views.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b75f755",
   "metadata": {},
   "source": [
    "# GitHub repositories analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "492000dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:21.999084Z",
     "iopub.status.busy": "2023-04-17T01:01:21.998638Z",
     "iopub.status.idle": "2023-04-17T01:01:22.500962Z",
     "shell.execute_reply": "2023-04-17T01:01:22.500484Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b23a376",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.502980Z",
     "iopub.status.busy": "2023-04-17T01:01:22.502793Z",
     "iopub.status.idle": "2023-04-17T01:01:22.505041Z",
     "shell.execute_reply": "2023-04-17T01:01:22.504745Z"
    }
   },
   "outputs": [],
   "source": [
    "USER=\"dbgjerez\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a74011",
   "metadata": {},
   "source": [
    "## Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aea83b31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.506602Z",
     "iopub.status.busy": "2023-04-17T01:01:22.506482Z",
     "iopub.status.idle": "2023-04-17T01:01:22.509751Z",
     "shell.execute_reply": "2023-04-17T01:01:22.509460Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encontrados 287 ficheros\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(\"./data/**/*.csv\", recursive=True)\n",
    "print(\"Encontrados\", len(files), \"ficheros\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f813113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.511370Z",
     "iopub.status.busy": "2023-04-17T01:01:22.511144Z",
     "iopub.status.idle": "2023-04-17T01:01:22.753625Z",
     "shell.execute_reply": "2023-04-17T01:01:22.753147Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.concat((pd.read_csv(f) for f in files), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c31fe83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.755510Z",
     "iopub.status.busy": "2023-04-17T01:01:22.755379Z",
     "iopub.status.idle": "2023-04-17T01:01:22.759156Z",
     "shell.execute_reply": "2023-04-17T01:01:22.758855Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"date\"] = pd.to_datetime(df[\"ts\"], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f80824d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.760741Z",
     "iopub.status.busy": "2023-04-17T01:01:22.760618Z",
     "iopub.status.idle": "2023-04-17T01:01:22.804203Z",
     "shell.execute_reply": "2023-04-17T01:01:22.803760Z"
    }
   },
   "outputs": [],
   "source": [
    "# should be the lastest day with data\n",
    "ts_max = df[\"ts\"].unique().max()\n",
    "\n",
    "# todo modificar \n",
    "month_ago = datetime.today()-timedelta(days=30)\n",
    "month_ago_ts = int(month_ago.timestamp())\n",
    "\n",
    "df[\"ts2\"]=df[['date']].apply(lambda x: x[0].timestamp(), axis=1).astype(int)\n",
    "df_month = df[df.ts2>=month_ago_ts]\n",
    "\n",
    "SIZE=10\n",
    "head_total = (SIZE if df_month.ts.count()>SIZE else df_month.ts.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d6ff7dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.805880Z",
     "iopub.status.busy": "2023-04-17T01:01:22.805666Z",
     "iopub.status.idle": "2023-04-17T01:01:22.816281Z",
     "shell.execute_reply": "2023-04-17T01:01:22.815916Z"
    },
    "scrolled": true
   },
   "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>name</th>\n",
       "      <th>views_uniques</th>\n",
       "      <th>views_total</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>quarkus-kafka</td>\n",
       "      <td>10</td>\n",
       "      <td>56</td>\n",
       "      <td>2023-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>workshop-amq-streams</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>2023-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>kotlin-ktor</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2023-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>istio-wasm-filter-golang</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2023-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>workshop-amq-streams</td>\n",
       "      <td>4</td>\n",
       "      <td>83</td>\n",
       "      <td>2023-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>workshop-amq-streams</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>2023-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>workshop-amq-streams</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>2023-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>kotlin-ktor</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2023-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>quarkus-kafka</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>2023-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2466</th>\n",
       "      <td>kops-aws</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2023-04-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  views_uniques  views_total       date\n",
       "2220             quarkus-kafka             10           56 2023-03-29\n",
       "2225      workshop-amq-streams             10           42 2023-03-29\n",
       "2216               kotlin-ktor              5           15 2023-03-29\n",
       "2122  istio-wasm-filter-golang              5            8 2023-03-23\n",
       "2059      workshop-amq-streams              4           83 2023-03-30\n",
       "2337      workshop-amq-streams              4           37 2023-04-13\n",
       "2072      workshop-amq-streams              4           32 2023-03-24\n",
       "1910               kotlin-ktor              4           21 2023-03-31\n",
       "2055             quarkus-kafka              4           14 2023-03-30\n",
       "2466                  kops-aws              4           12 2023-04-04"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=[\"name\",\"views_uniques\",\"views_total\",\"date\"]\n",
    "df_month[columns].sort_values(by=[\"views_uniques\",\"views_total\"], ascending=False).head(head_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0afdeb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.817822Z",
     "iopub.status.busy": "2023-04-17T01:01:22.817698Z",
     "iopub.status.idle": "2023-04-17T01:01:22.821324Z",
     "shell.execute_reply": "2023-04-17T01:01:22.821031Z"
    }
   },
   "outputs": [],
   "source": [
    "repos = pd.read_csv(\"./repos.csv\")\n",
    "#Remove repositories from organizations or watching\n",
    "repos = repos[repos[\"owner\"]==USER]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6bdff759",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.822973Z",
     "iopub.status.busy": "2023-04-17T01:01:22.822731Z",
     "iopub.status.idle": "2023-04-17T01:01:22.847248Z",
     "shell.execute_reply": "2023-04-17T01:01:22.846799Z"
    }
   },
   "outputs": [],
   "source": [
    "keys = repos.name.unique().tolist()\n",
    "# Remove repositories already deleted\n",
    "df = df[df.name.isin(keys)]\n",
    "repos = df.name.unique().tolist()\n",
    "dict_df_repos = dict()\n",
    "for repo_name in repos:\n",
    "    dict_df_repos[repo_name] = df[df.name==repo_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7034071",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b33db4b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.848894Z",
     "iopub.status.busy": "2023-04-17T01:01:22.848773Z",
     "iopub.status.idle": "2023-04-17T01:01:22.854890Z",
     "shell.execute_reply": "2023-04-17T01:01:22.854600Z"
    }
   },
   "outputs": [],
   "source": [
    "df_grouped = df.groupby(['name']).agg(\n",
    "    {\n",
    "        \"date\": [\"min\"], \n",
    "        \"views_total\": [\"sum\", \"max\"],\n",
    "        \"views_uniques\": [\"sum\", \"max\"]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c6f79d",
   "metadata": {},
   "source": [
    "### Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e7486fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.856537Z",
     "iopub.status.busy": "2023-04-17T01:01:22.856341Z",
     "iopub.status.idle": "2023-04-17T01:01:22.860772Z",
     "shell.execute_reply": "2023-04-17T01:01:22.860478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best views over: 43.0\n",
      "Worst views under: 2.0\n"
     ]
    }
   ],
   "source": [
    "views_best_quantile = 0.80\n",
    "views_worst_quantile = 0.20\n",
    "best_views_percentile = df_grouped[('views_uniques', 'sum')].quantile(views_best_quantile)\n",
    "worst_views_percentile = df_grouped[('views_uniques', 'sum')].quantile(views_worst_quantile)\n",
    "print(\"Best views over:\", best_views_percentile)\n",
    "print(\"Worst views under:\", worst_views_percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7958a4f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.862409Z",
     "iopub.status.busy": "2023-04-17T01:01:22.862162Z",
     "iopub.status.idle": "2023-04-17T01:01:22.867387Z",
     "shell.execute_reply": "2023-04-17T01:01:22.867101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>sum</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kotlin-ktor</th>\n",
       "      <td>261</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>istio-wasm-filter-golang</th>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarkus-kotlin-example</th>\n",
       "      <td>126</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golang-rest-api-cassandra</th>\n",
       "      <td>118</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mqtt-golang-influxdb</th>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kustomize-vs-helm</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service-mesh-workshop</th>\n",
       "      <td>94</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semantic-versioning-cli</th>\n",
       "      <td>78</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ml-github</th>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ocp-monitoring-golang</th>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarkus-kafka</th>\n",
       "      <td>51</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-amq-streams</th>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iot-devices-crud</th>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           sum  max\n",
       "name                               \n",
       "kotlin-ktor                261    5\n",
       "istio-wasm-filter-golang   166    5\n",
       "quarkus-kotlin-example     126    3\n",
       "golang-rest-api-cassandra  118    8\n",
       "mqtt-golang-influxdb       116    4\n",
       "kustomize-vs-helm           97    4\n",
       "service-mesh-workshop       94    9\n",
       "semantic-versioning-cli     78   22\n",
       "ml-github                   75    3\n",
       "ocp-monitoring-golang       67   25\n",
       "quarkus-kafka               51   10\n",
       "workshop-amq-streams        46   10\n",
       "iot-devices-crud            44    3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "views=df_grouped[('views_uniques')]\n",
    "views_best = views[views['sum'] > best_views_percentile]\n",
    "views_best.sort_values(by='sum', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fa360704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-04-17 19:36:35.451988')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = pd.Timestamp.now()\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1f8d712f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped[\"life\"]=(now-df_grouped[\"date\"][\"min\"]).dt.days\n",
    "df_grouped[\"views\"]=df_grouped[\"views_uniques\"][\"sum\"]\n",
    "df_grouped[\"views_day\"]=df_grouped[\"views\"]/df_grouped[\"life\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "148b0477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>life</th>\n",
       "      <th>views</th>\n",
       "      <th>views_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>workshop-amq-streams</th>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>1.069767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kotlin-ktor</th>\n",
       "      <td>292</td>\n",
       "      <td>261</td>\n",
       "      <td>0.893836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>licence-plate-workshop</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ocp-logging</th>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>0.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>istio-wasm-filter-golang</th>\n",
       "      <td>292</td>\n",
       "      <td>166</td>\n",
       "      <td>0.568493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-cloud-dataflow</th>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-boot-grafana</th>\n",
       "      <td>251</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-cloud-ms2</th>\n",
       "      <td>177</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-cloud-ms1</th>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-uca-aws-deploy</th>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         life views views_day\n",
       "                                             \n",
       "name                                         \n",
       "workshop-amq-streams       43    46  1.069767\n",
       "kotlin-ktor               292   261  0.893836\n",
       "licence-plate-workshop      5     4  0.800000\n",
       "ocp-logging                40    27  0.675000\n",
       "istio-wasm-filter-golang  292   166  0.568493\n",
       "...                       ...   ...       ...\n",
       "spring-cloud-dataflow     291     0  0.000000\n",
       "spring-boot-grafana       251     0  0.000000\n",
       "spring-cloud-ms2          177     0  0.000000\n",
       "spring-cloud-ms1          243     0  0.000000\n",
       "workshop-uca-aws-deploy   275     0  0.000000\n",
       "\n",
       "[66 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped[[\"life\",\"views\",\"views_day\"]].sort_values(by='views_day', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942bcf96",
   "metadata": {},
   "source": [
    "#### Best views repositories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96f5711d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.869145Z",
     "iopub.status.busy": "2023-04-17T01:01:22.868848Z",
     "iopub.status.idle": "2023-04-17T01:01:22.896232Z",
     "shell.execute_reply": "2023-04-17T01:01:22.895827Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_377671/3838494117.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_by_month[\"year\"] = df_by_month.date.dt.year\n",
      "/tmp/ipykernel_377671/3838494117.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_by_month[\"month\"] = df_by_month.date.dt.month\n",
      "/tmp/ipykernel_377671/3838494117.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_by_month['d'] = df_by_month[['year','month']].apply(lambda row: '-'.join(row.values.astype(str)), axis=1)\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>views_total</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>ml-learning</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>quarkus-kafka</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>quarkus-kotlin-example</td>\n",
       "      <td>21</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>golang-rest-api-cassandra</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>iot-devices-crud</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>2023-02-03</td>\n",
       "      <td>library</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>2023-02-03</td>\n",
       "      <td>mqtt-golang-influxdb</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>2023-02-03</td>\n",
       "      <td>quarkus-kafka</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2855</th>\n",
       "      <td>2023-02-03</td>\n",
       "      <td>REST-Services-MySQL</td>\n",
       "      <td>1</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>2023-02-03</td>\n",
       "      <td>workshop-golang-grpc</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2547 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date                       name  views_total  year  month        d\n",
       "0    2022-12-31                ml-learning            0  2022     12  2022-12\n",
       "1    2022-12-31              quarkus-kafka            0  2022     12  2022-12\n",
       "2    2022-12-31     quarkus-kotlin-example           21  2022     12  2022-12\n",
       "3    2022-12-13  golang-rest-api-cassandra            1  2022     12  2022-12\n",
       "4    2022-12-13           iot-devices-crud            0  2022     12  2022-12\n",
       "...         ...                        ...          ...   ...    ...      ...\n",
       "2852 2023-02-03                    library            0  2023      2   2023-2\n",
       "2853 2023-02-03       mqtt-golang-influxdb            1  2023      2   2023-2\n",
       "2854 2023-02-03              quarkus-kafka            0  2023      2   2023-2\n",
       "2855 2023-02-03        REST-Services-MySQL            1  2023      2   2023-2\n",
       "2856 2023-02-03       workshop-golang-grpc            0  2023      2   2023-2\n",
       "\n",
       "[2547 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_month = df[['date','name','views_total']]\n",
    "df_by_month[\"year\"] = df_by_month.date.dt.year\n",
    "df_by_month[\"month\"] = df_by_month.date.dt.month\n",
    "df_by_month['d'] = df_by_month[['year','month']].apply(lambda row: '-'.join(row.values.astype(str)), axis=1)\n",
    "df_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b73ddb4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.897830Z",
     "iopub.status.busy": "2023-04-17T01:01:22.897710Z",
     "iopub.status.idle": "2023-04-17T01:01:22.904017Z",
     "shell.execute_reply": "2023-04-17T01:01:22.903579Z"
    }
   },
   "outputs": [],
   "source": [
    "df_grouped_by_month = df_by_month.groupby(by=['name','year','month','d'], as_index=False).agg(\n",
    "    {\n",
    "        \"views_total\": [\"sum\"]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18707cae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:22.905580Z",
     "iopub.status.busy": "2023-04-17T01:01:22.905460Z",
     "iopub.status.idle": "2023-04-17T01:01:23.129855Z",
     "shell.execute_reply": "2023-04-17T01:01:23.129397Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb97d79ec90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "for repo_name in views_best.index.unique().tolist():\n",
    "    df_r = df_grouped_by_month[df_grouped_by_month[(       'name',    '')]==repo_name]\n",
    "    x = df_r[(       'd',    '')]\n",
    "    y = df_r[('views_total', 'sum')]\n",
    "    plt.plot(x, y, label=repo_name)\n",
    "    \n",
    "plt.legend(loc = \"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c19e28e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.131439Z",
     "iopub.status.busy": "2023-04-17T01:01:23.131310Z",
     "iopub.status.idle": "2023-04-17T01:01:23.406033Z",
     "shell.execute_reply": "2023-04-17T01:01:23.405578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb97d7ed3d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "for repo_name in views_best.index.tolist():\n",
    "    df_r = dict_df_repos[repo_name].sort_values(by=\"date\")\n",
    "    x = df_r.date\n",
    "    y = df_r.views_total\n",
    "    plt.plot(x, y, label=repo_name)\n",
    "    \n",
    "plt.legend(loc = \"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7aacde",
   "metadata": {},
   "source": [
    "#### Sum views total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4004748",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.407734Z",
     "iopub.status.busy": "2023-04-17T01:01:23.407608Z",
     "iopub.status.idle": "2023-04-17T01:01:23.412945Z",
     "shell.execute_reply": "2023-04-17T01:01:23.412616Z"
    }
   },
   "outputs": [],
   "source": [
    "df_total_views=df.groupby(['date']).agg({\"views_total\": [\"sum\"]}).sort_values(by=\"date\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "981ecf59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.414434Z",
     "iopub.status.busy": "2023-04-17T01:01:23.414311Z",
     "iopub.status.idle": "2023-04-17T01:01:23.555113Z",
     "shell.execute_reply": "2023-04-17T01:01:23.554669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb97d531790>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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exw0AAAAAANYsmsgQokNon7QqCBljekqLQf+7tfafSZK19hlrbWytTST9A43Hwm5Ieqzwxx+V9FT5a1prf9Za+zpr7esefvjhZb4HAAAAAACwBvFEhtCWHwxWqs2WMSPp5yR91Fr7Pxd+/pHCb/uPJH0o+/9vl/QGY8zAGPNKSV8k6b2re8gAAAAAAGATomxOrOd7iukQ2itttox9vaTvlvSHxpj3Zz/330j6LmPMa5WOg31W0n8hSdbaDxtj3ibpI0o3lP0AG8YAAAAAANg9Lki6z8jY3plZELLWvkvVuUD/ouHPvFHSG5d4XAAAAAAAYMuimJGxfTXXljEAAAAAAHB5sHZ+f1EQAgAAAAAAlWJbXDtPQWifUBACAAAAAACVxh1CRjQI7RcKQgAAAAAAoNI4Q4iRsX1DQQgAAABApzx/OtL7P3ey7YcBQOUMoS0/GKwUBSEAAAAAnfLz7/6M/so/+HfbfhgAVMwQMnQI7RkKQgAAAAA65TyMdTqKNYqSbT8U4NJzHUKB75EhtGcoCAEAAADolDirA52Nou0+EAB5hhBr5/cPBSEAAAAAneIuOk9H8ZYfCQA3MtZjZGzvUBACAAAA0CnuovNsSIcQsG1xkijwjDxjCJXeMxSEAAAAAHSKKwjdoyAEbF2UWHlZQUiSLF1Ce4OCEAAAAIBOcV0IZ4yMAVsXxzbrEEr/my6h/UFBCAAAAECnJAkdQkBXxNbK94y8rCJEjtD+oCAEAAAAoFPyDCG2jAFbFydpQcjkHUIUhPYFBSEAAAAAneJGUk6HjIwB2xYlNg+VliTqQfuDghAAAACATnEjY6eMjAFbl2QdQh4dQnuHghAAAACATnEXnKeESgNbl3YIeXmHEKHS+4OCEAAAAIBOybeM0SEEbF2cWHmeZLKCUExFaG9QEAIAAADQKXHeIURBCNi2cYdQ+t+WkbG9QUEIAAAAQKe4C05CpYHtG2cIMTK2bygIAQAAAOgUN5LC2nlg+6IkkW8Ild5HFIQAAAAAdIrrQLhHhhCwdXHWIWTyDiEKQvuCghAAAACATnEjY2dsGQO2Lk6sAn88MkY9aH9QEAIAAADQKW5k7JQOIWDrojxDKP1vOoT2BwUhAAAAAJ3iRsYIlQa2L05sliFEqPS+oSAEAAAAoFMS1s4DnRHlGULpfydUhPYGBSEAAAAAnZIUMoQs4ynAViVkCO0tCkIAAAAAOiVJ0h/jxGoYJdt9MMAlFyVWnjHysuoBGUL7g4IQAAAAgE6JCxecBEsD2xUnVoFXzBCiILQvKAgBAAAA6BQ7URAiWBrYpjix8j2PUOk9REEIAAAAQKcULzgJlga2q9whRK7X/qAgBAAAAKBT4sTmG43OKAgBWxUliXzPyHNbxqgH7Q0KQgAAAAA6xVqrK/1AknSPkTFgq+J87TwZQvuGghAAAACATkmsdPUgLQidESoNbFVs3chY+t8UhPYHBSEAAAAAnRInVlcPepKk0xEdQsA2xbGVN5EhtOUHhJWhIAQAAACgUxJrdSXrEGLtPLBdkQuVzqoHdAjtDwpCAAAAADrFFkbG2DIGbFdiyxlCW35AWBkKQgAAAAA6JbZWhz1fvmd0Rqg0sFVRae18TEVob1AQAgAAANApiU0zS476vu4xMgZs1ThDKP1vy8jY3qAgBAAAAKBTksTKM0ZXBoHOGBkDtmq8ZYyRsX1DQQgAAABApyRW8o101PfZMgZsWZRY+Z4nw9r5vUNBCAAAAECnJDbtEDoeBGwZA7YsTqx8T4UOIQpC+4KCEAAAAIBOSZI0s+S4HxAqDWyRtTYrCHl5QYh60P6gIAQAAACgUxIreUY6HvisnQe2yOUFBYVQaTqE9gcFIQAAAACd4kbGjvqMjAHbFCWJJMn3jAyh0nuHghAAAACATnFr548HAaHSwBbFWfXHp0NoL1EQQmu//L4bun0ebvthAAAAYM/lI2N9nw4hYItcQai4dt5SENobFITQyrN3LvQ3/8kH9C8/9PS2HwoAAAD2XJxkI2ODQGejWAkzKsBWTHYIZSNjyTYfEVaJghBaGUbpu34UczAGAADAerkMoSsDX5J0HjI2BmxDVCwIZdUDRsb2BwUhtOIqw3FMORgAAADrZa3yUGlJjI0BW1LZIUQ9aG9QEEIrcVYFpkEIAAAA65aOjKVr5yURLA1sCRlC+42CEFpxc9sxA6MAAABYs8Ra+Z7RMR1CwFaNO4S8wpaxLT4grBQFIbTiZkcj3v0AAABYM2slY9K18xIFIWBbxhlC6XtSIkNon1AQQiuuMsyGBwAAAKxbbK18TzrqpyNjZ4yMAVtR3SHENeG+oCCEVmI6hAAAALAh4y1jWYfQiA4hYBuqM4S2+YiwShSE0IoLlaZDCAAAAOtkrc1Hxo4YGQO2KsoyZD1T3DLGNeG+oCCEVugQAgAAwCa4003fGB1nI2OnQ0bGgG0odggZQqX3DgUhtOI+CGKqwQAAAFgj133gGeko2zJ2xsgYsBV5hpBv5Hl0CO0bCkJoJV87H/PmBwAAwPq4C1DPM+oHnvq+p3t0CAFbkReEjBmHStMitDcoCKGViA4hAAAAbIA73XR5JUcDnw4hYEuiilBp6kH7g4IQWnGFoJh3PwAAANaoODImScf9gAwhYEuSfO18MUOIa8J9QUEIrbhRMUKlAQAAsE7uRqSfVYSOBz5bxoAtyTuE/OLaea4J9wUFIbTC2nkAAABsgk23XMu4kbF+oFNGxoCtyDO9DCNj+4iCEFph7TwAAAA2oTwydmUQ0CEEbMk4Q8gbh0rTIbQ3KAihFVcQokMIAAAA61QeGTvq+zobkSEEbEM8kSFEh9C+oSCEVlwVmA4hAAAArJM773QXn8cDRsaAbSkWhFyHEBlC+4OCEFqJYtbOAwAAYP2SLEMo3zI28NkyBmxJlL0h/Ym181wT7gsKQmglXzsf8+YHAADA+riLTd91CPXJEAK2xb0fA49Q6X1EQQitECoNAACATRiHSo+3jA2jRFGcbPNhAZeSmxTxPSMvqx7QIbQ/KAihlTxUmjc/AAAA1igfGfNchpAvSTolWBrYuMkMofQ9ySXh/qAghFboEAIAAMAmlNfOHw8CSdIZwdLAxo3XzhdGxrgm3BsUhNAKa+cBAACwCeWRMVcQIlga2Lw806uwZYxLwv1BQQitjNfOM7sNAACA9ckLQm5krJ+NjBEsDWxcMUPIsGVs71AQQitR3iG05QcCAACAvea6D1w3wlE/6xBiZAzYuGKGkJS+Ly0Fob1BQQitjDOEqAgBAABgfcojY1dchhAjY8DGxfna+bR04BnDyNgemVkQMsY8Zoz518aYjxpjPmyM+aHs5x8wxvymMeYT2Y/XC3/mR40xnzTG/JEx5vXr/AawGa4gFPPmBwAAwBq588587Xy+ZYwOIWDTpjuEDCNje6RNh1Ak6W9aa79U0p+S9APGmC+T9COS3mmt/SJJ78z+W9mvvUHSl0v6Nkk/aYzx1/HgsTl5QYgOIQAAAKyRLY2MHfcJlQa2pZghJEnGECq9T2YWhKy1T1trfz/7/3clfVTSyyR9h6S3ZL/tLZL+fPb/v0PSL1lrh9baz0j6pKSvXfHjxoblI2O0CAEAAGCNyh0Jh1moNGvngc1zDQGuQOsZQ4bQHpkrQ8gY8wpJf1LS70p6sbX2aSktGkl6UfbbXibpc4U/diP7OewwNztKeyAAAADWqZwhFGRXojFtCcDGxdYqKGwY8wzvxX3SuiBkjLki6Zcl/VfW2jtNv7Xi56ZeMcaY7zfGPG6MefzmzZttHwa2JMlDpXnzAwAAYH3c6WZ2/Zl3CsXcmAQ2LkqsPG98iU+o9H5pVRAyxvSUFoP+d2vtP8t++hljzCPZrz8i6dns529Ieqzwxx+V9FT5a1prf9Za+zpr7esefvjhRR8/NmS8dp53PwAAANbHdQi5QlBeECK6ANi4OLZ5l57kMoR4L+6LNlvGjKSfk/RRa+3/XPilt0v63uz/f6+kXy38/BuMMQNjzCslfZGk967uIWMbYjqEAAAAsAFJacuYb+gQArYltjYvykqS55EhtE+CFr/n6yV9t6Q/NMa8P/u5/0bSmyS9zRjzfZKekPSXJcla+2FjzNskfUTphrIfsNayEmDHxXQIAQAAYAPKI2OeZ2TILQG2Ik4mO4QYGdsvMwtC1tp3qToXSJL+bM2feaOkNy7xuNAx7o4MHUIAAABYp3xkzIwvQQLPUBACtiBKSh1CjIztlbm2jOHycjPbHIgBAACwTvmWsVJXAuehwOYlpYKQoUNor1AQQiuuQ4jZbQAAAKxTnGcIjX8u8Ayd6sAWRIlV4I3LBp4RGUJ7hIIQWnHZQWx3AAAAwDq5a03PTAbZ0iEEbF6cWBXqQVmGEO/FfUFBCK24OzJ0CAEAAGCd8pExMoSArZvuEGJkbJ9QEEIrhEoDAABgE1zhp5hb4nseNyaBLShnCHkeodL7hIIQWnGjYqydBwAAwDqV185Lku8RXQBsQ5QkU2vnqQftDwpCaIUOIQAAAGyCrRwZo0MI2IY4sZN5XmQI7RUKQmilOLNNlxAAAADWxRV+ymMqZAgB9U6HkT73/NnKv26cWAV+ce28yBDaIxSE0ErxAEyXEAAAANYlybeMjX8u8DwKQkCDn3vXZ/Qf/eS7V/51o3KGEB1Ce4WCEFopvuk5GAMAAGBd3MiYmRhT4RwUaPL86UjPn45W/nXjxMovvRctBaG9QUEIrUSFED/mtwEAALAu+ZaxcoYQBSGgVhgnSqwUxclKv25lh9Bq/wpsEQUhtFIsArHhAQAAAOsyHhkrZggZYguABmFWCApXfK2WTGUIMTK2TygIoZXiHRk6hAAAALAuboGJV7hSCTyjmLYEoJYrBI2idXQIjd+MHqHSe4WCEFqZDJXmYAwAAID1SCrWznueEU3qQL1R1iE0jOOVft00Q2j8354xZAjtEQpCaGVy7fwWHwgAAAD2WtXIGB1CQLMw6wxadYdQXNEhxMTI/qAghFboEAIAAMAmuIvN4siYbwyh0kCDdWUIxYlV4JUzhFb6V2CLKAihFdbOAwAAYBNsxciY71EQApqsL0MoKW0ZY+38PqEghFaKWx04GAMAAGBd8lDp4siYT0EIaOIyhNYzMlZaO09BaG9QEEIrSWLljskcjAEAALAubuLFN5MXoZyDAvXcyJgrDK1KbCdHxjxjyJTdIxSE0EqUWPX99OVCiBgAAADWxY2jmPLaec5BgVrhujqE4skOIWNEh9AeoSCEVuLEqh+kL5eInZ8AAABYE9cJ5JfWznMOCtQLoyxDaMUdQlHFyBj1oP1BQQitxInVICsIUREGAADAutStneccFKi3rg6hxJYKQh7Xg/uEghBaie14ZCxifhsAAABr4i42C/WgtEOIc1Cg1ihfO7/6DqGpDCEKQnuDghBaSQojYwkHYwAAAKyJO9csdiUEnuEcFGiwzgwhbyJDyIi34v6gIIRWomKGEJ8AAAAAWJOqkTHf0CEENAmzjK1VF4TKHUK+GQe/Y/dREEIrxQ4hVn4CAABgXdw4SuEaVL7H2nmgiSsEDdewdt73xmUDjw6hvUJBCK1MrJ3nEwAAAABrklgrY9LRFIeCENAszxBa9chYqUPIkCG0VygIoZXY0iEEAACA9UusnRgXkygIAU2steMMoRV2CFlrFSeTGUKeER1Ce4SCEFqJE6t+4Of/HwAAAFiHxKaZQUW+ZxTTlQBUihMr9/ZYZYaQu+wrbxkjQ2h/UBBCK3HC2nkAAACsX5JYlepBaUEo5hwUqBIW3hurXDsfJenXKm788zwxMrZHKAhhJrfic+DWzvMBAAAAgDWpGhkL6BACahXHxFbZIeQmQ3zWzu8tCkKYyXUEsXYeAAAA6xYnkxegkuR5rJ0H6hS7goYrLAi591x5ZIwGgf1BQQgzuTe8GxlLOBgDAABgTdyWsaLAM5yDAjWKBaFVhkonFR1CnpGoB+0PCkKYiQ4hAAAAbIqt2jJm6BAC6oRRIUOIDiHMgYIQZopLBaE4Wd2HDAAAAFAUWzs1MuZ7dKoDdUZr6hBy14HeRIYQmbL7hIIQZpouCG3z0QAAAGCfJTYdSynKkgvoEgIqhGsOlZ7qEOJ6cG9QEMJMeUHIp0MIAAAA62WtlSmPjHlsuwXqFAtC866df/sHntKbf/Pjlb823jI2Lht4dAjtFQpCmCkPlc47hPgAAAAAwHrEiZU/VRBKf6RDCJi2zJaxf/Xhz+uf/cGNyl+L8oLQ+OfIENovFIQwk/sgGBAqDQAAgDWrHhnLbkzGnIcCZaNCqPS8I2NRbGvfV24ypNghZIwRl4P7g4IQZkpKGUJUhAEAALAuSWInQmwlyc/+M+Y8FJjiOoQOe/7codJhnNTe8HdfKphaO8/7cF9QEMJMUSlDiA4hAAAArEtStXY+Pw8lyxIocwWh40Ewd4ZQmNjaSJAo7xAqr51f8IGicygIYaapLWO06gIAAGBNKkfGsgIR9SBgmisCXRn4c4+MhVFSW0TKQ6XNZIcQEyP7g4IQZnIfBD23ZYwPAAAAAKxJbKdHxtzICh1CwLRRdsP+eBDMnyGUJLUdQnlByB+/H40xeaQIdh8FIcw0LggZGcOWMQAAAKyPrRoZ8+gQAuqEWRHouB8onHOaYxTbhgyh9OeD0sgY/QH7g4IQZnItgZ4xCjxDQQgAAABrkyRVW8boEALqjDOE/LnXzkdxfYdQVDEy5nuMjO0TCkKYyX0QBL6RT0EIAAAAaxQ3dQhxIQpMKYZKj6J47j8bJbZyc5gbDSNUen9REMJMrgDkGSPfUBACAADA+jSNjLHtFpiWZwj1g7nXzkfZn616axUbAxxjDIXZPUJBCDONZ0c9+Z7hQAwAAIC1SexkR4I0/m9uTALTJtfOz5shlP7ZqnHMPFTaG5cNPCMyhPYIBSHMlHcIeenBmIowAAAA1iVObO3aeQpCwDQXKn1l4CtO7FzvE9chFFUUkqoyhDw6hPYKBSHMNNkh5NEhBAAAgLVJrJUpj4z5jIwBdcI4kTHSQd+XpLlWz4d5h9D0eyuuzBAiy2ufUBDCTLF1HwTp/+I52xABAACAthJrp0fGjFs7z3koUDaKrXq+p77vZf89f0Goqqsors0QWubRoksoCGGmpBAqHXheXiACAAAAVq1q7XxAqDRQaxQlGvieBoGX/3dbLnOoKkPI/ZxXGhmTVLmVDLuHghBmigojY57H7DYAAADWp2pkzPPoEALqhHGiXuCpH8zfIeSKPlXXeG40LCiNjKW/tuijRZdQEMJMxVDpwPMoCAEAAGBtEmsnQmwlOoSAJmGcqOcb9fz5OoSsteMOoapQ6bgiQ8gVZ+kQ2gsUhDBTee08BSEAwK6LE6t3feLWth8GgAqJTW9EFrmLUKILgGmjOEkzhLIOobBlh1CxwNqUIVQsCJm8Q4j34j6gIISZJkKlDQUhAMDue/sHntR/+nO/q0/fvLfthwKgJLF2IrNEGncIsdwEmBbGVv1iqHTLDqFi4ag6Q6hqZMyNby78cNEhFIQwU5y92/2sQ4hWXQDArvv9Pz6RJJ0O4+0+EABTkmS6IOTTIQTUCqPJDqFh64LQ+P1UdY2X2IqRMTqE9goFIczkCse+MdnIGOVgAMBu++CNE0lSyDEN6JzETm8ZywtC3JgEpqSh0ibvEGo9MlbsEGrIEAoKM5x5hxAFob1AQQgzuW0Ovp8VhHjvAwB22ChK9NGn70qqPgEGsF1xYic6EqTCyBgFIWBKOUOo/chYuwyhYqaXyQtCiz5adAkFIczk2gfpEAIA7IOPff5OvpI3mmM1L4DNqFw7bygIAXXChQtCxQyhioKQreoQSn+0dAjtBQpCmMl9EHie2DIGANh5H7hxO///Icc0oHNsxciYuyDlPBSY5kKl87XzLW92FAtCbbeMeXQI7RUKQpgpzj4oAs9TQEEIALDjPvi5k/z/0yEEdE9sp0fGXIMC56HAtLRDyCy1dr5yy1hMqPS+oyCEmdxo6XhkjDc/AGB3ffDGbb342kBS+5NmAJtTNTLmOoTYdgtMG7ktY/58W8aKo2VVmXouKqRYnzWESu8VCkKYKV8771MQAgDstrNRpE88e1df/fLrkiYDNQF0g7XpjciivEOIi1BgSrplzNNgzgyhYoG1cmTMWgWemSjQum4h3or7gYIQZppYO28MB2IAwM760JN3lFjpq1/+gKTqFnkA2xUntj5DiK4+YEo5Q6ht9+usUOmoYuMfI2P7hYIQZnJvdt9LO4RY0QsA2FUfvHEiSfqqL7hfEh1CQBcl1ubBtY7rGOItC0wrZwgtsmWsapN0HE8XhFg7v18oCGGmYpgYI2MAgF32gRu39dL7DvTIfYeSqjMTAGyXtZJXugj1fbd2ng4hoGzxtfPFUOnqkbHpDqGsIMQ14V6gIISZ8rXzJls7T3sgAGBHffDGiV7z6P0KsotLRsaA7qkaGcs7hHjLAlNcqHSa99N+7XzUYu18UDMyxiXhfqAghJniJJGfhYnRIQQA2FUnZyP98XNnes1j96nnuZwFjmlA11SOjHl0CAF1wtiqH3gyxqjne60LQhMZQhXHw+oMIbaM7RMKQpgpTsZ3ZSgIAQB21Qdu3JYkfWWxQ4h2A6BzEmunRsYCjw4hoI7LEJKkge8tODLWNkMo/ZGC0H6gIISZXIeQlB6MKQgBAHbRBz93Ikl69cvuK4yMcUwDuiaxmhoZ8+gQAioliVWU2HzDWD+YpyDUvGUsXTs/WTLwCJXeK8G2HwC6L07Gbbp0CAEAdtUHbtzWqx461n2HvTwMs+1qXgCbk1ibd6cXBWRZAlPCrEjqCkK9OTqEimNidRlCdSNjlvfiXqBDCDMlhXR5CkIAgF319O1zveKhY0lpt4HvGQpCQAfFic1XWxd5nqGrDyhxY1/9QodQ22PbaKEMofRH3or7gYIQZooKI2O+4UAMANhNcWLzjAUp7TZg7TzQPdZqKlRayjqEeM8CE8LIdQil75l+0D5UetaWsaSiIGQIld4rFIQwU5yMD8q+5+Vt9gAA7JIomcxC6PkeW8aADkq706d/3jeMjAFlrhuoF2QdQguHSld1CCW1a+cpCO2HmQUhY8w/MsY8a4z5UOHnfswY86Qx5v3Z//6Dwq/9qDHmk8aYPzLGvH5dDxybExc+CHyPAE4AwG4qZyEEvqncqgJgu+Jkeu28JPk+0QVAmesGyjOEAk+jljc7wqQ4MlaxZazivZiHSnP43AttOoR+XtK3Vfz8m621r83+9y8kyRjzZZLeIOnLsz/zk8YYf1UPFtsxGSrtcWcGALCTync6A48OIaCLrFVlhpBvKAgBZeUMoXTtfNzuz0bNHUJhPDlqLUmu0ZYOof0wsyBkrf1tSc+3/HrfIemXrLVDa+1nJH1S0tcu8fjQAcW1875XPV8KAEDXxfFkh1DPN5V3RAFsV1w3MsZyE2BKWOoQmmftfJQk8kw6Blb13oqSREHpzUiG0H5ZJkPoB40xH8xGyq5nP/cySZ8r/J4b2c9hh8W21CGUWNYMAgB2TpRYBX55ZIzjGdA1ia0ZGaMgBEwZlUKle75pHSo9itOCT+B5tR1C0xlCriC0zKNGVyxaEPopSV8o6bWSnpb0P2U/P/3JLVW+VIwx32+MedwY8/jNmzcXfBjYhGK6vPtA4AMAALBryhlCPa/9al4Am2GtrR8ZoyAETBmVQ6UDb2IUrEkUW/V9L3tvTR8PozjJO48cdxilQWA/LFQQstY+Y62NrbWJpH+g8VjYDUmPFX7ro5KeqvkaP2utfZ219nUPP/zwIg8DGxIlifx8y1j6IwdjAMCuKW8ZC3zWzgNd404x/bqCEBehwAS3dn6Qj4z5c62dD3xT2zEbxpOdtRIdQvtmoYKQMeaRwn/+R5LcBrK3S3qDMWZgjHmlpC+S9N7lHiK2LU4kz6MgBADYbVMdQj4dQkDXuFwSr2LuwPcY8wTKXKj0ImvnR7FVz/cU1HTfhRUdQq5WS4bQfghm/QZjzD+W9M2SHjLG3JD0tyV9szHmtUrHwT4r6b+QJGvth40xb5P0EUmRpB+w1raLOEdnTaydzz4B0jW9LJADAOyOqS1jvqeQi0ugU/KCUEVFKPCMEt6zwITpUOn2GUJRnKjnGVlbvXUzSiq2jBEqvVdmFoSstd9V8dM/1/D73yjpjcs8KHRLbKc7hCpGTAEA6LTpDCG2jAFd484xq0KlPUOHEFCWZwhlhZt5OoTCOFEv8JTYpDZDqDhqLY2vB6kH7YdltozhkpjoEPKKHUIAAOyONEOotGWMDCGgU5pGxgKfDiGgzHUI9RdYOx9mx8W6cczqDKH0RzqE9gMFIcwUJ3Y6VJoPAADADkmSdHORX7jT2fM9hdzgADrFXWT6FRUhnw4hYEp5ZKzne61HxsIozQgK/OoMoShJ1PPKGUKESu8TCkKYKUk0tXaeUGkAwC5xF5HFO52BR4cQ0DWuRsvaeaAdt2K+uHY+Tmyr90qaEeTVdghFjVvGeC/uAwpCmClKkrwg5FEQAgDsIHfc8suh0mQIAZ0ya8sY56DApKkMoaww1Ob4lm4RM+p5nuKKGyRVW8bce9NSENoLFIQwUzFUmg4hdN1/+2sf0S+994ltPwwAHeOy74oZQj2f8ROga+KmkTEKQsCUqQyh7MdhixyhME4UNHUIlbL3pEKHEPdT9gIFIcxUHSrNwRjd9Bsf/rze/anntv0wAHRMZYeQ57FlbMPCONELp6NtPwx0mOsQqh0ZoysBmDC9dn6eDqF0rXzgm8qlQenIWDlDKP2RkbH9QEEIM8XJuBI8XjvPBwC66SKMK9dmArjc8gyh0paxkAyhjfqF9/yxvvXNv7Xth4EOc9eYfmVByOOmJFDijmO9UodQm01jUTYSVtd9FyZJPormeIRK7xUKQphpokPI0CGEbhtGCe3kAKaMO4TGpz59MoQ27tm7F7p1b0RnFmq592plhpDhpiRQ5go/5QyhNgWhUWwVeF7lkoU4284ZeOUMofTvIUNoP1AQwkxxYvPOIJ8MIXRc2iHE6xPApLoOIW5wbJbbhtN2JTIun3GoNB1CQBsuGNqNWeYFoRafs1GcqB+Yyg4hd8NkestY+iNvxf1AQQgzJVYUhLATojhRlFhOFgFMcdtTyhlCdAhtlnu+29y5xuXkmg68ylBpOoSAsvImsN4cI2NhnCjwPPV8bypDyJ1Pl0fGDGvn9woFIcxUXDufF4T4AEAHuTshFCwBlOVbxgontj1/ukUe60VBCLM0jYwF3vRFK3DZpcHQhXHoOTqE3J+t6hByo73TI2PpjxSE9gMFIcyUFEKl3QcCF9zooouQghCAapVbxiruiGK93AVKm3XIuJyaRsY8zzCmApSMSh1Cgzk7hHq+STOEpkbGqjuExhlCSz1sdAQFIcwUFUKlXYGYC2500TCKJRF6DmBaVYZQz0u3jBGMuTnuAoMMIdRJGkbG0otWXjtAURgl6he7X+dYOx8l4w6hcsfsuLO2OlSa68H9QEEIM8XJ+KBMhxC6jA4hAHWqtoy5k1w+MzYnjBgZQ7Nxh9D0r/meEfUgYFIYJ3kRSJpv7XwYJQp8UzmO6ZYABF45Qyj9kZGx/UBBCDNNrJ3PXjF0YKCLXIcQF3cAyuq2jBV/DetHhhBmcReZftWWMUOHEFBWmyHUpiCUJOrXZAiF2XutH5Q6hDxGxvYJBSHMNLl2Pn3JsOEBXUSHEIA6cXZi60+MjLUP3sRquOea5xx13DHcVBWE/OmLVuCyK2cIzRsqHdRkCLkRMkKl9xsFIcw0sXbecDcV3TUMyRACUG18Yju5Zaz4a1g/91zTIYQ6+dr5qpExQ0EIKAvjyQyhtiNjSWIVZxlCQUWx1XV0BjWh0rwV9wMFIcxUuXaeTwB0kNtaQwcbgLK6LWPSeLUu1o+RMcySj4xVVIT8ii4G4LILF+wQciNhaai0N90hlFRvGSNDaL9QEMJMxbXzFITQZRd5hxAXGgAm5RlC/nSHUMgxbWNC1s5jBneOWbV2Pg2V5v0KFIVRKUOoZYdQVFgrH1RkCLmbJdMjYy5DiPfiPqAghJmiiVDprCDEBwA6yF1gULAEUFa5ZcyjQ2jTRqydxwzuEF5RD6rMOQEuu1Fpy1jbtfPFjCDfM1O/P4ynb6RI4wgR3or7gYIQGllrldji2nnXIcSJHLrHdQhRsARQ1rRlLCRDaGMYGcMstmFkzPMMYyoFcWJ5L2HhDCFXmO8FXnWHUGGkrGicIcR7cR9QEEIj97lQ7hAigBNdlHcI8foEUFK5ZcxlCHGTY2MoCGGWppExOoQm/cxvf0r/4f/6rm0/DGzZKJrMEHLj0DNHxlzBxzPy/aYtY6UMoeyv4q24HygIoVFUOoF2P1IRRhddsGUMQI3KDiFucmxcGLmCULzlR4KucofwqoKQZ4ysZXmE87nnz/Xkyfm2Hwa2rBwqbYxRP/A0nBUqHbkMoeoOIVfAr+sQIkNoP1AQQiN307RcEOKCG12UbxnjAAWgpGrLmDvJnZWzgNUhQwizuGN41dr5gCzLCaMoocMRCmM7VbTp+15e8Kn9c8l4rXzgeYoTO1HkqVrGII3fm5xv7wcKQmiUdwiVtoxxZwZdNKRDCECNYnimQ4bQ5jEyhlnyglBNhpDE8ghnFCd0OEKjOFE/mHy/9ANPo7i5E7PYARRUvLfCGVvGeBvuBwpCaORuOrgDsCsMccGNLiJDCECdvEPIr8gQoltlYygIYZamkbGqi9bLLIwSRaWuDlw+aaj0dIdQ+7XzXn5sLF7jFdfSFxk6hPYKBSE0ch1Ceai0z4EY3ZUXhDhAASipyhByJ7khx7SNcRcYs7ItcHklSf3IGNEFk9zoJefll1tYCpWWpF5gZna/utdPOjJWURDKR8rqMoSWe9zoBgpCaBSX2na5M4MuI1QaQJ2qLWOuDZ4Ooc2w1uYXIHQIoc44Q2i6IkR0wST3PuK853ILY6tesHiHUN/35GfHw2KXvSso9bxyhhDvw31CQQiN3MiYKwS5DwA6MNBFeYcQBygAJZVbxsgQ2qioIpsCKHMvE7+iRaiqi+EyoyAEV2ifCpUO/Py8uM44I6jYITT+M1Fc1yGU/sjLbj9QEEKjcqh03iHEyTM6yHUIlbckAEDTljG29GxGsQhEhxDquPdqRYNQ3rFOdknKjV5yXn55uWJgv5Tzk4ZKtysI9QIvPzZOhkpXbxkzhvfhPqEghEasnccuKd4J4SUKoGjcIVTYMuaOaVxMbURxBTIFIdSxDSNjdAhNcu+jkKL2pVXcFFbU943CmR1CbiTMyzP1Jjo5s9dVz5suGXhG3HzdExSE0CgqZS4YY+QZKsLoJtchJHHHH8Ckpg4hxpc2o3i3etada1xeTSNjZJdMCgmVvvRcoX16ZGx2h1CUdwiZcYZQiy1jUvpeXPRl9z3/6L36H/6Pjy72h7FywbYfALotKYVKS+kBmjsz6KKJDiGuNQAUuBPbqgwhjmmbwcgY2sgXmlSMjPGenZR3CFFgvbRGhbGvor7v6c551OrPBp5XvWUsnl7G4HjGLJwp++mb9yrf35vw7N0LPXN7qK949L7tPIAOokMIjapCOH3PcGcGnTSM6BACUC1OEhkzeYPDjY9xMbUZxed5VtgpLq+mkbF8uQnnoZLGBSGej8vLfa6WM4R6c28ZcyPU4z8TJlY93+SZQUVmiYmRYZTo9nm40J9d1s/+1qf1f3vL723l7+4qCkJo5A4wxYOyb+gQQjddhOODGCdHAIqixE7c3JDGbfBsGdsMOoTQRtW5pxNUjLVcZq7Dg8+wy6s2QyjwZt7syLeM+aa2QyioyA+S0vfnogkiwzDeWkHo3jDS6bC5c+qyoSCERuW181LaIcSBGF002SHEaxTAWJzYqbb3fMsYHUIbUbxoJUMIddzhu6og5K55OQ9N0SGEpoLQzLXzyTh/qG7LWHnDmOOZxbO8hlGiO1sqCI3ihK7gEjKE0KgcKi1Jge9x4EEnFTuEGGsEUJR2CE2eMJNHslnuJNwYOoRQb5xfOf1rVcG3l9m4Q4j302U1qgmVHrRZOx+5YpIZ3yApdgglydTXdRYNlbbWahglis9CWWsrx9HWKYytwthu5e/uKjqE0MgdlIsFoWVCxIB1GoaxDnu+JC7wAEyq7BAiQ2ij3PN83A8oCKFW0jAylncIcR4qay0dQhhnCAXTHbCzjm3uxv9kh9D4z0Tx9Ki1s2iGkOsUjRKrs1E843evniuC0aU6RkEIjVzY2ESHkGcUM6uMDrqIEh0P0oIQJ0cAiqIkmTqx9Twjz4yPdVgvdyf7eOBzMo5azSNjrkOI109xBJNFGpdX7chYi1Bp9xqayBCKJ0fGajuEPJMHwM+jGO+wjRwhcremURBCo7hi0wNr59FF7k7ZUT+dhKUgBKCoqkNISsegQy6mNiLvEBrQIYR6TSNjVRetl1WxqMrzcXmNGjKEZheEsj/rVWcIRUlSmyHkLzgyVsw12kZByH3PIcegHAUhNHIfCsUPA98zC68ZBNbFHWCO+oyMAZhW1/re8wwXUxviTsSvUBBCg6TN2nnOQyfeQ5zzXF6u06WqIBQltjFTM4rTGyWeZ/JrvbBYEGocGVvsenDbBSH3vmFUfIyCEBpVrp2nQwgdNAzHFxoSHUIAJsWJlVfTIcSWsc0gQwhtuAtYv2rtvD/dxXBZFS9oOS+/vFynS79UEHIFoqbx3DBO1MveU1XjmOmv14VKa7EOoXC7I2PufTNrA9tlQkEIjVzlt7x2ng1O6Bo3k3xEQQhAhXTLWEWHkG8m7ohifUaxyxAKNKQIhxquYa+xQ4j37GSHEO+nSysf+yqFSg+CNgUhmy9XqBrHjJKmtfOLZQgVH892CkI2+5H3jENBCI2qQqV9YwivQ+e4lfPHfUKlAUyrzRDyPLIENsQ9z1cGvkZRstDFBPafe12YhgwhjvGTHQ50CF1eTRlCkhq7McN4nBFU1X0XxomCqjAvuQ6hBUbGwkJB6GyLGUKMiucoCKFR1dp53zOiqIquyTuEslBpipYAitItY9OnPb2AMehNKYZKp//N845p+bln5ZYxCkLOZIcQz8dl5T5H60bGmjphomQ8EpZ3CJUyhHo1HUJmR0OlyRCaRkEIjdyHwsTaed+w7hOdk3cIZWvnCT4HUFTXIdTzPE4MN8SN5rmsN1bPo4p7WVSvnacg5ExsGeO8/NJqWjsvNXcIjaLxWvlxhlBpy1hdh5C3YIdQR9bOkyE0RkEIjapCpT3D3VR0z1SHEHfLABTUZSEEPlvGNiXMt0FmBSFOyFHBXWRW1IPGBSFu+kyGSvMZdmmNC0KTb5g2I2Nph1A2Mpa9t4qvq1E8K0No/sc7MTK2zbXz3JDIURBCo3ztfLFDiLXz6CAyhAA0acoQ4u76ZoxHxtLPaQpCqGIr4gocOoTGiu8fno/Ly70OekF1hlBTJ0yaIeQ6hKbfW1GcTI2iOd6Ca+ddh85BzyNUuiMoCKFRXDEy5nncTUX3TG0Zo2gJoCCKG7aMcUzbCHcCno+MURBChcaRMbaM5Yrvn5Ci9qXlCizlwk2/RYZQGNuZGUJ1HUJm0bXz2fn6i64ebKcgRIbQFApCaFQVKk2HELrI3QFxHUKMNQIoqu0Q8ukQ2hS3dv4ozxCKm347Lil3jlnxds3fwxzjJzs/KJBdXmGU/tsvumXMjYxVdQiFybiDqGzRDiE3Mvbw1YHubDFDiBsSYxSE0KgqVNr3yBBC91yE6YWF214Tc8cfQEFdOGbg0SG0KWE2fuDuXBPqiSrWWhmTbjEqc+ejCeehE6HSfIZdXmGcyDPTI5Z5Qahpy1ixQyj7cWrLWFVlVmnB1i4UKp0+nhdfG2y8Q8haOy4I8Z7JURBCo6SmIMSBGF0zzMNKswwhutgAFNRuGfM9RbSOb0QYpXejBy3uXOPyiq2tHBeTqsdaLqtwokOI99JllXb5TF/St1k7P4qT/D0V5B1CxbDyGR1CC7zsiiNjJ+fhQkWlRcWJzYOwQ44/OQpCaJR3CJnJkTEOxOga1yHkttfQPg2gKEqqM4QCMoQ2JowT9QKv1SgDLq/ETp53FhEqPUaHEKT0dVAV/Nxm7XwUJ/nncdU4ZpjYqe1ljjFmoZuvxZGxOLE6HW1udLj4PiFDaIyCEBrla+eLodLGcCBG57gOIRdWStESQFFThxAnhpsxysYT2owy4PJKElu5cl6iIFTEljFI40J7WbstY3aqQ6i4OCiKq0etpcVHxkbZiNuDx31Jm109XzzmcPwZoyCERpVr530KQuge1yF0mK+d54MewFiUVG9L6fl0vW5KOUOIDiFUSRpGxigIjRXfP4y9Xl5hZCs7hNqM5hbHzao6hJq2jKWh0vM/3mGUaBD4uu+wJ0m6fba5glDx5g/HnzEKQmgUV2wZo0MIXTSMJi80ODcCUJR2CFWFSpMhtClpHoVhZAyNEjsdkOvkBSFyAic6HChqX15ph1B192v66/WvjWJByBgj3zMTN1TDpDqfSEo7hBbbMhZr0PPGBaENdgiFjFlWoiCERlWh0oG32MwosE7DMNEg8OT706F4AJBuGSNDaJtCRsbQQszIWCuuoDoIPApCl9ioJlR6XHivz+iJShlB5U3SUVydvSelBaTFO4Q8XdtCQah4E4JR8TEKQmhUFSrteWZivhTogoso1qDns4EEQKU4rskQ8jxFFJA3wl24sHYeTaytfq9K4/NRCkLu/WTU9z3Oyy+xsC5UOmjRIRRNbhHreUZx9vuttdmo9WozhMojY3e21CFEh+oYBSE0csF+XqlDaJEWQWCdXIeQyx1IOFkEUNC0ZYyLqc1IL1xYO49midXMDCFu+qTvn76fdkZT1L68XOdlmev8aerETLeIjf9ssUPI/dirKc6mGUILhEpH6Waz+4/SgtDJ+Wjur7GoUcSWsSrBth8Aui1K7NTqz3I7IdAFaYeQR4cQgEpsGds+l1dBhhCaxNaq5hpUxpg0u4RjfH5h7XuMjF1mYdYpVtbz2oZKFxcHeXn3nbtRUtsh5BktUoccRrEGgacrg0C+Z7aWIcTI8hgFITSKrZ3oDpLSghAHYnTNMEx0EPiFDCFeowDGajuEuMmxMWFEhhBmsw1bxqQsCJ73bF4QMjIE419io6g6Q8jzjHq+abzhkWYElTuE0t8fZj9WFZukJUKlswwhY4yuHQRbDJXmPeMwMoZGcUWYGAfidn7uXZ/R3/7VD237YVwaw6xDiHwBAFVqt4zRIbQxozhRL2DtPJrFSXNByPMWuxDdN67jLvApal9mYZzkRfaynu81fs6OShvKgkJObN4h1DAytsjbMI148CVJ9x32dPs8mv+LLKj4XHD8GaNDCI1iOz0yxtr5dn7308/pk8/e2/bDuDTyDiFGxgBUiJJ05XlZP9syZq2VabgIxfJchlDge/IMJ+SolmYI1f964BGiLEnDrBCQJJbn4xKryxCSZo9ER3GSj5ZJytbO2+zrpn+uPlTaKLHzf4YPo1jXj/uSXEFog1vGWDtfiQ4hNEoSm4/gOIFPQaiNKLF5uyXWb1jKEOI1CqCoLkPInezymbF+YWE9cj/wGBlDpaQirqDIM1LM+VUeKl3MfcHlU5chJLnP2erXRpxYJVYTxaSeP54CcQWhuq9tlhwZk6RrGy4IFYtAHH/GKAihUVWoNB1C7YRxwh2bDbrItoz5FIQAVGjaMuZ+HetVvJPdnzHKgMsrmTEyFvie4sKFqLVWP//uz+iF081tK+qCUXZhHXjNOTHYb6O4OkNISj9n614b4w6g8Xut2CHkrmHqvnbaIbTA440S9QsjY9tYO+8ZKeT4k6MghEaJnb6jGnhm4kCMalFsaUfcoGEU66Dn5xtIKAgBcJLEylpVbxnL2uW5oFq/yQ4hX0NOyFFh1shY8aJVkp66faEf+7WP6F9++PMbeHTd4UKl6dy/3NJR3LqRMVNbeHc3QYp/NiiESrsf60fG0mLsvIodQvcd9nRytrlCrjvOH/cDOoQKKAihURRPF4S87EC8yIfAZRLGSf5hivVzHUISwecAJrnPg8YOIQr4a1ccbRgEdAih2qyRMb/UqX4+SkNpL9vryRVYfc9TyDnP0m6fh/oLP/luffbW6bYfylzc9sYq/aChQyhq7hByN7V7DaHSi7zs3Np5Sbr/qKc7F9HGrindZ8TRwOcmUAEFITSKK1Z/uhNqjj3NQkL+Nsp1CElsIAEwyZ3g1m0Zk0Tm2wZMjIyRIYQayYy18+lq7GJBKFuTfcleT6MsVLrnGTKVVuCzt071+0+c6CNP39n2Q5lLWNoUVtQUKj1eKz/ZIRSWt4zVFJuMMYtlCJW2jMWJ1b3hZjaNuWPO8SBQGHGd4FAQQqM4sVNbWcZbnDj4NIni5NKdnGzTVIcQxTgAmbz1vXJkjA6hTQmj8chYOsoQb/kRoYuSRFP5lUW+Z5QUCkIX2evoshUYXai0X7iIx+LOw/R1tGvn7k0ZQj3fqx3NzTuA6jqEkukOoiLPLNYcMIwSDXrjkTFJGwuWdl1RVwaMjBVREEKjuCJU2hWEqAc1SzOEeJI2wVo70SHkc7cMQMG4Q6h+yxgFofUbFe5k9xkZQ43EWjXUg7Kck0JByF3IX7I7/i5DqMeWsZUYF4R267lsyhBqGhmL4qoOIW+cIZSPjNWHSs876mWtTTvb/C0VhLLv6ajvc/wpoCCERnWh0hIdQrOESaLEauIuFtYjjNPVmeMOIYLPAYzlGUIVdzrd3VHuFq5f8cKl7zMyhmqzRsY8b3JU5Xy0m50dyxpmBSHfM/nFPRZ3saOvo+Ioblm6Zaz6fHi8ZaxQEPKLW8ZmdAgtEM/gupVch9C1DReE8pGxfrBz/87rREEIjSpDpQ1rvdtwlXVyKdZvmLWLu5lkz2PjBoCxxg6h7O4nNznWK07Swv1EhhB3aFEhqdkI6ASemejou4guZ4aQK7D2fMMijRVwHUK7VFyLE1sZ7+E0bRlzhaJ+aWQsykfGpkfKiswCodJ5QaiQISRpY6vn3WfE0YCCUBEFITSq7BDyKQi14Q4ojCGsnzvAHPTGHUI87wCcpi1jPbaMbURYGk/oB7Tso1qc2Ma1814pzNaNjF22jjMXKu1zzrMS5/nraHeeS/e52g8W2DLmOoC8yVDpqQ6hhpGx+TuE3A3cbY2MJQo8o0FQ3zl1GVEQQqMooUNoUWH+gcrztG7uZNDdcfAZGQNQkOQdQtOnPa5Awd3C9RrlBaEsQ6gh7BSXW5oh1NAh5FdnCF22AqMLlQ58jw7HFXCjh7vUIeQ+V+syhHoNo7l5qHShmOQXlrKE+ZaxhlDpOa8Fh6HrEHJr5/uSpJOzDY2MZYsNmp6Xy4iCEBqld2mqM4S44G7mDiiMjK1feSbZZ2QMQEFTh5A72WXkYr3cdhd3J3vA2nnUsDNGxjwzeYy/2NHtUMtyodLlkG0sZhezqEbRjA4hv340N+/aLLzX0tdSNuFQsZa+yFtgZGxU6mg67vvyPbPRUOl+4KnfMEp3Gc0sCBlj/pEx5lljzIcKP/eAMeY3jTGfyH68Xvi1HzXGfNIY80fGmNev64FjM+LETp1Ae6zobWVcYecDZ92qOoQ4OQLguK2DTRlCfFavV5TnUZAhhGazRsYCr1wQchlCl+e4nyRWUWKzgpDHOfkK7OKWsXBGh1DzlrGKDqFC95379aobKZJkzAKh0nmHkJ99DaP7DnsbDZXu+V7j83IZtekQ+nlJ31b6uR+R9E5r7RdJemf23zLGfJmkN0j68uzP/KQxxl/Zo8XGxYnNC0CO+2CY90PgsglLaxuxPlUZQjHPO4AMGULb54o/7t+g6c41Lrc2W8aKN33OdyRD6A9v3NZ//guPr+RCdFTI5Cp2dWBx5zvYaeY+Q+u6eHpttoyVOoRcsbWc+1aWrp2f7/HmGUK98dfcZEEojBL1fZM9L7vz77xuMwtC1trflvR86ae/Q9Jbsv//Fkl/vvDzv2StHVprPyPpk5K+djUPFduQ2OkOIXeHlQ6MZnQIbU65Q8gzZAgBGHOfx5UdQj5bxjahHH7aZ2QMNaxVY0Eo8MxEdkk+MtbxAuN7P/u8fvMjz+jm3eHSX8u9dwaBN7EqHItzr6Ndur6ZFSrdazMyVlw7X+g2y2+kNGUILbp2vvB4r22yIBQn6gVeXiizXCtIWjxD6MXW2qclKfvxRdnPv0zS5wq/70b2c9hRVaHS7r85+NSz1o5bLnme1q6cIcTJEYCiuOHE1t302KUxgV2UB5gyMoYZYmtVs9hI0vRY+HhkrNuvJ/f4TofR0l+rmB0TeIbPrxVwGUK79Lk0nNEh1FR4L38mS5vYMjY5MialHUKbWztv85ExqftdhZuy6lDpqhJi5SvFGPP9xpjHjTGP37x5c8UPA6uSVBSEAgpCMxVPVLp+grIP3EzyQZ4h5FGIA5BznwdsGdue6bXzFIRQbdbImO9NXogOdyT7xXUw3VtlQSjbMsY5+fLO8w6h3flccq/5QW2otFEYJ5WdMO77LN4oKWYIjfKCUU2HkLdAqHRFh9AmR8aG2WY+9z11/TNjUxYtCD1jjHlEkrIfn81+/oakxwq/71FJT1V9AWvtz1prX2etfd3DDz+84MPAukWJlV86KLN2frZiFgW5FOtXnkn2F1iFCWB/xQ0ZQgEZQhtRtXY+Siyf1ZiSVGy4LQo8M/F+3ZUMoXGHULz015ruEOr2974Lzl2nWbQ7n0ltMoSsrZ5WKBYVnbRDyGWguoJRXYeQ5h65ys/XJwpCgU42PDLmvueuj5luyqIFobdL+t7s/3+vpF8t/PwbjDEDY8wrJX2RpPcu9xCxTZWh0j4FoVmKq+Z36U7Drso7hHpph1DgeTzvAHJRw5axPh1CGxGWLj5o2UedxKpxy1h5VGVX1s67jouVdAgVQ6UZk1+JC7d2fofOH8NSob3MbRCrem9UZQT5hWLreDNkXYbQ/B1C5S1jknT/YV93zsON3BwI4yxUuuF5uYzarJ3/x5LeI+lLjDE3jDHfJ+lNkr7VGPMJSd+a/bestR+W9DZJH5H0LyX9gLV2+TI4tqZy7bwhVHqW4p2r0Q7dadhVF6U7Dr7HyRGAsVYdQnxmrFVYWnHsPq+H3KFFSWKn4wqKAn83M4RcR8aqM4TcmDwBucvZxbXzxddBlXEnzPT3VB0qPX5v5b++jgyh0paxxEr3Rsu/L2YJs7Xz7nvm+JMKZv0Ga+131fzSn635/W+U9MZlHhS6Iw32K2cIpW8i1s7Xi2I6hDZpfMdhXBAaUogDkBlnCFWFSmdbxjp+MbnrqjKEpN0KcMVmxImVaVo7bya3jOUjYx1/LeUjYyu48HUdQv3AU6+Q7Vm3EQqz5RlCO3QsGFUUdYpcAX4Yx5J6E7+WF+kLBR/f8wqh0lae0dR1oGMWiGeoGhm7dpiWI+6ch7p20Kv8c6syiq2O+h6dwSWrDpXGnkmqOoSyVw15C/XCwgfkMs9TGCf61fc/yV2fGVy7uBsZo0MIQFEcuw6hqlBpwiU3oSpDqPjzgGOtpvIri4KpLWO70dnhLj5XGSo98D35dDmuhNsytktFgqqQ5qJ+w/HNFb56wfi91vNNfiM7TJLa/CApLczOe3kyrOhocufurtNvncIomdgy1vXPjE2hIIRGVaHSdAjNVry7sMyB5bf+6KZ+6Jferw8/dWcVD2tvDaNEnhmPg5RPFgFcbo0dQtkJL92c6+VujkxlCHW8qwObl8xYO++Vbvq4i8yuv5ZGeaj0akfGXIcH5z3L2ZXCYlHV2FdRXvioeG+4PxtMdAiluUBJYhXFNu8+q+KZ+a8Fq4KsXZ6Q6x5ap1GcaBB4bBctoSCERlVr591/c+CpVzyYLPM8PXc6lDRuY0W1YRTroOfnLeZ0CAEoyjOEKsYpXCF5ly4CdlF+8UFBCDPEtnlkLCgd43els2OcIbS6LWM93xufl3f8+++63Vw735wh1GvoxAwr1sq742FsraJ4dofQ3KHSUazAMxNf96C3uTyfNEPI5N8zGUIpCkJoFDUUhOId+sDctOLBZJkTlJOzdA0jaxGbXYTJRLssBSEARU1bxtwJM2PQ61U3Mtb1i3hs3qyRMb88MhbtRkFolSNjxUJAj5GxpVlrx6HSO5RB2WbtfPH3FYVxosAzE8VXP+sWihOrMLG1G8YkySwSKl06X5cKHUKbHBnj+DOBghAaJZWh0q4gtI1HtBuKFxbL3HV+ISsIDXmyG7kOIYeCEICipi1jvmdkDCeG65ZfwJY6hLhDi7LE2sa18+lYyy5mCKWPbyUjY4WCkO9R1F7WMEryPJxdWjs/cqO4dRlCM9bOlwtJ447ZJO0Qapjd9IwWyhAaFM7XpfHGsc2MjFn1Aq/xebmMKAihUdSwdp4OoXrFD5hlWnhvn4/Sr8cJc6Nyh1DgGcVkXAHINGUISemWlV26CNhFYelONiNjqBMnNj/XrOIbk59bWWvzMNquB5SvskNoWMhiCfIOoW5//112UYhm2KUiQVUmT1G/oUNoFCVTY9TjKZA0Q6hpa91ia+fjqcc62ODNgTBO1PfJECqjIIRGVQdl9+HAe6hesW03XKJTxY2Mdf0kZ9uGUZy3nEpp4CR3ygA44w6hurZ6PjPWLc+ryE7+3UUAxzeUWVu/6lpKx1rcqdUwLzQahXHS6a2swzxDaIVbxgIvv3HLZ9jiilmdu/Q8ts0QqtwyliRTxZni+GFY0UFUtGiotOsIcsah0us/FowilyHEDYkiCkJoVLl23nAnYpZVdQi9cDaa+nqYdhEmeSidNB04CeBym9UhFPgegaxrNp0hlF4EcEKOsvRmZP2vB4XV2K6z4+pBT9aq08d+dy636lDp8abE7n7vXeeCyaXdKlKPsi27dce2ptGoMJruACpmCEVZxlAds1CodFWGUNYhtIEFOmGcqB946gfp9zXaoeLfOlEQQqOqUGn34cDa+XrFuwvL3GnIO4Q4YW5U7hDyPW+pkTFrrRJOrIC9EefrdWtGxnyzVDcnZsvXI3uMjKFZYqfPPYs8Y+TuSbrOjmsHgaRu5witK1Q67xDiRu3C3OvouO/vXIdQXXeQNC7AV24ZS5LaDKEosQpjO3PLmKS5uvLSglBdhtB6X79JYvPcJHdDgkiOFAUhNKo6KPu0ps40sWVsiQP07XM3MsZz3WRYakH1veXuEv7T993Qn37TOzt9pxFAe3mHUE0eQuDRIbRubqONGwXKC0Lx+u8KY7ckVjPXzo87hNIfrx70JHW7wOi2V52OVjcy1mdkbCWKnWa71JU/jKaLOkVNGUJhPD0SlmcIxVZRkjRuGXOXh/OcK6c3cLczMuaux3q+p14wDs8GBSHM0Lx2ngNPneIdqmXWV7qRsS6f4HRBGio9vuOw7MXdHz93pmfuDHX3IlzFwwOwZU1bxqRsBIWLqbWKShcfdAihzqwtY56XjqqkgdLuQj7tEOryuM8oHxlbzZYxY9LPtIC180s7H6X/NtcOg053mZWF8fQIVlHjlrF4uuDjXkthkqSh0k2devnESPvHOwynO5rc479Y88iY+3cthkp3+fNikygIoVaS2DTYrxwq7QpCjIzVmhgZW7BD6CKM8ztfVLCbpWvnix1Cy2UIuYPS3YvlT9oAbN/MLWO+x8jYmo1KFx9Nd65xuSXWyp/RISSlhd6LfGQs7RDq8vmSe62HsV16xfYoSgOBjTF5WD7bfxd3vqMdQqMZHUJN27TCirXyxZv+YZw0joy5t+g8ESKNGULr7hAqBNATKj2JghBquYLPVKg0HUIzTYyMLXinwY2LSXxgzTIsdQj5S66dv8hO1Ir/BgB216wtY4FnGBlbszBOKjuENrFZBrslSWzjyJhfuDGZZwgdugyh7r6eio/tbMlg6WE03hDlztN3qbOla4pZVLt0LJidITRjZKz0Z4vjh2FFB1HROEOo/eMdVWQIGWPUD7yli6Qz/+48d8vPi1C8Z1IUhFDLnUCXV38GFIRmCidCpRc7sLhxsfTr7c7BaRuGUVzKEFq2Qyh9vu8wMgbsBdchVNf9Hvgen7NrFkaTI2OsnUedpKI7vajYxTAsZQh1+X0cxokOe+nF8LLB0sVCgOvi4Lx8cRejYofQ7jyPo3hGhlD+OTv9PYVxot7UNV5hy1iLtfPSvB1C8dTaeSk9Hrj38rqMKjqEuvx5sUkUhFAroUNoYcUi0KIz3W7DmESH0CwXYaKDiQwhs9QsvbtjzcgYsB/iJA00rus66Plmpy4CdlEYJ3mQp8TIGOqlC03qf92Nk1WNjI2WyG1ct1GU6IHjvqTlg6VH0bgg5OcdQryXFlXsNAuTZK7NWds0ipqLNv2GwkcUV6ydz/OoknTLWE1XrTQu2i47MialwdJrHxkrbObzPSPPcPxxKAihVl3mAh1CsxWzKBY9QE8UhDjIN6rqELJWC6+OdyeYdxgZA/ZC1YKEouLWIqxH+U625xkFnuGEHFPixLbuEDovhUp3uSgSxlb3H6WFq2WDpUeFDiE31sN5+eKKGULWdu+5/MDnTvT86Wjq50czR8aytfMVn7NV3UXFa7yq0OmicUFo9uN3qtbOS1mH0JpHxtxNH/c99+gMzlEQQq2kpiDkPgDYZlDPdQgNAm/hzTUnjIy1kgbf2YkOofzu4YJ3eAiVBvZLPGNbSnpiyDFtncJ4nHni9AOPghCm2Blr5915aZTYwtr5bheErLUaxUleELq3ZIbQqJAh5JMhtLTzbGTsyiB9HXXtGuc//bnf1f/33Z+Z+vkwStRvKNoEvifP1HQIJdMFoeJ7K0psY6i0O6TO0001DOPKAtag522sQ8h9z/3A44Z7hoIQas3qEFq0++IycEWgw76/eIdQ1p1y7SDghLmBu6Mw0SG05N0yd1AiQwjYD7M6hHq+19kg0d/6+E39o3dNXwjsmjCeHm3ghBxVZo6MFc5D85Gxw2xkrKOvJ3dOff9RNjK2bIdQYbtUjwyhpV2E6bbaLmabWWt19yKqvEk5q0NISl8fVd9PmutWnSGUh0qveO38KG4YGVt3hlBeEEofd58OoRwFIdRyBZ9y226xeoxqYTZ6cNjzF15lfHIWqu97uv+oz12fBu4AUjzALDvWOMxHxugQAvZBPONOZ+Avlzu2Tr/8vhv6md/+1LYfxtKqNtb0fTqEMC227UbGooqRsa6+ntzjup53CK1uZGz8fHTze98F52Gsw56fF9cW7e5fB3cNUDVSVd7eWKXuczZMptfKF19LVRlDRWbODCHX0b+tkTH3HIxHLTn+OBSEUMudHJfb7I1Jg7i4E1Ev7xDq+Qvfdb59PtJ9Rz1a6mdwK+IPeuMDzLJjja4F/S4dQsBemJ0h1N2RsWEUL72iuguKHQ0OxzeUWWtl59oyFsuY8ahPV9/HrhPhgRV2COUXtl73ihi75nyUFoRcAaRLnSOuUHJR0UFTHB2s0w+qO2GqxngnMoQqCkZF824Zc5/1tVvGNhUq7TrrApZJOBSEUKtu7byUnjwvms9yGeQZQj1/4Q+bF05D3X/Yq231RGodHUKuyMTIGLAf3JaxOj3fdHZk7CJMdDqKdmbrTZ0osVOjDf3A07Cjzzu2w73MmwpCxWP8RZRuGXWvrS5dyBe587iVjYwVRm+Km6GwmPMw1kHf7+Q6cldIqeqgGcWJei1GxsKK7XtRRbZe4I9vqIaxbR4Zy96jbQ9NecRDZYbQ5raM5RlCXF/lKAihVt3aeUnyPDqEmoRJOpfb8xffXHNyPtL1oz53UGeo6hDyl5ynH+YdQoyMAftgZoeQ73V2ZGwYxUps9d3hXVI12sDIGMrczcaGt+u4Q8hanY/S7JcuXsgXuZuDVwaBer5Zaah0jyiHpV3kI2PZc9mhzhFXtKjK2AnjRIMZHUK9wFRnCFUUk1yGkNsytsoOoWFpZKtoEHh5XMO6jKKKLWMcfyRREEKDulBpKesQ4sBTK4oTBZ6XBZUuniF031FPfd909gSnC6o6hPItY3QIAVCWIdTUIeR193PWFYJOR7tdoB5F011aA254oMRdXFZ1pzvFkbE0DNjPiyNdfT25x9ULjI4HwUpDpfPclw4VMXZNOUOoS8eDcYdQ9chYqwyhyoLQdAdQMZ8rTNpmCDU//uJjlVSbIbTu924+MhZkodIsNchREEKturXzksgQmiHMgtgCr7oq38bJWToyRodQM7dhpNghFCwZsHhBqDSwV2Z3CHW3IOQuApa9gNy2qrvRHN9Q1mZkrHjT5yJK0oJQPjLWzXPT4rjKcX/5glBYCJV2XRx0CC3ufBTrsO/nHTJdeh01jYyF8fQoblldJ0xU0bU5HsdM0l/3mjqE5ts63TgyFmx+ZKzHlrEcBSHUyjuEKg7KgU+HUJMoST9kl1llfHI+0vXjPh9YMwyjig6hfCXt/F/PWpt/TUKlgf0QxzY/0a8SLNHNuW6ujf50x4Olw9hOBZhyh7Z7PvnsXb3uv3uHnjo538rfn+dXthkZK3QIdbGzoyjfcOR7ujIIlt8yVgiVzm+CdfR73wXnoSssdjFUeskOoZrP2fTmdfWWsVGUKLFq7BBy79G2GUIXFR39zqC3+S1j/ZpspcuIghBqNYVKe6a7K3q7wAW1LbrK+CKMdREmuu+wp76//uT9XTasaEFdZgXrKE6yDSfSnYvdD3IFMLtDqN/hwrv7jDvb8ZEx1s7vhj944kS37g31xPNnW/n73chY0/t1uiDk5a+trr6P8+6EwNPxwF96BHQ00SFEhtCyXIaQu3HQpYBudwy4qMjYKb4O6lQd36y1CpNE/dJnsnstueJNU7HJm3PtfH6+3qtZO7/mnLypDiFuSOQoCKGWKwhV5S4EnmndIngZhbFVz/cWXmV8cpZ2ptx/1FOvZl0kUuf5yNh0h9AiXWzuIPjglYHixOZfH8DuipOk8U5n4HX3Joe7a7psR8G2VYZKMzLWOU+dXEiq7kbYBHcdblqsnY9cQSgYdwh19QKv2CF0PAiWDpUeFkKlA9bOL628dn7Uoc6Rugwha20WLt7QTqfqLWNxYmWtpjqE3GvJFZ+asvfcW7R9QSj9muVOUSmNfVj3Z84ongyV7vuG40+GghBqxQ3Bfn6HT567IMouPhZdZXxyPpIk3X/Y14C1iI3cWNe1w17+c/kM9ALdPe6A9aKrA0nkCAH7oNWWsY5eTLki9dlot4vTVaMN/cDn+NYxT99OR8XWvfGnTt4h1GJkLLFWF2Giw+K68A5dyBeFhYvRKysKlXadIb5nZExa+MZizsM0Q8gVK7rUIVS3Zcxdh83MEAo8DePqP1uXIeSOO81bxuYLlR53CFVvGRvFyVqbDfJQaTKEplAQQq2koUPI9wwHngZuZKy34Cpj1yF0/ahXWdnHmCvYXDsYF4S8fJ5+gYJQdhB82BWEyBECdt7MLWO+UdjRY5orUu9+qPR0+CkjY93zZJYdtLUOoTm2jEVx2sV70PPke0a+ZzSKu1k4HW848layZSyMk4lOi8AzCrlRu7DzLIvKFUC6VCioC5XON9e12DJWDpUe5eNTpS1j2X+77vjyrxe5glDbaIVRRean42If1nmDIIwmv2cKQmMUhFCrMVTaM+rozdROcK3xi26uOTlLO4TuO+oRujnDnYtQgWcmRsaCpUbG0oPgw1fSghDB0vvrP/+Fx/UPf+fT234Y2ICZHUKeJ2u7tz0zTmzeWbD7BaGKDKGAjLyuefr2dkfGXGdv48hYcctYNjImZYXdjp6cFi/Alw2VjuI08LdYYA08lr0sKk7S0at07bzLourOc1k3MlYsMjbpB9PXIlE8q0PIjYw1dQilP7btxq/K/HRckWidOUKjOJFnxl1P/WCxWI99FGz7AaC7GtfO0yHUKErStfM9b7Hq8zhDqF+7LhKpuxehrh32Jk4e/SVGxlyb7IuuMTK27979yVs6rAg3xP6JE6t+w791UAik9b3uvCaKd4RPd3hkzFqrKLFTFx+DwNNozZtl0J61Nt8utq3OLXfYrroZ6bj3a5yNjB30XUGoux1nkxlCvk6H6dKKpsJX7deqKAQE3mI3IDHuhjnse53cVueOA+WCUNsOoV5F9IT7/srZeu782f2dTR1CJl873/jX59wYat2WsfHf25v69VUYlXLsqp6Xy4oOIdSKGgpCgWe4E9EgjBMFXtohtMjY0sn5eGSsXzH7i7E755GuHkzWtt0djUVeo+4g6DqEGBnbT2ejSGejuHJrB/bPrA6hXke39FwU7pbu8paxsOZuNB2w3XLnPMqzqta9ArpOPjLWUCfx8g6hZKJDqMvbAosbjo4HgRI7+f6eR7G45AQ+5+WLOs9e82mHUPcCut2/d9oxOn7NjOLp10GVypGxmmLSOFS6zZax9Me5t4w1jIytszMxjOzEc0Wo9BgFIdRqCpX2DAeeJlFs1fPNwvOpL5yN1Pc9HfZ89bOxM9afV7tzEU7kB0mS63Bd5IB+kWcIHWRff3cvwlDv1t10LPOCk4FLIU6SxgwhdxLctW7MiQ6hJbcSbVM5zNMhQ6hbXH6QtMWRscQVhGa/X+NE+dp5yY2AdPP1VBzvuTJIb2ItOjaWX8wXLqz9BbfaYjweddDz8+NElwrVxcdSfF/mhcEWodKj0msj74oqdc66w6QrkjVt5xxnCDX+9blWI2NrLESHcTLxniFDaIyCEGrFccPaee5ENIqSrENowW1st89C3XeUjkH1/DTbomt3rrvi7kWka4er6xDKM4TyLWN0CO2jm/eGkqSLHR7DQXtR3K5DqGvB0sU8hV3OEKobT+gHnhKrhbZxYvXchjFpvVkeTdzFZZtQ6WEUK0psflGbXuB181ypuPL6uJ+esyz6nnYFgsHE+AtRDosaj4z5eXGlix1C0uT2v7rOy7KqzjnXCXjUnyzOpNcdRhdRiwyh7JfadgjlodI1W8akxbvm2ijn2HW5gLxpFIRQK+8QqrhL4xnWzjcJ4yxDKFhslfHJWajrR2nXizs48aFV7c55qKuDyQ6hZTKE3B2M+4966vue7tIhtJduuYIQ+SWXQpzlutXp4piANPn6PN3hkbFRYVymyB3funQ3/jJ7aqJDaDufjeMOofrf447xrqBy0BuHSnf1tTSZIbSaDqH+RIfQYhEFmBwZczfBu3TOXewKKv7/9qHS052Y+ffcn+7W8T2T3xxts2Ws/chY+jWrRtwGPX/i96xDVYZQGFsmMERBCA3ytfMVHwZkCDWLkvRDp+elq4zn/bB54Wyk+w/7ksYn0LTVV6vqEMoLQgvcLctbhwNfVw8CMoT2VF4QIkPoUogTK7/hTmfQ0YLQZIfQ7r5W3Z3sqpExieNbVzx1+0I93+iw57caGXv69rlun632GOkuLps6+sYFITfqk76OuryEI88QCkw+Mna2YIdqVah0z/e4Ubug4vhUr4M3YUc1BaFhngPUHEzeq9h4fB6mxcij/vR+qcDz8k6doDFDyBWEGv/6icfb973K7r+NbBmLkon3DDckxigIoVbT2nmfglCjKLYKPKPAX2yV8e3zdGRM4gNrljsXoa6WMoTcHZ6FMoSyuxODnqdrhz1GxvZUniG0pbEIbFaU2MYMoa6OjLmCpTE7HiodjS+Gi/LjW0cv4i+bp07O9ZL7DnTY91vdqf++n39c/+NvfGylj8GdLjWuna/pEOryCEg40SGUPt6FR8YqQqV9zyjq2OfXrnAFoYO+r57Lk+vQzYFiEehiYmSsZYZQVixMCtcidSNjUqlDqOG4Oc4QatkhFCa1j3WcIbTekbF+acwy/fnu/FtvCwUh1Gq6S0NBqFmYtSUGC26umRgZ4w5qrTBOdDaKp0Kl3Wu2bRtrkbs7cRD4unYQMDK2p+gQulziGVvGXE5C5zqE3AjrYU/3drpDqHlkbFsBxpj09MmFHrnvUIPAa3Wn/ta9oZ6/N1rpY2izZcwVd++NyiNj3d1aF8aJjEnPT9YRKh0wMrawi4ktY+6GYndeR3UdQlWFwSr5pEHhezorfM9lgWfyv6e5Qyj9sX2HUFy5YUwqbhmbPM49fftcP/vbn1rJWFcY24ljkHveutpVuEkUhFDLHVhqC0LMXNaKsryK8Z2G+T5sTs5Huv8oHRkbZwjxfJfdy4o1dSNji7RPFzuErh70GBnbUzfvUhC6TKJZW8byO4XdOjF0r88Hjvs73SFUlyE0oAO2U548OdfL7s8KQi0uks7DeOX/dvnNyIYOIa8hQyiMunmuNMouRo0xeYbQKjuEAp9sz0UVR8b8DmYIjeLxecpwgQ6hQcUYXNsMoabsPTNnhtAoSuoLQr3qmwO//sGn9d//i4/p1goKz+VQ6S6OB24LBSHUagqVpkOoWToyVugQmqOYcxHGuggT3XeYdr24E2g+sKa5Yk15ZGycIbT42vlB4OnaIR1C+2ocKs376jKY1SHUW7Cbc93cyfEDx30yhLBWcWL1zJ0LPXLfgQZBu5GxYZisPATWTT01jYwFpQyhwx3oEBpF43GVpUOlKwoBgUeG0KKKW8aMMelWrg49l7M6hGZtGRtfR7QbGQs8kz8nvaYtY3mHUNtQ6SQPjy6ryxBy7/FV3LwbRdOh0u5xXXYUhFArJlR6Ya4K7Vot58mlOMkCGu8vZwjxgTXFFWuuHZTXzi9eEBqGaUurMUbXDsgQ2leuIDSKEj7LLoFZGULjkbFufc5OFoR2tzgd1XQIsTShO27eHSpKrF56/6EGvdkdQlGcaBQnK/+3azMy5m5U3ss7hNLXUdV67a4I43F+ynHfZQgtGCodjW9cOenIWDe/965z3TKu0yzwTafGiGoLQnNkCJW/znlhgUpZ4HutOoRcp17bgZFFRsZcZ+wqijbF96BUGBnjfUNBCPXihlBpz1AQauJGxvoLdAidnKdtkdeP3Jax9Gt09a7XNrlizbXDyQ4hd7K4yN2yYZTkJwVsGdtfxfbjba1XxubE8awtY938nB2PjA10HsY7e9wdj4zVhEp37Hm/jJ66na6cf+n9B60yhFx35arvrrfZMhbUjox1uyDkXv+B72kQeDpdcAy0skOIkbGFXYTTnWZdei6HUZIXSIvnK20zhPqVI2ORDnt+5cavwDN5t3zz2vn0x3k6hGpDpWtGxlzRdyUdQqVQaSI5xigIoVbTQTnwKQg1CeMkHRlbIEPohdOsQ+iQDqFZxiNjpQ4hf5mRsfEdjGsHPV2Eq78Diu26CGPdG0Z60dVB9t/8++47V6Sv4+6gdi2U1Z0cP3ic3iA439HMK3fC3StdDHB8646nTy4kKe0QajEy5i7QVt8hlP5YFVfg5BlCpc6OdMtYt97DziieHFe5MgiWD5UuZgh5Hh1CCzoPY/meyYsfVWvat2kUJXkQefF8JR/FndkhNH3D42wUV46LSZPXfUHDjRQz79r5sCFDqGbBgBttm7fw/Kmb9/S2xz838XNhZCtHxrr0b70tFIRQqylUmg6hZlFss5Gx+Vca3s46hO4rbRnjA2vanXxkbJUZQvFEh5Ak3aVLaK+4QOnHHjiSRLD0ZTB7y5jrKuzW52wxVFpaPIR22/K18x4Foa566iTtEMq3jM34Nzlf8EJtFnfcrupccKY7hNLXUc/3OvtaKmYISWmO0NKh0qUOIc7LF3M+SnTY8/MCR9c6zUZxkmdlTnYIZTk/szqEqkbGRnFloLRUKgg1dgjNFyqdjoxV/53uMQ5L52PuPVL++Vl+6b1P6Ed++YNKCu+JME4mbkq4QhkZQhSE0KCxQ8ijNbVJlCQKfG9813mBDKHrpS1jXT3J2aZ8ZKxcEDJLZAhFSX5y6UbRCJbeLy4/6NHrh5IoCF0Gs7aMVYVudoE7Ub1+nH4W7WxByI2MBaWRMUI9O+Op2+e6Mgh07SDQoDe7sLKuDiHbIkPIrxkZ6wfd6uwoKueXLFMQyrdLTXQImc59fu2K88KNQCkbv+vQczmMkvwG5XCBDqGqkbGzUVy5cl6aLAI1FZvce7TtSvhRXN8hZIypLES7scqLOUf7715ESuzk+fuotGWMG+5jFIR22M27w7WuoXUFn6qC0Kar58+fjhZurd00a63C2KrnmfFd5zkOLC+UQqVpaaznOoSuTIVKu0Lcch1CrtBEjtB+cflB44IQ7619liRWiW3OJOnsyFg2wnrcTz/jXPv8rmHtfPc9dXKuR+47yLcszSrSuc/NVWew5R1CDSNj7r3szgsPdyJDyJZGxvyFz2uHVR1CnkeH0IIuwsnxqa5tqxtFSX6DsipUuinnJ/31ioJQ2DQyNllorJN3CLV8qoZhkmcFVaksCGXB67MyzcpcIcjlskpZUbYyQ6g7/9bbQkFoh/3HP/Me/S/v/MTavn7TQbnNBopV+s/e8nt6469/ZGN/3zLG29m8hYo5J+cj9X0vP8Hp18zVIh3lujoIpi70fN8dpBZbO+8uUtwdmTvnu1GMRDvjDqF0ZGxXc1nQTpzdvWzcMuZ3c2RsGKWfR1eWXFO9bbVr5+mA7Yynb1/okfvTInmbDCH3ubn6UOn0x8aCkGkKle5mUSQsdSekHUILbhmLp7eM+b6Za6Mtxs5L3TI9z+vUzYFRlOhqdgxYJFS6ar36RcPIWDAxMtaUIZT+OFeodMPXG/SmP3fOFuwQcjdy3dSFlB6H+hMjYxSEHApCO8paqyeeP8uzMNYhL2xUnET3fX+jJ3DP3h2u9XtdpSgvCJnCyNgcGUJnoe4/6uWzzP2OjjJ0wZ3zaCpQWirmgSwyMlboEMpHxugQ2ie3ss+Sl2UXP/POpmO35BszG8Ixe143P2fd59HRwHUI7WpBqLpDiIJQdzx1cq6X3X8gqd1Nv3UVhNqMjHmekTFp8ajnm/ymUJczhIbRZKj0KjKEJgJyPTKEFnUexjroT46MdalI4I4D5c49NwJlGoqnUvU2rbMw0lF/+vxZmrzuayrgePOGSjdkCEmq3G64fIfQ+Px9VHoPuv/f1c+MTaIgtKPuDiNFiV1r10jcMDKWnixs7iLqIkx2pkMmP/H1vEKo9Bxbxs5G+biYxAlzk7sX4dTKeWl8kIoXuFuWdghNFoQYGdsvt+4Nde0gyIuJ8955wm6JGm5uOHmHUIcuAqTs86jn6Ti7WFm0o2DbwprRhnHY6W5+X/viIox1695Ij9znOoRarJ0vZAi1zRBpw3X0NYVKS+P380HhArPvG43i1T6eVSlnCF3pL7dlzPfMxPm537Gull1yHsY67E0WCsIOFddG2Wtn0PMmMg/DGR03Tn5juXAdcdbUIeSbyv9f5s61277fhtECI2OjxdbOu/fW7WJBqLTprx+47Wvd+bfeFgpCO+qF03Qmcp1FgsRaeUaVledBttpzkZGcRQyjeO7q8La4A3LaITT/lrGTs1D3FYocdS2Nn3z2rr7zp9+zsyGjq3DnImzsEFrk2u4iivMD1njL2OV9jvfRzXtDPXR1kJ8MkSHUDRdhrO/8mffoQ0/eXunXjRs2Zjp58b5DFwFSdmc48HWcdQjt6ud93tFQt3Z+yULcL7zns/rv/vlujJV30edvj1fOS+ORsaYLveIF2irzVtqMjBV//aCU/SIt1h28buX8kmVDpcuFgJ5vOjfyuisuwtLImG8miifb5jbUpe/LUofQjEBpaRzmX3yfno9iHdWESrfPEEp/bPt2G0X1odJS9ahqvmVszn8P19l/+yy9Xk7zXRP1J0Kl0++/S//W20JBaEe54OF1ds1EDWt6V3US19YwTDbakbQMN8Md+N443HiO5+lsFOcn/1J9h9AfPHGi9372ed144XzZh7yz7pxHUxvGpHE7+SIdQsMwye84XukHMma8zQz74dbdkR66Msj/ndky1g1PnpzrvZ95Xr/18Zsr/bpR/pncECrtTd9B7YJxh1BWENrRUGl3gV6+iHUXYcuGZf/WH93Ub370maW+xmX21O30POKl92UjY4GnxDYXVs5H01kmq5C0GBmTCh1Cxc6ODofEhtF0qPTpKF7oxuooSqY2S/letzZj7ZLyCvae73WquOb+vcude2Gp46VO1Tatxg6h7L0VeM3jaO7X4nk6hBpGxg5Ko6qjKMlvqM974y4fGcuul+PEytrSmGUw/xTHvqIgtKNch9A6sy+ShoKQe0NvYowrSaxG8e6MjLkDcs8zhZGx9gdpd0fYcV1G5eLbRT6/v5sXCKtwd1g9MialoZOLZwilH42eZ3RlEOTbzLAfbt0b6uGrgzwrig6hbjjLxqFuvHC20q/bNP7sdDdUOj0eHA2ywsmOdgiFFZknUnrj5LDnL935dDaKKewu4amTUodQb/Yyi+Lzvcrzs6RhoUmRVzEylndUR90rjJS7OdyNv7MFXrejeLoglBYxuvd974LptfNep8aIXGdNOa5jFNlWI2NVWTnlIliRO1Y23USR5ls7H8WJ4sTO7hAKi0Wr8XFhnmudKE7ymwxuZMxdh/UqQqW7tFFuWygI7ajnXUFo3R1CNQfk8ear9Z+AuTfqrhWEilvG5rnIuAiTyTteXnWH0LoCHXdJXai0lB7QFglYLGYISenqeTKE9svNe0M9fGWQv8/YMtYNbub/c8+vtuuxTYbQeDS3OxcB0rhDqOd76gee7u1wqLRnqotyx4NA95bMRjobRRR2l/D0Sfqee0neIZTd9Gv4bDwPJ+/kr4o7bDcVcKXx+7l4Ubvp7vV5pIG2k1vGpMXGQKu2NQWe6VwG2q4oj4z1/fU9l//6j57VR566M9efyTOEKkbGyoXBKuX3RRQnGsWJjnrNodK9hkUMUjFUevZx0z3upsdbLngVM7bm+XwvZu25UGn3vfcJla5EQWhHvXC2/oJQnNjaUD9X4d1Erk/eCbMjF21uZKznm/G2qzk7hIoFCc9Ls4jKLY3no6xQdklPgq21aah0xciYlB7QFisIxRMFuWuHPdbO75GLMNbdi0gPXekXOoR247Nl37m7gZ9bW4dQw5YxV7zvWEGoeDw47vt5F9WuGcW2dn3xlYG/cLiuc0qH0FKeun0+8ZmYn+M1nGMWC+mrvKBy79cZDUJ5wagcKi11cwQkjCfzU65kBaFFXvuVI2P+Yl3RmF47H3je2l5D/69f/ZD+p3/1R61/f5LYfF36IFhNqLR77x7Vhkp72Y/tcrza3PN2nyXNHUKTI2PFUeJ5ln8Ub+K6kbGqHLtB0M0bQdtAQWhHjQtC6zsBihtHxjZ3F8Z9OOxKJ0zeIeR5tYHQTcodQlL1KtXzSz4ydjqKlVjp2mH1HQ7Pm//kKIoTRYmdaB2+ehCwdn6PPJd1Vz50ZVAobF/O91DXuAujp07OV7o+uU2HkJ/ljnVuZKxwPDjqB/nGlV1TFYLrLBOu65wNIw1XvO3qMnnq5CLfMCa1GxkbrmlkzP0bzuoQcr8+6E3f8e9qQai8dl5arEOoMlTaY2RsEdbadMtYMUMoWN/GttNhrI99/m7r3593tgTedMZOnOQ5OE16pcKHy/+amSE0o9jk7rG06RBy1zCDmiBrSVMdUMX3xjw3v4uLYG6fp+d8Yd4hNH6+uvx5sWkUhHbU86dZqPQau0OaTuC20iG0IwUh98GSbhmbv/o8jOKpD8x+MH23orjy9TJyQc9XV9gh5F5jEx1CBz0yhPbIrbtDSWlByBijg56ni0v6HuoadzcwjK0+f+diZV/XhcvPusDseV7n7hReFDqErqygcLIt6cVw9fN/ZbD4+m3HhW3vynlC15ycjfTAcT//73FOZNPI2LpCpdMfZ2UIuUiDye1Q3b3AS0fGigWh9HGvrEMoO+ehKDqfUZwosZq4EdjzzNpueJ+PYj15ct76RmM+alWxZazpOq2onEXqjrV1HUJ+PjLWrkOozUvOfZbM7BAqfK4UR7/m6RByz+3VQVDIEJrOsfM9I89c3uuoIgpCO+pkAyNjwyiZ+IAscicLm+0Q2o27+O4OTc83cweVWmvTDqGKsMDyc31+yU+A3R2AupEx3/Nabz5wXJFtIkPoMGDL2B65dS8rCF0dSEpPAhk16YZiseOJ51Y3NtamQ0hKi/hdy+CY6BAa+Etv49qWpm04qyh0ueMh7+XF3BtG+QiT1O6m32So9Oqe97jlljHfd1vGpgtCow6GSodxecuY6xBaVai0O9/s3vfeZRdZ/EK5sLiODiHXjSRJn3j2Xqs/U+ysKRdMhtF8W8bc13LHkcOaa7zWHUILZAg1bRkblG7QuY5YY+ZrQHBF1kcfOMpHxqoKQu6/u1hA3jQKQjtqHCq9vpOfizCureT2Nzhq4U46wtiudIxgXdwFReB541XGLQ8sw5qWyr7vTZ3gXPaRMTcjXB8qLcVzHtAvajqENjkyFidWv/i7T3DHYk3ygtCV9G74QUBBqCuKF0arzBFyJ/ZtQmq7djGVHoddhtDudgiNSiu3i46X7BAaRUl+w4Rg6cWcDuO8Y0Vqd9NvXaHSNi8ItesQKh6v+0H1VtZts9ZOFXGWGRkbVWTHuIy0XThP7hJ3Ll0cnwoqcjtXofj59PGWY2PutTzwPQ16/sR7LWwZKm3MZBbpeZi+5la1ZazNS84VdBpDpQN/4rrS5QpeP+rPda3jbhg/dv1wHCoduZv1k39/P5i+4X4ZURDaUZsIlR5GycRsdlGbwMFVPg5nFy6Sw3zLWKFDqOWHTV3oWtUH1mXfMua6durWzgcLzNO7A1HxjuO1g0B3h1G+Cnfd/uCJF/Tf/Mof6l2fvLmRv++yuXVvnCEkpRcT51xEdsLZKFLPT1u4bzy/uoKQu0CadXLbxTuFxePw8cBfqJugC5ouXK4cLFfoOi8Gj1LcXci9YZQXKKRChlDDZ2PxeV/p2vm2BSFvd0bG3LlIMb8k7xBaIBdsFE2usJfGHUJd+967Li8IlV5H63gei2OWbXOE3HmpC5UuXxO1GRmTJrNIxyNjy20ZM3N1CLUcGSt8f2775PWj3pwZQun1waPXjzSKEl2E8biwVr6+6uBxfxsoCO2oFwqp6euaFx5G8cT2hqL87tEmCkKFD4Fd6IaJ8i1jXmFkrGWHUEVBQso+sErP9Xj72uX8IBuPjNWvnW9zkCpyd2+KB4xrhz1Zq42te3btrXf3JLfofBTr+37+9/ShJ29v+6FIkm7eHerqIMjfY4yMdcfpKNK1g54eue9Qn3thdavnoxZbxqT13RVelLU2LQgVO4R2OFR6XRlCxedknpwJpKy1Oh1FulooCLmLzKZzrmE03si5yoKQewvOKggF2fu5amSsfL60bfmGoxWFSo9iW9EhlD5fdAjNxxU2J19HZi15cmeFz6qPPzNfh1DllrGWHULuz+cdQjMzhNpuGUt/bHMd2m7LmK8osflN9LPsvfHg8WDOLWPZyNj1NCj/5CxsHBnbhWaDdaMgtIOstXrhdJR/+K+rQ2QY1ncI9TfYIXSxpk0W6zLeMmYKI2PLdQj1gqq184yMSc2h0vN2CLkDzqC0ZUzaXIHGfV/Lhqx2xe984qbe+bFn9d7PPL/thyJJunlvmOcHSem/NQWhbjgdxjoa+Hr0+qE+t44OoZkjY+vbLLOI8vHgaODv7MhYOT+l6Lgf6CJMFs5vKl5kMTI2v7NRLGtV3SHUtHZ+FOu+rEN3lecheYfQjCsUL98yVpEh1KHCrlSdX3LUc6HSC2QIRdORDkHeHVX/GXb7PCR0uqRqZKzne2vZOOnONY77fvuCUCFU+qDnT3UItckQklwWabZlrOJ7nvy982YIzf77W20Z602+f09HsYyR7j/qzXWedm+Ydhu/+NqBJOnkfJQXics3JtLrK94TFIR20N1hpCixelF2UbOuIklxu0nZeO38+i+kit/fLnTDFA/8nmfke6b1RcZFQ4cQI2OTXIGmLkPI80y+Xagt9/oqdsa50OpNBUu7v2dXL/zK/s+PPSupOwWuW3eHeX6QJB32vJ34XLkMToeRjvuBHnvgSE+ssCAUtdwyNgg8DTt0ITneeph1CA2CfJvWrmkKlXbZNYuOw50xMrYU99k8URBquWXs/sP0s3QbGUJBxchYv0VRZBuKXR6O5xkd9xcr8laFSgczOoQ+f/tCX/PfvUPv+uStuf++fXZRMTIW+OnGyVUXz86zAOvXPHq/bt0b5ZmGTfKCUD4yVuwQsu07hAqjUaveMtamG989z00jbuUw+9NhpKOer8O+P9e1zt2LUFcPerr/KD1/PzkL8/fg9KglGUISBaGd9EIWKP2S+9LK57pa3YZh0iJUetMdQt0/2YtKeRWB134MobZDqKKl8bIXhO6ch+oHXu0mvEXWzo87hMbPv+tA2lyHUPr3LHLXsGuSxHavIHRvmOcHSdnI2IY+V379g0/nK1Ax7WwU63gQ6AseONKzd4cru7hv2yF0/1EvP752gRshdseD436gUZR0aqytrab1yK6ov+hY7sRqYgpCc3OfzYtsGRt3CK1+ZGxWAdd1CE2GSnczQ8gVqMrvgUVHlquyY9znW933/sln72kUJysttu+D84qNW/01bWxz3Yyv/YL7JbUbGxsVrgsGgT+xYKftljEpyyKde8tY83vQZH91m6fJnWfVdSVJxUK0e5yRjgbB3Ms/7l5EunoQ5J9Pt8/D2vdgVSTHZURBaAe5/KCXZK1w6yqSNK+d306o9C4UP9zB2M2397I7DW3UdghVhEpfuJGxS3oCfOcirF05L6Unk/MWhPIMp9LaeYkOoUV8+Kk7evZuegesK5lIt+6N9HBhZGxTW8Zu3RvqB37x9/Urv39j7X/Xrro3jHTU9/XYA+nc/40V5QiNM4SaT24fujJodcd2U6o6hCTpbAeLxWGc1F5cLJOlIjEytqzTpoJQwznXRZjkSx1WeWPSdRvMaBDKL1rL2S9S9wpCeYZQMPlNpQWh+R9rGio9+bXc+6vuvOdGtrlxH84tVmk8PjW+JHajUqseIXZ/12sfu1+S9EctgqWHxQwhN1KVvZ7CuP7GfdnElrHRrC1j4+uXJq5DqE0nlXud1xWhpOLnTvo8nQ5jXRkEGvS8OTuEIl0ZBHmH0O1ChlC5o4otYykKQjuo3CG0tpGxFmvnNxHEtXMdQrFbbTiusLedRa4KNZaqU/AvfYfQRZQXa6r4C2QIjS/AJtfOp3/fhgpCF/tTEHrnx56RMdIDx/1OdAiNokS3z8NSh5C3kYvIe1lB7IQOoVpno/Qk7rHrR5JWt3o+znPdmk95Hr46yLfQdcHFVIdQNlq1g8HSo6YMocFyOW2no906R+iaypGxXruRsXV0CLXeMmbqt4x1LSS2LtB20PMW6lANY6u+P3lh7T7f6s43nzxJC+y71H08jGL93//xH+i3Pr6+ravnFTdi15VF5bqRvuCBI10/6rXqECquay8XTNIMoRmV00yvNDLme6a2a9MVF2cXhNIf24yMjcO7G0bGStllp9lNonk76e6VOoROzkeVwe7uv7tWQN4GCkI76PmsIPSIKwit6WIm3W5St3Z+9snCKh+Hswt3/9zB2N1hCLz2HULu+azsEGJkbMKd87A2UFparEOoqkNr46HS525kbPcu+sr+9cee1Z987H49ev1Q9zZUUGvy3Gna/VEeGTvfQIeQa9Fe9nUUxknnLnZW5XQY6yjLEJJWt3p+ng6hF85GC4cbr1q5Q+jIdQjtYEEobBhtuLJsh9Cw2CG0Oxe7XeGK1cuMjK20Q8i9X1uuna8eGetYhlAhGLhoEPgLdXmPovoMobobYU9mHZe7dLPpf/7Nj+vXPvCUfmeNBaGqDCFXZFn1scCdaxz1fX3xi6+26hAqrkt3117uWmjeLWPumHIexjrq+fna+DL33po1Zj1PqHRV4a0sv7Z0GUKjNFfQraNvm+l0J8sQujII5HtmMkOoHCrtG4VRtz4vtoGC0A564SwtCL147SNjce0bt+cbGbOhtfPRbt39cyciLoyt75vWBxX3IV9+3stjZ0li89+7C8/JOty5iGpXzksLZghVdGhd3XSo9J50CD1790IfuHFbf/ZLX7z0WulVuXU3/ewshkpvau28Oxla9nX0//yVP9Rf+99/fxUPqXNOR5GOB74evjJQP/BWtno+LuW61Xno6kDWjm+6bFu5Q+jKYPGtRNuWXrjUr52XlhkZK2YIdaOYt0tcx9mVwvE08Iw8U3/DyVqr8zDW8cBX4JkVbxlLf5zZIdS0Zaxj50VhTaDtIh2q1trqUOkZY043XIdQR8a3Z/ndTz+nn/3tT0tab1dkniFUGJ8KvPUUFovZPV/ykqv6+DP3ZhY5xsVEPy9+DqNYSWIVJfWdl2XFTpjzUdyY5eMKQbO+tpmjQ2gYphvDmtfOT3ZApbmCaYeQte07tu5eRLo6CGSM0f2HvSxDqLoo2w98RsZEQWgnvXCWrpx3d7nX0SFibVpwqHvjGpO2Gm5m7fxubRlzxZ+8Q8j3Wo8uuQ/BqZGxUofQrm1eW4e7F2GeX1DFMwusna+4g5EGV3u6u6GCxrgg1K0T2nn9m4+ld/T+/S95ka4Mgk5kCLl8mOLa+YOev5H3kDvpXHb08Innz1a6kr1LzoZpqLTnGT12/VBPPLeqDqF2W8YezgqFLvdq26bWzvddhtD230vzatoy5gpCixaNJzOEdvtzcxtcgdFte5PSc7xB4NcWetK79enn56Cig3kZeYbQjCsU936u6uzYlQ6hRTLsih0jRW07hBYNb9+kuxeh/uY/+YAeu36kR+47WGsR/LwiO3JdWVT5OWY/LQjdG0Z66vZF45+Z3DI2Dl0e1Ywh1ukXbiyftSwIzbqJMs4Qmv33n4exDoL6riRpOrvs3jANlZ43tzbdMpYeV+476unkPCysnS9Hcpi97bqeBwWhHfT8aajrR/1CpXj1L+T8gDMj/GszodLFDqHuv2mntoz5pnX1ualDqPg1zsPdek7W4c75jA4h3+St523VbXm7Mujp7oZGntwWql3MCSl658ee0SP3HehLH7mqKwfd6BBynR8PHBU7hNL31rzdZPNyF63LFsbOw2RjW9E2aZSd4LqcnMceOFpdhlDLLWPuJktXgqXLI8THWUFoF1fPhy0yhBb9jDjN8jAkOoQWUTUyJqkxyHVYOFfpr/hc0BWE2o+MrT/7ZVl55/jUlrH5n7uLUU1BqGHMKYoTff5OWnjYhe7j//c//4ieOjnXm//jr9QDx/21PubzLC/VKxwf3L/TqgtCxY1mX/Liq5KkP/r8ncY/MyrcKC6OcoY1hcE65S1jTeHOfiHyokk+Mtbi/Ok8bC5CSdPZZWfDWMd9P//5NsVTa63uDaO8u/++w14WKp29Byu2OJMhREFoJ52cjXT9qFeYtVz9yWHdhXFRP/A33yG0AxdC45ExL/+x7chYXYdQ+Q5c8Y7oZa1s3525Zax9Z5ZzEcYKPJN3dznXDjbX4eIyhHbhpK3OMIr1O5+4pW/5Ey+SMaYzI2NnFaMRBy3CU1chHxlbsrA4DOP8pHKfuH8b1wXz2PWjlXVCzZMhJKkzwdL5CGt288d1cOziZ8OooUNo2e/rbJiGkfd8s5fF0nU7HUbyzPT2n0Hg1XZPnhdyVwaBv+IOofTHtiNjxQyhdV3IL6tuw9EiI8vuptF9pQ5pv6FD6PN3LvLCeNdHxt71iVt62+M39F/+mS/UV7/8AR2v+fyhanzKvY5WvnY+jNXzjXq+py/KC0L3Gv/MqPDaGRRGxupCkutMbBkL07DmOuORsVkdQumPrdbOh0ljEUqazi5Lx8iDqZ9vcjaKldhx/qcbGRvVjIxREEpRENpBz5+OdP24v9bV78P8RHRWh9AmQqXHlexd6IYZj4wVtoy1Xjtf1yFkJj6wdm3z2qpdhLGGUZJ/4FfxTf361fqvm1TmZl3dUEEoSWzeibSLOSHO7376eZ2NYv3ZL32RpPTO872LqHUg4LrkoxH9QkEo+xxdd2eByw5wBb9FXYTxQiHYcWLX3gW1DNf14roUHnvgUHcuovziZxnjDqHmUx43Sti5DqFgcu38LnYPRnGifs3FxSDw1fe9hT/zTkfpXeRFxm+QdmYdZ3kbRU0jY+OAWC/rEFrd8+7erzPerpVbxnzPyPdM5y7w6gJtD3r+3EXMk/O0YH1/odM1/dr1GUJuXOyo73fi5kyTD9w4kST94Lf8e5LSY8JaO4QqumXc+fuqb7gW/677Dnt66X0HMzeNTW4ZG4dKu5vPbUOli5MGZ6M4v/lSxW85MuY+M+I2W8bCOC9o1Sle11prdTpMQ6XnuXHnztXdjb/7j/qlLWOT31M/aL/4Z59RENpBL5yN9MBRf2KWdNXKYZZVBr3Vzo3XGUZJvl58F/JywtJ4QuB7+c/NUve89/zJ5/p8NM7E2IUi2aq5D/ymDCHf8+a+AE6D1Kdf81cPNjMydjqKlNj0rssudgE47/7ULfV9T3/6VQ9JSg/MUWK3/lo9G6V3wov/xu5EY92bxsZbxpZ7HZ2H8UKfg9/3lt/Tj739w0v93evkXu9HWbdIvnp+BV1CbTuEjvtpaOetjmQIlTuE3B3dsx0sFjeNjElpl9C94WLvjfNRnOZMLBDQi/S9Vx4Xk7LO5JrCysVEh1D971uEbbt23p8eGZPcDbRuXeDVZggt8Jp1RfL7j+o6hKa/3o2sIPTFL77a+YLy+Sie6Fg7XndBKJxeoNNfU4dQuRvpi18ye9PYKE5kTHpNUQxdnrdDqDgy1jZUetZNFCk9X21zs+9ixpiaNDkyNowSJTb995/nxp07jhRHxk7OwizHzkwVvsuRHJcVBaEd9MJZqOvHvYnWwVVrNTK2oVDpYWG16S50w0RxosAbf+j0vPZbxoZRor4/OcsspR/kUWLzOV138Xr/YW/rF9nb4C6qm0bGFt0yNgimD1ibCkW+k/0dL752oPMw7nRHR5OT01APHPfzE46r2cXGtoOlT4exjvuTd8IP5phNX8ZFPjK2XKfURZhm7cy7DvcTz9zTZ587XfjvXTd3wu+6t9zq+VUUhGLXtTmjIGRMuqyhMx1CpbBTd0e363f4q4ziZCq7oSi96Fu0QyhKcyYWXOF92bkOobJ+i5Gxg77f+PsW4T7aZhWE3Pv5IJge9+naKH2+Zaxi7fy8x56Ts+qRMRdTUNkhlG0Y+6IXXen8wgpXoHHH6SsDf60d0/cqCqLBmkKlz8PJosiXvPiqPnnzXuPxfJRdFxhjCp0yycQoWRv94paxMG4cGfNbjoxJ6fu0zZaxi6hFQajQIZSfEwz8qWyhJu48Og+VPuzp7kWki7B6bJlQ6RQFoR1jrdULpyNdP+rPNVM5r3KYZZVBb7Vz4/WPJUk3zzSsQO2SKLETbZY935tjZKy6pbIclOhOxu476l3KE+DyB34V3zOVd8qaXETVz//VDYUiu5Xkj9x3IGk3R0Ok6fBA17q77QvZ02GUd6A4myoIuYycOLFLdSO5x3kx52fhnfNw689/E9dBdTwoFYRWECyddwi1OLlNC0IdyRByN2ayzyTfMzrs+RMZcrvgfJTezT5qOJ9YJmfsbJh+3hz0PDKEFlBXEGpaHHIxGhcrV90hlOQdQs2/z42MlY/Zg6B7mSB1GUKD3vzFtBPXITRHhtCTL5zr4asDPXCl3/kMoYtS0eS4v94OobsX0dS55NpCpcNYh4VRrVc+dKxRNA78rjKMxhufqzqE6kZxy3rlLWMNn8euM6icp1klLQjN/vvPR9OdWGXF61pXuDzqz9chlE8Q5CNj6fvkudNhZUGIDKHUUgUhY8xnjTF/aIx5vzHm8eznHjDG/KYx5hPZj9dX81AhSXeHkaLE6oHjdY+Mze4QGvibyRC6yFYVDjYUYr2sME7yOzXSfFvG0g/+6Q9M9++QV/dHl7tDyBVOmkfG5u8QGobJ1N1GyY2MbbAgdP+hpN0dGyu3YF8ZpP9O2z4RTTsJJk/83PjYpjKEpMVzhJLC2N08Baw4sbo7jDo9auSKAe6u5X2HPV07CPS558+X/tptt4xJ6liHkDsOFy6OBv7ObRn79K00NPVVD1+p/T3L5IS493Ua0Hv5jofLOh1GeRdnUVOGkCu8Ha6hQ8haK2PUuJ5aSo/xxlSP2HftAm9Ut2Us8DWKk7k2otad/7hujqobYU+enOtl9x/q6iDQKE42cjP3c8+f6W+89f1zXyeUzx+OB8FaO6aLK8qd8dr5NYyMFQqYrhDbtChiFCfqZ8eAfDKksGVsrgyhtiNj/njCYRbPU6sOofOajM6iYlaQuyF6Zc4OITdB4M47XUHo5t1h5XPV72ABeRtW0SH071trX2utfV323z8i6Z3W2i+S9M7sv7EiJ6dudrifv7DX8cE+3nbV1CG0qbXziQY9L7uT0v0T4Siu6BBq2akyDKszbPIOodLF4P1H/UtZELrbokMo8EyroLui+gyh9O71uke4XOfTS12H0I4WhNI7fOPn0bVj310wI2RVzkbx1J3w/ARkzZ8txRO+RTeNFd/r82wacydIXe44c10vxX+fVa2eb5shJEkPX+13piB0EaUbaYqPe915GuvwyWfTgtC/96L6gtAym4TOsgyhRTY2wXUIVdyIajjHczmGbsvYcIUXVLG1M8fFpPT9fBD4lZkgu5MhNP+N3ZOzkQ57/tTFtfucqDpPufHCmV52/XAcTL+Bz5B/9ZFn9Ct/8KT++Ln5PsOHYTJxHubOH9bVGXnvYryi3BkHdK++Q6gY5pznwjUcz4dhsUNoemRsrgyhOA1rPhu12zLWtkOozan2sMXa+cAz+TRIcfPoPDfu7lWMjElZQai2Q8hufenJtq1jZOw7JL0l+/9vkfTn1/B3XFrPn6Wt7A8c9+R7Rj3frDdDqCERvrwKfV3GHUKbKUAtK0qSiQ/RwGu/ZazYGlrUzzuEqjKELt8J8J0WGUK+ZxTPeVJ4EcaVRdCrGxp5Go+MHWZ/327+25bvPuXP35Y7hO4Np0+C8pGxNb+Piid8iwZLL7pd0HUkNZ10bptrDy9emD52/SgPQ11G2y1jkvTwlYGePx11Ir9rWJFpdtRfPGtnWz717D15RnrFQ0e1v+fKEmO5Z1mGUBrQu1vPTRecDqcL5VLz2nn3POdbxlb4vCd2PA7W5Kgf5AtHinodzATJM4SC8pYxd6Hb/vk7OQun8oOk4pjT5GdXklg9dXKhR+8fF4Q2MT78mawzcJ6bF9L0yPm4iLWe9/bdi4oMIW89I2NnpbGpwxYFobRDaHJk7CKMFdYUGev0s23FLqx5FVvGpGxkrMXx8jyM89GvOsaYfBrkXuGcYLxdrf2WsXFBKN3Gd/PesDITKW+uuORdQssWhKykf2WMeZ8x5vuzn3uxtfZpScp+fNGSfwcKXjhNC0LXs3WT6xqjcicBVeMzTn9DBZq8Q2hnRsbsRJvlPO3LFxXbDtzXkDTR7imlGUJh3O110uuQh0rPGBmbd0PERenOlLOxglD2fb30/vV2CH3wxok+f7t+Zn1Z5eDEKxs8CW1yNpo+8XP/3u6O9/r+7uVHxorZQ/M8XreVpsudJeVQaUl66Gpfz62gW8d9DrRoENJDVwdKrPT86fZzhKo6Fo/7u5ch9Mmb9/QFDxw1dhxfWSIn5GwYZzkTjIw1+eyt08oV1/cWGBlzn0Xr2DKWZCNjs/yX3/wq/dz3fs3Uz3dxa1DdxfsiNyRun4dTG8akYofQ5Pd+695QozjRo9cPN3os/sytdInBvDciymvg3U2CdTzmJLG6N4ryvBmnH6xnZOyiFObsijLnYf33Nori/HVTDF0e5kXG9iNj1o4LJm0yhHotbqIYo3YZQi06hCTl0yBnw3HX8EFv/H3PcvcilDHjcwn3Xjk5C2syhNbzb71rli0Ifb219qsk/V8l/YAx5pva/kFjzPcbYx43xjx+8+bNJR/G5fHCWbkgtJ4cn3xkrLFDaEOh0lm75Lq+11WL4lKHkN++MDGrQ6gcKn1/Vvnu2t2wdbtznq4PP56xJaHNXHNRegFWnSEkLb8yfBZXKHhJ3iG0npO2v/r/+329+Tc/vpavLVVkCHUkVPpsmI6WFB3McedpGRdhnJ+YLDoyVnyM8wRTu4LQMJp/O9mmuFyc4knqA0d93T4Ply54x8nk5scmD10ZSErby7etauvhro6MNY2LSYtvGbPW6jQbfzjo+YRKN3jjv/io/h//5AMTP2et1ekiodKlLWOrPAdJEttqvPNFVw/06pfdN/XzXcwECbPV4eXva9zxMcfI2Hl1h1BQc2F7I9sw9rJCQWgTnyGfuZkWhOY9tl5E5QzC9T3me6NI1mpqZGx9HULRxDHO/f/GDqFo3CEU+J4CL50MmbtDKPsa7nygzZax1h1CbbaMhbO3jEnjz517hZtE85yn3bmIdKUf5Nuai++V6i1j2b/1JbuOKluqIGStfSr78VlJvyLpayU9Y4x5RJKyH5+t+bM/a619nbX2dQ8//PAyD+NScXctrx8XCkLr2DLWIlS6v6lQ6ezgsMg2hm0Il9wyVlWQcFsEihlCxow7V3ahULZKdy5CXT3oNV7gBQt2CFW95t3zvO5g6TsXoa4MgvwAtq6TttvnoW6cLJ/NUudiVN0htO218/eG0VQRcXMjY5FefDXt/Lqz4PNQvGiY5yS7WIA66+hIzVn2b+MVLpiuH/eV2PEo5aKilheY0rgg1IUcoWHF1sNdC5WO4kSfuXWqL5xREEpXS0dzhetKGo8/DHbnHGFbTs5Geqa0zWgYJYoSW10QasgQygtCaxjnT+zslfNNuhoq3ctWhxctsuXyds3ImCtilAvoT2Zjty+7/2hjI2Pno1hPZV3I827VLG+jWmfuUTlvxnFdN23P3dsqj9O7osysUOnieam77pt37bwrhriCUJtQ6XYZQpqZv5MkNj2/blUQSrPgXJHsqO+Pw7RbfM7cG05ujZsoCFU8V71gPcW/XbNwQcgYc2yMuer+v6Q/J+lDkt4u6Xuz3/a9kn512QeJsRfORvI9k7c3DnprGhlrtXZ+QyNjeYfQboyMRaUtY71sbreN8p0Rp1/6wHIttYsEEu6DuxdRZXZAkbdghlDV8z8uaKy3Q+j2eahrB0HeIr2OEyAXKPj0ukfGCicbg8BTzzfb7xCqCJU+zE/I1z8y9qJrabFh0QJHsWi1SIeQpM5uGjsdRVPdW64T1mXnLSqObasNY5L00JX07+xCQaiyQ6gf5K30u+CJ588Uxlb/XsOGMWncRThvwdJdNIy3jHXz9d0F94axXjgNJy7e3GdyeZRWykbGap7P8zDOcyxX3S3edmSsTs83CqNujX+MoqSyk2OhDKHzUeXI2LhDaPLfwuWwvWyDI2Offe40///zZgiVO0nW+ZjdTaor5YJQdrxY9ejhRZhMnBu5/990PC92CEnj6758y1jLDiFX+HDnH80jYyb72m07hJp/j7tGmadD6LSwaGK+DKFwouOr53v5a2hQEypdfIyX1TIdQi+W9C5jzAckvVfSr1tr/6WkN0n6VmPMJyR9a/bfWJEXzkJdPxp3RqyrS6fN2vm+v/5QaWvtuENoZ0bGJjuEAq/93aphTYdKniFUGBlzs/vuz10md85DXR3U5wdJi24Zq16LOR4ZW3+o9LXDXuEEaD2B8YmVPn/7Ym1bFcoZQsYYXRkEWw2VdqMl5Q6hQc0J+X/4v75L//B3Pr2yv/88jHX9qK++7y38OroonFjP1SFUKAh1ddPY6TCe+rdxnbAnSxaE5uoQutqxDqGg3CG0ePjyNnwqGxtpMzImzV8Ed7//qO9nGULdP0fYlrNRpFGcTLx+ThsLQs1bxg576Yav/orPzdqOjNXpZIZQnFR2chwE89/USzOE+lM/H+QZQqUOoZMz3X+Unle4wse6R8ZcfpA0f5G3nOWYfzas4djlbvJtYstYFKddPcVzo1ZbxsoFoez95q6/2mYIuWLIyfko+7ubtvR6Ez82MS1Gxtzn8mFDDEn+OLNmg9NhJN8zGgReYYlSmwyhaKrjy3UJlUPdpcLIWMc+Mzat+RZ7A2vtpyV9ZcXPPyfpzy7zoFDvhdNRftdUWl+XTlfWzqerAJVnCO3CiXA6MlbKEGo7MlbXIVSacXUZLe4gsQuFslVq0yHke94CI2PTF2CS8o68TYyMXTvo6bDnyzPrOWlzB+azUaw7F1Fl6/kyXGtw+XW8zBahVTgPY1mrqS6UQeDJmMm183Fi9eGnbutLXnJ1ZX//2SgNk7x2GCyeIRQtVhDahQ6hs9F0jskDrkPodLnOvLj0mdzk6iBQP/B0614HQqUrQu6P+r7ORrGsta0ykbbNrZyfPTI2/ox98bX2Xz/vEMqCRy8u+V3eJu548sLp+A76vUJwa9kg8BUldioXUXLnKuOg21GUrOw1uezIWH+OkTFrrd71yVv6+i98aGJcddXCOKnccDSYc2TsIox1ESaNI2Pl854nXzjXy+5Pcwmv9Nd3s6moWBC6WGTLWGWo9Oofc3kjleNu6s57DtnEdQEVs3tcQXBmhpA/WRC6CBONsuuK9h1C6fd0+2z2yNh8GUKzQ6Xd9940deK4MPvTYXrO5D5T2hb8715EevDKZMH0vsOenjw5r84QKm1xvqzWsXYea/T86Si/ayqtMUMoSuQZVR7Axn+3rzg7WViXi8LoWtq+3P2TvXRkrLRlLFmyQ6gUKn2RjeQUNw5cJs+dDvNA7Tq+N32nrIm1Nttot80OobTQZYzRcX89BZTiicc6No3lrcGlk40rg95WM4TGa80nT/zSNafeRMv2nfNQiV3tiKDLDrh6sPjzUBxrm6cN//YOdAil+U6T/zZuLOKFJTd+zdMhZIzRw1cGutWBUOm0Q2g6VDpKbOc6IOp88tl7etHVga4dNBee3b/93B1C2ev5MAuVjhN76e/01nHHk+IIpuvarOwQ6tWvY74o5Lz0fU+JXd3Fc2xtq42AdeYJlf7wU3f03T/3Xv3bTz23+F/YwihOKi9GxyNj7R6v+yyvLghlRYy43CE0Lgitcxy96NM3T/Px27kzhMJYB8W18wt+NrRxN/ua5S1j5a78VXDH7GJRxPOMDnu+zhuOy1MjY1nBZN5Q6XGG0Lirss7DVwcaBJ4ee+Bo5tf1jJnZbZ5vJWyzZSy7ri1vhW3bhJBmCE2+P9y5RPWWMTqEJApCO+ckGxlzmtaCtvEHT7ygv/vOT0z9fNop4Tfe7SlvvlqHYrj1QW9XR8bm6BCqDZWeXjt/2PPz4sVlKgglidXnXjjXFzzYfKDyPU9xYluPRbnnsGrt/EEvbVld+5axrENIWt82oWJB6Onb5yv/+sV1xEVXB4HuDdf7/DUZrzWffn+l2SPj95Db5riqApa1VufZutlrB8HCGULFItD5HMXxYoh1V1eWn41iHQ0m/20eyG5+vLBshlC2Zayth670dbMTI2PTHULu9bvIRq5t+OTN2RvGJC08yuI63tIMofnzWHZBklj9v371Q/rDG7cX/hpx1rkpSc+fjl/b45yO6c9Fd8OpKhqg2MWRF45WdB5irV1BqHS74/5zWbF52ZyyWeozhNw5XLvXrCsIVWUIeZ6RZ6SocAPSWqsbL5zrZdfTglDgb6bb/jO30vd93/fmKgglidUoSvLOGUlZl8iaCkLZOd2VQd3I2PxFziSx+l/e8Qn92geemvj5unMj1/VZpzwydpAVRuYNle7nBaHZW8YevjrQR//bb9NXfcH1mV837RBqNzLWNHXi5BlCWYfQ+OfbdgiFUx1f7v1S9R50jQ+X6TqqCgWhHfP82Sg/SZaaZ7zbePsHntKb3/HxqYvmtFOi+eWxifyafHTNdQjtwBs2TCbvBPX8dHSpTWFi1tp5d5IzlSG0A4WyVfn8nQuNokQvn1EQcheAbW9autfxQcUByxijqxsYeXIZQpLbJrT6v+98zR1CdSc92x4Zc89l1dx8uRXZFSAWHe0qG8WJ4sTqqB/o2mFvKyNjRx0vJFStvj7KVlove7E2T4eQlG4a68LImLsxU3S0xo07q2at1adarJyXFg+OPRuN73aPNzZ1/zxhHrfuDfUL7/lj/cAv/v7Cn6HFY0lxBNON4ZQvoCQVRtIrOoTCyQ6hut+3iCRZfstY2+KUex+tO6g9rOkQGq+db/e5fJKN+9R1SAelUfmTs1BnozjvEJK0kXOZz9w61SsfuqKDnjdXN6s7xhU7SdbZMV03MuZnxbV5u0bixOq//uUP6s3v+Lj+yftuTPxa1ciYlBYFG0Ol48nlAm5awnUINU1yFLlJg9stQqUltR6hNC1CpS/m6hDKRsZKY+Rtt0jeacoQqniuyBBKURDaIdbalWcI3T4PZe30gbxudKloEx1CxXDrTW01W1ZU2mjTazmL3DSylHcIxemH6nmY6OCSjoy57RUvf+C48fe5C8Co5bjeRV58rH7dXz0I1jrylCRWd4dR3rp8ZRDMnJn/G299v/7bX/vIXH9PsUNkHZvG8rboqZGx7YZKuztwVaMRh31/InvEXTCt6t/bPSeHPX+p19HCa+fPQz1yX7ryvqsdQlWh0sYYXT/qLT0yFiftt4xJriDUzQ6hK2sMWG3ju3/ud/X3/s/pruIqz9wZ6t4walUQWnQd9kSG0BybaHbJafY9PvH8md746/N93udfY1gsCI1f2+4zuS5DSKq+6TfZIZT+uKoOoeVHxkzr81L3elt3gSSMbXWo9JxFTBewX5f9l2ZWjr/WkydpF/Cj18c30NbVfey8cDrSC2ehXvXQsQ77/lwFoeKxsuh44K+tQ8j3TGW3TDBHp5mUFhT+q7e+X//0fTd03PcnRrWl8WdV+dzoaMZzNCrdKB5k0xKjOJExan2zo9wh1KY404bntekQmmPLWHatdzaMJ8bID1pMxLiw7auDckEovW6ueg+WtzhfVhSEdsi9YaQosZMFoSVHxtzoQvmDdlgTblzUdLKwKsVw63SutPsnemEpgDFoWX1uGllylX23SvViFOuw523k38A5ORvpo0/fWfvfM8sTz51J0swOIXeQbFkPyi8iqjqEJOnqoLfWkbF7o0jWqtAhNPuk7YM3TvShJ+cbIyhu/FhHh9BFRzuE3N9dHkuSXEjjdIfQqgpC7kTwsO/r2kFv8bXzhTuM87Th3zkP9dLsDnFnO4RGUWX31vWjvl44W+59N2+H0MNXB3r+dKRkhYGii6jsENpyp9cfPnlbv/uZ51v9XhcoPWvlvFQodC2YIXTc9/Ni/r51zLrn5E+85Kr+8Xs/p3d+9JkFvsb4OSl2COWjtDVbxqTq59PdlJKKHUKred4Ta5cKeO7NESrtvv91v5/qQqVXOTImpec9xZuPbuX8o9fHHULH/fXenPlMdtPulQ8dp/k4cxyrLmrOg9PzodX/G927SHNqquIxep5p/ToK40Q/+Iu/r1/7wFP64W/7E/qWL33x1HHehWsfzT0yFk9tGUtDpdMxxLZB7u5r3MlHxhbeKzUhzRBq/j3j/KQWW8ayDKF7w2hilPWg580snN7LO77IEJoXBaEd8kJ2EC+HSi9zV+ZOFi5W/jC6aNEhlM+Xx+s7kLo3/0FW/NiFDSJRYicO/O7O9Kw7DeO8pPoOoWF57fwGT4B/6rc+pe/6B/9u7X/PLH/8/Jl6vsk7HuoEc3YIjQty1QWhKwfBRBbLqrmD9DwZQncvornHjy4KB+an72xuZOzqYL0dVrMUs0bK0gyhQkHo1BWEwtYZVI1/92hcyFmuQyiWMelrZK4OoYtQL7nW3Q4ha61Oh1Fl99YDx/3lO4Ri22p9rvPQlb7ixC6dXbSsqhFid+G+rX/H02Gkzz1/1ur3fvLZu5Jmr5yXiiNj8x3L3Pv6cI9Hxtxn6n/9bV+iP/GSq/rhX/5DPTdnB1vxWFJ8P+Vbxio+F5s6kIdhrIPs11efIbT8yFjY8rG4C8h1v5+GUU2odD4yNmeodE1BqOd7E7k3N15I36vFkbG0+3h93+9nsw1jr3z4WIf9YK6CUFXwsrS+x1y1otzpBV7rpTnv+dRz+o0PP6Mf/rY/ob/6zV9YmRVYvDFUNKuLanrLmJ93wrQNlJbGhY+T85H62Sr3VfBarJ2vOy+s4r6/s9JNojYNEHUjgOORsfqC0Ko+v3YVBaEd4nIUJkOllx8Zk6YLQlXbTcr6cx7IFlHuEFr3VrNViOJk4uJjHE7X/LjHG9UqWhor1s5vesvYzTtDnZyFc23uWoc/fu5Uj14/mrlG2h3s2j7ecehd9de9drDeu2quOHvtcDwyNmss5O5FNHdxwb3XX/HgsT6/jlDp/KRnetRlGCVbO+g2hacelGbT3WdtGNuVvLeKbfDXDno6D+OFnoeLMNZB4GcdQu3XKt8+D/XglYH6vpePn3TJMEqU2OruretH/ZkZQveGkf7GW9+v/+wtj+f/++VCfsPcGUJXB5K01Rwha23lkoF1btyZZRjFCmOrJ0/OW32ufurmqa4OAj2cPZ9NDnqevAWCY4vZYOOCUPde48twz8l9h329+T9+re6ch/qxOUeFi8/rc4WC0Okw0lHfr3x/DBq6V9w5iLT6DKF4zvdr2Tyh0vdGmxoZSyrHVQLfU+CZuTKEPDNeH19W7hB68uRcR31/oqPoysHsc4tlfObWqXzP6LHrRzrseXO9H+s6jI/76xlzu3NRfSNCSvOYRi1fR27E+Nte/RJJaQEijeQY//n6UOlAZ2H19xbF6bGx3CE0jJLa11Qdd6P6znnUGCg9L9Ni7Xzegd+qIJSFSo/iic7FNh1Cd+s6hLKCUPXIWPq8tP233lcUhHaIu1s50SHUW24Vu+suKN8dqcouKNtEMWJY7BDqba74sYywvGWsZYZQU4dQL//AytbOZytf85GxDTwnrni4zbEfSfrj585mjotJixSEmjuErh70dHeNW7Lce3HcIeQ3tkiHcaLzMJ57jM2NjH3hw1fWkyFUc+BfdIvQqpw23AkvhzqeFEYqVhEsPQ6TDPKRwEXGD8/DOPssbJ/LcB6mF/H3HfZ0tKYchmU1/dtcP+7lQap1Pvi5E/3KHzypT928p6dOzvW7n3lOP/9vP5v/epwkE5/Jszx0JS1g3Nzi6vkosUrsdIH6+nH6+nluya6pRbhunDC2eqZFd+Enn72nL3zRlVYjDcYYHS/QBXA+ivMtkPN2W+yK80KH4Zc+ck1/8atfpt/6o2fn+hquEHztIJjofLtXEebuNC0OOR/F+Xj1qredJtZqiQYh9f00Q6hNd+d4ZGwDBaGam1jlLZdNTs5Huu+wVztS1/MmM4SevTvUS64dTLwH1zV+5Xz61qkeu36ofuDNnSFUVzhY5LOhjbuFza5l/VIeU5N8lC87vl877ClK7MR5xfkCHUJVm8QGPT+/uVbV8VLHvZ9vn4dTY2vL8IyZOV49V0Go57aMRRO5gu06hNzWuFKH0FFTqHT6d7TtKtxXFIR2iGvzncwQSsPFFh1rqOsQqsouKMtDpddZEJroENqNFetRkqhX7BDy2s2nzt0hVBwZW9Ed0V99/5P6D/6X36l8PXWhIGSt1RPPnenlD6y+IDRseP6l9ACzzpEn9/wWM4Sanmt3AntvGM31/j/Pir+vevhYdy+ilf971mYIzRka+8ydC33df/8Ofezzq8mtyse2qjqESlvGih0prnNrub87/RqH2ciYtFg+0UWY6KDn6zALlWyj2HmW3mXtXveEe0xVF6YPHPV1cjZqfB+fZO+dn/pPv0r/4oe+Ud/25S+ZKOYssmVM0laDpetGWF909UC+Z/T0yeyCzI+9/cP60X/2wZU9puJ7t83YWNuV887VBS76TkdRXkjc2w4hF5ydfZ8vunqgOxfRXN267njx2ANHer40MlbXIdF00++iskNohRlCS46MSbNvwknjkbGqzsnP3jrV1/3378jHrpYRRrb24n0QeBMbJJvcPo90/1H1hjFJ8n0z8bo4ORtN3ESWpCsDf63nMp+5eapXPpQu/TjsNefjlOVdNFNLKdazdfXesH5kLJgji6p8/uZGlIrB0nUdQocNN3jc9dWg3CGU3eiZr0PIyx9HOdh6GW3Wztf9u1YZBL7ixOpsFOdbNaWWHULD6pExt5XPFX+KyjfcLysKQjvEfYBfOyjOVHpKbLsDX1kYJ/kH9fTIWJsMofnC8BZRDFrelRXrUalDyH3YREtkCPmekTHpB1YYJ4oSW1o7v5oPsj+8cVsfefpO/qFadLsmgHyTnj8d6e4w0hc82LxhTCpmCM3XIVRXCHXZL+Xiy4eevL2SCxA3b+5OJK70A42ytuAq7vMgsdUns3Xce92dsK06WLruLpg7QLe94PvUzXt65s5QH/jcyUoe1+kwUs83lf++6Zax6QwhabFOnrLiHX53N3KRzqOLrBA8z13X24XXVRpe2cEOoUIwcNn9R30lVo1B3OVVzA9fTbeEubuW824Ze7gDBaF8hLVUoPY9o5dcO9BTJ7PHPX/vs8+3DoBuo3hB9rkXmv/+2+ehbt4dzlUQWmT70dkwzou8eUGo4+cI8zovFJSlcUDqPOH07t/useuTBaG67C6p+RzPFael1WcIJYnkL1MQmmNrkMusqnrdffyZu3rmzlCfyMLRlzGKk/xxlZUz7JqcnI3yokOVnucpLJzzPH8aTtxElrJx9DWdx1lr85Xz0nzfm1To1A6mO4TWcTOjMUPINxPPZZPb56GuDoL8xkNTQagc5nzU9yeWfRS599Rkh5BX6BBq/z4pFiRXOTLWJkNo/O/aLlTauTKY7BCa9VoaXydPvkfyDqFg+vk62uIYdpdQENohefjfYDJkS1qsIFA8magaGatbvz3+u9ffITQ+KS52w3S7iptukyhsGfPc3arFO4SMMer7nkbZmJCUnhyuenbfXaTerhjRcAe2bQYD/3F2V/oVLUbG3B3GeTOE6jqErh70FCd24g7F7bNQf/7vv1v/pJBXsqg7pQOZe5/XHaSKBYV5LgzOR7EGgZeHTK68IFTbIZR+X20LQq6zZVVjbWlWRvWJX/nO0wtnI734WloUWMXr/ayYIXToLuYW6xAa9HwdBO03txRHEY8GQSczhNzx56gmVFpSY8Cz+zV3ofzw1YGixOadQ/N2CF07DNT3Pd3sQodQRQHzkfsO9FSL/K9b94YrHXsrXpA9MaNDaJ4NY84iYyGno0hHPdchtJ8jY3mHUHZx5C7w5wk9H3cIHer2eZiPwpQ3+RTVjenHidUoHscKrPo8ZPmRscmtrE2aRsbcZ/+iWyGLmi7eB6UMuya3z8N8LKmK7xnFhXPNF05HeuB48vcfD9Kg53XkQT5zZ6jzMNYrH05vOM27EXN8fjudQbiukbErtQWh9uHkt8/CiUJdfuOncJx35wHlm+2HDVvG3HtqOlQ60TBK1J8xyVFULCq5z8xVMMbMzBA6D2P1fDMz+1OafH6K52wHvdmZufnIWOnf9MHjvu477Oml9x1O