LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fed8cbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "551453a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuración warnings\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a75333b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e481acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cve_ent</th>\n",
       "      <th>poblacion</th>\n",
       "      <th>nombre</th>\n",
       "      <th>2020-02-26</th>\n",
       "      <th>2020-02-27</th>\n",
       "      <th>2020-02-28</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-01</th>\n",
       "      <th>2020-03-02</th>\n",
       "      <th>2020-03-03</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-12-31</th>\n",
       "      <th>2022-01-01</th>\n",
       "      <th>2022-01-02</th>\n",
       "      <th>2022-01-03</th>\n",
       "      <th>2022-01-04</th>\n",
       "      <th>2022-01-05</th>\n",
       "      <th>2022-01-06</th>\n",
       "      <th>2022-01-07</th>\n",
       "      <th>2022-01-08</th>\n",
       "      <th>2022-01-09</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1434635</td>\n",
       "      <td>AGUASCALIENTES</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3634868</td>\n",
       "      <td>BAJA CALIFORNIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>427</td>\n",
       "      <td>98</td>\n",
       "      <td>164</td>\n",
       "      <td>813</td>\n",
       "      <td>1021</td>\n",
       "      <td>1269</td>\n",
       "      <td>988</td>\n",
       "      <td>589</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>804708</td>\n",
       "      <td>BAJA CALIFORNIA SUR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>586</td>\n",
       "      <td>278</td>\n",
       "      <td>289</td>\n",
       "      <td>863</td>\n",
       "      <td>1041</td>\n",
       "      <td>905</td>\n",
       "      <td>765</td>\n",
       "      <td>642</td>\n",
       "      <td>353</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1000617</td>\n",
       "      <td>CAMPECHE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>110</td>\n",
       "      <td>88</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>5730367</td>\n",
       "      <td>CHIAPAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 687 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cve_ent  poblacion               nombre  2020-02-26  2020-02-27  \\\n",
       "0        1    1434635       AGUASCALIENTES           0           0   \n",
       "1        2    3634868      BAJA CALIFORNIA           0           0   \n",
       "2        3     804708  BAJA CALIFORNIA SUR           0           0   \n",
       "3        4    1000617             CAMPECHE           0           0   \n",
       "4        7    5730367              CHIAPAS           0           0   \n",
       "\n",
       "   2020-02-28  2020-02-29  2020-03-01  2020-03-02  2020-03-03  ...  \\\n",
       "0           0           0           0           0           0  ...   \n",
       "1           0           0           0           0           0  ...   \n",
       "2           0           0           0           0           0  ...   \n",
       "3           0           0           0           0           0  ...   \n",
       "4           0           1           0           0           0  ...   \n",
       "\n",
       "   2021-12-31  2022-01-01  2022-01-02  2022-01-03  2022-01-04  2022-01-05  \\\n",
       "0         136          30          58         358         362         333   \n",
       "1         427          98         164         813        1021        1269   \n",
       "2         586         278         289         863        1041         905   \n",
       "3          14          15          28          86          94         110   \n",
       "4          19           4           9          20          24          32   \n",
       "\n",
       "   2022-01-06  2022-01-07  2022-01-08  2022-01-09  \n",
       "0         320         101           6           0  \n",
       "1         988         589         141           1  \n",
       "2         765         642         353          31  \n",
       "3          88          55           8           0  \n",
       "4          34          17           3           0  \n",
       "\n",
       "[5 rows x 687 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path =  'C:\\\\Users\\jose_\\Desktop\\covid-19_model'\n",
    "datos_total = pd.read_csv(path + \"\\datos_covid\\Casos_Diarios_Estado_Nacional_Confirmados_20220109.csv\", sep = ',')\n",
    "datos_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f467c03b",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = datos_total[\"nombre\"]\n",
    "datos_total = datos_total.drop([\"cve_ent\",\"poblacion\",\"nombre\"],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "feed7d27",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2022-01-03</th>\n",
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       "      <td>863</td>\n",
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       "      <td>905</td>\n",
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       "      <td>31</td>\n",
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       "<p>5 rows × 684 columns</p>\n",
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      "text/plain": [
       "   2020-02-26  2020-02-27  2020-02-28  2020-02-29  2020-03-01  2020-03-02  \\\n",
       "0           0           0           0           0           0           0   \n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "3           0           0           0           0           0           0   \n",
       "4           0           0           0           1           0           0   \n",
       "\n",
       "   2020-03-03  2020-03-04  2020-03-05  2020-03-06  ...  2021-12-31  \\\n",
       "0           0           0           0           0  ...         136   \n",
       "1           0           0           0           0  ...         427   \n",
       "2           0           0           0           0  ...         586   \n",
       "3           0           0           0           0  ...          14   \n",
       "4           0           0           0           0  ...          19   \n",
       "\n",
       "   2022-01-01  2022-01-02  2022-01-03  2022-01-04  2022-01-05  2022-01-06  \\\n",
       "0          30          58         358         362         333         320   \n",
       "1          98         164         813        1021        1269         988   \n",
       "2         278         289         863        1041         905         765   \n",
       "3          15          28          86          94         110          88   \n",
       "4           4           9          20          24          32          34   \n",
       "\n",
       "   2022-01-07  2022-01-08  2022-01-09  \n",
       "0         101           6           0  \n",
       "1         589         141           1  \n",
       "2         642         353          31  \n",
       "3          55           8           0  \n",
       "4          17           3           0  \n",
       "\n",
       "[5 rows x 684 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45ddd9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "2020-02-26    0\n",
       "2020-02-27    4\n",
       "2020-02-28    2\n",
       "2020-02-29    2\n",
       "2020-03-01    2\n",
       "Name: Casos, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casos_nacional = datos_total.iloc[[-1]]\n",
    "casos_nacional = pd.melt(casos_nacional, var_name = \"\", value_name = \"Casos\")\n",
    "casos_nacional[\"\"] = pd.to_datetime(casos_nacional[\"\"])\n",
    "casos_nacional = casos_nacional.set_index(\"\")\n",
    "casos_nacional = casos_nacional[\"Casos\"]\n",
    "casos_nacional.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b0c5c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_total = datos_total.set_index(names)\n",
    "datos_total = datos_total.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fce5cb91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-02-26       0\n",
       "2020-02-27       1\n",
       "2020-02-28       1\n",
       "2020-02-29       1\n",
       "2020-03-01       0\n",
       "              ... \n",
       "2022-01-05    1665\n",
       "2022-01-06    1675\n",
       "2022-01-07    1045\n",
       "2022-01-08     240\n",
       "2022-01-09       1\n",
       "Name: MEXICO, Length: 684, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_total.iloc[:,14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f65d50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_sma = pd.DataFrame()\n",
    "for i in range(33):\n",
    "    datos_sma[names[i]] = datos_total.iloc[:,i].rolling(window = 7).mean()[6:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "55dd3132",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_sma.index = pd.to_datetime(datos_sma.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0ad11a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BN0lFy2njahlRBrOpY3fiz2vRqCgROKTlaQ4xVnBt4WzEKgv3Bf9zj6utnMmeRlxgDFaivUR0HoZGsxsInN7REtoiMZ8G6BLnuwt/YrOMuMA4rfZWljt7cKf97qLeGP6hglA3ZCazZSOCqwG2RsJqB5sZ0e9WaIjOw9Bodgt+3l5SQCSb9KyPRPMMtolod+IL7Ojnn7FbmJhbw4P2YRSwYoUIJdLAVQTqK2cxRLJMkh0AtEU0wbw9uL4nX8MYJ3XsX/2vLl1LCwyNpgSBD6ODVazPutpQYAzm8hPdjS97oxrelLblGCheVrOBEj29Aw0D6oa4NaX2FDdSqqk9FBjZwuAyT/ndCk+XZzlz3Y+6dC0tMDSaEvjaghkrpy2xSaoyY8XyBbLaAd5tlIpSm9C+CoBlzlQgLryjdaZEhIYq11l+gLEaYFDXlXI1DOEUFrCxYu8uXUsLDI2mBGGUVPgTcSNNwllq6qgK1u0IixQOtpXr7sT//KMCenzbKhqsUdQxNJbEB8UaRiFVxX/t/fmQ+TiCEysTMvgEhlsafiLVbC7fs0vX0gJDoylBKQ0jaZKaNnpIXMMYZBPR7iRInIx83uPaV7Ml45qahKSGIaEw9yKCHnIOY7Q0MpEd1LfmSJnu/sFWlt4Nq1UMo5lWc2iXrqUFhkZTAlUqrFYkZlOfNqoiqFwLWsPoXuJRaiY2Y9vXsLVsJoBXCiRuk5LwJYbAKmciADONzTS05Ul70n+wCXalFGVOGxY2LVpgaDTdw4JVO7jv5U1A6cS9pElqtNeFz0cn8XUfTuD0dqeoabKVlMqxtay0hkHkvXj2qVXKFRizjS2uwLAGqcAAKuwGAFqMrgkM3XFPo/H4+J9fpC1vc8jUESWdriRMUlZixhpsE9HuJIyScv+dJlsBqMm4ZT+itaMgLsx9YVJLFXWqkr1TW7mrJUdZygQGoUlKwZBAYFR16Vpaw9BoPEZVusUFX9lYX2SSypBj3tY72Te/LDg+maOhTVLdh+9D8j//CeJWA27MjAMIci58RMItoTBxtYw9zS00thcoeGrLYBPsSikq7CYAmrRJSqPpHqrKUgC0Zu0ik9QV1t84ed2v+EHdN5khm4tWuDD4JqLdSfLzHy+12Ji0pEa6OyQusOMaRig8VjkTmapcM2NQlr7goJTilufW0pwd+KVdFFBWcAVGq9YwNJruoTzl/hxacoVwhWsIoHif+Tzrqw7GQfiS9Q8s7CIbus7D6D4cFXd6T5BamtJjQFyzUrQ6LSTCaiPCfLWawDC7lqHEw58XrN7Bd+9fxnfuW7rb76XXUZBWXtMvybzNwZ2jfRgajceQjPtzaM3ZjKhwt1mGMJZ6Rksjj486nhfU3pzXfBvbGIXIQbHztUmqG0mYBCewg6bUmEhyXnGmd8zd5L32Hd8zZAtLlJvMl807QXmQ6ubs7ryLPoGjFJZy7zNPqkvX0hqGRuPhO7Gbs4VYx70PWY8DUFM5h3uGXcyD9mGcbTyFqeICIqdNUt1GKZNUY3psIAiMSIc9iNeSgtA8FQqMzcG+9oJNyrtuYRDUlVJAytMwcqS7dK23FRgi8kcR2S4iSyPbvicim0RkifffeyP7rhCRlSLypoicGtl+iIi85u27RrxvVEQyInKHt32hiEzr0h1pNLuIH8rZmi3EOu6daz7Ni86ebK46AEOEu+x3M1oa2XvVjbHztQ+j+4jXklKeSWpsqGFAXGDETFJh7+8NagyOpJhphAKjJVvA8sKvCs7A/86UAssXGLKbBQbwZ+C0EtuvVkod6P33IICI7ANcAOzrnXO9iGd0hBuAS4DZ3n/+NT8B1CmlZgFXAz/dxXvRaLqEv9ZsydnBhJWy25gsNTxl7w+GgQg86RzEfOcQZqz5OxbRonYDf/LpKQIfhgjDaKFccjRlxsWEQjJSqpQwKWDRUrkHM2VLcFxDWx7Ly/rODQoNQ5FyXIGR76IX4m0FhlLqKWBnO9yfCfxdKZVVSq0BVgKHicgEYKhSaoFyvYm3AmdFzrnFe303cKKvfWg0PYnv6G7JFoIJq7xpDQAr1SSE0ETyD3U8ZdkdHG4sD87XPozuI2qSmuiVKG9Kjw1LmJP0WYSp3kn/RkvVDGZGTFL1rXlSXkKgPYg0jBwpFF2bWrviw/i8iLzqmaxGeNsmARsix2z0tk3yXie3x85RShWABmBUqT8oIpeIyCIRWVRdXd2FoWs0xfhaRUsuDKsta9sOwGY1KjYRPcv+FMxy3m8sCM7XUVLdhy+8ReBAYyUA1RUzQiEhiW58EHOIR/e1Dp3OVNkaaIMNbfmgl8Zg8WGYKk+OdK+1aL0BmAkcCGwBfultLyW+VCfbOzuneKNSNyql5iml5o0ZM+YdDVijeTv8H1NrthA8gabjRpe0kw66lgHkyLB5yvs4y3yWqV4Wsm7X2n2EHfcMjjaWsVWNoK5sWqwibTIqKpQlcWNV29AZpMVmiriLzIa2fHD9vD0IhLwCy8mRl1TvtGhVSm1TStlKKQe4CTjM27URmBI5dDKw2ds+ucT22DkiYgHD2HkTmEbTbfg/ptacHURJWZ7tN0sqtnIVgdX7fI520lybugYDh7pIzwVN1wg67onDkcYynnX2RQwh2vMiFhUVEeZJk1T7MLdgoW+WigqMQhdblvYHFIqUypKTFF293V0SGJ5PwudswI+guh+4wIt8mo7r3H5BKbUFaBKRIzz/xMXAPyPnfMR7fR7whNK9LjU9gFKKvyxYS32rKxT8SSpvO2HxO0/DyKp0bFUrQG7IRH5Y+B/mGms5RN4KMok1XcefASobVzFKmljg7IuRENiSKD2frCXl0zp0Jnllcqb5LOAJDO+7HgwmKcfXMEjTgfFmp9mZsNrbgQXAHBHZKCKfAH7mhci+ChwPfBlAKbUMuBN4HXgYuFSpIFj9s8AfcB3hq4CHvO03A6NEZCXwFeDyLt2RRtMJT7yxjc/8ZTEAq2ta+N9/LuNzt70EhGG1OdsJwzoTGoa/cvW77z1kH0ZWWZxkLmaHFhjdhv/5l7e4LtEVziSv54W7vSjTO/o+kaOhMkP5vXMG7zefZ7ZspLY5F3zXgyOsVgUmqa5qGG8bY6WUurDE5ps7Of4q4KoS2xcB+5XY3g6c/3bj0Gi6g0//ZTF5W7G9qT3oj7B8S6O707drF5zQJGK7GkYOy4vvD2csw4AWynlFzeRw4w3+2jLws4Z7Ct+flGnbBsBWNdIV2N7+2HdBsYYhiX1/cU7l8+a9nGi8xO+yk2n3KtYOBg1D4UVJSZquGm90prdmULH3BLda57JNjcE23/fgT1I5W5XQMNJFbUD9SelFZw77ylpampt74A4GB/60lmndRkEZVDPc0yJKJ+4R1T6SZUMQ6mQ4NWooU8UVQNVNXqmMQeD0VhGTVFfFoxYYmkHFnHFutc43tzUVRYz4b3MFOyw+6ORwlJDH9ByrodPV9F4vdaaTEpsJ+fW6PEg3sK2xnRXb3Oqq6dZtVDMcBwOkY8c2xP1LycKEhsA6NS4QGNs9gWEPAqc3gKl6yCSl0QwkKsvcR766KRuYnXx8DSNvh3sMO0uWFP4KttSE9aZyAwP3kg205e2gs5vmnVHXkuPmZ9Zw7ZMrg22pli2sU26al1dLyNsTFxZuWG0ozI2oQxz3u1qnxnGEl2i5vakdgPwAFxhBb3onR14qtElKo3kn+L+X6qZs0WorGpsf1JKys+S8dZUQ9mCIRuKsU+PIkWKOsWHQdXPrTm7476qYsABItW5jq3J7YERNglCq417kdVSgeMJ9vRrLeKklQ45tDZ7AGOAmqSCfpTfzMDSa/oq/wqppzsayXh0n1CoKjgri8w0nR9ar8JmMkvJ9GDYmm6w9mONpGJpdo7GtOI/FatnKFl9gEHV0q6LSILGQ25i8cLXDtc54DBTTjBo21rW5V/G+9OdX7+A/b27v9nvqbfxn2lI5CpIp0qrfKVpgaAYVTkTDiK62mrKFmLru+yIMO0tWuT0EipPDwvM3Z2Ywx9hAS9bm3Bue44k3tu3W+xiIJMtWVNCOmWtkG55JKvKZK1VcGiTUMIojqHwNA+DAqlo21LXG/tYFNz7PR//0YjffUe8TFHF0sq4Po4sKlRYYmkGFv8Kqbs7GBERTez4mQALndcH3YSQdrRIUIgSoLpvOeKmjob6WxevquPS2l3frfQxEHAWZiP9nvNfHe1ugYcSFRLI0SLzOVGSfd+waNR6AuZntbGscHCHQcZNUWmsYGs3b8erGehra/NBZd1t9a56WXGg+amwrxFa4fuVZsUOBAYlM48is1FzuTkY0bIqdr9l5lILRlRk+dPgeAIyTOgC2qVDD8D9yRXGuRbySbUSweCVF6hhKrTGKvVi3+2+mjxA0AuupxD2Npr9zxrVuSYi1Pzk9plVsjJglGhMaht/bQmy3LDTEG/Mky09kPYGhGjcClV3+YQ5GlFIYRjjZj/dKym3zileX6oHhkzQXJjUMX7isTc1kUnbV7rmBPoj7TCtMx/VhdDURQ2sYmkFDfWsuJhQ21LYFrxvb8jENIxcIjFDDMCImqWQeQHaIW63fatq028Y/0HGUik38E3yTFCODY0IfRmLmi2V6J9u1RiLaUjMZm11HhsFTxsXCxsAhb6R6rby5RtPv+PzfXsZRYXTNmpowM7upPV6aPPBh5FtpV16UFOHE49eS8rErx5FXJpnmDWh2DYUnlL0PeZzUYmeGkZUMEBfSyWmvVKJedK//Va1Pz8TEZrZE2/MMXJSCDK451o2S6hpaYGgGDc+srGFtTSvjhpYxYVgZC9eEVfSzBSdhkvJ8GO0NNDDEfR0N3YRYclh5JsMyNZXRdUt2920MWBwVF8TjpQ57yPiIbyJeVDBKtLaURIRO8N7btz7tljrf1wj9GAPZ36RQEYHRew2UNJp+QdJ08cLaWgwRDp8+knU7Qh9GrmDjKBVkaQc+jPY66pUvMKImqbiGUZG2WOTMYWzjMobSslvvaaDiKOUl2bnvx0ktduWEQH1IBhpESYbVSmKf/11VWxPIGhXsI2uD/VHtsjDAEvliGoahE/c0mk4JO7fFV5zDylOx43K2gyIM68wVHAQH2huop9I9j7ipIy4wTO6x30VKZTnDfG633MuAx9cwvA95gi8wPIRkQZCQZNmWeARVRDMxTLZWzGY/Y22wvzkiMHIDTGA4SpGWUMPQJimNphN8FfyyE2cH25ImC3D7cTtKkbFM933BoYo2RDk0ehoGEuZeJO3kliksZyoORhAOqnlnuE5vV2iY2IymAadyfNh6NZG4F8U1V8UXBeG+iGYIrB16KIcYKzjRcPuiNGdDgZHNOzy3soZplz/Ass0N3XuDvYCCmElK15LSaDrBD28VIZjsjUilWZ+c7YCKaxjDxHWK16tQw+goD0MQTDHIS4ayQRSB050oFX43Y6jHFIVdOSGewe19h8mJz83D8F+XyvQOXz8/8WJed6byXetWQMVMUtmCw+PL3RIhC1bt2C332ZPETVIZbZLSaDrDT1yKliMXiGVpgysgHKVIme72nO0w3PNF1BP1YbjHGxLP9Paje/JGWguMXcRRKviMx3tamlM1IVKFthOTFNGw2kQWeKRUiCGCY6b5m30CexjVTJVtQYtecB3g/jOQHwjNlRRBCLGbuKc1DI2mQ4Kqs5GS11E7uU+24Pow/O2OUowUtydDqGFIzLSRLE1hipCTTLCi07wzHBVGovllQVTlxLh5qYOwWndf+G8yDyNaydYUYYGzDwCHG8u5b0mYO5MtOFiewBgIDnCFIiNaw9BoOmVVdTMNrWEynj+hu28oaZJSKnSO245iktQAsEmNBvzEPe8SCbOHX1sqJ2nKJBdc4+kV1bRk4zkemo5wc2RMIxQYTtX4mKkpUqw2TvS7IeHDiEW3ua9Xqwk0qgpOGrqJZ1bUBMdm8w6Wt7IYCKXPoyYpW2sYGk1pTvzlf3n3L56MaBih9mBESnz4+E5vf2KxHcUU2Q5GKlYtNd43Ojzf95HkCE1Sr29u5MM3v8DX735l993oACLMw3BNUlllQfmohKmpo7BaiZiuEj6MWPVaV7tUGLzmTGeusSZWUyxbCBtgDYTmSjGnt5Hu8vW0wNAMOPxErPrWPHWefTrqc4gKD59AwzBDgTFZqmH4FJT3M4lOPIpEgTuJaBiewNjW6DbpeWNr026604FF4MMwhCmyzS06WJSA574uUjDeRsPw30e1xNfUDMa1riAdMSHetnA9/3plMwD5AdBu11EquD9bMlrD0GiS7GgOnZiL1oYhrqHTu0SUlNfH299uK8U02QojpoUHJUJpo05vf/WbJR3YjFu9ZkqFgeA87QH8HhcZu4UTjCU848x1G1V5+4W4byKKQCAVknW+SpmkAF51pmOoAntKWM7l3pc3BQK+MBA0DEXwPNpmWvswNJok1U1hr4OFa9zQyKij2/VnOGTIcYCspIJ2cp7TO8izsPPsKZtg3H7BtQRiYZ3FJilcgeFpGL7vYiA4T3sCv87XHnULKJcc99jHeILA3R/1YRSH1cbc3MVRUkb42v+OX1UzANjfWFNyPAPChxErDdJ1DUOXN9cMOHyBYRnCwtWu89SIOLrPzP6LS57+I58rcyf0jWo0P85dg6MywWQy0d7orszGzw2uG5uwKF7FmiK0qzQjvR9ofav7b05rGDuFVxmEKbXP06AqeFnNjgmCaLXg0iap0CPekdPbMELz1EY1hlx6OHMLq4ETi8bT3wXGcT9/ElspTvUFhplG0bUADK1haAYcO1pcgXHafuNZXePmUojnYxhOEx9ru4XqoXP5t30Ev8ifz2Sp4dCmJ4PEMYCZtrfqjAoMQqETPda/vmG4Jinfh+HH9/f3iaenULhBB8Nb1/GG2gMbM26Akk6KD5L0W0hiXylzpNA0ci4HGStLXrO/mxLX7mhlQ21bGCVlaJOURlOEv6KfMrIi2OY6uuEc8xnKyPLcnG/y+fwXudY+m62MYlp+BYrQ6T3LWeP28h41Oxbf7wuJaJl0CJMB21UqCKutbnYFlzZJ7RyO436+VW2b2OD13zYkUSE4IrCjJMuGSAf7SPg3GsYdxl7GBkbTUDSegRAlBZCRHApBiaVLg2g0SRzvh54yw8dbRBhOMx+3HuJNaw4Nw/YK9r0l05meX+V2fBMBFAc5y3iLKWBasVpGEpmU4iW03ZVre0TD2O71jR4oE8/uxlGKFDkqstvZ4IxxN3YQ7VRMKFiS5kISmd7R761x4rEAnGC+VHTF/izoo4IhTQHbyCAiuvigRpPEd+ylzXjY61dzv2MiO7i7/AOxCKd6YwSVqjlI3DvGWMq+rOIf6jggbs5IhtL6+A7xNpUqCqvVJqmdQwFjnW0IivWehhHP0o4KhRK1pCIO8fj3FA+5jbXWHb0fq5wJfNx8mDLCYAno399bWz7MLcmQxzbTblmU3a1hiMgfRWS7iCyNbBspIo+JyArv3xGRfVeIyEoReVNETo1sP0REXvP2XSPeNy8iGRG5w9u+UESmdemONIOSnz/yBtc+sQIICw5GNYzyXA3HFp7j9/b7eLHsyNiEkjPKKVdtXinoAldaf2MbI7lHnQAQ0zDi5c2jr10NY4caRpnkGUozWz2B0VW78UCnoS3PtMsf4IU1tYwrbAMIBEYsrDZhdoqSDLlN+jOiRSOjiwXLMvhV4Xz2MjZwsle91qc/15JqaAtzSzLkcIwMSNefxZ3RMP4MnJbYdjkwXyk1G5jvvUdE9gEuAPb1zrleREzvnBuAS4DZ3n/+NT8B1CmlZgFXAz/d1ZvRDF6ue3IVv3j0LdbtaAnUcSsiMCZVP42B4l/2kUWTRtYod1eXymFu2yL2Mdbxc3UxBYn3zBCJV0stivU3hHWMA2C/8tqitq+a0kTLiI9ztgIEPoy4w7qTPIyEQzz2mnjRyKhD3DQMHnHmkVUp5ibCa/uzhuFH6IGbh2Ebroax2wWGUuopoDax+UzgFu/1LcBZke1/V0pllVJrgJXAYSIyARiqlFqg3F/zrYlz/GvdDZwo0W9Uo3kHbKpvK2mSmrD9aWpkFK+rqUWlQbJGOQaKtMoyt+1FmlUZjzrzYsleEJ+8FMWTkmnAWscVGHtnqnfXLQ44Nta2Ba/H2lspGBmqGQb4hQQJXneY6U18X4eJe8Q1Q8sQCli8rqZygLEqdk0/SmpDbSu1Lf2rAnFcw8jjmGmE4vyVd8qu+jDGKaW24A5gCzDW2z4J2BA5bqO3bZL3Ork9do5SqgA0AKN2cVyaQU51UzYwSfkaRgXtjK9+lkXpefhr1OiEkjXKASinnT3bX+FFZw7tKuLsjpTXjppEzITEMEVY57g/hT3YtrtuccDxu6fCiXp0YTtt5RNi5VjiPozS13AFSwcmqajvSeLfm69pLnT24iBZQQXtwb6842oY7/rZkxzx4/m7fH+9QbTgZYa85/QOzbW7Snc7vUt9naqT7Z2dU3xxkUtEZJGILKqu1is4TcjQMjcH1RUYnknKmwzOMZ8mZbfyRPkpQHEvi6zhht+OU7VMyK1jsbOnG2kVCcV0/4nWkko6Vl1zVZOTYosaySR3PaV5G2pbcqyuDnugj7RraC8fG7xPBDvRkfEhKkyKc2TijvPoPv8Zec7Zl7TYHBjJyfCj3MDtl9KfaM3Fnd6OmXFNUl2Mk9pVgbHNMzPh/bvd274RmBI5bjKw2ds+ucT22DkiYgHDKDaBAaCUulEpNU8pNW/MmDG7OHTNQGFLQxvbPcdyVZnrb/jvW9WBOu5XHX2/uYDGqlmsTO8NFDs+856GcZh6FYCX1GzsiI8i2q0tFuufNEmJYDuKdWocE20tMHaGaBkXgBH2DtrLxgXvvdAY/P8Hk32JPIyOWrQSOS9ZeNJ/Dtaq8QBMlLDL3qb6tn7rx4hFSUkex0j3qoZxP/AR7/VHgH9Gtl/gRT5Nx3Vuv+CZrZpE5AjPP3Fx4hz/WucBT6iuGto0g4Ijf/wEh/3INRX4j8zTK2r4/X9XA2AZBkNoY568yZYJJyX6cUcEhlkGwFHyKg4GrzgzYwIh2qkvWpoiPkG5GkbeVqx1xjHR2Yzm7alpDgWG4