2-2-NMF parameter tuning on validation set.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from src.config import DATA_DIR, MODEL_DIR, PARAM_DIR\n",
    "from src.recommendation.NMF import train_nmf_model, model_type\n",
    "from src.recommendation.all_evaluation import _results\n",
    "from src.recommendation.evaluation import evaluate\n",
    "from src.recommendation.mixture_functions import get_train\n",
    "from src.util.data_io import get_dataset\n",
    "from src.util.eval_measures import eval_lst\n",
    "from src.util.io import save_pickle\n",
    "\n",
    "train_model, model_type = train_nmf_model, model_type\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nmf_model'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = os.path.join(DATA_DIR, 'day')\n",
    "results_dir = os.path.join(MODEL_DIR, 'tuning')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['recall', [5]], ['precision', [5]], ['nDCG', [5]]]\n"
     ]
    }
   ],
   "source": [
    "print(eval_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset Jan 20 - 22, 2015\n",
    "datasets = ['120', '121', '122']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_vals = [5, 10, 50, 100, 500]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "overwrite = False\n",
    "save_multinomials = True\n",
    "\n",
    "\n",
    "def hours_minutes_seconds(td):\n",
    "    return '{:02}:{:02}:{:02}'.format(td.seconds // 3600, (td.seconds // 60) % 60, td.seconds % 60)\n",
    "\n",
    "\n",
    "def eval_dataset(dataset, k_vals):\n",
    "    result_dic = {}\n",
    "\n",
    "    for n_components in k_vals:\n",
    "        print(n_components)\n",
    "        train, val, test = get_dataset(dataset, data_dir)\n",
    "        start_time = datetime.datetime.now()\n",
    "        all_multinomials = train_model(train, val, test, results_dir,\n",
    "                                       dataset, overwrite=False, save_multinomials=True,\n",
    "                                       n_components=n_components, is_tune=True)\n",
    "        end_time = datetime.datetime.now()\n",
    "        time_used = hours_minutes_seconds(end_time - start_time)\n",
    "        print('time_used', time_used)\n",
    "        dataset, eval_result = _results(dataset, eval_lst, val, all_multinomials)\n",
    "        eval_result.append(['time_used', time_used])\n",
    "        result_dic.update({n_components: eval_result})\n",
    "\n",
    "    df = pd.DataFrame(result_dic).T\n",
    "    df.columns = [item for sublist in list(result_dic.values())[0] for item in sublist[:1]]\n",
    "    for col in df.columns:\n",
    "        df[col] = df[col].apply(lambda s: s[1])\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "2019-10-22 16:34:35,372 INFO --> Loading data for 120\n",
      "time_used 00:00:08\n",
      "recall@5: 0.05916\n",
      "precision@5: 0.06474\n",
      "nDCG@5: 0.09130\n",
      "10\n",
      "2019-10-22 16:35:36,010 INFO --> Loading data for 120\n",
      "time_used 00:00:08\n",
      "recall@5: 0.06610\n",
      "precision@5: 0.07424\n",
      "nDCG@5: 0.10747\n",
      "50\n",
      "2019-10-22 16:36:33,876 INFO --> Loading data for 120\n",
      "time_used 00:00:37\n",
      "recall@5: 0.09622\n",
      "precision@5: 0.09913\n",
      "nDCG@5: 0.14940\n",
      "100\n",
      "2019-10-22 16:38:00,502 INFO --> Loading data for 120\n",
      "time_used 00:01:42\n",
      "recall@5: 0.11727\n",
      "precision@5: 0.11702\n",
      "nDCG@5: 0.17317\n",
      "500\n",
      "2019-10-22 16:40:32,849 INFO --> Loading data for 120\n",
      "time_used 00:41:49\n",
      "recall@5: 0.21183\n",
      "precision@5: 0.19793\n",
      "nDCG@5: 0.27853\n",
      "5\n",
      "2019-10-22 17:23:18,311 INFO --> Loading data for 121\n",
      "time_used 00:00:09\n",
      "recall@5: 0.05854\n",
      "precision@5: 0.06625\n",
      "nDCG@5: 0.09353\n",
      "10\n",
      "2019-10-22 17:24:31,048 INFO --> Loading data for 121\n",
      "time_used 00:00:10\n",
      "recall@5: 0.06850\n",
      "precision@5: 0.07855\n",
      "nDCG@5: 0.11152\n",
      "50\n",
      "2019-10-22 17:25:43,349 INFO --> Loading data for 121\n",
      "time_used 00:00:41\n",
      "recall@5: 0.09601\n",
      "precision@5: 0.10492\n",
      "nDCG@5: 0.15379\n",
      "100\n",
      "2019-10-22 17:27:26,982 INFO --> Loading data for 121\n",
      "time_used 00:02:11\n",
      "recall@5: 0.11872\n",
      "precision@5: 0.12568\n",
      "nDCG@5: 0.18124\n",
      "500\n",
      "2019-10-22 17:30:40,131 INFO --> Loading data for 121\n",
      "time_used 00:41:36\n",
      "recall@5: 0.21181\n",
      "precision@5: 0.20762\n",
      "nDCG@5: 0.28579\n",
      "5\n",
      "2019-10-22 18:13:33,659 INFO --> Loading data for 122\n",
      "time_used 00:00:09\n",
      "recall@5: 0.06155\n",
      "precision@5: 0.07040\n",
      "nDCG@5: 0.09842\n",
      "10\n",
      "2019-10-22 18:15:00,504 INFO --> Loading data for 122\n",
      "time_used 00:00:13\n",
      "recall@5: 0.06517\n",
      "precision@5: 0.07452\n",
      "nDCG@5: 0.10856\n",
      "50\n",
      "2019-10-22 18:16:37,325 INFO --> Loading data for 122\n",
      "time_used 00:00:53\n",
      "recall@5: 0.10193\n",
      "precision@5: 0.11189\n",
      "nDCG@5: 0.16325\n",
      "100\n",
      "2019-10-22 18:18:52,971 INFO --> Loading data for 122\n",
      "time_used 00:02:10\n",
      "recall@5: 0.12490\n",
      "precision@5: 0.13166\n",
      "nDCG@5: 0.19063\n",
      "500\n",
      "2019-10-22 18:22:24,725 INFO --> Loading data for 122\n",
      "time_used 00:43:48\n",
      "recall@5: 0.21728\n",
      "precision@5: 0.20947\n",
      "nDCG@5: 0.29075\n"
     ]
    }
   ],
   "source": [
    "dfs = dict()\n",
    "for dataset in datasets:\n",
    "    dataset = str(dataset)\n",
    "    df = eval_dataset(dataset, k_vals)\n",
    "    dfs.update({dataset: df})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>recall@5</th>\n",
       "      <th>precision@5</th>\n",
       "      <th>nDCG@5</th>\n",
       "      <th>time_used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.061546</td>\n",
       "      <td>0.070398</td>\n",
       "      <td>0.098419</td>\n",
       "      <td>00:00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065170</td>\n",
       "      <td>0.074520</td>\n",
       "      <td>0.108555</td>\n",
       "      <td>00:00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.101930</td>\n",
       "      <td>0.111891</td>\n",
       "      <td>0.163253</td>\n",
       "      <td>00:00:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.124900</td>\n",
       "      <td>0.131662</td>\n",
       "      <td>0.190629</td>\n",
       "      <td>00:02:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.217281</td>\n",
       "      <td>0.209468</td>\n",
       "      <td>0.290750</td>\n",
       "      <td>00:43:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     recall@5  precision@5    nDCG@5 time_used\n",
       "5    0.061546     0.070398  0.098419  00:00:09\n",
       "10   0.065170     0.074520  0.108555  00:00:13\n",
       "50   0.101930     0.111891  0.163253  00:00:53\n",
       "100  0.124900     0.131662  0.190629  00:02:10\n",
       "500  0.217281     0.209468  0.290750  00:43:48"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['recall@5', 'precision@5', 'nDCG@5', 'time_used']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_measure = df.columns.tolist()\n",
    "eval_measure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metric(col):\n",
    "    try:\n",
    "        df_plot = pd.DataFrame({c: df[col] for c, df in dfs.items()})\n",
    "        df_plot.plot(logx=True, xticks=k_vals, title=col.upper() + ' for different k')\n",
    "    except:\n",
    "        # time is not numeric value\n",
    "        return df_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n",
      "500\n",
      "121\n",
      "500\n",
      "122\n",
      "500\n"
     ]
    }
   ],
   "source": [
    "for k, df in dfs.items():\n",
    "    print(k)\n",
    "    print(df['nDCG@5'].idxmax())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          120       121       122\n",
      "5    00:00:08  00:00:09  00:00:09\n",
      "10   00:00:08  00:00:10  00:00:13\n",
      "50   00:00:37  00:00:41  00:00:53\n",
      "100  00:01:42  00:02:11  00:02:10\n",
      "500  00:41:49  00:41:36  00:43:48\n"
     ]
    },
    {
     "data": {
