\n",
                     "      <td>176</td>\n",
                     "      <td>208</td>\n",
                     "      <td>84</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>3</th>\n",
                     "      <td>57311</td>\n",
                     "      <td>4882</td>\n",
                     "      <td>{'kmecoval': 1376, 'robertsr': 24, 'lovef': 75...</td>\n",
                     "      <td>371</td>\n",
                     "      <td>{'robertsr': 18, 'lovef': 220, 'jamasba': 4, '...</td>\n",
                     "      <td>58</td>\n",
                     "      <td>{'robertsr': 10, 'ranftp': 1, 'heatha': 1, 'lo...</td>\n",
                     "      <td>{'mooree': 2308, 'adamjohn': 1231, 'kmecoval':...</td>\n",
                     "      <td>390</td>\n",
                     "      <td>241</td>\n",
                     "      <td>163</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>4</th>\n",
                     "      <td>57353</td>\n",
                     "      <td>4898</td>\n",
                     "      <td>{'kmecoval': 1054, 'hsuj': 1, 'koppenhaverb': ...</td>\n",
                     "      <td>302</td>\n",
                     "      <td>{'hsuj': 1, 'robertsr': 13, 'kmecoval': 17, 's...</td>\n",
                     "      <td>24</td>\n",
                     "      <td>{'robertsr': 7, 'kmecoval': 2, 'meechank': 13,...</td>\n",
                     "      <td>{'adamjohn': 1843, 'sharifin': 1231, 'masoodpa...</td>\n",
                     "      <td>177</td>\n",
                     "      <td>247</td>\n",
                     "      <td>101</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "  skeleton_id n_nodes                                  node_contributors  \\\n",
                     "0       61221    7875  {'adamjohn': 2442, 'ranftp': 4109, 'ratliffj':...   \n",
                     "1       27295    9975  {'koppenhaverb': 102, 'robertsr': 53, 'lovef':...   \n",
                     "2       57323    4585  {'kmecoval': 2194, 'robertsr': 159, 'koppenhav...   \n",
                     "3       57311    4882  {'kmecoval': 1376, 'robertsr': 24, 'lovef': 75...   \n",
                     "4       57353    4898  {'kmecoval': 1054, 'hsuj': 1, 'koppenhaverb': ...   \n",
                     "\n",
                     "  n_presynapses                                   pre_contributors  \\\n",
                     "0           404  {'hsuj': 1, 'adamjohn': 10, 'ranftp': 249, 'ha...   \n",
                     "1           412  {'robertsr': 32, 'alij': 6, 'schlegelp': 205, ...   \n",
                     "2           361  {'calles': 2, 'michaelLingelbach': 1, 'kmecova...   \n",
                     "3           371  {'robertsr': 18, 'lovef': 220, 'jamasba': 4, '...   \n",
                     "4           302  {'hsuj': 1, 'robertsr': 13, 'kmecoval': 17, 's...   \n",
                     "\n",
                     "  n_postsynapses                                  post_contributors  \\\n",
                     "0            128  {'ranftp': 89, 'alij': 1, 'koppenhaverb': 5, '...   \n",
                     "1             59  {'alij': 2, 'heatha': 2, 'hallouc': 2, 'schleg...   \n",
                     "2             76  {'vallas': 1, 'ranftp': 1, 'edmondsona': 20, '...   \n",
                     "3             58  {'robertsr': 10, 'ranftp': 1, 'heatha': 1, 'lo...   \n",
                     "4             24  {'robertsr': 7, 'kmecoval': 2, 'meechank': 13,...   \n",
                     "\n",
                     "                                 review_contributors multiuser_review_minutes  \\\n",
                     "0  {'ratliffj': 2834, 'masoodpanahn': 84, 'koppen...                      165   \n",
                     "1  {'schlegelp': 517, 'adesinaa': 2586, 'kmecoval...                      367   \n",
                     "2  {'ratliffj': 2116, 'kmecoval': 350, 'masoodpan...                      176   \n",
                     "3  {'mooree': 2308, 'adamjohn': 1231, 'kmecoval':...                      390   \n",
                     "4  {'adamjohn': 1843, 'sharifin': 1231, 'masoodpa...                      177   \n",
                     "\n",
                     "  construction_minutes min_review_minutes  \n",
                     "0                  416                135  \n",
                     "1                  502                313  \n",
                     "2                  208                 84  \n",
                     "3                  241                163  \n",
                     "4                  247                101  "
                  ]
               },
               "execution_count": 2,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "cont = pymaid.get_contributor_statistics(nl, separate=True)\n",
            "cont.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "For convencience, there is another function that shows contributions per-user instead of per-neuron:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 3,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
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                     "<style scoped>\n",
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                     "\n",
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                     "    }\n",
                     "\n",
                     "    .dataframe thead th {\n",
                     "        text-align: right;\n",
                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>user</th>\n",
                     "      <th>nodes</th>\n",
                     "      <th>presynapses</th>\n",
                     "      <th>postsynapses</th>\n",
                     "      <th>nodes_reviewed</th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>0</th>\n",
                     "      <td>robertsr</td>\n",
                     "      <td>14527</td>\n",
                     "      <td>1038</td>\n",
                     "      <td>481</td>\n",
                     "      <td>4729</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>1</th>\n",
                     "      <td>ranftp</td>\n",
                     "      <td>5927</td>\n",
                     "      <td>401</td>\n",
                     "      <td>132</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>2</th>\n",
                     "      <td>kmecoval</td>\n",
                     "      <td>5204</td>\n",
                     "      <td>87</td>\n",
                     "      <td>4</td>\n",
                     "      <td>5177</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>3</th>\n",
                     "      <td>hallouc</td>\n",
                     "      <td>4267</td>\n",
                     "      <td>61</td>\n",
                     "      <td>11</td>\n",
                     "      <td>8047</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>4</th>\n",
                     "      <td>michaelLingelbach</td>\n",
                     "      <td>4253</td>\n",
                     "      <td>1</td>\n",
                     "      <td>0</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "                user  nodes  presynapses  postsynapses  nodes_reviewed\n",
                     "0           robertsr  14527         1038           481            4729\n",
                     "1             ranftp   5927          401           132               0\n",
                     "2           kmecoval   5204           87             4            5177\n",
                     "3            hallouc   4267           61            11            8047\n",
                     "4  michaelLingelbach   4253            1             0               0"
                  ]
               },
               "execution_count": 3,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "by_user = pymaid.get_user_contributions(nl)\n",
            "by_user.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Sometimes the plain number of nodes (or cable length) are not very helpful. Did a person trace mostly backbone (fast) or did they scramble tracing fine terminal branches (slow)? Personally, I consider time invested to be a better metric:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 4,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
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                     "\n",
                     "    .dataframe thead th {\n",
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                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>total</th>\n",
                     "      <th>creation</th>\n",
                     "      <th>edition</th>\n",
                     "      <th>review</th>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>user</th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>robertsr</th>\n",
                     "      <td>1113</td>\n",
                     "      <td>690</td>\n",
                     "      <td>420</td>\n",
                     "      <td>198</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>hallouc</th>\n",
                     "      <td>627</td>\n",
                     "      <td>183</td>\n",
                     "      <td>180</td>\n",
                     "      <td>333</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>koppenhaverb</th>\n",
                     "      <td>378</td>\n",
                     "      <td>108</td>\n",
                     "      <td>15</td>\n",
                     "      <td>144</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>ranftp</th>\n",
                     "      <td>357</td>\n",
                     "      <td>219</td>\n",
                     "      <td>189</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>kmecoval</th>\n",
                     "      <td>333</td>\n",
                     "      <td>147</td>\n",
                     "      <td>72</td>\n",
                     "      <td>126</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "              total  creation  edition  review\n",
                     "user                                          \n",
                     "robertsr       1113       690      420     198\n",
                     "hallouc         627       183      180     333\n",
                     "koppenhaverb    378       108       15     144\n",
                     "ranftp          357       219      189       0\n",
                     "kmecoval        333       147       72     126"
                  ]
               },
               "execution_count": 4,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "time_inv = pymaid.get_time_invested(nl)\n",
            "time_inv.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Notice how the order of contributors changes whether we look at e.g. nodes created vs total time invested?\n",
            "\n",
            ":func:`pymaid.get_time_invested` takes timestamps from node creation, edits and reviews and calculates the time invested in minutes. In brief, pymaid sums up the minutes in which a user has performed 10+ actions. You can tweak this behaviour - have a look at the documentation!\n",
            "\n",
            "Compartmentalizing contributions \n",
            "--------------------------------\n",
            "\n",
            "You might end up publishing only reconstructions in a certain neuropil. In that case will want to subset contribution accordingly.\n",
            "\n",