DDc3kF7eURgRE1S0pHL2z82+jrpwyh+DQS9L7ar4QCMJd6PPar99CfacsmwWi8PY3cLDBG5HVgAzBGRjSLyCeAnwMkisgI42XuPUmoZcCfwOvAwcKlSyh/5Z4E/4DrCVwEPedtvBkaJyErgK3gRVxrNO8FRMGPMkNi2lCkcaKzEFEXtqANjyVsxDcN0TVJHylK2ls2kBVfj8I8wSgiaUpnGvmBZp8Yz3KlnNA28lLmEExPhmpqQqMAYSRMWBXIVcQ0jahn0iwiWMq3End7h9lhYLfHv3vQCI9rJ0KAqGCdxgdHfnN0+yTwMZXSP0/ttiw8qpS7sYFdxtS73+KuAq0psXwTsV2J7O3D+241Do+kMRykOnz4SFEH9qLQ4/MT6A3ll0jhif8z1btnq5Coz55mkAFZXHgz1eMe5B4UTTDjZJH94fqc4gDWeeeNo4zVGSjPftW4FvtONdztwiJqk/D7e2YrxwTZDohpGtDRL/DoiYZCD7ThFtaSCsFojrhlakQdhmxrBOKmPXbexvX+22436MMrIYlvlng+ja+hM7wHGS+vruP2F9b09jB7Hbb4Tb5I0atuzTDGq+XHhIvJlIyL74o7PunQ4Qb01/F3Ba38uMSW8pn8Nu2ilFuZo+PbwfYx1AFRJW5dXdgMRx1Hc/MwaylIGGctgnNeWNRcRGEI8Ss33S9kJiSEIadNvrRqv8xXPvYiHVEe1jWo1nDFJgdHWPwVGe0TDKJM8tlnm+TB0LalBT8F2ggnpnOuf44p7XhuQE9TK7U38963i6LhcwcHxwlyjQmHCmrvZoar4i31ykZCIThQ5q4pPOt/iysKnWD/04MiV/Ykq/NdfkTrJCUvAT/tYq8bhYHCo8SYAlbTR2KaT+JJkCw5bGtp579wJpEwj0DBy5Umnt/caIWOZsWtEfRMpy32TtxMaRsKHYXagYTRSQSVhTghAYz9NvmzNheMuJ4ttlvdM4p6mb1OwHWZ96yGmX/Eg63aEDrrtieiTgcCV9yzlI398gRfXxoPo6ttyQfVYXxCUS56RG5/gfvso8lixfckCdKYhPKv2507nhNgEEji9g+Y74TWKTCKRfe1k2JKZzsFeiGZK7KByrSbE19L2Gl+FITBOanEwsCtGB8dIwiSVseJTlm+GEoG06QqTgp3oh0E8SioqTKLPQbMqp0paY9fvrxpGWy6M7ionh2NlgN5L3NP0EXKRsL8v3P4yY6rcB2NtTf+M7ugMP1z2zUSP7IbWPI6jYhrAIcabGE6O/zr7e0eFGkbxKtPAVgpHqaK2qxCJkpJ4z+8oSQ1mQ/lesf01jQPv++gqvnnED0IYTx2N5kgwEyVZIq/LUvEpyzdDCULKDDUMIyExwkxvKfqO/fdNCQ0jYxn91ofRlnc1DAPHDavVGoYG4hNXS7bAlBGuA3etp21kC3bJ8/ojvjDc0hA3G2z3srujmsPRshQlFi84bu6Fq2HgvU5EyhiCUirWotU/DuJRUqYXppP0YSRzO1ZUHRHb31KzMfa+YDvc9NTqwNb8/X8t4+rH3trJT2JgoLy1jvt9GIyXWuqt0UX91sPPv9gk5QsJ1yTlfjcFJ9mrJOk4j1xbwoZKrsBoR3AHNrwi1W9NiX5Yrd8u2DbLEekgI/odoAVGP8eJ5BVVZiwmDHcFxvItTWysa2XOtx/mrkUbOji7b6CUCh7wtpzN1ob2To/f4u33J+g3tzZ52kFokz5SXqNl7EG04uZYRIWEELeNW4bgKK/cR0mTVLg67dCHkVi5rh52eGx/vmZt7P0/XtrIVQ8u5/onXbPVn55dy2/mr+j0vgcadqBhuMJ8nNTRkBqd6GUR1SKKTVKpyL7Q6e2UyMMobY6MZoE3qXIMUQzxyoPcpL7PyW99r1vutadpzdmkTKHcExiOVYahnd6a6Eq3LW8HS4jlWxrZ0ew+LDf8d1WpU/sMX7/7Vfb+zsM4juLTf13MET+eX9LW6mtTm+pcDWOcp3H84N+v05qzA7PQBHawn6ymZcpxwbnRSUNEYqtYw5Dg2skeFxAxX3USJZXUMFS6kl/mz2OlM9F9X78ucS/uv9saB69vw5+8TMP9PsZLLQ3W6JgGABGhIJTQMCI+DF/DKOnDCN8nS7pETVIAVZ5Zav/8q5xWeKJrN9lLNGcLDCtPUe71+HCs8p5J3NP0bfyJzjKEjXVtZL2aN69ubKDgqR99PVv17sWuuaYpW+ApLwqqprk4YcqfpF/eUB8rruYjIlRKG99N3epeb9b7g30xDSMxuVsJM1Q0sxgiJpHIsZ0l7oFrKvmtfQ6n5X5CXpmUNazixw8u55zrnwVgSMad+Fpy/dPk0R34WpqIUGHkGC4tNKbGxDQACKOfBCGTSmoY/rESCI+kD0OkY/8VhO+blaudV0pbYJZyr+y+7k+Rh43teYZXpIN2wY4Oq9VA+AAcNWs0rTmbhavdWjhteZu7Fm3s7NQ+QfRH2NiWZ8pI90frR3zd89JGvvPPpbFjcwWHF9fWFkUqGQIfrb+O08wX+bV8CGfEjGBfdKJI1hNKmiiSZc1Dp3fc7BRFiJs6/OidAhZr1HiGt6zm90+t5qX19QBUpN2c2WiC1WDD//5MQzjUcet4bSmbVWSSimkRZmmTlKNUIDwKTrGGEe+aGNkXNUl5Gf4fNh9jtISBFVPEXcTk+lFdqca2AsPLw+6PjlWGoBP3BiWtuQK/eXwFy7c0BhrG4dNHAu4qfa/xVcweW8nfXwx9F3212mZ00m9oy7PHSNcssMaL8vrKna9w64J1KKWwHcWssZWAWxjOVio4HqCyUMdRrU9wU+G9/MU4u8iB7U/oUUcnJDWMSCkK70Vcwyj9kynSMCLXXCtTGJddG7tP3xZfSlMaLER9GMfZC6hVlbxVeWiRSSrqw0hqH75gzhWc0IdRSGoY0QZKibDayCLgFWcmi53ZXGw9xmet+4NjvmT9A1B99jeUxHEUTZ6GETdJ6bDaQcndizdy9eNv8fv/rgo0jIp0aNtNWwYHTBkeO+ftHMm9RTTKq6Etz7ihrpPa74fts70pi61g/NAyTEPYUt+O46jAbg2wV+18TBzutI8r8lNEJ/SkSSru6I6YLzzR0VF0VZSkMzXaHnZLZhrj7K1BxMrGutbgexvUGkbEb3SgvZQFzj5gWEVCwf+OpcRHn46E0vrH5R0nXogwaZJKhNX6+xqo5IO5/2WxM5uPm26pu0XOnpxjPsPesr7fCIyWXAFHwYiKFOWBSaocQ3qmRaumj/Dhmxdy64K1QTLR+trWIEoq6gw0RCK2XZc1O/qmHyNqU21oyweTuu/D8CeBVdXNKM/sMH5oGZsbXA0jNFEo9tn+bzakZ7BCTY4l6kF8Qk+280wm6wWahYTn+vsss7TAEAkFC8Tbw6Yn7I2Jw0xxK9hurAu7Ag5mH4b/1ZfndjBOVbPYmVNUSRiiwrf4s/f3FRwVvk46vSUsWugKj3CfkXgWClj83T4+eH9V/kMAzDVW9xuTlJ+dPrwixVDcREQnVYEg2ocxWPjIH1/g6RU1fOefy4IIm/W1rYFaHw03NI3ilfAfn1nDvS9v5C/Px6N1epvoA1zfmg/uZ4dXJXTWGNcE9adn12I7bo+KCcPKAg3Dj71/j/EC45uX8/TwM4FibSA6oQtJDSP87KINdgKnd6Bx0IkPo9jp7TNpT7fcyL7GWsDTlrzvsDUb1zD6k2O1q/jfdWXbJsAt2pg0F0L4WZb6bFIRM1RUeCSLD0pE6Cd9VklB9Kg9L3i9RM2kSZWzn6ylJWtz38ubmHb5A33alOgvKIdXpJkmbo/09soprobRxWtrgdFPiNZQsj21oqY5F2Q/W2a4UjJFYrb2ScPLWVPTwpfveIX/vW9pUdOa3iRqkqppzgZmih1eKY1h5W7W7/OrdrgCwxDK0yZteRtHQcabJL5g3UfNkFksHH46QJGGIUjE6S2JNp3heKL2bgn2hxtSnfgwOjJJ2aP2ZLMayXuNhcG9BRpGYuLJ9hOzR3fgBALD07zUmCLtD8LPstQK/xcfOIALD5vCodNHFjnEfaJaRbLjXqm/10Alf7JPY+PBX0dh8Lqayn7GGjbVt/E7L0R93Y54CZG+RJOnYQwrTzHT2MwmNQonVQk6rHZwEs0B+IcXkmoaQtpQnG48zzfrvsuMlpeDY0ZXpqlryTG8wp18H166hZrmLF+/65VeXylFEw//+vw6Nte7vgs/h8TveNaULVDTnPOEoZs3YUd8GKOlgW1D52J4ZSWSEU3RyKjk5B7VMIyoSSQZJYUE/ROSJH0mUYGRtiz+VjiR481XONJYFhOMTYnPP1pldKDjfwZDWl2BsUmNLmmSSkdMTUkmDS/nx+fsT8o0Yv6sKFE/Ramw2lLf6FX2R6g+8FIAljrT2VvWs6GmkXLPV9jWh78n/zdTljLZUzayyplYlOG+q2iB0Q952QvNBAITkwlcZ/2a69LXcEh2IRe/eSnz019lf1nFmKoMTdlCEFH05JvV3PLcWu5avJE/P7e26PoNrXmefGN78P6+lzfxiT+/2K3mkt88voIFq3YEwu/cgyezvSnLC15hwW1N7SilyCc0EMNw/QN524mFUg6jhbxVFSnnQVFyXocaRsx8IREfRngtcE0iVmcmqVhYbfg6bRncYp8KwP6ympqmXIetMvvyRNTd+J/BkLZNNMhQWikr0gyBWH4FwPfP2Jdff/DAousl/XY+USFkRHxU0f1JFGFE3GvOdMolR+vmNyhPuQKjLwt2X2svU63sJet5Rc30uhV2XWJogdFH2VDbykf/9AINra7JqTJjMX2021HuuVU7in5UEzY/zEnyIj/Pf4DPT7ydp/a4lDQFbk3/hDkpLxnOM0Wtrm5mkldCZOX25qK/feV9r/GxP7/ItMsfIFuw+dIdS5j/xnaWb2kqOnZXaGjLc/Xjb3HhTc8HD/f+k4fFjqlvzbOloZ18wQlyM8D94VsCM/NvMVQ1krYMMuTISJ5camgwoRsS1waiE3p0xQnxyT2qfSRNUnaiOGEUv0WrT3TySpkGTVRQqyrZQ7azpqYlpiU2R7SMtpzN469vY9rlDwT9ygcq/ndf3rqJasstaW4knNIQJu75JqmPHDWNsw6aVHS9znJkQqVRijSYUiilgoXCUjUNgLKapZR5AqOtD0e3+c/W6MblWOKw2JldlO2+q2iB0Ue5+Zk1/OfNau5a7OZSFByHU/YZx+hKtxxGRcrkzAMnBsdPXXU765jADfYZNKdGs3jKR7kofyUK4WMbrqSS1qDE9sa6tkC1XlXtCowP37yQb9z9CgDNkR4A6yO22udW1XTLvb26sR4gVtsm6oPxWbKhnrztMCQdNoY0BS6u/gXXtXyVnxnXk7ZMhuJGgOVSQzvMtYhO6O7r8O/Eo6SEek9Ir/CEqT/B2M7baBgdmaS81+vVOPaQbby5rYlHlm4N9t/wn5XB6+ZsIWiAtWRDfcm/1Z/51yubmfvdR7weJqHAqLHcpklCca5FKpJr0RnSgSAQiSf/JQVLqZV3VMNYrSbSptKMbV4eahh9uKinbwmoyLq/V9c3VFqTeqdogdGHWFXdzGpvAp82qsLb5k6GtuOubv2Vq2EIcye5q/IJ7GB49Ys8ZLwbBwPTC6vdoMbxufxljGxfz09SN5H3bMAFRwWCYJU3KT69ooY7F22kPW8HmgyECXTgCpruwC9VMqIiHawyTZHAoez35l5T00LBUYFwAzil9jaObHRj5CdKDSlTGCbu9fIxDSMZVhsm03WWh1FKHoQ9MDrWMJJRWdGgA9+2vlpNYP/URsZWpnjgtS3B/uueDGt9ralpoSLjCkjfPJUt2IFg7+/88tE3acoWWLG9yXu2FOWtm9nhC4zIc+AT5FeU8GGUotRXlAlyOYo1mI4mUl/mO57je3L7iuBZ7Mv5M35sQMZuBKBBDfG0LG2SGlCc+Mv/csIv/wvAUC866L6XN/HB3y8gb7uTlW8+sYywds6HrMcRFPMtt72oYUjgyF3g7MvKmR/hPcYLjKOW8V5inB/t0ZKzY5FKK7c3M6IiHbyPCoytDe00tOZ598+fZMGqHbt8n74tekdLjkv+sigyZvfehqQthlek2NLQRq7gBEmJU2Urp9bcwpKhx3OfcTIjpZGMZTKMUGBEmyQlE/L8z0QpElFScdNVEjPQMDrP9I6boYpfP2PvxzCnnvePDTW1qaPCTHWAFduaGeLdr2+q+va9Sznxl/+lrqW4vlZ/w0/MPP2aZ9z+LdRj2tlAwzCkuF5UOuHD6IwbP3wI8796XGybIWH7VseLtItS6jt3KxeH41jqTGN6YRUVnnmstQ+H1fq/50zeNSE3MkRrGAOZ9TtaKXhfelveZuEa1xFsGuHqy/CEx3TZwmfN+6meejpbLddE5WsYPltmfpACFn9O/5RjJlvsOa6SlsgK6b6XNwWvV1U3BzbQ8pTJa5sagn1bG9t5ZNlW1u1oDQQOuFnZ695BYmBUQC3d1BiM2TKFDDnKJMeEYeVsbWgnbzuBGeAL1n3YYvHgpMuocSoZQTNlpuIiaz4A+XRCYEhcc/CFraNULLEuKViSGDuhYUA8WS/62vAiu/7jHIiDcGD7wmDfV0+ZE7vG61vCSBw/R8MPBKhr7f8CY8Kwsth7v05TXcrXMDouYb4zmdan7Ds+piH710xZodBJ+jA6WnlHn583mM4Q2pigXM3Q//08s6KGmj7WTdE39aXzjbSrFFnSRX67XUULjF5EKcXjr2+j4K2c/Ilx4ZodRR3dwNUqLFMYRjN3F77Ae589n4fSl2NjsHbetwJhkkzcc0bM4BP5rzFHNnJq/e2BHwTcDmY3P7Mm+Nu/++9q3trahGkI795zDI8u2xYcu62xnaWbXQHiNzNaub2Jw380n3f//D88/vo27nnJDfPNFmz+tnB9yftIlgYHsMjzQ67n5cynuWXHRZxhLWBzfTsFRwWOxjmynrUV+9OWGUONU4UlDuesv4pzzWd40dmTxqqZsYq0sQKDESHqZgXvvEnK3+Z05sOQuC8klXCkm4ZQy1DWZOZwcMvT+ClUydyBhat3BCYY3+xR5mXxD4QcDV/D8JksrrZVm/Y1DIrCYyd6ARqjK9PsCkI8lyMZVtsR0aCJzWWzABjb4vYsac4WUErxPzcv5Lwbntulce0u/N9cutBIA57wlNKa1DtFC4xe5MW1dXzy1kX8/JE3gdA88cdn1/L6ZnflPSRivy+3W/h5y7d5IXMpU9VmUoVW7rWP4YLc/+IMGR+soA1DYpNXJmXwjDOX+50jObbuXsap0CRywaF78PqWxsBevnxLIw8v24opwtzJw4LIlDFVGbY2tgcZ2Fsb2nljayMn/eqp4FqfvHURX7nzFWpbcjy/upYr732Np1eECYc+tmeL/u779wm27bXiJs7kPzzqHEKtMZrPVl/FgY1PkI+YpCZJDfUZ9z6rHTcDfO6Oh3nSPoDzc9/FSQ2JaRhJfHOSEykj4W6PHFvivGiUVNKc4WOIxCa6VELD8N8/U/U+JmVXcYLh5slEzS8zRg+hJWcHfqVWr2yIf93+UsuoM5Ia2mRPw6hPhT6MpBA99+BJ3PChg/nwkdN26W+613T/bt52gqircH/p86LPRX3lTGwlVNW7v9W6ljA0em0fS+LzNYxUrpEG5QqMZAvhXUULjF7E//6e9aKP/IX38i2NQX7FD8/eLzj+mFW/YL/CUv5pH8UXy37Ik6c+yhWFT/GS2tPTKjwNQ+ImEb/O1NWF81BicOmOHwU1/md61V+TmEb8h3vUzFEo5WZcg1stNlnQsNJz1j69ojqY3F7b2EAS39zmj2skjcxc+SfmG0fxpfzn+fqYG9haNotL7b8wwq6mPGVSTjsjpZnG9HgsQ9jsjADglQnn88n818DL5A5yLRJPdlTDsBMCI5m4lyQaJdURrobRscDwhfkLw05hR2oCP079gatT1zF+yxOMpY7DZTnHTXaPecX7zPw6U76Jpi/H/u8sSe1ymmwlVz4GO+UuliTx7LrbhPfMndCpObAzhLjQTTZh6mgejU6wU8aOZImaxX51j2FRYFN9W9Bvpq8R9MjJNQQahqB9GP0e/4H0o4YKjsPpcyfEHKG+qWgUDcze9iAPVpzBNwqfZqk1N2b2MBIRVNEfV5m3il2nxvPA+M8xK7uMFzOf41vWX6mQ0Hk3I2L7jTrYAfafPBwIazxtqG0tqksz1jNTLdvcGJQvedXzgfzikTc57ddPUduSw1Eq1iHtYutRzEIbf0pf4N1Migenfp0xqo4bjJ9RnrZ4l/EaAPXpCVimwQJnH07P/ohn9/wmNmbweUbbqcY+ayPejzseJRUeVyrEMhol1RFC3G+ULJkevDdT3DflGwynhbPNZ9n7P5/mhbJLuSPzf3xl1cc5qWp9ULrF92H4Wkhr3g1QaO7DDte3w7YVFWmTq7yF0GxjE23DZsWSKrubaFitKzASAqkDY030OzxojxH8tnAWE50tfNJ8kI11bZ0uIHqTmMDwNIzuiJACLTB6Fd930ZqzuePF9eRthWVKkFQHBPb7T1oPYiibpypOAbwJPbKi9UtmQHG1Wv8aAItGncGfJ/wvzzv78CnrQQ5b9n/49vT3zp0QhLS6AiO8frR8OrhawtqauKO71nPKvr65MdAifA3jd/9dxRtbm/jnkk0UPF+AO0bFmcaz7Bh7JJusqcHf3jbsAP7knM5esp5RTg1XpW5mhTOJVSOO8u5TWKamkY6sFqN+BJUQZ9FVfrIfd7xlJ0WEGkbxvqjPJF4OJFLQUMLvyhBhw/DDOTR7Pfu238z6E67ju/mP8K38x3EMi2vz32GGV9XWz5vxV8StWZvv3b+M/b77SJ+drN4OW6lICLVipmyiffisWFJld3HeIZPda0q0G58q8pF0qGFEHobh5SmWVx7BUmcaRxnL2FTXRr7QN7+DoPVtNqJhiHZ693uiP/pv/uM11te2YhnxmjhDyPI16w4+a/2LlZPPZovnfEtqAFEBYkZCSCEuMEzT4NURJ/H5/Be5tnAmU9bfy+9Sv6aMbCwKyzSEEW0bOM54mTONZzh4xTXsLeti4/f9LD5+wtvK7c3BvW1tbGd7U3vg7Pz+v17n8de3BeM9xljKdGMb2/c4PRR43r0ttydhiuKc17/IMFr4Qv4LFKyqTk0//o+8qIUqxJzeUZLNdpL4+5OCBuI9NqxONAx/QjQ9odbIEFooJzvnTG6xT+U2+yTuOuBPOGLy29RvmSGb2VDr2sYzgRO8wP2vuMJkR0vfiszZWfyw1pQlTJYahkob7SPmdOp72lV+eu7+LP2+W5Il6vTuKHAhiWXEf19py2CZM419jHXkbJutfTQT31/YGNlGGpU2SfVr8rYTlBXIdxAJlTYNKmjnf8zHmPvQWXze+ifz7YNYvN//BpOSGcnDgPgKOun0LrPimojvm/hV4XzWHPg1TjEWsSDzBQ7c+g+GGO5ENI/Xee9TZ/Ln9M/5Tfp65qy4iYcyV3Bd6tccPjrHxGFlPL48jKCKsrWxPSixDLB0U0PM3rtiezOmCOOqn+WPqZ/RpMrZMeWUxKRqsE655SJGtazit+oDvKH2CIRJeN/h3zUj913cczsUqEnTUkxglFjjXnDYFACOnDmqaF+05ET0+yh2ekeFYbF/CaC9fCy/HfpV9jXWcZX1RzbWtVGwnYjAsBk1xI0U2t5YLDDW7Wjh5fV1Rdv7ErZXj8syDObKagDaxuwfa5/bXZiGBH61TCSsNmme6UhIJcvjpwyD19R0RksjM2QLa/tojxlbKQwcjFxj39IwRGStiLwmIktEZJG3baSIPCYiK7x/R0SOv0JEVorImyJyamT7Id51VorINdJdBrc+yEf++AJ7f+dhICxTHsU0hdOa7ub1so/zw9SfwEzx+dwX+ET+a4iVDiZEMyEUoiYk00iEeFpGWPrcMMI+yBjsOPBSPpS/knVqHMev+gm3113E/elvcaP9HdrKJ3Bh7lt8KvcVnjztca5zzuEEYwnfafsZR88YTl1rKBSSvB6pO/XqxgZsR7HvxKGR8cKc137GFjWK03M/wikfkRCGwutqKvPtg1gy49P8WcI+F7HVe+JH3ZG/wZCIhmEX7wsvUnwvh04bydqfnM6UkRVF++ImqchnbkZNUqEjN5kjEzddwUsVx3B94QzmGW9S4TSxbHNjaJLK2YzyQku3NxWvbt/98/9w9vV9K8QziV+iPmXAOeYztKsUuZF7hd/jbvrpJwsYRvH/YtKpnmzAZZnC4/bBOEp4n/H8O8o96kkcR1HlNU7yfRhQXHpnV+gODeN4pdSBSim/68jlwHyl1GxgvvceEdkHuADYFzgNuF5E/OXVDcAlwGzvv9O6YVx9kue8KCPHUcHE9bPz9g/2H9gwn3Oqb+AlZxb/k7uSLRc8zr+dIwEJVmZQ7GOICglTEuaqiA3XNOKmk4xlssDZl7NyP+Chub/h1czBNKty7jNP4eUjf8MCZ18ec+aRrZzC7+QCvpm/hH3t13lv7S0l7++w6SMZkjaDekhVZRavbWyg4CjmTR3BbC8q6yRjMVX1b/KbwjmsV+NiPTx8m3OWNJ/If52le16KaZqR8cc1Jp/OWqhGM72TkTpRobMz2cRRwtLnJDSMqCCL+5eiJrVMUvuzDP5lH0lKbD6Wms+tC9YF+QCtuQIjPQ1jm6dhVDdl+7w/4x+LN3Lcz5/khF/8h+qmLKYIkzf+m5PNxfyi8AGMVFmkDe7uGYP/fXQWmpw0VSWfrZRpsJVRvCR7c4b5XJEPr69gOyoolxM6vbvH8b07TFJnAv5scgtwVmT735VSWaXUGmAlcJiITACGKqUWKLdq1q2RcwYsp/3mKS7920tAmJB0rPEK5679Aesr9uEDue/wguwfZKhC3G+RXKlGV95G0iEeCZE1Iq9Nz5bsImwaeyy/GvV9Lsp/m19lPkfbqDCk108avN85isfLTuG4bbdylLG06L7GVGWCiCqAA6cM59VNDdi2wvT8MyY2X3P+ROuw2dzvHOWNKynwIg5k04g5ja3Eyi/6ujMNY2ec3tEqpB89alrR/QF85t0zuebCg7xx+xpGQhBE/EbRv500qUU1DH9SWq6m8h91MJ9OP8xDL60MSoJsaWgPBMb2xiwNbXkOvepxfvTg8tj4+lq+xlfveoW1O1pZXdPC48u3YxrCxLX3ssqZwM32e2IBA7sjSgrCTO9cqXpUgfadMFUlIun8Z/Lx9PHMMjYja/4b7FdK8cqGem4p0S6gp3GUCsrlNBLmYXTHJ9tVgaGAR0VksYhc4m0bp5SbP+/9O9bbPgnYEDl3o7dtkvc6uX1A4v8e3trWHBRTq3Ra+IH1J36fupod5dO4beavKGAV5UJE/RZFUVLRfSUm1VI1qKKaR3DNyHFJp7p/7J+GfpYdZXvw19SPuSP9A8YQ2s1PrruTDzT/NXh/0B4jqG7K0pQtYJnuNY4zljCRajYf/BUKWEVjjBZZBLe8dWyFnggnDu8z/NEnfRiGhA79Ig0j8kuKZlN/+/S9KcXl79mLMw6YGIwViPXmgLjmYES0G9OgQ5NUNDT6D5xDhd3Aheb8oKvi86t3BKvEbU3tQV6Gn13vU9+aQynFY69v61PaR5XnTxhHLcO3v8BjzjwUhls/yjO77S5bdFCPqoQwTUd+Dx3hFkV098+3jqPBGs1ZDbcG+UwNbXnOvO5Zvnv/su4e+jumpIZBH/BhAEcrpQ4G3gNcKiLHdnJsqdGqTrYXX0DkEhFZJCKLqquLM4j7KrUtuWBVO9wrKuiTosA+T32Wi63HeNw5mDv2uRanzK1CG53c/fdRH0baigsFM7IvWY01Jkz8CUviLUctI66JpDoQSHmznH/sex2/t9/H4cYbnG0+Exx3VvUNnN34F84wnmOOrGfK8LLYNaoky1etu9nKGJqnnRzbF2gRCWFoGUYwqRf7bsLPMursL1l+JJK4F6UjDWNnEsWiEVQdZY8bkckm2T43di8RX8drxhyqRx/OJdYDpNpdM+bGurbADLK9MRsIxab2eF5GfVue+cu386lbF8VKp/cWVWUWHz1qGpVlrsA4z34AweE2+wQgjECC0oEg3UFnPgw/ijC6EEkSzdZ3zDSvzLiEQ423eDB9BRlysZatvZ1gaUc0jKjTu9d9GEqpzd6/24F7gcOAbZ6ZCe9fv3XbRmBK5PTJwGZv++QS20v9vRuVUvOUUvPGjBnTlaH3GO15m4P/7zH+95+u+WbmGNeGv+c499+LzPlUbn2eL+c+yxfyXySXGRX8eNzww3hGstWJhhE3e8Szl0NBEzq9UcTKJLjXj2oiSad6uC9bMZ6fFi5kgzOG/Y01BBf0uCZ9LY9kLufk5y7iFONFBAdL4NKmX7OnbOA36U9hWWFtoJiGIfEfb8o0In0u4hnVSZOU/9kVObYj95MUGNEfUvTHvjM2X/+jtJVKNGKKO+Oj303SvxEdY9RcuGK/LzOUVv6v7htYuELB94Ftb2oPIs/8MGFf9tS15IIs8eVbu6fpVVfw+4ikLYMysrw//wj1U09jg/KbJoUa2e4yp/lJpafsO65on58cGw1FTxL1/ZmGsGqP8/lV/jz2NjZwqvFiLGKqvjVPXUuOaZc/wPwOIgl3J05JDaO4rPuusMsCQ0SGiEiV/xo4BVgK3A98xDvsI8A/vdf3AxeISEZEpuM6t1/wzFZNInKEFx11ceScfo8/Ad22cD3bG9tJWwbzpo7g+DljqaCdz1v30j75KO51jgF8f4OnnkvcfBGdxEvlYQQ5FAmTlNsDwD8vVMFdM0rCcR6xJScrukbNYf7rpWpakJ8xAndy+teYSzgv+x2+nf8YmVw9N6av5pH0Nzl3+WUc0fZffln4AAvThxebw4zSk2rairRNLQqrjZuk/EJ9yZVkVNB0qmG8w9Xh1R88kMOnj2RMZSZ2P8nrx3wwnRwX/X5bxh7MVYUPsYezkcNHtcXMV6u2Nxf1h6gqc7XXutY8Q73XSe2jNyg4bnXgtGlwirGYKlqo2+fiYL9rkooLjL3GV3XrGEYMSfPKd07hqyfPKdrnVwfuLD8jmvxniJCyTH5rn8VaZxwXWU/w4wffCH5XDW35oH/JtU/2vIZnO5TWMHq5Res44BkReQV4AXhAKfUw8BPgZBFZAZzsvUcptQy4E3gdeBi4VCnl/zo/C/wB1xG+CnioC+PqU+QiE9dhP5rPqxsbSJkGZRZ83bqDMdJI27u+hW+ZizazdxwVMwtFTTVJc1W0JWlyUnW3UbTPViphdjKCh971NyQ0DKNY+1ivxjJZahAcpoqrTO4om8oitRd/tU/m5bMe54u5S8ljMbp1FfeN+Bi/s99X5KcwE5Nq3LxjhAJPijWr6GfgmxeSK1WhM4f4rguMo2aO5o5PH4llGp0IjPjqtKOJKXrffsTUeuW6AMfSQMYyeI+xkIfT36Q9l+PvXiSaj1/V9ZWN9cF1ovkwvUVUwzjRfIlaGU528pHB/qjAyHqd7P79hWN464fv6dZxDKtIlSwe6WsYnSmUSWGeNg0UBn+3j+cIYzkVTauD33pDW54hnr+muRcEtq1cDUOZadpxtfjytNm7Jiml1Gql1AHef/sqpa7ytu9QSp2olJrt/VsbOecqpdRMpdQcpdRDke2LlFL7efs+r1QnRXv6GclVYLPn/D195Xf5mPUItxeOR6YcFuyP2nMdFXfqRifqZLSQaYQ/uuSk6h8fXsPdpxImqaSPpCjSyr9+JBt9kxpNRvJMkh0c49V72lYVVqEtz2S43zma03M/5i9HPcLjYy5GYRQnHhphOenk+FOmEas11KGGEfkMciU0DP/vFWkYkY9q3tSR7Codlz5Plm0p/bNzHeKhFpc2DarVcABGUU/GMrkqdTN7GRs4PLWK51fHm1iVezWnHn99W2CuamzvXYGhlAq6Rc5gIycbi3k5fTApK9J21wid3n7QgWUaRSU8dhcdLTKiGBH/UvS3cLf9bmwjxZXWbTEHuD+DtfRC3S+lFMOkGSkbjr8QLU+ZfTasdtCzcnsT1z25kh3NWd7a5pppDp8eTkRHtf+XPbc/wq8L53BF4ZOYRmhysSIPY3Jii/kRzITJSCQwSSTt6RBOrNHuY5DISI74PtwVblIDCIWOf41NajQAz2Qu42upu9iR2YO2MtdOLBLPZI6eV9qktpMmqURkVPR1piMNIyJoOjJJVWUsLjxsCrtKRxNc1ByWzINJjiO2irWEGuUGQLwn9wjlpmKtcsuAf2nIo7y5tSE4t6k9H9zXiu3NrPRKpDe2uRPWqVc/xft/GwYn7E7a8zb/emUzSqnAv2IJfKnpl7SS4c6KC4v6hfgFFnuj50d5+u3/thkJXIgK/RqGseagKzjJfJnPmfcDbpSaL7B7o1Ck7SiGSyuUDw+2lafMbvFhWG9/iOad8um/LGZVdUvQ5wLgf46YyotrazlSXuOTNb+getj+/Hbb2eCVlDBEgiqqGX8lnFC0ool7yeqohhGGLTa3FwI1O9gfEUgd1TxyJ/SONYxAmJjhD2aNmgDAImdP7rOPZvq+xwWr/Kjw86/RUVhwMvTU6tAkRZFvJXoNvzJvUsMQoZOwWvcaE4aXdWkV1qmGYRZPNkliJinPl7UDNzv+kPxirjO+w0TP7HdY+3N8nWH8mAspYPHWtmYKjmKv8VW8sbWJB72e4X6Xvje39Zzz+8/PreUnD72BoxSn7usKuPFtK5iZX8G3Ch9ne2pSUUdCfyGRK/RMhNGfPnZo0Nfe/61kO/nbkvxuIs/19r0vZtkL8/mqdReLnDms3TGTGV5wS8s77P1954sb+MY/XuWl/z05yLl5pwRRUmXDg21lKXOnov7eDi0wdgOlJoS0ZXBs6g1+KdeyPTWZFw+/Aft+11lsGm4Eg01cFS82nYRCQqGKJvQqL2yxqT0fvA7OlVAQxMtWJCOhOtYAAv9GJHprtZrIe7I/5g01BYXBZ6tmkor8rWg+gmVGzVrFiYepiEkqFXm4fYHqfwbJ0iCGuOY7kdC8UCoPw/+47A5Kg3QWJbMzdOTMBhImqfgPV8TvIR1+NyLuM+Ng8G/7cOZk6ti78BYpKXBv+blMH+rwiW33cozxGufkvs/K7U3YjmLqqAosU3hpfb17r44qSlS0HcXrmxuZO3lYl+63I3zz4WV/X8KBU4YDsGf1IxQwecA+nNlG/Ps1DelxDeP4OWOD177ASJqPo8RMUonv0DJNvi+f5ihe42LrEX697FCOmeVGcb7TPJg7F7mpaiu3N3NYxCrxTnAcxQhpgopQW85YRpGZelfQJqndwMRIeXJwcy3mLv42fzZ+QKOq4M+Tvo85JHwYLCOMYor6GJIPmxWZ0JWiaML1TVLN2QJD0nGBEVQDNTqxoSeS59KxVX48yzwd+dvL1VQUxc74ZEG+6IQYvRd/X2yMEUETM0mJFCXFBWGREad3EkPCzyCpYUgg4EqeutMkO8VF6Uizgkh5EYl8pyo0cX0+fxnfHnMNXx/+K36ZP49HhryfZ/a8kktzX2S2bOJb1m00tRe8svEGM0ZVcJzxMkNxzVJrIiGftqO4+ZnVvP/aZ3hxbS27g+EVYa7Rkg31CA4ztz/K8opDqaeqKFIsarrM5nveJFWWLv3MRIn3m6EoodY2y/m3fSQnGS9Rv30Dd7y4vqNLkS3Y3PzMmpI5IX6uSnN2131PtgOjaIQho2Pj7w4NQwuMbmb6FQ/wxBvbY9u+bt3BxNV3caf1Pt6b+zG15dNik54kku7KUh1M6IYEfSna83aRdlAZaBiFomiQuJmo9INjmkkfQ+kIraRKnhxjoCGpeO+BqIkqudKO1btKOPRjJilDgggU/1hfSBiGxCrzRhHCbnz+7/SuzxzJ/521X6hhdNEp6N9PtMhisM+Kfq6l/UtRgZrMHDcNYVPZbH5rn0NtajxlaYsHnCO40X4fF1lPMLp6YeBcPrnxHv6c/jkPZq5kBI385vEVwXV2tGTZXO8WLnxlQ32X7jfKTU+t5lePuiZYf6X+saOnAXCwrKCyfQtLhp0Quc/4d5/uIFihJ5g8wi0q+Zl3z+TkfYrzNKCEuTBhak1bBn+yT8MShx8Ou5/7loSpZElT198Wruf//v16yTIiVYlwaMdRvJMYIMdRKMdmhDTCkHiu2s6Wde8MLTC6kYbWfMwU8sPTZ/BJ8wEusR5g214X84chl9BGGWlLYrWGIB7FVJkpbSm0DCNIQKr16gv5uE7v0CSVJJhUO1mFx30kxf0dfEFjmR1rKdFILjf7OTrpGTEfSTxkuGMtKGUZYViwEPt8DCN6bxTdm//3JXJ9P6z20Gkj+fARU4PezF2NIrFMg9s/dQS3ffLwon1BBn0JH4ZEBFagXSaEbXSSsozw+bm6cC5ZZTGx5lnyXq+HgxseA2AMDfyl6lpeXhMmj921aCMThrnZ95vq27p0v1GuenA51zyxksXragOH7/6eyetc8ykKZhlvDjs2uJdkSHgyrLYnef/+E7juooP52il7ctPF80oe45qkSvuh/GCO9WocD1WezanZRzhY3gr2r9ruanjPrqzhsde3BdkQfrLfglU7mHb5A6zf0Rr8hhs9gTHjygf59n3FNdtKsWRDPTOufJBlqzeQwi4SGKapBUafYl1tqPrvMSzN6S9/hm+nbuMZe1+2HP6twHRiGUbRSjiIkjKNYJWRxDRgrNeIqKY5LjAMQ9hrfBUThpXxtVOLk5Oi5Q+qMh1cP2KSclRc7Y4mnyV/8Mlx+D9+p4SGkYzKCu8t1KyimdHgTvoVqbDmVExgiASJV1JCGEYb8/hjmZQwGQYtLbthBXbkzFEMryh2VmYSn0OU0G8RmuIcBzJmPMLMN9uYEU0qS5oVajKjm9/EdhQZcoxvX8n1hTP4ev4S9ssv5dLWGwJh/+9XtwSf16Y6V2B88pZF/OnZNXSF6V5730eWbQsi1CozKcpp5/3m82yYcCoqUxmMP/kc7DmuioP2GM73z9iv+OK7GRHh9P0ndOqDitYiS44/KszvG3YxDelx/D79Ky4z/4GJzXVe8t6H/rCQT926iBGeM7umyf0N+9ng9768KQhcaWzLB5rFbQtd89bSTQ3c/Ezp76klW+DaJ1xNcsd2r7ZY5djYMd3xfGundzfy64jqfyZPMqJ2CZfnP8nf7eP5d7o8zCg1O9cwkg7rERUp6lrzmBENw++4ljIlMAFUpC0WXHFiybGVRzSMIZnSGkbUb+F4jW583DBef4wdJ6lFhYKjKKpH1WHIsEiQnZzNO/E8DMNgaLkVXCNqkrKigoZ41JQ/VnBQSlFVluJ3/3MIh0wdETvGH4svwK46e79uT7jyP5OC7VCR8C+VpUyas4VYaLGjVHHZlpjZMvwOlznTeF/rEgo4TMmtwlQ2rzgzecQ5lEtm5rhg1U3YBZMf8GFWbm+ixesV7ofePr58G48v38bHjp6+y/fnC8Qbn1rNAZ5mMULVc0f6/6iSNhbvcS7pbGhyjEe3uZ/BvZ87epf//u6iIm3SmrPjAQlQYiHkfW+pIfxrr58y+eWr+XLqH5xQuY4PvfYZNtTuFRy/utpdWNZ4LXinesL2lY31zPEy3Hc054qc8Of/bgFteZsPHb5H0cLo439+kYVrXJ/UaBq9wY8CwhpX2ofRh/juP5cGvothNPPR3O00jjmYv9vHA+5E6U/aKdMoakQf1rMpITC8FUnedhjjCYx2zzn42JffzS/PP+Btx+cLq4KtAl9Hkmh2t+OF+PpEhUky+imKmRAmxYmHoUCKYhjCUK8wY0u2kKhWawTCpODEtZaRQ9KRciDFtt6jvC55/r2ctt/44DP0CXoge0L7Q4dP5dPvnlny/naVaPG7ZH90X+AZEk8ujDtWiZmkYgJDTWVIoZ6Rzg6mtr8BwMuO28q3+tCvcWfh3XzIms9VFX8nb6uguu3qmpaSJqBbF6xl5fa3D8ON2tZztsM+E1zfzSsbGwDFnguvYI5s5Fv5j9Mw5pBYjbQou6ukeXfg/xajWq8ioX0b8ei/+uH78dH8N7ki/wnm5pZwR/r/OOdn9wTHL1zjJlxuqGt1c1U8v82bW5uC1y9vqCtyivt/f2NdK0l8YQEwXryEzqHxot86SqoPccsCN0R2xpghfNG6l2Gqgc1Hfp9oyY9ozZoRCbPFOM+uXMqH8cF5bnjcsPIUGcvkyBmjuPqDrpCYNnoI5x4ymVLsNyl0vvoTTFve7tgkZRjBpN3cXog71SWqYXTuw+jIIR4NGS7VV9v/cbbkCkXNiPxxJWsjjR1aFru3JNdceBCPfvnYolV9FF/D6I4VWEf42kLWdmIaEkS0v0h4qV9/yR+SZYSLjGRgxDJnGgDnOQ9zRM0/aMmMYzuuFjW0PM03Cp/mxsLpnOc8zG8rbsasfp0MrjnkqbdqYmMp2A7f+ecyTr+m8yS/TfVtTL/iQR573TWn5G2HOeOrOGrmKAwcLrduZ+j6+fy0cAG32SdhmkaQX5QMLtidn/uu8OzlJ7DwSldT93+L0bwn21FFFRL85zVlhqbD2+0TWX78jUyTrdyT/l7QBsAPed7WmOUrd74SmPA21bfR6uVtLNlQz6sbG4K/oZRi4jDXlBqtjOtz1MxRiLjzzwTxhMfQibFjtIbRh0iZwkePmsaF5S/wCeshnit7F0w8MLY/9CMYQdMkn/FD3VVva84usqVecuwMXvnuKYzz/Be3X3IEZx9UWkhEue9zR7PiKrcejz8pteftoiisaOMiP1moIVGDKGqGSka5ROnMv2EZEoRcJk0+ZsQ30dReiGlZKcNgaFlo241SmbGCz9Uv9HjTxfN45Euug7UsZbLnuM4L2flhtqXqDHUXYWJaKQ0jNBf6/iu/zHp05RpqGEZMw1hpzaTeHMWnuJeU08YLB/4w2Odf72eFD7JowgW835nPw5nLmZ/5Gh8c8hL/WBz20mhqzweJZn4+xL9e2cwOz3Ry1QOvM/d7jwAESW+fu20xSinyBTfAYYSV5fepX/EZ69+0HvBR/mSf6o05WvKm42rBfYFJw8uD35r/TLYX7FhJnWRYbah9G4HQByhMP5H/yV3JFKOac82ngXgVgntf3hTLBl+8rs6NeExb3PzM6mD7e37zNBO8VgGrq1tQSnH1Y2+xtcGNeMvbDkdMH8X4oWVMkB00MQQylVx42BQ+e5yrLesoqV5EKRVMqnnbIW8rxmXyfHDH9bzuTOXW4ZfGsq3TZsQklQhZBZjgrR62NRb3ahYRhpWX1go6I1oQzxcSbTm7KBrIn5xNQxg1xBVcSYFhGDC83BUmjiqORvLp3FwljKwoLZAMIxxHc7aQiIQKNYxStZGOnuWanfwV2Mn7jAtswTuDv+Kt6iA6bVcZEhEMQb8H2yn6fPzvJhrp5mtLqZgZMOr0Dq+dylTwizE/5Af2R/nD/rfTOCH0BfjXK2DxzMyv8sMJv+Xy/CdpoYKf2r/g2BU/xi9LP/d7jwbd/cD9rL9w+8tccOPzANz09Bqa2gtsrm8LJvm8rZh+xYNsbWynTGy+tu2bHG8s4QfOx8mf+otIfk7HCaldDWfenfjmW9dMGo4/FfNhhPeWSnw3QzIWL6vZLHWmcaL5UrD9RGMxfz6mHoDXNrmaRMoUNm/dwqfLn+Tg8SbLNjcGx7+xtSlYLC1eV8fqmhZ+M38Fn7p1EeAKobRlUJG2mCQ1VBtuDsaPz9mfb57m+k90pncv8s8lm/nSHUv4zQUHBoLg8C1/Zahdx5X5LzMsPTwmMFKmwSjfF1Ei+/MTx0znqRXVnL7/hN0y3vJOzDaVZZbnVBeGeRpAKQ1jxJBw0o4mZ0XpTGBYhjCysrTAiDqzW7KFIqF2gJcxvPf44hyHjx41jePnjGXa6CFF+3aGY2aN5ssn7clHjpq6S+d3xBNfO47NXuhq4MMoqKJ784WvrVTgq4nua2ovFIXVRrXEIRmLt2Q6i+3hfCY9NCbMo+avlGmwtWo//m2P4jHrOG6b+iAXrfsbWAW+V/goOVKBfR2g3dM2VnjO8Rmjh7C6poXrnlzJKV7JjyinbbuR6e3L+Vz+izyTPoZvRMZomuH3m3wG+7IP4wsnzOb51bXsO3FYUJeroS2f8GEQ9+9F7ttf+Mx3DuYy6x4+YD7Ji85e3Jz+JSyCm1MH8Xzd+1mY2pfxw8o4pe5+vmHfDttu5Cl7Lo+a87jNPhGFEQiWh5dtZeZY91n3t2U9gfHexrs4yXyZ+83TmJG4l84aRO0sWmDsIr6T6bK/LwHctpP7r/8Ly0aexJLNszjZMmIZpCnLCMw9tV6EU5QpIyt44qvHBe9v+fhhgRmmOzjroEn8/qnVJf0drk+jjVzBCTSApEwzDAItp6EtX1SryscyJHDSJ4lqGMkSEEZEiyov4W84dNpInvr68UwZ6WoRz11+QrDKFZFdFhbgajCXnTR7l8/viHFDywLTRjri9E4S+GBydtF3XpWxqG7Kxkw6yfDhyoxFNm97iXtxc1X0e7IMCSoAKKuMFQd9i+Wr13GR9SSTpYbP5L/MMytDgfGLR8NaaBvrWpk6qoLVNS3ctnB9oAl//dQ5/PyRNzneeJmjtt/OC2PO5cENRzDaMopCiX1hmKzg2oflBUfMGMWqH70XIAiWqG3JFZXfDyoYJAJaKryIxIfsw/i8eS8/S90EQEEZ1M94P/usfpoTG3/Ay9YvaE3txWxjEwDLJn2AvTc+zLHma3xq6jY+seY4VtsT8Y1C1z25KvgbyzY3kLMdptlrObPm9yxxZvDXsos4I3EvOqy2Fxk/tCz2/uupOxFsFkz/PGxuJW0aVMQ0DGFUpRcS6+VQ3P6pI6hvjedT+Lx7z+7tKDhlZAVLv39q8P6LJ8wKBNiUkeW8vqWRnO0EjvlRiUnfijjEG9vyHSa5FRzVYdE0yywWJodNH8kLa2oxDWHCsHKuOns/TthrbMnz9xhVEbxOll/p6/gmjGwnAiNbsIPP2CdqLvRNXDnbia1ih2SsIJEzZUiRZht97U9gKVMoT6f4ZP5SnnX242epm3jS/BqrV81muZzLSjWZOxeF/o2LblqIaQhzJw1je1M7jy/3iiCObOX/rD/yYetxtlfM4unpl8GGjViGUVSJwF8QJAvydUfZ7Z4gKjCKIgijSZVRc6EXrPBGYQ8Oyv6eY43XmGVs4lFnHj876X+49Nq7+W/mK3yKe3nTPpKD5S0WWwfy5v7/y5WrzuQL5r18ecu9PJ55kDaVZlnmQBbae3JX60FsUGOxMbns70vI5h2Obfw3jlh8JHc5E8x46Lg7zq57ILTA2EWUZ/edLNX8MnUDhxtvsHr2JTB8KrCc9nzceZ0yjGC16UcIHemFfPYGXzklTO772bkHcOi0DRy8x3DAFWRTvcn5XbNH8/SKGgwJNYzOmvI0txeKzFWWIRS81a8/gfmrsD9+9FDWVLcE2z90ePeahfoKUad3Ej8Jry1nF5nzfKe1GYmsa2qPa3iVGYsNta4T2jSlQ+0vZYaBBZYRRu3dbb+b847ej2kv/IAjCwv5sqX4Uv7zFDD4nHk/6X3ey9VuqxOOmzWc9w/fQuHNRznOeIV97l3HgabJS84sXtvrx2TKKjr42wYVXhlxX8M4ae9xPN4LLUx3FV9gJMuXRKOkLLO44GbGMsgWHKqGj2HC3Iv49dNrPH+DGTTIOlU9y6mNz+KIMD/9PsZmTED4rX0Ox7zvs9z/r3vY236Dk+zXmWe/wKWZv7LBGcPf97mO65Y0M54dHJ59kGUjT6JhUyWTSghhrWH0Am05m/K0GUT53Dv7ESrWreHH+Qs56qCvMjHvCoPq5rjZyTCEeVNH8NWT9+QDh+56z4XdwbCKFJ98V2jxjAqyGz88j22N7YhIoFUlzT9Pf+N4bn9hPdf/ZxXN2UJshQVuHolvVgH46ycOZ4+R7sRSmbE6rJr603PnsnRTY8l9/Y0jZ45ixughXHZiselr/8nDuGvxRsYPKy6v7msYGcsIBHFTezwoYEjGCsKNLUM6ybMxgvBipVTMdFU75WS+99Y0LtnxU84xn+H49Od5LLcfZ5rPwYo7eU96EnVUMXfTeipUK3nTZLHak8ZjvsX75o9mvRrH16tmBMLKLw/iYxoSmNv83871Hzq4ZBmbvkpHJXuKy7bETXGZlAntBU7ae2zgxzG9CgUKg5/lP8i4IcLacadw34oce4zdg0sj3409fBrz0ydyW/vRPDp9NOeOWsfyF+fzOet+vrTyY8xOHcB02QrAwj0ugU1tJR3c2undw1x572v8beF6TtxrLCMrUlxS8V/GrH+Qa+yz+L39fk4qSzOi0n1Ytje6AuO8QyZztxe6aBjCF0pMGH2Z8rQZCIiJw8u56zNHFhXXmzKyIlgJl+owNsoTGD7HzB5ddEwpPnjoHnzw0F0ded9iWHmKJ752XPD+66fOCUJn/+eIqcydPDwoBQ4wd5IrRP2IovHDyoOSI03tcZPg1JEVQWimaRhFEXWmIW5kj2kEWf452ykyXVWVWdxceA95ZXFC5VZOKSzCUUL17PPZsGI1VaqJl6uOp37isVy+ZBRNVPDKUaew/vFHAVeL8kOGk5qUFYl0a865Y01bRmCm7S9c/cEDgjBtX0NKmdEaaUZswSSRHi2ZlBl85rZSwevr7TPZZ8hQ9qqsoo5NzDQkljeUscLPNW0Z1I89jN/ZQ5jvHMzfZi3kjJX30Eaaf03/Nu2VU4C3AgtIFK1h9BBKKZ5eUcMLnqN7/hvb+UL5I3xV3YIz/TiuXX424DoY/cJuvobxs3P356fn7t8r494dHDptZPD65o/MY60Xj+9nVB8xo9jM5ptS6lv7z2pyd3Pp8bOC1yISExbLf3BasBr0BcHEYWWM6CCHZZ+IAC9VWsYKBEY4EeUKob8K3ImoMmPxoprONwuX8OsTD+RLdywBFPccezSXr1tMdVOWcyZNYp+JQ2lashxwO+UZ4pWBiZjDkomZ0QoG/bkBczT/6foPHcz2pvZOI9ggNEdGBarjxDW8tBX6lyxTgtcQT/qNVoxYoSZTf8pFnLjsNFoo45KxezLCT/wsUfVAaxg9xK0L1vHd+5cB8J1pr3Phlp9RrnK8mJrHoR++h9yVDwNu3Rnf4fspz8SzO5PBepsT9w5LQR8wZThv/N9pwY/g+StODCrMfveMfbjyntc4yPORaDonOpH7AmPs0ExEw4gLjL0iOSelEifTpmtDdxP+3OexPe8U5QlVRkJ6xw71V/6uDb6qzI3WyqSMmGkm7eX6ZAsOKcsITC7tidBZK7LyLlX+vT+StoygNPrYKnehWMokG/XblUUEasYyguZZGSs0F6YigiX5PmXGEwMrMhZZqwq74JA2w+uXatzUHaVBtMDohKb2PJff8xoPvOq2uzzDeJaPbr2BJqngmvw5vDj6Qu42TEZXZqhpzlKZsRAR1vz4vf0m8qM7ia6Yxg8Lo8j2Gj+Ue/pgYbn+wKyxlby6sYGxVWWBDyNZC2v66CFBa9ZSYbtDy1M0efW5fBNQ0iSV9oQCuLb6qFkrY5lBUmPGMmO5HYaX5ZwtOKTMsDimP18NK0/R0JYPcgAe/fKxQfDHQGLyCDdqb2Nda4dRgumIUADPXGWZtOXtmOZgGmF1Zv88P9Q8mgAMbsBE2nOqpy0jSBqsKOFv6Y7y5lpgdMKrGxsCYXGpeR9fT93JluGH8MqxN3LDnW9ibHKrTj7+lWN5ekVNUHp8MAoLze7hh2ftxwfnTWGKFyTw+w8fEvSZ8BERvnDCbC7920slEzNHV2XYVN8Wm9ABytLhijNjGYFQGFaeitUbK0uFJfczllHk/E1ZBmTdySy6UPCvFU3SfLsyLf0V//vZWNdWVCvMJ2MZlKfjq/zytCswMpYZaBEFW8VMUmnTCEKqU5YR00AzKZOMZdDkHedrH6WqFmgfxm7GLz/8172e45i1d3KPfQzWMddyxsHT+UKNzUyv0fvwijTvP2BiZ5fSaHaJirTF4RG/0KmRDOv9Jg0NosjeO3c8N374kJIBBWMq/QrHdkw7iWYrp8xQEFSkzVikVcYyg32ZiNkpHKNJbYt7jdEJJ/bMMUNYX9taUvMZSMweV8n00UP45nv2KtrnLyDTlkllovCnry2Up81AK8glKhqnI0IiqWFkrDBUPW0ZgZ+iVAuD7sio1wKjA75172vctnA9F5hPcMzaP/CAOpqv5T/Dn4e6QuKrpxQ3KdJoepI7P31kEEggIrFyHfOmjmCLV5jOFxI1zdmY2TCqCactI6iY3JqzY47zTCqcsDIps0jDGDe0jI11bVhmcd/oX3/wIO5/dTNzBqhm4ZOxTJ6MRMHd9snDWV3jWiD81X7aMpg5Jh6S7jvLJ48oZ4JnodhU1xavFWbGo6TKEtFt/jXSlkGr1+uklJajNYzdwGsbG7jx6dX865XNnGa8wFXWzahZJzHtuN9zwYtbiprvaDS9RUXa6rBs+92fPSp4/bnjZvLqxnreMzesU3ZwIgAhbRlBbkxjez7mOM9YRhD9l83bzEhMen5+ju/o/uhR04JrDatI8eEjBmYyZmccPWs0R89ytb29J1Txwtpatje1F4UR+1P4HiMrONibWzbVt8WCZdIxh3hxV8loFJaf11LKJGUY0uUyLFpgJLjmiRU89vo2PmfexzdSd/KyM4uDzr+FfTOV/GjyzuUPaDR9iSkjK3jgi+8K3r/1w/cEmsBlJ87mviWbGFmRDiavZBRW2jSY4ZlfV9W0UJYymTOuKojg8p3Yfq7N987Yd3feTr/j7IMnc8uCdUGDqUuOnREEFYwdmmF1TQsjKtKMHJJmnwlDOedgt/HRj86ey12LN1CeMgPN5M2tzTEfBsBeE4ayYnszhgHnHjKZlzfUc9lJe5YcS1e1DC0wcKOh/AindTta+NHEZ7mo9k7ut4/ivj2u4I9eL2KNZiAQbXD15ZP35Msnu5NLxnIn/tM9TWTKyHI21LYhIkEJ+ZO9UOoHvnhMYNL64omzqGnOcv4hfauCQV/hwETI+ZXv3TvY98Oz9uPb9y0Nqis8eFko2C86fA8uOnwPwE0ShNfYf/IwRlem2Wt8VVC+52fn7s8+E4Zy/JyxVJWl+M0FB3U4lq7mYojqp1k08+bNU4sWLerydRrb8+z/PTdT9cQJOd5V/Tc+aj0Ke72P9rP+gGFlOuwgp9EMNLY3tQedHetacmyqb2M/L+vccdSAzivq69S35qgqS3Vp0t/vu4+w7AenLVZKzduV8wethtGSLbB4XR2rtjexj6zlE9aDnFG7ADEVb045nznn30CZ+c6bFmk0/Rk/AQ3cGmDR6sJaWPQuwytK53e8E7qqYfQZgSEipwG/AUzgD0qpn3T338gVHJ5fvYNXN9Ty38f/zWnmi5xovMTHMtsomBW8OvZ8Fk24kA+edDRoYaHRaAYYA8KHISImcB1wMrAReFFE7ldKvd6V625paOOxV9czLruOHatfonn9q+wt67jQWMfnM03kSPGsvQ93GGfwja9eycEVIzm4O25Io9Fo+iADRcM4DFiplFoNICJ/B84EOhQYNc1Z/vD0aqZufYShLesw7XYk30a+vYnWlmaGqBZG5bdwIVtJidfY3kpTXT6DV+Uoxh5wKvse90H2ajc5JGMhZVqj0Gg0A5sBoWEAk4ANkfcbgcOTB4nIJcAlAOnxs/jhA8u5NXUbh5uvUVAGbWTISoaCWU7WqKB1xCwaZ5zDpvQM3pJpnHPiu5hspYg2KZ3Qv6orazQazS7zgUOnsKAL5/eJKCkROR84VSn1Se/9h4HDlFJf6Oicgw4+RD313PMYhTZMK41hpUmZous4aTQaTSeISL+PktoIRIO4JwObOzvBra+fArQpSaPRaHqCvpJg8CIwW0Smi0gauAC4v5fHpNFoNJoIfULDUEoVROTzwCO4YbV/VEot6+VhaTQajSZCnxAYAEqpB4EHe3scGo1GoylNXzFJaTQajaaPowWGRqPRaHYKLTA0Go1Gs1NogaHRaDSanaJPJO7tCiLSBLwJDAMadvEyo4GaXTy3K39Xj7ln/m5/G3N/G29Xz9Vj7plzk2Oeo5TatZ65Sql++R+wyPv3xq5eYxfP7crf1WPWY+7349Vj7p9j7so9DAST1L/64d/VY+6Zv9vfxtzfxtvVc7uCHnMv0J9NUovULtZD6c5r9DR6zD1Dfxtzfxsv6DH3FMkxd+Ue+rOGcWMfuUZPo8fcM/S3Mfe38YIec0+RHPMu30O/1TA0Go1G07P0Zw1Do9FoND2IFhgajUaj2SkGhcAQkbNFRInIXr09lneCiDS/zf7/iEifcMCJyGQR+aeIrBCRVSLyG69UfUfHf0lEKnpyjB2Mo9PPuK+hn+Xdj36WO2ZQCAzgQuAZ3D4bO42ImLtnOAMLcdsc3gPcp5SaDewJVAJXdXLal4Be/5H1Q/SzvBvRz3LnDHiBISKVwNHAJ/B+ZCJynIg8JSL3isjrIvI7ETG8fc0i8gMRWQgc2Xsjd/HG+u/I+2tF5KO9OKRSnAC0K6X+BKCUsoEvAx8XkSEi8gsReU1EXhWRL4jIF4GJwJMi8mQvjhtwnxERmS8iL3njPNPbPk1ElovITSKyTEQeFZHy3hwn+lne3ehnuRMGvMAAzgIeVkq9BdSKyMHe9sOArwJzgZnAOd72IcBSpdThSqlnenqw/ZR9gcXRDUqpRmA98ElgOnCQUmp/4Dal1DW4LXiPV0od39ODLUE7cLZS6mDgeOCXEjaHnw1cp5TaF6gHzu2dIQL6We4J9LPcCYNBYFwI/N17/XfvPcALSqnV3griduAYb7sN/KNnh9jvEaBUfLYAxwK/U0oVAJRStT05sJ1EgB+JyKvA48AkYJy3b41Saon3ejEwrcdHF6Kf5d2PfpY7oc903NsdiMgoXBVzPxFRuO1fFW5nv+RD4b9v9354fYUCccFe1lsD6YRlJFYrIjIUmAKspvQPsC/xIWAMcIhSKi8iawk/52zkOBvoFZOUfpZ7DP0sd8JA1zDOA25VSk1VSk1TSk0B1uCuwA4TkemevfeDuI7Evsg6YB8RyYjIMODE3h5QCeYDFSJyMQQO1l8CfwYeBT4jIpa3b6R3ThOwaxUzu59hwHbvB3Y8MLW3B1QC/Sz3DPpZ7oSBLjAuBO5NbPsHcBGwAPgJsBT3h5c8rlfxHsqsUmoDcCfwKnAb8HKvDqwEyi0XcDZwvoisAN7CtaVeCfwB1/77qoi8gvvZg1ue4KHedBT6nzHu5zpPRBbhrtDe6K0xdYJ+lnsA/Sy/zd8ZjKVBROQ44GtKqff18lA6REQOAG5SSh3W22MZqAyEz1g/yxrouc94oGsY/RIR+Qyu8/LbvT2WgYr+jHsG/TnvfnryMx6UGoZGo9Fo3jlaw9AMCkRkiog86SUvLRORy7ztI0XkMXHLQDwmIiO87SeLyGIv+WmxiJwQudZVIrJB+llZEc3AoLueZRGpEJEHROQN7zo/edu/rTUMzWBARCYAE5RSL4lIFW4c+lnAR4FapdRPRORyYIRS6psichCwTSm1WUT2Ax5RSk3yrnUEbsTPCqVUZW/cj2bw0l3Psrj1rw5XSj0pbq2s+cCPlFIPdfi3tcDQDEZE5J/Atd5/xymltng/xP8opeYkjhWgBpiolMpGtjdrgaHpbbrjWfb2/Qa3MsBNHf0tbZLSDDpEZBpwELAQGKeU2gLg/Tu2xCnnAi8nf2AaTW/TXc+yiAwH3o+rZXTIgM701miSiFvA7x/Al5RSjWGZnQ6P3xf4KXBKDwxPo9lpuutZ9nI4bgeuUUqt7uwaWsPQDBpEJIX7A7tNKXWPt3mbp777tuHtkeMn4ybBXayUWtXT49VoOqKbn+Ubcf1xv367v6sFhmZQ4NlubwaWK6V+Fdl1P/AR7/VHgH96xw8HHgCuUEo924ND1Wg6pTufZRH5IW45kS/t1N/WTm/NYEBEjgGeBl4DHG/zlbi23zuBPXDLPpyvlKoVkW8DVwArIpc5RSm1XUR+hlsWYiJuaes/KKW+1yM3ohn0dNezDKSBDbjlQ3yfxrVKqT90+Le1wNBoNBrNzqBNUhqNRqPZKbTA0Gg0Gs1OoQWGRqPRaHYKLTA0Go1Gs1NogaHRaDSanUILDI1Go9HsFFpgaDQajWan0AJDo9FoNDvF/wM2I4MfOpWeBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "casos_sma = casos_nacional.rolling(window = 7).mean()[6:-1]\n",
    "casos_nacional.plot()\n",
    "casos_sma.plot()\n",
    "plt.show()\n",
    "casos_diff = casos_sma.diff(periods = 1)\n",
    "#casos_diff[0:120].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "90388069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pandas stacked bar chart\n",
    "ax = casos_nacional.plot.bar(stacked=False, figsize=(10,5))\n",
    "\n",
    "# Create list of monthly timestamps by selecting the first weekly timestamp of each\n",
    "# month (in this example, the first Sunday of each month)\n",
    "monthly_timestamps = [timestamp for idx, timestamp in enumerate(casos_nacional.index)\n",
    "                      if (timestamp.month != casos_nacional.index[idx-1].month) | (idx == 0)]\n",
    "\n",
    "# Automatically select appropriate number of timestamps so that x-axis does\n",
    "# not get overcrowded with tick labels\n",
    "step = 1\n",
    "while len(monthly_timestamps[::step]) > 10: # increase number if time range >3 years\n",
    "    step += 1\n",
    "timestamps = monthly_timestamps[::step]\n",
    "\n",
    "# Create tick labels from timestamps\n",
    "labels = [ts.strftime('%b\\n%Y') if ts.year != timestamps[idx-1].year\n",
    "          else ts.strftime('%b') for idx, ts in enumerate(timestamps)]\n",
    "\n",
    "# Set major ticks and labels\n",
    "ax.set_xticks([casos_nacional.index.get_loc(ts) for ts in timestamps])\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "# Set minor ticks without labels\n",
    "ax.set_xticks([casos_nacional.index.get_loc(ts) for ts in monthly_timestamps], minor=True)\n",
    "\n",
    "# Rotate and center labels\n",
    "ax.figure.autofmt_xdate(rotation=0, ha='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a25d1c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2020-03-17</th>\n",
       "      <th>2020-03-18</th>\n",
       "      <th>2020-03-19</th>\n",
       "      <th>2020-03-20</th>\n",
       "      <th>2020-03-21</th>\n",
       "      <th>2020-03-22</th>\n",
       "      <th>2020-03-23</th>\n",
       "      <th>2020-03-24</th>\n",
       "      <th>2020-03-25</th>\n",
       "      <th>2020-03-26</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-11-01</th>\n",
       "      <th>2021-11-02</th>\n",
       "      <th>2021-11-03</th>\n",
       "      <th>2021-11-04</th>\n",
       "      <th>2021-11-05</th>\n",
       "      <th>2021-11-06</th>\n",
       "      <th>2021-11-07</th>\n",
       "      <th>2021-11-08</th>\n",
       "      <th>2021-11-09</th>\n",
       "      <th>2021-11-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 604 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2020-03-17  2020-03-18  2020-03-19  2020-03-20  2020-03-21  2020-03-22  \\\n",
       "0           0           0           0           0           0           0   \n",
       "1           0           0           0           0           0           0   \n",
       "2           0           0           0           0           0           0   \n",
       "3           0           0           0           0           0           0   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "   2020-03-23  2020-03-24  2020-03-25  2020-03-26  ...  2021-11-01  \\\n",
       "0           0           0           0           0  ...           3   \n",
       "1           0           0           0           0  ...          13   \n",
       "2           0           0           0           0  ...           0   \n",
       "3           0           0           0           0  ...           2   \n",
       "4           0           0           0           0  ...           2   \n",
       "\n",
       "   2021-11-02  2021-11-03  2021-11-04  2021-11-05  2021-11-06  2021-11-07  \\\n",
       "0           3           5           2           0           0           0   \n",
       "1          20          14          24           9           6          16   \n",
       "2           1           0           0           0           1           0   \n",
       "3           3           1           0           2           0           0   \n",
       "4           0           0           0           0           0           0   \n",
       "\n",
       "   2021-11-08  2021-11-09  2021-11-10  \n",
       "0           1           0           0  \n",
       "1          19           5           0  \n",
       "2           0           0           0  \n",
       "3           0           0           0  \n",
       "4           0           0           0  \n",
       "\n",
       "[5 rows x 604 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_muertes = pd.read_csv(path + '\\datos_covid\\Casos_Diarios_Estado_Nacional_Defunciones_20211110.csv',sep=',')\n",
    "datos_muertes = datos_muertes.drop([\"cve_ent\",\"poblacion\",\"nombre\"],axis = 1)\n",
    "datos_muertes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f4084fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha\n",
       "2020-03-17    0\n",
       "2020-03-18    3\n",
       "2020-03-19    0\n",
       "2020-03-20    1\n",
       "2020-03-21    0\n",
       "Name: Casos, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muertes_nacional = datos_muertes.iloc[[-1]]\n",
    "muertes_nacional = pd.melt(muertes_nacional, var_name = \"Fecha\", value_name = \"Casos\")\n",
    "muertes_nacional[\"Fecha\"] = pd.to_datetime(muertes_nacional[\"Fecha\"])\n",
    "muertes_nacional = muertes_nacional.set_index(\"Fecha\")\n",
    "muertes_nacional = muertes_nacional[\"Casos\"]\n",
    "muertes_nacional.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc592137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fecha'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muertes_sma = muertes_nacional.rolling(window = 7).mean()[6:-1]\n",
    "muertes_nacional.plot()\n",
    "muertes_sma.plot()\n",
    "plt.show()\n",
    "muertes_diff = muertes_sma.diff(periods = 1)[1:]\n",
    "muertes_diff.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dea8907a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "casos_acum = np.cumsum(casos_sma[6:])\n",
    "casos_acum.head()\n",
    "casos_acum[150:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce70fb9",
   "metadata": {},
   "source": [
    "## Datos de vacunaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d51d2a14",
   "metadata": {
    "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>MEX</td>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>87.847</td>\n",
       "      <td>1.38</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>MEX</td>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>87.847</td>\n",
       "      <td>1.38</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>MEX</td>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>87.847</td>\n",
       "      <td>1.38</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>MEX</td>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>87.847</td>\n",
       "      <td>1.38</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>MEX</td>\n",
       "      <td>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>87.847</td>\n",
       "      <td>1.38</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.779</td>\n",
       "      <td>-354.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           iso_code      continent location  total_cases  new_cases  \\\n",
       "date                                                                  \n",
       "2020-01-01      MEX  North America   Mexico          NaN        NaN   \n",
       "2020-01-02      MEX  North America   Mexico          NaN        NaN   \n",
       "2020-01-03      MEX  North America   Mexico          NaN        NaN   \n",
       "2020-01-04      MEX  North America   Mexico          NaN        NaN   \n",
       "2020-01-05      MEX  North America   Mexico          NaN        NaN   \n",
       "\n",
       "            new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "date                                                                            \n",
       "2020-01-01                 NaN           NaN         NaN                  NaN   \n",