/Zl5u9f3FQWhHXI6jBR4ZrJ1cImRodsTBaHpkbG69dv5372JDKFoXFXenZGxybvR7iRgNOPkZNaMbd9PN8qNt0T5MsbkY4Or4A5eVZkdJx3oEGq7cl4aZze1HxmblSHkRn3Gz82TJ+eKEruSiy530ukOZO4kve4gVSxUzFO0OBvFOur7eiQ7MD694mDpWRlCbYO5XSFjVQWr01Fc2YEiTd95euEs1BdkY4krKQgV1sVXvY7ausgyhA767e+6uuLufYc9XRn4OuvgSY+7S3+lKlS6RUHo9nmYPi/Za+5FV9ONau59mXYItT/dMcbowSt93bq7vVDpug4hSXrk/kM9NWNkLEmsnrs30t2LaGUjVMXP/hszCkKfcgWheUbGDua/6EvHCkodQns2MnY2SrcLunMyd7f7ZI5ChXuPPXo9/Vx74SzMf37myFipWFEeCz5YdYfQsiNj+Qbc2Y/HdU5VFcrda3EV20XDuL7r/qBFNopz+zysXTkvuTGn9JzH2nRTYlWHkLSeC+BP30rf968qjIzNtXa+ZsvY8aC5Y3oR1tqsQ6hhY9scHUL3FQpCVR1C7vhdzn866qXfW9W56rCqQyj7//eGofoLdgi1Gd1qy/c0M7bgfDS9IKHOoPD7ip9NgxbdZnVbxg56vt7zo9+ib//Kl079Gd8zOu6vd4xyF1AQ2iEu/K8YDrdMUaZ4kCtf5LfrEErftBvrENqpkbH5O4TGI2PVz3s/8PIgYWl8MpbOE6/m38CFJpcvvi4KW5G2WUX/7HOnMmZ8UtvEz5/3ObeM1XYIpQeY4vvmmbvpRdkqto/duZhsOR53CFW/3u9NFITm6BDKxo5elHXAtAmHncdFdvFSfh27A3vVOGIVVyBbVYfQ2ag+PPWgEM6fJFYnZyN9wQPpCe1qRsbGIx95h9AyI2NzhJBOjox1tENoON4UVea2aj5/Wv98nZyN8nExSflWq2ez9+e8HUJSmiO01QyhhuPBS+8/0OdvXzQGeZ6ch/ln36q6hNx5wrWDQJ+bkavyZDbS9rLr03dk6yyySehsFI8zhII9LQgNIx32/PxC0nWIVHXyNn2No76vB69MFlhPG0Olq0fGynkgLpdjdR1CWmrLmLtIblMQyjOEKl53eUFoBR1C5c7xojbZKM7JWXOHUOCNM4TOw1jDKJnKEJrVfbyMz2Qr51+RFYQO5g2VjuoLQtI42H4VhlEav1CfIWTmyhAqFoTcttjyJEbVMc6Nb1WNc9dlCEnp63OeDKH+WkfGmn/PMIpbrZyXyh1C4z9zkG0fayo+3RtGOuh5le+1o35QG8Z+Zc1bhHcBBaEdUhX+11+iIDQx21rOEJojVHrRk4AnnjvTN/yP/2d+4lileFK8Kx1CYTLZGuw6VWatQa07EDq9rENo6mQsax9dhbxDqHQCVHytbPND84nnzvTItYNWdxr8BUfGmjKEpMkCwTNZsWIVRYM759FEy/HxjLt4xY1n89zZOB+lYaAHPV8PHPdXvmnMFZzKm4Wuzt0hlP6+VXUI3RvGlSNJUnpCPorTO3R3LkIldtyFtsqRsaN+sFyodJTeZTvoefOPjB32dNzZDCHXIVRVEGozMjZ519wVhMYdQslcGUKS9NCV/nYzhKLxzZCyl953qFGc6NZp/eMrPvZVjb65f6c/8cg1fe755s7Ck7ORrh0Ec23BWSQnxK1Nl+pHnHbdWRhPXEguNDKWFcQfyP7sc/fGBaHaDqGa59OdL463jFV3CH3fz/+efuE9n239GJ20Q2juP5brlZZwNLnXMDLmPjtXsWmyaSNU25ydJDs+3ddUECoUMVxW1AM1HUJrKQjdPNUg8PTItbRL8zDbiNX2POx8NHnD03Hdo/dWePxy/65NHUKLFoQqM4RGSWVRxJ3LVx3TXVGzeF7qzn/vXkRzfb4Wi0er7BAyLTKE5uoQKnyvx6UOIan58/3uRVj779lkXSOJu4SC0A6pOnAvE+xc/KAq3zWeJ0No0Y6djzx9RzdeOM+zBqoMo0S+Z9TzvZ3IEIoTK2s1MZ6Qb7yYcWBp1yFkpw6Yg8BbWZfW3TxDaPJEc6IgtOUOoZe3CJSWFtkyNg4wr+IOMsUL+WfuDKd+blF3LsKJO1WzTtqKf+c8J6zFu1QvuXawlgyhqpOecZt6u8c67hBazUjbWWmFaVFx1MSdRD96/VDGrG7tfD9IV2Nf6Qfp113grrNbsX04R1Bnvv3kIEgzhDq5Zcx1CE3/++QZQg1r1m/PKAhF8b51CGXjng1jY7cKXUGr7hD60pdc1TN3Lxpfg8+fhfm/XVvp9qP53hdnozi/aBgEnozZzw6h4nvDvdarRrvr3BumI7MPlDqE7i6wZSzfyDljy9i//dRz+oMnTlo/RidObO2d/DbyglCbkTFXEBpNb+u9l2cIrWJkrH7L2EHPbxWEni61kO5r2DJW7BDKw/ZLI2brHBlLA6WP83+/w8KxtY26kfN1dDXl40U1r/+eb2beyHXKBaGrFcsjipv5itxzVNVJVRkq7TqELubrEPI9kxda23brtJFuGWv+PXXnhVWKx7zJzNzZBf87F1Htv2eTqwe9lZzr7TIKQjvkdBhPhf/VzXi3URcqba3NtozNyBBacm7c/f1NHQMXYZx/j7swMuZOQCa2jLVcgTq7Q8hMdAgVN3ysLEPoojpDqCsFoSeeP2uVHySNt4y1LgiF6ahTv+akrWpL1uezcau7LYscTe6cT4YSHs/YfDBZEJovQ8idlDxy38HqO4RGSeVr+Kjvy5j5M4TuXEQrOQk8LVw4lhVPWl22xoNXBroyCFZS7DvPcpskyfOMrg6ChXIpxmvn01yGWXP7Unoxc9z3FfhehzuEphcmOEdZeP7zDd0QJ+eTI2PH/fQ5msgQmiNrQUqLSs/dGzWOZa3TsOF48Mh96d33pk1jxa6gZ1dVEMr+nb7kJddkrRq7e184nV53PcuVg6DywrzJ2SjKP89cpt6+FYROC58fUnrxYsx8GUJnw1KH0OlIYZxuKqr7XKwLiz4fpf99WDgHkSbPBd14+yLds9ZqJWvnZ42MJYnV6Sgt1seJnfo+xxlCyx3frU1DuOsu3tPX7Ozz6JPzLDy/cWTMyzfa5h1CNSNj6ywIOUcN3S9VLsJEfd+bKuCv4zHX5c04Pd+beSPXKWc7+dlx/nbpOquqKDIeGZt+jtxxYHLLWNYhNJyvQ0gavzdWPTI26zP7Ikxar7ovHvOKN/Hczzdl5jZtjWuySH7dvqEgtEPuDiNdKbXCLTNG5T6oHjzuT3wQDStmVqssu1nC/f1NHQPDqLDadAdGxlzRp3pkbPy4/80fPavf+PDnJ/7s7A4hX6P4/9/ee4dJchXW4qdi554cd2eztKtdaVc5kiSRBAgBAkwSBuecE7afzbOxjeEZHJ75+WEb22BsDNjGCDAYJBCgAJKQdqWN2ryzk1PnWFW/P6pudVX1rdTdM9Oze8/3zadVT0+Hiveee4LaCHSUG9ulE9tE0zSTpFt2EkKW/1+vUOl8pY6FfBVbAhJCglk739g2n33yAp51qTEv11VjhdnFYxxptozNZTuZIVS3VWX6reLlynWziSVshhAZDIz2RE1Sq1NwWwXjOF0dEzxDqJl4awcFT4WQ0U5UV00lSl9cQrpDq0bFqoK4ZJ/QtfK6FcMyFpEEaFqwa6F15TIuiyhWlXUjOdxQrCgQHIUJBBzHoS8hYcUjQ8hpGeM4DkOpiEmKKKpmZooFxWBSbyrLdCA/pBWUPe4HpCFwyoPMXcg3CLROKoQEnjODoi94BEvTwmz9kIiI1Im592dSmiYNl1rLWMlBCAk8h56Y1KTk9QLJoOyzKO7IvdxNIcRxHGRKaYWzEZU2FjTVNS3cG9u1jMliMJs+IThJo6RzbNOpljEyLnQLAA5qGVuxFAS4QRQ4c8xDVGDNGUL6sdTpsVxNUXF+qWgjhKIe6hcaSPCyE6thc8ubhBB9e4o8j6rlGCrXFHzoq8eaFlXKRlZT2rFf0jGpqXaeqhDyIIRMhZDQrBCq1t1JRjeQ58dcGldbga4Q8rGM1ZRAlfOAPcczHlIhlG/HMsYyhBg2CnTLmEMhJLWumsmWa5BFHn0J2XaBC0oIiQaL36pCqEEIuX/2jaYQIqsJNssYT+TLjQvm//etU/iLb7xg+1s/hZBMFELO1TnRv4oxCMo11Ry4kJUoAutNLeiEvtM4t2iEFYa0jFlvVB/48hHXTAN9IOK+gkGzjJkKoU4QQqWaGUQI+A/acuUa+uMyRJ5rqWUM0JUGS4VqR1fUvaTBYYL7suWaqfLqhK2tWK17ZAhZLGNkEB2XkYp2TiFkXR1Lx6SWbAilqt7+GDNXyoIRQmSgSo6pMK0va4G8YYlxI2P74rKrQkjTNMMyZp/0DKUiDcuYo/kxCEj47qJHTs9qwksh1BuXEJV4THsodBbyFUgCh4GE3EFCSCdfJvp1QurCso9CKCQhFNbKoqqaQXBbqonF4HbKjYICJRC/NyY1Ldz4voYsQBJ4pKIilgpV89rmRggB9NIKp22dNhYkr93K9VNR22wZC2gZI/bZEaOV0GmnbYfUsoJsF69Q6aDkPtBsAbNC5DlzHNdY3LCfh6mI/vdeY+9WMLmst65aCSGvfBwaSlU6aeKnmG4FOTNDyEUhJ3I2hdBTZ5fxsW+dwqMnF23PsxY3WOG8z5eq9LERuX6FtYwB7op2N8iroBDiOA4+nTmuC4U0WL9f0lY7728/bEchxFrGGDYM9BVuZ4ZQ67atrLFyrNsILAohn/pzK2ShdbtSJoBlzKYQ2gAZQoT0oSmErDeWTKnWtPLcXstY+zd2q2LB2V5iveGtF4tOKudJHbgfyASwbqlgzVfqZpimE5WaaoZk0kAGzbaWMSNDqBO1tNlyzaYQMj3zLitr5Man38hCKIQs4X6jRvV8J5vG3AY9QLjgvmyphp1DuhKhE7a2gme9cmOgsVJsyOzDbls36LlNVoWQ/XUzxRpe+dFHXNVrBOW6ipjMhxpkZ8sNQogMPAtdZhsrVpvvbVb0xWXXDKFSTUFVUZsmSUPJiM0yFjZDiBBM66UQ8rofcByH8d4YpjzytRZyFQwkIjZirF2QHMORVBSywHtWzy8Xa+hPhFupJcdA0HsMOf6tVvqoxKPcxSriVkC7pvbE5ZCWsYZltj8hY6lQ9bRqEtAUyLScF+dYkIwngmbGWdEpy5hfqDS5F40YAcjOexP5Drk2rwGEmHIjhCIBScyVAISQYLWMFWvguGaigpwv+Q5nppwhlfNDFkIorEKoTl+Ya5DFnSOx/AhRkbeHSpNFiUVHtpwbIdQTE23qMrfFMs+WMYVGCNnPuzBYHcuYfzRD2WOh0Anr97OSSORe6KUAzZXdM9G8kIxIzDK23h+AITiI5NeKdi1j6aiImCzYqhyDKoQAkl/TnkLIa3JiVwhtBMsYyRCyhkob8mXLBXOlWGuyjJTrupfdbZXc2TIWlY3tInXGMmadoDrbS8i+GuuJrptl7Jwx+QiaIcQ7MoSKVQWq5r7iX64rnkHqAs/ZZKU1RTVfq13SQFV1ssoqOY6IPESe87CM1ZCMikjHpHAtYzW7QghojXApVRWTpHO+vptXPBnCp50t1XDFiE4IzbQZLK2YSgIfy1hNwVKhBlngEZcFpKLhtq0bdMtY49qtW9Ear/uDC8s4MZvHc5Mrrq9RM1rQoqJgft5AhJDFMkYmAp2s7u0E9Hwn98Fif0J2bVQiKglnrsZwOmJm59RbaBkjr7fsYVVbTXgphAC9aWzKK1Q6X8FgSrZZ59pFwVDZ8TyHzX0x1+r5UlVBqaY0qbb8kKTktPl9HgB2hVCIwPWNAjeFUFjLWNJCCC0XG5Yxr3MvQrOMUQihiGQvtzDJlFYUQpqGkA5PG4JmCJHjjITQOyfkncoQok3qrYhKQjC1p7G/ndYkKySrZaxQRW9Mcs3jcVtsAvTrx1wu3LjgzIJ+Pdg+mDQfa0khRLnmrYbNzWzg9GwZa4zbyaLEYoE+Pm4mhOzW8GKVPgbx2ka0+ZjVUieJ4e5rpmWsg6HSA0n/e0zJR4FvBfmussDbzhkzQ8hDhNByy5gxNg2aOXopghFCGwSapqFQaZbCtWOjypbqhkJIRLFmtYy51906IQutN1yRi6jXgKFi5LoAG8Uypl9MrJMPYh+zKoRWSrpc23rx0RUq7qckUQiVKS1jnSCEMqXGagmtdj4VFdETk9bVMjaQkANf7M1QacMyRgZ3bgqhck3xVAgBMEKG9W0zn6tA0/Q8j0pdbavpjbSHpC3nN8dxRg0zfXvnK3WkIlJoqautZcwghFqxZP39d0/jdX/1naYwQX0liH4chwlpzpbrGE5F0ReX2s4QIgN9NxVKQ4qsGmG4EjiOQzoqdiRDyCmXTjtWDo9MZQF4q1Gsq/JhmluypYbyrFsVQgXKYocVfQl3ewxRdDVZxpIRZEo1VOpKiwoho8mpCzOEAGC8N+oZKr2Qr2IwqSuEFjqkEMpbVCab++M476IQWi7Sw2z9EDYnhBCb1klW5BIkhGg2mr54SMtYpXHd74/LWMxXTbWFl8WCtujnzDEEiEKIbhkLExIOkAyh1hVC5JzxzRDyUQiR/8+UaqG/gxXkc7ipOaISj6riX80eJENIsFjGllxyvCIiD0lwX2wCgN/8/CG89x+e9Pw8TpxZyKMnJqHPomAyFUIhWsZoxMFqhEqT10q6hkpztgxKEtLtbJ/MuOyXdNSeIVR2WSwL3zLWeI3wodJG+1sHFUITfXFMLhc9j1+3shEayMKsk6i2jtNoqCsqClXFFr0QFGTs3W1jo7UEI4Q2CEo1Xd3QpBBqw0ZFsiWcCiFysgUJAOuEQsjrAl+pKyYx1U6j2lqBJg1uWMb0i2W5ppjb2CqNt35XGiRBD7hrahmjrOC1AjLxneiPUy1jPTFpXYPXzi0WAwdKA9ZQaX27kwHqYr5KHdzpDU7ex7yVfCEkBQlYbWegYq5UOQYUXhYr0zIWkQKHXqqqhnJNNQcgo+nWFUIXV8rIlutNAz2vDKGgTQ51RTUUUyJGe2JtZwgRS2ywlrHGILqjCiErIRSVbKqyI9P+hFBjVZ5vBHUGGGRbQ6UJIUYLr1wraJqGv3nkFCYt6pJixV29BeiWsZUivfEr41KtTFb9F/LVljKECMG0EkKF0UmYCzMuJPVYTwzz+YorET2fq5iEkE5et7/yWbTkGE70xXBhiU5ILRfp2SV+CDvpoyqEKJk3Gx3O4GxAPz5px+bT55bw5UPTtsdUVUOxppj7jiiEyL3c1zLmkyEE6BNWWoYQUWeGgdopy1hAhdBoDwmVbnxOTdOQK9fN6vF2gsrNDCEXNUdD/e69nTKlGuKy4NkArDdjkdp5etOf32ITAJxeKODwVNZ2nfYDaRizqtwJ8VAOeM+puIzDIqKeU9Xp2vmELLguFjgVQiumZSy4QsgWKu0olyDwbhkzFgYEuxqPIGyo9GpYxib6Y6gpmmf0QKUFy1jcJSLF7TwhhN1AMhLofawwLYmXcY4QI4Q2CPIV+o27HRtVtlxrKIRsLWPBFUIRUWi7dt7rAl+uNRRCJLiwuy1jhkLIkiFEVoVqxkqDdfJuXz3wJiT0AZcuxZcEzryw0wZsrYAM4Lb0x7DiWBEzCaGouG4M+rnFYuBAaaChzFIUQgjp27qqqFSVkx8hBxiEkJGJMOcghNqxjbkNKBIRwbN2PhUNpxAiA3MyGEhERKSjYkuWrIwRPO58b7dQSCB4kwO53qWjEsZ6om1nCDWun26WMYtCyEYIiS2tcDtRdGyTdFRvWyMERxCFEDnHo5LQ+Lw+g+yasWJmtoytUrtMGJxeKOCD/30Mn/7eefMxq52Fhr64DFWjWzfccjUIITSfq0BRwreMpSIieG4dM4TqKnjOnkdnxabeGDSNnv+laRoWCzohNJyKoqqoLYWYO5G3qEwm+uPIlJqtz0DDZtfnkXVCQ9hQ6ZJJ9DpaxrpYRRwWtOBsgNhRmi0O/9+3TuOPv3LU9lippkCzLCiaGULkuuiR30W1jNXJtcgedFtRrIRQ47gIO8lSW1D0WUHOGb+xKfn+wyRU2jK2Kdd0xc6YkbPXjlLUL0OoYVn2/rwrpZpn5TygL4SRY2KpUHMlZROy+71Y0zRTffjNY3Oe72fFmfkCdgzax2iEBAi6COG2oMRxHBKy+3ioFRDbvRtEgXNkCOnHgDN2wC3sOx2TUKwqqCkqNE0nZb1axmjEaadDpSOmZaxzLWMTffpCrVfrZMmlPY4G8hnDKoSIbW0oGW4hAqCXxlxuYITQBgG5cDe1jLVho8oYVoKYLNhuhH7hxla0EypNBvJBQ6UBY/Wviwd7NUrLmOgIOFxxIYQqdcVz5Uc2ViusocBAeyotKwhRtaU/DkXVbKTJeiuEKnUFU5lS4EBpAGYGgdMyBtBtY1by0Q2paCNUm6hWGoRQBxRCUSchJDY1n1j/Rg+VlgKTUWRQZl0dGuuJtUS4kGPXqU7y8ooHDe4jk9d0TMJoT7R9hZBpLXGzjFkzhKqm1SUVlVpa4XbCOchNxyRomj4ByVfqOGs06K14WEColjGfayE5JomEutHUsn7XUEJ+HbLkJVltjDSQ/bFECZYmapTeWHPLGKATQnVVs5H0QcAb1d5u2UWrDT0/z715baxXn8TSbGOZUg01RcNgUja3Q9g8EBoK1QZxR67FtEnAUtuWsWDHZ6HafF5HJf6Ssow5SXwCQrY5CcvZbLlpwkom0aRlsS8ho1JXzQmUb8uYM1Ta2O5Wi7XsWJiy3g/Dli60WzsfNkOIVjtPFn7GjfOsnep5WnW4FUHakwD9/uCVHwTAUDQ1MoTcSFkv9fFSoWru84cDEkKlqoKpTNnWMAa0ZhnzXFDqcKi0VwSBrhBqHENmhpCLQsj5WmQhJluqoVJXoWn0TDjZWOymhkr7WMa6IVTavBe4tE7WFL3BOLhCiBBC4RRCpDxhsBWFkJlftz4LQN0ARghtEJABUjJiv+C0aqNSVa3RMhYRUKoq5ip4pd5YifZDq2SEpmmNDCEvy5glVFp/v84EKK8W6l4tY8aqjXUAZ1118lUIGXlNzrT+TlnGGgoh/eJutY3ZCKF1UBdMLpegacC2weCEkKkQMra7lchyesAB/9p5QL9pkO00m9Nrnbca26stQqhkn7ib7+eyvUljWqNlLNh708JAR3uiLWX0EPKiKRzdr3beooxxQ4MgEzGWjmKxUPUcLM/nKlSygKDRpuOjEKorWCnW0Ge0I5FsjXZXjWgtY4A+UTo+k4WmAZyPGsW678wVxar3tdCpPCOfYT198sQed2gyYx4HfqHSZOWVRs6s+FjG5nOVljKEAGJVWz+FkNf9YLxXVy7QmsbI9W0oFcFQsrEd2oXeVEUsY4QQan5/Yq2g2VW84Bccm6/UbYqoIkX5p4dKd+8YISxMEt8ZKu1iaZzJllGuqbY8EkKcWS1jQKO509MyRhlzkbIP3nJORUTeRsBYFyn8FiwqdXtBgarBlQgNAtnMEApGCA2nSe28hRAyrvnkPOuIQsg1VJpMdL0/b7ZU82wYAxoKIU3TsFysupKyXmpvskA00R/Do6cWqUSFE2RRY/uQgxCSg5FdBF5Zjn42t7AgYyg3SAJnjumBxv1ngUIIpaJi0z3GJITKdfN8pBExHMchLglUFVVVUSDwnO21I7ZQ6fUnhMZ7Y+A4d4UQLXPMC8QN4lQu+imEyH5piRCKdGast5HBCKENAjfLQ6s2qkK1DlXTL1hxWURd1cybOTl5gyuEwg++ClXFnKh7XeCdCqFIl+cDUFvGeHvA4UrRXSHkFWosiZxZO2+vYuwMSZYt1yAJnBmw6PychBBqN0C5Fbwwq9eZhrGMuWUIAc21oYC+EuNHCOkhwwYhlCljOBU1V+zasYy5KoRk+gCoUNUtACmjZSxf9SdZAKtCqHGjbdWS1SCEGp+vpqioKe4rQSmz3cT7pktWY4lCCADmsvQJraZpeMffPoEf+Uf3AEw/awQ574oVZ4aQaPs8rcDMbbK8N9nP2VLNVMzsG0/7EEL6OReTBPPz+q26mtsxSlrGjAyhdbSMHTa+b67cUEYVKt6182RSQ2v8ypRqtlwlgoGEVSEUvmUMAHri0rpZxohCyA3jhpWF1jQ2n2sMjE1irANNY3nLfpro19+fljFCyFk/e4sT5LXdFok+/NVjeNPHHjMXr0yFkMX+EA1Y4b1RQCbjzuyRHkroeV1RTTLQqhIyFUKWUGkAOL9UhCzynjkk+piruWXMeb7JjudZLYp+k6yPffMUXvnnj5jfVVU1CGtQO1+o1CHwHFIREZLA2Vq3yALSZkIItWG59FUIiQEVQqVqkxLSCVKVXqopqNRV16a/hIfa5qKhOnzHzVtRrat47OSi53sCen4QgCaFkJl3F7R23qOlNBHpbGRB1kchJPI86ga5BjQUQkuFim28Zc3ps4Is8GVKNfNe7TY2isn061a13qxcj7ahECLnetCA56CvOZaOurdOkjlliPeMiHwTaeWnECLXvsFUeEIo3aHFv40MRghtEJAbOk3a24pCJGNOuETzpCMX7HC1862REeT9ZYH3VJyUnQqhrreMGQohnqIQMgg364qedaJZrqmetedm7byjlpOEObabc0LaiMiqrnU13pohBKx9BskTpxcRkwTsG+8J/DeEECI3buskw7nCAzQfazRY7VmzuTJG0pGOqEisBIgVbiti5DOkohLSURGaBuQDDJTMyYXlRjvaE8WCRzit32e2H8PeK0FBa6WtBBnJcJh2yTl67NQiXpjL49kLKzh4YYX6nIKZNUInHch5N5+vQNUaYbhpywpfqyC2LrtCqOFXPzyVRV9cwp7RtCfxZAuVlnnbY24wFUJxe+28V93wYr6Cv/vO6Y6EENNwZCqLqzelATRUQsWq0qSAsILsjyWKQkivVm6e9Mgij/6EjNlcGaqGlhRCvetoGfNTCMVkAX1xiWoZsymEUp1RCNUVFZW6ap5DPTEJqYhIbRpbLlSRjoq2hZEg4HnvnJDJ5RIurpRMAtu8ntkUQpeWZaxIyUkCGueEdTwxn9ebLwE7eeocP/YnG4SQl10MMJQ/TssYRQXapBCq1EA4Hb/r/dcOz6BcU3FyTl/4Uduundff2K9lLG+ECtNClsln7oxCiCjH27eMeTWMAXq7qqJqJinbn3CzjAnIu3ynaeOa8obrxpGQBTwUwDZGCCHnop0k6I1m7dbO65+5swr1XLlmLlLR0FCaNVrbIiIPVaOPj50gj2VKNfM8dhsbxWQXhVBdbSJsJYEzz61uUAgBeuukq0Ko2ljMCoqIyDddm3wVQrkKohLfFMAfBEHHppcyGCG0QZD3JYTCTub019MVQvZJQrhQ6dZq54kdabw36tMy5lQIbQzLmHUgTFamaxTLWFMlpVeGkDHgcma0NFjz9rYLaa0iq7pk5bFcU1Ctq+iJS6tS/RkEj51awE3b+0M1KohNCqHGtqZmCPlMwABd4UIUUjOZMkbSUcvkvh2FUB0ch6bBSTIiUCfvhHxKRsRQKpYSZVAy3uMeTuuGmiWY20qEORvwnAja5GC10BGFkJut7VOPn0NfXL+OfeqJc9TnFFwUlgQRkQfHNfJYiCKlsWrU+r6l5TaRlcNsqYYj01nsHU83NZI4Yd22QScQZBJDBqaywEN0ySog+OLBKXzgy0dxbtE9ILJVzOXKWMhXcN+BTYhKPA5Orpjfy2sQZ5LUFFvgioeNYigZMfOnWlEI9a6jZcxPIQS453+ZK6XJCNJREbLIt00IFRzHMcdxrpOA5WItdH4QgZcthEzCSP6USZbYMoQEM/T4UgA5V2MOBZ15n7Ycn7MWFaVNIWRaZu0KoelMydOqCdDHXCWKvd2p3s6V66Zd0ev6eXGlhGMzOQAw/6toWluWseAZQop5/07IdrKBfGaTEGpDKUgsY27jl0jAUOlMAMuYKPCoqZp5XLiFSic98gmnMmXIIo/RdBQvuXIIDx+b9V0gOD1fwEg6Ql10ibrYoZzQNM1zHOZVstEKyJjXDebYXdEXYss1FTuG9MzIxYI/IWRVApf9FEIu26hSV5tUQBzHmeP+SGiFkP6dvDL7WsFEX9y1dZIsioUhhF6xdwS37RywPUa+s9u4ZyGvFym0cu1gLWOMENow8CKE5BZsVBmLlYBcGIiNIEztvNyiYoe8/6a+GKp11fU19KBla4bQ6q/+1RUV7/q77+HRkwuh/7ZmWsYaFySO4wwvsv67TElfORN4zrbqVKl7K4RkwSCEqs0ZQoD/4McP2bIeWEgkxhlj8E0GFmRFGFhbQmguV8aJ2Txud9wc/EAUAYqxT/JlPcelJyY1hW4C/oQcYF9FmMtWMJKOhm7GoSFbqiEZEW2ZDEBjcuQcjBEShoRKWx/zAo2c2GxYP7waImif1/y3NQfLZyWIbD+v3DDra/ZYLGO0ie90poSvH53FD920BW+8bhMePDhFJQ2clgknOI5DVBTM9yCD7k40T9AqmslAcblYxbGZHPaO6YRQwWgkocGWIRSQELJe5wEjq0AWPEN7CYFCU+O0C2IXu2ZzD64e78GhyYyFrHMfoCZkAbLAY5lCzmSKHoRQKmLu07AtY4B+HGTWMUPI634A6JNVN4WQwHPojUngOA5DyUj7hBBlDLKlP0YNEl0uVl2tKn7wUgGQ/X9wMgNAH7NwnKPtStKbT4NYaDcCyLnaXDvfTAhZw/etCoa84zUIwapqzbmUTug5kf6WsYgo2MYg2XIdm/r0e4vX9ZOEFvMccMIghDQNnbGM+YyJCpW6SYglIoJZPmD9zA2FUBuWMbNlzLt23qskoGxYwHr8CCGHQsgtx8uLeJ1aKWG8JwqO43DXnmHMZivmtbtUVfCLn3kGf/Y/x21/c2Yh32QXI4hJwWycNUWD4hE+7FWy0QryPoQQOY7qimaeT1cYJSLWHMogCqGSj3o6LgtUWx1NIQQ0jhlJDHeeyALfdM3sBLb0xzGbK1PncmYIfYj3/NCbD+AtN07YHuN5zjOmZCFfbSk/CNAJYY5rb/Fvo4MRQhsEXoNmfQWnVcuYZN4Qiy0qhFrJ9DEJIeNmS7vIK6qGmqLZVklbUUOFxWKhiu+eXMD3ziyF/lszVNox+SBeZKAh+3UqAnwVQgKvW4MqdXuGkLGf2s1WIqsl5CZGBt/WYFpy/K2lZezxU7p/vXVCSP9/EiA4kJSbFEL5Sh3FqoIBn5sJIQhmMmXkKnWMpKOQRR4RkW+7ZcyZHwTo53td1ZqOeatlLIxlrUhprCHhsDTrhxusuRXWbIVijaxme2cI+SuEauA5/SZNVFC0prF//f4FqJqGd96yBe+6dSsqdRWff3qy6XkNJYH7+RWVePM9Gi1j7dsBablN5HWfvbCCal01FUKA+0p0o3aeh2Qoffxk+M5QaUA/prwUQoRAWaKo6NoFyUu6aiyN/Zt7cXgqY5J/XkoFjuPQl5CoZN9y0T1XYygVwYxhNWxJIRSTkavUfSeWq4FKTfUlqMd7o3RCKFfFQEI2CeahVKTtDCHaGGSiL47J5WITYW1t6gsLEjxPA5nkEoVQoaoYA/nGvg0a0NuNmM2WmxTXblaTdFQCx9mvxVaV55LFMlZ07Lt0VDTPB2dzrRO0CViZ0gTlXBzMlWsYM8h8LzLl4aOz2DYQx1VjaRyf1QmhdlvGSAivLyFUrZvbxJlPQ47BgYSMqMSvcssYKYdxv55bF+a8IAoOQshDIZSvNi82AQYhZIzNX7Z7GBynE3f5Sh3v/cfv47+encLffeeM7T5yZqGA7YNJ6nvFZSGQZYwQYl4K404tRpKcJe+WMf0grCqquT2vHDEUQnm7Qoi2KNGwnNeoi3FWxGX6fbmiuBFC+mOyEM4eJQk8YpJ7e2WrmOjXleYXKQsEfuqoMPASBSzkK6ZFOix4nkNSFn0XKy9lMEJogyBvrITRLiYtWcYsK/AxyR70GqZ23ukbD/z+Jbscl0YwVOrNrPJaWMYI859pYYWcqICcFceiwJmDgkypht6Ynv2SsUym/VaEiVc4W6pRFULtZiuRDCFZ1D24KxRCKKjCo5N4/NQi0lExVH4Q0KwQypX1yuTBRKSpZYzcxMiKphvIRP7kvJ51MNoTMR6X2lpBdMsGSLoQcGaduE0hFMQypv+ddcA11hOFwHOugYA0WIlM6/vS1DBWBM8Q0sMeyYRWD762DzRqiop//f553Ll7GBP9+oTipm19+OfvnWtSBxQqdURE3jPTJCoJpi2tESrdvh2wYflovDd53SdO62TnvvEec/+vuEw8nHa8qCT4toxlS3VIAme7hsZlug2RYDUVQkems5joj6EnJuHARA/KNRXPnF8xPpe3hL0vLlM/k6dlLBUxie2WMoRcqr3XAuW6EkghlC3Xm86neUM6TzCc6oBCiJJlM9EfR7mmNpFNK8Wa60TUD25B+nVFNcctjeypehMxETSgt9tQqSu4+88ewb9+/7ztcXL9cIau8zyHnphkyxCazZYhGmTIkkUFm3cQQjrBKtsecwOtSbZUbV68csYH5Mp1fRFJFlwXAIrVOh49tYg79wxj92gKJwxCSFG1JrVsWMgC75shRMYEALFQNQdhJ6Mi0lGpMy1jPhlCXmPblRIJavcLlda3GxnjuBGziYiePUizKU1nymZ231Aqgv2be/GV56bx7r//Hp48u4z33L4NpZqCbxzV1V3LhSqWizXscFEI6feqAIRQ1ZsQclNMt4K8xXbvBlMhpKrmeHjXMCGE9O1LGpOd+Y+A/j1kkdcVQj7fzStDiGYdJvcGN9WZGwaSEbM4ppOY8KieN8cuHcgt8poDLjjue2GRjIrMMsbQ/chX6kg6VsIIIlL4HJ8sRSFkDZUWeM715mV7b1HwXNVwA7m5EYUQbQW+TCGm1iJUmqwEuE3MvEBygpwXaUngzQaylVJDIeQM5PXKjCCrS9lyfVUyhKwqld64bO4jKyEUVOHRSTx6agG37hgIPaFryhCq1JGMSrpCyKE0uLiikyHkeHSDSQgZ4ZfkxpryWNUOgulM2bRGWdFQZNmPeftgNbiKpURRq4gCj/HeqKv/mwarjcZKhHUqQ0gfYDU+42hPrEkh9LXDM5jPVfDArVvNx95161acWyziOw67p3Ul2A1RSTCbD83JkiyA59oLFG2QZI33l0UeUYnHqfkCZJHHjsGETV5Og3OVTc9K8VcI9Ri2IYJERPRsGZvO6sfBEkWN0y6OTmWxd0wPlN6/uRcA8LhBivmF2+oV8PbPpGmaYRlzUQhZBodOkj4IaLactUKlRp8IWEEUGNMOldBCvmJrWhlKRTDXIcuYlZiYcLGbLhWq6POxtrjBrf0oU6pB04C9Y2mzoa5QUZpUf2a+VheXT9CwXKghX6k3bUsvZUFvTLJbxrJlDKciOnlqC5VuVkiSHCH/UGmhqbSiXHdTCNlr53UFq+RKqD92chHVuoq794xg90gKs9kKVopVaBrAt6lgkCyLcG4oVBqEkNNKm6/UTTVm2iffzQ9+GUJBMuHIPTdIhhAAzOUq4Dh3RZHbYlNdUTGbLWNTb2M8cveeYRybyeG5ixn89Tuuw++9bi9G0hE8eHAKAHCGVM67WcYCKoT8mriSLorpVmC13buBbMtaXTMXI7YPJsFzjQyhck3PlHTbzmScX6p529bjHi1jtOOGELJhcjUB4Bfu3oV/+4lbQ/1NEHgpzU27u8/9LAiiUnPrIQBTFTeUbG0hAtCPBdYyxtD10L3OLg05LahmskaOTSoiNoVKB2lbarx3awqhTKkGgefMSTBtMl2hyEcj0urXzpOJUCsDAFMh1GQZ40w7WaZYRU9cbhpkOAO0nSAKId0yZldNAZ2zjAH27Iz1tIxdWCriwlIJd+waDP23vKkQ0rd7vlxDOipiMOmuEJrwUwgZeQunKIRQOyqS6UwJ473NhBCR8zvPD7tlrCFL9gPNMgYYgYAhFEKELHR+b7+WMbL9fDOESnYL3Vg62pQh9KnHz2GiP4aXXDlkPnbP1WMYTMr41ONnbc8tVpQA4alEgt1oqeA4zpjQtJEh5LLNyffbM5qCaEw6AC9CiFjG9NeJyby5ouoGmhXRSyGkqhpmM/q5QbNntYNCpY4ziwXsHdOVftsG4khHRdMS6td60p+Qm0iqYlVBVVE9FUIErSmEmpuc1gpBFEKEwL7oJIRyFQxaBsZDqQiWCtW2rG9uljEANjK5XFNQqimu2SV+SEYE5CvN5wDJ8HjZbv18Pzi5orfTOSZY0YABvd0GkmvnXIhqNKk1j//0hZvG8+eyFQyno+hPSDaFULHarJAkypEgLWOAfcGJ1gRlVQhV6yrKNRUpw+7rdv186NgcErKAm7f348rRFADg+EyubcsY0Cjh8IJ1XJ2INIdKk3yldFRsr3ber2VM9D9mVyzjMC+QhbD5XAU9Mcn1uueWfTib05s2xyyLY/ddO46rN6Xx/x64Aa++egw8z+F1+8fxyPF5ZEo1nJk3CKEh9wyhQAohxz3OCXJf7sT4M1dpjKHcYLbVqap5PxxIyuhPRMymWpot2wpy7JR88hXdQ6UVanA0uTeErZ2PyyKGV0EhNJyKQBZ5TFIJIeO7d0QhRHfELBWqULXWKucJOt1it9HACKENgoLHhKbV2vmUEWJLBlTETlKpq4EJIbIqFFbCmSnVbHYX2gXeVAitsWWMeINbWRVutIw1K4SIfNm0jMUaMmRV1QxpqPt2t94UYg6SDGjPMlZTVBSrijkp7Y036pZplrG1umg+dkpXeoTNDwIaAyPFbBnTVwMHknpzkHVyNLlSgizwvnJTQpi9MKdL2+2EUGvbpFitY6VYMyXaVpgEnMNbTiykCVkIlXNTqirguGY76JZ+94YIGghZuKU/blO5kUGP2+SeXMN8M4QcRMZoTxTz+Yq5z45OZ/G9M0t41y1bbQNeWeTxtpu24KFjczZFUb5Sb7JcOEEGoX0Ju6Km3VUjtxV+cq4RxQwhNdyyKko1BZLAmd83KvqvumYpUvaES1YBoNvEyCSq0wqhYzNZaBqwb1z/vhzHYf/mXpPo81Nw6dck+7Yhk6Rel8G4lRBqLUNofRVCfiuqZNJmJUs1TcNCvmpTR5HtQGtXDApnUxUAbDYJocYkgNw32skQomUKkn1/0/Z+vaHuQgbFar1pXBS0ga/bQGrincda0cOG2xu3W8ZmsmWMpqPoT8i22vm8RQlD0B/UMkYhhMq1Znu7VSFEFgnSMT3jLkch+DRNwzePzeElVw5BFnnsMQihE7M5KKrWEoFrhSTwqPmMFXMVu2XMel20LpBZx2qtwD9DKLhCKAwh1O9h23RrjCWZZOMWQmjrQAJf+vkX4649I+Zjrz8wjqqi4muHZ3B2sQCB50yC2ImYFFIhJNO3k5tiuhVYbfdusIaTk+tab0zCYFI2LWN+hBDJCvULlY61GCodViG0WuB5Dpt7Y9SFRT/lVxhEXQLKiSW6PcuYxDKEGFYHYWqc/ZCj3NAJWssQqpttBaZCqNIIlfaTqlvfW9MatpygyJR0fzlRQNBOQlMh1BQqvTaWsVYUQqRlzLkSJAmczTLWG7dbxsj+81YINQZI1graTljG8o6bY2+ssfJItkMqKpmT6rUjhBYxlIqYvu0wEByWMTIoJsHRVgXE5LKu0PHLLSADxDMLBSQjonlOpiLusng/TK3o1wmaXc1t0EbILY7jdJ+6wAdTCFUVxCmBghP9cSzkK55hw1aQY2OTkWFC4HfjF41AQ5oCwIpsqW6zjI31RKFpjZv+n3/jBFIRET9000TT39511TA0DXj+YsZ8TFcSeF/TyGd2Zp94WR6CoOQSCkuOJUKQBLGMWa8PMReJuRW09pN4RLS16VhhJdE6TQiRQOm9xvcFgP2bG7lgfhPT/oRuGbPmQ5HJsJtlbNimEAo/3CHHQiv24XZRCaAQGklFwHOwBUtny3VUFdU2MCbkUDs5QmZTVcR+DA6lIrZJQCPMtjXLWL9hDVQcYwryukPJiNFQt4KCp0JoYxFCS2arZ7MKLirxVILEaRmbzerW4/6EPW+rUKkj7iDOAhNCZr5NY3uWazSFkG65rStqUwsmbQHgyHQWM9ky7tozDAAYTUeRioo4PpuDqqHt0Ft9Ec59TKRpmq1lLC6LTZYxkxCKSh2pnXdrhIoEUAiR+4KfZUwwxp7zuYrncxMu6mOTEKJY2K3Yv7kHWwfiePDgFE4vFDDRF3MlJwJbxnxydpIuC2StwGq7d4OtZaxQ1cPYBd4WOxCEEMqWa+aCu9vYKC4LKNaUpsX1qk+odJBoj7XChMvCYistY26IiDzVDkxU/+0QQu0q/Tc6uudIusRwYjaHW/74ITxzfrkjr1eo1F0vXHqOT/jaebICbyqEasQypgY+ceUWyQgyUSGSXGqoNFUhtBYtY3bmPwxMhZBj8CYKPOqKBlXVzO+uDzL0gDyz2c2D7be2CdhDpf0DCf2QtViQALtlLGuouUhzR9wjJLKT0DQNj51axO07B1oaHJLaWqtCKBWVMGgMhBcsq+UXl0u+gdJAY/BQUzSMpCO2x1vdJmQANkYZgLn5/J0KmqAqlmJVsZGJBJuN7z5JCQSkYaWoKwz7E7I9VNonQwjwbhEioCmEAF0JcWhyBV87PIsfe/EOKhFAgi3PLBTMx4JlCOnnXjMhJLYVGG5aPhzbnXy/vU5CyEWNUqnbCaFogFVXukJIcB1QE6VJT0zqeKj0kekseuOS7TgnOULkc3mhLy5D1ezWyBWfXI12FUI9ZobQ2lvG9Awh7/uwKPAYSUdNUhmwDIxTdssYAMznW1+kMpuqHMfxRF/MNgkgypRWQ6WHUhGoWuM+3HjdRo223lCXRbZUa1YIdeB+uB5YyrtbxtzUjb2WXK1itY5cuY7hdKTJXkna2Kwglr5UUIWQZYxZohBCZCxYtRFCuqqYdm96+OgcOE5vsQJ0Amj3SAonZvLQOmAZkwTOM1S6XFOhajDHoMmIgKqimmoea+C0Pqlv/R5AlEpuk3fRaI30yr1aLlYh8JyvxU8iCqF8xVOllzLH3vb3JNeSMZ88RY7jcO/+cTx6cgHPnl9xzQ8CjNr5IJYxn5axTkYW5ANYxkRry1ixZm7PgUQksEIobVEI6dms9AM7LotQVK3J5litq1RlmWlx7xKFEKBnytEUQn77NQyiEn2+2yCE2sgQirBQaYZVwFljMnJ2seDzzGAoeFge9BaI8JYxcgGTRf1mRC6y4RRCJL8m/PtbA61pJyFZ4bPVzrtcDDqJhmWs2tRW5Iea2TLWnCFUVVTkKnVoGsxQ6aqie+39vNOAPag65iDJgPD7wArij7daxlZKNWiahpVi1ZwYAWvnsz05l8d8rtKSXQywtozpRFzeIFWJQsg64bi4UsLmXrrc2YqIKJg3YGtTQzu2ItKeNU4ZgMVdPPNWOXuY9y9V61Q5ttkQEbB6PluqoSeu2wGs2QplFzWMFamI/2d1EhnETjeTKeP//M8J9MUl/MiLtlH/tjcuoy8u4bSVEAphGXMOotPtWsZcVFOpqAiOA/aM6oSQJPCIy4IrEV0ylALWz1vyuRbq13n7947LXgoh/VjcO5buuELosBEobSV3D0w0FEK+LWMJ/Xiwfi4/QqgnJpnXzVYsKKmICJ5bJ8uYT6YcwXhvzKYQWqBI501CqA2FUMGl6XSiP24LEm3XMub2WYllrD8u48BEDyp1FWcXC7awdqChaNl4CiEXy1ilOcCZoDeuExV1RTXVfaPpaJPKqkCzjBnnTFjLmKZpKDnUitbnVeuqJeNOLz2gkSkPHZvDgc29NtL2ytEUjs1koWiauaDTKiTBO0OIjGGSFoUQ0CDw8zbLmIisMR5qBTVFBcd5k9JuE12ChXwFAwnZd3GMXOf0YHcvyxhRCNmPt+lMCemo6Es8AcC9B8ahavr4ya1yHjAatILUzvu0lLoppltBkFBp2aIQWilWTRJ1ICmb8wRTueXS/kYsY27qbALynZ22sapL+zCZF3WVQqgvjpVirUmtXnaJKmgFvgqhNjKE2i2H2ejoniPpEgORE7bj2bfCulrhREuWMYeVIG6pPPSrP7fCuirUyvuTSRrdMkZIkuaWsU7UTrqBTDhUDciHlKbWvVrGFNXSFCGblphMqRZMIWT5nXWAGJVaU2lZYR3AAfrNTVE15Cr1JtvJWhFCjxlhs7fvDB8oDegrWALPQVE1UxGRMjKEgMa5Wa4pmM9VAimEgIatbtRGCEnIV+uhCUQAuLhSBseB2jLWCH603wDzDkIoHQtmayrVFMSl5uvIBCULxAvE9piOSijVFJMINRVCHsexn0KorqgoVBW7QsjY1l88eBHfPjGPn3rpTs+Vve2DCZxZyJv/X6goTZYJJ8gEx0kutGsXILlNTtXlLdv78aq9o7YJWU9McrUnlR2ZMjGXtg0CTdOQLdebQqUTEV0hRLuGTmf02uorR5IdJYTqiopjMznTHkcwmo5iKBWBLPC+K51kcmPNESLh5m4TH47jTLtUKwohs9q7tLYKIbJSHGQAvWsoieenMuY9hCgfrYTQYIcsYwlK0+lEXxzTmZJ5DVj2sfH5wZ0QqiIi8ojJgqks0zRQMoQ2Zqg0CYHWW7Ya52aRou4hIBlX2XIds1n970fSUfQldDUdmazqljEHIWQcE/5h+3bLWFVRoWnNpL9VLZ51WMac9yZV1fD8xQxu3WFf7NkzmkK2rGfqtWsZk0Vvyxi5B5HrrzNkWbeYk1BpCXVVC2R7oqGqaJAE3vM7RSX6RJdgIV8NZIexEgRewe5uY4uplRJ1cYqG3aMp7B7Rs5/cAqWB4KHSQVrGgM5mCHm2jBn3jJqi2gi2wWQEuUod5ZoSIFTaaBmrKp6164Rodx5jbgqhaIuh0qsJt4XFUk1BVHQnw8LAXSFUhSzyvopHLyQjEopVxSwHutzQPUfSJQayStepQbWX5aGVoGWrZQwwVo2NSXPZOHmDgCYnDvr+PTEJvCGBpYdKUxRCIg+1hcyiMLDuMzf7hhtcW8YEDnVVMycWRCEE6BaIIAoh64U/ukqWsbTFMgbo37+JEFojFv3RkwuY6I+ZN5lWIPD6drfe/AcT+qCKrCgQm4xf5TwBISKsTQ3pqAitBQIR0Cujh1MR6kqPm0Q6V6nZCGJdqRMsQ4i22jyYlBGTBFwIbBmr6rZH47gg27dUUyALfJNCzoqkjyzXDHuMWQkvETFJwNcOz2IoFcG7b9vm+fm2DyZtlrFi1Z1QJyADLKeyoV1fOWnkcQ6GHrhtG/7mgRtsj5HVRBqcVc9+QZ2FqgJF1ZozhGQRqka/XsxkyhhJRzGQjCBXrrfVSmXF6YUCqnXVlh8E6ITNgc09vmQd0NgvNIWQV9AqIRhaDanV6+7XViFUDZApR3DPNaPIlet45Pg8AHqWQlQSkI6KbVXPF6t1ag7XRH8MqgZMG1YTsn/8sk7cMJTUr6tOQmipUDWPAdJQBzQry8g2W+2swU6DWO3qqmZrASxU6x4KoUYLHsmsHDFCpYHGvihUFVMJQxC4dt6x4FSu0o9NMg6p1lXbeCIVEVGpq7YK+OViFXVVa7JJX2mQC0Anaue9CaGCqRBqtIzpj+vbPluu2UKlAbTcNOY2qbciInpnwi3kK4HUD9brnJdCiNjfnWOLqZVyYEIIAO49MAYA2D7gQQjJ+hzFb8HMt2Us0rmWsWy5BlngPd0QpNm3pugtY2R7Dhjn12Khikyxqjc2uxBLPTEJqqbvP68cQ3KOO5vGKhskVBqgt04C+n7tRMMYYIRK0xRCuQqGkpG2SKfGObGx7h2dQvccSZcYiEKoY4SQZ4aQ90pxXVGbKoSz5ZrNBhSPtKYQMgcBISYOmqbn6PSakmV6Jg1dIbT6+QCLhaoZqBc2R6imuCiEeH1wYg0GtIbIhlYISXaSDGhvAJx1TMIbA00KIeRC4HUSiqrhidOLuH1Ha+ogAoHjoGpaQx4eFZGOiZAEzjxHJw3Pc1CFEBlAjlozhMhqWwvWoqlMidowBuiDWlnkXSxjlgyhSLBq9JJLuDLHcZjoj9msH17Qm/JkcxBEyCinrYkGP4WZk5wkn49MHn7+rl2+g4sdQwnMZivmditUmsNnnSDXFqeyIRWVkK/YFTUrxeAV3sWaf6A1QdqLEHIQ9VGfVVe3lUuvQfV0Rg+lJSvLnaqePzylB3yTynkrfvbOXfjte67yfY2txoTjxGzOfGylWEVU4j2JE0IIOZsfg6InLq05IdRYDPG/D9+xaxB9cQkPHpoGoBMpPNdMbA6no20qhOikqrkqbFxHV4p65lyrVgaSfTSft3/WlWJjQsZxHA5M9AJozp5aj5YxTdPaHutZLczWzKpi1b1hloyhlos1CyEUaSaEKJbZm7b34efv2oXbfCzZzkW/Rh5Ic8sYoI9DGg1O9GZSsm+HHATHbgsh1K7wQRI41OruBETeQQgRUpqoJ52h0gCarDCL+WDnU01RXbNjCKIS720Zy1UC5aNY36c/4U7KxiQBPEchhDJ6wUZQvPOWrfiZl+3ETdv7PN8LgKcCCgiuEOrEgqRTZU2DxFtCpYs1c3uasQP5iq2xmQZy/53Jlj1btrwsY1RCSCKh0u2rbjqFLca9YHK5WSHUiYYxgMx3m8+T+Xyw88MLZmOvT+nJpQpGCK0SyCrdQgcsY5W6gpqiuVvGJG/L2CcfP4eXfOibtoygck211S3aLGMBwiwJ5BYUQvlK3bZy7TZBJN/JniHUfl6OFwhps2NI90OHnQjUVRUCzzWx1KLAGT7kRk2yOcgoBVMIWQfY1glxK/vACTKhTzkUQsvFqtkIR5AIkAHTLo5MZZEt13H7rtbygwhEXt/uZqOE0cw1kIiYKr6LhiomuELIIIR67JYxIFj1uxPTK2XP96adH61mCHm1bU30xQNbxjJGhlDa8b3LNfe8i8ZndSc9gMYqrJPI2DoQx6beGLVZzAkScHl2UVemVBXVN7SYfG7nIDoV1RU1ZNW+pqi4688ewd9+57Tv5wAMhVBAQqg35m5PKzmqnt3qVwnI6zhDpQkxRlsFmzFaisgKaKeCpQ9fzEIWeeyg2Aqu29KHtwbYpz0xCdsHEzg0uWI+tlKsuWY3EDQUQq0Nd3rXwTJ2bEYnvYLkeEgCj9dcM4ZvHJlFsVrHQr6C/kSkSRE1lIy0nSFEUyk77aZWJU8riMt6fglNIdRnOTdJQ53TCkXsqmtpGfvv52dw25881BYptFyomZM767ijWFWacpIICHmdKVUxky0jIQtIRSU6IeTYThFRwK++cndgopwsOJVccl6sWUNEUZk0LGMAbCrLuSydEOpLyGYzYLsKIVkUvDOEynTLWKFSR7GqQNMaj5GFMuu1+bGTC7jpj75hU6K6oebSFGWF1/Vc0zQs5Kum/dUL1uucl22T4zgkZPu4oVjV7XpuC1Q09CVk/Mar93gqbcj9z882Rr5/1KV2vnHv6kyGkC8hZLTC5Sp6KHSvaRlrxA6QsZAbyLEzk6l4ju8bGVYOhZBPy1g3KYRIriTNMhZUZOAHt/NkIV9tup6EBbGbrfb8plvRPUfSJQaST7JUaH0ARmDeuFwmFRFRQN2o+6ThybNLyFXqOHhhBQB95Viv3GwlVDq8OsX5/m6EkLlKSgtQbkEhtJCv4PNPT3rKVsmKOJm4hJ0I1BWNmlUhCTxqqmbmg1gtY1aFkJe6wl0hFF6l5US2rAeGkgtin4UQcgb8ptYgQ+jRUwsA4Lt66QdB4KCo1pBLssLTqA29uFKCwHPUli8ayCBi2BEqDSC0tUjTNFxcKXm+dyIiNA2A8k6FUMBq9FKN3jIG6Cv9k8sl33wuPWhcV42ZCiHjvYOsBI32RDCXq7her0yFkIPI+NP79+PzP31boGvTdkvTGBmE+raMie6180Bj3x6fyWGpUMUPzq34fg7AsNq4TOic8LKMVRzbVq+d968pdhJrSctKuBWapmE6U8JYOmpug6UOZeAdmsxg71i67QDM/Zt7cGgyY/7/ikVp6oahlH5utZIhBKy9ZaxcU/A7//kcNvXGcM81Y4H+5t4D4yjVFHzj6JxuLaGslA6lIk2qmzAouJDJYz1RCDxnqguXi9WW84MIhlPN5NVysWY7N0mOkPMzrYdC6IXZPCp1Fafn8/5PdsFioWqusNsJobq7Qigmmc+fy1YwYtxHCCG0bOQRFTxURn5wjrncVByyjRCqIyELEHjOcm+0KISMfUsjOHaP6iohN8VFUMgCZ7OpOUGuf+S+kLCQDc5KcppC6NDFDFQNeO5i43rkhqqi+l773MJy9feto6qogTKERJtCyPs8TDjU3qRhLOjiWFCQc9JJdjhRrul5e272OlnkIQt8S9Z8J3LlmmcOIdCIfiAEJtmeZD8sGAohL8syGccsFoJaxhrfTdM0PVSa2jLWfaHSgL5A4FSaO8cu7cAtM1e/77VHCNHUjJcTuutIuoRA5L+dsIyRldyky8XLbHdwmWAdmc4CAJ46twyguVEK0Mkma+186FDpEARNEyHkkklDVQi1YRn76NdP4Nc+dxC/9rmDrpNRQhKQCWUrljHaBVoSOCNUWn/9tCV/xaoQ8vQzu2QIdUIhlCvXkJQbstceY9V9NltGVVGbFEKrbRl77NQirhhOYjgVXLpMg8BxUCyWMTI4HUg2akMvLpcwmo565t5YQQYRo1RCKNx2WS7WUKmrnp79hCzagh/LNQVVRXWESosoBAjDK1brtoY6Kzb3xZCv1H0nv4Wqgrqq6So3yzEMEMuY941/c18ciqph1kWtkDGVLXYSZTgdDbxyuc2wF52ZL5iDR78JkVvtPPkcZN8eNBQqVuuSF9xym2jwzBByNPtEjRVwxYXgdstycbbpEGSM69BoT9QMXu+EQkhRNTw/lcGBzc12sbDYv7kX05ky5nL6xGWlWA1ACLWXIbTWlrGPfv0ETi8U8MH7rwmkEAKAm7f1YyQdwYMHpzDvslI6RCFZwoDWVAXojZrjvVEzf2y52J5CCNCbYpx5R87XvXlbP3YMJnDVmD2XqkEIrZ1CaNY4HmmVy0GgaRqWi1Wzqcm6EOWl6uyzWLtnsmWMGPdLk9AtVFGp69cIPyWQGyKOMZ6p4vBpGSP3SSoh5GIZAxo5Qu3XzgcLlSafr2GlVZoqydOWxTuCM/O6MujknD8JGCRDSFc+0D/vgsf2ckIMmCEE6GNv68IAaTwNujgWFLGAJK1b3p4VtAWyVuBV1ENA9hm5FpkZQkQhVKj6E0LGMaRp7lY4wBIqbSHNSARFhPJ3V2/qwXVbejvS3NVJ6NXz9gyhjlrGJD2Pyrpwqaq6ZbddQoic75dr9Xx3HUmXEIhCaLEDhJCzHtMJL0IgV67h3KI+SCGEUIZiJbArhNQWFELhCaG0RSHkFSrtbBnT3y/c6p+qavifI3og7X88cxG/+JlnqYOFJVMh1LpljJZVIVoyhGKSYIZ8AkCmVKd+VyesF37rBFPgOUgC116GUKluOx7IDe6scexYbRl+odKlqoL3/MP3zdyQsKjWVTx5Zgl37GovPwiA2TKWt1jGAGAwIZt2zsnlUqgVMVIXbh2cNbzH4W4kpC7ay7OvK+gaxyGtHcO8kfm8v54hRB8EbXFkgbjBmoNFC5X2Iz/Itp50saeZVief1TsvxGQBYz1RnFkooGhsE78JEfncbgoh8rkOXdCP6/NLxSZShQYyyA2CnpjeckEj2PWqZ+s1gFhj6Oc9sUJu7rWHslsnPlbMGBkkYz0x24SyXZycy6NYVczMl3ZASCWyDwJZxtpoGQP0a1++0rmAbS88e2EFf/ud03jbTRN48RVDgf+O5zm8bv84Hjk+jwtLRerAeCgVQbGqtDyZcrOMAXa76XKh5jsR9cNQqmHpBXRSUc8dbLxuX0LGw7/2MlzrOK7I/dAvr6STIAqC84vNofxPn1vGz3z6aVfiFtAVIIqqYSdRJlsVQpW667WL3ItWilXMGPlfgE4uJGQBS4VqU3hyWDgX4Uo+hBBRCJH7UyrSbBmbz1WQkAXq8bTbJITaD5X2GpeS7WIqhIhlrNpQCBHFNBmrWUOliVXsFIUQOjaTxQ9/4vvmvbIWQCEUlQTXMRw5FwIphCyWsT4fsjwRsS82NcYjnVUIuTVoORGEONAXJNs/t60ZUW4gY/kGIaRvz7isl1wsBlAIWX/nNTaibSOyyE8jE1999Sj+82fu6EhzVycx0RfH5HLRRtgEWSgMCtqcc7lYhaJqbWcIkWukMyvscgEjhFYBiqphqViFLPDIlettt104pa1OeFmGSBbBpt4Ynjm3DEXVzIPdWTtPmGndMhYuVDoMIZR1KITcMmnIa1ovho0MoXCD82cuLGM+V8HvvvYq/PZr9uDLz03jZz79g6Z9Q1ZiNvVGEZX4lhRCzoYxwJ4hRFa0RYFHQhYMy1g4hVCzfz9805wVOUujBqCTjMmIiHOL+qDHGSpdUzTX4/r5qQy+dXwe//jo2ZY+y7MXVlCqKW3bxYDmDKGGQkjGYqFiWraCBkoDwP3Xb8b/eu1e2/6g5SQEQZAB2FhvDJOWFZeG/c2eIaT/zocQ8iBsSDisX7A0CTylWcbKAQZ0ZFtfXKE3mrlZxsJi+2ACpxcKZvaP34To5VeN4BfvvgIT/fZ94dy2BydXTBL+xKz/6nApRKg0ySKgXXfKNdVuGZO8B9mTy0WkImKT0spNIUTa9kZ7oubAtxOEEFFUEYtPO9g33gOB58zXXCnVbLkyNLzoikH85Et24OpNrSmUyOuHvReERaWu4Nc/dxAj6Sh++7X+IdtOvP7AOKpGPTLVMtZm9byX7WhLf9wMEl0uVn0non5w5h1lSjVoGtAf8HWjPo1Nnca8h0Loa4dn8JXnZkzClYYlhzKZXGM1TUOxprjGBfA8h56YhOViDXO5MoYtRQd9CdkghIJZZt3QaBkjCnLDMuasnRcaLWM2QsjFMuamdjEtY21OdHcMJXBhuega/EwWieLGddRaae60jDkXBQC9ORGgK4S+9vwsHjkxj88/PQlAHxf6ZwjxHgoh/XgggeteICQGx3m3LwL6InPeMmaZWimD4+z5iJ2AW2CyE+Wa6ksc+JVSBIWzmIMGkxAyzl2rQnEgKRsZQnVTUU+DNV8oSKi01VZHslK7KSfID1sG4ijXVJs9Och+DQqzRdJyrpD3CtLC54UUs4wxdBq6b7uRQ0PqRFuFM/zOCa/q98OGv/ldt25FrlLHidkcdQU+LgvmxKkS4uSVO5Ah5JZJU6npxJSVAW/VMvbV52cgCRzu3DOMn3jJTvzhffvw9SOz+MR3z9qeRwZm/YmIbt9ooXaelvqvZwipWHGsJvTEJKN2vvUMIYD4attpGas1KTJ6YpKpLnMSQoC7rJIMkL56eKalz/ToyQXwHHDr9vYJIZ7XLWNEuUNyAgaSEZRrKrLlOmayZWwOQQhdvakHP/Ki7bbHWrWMEULIywq1eySJyeWSeY6YUvdIY5+ko/4rGzVFRU3RzAGwE2Zb0BKdqCHImFXfMpKyvkKdtSqE/Aghg/y66FJxny3VwXPumWlBsX0wgdPzeXMl2I+UGe+N4ZdfcWXT4X/LLAAAWVxJREFUipt125aqCl6Yy+NV+0YBACdm/G1jYUKleyjWBAKnZSziM8gmRKfz+yRcQqVnMkQhpNsne2JSR1rGDk2uIBURsWPQvZY4KGKygCuGkzg4mTGyrKqeg3FAv1697zVXtTwgJftkpUMB2274q4dO4oW5PP74Tde0pI7bv7kHWwf0c9hNIQTAbKMKC1pTFcFEfxwL+SqWC1UUq4rZUtcqhlIR5Cp189gm9+WgrxvxsN+sBmYNhRAtlP+0YS2aC0AIjfREEZcFUyFUrqm61cRD3dgXl3FmoYCaotlszAMGIZQ3731tZggZ25OooZyKdStxlC03cgdp+XpehNAVI0lzQaod3L1nBJoGfOv4PPX3+YpOtBGbfETkzdYtp51MFnnEJMG8v2bLNSzkK4hKPM4sFJqs2kem9XH3Pz9xDqqqBWsZ8yAxyUJlMIUQsf1Lvjb43riMc4tFc99MrZQwnIp0PJcmSvJxfEjacs2/pbRTkQVZxyIoDWQxmpDTfTZCSM9ky5SqnsQbGSMB3gohWvC2qRDaQISQs2QACFY2EhTk+LAqQBdyBmHatmWs9bbgSwEb5yjbQCAXb+KFXmwzWLoxAXRvGQPopMyR6SwGEjJea4RTPnVumR4qHRFRqipQVQ1VJXjLWDuWMSL/Jhd4Z5htpd5MTEU9vqsbNE3DVw/P4I5dg+ZA+4HbtmFzXwzHZrK25y4VquA5PayxNyaHD5VWNRfLmK5UccpLSc10MIVQ43WdF1e3Ksag0C1j9uOrNy6ZhAWVEHK5KRNCKFeu4xGXwZgXHj+1iKs39Xg2NwSFaFjGcuUakpZqUHLjODyVgaJqbYcoxiQ9QDOsQmg6U4Ys8marEw3kOkIya5yrl0CwljOy8uR2Y05GRPTFpVCWMZ7nkIyI9gwhnxt/VBIwmJQ9FULpmNS2FHr7YALZct1ULrS6Qm7dtuR4ed3+MUQlHscD5Ah5ZYA4QcuqAHTLa6Wu2rIECPHmdi10s0KSemWaQoi3WCH7E3JHLM+HJjO4elNP2yGxBAc29+LQ5AoKVb190y9DqF30WnJaVgvPX8zg/3vkFO6/fjPu3D3c0mtwHId7948DoA+MCek96ULEekFVNf04djmHyGsfMhag2s0QIscgGUstG2RcUCuaXuG9NgohVdXMz0nbtmcW9PuhMxPJCkIIDSRko9VOP9aKAfLPemKSqQQfsRBCRCFU9FGY+8G5CPfZpy5gx2ACO4y8IwIyeW4ohBoZkUBz7bwbIRSXRTz4cy/CO27Z0tLnJdg3nsZwKoKHj81Rf1+o1G33UI7jDAtVvdGSZtlm6ZhoWsbOGuqgF+0aQlVRm/b7keks0lERZxYKePTUAir1AKHSHiTmfK4Cngt2/BOFepDn/uiLtmO5WMX/fvAIAP0eEKZhLCjMDCEfhVAQy3knCCFV1XMl076WMX1bEhK91zIOHkzIuLhcQk3RPAkhnufMeYe3Zay5ZaxKcUl0O7YYixJkMRkw7O4dIrXM65HlXAlDmHohJgngOdYyxtBBkPygK0f0G2a7snun19kJL9XMkeks9o6nMdEfw1AqgqfPLlnqiC2185IeUErsaUFDpZ2Bg0GQKdUg8Jy5YpWMiqgbEx4raNY12sXAD0enc7iwVMKrjVV9gq0DcdtFC9Azn/risi7FbiFMtKaokCiWMUnkUVM0ZIr2Vpy0UTNNa1Rreg2rdc65XaQ2LWOVZoVQX1wGiT2wEUI+sspT83nsHkmhPyHjiwenQn2OYrWOZy4sd8QuBuh5EnUjQ8i6GkRCAQ8aWSRhLGM0cJxOjIRdWSANY16T5T2jemgqUaO0ahkjK09eWToT/f7V8ysWQgjQlYYNy5gaKC9nU1/cdWKaLTUfi62AKDSfv6iTvq0TQo1te9BouLpuohdXjqQCBUsXq+4ZIE6Q88xZPU/Obeu2jZoKIfp572aFNBVCVadCqIQhy+pwf0I2J+KtolJXcHQ6i/0T7QdKE+yf6MFKsYbnjH3R26a10A/WJqfVQLWu4tc+dxADCRm/97q9bb3W/TdsxnAqQrXH6Wqx1oKPycq+W44hURceMppM27aMGWQBIVGWTeVuUEJIWLMMoaViVQ/Zj0uYzpRsY6G6opoWXC9CiHy/vriMHkurnUnie1xTe+OSOSGyEkL9ToVQi9c/qwr8+YsZ/OD8Ct5569ame1ZjYVK1WdAjogBZ5G33prls2bNCffdoquXPS8DzHO7cPYxvn5in5n/lKZlYCVlE0ZYhZFXhNu5zJD/olXtHANhtY5lSDReWSviRF21Hf0LGpx4/F6h2Xl/Uc1cI9ScigYLxyYJkkHPw+i19+JmX7cLnn57E1w7PYGolXJ5iUPjZmwnKNcVs+3RDMiK0bekp1hRomn1RjQayCFuoKkhHRZviaiApm+e2nzWPzLW8zmOB5yCLPIq1xncj15JOVbavBTYb9xnr3GrVFUIkdL1NQsgcxzPLGEOn4FQItUsI+d3Q3YKWa4qKEzN57B1Pg+M43Li1z1QIRSXepkYhK3/E3uZ3UW68d3gLF6msJgoAonxyTmZpvtNWFElfPTwDngNebty8CbYOJMyMHIKlfKPJxKvxxw11ha4QkngOdVXFikNe2tOkEPKwjBk3I1oLQ9uWsVJzwJ5VoRPWMrZ7NIV7rh7FQ0fnAgXvEjx5dhk1RcPtO9sPlAb0myxZDbKu9g0m9BvHQWMC04lBUCpKz8LywnSmjHGfFbnNfTHEJMFUoxB7lpU0SQfIMCL7gYQR00Cq572wUrQrDNMxyR4qHYAQ2twb81AINavVWgFp7CG1wK1aJmKSANFQfx2aXMFoOorhdBRXjqTMlXmCuqLi5//1GXzv9KL5WJBVT4JeF4UQzVLqNcjOlGrIletUK2RU4sFxMMO2CaYzZYxajsW+uGwubrSKY9M51BQNBzqQH0RAXuvbL+jqw3Yrzv1gNjmtUobQx751EsdmcvijN17Ttipy+2AC3/+dl5s5LFZERAEjqaivJZQGv0UpYhMghGm7odLDKZJ3pK/MmwqhwISQex5Lp0HUAzds6YOqNWzAgE7KkqYgL8sYUeINJGX0xSXTnkgIIS9yxLqtrdkv/XGd0G1kCLV2/WuUVqj49PfOISrxePP1m5ueZ1UIZR0LMOmoaN63yjUF2XI9UGNWu7jrqmHkKnU8eXap6XfOMQFAGqyURpGLrcmzQQidni+A4/TXB4CT8w1C6KjR6nvtRC9+6KYJfOPoLC4ul4K1jLmFSucrgQNziWUsKHn6C3dfgas3pfG+/3jOXKDqNEKFSvsphOT2Q6VJSLdfhpB1cde5PQeSEdSNFVM/Qoj83k8pbM1yBeg5qt2OiChgvCdmy6Is1ToZKk3PEJIFviPjxlRUYgohhs6hoRDSB2ULbQ6q/TzgbhlCJ+fyqCoq9hq1rDds7cPkcgkvzOWbLmDkQkVqhgMrhKTWFELOGnMATTJQT4VQCPLja8/P4MZt/U1ywq39cSwXa7bclcVCxVSP9LZCCKmqS6g0j1pdbWpKSUd1hRAtL8kJnucg8hz1hqkTQq0NgDVNt1Q5Q3zJ5JTj7GoUM3yRQvSUqgourpSwaziJ1x8YR6mm4OtHZgN/lsdOLUASONy0ra+Vr9IEgedRV/VQ6SRFIXTICKftRKtGKiqZg14CP2nz1EoJYx4NY4C+368cSZpqFELE0RRCTmWJFY3VZg+FkNEQ4dWIkynVIAu8SUikonbLWBDyY1OfTgiplPfplEJoc18MIs+ZA3Q3u4sfOI7Tv2O5hkOTGew3mq52j6Qwn6vYCP9nLqzgwYNT+MyTFwA0cpvCtIwBzYQQrdnHq2WM5DNtcjSMke+TkEWKQqiMMUcGSbsKoUagdOcUQrtHU5BF3rSjrrZljJA0q5EhdHQ6i//78Encd+04XuFYsFgNTPTHfBWANDTGIPRzaDApIyYJeO7iCoDOWcZIdseSsVAVVHm0lqHSRPlzg3HPsiqwSPAw0MjeoWG5WEXEyKnpjTcsYwWTxPe2jBFYV8j7EjKKVQVLRmSB274LgogoYD5XwReemcJ9BzZRiUtiZ81X6qjWVds1XJ9k6d8pTIV6u3jRrkHIAo+HjzbbxgoUQihpWsbqiMuCTZGTjjYsY2cWCtjcF8NgMoLhVMSmEDoypd9v9o6n8c5btkCDfoz4t4zpKnLavXc+Xw28vYiKJShRLos8PvrWa5Gv1FGpqx1vGAMaGUJBQqX9Go7btYxpmoY/+spRyAKPW7b3ez6X5znzGHCS0Vabv69CiFjGfMYBccmFENpAGUKA7r44ayy2a5rW4VBpeobQQFLuSOOavrDLWsYYOoTFQgUiz2GiPw6B58wbcqsoVOr6KrXLDSUi0VU65Ma0b1wfjN+4Tb/4PXZqsWnCRQghMgEIWjtPmOuwodLpAJk0VIWQFE4hdGahgOOzuSa7GAAzgPO8Rdq4WKhiwFCP9LZkGdOo4YGiwKFUU1CuqZRQ6XpTYKwbSLihExFRaDlDqFBVoGpoUgiRlcd0VLLJwxMuii5At4tpGrBrOImbtvVjNB3FgwenA3+Wx04u4rqJvsAWGz+YGUIVe6MEmbBMZcoYSkU6crNy3kgW8hXc8IGv47+evUh9fl1RMZstB1InXTmSwvGZRjYTYF81DpIhRCZIXqtUE/0x1BTNM3g2U6qiJ95Q+KUNIkzTtMArQZt6Y6jWVSxQro20gPNWIAk8tvTHzWuFW5h2EKSiEi4ul3BmoWDWp185as92AmDmVTx6csHcHoD/yiCBW4aQ2exjDZUW3VddifrKzQoZl4Um5Z61thpoZJA4s93C4OCFDAaTckdtCJLAY+9YGkcMom+1CaFURATPdd4yVlNU/PrnD6I3LuH99+7r6Gu7YaIv3pplzKepiuM4TPTHzHDldlVbA4kIeK5BCK1YCJMgiEprRwjNG9/5hi0GIWRRYJ0xAqXHeqKYy3mHSg8k9AlNT6xhGSOTQy8yhxz/g0nZNnEkE9YLBjncTkhzROTx5UPTKNUUPHDbVupznAG8zgULMr4jv18LQigREXHrzgFqjhDNMhY3LGP5cjNZ1BOzW8a2DeiW5J1DSTshNJ01iKIoNvfFcfceXUUk+baMuS92LuQqgfNRwiqEAOCKkRR+89V7AOiNgZ1G8JYx/wWl3riEfLVuG7eHweeensTXj8zi11+1GzuGkr7PJ9vTqXq07o+gCiG/sVFMFmzB29UNTAiR/UOzu7cDMu6xXt91BV1nrifWa9Xlho11lG0QLBq2I4Hn0BeXO2AZUzwlw26WsSPTWUQl3qwy3TeeRlTiUa2rFIUQsYwRQijYocEbcuIwCqGsQyHkRgjRFULuq+I0fO3wDADgVVfTCCF9u5y12MaWCg3LWG9cRqmmhCK76qpKJe4knjfzeHodVqx8pY5Ctfm70iAJPLWFISK1bhmjtc5ZP6fzWPGqZjxlSKd3DiXB8xxet38Mj5yYC9TWlinW8PxUBrfv6kx+EGDNEKrZQtmjkmD+f6cmq862vOcvZlCuqfg3Qy3ixGyuAlXzbhgj2D2awkK+gsV8BblyDTFJsK04yiKPiMibbWo0FKsBCCFKQ4QTxPJJkDaIsDA3fmJlojWN0QLOWwW59sUtbTKtIBUV8f0zuu3AqhACgOMW29jDR+cg8BzmchWcmi+YA+CgljFJ4JGwtAwREPuLzTImNw+MCC4ak363Y1tfZW38Xa5cQ65St9kFBhIyaorW1uDo0OQK9m/u7cjKnRUHLIqjdu1JfuB5Dr3x8AUDfvj4t0/j+YtZ/OF9V7fdyhUUE/1xzGTLoe8VDdu6/7UDaJ+kE3gO/YmIWSdM7stBj6O1tIwRoueazT2QBM5GuJ1ZKCAVFXHVWNoky2hYKlTNY6DXsIxpmhaoIZEc/8Mpu9KUvB65lsdbtIwB+rirVFNw3ZZeaj4VoOetcFxDAWQdTyQjDTs1IYScn3e1cPeeYZxeKOC0xdYFuFnGROQNy5hzgYzkPWqahjMLBbM1cddwEqfm8iZxfngqi33jafPv3nWrTqD5t4yRsa39uNU0LZxlTKATGH547+3b8C8/fgtetnso1N8FgSTwEHnO3zJWVRDzcSe85cYJJGURv/q5Zz2VzDRcWCriDx48glu29+NHHU2xbiBEpxch5He9a1jGvMc1MYdljLSMBZ2PdQu2DiSwWKgiZzSzAt4NymFgFgs5QqU7RTCzDCGGjmIhX8WAcbEYTLafw6DfuNxv5m65OkemstgzmjYlj5LAm/kLTnsQsaMR8iqMYkIWwtmVnJYxM6Q4UIZQuMyirz4/g2s29VAnRmQlhISf1RUVK8WaSQi5rdZ7oaZo5oqCFdZcIXvLmP7d53OV4AqhDlvGyEDNeUyQz+kkhJIuFj9AtynyHLBtUN+2r792HDVFM4k5Lzx3MQNNA27a5i3jDQOSIZSjrPgR21i7gdIEzgwhohx5/PQiNT9i2lBxjPtYxgCYmSDHZ3NGg0vzwMIqy6fBr2UMsFTPe+QIZUo1W5AvGSib5EeAG/8mj8ajbLnmu+IWFA1CqD2CKRVtWKz2b+oFAIykI+iJSWa204WlIo7P5vBOox3n8VMLgUg4J2jZZWQgTWsZoxFCk8slRETedRLhVAgRRZhTIQQ0cuXCIl+p4+R8vqN2MYL9lkyiTh0rXuiNSVjuoELoxGwOf/GNF/Daa8Zwj9EAuhaY6I9D04CplXDV82ZTlU8gPaATxJ2orR5KRUzyYLlYDaU6iqxhqPRstoKemIS4LGJTr92Sd3ZRJw6GUxHfljEy7uiLS6irGgpVJdD1g0xGRx3ZLw2FUBGSwAVWfdNArjsP3EpXBwG6SkwWeHO861QIkXvT/BpaxgDgLkOh41QJFSr1JoIzGRFQqNSRLdeQdCyQESXsfK6CfKVu3lt2DSeRq9Qxl6ugWldxci6HvRZC6CVXDOHA5h5cMdyc62VF1OV6TqxcQRUQvTFdkXmNC3HnBp7ncPvOQd+q+lYRkwX/UOm6v8J4U28M//u+fXjy7DI+/u3Tgd9fUTX86ucOAgD+7K0HAi8QkbF7f8J+PAxY7q3OsbMT5Pde+Y0AEJdE23250TLWGXXNWmGrZW5Voqib24GpEKo7FUKdWVRJsgwhhk5isdA4ODtR3VugSFutMC1jFsZU0zQcnsrYbkwAcKPhc29SCJFQ6WI4hRB5/7CWsR6LAiCMQkim5CV96KvH8OVDzbakmUwZz15Ywasp6iBAXw0aTEbMYGky4B+0ZAgBCKRuIagr9HpR62O9sWbv8VyuEmibW7NbbI+3QQhlKa1VQGNFhJY3xXH0UOmTc3lsHUiYF+1rNvVg60A8UNsYWVndZgy0OgHBCPOmrfiRAdbmTimEHITM8Zk8ohIPTQO+/Fzz8XnRJIQCKIRI9fxMDrlKjUoIWYM7aSjVyGqz+7VkU6/eEPGR/zmO+/76Udz314/i5/7lB7asnxVnU54hsQ2Sd2F9HwBNwdI1RUWxqnTEMgYA242mMS9CPQjI59k+mDDzMziOw+6RlNn+9s3j+oTjh2/fhk29MTx6crER5O2R29T0XhRCqEIZVEU9ZPikYcxNUeEM5pzO6ASBVa1GBsCLLVqenzcI3k4GShMQ215U4juWTeCFnrgU6j7ghbqi4tc/fwjJqIj/fd/aWMUIJgwiNmyOUJCmKqL665TayU4I1ZomZF6ItmGhDou5XNkMwXa2NJ6eL2D7YALD6SgWCxXUKW1XgEOZbIwPVopVCyHkvt3J/XkkbScMGgqhUtuNXRGRR19cwmt8yMuIyJsKoVRThlBDIcRx7edMBcVEfxxXjiTN6zNBoaIgGWkeBxerdWoleTomQlE1HDaiGLYbdqNdw/p/T83l8cKcHqJPcjsBnWj5ws/egZ9+2U7Pz0niEJyEEMkgDUqgxWQBj/7WXXjRFZ0p5ugUYgFsnLpCyP96/sbrNuGeq0fxka8fN6Mx/PDp753D988s4ffv3YvNfcFtcZJLJhMhhDgONvU5DeQc9RsHNCmENmDLGNBwX5xbLDbs7h1uGSPXd1XVsJivdtQyxgghho5hIV8xV2dI9Wc7oElbraBZxi6ulJAt123SVQC4cauuvnANlTZWg8MQQrLAB7aMaZqGbLluI0VcCaGa2nQhbDRe6N+1pqj42++cxke+frwp6+J/jhh2MUp+EME2S/U8mfj0WzKEgHDtMnXVpWXM8ph9Mq3/ez5XDqwQoj0vIoYj5awgJEZQyxjHcUjKurTaiVPzeey0+LI5jsNrrhnDE6cXbeHdNJxf0lcyR9Odk5KLvG5nLFaVporRTiuEksaNhByHJ2ZzuGlbP/aMpvAghRBrTML9v+9QKoK+uITjs3kjILt5cpSKea9sBKkwlkUeP/OynbhiJIXemIS6ouJLh6Zt4ajODLBUVIKqNcL0gxzHqaiEdFRssoyZ9sWuUwjpn8epdrlyNInjszlomoaHjs5h+2ACO4eSuGPXAB4/vWiSLmEVQs5wcLIaFqUohEqUie9Fn/rgeMSuEKIdi+Q62Gqw9KFVCJQm2DGYQCoirrpdjKCvg5axv//uGRy8sIL3v35fxwaxQdFQAIYjhMhx7DUOIYrbTu2ToaSFECpUQ72ubhlrXyFUV1RfW8pcrmLWvW/ui5vqynJNwVSmhO2DSQynItA0uC4OWr9fI8S8Zp6jXnYv8ncjabpCKFOqtRUoDQDvuX0b/vd9V/te22VRML9jU4aQhRDqj8sdUZEFxZ17hvG900vmWKdSV1BV1KaFAmIXoWUIkfHRM0YzqdUyBuhNY9ZAaSuCWB2jZjaK/XpOCLa1vlZ0Gk6ywwlV1VCpBwsf5jhOb2WMyfiVzz4baOz79SOz2D2SwptvaG7I8wI5Tp0EZr9LxiYNhFz0I0V05a7VMqb/eyO1jAHAFiOf9dxSoaFubkOhaIWppDP2+UqphrqqdY4QirBQaYYOwspWDiRkLObbC5Wm3ZysoFnGzBvTmP3GdP2WPgg8Z0vIByyh0i1YxvT8mmCEUL5Sh6JqdMuYM1S6rpg3Sdv7iYL5fmcXCqgpGk7NF3B02l4B/dXnZ7BzKGHesGnYYiGElowJrbV2HginENItY5SWMctjtlDpOFmFrwby2KaiItUiERGDk3JOkOYMp+qEEEK0yXkyKiJfsW+XuqLizEKhaXvfvWcYdVXDd04seH6OC0tFjPfGbM0e7ULgOVNt0WwZMxRCHbSM1Y1BjaJqODGbw+6RFF5/7Th+cH6laVV+aqWEVFT0rT4F9AHQlSMpnDAsY87VS4C0oLgfq0HzbH79VXvwTz9yM/7pR27GR956LQDgoDEIBvSVayuhS2yPxHYUVBq8uS/epBDKmvbFzmQI7TCq59sJVAUa58Z+h9pl90gKuXIdp+YLePz0omlPuH3nIDKlGp4+twwgHCHUG29WCNEyhLzy1C4ulzyPa2fL2IxBCA1bVAZkwNuq5fngZAabemPmedZJ8DyHAxO9a2Y76Y1JLVvnrDg1n8efff0EXrl3BPfuXzurGMFIOgpJ4GyVwEEQhJggZFOnlB9DKT1DSNM0LBXDEkKdCZV+9ye+j9/9wvOez5nLViwKoRiWClUUKnWcWyxC03SVIvk9Lay/UleQq9TNMRn5njohZBDKHtdUcg44VQ/pqARyK221cp7gbTdvwesPjPs+LyLyJoFsI4QiIvLVOlRVw1yuc3kfQXH3nhF9DPKCPgYpuISkx2UB5ZqKlVKtmRAyxkEHL6xAFnhT2TuciiAZEXFyLo/DU1nEZcEMnA4D50SXgFSkb3hCSLKTHU6QMX3QuUd/QsaH3nwNjs3k8InvnvV8rqZpODKVxf7NPaHz7CSXTCZR0FVzQSzL5Hjv9Xmu01ZHVDAbLVQ6GRExmJRxfrFojl06pRBytmqbhGkHM4QqdbXl+dRGxsY6yjYAitU6ilXFHAQPJCPIluttHVyFqo9ljJKrc3gqC54D9ozaCaGeuITP/9RtePft22yPkxX0sLXz+vsHJyPIRMd6EY1JAniKBYmmECLvR1YEjlkCXa22pOVCFd87s+RqFyPYNpDATLaMck0xV7YatfPGwCyMQkhRqeGB1sd6HKHSAKBpwRj0j7z1Wvz2a65qejxMhlCmWLPJp4lypzlDiG4ZA0j4on1/nV8qoqZo2DlkHwxdt6UPvXEJDx3zrp+/sFyyBZN2AlZCyKmAGjQG4LRq7lZAiJ1suYbzS0VU6iquHE3h3v36QPpLDlvj1EqwhjGC3aO6PSlbplvG/OoySy3k2ewaTiIuC6bao6aoKFQVm8qNfG+SkRH0xr+pL+auEOqQZWwkHUFMEtoKVNU/j769DzjULruN6+s/PHoG1bpqNsrctlMPRn/oqH7MhyHYqRlCJJjRco3geQ4RsVkJUarq1zKvYysREVCs2BVCg0nZdg3qN66D7SiEDkx0Xh1E8MH7rzEJy9VGD4WkCwtF1fAbnz+EmCTgA2+4uuNB20Eg8Bw29cYwuVTyf7IFfrXzQIMQ6lTr23AqgpqiYbFQRaZUC2VFi0o8ym0O6Kt1FU+eXcLjp9wXMjRNw3yugiGDSN1iUWCdWdBDjHcYljGAXj1PAuStodIAsFKqolCtQxZ5z1yX8d4Y/vXHb20ibHij1ARoXyEZFBFRt0gDzZYxTQPyVT2DZ60Joeu39KInJuEho36+4GKBJCTQQr7StFBD7kkHJ1ewdSBuLlxxHIedw3rT2JHpLPaMplpa1IpQwnLJZwGAwdTaqCFXC1HJO0OokTUTfO5x154R3LFrAP/8xDlPJd9croLFQrXJMREEohkq3XxdG0hGAhFCL79qBP/y47f4xiHEXUKlNxohBOi2sbOLBWpDajtwEqcNwrQz5wcZW9MyUi91bLyjrMtBVlMHLBlCQOuDakA/MJ12FytIu4NNITSdxY6hJHVydt2WPlfL2ErI2nkgXH4NGfxYyQeO46gEQ7mmUD9HROTNm+aJ2RwEnsNtOwbw4MEp067zjaOzUFQNr97nvQpLqucvLBVNa5+pEDKl28H3nW4ZoyiEjMcEnrP5ja0T3yAKoV3DSWrmTEQKnpnwycfP4r3/8CSev5gB0AiVbs4QkjDRH8NVY81hiMlIs2WMVK86FUICz+FlVw7hW8fnPW/ak0tFTPR3rp4a0C1j5JhznkP7NvVgJB3pWM0qIQ1y5brZPLV7JIWJ/jiu29LbZBubWikFsosRXDmSQq5Sx4WlIlXxkor4WMZqCiSBCyXVF3gOV4/34OCkfqyQSTHN9jgXUiG0qTeGyeWizerpRk62Co7jcNvOATODqVXsHk1jNB3FvnGHZWxEP9Y/9/QkkhERNxqB6CPpKHYNJ/GD8+EVQjRCiAx+nNdzWlDnxRVd/eGVkxCX7dfbmUypKZQ2IQt6SGwLlueFfAUXlkqrkh9EsLkv7qn+7CT64jLylTpqLhkwQfCPj53F0+eW8Xuv22sSBOuBif7w1fOFSh1Rifec6CYjIvaNp5vOkVZBSIOTc3loGn1C5oaoKEBRtbb218m5PGqKhrOLRdfWmZViDVVFxYjRmNVoaSyZNtttgwkz34cWLG2OGc0MoYZlrFRVzMIPL9y2c4A6aSRjmXYVkkFBPoMzVyVluTfO5yoYWmO1iyjweNnuIXzr+BxUtdGc6Mx+IQSRpjWPF4hqdaVYM63IBLuGknhhLo+jU9kmu1hQuCmEzMylNbLHrhZ09VUAQiikkuSBW7fi4kqpKTTcioaVL/y1yc0yBuh26CAkkyjwuH2nf6aTU0W1kKuA59bu/O0ktvbr1fOdbhkj9jky3zFD6jt0TSFxDJdjjhAjhDoMMngmbCW5ybfTNOaXIUTaHaw+2iNT2Sa7mBciIg+eay1DKEx+TZaiEAKaK7sBneCiKoSkhmXs+EwO2wbiePMNm3FxpYQfnF8BoNfNb+qN4epN3tuAkAFnF4tYzOs3XrKqloqI4LmwLWMqJFrLmPFYOiraVoat26GtFhBDNeXMUaLh6Ix+c/z0984B0PdJROSb3l8UeHznN+7CfdduanqNZERE3qFGOUkq5ymTtLuuGsFSoYpnLdYjKwqVOhYLVXOVuVPgOb12nnxmK161bxTf++2Xd0zKSl4/V67jxGwOHAdcYRAG9+4fx5HprEmaAcB0phQoUJpgj9E0VlM0qs3MLwwvaGCjE/s39+DIdBbVumqSa7SmPFKrHFQNs7kvhkJVsZ1fxL7YKYUQAHziPTfhfRRVXRi8dv8YHn/fXU3HSm9cxkg6gmpdxUuuHLRNym7fOQDCf4ZZoe+JSSjVFNs11bSMOc5RWlAnaW7zysZKRPSBp6ZpqNZ1q+do2v58juPQn5BNG3EYPHVWJ8JIicFGR68l16UVnF0o4MNfO4Y7dw/hTdc3X0/XEs7g4yAoVJVAk5Iv/8KLA9c5+4EQQqStMYwVza2xKQyOTDfCao9a/m3FbM5utST3r/NLRZyZL5h2osFkBBxHt4yRxcI+ykJUoaK0pe4hr9muZSwoyLgxKYu2XJWUOcmqYb6DFdFhcNeeYSwWqjg4ueIakm4l7p1kkfWetN2hgt41nMR8roJcpY69Y60RouTaXnEcs/P5Kvrj8qq1f60VYj4KIXKuhi0JePlVIxhJR/CpJ865Poecy3soi5t+MC1jlOvPR956LT54//7Qr+mGmCyiVFPMEo+nzy9jz2h6TYoTOo0tA3FMZ8vm+K5TCiGe5yCLPD771AW85W8ew0e+fgJA5yyV5jjeiMTIlGq45y++g0dOzHfk9bsZG/sK04UgeUEDRiAnGcR4BUvXFdV1Il9XVJRrqm8ooFU1s5iv4OJKKZQ8kuM4JGTRVMOErZ1vxzIGGBYky2RW04yAOTeFkDFZOjGbw57RNF65bwQRkceDB6dQqNTx7RcW8Mp9I76y/G1mGn4Bi0awI1kF5XmO2vjjhbriFipNbyqISrx5w2mnSSAi8lA1mOSHF4h65QvPTCFTqiFbrodWZCQpBN7JuTxG0hHqZP6lVwxB4Dk87GIbI5PYTlvGrPuCZrPqJMigN28ohLb0x83B/Gv3j4HjgM8/PYkF4/xcLtZCEUJXWFQubrXzpZriuiperNZbmlzsn+hFta7ixGyOev42LGOGQiioZay3uXq+oRDqvhUxt2vJlcZ+uWvPiO3x2w3bGBBu1dPMLrNcd8hg2XmN0GX49v1Ncpk8Q6VlPe8qV6njp//5aZxdLOI11zTba/sSsrlIEAZPn1uCLPK4OmT1cbeisU/Ck2OqquE3/v0QJJ7HH7/pmnWxilkx0RfHcrEWKjjTr+l0NeAkhMKGSgPNAb1hcHgqY44F3JqMiAVs2FAI9cUlJGQBF5aKOLNQMJUkksCjPy5TFUJOZXJEFBCXBV0hVKuHUhc6QRYk2w2VDgqyqOS8PxG1zdRKCdW6ui6E0EuvJGOQOVdCKElRNRFYx0g7HAohq02+FVsS4H7M6pXaGzs/CACisneGUENJEu54FwUe77h5K759Yh5nLeUXVhyeymBLf7ylhSZz7N4h1bIXyLleriuoKyqeOb+yYRdVtg0koGnAiTn9+t1JUuu9t2/D9sEEJIHHpt4Y3n7zRMesykTpT+aj3z4xj6PT2UBKzY0ORgh1GE7LGMkScqvuVVUNP/TxJ/Cb/36I+vtG+J33wWhVzRwyrECkmjcoYrJgEgrhaudD5NeQCaXj5E1GRbO2GmjY39wzhFQUq3WcWyriypEUUlEJd+0ZxpcOTeOhY3Oo1lW82qNdjKA3LiEVFXFusWirfjV/H5NCrQrXVZW6kkNuKrTGLvJYewqh5hwpGso1BWcXi7hz9xBKNQX//vQksuUaNaTYC8movbYa0GtX3SwcPXEJN27tw8PH6Cw7CTnttEJIsIR5rz4hRBRCNRyfzZlEAaBbiG7dPoC/eeQUbvzAN3DHBx8GEC7QuicmmRYzmkKIkChuKqFiVWlJDUVycw5OrpgTYiuxSY4dohAKEyoN2KvnO50htBbYO5YGzwEv2z1ke/zWHQMgc/8wkzoy8cg6CCGOa74uR6Xm5paLyyWIPNfUOmQFGdy85xPfx0PH5vCBN1yNN13f3L7Sn5Cw1ELt/FPnlrF/U0/HmkXWG72WoN+wIHXHv/u6qzDW01lLbCsgttwLIXKECpXWyOR2YBJCM7qqMgwhFOmEQsgIoe1PyO6EkEHwEEsYx3GY6I9jclknhHZYiILhdBTzuWaFkJMQAoxxR6lmKIRaP4caCqG1tYw570/k3nh6Xp+wrwch1BuXccPWPjx0dM7MB3GOCazbyWkZsz53+6B9nEPGPTynZ/21ArdjdiFf2fD5QYChZvUghNrJmnn7zRMQec5UvTtxZCrbMlEn8hzSUXFNFFrkXC9WFRybyaFYVXDD1o1JCJGmMbIA3UlC6H2vuQr/8uO3mj9/8qb9HVtoSUbt4+iHj82hNy7hui0bcz+EASOEOoyFgl0h5GcZ++/nZ/D0uWU8dmqR+vt8lX7jcsKqmjl0IQOOQ+jVWevNMJxlrH2FUDJit7uYhJBby1hNNbMFdo8atpwD41jIV/Dhrx3DQEI28zy8wHEctg0kcG6piEUKIdQTl0OFStcUjW4ZM5QqNBabTADb8dg2Agm9B8Cn5vNQVA1vun4zrtvSi39+4hyypVqgpisrko5qRk3Tm96slfNO3H3VMI5OZzG10jwRIRaGiQ41fhFYxVrJyOqSDOQcXShUcWah0JRb88H7r8Ef3rfP/PnQ/fvxqgCkpRWEZHLK2fX3b8jyaWjVMralP47euIRDFzJUy1hTqHTQDKE+ukJI4Lm2JkFrjZ986U7864/f2rSK2xuXsW88HTq3yU0hFBWFpkFPTOKb7LqTyyWM9UY9817ixvHz7IUVfPjN+/GuW7dSn9efiGA5JAlSril4/mIGN2zQlU0a+lq0jF1YKuJP/vsYXnzFIN5648RqfLTQMHNuLDlCNUXFN47MuiqVCxWlqaJ7tZGKiIiIPI4ThVAiRIaQRBZIWiOENE3DkWl9Erl3LG2zj1lBVJFEIQToRPfhqSwWC1Vb1sxwKuKpELIqEHriMlaKVZSq7VnGBtaYEIqYhJDTbqX//6l1JIQA3TZmtW47t4tVSeW0SEoCb96XnBlCW/rjkAUeO4eSLU98oy6tkZeKQsjfMhauZcyK4XQUr7p6FJ99arJp++UrdZxdLIaK0LBCFvmONSf6gYydSlUFT51dAoBA85huBHFfEEKoU9EMqw1y3pNG7G8en8Odu4c72n7crWCEUIexkKsiIQvmwd8TkyDwHNUypqgaPvL14wD0QTzNmuTWhuCEtWXq0OQKdg0lQweRkYuRyHOh2HDZUgPvh0ypBpHnmuR3yYhoS3WvmH5iWoaQPgkiF5qGZWPYkGuX8Iq9I4FPYL16voClQtUcQBH0hraMuSmE9M9CayQgaoh2GHSzitFnPxD5/e7RFN5921acXijg+2eWWrKMFaqNzKLZbAX5St0z5JVUctPC/y4sFxGXhY7feNdUIRRp1NIqqoYrHSuFWwcSeOC2bebPW2+aCL3PSY4Q7buQY3eW0mQD6KGNrRAtHMdh/+ZeQyFkhEpbjhdZ5BGVeFMFGfTG3xeXEJMEW9NYtlRvytnqdvQnZNyyY4D6u9dcM9Y0efADjRAq1RTqtZCqEFop+bbXbemPIyLy+PO3XYe3eBAV/XHJtEEHxaHJDGqKhhu3bsyBLA2kcTJMOYSmaXjffzwHDsAH7+/cCma7MJuwLDlC//bkBfzYJ5/Cd0/SG7X8mk5XAxzHYSgVMc+DUBlCIt1+ExSTyyXkynoezN7xNI7P5KhW3LlsBamIaLvmbemPYzqjE0VWJclwKkLNEFoqVNEbl2zjhr64rkwuVNuzjBFV1VrZHWQXQogsGpw2cgaH14kQIi2QpJE2KTsVQpYMIZoKNyohZVRqWyEKPK7f2os7dvkHB7uhQWI6LGO56iVBCMUpBQhWlNpso3rg1q3IlGq2tmEAODZNAqVbI4S2DsRxVYtkUlgQ8rdUU/DUuWWM9URDNdF2E/ri+rlCroXRECKD9YS5sFqp45nzy1gp1sy5y6WOjbGHNhAWCxUMWm52evWnRG1q+cIzF3FqvoC336wPyGnBhUQ1408I6aoZTdNwcDKD/S20u5CbYRh1EHm+mzJlLlfGL37mGfznM5OoKyoypRp6YlLT4NiZSeOtENLJr+MzOUREHlsNJjoqCXilobh4lU/dvBVb++O4uFzCXLZsWv0IemISMh6TgG8cmcVffOMF8/9rKj1DSOTdfcgNy1g7GULBLGPHZ/KQBA7bBxO45+ox9CdkVOpqaLIkGRWhqJo54DYbxjwUQjuHktjSH6cTQkt65XynJ00kzJvnwtl2WgGRmj59Tg/U3dOidNwLpkKIMlglkmhSEe9Eq5YxQLeNvTCXx4xxc3cSiGmjVhgIfhxzHKdXz680JqZLhWrHGsa6AT/90p342i+9JNTfEHuSXSGkUgfKMUloaqW5uFzCpl5v6+WtOwbw3Ptf1VRV7UR/IoJsOVy71lPn9JXNjSp1p4FYnMMsDnzmyQv47skFvO81V3XVoL43LiEZEW3KPDKJIrXcTuQr9TXLobGCEAeyyIeaKLYbKn14qjGJ3DuWRlVRcWo+3/S8uVzZrJwnsDZl2hRC6QgW8tWmps2lYrWpQao3rlvGilXFVPO1gv51Ugg5r+GmZczIeBlKrk/L3q7hJCb6Y6Z1zRnF4JUhBOi27O1DCeo45V9//Fb8/r17W/5sjWO2ca0tVOoo1ZRLghCKSgLKNdUMTHai0TLW2jj4lu39uGI4iX92hEuTc7nV9sMPvOEafOyd17f0t2FBvnuxquDpc8sb+h7KcZxpG5OEcCKD9YQ1+uHhY3MQeA4vuXLI568uDWyMPbSBsJhvVpkMJCJNOQzVuoo/f+gE9o2n8cuvuBIAPbiQqGb81D5ENTOVKWMhX8GBifAXv5gx4IuEZOhlkUeVMmHQNA2/8flD+K9np/DL/3YQd/3ZI3ji9CJVJeMMlS57KYQMRdLx2RyuGEnalEA//uIdeNP1m3BHgIpHgm0DCdRVDdlyHf0Jp+1D8rSM/e13TuOj3zhhDhb1ljFa7byhEKLkIDQsY51QCHkPgE/M5rBzKAlJ4BGVBNPGEDazhQwwSRL/SSM4zkshxHEc7tozjEdPLjSpGiaXix3PDwJgNp0kI6uvOhEM5duZhQIkgTMls53Ey3YP4b5rx7Gfcn4Pp6MY64nikFER74RuP2jtGNu/uReKquGxU4tIRcUm9R25icakZluTFzb3xcwMoecmM/ja4RncvEEl0jRwHBf6uDMVQkWHZYxyfYjKdoVQta5iNlf2bBgjoNVUO9GfCG+VevrsMnYMJdZMZr8WSBvHfNDtMLVSwh99+Shu2zGAd9y8ZZU/XThwHIfNfTFTITSdKeHJs0vgOeChY3TbWLGirFlTlRXEWtQfl0OdR7TJdRgcmc7qeTAjKZNop43P5rIVs3KegFjyeK6hxgL0HDlF1ZrU4kt5ilU9JmOlWNOLANoYF6xX7byTTIlJAgSew3yuAlng1600gOM43G2E/0clvmmSaiXfaNvsgVu34odv2+b62u2MMQSegyRwNoJ/gVRqr5OiqpOIWQKTaTCLE1rMneM4Dg/cthWHJjM4aGmzPTKVRX9CNnO+Wn3ttUBMMqyVc3lMZ8q4cQMTQkDDNraRWtIiIg+R55Av1/HwsTnctK2POme9FMEIoQ5jIV8xg6QJ+hNy0yDgs09dwIWlEn7tlbsxnIpiKBUxmWwrAhNChmrmkHEhbEkhJLejEGoeeP3L98/jW8fn8fv37sXHH7gBfXEJp+YLTSocQB9A5Kt1czDqrxBScMIR3AvoK3ofeeu1gSY7BITFBkC1jGVLNeqqRk1RcdBQY3z6ifNQVA2aBs+WMdqFpccYHLWlEDIzhPwUQvZt9s5btoDjGhO/oCAZNiRY+vHTi0hHRd+By91XDaNSV/H46YY1QdM0nF8q2lZWOwWiEAqbkdQqyPvsGEyGOgaDYiAZwV+87TpXAm//5h53hVCt9WBYEiz93MWMZw5WWAXSpt4YLi6XUK4p+OXPPouBpIzfeW17FfEbHSRvI1OyE+Q0oj4qCrZJ73SmBE0DNndIkUIIcq+WTCtUVcPT55c3/EDWCY7j0BuTzMwYLxCrmKJq+NP799vqt7sFE/1xM0Poy4emoWnAj9yxHReWSlQlzHq0jAGNiTCt8tkLZLz0/TP0bEY/HJnKYudQEjFZwPbBBCIiTx2fzebKZuU8AVnYmOiP2+4BRO3kPIaWi9Wm76dbxqooVhTE2yDiyGdbK9Vlo2WsuTyD7JOhVGRd7ZPE/kEbU1vJN5pC6IHbtuH+G5rD9zsF/XreTAg5LWobEdZ8HBrMUOk2lNxvvG4TErJgq6A/Mp3F3rF011h2vUAW7L7zgl6+slHzgwjI3KpTlfNrAY7jkIqKOD6Tw7GZnEkgXw5ghFCHsVioNl28+5OyLVS6XFPwVw+/gBu29pnNNG7BhbnAhJCumjk4mYEkcLhqLLxdhUwWw7K5EVFAxaEQOrtQwAe+dBQv2jWIH75tG165bxRf+Nk78JmfuBUfeMM1Ta+RjIjQNJi1lETp4pYhtJCrYjZbaQrubQVbLYSQc6UuHZOgao39YMXhqSzKNRWDSRmfe/qCGeZLC5D1qq7sRIaQLPhbxnLlGi6ulGwtGBP9cXz6R2/BD9++LdT7kclBvlzHV56bxtcOz+K9d2z3venetK0fssjj0ZONgfpSoYpiVel45TwAU8myViukxDbmzA9aK+zf3Iuzi0WbuoSgVKWrTIJgOB3FqNFaRfJUrCATgLA3/k19MSwXa3j/Fw/j5FweH37zAVuD2eUIUeCRjIhYKVnvGSpilGthTOZtEwiSxxSmvc4LJMg3KCF0eiGPlWLtksoPIrhuSx8eO7XoGrxM8PmnJ/HIiXn85qt32xYbugkTfXFcWCpB0zR88eAUrtnUgx950XYAzRlvmqbpGULrYBkj1qK+kJXCe0ZTeO01Y/jLh0/irx56wf8PHDgylTEzR0SBx57RVJNCSNM0zGUrTXk45NxzZocNGUqiOUfG21KBbhmrqxpybVr1do+k8PEHbsDdV61NBoabQsj62OA6q11u2dGPuCxQCU7eKDTgOKzL8R6ReBvBP5/Tr7uXgmXMJIRcbJyEKGqHPEhFJbzx+k148OAUlgtV1BTdSdBqftBagxBC3z25gLgsrErswFpiq0GObySFEKCP4x85oZNyd14m+UEAI4Q6CtWQAw84bEcDCdmWIfTlQ9OYzVbwK6+40pxA7x1P4+RcrqmtK1SodE3BockV7BlNtyS7jLeoEJKNljEyUK4rKn7ls89CEjh8+C2NFVKO43DrjgFqLadJMBjfl9wU3VrGyE2lExPvkVTU/M5O9ZKZ50GZYJMWgPe/fh9y5To+//QkgIYqxYrRdBSywFMtVR3JEJL8LWMnZvWVXyeJdvuuQVtLShAQguX0Qh6//Z/PYf/mHvzcXbt8/y4qCbhxa5+tVe+CMYldDctYQyG0NoM78j7rdSM/YCgDD11cafpdsQ3LGKCrjwCXprwoIZPDHcMkW+UzT17Au2/betl4tf3Q4wizd7OMOZtbJg37XRDLWBAQgjwoIfTUWT0/61JqGCO4+6phTC6X8MJcs4KGYDZbxh9+6Qhu3taPd7tYS7oBW/pjKNX0nIpDkxm8/sA4xntjuGos3ZQjVK6pULW1y6GxolWFEM9z+Iu3XYs3XbcJf/b1E/jw1475EnkEy4UqpjJlWyvR3vEeHJnO2l4jW66jUlcxkrbfOxMREVeOJHG9o6aYphDSNA3LxSr6neMOC+nejmKC4zi8ct9oqJbDdtBoGWu+R5DHhtaZ3IiIAu65eszV0p2IiEjK4roo+/Qs0EvcMuZCCLXTMmbFu27dikpdxeefnsSp+TyqdbXlyvm1BtlGC/kqrtvSu2Fyd9xA8l03kkII0Ati6qqGrQNx7BzqfPRDt2JjH21dhkypBkXVmkiF/oSMTKlmBnN+8eAUNvXGcPvORjPNvvE0aoqGF4wsFoIGIeR9QkUkXSH03GTGnLiFRbyNUGmgoU75+++ewQ/Or+AP33A1xnqCTUzIRLpBCHkrhAg6MfHmec70+jvJPKLosa7WEzx9bhmb+2J47TVj2DOawj89fhYAqBfx0Z4ojvzBq3Bgorfpdz2dzBDysIxZG8baBdlff/DgEZSqCj7y1msDDzrv2DWIo9NZs73IrJxfBcuYqRBaM0JI35dOK+Na4Rrj3HfmCGma1nLLGAE5dmn2g1YtY2Q1fcdgAu+75/K2ilmRNqyqBOW6S4aQQQiRierF5RI4DoGvu34wCaGA7VpPnVtGf0LGjpDNahsBd+7WVwrdgpc1TcPv/OdzqNRV/Ombu9MqRkDI94996xQA4LX7xwDoLUxPnVu2LYDkA45BVgPWDKGwEAUe/+ctB/D2myfw1988hY9+/USgvztKaSXaO55GplQz884AYM5oDKNN1r/yCy/Gz91pXyAh9i2rQihXqaOmaFSFEMFaNYR1AkQhlKYphAxC0WmxWw/86f3X4BPvuYn6u2REXLPxghNRibdl7Mzn9GPlUshja1jG6GPUUk2BLPBt13vvGU3j5m39+OfvncPzF41zeY1awtqF1dJ/wyWgsiXui+gGuoYBjfnCXXuGN4TVsFNghFAHQWqXnRlC5P+Xi1UsFar47skF3Htg3HagkQuW06eer+gXST/FT0TkcX6piFylbqoEwiIutRYqTciIqqI3CPzTY2fx4isGfRtsrCDyXBIs/cz5FfAcqO0sDZ+6aNpY2gVhsp033l6XdhlN0/DUOT0rg+M4vOvWrbiwpA8WJUqGEEAnigBrqHT7LWO0cG+C4zM5xGWhI403ZLV4sVDF++7Z4xkm7cRtBhH6xGldYUWyLC4FyxghyjphZWwFPTEJ2wcTtlBFQCdrNa291WZTIUQhhKyh0mGwd6wHr943ir98+3VtfbZLDT0x0V47X1Wo2zYqCdC0xnl/caWE4VSkY/lVpLZ6KR+MEHr63DKu39J3SQ6iRnui2DeexsPHZqm//69np/CNo3P49VftbrILdRsIIfTwsTncvK0f48Y94a6rhqGoGh4xMiwAy6LUeljGiEIopGWMgOc5/PEbr8Ebr9uEj33rFM4tFnz/xmwYsyqExpqDpeeMyTpNXSsKfBMhGBEF9MYlzFoUQuS8ah53NP6/1dy39UCDEHK/R6y3QgjQ948b8RCXhTUbLzgRlQTbot5CvoK+uLRmCq/VBLm/F6vN8QsAycnrzPd8121bcW6xiL/7zmlERL7rr8cE1nv8pZDDN5qOQhb5DVM5T0DI68spPwhghFBH0fD7OlvG9P9fzFfxleemoahaE1mydSCBuCw0+dTzlVqglbmIyJt1prQGoiBop3Ye0NUpT59fxlSmjPuv3xxqUkAY2UJFD5Z+8NAU7tg12ESuWd9v90iqYxOPHUMJiDzXNPAk6h1nu8yFpRLmcxXcYIS+vfG6TeYgQqS0jHnBrIZtY+AXpGWMBEp3YuWabJcXXzEY2hqxf1MPkhERj57Sg6UvLBUxkJBXxZKw1qHSvTEJCVnoWIZLK9CDpe0KIZLN1U5jzf5NveC45uB1oPUcrJgs4G8euAFXb2rtmnWpoi8uYyZbNpU/5ZpKHSybbUpVFYqq4bnJDDZ3kFiVBB49MQlTFmWEGxbyFZxZKODGS9AuRnD3nmE8fW4Zyw4L3VyujPc/eBjXb+nFe+/Yvk6fLjis16d7D4yZ/z6wuRcDCRkPH22QXoVqMNv6aoAs+LSTO8NxHN53zx6IAoc//4Z/ntCR6SxG01Hb2OOqsRQ4DracR2L9CtNeNJyK2BRCRHnnthAFoK1Q6bWGdbHOCZMQ6nL7U29cMonwtUZUEppaxi6F/CCgca9yyxAq1+iLHq3g1ftGMZiM4NhMDntGUxvGehWVeHCc3lB43Zbe9f44bYPnOWwfSKxZZEOn0BuXkYyIuHn7xldphcHGOEs2CIhCyHkBt+YwPHhwCjuHEk2hzwLP6cGFjmDpQkUJNBAjN+KYJGDXUHC1hhUxM0MofKg0oK9SP3hwClGJxyv2hmNWCZmSq9RxaDKDc4tF3LufrjAi5Ecng3t/7MXb8Yn33NR04+iJE8uYnRB66pyubiEsfiIi4v7rNwGgt4x54eZt/firt1+Hm9poFAjSMnZiNtcx5Up/QsbH3nk9/vJt14UmmESBxy3b+/G4kSN0YamEzauQHwQ0aufX6ob00y/bib9/z03rahfZv7kXM9myaWkAGqty7aw298Ql/ON7b8a7btva9Lt0iwohBjpefMUQLiyV8IPzeiZPxcUyZg3q/H/fPoXjszk8cGvz/mkHL7lyCF95ftp1ZZfg6XP6Z70UVjbdcNdVI1A14NsWBY2mafi9LxxGsargQ28+0LblYS0Ql0UMJmUIPId7rmkQQgLP4WW7h/GtE/OoG6oz0iS5Hpax0Z4oPv7ADXjjdZvaep3hdBQ/fNs2fOHZi3hhNuf53CNT2aYQ2rgsYvtgwq4QMoid4RAq5eFU1FQWAR4KIYsKsx2b71pDDpIh1OWE0P963V68//X71uW9o45Q6YV89ZIhhOI+GUKlmtIxlbAs8nj7zRMA9PyvjQKO4xCTBOweTa/ZIuZq4/+85QB+89V71vtjhMIv3L0L//Dem1alKbibcXl921UGaRJzrqCT/z8ylcX3zy7h9Qc2UZUt+8Z7cHTKHlyYr9QDyVcJIXD1pnTLbHjCbBkLHyoNAMWK3jh1956R0KuJSUtr1YMHpyAJHF519Sj1ucTS1sng3uFUlBpoS5Qw2SZCaBmpiGjLinngtq2IiDzGesLZ2Hiew70HxtsiEQgp59YyNp+rYLFQ7