            "Let's first prune the projection neurons to the lateral horn"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 5,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "image/png": 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15jNm2jdzZqAb5wV60DXUlISq2U9uL9DVMAz1p7esgaQUWEAp1RlYQ+QyFyGEEMdpsW0nz7nmsM62j7/4T+I+3+jqOOxCYIxhGCXVcbDqJKWgmpUvZTwfGGR1FiGEqKmK8PKiax7vO1fQPGxwaaAPV/j74ay+s+JzgHGGYXiq64DVQUpBNVNKXQW8bXUOIYSIZgFCbNGzSTBdpJiJ2NDZqxWyyL6TLx1rWWPbC8Al/j7c6RtJvao5VfBnZgBnG4bhs+LgVUEmGla/M60OIIQQ1SVMmAw9l2W23UxzrKee6aZ/qBX9g63oGm56aN2BUvysse1lpW03y+27WWvbh798xUI7NuqZbvK1UgDqmTGH7v8h39iKTBCsKqcBnyilLjAMI2BViMokIwXVSCmlAdlAQ6uzCCFEVfi5BCy17WK5fRfLbbsp1MoASDDdaGgUaZER91jTSe9QCwo1D5ttBwgRRkcnLdSYvqGW9Aql4NECbNMPkq+V0jXUjL6hFrzhXMJUxzpu8J3Mbf7hFj7aQz4FLjMMI/int4xyMlJQvdKQQiCEqEUK8bDNlssmPYtl9t0st+1Clb/oNw8nMiLYnv6hVvQLtiLFjGzvkqeVstSWyXLbblbYd2OYMVzvG0yfUAt6hlKIx/WHx/x5xOBq/4CqfXAVdyHgU0qNNwyjSndmqmoyUlCNlFI3AK9YnUMIIY5VEV6223LJ0CMf2/RcMmy5HNQOT8BvHk6MnBr4VQmobJHVCEtpH678jQuDfi+lhQd+8/W4xMbYnTFH+YkjTAEm1uT9EWSkoHoNszqAEEJU1EGthOn2zXzv2MQq255Dmxe5TQftwg0ZEmxLu3Ay7UMN6RBuRFPTqJZcDcy4E9rb4GjKig6yecGHbJz3Hr6y3y5BMPamN0jpNPjP7uZawKuUutUwjBr5jltGCqpJ+XyC/UATq7MIIcTvKdDKmGnfwvf2TSyz7yJMmHbhhpwWSKNruCntQg1pbhro6FZHPWEBbyk5u9aydcmX7Fj9PeFQkJZdh9O65xh0/cjH17TDAOKMCo9MPAPcUxOLgYwUVJ/2SCEQQkQhhYcfHOl8Z9/EYvtOQoRJDScxyTeYscHOVTJMX5nMcJhQ0I/N4cI0w4QCvshH0EfQ7yUU8BEMeAkGfJQWZpGzcy3ZO1eTv38rZjiEwx1H2pBL6DL0CoxGqUfeNyYBfSdQCBX/e7gb8AB/r9QHWg1kpKCaKKWuA163OocQQvwsWyvmUfd05tozCBIiJVyfscHOnB7oTKdwYysv9ftThdk72bt5Pvu3LiFr23L8nmLQNKjAa5rDFUvDVt1p3LoXjVr3pGnbvjjch09HhCjEY59LqWM2PtsKQlohscFRNPa8dKwxHzAM44lj/SEryUhB9ZH5BEKIqLHMtovbYz7HS4Ar/f0YG+hCt3DTqC0CpmlSkJXBzjUz2LlmJgVZGQAkJLegdc/R1EtuScDvQddt2J0x2OxO7A43Nqcr8qfdhc3hIia+AYlN26HrR27LHNRyKLPPRmmzWJ+/gzV7GrL1QAt8BefQ0N+Af196XMsnP66U8hiG8dyJ/w1UDxkpqAbl8wl2AylWZxFC1G0mJm87lvIv9w+0DNfnJc+FtA1H75XSxXl72bL4M3aunoHK2QmaRpO2fWjdYzStuo0kIaniT6smPgL6XoL6LgJ6JrnePSzbn8fyfSF25NnJL40hv6QegW0x6AttxOXZSMhzQRM/c1fdeCIP4ybDMGrElWcyUlA92iCFQAhhsTL8POj+lm8dGxgV7MSTnjOraifBE5azax3rf/wfO9fMBKBp+/50HXEVqd1GEWv8fokxMfHpaymxT2NF3jKW7TIIh11oQJAy8kvdHMhKIHNZW+rvaon9QFucpoNurkY0L2pG/ME44grdNCSO+kEnNkzsfd890YfzslLKaxjGmyd6R1VNRgqqgVJqAvCG1TmEEHXXLi2fW2I/I0PP4XbfCK73D4q6UwVmOMyuDXNY/+ObHNi+Eqc7no6DLqTbiL8Ql/jH87T9ejoljm/JCs5genoM36/vwO70RuiLbWg5QBBiQi4GHezEyWVdSQ2V31/5X4EO+PDh1/NJYA9tnRm4GgUxrz2dmCuSsJ34kgsmcDvwYjRflSCloBoopd4GrrI6hxCibppry+CvMV+ho/Fvz7kMCbW1OtIRgn4PW5d+yfo5b1OUu4v4+k3pOvwvdBx4Ps6YhN/9uYC2h1LHt5TYv2XRnlJmbWrHwm3tCOW7abQqjuDKAPViXHTu3oi2xc0Zurg/uq7hb+aFISXUS97Nhn2LWeAtwOv8hIKEXXyZOYzgKacQHDWKcNu2kcmLlWs6MMEwjKzKvuPKIKWgGiilMoFWVucQQtQtYcK87JzPy66fSAs14UXPhVW2yuDxCPq9rJ31XzbOfx9faSENW3aj2ylX07rHaHTbkWe3TfwEtf0E9f349a2UOr7HZ1sLwOKNw3nk5TS0fTbqlTqJ2aph13TOPCeNi1IHEP48gcBOHUwT7/CFLGv1ACsK17IpoZSQDnaHm+4JHend5XQuGXIHdr3Kz6znAdcZhvFlVR/oWEkpqGJKqVZAptU5hBB1yzp9P8+75rDIvoOzA915xHs6bhxWxzpE5e5i9hu3kr8vnVbdRtJt5ASatO2D9qt35mFKyHM/TKljOiaH9xsKl3Riy+zT+GlqE+bOzqaouHz34li46KyuXDdyMDybRCBTQ2+0nw2tXuSt1JfJjfWioZGmNaF380H0GngxXdsMwWW3ZG7FG8AdhmEUW3Hwo5FSUMWUUlcBb1udQwhRNyy07WCyayHLbJkkmG5u943gskCfqJo/kLnuB+a9ey+abmPEVU/TosvRr9gOU0Z27HX4bGtI8F+KM9wF09eUaW8HeO25zagiHwFXGDrY6DG0GZeM64JdtzHws1TUFCdB2z4WdbmD9/rOJODQGai15oy0i+k54i/E14uaKy52AFcYhrHY6iAgpaDKKaXeACZYnUMIUbsd0Ip4zD2D2fYtNDITuNo/gAv9vf50x8HqZJom62ZPYdnUf5PcsiujrvkPCQ2a//Z2hPHalpDvfhK/vp1Gnmdw+8aw4sNcZjy7iyU529nVNo/mYxtwyslNaZNYSuHBteQs2EPvTyfRMLc9y9I+4uuTH4MEk7GtxnLu8DtpldTegkddIWHgMeAxwzACVgaRUlDFlFLbgOia1SOEqDUChPjQsYLnXXMJamFu9g3lav8AHNj+/IerUTgUZMHHD5O++DPa9B7LsMufxO48csg+oO+kxP41JY5pBPV96OEkChfdxbbnemH/waBpSTKmDpoLwvVK2NlsIcvbfsmupJWctPFcTlk9CWwl7Bn5Gvrl9Ulq14vuTfuQ4KqejZoqwTIiowYZVgWQUlCFlFIpwB6rcwghap8CrYyPHav4wLmCHK2Yk4NtedA7lpZmfauj/cb+jGXMefsuylQuPU+bRN/Tb0Ur33AopOVRav+WEsdUfLYNgE5McBCbZ53K1/+ox+mrhtEgHE92TAHT01aztfcM0nIV3TJ70z1zKAmlKdh1BzaHi4RTi6j/VCy2hjV6s6YyYLhhGMutOLgsXlS1ZGljIUSlStezedu5jGmODQQIMjDYmkf94xgaahdV8wYADuxYxYpvnicrYxkAfU6/hd5jbwLAq69BuV7FY1+ASQhnKI2kjRew/ZkSblvSGM+eUv4aGIfDpfPEmPdY0XETyeGZnFJwkCsSe9L39HxCfbIoi0nFnxmLMy2Eu2e8lQ+3shwE1lt1cBkpqEJKqdeB66zOISoup1jj/Dfi+PsYL6d2Cv75DwhRrhQ/G21ZZGvFeLUAHgJ4tQAtw/UZGmxHLM7jvu8QYX60b+Ud5zKW23bhwsE5ge5c6e9Huyhcorgkfz9LvnqanaunE5OQRPcRV9N0+0GaxjTF3yZIbsclLNqxDbtWj8DaNqz/KpnYHQeZXZxIlmkwTOvOBQzE7YbPL3mDH5r8SH3XPh455VEGtz2uPQhqkssNw/jAqoNLKahCSql0oIPVOUTFvbPUwRMzI+c5191XjFPG0kQFzLNlcH/MN+RpJUf9fozp5NRgJ84OdGNAKBUbOkV4ybDlsE0/SJAwqeEGtAkn0dQ8fP47SIjp9s087p5BgVZG07DB5YG+XODvRSIx1fXwKiwU9LPuhzdZM/M1TNOk52kTSEtri+2rf7LXd5BvVTsycpPZsaExRbsT0Ms0Qmj47HY8djudm7VlfNEptA01wtnfyxsj72aeOZVujXvxwPCnaVavhdUPsaotBwYYhhG2KoA85VURpVRTpBDUOJ2aRH4X05qEpBCICplm38A9MV/RPtyIxz1n0MpsQIzpwG06cGJjnW0/3zo2MN2+mamOdTQw43CbDvbrhUe9v9bhJDQ08rUyCrWyQ19/wXMBI4MdsEfZBEKIXFWwZ+M8Fn/xOHryNnrc1IikTvUw7a+yckWYb3b3Yf53Y/H6XbiSbTRs7Kb7IDub9DIOxOqk+Jpy18phdN/ekvg2GsWTNvOP4CQOluXw1wH/YFzHC9C1Gj1PoKLusrIQgIwUVBml1MXAR1bnEMdm1R4bl70VC8C0iaW0b2Tp76eIcjPsm7kz5gv6hFoyuewSYv5gcSAfQX60b+VH+1aChOgUbkKHcEPahxrhxEamns8mPYvF9kxc2DFMN586VgPwWem1dA03ra6H9fsCAWzr1kE4TKhDBzAM8rMyWDHzEQLGTzQfGiahsY7TE8fSTzvx3pQ0dm+pTzDGSaCnHX2Qk+QmcWSXlmLTNE7T2nPBgv40XtwQ4oLsOHseH3X8JxlFm4l3JvDE6Ffo2bSf1Y+6unxpGMZ5VoeQUlBFlFKvADdYnUNUXHaxxrn/jSO/NDJZq16MyeuXlNEzRYqB+K059q3cEvMZXUNNeaPscuJOYM7ArwUJ8Yj7ez51rOYVz8WMDFo36KhlZWGfPx/bgp/Q1i0ibCsmmBimpEWQrb3ClPUppV4nE0dYZ897rdj+fms+29uNvSX1MN0aDXrH06dfe8YYHekZaIYzy0Vppom5x4aZYyMYDrJ4+Ee83/VxPDFFdG7Ug7EdzmVU2zOIc9aKiYMVEQQ6W3kp4s+kFFQRpdRGoLPVOUTFdHr08KYrH11dSlKcyTUfxJJTrPHShR6GtA1ZmE5Em4W2HUyM/YhOoca8WXY59Spx++FivNwR8wUL7Nu5zj+YO30jqveqAr8fc8NMAnum4vcsxdcwG2+HIMGGGqbbQchuJxQOEAz6ME0TfZ+Dhp/HsufJzewOlbLGnkkDdyKt4pLp4m6GXnbk6Q5bkom9RRi9RYBVngV8nPwsBW33cHG38YxsezpNE+rkLvP/MQzjdqtDgJSCKqGUagRkW51DVNzPpeB/V5QxsHWkAOSWaFz7QSzbD+p8fV0pbRvKiIGIXBJ4Udz/SA034O3SKyt1wl+WppgY+zHb9Vwe9p7OhYFelXbfv6uwEP+ej/GVzSNAOt6kvfibB0ADTXfh9KRgd/YmKy/Izo3p7N9XRmlBYxy+7qQ0uRC7twmb3s9h1Ka+aC6NkAPiYuwkD9GxtwrjaGFibxnG0SIcKQOxkFeWy/0zb2JL7nrOTruE6/rdXpMWGKpsHqCFYRh5VgcBKQVVQil1EfCx1TnEicsv1Tj15TjaJYd584oy4ipvhFjUQAFCXBD3Bge1Ur4uvY5ks/KGt2fb0/m7+1v8BPmP5wIGh9pU2n0f4vFg27wZfd06bOvWEd63iuwr0ykcGoKNPdHnDSM45W40nMT/JY/wnuZs25bL/nw/mmlieOJI8MahmzqHBi/8JvgAF+hOjQSnC5c9MjqQeJOP+rf4Dx3eNE1W7FvE0/P/hvIW8OCIZzg5dVTlP86aZZZhGFFznaWUgiog6xPULt9ttHPnFzFMHOLnjhE+q+MICz3j+oEpzkW87LmIU4IdK+U+CynjMfcMvnFsoFOoCf/ynk37cKNKue9DfD6cr7+O6/XXIRDAxCT7jC7kpPQjvPYktOUnowUS4Rcz/P3BANm2fDYnHyBkM0lJcGO0qocrWcPdUCMmWWdfbgFlj8eRoiVjdLYTOziEVqoTytLxro4Ug9ZbigmFQ8zdOZ0P1k4hI28zDeMa88SpL9OxYdfKfZw10wMD20PeAAAgAElEQVSGYTxhdYifyUVXVaPOV9/a5PQuQeZkBHhziZOLevlpnihF2grpejb5WhnNwgYtzfrVvnrfBj2LKc5FXBjoVWmFYIuezbWxH1CoebjJN5RJ/iGVvmeBbdky3A89hL5zJ95TzyOvyXnkrU0g9G1LNBy42juJucSBu78PV98g89UOnvlhGltz/Ti1Us5v2YD/O/caYmPijrjf77/cyjOvz2dC8mi692lIy3c8R3w/lK+h2U2256fz1E9/Y0vueloYqdxz8qOMbncWTnv0bNRksblWB/glGSmoZEqpNsB2q3OIynWgSOO0V+Jplxzi8TO9dGos8wuqSyEeZjo285D7O0wiz1cNzQQGBFPR0SjQyhgX6MLpwS6/+4L6888db5FYq+/j/php7NcU80puq5SJhVv1HP4S+y4u086rnotJCzc54fsEoKwMPTsb28KF2H/4AX3RIlS7keS2vAnPko6EvSFonwlnesgd2IQdWj7b8w+yJXs/GXkHySkpISFQzJmJGneeP5GGDVv+5hDrVh7glsun0bNfUx576VQSjN++wAfDQd5d/SrvrplMvLMetwy8j1Pajqsr6w1UVBlQ3zAM/5/esppIKahkSqnrgclW5xCVb8ZmOw9/50Z5NS7t4+fWYT6M6FtUrlZZo+/lkrj/AdAmnMzfvWPZpeezxLaTpfZd2NBxm3b26AU0DRtM8A+kW6gphZqHAaFUcrQSRse/BIATO72DLTgl2JHuoWZ0CDf6w3UFILId8Uz7Zl5yzSfWdPCI93SGhU58+92NehYTYt/HiZ33Sq+ildnghO9TX7MG9z//iW3dOjzt/BR1TyDPfxHFKy+A3a3wufyk91vN/F47WFIvSGkgjGmaBHyl6N4ikv2KpICihxHDxLGX0arToKMeJy+3jMtP+wQj0cXkz88lsf7RC9IrS5/mo3VvMqrtGdw66AES3dG3UVMUmG0YxqlWh/glOX1Q+eTUQS11WlqQAaklvDDPxYcrnXy/ycFlff1MHOzHEX2LzNUKBfrhFf3eLLucJmY9BoRSuTjQ+9DXTUzm2bYx2bWQx93TD329vhlLQfmKgHZsnBZIY6Mt69BtUsL1GRxqw4BgavneBJGCoKGxVyvk/phpLLNlAtA51JTnPOdVyov3Gn0v18V+SILp5n9ll5/YfZom+qZNFH36KVvmzyezSQpq9OMk7GpJqw96QtBORsp+5o6ez7LOO0lMdNPGSOJUzU+K24Zn7Tfou1fQu/doUrufRr3kljRo3glN+/0RlbnTd+IpC/Di+2f+biGYt3MmH617k3PSLuXOIQ8d/+Or/eZaHeDXZKSgEimldCAXOPFnDhHVNh/QuX+am80HIm1g+o0lpCbJ71JlCxNmr6YqvB3wGn0v+XoZJibT7ZuxoTMq2JERwfbYiAxb79TzWGnbzQeOlezRCyjWvNixoaMRZzrpFGrMatteHNi42j+A0wOdSTWTTvixZOg5rLbt5UnXLBqa8bxVdgXNzOO4DM800XfswDZnDsvmzeNrszkFgeEk+pIZuLUzDUri8CQUkTFoGVldsnE22EWyv4jGup+s1d8ecVcOVywnX/oobfuM+9PD7slUvPGfFcz5fgdt2tfnzannH7U87FWZXPfVBbQwWvPSme/jtMklO39giGEYC60O8UtSCiqRUqo3sNLqHKJ6mCZc8U4sK3dHisGMm0po1UB+n2qSEGFW2vawyLYDvxYiXysjQ8+hVbgBd/pGkmImnvAxyvBzT8zXzLZvAaBVuAHvll1FIzPhT36yXDiMvm0b+rZtaMuXs2vlSubabLzfvgO908/lrFWDcNrC2HUfoeE/oJ/3AfttC9g310mwTMcZWw9XrIHfU4yv9PB+C8OveoqUTicTk/DH72EK8jy88Z8VfPd5Og6njYsndOfi8d2IS/jti7036OGGry8mtzSbKed+QZOE5hX/i6p7PEBiNM0nACkFlUopdQ/wlNU5RPV6cqaLt5dGniCfOdfDuC5B/mD0VdQxN8d8yo/2rdzkO5n24Yb0CbUkyYz7ze2K/X5m7NhBiddLKCuL0K5dhPbsIbR/P95AgM3167M+ORlHqAmDM7ozOr0TTVQCTrsfTTPxvXE5MWlNiC2+EJc9BUdMPO7YRBzuI49lmuYfnh74pW1b8rhv0gzyD3o465I0rpzUkwbJsUe9rWmaPDnvPmZkfM3TYyZzUouhx/6XVbdE3XwCkFJQqZRSM4CoWYRCVA/ThLTHDr/r69sqxIOneekoVyjUectsu7gq9h1u941gkn/IUW8T8Hr5bM4cXtq6lXy/HwKByD8qAJuN+HAcQ7d1Y8j2trTe3Ry7HsJuC6INWEDZ0M8pa7+VFi2vIyX14kqd2b9ozi4eufNH4hOcPPnqaXTokvy7t92ndvHMgodZuX8x43vfxIQ+t1Rajlrsb4ZhPG51iF+TUlBJlFJuIB+icJNzUeUe/s7FRyudXDPIz2erHZT4NC7tG7lCoV7lLYsvapB0PZubYz7FowWYXXIz7l9e6RAIYJs9m3kzZvBPt5vtCQkMyM7m3pwcWqelofXsQ6B5d/IW7ML3XhqUxGAm5RKKy8fTdzae3ito2vkcWrQ9B4fr6O/cj1dZaYDP3l7Pmy+upEPnZJ54dTTJjX47sgFQ6i/h843v8vaqV3DaXVzf9w7O7nypXHZYMScbhrHA6hC/JqWgkiilRgA/Wp1DWGNPgcYZk+M5LS3A/ad6+c+8SEmoH2ty48k+xnUJUj9WftfqikLKGBv/KibwsHcsY4KH90bz+/1MvecePrLZ2JiUTI9AMrfbu5IW3xfvfjvejGKC212EfWEgjLfvLEpOmUFyr0E0ThlOQlJKhYf/j9WiObv45/0/oQq8DB/TmvueHI475siL1Ep8RSzaM5e5O6azdM98AuEAI1qP4ZaB95McV8krMdZeHiLrE0TdEqlSCiqJUupx4H6rcwjrXP9hDEsy7fRtGeSGIX5inSaPTnezZq8Nmw4npQY5vXOQUZ0CJMp4Uq32rOtH/utcxFel19Ex3Jhiv5812dlsz8hg/btlNNrbnLTiZrQubUG834WpBTG1EszGezDbbyXQYhuF5hb8SSZ9Lr2XhKSq3TnQNE3eemkVb728ivZpSdzx0GC69Gx86PthM8y8nTOZkfEVy/YuIBgOkhzbiOFtTuOUNuPo0rhnlearhX4wDCMqL1+XUlBJlFJLgf5W5xDW2XxA55PVDr7Z4KB+rMnMm0oxTUjP0fluo4PvN9nZU6Bjt8G1A31c1T9Agzj5/attNupZXBj3JsOL2jMyvTszMzNZtHcvIaXouaUF93xzAXG6TsKIGOwdfBS1mkywx1T8Tbewb4WXvDUxaN4mdBt5DV2HXYmmV+1Q/LL5e5j68Rbmz85kzDntuesfJ+N0Hl54Y/X+pby45Em25W2hUVwThrc+jRFtxpLWqLucJjh+DxqG8ZjVIY5GSkElUErVBw4C8hsimL7JzqYDNm4d5sP+i0WNTBM2HtB5db6LH9LtuB0mYzsHOb9ngL4tQ9YFFpUibJp8u2Mb926fSb7yk6jiwYQUE/4y3U+/5cNwmN1xNHUS/8YOtu99j3DHl4lPLSZrTgO0/aNplTaGFl2H44qpVy2Zv/1sC/96cD7x9VycfUka19zWF12PnJoIhAJMWfE8H617kyYJzbmm762c0mYcNl1W6qoEUTmfAKQUVAql1LnAF1bnEDXH9lydN5c4mb7ZTmn5pMS/j/HJpYw1UFkgwMydO3lz/XpWFWThS/QxJKkFI10pjP1iBsa7p1AaHoC9nZ2ysRvZGvMcZe65tLmwhPhmNhy7J5La5H5s9upd5OeL9zbyn8cW0f/kFB578VRc7sNzB3YX7uTROXeTfnAjZ6ddwk0D7sVtl3NelWQnkBaN8wlASkGlUEq9DNxodQ5R83gC8OyPLt5d5uT1S8sY2k5GDGoC0zRZl5vL5+npfLt9O6WBAE2NODIH7+aylA48/U4pZR/8j6xNdxAMn4F//ErWaH8lFLubTleGSGgdwqW3plHgCWJCR79UsaoU5Hl45amlzJyawaDhLXnkhVGHTheYpsm36Z/xwuLHcdrc3Dv0MU5OjcpT3zXVbmCoYRi7rA7ye6QUVAKlVDrQweocomYKhGDgv+Pp2DjMW1eUyT4KUSxTKX7IzOSrjAwyCgpw2WyMadOGczu25+PmP/G9tp5Zpy8gL/M09PxrsYXqsbPHMzjunEzLMQEc7jgctkbU991OfOAc9ErYbfFYzJ2+g2cfWUhpsZ+LJ3Tj6lv64Cj/B5ddsp8XFz/JT5mz6NNsAA8Mf1quJqhc+4kUgqjeRVdKwQlSSrUEorb1iZrh63V27v06hsv7+XlwTFSOKtZZpmmyJieHp5cuZXV2NgDdGjbk/I4dOb1NGzyhAm4u/A+rkks45w2Ty56+Ebe3NbZEHdcN6YSu/Rta0kYSzDOICQ0iJjgMu1lJ2yRXkCr08vw/FvLjdzvo2CWZ+58aTmq7yH4SG7PX8G36Z8zI+BqAa/rexiXdJ8gkwsqVDQwzDCPd6iB/RnZJPHHtrA4gar6zuwdJz/Hz5mInHRuFuah3oEqOEwghIxEVEAqHWZWdzQ+7dvFDZiZ7iotJjonh3pNO4pSWLYhRe1lWsoqHF/2L73ra8NW3c80jfs59/984Gtqo9/xsnCPTKXF8hqaVUd/7d+oFrkSzYC7ywZxSrj//KwoLvEy4pQ+XXd8Du10nI28zb696hZ8yZ+G2uxnd/izG976JxvHNqj1jLZcHjKoJhQBkpKBSKKVeAyZanUNEv7xSjVh7CIceIKS5sOtgK3+dCIVh0kcxzN9uZ/wAP389xYdNh3AYdhXopCSGT+gFPadY44p3YmmdFKZ/qxATBvhlYuNRFHq93DprFst278YRCjHY62X43kwaqZVkavsJuhx8PKkPGd0i1/G3ykzgyTeG0fTr06DlbrRPr0Jrlg3ouELdSfY+gTPcptofx46t+cybsZPvPk8n76CHF94bR6jxXuZnzmJ+5myySvbhsrm4stckzu9yJXHO+GrPWAcUAiMMw1hjdZCKklJQCZRSTuAHoHpnDIka56YPdaatKiEpwY5HS6JxPZPbhvno2izEeVPiCP5inqHdBg1iTbxBKPJotGoQ5q6RPkZ1DHI8l64XlGnc+YWbxTsjA4QbHyg+VEhExOa8PG797jty9u3j74sXM3bXNtKbeNiY7CcQ9JFaaGfhad1Z3nccHTa05poP+mE/WB8Io437kdgnZhIX14+Y4DAcZotqz5+xOY83nl/Bvt1F7N4Z2RGxY58EEoZsZVvCdAo8+dh1O/1SBjO01akMajWS+jGy03sVKSIyQrDc6iDHQkpBJVFKNQZWAFW79Jio0d79Yg4z5q+hmEaMGHcZC3fFsX7/b9/+P36mlx15OvmlGk57ZI2DT1ZF1s5v1zDMKR2DBMMwabCPhGOYq7Y9V+eMyXFMGBgZiairtL170fftI9S/P2ga2aWlPDhvHj9t20ajvDz+u2gRSZeNY2b6J3i8RbRsPZLOja8ke5uPkmm9cBXFEEOIWEI4xm3EuMxOvd4dq33i4C9l7S3i6vM/oqTMgyM2RLN+Htz9N5PhW4Hb7mZQyxEMTT2Vk1qcTJyzgts2i+NVCow2DGOR1UGOlZSCSqSU6gMsAAufGURUe+/+wXiK82jSri/jbn4LTbczY7Od2z+PXAP+yDgvF//OfILsYo23ljiZm2FnZ17kLf6xTky843M387bZ+eGW0jq9F4P77rtxfPMN4Q4d2NS0KZe2bInXNLll3RrOaeOg4OJU0nd/SH0tjYbTHsZc0gVf0IlyaGw4fR3dzllK55bdSWh0Errd8ecHrEKBUIC3pn3Ja/dl4AsEqHf197iblGK4EkmKbcTo9mdxeofziHdVz4JIAg8w1jCMeVYHOR5SCiqZUuoy4H2rc4jo4CnOY/6HD5Kzax2aplGmcgG4+KFZ1Es+PLx83pRYnDb46OqyP73PUBiyijTu/CKGdfsOjzL8bYyXM7r+/r4K6dk6Z78ex6QhPm4f4T+xB1aD6du3EzduHADzhwzhxjZtcGgaL3deSfwFawk7wbsviO3DCdheuAcNG1kXr+eJK5eRlOrmZdvF1IuC3r9P7eLFJU+yZN5eDn7Un5j6YW5+uiPnDxlHnDOhyjZNEn/IB5xhGMZsq4McL7n6oJIZhvGBUqoHcI/VWYT1XLEGWduWk9i4DTmZaw99/ZeFIEtpbMqycXcFh/NtOqQkmjw6zsvZrx/e0vax6W4+WOFk6vWlRyyv/LMXf3IR7zIZP6AOF4L0dOLOOw8zLo6yt97i71u3Ehfw89Q5W4k10tEfeZ3SGU3RtqdgdycT21dn3VNrua3zNwB8VPzXqCgEq/cv5cHZt1KypjmlU0eS1tFg8ruX0iC5crdRFsckAJxXkwsByFr9VeV+YLrVIYT1dJudq55axtl3fXzoa617jTniNj9sjXTzkR2Cx3TfHRuH2fJg8RFf23FQ5+kfXBwoOvJd4uSFTmZvsTOgdahO79AYd/bZEArhefZZwt26sa+4iIENd5C4KgvH7Z/i/+9oyvaH0IZmkfKFn8WfLOKuzp8CMLPk5qgoBFM3f8xd31+DZ1Y/gt+MZtDADrz1yZVSCKwVAi4yDOM7q4OcKBkpqAKGYYSUUpcCa4BWVucR1tI0jV+epksbfNER3/9xq53UpDBtksPHdf+bHihm9V4byfFh/jPXxTtLnbyz1Mklffyc2ilIvMvkuR9dAIw/qe6OElBUhJmQQKhfP0LDhrFuxydM+GgAg3a0AT2OoOZmT+pk1GlfMfbu95ns/pFXXPMZEGzNc97zqG9a/6L74do3eHXZv+isncKmZd3o0acp/5oyBqdLnsotFAYuNwzjK6uDVAb5l1RFDMMoVEqtQkqBAAoPHF7ZtLQw+9DnB4o0Fu2wc2X/43+x1nXoU77L4rPneRnUOsTfvnHz0UonH608cpOdurwbo+vVV9FKSigaezdZtxfj/fp8+oftJIwpJPHCMubOn0he/joGnvMSV8W/x1rbPk4PdOFp79nYsX7Fp7AZ5tMNb9Gn2UBc35xJfEI2j78yWgqBtUxgvGEYH//pLWsI+ddUtTxWBxDRYfWM1wDoMOA82vc7+9DX/29qZDg6u7jyzuRd0CvAOd0DbD+oU+zTyC7WuOuLyBLKdZW+Zg3O//2PwnaT2PvXHuSSx9JO6Qx8oB7thqQw+43bOJi/jpZ3PsXdXbeyX1c85zmfscHOVkcH4GBpDk/M+z9yS3NJ2zaJBXP2cP2d/Uisb/3pjDruesMw3rU6RGWSUlC1/nwquagTtq+MTFQ76Zx70MpXHirxwYbyNQqaG3986sA0Ye0+nU9XO7lnlBfjT+YF2G2ROQcQWUURIDNPxxeEOvfG0u8n5m9/wzRh95b78BLktklvcvk5q+nT