      "image/png": 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fBYM4S1mShy2hbt28C3xw4kpfRFyB6UAItk3St4jIUmNMpEO17UCwMSZRRB4GpgCDRKQc8BIQDBhgq73t+X884jy4Is9r2S2trJRS2dl4YiOPrn6UAJ8AZnefTXnv8lfLzuxeRakFQzlNOVKGLqZ+Hl7hX+HMlX4b4KAx5rAxJhX4BujjWMEYs8YYk2h/uQm48k1Ed2ClMSbOHvQrgbC8GXrhcK2llZVSKqv1x9fzyOpHqOpblTnd52QK/LO7f6HUgiGcwo+UYUuon8dX+Fc4M6cfAEQ7vI4Brrc310jg5+u0DcjaQETGAGMAqlWrlrW40MjN0soAQUFBXLx4kdTUVBYvXkx4eDgNG97Y7VZKqaLp15hfGb9mPLXK1GJWyCzKepW9WnZ290pKLRhKDBVJG7qEBnVqX6enG+NM6Gc3X5Hteswich+2qZxOuWlrjJkFzALb0spOjKlAfP311397b+TIkdesn3UTdaXUrWlt9FoeW/sYdcrU4ZPQTyjtWfpq2bld4ZRaOIxoKpE6bDGNbmLgg3PTOzFAVYfXgcCJrJVE5E7gOaC3MSYlN22VUqq4WnV0FY+teYz6Zev/LfBjdy2n1MJhHMOflKFLbnrgg3OhvwWoIyI1RMQDGAxkWrxZRFoAM7EF/hmHohVAqIiUFZGyQKj9PaWUKvZWRK3giXVP0LB8Q2aFzsoc+DuX4bPwPqJMZVKGLqZx3Vr5MqYcp3eMMekiMg5bWLsCc40xe0TkVSDCGLMUeBvwAb6z371yzBjT2xgTJyKTsJ04AF41xsTdlCNRSqlC5OcjP/PMb8/QtEJTZnSbgY+Hz9WyuJ3LKLXofg6ZANKGLaRpPgU+OPlwljFmGbAsy3svOvx+53XazgXm/tMBKqVUUfPDoR94fv3ztKjYghndZlDCPePOvvM7fsRn8QMcNFVJG7aQZnVrXqenvKfLMCilVB5acnAJz/3+HMGVgrMJ/KWUXPwA+001UoctyvfABw19pZTKMwsPLOSF9S/QtnJbpnWblinwL2xfjM/if7PfVCNt6EKa161RIGPU0M+F3CytvHLlSlq1akWTJk1o1aoVq1evLqhhK6Xywbf7vuWlDS/RIaADH3b9EG8376tlF7YtpOSSkew11UkduogW9Qom8EFDP1dys7Ry+fLl+eGHH9i9ezfz58/nX//6V0EMWSmVD77+62smbZpEx8COvN/lfbzcMlbXjd+2AJ+lo4g0QaQNXUjLekEFN1A09HMlN0srt2jRgipVqgDQqFEjkpOTSUlJQSlVvHwR+QVv/PEGXap24d3O7+Lp6nm1LH7r9/gsHcVuU4u0IQtoVcCBD0VwaeW3Nr/FX3F/5Wmf9cvVZ2KbiTfcz7WWVl6wYAEtWrTA09Mzm1ZKqaJq/p75TI2Yyp3V7mRKxym4u7pfLbu49Tt8fhjDTlMby5DvaF0/qOAG6qDIhX5hda2llffs2cPEiRMJDw8voJEppW6GObvn8N629witHsqbHd/E3SUj8C9F/I+SPz7EdlMHy5DvaFu/+nV6yl9FLvTz4oo8r11raeWYmBj69u3LZ599Rq1a+ffwhVLq5pq5cybTdkyjR40evHH7G7i5ZETppS1fUeKn/7DN1MUy5FvaFaLAhyIY+oXNlaWV161bl2lp5QsXLnD33XczefJkbrvttgIcoVIqrxhj+GjnR3y08yN61ezFpNsm4erierU8YfOXlFg2jghTDzP4f4Uu8EG/yM2VIUOG0L59e/bt20dgYCBz5sxh3LhxXLp0iZCQEJo3b85DDz0EwLRp0zh48CCTJk2iefPmNG/enDNnzuTwCUqpwsoYw4fbP+SjnR/Rp1afvwX+5T8+p8Sy/7DF2gDL4G9p16DwBT6AGFO4VjIODg42ERERmd7bu3cvDRrkzabAhVFxPz6lijpjDO9te4+5f86lf53+vNj+RVwk45r58h+f4f3zo/xhbYh1yDfc1iD/9wURka3GmOCc6un0jlJKXYcxhnci3mF+5HwG1h3Ic+2eyxT4iZvm4b38MTZaG2MGf8XtBRD4uaGhr5RS12CMYcqWKXyx9wuG1B/CM22eyXSzRuLGOZRY8Ti/WZvC4K+4o2HV6/RWOGjoK6VUNqzGyht/vMH/9v2P+xrcx1Otn8oc+BtmUyL8CX61NoPBX9KxCAQ+FKHQN8Zk+gcvLgrbdypKKVvgT9o0ie/3f8/wRsN5vNXjmfInacMsSoRPYK21BTLoczoVkcCHInL3jpeXF7GxscUuII0xxMbG4uXllXNlpVS+sBorL294me/3f8+oJqP+HvjrZ+IdPoE11haYQZ/TqVHRCXxw8kpfRMKA97HtnDXbGPNmlvKOwHtAU2CwMeZ7h7IpwN3YTjArgf8zuUzvwMBAYmJiOHv2bG6aFQleXl4EBgYW9DCUUoDFauHFDS+y9NBSHmz6IP9p/p8sgf8R3iufZpW1JTLwM7oWscAHJ0JfRFyB6UAIto3Ot4jIUmNMpEO1Y8Bw4MksbTsAt2E7GQD8DnQC1uZmkO7u7tSoUXBLkSqlir90azrPr3+enw7/xNjmY3m42cOZypN/m4b3qudYaQ3G5d5P6dq46AU+OHel3wY4aIw5DCAi3wB9gKuhb4yJspdZs7Q1gBfgAQjgDpy+4VErpVQeSrem88xvz7A8ajmPtniU0U1HZypP/vUDvFa/QLi1NXLvXLo1KZqBD87N6QcA0Q6vY+zv5cgYsxFYA5y0/6wwxuzNWk9ExohIhIhEFMcpHKVU4ZVmTeOpX59iedRyHmv12N8Df917eK1+geXWNnDvPEKaFO778HPiTOhnd8uMU3PyIlIbaAAEYjtRdLXP/2fuzJhZxphgY0xwhQoVnOlaKaVuWJoljQnrJrDy6EomBE9gROMRmcpT1r2L15qXWGZpiwyYS2gRvsK/wpnQjwEcjzQQOOFk/32BTcaYBGNMAvAz0C53Q1RKqbyXaknl8bWPs+rYKp5u8zT3N7o/U3nK2ql4rnmZHy3tcRkwh+5Ni37gg3OhvwWoIyI1RMQDGAwsdbL/Y0AnEXETEXdsX+L+bXpHKaXyU4olhfFrxrM2Zi3PtX2OYQ0y74ORumYKnmsnsdTSAdcBnxDWrHgEPjgR+saYdGAcsAJbYH9rjNkjIq+KSG8AEWktIjHAvcBMEdljb/49cAjYDewEdhpjfrgJx6GUUk5JTk/m0dWP8tvx33ix/YsMrj84U3nqqjfxWPc6iy234dZ/Fj2KUeBDEVllUyml8kJSehKPrH6EzSc380qHV+hbp2+m8tRVb+Dx21ssstyB+4CP6FmEAl9X2VRKKQeJaYmMWz2Orae38trtr9G7Vu+MQmNIW/U6Hr+/zfeWjnj0n1GkAj83NPSVUsXe5bTLjP1lLDvO7uCN29/g7pp3ZxQaQ9ovk3Bf/w7fWTrh0W8avZsXz8AHDX2lVDGXkJrAw788zO5zu3nrjrcIqxGWUWgMaStfxX3Df/nG0gXvfh/Sp0XxDXzQ0FdKFWMXUy/y8MqHiYyN5O1ObxNSPSSj0BjSw1/CfeP7fGXpinff94t94IOGvlKqmIpPiefBlQ+y7/w+3un8Dl2rdc0oNIb0FS/gtulDvrB0o8Q979G3ZdF+0tZZGvpKqWLnQvIFxqwcw8ELB3mv83t0qtopo9AY0pc/h9sf0/ncEoJ3n3fp16r4X+FfoaGvlCpW4pLjGB0+mqj4KN7v8j53BN6RUWgM6T8/jdvmj5lvCaVE73cYEHzrBD5o6CulipHYpFhGhY8i+lI0H3b9kA4BHTIKjSF92VO4bZnF3PQwfHq/zb2tb40pHUca+kqpYuFc0jlGrRjF8YTjTOs2jXaVHZb5MgbLTxNwi/iE2ek9KNX7LQa2ufUCHzT0lVLFwJnEM4xcMZLTiaeZcecMWvu3zii0WrH89ASuW+cyK/1ufHtNZlCb6gU32AKmoa+UKtJOXT7FqPBRnE08y0d3fkSrSq0yCq1WLD8+juu2eXyc3hPfnm8wuO2tG/igoa+UKsJOJpxkxIoRnE85z8yQmTSv2Dyj0GrF8sN4XLfPZ0Z6b0r3nMTQdrd24IOGvlKqiDqecJyRK0ZyMeUis0Jm0bRC04xCqxXL0kdx3fE509L7UPruVxnWLqjAxlqYaOgrpYqc6IvRjAwfyeW0y3wS+gmNyjfKKLRasSwZh+vOL3k/vS/l7nqJf7UPKrCxFjYa+kqpIuXoxaOMXDGSZEsys0Nn08CvQUah1YJ18X9w3fU176X3o+xdL/GvDkEFNtbCyJmdsxCRMBHZJyIHReTpbMo7isg2EUkXkQFZyqqJSLiI7BWRSBEJypuhK6VuNUfijzBi+QhSLanMCZ3z98Bf9DAuu77m3bT+lOnxIg9o4P9NjqEvIq7AdKAH0BAYIiINs1Q7BgwHvsqmi8+At40xDYA2wJkbGbBS6tZ06MIhRqwYQbpJZ073OdQrVy+j0JKOdeGDuOz+H1PT7qV0jxcYfluNghtsIebM9E4b4KAx5jCAiHwD9AEir1QwxkTZy6yODe0nBzdjzEp7vYS8GbZS6lZy4PwBRoWPwkVcmNd9HjXL1MwotKRjXTgGlz0LmJI2EL8ezzLidg38a3FmeicAiHZ4HWN/zxl1gQsislBEtovI2/a/HJRSyin74vYxcsVI3MSNud3n/j3wF4zCZc8C3kwbTLmwZxipgX9dzoS+ZPOesxvrugF3AE8CrYGa2KaBMn+AyBgRiRCRiLNnzzrZtVKquNsbu5eR4SPxcPVgXtg8apR2CHRLGtbvR+ISuYg30obg130io+6oee3OFOBc6McAjsvQBQInnOw/BthujDlsjEkHFgMts1YyxswyxgQbY4IrVKjgZNdKqeJsz7k9jAwfSQm3EswLm0c1X4e1cixpWL8bgcvexbyWNgy/0AmM7qiB7wxnQn8LUEdEaoiIBzAYWOpk/1uAsiJyJcm74vBdgFJKZWfX2V2MDh+Nr4cv88LmUbWUw3Vneirmu+G4/LWUSWn3UT70CR7sVKvgBlvE5Bj69iv0ccAKYC/wrTFmj4i8KiK9AUSktYjEAPcCM0Vkj72tBdvUzioR2Y1tquiTm3MoSqniYMeZHYxZOYbSnqWZ130eAT4OXyGmp2K+ewD560deTrsfv5DHeEgDP1fEGGen5/NHcHCwiYiIKOhhKKUKwNbTWxn7y1jKe5dnTvc5+Jf0zyhMT8F8ez+yfzkvpj1ApTsf5T9dahfcYAsZEdlqjAnOqZ5TD2cppdTNtuXUFh7+5WEqlqjIvLB5fw/8//0L2b+c59P+TcVuj2jg/0Ma+kqpArfp5CbG/jKWyiUrMy9sHhVLVMwoTEvGfHMfcmAFz6WNoGLX/zCua52CG2wRp6GvlCpQG45vYNyqcQSWCmRu97mU9y6fUZiWjPlmGHIwnGfSRlKhy8M82k0D/0Zo6CulCsxvMb/xyOpHCPINYm73ufh5+2UUpiVhvh6CObSKp9JGU7HzQ4y/s27BDbaY0NBXShWIddHr+L81/0etMrWYHTqbsl5lMwpTEzFfDcYcXsPEtNH4dxrN+Dv1Cj8vaOgrpfLdqmOrGL92PHXL1uWT0E8o41UmozA1EfPVIMyRdUxIfRD/TqN4LKQuItktDqByS0NfKZWvVh5dyZNrn6RhuYbMCp1Fac/SGYWplzFfDcRE/cYTqQ/h3+nfPK6Bn6c09JVS+Wb5keVMWDeBxuUbMzNkJr4evhmFKQmYL+/FRK3nsdSH8e84nCdD62ng5zENfaVUvvjp8E9M/G0izSo04+OQj/Hx8MkoTEnAfDkAc3Qj41PHUvmOB3iquwb+zaChr5S66ZYeWsqzvz9Lq0qt+OjOjyjpXjKjMOUS5ssBWI9t5tHU/1D59vuYGKaBf7No6CulbqpFBxbx/O/P09q/NdO7TaeEe4mMwuSLmC/6Y45t5pHU/1D5tqE83aO+Bv5NpKGvlLppvtv/HS9ueJH2Vdozres0vN28MwqT4zFf9MMaHcHY1Eeo0mEIz97VQAP/JnNmu0SllMq1//31P1774zVuD7id97q8h6erZ0Zhcjzm835Yj29nbOqjBLQfyHN3a+DnBw19pVSe+3Lvl7y5+U06B3bmnc7v4OHqkVGYdAHzeV+sJ3fxUOr/Edh+AC+zkBACAAAgAElEQVT01MDPLxr6Sqk89dmez3g74m26Vu3K1E5TcXd1zyhMOm8P/N2MSfk/qrbrz4s9G2rg5yMNfaVUnpn751ze3fouIdVDeKvjW7i7OAR+Yhzm83uwnIpkTMp4qrbty0u9NPDzm1Nf5IpImIjsE5GDIvJ0NuUdRWSbiKSLyIBsyn1F5LiITMuLQSulCp9Pdn3Cu1vfpUdQD6Z0nPL3wP+sN5ZTkYxKGU9Am7683LuRBn4ByDH0RcQVmA70ABoCQ0SkYZZqx4DhwFfX6GYSsO6fD1MpVZh9tPMjPtj+AXfXvJs37ngDNxeHSYTLsZj5vbCc3sfIlMcJbNOHV/to4BcUZ6702wAHjTGHjTGpwDdAH8cKxpgoY8wuwJq1sYi0AioB4XkwXqVUIWKMYdr2aczYMYPetXrz+m2vZwn8c5jPepF+5gD/TnmcgNa9eLV3Yw38AuRM6AcA0Q6vY+zv5UhEXIB3gAm5H5pSqjAzxvDB9g+YuWsm/er0Y9Jtk3B1cc2okHAWM78X6WcO8u+UxwkM7slrfRrj4qKBX5CcCf3s/gs5u5v6WGCZMSb6epVEZIyIRIhIxNmzZ53sWilVUIwx/Hfrf5m9ezb31r2Xl9q/hIs4xEnCGZjfi/Rzh3gg5QkCW93F6/do4BcGzty9EwNUdXgdCJxwsv/2wB0iMhbwATxEJMEYk+nLYGPMLGAWQHBwsLMnFKVUATDGMGXLFL7Y+wWD6w3m2bbPZp6uuXQa5vciLS6K+5OfpFrLMN7o20QDv5BwJvS3AHVEpAZwHBgMDHWmc2PMsCu/i8hwIDhr4Culig5jDJM3T+brv77mvgb38VTrp7IE/il74B/jvqSnqNYyhMn9NPALkxynd4wx6cA4YAWwF/jWGLNHRF4Vkd4AItJaRGKAe4GZIrLnZg5aKZX/rMbKa5te4+u/vuaBhg/8PfAvnoRPe5IaF82wpAlUbRnCW/2bauAXMmJM4ZpNCQ4ONhEREQU9DKWUA6ux8urGV1lwYAEjGo9gfMvxWQL/hC3w408yNPFJqre4kykDmuKqgZ9vRGSrMSY4p3r6RK5S6rosVgsvbXiJJYeWMLrJaB5p8UjmwI8/DvN7khp/miGJE6jevKsGfiGmoa+UuiaL1cIL61/gh8M/MLbZWB5q9lCWwI+xXeFfPMPgxAlUb96Ft+9tpoFfiGnoK6WylW5N59nfn+XnIz8zrvk4Hmz2YOYKF6Jhfk9SLp5jcOJTVGvWiaka+IWehr5S6m/SrGk8/evThB8NZ3zL8YxsMjJzhQvH4NOepCTEMjDxKao37cg7GvhFgoa+UiqTNEsaE36dwKpjq3gy+EkeaPRA5grnj9oC//J5Blx+mqCmt/Pfgc1wc9WN+IoCDX2l1FWpllSeWPcEa6PXMrH1RO5reF/mCnFHYH4vki/HM+DyRKo3uY13NfCLFA19pRQAKZYUHl/7OL/G/MqzbZ9lSP0hmSvEHYZPe5GceIn+l58mqHF73h/UXAO/iNHQV0qRnJ7M+LXjWX98PS+0e4GB9QZmrhB7yHaFn3SZfpcnUr1RO94brIFfFGnoK3WLS0pP4tHVj/LHyT94pcMr9KvTL3OF2EPwaU+Sk5O4J+Fpghq24YMhLXDXwC+SNPSVuoUlpiXyyOpH2HJqC5Num0Sf2n0yVzh3wBb4KSn0SZhIUMPWfDhUA78o09BX6haVmJbI2FVj2X5mO6/f/jq9avXKXOHsfpjfk6TUVPokPE1Qg2A+HNJSA7+I09BX6haUkJrA2FVj2XV2F2/e8SY9avTIXOHsPvi0J0lpFnpdeoag+q2YNrQlHm4a+EWdhr5St5iLqRd5+JeHiTwXyZSOUwgNCs1c4cxftiv8dEPPS09To34LZgzTwC8uNPSVukVYrBYWHVzEh9s/5GLKRaZ2mkq36t0yVzodCfN7kWgRel58mhr1mjNdA79Y0dBX6haw6eQm3t7yNvvP76dlxZY81eYpGvk1ylzp9B574Lty98WJBNVtyoz7WuLp5pp9p6pI0tBXqhiLio/ina3vsDZ6LQE+AbzT6R1CqodkXikT4NRumN+bROPGXfbA/+i+Vhr4xZBTf7OJSJiI7BORgyLyt+0ORaSjiGwTkXQRGeDwfnMR2Sgie0Rkl4gMysvBK6WyF58Sz1ub36Lvkr5sObWF8S3Hs+SeJYQGhf498E/usl3hG3fuin+a6nWa8vF9rfBy18AvjnK80hcRV2A6EIJtk/QtIrLUGBPpUO0YMBx4MkvzROB+Y8wBEakCbBWRFcaYC3kyeqVUJmnWNL7b9x0zds7gUuol+tbuy7gW4yjvXT77Bid2wGd9SMSLHvETqV6nMTP/pYFfnDkzvdMGOGiMOQwgIt8AfYCroW+MibKXWR0bGmP2O/x+QkTOABUADX2l8thvMb/xdsTbHIk/QtvKbZkQPIF65epdu8GJ7fBZHy5LScIuPEVQ7YbM0sAv9pwJ/QAg2uF1DNA2tx8kIm0AD+BQbtsqpa7t4PmDTI2YyvoT66nuW50PunxA56qd/z6N4+j4Vvi8L5elJN0vTCSoVgM+uT9YA/8W4EzoZ/e/nFztpi4ilYHPgQeMMdZsyscAYwCqVauWm66VumXFJccxY8cMvt//PSXcS/BU66cYXG8w7q7u12944Bf4fgSXXUvR/fwEqteqr4F/C3Em9GOAqg6vA4ETzn6AiPgCPwHPG2M2ZVfHGDMLmAUQHBycqxOKUreaNEsaX/31FTN3ziQxPZGB9QYyttlYyniVuX7DM3