       "2020-01-02                 NaN           NaN         NaN                  NaN   \n",
       "2020-01-03                 NaN           NaN         NaN                  NaN   \n",
       "2020-01-04                 NaN           NaN         NaN                  NaN   \n",
       "2020-01-05                 NaN           NaN         NaN                  NaN   \n",
       "\n",
       "            total_cases_per_million  ...  female_smokers  male_smokers  \\\n",
       "date                                 ...                                 \n",
       "2020-01-01                      NaN  ...             6.9          21.4   \n",
       "2020-01-02                      NaN  ...             6.9          21.4   \n",
       "2020-01-03                      NaN  ...             6.9          21.4   \n",
       "2020-01-04                      NaN  ...             6.9          21.4   \n",
       "2020-01-05                      NaN  ...             6.9          21.4   \n",
       "\n",
       "            handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "date                                                             \n",
       "2020-01-01                  87.847                        1.38   \n",
       "2020-01-02                  87.847                        1.38   \n",
       "2020-01-03                  87.847                        1.38   \n",
       "2020-01-04                  87.847                        1.38   \n",
       "2020-01-05                  87.847                        1.38   \n",
       "\n",
       "            life_expectancy  human_development_index  \\\n",
       "date                                                   \n",
       "2020-01-01            75.05                    0.779   \n",
       "2020-01-02            75.05                    0.779   \n",
       "2020-01-03            75.05                    0.779   \n",
       "2020-01-04            75.05                    0.779   \n",
       "2020-01-05            75.05                    0.779   \n",
       "\n",
       "            excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "date                                                                            \n",
       "2020-01-01                                   NaN                          NaN   \n",
       "2020-01-02                                   NaN                          NaN   \n",
       "2020-01-03                                   NaN                          NaN   \n",
       "2020-01-04                                   NaN                          NaN   \n",
       "2020-01-05                                -354.3                          NaN   \n",
       "\n",
       "            excess_mortality  excess_mortality_cumulative_per_million  \n",
       "date                                                                   \n",
       "2020-01-01               NaN                                      NaN  \n",
       "2020-01-02               NaN                                      NaN  \n",
       "2020-01-03               NaN                                      NaN  \n",
       "2020-01-04               NaN                                      NaN  \n",
       "2020-01-05               NaN                                      NaN  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path =  'C:\\\\Users\\jose_\\Desktop\\covid-19_model'\n",
    "datos = pd.read_csv(path + \"\\datos_covid\\datos_world.csv\", sep = ',')\n",
    "datos[\"date\"] = pd.to_datetime(datos[\"date\"])\n",
    "datos = datos.set_index(\"date\")\n",
    "datos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74dd2106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['iso_code', 'continent', 'location', 'total_cases', 'new_cases',\n",
       "       'new_cases_smoothed', 'total_deaths', 'new_deaths',\n",
       "       'new_deaths_smoothed', 'total_cases_per_million',\n",
       "       'new_cases_per_million', 'new_cases_smoothed_per_million',\n",
       "       'total_deaths_per_million', 'new_deaths_per_million',\n",
       "       'new_deaths_smoothed_per_million', 'reproduction_rate', 'new_tests',\n",
       "       'total_tests', 'total_tests_per_thousand', 'new_tests_per_thousand',\n",
       "       'new_tests_smoothed', 'new_tests_smoothed_per_thousand',\n",
       "       'positive_rate', 'tests_per_case', 'tests_units', 'total_vaccinations',\n",
       "       'people_vaccinated', 'people_fully_vaccinated', 'total_boosters',\n",
       "       'new_vaccinations', 'new_vaccinations_smoothed',\n",
       "       'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred',\n",
       "       'people_fully_vaccinated_per_hundred', 'total_boosters_per_hundred',\n",
       "       'new_vaccinations_smoothed_per_million', 'stringency_index',\n",
       "       'population', 'population_density', 'median_age', 'aged_65_older',\n",
       "       'aged_70_older', 'gdp_per_capita', 'extreme_poverty',\n",
       "       'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers',\n",
       "       'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand',\n",
       "       'life_expectancy', 'human_development_index',\n",
       "       'excess_mortality_cumulative_absolute', 'excess_mortality_cumulative',\n",
       "       'excess_mortality', 'excess_mortality_cumulative_per_million'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b3a729d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vacunas = datos[\"new_vaccinations_smoothed\"].dropna()\n",
    "vacunas.plot(legend = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1507cd25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "personas_vacunas_1 = datos[\"people_vaccinated\"].dropna()\n",
    "personas_vacunas_2 = datos[\"people_fully_vaccinated\"].dropna()\n",
    "personas_vacunas_1.plot(legend = True)\n",
    "personas_vacunas_2.plot(legend = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2e03b5",
   "metadata": {},
   "source": [
    "## Datos de la UNAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a8b94a7b",
   "metadata": {
    "scrolled": false
   },
   "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>positivos</th>\n",
       "      <th>positivos_cum</th>\n",
       "      <th>muertes</th>\n",
       "      <th>muertes_cum</th>\n",
       "      <th>recuperados</th>\n",
       "      <th>recuperados_cum</th>\n",
       "      <th>activos</th>\n",
       "      <th>activos_cum</th>\n",
       "      <th>recu_neto</th>\n",
       "      <th>inferidos_8</th>\n",
       "      <th>...</th>\n",
       "      <th>letalidad</th>\n",
       "      <th>incdia_acum</th>\n",
       "      <th>incdia_recu</th>\n",
       "      <th>incdia_muer</th>\n",
       "      <th>incdia_act</th>\n",
       "      <th>rec_rate</th>\n",
       "      <th>rec_rate2</th>\n",
       "      <th>beta</th>\n",
       "      <th>beta2</th>\n",
       "      <th>r0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha_i</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            positivos  positivos_cum  muertes  muertes_cum  recuperados  \\\n",
       "fecha_i                                                                   \n",
       "2020-02-19          1              1        0            0            0   \n",
       "2020-02-20          0              1        0            0            0   \n",
       "2020-02-21          0              1        0            0            0   \n",
       "2020-02-22          0              1        0            0            0   \n",
       "2020-02-23          0              1        0            0            0   \n",
       "\n",
       "            recuperados_cum  activos  activos_cum  recu_neto  inferidos_8  \\\n",
       "fecha_i                                                                     \n",
       "2020-02-19                0        0            0         -1            0   \n",
       "2020-02-20                0        0            0          0            0   \n",
       "2020-02-21                0        0            0          0            0   \n",
       "2020-02-22                0        0            0          0            0   \n",
       "2020-02-23                0        0            0          0            0   \n",
       "\n",
       "            ...  letalidad  incdia_acum  incdia_recu  incdia_muer  incdia_act  \\\n",
       "fecha_i     ...                                                                 \n",
       "2020-02-19  ...        0.0            1            0            0           0   \n",
       "2020-02-20  ...        0.0            0            0            0           0   \n",
       "2020-02-21  ...        0.0            0            0            0           0   \n",
       "2020-02-22  ...        0.0            0            0            0           0   \n",
       "2020-02-23  ...        0.0            0            0            0           0   \n",
       "\n",
       "            rec_rate  rec_rate2  beta  beta2   r0  \n",
       "fecha_i                                            \n",
       "2020-02-19       0.0        0.0   0.0    0.0  0.0  \n",
       "2020-02-20       0.0        0.0   0.0    0.0  0.0  \n",
       "2020-02-21       0.0        0.0   0.0    0.0  0.0  \n",
       "2020-02-22       0.0        0.0   0.0    0.0  0.0  \n",
       "2020-02-23       0.0        0.0   0.0    0.0  0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_covid = pd.read_csv(path + '\\datos_covid\\evolucion_nac3_1b.csv')\n",
    "datos_covid[\"fecha_i\"] = pd.to_datetime(datos_covid[\"fecha_i\"])\n",
    "datos_covid = datos_covid.set_index(\"fecha_i\")\n",
    "datos_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca6abf8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "casos = datos_covid[\"positivos\"]\n",
    "casos_sma_1 = casos.rolling(window = 7).mean()[6:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f4354de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fecha_i'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recuperados = datos_covid[\"recuperados\"]\n",
    "rec_sma = recuperados.rolling(window = 7).mean()\n",
    "rec_sma.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c0c7268b",
   "metadata": {},
   "outputs": [],
   "source": [
    "muertes = datos_covid[\"muertes\"]\n",
    "muertes_sma_1 = muertes.rolling(window = 7).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9fe0f6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "activos = np.cumsum(casos_sma)-np.cumsum(rec_sma)-np.cumsum(muertes_sma)\n",
    "activos.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5be891c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "activos_diff = activos.diff(periods = 1)\n",
    "activos_diff[0:150].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba7518e",
   "metadata": {},
   "source": [
    "  # Modelo LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cfc6963b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos las librerías para el modelo LSTM\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1f1e19",
   "metadata": {},
   "source": [
    "Creamos una función que separa convierte una lista en una matriz y un vector de resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26a7cba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_list(arr, n_steps):\n",
    "    X,y = list(), list()\n",
    "    for i in range(len(arr)):\n",
    "        end = i + n_steps\n",
    "        if end > len(arr)-1:\n",
    "            break\n",
    "        X.append(arr[i:end])\n",
    "        y.append(arr[end])\n",
    "    return np.array(X),np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36cf66e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20, 30],\n",
       "       [20, 30, 40],\n",
       "       [30, 40, 50],\n",
       "       [40, 50, 60],\n",
       "       [50, 60, 70],\n",
       "       [60, 70, 80]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_steps = 3\n",
    "arr = [10,20,30,40,50,60,70,80,90]\n",
    "x,y = split_list(arr,n_steps)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cd95c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 20 30] 40\n",
      "[20 30 40] 50\n",
      "[30 40 50] 60\n",
      "[40 50 60] 70\n",
      "[50 60 70] 80\n",
      "[60 70 80] 90\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(x)):\n",
    "    print(x[i],y[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db299251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 3, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features = 1\n",
    "x = x.reshape((x.shape[0], x.shape[1], n_features))\n",
    "np.shape(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305c4882",
   "metadata": {},
   "source": [
    "This is a one layer LSTM model that reads a 3 dimensional input and outputs a 2D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6991210b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c2668291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[102.878044]]\n"
     ]
    }
   ],
   "source": [
    "# fit model\n",
    "model.fit(x, y, epochs=200, verbose=0)\n",
    "# demonstrate prediction\n",
    "x_input = np.array([70, 80, 90])\n",
    "x_input = x_input.reshape((1, n_steps, n_features))\n",
    "yhat = model.predict(x_input, verbose=0)\n",
    "print(yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78255e69",
   "metadata": {},
   "source": [
    "## Stacked LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819c27db",
   "metadata": {},
   "source": [
    "We can stack LSTM layers by asking the model the first layer to output a value for each time step as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b30116b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d79e13a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[104.244514]]\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))\n",
    "model.add(LSTM(50, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# fit model\n",
    "model.fit(x, y, epochs=200, verbose=0)\n",
    "# demonstrate prediction\n",
    "x_input = np.array([70, 80, 90])\n",
    "x_input = x_input.reshape((1, n_steps, n_features))\n",
    "yhat = model.predict(x_input, verbose=0)\n",
    "print(yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70275cd7",
   "metadata": {},
   "source": [
    "## Bidirectional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2c30e75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[101.56053]]\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Bidirectional\n",
    "model = Sequential()\n",
    "model.add(Bidirectional(LSTM(50, activation='relu'), input_shape=(n_steps, n_features)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# fit model\n",
    "model.fit(x, y, epochs=200, verbose=0)\n",
    "# demonstrate prediction\n",
    "x_input = np.array([70, 80, 90])\n",
    "x_input = x_input.reshape((1, n_steps, n_features))\n",
    "yhat = model.predict(x_input, verbose=0)\n",
    "print(yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0a5fb8",
   "metadata": {},
   "source": [
    "# Multi-step LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a66c34",
   "metadata": {},
   "source": [
    "If we want to predict more than one value into the future we need to apply other models of LSTM.\n",
    "But first we need to prepare our data. We want to create to arrays, one with the input values and the other with output values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1466a1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split a univariate time series into a matrices of input and output values\n",
    "def series_split_multi(series, n_input, n_output):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(series)):\n",
    "        end_x = n_input + i\n",
    "        end_y = n_output + end_x\n",
    "        if end_y > len(series):\n",
    "            break\n",
    "        X.append(series[i:end_x])\n",
    "        y.append(series[end_x:end_y])\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7be70e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 20 30] [40 50]\n",
      "[20 30 40] [50 60]\n",
      "[30 40 50] [60 70]\n",
      "[40 50 60] [70 80]\n",
      "[50 60 70] [80 90]\n"
     ]
    }
   ],
   "source": [
    "n_steps_in, n_steps_out = 3, 2\n",
    "l = [10,20,30,40,50,60,70,80,90]\n",
    "X, y = series_split_multi(l,n_steps_in,n_steps_out)\n",
    "for i in range(len(X)):\n",
    "    print(X[i],y[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed93ec6",
   "metadata": {},
   "source": [
    "The LSTM expects the input data to be in the form of a 3D array: samples, timesteps, features. Currently it is in the shape (samples, timesteps) and we only have one feature so this is straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "97be4064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 3, 1), (5, 2))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features = 1\n",
    "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19892cf9",
   "metadata": {},
   "source": [
    "# Multi-step Forecasting with walk forward validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f71252",
   "metadata": {},
   "source": [
    "Now we will start predicting the covid-19 cases week by week using walk-forward validation. This means that the model will be given the first week of data and it will predict the second week. Then the actual week 2 data will be given in order to predict week 3 using weeks 1 and 2, and so on.\\\\\n",
    "First we have to split the data into train and test and organize it in chunks of weeks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a3bac70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7039d6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def smape(y,y_hat):\n",
    "    s = 0\n",
    "    for i in range(len(y)):\n",
    "        s = s + np.abs((y[i]-y_hat[i])/(y[i]+y_hat[i]))\n",
    "    smape = s*2/len(y)\n",
    "    return smape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4c22099e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684, 678)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casos_sma.count(), casos_acum.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4cf6ee27",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_weeks(data,n_test,n_input):\n",