SiJ9pprxkKHfRLctnMAZxYKmFop4cJyEROrpKgR19gytrkvjlt3DPg/cRVBKuIPWlRCpMGjXS/3S68colokWs0QYqDjvmvHkYqI+NTjen2um2UsJuvX3h+cX8ZHv34Cr7lmFPddG9yqGwQP3LoVOeO67AZV1fCpx88hIQuXtNpr/6YeDCZlW47Ql5+bxlcPz+CXX35lKOvseuPqTT24e89w04Tz7quGsVKs4RnDdrqeCiEAeOW+0Y5Mjn7qpTuRkEV8xCNLqFxTcHI+T80c2TuWtln6Z7OV0Pai4XTEzIUB3BVCPVaF0AayjE30xRCTBKpqKrlBFEJ7RtPr1kpFq51f71a2TsGvZaxcUxBtoQzHDW+/eQvisoBbNpjKY0t/HHfuvnTKNa7Z3IMr1ilCoVVsHUi0tUC/UcEIoQ5iMV8Bz6GpNplIjz/1xDloml2ibcXe8TRylbqZRQPoBEmQAYdskED7W8wPAtpRCOnv/ciJeSzkq7g3RHYQARks5Mo1fOnQNF565bBJxri931oE90ZEATFJwIojVPXps8u4bmufbTV413AKz/zeK/DiK9b+Yu5nGTMDpbvkwnzHrkEAwHdfWMDUSmlVGsYAQODda3AvVewb74HAczg0uWI+1gnLmBfSLdbOM9CRiIi4/4bN+MpzM1jIV1Cuq1Singygf/s/n0NvXMYfveGajuf33LStD7tHUvjk4+dcm5o++fhZfPfkAn77tVdtuIrZMOCJgub4HOqKisV8Bb/3X4dxYHMPfvzF3W8Vs+LjD9yIv3rHdU2Pv+iKQYg8h//1hefxE598yrRZrVeuSqfQl5Dxoy/ajv9+fgbPX8xQn3N0OgtF1aiEwN7xNC6ulPCYYXWey5WbGsb8oCuEGlZQ0iLlJIQiomAqKjaSQuilVw7hmd97RdMYGOiuDKFuhV47r9+rFVXDfK7SFEGxUUHmF+6h0mpHw4fHe2N48nde3vEFktXGf/3cHfiVV1y53h+D4TIEI4Q6iIVCFf0JuUky3huTwHPA+aUirt6Uxg4XS5cZXDjdGKwUqvVgljGJEEKtr84ShVDYYDeiEPrcU5NIRUS8rAV2mww2v3l8HjPZMl7vcRG/aVs/Xr1vtOXw7LDojUu2DKFMqYYTczmqNWK9VvP8WsaOz3SuYawT2D2SQn9Cxn88M4maopktb50GEctt9MlMGMRkAVcMJ20KIZMQWqXJRcqsnd84k5dux7tu3YqqouLTT5yHomrU1VMygF4p1vChN+9vWbHnBY7j8MBtW3F4KmuqRqw4OZfHn/z3Mdy5ewjvuHlLx9+/23D3nmFky3U8fW4Zv//Fw8iVa/jQmw9smJwKAlmkl1WkoxJ+5EU6uXV+qYhKTcEduwZWJfR/rfGjL96O3riE//M/x5t+p6oaPvjfx5CMiNTV4XuuHsNgMoJ3/O338Ja/eQzPX8yEVrsMpyKoKRqWizWsFKv4x0fPYvdIinpdJraxjaQQ4jjO9R7wkiuGcN+146EV1JcTdIWQPob72++cRrGqXDJKBXJcuIZKVxXEOhQqTZCIiBuu4CAiChvuXsJwaWDVjjqO417NcdxxjuNOchz3W6v1Pt2ExXylqbYc0FcVSUidWy4OoE/WBZ4zfeqz2TIml0uB1A2EzLmWUmseFA2FUNhQaf3vjs/m8Mp9oy1NCiMiD5Hn8O0X5hGTBLz8qmHX524fTOBvHrhhzewpPTHJ1vjzg/PL0LTuysogrRlulrETszn0J+SuWW3ieQ637Rgwm8ZWa7JxOSqEAD0c9tDkiiWUWCeEVuucYZaxzmPXcBK37xzAJx8/C4BOthGi8x23bDFr0VcDbzBC8//ZsLAR1BQVv/rZZxGTBfzp/fs33OC7FbzoikFIAocPfPkovnRoGr9w1xVdQ7R3Cr/9mqvw1V96ifnz6R+79ZI4t9NRCT/5kp341vF5/McPJm2/+/vvnsH3zizh9+/dSyV6tg8m8J3fuBPvv3cvJpdLOLtYDN1yShRFc7kyfvcLz2OpUMWfvfUA9bwhKpuNpBDywtWbevAXb7uOTXY9EJEEVOoqjk5n8ZH/OYFX7xvFPS7RCRsNRD3sliFUdsnJY2BgWBusypWZ4zgBwF8DuAfAXgBv5zhu72q8VzfhdfvH8Z47tlF/RyTBr/OwU0UlATuHEjgyncXkchFv/X+Po1JTArWW3HftOD72zuvN+vRWQEIjw16UrcFbbnY4P3Ach2RUhKbpGQbdtCrWG5dsodJPn12GwHO4tstaACIiT7WMaZqGo9NZXDmS7KoJ2+27Glk7E/2rmyF0qQT0BcX+iR6sFGum/bTQgVBpL7RaO8/gjQdu3YpFo9GKJqe/bqIXH7p/P/7Xa1f39pqMiHjT9ZvwpUPTWDI+j6Zp+ItvvICDkxn80RuuCRWuu5GRikq4ZfsAnruYwb7xNH7qZTvX+yMxhMB779iG23YM4Fc/dxD/9uR5ALqC9sNfO45X7h3Bm2/Y7Pq3MVnAe+7Yjkd+/U781duvwy+9/IpQ7z1sZOv83XfO4EuHpvFLL7/CNXOLNI210z7KsLEQlXgUq3X88r89i3RMwh+/qfMW4PWCbCz6kgyhTLGG759ZMtuR3XLyGBgY1gardae5GcBJTdNOAwDHcZ8BcB+AI6v0fl0Br+ycnUNJjKSj2NTrPfHdO5bGt19YwA/9vyeQLdfwqR+7Bddv8Vei9MZlvOaa1sgYAtMy1mLtfH9CNrNhWn3/lWINr28hg2g10ROTcHahaP7/U+eWsHcs3VWkFUAIoYZCSNM0PHR0Dv/3mydxcDKDn7tz1zp+umbcvlM/VnhO93uvBvg1DpXuFhA75bdOzGE+V8E/PnYWUYlftUDPvriMdFS01VkztI+X7x3BSDqC2WwFUcp1WRR4vPWmiTX5LO+6dSs++fg5fObJ89gxmMBff/MUnruYwRuv24TX7m/v3rPRcM81o/j+2SV86M37ITHFw4ZCVBLwD++9CT/5qafxm//+HHLlOv79BxeRjon4k4ATcFnkW8pKHDauv59/ehLXbenFT73UnUwkhNCloMxiCIaoJKBYVXBsJodPvOfGpmypjY6YJODicgkf/O9j+OcnziFfqWPbQBw/87JdKFTqjBBiYFhHrNYsaROAC5b/nwRwyyq914bAn7/t2kDP2zuexheenUJfXMK//vita9rY0nKotOH7vefq0bYGx6moiFRUxEu7LGG/Nybj/NICfvZffgAA+MH5la7MyoiIAr55fM78nKfm8jg2k8Pmvhj+6I1X4y03rM3EMSi2DcQx3hMFx3GrNqkyW8YuM8vY7tEUIiKP3/uvw+A44NX7RvFzd+1atQFmVBLw2PvuXrXQ6ssVksDj7TdvwZ9/44V1l9NfOZLCLdv78aGv6vkr2wbi+ND9+/HG69urBN+IePtNW/Daa8ao4bkM3Y+oJODj774BP/cvz+ADXz4KAPi7d99oFoCsFkhDY0wS8NG3Xutpn+qNy5AE7rKrPr6cQXLi3n7zBO7aM7LOn6bziMoCvvDsFDgOeO01Y3jJFUP4p8fP4jf+/RCARoMwAwPD2mO1Zkm0JRZbPQnHcT8B4CcAYMuW7ptcdxpBB/Ov3jeGx08t4rfuuWrNcwn64jJet38Mt+4IF2K3pT+Ol1w5hB++fVtb7/+6/WOIiEJoQmq18bLdQ3j6/DKOTevZTjsGEy2tDq42XrVvBN89uWB+zlRUwp+95QBef+14V65icxyHH3vxDuTK9JDBTuCGrX14+VUjl51yRRJ4vOeObVjIVfFTL92xJrWfl5sKa63wzlu24gfnV9YsRN8Lv/TyK/GXD72At908gddeM3bZ5oHwPMfIoA2OiCjgY++8Hn/4pSPojUl4+d7Vn4DHZAH3XTuOu68awbZBb3u/V44iw6WJW3b04+VXDeN3V9kCvF5403WbsFys4idfuhM7jXKdt9y4Gd86MY9PPX6upUIaBgaGzoBzq5Ft60U57jYA79c07VXG/78PADRN+xPa82+88Ubtqaee6vjnYGBgYGBgYGBgYGBgYGBgYLhcwXHc05qm3Uj73Wot7z0J4AqO47ZzHCcDeBuAL67SezEwMDAwMDAwMDAwMDAwMDAwhMCqaPw1TatzHPdzAL4GQADwCU3TDq/GezEwMDAwMDAwMDAwMDAwMDAwhMOqhT5omvYVAF9ZrddnYGBgYGBgYGBgYGBgYGBgYGgNl2ciJAMDAwMDAwMDAwMDAwMDA8NlDEYIMTAwMDAwMDAwMDAwMDAwMFxmYIQQAwMDAwMDAwMDAwMDAwMDw2UGRggxMDAwMDAwMDAwMDAwMDAwXGZghBADAwMDAwMDAwMDAwMDAwPDZQZGCDEwMDAwMDAwMDAwMDAwMDBcZmCEEAMDAwMDAwMDAwMDAwMDA8NlBkYIMTAwMDAwMDAwMDAwMDAwMFxmYIQQAwMDAwMDAwMDAwMDAwMDw2UGRggxMDAwMDAwMDAwMDAwMDAwXGZghBADAwMDAwMDAwMDAwMDAwPDZQZGCDEwMDAwMDAwMDAwMDAwMDBcZmCEEAMDAwMDAwMDAwMDAwMDA8NlBkYIMTAwMDAwMDAwMDAwMDAwMFxmYIQQAwMDAwMDAwMDAwMDAwMDw2UGRggxMDAwMDAwMDAwMDAwMDAwXGZghBADAwMDAwMDAwMDAwMDAwPDZQZGCDEwMDAwMDAwMDAwMDAwMDBcZmCEEAMDAwMDAwMDAwMDAwMDA8NlBkYIMTAwMDAwMDAwMDAwMDAwMFxm4DRNW+/PAI7j5gGcW+/PscYYBLCw3h+CITDY/tpYYPuru8H2z8YC21/dDbZ/NhbY/to4YPuqO8D2w8YB21fdi62apg3RftEVhNDlCI7jntI07cb1/hwMwcD218YC21/dDbZ/NhbY/upusP2zscD218YB21fdAbYfNg7YvtqYYJYxBgYGBgYGBgYGBgYGBgYGhssMjBBiYGBgYGBgYGBgYGBgYGBguMzACKH1w8fX+wMwhALbXxsLbH91N9j+2Vhg+6u7wfbPxgLbXxsHbF91B9h+2Dhg+2oDgmUIMTAwMDAwMDAwMDAwMDAwMFxmYAohBgYGBgYGBgYGBgYGBgYGhssMjBAKCI7jJjiO+ybHcUc5jjvMcdwvGo/3cxz3dY7jXjD+22c8/gqO457mOO454793GY/HOY77Msdxx4zX+aDHe95g/P1JjuP+kuM4znj8oxzHPWv8nOA4bmUNNsGGQpftry3GZ3mG47hDHMe9Zi22wUZCl+2vrRzHPWTsq29xHLd5LbZBN2Od9s8fcRx3geO4vOPxCMdx/2bst+9xHLdtFb/6hkSX7a+XcBz3A47j6hzHvXk1v/dGQZftn1/hOO6Icb17iOO4rav53TciOrW/jN99leO4g8br/A3HcYLLe7rdn9j55IEu21eX7bnVZfvhp4zHn+U47rscx+1di22wUdBN+8ry+zdzHKdxHMfaytYKmqaxnwA/AMYAXG/8OwXgBIC9AD4E4LeMx38LwJ8a/74OwLjx76sBXDT+HQdwp/FvGcB3ANzj8p7fB3AbAA7Af9OeB+DnAXxivbdPt/100/6C7qf9aePfewGcXe/t020/Xba/Pgfgh41/3wXgU+u9fdb7Z532z63G++Ydj/8MgL8x/v02AP+23tun2366bH9tA7AfwCcBvHm9t003/HTZ/rkTQNz490+z82n19pfx/2njvxyAfwfwNpf3dLs/sfNp4+yry/bc6rL9kLY85/UAvrre26ebfrppX1k+w7cBPAHgxvXePpfLD1MIBYSmadOapv3A+HcOwFEAmwDcB+CfjKf9E4A3GM95RtO0KePxwwCiHMdFNE0rapr2TeM5VQA/ANCkQOA4bgz6ifW4pp8hnySv7cDbAfxrR77kJYQu218agLTx7x4AU86/v9zRZftrL4CHjH9/0/gMlzXWev8Yv39C07Rpyq+s7/l5AHc7V5cud3TT/tI07aymaYcAqJ36fhsdXbZ/vqlpWtH43yfc/v5yRqf2l/G7rPG4CJ3E05zv53V/YueTN7psX12251aX7Yes5akJ2t9fzuimfWXgD6GTUeUOfUWGAGCEUAvgdIvCdQC+B2CEDLKM/w5T/uR+AM9omlZxvE4vgHvRmHxasQnApOX/J43HrH+/FcB2AA+38j0uF3TB/no/gHdxHDcJ4CvQVV0MLuiC/XXQeE0AeCOAFMdxA618l0sRa7R/vLAJwAXjPesAMgDY/nFBF+wvBg902f75UeirtQwu6MT+4jjuawDmAOSgk9pO+I7/GPzRZfvqsj23umE/cBz3sxzHnYJONPxCO9/nUsZ67yuO464DMKFp2pfa/jIMocAIoZDgOC4JXQb3Sw7W2e35+wD8KYCfdDwuQlf2/KWmaadpf0p5zMm0vg3A5zVNU4J89ssRXbK/3g7gHzVN2wzgNQA+xXEcO/co6JL99WsAXspx3DMAXgrgIoB64C9xCWMN94/ny1IeYyt+FHTJ/mJwQTftH47j3gXgRgAfbuXvLwd0an9pmvYq6DaNCHRbctOfUh5j17gQ6KZ9dTmfW92yHzRN+2tN03YC+E0Avxv4C1xGWO99ZcyLPgrgV0N+dIYOgE1KQ4DjOAn6yfJpTdP+w3h41pC/ERncnOX5mwH8J4B3a5p2yvFyHwfwgqZpf248V+AaQdF/AJ0xtcpLN6PZavQ2MLuYK7pof/0ogM8CgKZpjwOIAhjs2Be9RNAt+0vTtClN096kadp1AH7HeCzT2W+78bDG+8cLkwAmjL8Todswl9r6cpcgumh/MVDQTfuH47iXQ7/Wvd6pPGLQ0eH9BU3TygC+COC+Fsd/DC7opn11OZ9b3bQfLPgM6PEblzW6ZF+loGcSfYvjuLPQc+++yLFg6bWB1gVBRhvhBzqj+UkAf+54/MOwh259yPh3LwzrCeW1PgD9xON93vNJ6CcECd16jeV3uwGcBcCt97bpxp9u2l/Gv99j/Psq6Bc+tt+6d38Nkr8F8EcA/mC9t896/6zH/rE83xmC+7Owh0p/dr23T7f9dNP+sjz+j2AhuF23f6DbA04BuGK9t0u3/nRqfwFIAhgz/i0C+DcAP+fynq7jP+P37Hzq8n11OZ9bXbYfrrA8514AT6339ummn27aV47nfAssVHrtjoP1/gAb5QfAi6DLDw8BeNb4eQ307IqHALxg/LffeP7vAihYnvssdP/lZuN1jloe/zGX97wRwPPGDeX/wkIiQM+l+eB6b5du/emm/QU9pPhR4wL6LIBXrvf26bafLttfbzbe7wSAvwMQWe/ts94/67R/PgR9JUk1/vt+4/Eo9Ca4k9CbKnas9/bptp8u2183Gf9fALAI4PB6b5/1/umy/fMNALOWv//iem+fbvvp4P4agT4ROgQ9jPWvAIgu7+l2f2Ln08bZV5ftudVl++EvjL99FnpRyL713j7d9NNN+8rxnG+BEUJr9kNOFgYGBgYGBgYGBgYGBgYGBgaGywQsQ4iBgYGBgYGBgYGBgYGBgYHhMgMjhBgYGBgYGBgYGBgYGBgYGBguMzBCiIGBgYGBgYGBgYGBgYGBgeEyAyOEGBgYGBgYGBgYGBgYGBgYGC4zMEKIgYGBgYGBgYGBgYGBgYGB4TIDI4QYGBgYGBgYGBgYGBgYGBgYLjMwQoiBgYGBgYGBgYGBgYGBgYHhMgMjhBgYGBgYGBgYGBgYGBgYGBguM/z/0Lt9LohKqDQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "x = df_total_views.index\n",
    "y = df_total_views[('views_total', 'sum')]\n",
    "plt.plot(x, y, label=\"total_views\")\n",
    "plt.legend(loc = \"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d55a33",
   "metadata": {},
   "source": [
    "### Clones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4430fccd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.556742Z",
     "iopub.status.busy": "2023-04-17T01:01:23.556619Z",
     "iopub.status.idle": "2023-04-17T01:01:23.561949Z",
     "shell.execute_reply": "2023-04-17T01:01:23.561655Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best clones over: 32.75\n",
      "Worst clones under: 14.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the best 25% repositories\n",
    "best_clones_quantile = 0.75\n",
    "worst_clones_quantile = 0.25\n",
    "best_clones_percentile = df_grouped[('clones_uniques', 'sum')].quantile(best_clones_quantile)\n",
    "worst_clones_percentile = df_grouped[('clones_uniques', 'sum')].quantile(worst_clones_quantile)\n",
    "print(\"Best clones over:\", best_clones_percentile)\n",
    "print(\"Worst clones under:\", worst_clones_percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d6f5a3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.563513Z",
     "iopub.status.busy": "2023-04-17T01:01:23.563267Z",
     "iopub.status.idle": "2023-04-17T01:01:23.568975Z",
     "shell.execute_reply": "2023-04-17T01:01:23.568602Z"
    }
   },
   "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>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>golang-rest-api-cassandra</th>\n",
       "      <td>3942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-golang-grpc</th>\n",
       "      <td>2519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ta</th>\n",
       "      <td>2345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iot-devices-crud</th>\n",
       "      <td>2052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mqtt-golang-influxdb</th>\n",
       "      <td>2031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ml-github</th>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service-mesh-workshop</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-cloud-ms1</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-amq-streams</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semantic-versioning-cli</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b0rr3g0-blog</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kustomize-vs-helm</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kotlin-ktor</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-argo-rollouts-doc</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workshop-tekton-argocd</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-kotlin-crud</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golang-k8s-helm-helloworld</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             sum\n",
       "name                            \n",
       "golang-rest-api-cassandra   3942\n",
       "workshop-golang-grpc        2519\n",
       "ta                          2345\n",
       "iot-devices-crud            2052\n",
       "mqtt-golang-influxdb        2031\n",
       "ml-github                    267\n",
       "service-mesh-workshop        118\n",
       "spring-cloud-ms1              59\n",
       "workshop-amq-streams          57\n",
       "semantic-versioning-cli       51\n",
       "b0rr3g0-blog                  47\n",
       "kustomize-vs-helm             41\n",
       "kotlin-ktor                   40\n",
       "workshop-argo-rollouts-doc    39\n",
       "workshop-tekton-argocd        39\n",
       "spring-kotlin-crud            35\n",
       "golang-k8s-helm-helloworld    33"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clones=df_grouped[('clones_uniques')]\n",
    "clones_best = clones[clones['sum'] > best_clones_percentile]\n",
    "clones_best.sort_values(by='sum', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157569fb",
   "metadata": {},
   "source": [
    "# Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d5ba727",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.570509Z",
     "iopub.status.busy": "2023-04-17T01:01:23.570388Z",
     "iopub.status.idle": "2023-04-17T01:01:23.575168Z",
     "shell.execute_reply": "2023-04-17T01:01:23.574857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best stars over: 1.0\n",
      "Worst stars under: 0.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the best 50% repositories\n",
    "best_stars_quantile = 0.80\n",
    "worst_stars_quantile = 0.20\n",
    "best_stars_percentile = df_grouped[('stars_count', 'max')].quantile(best_stars_quantile)\n",
    "worst_stars_percentile = df_grouped[('stars_count', 'max')].quantile(worst_stars_quantile)\n",
    "print(\"Best stars over:\", best_stars_percentile)\n",
    "print(\"Worst stars under:\", worst_stars_percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5234152",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.576695Z",
     "iopub.status.busy": "2023-04-17T01:01:23.576506Z",
     "iopub.status.idle": "2023-04-17T01:01:23.581364Z",
     "shell.execute_reply": "2023-04-17T01:01:23.581029Z"
    }
   },
   "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>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mqtt-golang-influxdb</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kotlin-ktor</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spring-kotlin-crud</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kops-aws</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kustomize-vs-helm</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semantic-versioning-cli</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jenkins-ci</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service-mesh-workshop</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ta</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golang-k8s-helm-helloworld</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golang-rest-api-cassandra</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarkus-kotlin-example</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            max\n",
       "name                           \n",
       "mqtt-golang-influxdb         14\n",
       "kotlin-ktor                   7\n",
       "spring-kotlin-crud            7\n",
       "kops-aws                      4\n",
       "kustomize-vs-helm             4\n",
       "semantic-versioning-cli       4\n",
       "jenkins-ci                    3\n",
       "service-mesh-workshop         3\n",
       "ta                            3\n",
       "golang-k8s-helm-helloworld    2\n",
       "golang-rest-api-cassandra     2\n",
       "quarkus-kotlin-example        2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars=df_grouped[('stars_count')]\n",
    "stars_best = stars[stars['max'] > best_stars_percentile]\n",
    "stars_best.sort_values(by=['max'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930ebe06",
   "metadata": {},
   "source": [
    "# Red zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b27fe45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.582933Z",
     "iopub.status.busy": "2023-04-17T01:01:23.582745Z",
     "iopub.status.idle": "2023-04-17T01:01:23.637116Z",
     "shell.execute_reply": "2023-04-17T01:01:23.636656Z"
    }
   },
   "outputs": [],
   "source": [
    "repos = pd.read_csv(\"./repos.csv\")\n",
    "repos = repos[repos[\"owner\"]==USER]\n",
    "\n",
    "# remove colum\n",
    "repos = repos.iloc[:,1:]\n",
    "# remove owner\n",
    "#repos = repos.drop(labels=\"owner\",axis=1)\n",
    "# add num topics\n",
    "#repos[\"num_topics\"] = len(repos.topics.str.split(','))\n",
    "def count_topics(topics):\n",
    "    return 0 if topics.count(',') == 0 else topics.count(',')+1\n",
    "repos[\"topics_count\"] = repos.apply(lambda row : count_topics(row.topics), axis=1)\n",
    "# remove topics\n",
    "repos = repos.drop(labels=\"topics\",axis=1)\n",
    "# format date\n",
    "repos[\"latest_commit_date\"] = pd.to_datetime(repos[\"latest_commit_date\"], format='%Y%m%d')\n",
    "# days until latest commit\n",
    "today = pd.to_datetime('today')\n",
    "repos[\"days_latest_commit\"] = (today - repos.latest_commit_date).dt.days\n",
    "# views\n",
    "def views_total(name):\n",
    "    try:\n",
    "        return df_grouped.loc[name]['views_total']['sum']\n",
    "    except Exception as e:\n",
    "        print(\"[Views] Error \", name, \" ==> \", e)\n",
    "        return 0\n",
    "repos[\"views_total\"] = repos.apply(lambda row: views_total(row[\"name\"]), axis=1)\n",
    "\n",
    "#stars\n",
    "def stars(name):\n",
    "    try:\n",
    "        return df_grouped.loc[name][('stars_count')]\n",
    "    except Exception as e:\n",
    "        print(\"[Stars] Error \", name, \" ==> \", e)\n",
    "        return 0\n",
    "repos[\"stars_total\"] = repos.apply(lambda row: stars(row[\"name\"]), axis=1)\n",
    "\n",
    "# clones\n",
    "def clones(name):\n",
    "    try:\n",
    "        return df_grouped.loc[name]['clones_uniques']['sum']\n",
    "    except Exception as e:\n",
    "        print(\"[Clones] Error \", name, \" ==> \", e)\n",
    "        return 0\n",
    "repos[\"clones_total\"] = repos.apply(lambda row: clones(row[\"name\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "891d2d2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.638814Z",
     "iopub.status.busy": "2023-04-17T01:01:23.638688Z",
     "iopub.status.idle": "2023-04-17T01:01:23.641854Z",
     "shell.execute_reply": "2023-04-17T01:01:23.641435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(repos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cea9f540",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.643451Z",
     "iopub.status.busy": "2023-04-17T01:01:23.643335Z",
     "iopub.status.idle": "2023-04-17T01:01:23.656162Z",
     "shell.execute_reply": "2023-04-17T01:01:23.655867Z"
    }
   },
   "outputs": [],
   "source": [
    "# Scores by labels\n",
    "cut_labels = ['S', 'A', 'B', 'C']\n",
    "# FIXME ver si es mejor 1 mes, 6 meses 2 años por ejemplo\n",
    "cut_bins = [0, \n",
    "            365/2, # 6 meses\n",
    "            365, # 1 año\n",
    "            365*3, # 3 años\n",
    "            365*99]\n",
    "repos['score_commits'] = pd.cut(repos['days_latest_commit'], bins=cut_bins, labels=cut_labels, include_lowest=True)\n",
    "\n",
    "# Topics value\n",
    "cut_labels = [False, True]\n",
    "cut_bins = [-1, 0, 99999]\n",
    "repos['score_topics'] = pd.cut(repos['topics_count'], bins=cut_bins, labels=cut_labels)\n",
    "\n",
    "# Views score\n",
    "cut_labels = ['C', 'B', 'A', 'S']\n",
    "# rank for zero values\n",
    "repos['views_total'] = repos['views_total'].rank(method='min')\n",
    "cut_bins = [0, \n",
    "            repos['views_total'].quantile(q=0.25),\n",
    "            repos['views_total'].quantile(q=0.75),\n",
    "            repos['views_total'].quantile(q=0.9),\n",
    "            repos['views_total'].quantile(q=1)]\n",
    "repos['score_views'] = pd.cut(repos['views_total'], bins=cut_bins, labels=cut_labels)\n",
    "\n",
    "# Clones score\n",
    "cut_labels = ['C', 'B', 'A', 'S']\n",
    "repos['clones_total'] = repos['clones_total'].rank(method='min')\n",
    "cut_bins = [0, \n",
    "            repos['clones_total'].quantile(q=0.25),\n",
    "            repos['clones_total'].quantile(q=0.75),\n",
    "            repos['clones_total'].quantile(q=0.9),\n",
    "            repos['clones_total'].quantile(q=1)]\n",
    "repos['score_clones'] = pd.cut(repos['clones_total'], bins=cut_bins, labels=cut_labels)\n",
    "\n",
    "# stars\n",
    "# FIXME this structure have to change because depends of the user\n",
    "cut_labels = ['C', 'B', 'A', 'S']\n",
    "cut_bins = [0, 1, 5, 10, 99]\n",
    "repos['score_stars'] = pd.cut(repos['stars_total'], bins=cut_bins, labels=cut_labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5bd8065c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.657784Z",
     "iopub.status.busy": "2023-04-17T01:01:23.657536Z",
     "iopub.status.idle": "2023-04-17T01:01:23.664126Z",
     "shell.execute_reply": "2023-04-17T01:01:23.663831Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = { \n",
    "    'score_commits': {\n",
    "        'weight': 30,\n",
    "        'labels': {'S': 100,\n",
    "                   'A': 70,\n",
    "                   'B': 25,\n",
    "                   'C': 0 } \n",
    "    },\n",
    "    'score_topics': {\n",
    "        'weight': 10,\n",
    "        'labels': {True: 100,\n",
    "                   False: 0}\n",
    "    },\n",
    "    'score_views':  {\n",
    "        'weight': 25,\n",
    "        'labels': {'S': 100,\n",
    "                   'A': 70,\n",
    "                   'B': 30,\n",
    "                   'C': 0 }\n",
    "    },\n",
    "    'score_stars':  {\n",
    "        'weight': 30,\n",
    "        'labels': {'S': 100,\n",
    "                   'A': 70,\n",
    "                   'B': 40,\n",
    "                   'C': 0 }\n",
    "    },\n",
    "    'score_clones':  {\n",
    "        'weight': 5,\n",
    "        'labels': {'S': 100,\n",
    "                   'A': 70,\n",
    "                   'B': 40,\n",
    "                   'C': 0 }\n",
    "    }\n",
    "}\n",
    "def score(row):\n",
    "    score = 0\n",
    "    for k in weights.keys():\n",
    "        score += (weights[k]['labels'][row[k]] * weights[k]['weight']/100)\n",
    "    return score\n",
    "        \n",
    "repos[\"score_total\"] = repos.apply(lambda row: score(row), axis=1)\n",
    "repos = repos.sort_values(by=['score_total'], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d084cc22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.665739Z",
     "iopub.status.busy": "2023-04-17T01:01:23.665497Z",
     "iopub.status.idle": "2023-04-17T01:01:23.711819Z",
     "shell.execute_reply": "2023-04-17T01:01:23.711322Z"
    }
   },
   "outputs": [
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       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 16.5%, transparent 16.5%);\n",
       "}\n",
       "#T_ff501_row46_col6 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 14.6%, transparent 14.6%);\n",
       "}\n",
       "#T_ff501_row47_col6 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 12.2%, transparent 12.2%);\n",
       "}\n",
       "#T_ff501_row48_col6, #T_ff501_row49_col6, #T_ff501_row50_col6, #T_ff501_row51_col6 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 11.6%, transparent 11.6%);\n",
       "}\n",
       "#T_ff501_row52_col6, #T_ff501_row53_col6, #T_ff501_row54_col6 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 9.1%, transparent 9.1%);\n",
       "}\n",
       "#T_ff501_row55_col6, #T_ff501_row56_col6, #T_ff501_row57_col6, #T_ff501_row58_col6 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, lightgreen 2.4%, transparent 2.4%);\n",
       "}\n",
       "#T_ff501_row59_col6, #T_ff501_row60_col6, #T_ff501_row61_col6, #T_ff501_row62_col6, #T_ff501_row63_col6, #T_ff501_row64_col6, #T_ff501_row65_col6 {\n",
       "  width: 10em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ff501\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ff501_level0_col0\" class=\"col_heading level0 col0\" >name</th>\n",
       "      <th id=\"T_ff501_level0_col1\" class=\"col_heading level0 col1\" >score_commits</th>\n",
       "      <th id=\"T_ff501_level0_col2\" class=\"col_heading level0 col2\" >score_topics</th>\n",
       "      <th id=\"T_ff501_level0_col3\" class=\"col_heading level0 col3\" >score_views</th>\n",
       "      <th id=\"T_ff501_level0_col4\" class=\"col_heading level0 col4\" >score_stars</th>\n",
       "      <th id=\"T_ff501_level0_col5\" class=\"col_heading level0 col5\" >score_clones</th>\n",
       "      <th id=\"T_ff501_level0_col6\" class=\"col_heading level0 col6\" >score_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row0\" class=\"row_heading level0 row0\" >45</th>\n",
       "      <td id=\"T_ff501_row0_col0\" class=\"data row0 col0 service-mesh-workshop\" >service-mesh-workshop</td>\n",
       "      <td id=\"T_ff501_row0_col1\" class=\"data row0 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row0_col2\" class=\"data row0 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row0_col3\" class=\"data row0 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row0_col4\" class=\"data row0 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row0_col5\" class=\"data row0 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row0_col6\" class=\"data row0 col6 82.0\" >                  82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row1\" class=\"row_heading level0 row1\" >29</th>\n",
       "      <td id=\"T_ff501_row1_col0\" class=\"data row1 col0 kustomize-vs-helm\" >kustomize-vs-helm</td>\n",
       "      <td id=\"T_ff501_row1_col1\" class=\"data row1 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row1_col2\" class=\"data row1 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row1_col3\" class=\"data row1 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row1_col4\" class=\"data row1 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row1_col5\" class=\"data row1 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row1_col6\" class=\"data row1 col6 80.5\" >                  80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row2\" class=\"row_heading level0 row2\" >43</th>\n",