sh/AU4uJJLPjXoEfsY1WrWY/b9rfH20MC/VTgT+luAOiJSAzgODAaGOtO5iHgAi4DPjDHf/eNRKqUwxrA6ejXvRLxD9KVo7gi4gyeDn6RmmZrXb5hwFta+AVs/xXj4sL7GeEb91YIWNfyZ84AG/q0mx9A3xqSLyDhgBeAKzDXG7BGRV4EIY8xSEWmNLdzLAr1E5BVjTCNgINAR8BOR4fYuhxtjdtyMg1GquPor7i+mbJnCllNbqFW6Fh/f+TG3Bdx2/UZpybBpBvz2X0xaIpGBg3j0RAiH9noR1sif/w5qpoF/CxJjCtdsSnBwsImIiCjoYShVKJxLOseH2z9k0YFFlPYszbjm4+hftz9uLte5XjMG/lwAv7wC8ceIqdiZJ8/3Y9Ol8nSo5ceT3evRslrZ/DsIlS9EZKsxJjinevpErlKFUHJ6Mp9Hfs7s3bNJtaZyf8P7GdNsDL4evtdvGL0ZVjwLMVu4ULo+L3u9yuJjtWlRrQxfDapHh9rXuF9f3TI09JUqRIwxrIhawbtb3+XE5RN0rdqVJ4KfoJpvDne1nY+CX16GPYtI9qrANO9HmXG6DXX9SzPngXp0rV/x+rdwqluGhr5ShcTus7uZsmUKO87uoH65+ky6bRJtKre5fqPkePh1KuaPj7HiwrfeQ5l0/k78y/vx/pC63N2kMi4uGvYqg4a+UgXs1OVTvL/tfX48/CN+Xn680uEV+tTqg6vLdb5ktaTD1nmwdjImMY51Xt2YeOEe3NwDeHlAHfq1CNBNy1W2NPSVKiCJaYnM2zOPT//8FKuxMrrJaEY2GUlJ95LXbmQMHAi33W9/bh+Rns2YkPI4Z9zr80if2gxqXVVXxlTXpaGvVD6zGis/Hv6R97e+z5mkM/QI6sH4VuOp4lPl+g1P/Qnhz8HhtZx2D+T51MfZIu14OKw297cP0tsvlVM09JXKR9tOb2PKlinsid1Dk/JNeKfzOzSv2Pz6jS6dgtWvYbZ/QaJrKaam388iE8bwrnX47+01KOWVw7ILSjnQ0FcqH8RciuHdre8SfjScSiUqMfmOydxV4y5c5Drz7qmJsHEa1t/fxZqeynxLD2Za+tG3Q2PWdKpF2ZIe+XcAqtjQ0FfqJkpITWD27tl8Hvk5ri6ujG0+luGNhuPt5n3tRlYr7P4Wy8qXcU04yUprG6ZYhnBbmzb82KU2FX298u8AVLGjoa/UTeC4CXlcchy9a/Xm0RaPUqlkpes3jPqd9OXP4nZqJ5GmJq+lvUi1Fnfyabc6VC1XIn8Gr4o1DX2l8tgfJ/9gypYpVzchn9FtBo3KN7p+o9hDpK94Abf9P3EOP95MHYulUX/eCK1PrQo++TNwdUvQ0Fcqjxy9eJSpEVNz3oTcUWIc6WvfQrbMJtW48W7aQA7Xup9xYU1pVKV0/g1e3TI09JW6QfEp8czcNZOv//oaT1dPxrccz30N78PT1fPajdJTsWyeTfqaybilJfBteid+DRzDqB7tmVBdF0NTN4+GvlL/0JVNyD/a+REXUy/mvAk5gDFY9v5I0k/P4nP5GBssjVlQ4WEG3hXGR7oYmsoHGvpK/QO/xfzG1IipHI4/7Nwm5IA5sZ3zC5+i3LnNnLQG8Fmpl+h891DebVBJF0NT+UZDX6lc+CebkJv4GE4vfp6KRxZjjA/veT1E7bD/8EqzqroYmsp3ToW+iIQB72PbOWu2MebNLOUdgfeApsBgY8z3DmUPAM/bX75mjJmfFwNXKj9Fxkby7b5vWXxwsfObkKckcHzZm5TfOYuyxsJXbvdQ8s6nGNemvi6GpgpMjqEvIq7AdCAE2ybpW0RkqTEm0qHaMWA48GSWtuWAl4BgwABb7W3P583wlbp5LqZeZNnhZSw8sJC9cXvxdPV0bhNyq4WYNXPwWT+ZAGsc4XIbCXc8y72d2utiaKrAOXOl3wY4aIw5DCAi3wB9gKuhb4yJspdZs7TtDqw0xsTZy1cCYcDXNzxypW4CYwzbzmxj4YGFhEeFk2xJpn65+jzX9jnuqnlXjjtXxWz9GVY8S2DqYXZSh7Ut36F7WB9dDE0VGs6EfgAQ7fA6BmjrZP/ZtQ3IWklExgBjAKpVy2GHIKVugnNJ5/jh0A8sPLCQqItR+Lj70LtWb/rX7U9Dv4Y5tj9+YAcXljxNo4SNHDcV+Kne69zRdwzNvHV9HFW4OBP62X3T5Oxu6k61NcbMAmaBbWN0J/tW6oZYrBY2ntzIwgMLWXNsDekmnZYVWzKqyShCqodQwj3nZQ9OnYrhyHcv0PrcYnzxZE3VsTS79xnuLp3DXrZKFRBnQj8GqOrwOhA44WT/MUDnLG3XOtlWqZviRMIJFh9czKKDizh1+RTlvMpxX8P76FunLzVL13Sqj9gLF9n2/Vu0jZ5La5LZVqEPNQZMoot/1ZwbK1WAnAn9LUAdEakBHAcGA0Od7H8F8IaIXHnEMBR4JtejVOoGpVnSWBO9hoUHFrLhxAYAOlTpwITgCXSp2uX6d+E4iE9M5dfFn9B833uEyBn2lmpHmT5v0qZOi5s5fKXyTI6hb4xJF5Fx2ALcFZhrjNkjIq8CEcaYpSLSGlgElAV6icgrxphGxpg4EZmE7cQB8OqVL3WVyg+H4w+z6MAilh5aSlxyHP4l/Xmo2UPcU/uenHeqsjPGcPBUPNvX/0ydP9+lF/s47lmT493fp0Gru27yESiVt8SYwjWFHhwcbCIiIgp6GKoIS0pPIjwqnIUHFrLtzDbcxI3OVTvTv25/2lduf/0Nx+2MMUQeOsrhTUvwilpFq7StlJME4l3KknD70wR0Hg1O9KNUfhGRrcaY4Jzq6RO5qtjYE7uHhfsXsuzIMhLSEgjyDeLxVo/Tq1av66+HY2exWIncuYkzW5dS/sRaGlv/opEYLrn4crZKJ1xa9KJMs16U9tSljlXRpaGvirTsHqDqHtSdfnX60bJiyxzXtElNSmDfpp+4vHsZQXG/04RzABzzqMW+aqMJbHMPvrXbUUqv6lUxoaGvihxjDFtPb7U9QHU0nBRLSq4eoEo+c5jDGxbBgRXUvLyNJqSRaDw54NOa03VDqX1bX6qV1+dFVPGkoa+KjHNJ51h6aCmLDiy6+gDVPbXvoV+dftd/gMqSxuWDv3NiyxJ8jq6mctpRGgLH8GdLuT74NLmbBu3CaOat2xGq4k9DXxVqFquFDSc2sPDAQtZGr830AFVoUOi1NxhPOMulP5dxfsePlD+9npLmMtWNKztcGrK9yiNUCu5Ds2atqKYLn6lbjIa+KpROJJxg0cFFLDqwiNOJp3N+gMpqhVM7ubjrJ1Iif8bv4h5KYUgyZVjj1o7kGndSs21Pgmvpcsbq1qahrwqNKw9QLTiwgI0nNgK2B6gmtplI58DOf3+AKuUSHFrDxd0/4XpoJSVTY/ExwmFTk5+9h+BSP4yWbTpxV5XSukmJUnYa+qrAHb5wmIUHFrL00FLOp5y/9gNUxkDsQcz+5Vze8zPeJ/7A1aSDKcEaa1MOlhlOmSY96NiiAfdX0NsqlcqOhr4qEIlpiYQftT1Atf3MdtzEjS7VutCvTr/MD1Clp0DU75j9K0jZuxyvS0cR4IQ1gDXWME5V6khQ8y6ENKlKzzLXmN9XSl2loa/yjTGGyLjInB+gungCDoRj3bcCc3gtrumJpODBRksD1tGVhGpdCW7WnP4NK1Hex7NgD0qpIkZDX9108SnxLDtie4Dqr7i/8HL1IjQoNOMBKmOF41th/0dY96/A5fRuAE7jx6r09qyXVrjX6Uy3pkE8Xr8ivl7OLY6mlPo7DX11U1x5gGrBgQWsPLqSFEsKDco14Pm2z9OjZg9809Ph0GpYPxNz4BckKRYLLmw3dfklfTCb3YMJqh9M9yaV+W+dCrrzlFJ5RENf5akrD1AtPLCQoxePZjxAVbsvDS3A/hWwYSAm+g/EWLnk4svq9Kb8kt6cP72Dad+4NmGN/Hm8ph8ebnoPvVJ5TUNf3TCL1cL6E+tZeGAh66LXXX2AakzD4YRYPPA+vAY2DoJ4286ZUe61+Cm9N6vSm3PWtzGhLQO4v7E/LauVxVXvoVfqptLQV//Y8YTjth2oHB+gqtmbvqYENY9uga3/gfRk0ly92ebajEVp3VljaU7JUlXpEezPK40q0zjAV++hVyofaeirXEm1pF7dgerqA1RlGzDRoxqdY/7Efe97AFzwqspa11AWJDbkD2sD6gb4EdbOny8b+1O7YqmCPASlbmlOhb6IhAHvY9s5a7Yx5s0s5Z7AZ0ArIBYYZIyJEhF3YDbQ0v5ZnxljJufh+FU+OXzhMAsOLOCHQz/YHqBy9+Vhd3/uidlH5SPLsbq4c8ynOT95jOD7S42ISqlMcPWydL/dnzca+VO1nC5mplRhkGPoi4grMB0IwbbR+RYRWWqMiXSoNhI4b4ypLSKDgbeAQcC9gKcxpomIlAAiReRrY0xUXh+IyntXH6Dav4DtZ3fghtDF6k6/M2dpn3QMi3cF/ixxO++nNuTHhLokJ5ekfS0/RnXzJ6RhJSqW8iroQ1BKZeHMlX4b4KAx5jCAiHwD9AEcQ78P8LL99++BaWKbqDVASRFxA7yBVOBi3gxd3QzGGCJjI1nw1zcsO/Izl60pBKVbeSI+np4JiXiUbcQfpYZwf0o9NpwPwOOSG53qVuDVxv50q1+J0iX0HnqlCjNnQj8AiHZ4HQO0vVYd+0bq8YAfthNAH+AkUAJ4LLuN0UVkDDAGoFo13byiIMSnxLNs93wWHFzIvpRYvKyG0MuX6ZdipbJvW1aXbEH/+FocPV4SH083utavyPTG/nSuV4ESHvrVkFJFhTP/35rdrRVZd1O/Vp02gAWoApQFfhORX6781XC1ojGzgFlg2xjdiTGpPGDSkonY/RkL9y9kZfJxUgQapKTyrLUk9Uu0Z5V7S+4/VZHLZ10oV9KDkCaVeLmxPx1q++Hppg9LKVUUORP6MUBVh9eBwIlr1ImxT+WUBuKAocByY0wacEZE1gPBwGFUwbh4gnORi1hycDGLko9z1N2VUlYrvV3K0tCzAxuSO/DCMQ/SrQZ/Xy/ubeNP90b+tA4qi5tuOKJUkedM6G8B6ohIDeA4MBhbmDtaCjwAbAQGAKuNMUZEjgFdReQLbNM77YD38mrwyglWCxzfimXfz6w/vIyF6edYV8KbdBGae5ajg0cH/ozrxrxj6RgDQX4lGHVHZcIa+9M0oLRuOKJUMZNj6Nvn6McBK7DdsjnXGLNHRF4FIowxS4E5wOcichDbFf5ge/PpwDzgT2xTQPOMMbtuwnEoR0nn4eAqOBDO8cOrWOSWxqJSJTnj6UYZr7IEe7Yj+mxnfovx5jegQeUSjO/mT1hjf+pW8tGHpZQqxsSYwjWFHhwcbCIiIgp6GEWLMXBmLxxYAfvDSY3+g9UlPFlYugybPG3n9UD3JlyMa0tMTBDgRotqZQhrZJu6CSpfskCHr5S6cSKy1RgTnFM9ve2iqEpNhKjfbAuYHQiH+GgOubuxsFIQP9SsyXlrCiVcyuMRH0zsqebss5albY1yjOnjT2hDf/xL6z30St2KNPSLkvNHbQF/IByO/ArpySR6lGRFtaYsCAhgZ9IJBAuS0JjEc61ISa7LHXUq0v0ef+5sUIlyJT0K+giUUgVMQ78ws6RB9B8ZV/Nn/wLAlA1iT9N7+M7dyk/ndpJiOQ7xFUg+fxfuSa3pUqcmYR1s99CX0g1HlFIONPQLm8vn4MBK2/z8wdWQEg8ublC9A/FNB7LQXfjq6DpOnd+AsbqTfrEJHkntCanZjh63Veb2OuXxctd76JVS2dPQL2jGwMmdtiv5/Sts2wZiwKcSNOyFqR3KareSzN67hD2HvsBIOpakADyTBxBaPYxet9eibc1yuOs99EopJ2joF4SUS3Boje1q/sAvkHAKEAhoCZ2fgbqh7HHxY3rEt/yx+QNSXc5gLF54prSjc5W7Gdq5Ay2qltV76JVSuaahn1/OHbTfUrkCjm4Aaxp4+kKtrlC3O9QO4VCiB59sXc7an98hwXU3IlY8LLW5o+wgRrXqQ4vAinoPvVLqhmjo3yzpKXB0PewPt4V9nH3lifL1oN1DUKc7pmpb/jqbzHc7drJ80xTi3Tbg4n4RFxcfWpTuzejmg+lYo1HBHodSqljR0M8raclwPAKi1sPR3yF6C6Qngasn1OgI7cZCnRCspauzI+YCy3ZH8+MPHxLv+jtuPgfBS6hZsiX3NRpIv3ohuLvqXTdKqbynof9PpSZCzGZ7yK+HmAiwpAAClRpDqwegZheo0ZF0Vy82H4lj+a+n+Hnfl1x0W497mW1I6UTKuVWgX90HGdqgP5V9Khf0USmlijkNfWelJNjumY/63Rbyx7fZ5uXFBfybQpvRUP02qN4evMuSnGZhw6FzLF96gPC9x0hw24Zn2S24VDqKt7hxR0BnBtcfQLvK7XB10VsslVL5Q0P/WpIvwrFNtqmaqPVwYjsYC4grVGkB7cdC9duxBLYhOtGdfacvsf/4JfZtO8L+07s4fDYBq0c0Jf224lptB94kE+RbgwF1n6RnzZ74efsV9BEqpW5BGvpXJJ2HoxttV/FRv8OpXWCs4OIOAa0wt40nrkJr/nSpx86zl9hz5gSHDxwn5uJM0uUS4noJcUughHcSXuUS8St/gURrLJ6uXnQPCmVA3QE0r9Bc775RShWoWzf0L8faAv7oeqxRvxN/di9xrkKsuxdn/GpzpG4YB118OZTuwrmUeC4f2og1KhxxvYy4pNv6KAluJW3/iIJQxrMs5Uv44edVAT/v+rSo0IK7at5FKY9SBXqoSil1RbENfYvVwvmU88QmxRKbHEvshSPEndxGbOx+Yi/GEJt2iVhXV2JdXYnzdsVSPcChdRykxmGMC2LxwculNBV9ylGhRF2q+lakVjl/Anwr4ufth5+XH37efpT1LKtz80qpQq9IhX6aNY24pDhbiF8Jc4f/G5ecUXYh+QJWrH/rw90YfF1ccXWtwOW0MsQnVcSS7os7vvj7VKBGmUrUrVCFpv6BNA+sTKVS3jolo5QqNpwKfREJA97HtnPWbGPMm1nKPYHPgFZALDDIGBNlL2sKzAR8ASvQ2hiTfK3PupBygU92fZJtsMenxGfbxtvNm3IevpQzrlRKSaLR5fNUSo6nnMVCSYsbp9MCOJJWmz/TGnNI6uNfoSz1/EtRt1Ip6vn7ULdSKaqU9tZlDZRSxV6OO2eJiCuwHwjBtgH6FmCIMSbSoc5YoKkx5iERGfz/7d1fiFRlGMfx76/dTF1q/2lZq9aKu6WCpZgViUsQYnhhgqARUiBJF3YbBV1Yd3XRhRTUhiJ0kYQXuZHihUFBhLkY/tnMEMlct3+6rUlmZT5dzKDjtsvMzs7MmZ3z+8Cyc2bec97nzHn2mXffc3YPsCYi1mVvkn4I2BARhyW1AkMR8e9o/U1pnxJzt8yl4eaGa1Mnw783T2rm6tBF6k4fp3XgCLMHv6b1ys8ADEUDB6/ex1cxj/7GxdTduZCOGU3Xivvslqm+wbeZ1ZxS3jlrKXAyIk5lN7wTWA18k9NmNbAl+3gX8JYycyIrgCMRcRggIs7n66yzuZMDTx9gcv1kIoKBC5f57sff+en7Y9Sd/oLpg3uZ99dRZmgQgPNxK8fqFzDQspbLbQ/TMmcRnTMaWT69gVvqPcduZparkKLfBpzJWe4HHhqtTfZG6heAVqATCEn7gOnAzoh4Y3gHkjYBmwCmtbXz6u4TXDrbx7TzvSy62sfSm77lMQ0BMHRTM2dbFnO27REaOruY1fkAXZN9Rygzs0IUUvRHmugePic0Wpt6YBnwIHAJ2J/9FWT/DQ0juoFugI67bosXj66imYsg+GPq7Vxu6+LS3OVM7eyiqXUuTT6xamZWlEKKfj8wK2d5JjAwSpv+7Dx+IzCYff6ziDgHIGkPsBjYzyga6/6h6f5VcPcyuOdRGprbaXCRNzMriULOaB4EOiS1S5oErAd6hrXpAZ7JPl4LfBqZM8T7gIWSpmY/DLq48VzA/92xAK15FxZvgJY54IJvZlYyeUf62Tn6zWQKeB2wPSL6JL0G9EZED7ANeF/SSTIj/PXZdX+T9CaZD44A9kTEJ2XaFzMzyyPvJZuVtmTJkujt7U06DDOzCaXQSzZ9wbqZWYq46JuZpYiLvplZirjom5mliIu+mVmKuOibmaVI1V2yKbDeiKcAAAJKSURBVOlPoK+IVRuBkf/3cvFt87XL9/o04FyBMVWzsby31d7veLc5G/ihjH2WKjfztamV3IRk8rMac3NBREzJ2yoiquoL+LXI9bpL3TZfuwJe7036/SzRMSn4va32fse7zWLyM4nczNemVnKzFMe0WvqsVG5W4/TOUJHrfVyGtvnajaXPiSyp/SxHv+PdZjH5mURujrXfiSyJ/ZywuVmN0zu9UcBflU0EtbQvllErx7RW9sOuK/SYVuNIvzvpAEqolvbFMmrlmNbKfth1BR3Tqhvpm5lZ+VTjSN/MzMrERd/MLEVc9M3MUsRFv4IkzZG0TdKupGMxyyXpSUnvSdotaUXS8Vj5uOiPk6Ttkn6RdGzY8yslnZB0UtJLABFxKiI2JhOppc0Yc/OjiHgOeBZYl0C4ViEu+uO3A1iZ+4SkOuBt4AlgPvCUpPmVD81Sbgdjz81Xsq9bjXLRH6eI+JzMfYFzLQVOZkf2fwM7gdUVD85SbSy5qYzXgb0RcajSsVrluOiXRxtwJme5H2iT1CrpHWCRpJeTCc1SbsTcBF4AHgfWSno+icCsMuqTDqBGaYTnIiLOA/6BsiSNlptbga2VDsYqzyP98ugHZuUszwQGEorFLJdzM+Vc9MvjINAhqV3SJGA90JNwTGbg3Ew9F/1xkvQB8CVwr6R+SRsj4gqwGdgHHAc+jIhibgxjVjTnpo3E/3DNzCxFPNI3M0sRF30zsxRx0TczSxEXfTOzFHHRNzNLERd9M7MUcdE3M0sRF30zsxRx0TczS5H/APXnbDAeeNWFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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laeWiRYvSunVrPD09b3k/lVI5W6otlVf+eIVZe2fR178v79UdhsfsByDxDAz8Earc5VKcPw6cpv9XWyjrVcjlY7s0OcsY8wvwS4b3/s/hz52use/XwNcu98iZ5eOssa7sVL4BdJl4w2Ecl1ZWSqnMJKQm8N/1/+WP438wutFohpVrjXzTDYwNBi2z8pELfttzktHf7qCad1HmDGmOz/OuHV9n5GaTjEsrK6VURnFJcTy5+kl2x+zmtZav8ZBXdZjVDdw9YeAy8K7lUpzFOyJ47rt/aFCxBLMGB1KiSEGX+5D7kn42nJFnt8yWVlZKKUcnzp9g+KrhHD93nI/afkRHKQqzHoDCJWHgUihd1aU4QZuO8OqPu2lRtQzTHwvAq1DW0njuS/o5zMWlldevX3/F0spKKXVR2Jkwhq8aTmJqIlM7TyUg4TzM6wnFK1hj+CV8nQcBvlx/kInL99HR34cpjzbBs2DWV+7VpJ8FWVlaGcDPz4/4+HhSUlJYsmQJv/32G3XrZlzBQimVl+2M2smTq5/Ew82DmffNpPbpcFgwAMpUtxK+l4/TGMYYPvxtP5PXhnF/wwp89EhDCrpdXzkUTfpZMG/evH+9N2TIkKu2Dw8Pv4m9UUrldOuPree59c9Rrmg5vuz0Jb5Ht8KiodbN2v4/QJHSTmPYbIY3ft7LN3+G06dZJd7u2QC3AjdxwTWllFJZtyRsCU+vfZrqJaszu8tsfA/+Dt8/DhUDrDN8FxJ+WrqN/y36h2/+DGdo66pM6HVjCR/0TF8ppbKVMYavd3/NJ9s/ocUdLfik/ScU3Tkflj0L1dpBn2/Bo6jTOClpNsYu2MEvu07yTKdajOlYI1seFNGkr5RS2cRmbHwQ/AFz9s6hi18X3m79NgU3T4XfXoZa98HDs6Cg8wmbiSnpjAjaxvr90bzSrQ5D766WbX3UpK+UUtkgNT2VVza+wi+Hf6Gffz9eaPY/Cvz+Iax9G+r2gF7TXSpgfi4plSHfBLP1SCwTezWgT2DlbO2nJn2llLpBCakJPLvuWTZGbuTpJk8zpN7jyOrxsPETaNgXHpgMbs7T7ZkLKTw2cwt7I+OZ1Kcx9zeskO191aSvlFI34EzSGZ5c/SR7YvYw/q7x9KreA1a8AFumQcDj0PVDKOD8mZmo+CT6z9hMeEwC0wY2pYN/uZvSX316JwuysrTyypUradq0KQ0aNKBp06asWbPmdnVbKXWTRJ6PZODygew/s59P2n1Cr+rd4aenrITfcjR0+8ilhH8sNoGHp/7F8TOJfDO42U1L+KBJP0uysrRy2bJl+emnn9i1axezZs1iwIABt6PLSqmb5MCZAwz4ZQAxSTFM7TyV9hVbww9PwI4gaDsO7nnLpQLmYVHnefjLv4hLSCVoaHPuql72pvZbk34WZGVp5caNG1OhgjUeV69ePZKSkkhOTkYplfttP7Wdx1Y8BsA3931D0zL1YeFjsHsRdBoP7V90KeHvPn6W3lP/Is1mmD+sBY0rl7rZXc99Y/rvbnmXfbH7sjWmf2l/Xgh84YbjXG1p5UWLFtG4cWMKFXJ9zWulVM609uhant/wPHcUvYOpnadSwaMkzOsDB9dA1w9cLmC+7Ugsg2ZupVghd4KGNqeat9dN7rkl1yX9nOpqSyvv2bOHF154gd9+++029UwplV1+OPAD4/8aT93SdZnSaQqlcYegB+HYJug+xeUC5n8cOM0Ts4MpX8KToKHNqViy8E3u+WW5Lulnxxl5drva0soRERH07NmT2bNnU7169dvYQ6XUjTDGMGP3DD7d/il3VbiLj9t9TJHUJJjbA078DQ9+5XIB84zFT7yL3doRgFyX9HOaqy2tHBcXR7du3ZgwYQKtWrW6jT1USt0Im7Hx/tb3CQoJomvVrrzV6i0KJsbBnJ5wOhQemeNyAfMlO47z3+/+pkHFEnwzuBklizifrJXdXLqRKyL3iUioiISJyLhMtrcRke0ikiYiD2XY9p6I7BGREBGZJLm4ykjfvn1p2bIloaGh+Pr6MmPGDEaPHs25c+fo3LkzjRo1YsSIEQBMnjyZsLAw3nzzTRo1akSjRo2IiopycgSlVE6Smp7KuN/HERQSRP86/Zlw9wQKXoiGb7pCTBj0ne9ywp+7+QjPLNxJoF9pgoY2vy0JH0CMMdduIOIG7Ac6AxFYBc77GmP2OrTxA4oDzwFLjTHf29+/C6toeht70z+AF40x6652vICAABMcHHzFeyEhIdSpUycLHyt3yeufT6nc6ELqBZ5Z+wx/nfiLsU3G8nj9x5G4ozD7AbgQA48udLmA+dT1B5mwfB8d/H34/DqLnzgjItuMMQHO2rkyvBMIhBljDtkDzwe6A5eSvjEm3L7NlmFfA3gCHoAABYFTLhxTKaVum9ikWEatGsW+2H28cdcb9KzZE06HWQk/5by1NLJvU6dxjDF8tHI/n60J4z933sHHvRtdd/GT7OJK0q8IHHN4HQE0dyW4MeYvEVkLnMBK+pONMSFZ7qVSSt0ix88fZ/jK4Zy8cJJP2n9Cu0rt4NQemN0DjA0GLbOKoDiR3cVPsosrST+zXl57TOjijiI1gDrAxQKQK0WkjTFmQ4Z2w4BhAJUrZ++Kckop5arQ2FBGrhpJUnoS0++ZTmOfxnB8OwT1AndPGLgMvGs5jZNuM4xb9A/fbYtgSOuqvNKtTrashZ8dXLnOiAAqObz2BSJdjN8T2GSMOW+MOQ8sB1pkbGSMmWaMCTDGBHh7e2cayNm9h9wqr34upXKb4JPBDF4xGBFh9n2zrYR/dBPM7g6FisHg5S4l/JQ0G2Pm7eC7bRGM7VQzRyV8cC3pbwVqikhVEfEA+gBLXYx/FGgrIu4iUhBoC2R5eMfT05OYmJg8lyCNMcTExODp6byoglLq5ll9dDXDVw6nTOEyBHUJokapGnBonfVYppePlfBLV3UaJzElnWFzglm26wSvdKvD2E61clTCBxeGd4wxaSIyGvgVcAO+NsbsEZE3gGBjzFIRaQYsBkoB94vIeGNMPeB7oAOwC2tIaIUx5qesdtLX15eIiAiio6OzumuO5+npia+vr/OGSqmbYtH+Rbyx6Q3ql6nP5I6TKeVZCvb/CgsGQJnq1k1bLx+ncc4lpTJkVjBbw2OZ0KsBfbO5+El2cfrI5q2W2SObSimV3YwxTN81nc92fEbriq35sO2HFClYBPYshkVDoVx9GLDYpQLmjsVPPurdiAduQvETZ7LzkU2llMpTbMbGxC0TmbdvHv+p9h/eaPUGBQsUhJ3z4MdR4BtoPYfvWcJpLMfiJ1MHNKVjnZu3Fn520KSvlMpXUtJTeOmPl/g1/Fceq/sYzwY8SwEpAFtnwLJnoWpb6DsPPIo6jXUsNoH+MzYTfS6ZbwY3u+lr4WcHTfpKqXzjQuoFnl77NJtPbObZps8yuP5ga8Ofk+G3l6HmvfDIbCjo/OGKsKjzDJixmQvJaQQNbU6TW7AWfnbQpK+UyhdiEmMYuWok+8/s561Wb9G9RncwBja8D2vfhro9oNd0cHe+Js7u42d57OstiMCC4S2pc0fxW/AJsocmfaVUnhdxLoLhK4cTlRDFpA6TaOPbxkr4q16HjZ9Aw77wwGRwc54Sb1fxk+yiSV8plaeFxoYyYtUIUtJTmH7PdBr5NAKbDVa8YBUwD3gcun7oUgHzi8VPyhUvxNwnWtzS4ifZRZO+UirP2npyK2PWjKFowaLM7jKb6iWrgy0dlo6BnUHQcrTLBcwdi5/MHhKIT7HcOalSC6MrpfKkVUdWMWLlCHyK+BDUNchK+Omp8MMTVsJvO87lhL9kx3FGzt1OnQrFmT+sRa5N+KBn+kqpPGhh6ELe3vw2Dco2YErHKZQoVALSkuG7wRC6DDqNh9ZjXYo1d/MRXlmym+ZVS/PVY83wKpS702bu7r1SSjkwxvDlP1/y+c7Pubvi3XzY7kMKuxeGlARY8CgcXANdP4DAJ1yKdyuKn9xqmvSVUnlCui2dCVsmsCB0AQ9Uf4DX73rdmmWbFA/f9oZjm6D7FGjc32msnFj8JLto0ldK5Xop6SmM+30cK4+sZHC9wTzT9BlrdcuEWJj7EJz4Gx78Cuo/6DRWTi1+kl006SulcrXzKed5eu3TbDm5hecCnuOxeo/ZN0RbSyOfDoVH5rhUwDwnFz/JLpr0lVK51unE04xaNYoDZw7wTut3uL/6/daG+Eir+EncMeg7H2p0dBorJc3GMwt2smzXCcZ2qsnTHWvmuYQPmvSVUrnUsfhjDF81nNOJp5nUYRJ3+95tbThzxCpgfiEGBvwAVe5yGisxJZ2Rc7exLjSaV7rVYejd1W5y728fTfpKqVwnJCaEkatGkm7S+eqer7jT+05rw+kwK+GnnLeKn/g2dRortxQ/yS6a9JVSucqWE1sYs3YMxTyK8XXnr6lWwn5WfmoPzO4BxgaDlkH5Bk5jORY/+bRP49tS/ORW06SvlMo1fgv/jXG/j6Nyscp82flLyhctb204vh2CeoG7Jwxc5lIB89xW/CS7aNJXSuUKC/Yt4O3Nb9PQuyGTO062ZtkCHN0Ecx+GwiVh4FKXCpjnxuIn2cWl2QYicp+IhIpImIiMy2R7GxHZLiJpIvJQhm2VReQ3EQkRkb0i4pc9XVdK5QfGGD7f+TlvbX6LNr5tmHbPtMsJ/9A667FMLx8YvNylhH8w+jyPTP2LMxdSCBraPF8lfHDhTF9E3IApQGcgAtgqIkuNMXsdmh0FBgHPZRJiNvC2MWaliHgBthvutVIqX0i3pfPO5ndYuH8hPWr04LWWr+FewJ629v8KCwZAmerWTVsvH6fx9kSeZeCM3Fn8JLu4MrwTCIQZYw4BiMh8oDtwKekbY8Lt265I6CJSF3A3xqy0tzufPd1WSuV1yenJjNswjlVHVzGk/hCebvL05efm9yyGRUOhXH0YsBiKlHYab9uRMwyauSXXFj/JLq4M71QEjjm8jrC/54paQJyI/CAiO0TkffuVwxVEZJiIBItIcHR0tIuhlVJ51bmUc4xcNZJVR1fxv2b/Y2zTsZcT/s558P3jUDEAHlvqUsLfGHaaATM2U6aoB9+NvCvfJnxwLelnNiXNuBjfHbgba9inGVANaxjoymDGTDPGBBhjAry9vV0MrZTKi6ITohm8YjA7Tu1g4t0TGVB3wOWNW2fAkhHgd7c18cqzhNN4K/eeYvDMrVQuXYSFI1rmympX2cmVpB8BVHJ47QtEuhg/AthhjDlkjEkDlgBNstZFpVR+cTT+KAOWD+DouaNM7jiZbtW6Xd7452RY9izUvBf6LQSPok7j/bjzOCOCtuWJ4ifZxZWkvxWoKSJVRcQD6AMsdTH+VqCUiFw8fe+Aw70ApZS6aE/MHgYsH8CF1AvMuGcGrSq2sjYYA+vfg99ehro9oHcQFHSevL/dfJSxC3bSzK8Uc4c2p2QRj5v8CXIHp0nffoY+GvgVCAEWGmP2iMgbIvIAgIg0E5EI4GFgqojsse+bjjW0s1pEdmENFU2/OR9FKZVbbTqxicdXPE4ht0LM7jKbBt722bTGwKrXYe3b0LAvPDgD3J0n72kbDvLS4l20r+3DN4MDc321q+wkxrg6PH9rBAQEmODg4NvdDaXULbIifAUv/f4SVYpX4ctOX1KuqH1mrM0GK16ALdMg4HHo+iEUuPZ5qjGGj1fuZ9KaMLrdeQcfP9IID/e8UfzEGRHZZowJcNZOv/6UUrfNvH3zmLB5Ao19GjOpw6TLk65s6bB0jFXAvOVolwqY22yGN5ftZebGcHoHVOKdXnmr+El20aSvlLrljDFM3jmZaf9Mo12ldrzf5n083e3j9OmpsHg47F4EbV+Adi86TfjpNsOLP/zDwuAIHm9VlVf/k/eKn2QXTfpKqVsqzZbGW5veYtGBRfSq2YtXW7x6eZZtahJ8PxhCf4FO46H1WKfxHIufPN2xJmM75c3iJ9lFk75S6pZJTk/mf+v/x5pja3iiwRM81fipywk6JQHm94NDa6HrBxD4hNN4SanpjAiyip+83LUOT7TJu8VPsosmfaXULRGfEs+YNWPYdmob4wLH8WidRy9vTIqHb3vDsU3QfQo07u803rmkVIbOCmZLeCzv9GxAv+Z5u/hJdtGkr5S66aISohi4wC6FAAAgAElEQVSxagSHzx7mvTbv0aVql8sbE2Jh7kMQuRN6TYcGD109kJ1j8ZNPejeieyNXV4ZRmvSVUjdV+NlwRqwaQWxSLFM6TuGuCg41a89HW0sjnw6F3nPAv9vVA9k5Fj/5sn9TOtXNH8VPsosmfaXUTbPn9B5GrR6FMYaZ986kXtl6lzfGR8Ls7hB3DPrOhxodnca7ovjJoGbcVSN/rYWfHTTpK6Vuij8j/+SZtc9QyrMUX3b6Er8Sfpc3njliFTC/cBr6LwK/Vk7jHYw+T/+vNnMhOY2goc1pUrnUzet8HqZJXymV7ZYfXs5Lf7xEtRLV+KLTF/gUcShwcjrMSvgp563yhr5NncZzLH4yf1hL6lbIf8VPsosmfaVUtpobMpeJWybStFxTJnWYRHEPhwR9ag/M7gHGBoOWQfkGTuNdLH7iZS9+Uj0fr4WfHTTpK6WyhTGGz3Z8xvRd0+lQqQPvtnn38ixbgOPbIagXuHvCwGXgXctpzI1hp3lidjA+xQoRNLQ5vqWK3MRPkD9o0ldK3bA0WxpvbnqTHw78wIM1H+SVFq9cnmULcHQTzH0YCpe0hnRcKGC+cu8pnpy7naplizJnaKCuhZ9NNOkrpW5IUloSz294nnXH1jH8zuE82ejJK5dBOLQO5vWF4hWsAuYlfJ3G/HHncZ5d+Df1KxRn1uOBuhZ+NtKkr5S6bmeTzzJmzRh2RO3gxcAX6Ven35UN9v8KCwZAmepWwvfyyTyQg283H+XlJbsI9CvNjEHNdC38bKY/TaXUdYlKiGL4yuGEx4fzXtv3uM/vvisb7FkMi4ZCufowYLFLBcynbTjIO7/so31tb77o3xTPgm43qff5lyZ9pVSWHT57mBErRxCXHMcXnb6gxR0trmywcx78OAp8A+HRhU4LmOfn4ie3miZ9pVSW7IrexajVoyggBZh530zqlql7ZYOtM6wC5lXbQt95TguYa/GTW8ulr1IRuU9EQkUkTETGZbK9jYhsF5E0EfnXakkiUlxEjovI5OzotFLq9th4fCNDfhtC0YJFmdNlzr8T/p+TrYRf817ot9Bpwk+3Gcb98A8zN4bzeKuqTHxQE/7N5jTpi4gbMAXoAtQF+opIht80R4FBwLdXCfMmsP76u6mUut1+PvQzo1ePpnKxyszpMofKxR2WMjYG1r8Hv70MdXtA7yAoeO1HLFPSbIyZt4OFwRE83bGmVru6RVwZ3gkEwowxhwBEZD7QHdh7sYExJty+zZZxZxFpCpQDVgBOi/YqpXKeOXvn8N7W9wgoF8CkDpMo5lHs8kZjYNXrsPETaNgXHpgMbtdOLVr85PZxJelXBI45vI4AmrsSXEQKAB8CA4CrLqEnIsOAYQCVK2shBKVyCmMMn27/lBm7Z9CpcicmtplIIbdClxvYbLDiBdgyDQIeh64fQoFrDyBo8ZPby5Wkn9n1lnEx/ijgF2PMsWtdthljpgHTAAICAlyNrZS6idJsaYz/azxLwpbwcK2Hebn5y7gVcHiE0pYOS8fAziBoORruectpAfMzF1IYNHMLe7T4yW3jStKPACo5vPYFIl2M3xK4W0RGAV6Ah4icN8b862awUirnSExL5Pn1z7M+Yj0jG45kZMORV463p6fC4uGwexG0fQHaveg04UfFJzFgxhYOx1zQ4ie3kStJfytQU0SqAseBPkC/a+9iMcZcKoIpIoOAAE34SuVsZ5PP8tSap9gZtZNXmr9Cb//eVzZITYLvB0PoL9BpPLQe6zRmxJkE+n+1mSgtfnLbOU36xpg0ERkN/Aq4AV8bY/aIyBtAsDFmqYg0AxYDpYD7RWS8MabeNcIqpXKgkxdOMnLVSI7EH+GDth9wj989VzZISYD5/eDQWujyPjQf5jSmFj/JWcSYnDWEHhAQYIKDg293N5TKdw7FHWL4quGcSznHp+0/pfkdGZ7XSIqHb3vDsU3wwGfQuL/TmHsj4xkwYzMiMPvx5lr85CYSkW3GGKdPSOqMXKUU/0T/w6jVo3AXd2beO5M6Zepc2SAhFuY+BJE7odd0aPCvOZj/su3IGQbP3EJRLX6So2jSVyqf+z3id/67/r+U8SzDtM7TqFS80pUNzkfDnB5wej/0ngP+3ZzG1OInOZeuaKRUPvbTwZ8Ys2YMfsX9mNN1zr8TfnwkfNMVYg5C3/kuJfxVe08x+JutVCpVhIUjWmrCz2H0TF+pfGrWnll8