7jVWbvyG+YVL2PiPS9jQch12bEwpu4wBoVSrkwOwR2Vy53dXU6CK6bL+Thb8UMbYcztw8YTuVker6242DGOK1SEqW117eqhuUgoEPk/Roc/dcYkAZOZH3r2X+DQ+HF9GrxZHH9Y3TZi63s6rC1xk5unEOk3O6GpjYOuKnwZIijN5ZJyXh751M2WRk5uG1qERA9MkoXvkxbN04v2EJ9uY3m0JcWnbWNm9A315itzOWWhdB5MUF8NffUM5I9CVxmZ0LO6z9eAm7vr2WkpWtSRm2eVsLjC59JruTLy7v1xyaK07DcN42eoQVUFKQdWSUlCHBf1ecnat47sXxx/xdU8Arn4vFm9A48mzvPRqEcL0g2+dDd8aG2YAYkcG2ZkI909zszFLp3PTMI+e4WVs5wDxrmPPcnHvAG8tdTI73c4NQ/zHtUxyTWSbP//Q54UtJqB8XhZ0XkPeKAcluknnZfuJ2byVU0fdxanmYBKxfjLhz9ZmreD/Zk7CM7cnwYV9SOvVkJteHUCXno2tjlbX3W8YxnNWh6gqUgqqlswpqMNmvDaR/RlLD/13vb9MYeqWbL5emUqW0nn7yjJOSo28498zPJ5Q/uF3ftttcH2hkxinj9svuoMxPTZSP3wucf7xHO+v7Q1DfNzzVQxfrHVwQa+q2YUx2jimTQNATVvM7ss00hvvZ8PV60lISuS+6TZyPp7J4IsforNxqsVJj7R49zwenH0LcXu7Ubq4H2PP6cj9Tw076ujAz/tlHG1tClHpHjEM40mrQ1SlOvJ+wTIyUlCHhc3Is3UQnY2tR3P/4jwmfpjEij069432HioEAM60w58HQ7DkYxdt7CbvX7eS0/pOxXRsJ9/1DLsSemJyfKuQntk1SM+UEM/OcVHkPbHHVhPo69fj+PZbfFeMZ/u4FMqyw/xv/FRC3QO8tK43OZ+8Trt+Z5I2+BKrox7iD/n5YO0UHph1E8bufhR9PJy2HZK5/cFBRy0EX6+z0/WJBCZ8ED0jHLXYU8AjVoeoalIKqpaUgjqsYctu5Z9pfOY/l8KsswiaPkoT3uLHg0de0tx4socmb5TRZEkx03qY9MnQePJDjcZbe5FavJHU4vUs2TyUUf+3nPfnfROZbHAMft42uZkRJr9U49X5x3EOwkKl+NmiZzPLvoVOCY8e+njONedQScrVStim57JTzyO0ZSOxEydiulzsL7iXIp+fT0cvYduNmzjVG2DLK/eR2LgNQy5+JGrOzf+4/Tuu/GQsry17htT80eR9fBJpXRvxwrtnEJdw+NLSUj+s2G1j/Hsx3Pt1ZMbpskwZJqhizwP3GYZR6/cFkNMHVUtOH9RhA869l40/vUdWqDOO0lNJa+Fnl/Ylus3D0v2FFPl81HNFXpw1O9gHeLl/WpBpPRLoPsFDyvNOcm6MoenHYRwtTYoyu6KFw2z3ryOwvgBH96sqnGX1Xhsrd9ug/Hr7/y1xcl6PAO0bHd+CSdUpRytmaPzzR/3eZOcCfrRvpYEZy3LbbsLhIFpREY7mPtpM6ULzegO4doQDvxnikwdncUHcVoZ+soFiZzOGXvY4Dld0vMN+d/Vr/HfF87RL6sSzQ9/k3f/Lp1mLUp5583Ri4yIbLs3LsPHpmsiGWMFfzTM9t0fdOB1kkVeJTCys9YUApBRUNRkpqON6j7mJ8DfPc0vcffRv4OA5byM2+iMvzPle76FSAPDO6lf5bN1ARuwfRtpaB0lPeMm6IpbcO2Jo9uwWvpk3EMOVxxk93+VAiwakeK9Aq+BgX8+UEJoG3ZuH2J2vU1CmcebkuN8skxyN/umadejzJ7xnkhZqwieO1WjAQb2UEnyUaD4uPZDKkPtex+/Q2TR+LBndG5P0Yge8vjCPT/qCv7XKYlDREjJ2t+eiB6fjjImOKwzeX/tf/rvieUa3O5O7Bz3OWy+sZs3yLMbf2o+dRS627dBZvNPOV2sdJMebXNrHz+A2QWKd4LSZpDUJ173LTKvPD0QuPawThQCkFFQ1KQV1XINmHQBI2Pcpm/fBmYnNOOPC53lq6VK25ueTahiHbptXlkNC/KdoxcMpXavTwAfxZwYo/VZn60V3s3X4W1w77lEatDaxh1OJrK5asVJQWKZhmjCsXZCxnYOMfSWOnik1Y3XD23zD+c6xEYBuoWa0DzfiId/Y39wu9qrzsW2MrBZZcNe9/Hh9Icb3DchrVsTkG08ma/ftBBtoDBj7TFQUgmV7NvDu6vdYvncF3Z1Xoy8cxVkPfEXuviIc3bvz9MHBmFMi/3/tNvjLSX7+eopPJhRWn0IiqxVG/3BaJZJSULWkFNRxM1+/8dDnrXuNofvI8SSkdObppUtJz89ndOvWh74/vvdNvLP0c+anFPPAvnoc/LubcIEGuQUUaOPRQ046tElE0xwY/uvQKvjru/mAziPfu4l3mVzW109iDDVihOBnrcwG/FhyK+fE/ZfrYz76f/bOMz6KqovDz5RtaZseUiBAQu+E3hGkNwFRULAgNkTFDlZU7IqNV7GCIoIoqPTee++9B0ghZbPZPuX9sJgYCQoqBnSfD/zYzcyds5Ps3P899xR+dtxN6G8aEwnZ2Uh7/MLhzH0byOkSQoxLZmGD3bR4IpRDs97jbKZE42dDMUStAE+Lf/QzqBqsPy6xL0Oi0JvBpE2ZnMpNQRDGEJyTj232LFaxDRLLE9ajLc2vSyY1xkdqtEZqjEZypIYhIAb+ae6zWq3pZW3EP01AFFxZAjEF/2E8rgKMllC8LjvdHviSxGrNi34WHRTEgdxcf8Cg04mQl0dMbj6Sqw9u00biO2aTOb47ePztlN1qKrrBRHXjgxjUDWRZHiLBMQOjlvq7NmxPF7llUjAGSee5Lp5rskPicukQb5qXUCC4KBBcKIJGUQKGriNt3Ih57Fg03cKJ1NnYP0rC3ESl0hgXP6/eyMebsxh+bDHtOz1MuJ6F3TiZcO+9SHrEFbW7wA2L9xuYvUdmw3EZRdNx+hy4fUGYjBZ61t1Hh9R6uDKD+GyxTvWmFXnhg85UjNQQhP9AesjVzRSr1Tq1rI0oCwKi4MoS8BT8B1EVL16XnfUzXsPrstN5+Hc8PHQ7WWe28uCYdtzQI4HsvDyWZGYS/OqriGfOADA1oTfnGj5PbMjTWKatA09HABRB5rkWCTQs3EPV2Gocl/YCcCa4LxXtOy9qh0+FZ+dYiArW+fnuQsKvjpi6y2KhvI9HLDNR8XtwOyrVS3QrlGfPxvL44wCcarsA+8rqhN/jJfwBD2szMziTm4XqcxPddihp3e7Hrs7EbpiOKmReMVGgqPDVRiMfrTZidwvEWzWGNPFywj6V7Vlv0K5Sbx5vcw8RlorkZDsZ/cYSrCESTz/dgEpR/ylP9dVKOjC8rI0oKwKi4MoSEAX/ERSvm9nvDcZhy8RpyyrxMzm4Itl7ZyEAHzz0IzWfX4Qw9HoQBBy1amEcOBA9Kor7d0ShqRmExK4hfc4PJBlMnGpnJccu0OmQg8kNJH5evpS6PcGnGBOvtYAAACAASURBVJCl3y9X3Gl8MGdtIq/3dl1TgkBHp0boyyXe22h/HAsGDL/pVqg2aIAeF4eekUv+vEoIYTrhD3rYePYUt/8wlShHJs/UTKZPH/82jktejqRHYfgDD8ufpcANj86wsOqITOsUhQfaeqiboCEIsPJYCLsX59EupSYndrl4/fONbFyTjijAi+93pGqt6CtiU4DL5nar1Zpf1kaUFQFRcGUJbB/8R3AX5mIwBRFdviaxrQdhtISwdvrLxFSoQ0JSKFtiv8apyfSz9eVGZTDnzvZDjPYxqks4j7UzEhWsE3z4LRx5TWhfZRjxSbWRRJEKG+zY64fR8UgQkxsU8uTcNihrtgHwcHs7w5tDaWn2O06LnLX5g9SOnJMA5R+8G3+NaYatRf+vpyZxm7cJYZTeDVBPSiJ/4jzONTsKPgfhbQ6CmMo3e3YjeuwMzlxIu6H+mhA6Kh5pG2Y17ZLjMS6H9HyBe6cGcTxX5MXubgY0LJkmWKdcQ2RR5ptJGzj5QxaR0RYG3Fabrv2qkVQxnOov+YMf3+vvonONa+f39S/jfavVuqSsjShLAqLgyhLwFFxD6KiowjkkPRaByytoExKZQPcHJ5V4b+/KbwiJSgRBwL5tG4LNRpe7vmHXFhVj+hl8UQl8uymMzcdE3uy7BZPxMAAdKz+AJPpXxFI4xHXxcXixAWQDKD4ETUcQgvlwSSwfLoFXerqJt2qk54tkFAg0SFJ5cX7xJHrrNdYy+TPjOgCmOu6gvpb0+wc7nXj6z8Tt60tYzCbCx1XnnU0bWXgynRvqphGUPp0fXu3FgOcWYIkCRcjCqjQF4FSewNiFZtYclfH9ag4e1MjLs108pYqti7E9XeT+74LwqfDpQOcFDasOntvLG4texj6rCYf3JtGibSJj3u2I2WJg+WGZYR8Vp6Yu2i8HREHZsB94qqyNKGsCouDKEhAF1xAOeQ7ZlicAqGTf/5fHM5j9QYYAWCzoFgt3PVCb/917BGdeDl6SSEhYy8nMVvT+LBOD3AZRdNEwKbJoDHnWLPSCilhdjbA6c8mNCQYNRI8A54MGR88qfRUNfi+C+xqaX84INtLFPERE6moJf3i8ccoUzAWTsNfugS2zO8ufPsLnabsYUKs6TzdtwpRFz6N43Xg8p7HoVdh3sg479yazZX8Qu86UHs4/ZbORpzt7kC5RFKw8LDF8ehDxYRof3+SicrQ/LiDfncfKYwtZcnAe6xdl41rSApM3gro9G1HvpsaMmmdhwb7iR3BksI7VrLPisEyWXSA29D+TGn81oAJDrFbrf967GxAFV5ZACPE1hEhx7vo58zPIWjI6TjShEF1wouFEFxzIWhJGrTa/dck75RVogg1ZK4+oh2FtuRrVLeDjLAbiAdC6dCYuZhKRSfls9kZw6FQ1IiIXUpDZGknw0DBJJsQEmXaB9ccltsyQ8GTXoK9mIM2wg8hWWym/wsW4wifRRRl+VfyoSUX1gnK3ug6dPgxhwkAnbVOv3roE28V0tkvpZIh+EaWhcUrIJ1mPLP0Enw/Lgw8iL1uG0qYlGS96EFp5SVwQz1P12nFrq8rsWzMVxesmufNYPj2ax9wfz3E29yskQaRBPIxo6yE2VGfpQZlVR2S2PWmnziuhyBJIl1gA/kCmyMgZFlKiVb4Y5MSpHGfSlk38b2V5zmZIGPcImA+lIbkkfJFJeDtdz7KoWJYthhCTToPyKoMbe2mQpGKUYckBmefmmOn+cTCbHi/8m+5ugEvgJavVuqmsjbgaEPTLrKEe4PKw2WxOitZ0Aa4GNFzoeJEoLhykks+Z4H4o4um/5Rq6pmI7lYchVCMksjzhnvuxeu/kSIZE/6ZfENWyISert8Fk8OCL+ois9E7o3mQizBbiQkXynf5lqoybIYvO0utAEKErU5A8Kqk3R+ApV4HPrn+BCv3a8vEaIzvS/ziJvWqcxo93Oa66tslfGNfxhmlx0eu6aiK3ehvTS6lT6vGGr77CNH48gs2GHh3NsS++YHPf8kQWhEC0StoanQNbZ/LB9C3skHpxWqyLxRpGvSqL6NhgNv0rPEmkOabEmLoOYxeYmLzJyB3NvDx5vafUa3sVMMqw7KDMjB0GFu2X8WpeqpbbwP6MCBRfHtqpIIynMgg5fgBBEVArpKDVqEvtJkmMaO+lUpSGLEJEkI7FX8GYTSckBn8VxNNd3Iw9v/Wz8IFCKkQEns//AJuAllarNVArmoAouOLYbLZzQFRZ2xGgmGOh1QEI9nVBJAyfeASPuAf9V7nhAjJh3lsR9BBEPQSRIAQ9GJEgRC0CSY9CwAC/ij3QceOSV+ORduPZ0YN5Hw7FFKnS5Y0uOORFGLUq5GbcxNAeESiZefR69VZa1pD5ZH0Bi/YHAyAKIkZJJK2iDa+8gZOeI0x4bQiW8huIW9CbSsPCEZ6dh1apEhiNfLDCyPiVJkLNOna3wK2NvTQor+JT4anzzXJqxavsOesXDUOaehnd6cIJT9Mgq1AgLlS/rL30v8p0wzaeNc8u8d5O+yiMF3FiGr7/HvMzz6DWqYNnxAgKnO3ZNqsAw6wwVFGj/I9r+OjLj5knjyJHrEiMdpi66myGvfI9UWFn0VSVcmfXElEupWhMXYd3lpn4dI2RG+r5eLmHu1RPwbhlJiasLm5MpOgKDm8hPtWLIAiEuPIwLt2AKS+biFCZjl2Tuf2BNMLLWfGqAlHBpT9rHV54/EcLSw+U/Mxda/oY1y/gbLzCuIH6Vqv1QFkbcrUQ2D648jgJiIKrCoOWjE88gUfajiYUYtBSCPHdQJhvILJWjnPmF3AY5uKR9hLjegNZL3fpY/uSwQdU87/25EqYTjxKcEIPbMZPCEkYy+uTIni6Sy82zZvFt3v6IwpmxOC1NEy0cfDEDTh9XtYfDmXht50wiAKiy4lxyA9YF5swtBuNt5p/cI8C5SP8+9fj+rpolVK8PZBp98/sw9t4GNHWy4pDEvdMDeKrDUb61/dRNVbDp8JPOw18t83AgSwJjw/WPFJ40cmrNHQdNP3S3e2/xoH3AkEAXFQQyEuWYH7mGbSUFPKeG8+yHhYqiSai5RiWph7iw0b5FH7TAIwTiJKyeLXVLix7XsdUbRNWcyGKFwpPyax9tw9tBr1Mlca90XV4ZaGJrzcauTnNy3NdPKV6Uhbsk4sEgYaG0+cgyDKF8iEHaOhsgGenzNGdZzFbZB75oB3tOlfCZP715yj9nm45KXHLpNLzReftNQREwZXniYAgKElAFFx5/vOBK1cbSY4Fv/vzGPfbWNRW5JpeIj24C+VcX2JWG1z2deIqNyTz6FYc+RkkxHYmWOmMKuSx1XUSe2IGeQvT6TfhVYKivubdNf0wW+OZf5+bPp8Gk3JWR1EkIkJ0tNQtSAOmUK6PFc/SuwB/YaJ6rxbHQNw1JYiBjbzc0shHaozGrF1+v3SvOn6PaNsqKh2qKSw5INNrQjANK6hsPVlyy6FrTR8G6fI8hzVe9tuw/KFCyoVd3rlBGBjuacN408qi9+Y57i/9YLcby/DhqIlJnKgyj1PtRaI1nQI86EFmZiYmky3EE2aUuaneTob2f50NY46Rk3OO6p39YslgCiI5eQg7lRlsX/gJKWm9qTm2+B7e1tSHVwXzb0TBF+uMvLHYhA54FBdVk7vhUQ9Tad8QMlYks8+RTUL5UIY+mEbXvtWIKRd8yffgjSUlW1jHW7WiVNIAV5zFwPiyNuJqIyAKrjyBDIRrDAGBUF9fXPIyHPIivOLuPyUKara6mcyjWzGH+h1Fbh+MXRDP9G0VqNEth+wJE1n5oEz2yDRAp2vaTMJSpvHZ3XehNvdP/ksiFVI/GEkVl4rxjIRH10EQOG270Mf/7WYj647JzLvPwcydBuonqSRHFk/UL3V3s+RACMAFggAgLkwn7OKJDL/LvdMs7M+QSs3PvxgCAmvko0Wvh3lbUkkr6VTTdZ1zWU4SVs8H4Gz8B+TNsqCo/hW0bjLzQRMPmxNUojnEI7YbSfDYcOt25KhwGjV/lAqNCykwf+n/jL7n6TmyP26Pl3unlQz16fo//2S+eEQhSeE6WXaBD1ea+G6rAa/mxel1UDGqA7E51Yg9dhtblubStlMF+t5ak7qN4hHFS993yS4UePxHc4lYkE2P2wk1+/skjF9ppEO1ayht5NrDBtzxX2t2dCkERMGVJyAKrlGsnnswK80I9Q34U+c77TkABIXFkO+COyYHsS9D4u6WXh5sa6TDpwI2t4o6oQb3jEtgSJWaOLTZJKW8gFMYgIMQ3uyWiz79J6R8EaETWN8IomVVnZgQnftbe5i4wYjTWzwZHc8RmbnDwJFskRe6lXQ9R/5mW+C9/i46VVco9IDLd/kpcPm/8oHtz/BPbt9tM1ySKNDRed48l+1Scb+ZBz1tSxyzdf0ZRt4+B8Ht5ibfRh6tFEPm+ibkFfjHX5qSzu7201kZ6yZU8NFQP0ebpk8SmuLEY3mZtHtTECM/oOD8eOWcExEwkWNpxMsrzWw7JXF7My/1ElVmbDew6oj/cZjnFHB4BO74xkCm3Uto8GyiDKPR5reAI/05JZrJMtkYcl8D7nwwDeEygjBUDb7dYuC95Sbs7uLz1j1aSOh5QSaJ8GC7a6u2xDXI8P9is6NLISAKrjwBUXCNYtJqY9Jq/+nzj26ZS0hkIqYgK6/PN3EwS+Kjm1y0r+pfAS7YdgcNq72P4ZzAidckhIY96P7xbRR6dObIHgSzC00AQcf/j9lCvldmzu7fv+4vdQu61vz9yblTdQVBgFAzhJovP+D45V8VSHqvv4u3l5p4qfvv74ErqCySDzDetJLDYnaJn/22hLE1wozgdkNBAdOozvFTQ+lf4CRbKODZhGXc3/wxvJY0jJZqCAYz26Qk8hotJChuAzIWiDxVYrzsc415f7mZ2bsNhAfpvN7HRe86/t9F15oKO9JF+nwmc/OkDHIKI1A0B5UTb6J6iJmCKXdScFrmpnvqUietHLXrxxIWfnlule3pIi/ON7P3rETzSgrPd3UzfpWJWbsMKIH16j/JdGBKWRtxtRIQBVeeQEzBf5DCvLNkn9xFk96PczBLYtpWI4PSvEWCACDLc4rc4XbCPwzj1HEb3Zt8haP/cASTiR8Tg2h4HCSXwg0t3+WpE58j37mbc45C1h6VeXFe6RPSKz3dRaLA+ptE2NVHSk66q49KtE75c7ULfCrsPF/8Z9doOwYJOlZTLhpwqKLxrWELE40bSBfzqKBFEqEHkSf4NXMLpfJvTlCp9dmrrDfNYVjUjVR09KetvT7HjRm8a5nKTWlTCIlO5Fj5GrSqdIoutQ8zZ1dVIiO3lBgmzHsrJw4/w/fbzfy8y4AowD2tvAxt7imxVbLs6DxeXTEKp28hdk8oRqeD6rbNhG0ewslMB4KuccuznThtrcy4VUZanlb4+GbXJbUz9ijw6kIT07YaiQ7Reaevi641/YLszT5u3uwTCCb8B8nA3xI5kHZ3EQKi4MoT8BT8B0nfvwaApBqteWShmVCTzgNtS6YCjlszhvgIjcKHmuGdoGKUJNpmLWZ/WHdaHwBVhOlZ75AkvYntDolylr1UNNUkMsjHp2uNdKiq8FgHD2aDvzOfVwWzXHqFwxWHJB74vmSU+1tLzLSo5PhTmQM/bDdwMlfko5uKJ8aLjXNKyONZ8xzWy8eorybxpKsj9dUkegd/Qg21HM94OlNB8xcpUrwu8s4eJmvqR9Sfu5gz5RbySEYdfGaVc+Xy+KnWHAqXODnmuo6Wj5wld4qZ5OBEmlVeQbPKJWtMuE78SNfxjYte963v48G2ngsCIpcemcuLyx6jvLUiXWv/xLqpzQjeuRGvqHEsLB7iyqPXqMNbh+KKzllzVObJn8y80/f3J/RCDzww3cL6YzK3NfUyoq2HENPvnhLgynKn1WrNKWsjrmYCouDK4yhrAwL886TvW02QNYa5p2ux4bjEM13chP9q5X4s7zBbzqzn4caP8NEuM2kvxrHvhdPUWBhHP4N/r3nfcB8Prl6GNiaMgws18k3vE+f6mDAz/HS3o8RK95cqfGPmFc84c/bItKqssOyQzDOzLVSNVflsoIsX5plZuE/mQKbIN5sMDGl6eTVb3D4Yv8pEg/Iq7ar8fjDcEvkAj5pnIiEy1t2Tfr76AIw0/0CB4OZL9y1U0/yTbe7pA/zwUjdQVQS7nXUNDbTJDcEggMEosaTNOpYnnsaYncDKjWaUhXm4zvXh52V1Gd3wKc5G1MWnGBC9dejxws/8uobE57c4aVm5pFdE13Wm7PyMTzeNo3ZcA0alfUD/mzcgH91A5TZV2Z3YCsKsPNfVTVSwTnSwk6gQjS7j/cGac/cYflcU5Lvgnm+D2H1W4vXeLnrXDQQOljETrFbrvLI24monIAquPJOAO8vaiAD/LIW5Z1hpGM6KBWbaVVUYlFZy4j2S4++t0KbidazJPMxJRyG392tP1Y9SEY0+YseoNL3Xi/p8GoZphwk9mkp+/Bp0dASEC7IEdB3eWmpi6pbi4jqPzihWIXUSVD4b5MRqgTf7uEiJNvLRKhOvLDTTpaZyWUGGKw/LZNsFXu158aZBGho/GnbykmkBqVoM77v6k6D7K0huF9OZZ9jLcE+bIkGAx4PvrbEIeXn+15KEHhqK745FGO6/G5/HSffprTh0+3GW1O6HtG8G+19tyE0VLNQ/JrKnwEDw9ZvptzyGHF8BkIcsGjDKJo4+f2GxJofXzmsrn2bFsYVcV7krtyQ/wZ03LiY/3UnNQR3ZE1aHztUV3rrBfklbBL9l3TGJ5+aYOVsg8l5/Fx0DmQRlzRHgsbI24logIAquMFardaXNZpsO3FjWtgT45zhWbgTzdjSif9o5Xu9vuqAgTmbhGQAiLdHUiMpjTXo6kY0qIwCTzXOp7QphGI3RkpLQZZ2CqP1kH7Aj5a+ifM02F1xvwhojn681Ikv+rQSA66sr1ElQqR6n0rSiiun8t90kw0PtvHgUgS/WGWnzbkhRXEBpuHxgECHXKXDGJvDg936x8e5yEy8vEMh3CWg6JIVrlA/XqVreyYLmX3PQfJJ67jiaPvE+cwreBGDoe3t417KcSD2YO73N/RdQVSwjR5K6dCP3i0lkdWzAdG0dcSmNqNFuAPtsWQAEm2Wem3ET/fUwwn33YjrigeMSmklH+cpA3pfxqAPcGMJMCIKdqsntEbDjVTZilP0eFF3XWXxkNh9teINcVw6DUx7Dtrwaw76fS7bbiN6jH3tCE6iXqPJ675IxA4UeGL+y2BNzX+vSSyFPXG/gtUVmKkRqTLzVSaMKV2/Pif8IGv5mR4FmEpdAQBT8MzwB9AICu4nXIKPMPzPTsIPbvc3o66tHVS32d4/feELis+OdqaTNp7uyEIP08gXHbDmznsoRVbCaw6kRFYWq61Te6UMMD8OoSUyesJ15Mw7yU9xiCtp4KUywkzkjiNCErAvGmrzJwLvL/H9avwiCL2+9sH3vb3mio4eNJyR2n5Go80ooW560E3ze0eBT/XED7y03kecs3R3g8gnUiFOJCNLRgfQ8kU2ZPr45IKEtvp2Pnt+C9txTFBYUT54rfbtZLx9jlLsTwRiR1q0j6I47ACiUgug7Yjc1onOIXXEdNVsMYspTa6lPE2SDgGRWsDfYycHcKLK06rjyszl8chV7OjenYlAlQgo82IMFVj6okhIdzOrjYxi9aDh7s3dSP74xh3P28+7al9iZsYUUY31qH3uEae/lousHiWlSi4xyzSHYvzXwyc1OzOf7Eug6zNsr8+oiM9l2gX71fTzWwUNE0IXelSy7wJQtRqrGaXx3h6NojABlyutWq3VtWRtxrRAQBf8AVqv1uM1mexN4pqxtCXD57BczAZhoXM9k4ybedfWjlVIZMxc+8U/kCoyYbqFClM4jqes5tnYG7hsewxwcXuI4URDJdeVg99ioHh7FDSsaoW8IJShF4PGJadzY8QA5GXZaHqvDK0u3EGKXKNhRgao33FBinJk7DLw830xEkF40eQ9r6f1DQfAL0+90FlUl7PK/EJ7t7CbPJfDJGiOn80uPHGycrPJAGw9NK/qvkY+TOYa97DXsIldKx/DikwQVhnP8/ieI1P2p4NHla9H98Wn0C/mceM3KoO1mQm5tgOAqTs6xqG5qSZlM3R1PBcdiWt0QTSomHKKAvdYh9rw5nuXb+7LhhIAob8GQVx/jV9mo5zLJapxKVpzE0VF2TLL/PtQtl4aAwKrji1h+dD4/7vsWixJFoxNPsHseHPfm0Ll3FW4b3pDx22PYtc3/+2xTRSHEBKfyBKZuNbLjtMTmExLVy6l80N9N/aQL8wfzXTBxvZGvNxlRNXinrzsgCK4OzgEvlLUR1xKBhkj/EDabLRg4ACSWtS0BLo9N0gnGG1fRz1efxy0zAaijJjDJOZggjCWOvWuKhR2nJb4f6sBSsI2f3r6Z9re9RWqjHiWOO5Szj2Ez+zGw7lBuXD+Kw2+pZLXPptvHkYgW8HoUrq/5CoosoPlg2MDJ9HlgNpHxVYrGWLhP5uEZFkSBIvf9hJtdVIjQLiuj4ECmSO9PSpbmTYrQSM8rHuTO5l6qxaq0r6oUxTNsF9OZYFrDKvkICiqpWgy9fXVpk9WIfv+Lwp6XRWNlCp0V/9bBlhubsrx7Kh8faEHP64Zf1J4FwUMwnX4On2BGABbVcvF160JUGTQlkny3F6vJhCgIGD55h2CTwhfT+lK3UbkLCgn1+KoZBZ58BFWm2qlbObUgFpdD5bpuKdwxIo3yFf1xDv9bZeT95cWOvJaVFR5u72HgxGASrBqDm3gZ2NCH/JstlhWHJCasNbE/Q8TlE+hSw8eD7fydEANcFey0Wq31ytqIa4mAp+Afwmq1Omw221PA12VtS4DLo7GazERXMgBpheVZLx/jGfMcBgVNorWaQi21HGG6hY8O57D+aFtadtlDaFQ04dbaiJJM7un98BtRUCWqBs3Lt2XBoR/puf1p8pPy+fqGVfSw9Cbv7GG2zH+XO99az4THe6M4BZA6lRAEq45IRXv7GlApWmPyEOdlNTP6hWpxGtdXV1i0v/hx8GtB8H5/F51qFAfK2XDxtnkp3xm2EqWHMNjbmN6+ulTTYhEQIBx+HOZgzNxw9p66k3jravbkbmd+81D67xTp2dUvCNR69fAOGoTlySeLxtZ1gfBTD6ELNiyanVsHgD1OA0FA0E14VP9ka5BECr0+jJIbQTDw4ODZDH+qGf2G1GTVicWsPbGUU7bj2Nz5+PZVIHbrAA5k6jRpHc+9jzUlpVpkiXuQeL7abYvKCmuPyqw5KvNabzdbn7BjLOUpueWkxPsrTGw4LhETqtOzjsItjbxUjQ2IgauM3LI24FojIAr+WaYAw4FmZW1IgD9Hgm6lr68+Rl3mM+M6vjSuR0FFc5nImfkgUsJR1rWZTAtJ4x1XX1xVU7Blnyh1rB7VB7B/2iFOrcomr14O+3NzWDblOQ6vn46pZjCfju6NXChgCI6k9vW3FZ23L0Nk2JTimgMDG3kZ2d7zp/sWFLgpIQh+ISZUZ869hUXjKqjMlffymnkRNsHF7d5mjPC0Jfg33hKA1BiNrjU0jIvCqDvkK6YondF1nbvvmwyA57HH8N51F6gqznLl2K/HsXB1Ktd/oKJLTnLjV5AQ+zL2qAXUTVpPjOhg2clBuNV8IhJn47BXIzh6G9WHxZP1dR4exc3xyAUM+/FZDufsJ9RoxXSyOgWzuqFmRFGlTijPvNyCJq3Ll3oPEs7XsulU3S8KAFqPC+HDASUzBwo9/hbK32wyYjH6z8lxCDzR0V0UjxHgqiKvrA241giIgn8Qq9Wq2Wy2h4ANZW1LgL9GD6U2PZTaeFDYrWXxwk9hrHGZCb1tKoLkXy0+YpmBZ0QVUg/ZKC9mUl2LKzFGy+T2yKFxGL1mJtV5ldzCKqzcs4TrWw8icchCVOdOJo1tgG4wsHXDWZq0Ls/2dJGbv/S7+muUU/noJtdldyf8Lc/PMSOJkBypcfSc30NwS2Mvozt5kER/8aH3TMtZIR/GLripqybyuXsQNbTfbykdIuv02SLgOO3k6LcVqLz7LE6PncJlm9Dj4/0HSRLTxFZU6GOhsa7g0fM5XfNj0mt/zEPhS0GG21u9ScfVuewb2omBs79GEMBgymbcda3JzFzDuEnZSNWPMjtzLVZjFP3ML7Bvlpl9O7OpnhjC7U815PpeVZDli++p1IxXMRt0Zu4sDgToVddHjbji2AynFxq94Y+/GNzEy+aTEvsyJNLKq5j+RNpigH+EgKfgMgmIgn8Yq9W60WazfQUMKWtbAvx1TMj8OLcyh3YbePU6D/dE3U6+3cUcwx7OiDYcB7cwpZyD3paPaKdVZ7C3MS3Vyn43O1Ajoh45FjPlQgvYqLrIq9KKhgN6c1b8msFnT3LvdQKdTvbgy492sKFcG9YdM6Ch06+ewis93RetE3CpHMgUmbfXwPA2Hka09XLHZAvrjsmM7uRBFHW+NG5gnGkZki7SXalFayWFjko1JP44aGHTGRlDqo+kPQKKaOHGL3eSPahPsSAA1q+QKT/IgixqyEoeZyt9z6maE6g6uDb2eeGgQO3EjZjXW2k4QEUQQNVUakQ4+GD1YAq9duIb9cWxI43Xqj3Duy+sZ8YZO3HxOo+92JouN1TBcAmFBoKN0K2WwoztflGw9pFCzhQIbDklseaoQFahwDeb/K6ArjV9PN7BQ91XQ6mXpDLpVucFKacBrhoCnoLLJCAKyoZRQD/g0huvB7gq2XFaZPo2A3e18HJPS39nu3As3OJrBEBhRA0S3rqJ9W1t7OwaxF2WQ1R0RXGv0IruvpqIQTKK10XDnQ7m1ncyT7PTJ+tLKiZqhK40sWPEvZwYfRSfKrDuqIwqaujXLaFb6xgENeUv2a7r8OoiE6FmnSFN/LZXjNLYn6kjijojzTOYb9hLO6UqL7i7Uk4Pu/TBFQXz9s24g714DfV5PX4SegAAIABJREFU5iUdb2wqolzsY3eeEtDuDMIogijksSt5FeP6jqBtxXasOjkEBCN9mv2MqIH5gMwJNY8Cdz5ezUtG3mK6pbaiX/XbeX3GAVy6jbefWYcsi7z4Xkdadkj+Xc9AabSoVCwKbpkUxLGckufXTVQZ2d5HrzolC1EFBMFVTUAUXCYBUVAGWK3WMzab7RVgbFnbEuCv8ckaEyEmnXtblSxk43L6+PitjWRnOMjPvhdpxUIMd/mwypGclr081Ww+z+1dSs1oM6O9N9N67ziad93E7qO7GTXAhnjmHjx5BpxPHEYQRUJMOsrW1ST3bMa+6xezzdeK1n9RFNw8MYgd6RJjuruLmiflOQXCLTrTDduYb9hLayWFj1wDijwbl4Jw9iyWRx6hdeRpnh7XgT7XNaLeos9x2XMQJZnTPwbjPSBS4BaQfGCRXfh8PsbdOABTiJH5J9LIybueu7p+wrDrvyDpzfYceqIP98zsg0/rS5AhmJk3TyHRGs/xw3mcOrYRgPxcN+993YPaDeL+wMILOZ0vMGlDsWA5liPyfDc3TZNVXD6QRagaqxV5Zo7n+v8TbAxkb13lBLYPLpOAxi073gGOl7URAf4c+7J2Mn2bhyUHZO5q4b2gyc3BPef4ccpejh/OQxdDOJDeBJcDsBfSIOog0VsLEGIdbD9YyHvPzEMoqE7P3Y1R9iaSfyiFPEc4TikIs+4jMkjHowh4Nm9md9O3EUS91BoJl4Ouw450v1v9xvrFK99zToFduT6eM8+hgVqej103X5YgkNatI6R9e5ZEZfLwW50YPeIhwnV/UKQkG1F9Xny6DY/i76FgkXzgLWBN5/5Ywky0qBWP2/UIjapu4r5O8zCe+4QvmrRiWNY4TJKJcHMkQYZgQs2RFBZ4uK3H9wAkJYcxdcnNlyQIvApoGmTaBT5fZ+SdpSa6fRzCrjPF2wz3t/YwMM1H5WiNWvEa1eK0Els1YSYQBLD+iZbT/ySqVixg/qMEPAWXScBTUEZYrVa3zWZ7FPihrG35t6NkCeSMMRPxsAdjlb+eMuavMXAbh04uoFY5D3c1L92tHmo1Mfr1dtRuEEd+rouxI78h49BqWtbeQGtR52hBInNcPdj8TjYrTXto9Fl1GhhOsDMki9QxNeiw9DnaHw7n7iOt8akQZtGx2W0AdPXV/EufYfGB4q/+L+7vk0IeC09IKKLCvd7GPOHpeEmxA7/G9PbbAEwfN4TeX1aj2u4kBARCevoQB5/gRB8dfVM5nIJGptVOfuM+6BXzmZJwAgQP2w7XxuPTOXw6mvs+ziPffQsCAu0qdcFg6cb+vce5v35D1sw5zmujVwJgNstMnN3/orEDug7nHALLD8nM2yuz8YRcVPnxFzpUU1jyq3vy1UYjN6X5iLtIT4jIYJ22VRSWH766H6Grj0jcMzXokipc/ksJeAouk4CnoGyZCSwvayP+7ZxqE4JziczpnpcWwqGjYjNMJNf0Fk55KV7xEDrFD9SK4akUODqhaqFER75+QUEbgHqN45m9YUjRyjU80sKbk+5iyJ2hiKJ/ovmpZnO8vcx4U0R2VN1LhreA272tcd1sZu9tExn/QRLdm07AYY2mavMUwoIkEt6NJsIbTHk94k/fD58KI6b79wsmDXaiozPJsIHrXFNQBBUpMo9DUjb5gusPRgLh+HHkuXORVq/G8N13SLt3k33jjVi/qEafcW38XgYd9tYbycwJ3SmI2cSCOod4tmMuOzr2peOLY9jSOBlkOwBm83J0DpJrT2X/ifn0qjqOL/rOIjRsIJP3HKNFYRybxxzltdErSSgfyoDb67Bg+x2lCgKPArd9HUSNl0NpPS6EZ2ebSc8XubWxt8Rx3w118EI3f7fD8hEa8+8vxKMKPPGjhSz7xVfZDZNUXF4B2x/fpjJB1+Hz9SaiQ/Rrov+Cpvk9G38zAU/BZXJ1y9x/OVarVbfZbA8DWwkItCtG4s8OTvcKRk744yeORgHnLM/gkBcCYDv/vqCbMehJ6ChIehTNKj/Oao+ds45pvLTMy9HcA7zc8QMSrcmlj6upLP/qCY7vWFT0Xj8xg+l1k/HViKRyvYrkGGKouVLm82+7cJ/VgCu/Gz6zRnb/3iQ+/zkVF4ewbvgZ9A+97LnnLLW0+FKv9Uf8cD6Ybkx3N3mV91AjeDoAkiQhhRYS7rWyxV5Ab+snvOPqS3O1UukD2WyEdOlS4i2ldm2W5N5Hl+mJyIoJRXNxuNIbpG+YCSIsviOO6VnN6Fn5JKMGTuTZlTezNWsjw64PpXEVExWVCeTlxbL1pIFn50QydVsrPjs4FU6qlFtn4Eh6JknJYbwwrgPtulS6oILhr5mxw8CG436xMLqTm0bJKjXObwM8ep2H+6ZZWH1E5qedBmqV8/9tjOvnomKUzkNtPby1xMSRcyKxoaVPqK7zuy4Or4DVcvVtI8zbK7PxfNvuP9Pp8Z9m/QmJUT9b+HSg8+8sAhXwFFwmAVFQxlit1h02m+0T4N6ytuVfy/kHonLmj3WX3TADh7wQi9KMWNcHeMUjKOIJPNIuFDEDQTfgJYeNR5JpXns+DrWABUcmoaOzKv07brY+Xuq4uaf3c2TLbMJSfHhyJZIi23B7k8Gs29KNXZlepu+Ct9f5v45WewzOzHaEB4XgLMhGkUUiC3M51KsQlioI7xnp1+YLOtSvwnjXgMu6FT4VPl5tpFaSjwPNZjHFtAmAcD2IT70DsfcM487JwfRacQ+be07g7qCprLM/Qshve3kVFhJy3XUl3vI88giL7mvP+0fm0LXgVhYIPh5eJ5FvvwEpZjODHvuY+m8ngujlxp4TSQ//gjPOc5gNAmmpJsppD7LycDIVwox0qwVvbVjNySNZhGzyoh5UsJYPZuiYRnTtW/UP0wyXHZQZM9eMxaiz8bHCCyZFgwSfDnTxxmITX673BxjGhelF4uCuFl461/ARE1L6ZJ9pF/holYkqsVpR4aOyYPdZke+3GXi2i6eotPX64xKTNxlZc1SiRjmVgb9p2321MnG9Ea/ir5fxNxLwFFwmAVFwdfAcMBCwlrUh/zaUswKnu1965qdBSwEERD2chUezWXvaTofkBrSt0LvomO3pIm5nMDek9KZ1XS9H1Dd55psMNueNo2no50R6HiPUOwCR4liDqKSaVBtkJqppNpJkoEGTVcgfr8F79wlCjUa679eQixakOuHGz/iiUGaTtoPjGV5qPbqKIW9vxfmEkVs2TeXEMNgw+yRcRpagV4GRM80cs6vk3fgN+0z7ucXbmBGetoRzPv2gkkanGgpLtobzSodODA+ZyhjzPF5298D0q8eFtHUrgsOBVrEijjlzQPLPuopwgKM1TpFe7xZazPqJSKeRcGdd7je/x6vvBIGYg45KbPw0grx9CTX8iISTio6VzDuaz3OrlvgvkKchrPARukenQkwYg56pR7/BtTFb/viRVeCGEd9biLdqfHij66KrZEHwd4o8lC2y+ohMbEjJYMLyEaVP9roOL8z1l3kc2b709sn/FFtPSkzdYuRknkjlKI1NJ2UOZIpEh+h0r6UwrIXnsvpglBU7TousPCzzaAdPUYvvvwGdYmdfgEskIAquAqxWa7bNZnsBGFfWtvzbEC8SKHYxgtTWRHhGsCJ7Ko8tnUaEIYFqkSXr5K84LCMI0LJiMFblNhpyG7VjenE4Yx0Auaa3yDO9i0VphUfaQ5DSFg0Hsa2yUH0gebwoqszYaiPJylWpGOelQvB6jEITkPxtiCMiP8E018XQcwLTquShG6189WRTOt39P/43vg/dB07EcR/YvnZh0SUO5+yjZuzF+744vDDke1h91Iep11xqpBbwnGMoNUupShhs9NvQTq3C3d6WfGJcw3zDPnbZR/kP0HVM48eDKOL84gucxjXY5e+xZ9jwOXaiVWvJbV9OxqLZIUxA1WQqhJ1hV04Igm5EIpjo/Hm8vuIFdp7w8WDz5wk1lmPq3rXg0BFW+WCLQpBR5tZ7GjD0vjTCwi+9hvOWk/5Awjf7uKkV//urTuF8MymATLuIolJqjMiv+XiNkWUHZR65zsN1VZXfP/gKM6SpDx2YsMZUVJ55dCc3N6X5/s7J9Yozd48BgwwD07x/fPClk2+1WgPNKC6Ta+jP5l/PePxbCNXK2pB/E2IIIAA6CJdYm97svINHp+cBGvGhQaxMT2dndjZWk4kQg4kJ69JIinLgUN1E4C97Wye6G+uPH8Rsexa39SUkPQqnvBwAu8GfNmeSwyg46mDjNxGMrv05uxJb0LyWjbVnJvBz7Ujq7mjMgiYbKQh2cUwczOO9PqPbplwi3Oc/A3B8xyLa3nodA5+uwzejdtJn3mcE53yDT/UwfeAywswlWzTn4+JD504mTKmCIyuChP7zebl2PL2dnS6aauhV/e51ARjpac8nxjX4UPCiYERG3LEDaccOPPfdhzsihFMMQvW4Ucw6dhIwuYKQ1CBEJDShEEnUefvdxmwbqPO0aEZH57lFj7L97CaGN32SfrUGk+92s3ddFsJMD/iA+jJzPx5CXHwo8/fK1EYlKfzSBN6R86Waq8b+cXDdl+uNrD0q07OOjzuaef9QEPyybdCuqsKwFn/rBPanua2pj1sb+7jhs2AOZoqczBevKUEAcCJPJNSkX5Da+xcJxBP8Ca6xP51/L1ar1Wez2UYCc8valn8TmhO/ExHQL+EZrus6X+zch4ABHR/BlnSyHckcys0l3+MhLz8JW4GI07KSjlOPUik8nDZJSdSN8mu5b9ftpWsHHbMhk2T7RnLMr+BaI6CkrELW93F4Y2fG8Ra+cIlx+pN8YHOghUZxzOxBF3Xa1tnE0KR8rFoke9xDyam5iGG7dlK9dgfq9XyAyITqAIzu1YGfp+4j63UvkTcVcl+rh4sEgYrGaukI8w37mJWVQ8bX/bC4QrjvloM8mdyaMOX3V90tK6nM2W1gX6ZI1XgfoboZu+DmE+NaHvC2Qdq8BZvnOrI3P4T9PQO6dpDcyR0pHz6WQw29PH1zLUyEoAsFAIR01XHNNXFdlIdUWcOl7mbLmfU81eYVulXry4cbtvDhpI0Ic73osTKOds2Ji2lFt69FHJ5i4TKmu5ubGv7x/vjRHJGoEP0PG0QpKry+yD8L2dwCoabfFx0eBXp8HIxXgdP5IvkugYigqyPAUBLhx7sctH0/hNVHZKBstzUuB5sLlh+U6d/gb499CMQT/AkCouAqwmq1zrPZbHOBbmVty78F5WTxhqoYrKP7wL1VQisQCOqoFO0hn3M6eXPjRnZmZXHMZkMkjKe6zaVvxf5YfX2Lxhg00UKGJPLJLWmsOR3LylOn+GbvXqYAqdYhfLtrDitPmBnaIR6kM8ScG4yjfwNwFPDNyIl8oQymcuxuHh92N4JWyKGZtwPgMXhJj8nGsboj7Z5/i5UHzIQYQjmttmdaozhW3j8BUSpexoqiAGO80AuEvbUYcJ9/nEzBzqOWmaw748M9rxP6kcokmmSm3OqhVvylOaHaVVEwyDB1i5GzN07GLvjT9X427GLYtnZkz+mFwzYIebeCgorqEan401q+eWMxu1fa6L6pEuBAx4Ooh+CaG0TcBCfmpiqV59iZtjWcoc16Yz5Sj3ve+omVS48j+IAKIo7OaVj0NhS6L/RiPD/HTJcavqLqixfjeK5IxUsIVpuwpth1tPKQzPWHQhjS1MuxcyJnCkTKR2jUTVB5f7lfOAQZdZxev12HskR2nBZpV+XqSfX7caeBbLtAq3plu6VxuXyy1oQowqBGf7vnJeAp+BMERMHVxyNAJwK/m78F4XyqmBiqY6ik4dktkfFUAaJJIKkdSAZ/tb2d2dn8dOgQwQYDr7RpQ/vkZCLM9/pd2eexuWDrKZkHWjmpe9ed1ImO5l6fj6y8PF6uXp2fw8LwCV05XahwcOImYpQWCMCo8MlEum8kZcrjDBxr5+4bZnF4mcreE+VAg7iwArJywzgWfpa0k9VYvr8ig5tv4NzSivysmTlqqMCsAz/Qu2bJTIPqiRLro7xoZ9M4l+His0prmGzchHtuZ/QVbUgOlhjawUffeu7LWtFGBuv0r+9l+jYjb3ZuRtWQWCy6gWZza3H24WDEnCBigr5A1N5nfjeBROMEYma3p+3cDnTVDAThQkNB9QSheGXOmbJ49L2fOOGx4bDbCLZXY/aUaBaLszFbzVBbRqsu4Y1ujNHZxi94gFn3OBg5w8Lh7GJhZ7yEb8XWk37xdCRbJCWmdHGwPV3kf6tM9Kzj480+bhbsk3noewtf/arUcY5DYPnB4gt2qKbQo5aPcw4Rlw/apl49ggBg9Cy/ayQp/OrfRj+QKXLknIhHEfhuq4E2qQo1y/3tdgc8BX+CwMRzlWG1Wg/YbLYPgJFlbcu/Ad3nn2A0u4BWKHB2UBB6XAbq+8NJjzhComMWsp5Acpg/jP/JZs3oW630FfXkTUbweWk7eiBS3t6i9xOAD6xWRpnNbIqOZrbi5XuxLQ7q43V3YGDnNjy/roAOOcHc0FnDoL7MvuPpLIowoXtEMh0RyHI0OXEKwfstJGRHsHLm9bSvMpmng4bw/Ilcnl/5HWZTCq0q1CXY4K8zkDPnB4w3tiHn8yjad/8U/R03nuopSCvaMaCuxtOdHX96j7ZSlIaiQhNvCl09lbHPMHDuGTPGOiop29ojiwX8kGhjS/uGvDlkBf1fE6g5J4lj+nJS97ch1V6VDeb9zA3fQIXaoew7kY3u1fAJJgRNQ6wrIdb+AXuiRI7SCVmpjLGgFVEhAjc19DJ1i4GeEy7MGrmcgr23fBXE+kcLL3g/yy4wcoaFuDCNZ7v4vSCdayisf8yOTxWYusVAj9oKSeH+VtKpMdpvIvivLjHwW7rXunpTEPOd8PRsS4nqkeFBOqOud1+JywU8BX+CgCi4OplOQBT8LWi/mhO0fIHw+zyE3RmNK/w2soVHORVyHTGu14kKj2Jsu/qkxZZeEGj2bpkP5vjoe/R7mudtRq1XD+e335ZokRcOXA901HW8s3cydW19dB2ChGM0OVKJJam76PnFfEyymfIhcRzLCEEkGIs3nKcn9iY5MxpJhDf/dz8I8OH7O3i0V0Pe+mQjZ9xpDJmzmMk9w7i+UhWcPgf2gmwSa2/nxJrmCCNMCPdbkBpVpUpzeKGb+7KDzbwehZPHbORmOwkXI4B43lpiYpRNJWesGUsLhcT2XyPvKMArauyqG8XC3nUJei2IdRMOss18lIQ6OxAa2QiW+tCoo4Xb0wYw89BEpq/wxxfoOkjZrWhQtTOe4AR2ZWchCW40eRdfDDpHpahQzhYIfLqm9KjQbenSH5brjQrRySkUSs13L/TAvdMs5LsEJg9xlog7CLcA6IxoW+zGrhZ39a+6f6F2gorVrFMx6uqIcyiNsQvNLDkg06O2v9ukqgnUSVCJvkg9iL9IwFPwJwiIgquThLI24N+CmuWftOOnOjDV1M5nIAiEKN3J5lEAsi1PggWa16tHgnNayfM1eG+5iU8WKzQ5s5Y397yI0q8f7rEXb3CZ/Z3AzWNrc3uuAUHwoZMIgkLTI6komoIJCBFD0AFNKKTS6QokZ0ZzJDGTlNNx/JIukb+5CaOssxGFcoQaQnF4M1mw/wM6CiNxxgTjjgzhUKtErLE62tdelL4VMCzdRrXQQlBagXzpX++dWzIY+8QyMk77VVSY1UT7xGF0/dxEtkknpKNC7DgXxkdX4JBV3q/qYXzVGzF2jcByykLnvlUY+PQu1HILiLA/h9EXy0tLH+PxKcsAiJIbkqPUItwUS75ejuWH3DSpJzG2TQeChVPc9XVb7p4aiSz8vs2P/Whh2YjCi24j5Lsgp9DvT2hWsaR42H1W5NEZFtLzRf43wPmH6Yplja7D7xRsvODYE7kiPWtfvV6CpQdlZu82kBqj8UZv9z/RcjogCv4EAVFwdZJU1gb8W1DO+p+qhmT9gpTESvb96Ki4pU3Yjd/gkBdhM35OmPdOBATO2ARG/WRiw0GVQQcmMyZxI7492/Fd5EntXCaTNcJCXqEbiyYRZpYxSUZyXM6iY5a+NwpZNKDrCvbEjZgHT0X6pj85uu4XBLo/WeJIhcOcisxk/yGQnPkYnV5U2cTqbXNpv3UyoS3bc7xPE0SfylvzUnl+Uy+ONQgivOKPbJ1zkE71D3HznXVp26kSNevH/u49Ksh389idcxEEgTseSMMSLHPgOS9dDyn8n73zDo+iavvwPWX7JpueUBJIoUPovYTem6B0C/AKomLvFetrxa6gCIKAVJEO0pHea+gQkgAhfZNs35n5/lgEEUQUUL/X3NeVC3Z35pzZ3WTOc57ye1RB5VQTmXYfuhB0sCbdxxv6NqTtS0SfKiHUg7dHJ2MvvJNiUxHnDopMWfkmacrLgIDTXZvMrOk83TWd8amr0TQItXhQ/TaaBg2grGkx7yxvCyiomnoxPtC7to8f9uoYneJhRHMvQ6YE2jznlQi0+9TKk+3c9Kx1ZULdydxLyZiNKgRezyoS+Hidgfn7dERYNSYOdtK44j83BKCqsOaYzJeb9Hw9yHldIaBCl0Cx++rekb8bTYMP1xoYv0GPSa/xbm/XX2EQTAG+uuWz/A9SahT8Myk1Cq6Tdad+JMQYRmr2Xr7Y9i63VR/EI81evKiJr2SLCHIg0fB8sYDXDzHB2kWVOwEJk9IEo6s+GJ8k3/AuJdJSlqyZwvsrohFysnn8xDge8S/D8ebc39y6uXdInB8VSItPq51F5mOnGZ1Sj5ynjGgLIsh35gJgdxegkwyYZTOmk8n4nktCwgeiDGJgTZQ1P53eKES/5hTPWcugWNwIooDJKXDceDe60AL8KW4MOXZSVh7n4Jr2OE0iUf0no7U8RrlmVTn7vJsZE/cxY+I+7n+6Mf2HJl/1uhVF5bUn16AoGuPn9CKpajiqA469aqYID4Kq8nD6Qp4Yspd1p7LZk1EBT0XwjizBf5uDezM2Ioe9T6XuHjRN46XlfiATQQjc9dvXbs8cu8xriypjCbUzvKmfF/8jU+MNiYlbRGYe3Eh2XhsAWiYdYevJWrzdy0WvZD8bTsictYvoJJhxj5Nqrwc0IXKKA82KQkxOWv0q2e9njQKASpEq+86K3DfDTLFHYFADLw+08lwIE9xcNp+ScHoFapdTOF8s4PQKRAWpRAVpmHSQUSAwYbOBZzq40YmXBJI0DfadFXlwtpmcYoEQs4bHDy6vQOVolTyHgPV3SiUB9lxog50Q8c8yClw+eHO5kdm7ddxW2/enwlp/kHxgpM1mm3NLZ/kfptQo+GdSahRcBxN3fsI3uz677Ll5qdMJMYYytP5oAHwnRHTxKjXfDrrYge3d21z0qHn5LlNAh837H8b+WI05P90FgOTKQRDgkRNf4ty4AazWq16H77RA9iOBlSZuQwnxEeFAOACR77iJfAcq5Js53dSMy+/C7XOxp8xajndehZCzHLFSY5ZmJPLWd6NQ0TCoGuLAXIbZVhPUui7VmmzjxyYxbMlIZHF6DVz+sljXHqbCwR/p0KIqb5zowj2HZvDo0x9Sf0UKxK0kIakbJ48HFF7PZRZfcc2njuXz2VtbOLw/h+IiL8NG1yepajiaD07XD0IRQBVlZrIaLe0k750yoFoj8TdRiP7vUc7WNZEoFNI74Ri+QpHCDInHF/kRMCDrzSRGhvJ0t4ZEW0oocaxn+vq2uAobsX6PgW3l3XzR38XQaZCV+xygklRuNJtOfMvgBl5aJSlM2KQnt0QgNjTwpQkCbHuymEbvBtGnjo99ZyRGfGcmPkJl9rBAQmWBU+DFRZeSBJYclBm/0YBRpzHtbifx4bdmwdyaJjF0qvm6jp25M5AkqpMhyKChqGB3XTI0C50CA+p7qRur0LW6/7obGe3ICBwo/pFMzFuI2wdfbdIze7ee7GKBkS28PJziudUegqXAcJvNdu6WzvI/jqBp/9yklH8rdrv9J6DF330d/3RKPEU88+Mo2iZ0oWpkLSbt/JStmT8B8HiLMfSqNoDMLhb0lVS+76fw4ZqAH9ao09j6RAkGGQr0H1Fo+ALB2ZUXZ3dmy6FWaGgIqsrz3e+nQ/higm0vYrXdj4Dusvk1HxR+psc+ITBu+Itugvr/dkzXs08k/30jxv7FrIqbxsz9k8jMPYbH3pbPx32LoIEmaIR6XBiMFuJXHEatVAlB8eGe+gLvttvN5CpVCJ6uJz09lGj9CpKDh3IgszubGuwkVDLge2sMakEeHdKGoAQFw4VKhc9n9KRGnUAb5zVLT/LWc+swGGWatY6jcatYUjrFo2SKZHa14MzLp8RnAb+KU3DzqOFDPKKNknvKoK+4h0HdtjItqSLDt2+nwrIcjsY1YdmZVegMZiSdgTFtx9I2MSC1ket00mLaNADGtribsatCySwQ0dDIc+YAEGwMQSfqrlBYbFRR4etBzqsujKuOyDwwK2CIvdbdzR11fXyyTs9n6w10reFje7pMzoW2xz+McFD1FiYMFrmh+QdBWPUao1p4iArSCDJqbDol8/WmKxMmR6d4cPoC7n6AmmUUXlp8yZjZ9mTx7wovXe0a2n1ipUYZhW+G/P29nD//Sc/Haw00iffzQEsvDSvc0nCNE3gcGG+z/Y3dqf5HKDUK/oHY7fZTQMW/+zr+v6FpGv1mtOV8yTkkQeLtTuMoc3dnjA0VIt92k+cQ+PwnPdO267mrsZfnOnoo0H9CoeEzjqTX56HPpiFgwWzPx+1RsVoKmfZGC3S2S8ZAtPMrzEpLir/XUTDWgJIrYO3tI/QxD3LUH/tb8ik+Vu2aynur3+O1Tw4CIGsKNp0OndGCFKmhi1MxfZXHfUGT2etIpenhHFpmrGZMWldKlASknKfolyzzaf9LK6e0aRPj7vicaa5qEByMZgysMONn96Yw383TI5dRo3YUjz0Xz8lt47FFViT4QEuE8Y1xuxS8ig7tQifv76pOoNnjH/P648PIb9MVfZ3FyB0cmCJP0G3TJadmAAAgAElEQVTsaraEOfCHhl6cO0gfzMK7tiAKIqqmUX3ChIuvHb73Xjx+eG+Vga83eynyONFLeoINl0szA8y7N7CQXyvRbmmqzKNzTUwc4qRZvEK114OwmTS+HOjk6R9MnMoLvAeDDsb1d1K3vMLJPJHDWRIeBbrX8BH0Bxff38Lj5zK3uNcP7T+14lfhqfZueif7ySkRMOmuLuU7ZZuON5cHLubQC8XXnWD4S7p8YeFUrsiT7T0Mb/r3STCn5QkM/MZC5SiVyXc6f/+EG2MLcJfNZjt2qyf6t1BqFPzDsNvtIuCGX21L/wc5tn0+50/uoUmfZ5B1Ny56rmoq728Yg17Ss+fcdk7mH+WVb5YTZLOQ9/FGTLKZDHsaPx3pyZojiRcXEwANlWqv2cDjYf2SNqQOepSYO7uSUOY4ZyxdETUTquBCVmMJ+nQ1BW+bkcuqhL/sxtRKueZNvNBdwKHsfdQp0xCTzoymqhw8uIJte3+g3MwldEzT0bvF6zyzcAASDow6E1Z9MIIg4EOhSHDz2WdzGbLiK3r+cJIFtURebnSeU9JwnNnvEBS+hQV3xlMnOvrinPYNu+jVcykArfvVZs3aSx7VkBCJPikTKchzYCyuQIt1M/D5ZfyKDgQNVRM5IGbyoWE53iH9INhPUjBsyQpDjfiR2MEzaLEonbVFXsq4jvF4h1dJqT2E/rMGEGxMYHDdUaTm5vL1vn0X5xyWnMxTjRtffPzaMoWvt8gsuU+jegwMm2ZiT6Z0UTFw/SMlRF2jmVV2sUCnzy3opcCxBhleX2ZgW7rMsWwRg6xRL1bBqIPVR64dJV32QAkVw27+fXDbaYkKYSrR19GUy+2DOm8FciYm3/nnEiEzCgReXGxkyymZMjaVNQ85/vAYN8pPJySe+sGEIMD0ux23sjzSD7wCvGWz2f5/STj+wyk1Cv5h2O32aCDr776Ov4IZY9pTnJdJ++EfEV+n000bV9VU7O5Cvj84lfOTfHRceD/v/2cgZ8ocDryuGtF59uL2WZgwOJ06r7+AGFOWjI530HuijXLe88waVx1Bf7ld5hDXkvXFXvjgEaztjUR9EMjIB9DQcMgL0AQPshqLTk0kr9jP4iNzmXNgCiXeYiyanpo5EvaCMxwNvXQfS87R8UPZB3llzt3UyYzAL4EoyQgGIz5RRUIgWDNRrtIcYo49RtrsSdy2sh8ZjncodAwgpNxUDAY3oLL3Pz0xCAGthdblxoLLxfbIbxmd8gEb16bjKs5DFQQURO7z9qGBUgVBEtAUjbfMs6moRDHQ34onKu3gfHwEcnwIlWvuY+ORNrg9CpJOoVf0IFaq0fgoR7DbiUmnx2EKo8Cdh0VnxaS7PL6+vF8/Ktgu7wref5IZAZgx1MneMyJDpljoVctHu8p+Rs00MW6A85oSwptOSQybaua921x0r+m/qEgI0KOWj5e7uC/uyPefFVl2SHfRlf9sRzd1yit8ss7AgXMSi+9zEG75+++DT/5gZN1xmTUPlWC5zuZdv2ZPpsiASQHRp/3PFV93TsKNUuSGJ+aZWH9cJj480LL6t9QkbwKHgSE2m23nrZrg30xpouE/DzvQhkCyYeyFf8v/4nHk33dpfw4fCm8bVnK3txGx2iVXc8cRn7Pgg4Gs/PphqrcaTNO+zyGKN34XEwURqz6IuJB4YgfqYSFUOFProlEgim6cUkfO5i6g59drKV9uFovmhJI4dSr9Kj/KuFoPkuHwE6fX0Dxgn6DHkyrh3twdnK2hwzL0H51CEO8DwC9kU6j/gmL9d9idKjuOezic6WP7MQFVkWhYoSbdKz/M1vWLOZO7GlmvZ1iqiXHR0Tg3N2VHm3XI8i6eGqAw9PRWXPFeBo1bAZoP0aeieRUcgpfze7oTrJ9A2UVryXf3p9jbk9tbvMm84ypSkZH+O5pTxzsVzSOB1UpwVRnvOR017+hPXsgBxF0CotMIGoz29KSeUhkVmKT7kXWRB9GG6FF2+vBv02OqWQ9/pIc5FVrx9pByeD/xQUY7NC/8UDAaU8gmXmrWlq2Hd5N1bCvpgg/VlYzXUQVN56BWZDThIRkMrVv+CoPgyJbvKcioTXhsDfIcAg/MMhNhUXmirZu0/OvLRPs52/65hSZeXKzhuuBh6FLdxzu93Jd5bmqVValV1sPoVh7af2pl7l49zRNcTBj098fef0lOiUBCuPqnDQKATacCt/Q5/3H8ZQaBpsFLi41sPCnzZHsPQxp6b2WFwSKgn81m+2d9ef9DlBoF/zBsNpsbWPtbr9vtdiMBcaOrGQ0/Pxf9W+f/1Who7JDSmaPbzUz9Lv7jacoIb3NM6AgrW5k7/7uZbfPf48DaKah+Hy0HvnpT5t2cvpbX1jwJGoyVdvNkpbcx3PUkq04u4f0NYzDoT2OxjCe38F6CY25jdLsfGLHPQq5BB0Yj+Q4H5fUa5wZa8B4VA7H9FD9yvA7/Qz9RGDQHvC6svt6cNfdDFYrxFnbgpe+2UODKI9iko00tH90a6IkIPgm8TpVqSVi8YwhWBiMSzFeVx6FXPDRUQnDmrWdepMzhdrGc6BPFxtuH0/Pjo9Q7NJzEA8NwKW6KJC+nTLMIbZRJ/rJQgkwr6Ji8igNn99Pl+xV0OliXM2E5bC17AFwuHGYDXXMb0mFGApLDQoarhJzoLFqfa4ZGQA/hI9N8dksnoK4PQXRwroaItkVHZJ6L3bXfom25jnjGhSBqWxEjdqHmPEGtcIVpg+4l2GDkzLFtbDHGUKBIyD4Z2V0Tk9HCiQw4kVGDyb2KcRXnk35wLeWrNuf4jkVsXziWvKDV1LKpHMsRyS0R6JnsJ8QMcqBggtwSkWvJCf/cpMjrh5/FDebd66DaNfTzjTp4vK2HFxcb6f2VhWFNvIxs4bmhRfhmUuIRCLuBrouqCvP26mhcUaHmXyjMtOaYzLJUHY+1/UtyGT4rNQhuLaVGwf8zLhgNJy/8XBW73a7n9w2HGP6YlPwfRkNjlGkWB6VzvOLuytOm+Xxh+Ikiwc2Lns4ASLKeJn2e5cDaKZzctYTm/V++Kd6ClhXb81r7j5m29yuC9TZEScBqCKZXtQH0qjaAxUfm8t91YyhxtuZczktYglfyXEs7mvYklqCNzE41U7h8BGUPNST4lTxcnTM478olOaYBMq8i+kQK9eMp0k1DFUoI8g5k2laJYreTL3rOpUZUHRBAw4HfcRa3tJUS3WIKzB9RpH2LvqgfomjEJJo5tC2W+DouZBV2GgyEGIzMi3iN/HYLOR48hx3xEwhuOJJpGTN4dtpcHK9Xw1YjH5NlNnmnuvOf5A7sOPQelmMTGbWwN4m1ynLGlket0zE08tbFoW4hzRlOTa0yUm4lFEElNMxI3HInE+I6436gD8tb7ma+ZuRATmc0AcoHr0Sr5sdfYEbv86AXVJLF85wO1hGkT+bpdevZn5NDcVER5UQ7kroWo/EDOuV2ZZp/IjqDGa+7hEGPTaC1/1NELi12ZZIaInjiCbMoNKmoUDdW4fD5wHe+/2zg36TI64+ph1s1fnq45LrK3W6r7aNpvJ/Xlhn5cqOedcdlvr3L8Yez/W8F+U7hhpoZ7ciQyCgQeTDlr1szSzxw/8xA2GZYk1tuEJQAa271JP92So2C/0FsNpsXSLvwc1XsdrsOKMPlhsKvDYcywJ+uLBYQaOFPYK18lKdN8y8+f7uvzuXHCQIVk9uTtm8lU59tRrmqzShftQXlqjbDGnr1XgS/O7cgkBLfkZT4jmSM1eNLvzwXqVuVvnSr0pdDWSJ9vzYRffptCuLuQ5EFLOaNGL8dgXVdEmtrfMdUx7Mw9/LxY6zlCA+ORtBnIslOdL6N7DidTvcqt1Mzuu4vPgMrerUyerUywb478Yj7KDR8itP2JY37NmPNzJpkBTegbHQkUb75ZChRNM7UqBBangqdRlGlye0s+GAgWdvexFtN5uPeI7nv27lMORwB4gSMk42Y9VZqOfMpQY8sKvQ7XgWH3YTPq2e5fgP+5Odh7avUUJNwqW6yh22lwXstOV14kmUHP2Lls/nsUpPRG0z45o9EEMBfx0eduCyabt5PbBuNLUo1tpxKIDftDHmZIq3CVe6pVYtuFeOQpQKGf/8jKRUHcVdKeyovH8yJHJEN8r1slIdxTqhGb9/ztOw8iMQG3bBFJfDkGxJBhsB30q6yn/dWGch3COzOlAg2adQqe+Xi6PbBuuPyxaoCgO/ucVI39o8l5cUEa3x6h4sP1xr4apOeNh9bebe3m7aV/758tYwCgbOF4g0trFvTAgZV7XJ/nVrj2NWXEoTlWx+uWGaz2Ty3fJZ/OaVGwb8Um83mA9Iv/FwVu90uE/Ao/NJQSAAevN55hvga8pFhLcWCmwZKBb52DsJwlV+7lDvfIv7AWs4c3kjm4Q2c3LX04mtth44lsV7X653yCvQ1FHwnrn7Hqhaj8lDkPj4634PxP64j6fZIFtcKof7r96OisHLAZ3CV+3RWyRlighricUTjVJx4/T56VxnCA82evOa1eLJDMfECBblOdi5ahhgbRbmuOsaM+ILhk61kZEeRvm4mBxMakJjYlDP+XE63bsi8fYfwO10cMsRSP9pH8zMuVEHF7XehKaBqBhBUhAqHyf22C2+2G4ndH4mpbSEvbHkLs9KEA6bNaC0HoTgl9u56gcdOPc+RlJa4o+oQ4haxpg4gLacWuSE5dPiiOY6dbubWaYD3oIioO0/bqqcQQnys3F+Fz9tH/qKJTQyrhu1HJwWyLuPrdGLS43UY6HuA3WofVge9zvfh++jeyEVIpMrryw1oGhy64B34ua2zywdn7SIhJu2yroTHc0TeWG5g/1mJEk/AuRUXpvJRX9c1wwXXQhDg0TYe6sX6GfmdmftnmhjYwMvT7T0Y/+K6H02Dd1cFXBXNfqfZ07XoUdPHxC16XlhkYvIQ5686O958XD6YviMQe3m56y3pcvhrFvwVk/zbKa0+KOUPY7fbDwA1rvf4+0wzWSsfRUJkgLc+D3hbEqZd2Rb3ZzRNo+DcMeb+t+dlz9/z3i50hutTjvslOc8ZcW+QiV1/ZRtdAHXDZgZMMHDGWIbJD8qELS+P/Ss9EW+4CeobECMq8RSx+uRS8pzZ9KjaH63Ewu0p0wEIDQu4TwvyXYSEGdHrJeISQigqdJPSKYH+91TmyOY5nN6/mlMHdrJ+d1uyi2oQFmlhf9MBqGb44bkyPJjXkwOLq2BwnSPZuJ++GZlsMLg5ZC2iqi6CzdnDKHKN5pGthXQ4ZkMVYHL7F4jLHErK4ZpIgpv32t/H+S3l8JYYSGzYkzt3N8MiFzJbWo+703ZaDVjK8RiFNUHBpIbEYQuSGD9yH2qTbby30MeeCieo7VzJ/au7U74gFiFCpGTsGcq3ng7BX5J5vi4jPlhIchmVLwc6sV1FMvjo1h9YN/UZANoP/xh3TGf6TbLQt7aXMV09NHrPSpFLIMyisemxElYclhk928Sdjbx8u01P44rKxfr2Qhd0G2fF6YXWSX7uqOejcqRKmFm7aep4afkCnT+7pFbZJN7P4AY+2lfx/ym9gD/KznSJwZPNPNTaw/0tb8wF/8M+mWfmm66q2nmzUFVQNBg9x8TaowEDf9IQ5+92r7xBFCDaZrPl3cpJSin1FJTy55jLHzAKxrn6kyuU8Jn+J2bodzJft48R3ubc5W2E8SpyDIIgEFa2Mn2e+YHv3+p98flvnqjHfz4+dLGvwfXiTxfxZwtoHhCuIodg2raZz/Yt446GE7hvViIfz5QIb+u/aBAAWA3B9KzW/+LjYv8lL2ZBvovaDWLoNbAaZzOK8Xr9ZJwqRMDPF2//xLwJs6lbaR1Vatg4kNmbtEwDbdueo9eIrvRf6AUHLJ9Xncz/mOjhP8DuVcHssLfiSDmBNuUnYzrbnuDoFLynh2JQoLylPyb/MjSgz0+vMGroeOalDELLCKNkWXsEWwGThFdR9hk5bhUZ22orrtX7UGq7WNWpHopXpaTES6LjHC9I+zmXOomEWUZeRk96uRzON/6ESp+FEGfoS/ZdYZRfVpHINo/hdDaA6JG8NvBjXp7xMG+vNPJmjyt3iBWS2178f0xiA0xBKiEmjfn7dTzb0cO2J0p4ar6R5Yd0HM0WiQoK7Pa/3RbYdY5ofumzfXuFEbtLYM7wW6dKWDFMY++zxWw4IbP2mMyaYzKjZ8vEhakkl1UYneKhwi3QMfiZTackRBHuanTjMflWiYGF+ZfSyTeTfWdF+n19yaD/uU11Wr54q42CDaUGwV+DNGbMmL/7Gkr5f4bH48kDRv2Rc8zoaa1Uoou/OmliPt/pd/CDbh/V1BjKa1cq2gGYgyNQ/B4Ks06g+DwXnotEUxX0xiCKcjPQVJWjW+exdspTnDmyifMnd5N/7ij27DRy0vZiz05DOhmFetSCY5kOS3cf4i+TyjQN87BhhPrsdDx7gLjTg5DyRLbf5ccdCpO26Clr0wj7VR27wSjTtmsiftdZHnpYJKVtMIJ9IZ7T7xMi/kj54JWUC16LQbJz8kwcRzMbUqy15vARHW3bacQHTWbNYQeHpA4M9D7A8Sc749YZ+dYygD7xuXy914PHL5J68gXy8kaSkVcLUa8hSHB7z9fRH6uNXBCPHzPl7F62ygdwLe2OWDmTd4VX0CsmBPzc//A6/FPS0Cr4CPlQ5RnlNqRPpxC8aD3fjM9neXw7Tq1+inPBHsqxikqFzclNmEP7B97DuchE0RYoOqIR9aCCTquAgI7Qcu/jcycwe2sNWiT6iQm+/LM5uHYKZ45sptN944koXw2AYIPGj4d01ItVqBCmUTlK4Zstes4VixS5RHZmSJSxqWx+vOSi4M3KIzJjVxu4t7n3lu16f0YWA82E2lb2c3djL2EWDbtbYMVhHVO36+lQ1f+LcMnNZe0xmUNZIg+1vnGj4GiOyJzdevKdAv3q3dw2yuuPS9w15XIP38+loOuOyaRU8l+XUNOf5BOj0bj5Vg1eyiVKwwel/GHsdrsAHAEq/dkxtkppjDEuIVdwMMcxnApa2DWP9/s8LHi/P3lnDl/zOL0pCK/rygZAYdmtqL75M8SqRcTPNqG3BcIQ8po1mEYF7JtTheMoienOjx1UPrBJ/PynER2sMXuY4wqFPZ/HyZSnG6Eqly9YSQ17YIuKxxwcSWiZSlgiqjFz4n6mfLEbPyq1l4Rxf5bChJmb+F7/NqP6r2Zym12MyKxEm5cjMc1Lwev3M6Pefsa33ohBqUZC2ZOkHxtGhDWLbx+vi/jtMDxj3sLrlzH4fLyizOVAqEK9bo15ZGECYhHc0/V5vFk29CfDoW8/5jwcytsbunIq7yhD1+hZEDWC9cH3opdURhT3oPJpHw13bcSuO4J3sZ+yKyqT/YnErOdW8dadrdEhoaGRFlQNp9vMve+tp4wlklnDXJfFr3PSD3B63yoadH/44nOFTmj3qZVwi8aCEQ4kEeq9E8TgBl6+2RLwECx/oOTijvzIeZEB35hJilCZerfzVnfW+012ZUgM+ibwu3JfCw+tK/lJLqve1MY+H63VM26Dga1P/PGeB7/mnF2gzceBUMijbT2MbH5zKgI2nJD4z/RLobvU54vxq4GKh2EXmkE1ifffyr4LSTab7cStGryUS5QaBaX8Kex2+3+BZ25kjAyhgNstXxOtBTPDcQ9mrl0w7nWXkJG6Ho/TTnFuBnpTEJqqUK5qC8LLV8PvdWG0hOB1l+AuyUeSDXgchWSd3MmWeW8Tdrw9lba/zvn4uZxJ+Zz4Oh3RrVqFP+0EBnsKIScnYXrIi+1xA3dO1rPzpBtR0hMaYiMySGP8ANfFTnv5Z4+ycdYrZJ3YSctBrxMZVxNZbyI4Iu6q4Q23y0+nupNw4qUkzs2gYec5tcXGKsuzhL02jnirjp6PTMJ8IonqWz9DUKwUaQKjho1j6OYOpNbP50x6MwYdyqVaoRuDx4pTs6C6BQRBI1POZb5uN6OcnQGVjfIevgxZhhrnZdDg6iwtGEyRNwt/0GOU+KKR8nvi9DdH1OdQp8YXDF7+Pn7RRsqRgzjSdbw+/TuarKhNgyXVeGDfe3TVqvOOuxciIlmmobjkzSzddhsfzH2ducNkksv+/n1k3l4dzy4w8v29DoIMGndMtNC+ip85uwMhpMMvBoy5PIfAHRPN+BSBucOvNMb+ak7nC3y4xsDS1MB1Dmno5YXONy8JftOpQMfH6CCV929zUaf8nw+T/LJRFMCLXdwMbnBjHoMCp0DT9y/lXPy6N8OiAzJPzAvM+dHtLjpVu+lenVSbzXbd4cpSbozS8EEpfwqPx1MIjLiRMWyYqKZEM1m/jdNiPp381a7olPdLJFlPWJlKRMbVonzV5pRJakiZSo2whsbw5Qe7OHLQTp1GZZBkPQazDb3RiqqpbJr9Gq6iHKhQiOJQKXNiIMWGQ6Tb53Pem0uu5CQk43kkXwzryrbl8Mavqa0uQiw5TV3fbBpoc9jsaMr3qSHEGzPJ3vwx66c/j89dQvN+L1O1aV/MwREYLSFXNQi8XoWXH15F5mk7VUeHcWZ7MecWalRNdLIrtAtaUBEjli3Dd/IITe97iKQHa+OaGYSqanTZ2oDy58Kpv7sCLU/rCXPLKD4LXrcBzR+YS0MgSLXS0JfEz9ITZ8V8NsafwGfRsTUsF7chj9y82ri8lRCK2+LxV8NkO4Q1egZFHoURG86RYykirl1r1M3l2dX8CO1nNuVQszQO9jjBQekcJvTUUSLJM76OrEXg9Cms3DGYLtUgLvT3F26DDNN26KkQptG6ksKgBl7eX23A7hKoH6fQt44Prx9GzjSTlicxcbCThIi/f9MSYoLO1f10rObnu5169p2VWHVUJqdEoNAlIEsaQYbL2xavPiqzcL/MkWyRvWckXD4BnRT4DH5dFRAbqtEs3s/ywzq+3W7AIEOdctfup/FbRFhVGlVUiLBo5JQILDqg40SuSNN4/5+uqujwqRWnV6BVJT/LH3BccV2Vo1Q6VPUzY2cg1NY84abnFnxtNBpX3exBS7k6pYmGpfxZdhHQQah4I4O0UBJ5xNOasYbVnBOLeNjTmiZKxWsaB78mdU82303YS+vO8Ref01SVY9vns23B+/jcJXS5fwLlq7VA88G5O00kH/ycwp5fcW7zB2ThwOKoicuchqpzoXrAl32cxhwPDFYMdwm9mSdOZuR3QYzyrqRJ0z407Pk4RsvV8yF+yWdvbWHzunQeH9OCucN2EH+bCboLbFwajqnOEsQtDmadsmJ3jWDO8gLeK9Ghqh58XhURAVXTeEX/PYLOy0sld5AvOAnBwkXtKR2ospmXexyg7qI8+jgbkSv7EZ0yugwFfYaMfcAeTCYfXmdXgj0aT3u/pe+YvjSbGgoeDzOT6tOQjezKeJOy5aZx33P9CdPMHG5xGIcQ2BVvkE7S1fw+muAm3PUace4UBM2A1399LuOECJWkSJVVR2V6Jft4Zr6RjILACpkYoaJp8PISI7vSJcb2cV1Vq+DvJNyi8UgbDx+uMXAoS+JQ1pVlrlVjFE7mSheUFq9OGZtKkBGKXOBVBMItGuEWFZ8i4Ffg3ZUG3l1poF6cQrVohXCLRp5DILmsQq/ka+/Cg43QMlGhZaLCQ609TNgUaCe9NFVH8wQ/o1p6aRB3/Yv2tB06cksCv2fPd/ztssP4cJX3bnNROeqWfGfzf/+QUm4WpZ6CUv4URqMRj8cTCzS70bHqKbFEaUGs1h1lun4HW+XTlFdDKPcbCYi/xOdTGNRpFqrfT5NmZqonB6PTm9k09w12LPwAa0g0nUd9RUxifQAECUzNFBxzTQSfa0DDkbWITx0B+yoQZJ2CVbeX4CbtCImrSsG545feL8UkuH9kh9yf4shOPDC0D1bz9QWAv3x/O/GVQnnohaa8YlhKK08lrNvDycpy4snMQTjjRnNINHAkMdzRE9mvQ1Uv7ZA1BCYZVuMUiunma8AZ8TxhOhuqReMRw5esSUrlk16VyTSGcbLtNnrsq86xyuVYn9IQS8WJ6PcZCdrrJYxFuKy9MVlCuPO+OlSPkagZGcmiY8c4GmSl/sCufN3sHNNH7aDHlJoEe0Np/0QM+lCRrXIamWIhA8TAhi3S/SarjhhYf1zmkTaeq5YmXo3D5yVWHpaZuFnP2SKRx9p42HRKpnN1P/vPikzYZOD+lh6GNLq5SXJ/Fk2DdcclOn9uZeJmPVvSAvsok14jLlTDpNcodl8yYGuWVckqEvApl54rF6Iyto+L+rEKNcsqyKJAkEEjKVKlUqSKXoIiV6CtcmKkSmZhwFDy+AUOZUn8dFzmSLbE8kM6Pt9gIKtIpHY5Be+Fds2CEGhIpJe4bBevk6BxRYXoYA2rQWNXpsy07Xo+XW8g0y7Svsq1DYxlqTLPLzRRPlTFowS8Ir91jiQGPAa3oLFUNvCY0Wj8+11G/xJKcwpK+dPY7famwKYbHUdDY594lvXycZboUjkl5l587QFPK0Z7U37zXJ/Xy7P3fMC6NX6sZge92izFYgrsXONqtqbjvZ8jXCUrzLlG5vwoE8GDvbh3S7A+lSrhXRAEDcfMmai1awOwY/HH7F72+cXz9kg9WRv5OZHWQI5Bld8pkyvMd9Gr2VQA2nVPZIZ+J8FLLZR1hVO+QTTFR+JpVhhDNUFCRkR1ZLLUvZJKahvWG47Q2FeL6mo5XgibRJbPThtfMnfr2oJeZUyPWaQtOYO7TBXsTbsjGbLxe6J5ea2RpHyZe/rkoA/fhDl6LNbvkvAeKI/fUAYxuT6Gpgm0fX4Rp6Q8tswqwpsloI9XiOlYTO1522nx4ykkn5XBH67GYArmbvO3JCvlGKUI6JXqyFo0648HYuGf3OGiQ9XriyOvPCLz4IWY94jmXrrV8NHrSwu31fYxf7+OdpX9fNTXdVMT+X4Phxd2nJbYc0YivUDEZtSwGiDXIbA7Q7qooFg1RuHtnm7iwlSMMr/r3ldVqP5GEHfU9fFa9z8m7uP1g93vLvkAACAASURBVP6CH9fjD/iEbptg4UTO5R+MUadhMUBeiUBsqErn6n4qhqlEWFXK2zTiwy8lRRY6Yeg080UPx885HFdjS1ogsbBmGYVJQ5yMWWJkaaqO6fc4/tK+CsDXNpvtP3/lhP92So2CUv40drtdBDII9Fn4U2hoPGH8gcW6A795zEOe1oz0Nkf6leKy11XMiq8e5OyxrRiiuzN5ekXCwjRG/CefsgmJVGs+AFH67QhZ/lsG7N8Ekhsji8dSJuh9fP37437pJZACN057dhqzXut88Zz4Oh2J6vIpD84y41Pgla5umiX4L7bpBdi74xyb16Rz/lwJm9ak43ZdWjB1QTrePjscm8lIvkdEUQHBD1YHr342iYy7PChq4Jq16BiqO8J4TutAWu00RjebinGxwJh9/YkOCuG++z7EMDcUf0kUnrtDmVlHY+D2ljQ5YubRNWYe6ePg+DuPg8mJ5NUhvtsP07x89GfPoelkzIMUkh82Ui4klOlTTiMjMrlbN6TNUziw9lusYWUZ+Mrq3/z8/AqkfGylbnmFT+/4/RCCywcdP7OiqAFX/LFsEaNOw+0LrK5VYxSm3+3EfIsaFGlaYKHPKRE4XySy7JDMwgM61F+sceVDVYrcAiWegFs/IVylTx0vXav7/7Kug79FbonAtB06XL5AjwRVhbNFInaXQFyYyrY0iS1pMr+8pYdZNBpX8NOookJ2scAXPxkIMmrMu9dB+RANvwKZhQK7MmS+3KQnLe/yv7EtTxQTYgp0cHxrhYEXO7kJ+eP6YTdCT5vNtvAvnfFfTqlRUMoNYbfbPwZG38gYS+SDLNAd4Cf5OOOdA0hWyuEV/OQJDl42LmGPlEkTfzzvuHsRpQUB4HYUsuTToRScO0arQW9QqVEvdmzM5KmRy6nfpCzvTujyu/MqdkhvHBiv7LRczNp+lPr1r9gCrpv2HJmHNhBbvRVHNs9h6Pt7yCwxMWqmmVO5IjoZHmzloW1YFhM+2MaW9RnIskhkjAVTfZEO9yXRxVaVkFAze6rLePFj1xuYWEvl4TVGDCYPw3e/SsmkGhTvr03IkuVogLtyEsaMA3wt3Y/OJTNi2Ed48fD1J0+wQtrNt/VXoN9vQdN0vF9zIX61Ao/UfZc7Br/O3X3HMKvbPGbe8QXJy1NZ6U8LvC9RxFzswrj/ALb0o1h1Cs1ax/F9XBpEiNSKjGRWr16cP7kLsy2S4Ii4a36G//3RwOSterY8XnzNxcLjh9GzTaw/LjP1bid1yil8tVnPR2suWVNT73b+oXj3tTh4TmTbaZnULJHULAlVhfPFAk7vb2/vJ9/ppHHFwPya9vuegH8ibl9gAc8tETmVJ7LltMTmUzI5xZe/mVCzhl8Fly+Qx3A1Fo50UOnW5AhcLy4gwmazOf/Oi/i3UWoUlHJD2O32FK7R6vlG0dD4XreXV43L8OCjpy+Zjv6qsGgBaUsm0WnkF8TWuBReGP/+NqZ/tZcVe4eiv0Zxu6bC6VpBaBduiIYaChFvutFXufImOGNMe4rzMpH1RvxeN/W7jqZelwdw+2DrKYEJs86zffkhdKePERQqkvCICeMwjWhDEHN1ewCookbzyr33YFoWglvwsbBJFu1+rAjA4Q6pjPliDvYP70eyeBBzTRjyihE9M4hcJvCsrR850XtJ0r/MGvtweh8aypuGSRzRF+K3RiAVObG2LUdOQigOTx1e7JNEpUfGI+dV5+XB/ZiR/DFNtjXCq3oRBJGkyHxUtQrVg4po6d7F3G8P4tEpnOnuIyzOzKY777zu72fNUZlRM03IEux7pvgyt7+qwqKDMk3jFZ5dYGTDCZnXuru5o24gX2BnusSIGSYcF/oZCAIkl1Xomeyjew0fwcbrX5h9CixNlVmaqmPvGYl8R+DEIKNGjTIKBhliQ1UqhKnsSpcocAm0q+KnfqxCpFUjzKzd8l4BfxeaFvAGrD4qY9QFSj6ziwVkEUx6iA9TWX5YJrtYwKyHQ1kiHr9Am8p+qkUrtKviv2Vqkr/DApvN1uvvmPjfTKlRUMoNYbfbJeAsEHUr5zkh5tDNMu7iY1dxPoIoXsz+H+Ctz0BffY7Ny+fdpzcwcX5fEquEYcfFLjkDgFDVTB21PAA5zxopmReo0Yoa6yLnKROaH/RJKpbuPoLv9CJaQEHlo+frUhKsJ67HMLbtmYOvbBnKthvEiu5plBz1oaLhN+pxVa5BSZUmyOWcVGq3G1/yHuySAwCDQ8+XVZ4GwGP2obeb0VyBRMXhn/5EQUw+rlWtubfzQb6ZVxdF1QjKWY5p6RFGpzh4bM9kvLNnMamViSgtlAlxz3P4nnjO2TtCSQ0kyQ2ChMlwEL1hN/V3D+fRDcE83yGfs0nhqJqC3ZGLKjnRS2EE6W0A9Ez20cqczjtPzicn3wnlRb74pAet61e8ru/Fr0DNN4MuPt73bDFn7CKrj8q8u/KSF0AQ4PXubvrWuZRAOH+/zNM/XMpQ7F/fx+qjl+9qH2jlYXRKQIBHUQN1+G5/YKdbL1ah2C2w4ojMzJ16jueIRAdrVI1WKB+i0reOj6pRN1do6N9AnkPgiw161h2TySgQMehg8hDHDekn/EmG22y2iX/1pP92So2CUm4Yu90+Dhh5q+c5LxQzX7eP1QUb2S6dRrCFIOsvrwAQzotIDSxEtDWS+KqNjRWOX/b6S+4ulAge6tdrjmSXeSvtK3LEElwFKvUWVqHp/JpU2h5LenIW4yd/z5nIfNzOIrSVGsJpHaggFsiYD4agbRYQEanzSQQ1O0aTSCT7D4WzdW0SJ7NlKkUp3N60iHI1T7PEuJf+iX0AGJr2BpPKvIrq0XM0SuHR251oGmiKh7Cor/EVN8aR34DQ9DQMa5bzdNnDjLg9kvwHHmRe1ZMk6JPY2rMO38YVcVp6CuX840iCG4Gfb9oaZq/AtJmRLKriZEYz0EsGfO4SEvQnOKtrcNlnYtSpmIOmUbIrC98GD8GCnk8n9KBhi/LX9b3YXdD4vaBrHvNmDzd96lxeUXC+WCDlQ+tvnHGJe5p4qR+r8MUGPannrh7Yrxyt8lCKh7aV/KVGwE0ku1hgyBQzhS6Bz/u5blp45zrQgDI2m+38XzVhKQFKjYJSbhi73d4B+PGvmMvv8/DdS22wRcbR45HpCKLIDN1OxhiXUF+Jo4m/Iqs/PsWJz+34gvxo09xQ68odzsTE5znaPo21n20iUrMSpQVh0CS8gkLYqgjqPVgPr81H6sBDfL9mIxmb8xBUDUGDoOBwomKC6T+0Fn3vqnnF2KoKSw/JfLrewKlcEZte461VEJOmMfbD7/D1LaLa9OFM363Howu4uE+6HCjCIcIiVqK6HHhUL7Z0D5Y5QTzcy8vADx5HlHUsvu19EvY8zjPDW5AddpQStRtnswLVDSFmF7IEodZcGlf6hkpjXiWhQOaePrkYNAFZEzBqxbx9Zwy9k1UEAc4UCjT5QMQr5PFyR5nNp1dz4INMrIqecqN6Map1DClJv78QtPzQetkOPylS5fgvMuV/rYKnqjDkWzO70iUqhquXJbh9PdjJ8GlXJiiIIrzb20WNGIXvduqZt09Hz1o+BtX3kRDxz9I0+F8ivUDg3ulmzthFnu7gZkgD31+Rb7HZZrPdcLlzKX+cUqOglBvGbrfrgCzg2g0MbgK5GQeZ905f2g4dS2K9rgCU4OFu87fUV+J4ztMRgIw0O48NXUx+sYtXv2xH1XqReNfo8f1owD/PjIBA2DNubPdcvR7evV9g73Av0nEj2YYC1AczOZU+AAG4/4u9iOLv3xU1DbalSRhbW/D4A1ufl96wM7i+wrp9QWw8KSMIYPc7ceqPIEdMI1ZdT1D+AYLlWuzS1ccyIQoxyEn4sDnIghvbORsvfbWG2V2fY1WtbZw+sxIVA6CjamwqTmcog1Pe4vx6PSFrmtJh9+081snF4SgfelXgSVdHhj4/mdCYRAAK3G7qjfsJ7F0wiCba1shg4cZjWGbvxF2+MsYuvbitto+OVf00ruj/zcqA3RkSAy/0CAgyBmr3f7nY96nju6yj4mvLDEzbfuVgRp3G7qdLqPb65Z6H+AiV2cMcl1V5lPLXUeSGp+YHWiX3qOXj1W5uTH9SIfE6ecZms719S2co5aqUKhqWcsPYbDaf3W6fDwy91XNJcmBV0NRLu1crBuY4h+PhUulfbEUbn03vybN3rmD17bmExCVgzrIgmrSLaonmlN/eAc/buodx9m3c1b4ZbTMbok6JQIq9m+hHzb9rEGhe8OyVcG2ViPrUACYNVSNQeuew8uYPl3bFlsQDnNtfEYwuKuqXYfYV8N8ztei420Ha1DtJ2T0OZU0CGYdvI6rKApQyx8kPySTxSH8mho1B0yBYhA8fbE+QIYc3Zn/N+OVvICqRWOqItN2t0eK0yuEoH15R44eyJwj/YCDN+j5PUoMe6ESR/3YqT3JEDn3GxbHyYHm8pkikOkYsu7bSgKOsPFKJeXsvXwHiwlQSI1RMOg2DDCbdpc1FsVvAatAoa7tkFKRmXXrPe8+IFw2CB1M89KntIyZIY+FBmV0ZMvdMvdJLMHmIs9Qg+BsJNsLnd7j4fENAIXFnhsTHt7tupWbBgls1cCnXptRTUMpNwW63dwMW3ep5zh7bxuKP76LzfeMvqzr4JZoGWjG4d0lkPWYk57SXNLJZV8dA01dtdK7tJ9ykIf5G3Xna8QKG9/6eFu0qMObDdqh2gV23b8CS2hBLxVDUfImosS701RVcm2S8RyQEnYYgg2urhPfQ1QeWIjRifyphxWGZh+aYKMaDwxnwVOjlM9xe/00+6vEBxq3bMQ8bhvuNN1gbVp8nRy7E7vHjeaGElvEy98x8FHVlJM/0Xk8NbQkNIzJISigg7q6fkHRWzhbEU8H3GvGD3yJ320gKtUpM7dqYBdV8GGUTXkGld6aV2hVa0GHEZ+gMFgRBoOprgd35ovscfD95L4u+2sKYD9rSslMiW9Mu75L3a2wmDZdPuELe97mObt5fY6RNJR/Dm3o5cl7ihUVXKkGWC1E5c0HJL9ikUeS63PB6obObIQ3/GSqH/3a2pkk88YOJQpfAhIGXyjhvAoXATgKCaC/bbLbSxelvoNRTUMrNYiVQBARf7wmOYi9Gs4z0B2rBLnoIrhHUzH/DQNHUwE5UAN7oI3Js5yY4kcvqxcN4Y7WVVpX8jO/vumKYxXMOM+HDHZgsOh55qTmCICCFwNEmz5JQdySmhYE2y9mPXcqaF20aqKB5BLRrdKoNf8GNIECjin48gh+HrgAMW9AV1sckxrI+dTKP+vwMOJhBe0mPv3lzasQuRJSKCDGBzu5nxe42VD0YSkdFYEI9F8YqNanU6HkAfEoGZ419iY08jaoN4eSCEhjTDNe4VqQc7s3rW9dxR59iMiqEsCjBzTr7Uk4/VR+dJhCTWB9VmYso6UiKVGleL5jFAowds4HkBjG0SLRcVMA7nS+w+qiOVUdldmUENABKPAJBRo0qUSr7z14yitILRfSSxrJUHctSf9vf/LNBALD2oRJSsyQOnxd5fVnAgFh4QEc5m8aMXTo+7+f6ny0f/P9A44oKP9zroNs4C0OnmZl+95+qTLATMAB2Ajsu/Huy1BD4+yk1Ckq5KdhsNo/dbl8IDL7WcV4/TN6mp8QjcH7GYtb/eIx6TSvywrvtiCl37Qx2gJCYRGS9iUMbZhBbvdVlr+W/bUAqo14sNTTUVIh4x82CBJXTJ5vTudVc2LsTmrRk3TGZF5Z56VwzmwiziSK7h1nv7WHrygyq1o5k+GMNECwCmqahKn6c9hz0XQoIreShYKwBa3cfxmYKxroKckX1MuPCuU7Cd0zC3MGHroKGa7NE1lAzuw7sYoPpO/KsUNS+Pizpg6iz8s3QIyQFN2XePi8LNzhYVdCT8G7d6HzAR5J/B21easGCZ0rwfigS3DuFlNMCNqtGSFYbwu681MJXp8VSxjmNM5YeIIj4jQY8z76DZ3Fn4oqmoHk7Mn/OUWbVcvJ2ikxJqJVJxnwGp4fwTvp9eMRcWpuX43a0o1ayyLCRocydUYTHfflOsEKYxtAmXoY28ZJdLLD4oI7sYgGHV2DWrksLf/MEP1O3/TF5whlDHZj10CBOoUGcQvsqflp/ZMXlEzhfLLDumMxZu0DsdXRlLOXWEW7ReKeXiyd+MF2PgVZEoIHaz4v/DgIGQGl26D+Q0vBBKTcNu93em/9r777DoyjX/4+/Z2db6qQASehdAtIEUbqIICAgClI8qIiKHAt2f0c99nr82vsRFeVYAFFQFARBVERAem+BAIEkhLRJ2b4zvz82UpROyCbkfnnl8gqZzN4JJPvZp9wPzDjWxzdlW7h6YtTB9xMWz6R07VoUTFp0acX1Tw0kwgZ2a2ie2lH2f5saOtP9gnqhJ6dlM/+PdQs+ZMAdH1HnvNACZc8aC1kjD9078QkPsSOPHG5+6r6FLF6wi+e/GsCw6X4KCxtgc2YSF/ktlq89kGVgdrVBdyuooWf5RprGXR07svXtkTSo25R+49457smIroCfUmMrNe2HdiX4An7SznPi8pcw4dlOpA+9GNOiYL4ykmBJH6bcYHLZeQYGLnwT72TZhsZM7Xo/P+0IEjSsqEYC1vdfw2416dqzI6N/6ogFhcQIJ4qi0GjLkT3s3eof7LONwOvSsahWgrvvxTrifmJ3b8Gb1gNFgeieCQy7Mp+Aoybb8tYS4TMwFYX7vT2I4ND9ouNTGPnkT0c9EvpoHp7l5Os1NtY+VEyJV6HrK8ffcnhlGz8PXubFNOHqD6JIjDKYNtZ1REvhD36389ICB1Y11BfhRN0TRcUxjNCg3WH/PEr4+whAmgSAqkNCgSg3uq73Ag42y88rVXhnkZ17L/USaYNL34wiSz/sZUUwiKs4B9umTdjXrcO8ZjQk1jzuY0y4xEuPegdY8vJFxKc0Zcj907HanRRNtpH3nJOYoX4iLwsQcUngb1MDe9ILufbyqbjqGpjXRdA5YhDzJ24jasc6nDaTAfel0vayOhR7veS5Ctmet5mfMvIo9Foxg36s/mKSIhQSE5MxUfEGFawWFc1hxxNQKPVDpisfUyklOTKJulERpOWuRvfs538vTALgqk/vIRiXSMq6WrjdDvL3XUOHpBS+vbWE3JhReNU1KKYFFBt6cTKlGR/SNK4e6Wv28OAtP0BQ4UXPraCABYiw2ag93iD+ft/BrzdtxSw2pU2g6Qg3zphYRj03n147FW6eWwPLZ2NRInw0GWhld5yLkZ3eZHtMTSwmfLW6DZsaBcF55Jx/95FP0aLr8BP+/bv90P6F0GjP9Rf5mL7adtS2wsPa+/EGYNZ6G28Mc9M3NbQQYd7m0FqLazv6eKz/oRGQoAGzNlj5ZbuVgKFU+IFJ4phKgNUcOQKwXQJA1SbTB6Jc6LquAM8c/mdTVtr4bLmdn7dbuaRZgCzdwlVt/QdXsn8yxkuSkc2U/zzI92lX0mzffO5+YBD+oAVvELx+BW8A7v4qNH/funaQN3528AZ1qd1gA8n73sX7+WSuvWEcOaus2ONMEp/2oJQ9Ybj8pezI28q2vI1sy93Ez2mbyHX3hO0KEU+6WaZ8guqPIdC+Hf3H/MiP+beSsaE1qmJhY85aAJpFJFFgrY1e6qYwEEV2IIHMHC8WJYAFDwYOTNOKqnhRFS82yz6cVoN9+XXZkathoGKajVlXN5MIvwPP9kZEmQ482FAUg5TELHbl1WH6po107rKGOO/tBCy7CSqFJFvuwFmvLmAS2zEl9EWpJs0Xu8haGuA/7l9oPb0Zg99vR4mZTr0HarNt2Qx+/fwRUpp2p0FwAnlMwDAt7G1rwbJExf7aR6jBBDaYVt5LKiHf5iLGZ6I7TJ7vWIdfZuaQ/s7zzJjx0MG/x0VTHkNRrTS/6KqjjhjkFCv8b7mdiYsPTRVMXmanTpxx1FAwffWhKYbc0kMf75sa4KYuPj783U6bOkGGtAmFBdUCQ9oEDr4vwmYD8CuwlFAA2KZpWoV1MxIVQ0YKRLnQdX0w8M3hf+b2w/PznAfnme1WSIoxyCg49DKvZozJQ/Xe4qePv2TJxsu5+/FeXHND64Mf31uo0OetaMZ383LXJaE57B+3Wvlxi5VFm0oIBAJERdXgmekKudFww6Ii7KrJO8teZOr6SQfvE+dMYM/qQXi+dGKz+InvugPD4aYwsT7Ztkeon3w70ZGhU6Bb1Difrg0upUejvjSMa4KiKPg8JRRm76BGg9Z4Ax5cgf3sdPRDdfcnwnUjpb5Sckqy2e5/GpcrmtlrMzGUAgBUM5E7106k4fxevPTbZD6MG0G01c5Ns2eTngdObzv+edV9XFDPS23XFyj8fe+dzxfksTvns+SXPYy/vxOjbm7LAZeLAVO/ZNxn3blgcwOW9JzFuoaTSE2px523/h9WewQGLu798Tvmr+7PwpRYcp+JpqjksDbC1wcpMV3kKi7Oi2xP9+xC3l4QR9HmzSyf9Qprf5x48NpaDdtSN7Ub9VtdQmK91mzJsTB1lZ2v19rwH/Z8He0wKfEeGQaaJxncc4mX9DwL9RMMAkFolGj87ejpQBDGfh7Jyj0q7eoGebSfJ1x998Uhy4CrNU3LDHch4uyTUCDOWNn5B2uBVn/92J50nfvvXUJR0MauNv3B4WBoOz/Xd/Jx5fuhNQAmJkMLRrP1Z4UcvRnPvtKQpql1SGnWia6vxpBfqrBwQgkpf1mY/N3HTzN/i0Lh+c+ybosVewByIwMkab+D82ZUSynP932X5jVaolpi6Xnjx9hWGCzf+k9U1YJpmuzM38/oT2qxr9CK39zOo5fX57Zux+/KYhKk0P4WhY53AbCY0cT5bifGN5TMqJH4LTv51+R89uYFUIjgg2veJP2Ji0j4oQ7q6p9JjfYTsOzmreXpfLIigm9vm0kthhPnnYCFv2/XO1yfNh8x9LpWjH/gIgB+27yah6Z9zogF46iXX4P7xk5CjY5GURQe69qVES1asDj7R8ZO6oFqJlAv18qzX4aesGtEm1gUmPPxU0xe9T12awaWAwd4e34crRbuBGD13PdY8d1rxLXwUruHhWWT6rK93ousNXuTX6pgVUNP7ttzLLRIDjL1Rhd2FVZlqGw/YKF/Sz9axDG/nKMqdMFbvzr4YbMNRYF3R7jO5n54cXzfAqPkpMLqQ2bmRHm4jqMEAoA3n/ud/ZszKN2yk4hP36GZI4/rO/k4L8ng17tLaJkSREFhVaOJXHtjAgR1XnjoN2a9PoYPJqSSeUDHCPqJZT+GESRz21KK8/aSm7GJgq1zGNA4k0n/cNGkcQ777CXo7ny2ZaeyL2sx3ZO3kBTZm32lCkNnzsTQFCIsVgry3AAoikKTxGTmjLfQodE8II43Fibw5i92vMcYqTbxU2z74mAgADCUEvId/2F3TCf8ltCT6bOj43lsRDzvjo+i8KMSEn5IwYmfBM9T5ETcSb7jJVrV2wBGNL8tf4sE74MnDAQlRV6CholiUXAX57Fg0j1sfmcU1+XOwpk6mtZTMlh86620rFEDgKcWL+ben36iXb2yA4WUfPbUcHHX9X6iykb6S30l7JmUz/DW3bmkcT/MqChuv6yQbet+BKD95eMBsF9Rh+nZ/2Jyzd9Z5O6NaoHGNQy6NwmwPSf0a+S1oW4c1tCisw71g4zscOqBACAuEv7dz8uH17rwBmDYB1EM/yiSHzbJbGcFex8YKoGgepGRAnFGdF13AtuAekf7+MBOn1BcdGjz/pWjUrn38W5HXPNrmsq4LyIZ2cFHq5wlvPbkIpLjt9Cv60LylPpEmfk4KTl4vd0ZTcDvwRkdz3mjHuLjHZ+ybv8qvP5a7Mn6gUiHwuUdZrFozVhKsw/gjsklXttKh2WF7NlQwNQFI/+2/fHWmcOxqxquok9ZvPPPpe8KH4zOoW2DXdisXgxFp8DxOj51EzajMcmuifgte/Cpmyixfo9P3XTwfjG+UVh/Hk7pxCbkLHESwCDBtJGybSEq8diMeihmDLfNm8fiffv4duhQGmraMb/Pbpefm4Z8Tfa+Yp58pQsZi2/DU5xPw7Z9aN1rDDUbtD7iel8wyDurVvHemjWMaX0eH8y/EBS4bdCLfLd0FPe81pHCCHBb/LTN8mBR3GwY8D05V/3G0sX/ozQukoeGfATBfnz4m8GSjGIURWXw+XHc08vL8z86+WmrlcRok7yy6Yg/+xiUJ90NM9ba+HKNnR0HQmtSLk/107F+UDocnl2PAc9I34DqR0KBOCO6rt8LvHysj/dsMfGI9+esHENk1N+H5x+f7WTqShtv9t3HMzdMBeCmcZF4058+4rqE2s1RLCqxNeqR0zaV95a+hyu9LrlqKv36L+OBgbkYpgWrGmTBvH/z0jVFmKYVi8WKoig0veZSel7RnCY1gkTYYG1WEYa6l/k7B3NZy6aM7dmU4a/dwd68OigomBz6+bCqAd69cyQd4kYS7R+Chb/3VTDxEsxx4l5sJfchJz6CfH7bfJpOsHIdnf52fU5pKT0+/xyAjTfdhHqMZfU7t+Vz4+CvuOexrjRJ/oOlM15g0D2fk9z4gmN96zFMkwnz5zNniwc9awiDe77MzX0+pWFJL4qeeJOJtSzMc5tc/5tKjy0KJkGevXIumckvU+wbgG4OIsJaL9QF0Z+LxR7g2g42Hr/cTlaRQozDJNoBrZ4NfR/ORij4kz8ILy1w8MVKO74AWFV4rJ+H4RdIl8NyFgTGyZHF1ZeEAnHadF3XgB1A4rGu2Z9ZwoTRs8jOPPRKv3PP+tz5SGfq1I/F7w+iKAoL0+zc+WUE1sIDJHz3v4Pb6/r0Vdizfj5RES5K3ZEEteF4vQESUqz8lvYLak5tnIZGnsdLZEyQ+OalJMc1JKdgK3tWJ2JFJc7pJGBY6Dh+EBmxjUg7oOI3/BiUYCp+PD4Tj8+kb8cZ9G2zh6aJEUSrtXnx+4tZsbMxCiomfgzFhcWM5Y/7/MQdNiy+t1AhS7cQIpWbawAAG/JJREFUVwpcEwm+0BC6Pcngv899y+J+G5hbejuOY2z2uWbmTNYfOMDgZs148ZJLjnrNvt06114+jUHDW9DA/hxGMMiIx098MOXKPSrDJzko9ftxxP+EI/p3vl3cF8cf3UiZ4mbHhe3Iv+NV4mZegctnwWMzealzAWtqF2CP/YWY2J8pLf4I0yzCtHhRzUSSYq3c2cPL8Av8bN1v4cr3o4iNMPnj/pIT1nOmPH5YvVflv4vtLE23cnsPL7d196FaQo2x7DLDcCZcwDBN0+aEuxARPhIKxGnTdf1p4N8nus4wTH5bsItH75wPQHSMHavNQr8hzZny0ToA+g9rQVbrS5m7IBvl+xm06doARc9h54YMFEysjmjsTietL0hGi3eyavNmMrKz6dKuPbfe2ZWnZy9m85oc2BWEAhMiDGK6FPPS0zk0rZNCbu5+EhpuwR5sjdfQSSvI4qMf7ifgacHvaTkka1783l64fCe/zCbaDR4bBKxQJx/um6OQWAyrGsKMjiaxXfNYM+gDnovpzPBA+2Pe54DLRffPPgPgh+HDjzqNsG5lNnf+YxaYJqP7vgLALW9uOW5987eG9v0bBgRMg3y3hybubN6ZUYvE516m5ph6FOTMoOjS/7KPFJ7qo/DITEgsNqnpz2TakOeZfdEMMrN2gaWAhzoMYfO+V0mnI5v3q3x4rYtXfnKwPlOlU8Mgk6+ruKlntx+emO3km3U2xnX1ce+lXkZ9HIk3AG8Mc1M3Tn6vnaIDwBWapi0PdyEivCQUiNOi63oyoVGCk+4tZ5omphk61vjFR35lw+r92GwW/P7QyvLBI1ow0dOd4NxvoKSIgqFX06BGPa5L7EdcwwFc2rI39ZND+/VX7lvCPbNv5OnL3qBJjc5cNmXKwcfRHA7eHViHlNpfYaj7CCh7UHDiCLbGo65ENWsQGehDrG80Gfkl3DB9ILdf9P+4utWN7My1sCvfQr5LodSrYLeafLPOxsasIw85ap4Fj3wTGs5Y0Ap6bgFTgVf6mWyqC1ec72fadg9uv4VEI4rx3XyM6+o9uMDvry6ePJlCr5cOycl8NmjQ3793hkH3Zm+TGJ3OwB6zgeOHgg1ZFkZ9HEVqUpBJo1088usCZqx14szozMfTNHzxLr5+6isufGUIdTOjeGtsEWOvdvDUB9A6zc8zyxdTYu3Kv8cP4Q/jfwzqMpM+We9jLW1Ft7H/ZdiHUaTnHgpQzw3ycHW7ih3KN024b4aT2RuPnI66r7eXW7oc5xAK8Vc7gMs1TdsR7kJE+EkoEKdF1/W3gdtO9/NN08RV6icq2s7GNfu5beShk1Ij6sGujaHZfOeYiUSkZGF3RmO1WOnRsC/dG/bmuy1fsjJzKde2ncCn22LJdYd2FKiKwsabbz7yscrWBfx5ZPLh1mWv5OF5t/PmwP/RKKHZMevdW6gQ6zSZtspOzRiDBvkQc0sEFgVQILJXgNfbw9f7Q+PXDhtkR2XhL4qmlnFo7cGj/T2MaO/H+peDFF9YupSP168HYPPNNx/RJKg4by8/TryDr2YmkJHTmNEDPqVuiy4MuOPY0763T4tgZYbKxwOzmJeWyctbcggGNAYvbs11K2JR+LM1rUn67V/R/qkHaPBuDbZNrMOIju/TN2s5Y9dcwZroIPf1yyU2UqGxmUGB24k1sSX6X04x/GZc6d96DlSE3BKFbq8eaqXcqIbB60PdNK8lWxhP0nJgoKZpOeEuRFQO6hNPPBHuGkQVo+t6U2ASZ7ClVVEU7PbQM2Ot5Gg6davLH4v24ir1EyiCaIdChGrQJ+kPWjVM5f4r38ZhdfBL+jx+TJtFVvFeAK5q/TBzd2XQtlYtMktKeLV3b5rGxx/5WGX/HU1SdG2GtBxFrejk49Yb6wSHFS6oF6RFkoFjlhXvCisxw/zUnuoi+soAvTsEuKOnj4aJBtlFFtIjM7FEuhnXKoIDxSolXoVf06xMWWVHVUxaJhsHD5MxTZNZaWkA3NmhwxGPvWrOW+xYs5Dl2wZjWDRWbW5Lk8QZpDTrRExinSOu1d3w+Qo7ny6x4M3OY+pyC8t3xOH2NcGuNOBihwNbpIeP2qXRa3ciMXZo9lwnFHsBJedn0+G1QrZEN+OH1lfRf7mFWsUW0hJ82M/bSKyZgLNwBb06NqF7M5MVew5N4D9yuTcsJxdG2qFd3SB5pQrTbyrlps5+EqPkhc5Jmk0oEBSGuxBReciyHHE6nqac/+20apfE9F+uBWDZrxk8eMscmiYvppHDA2u30urmdrSq1Y5xF97Lol3zqRNbn5a12uIPBulaJ4Mf0tO5slkzLmvY8JQfO9IWdcJrCnGzIWs/pZsV9vXMotP7PSi+NJtlL27hgFLCRjWbHKWYLIuO+2I/8RdFEm/RAbjUNZpH+jSiyAPLdln5YqWNF350MmmZg5su9tKnRYALklKO+dgW1cbWXc3Q83VUmxObPQKtVkPmvjeOLmPepyCmMxsyVdZlqvy83YovAPhKwYQJ6R/QJX85KV1T2ThmNGtysllTWopaVIRvXgPsLOG8wRqZj5SQP9pg38KBNN9xFbMXKqREmmCYJDUYxhU9ihmsPsEP7/yLAc2T+MNz5AmVNvXotVeEbk2CdGviDl8BVdOHwHhN06R3tDiChAJxSnRdvwAYeTYf46Ie9fh5yy2smu1l1Q+hdU8F2TuIT26C0xpBn6aH5twVRaHQ6+Wf7dtzV8eO5VrHXqWQ3607+cq2lnXqPm56YiDdp7YjjgaU4uOta2ez0hma1080o6lvxNMhWJ8kI4Yv7CsP3udXaxp1jDhqOqPp0wL6tAjwe7rKO4scPDfPyXPzwGkzyfcMxRmVRmaRn9qxoXny7EIfa3fqNKmbzuLVnQgGvHj9Jj84n2WjXePxyXWxR6pYrQ5qxxlc3dbHlJV2Ordy8OqDUyj03IxNHYTRKpW1s75mZmEBwxZ1ZmPb5ajBIB93LMaaU5PW267BGxFgsXMH64unYyiDKfVEEe1QuePTz/miz4XUvLA1PiK4alo7ig5rtDTlxtJy/b6Ls+5J4EnpQSCORtYUiFOi6/pcoO+Z3qcQN/stRZxnJB33uv9OaIHXaSXBGsvAuz4jLqnR367xB4PY1DN/qWpissOSyzzrFn6wbWabZT8ATY2a9PO3pMvq84gd2hhL2VSEtmY/wQiDCNNGzF+6Ec6wraUYD/91/E6eEtqq1yqYwleuI9c7bMiysD5TZWNWkIkrc/B7klAsAcZ2stM03s+zs0KvgGsU7cPz3c+h1RGmSfCSi2lYv4hkz