    "    # Divide in train and test sets\n",
    "    train, test = data[:-n_test], data[-n_test:]\n",
    "    # Split into weeks\n",
    "    train = np.array(np.split(train, len(train)/n_input))\n",
    "    test = np.array(np.split(test, len(test)/n_input))\n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb10cd6",
   "metadata": {},
   "source": [
    "## Univariate input and vector output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5427891",
   "metadata": {},
   "source": [
    "We can see that the training set has only 83 weeks, which is not a lot for our model, so we can arranged the data in a different way. This will be to set it up so that the first week has the values [d1,d2,d3,d4,d5,d6,d7] and the second week [d2,d3,d4,d5,d6,d7,d8] and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "47468f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split a univariate time series into a matrices of input and output values\n",
    "def series_split_multi(series, n_input, n_output):\n",
    "    #flatten\n",
    "    series = np.reshape(series, series.shape[0]*series.shape[1])\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(series)):\n",
    "        end_x = n_input + i\n",
    "        end_y = n_output + end_x\n",
    "        if end_y > len(series):\n",
    "            break\n",
    "        X.append(series[i:end_x])\n",
    "        y.append(series[end_x:end_y])\n",
    "    X = np.array(X)\n",
    "    X = X.reshape(X.shape[0], X.shape[1], 1)\n",
    "    return X, np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba864c8",
   "metadata": {},
   "source": [
    "Next we have the function to build the model and make a forecast into the future based on the data of the previous week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7bbf2d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import RepeatVector\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ff58de9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ee82ca",
   "metadata": {},
   "source": [
    "## Vanila LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a610f121",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vanila lstm\n",
    "def build_model(train, n_input, epochs, neurons_lstm, neurons_dense, batch):\n",
    "    # prepare data\n",
    "    train_x, train_y = series_split_multi(train, n_input, n_input)\n",
    "    # define parameters\n",
    "    verbose = 0\n",
    "    n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "    # define model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(neurons_lstm, activation='relu', input_shape=(n_timesteps, n_features)))\n",
    "    model.add(Dense(neurons_dense, activation='relu'))\n",
    "    model.add(Dense(n_outputs))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    # fit network\n",
    "    model.fit(train_x, train_y, epochs=epochs, batch_size=batch, verbose=verbose)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e914298e",
   "metadata": {},
   "source": [
    "## Encoder-decoder LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3609d3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoder decoder lstm (two lstm layers stacked)\n",
    "def build_model_stacked(train, n_input):\n",
    "    # prepare data\n",
    "    train_x, train_y = series_split_multi(train, n_input, n_input)\n",
    "    # define parameters\n",
    "    verbose, epochs, batch_size = 0, 50, 16\n",
    "    n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "    # reshape output into [samples, timesteps, features]\n",
    "    train_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n",
    "    # define model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(128, activation='relu', input_shape=(n_timesteps, n_features)))\n",
    "    model.add(RepeatVector(n_outputs))\n",
    "    model.add(LSTM(128, activation='relu', return_sequences=True))\n",
    "    model.add(TimeDistributed(Dense(150, activation='relu')))\n",
    "    model.add(TimeDistributed(Dense(1)))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    # fit network\n",
    "    model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6dab6c",
   "metadata": {},
   "source": [
    "## CNN LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1549771b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN LSTM\n",
    "def build_model_cnn(train, n_input):\n",
    "    # prepare data\n",
    "    train_x, train_y = series_split_multi(train, n_input, n_input)\n",
    "    # define parameters\n",
    "    verbose, epochs, batch_size = 0, 20, 10\n",
    "    n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "    # reshape output into [samples, timesteps, features]\n",
    "    train_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n",
    "    # define model\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))\n",
    "    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(Flatten())\n",
    "    model.add(RepeatVector(n_outputs))\n",
    "    model.add(LSTM(200, activation='relu', return_sequences=True))\n",
    "    model.add(TimeDistributed(Dense(100, activation='relu')))\n",
    "    model.add(TimeDistributed(Dense(1)))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    # fit network\n",
    "    model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b73e2b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convolutional LSTM\n",
    "def build_model_conv(train, n_input, n_steps = 4, n_length=7):\n",
    "\t# prepare data\n",
    "\ttrain_x, train_y = series_split_multi(train, n_input, n_input)\n",
    "\t# define parameters\n",
    "\tverbose, epochs, batch_size = 0, 20, 16\n",
    "\tn_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "\t# reshape into subsequences [samples, time steps, rows, cols, channels]\n",
    "\ttrain_x = train_x.reshape((train_x.shape[0], n_steps, 1, n_length, n_features))\n",
    "\t# reshape output into [samples, timesteps, features]\n",
    "\ttrain_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n",
    "\t# define model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))\n",
    "\tmodel.add(Flatten())\n",
    "\tmodel.add(RepeatVector(n_outputs))\n",
    "\tmodel.add(LSTM(200, activation='relu', return_sequences=True))\n",
    "\tmodel.add(TimeDistributed(Dense(100, activation='relu')))\n",
    "\tmodel.add(TimeDistributed(Dense(1)))\n",
    "\tmodel.compile(loss='mse', optimizer='adam')\n",
    "\t# fit network\n",
    "\tmodel.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9ed0c861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecast\n",
    "def forecast(model,history, n_input):\n",
    "    data = np.array(history)\n",
    "    data = data.reshape(data.shape[0]*data.shape[1])\n",
    "    input_x = data[-n_input:]\n",
    "    # For Convolutional LSTM\n",
    "    #input_x = input_x.reshape((1, 1, 1, n_input, 1))\n",
    "    #For any other LSTM model\n",
    "    input_x = input_x.reshape((1,len(input_x),1))\n",
    "    \n",
    "    # forecast the next week\n",
    "    yhat = model.predict(input_x, verbose=0)\n",
    "    # we only want the vector forecast\n",
    "    yhat = yhat[0]\n",
    "    return yhat        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8d6247cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate one or more weekly forecasts against expected values\n",
    "def evaluate_forecasts(actual, predicted):\n",
    "    scores = list()\n",
    "    # calculate an RMSE score for each day\n",
    "    for i in range(actual.shape[1]):\n",
    "        # calculate mse\n",
    "        mse = mean_squared_error(actual[:, i], predicted[:, i])\n",
    "        # calculate rmse\n",
    "        rmse = np.sqrt(mse)\n",
    "        # store\n",
    "        scores.append(rmse)\n",
    "    # calculate overall RMSE\n",
    "    s = 0\n",
    "    for row in range(actual.shape[0]):\n",
    "        for col in range(actual.shape[1]):\n",
    "            s += (actual[row, col] - predicted[row, col])**2\n",
    "    score = np.sqrt(s / (actual.shape[0] * actual.shape[1]))\n",
    "    return score, scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1e8108fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to evaluate to the model\n",
    "def evaluate_model(train, test, n_input, epochs = 100, neurons_lstm = 128, neurons_dense = 150, batch = 16):\n",
    "    #Train the model\n",
    "    model = build_model(train,n_input,epochs, neurons_lstm, neurons_dense, batch) # Vanila lstm\n",
    "    #model = build_model_stacked(train,n_input) # Encoder-decoder lstm\n",
    "    #model = build_model_cnn(train,n_input) # cnn lstm\n",
    "    #model = build_model_conv(train,n_input=n_input) # conv lstm\n",
    "    #Hiistory in weekly data\n",
    "    history = [x for x in train]\n",
    "    predictions = []\n",
    "    for i in range(len(test)):\n",
    "        yhat = forecast(model, history, n_input)\n",
    "        predictions.append(yhat)\n",
    "        history.append(test[i,:])\n",
    "    predictions = np.array(predictions)\n",
    "    score, scores = evaluate_forecasts(test, predictions)\n",
    "    return score, scores, predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ed8fde2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "def best_scores(train,test,n_input, epochs, neurons_lstm, neurons_dense, batch):\n",
    "    print(f\"Corriendo el modelo LSTM\")\n",
    "    print(f\"Epochs: {epochs}, Neurons_lstm: {neurons_lstm}, Neurons_dense: {neurons_dense}, Batch Size: {batch}\")\n",
    "    print(\"===================\")\n",
    "    start = time.time()\n",
    "    rmse = []\n",
    "    min_error = math.inf\n",
    "    for i in range(30):\n",
    "        start_exp = time.time()\n",
    "        print(f\"Experimento {i+1}\")\n",
    "        score,scores,pred = evaluate_model(train, test, n_input)#, epochs, neurons_lstm, neurons_dense, batch)\n",
    "        rmse.append(score)\n",
    "        print(\"Error del modelo RMSE: %.4f\" % (score))\n",
    "        runtime = time.time() - start_exp\n",
    "        print(\"Runtime del experimento %i: %.4f\" % (i+1,runtime))\n",
    "        print(\"---------------------------------\")\n",
    "        if score < min_error:\n",
    "            min_error = score\n",
    "            min_pred = pred\n",
    "            min_scores = scores\n",
    "    print(\"---------------------------------\")\n",
    "    print(f\"Runtime: {time.time()-start}\")\n",
    "    return rmse, min_error, min_pred, min_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f60b07ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "672\n",
      "665\n",
      "658\n",
      "651\n",
      "644\n",
      "637\n",
      "630\n",
      "623\n",
      "616\n",
      "609\n",
      "602\n"
     ]
    }
   ],
   "source": [
    "k = casos_acum.count()\n",
    "while k > 600:\n",
    "    if k % 7 == 0:\n",
    "        print(k)\n",
    "    k = k - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a4a1e395",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 7)\n",
      "0.0 0.0751699320271863\n",
      "(4, 7)\n",
      "0.07308681465685969 1.0\n"
     ]
    }
   ],
   "source": [
    "n_test = 28\n",
    "n_input = 7\n",
    "resta = 6\n",
    "#series = casos_sma[:-resta] # Casos diarios (promedio de 7 días)\n",
    "series_0 = casos_acum[:-resta+1].diff(periods = 1)[1:] # Casos acumulados\n",
    "series = (series_0 - series_0.min())/(series_0.max()-series_0.min())\n",
    "train, test = split_weeks(series,n_test,n_input)\n",
    "print(train.shape)\n",
    "print(train[0, 0], train[-1, -1])\n",
    "# validate test\n",
    "print(test.shape)\n",
    "print(test[0, 0], test[-1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c912b435",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corriendo el modelo LSTM\n",
      "Epochs: 70, Neurons_lstm: 128, Neurons_dense: 150, Batch Size: 16\n",
      "===================\n",
      "Experimento 1\n",
      "Error del modelo RMSE: 0.1979\n",
      "Runtime del experimento 1: 15.7855\n",
      "---------------------------------\n",
      "Experimento 2\n",
      "Error del modelo RMSE: 0.2022\n",
      "Runtime del experimento 2: 15.8126\n",
      "---------------------------------\n",
      "Experimento 3\n",
      "Error del modelo RMSE: 0.1975\n",
      "Runtime del experimento 3: 16.3625\n",
      "---------------------------------\n",
      "Experimento 4\n",
      "Error del modelo RMSE: 0.1992\n",
      "Runtime del experimento 4: 15.5412\n",
      "---------------------------------\n",
      "Experimento 5\n",
      "Error del modelo RMSE: 0.2008\n",
      "Runtime del experimento 5: 18.3987\n",
      "---------------------------------\n",
      "Experimento 6\n",
      "Error del modelo RMSE: 0.1947\n",
      "Runtime del experimento 6: 15.4012\n",
      "---------------------------------\n",
      "Experimento 7\n",
      "Error del modelo RMSE: 0.1973\n",
      "Runtime del experimento 7: 14.9680\n",
      "---------------------------------\n",
      "Experimento 8\n",
      "Error del modelo RMSE: 0.2047\n",
      "Runtime del experimento 8: 14.4212\n",
      "---------------------------------\n",
      "Experimento 9\n",
      "Error del modelo RMSE: 0.1996\n",
      "Runtime del experimento 9: 16.9655\n",
      "---------------------------------\n",
      "Experimento 10\n",
      "Error del modelo RMSE: 0.1984\n",
      "Runtime del experimento 10: 16.2079\n",
      "---------------------------------\n",
      "Experimento 11\n",
      "Error del modelo RMSE: 0.1969\n",
      "Runtime del experimento 11: 15.9359\n",
      "---------------------------------\n",
      "Experimento 12\n",
      "Error del modelo RMSE: 0.2066\n",
      "Runtime del experimento 12: 16.6401\n",
      "---------------------------------\n",
      "Experimento 13\n",
      "Error del modelo RMSE: 0.1967\n",
      "Runtime del experimento 13: 15.5841\n",
      "---------------------------------\n",
      "Experimento 14\n",
      "Error del modelo RMSE: 0.1977\n",
      "Runtime del experimento 14: 15.5947\n",
      "---------------------------------\n",
      "Experimento 15\n",
      "Error del modelo RMSE: 0.2112\n",
      "Runtime del experimento 15: 16.7680\n",
      "---------------------------------\n",
      "Experimento 16\n",
      "Error del modelo RMSE: 0.2001\n",
      "Runtime del experimento 16: 16.4743\n",
      "---------------------------------\n",
      "Experimento 17\n",
      "Error del modelo RMSE: 0.1928\n",
      "Runtime del experimento 17: 16.1016\n",
      "---------------------------------\n",
      "Experimento 18\n",
      "Error del modelo RMSE: 0.2045\n",
      "Runtime del experimento 18: 15.9252\n",
      "---------------------------------\n",
      "Experimento 19\n",
      "Error del modelo RMSE: 0.1959\n",
      "Runtime del experimento 19: 17.4721\n",
      "---------------------------------\n",
      "Experimento 20\n",
      "Error del modelo RMSE: 0.2093\n",
      "Runtime del experimento 20: 17.4873\n",
      "---------------------------------\n",
      "Experimento 21\n",
      "Error del modelo RMSE: 0.1915\n",
      "Runtime del experimento 21: 16.7522\n",
      "---------------------------------\n",
      "Experimento 22\n",
      "Error del modelo RMSE: 0.1930\n",
      "Runtime del experimento 22: 16.0155\n",
      "---------------------------------\n",
      "Experimento 23\n",
      "Error del modelo RMSE: 0.1964\n",
      "Runtime del experimento 23: 15.8673\n",
      "---------------------------------\n",
      "Experimento 24\n",
      "Error del modelo RMSE: 0.2051\n",
      "Runtime del experimento 24: 15.8826\n",
      "---------------------------------\n",
      "Experimento 25\n",
      "Error del modelo RMSE: 0.2014\n",
      "Runtime del experimento 25: 15.9679\n",
      "---------------------------------\n",
      "Experimento 26\n",
      "Error del modelo RMSE: 0.1925\n",
      "Runtime del experimento 26: 15.7547\n",
      "---------------------------------\n",
      "Experimento 27\n",
      "Error del modelo RMSE: 0.1995\n",
      "Runtime del experimento 27: 16.3310\n",
      "---------------------------------\n",
      "Experimento 28\n",
      "Error del modelo RMSE: 0.1942\n",
      "Runtime del experimento 28: 16.1490\n",
      "---------------------------------\n",
      "Experimento 29\n",
      "Error del modelo RMSE: 0.2071\n",
      "Runtime del experimento 29: 15.8507\n",
      "---------------------------------\n",
      "Experimento 30\n",
      "Error del modelo RMSE: 0.2086\n",
      "Runtime del experimento 30: 15.6493\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Runtime: 484.06771636009216\n",
      "Mean(RMSE) = 0.1998, Std(RMSE) = 0.0052\n"
     ]
    }
   ],
   "source": [
    "errors, min_error, min_pred, min_scores = best_scores(train, test, 7, epochs = 70, neurons_lstm = 128, \n",
    "                                                      neurons_dense = 150, batch = 16)\n",
    "print(\"Mean(RMSE) = %.4f, Std(RMSE) = %.4f\" % (np.mean(errors),np.std(errors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "dcb0c050",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corriendo el modelo LSTM\n",
      "Epochs: 70, Neurons_lstm: 128, Neurons_dense: 100, Batch Size: 16\n",
      "===================\n",
      "Experimento 1\n",
      "Error del modelo RMSE: 127.6942\n",
      "Runtime del experimento 1: 30.1137\n",
      "---------------------------------\n",
      "Experimento 2\n",
      "Error del modelo RMSE: 97.3601\n",
      "Runtime del experimento 2: 30.0994\n",
      "---------------------------------\n",
      "Experimento 3\n",
      "Error del modelo RMSE: 101.4418\n",
      "Runtime del experimento 3: 34.9549\n",
      "---------------------------------\n",
      "Experimento 4\n",
      "Error del modelo RMSE: 126.4324\n",
      "Runtime del experimento 4: 32.8888\n",
      "---------------------------------\n",
      "Experimento 5\n",