EPwBzcs355P2n+DlkWH45Uw4zHoAEmKg/yLwa+U0phY/yfk06SuVzxhj+Hjbx8zcM5POVToz8e6JeLhlSM6nw2D2A5By3ipv6NvUaVwtfpI76G9FqXxkV/QuPt7+MVtPbqV37d68GPjilbNsAU7tgdk9wNhg0DIo38Bp3OkbDvH2LyFa/CQX0KSvVD5w+OxhPtvxGSuPrKS0Z2lebfEqD9d6+N+rWh7fDkG9wN0TBv4M3rWvGdcYw8erDjBp9QG6NbiDj3tr8ZOcTpO+UnnYqQun+OLvL1gStoRCboUY1XAUA+sNpGjBTNa5P7oJ5j4MhUtaQzqlq14ztmPxk0cCfJnQ605dCz8X0KSvVB4UnxLP17u+Zm7IXNJMGr1r92bYncMoU7hM5jscWgfz+kLxClYB8xK+14yfbjO8+MM/LAyOYHArP17tVpcCmvBzBU36SuUhSWlJzNs3j692fcW5lHN0rdaVJxs9SaVila6+0/5fYcEAKFMdBiyBYtdeCC0lzcYzC3aybNcJxnSsyTOdamrxk1xEk75SeUCaLY2fDv7ElJ1TOJVwilYVWzG2yVj8S/tfe8c9i2HRUChXHwYshiKlr9ncsfjJS139GdamejZ+CnUraNJXKhczxrDm2BombZ/EobOHaFC2ARPunkCz8s2c77xzHvw4CnwD4dGF4Fnims21+EneoElfqVwq+GQwn2z/hL+j/8avuB8ft/uYjpU7ujbUsnWGVcC8alvoO89pAfOLxU92a/GTXE+TvlK5TGhsKJ9u/5Tfj/+OT2EfXmv5Gj1q9MC9gAv/nBNiYd1E2DIVat4Lj8x2WsA8Y/GTzlr8JFfTpK9ULnH8/HGm7JjCz4d+xsvDi7FNxtKvTj8Kuxd2vnNaCgTPsBJ+cjw0Gwr3TgD3ay+T4Fj8ZOagZrTS4ie5niZ9pXK42KRYpv8znQWhCyggBRhUfxBD6g+hRKFrj8EDYIxV4eq3VyH2IFRrD/e+DeWc1zhyLH4yZ0hzmlbR4id5gSZ9pXKohNQEZu+dzTd7viExLZGeNXoyouEIyhct71qAyJ3w2ysQ/juUrQ2Pfg81OjmtZQuXi58AzBvWgnoVXPiCUbmCJn2lcpjU9FS+P/A9U/+eSkxSDB0rd2RM4zFUK1nNtQDxkbDmLdj5rfUIZrcPockgcHPtn7sWP8nbXPpbICL3AZ9i1cj9yhgzMcP2NsAnwJ1AH2PM9/b3GwFfAMWBdOBtY8yC7Ou+UnmHzdhYcXgFn+34jIjzEQSUC+DTDp/S0LuhawFSLsCfn8HGT8GWBq3GwN3/dfoopqOLxU+8ixVirhY/yZOcJn0RcQOmAJ2BCGCriCw1xux1aHYUGAQ8l2H3BGCgMeaAiFQAtonIr8aYuGzpvVJ5gDGGvyL/4pPtnxASG0KtUrX4vOPntK7Y2rXHL202+GcBrH4DzkVCvZ7Q6XUo5Zelfqzae4pR326napmizBkSiE/xaz/Vo3InV870A4EwY8whABGZD3QHLiV9Y0y4fZvNcUdjzH6HP0eKSBTgDWjSVwrYfXo3n2z7hM0nN1PRqyLvtH6HbtW6UUBcXKky/A/49SU48TdUaAIPz4TKLbLcD8fiJ98MDqRUUS1+kle5kvQrAsccXkcAzbN6IBEJBDyAg1ndV6m8JvxsOJN2TGLlkZWUKlSKcYHjeLjWw/8uZnI1MQdh5f/Bvp+huC/0+grqPwgFsr6s8cXiJ838SjPjsQCKeRbMcgyVe7iS9DO7vjRZOYiI3AHMAR4zxtgy2T4MGAZQubJO7VZ5V1RCFF/8/QWLDyzGw82DkQ1H8li9xzJf6jgziWdg/fuwZRq4F4IOr0LLJ6GgC8/qZ+Ji8ZN2tb354tGmFPbQ4id5nStJPwJwXKLPF4h09QAiUhxYBrxijNmUWRtjzDRgGkBAQECWvlCUyg3iU+KZuXsmQXuDSDNpPFL7EYbdOYyyhV2c7JSeai2dsH4iJJ2FxgOg/ctOV8S8Gi1+kn+5kvS3AjVFpCpwHOgD9HMluIh4AIuB2caY7667l0rlUsnpycwLmcdXu7/ibPJZulbtyujGo6+91LEjYyB0Oax8FWLCoFo7uOdtKF//uvtkjOHNn0P4euNhHm7qy8QHtfhJfuI06Rtj0kRkNPAr1iObXxtj9ojIG0CwMWapiDTDSu6lgPtFZLwxph7wCNAGKCMig+whBxljdt6MD6NUTpFuS2fpwaV8/vfnnLxwklYVWvF0k6epU6aO60FO/GPdpA3/HcrWgn7fQc3OLk2uyowxht8PnOazNQfYGn5Gi5/kU2JMzhpNCQgIMMHBwbe7G0pdF2MMa4+tZdL2SRw8e5AGZRswtslYAu8IdD1I/AlY+xbsmAuFS0H7l6DpIHC7vhusxhhWhUQxec0B/o44S/ninjzVsQb9Aitr8ZM8RES2GWMCnLXTGblKZZPtp7bz8baP2Rm9E7/ifnzU7iM6Ve7kemJNSYC/JsMfn4AtFe56yppcVbjkdfUn3WZYsfskn605wL6T56hUujATejWgV5OKFHLXG7b5lSZ9pW7Q/jP7mbR9Eusj1uNd2Jv/a/l/9KzR07WljsGaXLVrIawab02uqtvdmlxV2sVlFzJIS7ex9O9IpqwN42D0Bap5F+XDhxvyQKMKFHTTm7X5nSZ9pa5T5PlIpuycwk8Hf8KroBdPN3maR+s86tpSxxeFb7RPrtoJFRrDQ19DlZbX1Z/ktHR+2H6cL9Yd5GhsAv7lizG5X2O61L9Db9SqSzTpK5VFZ5LOMH3XdObvm48gDKo3iCENXFzq+KKYg7DqNQj5CYpXhF7Tof5D1zW5Kik1nflbjjJ1wyFOnE3iTt8SvPqfADr6++hNWvUvmvSVclFCagJz9s7hmz3fkJCWQPfq3RnVaJTrSx2DNblqwweweSq4eUCHV6DFk+CR9YXNLiSnMXfzEaZtOMzp88k08yvFuw/eyd01y+oNWnVVmvSVciIqIYoFoQv4LvQ7ziSfoUOlDoxpMobqJau7HiQ9FYK/hnUTIDEOmlycXJWFLwy7s4mpzP4znBkbDxOXkErrGmUZ3aExLaqVyXIslf9o0lfqKnaf3k1QSBC/Hv6VdJNOu0rteLz+4zTyaeR6EGNg/69WMZOYA1Yh8nvfhvINstyf2AspzNx4mG82hnMuOY2O/j482aEGTSprRSvlOk36SjlIs6Wx+uhqgvYGsTN6J0ULFqWPfx/6+fejUnEXZ9FedHIX/PoyHF4PZWpCv4VQ854sT66KOpfEV78fJmjTERJT0+lSvzyj2tWgfkWtZqWyTpO+UsDZ5LMsOrCIefvmcfLCSXy9fHmh2Qv0qNEDL48sVo46d9KqXLUjyJpc1eV9CBic5clVkXGJTF1/kHlbj5GWbqN7o4qMaledmuWKZa0/SjnQpK/ytUNnD/FtyLcsPbiUxLREAssH8lLgS7TxbYNbgSxOYEpJgL+mwB8fQ3qKtfplm+esxJ8FR2Iu8MW6gyzaHoEx8GATX0a2q45fWRdX4lTqGjTpq3zHGMOfkX8yJ2QOG49vxKOAB92qdePROo9Su3TtrAe02WDXd7B6PMQfhzoPQOfxWZ5cFRZ1js/XHuTHvyNxKyD0DazM8LbVqVjy+pZNViozmvRVvpGQmsDPh34mKCSIw2cPU7ZwWUY3Gs3DtR+mtGfp6wt65E9rclXkDmty1YNfQZW7shRib2Q8U9aG8cvuE3i6u/F4Kz+euLualitUN4UmfZXnnTh/gnmh81i0fxHxKfHULVOXd1q/w31+91HwOhcxI/YQrHwNQpZak6t6ToMGD2dpctXOY3FMXnOAVSFRFCvkzpPtavB466qU1lKF6ibSpK/yJGMMf0f/TVBIEKuOrMJg6Fi5IwPqDqCRd6Prn7yUGAe/2ydXFSgI7V+xxu6zMLlq86EYJq8N4/cDpylZpCDPdq7FY3f5UaKwlilUN58mfZWnpKan8tuR3wjaG8TumN0U8yjGwLoD6ePfhwpeFa4/8JkjsHeJtQJm4hlo3N+aTevi5CpjDH+Eneaz1WFsCY+lrJcHL3bx59EWVfAqpP8M1a2jf9tUnnAm6Qzf7f+OBfsWEJUYhV9xP15p/gr3V7+fIgWzvsQBxljj9KG/WJWrTu223q/axqpcdcedLoYxrA6J4rO1Yfx9LI7yxT15/f669AmsjGdBXd5Y3Xqa9FWuduDMAeaGzOXnQz+TnJ7MXRXu4vW7XqdVxVYUkCwuXpaWDId/v5zoz0WCFIDKLa1EX7sLlHFt6YWLa9lPXhtGyIl4Xcte5Ria9FWuYzM2NkRsICgkiM0nNuPp5skD1R/g0TqPZm09HLCGag6shH3LIGw1pJyDgkWgegfwfxVq3gtFXV/TRteyVzmdJn2Va1xIvcCSsCV8G/ItR88dpVyRcoxtMpaHaj2UtWWNzxyxn83/Yq1nb9KhqA/U7wX+3awhnIJZezY+Jc3GD9sj+FzXslc5nEtJX0TuAz7FKoz+lTFmYobtbYBPgDuBPsaY7x22PQa8Yn/5ljFmVnZ0XOUfEeci+Hbftyw+sJjzqedp6N2Qp5o8RcfKHSlYwIUnXi6Nzy+3Ev3F8Xlvf2j1NNTuChWbXvda9gu2HmPq+oNE6lr2KhdwmvRFxA2YAnQGIoCtIrLUGLPXodlRYBDwXIZ9SwOvAQGAAbbZ9z2TPd1XeZUxhuBTwcwNmcvaY2spQAHu8buH/nX608DbhRUq05Ih/HfYl9n4/FtWondxfD4zF5LT+HbzUab9fojoc9Za9hN1LXuVC7hyph8IhBljDgGIyHygO3Ap6Rtjwu3bbBn2vRdYaYyJtW9fCdwHzLvhnqs8KSU9heWHlxMUEsS+2H2ULFSSIfWH0Lt2b8oVLXftnbN5fD4z8Un2tez/OMwZ+1r2n/XVtexV7uFK0q8IHHN4HQE0dzF+ZvtWzNhIRIYBwwAqV67sYmiVl5xOPM3C0IUsCF1AbFIsNUrW4PWWr9OtWjc83a+xHMFNGJ/PKC3dxvajcazce5L5W49xLknXsle5lytJP7NrVeNifJf2NcZMA6YBBAQEuBpb5QF7Y/YyN2Quyw8vJ9WWSlvftjxa51Fa3NEi82ESY6wi4vt+yWR8fgzU7nbd4/OOYs4nsy40mrWhUWzYH018UhruBYTOdcvxZHtdy17lXq4k/QjAsXqELxDpYvwIoF2Gfde5uK/Ko9Jt6aw9tpagkCC2ndpGYffCPFTrIfr598OvhN+/d7ja+HylFtkyPg9gsxl2R55lzb4o1oZG809EHMZAWa9C3FuvPB38fWhVsyzFPXWpBJW7uZL0twI1RaQqcBzoA/RzMf6vwDsicvEa+B7gxSz3UuUJ8SnxLD6wmHn75nH8/HEqelXkuYDn6FmzJ8U9il/Z+OL4fOgvcGDVTRmfP5uYyh8HTrM2NIp1odGcPp+MCDSqVJJnOtWifW0f6lUork/hqDzFadI3xqSJyGisBO4GfG2M2SMibwDBxpilItIMWAyUAu4XkfHGmHrGmFgReRPriwPgjYs3dVX+cST+CHND5rIkbAmJaYk0LdeU5wOep12ldlcWKjlzxP5Y5TJryWJb2uXx+dpdoVrbGxqfN8aw/9R51oZGsXZfFMFHzpBuM5QoXJC2tbxp7+9Nm5relPEqlA2fWqmcSYzJWUPoAQEBJjg4+HZ3Q90gYwybTmxibshcNkRswL2AO12qdqF/nf7UKVPnYqOrj8/X7pIt4/MJKWn8GRZz6Wz+eFwiAHXuKE4Hf2/a1/ahUaWSuOtsWZXLicg2Y0yAs3Y6I1dlq6S0JH4+9DNzQ+YSFhdGac/SjGg4gkdqP0LZwmUhLQXCVt3U8fkjMRdYuy+KNaHRbDoUQ0qajSIebrSuUZbRHWrQvrYP5UtogRKVP2nSV9ni1IVTLAhdwHf7vyMuOQ7/0v681eotulTtgkfKBWtcPnTZv8fna78Cte6FomWv+9jJaelsPXzm0rDNodMXAKjmXZQBLarQvrYPzaqW0oXOlEKTvrpBu6J3MSdkDivDV5Ju0ulQuQP96/SnqUdZZP8K2PhghvH5ntawzQ2Oz584m2g9Urkvio1hp7mQko6HewFaVivDwJZVaFfbRwuJK5UJTfoqy1Jtqaw+spqgkCD+jv4br4Je9KvTj76lG+J7ZCssfvry+HzZ2nDXUzc8Pp+WbmPHsTjW2h+pDDkRD0DFkoXp0bgiHfx9aFm9DEU89K+0Utei/0KUy84mn+X7/d8zb988TiWconKxSoyr2pMeZ89S9PeZEH88W8fnY84ns35/NGtDo9mwP5qziam4FRACqpRiXBd/Ovj7UNPHS9e6USoLNOkrpw7GHWRuyFx+OvgTSelJNPeqyquFqnP3vmAKpGy8PD7f/uUbGp+32Qx7IuNZGxrFmn1R/O0wQapz3XK0r+1D65pltZasUjdAk77KlM3Y2Hh8I0EhQfwZ+Sce4sb9pjD9IqOolXw028bn45PsE6T2RbFufzTR56wJUg19SzK2Yy3a+3tTv0IJnSClVDbRpK+ukJCawNKwH5m7eybhCSfwtglPxcXx0LnzlC5dE5qNtK8/H3Bd4/PGGA5EnbePzUcRHH6GNJuhuKc7bWp508Hfhza1vCmrE6SUuik06SsAIuOOMD/4I76P3MA5k0b95GQmxp/nntJ3UjBwqLVi5XWOzyempPPXodPWujb7Lk+Q8i9fjCfaVKODvw+NdYKUUreEJv18zCScYefOr5lzaAmr084gQKfEFPqXqEvDBg8hte+77vH5ozEJl8bm/3KYINWqRlmebF+DdrW9qVDyxpY8VkplnSb9/CbuKKkhP7Fi//fMTY5kTyEPitsMgwpXpm/dgZSv0/26xudT0mxsDY+1z4SN4lC0NUGqatmi9G9ehfb+3gRWLa0TpJS6zTTp53UX17cJXU7Mvp/5LvkYC4oV47S7G1WLluHV6j34T+NRFCnkleXQp+KTLo3N/3HAPkHKrQDNq5VmQAtrglRVnSClVI6iST8vSkux1p8Ptda3CU2MYm6J4izzKkpKkZK08m7CWw2H0bJCSwqI6+Po6TbDzmNnLo3N77VPkKpQwpPujSvSobYPd9XQCVJK5WT6rzOvSIyzrz9vrW+TnnKODcVKEuR9B1uMG4XdPOlZozv96vSjWolqLoeNvZDChv3RrNkXxYYD0cQlWBOkmlYpxQv3WROkapXTCVJK5Raa9HOzuKPWSpX7lsGRjWBL47yXD0tqNGOuLZaI5FjKFynGs/7D6VWzFyUKOS/xZ7MZ9p6IvzQ2v/PYxQlSHnT0L0d7f2/uruFNiSI6QUqp3EiTfm5iDJz42xq22fcLnNplvV+2NseaDebbQjYWn/yTC4lhNPZpzNg6L9GxckfcC1z713wu6XIFqbWh1gQpgIa+JRjToSYd/H1oUFEnSCmVF2jSz+kyjM9fXt+mOabTGwR7V2HO8TWsO/YbbuLGvVXvpX+d/tQvW/+qIY0xHIw+f2lsfmt4LGk2QzH7BKn2tX1oW8sb72I6QUqpvEaTfk6UYXyelHPgXhhqdIT2L5NcvT2/nNpMUEgQ+w/up1ShUjxx5xP0rt0bnyI+mYdMSWfToRh74e8oIs5cniA19O5qtK/tTdMqpXSClFJ5nEtJX0TuAz7FqpH7lTFmYobthYDZQFMgBuhtjAkXkYLAV0AT+7FmG2MmZGP/845Mxucp6g31elizYau1Izr1vFWoZHlfYpNiqVmqJm/c9QZdqnbB0/3flaCOxTpMkDoYQ3KajcIF3WhVowwj21WnXW0fKuoEKaXyFadJX0TcgClAZyAC2CoiS40xex2aDQHOGGNqiEgf4F2gN/AwUMgY00BEigB7RWSeMSY8uz9IrnPV8fla0HK0lejt69vsidnD3E1vsjx8Oem2dNr6tqV/3f4Elg+84qmZlDQbweGxlxL9QfsEKb8yRejXvDLta/sQWLU0ngV1gpRS+ZUrZ/qBQJgx5hCAiMwHugOOSb878Lr9z98Dk8XKRgYoKiLuQGEgBYjPnq7nQtcYn6fzm9ZCZmVrWE1taaw9tpqgvUFsj9pOEfci9K7dm37+/ahcvPKlkKfik1gXao3N/xF2mvPJaZcmSPVrXoUO/jpBSil1mStJvyJwzOF1BND8am2MMWkichYog/UF0B04ARQBnjHGxGY8gIgMA4YBVK5cOePm3C0xzl4IfJn1/+R4h/H5l6DWlevbxKfE88P+H5i3bx6RFyKp6FWR/zX7Hz1q9KCYRzHSbYZtR85cmgm7J9L6Dr2jhCf3N6xA+9retKpRlqKF9HaNUurfXMkMmT2nZ1xsEwikAxWAUsDvIrLq4lXDpYbGTAOmAQQEBGSMnfucOwl7l8K+n68cn6/b/dL4fMb1bQ6fPczckLksPbiUxLREmpVvxguBL9DWty3xiems2RvNmn1hrN/vMEGqcin+d19t2tf2wb98MZ0gpZT6//buNbbN6o7j+PefNEmTNEkTO7Zbkl7ipEkvK5SmtA1stOUyLtLaF53WwTSQYKjTQNorBtNeTEh7gTRpCA1pMHETrcamikEGbCBA3YBkLeHSQtoVcHpZG+w0JkmTOM31vxePSy5NFudqJ/5/pMiOfR4/57FPf3l6fJ5zxhVL6J8Fiof8XgQ0jlHmbLQrJw/4GrgD+Ieq9gJNIvI+UAk0MN9cCvpjLzsLgaMj+uc3QsrwvnRVpbaxln3H9/HuuXdJS0nj9pLbuaPiDvovLuXgiSaeeO0QH59pYUChIDudHeUetvXG618AAAk0SURBVFd4+E6ZXSBljJm4WEL/A6BMRFYC54A9OGE+VDVwF1AL7AbeUVUVkTPADhHZh9O9swV4bLoqH3ejBX3hatj2EKzZBZ6KUTfr6uvi1YZX2X9sP4G2AK6FLu5dt5elKdupC/Rx18FzNLU7fxfXF+Vxf/QCqfV2gZQxZorGDf1oH/39wBs4QzafUdV6EXkEqFPVauBp4AUR+RLnDH9PdPMngGeBz3C6gJ5V1aMzcByzZ5JBDxDsDPLif17kwBcHaOtuoyR3FTe5f05j4yp+/1I7vf0nyclwLpDaVl7ItnKPXSBljJlWoppYXeiVlZVaV1cX72oMN1bQr901btADHDl/hP3H9vPm6TcZUGVJWiUXQlv4KrQEEFZ5F7G9wsP2cg8bl+eTZhdIGWMmSEQ+VNXK8crZEI+xtIfgeDXU/3VI0FfEdEYP0DvQy1un3+Lpo89zorWeVM2kp/VausJb6MHNtX43P9vqYVt5IUX5WbNzTMaYpGehP1SMQa+qtPdcINgZJNQZIhiJ3nYGaewIcrqtkfDFJvrpZqDbTU/LTnwp13FDeTHbb/Ow2S6QMsbEiYX+N0H/sjO8EqW9sJxQ1V6CV1xJKC2dYCRI8Iv9hD4ZDPhIX2TYywhC6kAe3d25DPQuRvpXUJqzge9VbGfHah8l7mwbUmmMibukC/3O3k6CTfWETlQTPP0uwbaThBakEszMJbRqHcGBHjr7u+Cr15wfnEAvzCzEm+2lJNdP2aJK2jqyONecQcNXC+jpziVlIJf1RQVU+V1U+d1sXJ5vZ/PGmIQzr0I/0hsh2Bkc7G4Z0u0S6jhHsKORjoGewQ1SQPLzcGUsxreoiBXZXrZk+/Bl+fBme/Fl+yjM9HK+JYPDJ1upCYR589TXRHr6EYE1S3L58dVOyFeuyCdnoY2bN8YktjkT+pHeCKFINMCjt0PvhyIh2nvaL9vOlZqFt6+PZZFWrunrx7uwAN/STXhLv4uvaDOeTA9pqYNhrap8HuqgJtDMS4Ewhxo+5cLFPgBKPYvYvbGIKr+LzStd5Genz9rxG2PMdEi40G/tbuXJI08OBnv0bP1Cz+XztBUsLMCb5aU4p5hKbyW+bB/elEx85z/Hd7IWz+lDpF/6MnbNvc4QS8/qYa+hqpxq7qQmEKYm0My/G8I0dzj/GyguyOTWdUuoKnWxtcSFJ/fy6YuNMWYuSbhx+pkrM7X016XkZ+Q7XSxDulq8Wc6tL8uHJ9tDRmr0wqVLX8YeewVOvcc3o27W7Bo16Btbu6gNhKkJhKkNNNPYdhEAT04G15a62ep3Qr64wIZSGmPmhjk7Tr9scRmH7zw86qIgw4wW9O5yuP4XlwV9c0c3tYEwtQ1hagNhTjY788znZ6Wx1e/ip343VX6XjbAxxsx7CRf66anpYwf+0KA//T7owKhB39bVy6H6YPRMPsyJkNPXvyhjAZtXFnDn5mVU+d1U+HJsLhtjTFJJuNC/TEeTE/Ijg/47D34T9JGePj441ULNh8epDYT57FwbAwoL01LYtKKAnRuWUuV3s25prq0Ba4xJaokZ+h1Nwy+YGhH0F/NX8fGZVmo/aaa2oYaPz7TSN6CkpQobivN5YEcZVX4XVy1bTMYCGytvjDGXJNwXuZUrF2vd3TIY9Gt30Vexk6O9S6NfvjZTd6qF7r4BUgS+dUUeW6N98pUr8slKT8y/Y8YYM5Pm7Be59Pei3/4lAc9NHGxxUxMIc/jgWTq6TwFQ4cvhzs3LqfK7uKakgFy7IMoYY2KWcKF/JnU5G97bRGvkPHCeEnc2O69y+uS3lBTgWmTzyxtjzGQlXOhHevu5cbWXKr+LrX4XS/Iyx9/IGGNMTBIu9Ct8Ofz2+1fGuxrGGDMv2fhFY4xJIjGFvojcIiInRORLEXlolOczROTP0ecPiciKIc+tF5FaEakXkU9FxCawMcaYOBk39EUkFWeB81uBNcAPRWTNiGL3AC2qWgr8Dng0uu0CYB+wV1XXAtuA3mmrvTHGmAmJ5Uz/GuBLVW1Q1R7gRWDniDI7geej9w8AN4gzic3NwFFVPQKgqmFV7Z+eqhtjjJmoWEL/CuC/Q34/G31s1DKq2ge0AS5gFaAi8oaIfCQiD069ysYYYyYrltE7o81INvIy3rHKLACuAzYBEeDt6FVjbw/bWOQ+4D6AZcuWxVAlY4wxkxHLmf5ZoHjI70VA41hlov34ecDX0cf/qarNqhoBXgeuHrkDVX1KVStVtbKwsHDiR2GMMSYmsYT+B0CZiKwUkXRgD1A9okw1cFf0/m7gHXUm9XkDWC8iWdE/BtcDx6an6sYYYyYqpgnXROQ24DEgFXhGVX8jIo8AdapaHR2G+QKwAecMf4+qNkS3/RHwME53z+uq+n/79UWkC6ifxLHk4XyXMJ1lxys33vNuoDnGOiWyiby3ib7fqb7mMuDMDO5zutrmeGXmS9uE+LTPRGyba1V1/CkMVDWhfoDzk9zuqekuO165GJ6vi/f7OU2fSczvbaLvd6qvOZn2GY+2OV6Z+dI2p+MzTZR9zlbbTMQrclsnud3fZqDseOUmss+5LF7HORP7neprTqZ9xqNtTnS/c1k8jnPOts2Em09fROo0hjmh54L5dCzGMV8+0/lyHGZQrJ9pIp7pPxXvCkyj+XQsxjFfPtP5chxmUEyfacKd6RtjjJk5iXimb4wxZoZY6BtjTBKx0DfGmCRioT+LRKRERJ4WkQPxrosxQ4nILhH5o4i8IiI3x7s+ZuZY6E+RiDwjIk0i8tmIxy9beEad6anviU9NTbKZYNt8WVV/AtwN/CAO1TWzxEJ/6p4Dbhn6QIwLzxgz055j4m3zV9HnzTxloT9FqvovnPmGhopl4RljZtRE2qY4HgX+rqofzXZdzeyx0J8Zoy48IyIuEfkDsEFEHo5P1UySG2tRpAeAG4HdIrI3HhUzsyOWRVTMxI26qIyqhgH7B2Xiaay2+Tjw+GxXxsw+O9OfGbEsPGNMPFjbTHIW+jMjloVnjIkHa5tJzkJ/ikTkT0AtUC4iZ0XkHnUWh78fZ+Ww48BfVHUyC8MYM2nWNs1obMI1Y4xJInamb4wxScRC3xhjkoiFvjHGJBELfWOMSSIW+sYYk0Qs9I0xJolY6BtjTBKx0DfGmCRioW+MMUnkfy4mnc9AeO6FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v4ODE+Xi6/LieLUfPMuLp2rzYtHy610lzJyC4KtHlbDYR6QEEAl+nOl4cqAE47gbzDlAFqA8UAAa7KtMYM8YYE2iMCSxcuLAb1b3z0rP8dc6cOXnooYfw9fW94/VUSt27ElMSee+f9xi5dSSPlH2En1v/TAFf+31l/18w8VGrn6Dvn1D6Qae8e09e5Mkf/iHy3GUm9AriiTrX+85+be6MMooASjq89weiUicSkZbAu0BTY0xCqtNdgLnGmKQrB4wxx+0/JojIBOCt9FTcpcVD4MSOWy7GSbEa0O6LWyrCcflrpZRK7Wz8WV5b+RpbTm3h5dov079m/6vf7rdOgwWDoEg16D4L8hR3yrvp0BmenxSMj7cnv77YgAC/vDddD3cCQjBQUUTKApFYTT/POCYQkTrAT0BbY8wpF2V0w3oicMxT3BhzXKyrfgIIu4n63/NSL3+tlFKODp0/xIDlAzh56SRfNfmKdmXbWSeMgTVfw8pPoVxz6DIZfPM45V284ziv/hqKf/7sTOodRMkCOVx8gvtuGBCMMckiMhCruccTGG+MCReRoUCIMWYBVhNRLmCWPaodNca0BxCRMlhPGKtTFT1NRApjNUmFAv1v6Urglr/JZzRXy18rpdQVG49v5PVVr+Pt4c24NuOoXcQ++j4lGRa9AVsmQa1u8Pj/wCubU95J6w7z0e/h1C6Zj/E965M/ZzYXn5A+bk1MM8b8AfyR6tgHDj+3vE7ew7johDbGtHC7lpnQleWvV69e7bT8tVJKAczZO4dPNnxCmbxl+P7h7ymRy36bTLwEs3rDvqXQ+E1o8T44fKE0xvDV0j2MXnWAllWLMrJbHbJny5gVk3WmcgZIz/LXAGXKlOHChQskJiYyb948/vzzT6pVq3Y3L0EpdYek2FIYsWUEE8Mn0sivEV83/Zrc2XJbJ2NPwy9d4HgoPPot1O/rlDcx2caQOdv5bWsk3YJKMaxDAF6eGbcknQaEDDB9eurRtNC3b18XKS2HDx++jbVRSt2r4pLiGPL3EFYeW0nXyl0ZHDQYLw/7bTjmgLWpzcUT8PQ0qPKIU97YhGRemrqZv/dF80arSgxqUSHDm6I1ICil1B1w8tJJBq0YxJ6zexgSNITuVR0GmkSEWE8GAD1/h5L1nfKeuhhPn4nB7Dp+ka861aRL/ZLcDhoQlFLqNtsZs5NBywcRmxTLyBYjaeLf5OrJPYutPoPcRaHHb1CwvFPeg6dj6TlhE9EXExn7XCDNqxS5bfXUgKCUUrfRiqMrGPL3EPL65GVyu8lULlD56smQ8bDoTSheC56ZCbmcb/Zbj56l7yRrl8jp/RpQu2S+21pXDQhKKXUbGGOYFD6Jbzd/S/VC1flfi/9RKHuhKydhxSfw9zdQsTU8NdHa6czB8l0nGfDLFork9mVynyDKFMqZ9kMymAYEpZTKYEm2JD7d8Clz9s2hdenWfPrQp/h62ZerSU6E31+BbdOhbk9rNJGn8614xqaj/HfuDgL88jK+V30K53a51FuG04CglFIZ6HzCed5c9SYbT2zkhRovMLDOQDzEPjQ0/gLMfA4OroTm70KTt9PMMfhu+T5G/LWPJpUKM7p7XXL63LnbtO6pnAHSs/z1smXLqFevHjVq1KBevXqsWLHiblVbKZXBjl44So8/erD51GY+fehTXqn7ytVgcOE4THwEDq2BDqOg6X+cgkFyio3/zt3BiL/20amuP+N6Bt7RYAAaEDJEepa/LlSoEL///js7duxg0qRJPPvss3ejykqpDBZyIoRn/niGcwnnGNt6LO3Lt7968vQeGNcKYg5C95lQx3nHmsuJKfSfupnpm44xoHl5vnmqJt4ZOOHMXRoQMkB6lr+uU6cOfn5+AAQEBBAfH09CQurFYZVSmcmCAwt4YdkL5PfJz7RHplGvaL2rJ4+stza1SU6A3n9ABeeVfs5cSqTbzxtYvvsUwzoE8HabKndt7bMs1Yfw5aYv2X3G/Q2l3VGlQBUGB7ncqsFt11r+es6cOdSpUwcfnzvTYaSUylg2Y+P7rd/z846feaDYA/xfs/8jr4/D8tM758OcFyBfKegxG/KXccp/7Ewcz43fRNS5y4zuXo+21d3f//h2yFIB4V50reWvw8PDGTx4MH/++ec1ciql7mXxyfG8u/Zd/jzyJ50qduLdBu/i7eF9NcGG0bDkHSgZBN1mQA7nVoSwyPP0mhBMUoqNac8/QGCZAtxtWSog3Oo3+Yx2reWvIyIi6NixI5MnT6Z8+fLXKUEpdS+KvhzNKyteISw6jDfrvUnPgJ5X/4/bbLDsfVj/PVR5DDqNBe/sTvnX7D3NS1M3kze7N9NfaEjFornvwlWklaUCwr3kWstfnzt3jkcffZTPP/+cRo0a3cUaKqVuxp4zexi0YhDnEs4xvPlwHi718NWTyQkw7yUImwNB/aDtF+DhvDT1b1si+M/s7VQokouJvYMolvfe2U7XrU5lEWkrIntEZL+IDHFx/g0R2Ski20VkuYiUdjiXIiKh9tcCh+NlRWSjiOwTkV9F5NZ3d7hLunXrRsOGDdmzZw/+/v6MGzeOgQMHcvHiRVq1akXt2rXp39/a/+f7779n//79DBs2jNq1a1O7dm1OnXK1yZxS6l6zJmINzy1+jhRbChPbTnQOBpfPwZQnrWDQ8mNo95VTMDDGMHrVAd6YuY36ZQows3/DeyoYAIgx5voJRDyBvUArrP2Vg4FuxpidDmmaAxuNMXEi8hLQzBjztP1crDEml4tyZwK/GWNmiMiPwDZjzOjr1SUwMNCEhIQ4Hdu1axdVq1Z141Izn6x8bUplJsYYftn9C18Ff0Xl/JUZ2WIkRXMWvZrgfARM7Qwx++GJ0VDzKaf8KTbD0N/DmbT+CI/X8uObp2ri45Uxm9q4Q0Q2G2MCb5TOnSeEIGC/MeagMSYRmAF0cExgjFlpjImzv90A+N+gcgK0AGbbD03C2ldZKaXuKcm2ZD7d+ClfbPqCZv7NmNh2onMwOBkOY1vBhUjoMSdNMIhPSmHQ9C1MWn+E5x8qy3dP176jwSA93OlDKAEcc3gfATxwnfR9gcUO731FJARIBr4wxswDCgLnjDHJDmWm2WZTKaXupouJF3l79dv8E/UPvQN681q9167OPAY4uBp+7QHZckHvxVCsulP+83FJvDA5hE2Hz/Deo1V5vnG5O3wF6eNOQHA1Q8JlO5OI9AACgaYOh0sZY6JEpBywQkR2ABfSUWY/oB9AqVKl3KiuUkrdusjYSAb8NYAjF47wUcOP6FSpk3OC7bOsDuSCFaw5BnmdG0aizl2m5/hNHI65xP+61aF9Lb87WPub406TUQTguD2PPxCVOpGItATeBdobY/6demuMibL/eRBYBdQBooF8InIlILks055vjDEm0BgTWLhwYZcVvFE/SGaUFa9Jqcwi9FQozyx6hlOXTzG61WjnYGAMrB0Ovz0PpRpAnyVpgsHuExd48od1nDgfz6TeQZkiGIB7ASEYqGgfFZQN6AoscEwgInWAn7CCwSmH4/lFxMf+cyGgEbDTWHe7lUBne9KewPybuQBfX19iYmKy1A3UGENMTAy+vvfWCASl7geLDy2m79K+5PTOydRHptKgeIOrJ20p8Mfb8NdHUL2T1WeQ3XnTmvUHYnjqx/XYjOHXFxvyYIVCd/YCbsENm4yMMckiMhBYCngC440x4SIyFAgxxiwAvgZyAbPskzOOGmPaA1WBn0TEhhV8vnAYnTQYmCEinwBbgXE3cwH+/v5ERERw+vTpm8l+z/L19cXf/7p980qpDGSM4cftP/JD6A/ULVKXEc1HkN83/9UESZdhzvOweyE8OAhaDgUP5+/UC7dH8cav2yhVMAcTe9fHP38OMpMbDju9l7gadqqUUrcqISWBD9d9yKKDi2hfvj0fNvyQbJ4OU6PizsAvT0NEsDXZrEH/NGWMX3uIYYt2Uq9Ufsb2DCRfjntnapW7w051prJS6r52Jv4Mr654ldDTobxS5xWer/G882qjZw/D1E5w7hh0mQTVnEbdY7MZvlyym5/WHKRNQFG+61oHX+97c1jpjWhAUErdtw6cO8CA5QOIvhzNN02/oU2ZNs4JorbCtC6QkgjPzYfSDZ1OJybb+M/sbcwLjeLZBqX5qH0Anh53Z+nqjKABQSl1X1oXtY43V72Jj6cPE9pMoEbhGs4J9v1lbXeZoyD0WgiFKzudvhifRP+pm/lnfwxvt6nMy83K37V9DDKKBgSl1H1n5p6ZfLbxM8rlK8eoFqMonqu4c4KtU2HBK1C0GnSfDbmd9yk4dSGenhOC2XvyIl93rslTgSXJCjQgKKXuGym2FL4J+Yapu6bSuERjvmryFbmyOSy1Zgys/gpWfQblmkOXyeCbx6mM/adi6Tl+E2fjEhnXM5BmlYvc4au4fTQgKKXuC5eSLjF4zWBWR6yme9XuvBX4Fl4eDrfAlGRY9AZsmQS1ukH7keDp7VTG5iNn6DspBC8PYUa/BtT0z0dWogFBKZXlnbh0goHLB7L/3H7efeBdulbp6pwg8RLM6g37lkLjt6DFe5CqP+DP8BMMmr6V4nl9mdQniNIFc97BK7gzNCAopbK0sOgwBq0YRHxyPKMeHkWjEqk2poo9Bb90gePb4LHhENgnTRnTNh7h/Xlh1CiRl3G96lMoV9bcB10DglIqy1p2ZBn//fu/FMxekJ9b/UyF/BWcE0Tvh2md4OJJ6PoLVG7ndNoYw/Ble/nfiv00r1yYUd3rkiNb1r1tZt0rU0rdt4wxjAsbx3dbvqNW4Vp81/w7CmYv6JzoWLD1ZCACvRaBfz2n00kpNt6du4OZIRF0CfTns4418PJ0a5PJTEsDglIqS0lKSeLj9R8z/8B82pVtx7BGw/DxTNXEs/sPmN3HGk7aYw4ULO90Oi4xmQHTtrByz2leaVGB11tVyvRzDNyhAUEplWWciz/H66teJ+RkCC/VeomXar2U9kYePNZasbR4bXhmJuRyXlY/OjaBvhOD2RF5nk87Vqf7A6W5X2hAUEplCYfPH2bgioFExUbxeePPeazcY84JjIHlQ2Htt1CpLXQeD9mcRwodiblEz/GbOH4+nh971KN1gPOEtKxOA4JSKtMLPhHMaytfw1M8GddmHHWK1HFOkJwICwbB9hlQrxc88n/g6Xz72x5xjt4Tgkkxhl9eaEC90vm532hAUEplanP3zWXo+qGUylOK7x/+npK5Uy0jEX8BZj4LB1dB8/egyVtp5his2nOKl6dtIX+ObEzuG0T5wrm4H2lAUEplSjZjY8SWEUwIm0DD4g35ptk35MnmvMwEF47DtKfg9C7o8APU6Z6mnNmbIxgyZzuViuZmYu/6FMlz/+5U6NYYKhFpKyJ7RGS/iAxxcf4NEdkpIttFZLmIlLYfry0i60Uk3H7uaYc8E0XkkIiE2l+1M+6ylFJZWVxSHG+seoMJYRPoUqkLo1qOShsMTu2Gca3g7CF45tc