       "      <td id=\"T_ff501_row2_col0\" class=\"data row2 col0 semantic-versioning-cli\" >semantic-versioning-cli</td>\n",
       "      <td id=\"T_ff501_row2_col1\" class=\"data row2 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row2_col2\" class=\"data row2 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row2_col3\" class=\"data row2 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row2_col4\" class=\"data row2 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row2_col5\" class=\"data row2 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row2_col6\" class=\"data row2 col6 80.5\" >                  80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row3\" class=\"row_heading level0 row3\" >12</th>\n",
       "      <td id=\"T_ff501_row3_col0\" class=\"data row3 col0 golang-rest-api-cassandra\" >golang-rest-api-cassandra</td>\n",
       "      <td id=\"T_ff501_row3_col1\" class=\"data row3 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row3_col2\" class=\"data row3 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row3_col3\" class=\"data row3 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row3_col4\" class=\"data row3 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row3_col5\" class=\"data row3 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row3_col6\" class=\"data row3 col6 73.0\" >                  73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row4\" class=\"row_heading level0 row4\" >34</th>\n",
       "      <td id=\"T_ff501_row4_col0\" class=\"data row4 col0 mqtt-golang-influxdb\" >mqtt-golang-influxdb</td>\n",
       "      <td id=\"T_ff501_row4_col1\" class=\"data row4 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row4_col2\" class=\"data row4 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row4_col3\" class=\"data row4 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row4_col4\" class=\"data row4 col4 S\" >S</td>\n",
       "      <td id=\"T_ff501_row4_col5\" class=\"data row4 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row4_col6\" class=\"data row4 col6 70.0\" >                  70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row5\" class=\"row_heading level0 row5\" >57</th>\n",
       "      <td id=\"T_ff501_row5_col0\" class=\"data row5 col0 workshop-amq-streams\" >workshop-amq-streams</td>\n",
       "      <td id=\"T_ff501_row5_col1\" class=\"data row5 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row5_col2\" class=\"data row5 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row5_col3\" class=\"data row5 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row5_col4\" class=\"data row5 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row5_col5\" class=\"data row5 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row5_col6\" class=\"data row5 col6 68.5\" >                  68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row6\" class=\"row_heading level0 row6\" >53</th>\n",
       "      <td id=\"T_ff501_row6_col0\" class=\"data row6 col0 ta\" >ta</td>\n",
       "      <td id=\"T_ff501_row6_col1\" class=\"data row6 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row6_col2\" class=\"data row6 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row6_col3\" class=\"data row6 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row6_col4\" class=\"data row6 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row6_col5\" class=\"data row6 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row6_col6\" class=\"data row6 col6 65.5\" >                  66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row7\" class=\"row_heading level0 row7\" >11</th>\n",
       "      <td id=\"T_ff501_row7_col0\" class=\"data row7 col0 golang-k8s-helm-helloworld\" >golang-k8s-helm-helloworld</td>\n",
       "      <td id=\"T_ff501_row7_col1\" class=\"data row7 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row7_col2\" class=\"data row7 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row7_col3\" class=\"data row7 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row7_col4\" class=\"data row7 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row7_col5\" class=\"data row7 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row7_col6\" class=\"data row7 col6 64.0\" >                  64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row8\" class=\"row_heading level0 row8\" >32</th>\n",
       "      <td id=\"T_ff501_row8_col0\" class=\"data row8 col0 ml-github\" >ml-github</td>\n",
       "      <td id=\"T_ff501_row8_col1\" class=\"data row8 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row8_col2\" class=\"data row8 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row8_col3\" class=\"data row8 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row8_col4\" class=\"data row8 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row8_col5\" class=\"data row8 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row8_col6\" class=\"data row8 col6 62.5\" >                  62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row9\" class=\"row_heading level0 row9\" >58</th>\n",
       "      <td id=\"T_ff501_row9_col0\" class=\"data row9 col0 workshop-argo-rollouts-doc\" >workshop-argo-rollouts-doc</td>\n",
       "      <td id=\"T_ff501_row9_col1\" class=\"data row9 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row9_col2\" class=\"data row9 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row9_col3\" class=\"data row9 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row9_col4\" class=\"data row9 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row9_col5\" class=\"data row9 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row9_col6\" class=\"data row9 col6 61.0\" >                  61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row10\" class=\"row_heading level0 row10\" >28</th>\n",
       "      <td id=\"T_ff501_row10_col0\" class=\"data row10 col0 kotlin-ktor\" >kotlin-ktor</td>\n",
       "      <td id=\"T_ff501_row10_col1\" class=\"data row10 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row10_col2\" class=\"data row10 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row10_col3\" class=\"data row10 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row10_col4\" class=\"data row10 col4 A\" >A</td>\n",
       "      <td id=\"T_ff501_row10_col5\" class=\"data row10 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row10_col6\" class=\"data row10 col6 59.5\" >                  60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row11\" class=\"row_heading level0 row11\" >27</th>\n",
       "      <td id=\"T_ff501_row11_col0\" class=\"data row11 col0 kops-aws\" >kops-aws</td>\n",
       "      <td id=\"T_ff501_row11_col1\" class=\"data row11 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row11_col2\" class=\"data row11 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row11_col3\" class=\"data row11 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row11_col4\" class=\"data row11 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row11_col5\" class=\"data row11 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row11_col6\" class=\"data row11 col6 52.5\" >                  52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row12\" class=\"row_heading level0 row12\" >60</th>\n",
       "      <td id=\"T_ff501_row12_col0\" class=\"data row12 col0 workshop-golang-grpc\" >workshop-golang-grpc</td>\n",
       "      <td id=\"T_ff501_row12_col1\" class=\"data row12 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row12_col2\" class=\"data row12 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row12_col3\" class=\"data row12 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row12_col4\" class=\"data row12 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row12_col5\" class=\"data row12 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row12_col6\" class=\"data row12 col6 52.5\" >                  52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row13\" class=\"row_heading level0 row13\" >52</th>\n",
       "      <td id=\"T_ff501_row13_col0\" class=\"data row13 col0 spring-kotlin-crud\" >spring-kotlin-crud</td>\n",
       "      <td id=\"T_ff501_row13_col1\" class=\"data row13 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row13_col2\" class=\"data row13 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row13_col3\" class=\"data row13 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row13_col4\" class=\"data row13 col4 A\" >A</td>\n",
       "      <td id=\"T_ff501_row13_col5\" class=\"data row13 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row13_col6\" class=\"data row13 col6 52.0\" >                  52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row14\" class=\"row_heading level0 row14\" >62</th>\n",
       "      <td id=\"T_ff501_row14_col0\" class=\"data row14 col0 workshop-tekton-argocd\" >workshop-tekton-argocd</td>\n",
       "      <td id=\"T_ff501_row14_col1\" class=\"data row14 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row14_col2\" class=\"data row14 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row14_col3\" class=\"data row14 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row14_col4\" class=\"data row14 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row14_col5\" class=\"data row14 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row14_col6\" class=\"data row14 col6 51.0\" >                  51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row15\" class=\"row_heading level0 row15\" >4</th>\n",
       "      <td id=\"T_ff501_row15_col0\" class=\"data row15 col0 b0rr3g0-blog\" >b0rr3g0-blog</td>\n",
       "      <td id=\"T_ff501_row15_col1\" class=\"data row15 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row15_col2\" class=\"data row15 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row15_col3\" class=\"data row15 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row15_col4\" class=\"data row15 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row15_col5\" class=\"data row15 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row15_col6\" class=\"data row15 col6 51.0\" >                  51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row16\" class=\"row_heading level0 row16\" >38</th>\n",
       "      <td id=\"T_ff501_row16_col0\" class=\"data row16 col0 ocp-monitoring-golang\" >ocp-monitoring-golang</td>\n",
       "      <td id=\"T_ff501_row16_col1\" class=\"data row16 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row16_col2\" class=\"data row16 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row16_col3\" class=\"data row16 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row16_col4\" class=\"data row16 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row16_col5\" class=\"data row16 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row16_col6\" class=\"data row16 col6 50.5\" >                  50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row17\" class=\"row_heading level0 row17\" >40</th>\n",
       "      <td id=\"T_ff501_row17_col0\" class=\"data row17 col0 quarkus-kafka\" >quarkus-kafka</td>\n",
       "      <td id=\"T_ff501_row17_col1\" class=\"data row17 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row17_col2\" class=\"data row17 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row17_col3\" class=\"data row17 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row17_col4\" class=\"data row17 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row17_col5\" class=\"data row17 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row17_col6\" class=\"data row17 col6 49.5\" >                  50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row18\" class=\"row_heading level0 row18\" >7</th>\n",
       "      <td id=\"T_ff501_row18_col0\" class=\"data row18 col0 gingonic-go-mongodb\" >gingonic-go-mongodb</td>\n",
       "      <td id=\"T_ff501_row18_col1\" class=\"data row18 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row18_col2\" class=\"data row18 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row18_col3\" class=\"data row18 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row18_col4\" class=\"data row18 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row18_col5\" class=\"data row18 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row18_col6\" class=\"data row18 col6 49.5\" >                  50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row19\" class=\"row_heading level0 row19\" >59</th>\n",
       "      <td id=\"T_ff501_row19_col0\" class=\"data row19 col0 workshop-argo-rollouts-resources\" >workshop-argo-rollouts-resources</td>\n",
       "      <td id=\"T_ff501_row19_col1\" class=\"data row19 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row19_col2\" class=\"data row19 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row19_col3\" class=\"data row19 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row19_col4\" class=\"data row19 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row19_col5\" class=\"data row19 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row19_col6\" class=\"data row19 col6 49.5\" >                  50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row20\" class=\"row_heading level0 row20\" >18</th>\n",
       "      <td id=\"T_ff501_row20_col0\" class=\"data row20 col0 iot-devices-crud\" >iot-devices-crud</td>\n",
       "      <td id=\"T_ff501_row20_col1\" class=\"data row20 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row20_col2\" class=\"data row20 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row20_col3\" class=\"data row20 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row20_col4\" class=\"data row20 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row20_col5\" class=\"data row20 col5 S\" >S</td>\n",
       "      <td id=\"T_ff501_row20_col6\" class=\"data row20 col6 43.5\" >                  44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row21\" class=\"row_heading level0 row21\" >2</th>\n",
       "      <td id=\"T_ff501_row21_col0\" class=\"data row21 col0 ansible-bootstrap-workstation\" >ansible-bootstrap-workstation</td>\n",
       "      <td id=\"T_ff501_row21_col1\" class=\"data row21 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row21_col2\" class=\"data row21 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row21_col3\" class=\"data row21 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row21_col4\" class=\"data row21 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row21_col5\" class=\"data row21 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row21_col6\" class=\"data row21 col6 40.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row22\" class=\"row_heading level0 row22\" >36</th>\n",
       "      <td id=\"T_ff501_row22_col0\" class=\"data row22 col0 ocp-HTPasswd-file-provider\" >ocp-HTPasswd-file-provider</td>\n",
       "      <td id=\"T_ff501_row22_col1\" class=\"data row22 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row22_col2\" class=\"data row22 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row22_col3\" class=\"data row22 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row22_col4\" class=\"data row22 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row22_col5\" class=\"data row22 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row22_col6\" class=\"data row22 col6 40.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row23\" class=\"row_heading level0 row23\" >3</th>\n",
       "      <td id=\"T_ff501_row23_col0\" class=\"data row23 col0 asciidoc-base\" >asciidoc-base</td>\n",
       "      <td id=\"T_ff501_row23_col1\" class=\"data row23 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row23_col2\" class=\"data row23 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row23_col3\" class=\"data row23 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row23_col4\" class=\"data row23 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row23_col5\" class=\"data row23 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row23_col6\" class=\"data row23 col6 40.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row24\" class=\"row_heading level0 row24\" >39</th>\n",
       "      <td id=\"T_ff501_row24_col0\" class=\"data row24 col0 pelorus-workshop\" >pelorus-workshop</td>\n",
       "      <td id=\"T_ff501_row24_col1\" class=\"data row24 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row24_col2\" class=\"data row24 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row24_col3\" class=\"data row24 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row24_col4\" class=\"data row24 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row24_col5\" class=\"data row24 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row24_col6\" class=\"data row24 col6 40.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row25\" class=\"row_heading level0 row25\" >63</th>\n",
       "      <td id=\"T_ff501_row25_col0\" class=\"data row25 col0 workshop-tekton-argocd-app-quarkus\" >workshop-tekton-argocd-app-quarkus</td>\n",
       "      <td id=\"T_ff501_row25_col1\" class=\"data row25 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row25_col2\" class=\"data row25 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row25_col3\" class=\"data row25 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row25_col4\" class=\"data row25 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row25_col5\" class=\"data row25 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row25_col6\" class=\"data row25 col6 39.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row26\" class=\"row_heading level0 row26\" >64</th>\n",
       "      <td id=\"T_ff501_row26_col0\" class=\"data row26 col0 workshop-tekton-argocd-app-quarkus-config\" >workshop-tekton-argocd-app-quarkus-config</td>\n",
       "      <td id=\"T_ff501_row26_col1\" class=\"data row26 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row26_col2\" class=\"data row26 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row26_col3\" class=\"data row26 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row26_col4\" class=\"data row26 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row26_col5\" class=\"data row26 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row26_col6\" class=\"data row26 col6 39.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row27\" class=\"row_heading level0 row27\" >6</th>\n",
       "      <td id=\"T_ff501_row27_col0\" class=\"data row27 col0 dbgjerez\" >dbgjerez</td>\n",
       "      <td id=\"T_ff501_row27_col1\" class=\"data row27 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row27_col2\" class=\"data row27 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row27_col3\" class=\"data row27 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row27_col4\" class=\"data row27 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row27_col5\" class=\"data row27 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row27_col6\" class=\"data row27 col6 39.5\" >                  40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row28\" class=\"row_heading level0 row28\" >41</th>\n",
       "      <td id=\"T_ff501_row28_col0\" class=\"data row28 col0 quarkus-kotlin-example\" >quarkus-kotlin-example</td>\n",
       "      <td id=\"T_ff501_row28_col1\" class=\"data row28 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row28_col2\" class=\"data row28 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row28_col3\" class=\"data row28 col3 S\" >S</td>\n",
       "      <td id=\"T_ff501_row28_col4\" class=\"data row28 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row28_col5\" class=\"data row28 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row28_col6\" class=\"data row28 col6 39.0\" >                  39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row29\" class=\"row_heading level0 row29\" >33</th>\n",
       "      <td id=\"T_ff501_row29_col0\" class=\"data row29 col0 ml-learning\" >ml-learning</td>\n",
       "      <td id=\"T_ff501_row29_col1\" class=\"data row29 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row29_col2\" class=\"data row29 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row29_col3\" class=\"data row29 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row29_col4\" class=\"data row29 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row29_col5\" class=\"data row29 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row29_col6\" class=\"data row29 col6 38.5\" >                  38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row30\" class=\"row_heading level0 row30\" >61</th>\n",
       "      <td id=\"T_ff501_row30_col0\" class=\"data row30 col0 workshop-redhat-sso\" >workshop-redhat-sso</td>\n",
       "      <td id=\"T_ff501_row30_col1\" class=\"data row30 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row30_col2\" class=\"data row30 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row30_col3\" class=\"data row30 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row30_col4\" class=\"data row30 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row30_col5\" class=\"data row30 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row30_col6\" class=\"data row30 col6 37.5\" >                  38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row31\" class=\"row_heading level0 row31\" >37</th>\n",
       "      <td id=\"T_ff501_row31_col0\" class=\"data row31 col0 ocp-logging\" >ocp-logging</td>\n",
       "      <td id=\"T_ff501_row31_col1\" class=\"data row31 col1 S\" >S</td>\n",
       "      <td id=\"T_ff501_row31_col2\" class=\"data row31 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row31_col3\" class=\"data row31 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row31_col4\" class=\"data row31 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row31_col5\" class=\"data row31 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row31_col6\" class=\"data row31 col6 37.5\" >                  38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row32\" class=\"row_heading level0 row32\" >23</th>\n",
       "      <td id=\"T_ff501_row32_col0\" class=\"data row32 col0 istio-wasm-filter-golang\" >istio-wasm-filter-golang</td>\n",
       "      <td id=\"T_ff501_row32_col1\" class=\"data row32 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row32_col2\" class=\"data row32 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row32_col3\" class=\"data row32 col3 A\" >A</td>\n",
       "      <td id=\"T_ff501_row32_col4\" class=\"data row32 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row32_col5\" class=\"data row32 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row32_col6\" class=\"data row32 col6 37.0\" >                  37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row33\" class=\"row_heading level0 row33\" >44</th>\n",
       "      <td id=\"T_ff501_row33_col0\" class=\"data row33 col0 service-mesh-wasm-go\" >service-mesh-wasm-go</td>\n",
       "      <td id=\"T_ff501_row33_col1\" class=\"data row33 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row33_col2\" class=\"data row33 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row33_col3\" class=\"data row33 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row33_col4\" class=\"data row33 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row33_col5\" class=\"data row33 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row33_col6\" class=\"data row33 col6 30.5\" >                  30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row34\" class=\"row_heading level0 row34\" >31</th>\n",
       "      <td id=\"T_ff501_row34_col0\" class=\"data row34 col0 licence-plate-workshop\" >licence-plate-workshop</td>\n",
       "      <td id=\"T_ff501_row34_col1\" class=\"data row34 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row34_col2\" class=\"data row34 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row34_col3\" class=\"data row34 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row34_col4\" class=\"data row34 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row34_col5\" class=\"data row34 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row34_col6\" class=\"data row34 col6 28.5\" >                  28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row35\" class=\"row_heading level0 row35\" >0</th>\n",
       "      <td id=\"T_ff501_row35_col0\" class=\"data row35 col0 amq-streams-workshop\" >amq-streams-workshop</td>\n",
       "      <td id=\"T_ff501_row35_col1\" class=\"data row35 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row35_col2\" class=\"data row35 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row35_col3\" class=\"data row35 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row35_col4\" class=\"data row35 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row35_col5\" class=\"data row35 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row35_col6\" class=\"data row35 col6 27.0\" >                  27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row36\" class=\"row_heading level0 row36\" >20</th>\n",