      "Error del modelo RMSE: 115.1776\n",
      "Runtime del experimento 5: 36.2958\n",
      "---------------------------------\n",
      "Experimento 6\n",
      "Error del modelo RMSE: 78.5678\n",
      "Runtime del experimento 6: 38.7873\n",
      "---------------------------------\n",
      "Experimento 7\n",
      "Error del modelo RMSE: 98.0776\n",
      "Runtime del experimento 7: 32.0097\n",
      "---------------------------------\n",
      "Experimento 8\n",
      "Error del modelo RMSE: 74.9819\n",
      "Runtime del experimento 8: 34.6581\n",
      "---------------------------------\n",
      "Experimento 9\n",
      "Error del modelo RMSE: 96.8244\n",
      "Runtime del experimento 9: 35.1347\n",
      "---------------------------------\n",
      "Experimento 10\n",
      "Error del modelo RMSE: 159.1567\n",
      "Runtime del experimento 10: 34.2875\n",
      "---------------------------------\n",
      "Experimento 11\n",
      "Error del modelo RMSE: 120.6965\n",
      "Runtime del experimento 11: 29.5310\n",
      "---------------------------------\n",
      "Experimento 12\n",
      "Error del modelo RMSE: 119.4789\n",
      "Runtime del experimento 12: 32.2343\n",
      "---------------------------------\n",
      "Experimento 13\n",
      "Error del modelo RMSE: 120.9689\n",
      "Runtime del experimento 13: 32.4443\n",
      "---------------------------------\n",
      "Experimento 14\n",
      "Error del modelo RMSE: 121.6343\n",
      "Runtime del experimento 14: 33.5145\n",
      "---------------------------------\n",
      "Experimento 15\n",
      "Error del modelo RMSE: 85.7201\n",
      "Runtime del experimento 15: 37.9167\n",
      "---------------------------------\n",
      "Experimento 16\n",
      "Error del modelo RMSE: 82.1374\n",
      "Runtime del experimento 16: 41.5073\n",
      "---------------------------------\n",
      "Experimento 17\n",
      "Error del modelo RMSE: 207.9487\n",
      "Runtime del experimento 17: 42.2929\n",
      "---------------------------------\n",
      "Experimento 18\n",
      "Error del modelo RMSE: 193.4571\n",
      "Runtime del experimento 18: 37.9420\n",
      "---------------------------------\n",
      "Experimento 19\n",
      "Error del modelo RMSE: 147.2506\n",
      "Runtime del experimento 19: 43.3423\n",
      "---------------------------------\n",
      "Experimento 20\n",
      "Error del modelo RMSE: 99.5401\n",
      "Runtime del experimento 20: 42.8115\n",
      "---------------------------------\n",
      "Experimento 21\n",
      "Error del modelo RMSE: 73.3722\n",
      "Runtime del experimento 21: 42.0480\n",
      "---------------------------------\n",
      "Experimento 22\n",
      "Error del modelo RMSE: 180.9424\n",
      "Runtime del experimento 22: 37.8238\n",
      "---------------------------------\n",
      "Experimento 23\n",
      "Error del modelo RMSE: 134.0570\n",
      "Runtime del experimento 23: 39.5947\n",
      "---------------------------------\n",
      "Experimento 24\n",
      "Error del modelo RMSE: 225.3289\n",
      "Runtime del experimento 24: 39.0266\n",
      "---------------------------------\n",
      "Experimento 25\n",
      "Error del modelo RMSE: 120.2997\n",
      "Runtime del experimento 25: 44.5220\n",
      "---------------------------------\n",
      "Experimento 26\n",
      "Error del modelo RMSE: 128.8754\n",
      "Runtime del experimento 26: 42.1875\n",
      "---------------------------------\n",
      "Experimento 27\n",
      "Error del modelo RMSE: 107.6357\n",
      "Runtime del experimento 27: 38.1139\n",
      "---------------------------------\n",
      "Experimento 28\n",
      "Error del modelo RMSE: 90.7421\n",
      "Runtime del experimento 28: 29.9845\n",
      "---------------------------------\n",
      "Experimento 29\n",
      "Error del modelo RMSE: 85.1677\n",
      "Runtime del experimento 29: 34.1382\n",
      "---------------------------------\n",
      "Experimento 30\n",
      "Error del modelo RMSE: 167.1907\n",
      "Runtime del experimento 30: 38.5288\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Runtime: 1098.7348086833954\n"
     ]
    }
   ],
   "source": [
    "# univariate multi-step encoder-decoder convlstm\n",
    "from math import sqrt\n",
    "from numpy import split\n",
    "from numpy import array\n",
    "from pandas import read_csv\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from matplotlib import pyplot\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import RepeatVector\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import ConvLSTM2D\n",
    "\n",
    "# evaluate one or more weekly forecasts against expected values\n",
    "def evaluate_forecasts(actual, predicted):\n",
    "\tscores = list()\n",
    "\t# calculate an RMSE score for each day\n",
    "\tfor i in range(actual.shape[1]):\n",
    "\t\t# calculate mse\n",
    "\t\tmse = mean_squared_error(actual[:, i], predicted[:, i])\n",
    "\t\t# calculate rmse\n",
    "\t\trmse = sqrt(mse)\n",
    "\t\t# store\n",
    "\t\tscores.append(rmse)\n",
    "\t# calculate overall RMSE\n",
    "\ts = 0\n",
    "\tfor row in range(actual.shape[0]):\n",
    "\t\tfor col in range(actual.shape[1]):\n",
    "\t\t\ts += (actual[row, col] - predicted[row, col])**2\n",
    "\tscore = sqrt(s / (actual.shape[0] * actual.shape[1]))\n",
    "\treturn score, scores\n",
    "\n",
    "# summarize scores\n",
    "def summarize_scores(name, score, scores):\n",
    "\ts_scores = ', '.join(['%.1f' % s for s in scores])\n",
    "\tprint('%s: [%.3f] %s' % (name, score, s_scores))\n",
    "\n",
    "# train the model\n",
    "def build_model(train, n_steps, n_length, n_input):\n",
    "\t# prepare data\n",
    "\ttrain_x, train_y = series_split_multi(train, n_input, n_input)\n",
    "\t# define parameters\n",
    "\tverbose, epochs, batch_size = 0, 70, 16\n",
    "\tn_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "\t# reshape into subsequences [samples, time steps, rows, cols, channels]\n",
    "\ttrain_x = train_x.reshape((train_x.shape[0], n_steps, 1, n_length, n_features))\n",
    "\t# reshape output into [samples, timesteps, features]\n",
    "\ttrain_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n",
    "\t# define model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))\n",
    "\tmodel.add(Flatten())\n",
    "\tmodel.add(RepeatVector(n_outputs))\n",
    "\tmodel.add(LSTM(128, activation='relu', return_sequences=True))\n",
    "\tmodel.add(TimeDistributed(Dense(100, activation='relu')))\n",
    "\tmodel.add(TimeDistributed(Dense(1)))\n",
    "\tmodel.compile(loss='mse', optimizer='adam')\n",
    "\t# fit network\n",
    "\th = model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "\treturn model\n",
    "\n",
    "# make a forecast\n",
    "def forecast(model, history, n_steps, n_length, n_input):\n",
    "\t# flatten data\n",
    "\tdata = array(history)\n",
    "\tdata = data.reshape((data.shape[0]*data.shape[1]))\n",
    "\t# retrieve last observations for input data\n",
    "\tinput_x = data[-n_input:]\n",
    "\t# reshape into [samples, time steps, rows, cols, channels]\n",
    "\tinput_x = input_x.reshape((1, n_steps, 1, n_length, 1))\n",
    "\t# forecast the next week\n",
    "\tyhat = model.predict(input_x, verbose=0)\n",
    "\t# we only want the vector forecast\n",
    "\tyhat = yhat[0]\n",
    "\treturn yhat\n",
    "\n",
    "# evaluate a single model\n",
    "def evaluate_model(train, test, n_input, n_steps = 1, n_length = 7):\n",
    "\t# fit model\n",
    "\tmodel = build_model(train, n_steps, n_length, n_input)\n",
    "\t# history is a list of weekly data\n",
    "\thistory = [x for x in train]\n",
    "\t# walk-forward validation over each week\n",
    "\tpredictions = list()\n",
    "\tfor i in range(len(test)):\n",
    "\t\t# predict the week\n",
    "\t\tyhat_sequence = forecast(model, history, n_steps, n_length, n_input)\n",
    "\t\t# store the predictions\n",
    "\t\tpredictions.append(yhat_sequence)\n",
    "\t\t# get real observation and add to history for predicting the next week\n",
    "\t\thistory.append(test[i, :])\n",
    "\t# evaluate predictions days for each week\n",
    "\tpredictions = array(predictions)\n",
    "\tscore, scores = evaluate_forecasts(test[:, :], predictions)\n",
    "\treturn score, scores, predictions\n",
    "\n",
    "# define the number of subsequences and the length of subsequences\n",
    "n_steps, n_length = 1, 7\n",
    "# define the total days to use as input\n",
    "n_input = n_length * n_steps\n",
    "#score, scores, predictions = evaluate_model(train, test, n_steps, n_length, n_input)\n",
    "rmse, min_error, min_pred, min_scores = best_scores(train,test,n_input, epochs = 70, neurons_lstm = 128, \n",
    "                                                    neurons_dense = 100, batch = 16)\n",
    "# summarize scores\n",
    "#summarize_scores('lstm', score, scores)\n",
    "# plot scores\n",
    "#days = ['sun', 'mon', 'tue', 'wed', 'thr', 'fri', 'sat']\n",
    "#pyplot.plot(days, scores, marker='o', label='lstm')\n",
    "#pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eaba365f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = min_pred.reshape((min_pred.shape[0]*min_pred.shape[1]))\n",
    "#predictions = casos_sma[1]+np.cumsum(predictions)\n",
    "predic_lstm = pd.Series(predictions, index = series[-n_test:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2a0ab7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d8afb05040>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kfv31VwCaNGnCOeecw9dff31QEv/222+5/PLLiY+PJz4+nksvvZSffvqpMI4ZM2YwYMAAAPLy8oiNjS3xOr300ktcdtllh+weWNL5AuXn53P33Xfz008/4Zxj8+bN9O3bl1GjRjFixAh+97vfMWrUKE466ST69u1bYkwiIhXtx2Vb2boru9TtIiWJvw/8ZGZ/ATYD1wNv+es+B540s67+ffEbAtYdtrKWlEOl4J54UXXr7m+I75w7oG1B0XYGCQkJhc9jY2MPep2bm1t4nI8++oj27dsfsP/OnTvL1PWu6LbF7VtSFyznHPfddx9XXnllUOfbvXs3b7/9NtOmTSvxmMF49NFH2bZtG1OnTiUhIYHbbrut8NbBo48+yoIFCwp/EFxyySXcddddQR1XRKQifDB7XVDbVUYXsyfNLA1IASaZ2XJ/+QtmdiaAc24lcD/wM7ACL5G/6K/LBK4GPvL3bQn8I9RxR6IRI0YwceJENm7cCMAzzzzDiBEjytzn/cwzz+Sf//xnYVLPyMhg+fLl1K9fn549e/L6668DMG3aNObNm1fsMU444YTC++Bbtmzhww8/5Pjjjz9ouxEjRjB27Fhyc3PZu3cvb7zxxgFxPPXUU4WtzHNycpg9e/Yh43733Xfp3bs3Xbt2PeQ2JZ0vUEZGBs2aNSMhIYFNmzbx7rvvFq5bvHgxPXr04KabbuK3v/0tU6ZMOeT5REQq2s6sHL5csJHB7UpvvFsZrdNvBG4sZvnVRV4/DxTbgdc5Nx4YH5IAo0iPHj148MEHOemkkwCvEdyhGpSV5LHHHuPuu++mb9++xMTEEB8fz0MPPUTHjh157bXXuOKKK/jPf/5D//79GTJkSLHH+N///sf1119P7969yc/P59577z2oKh3g2muvZe7cuXTv3p2UlBSOPvpoVq/27vOMHj2abdu2ceyxx2Jm5ObmctVVV9GvX79iz/niiy8eskFbMOcLdMstt3DeeefRt29fWrZsyQknnFC47g9/+ANLly6lRo0a1K5dm6effrrEc4qIVKTP5m1gX24+5/ZvybulbGtVecSplJQUl5aWdsCyvLw8li5dSufOnUu9/ypyKPociUionP/sZH5du53p951AYq0a65xzKYfatkp20xIREYlGa9P3MG1VOid2b0r9hPhSt1cSFxERiRAf+g3azu1/yML3AZTERUREIoBzjg9mpdGobg2O7tQoqH2UxEVERCLArDXbSd22h7P6tiQuNrj0rCQuIiISAT6c7TXEPqd/y6D3URIXEREJs325eUz4dQNdm9Wje/P6Qe+nJB4B2rZty/z58w9Y9sMPPzBs2LDC6UmPPPJINm3axNVXX104JWeNGjUKpzDt27cvmZmZtG3bliZNmhROaALwzTffYGbccccdlf3WREQkCN8u3syOvTmM7NeyTAN4RcqwqxIgNzeXkSNHMmnSpMKBT5YsWUKdOnUOmA60bdu2xQ7X2rp1a8aPH8+5554LeOONDxw4sPLegIiIlMn7s9YRY3B2v+Cr0kEl8YiUmZlJZmYmzZs3L1zWpUuXA8ZRL8mVV17JSy+9BMCOHTuYMmUKJ598ckhiFRGRw5O+O5tvF2/myI6NaFo/ofQdAqgk/saFkLEqNMdu0A4uLvtcLQ0aNOCGG26gU6dOHH300QwbNowLLriAzp07B7X/8OHDefzxx1m3bh0TJkzgvPPO06hiIiIRasKv68nNd0H3DQ+kkniEeuyxx5g/fz7nn38+S5cupV+/fsVOqXkoo0eP5tVXX+Wll14KepYwERGpfB/MSqNOjVhO6tG0zPuqJF6OknJladOmDWPGjGHMmDHUqVOHd955h6OOOiqofceMGUP//v3p3Llz4dzkIiISWZZvzuTXtB2MGpBC7RplT8kqiUegXbt28dlnnxXOjb13714WLVpEhw4dgj5GixYtePDBB3nooYdCFaaIiBymD2Z5w6yWpW94IJXEI8QJJ5xAXJz3z7F371769u3LrbfeSq1atcjJyeHkk0/mxhsPmtG1RFdccUUoQhURkQqQn+/4aPY6WiQmMLRdcrmOoSQeAVJTUytsv0Md64EHHijXOUREJDSmrNrG+h1Z3HBsB2Jigu8bHkjV6SIiImFwuFXpoCQuIiJS6fZk5/LZvA30SUmkY5N65T6OkriIiEgl+3LBJnZn53FOOfqGB6p2SbxgTNqClt8i5VHw+SnLGMciIgXen5VGXIxxRp8Wh3WcatewLSYmhvj4eLZt20ZycrK+hKXMnHNs27aN+Ph4YmKq3e9gETlMG3dk8fPyrYzo1pSGdWoc1rGqXRIHb4KQNWvWkJ6eHu5QJErFx8fTunXrcIchIlHo4znryHdwThknOylOtUziNWrUoGPHjuTn56taXcrMzFQCF5Fycc7xwax11E+I4/huTQ77eNUyiRfQF7GIiFSmhRt2smRTJpcMaU3NuMOfmEpZTEREpJLs7xt+eK3SCyiJi4iIVIKcvHw+nrOOtsm16d86qUKOqSQuIiJSCb5dvJmtu7I5b2CrCusZpSQuIiJSCd6ZkUaMHd4wq0UpiYuIiITY5swsvl2ymeGdG9M8sVaFHVdJXEREJMQ+nLWOvHzH+QNbVehxlcRFRERCyDnH2zPW0qB2PCMqoG94ICVxERGREJq1JoOVW3Zzdr+WFdI3PJCSuIiISAi9Mz0NgPMGVGxVOiiJi4iIhMzufbl8Mnc9vVom0r1F/Qo/vpK4iIhIiEyct4Hd2XmcP7BiRmgrSklcREQkRN6dkUaNuBjO7FNxfcMDKYmLiIiEwMotu5iWms7JPZqRWDs+JOdQEhcREQmB92Z6DdouGFTxDdoKKImLiIhUsNy8fN6flUbLpFoMa58csvMoiYuIiFSwH5dtZdPOfZw3MIWYmIqZ7KQ4IU/iZtbJzH4xs6VmNs3MuhezTYyZPWJm881ssZm9aGY1Atbf6a9baGYfmllSqOMWEREpr3dmrMUMRg0ITav0ApVREn8WeM451xl4GHixmG2uAnoD/YFu/rJbAczsROAyYJhzrjswB/h7iGMWEREpl2279jFp0SaO7NCIlAa1Q3qukCZxM2uCl5jH+YveB9qZWdsim/YBJjnnsp1zDpgIjA5Y96NzLtN//UnAOhERkYjy0Zz15OQ5zgtR3/BAoS6JtwLWO+dyAfwEvQZoXWS76cBZZlbPr0a/EGjrr5sBnGhmTc2bRf1SoJ6ZNQxx7CIiImXinOPdGWupnxDHb3o0C/n5KqM63RV5Xdwd/teAL4AfgG+ABUAOgHPuO+DfwKfAZGCDv09O0YOY2W1mllbw2LVrV4W8ARERkWDMW7eDxRszOatvSxLiK3ayk+KEOomvBVLMLA7AL0m3wiuNF3Kevzjn+jnnjgIWAwsD1j/jnBvonBuKl+jTAqrXA4/zqHMupeBRt27dEL41ERGRA70zYy1Ahc8bfighTeLOuc3AbLwqcIBzgVTnXGrgdmaWUNDi3MwaAffgNYIrWN/c/1sb+EvgOhERkUiQlZPHx3PW07VZPXq2rPjJTooTVwnnuA54xcz+COwELgcwsxeA8c658UAi8L2Z5QGxwGPOuQkBx/jSzGKAGsBY4IlKiFtERCRoXyzYSGZWLucPbIVX8Rx6IU/izrklwLBill8d8HwT0LWEY/QKTXQiIiIV450Za4mPNc7uF5rJToqjEdtEREQO09r0Pfy8fBsndm9Kwzo1St+hgiiJi4iIHKZ3/clOzqukBm0FlMRFREQOQ16+470Za2lWP4HhnRpX6rmVxEVERA7DLyu2sn5HFucOaElsCCc7KY6SuIiIyGF4Z4ZflT6gcqvSQUlcRESk3DJ2Z/PFgo0MbteQto3qVPr5lcRFRETK6d2Za8nOzeeSIUWnBKkcSuIiIiLlkJ/veH3qGpLr1ODknqGf7KQ4SuIiIiLl8OPyrazetofzB7WiZlzoJzspjpK4iIhIOYybshozuHhweKrSQUlcRESkzNZt38vXizZxbOfGtGpYO2xxKImLiIiU0VvT1pDvYPSwNmGNI6gkbmbXmVmi//xJM5thZsNDG5qIiEjkyc7N563pa2mZVItjOjcJayzBlsRvdM7tMLMjgZ7AvcAjoQtLREQkMn25cCNbMvdx8ZDWlT5CW1HBJvFc/+/xwGvOuS+onLnIRUREIsq4KauJjzUuGFT5I7QVFWwSzzezC4ELgK/9ZZU315qIiEgEWL45kykr0zmlZ3Ma1a0Z7nCCr04HLgSed86lmlln4NvQhSUiIhJ5xk1ZA4S/QVuBoKrEnXNTgbMDXi8Fbg5RTCIiIhFnT3Yu789Mo0vTegxs0yDc4QDBt05vbmafmNlu/zHezJqHOjgREZFIMX7OejL35XLp0NaYhbdBW4Fgq9OfA34BWvqPX/xlIiIiVZ5zjrFTVlOnRixn92sZ7nAKBdvCvJVz7oyA1/80szkhiEdERCTizFm7nQXrd3LJkNbUS4gPdziFgi2Jx5hZ4RQtZtYEiIy6BBERkRAraNB26dDIaNBWINiS+L+A2WY2AXDAqcAfQhaViIhIhMjYnc2EuesZ2KYB3ZrXD3c4Bwi2dfpYM5sNHItXAv+vc25hKAMTERGJBO/NTCM7Nz/iSuFQhlHXnHPzgfkhjEVERCSi5Oc7Xp+6moZ1anBKr2al71DJSkziZjbWOTfazKbjVaMfwDk3OGSRiYiIhNnPK7aSum0P1x/TgZpxseEO5yCllcQf8//eEeI4REREIs7Yyasxg0uGtA53KMUqMYk752aaWSxwtXNudCXFJCIiEnYbduxl0qJNHNO5Ma0a1g53OMUqtYuZcy4Pb4AXERGRauPNaWvJdzA6Ahu0FQi2YdskM3saeBnYVbBQLdRFRKQqysnL561pa2iZVItjuzQJdziHFGwSv8b/e3LAMge0r9hwREREwu+rhZvYnLmPO3/ThdiYyB3bLNh+4u1CHYiIiEikGDdlNfGxxvkDW4U7lBIF3U/cb+DWBkgoWKbqdBERqWoWb9zJLyu2cUafFjSuVzPc4ZQoqCRuZqcALwDJwG4gCVgDqIQuIiJVyhPfLAfg2qMj/45xsBOg/B04AljknEsGLgfeCVlUIiIiYbB88y4+nbeBY7s0pldKYrjDKVWwSdw551bjl9ydc+OAYSGLSkREJAye+m45zsHNx3cMdyhBCfaeeI7/N83MzgBWAymhCUlERKTyrdm2h4/nrOeIDskMaNMw3OEEJdgk/l8zawDcB7yFd0/8dyGKSUREpNI9/f1y8vIdN0VJKRyC72L2pv90JtApdOGIiIhUvnXb9/LezDQGtmnAsPbJ4Q4naKXNYnakc+5nMzu1uPXOuYmhCUtERKTyPPv9CnLyHDeP6IRZ5A7uUlRpJfExwM/AncWsc0CpSdzMOgGvAo2A7cCYov3LzSwGeBhvRLg4/5y/dc5l++vv8GPJBbKAm51z00s7t4iISGk278zirelr6Z2SyPBOjcIdTpmUNovZNf7f4w7jHM8CzznnXjGzUcCLHNyy/SqgN9AfrxHdC8CtwL/MrA9wM9DDObfLzC4FngQ0l7mIiBy2535YSXZuPjcfH12lcCi9Or3YavQCpVWnm1kTvMR8kr/ofeAJM2vrnEsN2LQPMCmg5D0RuB/4l78+HqiDN/lKEpBW0nlFRESCsW3XPl6fuoauzepxQrfInejkUEqrTi+oRk8ABgHz/Ne9gCmUXp3eCljvnMsFr7O5ma0BWgOpAdtNB671Z0rbB1wItPX3+dXMHgVWmVm6v354qe9MRESkFC/+tIq9OXlRWQqHUgZ7cc4d51elrwCOdM71c871wxu9Ldhx012R18VdpdeAL4AfgG+ABfh9082sDXAm0ME5lwL8B3i9uBOZ2W1mllbw2LVrV3GbiYiIsH1PNq9NXk2HxnU4uWezcIdTLsGO2NbVOTe14IVzbhpeNXlp1gIpZhYHYN7PnFZ4464Xcp6/+D8SjgIWs/9HwnnAfOfcBv/1y8Bwf0IWihznUedcSsGjbt26Qb49ERGpbl75JZVd+3K56fiOET3daEmCTeK5foMyAPznuaXt5JzbDMwGCvY9F0gtcj8cM0swsyT/eSPgHrzW6gArgaPMrCAjn4E3hntekLGLiIgcIDMrh5d+WkWb5Nqc0btFuMMpt2BHbLsCGGtmz+NVj8/HmwQlGNcBr5jZH4GdBfuZ2QvAeOfceCAR+N7M8oBY4DHn3AR//w/x7sfPMLN9QCb7fxSIiIiU2WuTV7MzK5d7T+tGXGyw5dnIY84VvWVdwsZm9QCcc5khi6gCpaSkuLQ0NWQXEZH99mTnctRD31IrPpZv7ziWGnGRm8TNbJ3fHqxYZYrcOZcZLQlcRESkOG9MXUP67myuP6Z9RCfwYER39CIiImWQlZPHsz+spEm9mpw3sFW4wzlsSuIiIlJtvDNjLVsy93Ht8PYkxB/UySnqlDmJm1mimfUMRTAiIiKhkp2bzzPfrSC5Tg0uHtI63OFUiKCSuJl9bmZJfjevX4FPzOwvoQ1NRESk4nwwK431O7K46uh21K4RbOesyBZsSbypc247cCrwMd6c4meHKCYREZEKlZuXz1PfrSCxVjyjh7YJdzgVJtgkHu//HQ585ZzLAfJDE5KIiEjFemv6Wtak7+GKI9tSLyG+9B2iRLBJfL6ZfQ6cDnxjZrVDGJOIiEiF2b4nm0e+XEKTejW5+uj24Q6nQgV7U2AMcDLwq3Nuj5m1xBsaVUREJKL956ulbN+Tw38u6EPdmlF0L3zee6VuElRJ3DmXhTdd6GAzO99f9vlhBSciIhJiizfuZOyU1fRvncTZfVuGO5zgzXkD3r+61M2CbZ1+Fl6r9AuBi4HZZnbGYQUoIiISQs45Hhi/AAf8+cye0TNf+MxX4KMbILH0wWiCvSd+PzDUOTfSOXc2MAz4c7kDFBERCbHP5m9kysp0LhjYil4pieEOJzjTnocJt0KDtnDFxFI3DzaJxzrnlhe8cM6tKMO+IiIilWpvdh5//3QR9RLiuOM3XcIdTnB+eQIm3gHJnbwEnlRxJfHNZnaV+XURZnY5sPUwQhUREQmZZ75fwbrte/ndCZ1pVLdmuMMp3Y//hi/vhcZdYcynUD+4Oc6DTeLXA9cAe81sr//6uvJFKiIiEjppGXt45vsVdGxSl8uGRfjALs7Bd/+Er/8CTXt5Cbxe06B3D6qtvV99PtQfdtU0HamIiESqf0xcxL7cfO4/ozvxsRF859c5L3n/9Cg07wujP4TaDct0iBKTuJmdeojl/vld6XfdRUREKskvK7Yycd5GftOjKUd3ahz6E25e7N3Hzs+DjiOg04nQrDeU1hLeOfjyPpj8BKQMgkveg1pJZT59aSXxO/2/CcAgYJ7/uhcwBVASFxGRiJCbl8+fxy+kRlwM953WPbQncw5mj4WJd0FeNsQlwJpf4Ju/Qt2m0PEEL6m3P+7g0nV+Pnx+N0x7DloPg0vehZr1yhVGiUncOXccgJmNA37nnJvqvx4MlN4LXUREpJK8PnUNSzZlcvPxHWnVMISjg2fthE9+D/Pfg/opMOpFaNEP1kyG5ZNg2SSY87r3sBhoOdAroXc8wSulT7zd6wve9mi4+G2oUafcoZhzrvSNzGY45waWtizSpKSkuLS0tHCHISIiIZa+O5tj//UtdWrG8fXtx4RuqtH1s+HdKyBjFXQ9Hc58vPj72DvS/IT+Faz8HrL9pmTxdSBnN3Q4Hi54HWqU/GPDzNY551IOtT7Yd5lrZpc658b5B70UyA1yXxERkZD695dL2JmVy99G9gpNAncOpjwNX/3Ju999yr9g8DWHvvedmAIDxniPvBxYO9VL6Cu+geSOcPbTEJ9w2GEFWxLvBowFeuJNQTofuNw5t+iwIwghlcRFRKq++et2cMYTPzGoTUPevm5oxQ+vuifdGwZ16WfQsAOc9zI071Ox5ziECimJ+8l6oJnV81+ri5mIiISdc44/T1iAAfef2b3iE/jqX7yJSHaug94XwGn/LncjtFAIus7BzJoCPYAEdTETEZFIMGHuBqanZnDJkNb0aFGB46Pn58GPj8J3//Banp/1FPS9uPSuY5UsqCRuZmPwJkFJBpYBfVAXMxERCaM92bn849NFJNaK5/aTKnB89L3b4Z3LYNX30KSHV33eODLHXw92KJvbgP7ACufcAOB4YHHIohIRESnFgxMXs3FnFred2JmGdWpUzEF3bYZXTvcS+IAxcM3XEZvAIfgknuOcy8AvuTvnfgBC3JNeRESkeJ/P38jYKasZ2r4hlw6toPHRM1bDS7+BTfNgxP1wxn8hvlbFHDtEgr0nvs+fwWypmd0MrAYahS4sERGR4q3fvpe7359Lg9rxPHZBP2JjKuA+9ebFMHYkZG6A0/8DA688/GNWgmCT+H1AfeAu4BkgCbghRDGJiIgUKzcvn9+9NYcde3N44bKBNEs8/L7WrJsJ40bBvp3e6Gs9zz38Y1aSYLuYfeM/3QGcGLpwREREDu3xb5YzLTWdMUe05YTuwU/ZeUirfoA3L/Jao1/0ljc8ahQJtnV6beBioH3gPs