0wcAYw6iV+3lr1jYalCvIry82uK+DAbjxhCAinsAooBUQAQSLyAKHrTDB2gIz0BgTJyIvAV8BTwNxwHPGmH0i4gdsFpGlxphz9nxvG2NmZ+QFKaWytlNxpxi0YhC7Ynbxn/r/oUfVHmlHEh1ZB9O7gpevNcfAz/n7ZorN8NGCcKZsOEKH2n583bkW2byy9hwDd7jTZBQE7DfGHAQQkRlAB+DfgGCMWemQfgPQw358r0OaKBE5BRQGzqGUUum0K2YXA1cM5GLiRUa2GEnTkk3TJgqfB7/1g3ylrDkG+Z2HjcYnpfDK9K38ufMkLzYpx+C2VfDIxJvaZCR3QmIJ4JjD+wj7sWvpCyxOfVBEgoBswAGHw5/am5KGi0jWXBxEKZUhVh1bRc8lPRGEKe2muA4G63+AWb2sJ4K+f6YJBufiEuk+diPLdp3kg8eq8c4jVTUYOHAnILj62zIuE4r0AAKBr1MdLw5MAXobY2z2w+8AVYD6QAFg8DXK7CciISIScvr0aTeqq5TKSowxTAqfxCsrXqFc3nJMf3Q6lQs4716GzQZL34Wl70DVx6ztLnMUcEoScTaOTqPXsSPiPCO71aHPQ2Xv4FVkDu40GUUAjuO4/IGo1IlEpCXwLtDUGJPgcDwPsAh4zxiz4cpxY8xrmbI5AAAgAElEQVRx+48JIjIBeMvVhxtjxgBjAAIDA10GIqVU1pRkS+KzjZ8xe+9sWpZqyWeNPyO7V/ZUieJhXn8InwsP9Ic2n4GH8yb3O6Mu0GvCJi4npTC5bxANyqVa10gB7gWEYKCiiJQFIoGuwDOOCUSkDvAT0NYYc8rheDZgLjDZGDMrVZ7ixpjjYvUGPQGE3dKVKKWylAuJF3hz1ZtsOL6BvtX78krdV/CQVI0al8/CjO5w5B9oNQweHJRmjsG6/dG8OGUzOX28mN3/QSoXy30HryJzuWFAMMYki8hAYCngCYw3xoSLyFAgxBizAKuJKBcwy97bf9QY0x7oAjQBCopIL3uRvYwxocA0ESmM1SQVCvTP2EtTSmVWxy4cY8CKARy7eIyhDw6lY8WOaROdOwbTOkPMAeg0Dmp0TpNkwbYo3pwZStlCOZnYOwi/fNnTlqP+JcZknlaYwMBAExIScreroZS6jbac3MKrK1/FYBjebDj1i9VPm+jEDmvCWWIcdJ0KZZukSTL274N8smgXQWUK8PNzgeTN4Z22nPuEiGw2xgTeKJ3OVFZK3TN+P/A7H677EL9cfox6eBSl87hYafTgKpjRA3xyQ5/FUDTA6bTNZvj0j12MW3uIdtWLMfzp2vh6e6YtR6WhAUEpddfZjI1RoaMYs30M9YvVZ3iz4eT1yZs24faZMO9lKFQRus+GvM4j4BOSU3hz5jYWbj9Oz4al+eDxADx1WKnbNCAope6q+OR43v/nfZYcXkLHCh15v8H7eHumat4xBtYOh+UfQ5nG8PRUyJ7PKcmF+CT6TQ5hw8EzDGlXhReblLsvNrXJSBoQlFJ3TfTlaF5d+SrbT2/n9Xqv0zugd9qbuC0FFv/H2timeid4YjR4Oc9jPXE+nl4TNrH/VCzfdqnFk3X97+BVZB0aEJRSd8W+s/sYuHwgZ+LPMLzZcFqWbpk2UdJlmPM87F4ID74CLT8GD+ehp/tPXeS5cZs4fzmJCb3r07hi4bTlKLdoQFBK3XFrI9fy1uq3yOGVg4ltJxJQKCBtoksx1gJ1EcHQ7it44MU0SYIPn+H5SSF4e3rw64sNqV7CRb+DcpsGBKXUHfXLrl/4MvhLKuWvxMgWIymW08U2lWcOWXMMzh2DLpOgWoc0SZaEneDVGVvxy5edyX2CKFkgxx2ofdamAUEpdUck25L5Kvgrpu+eTjP/ZnzZ5EtyeLu4iUdtteYYpCRBzwVQqkGaJFPWH+aDBeHU8s/H+F71KZAz2+2/gPuABgSl1G0XmxjL22veZm3kWp6r9hxv1HsDTw8XcwP2LYOZPSFHQej1BxSu5HTaGMPXS/fww6oDPFylCN8/U5fs2XSOQUbRgKCUuq2iYqMYsHwAh84f4oOGH/BUpadcJ9wyGX5/zZpo1n025C7qdDopxcbgOdv5bUsk3YJKMqxDdbw8dVObjKQBQSl122w/vZ1BKwaRlJLE6JajaejXMG0iY2DVF7D6Cyj/sNVn4OO8AF1sQjIvT9vCmr2neb1lJV55uILOMbgNNCAopW6LJYeX8N7a9yicvTCj2oyiXL5yaROlJMHC12HrFKjdHR7/DlJNSjt9MYE+E4PZefwCXzxZg65Bpe7QFdx/NCAopTKUMYYx28fwfej31ClShxHNR1DAt0DahAmx1u5m+5dBk/9A8/+mWbr6UPQleo7fxKmL8Yx5th4PVy2athyVYTQgKKUyTGJKIh+t+4jfD/7OY+Ue4+MHPyabp4sRQLGnrJFEJ7bDYyMgsHeaJKHHztFnYjDGGKa/0IA6pfLfgSu4v2lAUEpliLPxZ3lt5WtsObWFAbUH8GLNF12380fvh6lPwqXT0HU6VG6bJsmK3ScZMG0rhXJnY1LvIMoVznUHrkBpQFBK3bKD5w8y4K8BnIo7xddNvqZt2bQ3eQCObYJfngbxgJ4Lwb9emiS/Bh/lv3PDqFo8N+N71adIbt/bXHt1hVtjtkSkrYjsEZH9IjLExfk3RGSniGwXkeUiUtrhXE8R2Wd/9XQ4Xk9EdtjL/J/okAGlMqX1UevpsagHcclxjG87/trBYPcimPS4tUpp3z/TBANjDN/9tY/Bc3bwYPmCzOjXUIPBHXbDgCAinsAooB1QDegmItVSJdsKBBpjagKzga/seQsAHwIPAEHAhyJypSFwNNAPqGh/XeO3SCl1r5q1dxYv/fUSRXMW5ZdHf6FW4VquEwaPhV97WHMM+i6DguWdTien2Pjv3DCG/7WXJ+uUYFzP+uTy0QaMO82dJ4QgYL8x5qAxJhGYATgtLGKMWWmMibO/3QBcWXu2DbDMGHPGGHMWWAa0FZHiQB5jzHpj7eE5GXgiA65HKXUHpNhS+Cb4G4auH0oDvwZMaTeFErlKpE1oDPz1ESx6Eyq2hp6/Q85CTkkuJ6bQf+pmpm86ysvNyvN/XWqRzUsnnN0N7oTgEsAxh/cRWN/4r6UvsPg6eUvYXxEujqchIv2wniQoVUrHHyt1t8UlxTH478GsOraKblW68Z/6/8HLw8WtJDkRFgyE7b9Cvd7wyDfg6ZzuzKVE+k4KJvTYOT5uH0DPB8vcmYtQLrkTEFy17RuXCUV6AIFA0xvkdbtMY8wYYAxAYGCgyzRKqTvjxKUTDFoxiL1n9zIkaAjdq3Z3nTD+PPz6LBxaDS3eg8ZvpZljcOxMHD0nbCLi7GV+eKYu7WoUvwNXoK7HnYAQAZR0eO8PRKVOJCItgXeBpsaYBIe8zVLlXWU/7p/qeJoylVL3jvCYcF5Z/gqXki8xssVImvg3cZ3wQpQ1x+D0bmt3s9rPpEkSFnme3hODSUhKYWrfBwgq62Limrrj3GmoCwYqikhZEckGdAUWOCYQkTrAT0B7Y8wph1NLgdYikt/emdwaWGqMOQ5cFJEG9tFFzwHzM+B6lFK3wfIjy+m9pDeeHp5Mbjf52sHg1C4Y2wrOHoZnZroMBmv3RdN1zAa8PITZLz2oweAecsMnBGNMsogMxLq5ewLjjTHhIjIUCDHGLAC+BnIBs+yjR48aY9obY86IyDCsoAIw1Bhzxv7zS8BEIDtWn8NilFL3FGMME8InMGLzCGoUqsF3Lb6jUPZCrhMfXgszngEvX+i9GIrXTJNk3tZI3pq1jfKFczGxT32K581+m69ApYdYg3wyh8DAQBMSEnK3q6HUfSEpJYlhG4Yxd/9c2pRpwyeNPsHX6xrzAsJ+g7kvQv4y0GMO5HMeAGKM4ac1B/li8W4alCvAT88Gkje7t+uyVIYTkc3GmMAbpdOBvkqpNM4nnOeNVW+w6cQm+tXsx4DaA/CQa7Qwrx8FS/8LpRpC118gh3MTUIrNMGzhTiauO8yjNYvzbZda+Hjppjb3Ig0ISiknRy4cYeDygUTGRvLZQ5/xePnHXSe02eDPd2HDD1C1PTz5M3g7P0HEJ6XwxsxQ/thxgj6NyvLeo1Xx8NBFCe5VGhCUUv8KORHCa6teQxDGth5L3aJ1XSdMireaiHbOgwf6Q5vPINWWmOcvJ9FvcggbD53h3Ueq8kITF/shqHuKBgSlFNGXo/lx24/M2TuHknlKMqrFKErmKek68eWzMKM7HPkHWn8CDQemmWNw/Pxleo7fxKHoS3zXtTYdarucd6ruMRoQlLqPxSbGMjF8IpN3TiYpJYlOlToxqM4g8vrkdZ3h3DGY2gnOHoJO46BG5zRJ9py4SK8Jm7gYn8zE3kE0qnCNUUnqnqMBQan7UGJKIrP2zuKnbT9xNuEsbcq0YVCdQZTOU/ramU7sgKmdIeky9PgNyjZOk2TjwRhemByCj7cnv77YgAC/awQWdU/SgKDUfcRmbCw+tJiRW0cSGRtJULEgXq/3OtULVb9+xgMrraUofPNAnyVQNPWCx/DHjuO8NiMU/wLZmdQ7iJIFctymq1C3iwYEpe4DxhjWRa1jxJYR7D6zmyoFqvBjyx950O9B17uaOdr2K8x/GQpVhu6zIG/a/oCJ/xzi44U7qVsqP2OfCyR/ThfbZqp7ngYEpbK4sOgwRmwewcYTGymRqwRfNP6CdmXbXXtewRXGwNpvYflQKNMYuk4DX+cmIJvN8OXS3fy0+iCtqhVlZLc6+HrrHIPMSgOCUlnUkQtHGLl1JEsPLyW/T36GBA3hqUpPud70PjVbCvzxNoSMgxpPQYdR4OXjlCQx2cbgOduZuzWS7g+UYmiH6njqHINMTQOCUlmM4xBSb09v+tfqT89qPcmVzc2N6hPjYE5f2PMHNHoNHv4QPJyfJmITknlp6mb+3hfNW60rMaB5hRs3Pal7ngYEpbIIV0NI+9fqf+3F6Fy5FAPTn4aIEGtDm6AX0iQ5dTGe3hOC2X3iIl91rkmXwGvMV1CZjgYEpTK5mxpCmpoxcGC51Ux0IQqengJV0y5ZceB0LD3HbyImNpGxPQNpXrlIBl6Juts0ICiVSd30ENLUIrfAXx/CoTWQrzQ8twBKpd0ld8vRs/SdGIyHCDP6NaBWyXwZdCXqXqEBQalM5paGkDqKOQArhkH4XMhRENp9Ze197JW203nZzpMMmr6Fonl8mdQ7iDKFcmbgFal7hQYEpTKRmx5C6ij2FKz+EjZPBE8faDrYWo/IN4/L5L9sPMp783ZQvURexveqT6FcPi7TqczPrYAgIm2B77B2TBtrjPki1fkmwAigJtDVGDPbfrw5MNwhaRX7+XkiMhFoCpy3n+tljAm9hWtRKsu6pSGkVyRchHXfw7qRkBwP9XpZwSB3UZfJjTEM/2sf/1u+j2aVCzPqmbrk9NHvkFnZDf91RcQTGAW0AiKAYBFZYIzZ6ZDsKNALeMsxrzFmJVDbXk4BYD/wp0OSt68ED6VUWrc8hBQgOdF6Glj9JcRFQ7UnoMX7UKjCtbOk2Hh3bhi/hhyjcz1/Pn+yBt6e6XgKUZmSO+E+CNhvjDkIICIzgA7AvwHBGHPYfs52nXI6A4uNMXE3XVul7hMZMoTUZoOdc2H5MGt10tIPQauh4F/vutniEpMZ+MtWVuw+xcDmFXizdSWdY3CfcCcglACOObyPANIOQbixrsC3qY59KiIfAMuBIcaYhNSZRKQf0A+gVKlSqU8rlaVkyBBSgIOrYNmHcDwUigRA99lQoWWafQtSi4lNoM+kEHZEnGPYE9V5tkE6P1dlau4EBFe/QSY9HyIixYEawFKHw+8AJ4BswBhgMDA0zQcZM8Z+nsDAwHR9rlKZReohpA8Ue4DX671OQKGA9BV0fDv89ZE1pyBvSej4k7X0hMf11xdKsRkWhx3n66V7OHE+ntE96tEmoNjNX5DKlNwJCBGA41REfyAqnZ/TBZhrjEm6csAYc9z+Y4KITCBV/4NS9wNXQ0h/avkTDf0apq+Z5uxhWPEp7JgJ2fND60+h/vNp9jhOLT4phTlbIhiz5iBHYuIoVygn055/gMAyBW7twlSm5E5ACAYqikhZIBKr6eeZdH5ON6wngn+JSHFjzHGxfuufAMLSWaZSmVp4dDjDNw+/tSGkl2JgzdcQPBY8vOChN6DRq5D9+pPGLsQnMW3DUcatPUR0bAK1/PPyTo+6tKpWTBeou4/dMCAYY5JFZCBWc48nMN4YEy4iQ4EQY8wCEakPzAXyA4+LyMfGmAAAESmD9YSxOlXR00SkMFaTVCjQP4OuSal7mqshpF0qdcHb09v9QhIvwYYfYO13kHQJ6vSAZu9AHr/rZjt1MZ7xaw8zbcMRLiYk07hiIV5qWpuG5Qtqx7FCjMk8zfKBgYEmJCTkbldDqZuSeghpz4Ce6R9CmpIMWyfDqi8g9iRUeQwe/gAKV75utsPRl/hpzUHmbIkgOcVGuxrFealpeaqX0C0u7wcistkYE3ijdDrLRKnbLEOGkBoDu36H5R9DzH4o2QC6THG55pCjsMjzjF59gMU7juPl6UHnev70a1xOl55QLmlAUOo2ybAhpIf/gWUfQGSItY1l1+lQud01h5AaY1h/IIbRqw/w975ocvt48WLT8vRuVIYiua/fyazubxoQlMpgGTaE9GQ4/PUx7FsKuf2g/fdQqxt4uv5vm2IzLNt5gtGrDrAt4jyFc/swuG0VujcoRR7fdPRPqPuWBgSlMkiGDSE9dwxWfQ6hv4BPHmj5MTzwInhnd5k8ITmFeVsj+Wn1QQ5GX6J0wRx81rEGT9Ytofsbq3TRgKBUBsiQIaRxZ6xN7TeOsd4/ONAaRprD9ZyA2IRkftl4hHFrD3HyQgLVS+Th+2fq0K56cR06qm6KBgSlbkHoqVDGhY1j1bFVNz+ENOkybPwR1g6H+AtQ+xlrCGk+11tTRscmMPGfw0xef5gL8ck8WL4g3zxVi4cqFNKho+qWaEBQKp1sxsaaiDWMDxvP1lNbyeuTl5drvcyz1Z5N/xDSbdNh5WdwMQoqtoGWH0JR130Nx87EMWbNQWaGHCMxxUbbgGL0b1pedy5TGUYDglJuSkpJYtGhRUwMm8iB8wfwy+nHkKAhdKzQkRzeOdwvyBjYs9gaQnp6N5QIhE4/Q5mHXCbfGXWBH1cfYNGO43gIdKrrzwtNylG+cDqCj1Ju0ICg1A1cSrrE7L2zmbJzCifjTlIpfyW+aPwFrcu0xtsjnaN3jm609i8+uh4KVoAuk6Fq+zRDSI0xbDp0htGrD7Bqz2lyZvOk70Nl6dOoLMXy6tBRdXtoQFDqGqIvR/PLrl+YsWcGFxMvElQsiI8e/IhGfo3S31Z/eg8sHwq7F0KuovDYcKjzLKTqa7DZDH/tOsno1QfYevQcBXNm4+02lenxQGny5tCho+r20oCgVCpHLhxhUvgk5u+fT5ItiZalW9Kneh+qF6qe/sIuRFlDSLdOBe+c0OI9aPAyZHOeKZyYbGN+aCQ/rTnI/lOxlCyQnWEdAngqsKQOHVV3jAYEpezCo8MZFzaOv478hbeHN+0rtKdXQK/0zywGuHwO/hkBG0aDLQWCXoQmb0FO5+UqLiUkMyP4GGP/Psjx8/FUKZab77rW5tEaxfHSLSvVHaYBQd3XrkwmGx82nk0nNpHbOzd9a/Sle9Xu6Vtr6IqkeGsp6r+/gctnoUYXaPEu5C/jlOzMpUQmrjvMpHWHOX85iQfKFuCzJ2vQrFJhHTqq7hoNCOq+lGxLZunhpUwIm8Ces3sokqMIbwW+RaeKndI3dPQKWwpsnwkrP4Xzx6B8C2j5ERSv5ZQs4mwcY/8+xIzgo8Qn2WhVrSj9m5anXun8GXJdSt0KDQjqvhKXFMfc/XOZsnMKkbGRlMtbjmGNhvFo2UfTN5nsioRYq6N43Ug4GWYFgPYjoXxzp2R7Tlzkp9UHmL8tCgGeqFOCF5uUo2LR3BlzYUplAA0I6r5wNv4s03dPZ/ru6ZxLOEedInUYEjSEJv5N0re8BFgTyg6uhO2/wu5FkBQHBcpBp3EQ8CR4XC0v5PAZRq86wPLdp8iRzZOeDcvwfOOy+OVzvS6RUneTWwFBRNoC32HtmDbWGPNFqvNNgBFATaCrMWa2w7kUYIf97VFjTHv78bLADKAAsAV41hiTeGuXo5SzyNhIJodP5rd9vxGfEk+zks3oU70PdYrUSV9BxkDkFisIhM2BuGjwzQc1n7ZeJR/4NxAYY1i55xSjVx0g+PBZ8ufw5vWWlXiuYWny58x2G65SqYxxw4AgIp7AKKAVEAEEi8gCY8xOh2RHgV7AWy6KuGyMqe3i+JfAcGPMDBH5EegLjE5n/ZVyac+ZPYwPG8/Sw0sRER4r9xi9AnpRPl/59BUUcwB2zLICwZmD4OkDldtaQaBCS/Dy+TdpUoqNhduj+HHVQfacvEiJfNn56PFqdKlfkhzZ9GFc3fvc+S0NAvYbYw4CiMgMoAPwb0Awxhy2n7O586FiDaNoATxjPzQJ+AgNCOoWGGMIPhHM+LDx/BP1Dzm8ctCjag96VOtBsZzF3C/oUjSE/WYFgcgQQKxlJR56A6q1B1/nbScvJSQzK+QYP/99iMhzl6lUNBffdqnF47X88NahoyoTcScglACOObyPAK6/b58zXxEJAZKBL4wx84CCwDljTLJDmSVcZRaRfkA/gFKlSqXjY9X9IsWWwvKjy5kQNoGwmDAK+hbk1bqv0qVyF/Jky+NeIYlxsOcPKwjsXw4mBYpWh1ZDoXpnyOv865mUYmPN3tPMC41i2c4TxCfZCCydn6EdAmheuQgeuvy0yoTcCQiufrNNOj6jlDEmSkTKAStEZAdwwd0yjTFjgDEAgYGB6flclcUlpCQwf/98JoVP4ujFo5TKXYoPGn5A+/Lt8fH0uXEBKclwaLU1XHT3QkiMhTwl4MFBULNLmlVHbTbD5qNnmR8ayaLtxzkbl0S+HN50quvPk3X9deioyvTcCQgRgOPC7P5AlLsfYIyJsv95UERWAXWAOUA+EfGyPyWkq0x1fzufcJ6Ze2Yybdc0YuJjqF6wOt82+5YWJVvg6XGDZR6MgeOhVhAImwOxJ8EnL1R/0uoXKPWg0yghgL0nLzJvayTzQ6OIPHcZX28PWlUrxhO1/WhcsTDZvLRZSGUN7gSEYKCifVRQJNCVq23/1yUi+YE4Y0yCiBQCGgFfGWOMiKwEOmONNOoJzL+ZC1D3jxOXTjBl5xRm751NXHIcjUo0om/1vgQWDbzx7N6zh2H7LNgxE6L3gmc2qNjaCgIVW4O38wqix89fZkFoFPNCo9h1/AIeAg9VLMybrSvROqAYuXy0k1hlPTf8rTbGJIvIQGAp1rDT8caYcBEZCoQYYxaISH1gLpAfeFxEPjbGBABVgZ/snc0eWH0IVzqjBwMzROQTYCswLsOvTmUJB84dYELYBBYdWoQxhrZl29I7oDeVC1S+fsa4MxD+m/U0cGyjdax0I2g4AKp1gOzOTTznLyexeMdx5oVGsvHQGYyBWiXz8eHj1Xisph+Fc7vRDKVUJibGZJ5m+cDAQBMSEnK3q6HukC0ntzAhbAKrIlaR3Ss7T1Z8kmerPUuJXC7HH1iSLlubz2yfCfuXgS0ZClexngRqdIZ8zgMT4pNSWLn7FPNCI1m5+zSJKTbKFspJh9p+dKhdgrKFcl7jg5TKPERkszEm8Ebp9LlX3VNsxsbqY6sZHzae0NOh5PPJx8u1XqZrla7k971Gp60tBQ7/bQWBnQsg8SLkLg4NXrIWlytWw2kDmhSbYcPBGOZtjWRJ2AkuJiRTOLcPPRqU5ok6ftQokVcXmFP3JQ0I6p6QlJLEwoMLmRg+kYPnD1IiVwneCXqHjhU7kt3LxTIPxsCJHVdnDl88DtlyW01BNZ+CMo3BoYPZGEN41AXmbY3k9+1RnLyQQC4fL9oEFOOJOn40LFdQl5tW9z0NCOquik2M/Xd7ylOXT1E5f2W+bPwlrcu0xsvDxa/nuWP2mcMz4fQu8PCyOoVrfAaV24G3c/A4GhPH/NBI5oVGcuD0Jbw9haaVivD+Y360rFpUN59RyoEGBHVXRF+OZurOqczcM5OLSRd5oNgDDGs0jIZ+DdM211w+CzvnW0HgyD/WsZIN4NH/g2odIWdBp+QxsQks2nGceVsj2XL0HABBZQrQ56GyPFqjOPly6HpCSrmiAUHdUYfPH2Zi+EQWHFhAikmhZSlre8qAQs6TwEiKh31/Wk1C+/6ElEQoWBGav2d1Dhco65Q8LjGZZTtPMm9rJGv2RZNiM1Qumpv/tK1M+1p++OfPcQevUqnMSQOCuiN2nN7BhPAJ/25P2bFCR3oG9KRUHodRPzYbHF1nBYHw+ZBwHnIWgfrPWzOHi9d26hxOSrGxdl8080Ij+TP8JJeTUvDL68sLjcvxRB0/qhRzc9kKpRSgAUHdRsYY1kauZUL4BIJPBJM7W26er/E8z1R9xnl7ypM7rSCwYzZciLA2o6/6uBUEyjYFTy+nMrccPffv8hExlxLJm92bJ+qU4InaftQvU0DXEVLqJmlAUBkuyZb07/aUe8/upWiOorwV+BadK3Ump7d9XP/5SAibbfULnAwD8YQKD0Orj63O4WzO4//3n4plfqi1fMTRM3H4eHnQsmpROtT2o2nlwvh4aeewUrdKA4LKMFe2p5wcPpmoS1GUz1ueTxp9wiNlH7G2p4w/DzumWE8Dh9cCBkoEQruvIaAj5CrsVN7JC/EsCI1i/rZIwiKt5SMaVSjEKw9XpE1AUXL73sSWl0qpa9KAoG7Zmfgz/25PeT7hPHWL1OW/D/yXxv6N8UhJvto5vGcJpCRY2002GwI1noKCzhvWXIhPYsmOE8wLjWT9wRiMgZr+eXn/sWo8XrM4RfL4XqMWSqlbpQFB3bSIixFMCp/EvP3ziE+Jp3nJ5vSp3ofahWpaawctfAN2zrOGjeYoBPV6WUtIlPj/9u4/OqryzuP4+5ufTH6S32FCAgkEyCRgkIjoAgoSAmiAU6GyuCrao2utPXvs2lZ33XWrdbe77dnTdk+3e2yXoNVVEY8QAQlghRZ/lQghIRMCgQRIJgFCSAj5nZln/5iBBEplIuQn39c5OYd773PvPDfzcD9z5z55nlsvezjc0e3k40Nn2FRUw0eHTtPZ7WJcVBDfnZ/KskwrE2JCBu8klbqJaCCoPis7W0ZeaR7bq7YjIuSm5LImYw0pnV2eh8MPQeMJ8LNA2n3uEEi5G3x7vuJxuQxfVDawqaiGrSW1nG/vJjokgNUzk1iWaSUzcbQOH6HUANNAUF4xxvBF3RfkHczjU8enBPsH87DtYR5MWkhcxS54ew3UHgDxgZR57r8XmHIvBIZcdoyy2mY2FdWQf8BBbVM7QQG+5KTHsyzTyuyJ0Tp8hFKDSANBfSWny8nOEztZe3At9rN29/SU057kmy4LYaX5sOVlMC6wTodFP4H0b0Bo3GXHONnQSv4BBxv313Dk9AX8fIS7JsXw/JI0stPisARoDyGlhgINBHVV7d3t5B/NZ13pOk42n2RcaBIvJq8gt+4YgVtehu42GD0O5jzr/nuB6NTL9m9o6WRLSS2b9tdQePwcAFnjInh5eQb3Th1DZLAOH9JRYMoAAA/dSURBVKHUUONVIIjIIuAXuCfI+a0x5idXbJ8L/ByYBqwyxmzwrM8Efg2EAU7gFWPMO55t64C7gCbPYdYYY4qu94TU9WnqaOKd8nd4s+xNGtobmBqWzPdCM5lX8Qm+xXvAEgnTH3QPK50487KHw22dTnaUnWLT/hp2Hz5Dt8uQGhvC93Pcw0ckRurwEUoNZdcMBBHxBX4FZOOeX3mviOT3mvkM4ASwBnj2it1bgYeNMUdExAp8KSIFxphGz/bvXwwPNbjqWup43f46Gw5voK27jdmjxvBYsw9ZlbsRv1HuPxab9gBMuAf8ej7ddztdfHL0LJv211BQWkdLp5P4sFE8NjuZZZlWbGPC9OGwUsOEN3cIM4EKY8wxABF5G1gGXAoEY0yVZ5ur947GmMO9/u0QkdNADNCIGhIqzlWQV5rH1mNbMMbFYmcAa2prmdx1EpLnwl/9wD2MxKiecYGMMRSdbGRTkYPNxQ7qL3QSOsqP3FusLM20cntyFL46fIRSw443gZAAnOy1XA3c3tcXEpGZQABwtNfqV0Tkn4GPgOeMMR19Pa7qO2MM+07vI6/4N+x2fIIFYdX5Zh5qasIabYN5L0LG/RBmvWy/Y2cusLHIQX5RDVVnWwnw8+GeKbEsy0xg3hQdPkKp4c6bQLjaR70+TcQsImOA3wGPGGMu3kU8D9ThDolXgR8CL11l3yeAJwCSkpKu3Kz6wGVcfHz8I/L2/ZIDzVVEOF185/x5VhHO6Iw17ofDsWmX7XO6uZ0PDtSyqaiG4uomROCOlCieunsiORnxhFt0+AilRgpvAqEaSOy1PBZwePsCIhIGbAFeMMZ8fnG9MabW888OEcnjz58/XCz3Ku7AICsrq09BpNw6uzvYsu+/yTv8DpXOFhK6uvmH1i6Wj1+E5Z7V7slmfHr6/ze3d1FQeopNRTV8UlGPy0C6NYx/XJJG7i1W4sN1+AilRiJvAmEvkCoiyUANsApY7c3BRSQAeB943Rjz7hXbxhhjasX9xHE5cLBPNVfX1HyqhA2f/5Q3GvZz2gfSOrv4j+CJZN/2OH6TFoFf4KWynd0udh8+w8b9NewsO0VHt4vESAvfmTeRZZlWJsaGDuKZKKUGwjUDwRjTLSJPAwW4u52uNcaUishLQKExJl9EbsN94Y8AckXkR8aYdOCbwFwgSkTWeA55sXvpmyISg/srqSLgyRt9cjellnrOFL3BG+VvsV4ucMHHh1kyipfH53LHbd9FgiIuFXW5DHurGthY5GBrSS1NbV1EBgfwwG2JLMtM4NYkHT5CqZuJGDN8voXJysoyhYWFg12NoaezFcq3Unngd7zWWEJ+SBBOhOyQZB697RnSx827rPihuvNs3O/ggwMOahrbsPj7sjA9juWZCcxOjcZfh49QakQRkS+NMVnXKqd/qTxcuZxQuRuK11NcsZW1wf78PshCQGgo30iczyNZ3yMxrOfRT01jm3tugaIaDtU14+sjzEmN5vs5k8m2xREcqE1BqZudXgWGE2PcA8gVr8ccfI8/OhvJi4ikMDacML8gHk97kNVpDxJliQKgsbWTrZ65Bf5U2QDArUmj+dHSdO6dNobokMCvejWl1E1GA2E4OFcFJe9C8bt01ZezLTSMtbHxVJgA4oPi+EH6I9yfej9B/kG0dznZXOxg434Huw+fpstpmBATzN9nT2JZZgJJUTp8hFLq6jQQhqrWBih93x0EJz6jVYT3kjJ4PTKduq5mJoaP5ZWMR1mcvBgf/Pj0aD0b9x+hoLSOCx3dxIYG8sgd41k+PYF0qw4foZS6Ng2EoaSrHQ5vc088f2Q7uLo4GzOJt6bfx1stxzjf1cSMyBn8U8ZjzLbO5qDjPP+25QgfFDs409xBaKAfizPiWT49gVkpOnyEUqpvNBAGm8sFx/e4Zxqz50PHeQiJ5+SMv+E1i7DR8Uc6G0uYnzSfRzMeJVwmsqnIwb+8/QeO1bcQ4OvD3ZNjWD49gflTYhnlr8NHKKW+Hg2EwVJ30DPd5AZodkBACKQtxZ5yB3nnitl+Yge+4kvuhFyWjl9NcWUAL653UHRyFyJwe3IkT8xNYXHGGMKDdPgIpdT100AYSI0n3AFQ8i6ctoOPH0xcgFn4Mp+HR7P20Jt8XvRTQvxDWD35YeJYwO9LO/jm5qM4XYa0MWE8v3gKubdYsY62DPbZKKVGGA2E/nbFw2EAxs6EJT+jO20pO+v3s/bgWsoayoi2xJCb+Djn6maQt/k87V3VJIy28LdzU1g+PYFJcTp8hFKq/2gg9IfOFij/0B0CFTvB1Q3Rk2H+C5CxgvawMWys2Mhr29dQfaGaeEsSmZbHKTk0kf9rNUQEtbBixliWZSYwIykCH304rJQaABoIN4qzC47tcodA2WboaoFQK8z6Nu225RwO8MfeUIa9LI/d1btpaG8g2j8VS8NjHDk1kZP+fmTb4lmeaWVOagwBfjp8hFJqYGkgXA9joHqvu5to6fvQWk/7qHDK07Kxx6ViN+3YG8o4+vvNOI0TgFE+YdCeTKtjBcfbk5mdGstzd1lZmB5PiA4foZQaRHoF+jrOlEPxetpK1lPeVofdEozdOg57QArH2s/gbN4HzfuIGBVBUvAkbEHLOFEXRU1dFM3d4WQmRvDUAiv3TbMSE6rDRyilhgYNBC+1nT1K+f61lFZux95Rjz0wkMoIf5wR8QBE+oMtciJ3Ry7FvzuRSkcEew51s6exHV8f4fbkSB5fEs/C9DjGhGsPIaXU0KOBcBWtXa2UnyvHXluIvWon9nNHOEYnLhEIgEhLDLboqcyPy8QWZSN1dBpHanzYXnqadZ+doqGlkwC/VuamRvPMgkksSIsjIjhgsE9LKaW+0k0fCJcu/mft2M/aKa0vobKpCpdn2uiobic248c90dOwpd6Hbfx84oLiaOl08vGh07y/p45d5SVc6OgmNNCPeVNiWZQRz12TYnRIaaXUsOLVFUtEFgG/wD1j2m+NMT+5Yvtc4OfANGCVMWZDr22PAC94Fn9sjHnNs34GsA6wAFuBvzP9PFtPa1crhxoOXbr428/aqTxficu4AIgWf2xtLWS3tWHzCcI2YTGxmQ8h1ukgQkNLJzvtp9hWWsieino6u11EhwSQe8sYFqbHc+eEKAL9dOgIpdTwdM1AEBFf4FdANlAN7BWRfGOMvVexE8Aa4Nkr9o0EXgSyAAN86dn3HPBr4Angc9yBsAj48HpP6KLWrlbKGsouv/g3VWI8n/xjLDHYgq0stEzA5ijFdv4MsX7BYFsKU1fC+Dng40tNYxvbP61i28E69lY14DKQMNrCQ7PGkZMez4xxETqInFJqRPDmDmEmUGGMOQYgIm8Dy4BLgWCMqfJsc12xbw6wwxjT4Nm+A1gkIruAMGPMZ571rwPL+ZqB0NLVQtlZz8W/wX3xr2qquvziH2UjZ3wONv8IbA47sWVboOFL8A2ASTmQvRJSc8B/FBWnmynYXUlBaR3F1U0ATIoL4el5E1mYHq/DSSulRiRvAiEBONlruRq43cvjX23fBM9P9VXW/xkReQL3nQRJSUlc6LzwZ5/8j58/funiH2uJxRZlY/H4xdiibNiibMQ4nXDwPfjTenDsBwSS58DsZyAtFzMqnJKaJrZ9VEVBaR1Hz7QAkJk4mh8umkJOehwpMSFenrJSSg1P3gTC1T4Ke/td/1/a1+tjGmNeBV4FGD1xtLnzrTt7Lv5B7ov/kpQlpEelY4uyEW2Jdu/Y3gRlH8DH/wmVfwDjgjGZsPAVyLif7uA49lado2BHDdtLv8TR1NM99JE7x7PQFk98+CgvT1MppYY/bwKhGkjstTwWcHh5/Grg7iv23eVZP7avxwz0DeSpzKcuffK/dPG/qKvdHQLF6+FwATg7ICIZ5jwLU1fSPnoCn1TUU7C9jh32Es61dhHo58Oc1BieydbuoUqpm5s3gbAXSBWRZKAGWAWs9vL4BcC/ikiEZ3kh8LwxpkFEmkVkFvAF8DDwX9c6WGJoIk/e8uTlK11OqNrjHkPIng8dTRAcA1mPwtSVNEdN4+PD9RTsqGPXoR20dDoJDfRjflosOenaPVQppS665pXQGNMtIk/jvrj7AmuNMaUi8hJQaIzJF5HbgPeBCCBXRH5kjEn3XPhfxh0qAC9dfMAMfJuebqcf0pcHysZA7QF3CBx8D5prPRPM5MLUlZyNncXO8rNs21HHJxUf0el0dw9dmplATnocd06I1sHjlFLqCtLPXf9vqKzMqabwFw+5g6D+MPj4Q2o2TF2JI+4utpWfZ1tpHYWe7qFjIyzkpMezKCOeW5O0e6hS6uYkIl8aY7KuWW5YBYLV1xQ+EQLjZsPUFRyLvYetFR0UlJ6ipMbdPXRyXCg56XHkZMRjG6PdQ5VSyttAGF5fnodZsT9QwObjPmzbXcexMyWAu3voc4unkJMeT3J08CBXUimlhqdhFQiHLgSx5LVKfH2EWSmRPHrneLK1e6hSSt0QwyoQLP6+/GzlLSxIi2V0kHYPVUqpG2lYBcK4qCBWzBh77YJKKaX6TPteKqWUAjQQlFJKeWggKKWUAjQQlFJKeWggKKWUAjQQlFJKeWggKKWUAjQQlFJKeQyrwe1EpA0o7eNu4UDTDS7rTbmvKhMN1HtZp6GuL7/fofya13vMJOBEP7/m9bY7b8uMlPapbbNHujHGcs1Sxphh8wOc+Rr7vHqjy3pT7qvK4J5HYtB/nzfoPfH69zuUX/N6j9nfbdPb8jeizEhpn9o2LzuGV+1zuH1l1Pg19vmgH8p6U64vrzucDcZ59sdrXu8x+7ttelte22YPbZs9vGqfw+0ro0LjxZjeQ91IOQ/VYyS9pyPpXJSbt+/pcLtDeHWwK3CDjJTzUD1G0ns6ks5FuXn1ng6rOwSllFL9Z7jdISillOonGghKKaUADQSllFIeGghDgIikiMj/isiGwa6LUr2JyHIR+Y2IbBKRhYNdH9W/NBD6iYisFZHTInLwivWLRKRcRCpE5DkAY8wxY8y3Bqem6mbTx7a50RjzOLAGeGAQqqsGkAZC/1kHLOq9QkR8gV8BiwEb8NciYhv4qqmb3Dr63jZf8GxXI5gGQj8xxvwBaLhi9UygwnNH0Am8DSwb8Mqpm1pf2qa4/TvwoTFm30DXVQ0sDYSBlQCc7LVcDSSISJSI/A8wXUSeH5yqqZvcVdsm8F1gAbBCRJ4cjIqpgeM32BW4ychV1hljzFlA/7OpwfSX2uYvgV8OdGXU4NA7hIFVDST2Wh4LOAapLkr1pm1TaSAMsL1Aqogki0gAsArIH+Q6KQXaNhUaCP1GRN4CPgMmi0i1iHzLGNMNPA0UAGXAemNMXyf8Ueq6aNtUf4kObqeUUgrQOwSllFIeGghKKaUADQSllFIeGghKKaUADQSllFIeGghKKaUADQSllFIeGghKKaUADQSllFIe/w+T8I7vSjYwZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in eval_measure:\n",
    "    df_time = plot_metric(col)\n",
    "print(df_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_component=500 taking too much time (over half an hour), use 100 instead\n",
    "save_pickle(os.path.join(PARAM_DIR, model_type), 100, False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}