1JqmxtIMTYz4Y0lpOepDHg3imcGerhi8PPkuUdhTXXi2aaB3w+WUvY7QidRxhhBFrXczqvDfmLWiBE0TYjhoTlZfL2yHqbio09xDvd+U4OCUoOV58+kR2wzMpb3Yl3tAJMus3PVBX6+WhMKL+sfLg7raIE4KQah0YGJJ7xSVFsyUiBOmq7rl1IOgQDgSeds5tg28YinHylmLD0CTbAf5Z9j3gN38F6dbXRekE7ps/0Z8chs4pIaH3HNsQJBMR42qdmkGBp5lhK8BIky7axV99E90IR4M5JfrGn8ak2jSPGwXs0kXwm96m0frMfDnr5cGGxACyMptCahFXinudE/suP60Ua8EYXlGJn6Kn9bALoGG7NW3ccqdS/TbavZpeTT0Ew4eN35KQbnpxhcNX0eMTUL8bnqU5LXlQ+XGsQd9nW57CmHVkUoCkaT9gR9vxBvZlDb2EgERWz69VM2xowFoGP9ADVS51FrzwdwANzRLciyPkVpUWtqeT3kR0RRolr4btx6Arm1+Pf8vdzc5gK61WvMgGYmcZHgTb4Wyxt5/HzFOHouvY1iI4HChCDdd9i4rruPpIFeIuwmny+10+PhGBY8XEzksZsyivByA8M1Tfsu3IWIyk1GCsRJ03X9EuAnOMaqvVOwWs1gVOTHB9+PNO10CjbkNm932hi1MTF5w/4Lk+xLKSjdjxkMcv6KLPrU68/w2ldTx4w77v13K/mMi/yC3Zb8E9ZS04whzoygZTCZC4P16RpoTIp57Ge30rlWcu6KwN4iSO1pLpRjbDM83Ap1D6MjP+F630X8w9eRBocFg6Bh0OSFfIqKk7CpFgKGDxMLKhbsfheRURqqakP5+gswDMyrRoKqYpoGPk8JQb+HSLOAWDObgpjOqKqd2nEGMRk7cORkUieQS7BhQ2z1W3Pdsw4CBgQN+Mf4LAp9KkYgCtWikOg88qAC1QJ+w0O+q5BBOyMZ/5uGVzWJD2YSFZVEwr0BtFt9dH4ohtf/p/DFZSavfXL2uhqK05ZLaEHhsnAXIio/CQXilOi6/gTweHncqxAXaWoubvx8a1vPLNt62gTrMM01lrnWzdwVMR2ADwuvYfquqcxq5cdqc+DARpIRgx2VNMsBegWa85xnEPFmJAGC7LEUMDpyMn6CPOrph08JUNOI4YClmH87v6NLoDGD/a0psLioZcTQP5CK5RQ3UpTOs5IzIYLoq/zUeMqDcvyDFclVShgb+fnBKYnzg7W5xdeFPoHzsGBhUZqF6VsyaFNLo12yxjUfKxQH8omv/Q3v9evFxbXrkldkkF5oo3OjIHvyLWzab+G7DTb09MW4czax19KWA5amOCLjGNJeocSrkO9ScPsPZbj4Qrj7bZU1F+/m3YtK8ClWCsztWG2FfDCgDxFqNHkl8MfeNKaunY3bm0qpuzPRjgjquGK4bbmPtluKsNqdOCJCWwEtY3xkv+lgdUP4x4IiYo5/rpOoWOlAP03TtoW7EFE1SCgQp6TsmOQFQM/yvO81kR+yXg0d194+WI/VagYAb7mHc1ngvNBj4ybLUsQ02yqKFS/FipefraHfdXasXO5PZbF158EpgHfdI+gVaF6eZR6h4A07he84iOgSoNZrbiyxJ/6c3Uo+P1u385l9BXss+bQL1uUd93ASzCj2FyvM32rl7V8d2FST+g2/Z3nOdt7u04feJ9hVkZWZyUWvhrZidglM4tP/jMVqP/qz8/57HLxe8hC/dZqCYarsN+9AtVi5u2NHGkXv4Yu1H7CzYDuRtiiK3E4ysmaTHJNM50Ywd7NKq00645bFkBK0Eu0MHb/sC8DG5ibdp5VSM1p+p1QSKwl1Kdwf7kJE1SGhQJwyXddrAq8B15bXPcdHTD34BB9tOugeaMK/vf1INE+8XTDNcoBP7MuYaVtPajCJIf421DXi6BFsWl7lHVPx1zbyHnNirW9Q82U3jtSTa5oTxOAb2zqecM7BmlsLy5RR5GfUwG6qNK1p8OY1bholGpimeUQjo2Mp8sAFz9nwuYsY4nuYwW1NLhv72jGvd/tdjJs5jN2FO/EbkezwDifZWUCc8g2N4psyqs3N9G5yBZ8si+KlBQ6+GVdK/QSDb9daWPjpQxQHG3P3jHtIiDKxxZtot/iIucZ3UsFIVIi5hM4xOPsHUohzioQCcdp0Xe8BvAW0PtG1J2Ji4ieICx9xJ985+QgBglip+CXw7mUqB+6KwDSgxhMeInoEsEQf/3OmrrKxfLdKy/P388i0RLwEiOq7kJRWGVwX14AbfBcRy4nH4U0TXv7JwQe/2zFNA3dxLo2CyzgvoZCnJlxFcuzxf76zi/exMP1nPtmwjTW5kdzQKpkXLr3+YBC58v0onDaTqTe6yN65imUz/oN7BSRlXUmDrFuJudpPzec9J/29EhXiE+AWTdPkCElxyiQUiDOi67oV+CehhkbVdu25b7uF7LGRBA8oKFZwtA9iTTbABiVf21DjTOovDb1oM01IfebIY5cfH5pPVvtFbLXk8It1OxGmnVpmNDGmk8ZGDZ7w9CeSv69oNE3o/VYUmYUWbFbw+Q3izQwOBGridEYy+58lNEg4/s/4+Llz+XnPnoPvP9mtGyNSU8nSFXq9Ec39vb3c2KmELx6/FHdRLl2mr8bmiMLmjKL2V6U4WklL4UrkWeBR6UEgTpeEAlEudF2vBbwA3BjuWsLFDIBnlYp7oRXPChXfZhXzsH5x9ZcWo5Ztmpi72cqkpXZ6Ngsw5iIfEYctVNxgyWKGbS35SilzbJsAuNLfhv94rjzq467PtFA3ziQ+8tDP8qIdKv+cGknDBIPPx5QSe5xBh4W7dzNz+3aWZ2eTX3aGxB0dOtCrXkOyCuI4P1mFAyv4/s0bMIIBLp7zG9FqQxztDGpPrbiTEcVxGcDtmqa9F+5CRNUmoUCUK13XLyY0pdDhRNee60wvoELJDBu5jzqxphgkvevG3uLkXlkbGLSMeRbg4K6MU7EkXeX9xXZeG+pGizjx9QAT167l5T/+OPj+iBYteLJ7dwDcxXksnPwgTV/9CJs1Glt9g7rzpJthJeABRmmaNjPchYiqT0KBKHdlOxRuAp4HEk5webXgXqKiT7KHdimcwpKJJWo6PgJ0DzY55W2TEJpeOIl1igelFRQwcHpoK+g/27dnVGoqtaKOXOxZ/GUo5NgaGNSdK6EgzILAYE3TZoe7EHFukFAgzhpd1xMJrTUYTzk0PBKVQ7AQ9nSOIe42L/F3+sJdTnU3TtoWi/IkoUCcdWWHKL0FdA53LeL0BbIV9vaOxgyG3k/5woWzfTC8RVVvz2ia9mi4ixDnljCcgC6qG03TVgHdgBsAaaRSRVk082AgAHC0lUAQRpOBx8JdhDj3yEiBqFC6rmuE2iRPgDA0FRBnxPRDyTc2IjoHsNaR3x1hMp9Qp0KZuxHlTkKBCAtd11sBbwK9wl2LEFXIOqC7pmlF4S5EnJtk+kCEhaZpG4HewAhgX5jLEaIq2AsMkEAgziYZKRBhp+t6NPAwcD9wgvMGhaiWdKCbpmkbwl2IOLdJKBCVhq7rzYHXgX7hrkWISsQPXK5p2sJwFyLOfTJ9ICqNsjPfBwBDgF3hrUaISmOMBAJRUWSkQFRKuq5HAA8C/4KTOC5QiHPT/9M07cVwFyGqDwkFolLTdb0R8Aqh0QMhqpN3gDvkxENRkSQUiCpB1/V+wBtAs3DXIkQF+AYYqmmadIgSFUpCgagydF13AHcDjwJRJ7hciKpqGXCppmlyLrWocBIKRJWj63pd4P+AkeGuRYhylgZ00TTtQLgLEdWThAJRZem6fgmhg5ZahbkUIcrDHqCXpmk7w12IqL4kFIgqTdd1G3A9MBboEuZyhDhd6YQCwe5wFyKqNwkF4pxR1vzo+rK3emEuR4iTlUZoDUFGuAsRQkKBOOfoum4hdNDSGGAoEBHWgoQ4tm2EAoGc/yEqBQkF4pym63osMAy4AegR5nKEONwmoLemadnhLkSIP0koENWGruuNCU0t3AA0DG81oppbD1ymaVpOuAsR4nASCkS1Uza90J3Q9MI1SM8DUbHWAH00TcsNdyFC/JWEAlGtlR3bfDWh0YNLw1yOOPetBPpqmpYf7kKEOBoJBUKU0XW9AXAdoRGEJuGtRpyD/iB0BHJhuAsR4lgkFAjxF7quK4R6HowBhgOxYS1InAuWAP01TdPDXYgQxyOhQIjj0HU9ktAJjTcAfQAlvBWJKmgRcIWmacXhLkSIE5FQIMRJKjtz4S7g/nDXIqqMhcAgTdNKw12IECdDQoEQp0jX9eeAh8Jdh6j0FhNaVCinHYoqwxLuAoSogh4BJoa7CFGp7QCGSCAQVY2MFAhxGnRdV4FphLYzCnG4AuBiTdO2hbsQIU6VjBQIcRo0TQsC1wI/hbsWUan4CY0QSCAQVZKEAiFOk6ZpXkI7E1aGuxZRaYzVNO3XcBchxOmSUCDEGSjbZtaf0Gl3onp7UtO0T8NdhBBnQkKBEGdI07QDQF9Ajr+tvj4Dngx3EUKcKVloKEQ50XW9JaFGNQnhrkVUqN8InXjoDXchQpwpCQVClCNd1y8GFgCR4a5FVIg0QjsN8sJdiBDlQaYPhChHmqYtJbRN0R/uWsRZlw8MkEAgziUSCoQoZ5qmzSV02qIMw527fIS2Hm4PdyFClCcJBUKcBZqmTQXuCHcd4qy5SdO0ReEuQojyJqFAiLNE07R3gCfCXYcoV7nADbL1UJyrJBQIcXY9BbwV7iLEGQsArwHNNE2bHO5ihDhbZPeBEGeZrusW4FNgVLhrEadlLnCPpmmbw12IEGebhAIhKoCu63bgW+DycNciTtoO4B7gO03T5BelqBYkFAhRQXRdjwLmAxeHuxZxXCXAM8Br0pBIVDcSCoSoQLquJxDqetgy3LWIo5oMPKRpWma4CxEiHCQUCFHBdF1vA6wC1HDXIg5aDkwoaz4lRLUluw+EqGCapq0DXg93HQKA/cCNhFoVSyAQ1Z6MFAgRBrquxwCbgTrhrqWa8hPaYviMpmlF4S5GiMpCQoEQYaLr+jDgy3DXUQ19D9yradq2cBciRGUjoUCIMNF1XQFmA/3CXUs1sQ24W9O0OeEuRIjKSkKBEGGk63pTYAPgCHct57AiQp0l39Q0zRfuYoSozCQUCBFmuq53Bz4Cmoa7lnOMCUwCHtY0bX+4ixGiKpBQIEQloOt6JPAscBeghLmcc8FC4D5N01aHuxAhqhIJBUJUIrqudyX06rZZuGuporYCDyCtiYU4LRIKhKhkykYNnibUd19GDU5OLqFjqt/XNM0f5lqEqLIkFAhRSem63oXQqEHzcNdykkzgD0LdAXsCrSvgMX2E+g08p2maXgGPJ8Q5TUKBEJWYrusRwJPAfVTODqQHgB/K3uZpmpb75wd0XW8ODC1763AWHnsKoXMKdp2FewtRLUkoEKIK0HX9YkKjBi3CXIoBLAPmlL2t0jTNONEn6breCLgaGMaZnxL5O6HmQ8vO8D5CiL+QUCBEFaHrupPQvPkDVOyowX6OHA3IP5Ob6bpel1BAGAp05+TXTewE/h/wlSwiFOLskFAgRBWj63on4GMg9Sw9hAEs4dBowJqTGQ04HbquJwNDCAWEXhz95MhCQgsv39Y0zXs26hBChEgoEKIKKhs1eIzQK+fyGDXIIjQSMAeYr2laQTnc85Toul4DGExoiuEyQiMIbwNPa5qWV9H1CFEdSSgQogrTdf1CQmsNWp3ipwYJzc3/ORqwtjINyeu6HgdomqbtDnctQlQnEgqEqOJ0XXcAjwL/4ujD73/K5FAImC9b+IQQfyWhQIhzhK7rHQitNTi/7I8CwG8cmhZYX5lGA4QQlY+EAiHOIWWjBuOBPcACTdOKwlySEKIKkVAghBBCCKBydkgTQgghRBhIKBBCCCEEIKFACCGEEGUkFAghhBACkFAghBBCiDISCoQQQggBSCgQQgghRBkJBUIIIYQAJBQIIYQQooyEAiGEEEIAEgqEEEIIUUZCgRBCCCEACQVCCCGEKCOhQAghhBCAhAIhhBBClJFQIIQQQghAQoEQQgghykgoEEIIIQQgoUAIIYQQZSQUCCGEEAKQUCCEEEKIMhIKhBBCCAFIKBBCCCFEGQkFQgghhAAkFAghhBCijIQCIYQQQgASCoQQQghRRkKBEEIIIQAJBUIIIYQoI6FACCGEEICEAiGEEEKUkVAghBBCCEBCgRBCCCHKSCgQQgghBCChQAghhBBlJBQIIYQQApBQIIQQQogyEgqEEEIIAUgoEEIIIUQZCQVCCCGEAOD/A3ExO0na94wUAAAAAElFTkSuQmCC\n",
                  "text/plain": [
                     "<Figure size 576x576 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "import matplotlib.pyplot as plt\n",
            "\n",
            "lh = pymaid.get_volume('LH_R')\n",
            "lh.color = (240,240,240,.5)\n",
            "\n",
            "nl_lh = nl.prune_by_volume(lh, inplace=False)\n",
            "\n",
            "fig, ax = pymaid.plot2d([nl_lh, lh], connectors=False, linewidth=1.5)\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "You must **not** use :func:`pymaid.get_user_contributions` or :func:`pymaid.get_contributor_statistics` with neuron fragments as they are not \"`fragment-safe`\". That's because these functions use only skeleton IDs to query the CATMAID server which is totally oblivious to the local changes to these neurons.\n",
            "\n",
            "For our purposes, :func:`pymaid.get_time_invested` and :func:`pymaid.get_team_contributions` are `fragment-safe`:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 6,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
                     "<div>\n",
                     "<style scoped>\n",
                     "    .dataframe tbody tr th:only-of-type {\n",
                     "        vertical-align: middle;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe tbody tr th {\n",
                     "        vertical-align: top;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe thead th {\n",
                     "        text-align: right;\n",
                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>total</th>\n",
                     "      <th>creation</th>\n",
                     "      <th>edition</th>\n",
                     "      <th>review</th>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>user</th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>robertsr</th>\n",
                     "      <td>588</td>\n",
                     "      <td>249</td>\n",
                     "      <td>210</td>\n",
                     "      <td>177</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>koppenhaverb</th>\n",
                     "      <td>225</td>\n",
                     "      <td>96</td>\n",
                     "      <td>9</td>\n",
                     "      <td>33</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>lovef</th>\n",
                     "      <td>198</td>\n",
                     "      <td>0</td>\n",
                     "      <td>15</td>\n",
                     "      <td>162</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>schlegelp</th>\n",
                     "      <td>171</td>\n",
                     "      <td>27</td>\n",
                     "      <td>30</td>\n",
                     "      <td>66</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>ranftp</th>\n",
                     "      <td>105</td>\n",
                     "      <td>69</td>\n",
                     "      <td>39</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "              total  creation  edition  review\n",
                     "user                                          \n",
                     "robertsr        588       249      210     177\n",
                     "koppenhaverb    225        96        9      33\n",
                     "lovef           198         0       15     162\n",
                     "schlegelp       171        27       30      66\n",
                     "ranftp          105        69       39       0"
                  ]
               },
               "execution_count": 6,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "time_lh = pymaid.get_time_invested(nl_lh)\n",
            "time_lh.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "There is currently no pymaid wrapper for the number of nodes/connectors/etc per user but that's no problem:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 7,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
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                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>node_id</th>\n",
                     "      <th>creation_time</th>\n",
                     "      <th>creator</th>\n",
                     "      <th>edition_time</th>\n",
                     "      <th>editor</th>\n",
                     "      <th>reviewers</th>\n",
                     "      <th>review_times</th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>0</th>\n",
                     "      <td>331812</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[53, 13]</td>\n",
                     "      <td>[2016-05-03 14:43:00, 2015-05-12 12:32:00]</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>1</th>\n",
                     "      <td>331813</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>2</th>\n",
                     "      <td>331814</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>3</th>\n",
                     "      <td>331815</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>4</th>\n",
                     "      <td>331816</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[53, 13]</td>\n",
                     "      <td>[2016-05-03 14:43:00, 2015-05-12 12:32:00]</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "  node_id       creation_time creator        edition_time editor reviewers  \\\n",
                     "0  331812 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [53, 13]   \n",
                     "1  331813 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "2  331814 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "3  331815 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "4  331816 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [53, 13]   \n",
                     "\n",
                     "                                 review_times  \n",
                     "0  [2016-05-03 14:43:00, 2015-05-12 12:32:00]  \n",
                     "1  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  \n",
                     "2  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  \n",
                     "3  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  \n",
                     "4  [2016-05-03 14:43:00, 2015-05-12 12:32:00]  "
                  ]
               },