65u0IUl4iIyAGmrtzG498so3vz+vzh1K7ewqydkFC/fAdc/Kk3hGpcAox+D9oMq7hgK0mw1ekf4g2zOhPYF7pwREREDpaxO5vfvT2HmnGxPH5xP2rGxcLscfDxjdC0J/QYCT3PgYbtgzvgnDe9fWs3hEs/gOa9Q/sGQiTYYVcXOOd6VEI8FUrDroqIRD/nHNeOnclXCzfx8KjenD+wFWRuhCcG7x98JWu797d5Xy+Z9xgJSa2LP+CUZ7ypQBNbweiPoFHHSngX5VNRE6DMM7PmzrkNFRSXiIhIUMZNWc1XCzdxZp8WnDfAz2ef3wP7dsAF46DTb2Dld7DgA6+K/Ks/eY+UQdDjHOhxNtRv4U1i8v1D8N2D0Kizl8ATW4bxnR2+YEviPYDPgDlAVsFy59z5IYusAqgkLiIS3RZt2MlZT/5M0/o1+fSWo6mfEA9Lv4Q3zoMup8FFbxy4Q04WrPga5n8ASz7zpv3EoPUwqNfMS/TN+3pV6HWSw/GWyqSiSuKvAuOBWUBeRQQmIiJSkj3Zudz85mzy8x2PX9TfS+DZu+HT26FGXTj1XwfvFJ8AXU/zHtl7YNmXXuJe+iXk7oU2R8FFb5a/MVyECTaJ13DO3RTSSERERAL89ZOFLN+8i3tO6UrfVknewm//ATvWwCkPl14VXqO2V5Xe42zYtwvSpnsl8gqYxztSBDvs6s9m1iukkYiIiPg+nbuBN6et5ehOjbj2aL/F+fo5MOUpaDkABl1dtgPWrAsdjqtSCRyCL4kPBa40syUceE98cEiiEhGRamtt+h7u+WAujerW4N/n9yEmxiAvFybcCpg3pnlMbLjDjAjBJvHfhTIIERERgJy8fG55azaZWbk8ceVgmtTzS87TnoMNc+DIW6GZKoYLlJrEzSwWeMI5V66rZmad8BrGNQK2A2OccwuLbBMDPAyc7Mf0M/Bb51y2mZ0APBKweRNgo3Ouf3niERGRyOSc42+fLGT2mu1cO7w9x3Ru7K3Yvha++RsktYFj7glvkBGm1Hvizrk8IM3Myjsf27PAc865zniJ+sVitrkK6I03Z3k3f9mt/vknOef6FjzwWsi/Xs5YRESksjnndf0qxX+/Xsark73pRe84qcv+fSfe4XUVO/1Rr7GaFAq2YdtS4Eczu8vMbih4lLaTmTXBS8zj/EXvA+3MrG2RTfsAk5xz2c7ruD4RGF3M8VoAxwNjg4xbRETCKScLXj8PHm4PPz4KudnFbvbKz6t4bNIyerVM5PnLBlIjzk9PCz+GpZ9Dr/Og4wmVGHh0CDaJ1wfm4ZWSB/mPgUHs1wpY75zLBfAT9Bqg6Fh404GzzKyemdUALgTaFnO8y4HPnHObg4xbRETCJScL3r4Eln8FMXHw9Z/h6WGw/OsDNvto9joemLCQ9o3q8MoVg6iXEO+tyNoBn90NCUnwmwcrP/4oEOxUpFccxjmKDglX3OztrwFtgB+A3cAkvBJ3UVdQQiM7M7sNuK3gdWJiYhlDFRGRCpG7D94ZDcsnQb/R3sAsk5+EHx6BcedAtzPgNw/y7caa3PHurzRPTGDs1UNIrltz/zEm/Rl2bYQzn4C6jcP3XiJYicOumtmRzrmfzezU4tY75yaWeHCvOn0ZkOycyzUzAzYAQ51zqSXsdyFwo3Pu6IBlw4E3gDb+ffpSadhVEZEwyN0Hb4+GZV9A30vhzMchxq/43b4WvvgjLBpPXmwC/8s+izfjzuKN3w6nY5N6+4+xZiq8dJI3wtqYT/ZPdFLNHO6wq5fjtRS/s5h1BfeuD8k5t9nMZgOXAq8A5wKpRRO4mSUACc657WbWCLgH+L8ih7sSeCXYBC4iImGQmw3vXO4n8EsOTOAASa3ggrGsnvYJ+RPv5Pexb3NDvanU3P4vaHLS/mNMuBVia8AZj1XbBB6M0pL4z/7fG5xzi8p5juuAV8zsj8BOvB8GmNkLwHjn3HggEfjezPKAWOAx59yEggOYWT28HwB9yhmDiIiEWm42vDsGln4GvS88OIH7Vm/bzblfJrA352E+HTyXtvOf9Cc0ORVOfhDmvQdbFsGxf4RGnSr/fUSR0qrTZzrnBpjZrGjsl63qdBGRSpKX4yXwxZ9Ar/Nh5DPFjqq2aWcWo575hXUZe3nqkv6c3LM57FgHX97nTVQSl+B1K2vQBq7/CeJqHnyuauSwZzEzs8eBFmb2cNF1zrm7DjM+ERGJdnk58N4VfgI/75AJfMeeHC57cRpr0/fy0Lm9vAQO3kQm570MA8bAZ3fB1mXe0KrVPIEHo7Qkfh4wCu/+9+7QhyMiImGTuQmytkNyp2KrwYuVlwPvXQmLJkDPc+Hs4hP4nuxcrnhlGks2ZXLPKV25YFDRnsZA+2O80vfurVC/+eG9l2qixCTunFsJPGxmG5xzGmBFRKQqcg5mvgKf3wO5WVCzPrToCy0HQspAb9awes0O3i8vF96/GhaNhx4jYeRzEHtwWsnOzee342Yxa812rjumPdcf0+HQscTGK4GXQbD9xMeaWXOgXeA+zrkfQhWYiIhUgqwdXkvwBR9CUmvodiasmwVpM2BVwFd8/RRIGeAl9JYDoVlPmPA7WPgRdD8bznmh2AS+NzuPm9+czfdLt3DBwFbcc3LXynpn1UJQSdzM7sXrZrYSKOji5QBNRSoiEq3WzfLuZWekesn7zMehVpK3Li/XayGeNgPWzfQeC8d7w6AG6nYmnFt8At+6ax9XvTqDX9du5+y+Lfj7yJ6YuotVqBJbpxduZLYCGOKc2xr6kCqOWqeLiBTDOZjyFHx1v9cH+zf/gEFXl94fe18mrJ/tJfS0GdCgLZzwgFcFXsSKLbsY87LXiO2m4zpy+0mdlcDL4bBbp/s2RlsCFxGRYuxJh49u8PpyJ3eEUS9D897B7VuzHrQb7j1KMD01nWtem0FmVi4PntOLiwYX04hNKkSwSfwLM/s33hSghfPJFZ0XXEREItjqyfD+VbBzHfS+AE77t5eYK9Anc9dz2zu/Eh9jvHD5QI7r0qRCjy8HCjaJF0yAck7AMge0r9hwRESkwuXnw0+Pwrf/8Ppen/UU9L24Qoczdc7x3A8refCzxTSpV5OXxgyiZ0tNQhVqwbZObxfqQEREooJz0TWWd+Ym+PBaWPkdNOkO570CjbtU6Cly8/L584SFjJ2yms5N6/LyFYNpmVSrQs8hxQu2JI6ZDQRG4JXAv3bOzQxZVCIikcY5rx/1oglw9SSo3yLcEZVu43wYd643neeAK7xxyeMrNrnuyc7l5jdm8/XizQxrn8wzoweQWOvghm4SGkENyWNm1wAfAM2BFsAHZnZ1KAMTEYkYznnDgU59xrufPPWZcEdUurXT4JVTYW+614f7jMcqPIFvzsziwuem8PXizZzTryWvXjlYCbySBdvFbC4wwjm3xX/dGK80HmSTxvBQFzMROWzOwRf3wpQnoe3RsGuTV0V920KoWTfc0RVvxTfw1iWAwYXjoMPxFX6K5Zu9LmRpGXu55fiO/P5EdSELhdK6mAU5OC4UJPCA56VnfxGRaOYcTLrfS+Cth8FFb8GwG2HfDpg9LtzRFW/heHjjAq/v9mUfhySBf790CyOf+pmNO7J4+Nze3HZSFyXwMAk2iS83s7+bWQsza25m9wMrQhmYiEjYfft3+Pm/kDIILnnXK3n3vgBqJ3uDpeTnlX6MyjR7HLx7OdRqAFd8Bq0GVejhnXO8+NMqrnh5Gga8csVgzh/UqkLPIWUTbBK/HugAzPUfXf1lIiJV0/cPww//ghb94dL39/enjq/ljW62fbU39WakmPwkfHwjJKZ4Cbxpjwo9/L7cPO5+fy5//WQhbRvV4eObjuKoTo0q9BxSdkHdE49WuicuIuXy46Pw9Z+heR+vSrpWgwPX79oM/+npzfR11ZdhCbGQc17/7x8ehkZd4LKPKrzl/NZd+/jtuJlMT83gmM6N+d9F/dSArZJUyD1xM/uzmSUHvG7kV6mLiFQtvzzuJfCmPWH0RwcncIC6TaD3+bB2KqydfvjnzM32pgJd+T3k7A1+v/x8+OxuL4G36OeVwCs4gS9cv5OznviZ6akZXH1UO14aM0gJPIIE2zp9jnOub5Fls51z/UIVWEVQSVxEymTKM/D53dC4G4z5BOqUUF28eRE8NdSbhvP8Vw/vvF//BX78t/c8tqZ3D77d0d4Y5S0HeKOsFZWX61Wfz33LazV/4RuQUP/w4iji8/kbue2dOeTk5fP3kb04f6Duf1e2ipoApbhmh/opJiKRJ2cvfPM3L8k27eFViTfr5U32ERN76P2mv+Al8Ead4fLxJSdwgCbdoOMJsGg8ZKyGBm3KF+/GeV7juWa9oOe5sOpHWDMFVv8E3z0IcbWg9RAvUbcb7pW48/PgvSthyafQ5VRvEpP4hPKdvxjOOR7/ZjmPfrWURnVr8MylgxnYtmGFHV8qTrBJfKmZ3Qb8By+h/x5YHLKoRETKY8sSeHcMbF4IMXGw4uv96+Jq+Um9t5cwm/XxEnGN2l5V9qe3Q8MOcPkEr7o8GMNuhOWTvMFfTn6w7PHm5cL4m7372mc96f3gOOr3kJfjzfW96gdI/cFL6iu/8/apUdf7gZGR6rWUP+vJYqcCLa+92Xnc+d6vfDJ3A92a1+f5ywaQ0qB2hR1fKlaw1ektgHHAEXj9w38CRjvnNoY2vMOj6nSRamTOm/DpbZCbBcf9EY78HaSvgo1zvccG/++ebfv3sRgvcW9b7pWkx0yExJbBn9M5ePpIr6X6bQshoYwTfvzyOHx5nxfriX8+9HY5WbBuhldKT/3RK733uxRO+jvEBD3cR6k27NjLta/NZN66HZzcoxn/Pr8PdWoGPTq3hEBp1ellap1uZnUAnHO7KyC2kFMSF6kGsnfDxDthzutQrwWMehHaHFH8ts5B5gYvCRYk9Y1zvcZr578GSeWY93r26/DxDXDiX+HIW4LfL30lPHUE1G8Ov/2lbEOihmASlmmr0rnh9Vls3bWPW0Z04ncjOhETowFcwq1Ck3i0URIXqeI2LfSqz7cugY4nwshnoU5yqbtVqNx98FgviImHW+cEV7XtHLx2Fqz63qu+bzc85GEeOhTHq7+k8rdPF1EjLoaHR/Xm9N5RMLlLNVFhw66KiEQM52DWa/D88V5V+Il/gYvfqfwEDl7L8cHXwM40WPhxcPvMed1L4P0vC2sCz8rJ4/Z3fuWBCQtJaVCLD284Ugk8yiiJi0h02ZcJH1zrNQir0wiu/ByOvLVC7w2X2cCrvIZzk5/wfmCUJHOTN6FK3aZeFXyYrE3fw7lP/8IHs9dxfNcmfHzTUXRpVi9s8Uj5lPlTb2aJZtYzFMGISCVZ8BG8fBrsWBfuSMpm4zx47liY947Xteq6H6DV4HBHBbUbQt+LYf1sWDO55G0/uwuytsOpj0CtpMqI7iA/LtvCmU/8xIL1O7l1RCdeuGygBnCJUsGO2Pa5mSWZWV3gV+ATM/tLaEMTkZBYNMHrY7z6p+iYF7vAzFfh+RFen+zfPOgNblI7gvouD73B+/vLE4feZvGnsPAj6HYGdD+zUsIK5Jzj6e9WcPlL08jNc7x4+UB+f2JnNWCLYsGWxJs657YDpwIfA52As0MUk4iEytIv4N0rvFm4klrD7LFlG+YzHJzzJiOZcAvUawpXfQHDbqjw1tmHrVFH6HwKLJkI24qZ5DFrh9cXvWYinPKvSg9v175cbnxjFg99vpgOjesy/uajGNGtaaXHIRUr2CReUM8yHPjKOZcD5IcmJBEJieVfw9ujvaE5Lx8PQ2+EvRkw//1wR3Zoznn9qL/9OzTrDdd86w1DGqmOuAlw3jSlRX11v9e97aS/et3KKtHKLbsY+eTPTJy3kdN6NeejG4+kXaM6lRqDhEawSXy+mX0OnA58Y2Yavkckmqz6Ed66xOuLPPojb6SyvhdBfB2Y9lzpjbHCIT8PJtzqNRZrPaz0scwjQZsjvVHXZr8Oe9L3L0/9GWa+7A2d2v+ySg1p0sJNnPXEz6zYsos/nNKVJy7upwFcqpBgk/gY4BngOOfcHqABcE+oghKRCrRmCrxxgdd/efQH3rCj4I0u1ucC2PArpM0Ib4xF5eXAB9fArFehw/Fw6QdlHw0tHMxg2E2QuxdmvOQty8nybgXEJcAZ/6202wC79+Vy74fzuPq1GcTFGq9dOYTrjumARdptCDksQSVx51wWMB0YbGbn+8s+D2VgIlIB0mbCuFHe80veO7gqetA13t9pz1VuXCXJ2evVGsx/32sAdtFb3vjm0aLHSG/kuGnPeQPB/PCw15f92HsguUOlhDA9NZ1T/vsjr09dw9D2DfnklqM5qlOE12JIuQTbOv0svFbpFwIXA7PN7IxQBiYih2nDXBg3EvJz4ZJ3vJmwimraHdoc5bWY3rW50kM8yL5MeP08WPYF9LkYRr1S/DSckSw2HoZcB7s2eVOM/vxf737+sJtDfuqsnDwenLiI85+dzKadWdx/RnfeuHooLZPKMKSrRJVgq9PvB4Y650Y6584GhgEljNYvImG1aaE3rGdOFlz0JrQ96tDbDr4G8rK9qutw2pMOr57pTfAx+Dp/dq4ovXc7YIzX3qBg8JczHw/5e5m/bgdnPvETz/6wkt4pSXx6y9FccWQ7dR+r4oJN4rHOueUFL5xzK8qwr4iU164t3nSUOzcE3/hsy1J47UyvVHvBWOhwXMnbdz0N6jWHGS97U2Mejtxs+OERmP4ibFoA+UF2YsncCK+cButnwfA74ZSHwjsC2+GqlQT9R3vPj7gJWvQN2aly8/J5/OtlnP3kz6zcspvbT+zM+9cPo2OTuiE7p0SOYH8abjazq4CXnHPOzC4HtoYwLhFZ8jl8dL3XDQwgIQmadIcmXf2/3aBxtwPHC09f6SXwPelw/qvQ+Telnyc2HgZe6XXjWjLx8AYh+fkx7zgFaiZCykBoPdQbWa3lQKhZJLlkrPZqDTJWeWOgH3lr+c8fSY79AyR3hH6jQ3aK5Zt3cfs7c/g1bQddmtbj3+f3oWfLKGgAKBUm2PnEOwCvA338RXOAS/0SecTSLGYSlfJyvHupv/zPS9zDboLM9bB5EWxe6A0aEqhOEy+hN+nmjQi2cx2c+wL0PDf4c2Zugv/0gDbDvFm1ymPzYnj2aG9+7uPvhbXTYO1UbyjSvGxvG4uBpj39pD7EqwH44BrYuR5Of9T7MSGlys93vPJLKg99vpjsvHyuHd6e207sTM242HCHJhWsoucTr+vvk1kRwYWakrhEne1rvSFR06ZByiAY9TIktdq/vmA+7M2LAh4LYcsSyNkNGIx8BvpcWPZzv3cVzH8PbpjqlfbLIj8PXjoZ0qbD1ZO80neB3H2wfo6X0Aseu7fsX2+xcM5z0GtU2WOuhpZuyuRPH89nysp0Wjeszb/P78OgthE0/KxUqNKSeFDV6X5L9O+dczvN7A4zGwo84JybX1GBilR7gdXnR9wMI+4/eG5qM6jfwnt0HLF/eX4+7FjjJcTApF8Wg6/xkvj0F+C0R8q277TnvR8eQ284MIGD17q89ZD9reOd86r9106DjXOh88nQ/pjyxVyNbMncx6NfLeXt6WvId3DJkNb88dRuGrilmgu2On2uc663mfUBXgWexqtOPzqIfTv5+zQCtgNjnHMLi2wTAzwMnIz3w+Jn4LfOuWx/fWvgSaAz4IAnnXOPl3ZulcQlKhStPh/5DHQ5pfLjcM6rDk9fBbct8oZnDUbGanhqmHdv/oYpUEPDeVakrJw8XvxpFU99u5zd2XkMbtuQe0/rRp9WSeEOTSpBaSXxYJt/FjRZPQl4zjn3LBDs/9Rn/X064yXqF4vZ5iqgN9Af6OYvuxXAvOGFPgRec8518de/G+S5RSLbjjR4+VQvgacMgut/Ck8CB6+UP+gayN4Fc98Obh/n4JPfeVX5Z/xPCbwC5ec7PpydxvGPfMe/vlhC43o1eebSAbx93VAlcCkUdBczvwr9XOBbf1mpk8+aWRO8xDzOX/Q+0M7M2hbZtA8wyTmX7byqgYlAQZPOEcBe59y7AM6zMci4RSLX0i/gmaO8augjboYrPit/VXhF6XWeN7zptOeD69L265uw4hvoe2npXdkkaFNXbuPsp37m92//yu7sPP50ene+/P0xnNyzmYZNlQMEezPlPryx0792zi0ysy7AsiD2awWsd87lgpeAzWwN0BpIDdhuOnCtmT0N7MMbGa6tv647sMXM3gK6+Pvd7pxbGWTsIpElLwe++as3kldCkjesaLhK30XVqO11iZr8hNc/vaR71bs2w+d/gLpN4Td/q7wYq7BVW3fzz88W8cWCTcTHGlcd1Y6bj+9IUu0a4Q5NIlRQSdw5NwGYEPB6CXBOkOco+nO+uJ+RrwFtgB+A3cAk4Hh/XTxwAt6IcQvM7FrgLWBw0YOY2W3AbQWvExPVX1IiTNZOeOtib1SylEEw6iVvXu9IMvBKmPwkTH++5CQ+8U7I2g7nj4VaDSotvKpo084snvl+BeOmrCYnz3FKz2bcfXJX2mq6UClF0M0azewcoC+QULDMOXdXKbutBVLMLM45l+vf324FrAncyK9C/4v/wMwuBAoav60GZjvnFvivxwFPm1mscy6vyHEeBR4teJ2SkhKB8ytKtbUnHV4fBetmeveeT37w4NbnkSC5A3Q8wetzviMNEotpU7NogjfeerczDm9wmGpu0YadvPDjKsb/uo6cPEeflETuO727uoxJ0ILtYvYY0AEYALwJnAd8Vdp+zrnNZjYbuBR4Be+eeqpzLrXI8ROABOfcdjNrhDfN6f/5qz8DHjKzls65dXgt2OcXTeAiEW3XZhg7EjbN94YVPe7eSpuSslwGXwPLv/KGYh3xfweu25sBn97u3Ts/tYxd0QTnHD8s28oLP67kx2XewJeD2zXkmqPbM6JrE411LmUSbEl8BF7js9nOudvN7GHghSD3vQ54xcz+COwELgcwsxeA8c658UAi8L2Z5QGxwGN+FT7Oud1mdgPwqV+S3443k5pIdNixzhtWdNsyOOEBOOr34Y6odB1PgAZtYeYrcMxdB84k9uX/eTN0nfUk1GsWrgijzr7cPD6es54Xf1zFkk2ZxMYYZ/RpwTVHt6N3SlK4w5MoFWwSz3LO5ZuZM7N459wmM2sZzI7+/fNhxSy/OuD5JuCQQ0Q5574AvggyVpHIkb7KG8t8+xo45V8w5NpwRxScmFgYeBV89X+w8GPofb63fOV3MHsstD8W+l4SzgijxvY92bw+dQ2v/JLKlsx91K0Zx9VHtWPMkW1JaRBF86RLRAo2iWeaWW3gJ+BVM9sI5IQuLJEqoGA2sYJSa79Lwx1R2fS71JvMZNpzXhLP3g0TboX42nDGfyP7dkAEWLh+J29NX8O7M9LYm5NHi8QE7j21GxcMbkX9hAhsCyFRKdgkfhHegC934rX+boB3X1xEirNhrncPPGt72ScjiRS1G3rjmc8e501iMu89yEiF3zzoVbXLQTbuyOLjOev4cPY6Fm/0ppjo2bI+1xzdnlN7NSc+NoqnV5WIVOKwq2ZWH2hYTEO09sBW59zO0IZ3eDTsqoRF2gwYdw7kZHnTgUZKH/DyWD8HnjsGUgbDuhnQoj9c9aVX3S4A7NqXy+fzN/Lh7DR+WbEN5yCxVjyn927OOf1b0r91Aw3QIuV2uBOgPIzXCj21yPIj8e5z33BY0YlEqrwcbyATM2g5wHs07AAxpZSkVv0Ib14ILh8ufjv6RzFr0ddL4GnTICYeznpCCRzIzcvnp+Vb+XD2Or5YsJGsnHziY42TujdlZL8UjuvaWNOCSqUoLYkPd85dX3Shc26smd0dophEwu/nx7zBTgLVTISW/aDlwP2JvV7T/euXTYK3L4HYGnDp+96c2VXB0OvhvWkw/A5vzvJqKi/fMWdtBp/O3cj4X9ezddc+AAa2acDI/i05rVdzjawmla60JF5SX2wNpCJV05Yl8P3D0KgLXPgGbPwV1s3yBmlZM9VroV2gfgq07O/dI57yNNSsC6M/hBb9whV9xetxjlcL0ax3uCOpdDuzcvhh6Ra+WbSZb5dsJmOP1563bXJtRg/tzMh+LWmdrBbmEj6lJfE4M6tf9N63mSUSxAQoIlEnPw8+vsmrTj/rCWjU0XsUNEzLy4Uti7yEvm6ml9wXf+JVn9dtCqM/gqbdw/oWKpyZV61eTazaupuvF23im8WbmbYqndx8r7zSvXl9Lh3ahhO6NaV3SqLuc0tEKC2JvwmMNbMxzrkMADNrgDed6FuhDk6k0k17zrv/O/QGaHXQ8PwQGwfNenmPAWO8Zft2waYF0KiT16JbokpOXj4zUjP4ZvEmvl60mZVbdwNQMy6G4Z0bM6JbE47v2oTmibXCHKnIwUprnR4LvIQ3XGrBrGWd8KYUvTLShz5V63Qpk4xUeGoY1GkMN0zW3NhV2Prte/l+6Ra+X7KFn5dvJXNfLgBN69fk+K5NOaFbE47o0IhaNdQ4TcLrsFqn+0n6cjP7C9684ACznHMrKjBGkfBzzhvIJGcPnPk/JfAqZl9uHjNSM/h+6Ra+W7KZpZt2ARBj0KdVEsd2bsKIbk3o0aK+qsklqgQ7FekKQIlbqq7ZY70Ga/0v84YUlai3Nn0P3/ml7V9WbGVPtldx2KhuTc7tn8IxXRpzdMdGNKijFuUSvYKeilSkytq5Ab64D+o1hxP/Gu5opJyycvKYuiqd75ds4bulm1m5xbu3HRtj9G+dxLFdmnBM58Z0b15fM4VJlaEkLtWbc960mvt2wMhnoFZSuCOSMli1dTffL9nMd0u3MGXlNrJy8gFoVj+B8wemcGyXJhzZsRGJtdSZRqomJXGp3hZ8AEs+9bqQdT013NFIKfZk5zJ5xTavUdrSLazetgeA+FhjYJuGHNulMcd0aUyXpvV0b1uqBSVxqb52b4OJd0GthnDKw+GORoqRn+9YtHEnPy7byk/LtjJtVTrZeV5pu2VSLS4Z0ppjOjfmiI6NqFtTX2dS/ehTL9XX5/fAnq1wzgtQp1G4oxHfxh1Z/LhsCz8t9xL3tt3ZgNdve0j7hoX3tjs0rqPStlR7SuJSPS39Aua9A51P9qbblLDZk53L1JXp/LhsKz8u28KyzbsK13VrXp9RA1I4qlMjBrVtSEK8+m2LBFISl+onaydM+B3UrA+nPeoNKyqVZl9uHnPWbGfKynQmr9zKrNXbC6vIm9b3un8N79yIIzo0onG9mmGOViSyKYlL9fPVnyBzPZz+GCS2DHc0VV52bj5z07YzecU2pqzaxszVGYWtyGvFx3JEx2SO7tSYozs1olOTuqoiFykDJXGpXlb9CDNfhrZH7x/7XCpUTl4+c9N2MGXlNqas3MaM1Az25ngDrSTExzCwTUOGtm/IsA7J9E5JIj62lDnaReSQlMSl+sjeA+Nvhrha3tCqKvFViL3Zecxem8H0VRlMT01n1pqMwtHRasTFMKB1A4Z1SGZo+2T6tEqkZpzua4tUFCVxqT7SpsPO9XDC/dCwfbijiVrb92QzPdVL2NNWpTN/3Y7C6TprxsXQt1USQ9t7Sbtf6yQ1RhMJISVxqT7aH+PNTtagbbgjiRrOOdZt38vM1RlMW5XO9NT0wslDAOonxDG8c2MGtW3I4HYN6NlSJW2RyqQkLtVLcodwRxDRsnPzWbhhJzNXZzBzdTozV2ewaee+wvVN69fk9N7NGdyuIYPaNqRL03oah1wkjJTERaqxbbv2MWvNdmauzmDW6gx+TdvOvlyv5bgZdGlajxO6NaV/6wYMatuQVg1rqfW4SARREhepJnLz8lm8MZPZa7cze00Gs9dsZ9XW3YXr69aMY3C7hgxo04ABbRrQp1US9RM0cYhIJFMSF6miNu/MYtaa7cxe6yXseWk7Crt6AbRJrs05/VsWJu1OTeoRq6pxkaiiJC5SBezLzWPB+p3MXrO/lL1u+97C9XVrxtG/TRL9WjWgX+sk+rZKIrmuRkMTiXZK4iJRxjlHWsbeA6rFF67fWTh0KUCnJnW5YGAr+rVOol/rBnRsUlelbJEqSElcJMLt3pfL3LQdhdXis9dsZ+uu/S3Gk2rHc2THZPq1bkDfVkn0aZVEYi3dyxapDpTERSJIfr5jxZZdXrL2S9pLN2Xij6VCbIzRrXk9TunZrLCU3Ta5tlqMi1RTSuIiYbR11z7mrNnOnLVeA7S5a3eQuS+3cH3T+jU5qfv+hN2rZSK1amgwFRHxKImLVJKCgVQK7mPPXpvB2vT9jc8S4mPo3TKJvq2T6NfK+9s8sVYYIxaRSKckLhIiXhevDGat2c6s1RnMW7ejcCAVgA6N63Bu/5TCpN2lWT3N6CUiZaIkLlIBCkrZs1ZnMKuYLl71EryBVPq39rp49WvVgMTaanwmIodHSVykHDZnZjFrtdfwbGYxpezALl792zSgY+O6GmNcRCqckrhIKXLy8lm8IdOvGveSdlpG8aXs/m28bl7q4iUilUFJXKSI9N3ZzFqdwcw1+ycFyco58F72eQNSGNCmgUrZIhJWSuJSreXnO5Zv2eVPvekl7ZUBk4LUqRHrjS3eugH92jSgX6skkmrXCGPEIiL7KYlLtbJrXy6/rt2+P2mvySAza3+/7DbJtTmnX0v6t2lA/9YN6NJMk4KISOQKeRI3s07Aq0AjYDswxjm3sMg2McDDwMl+TD8Dv3XOZZtZW2A5MD9gl3OdcytCHbtEv407spiems6M1HRmrM5g0YadhaOf1YiLoXfLxMJq8f6tG9C4niYFEZHoURkl8WeB55xzr5jZKOBFYFiRba4CegP9gRzgBeBW4F/++u3Oub6VEKtEsfx8x9LNmcxIzWBGajrTUzMO6ObVqK43+tnAtl7S7tGiPjXjNPqZiESvkCZxM2uCl5hP8he9DzxhZm2dc6kBm/YBJjnnsv39JgL3sz+JixxkX24ec9N2MG2VV9KeuTqDnQFV4x0a1+HCQa0Y0KYBg9o2pI3GGBeRKibUJfFWwHrnXC6Ac86Z2RqgNZAasN104FozexrYB1wItA1YX9/MpgOxwEfA351zeSGOXSLMrn25zFqdwbRV6UxLTWfO2u1k+32z42ON3ilJDGzTgIFtGzKgTQMa1lEDNBGp2iqjOt0VeV1cUeg1oA3wA7AbmAQc76/bAKQ45zabWUPgbeB2vHvoBx7Y7DbgtoLXiYmJhx28hE/67mymp6Yz3U/aC9bvJM+/oV2nRixD2yczuK1Xyu7TKomEeFWNi0j1Ys4VzbEVeHCvOn0ZkOycyzWvLnMDMLRIdXrR/S4EbnTOHV3MuouAi51zZ5R2/pSUFJeWllbu+KVybdix1ytlr0pnemo6SzftKlzXoHY8g9o2ZHC7hgxpl0y35vWI0zjjIlLFmdk651zKodaHtCTul55nA5cCrwDnAqlFE7iZJQAJzrntZtYIuAf4P39dEyDDOZdjZjWBc4DZoYxbQs85x6qtu5mems5UP2kHzujVrH4CZ/Zp4SfthnTQgCoiIgepjOr064BXzOyPwE7gcgAzewEY75wbDyQC35tZHt5978eccxP8/Y8C/uKviwO+Af5eCXFLBcrLdyzZmMm0VduYnprB1FXpbN21r3B9+0Z1uGBgKwa380rbKQ1qqRGaiEgpQlqdHm6qTg+fnLx85q/bcUD1eEHLcTPo2qw+Q9o1ZFDbhgxq14Am9RLCHLGISOQJa3W6VB9ZOXn8unZ7Ycvxmasz2JPtdSCIizF6pyQyyK8aH9CmoSYIERGpAEriUi679+Uya43X3WvqSr+7V57X3atmXAz9WicxpF0yQ9o1pG/rJGrX0EdNRKSi6ZtVgrJ9TzbTUzOYtmob01alMz+gu1fdmnEM65Bc2AitV0qiRkITEakESuJSrE07sw64n714Y2bhuga14xnRtUlhI7Tuzeuru5eISBgoiQsAaRl7mLoynal+STt1257CdU3r1+SMgO5emj9bRCQyKIlXQ8450jL2MmXlNqb4iTstY38f7dYNa3PegJTChmitG2rMcRGRSKQkXg0451i9bQ9TV21j6sp0pqzcxvodWYXr2zeqw0WDW3kN0do3pHlirTBGKyIiwVISr4IKRkObuspL2FNXprNx5/6k3aFxHS4Z0poh7ZMZ2q4hTeqrj7aISDRSEq8CnHOs2LLbS9ir0pm6chubM/ePhta5aV1O7N7GmzCkXUMa16sZxmhFRKSiKIlHIeccyzfv8u5p+/20A4cw7dqsHqf2as4Qv/V4cl0lbRGRqkhJPAo451hWkLT96vFtu7MBbwjTbs3qc0af5v7UnA1poHm0RUSqBSXxCJSf71i6ObOwEdrUVemkByTtHi3qc3a/loVJO7G2hjAVEamOlMQjQEHSnrJif5evjD05AMQY9GiRyLn9WzKkXTKD2mnccRER8SiJh0F+/v7q8ckrth2UtHu2TOS8gd644wPbKmmLiEjxlMQrQdF72lNW7q8eD0zaQ9t7Sbt+gpK2iIiUTkk8BLwuX7uYvDLdryLfdkBDtB4t6nNOv5YM65CskraIiJSbkngFcM6Rum0Pk1dsY7Jf2t7i99M2g+7NAxqi6Z62iIhUECXxclqbvj9pT16x7YAR0bo2q8dpvZozrIN3Xzuptrp8iYhIxVMSD9L67XsPSNrrtu+fMKRTk7qc1KMNw9onM6R9Mg3VT1tERCqBkvghbM7MYrJ/P3vyim0HTM3ZvlEdLh7SmmHtkxnaPlnDmIqISFgoifvSd2czZeU2flmxlckrtrFiy+7Cda0a1uKCga0Y1sFL2s0SNWGIiIiEX7VN4jv25DBl1bbC0vbijZmF65onJnBO/5YMa5/MsA7JpDSoHcZIRUREildtknhmVg7TU9ML72svWL8T57x1jevV5Mw+LRjWIZlh7ZNpk1wbMwtvwCIiIqWo0kk8Ozefhz5fzOQV25i3bgd5+V7WblinBqf0bFZY0u7QuK6StoiIRJ0qncTTd2fz9HcrqJ8Qx/Fdm3BEBy9pd25Sj5gYJW0REYluVTqJ16sVzyc3H0W35vWJVdIWEZEqpkon8To1YunZMjHcYYiIiIRETLgDEBERkfJREhcREYlSSuIiIiJRSklcREQkSimJi4iIRCklcRERkSilJC4iIhKllMRFRESilJK4iIhIlFISFxERiVJK4iIiIlHKXMGk2lWQmeUCG4PcvBawN8zbRkoc0bZtpMRRlbeNlDiq8raREkdZtq0L7ApzDFX9Gjdzzh16nhPnXJV9AGll2PadcG8bKXFE27aREkdV3jZS4qjK20ZKHGXcVt+xYb7Gqk7f790I2DZS4oi2bSMljqq8baTEUZW3jZQ4yhpzuGOo1te4qlenpznnUsIdh4hIVaTv2NAr7RpX9ZL4o+EOQESkCtN3bOiVeI2rdElcRESkKqvqJXEREZEqS0k8CphZJzP7xcyWmtk0M+vuLzcze8BfPt/MvgtzqBHFzP5nZqlm5sysZ8Dyl81srpnNMbPpZjYinHFGEjNLMLOP/M/UHDP73Mza+uua+K+X+Z+3o8IcbsQo5br94i+b4183Z2a9wxxyxCjh+03/T4NRlqb5eoTnAXwDjPGfjwIm+89vBd4Havivm4c71kh6AMOBFCAV6BmwPCngeV9gK/6tper+ABKAU9l/q+0m4Ev/+UvAA/7zQcBqIC7cMUfCo6TrVmS7UcC8cMcbSY8Svt+SArbR/9NDPFQSj3Bm1gToD4zzF70PtPN/5d8J3O2cywZwzm0IS5ARyjn3g3MurZjl2wNeJgFqGOJzzmU55yY6/5sTmAK095+fDzzpbzcd2ASoNE6p1y3QlcCLlRdZZCvp+03/T4OjJB75WgHrnXO5AP6XxBqgNdAYGGlmU/zHBWGMM6qY2T/NbAXwAXBewJevHOgWYIKZJQMxzrktAetS8T6HcrBbgAmBC8ysJXAs+xOWlPz9pv+nQVASjw5FP7jmP2oAtZxzQ/FKSY8G3vuVQ3PO3eOc64B33f5lZjXCHVOkMbM/Ap2Ae/1FxX0OpYhirluBMcAnzrmtlR5UZDvk50r/T0unJB751gIpZhYHXmM2vF+vq/HGLB4H4JxbA/wMDAxTnFHJOTcJqAf0CncskcTM7gDOAU5xzu1xzm3zlzcO2KwNXqlJfEWvW8ByA65AVelFHer77YDPlf6fHpqSeIRzzm0GZgOX+ovOBVKdc6nAm8DJAGbWABgMzA1DmFHDzOLMrFPA68FAE2Bl+KKKLGZ2G3ARcGKR+5LvAjf62wwCmgE/VXqAEaqE6wZwDF7N2VeVHVckO9T3G5Cm/6fB0WAvUcDMugCvAMnATuBy59wCM2sEvAy08zd93Dn3bHiijDxm9iRwFl6y2YpXc9EDrzVsIpAH7Abuc859E644I4mZpeCVjlYCmf7ifc65IWbWFBiL93nLBm5wzn0fnkgjS0nXzV8/FljpnLs/TCFGrOK+34Dl6P9pUJTERUREopSq00VERKKUkriIiEiUUhIXERGJUkriIiIiUUpJXEREJEopiYuIiEQpJXEREZEopSQuIiISpZTERUREopSSuIiISJRSEhcREYlSSuIiIiJRSklcREQkSimJi4iIRCklcRERkSilJC4iIhKlojKJm1mqmS02s1/NbJmZfWxmR4Q7LhGRqsb/vu0Z7jikeFGZxH2jnHN9nHOdgJeAiWY2JNxBiYiIVJZoTuKFnHMfA08Bd5hZvJn908ymmdkcM3vLzJIAzCzRzF4ws3l+Kf6lsAYuIhIlzOw2M5tuZrP979chAeucmd1tZlPNbJWZXRHOWKuTKpHEfdOBHsCdwC7n3GDnXF9gAfBnf5vHgL1AH+dcH+DuMMQpIhKNxjrnBjnn+gG3AC8WWZ/lnBsCnAr8z8ziKj3CaqgqXWTz/54N1DezUf7rGsAK//npwADnXD6Ac25LpUYoIhK9+pnZvUAykAt0N7Mazrlsf/3rAM65RWaWCzQD0sITavVRlZL4IGA+0A64wTn3TZjjERGpKmKA94FjnXMzzaw+sAOvkFSQxLMCts+jauWXiFUlqtPN7Czgt8CjwHjgNjOr7a+rbWY9/E3HA3eaWYy/rnE44hURiULxwFr/+c3hDET2i+Yk/p7fOG05cBVwqnNuCvBPYA4w1czmAlOAvv4+vwdqA/PNbA7wj8oOWkQkysQBe4A/AdPM7AdgX3hDkgLmnAt3DCIiEoHMrDmwGGjmnNsb7njkYNFcEhcRkRAxs9uA74A7lMAjl0riIiIiUUolcRERwcwSzOwjM1vqD5T1uZm19dc18V8vM7P5ZnZUwH7/MLNFfhulaWZ2fMC6W/zt5/rHvCAMb61KU0lcREQwswTgeOAz55wzs5uAM51zJ/mjW65xzj1gZoOA94AOzrlcMzsF+M45t9fM+uBVwTd3zmWZ2QhghnNuh5m1AmYBA51zq8PyJqsgJXERETmImQ0E3nLOdTSzXUC7ggGyzGwacJdz7rsi+8QA24HuzrmDBnoxs/nA9c65n0Idf3Wh6nQRESnOLcAEM0sGYoqMcJkKtC5mnyuAFYdI4CcADYCZIYi12tKIOiIicgAz+yPQCbgeqAUUrbK1YvYZAdwPnFjMul7Ay8AFaulesVQSFxGRQmZ2B3AOcIpzbo9zbpu/PHCEyzbAmoB9jsFL0mc455YUOV534BPgSlWjVzwlcRERAQr7hl8EnOic2x6w6l3gRn+bQXiTm/zkvx4OjAXOcs79WuR43YCJwLXOua9C/gaqITVsExERzCwFb2z0lUCmv3ifc26ImTXFS9Tt8CY8ucE5972/3zKgPrAh4HCjnXPzzOwrYCAQ2Br9bufcF6F9N9WHkriIiEiUUnW6iIhIlFISFxERiVJK4iIiIlFKSVxERCRKKYmLiIhEKSVxERGRKKUkLiIiEqWUxEVERKKUkriIiEiU+n/sfv5x4rXu+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 560x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=80)\n",
    "plt.title(f\"Tercera ola de casos en Nuevo León\")#. RMSE = %.3f\" % min_error, fontsize = 13)\n",
    "plt.ylabel(\"Casos confirmados diarios\")\n",