       "      <td id=\"T_ff501_row36_col0\" class=\"data row36 col0 iot-esp8266-irrigation-system\" >iot-esp8266-irrigation-system</td>\n",
       "      <td id=\"T_ff501_row36_col1\" class=\"data row36 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row36_col2\" class=\"data row36 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row36_col3\" class=\"data row36 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row36_col4\" class=\"data row36 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row36_col5\" class=\"data row36 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row36_col6\" class=\"data row36 col6 25.0\" >                  25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row37\" class=\"row_heading level0 row37\" >19</th>\n",
       "      <td id=\"T_ff501_row37_col0\" class=\"data row37 col0 iot-esp8266-dht11\" >iot-esp8266-dht11</td>\n",
       "      <td id=\"T_ff501_row37_col1\" class=\"data row37 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row37_col2\" class=\"data row37 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row37_col3\" class=\"data row37 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row37_col4\" class=\"data row37 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row37_col5\" class=\"data row37 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row37_col6\" class=\"data row37 col6 25.0\" >                  25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row38\" class=\"row_heading level0 row38\" >24</th>\n",
       "      <td id=\"T_ff501_row38_col0\" class=\"data row38 col0 jenkins-ci\" >jenkins-ci</td>\n",
       "      <td id=\"T_ff501_row38_col1\" class=\"data row38 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row38_col2\" class=\"data row38 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row38_col3\" class=\"data row38 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row38_col4\" class=\"data row38 col4 B\" >B</td>\n",
       "      <td id=\"T_ff501_row38_col5\" class=\"data row38 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row38_col6\" class=\"data row38 col6 24.0\" >                  24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row39\" class=\"row_heading level0 row39\" >1</th>\n",
       "      <td id=\"T_ff501_row39_col0\" class=\"data row39 col0 ansible-base\" >ansible-base</td>\n",
       "      <td id=\"T_ff501_row39_col1\" class=\"data row39 col1 A\" >A</td>\n",
       "      <td id=\"T_ff501_row39_col2\" class=\"data row39 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row39_col3\" class=\"data row39 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row39_col4\" class=\"data row39 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row39_col5\" class=\"data row39 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row39_col6\" class=\"data row39 col6 21.0\" >                  21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row40\" class=\"row_heading level0 row40\" >26</th>\n",
       "      <td id=\"T_ff501_row40_col0\" class=\"data row40 col0 jwt\" >jwt</td>\n",
       "      <td id=\"T_ff501_row40_col1\" class=\"data row40 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row40_col2\" class=\"data row40 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row40_col3\" class=\"data row40 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row40_col4\" class=\"data row40 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row40_col5\" class=\"data row40 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row40_col6\" class=\"data row40 col6 19.5\" >                  20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row41\" class=\"row_heading level0 row41\" >13</th>\n",
       "      <td id=\"T_ff501_row41_col0\" class=\"data row41 col0 helm-spring-boot-chart\" >helm-spring-boot-chart</td>\n",
       "      <td id=\"T_ff501_row41_col1\" class=\"data row41 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row41_col2\" class=\"data row41 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row41_col3\" class=\"data row41 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row41_col4\" class=\"data row41 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row41_col5\" class=\"data row41 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row41_col6\" class=\"data row41 col6 19.5\" >                  20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row42\" class=\"row_heading level0 row42\" >35</th>\n",
       "      <td id=\"T_ff501_row42_col0\" class=\"data row42 col0 ms-helm-chart\" >ms-helm-chart</td>\n",
       "      <td id=\"T_ff501_row42_col1\" class=\"data row42 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row42_col2\" class=\"data row42 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row42_col3\" class=\"data row42 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row42_col4\" class=\"data row42 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row42_col5\" class=\"data row42 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row42_col6\" class=\"data row42 col6 17.0\" >                  17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row43\" class=\"row_heading level0 row43\" >8</th>\n",
       "      <td id=\"T_ff501_row43_col0\" class=\"data row43 col0 golang-azure-eventhub\" >golang-azure-eventhub</td>\n",
       "      <td id=\"T_ff501_row43_col1\" class=\"data row43 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row43_col2\" class=\"data row43 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row43_col3\" class=\"data row43 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row43_col4\" class=\"data row43 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row43_col5\" class=\"data row43 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row43_col6\" class=\"data row43 col6 17.0\" >                  17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row44\" class=\"row_heading level0 row44\" >21</th>\n",
       "      <td id=\"T_ff501_row44_col0\" class=\"data row44 col0 iot-esp8266-relay-web-server\" >iot-esp8266-relay-web-server</td>\n",
       "      <td id=\"T_ff501_row44_col1\" class=\"data row44 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row44_col2\" class=\"data row44 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row44_col3\" class=\"data row44 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row44_col4\" class=\"data row44 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row44_col5\" class=\"data row44 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row44_col6\" class=\"data row44 col6 17.0\" >                  17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row45\" class=\"row_heading level0 row45\" >49</th>\n",
       "      <td id=\"T_ff501_row45_col0\" class=\"data row45 col0 spring-cloud-ms1\" >spring-cloud-ms1</td>\n",
       "      <td id=\"T_ff501_row45_col1\" class=\"data row45 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row45_col2\" class=\"data row45 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row45_col3\" class=\"data row45 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row45_col4\" class=\"data row45 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row45_col5\" class=\"data row45 col5 A\" >A</td>\n",
       "      <td id=\"T_ff501_row45_col6\" class=\"data row45 col6 13.5\" >                  14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row46\" class=\"row_heading level0 row46\" >10</th>\n",
       "      <td id=\"T_ff501_row46_col0\" class=\"data row46 col0 golang-gin-gonic\" >golang-gin-gonic</td>\n",
       "      <td id=\"T_ff501_row46_col1\" class=\"data row46 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row46_col2\" class=\"data row46 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row46_col3\" class=\"data row46 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row46_col4\" class=\"data row46 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row46_col5\" class=\"data row46 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row46_col6\" class=\"data row46 col6 12.0\" >                  12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row47\" class=\"row_heading level0 row47\" >5</th>\n",
       "      <td id=\"T_ff501_row47_col0\" class=\"data row47 col0 cd-travis-app\" >cd-travis-app</td>\n",
       "      <td id=\"T_ff501_row47_col1\" class=\"data row47 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row47_col2\" class=\"data row47 col2 True\" >True</td>\n",
       "      <td id=\"T_ff501_row47_col3\" class=\"data row47 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row47_col4\" class=\"data row47 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row47_col5\" class=\"data row47 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row47_col6\" class=\"data row47 col6 10.0\" >                  10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row48\" class=\"row_heading level0 row48\" >9</th>\n",
       "      <td id=\"T_ff501_row48_col0\" class=\"data row48 col0 golang-azure-eventhub-kafka\" >golang-azure-eventhub-kafka</td>\n",
       "      <td id=\"T_ff501_row48_col1\" class=\"data row48 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row48_col2\" class=\"data row48 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row48_col3\" class=\"data row48 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row48_col4\" class=\"data row48 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row48_col5\" class=\"data row48 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row48_col6\" class=\"data row48 col6 9.5\" >                  10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row49\" class=\"row_heading level0 row49\" >65</th>\n",
       "      <td id=\"T_ff501_row49_col0\" class=\"data row49 col0 workshop-uca-aws-deploy\" >workshop-uca-aws-deploy</td>\n",
       "      <td id=\"T_ff501_row49_col1\" class=\"data row49 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row49_col2\" class=\"data row49 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row49_col3\" class=\"data row49 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row49_col4\" class=\"data row49 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row49_col5\" class=\"data row49 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row49_col6\" class=\"data row49 col6 9.5\" >                  10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row50\" class=\"row_heading level0 row50\" >22</th>\n",
       "      <td id=\"T_ff501_row50_col0\" class=\"data row50 col0 iot-k8s-stack\" >iot-k8s-stack</td>\n",
       "      <td id=\"T_ff501_row50_col1\" class=\"data row50 col1 B\" >B</td>\n",
       "      <td id=\"T_ff501_row50_col2\" class=\"data row50 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row50_col3\" class=\"data row50 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row50_col4\" class=\"data row50 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row50_col5\" class=\"data row50 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row50_col6\" class=\"data row50 col6 9.5\" >                  10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row51\" class=\"row_heading level0 row51\" >25</th>\n",
       "      <td id=\"T_ff501_row51_col0\" class=\"data row51 col0 jenkins-pipeline\" >jenkins-pipeline</td>\n",
       "      <td id=\"T_ff501_row51_col1\" class=\"data row51 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row51_col2\" class=\"data row51 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row51_col3\" class=\"data row51 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row51_col4\" class=\"data row51 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row51_col5\" class=\"data row51 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row51_col6\" class=\"data row51 col6 9.5\" >                  10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row52\" class=\"row_heading level0 row52\" >54</th>\n",
       "      <td id=\"T_ff501_row52_col0\" class=\"data row52 col0 terraform-ansible-aws\" >terraform-ansible-aws</td>\n",
       "      <td id=\"T_ff501_row52_col1\" class=\"data row52 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row52_col2\" class=\"data row52 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row52_col3\" class=\"data row52 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row52_col4\" class=\"data row52 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row52_col5\" class=\"data row52 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row52_col6\" class=\"data row52 col6 7.5\" >                   8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row53\" class=\"row_heading level0 row53\" >16</th>\n",
       "      <td id=\"T_ff501_row53_col0\" class=\"data row53 col0 influxdb-telegraf-grafana\" >influxdb-telegraf-grafana</td>\n",
       "      <td id=\"T_ff501_row53_col1\" class=\"data row53 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row53_col2\" class=\"data row53 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row53_col3\" class=\"data row53 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row53_col4\" class=\"data row53 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row53_col5\" class=\"data row53 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row53_col6\" class=\"data row53 col6 7.5\" >                   8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row54\" class=\"row_heading level0 row54\" >42</th>\n",
       "      <td id=\"T_ff501_row54_col0\" class=\"data row54 col0 REST-Services-MySQL\" >REST-Services-MySQL</td>\n",
       "      <td id=\"T_ff501_row54_col1\" class=\"data row54 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row54_col2\" class=\"data row54 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row54_col3\" class=\"data row54 col3 B\" >B</td>\n",
       "      <td id=\"T_ff501_row54_col4\" class=\"data row54 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row54_col5\" class=\"data row54 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row54_col6\" class=\"data row54 col6 7.5\" >                   8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row55\" class=\"row_heading level0 row55\" >50</th>\n",
       "      <td id=\"T_ff501_row55_col0\" class=\"data row55 col0 spring-cloud-ms2\" >spring-cloud-ms2</td>\n",
       "      <td id=\"T_ff501_row55_col1\" class=\"data row55 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row55_col2\" class=\"data row55 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row55_col3\" class=\"data row55 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row55_col4\" class=\"data row55 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row55_col5\" class=\"data row55 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row55_col6\" class=\"data row55 col6 2.0\" >                   2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row56\" class=\"row_heading level0 row56\" >17</th>\n",
       "      <td id=\"T_ff501_row56_col0\" class=\"data row56 col0 iot-air-quality\" >iot-air-quality</td>\n",
       "      <td id=\"T_ff501_row56_col1\" class=\"data row56 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row56_col2\" class=\"data row56 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row56_col3\" class=\"data row56 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row56_col4\" class=\"data row56 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row56_col5\" class=\"data row56 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row56_col6\" class=\"data row56 col6 2.0\" >                   2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row57\" class=\"row_heading level0 row57\" >56</th>\n",
       "      <td id=\"T_ff501_row57_col0\" class=\"data row57 col0 uca-workshop-docker\" >uca-workshop-docker</td>\n",
       "      <td id=\"T_ff501_row57_col1\" class=\"data row57 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row57_col2\" class=\"data row57 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row57_col3\" class=\"data row57 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row57_col4\" class=\"data row57 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row57_col5\" class=\"data row57 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row57_col6\" class=\"data row57 col6 2.0\" >                   2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row58\" class=\"row_heading level0 row58\" >46</th>\n",
       "      <td id=\"T_ff501_row58_col0\" class=\"data row58 col0 spring-boot-grafana\" >spring-boot-grafana</td>\n",
       "      <td id=\"T_ff501_row58_col1\" class=\"data row58 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row58_col2\" class=\"data row58 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row58_col3\" class=\"data row58 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row58_col4\" class=\"data row58 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row58_col5\" class=\"data row58 col5 B\" >B</td>\n",
       "      <td id=\"T_ff501_row58_col6\" class=\"data row58 col6 2.0\" >                   2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row59\" class=\"row_heading level0 row59\" >48</th>\n",
       "      <td id=\"T_ff501_row59_col0\" class=\"data row59 col0 spring-cloud-eureka\" >spring-cloud-eureka</td>\n",
       "      <td id=\"T_ff501_row59_col1\" class=\"data row59 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row59_col2\" class=\"data row59 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row59_col3\" class=\"data row59 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row59_col4\" class=\"data row59 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row59_col5\" class=\"data row59 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row59_col6\" class=\"data row59 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row60\" class=\"row_heading level0 row60\" >47</th>\n",
       "      <td id=\"T_ff501_row60_col0\" class=\"data row60 col0 spring-cloud-dataflow\" >spring-cloud-dataflow</td>\n",
       "      <td id=\"T_ff501_row60_col1\" class=\"data row60 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row60_col2\" class=\"data row60 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row60_col3\" class=\"data row60 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row60_col4\" class=\"data row60 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row60_col5\" class=\"data row60 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row60_col6\" class=\"data row60 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row61\" class=\"row_heading level0 row61\" >55</th>\n",
       "      <td id=\"T_ff501_row61_col0\" class=\"data row61 col0 terraform-aws-demo\" >terraform-aws-demo</td>\n",
       "      <td id=\"T_ff501_row61_col1\" class=\"data row61 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row61_col2\" class=\"data row61 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row61_col3\" class=\"data row61 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row61_col4\" class=\"data row61 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row61_col5\" class=\"data row61 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row61_col6\" class=\"data row61 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row62\" class=\"row_heading level0 row62\" >15</th>\n",
       "      <td id=\"T_ff501_row62_col0\" class=\"data row62 col0 influx-telegraf-chronograf\" >influx-telegraf-chronograf</td>\n",
       "      <td id=\"T_ff501_row62_col1\" class=\"data row62 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row62_col2\" class=\"data row62 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row62_col3\" class=\"data row62 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row62_col4\" class=\"data row62 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row62_col5\" class=\"data row62 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row62_col6\" class=\"data row62 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row63\" class=\"row_heading level0 row63\" >14</th>\n",
       "      <td id=\"T_ff501_row63_col0\" class=\"data row63 col0 ic-gradle\" >ic-gradle</td>\n",
       "      <td id=\"T_ff501_row63_col1\" class=\"data row63 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row63_col2\" class=\"data row63 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row63_col3\" class=\"data row63 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row63_col4\" class=\"data row63 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row63_col5\" class=\"data row63 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row63_col6\" class=\"data row63 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row64\" class=\"row_heading level0 row64\" >30</th>\n",
       "      <td id=\"T_ff501_row64_col0\" class=\"data row64 col0 library\" >library</td>\n",
       "      <td id=\"T_ff501_row64_col1\" class=\"data row64 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row64_col2\" class=\"data row64 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row64_col3\" class=\"data row64 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row64_col4\" class=\"data row64 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row64_col5\" class=\"data row64 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row64_col6\" class=\"data row64 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ff501_level0_row65\" class=\"row_heading level0 row65\" >51</th>\n",
       "      <td id=\"T_ff501_row65_col0\" class=\"data row65 col0 spring-data-mongodb\" >spring-data-mongodb</td>\n",
       "      <td id=\"T_ff501_row65_col1\" class=\"data row65 col1 C\" >C</td>\n",
       "      <td id=\"T_ff501_row65_col2\" class=\"data row65 col2 False\" >False</td>\n",
       "      <td id=\"T_ff501_row65_col3\" class=\"data row65 col3 C\" >C</td>\n",
       "      <td id=\"T_ff501_row65_col4\" class=\"data row65 col4 C\" >C</td>\n",
       "      <td id=\"T_ff501_row65_col5\" class=\"data row65 col5 C\" >C</td>\n",
       "      <td id=\"T_ff501_row65_col6\" class=\"data row65 col6 0.0\" >                   0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb97d9949d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_columns = ['name', 'score_commits', 'score_topics', 'score_views', 'score_stars', 'score_clones', 'score_total']\n",
    "table = repos[table_columns]\n",
    "color_c = 'background-color: #c91e1e;'\n",
    "color_b = 'background-color: #ffc258;'\n",
    "color_a = 'background-color: #9bc2fd;'\n",
    "color_s = 'background-color: #5e9400;'\n",
    "style = table.style.set_table_styles([  \n",
    "    {'selector': '.S', 'props': color_s},\n",
    "    {'selector': '.A', 'props': color_a},\n",
    "    {'selector': '.B', 'props': color_b},\n",
    "    {'selector': '.C', 'props': color_c},\n",
    "    {'selector': '.False', 'props': color_c},\n",
    "    {'selector': '.True', 'props': color_s},\n",
    "], overwrite=False)\n",
    "\n",
    "cell_color = pd.DataFrame(table[table_columns],\n",
    "                          index=repos.index)\n",
    "\n",
    "style.set_td_classes(cell_color)\n",
    "style.bar(subset=[\"score_total\",], color='lightgreen')\n",
    "style.format({'score_total': \"{:20,.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ff14c91b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.713520Z",
     "iopub.status.busy": "2023-04-17T01:01:23.713334Z",
     "iopub.status.idle": "2023-04-17T01:01:23.716012Z",
     "shell.execute_reply": "2023-04-17T01:01:23.715716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puntuación media:  32.02272727272727\n"
     ]
    }
   ],
   "source": [
    "print('Puntuación media: ', sum(repos['score_total'])/len(repos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "275284b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-17T01:01:23.717535Z",
     "iopub.status.busy": "2023-04-17T01:01:23.717292Z",
     "iopub.status.idle": "2023-04-17T01:01:23.721672Z",
     "shell.execute_reply": "2023-04-17T01:01:23.721393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ic-gradle',\n",
       " 'spring-cloud-dataflow',\n",
       " 'spring-cloud-eureka',\n",
       " 'spring-data-mongodb',\n",
       " 'terraform-aws-demo']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_red_zone = df_grouped[df_grouped[('stars_count', 'max')] <= worst_stars_percentile]\n",
    "df_red_zone = df_red_zone[df_red_zone[('clones_uniques', 'sum')] <= worst_clones_percentile]\n",
    "df_red_zone = df_red_zone[df_red_zone[('views_total', 'sum')] <= worst_views_percentile]\n",
    "df_red_zone.index.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "659d871e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
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 },
 "nbformat": 4,
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