               "execution_count": 7,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "node_details = pymaid.get_node_details(nl_lh.nodes.treenode_id.values)\n",
            "connector_details = pymaid.get_node_details(nl_lh.connectors.connector_id.values)"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Next, map user IDs to names:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 8,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
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                     "    }\n",
                     "\n",
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                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>node_id</th>\n",
                     "      <th>creation_time</th>\n",
                     "      <th>creator</th>\n",
                     "      <th>edition_time</th>\n",
                     "      <th>editor</th>\n",
                     "      <th>reviewers</th>\n",
                     "      <th>review_times</th>\n",
                     "      <th>creator2</th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>0</th>\n",
                     "      <td>331812</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[53, 13]</td>\n",
                     "      <td>[2016-05-03 14:43:00, 2015-05-12 12:32:00]</td>\n",
                     "      <td>adesinaa</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>1</th>\n",
                     "      <td>331813</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "      <td>adesinaa</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>2</th>\n",
                     "      <td>331814</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "      <td>adesinaa</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>3</th>\n",
                     "      <td>331815</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[13, 53]</td>\n",
                     "      <td>[2015-05-12 12:32:00, 2016-05-03 14:43:00]</td>\n",
                     "      <td>adesinaa</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>4</th>\n",
                     "      <td>331816</td>\n",
                     "      <td>2015-05-12 12:31:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>2015-10-20 06:44:00</td>\n",
                     "      <td>13</td>\n",
                     "      <td>[53, 13]</td>\n",
                     "      <td>[2016-05-03 14:43:00, 2015-05-12 12:32:00]</td>\n",
                     "      <td>adesinaa</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "  node_id       creation_time creator        edition_time editor reviewers  \\\n",
                     "0  331812 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [53, 13]   \n",
                     "1  331813 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "2  331814 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "3  331815 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [13, 53]   \n",
                     "4  331816 2015-05-12 12:31:00      13 2015-10-20 06:44:00     13  [53, 13]   \n",
                     "\n",
                     "                                 review_times  creator2  \n",
                     "0  [2016-05-03 14:43:00, 2015-05-12 12:32:00]  adesinaa  \n",
                     "1  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  adesinaa  \n",
                     "2  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  adesinaa  \n",
                     "3  [2015-05-12 12:32:00, 2016-05-03 14:43:00]  adesinaa  \n",
                     "4  [2016-05-03 14:43:00, 2015-05-12 12:32:00]  adesinaa  "
                  ]
               },
               "execution_count": 8,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "user_list = pymaid.get_user_list().set_index('id').login.to_dict()\n",
            "\n",
            "node_details['creator2'] = node_details.creator.map(user_list)\n",
            "connector_details['creator2'] = connector_details.creator.map(user_list)\n",
            "\n",
            "node_details.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Group by user and count:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 9,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
                     "<div>\n",
                     "<style scoped>\n",
                     "    .dataframe tbody tr th:only-of-type {\n",
                     "        vertical-align: middle;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe tbody tr th {\n",
                     "        vertical-align: top;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe thead th {\n",
                     "        text-align: right;\n",
                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>nodes</th>\n",
                     "      <th>connectors</th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>robertsr</th>\n",
                     "      <td>5527</td>\n",
                     "      <td>1091</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>koppenhaverb</th>\n",
                     "      <td>2847</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>ranftp</th>\n",
                     "      <td>2496</td>\n",
                     "      <td>349</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>jefferis</th>\n",
                     "      <td>947</td>\n",
                     "      <td>11</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>schlegelp</th>\n",
                     "      <td>914</td>\n",
                     "      <td>333</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "              nodes  connectors\n",
                     "robertsr       5527        1091\n",
                     "koppenhaverb   2847           0\n",
                     "ranftp         2496         349\n",
                     "jefferis        947          11\n",
                     "schlegelp       914         333"
                  ]
               },
               "execution_count": 9,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "import pandas as pd\n",
            "\n",
            "node_counts = node_details.groupby('creator2').node_id.count()\n",
            "cn_counts = connector_details.groupby('creator2').node_id.count()\n",
            "\n",
            "lh_counts = pd.concat([node_counts, cn_counts], axis=1, sort=True).fillna(0).astype(int)\n",
            "\n",
            "lh_counts.columns=['nodes', 'connectors']\n",
            "\n",
            "lh_counts.sort_values('nodes', ascending=False).head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Contributions by date\n",
            "---------------------\n",
            "\n",
            "You might find yourself in a situation where your neurons of interested have already been published in the past. In that case, you could consider crediting only reconstructions that have been done since.\n",
            "\n",
            "For this, we need to subset neurons to nodes/connectors that have been created/reviewed/edited after a certain date:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 10,
         "metadata": {},
         "outputs": [],
         "source": [
            "import numpy as np\n",
            "\n",
            "after_date = np.datetime64('2017-01-01')\n",
            "\n",
            "# Get node details\n",
            "node_details = pymaid.get_node_details(nl.nodes.treenode_id.values)\n",
            "cn_details = pymaid.get_node_details(nl.connectors.connector_id.values)\n",
            "\n",
            "# Subset to nodes/connectors created after given date\n",
            "new_nodes = node_details[node_details.creation_time >= after_date].node_id.values\n",
            "new_connectors = cn_details[cn_details.creation_time >= after_date].node_id.values\n",
            "\n",
            "# Subset neurons to the new nodes/connectors\n",
            "nl_new = nl.copy()\n",
            "for n in nl_new:\n",
            "    pymaid.subset_neuron(n, new_nodes, inplace=True, remove_disconnected=False)\n",
            "    n.connectors = n.connectors[n.connectors.connector_id.isin(new_connectors)]"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Let's visualize this: old neuron in grey, recent additions in red."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 11,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "image/png": 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\n",
                  "text/plain": [
                     "<Figure size 576x576 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "fig, ax = pymaid.plot2d(nl, color=(.8,.8,.8), connectors=False)\n",
            "_ = pymaid.plot2d(nl_new, color='r', ax=ax, connectors=False, linewidth=1)\n",
            "\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Now that we have subset our neurons of interest, we can use :func:`pymaid.get_time_invested` and :func:`pymaid.get_team_contributions` just like we did before:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 12,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/html": [
                     "<div>\n",
                     "<style scoped>\n",
                     "    .dataframe tbody tr th:only-of-type {\n",
                     "        vertical-align: middle;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe tbody tr th {\n",
                     "        vertical-align: top;\n",
                     "    }\n",
                     "\n",
                     "    .dataframe thead th {\n",
                     "        text-align: right;\n",
                     "    }\n",
                     "</style>\n",
                     "<table border=\"1\" class=\"dataframe\">\n",
                     "  <thead>\n",
                     "    <tr style=\"text-align: right;\">\n",
                     "      <th></th>\n",
                     "      <th>total</th>\n",
                     "      <th>creation</th>\n",
                     "      <th>edition</th>\n",
                     "      <th>review</th>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>user</th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "      <th></th>\n",
                     "    </tr>\n",
                     "  </thead>\n",
                     "  <tbody>\n",
                     "    <tr>\n",
                     "      <th>robertsr</th>\n",
                     "      <td>168</td>\n",
                     "      <td>81</td>\n",
                     "      <td>51</td>\n",
                     "      <td>9</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>schlegelp</th>\n",
                     "      <td>30</td>\n",
                     "      <td>9</td>\n",
                     "      <td>9</td>\n",
                     "      <td>9</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>alij</th>\n",
                     "      <td>6</td>\n",
                     "      <td>0</td>\n",
                     "      <td>0</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>sharifin</th>\n",
                     "      <td>6</td>\n",
                     "      <td>0</td>\n",
                     "      <td>3</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>edmondsona</th>\n",
                     "      <td>6</td>\n",
                     "      <td>3</td>\n",
                     "      <td>3</td>\n",
                     "      <td>0</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "            total  creation  edition  review\n",
                     "user                                        \n",
                     "robertsr      168        81       51       9\n",
                     "schlegelp      30         9        9       9\n",
                     "alij            6         0        0       0\n",
                     "sharifin        6         0        3       0\n",
                     "edmondsona      6         3        3       0"
                  ]
               },
               "execution_count": 12,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "time_new = pymaid.get_time_invested(nl_new)\n",
            "time_new.head()"
         ]
      },
      {
         "cell_type": "raw",
         "metadata": {
            "raw_mimetype": "text/restructuredtext"
         },
         "source": [
            "Relevant functions:\n",
            "    \n",
            ".. autosummary::\n",
            "\n",
            "    pymaid.get_contributor_statistics\n",
            "    pymaid.get_user_contributions\n",
            "    pymaid.get_time_invested\n",
            "    pymaid.get_team_contributions"
         ]
      }
   ],
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