    "series[-40:].plot(label = \"Promedio de 7 días\")\n",
    "predic_lstm.plot(label = \"LSTM\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "503957de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.410168137153825"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(series[-n_test:],predic_lstm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "90c613a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.983270761918064"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(series[-n_test:], predic_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "255ab750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018306380239038883"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(series[-n_test:], predic_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f13e1715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018251266841324897"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smape(series[-n_test:], predic_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "9d88571e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912774773102091"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(series[-n_test:], predic_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0dd0ab04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.plot(['Ma','Mie','Jue','Vie','Sab','Dom','Lun'],min_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "249be52c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81, 7)\n",
      "1.0 280.14285714285717\n",
      "(4, 7)\n",
      "272.0 120.0\n"
     ]
    }
   ],
   "source": [
    "train_m, test_m = split_weeks(muertes_sma[:-2])\n",
    "print(train_m.shape)\n",
    "print(train_m[0, 0], train_m[-1, -1])\n",
    "# validate test\n",
    "print(test_m.shape)\n",
    "print(test_m[0, 0], test_m[-1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7f76a54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "errors, min_error_m, min_pred_m, min_scores_m = best_scores(train_m, test_m, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "916adc6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_m = min_pred_m.reshape((min_pred_m.shape[0]*min_pred_m.shape[1]))\n",
    "#predictions = casos_sma[1]+np.cumsum(predictions)\n",
    "predic_m = pd.Series(predict_m, index = casos_sma[:-1][-28:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2bae171a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20731df5a60>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "plt.title(f\"Tercera ola de muertes con LSTM. RMSE = %.3f\" % min_error_m, fontsize = 13)\n",
    "plt.ylabel(\"Muertes\")\n",
    "muertes_sma[-50:-1].plot()\n",
    "predic_m.plot(label = \"Predicción\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "67ee7648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.97611174291494"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(predic_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b8376309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03208361023810324"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(predic_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a7ac743b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9659534022953284"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(predic_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "73622a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b30642c760>]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(['Ma','Mie','Jue','Vie','Sab','Dom','Lun'],min_scores_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebe8644",
   "metadata": {},
   "source": [
    "Analizando los casos por estado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "49f2ee8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "error = []\n",
    "predicciones = pd.DataFrame()\n",
    "for i in range(32):\n",
    "    casos_estado = datos_total.iloc[[i]]\n",
    "    casos_estado = pd.melt(casos_estado, var_name = \"Fecha\", value_name = \"Casos\")\n",
    "    casos_estado[\"Fecha\"] = pd.to_datetime(casos_estado[\"Fecha\"])\n",
    "    casos_estado = casos_estado.set_index(\"Fecha\")\n",
    "    casos_estado = casos_estado[\"Casos\"]\n",
    "    estado_sma = casos_estado.rolling(window = 7).mean().dropna()[:-1]\n",
    "    # Dividir los datos en train y test\n",
    "    train, test = split_weeks(estado_sma[:-8])\n",
    "    \n",
    "    # Hacemos predicciones utilizando LSTM\n",
    "    errors, min_error, min_pred, min_scores = best_scores(train, test, 7)\n",
    "    predictions = min_pred.reshape((min_pred.shape[0]*min_pred.shape[1]))\n",
    "    #predictions = casos_sma[1]+np.cumsum(predictions)\n",
    "    predic = pd.Series(predictions, index = estado_sma[:-8][-28:].index)\n",
    "    \n",
    "    #Guardamos las predicciones en un DataFrame y los errores también\n",
    "    predicciones[names[i]] = predic\n",
    "    error.append(min_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d870b6e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'predicciones' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-b12ebcd31f91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredicciones\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'predicciones' is not defined"
     ]
    }
   ],
   "source": [
    "predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9332cb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rmse = np.zeros(32)\n",
    "for i in range(32):\n",
    "    casos_estado = datos_total.iloc[[i]]\n",
    "    casos_estado = pd.melt(casos_estado, var_name = \"Fecha\", value_name = \"Casos\")\n",
    "    casos_estado[\"Fecha\"] = pd.to_datetime(casos_estado[\"Fecha\"])\n",
    "    casos_estado = casos_estado.set_index(\"Fecha\")\n",
    "    casos_estado = casos_estado[\"Casos\"]\n",
    "    estado_sma = casos_estado.rolling(window = 7).mean().dropna()[:-1]\n",
    "    error_rmse[i] = np.sqrt(mean_squared_error(predicciones.iloc[:,i],estado_sma[:-8][-28:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1a362649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.224900477644791"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(error_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711b9afb",
   "metadata": {},
   "source": [
    "## LSTM vanila with prediction intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a23723af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((595, 7, 1), (595, 1))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x, train_y = series_split_multi(train, 7, 1)\n",
    "train_x.shape, train_y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "e1cf5ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the model\n",
    "def build_model(train, n_input):\n",
    "    #prepare data\n",
    "    train_x, train_y = series_split_multi(train, n_input, 1)\n",
    "    # define parameters\n",
    "    n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n",
    "    verbose, epochs, batch_size = 0, 70, 16\n",
    "    # define model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(80, activation='relu', input_shape=(n_timesteps, n_features)))\n",
    "    model.add(Dense(50, activation='relu'))\n",
    "    model.add(Dense(n_outputs))\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    # fit network\n",
    "    model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "    return model\n",
    "\n",
    "# Forecast\n",
    "def forecast(model,data, n_input):\n",
    "    input_x = data[-n_input:]\n",
    "    input_x = input_x.reshape((1,len(input_x),1))\n",
    "    # forecast the next week\n",
    "    yhat = model.predict(input_x, verbose=0)\n",
    "    # we only want the vector forecast\n",
    "    yhat = yhat[0,0]\n",
    "    return yhat    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1eed9f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to evaluate to the model\n",
    "def evaluate_model(train, test, n_input):\n",
    "    #History in weekly data\n",
    "    history = [x for x in train]\n",
    "    pred_mean = []\n",
    "    pred_std = []\n",
    "    for i in range(len(test)):\n",
    "        data = np.array(history)\n",
    "        data = data.reshape(data.shape[0]*data.shape[1])\n",
    "        for j in range(len(test[i])):\n",
    "            yhat = []\n",
    "            for k in range(10):\n",
    "                #Train the model\n",
    "                model = build_model(train,n_input)\n",
    "                yhat.append(forecast(model,data,n_input))\n",
    "            pred_mean.append(np.mean(yhat))\n",
    "            pred_std.append(np.std(yhat))\n",
    "            np.append(data,np.mean(yhat))         \n",
    "        history.append(test[i,:])\n",
    "    return pred_mean, pred_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "890cc09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_mean, y_std = evaluate_model(train, test, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "df3d9503",
   "metadata": {},
   "outputs": [],
   "source": [
    "predic_lstm = pd.Series(y_mean, index = casos_sma[:-8][-28:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "33f8ff96",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper, lower = predic_lstm + (1.96)*np.array(y_std), predic_lstm - (1.96)*np.array(y_std) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a25c156d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24b3902dee0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "plt.title(f\"Tercera ola de casos con LSTM. RMSE = %.3f\" % min_error, fontsize = 13)\n",
    "plt.ylabel(\"Casos\")\n",
    "casos_sma[-50:-resta].plot(label = \"Promedio de 7 días\")\n",
    "predic_lstm.plot(label = \"Predicción\")\n",
    "upper.plot()\n",
    "lower.plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "e4d1e52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = []\n",
    "for k in range(2):\n",
    "    model = build_model(train,7)\n",
    "    data = test\n",
    "    data = data.reshape(data.shape[0]*data.shape[1])\n",
    "    input_x = data[-7:]\n",
    "    input_x = input_x.reshape(1,7,1)\n",
    "    y = model.predict(input_x)\n",
    "    yhat.append(y[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1fb11bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2399.0747, 2348.0696]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e724df1",
   "metadata": {},
   "source": [
    "# Modelo ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "36b41537",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "096a2a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 28\n",
    "periods = 7\n",
    "pred = []\n",
    "error = []\n",
    "coef_int = []\n",
    "#series = datos_sma[\"CHIAPAS\"][:-29]\n",
    "for i in range(int(start/periods)):\n",
    "    model_arima = auto_arima(series[:-start+i*periods], seasonal = False)\n",
    "    predictions, coefs_int = model_arima.predict(n_periods = periods, return_conf_int=True, alpha = 0.01)\n",
    "    order = model_arima.get_params()['order']\n",
    "    pred.append(predictions)\n",
    "    coef_int.append(coefs_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de45409c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.93507243]), array([-0.48635935, -0.15280756]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_arima.arparams(),model_arima.maparams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3e21b5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(665,)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(model_arima.resid())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "604f3be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.array(pred)\n",
    "predic = pred.reshape(pred.shape[0]*pred.shape[1])\n",
    "pred_arima1 = pd.Series(predic, index = series[-start:].index)\n",
    "\n",
    "coef_int = np.array(coef_int)\n",
    "inter = coef_int.reshape(coef_int.shape[0]*coef_int.shape[1],coef_int.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "433522c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "inter_up = pd.Series(inter[:,0], index = series[-start:].index)\n",
    "inter_low = pd.Series(inter[:,1], index = series[-start:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "12570c1e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d8b2fce340>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "plt.title(f\"Tercera ola de casos con ARIMA{order}\", fontsize = 13)\n",
    "plt.ylabel(\"Casos\")\n",
    "series[-40:].plot()\n",
    "pred_arima1.plot(label = \"Predicción\")\n",
    "#inter_up.plot()\n",
    "#inter_low.plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a7065c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004272620960698199"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(pred_arima1,series[-start:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8089f684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0022302087595157433"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(pred_arima1,series[-start:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0deae2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0022869981517929443"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(pred_arima1,series[-start:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "de911142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0022775644756627537"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smape(pred_arima1,series[-start:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3ed0233e",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 28\n",
    "periods = 7\n",
    "pred = []\n",
    "error = []\n",
    "for i in range(int(start/periods)):\n",
    "    model_arima = auto_arima(muertes_sma[:-2][:-start+i*periods], seasonal = False)\n",
    "    predictions = model_arima.predict(n_periods = periods)\n",
    "    order = model_arima.get_params()['order']\n",
    "    pred.append(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7adce2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.array(pred)\n",
    "predic_m1 = pred.reshape(pred.shape[0]*pred.shape[1])\n",
    "pred_arima_m = pd.Series(predic, index = muertes_sma[:-8][-28:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2c7fb438",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_arima_m = pd.Series(predic_m1, index = muertes_sma[:-2][-28:].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3a6d17cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2072f47fb80>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5),dpi=100)\n",
    "plt.title(f\"Tercera ola de muertes con ARIMA{order}\", fontsize = 13)\n",
    "plt.ylabel(\"Muertes\")\n",
    "muertes_sma[-50:-2].plot()\n",
    "pred_arima_m.plot(label = \"Predicción\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f099220e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.19862704942933"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(pred_arima_m,muertes_sma[:-2][-28:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "19adfd0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.503409471501788"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(pred_arima_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3348ec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05343038234804392"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(pred_arima_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "767f2439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9464527428445545"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(pred_arima_m,muertes_sma[:-2][-28:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e35915b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasting with ARIMA models using auto_arima\n",
      "----------------------------------------------\n",
      " \n",
      "Entrenando el Modelo para AGUASCALIENTES\n",
      "    Semana prediciendo la semana 0 ARIMA(4, 1, 3) ---> Error: 10.483611674679665\n",
      "    Semana prediciendo la semana 1 ARIMA(2, 1, 4) ---> Error: 16.949721124850182\n",
      "    Semana prediciendo la semana 2 ARIMA(2, 1, 5) ---> Error: 4.762471390311713\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 3) ---> Error: 12.946562989809765\n",
      " \n",
      "Entrenando el Modelo para BAJA CALIFORNIA\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 28.9366273715507\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 5.626993054409871\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 49.55655238841448\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 5) ---> Error: 9.779560379111041\n",
      " \n",
      "Entrenando el Modelo para BAJA CALIFORNIA SUR\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 2) ---> Error: 1.9876452897050167\n",
      "    Semana prediciendo la semana 1 ARIMA(5, 1, 2) ---> Error: 1.1147959498262925\n",
      "    Semana prediciendo la semana 2 ARIMA(5, 1, 2) ---> Error: 2.739344868987921\n",
      "    Semana prediciendo la semana 3 ARIMA(5, 1, 2) ---> Error: 5.925797709178302\n",
      " \n",
      "Entrenando el Modelo para CAMPECHE\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 5.472177762225574\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 1.7451754370075672\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 6.855457231016506\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 2.7082594411548775\n",
      " \n",
      "Entrenando el Modelo para CHIAPAS\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 5) ---> Error: 2.5848043570119095\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 3) ---> Error: 3.375699118240725\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 3) ---> Error: 5.4365874379323476\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 3) ---> Error: 1.1758307135926989\n",
      " \n",
      "Entrenando el Modelo para CHIHUAHUA\n",
      "    Semana prediciendo la semana 0 ARIMA(2, 1, 5) ---> Error: 7.249985375297352\n",
      "    Semana prediciendo la semana 1 ARIMA(2, 1, 5) ---> Error: 8.864974679361326\n",
      "    Semana prediciendo la semana 2 ARIMA(2, 1, 5) ---> Error: 6.151583253302058\n",
      "    Semana prediciendo la semana 3 ARIMA(2, 1, 5) ---> Error: 6.9539161210117975\n",
      " \n",
      "Entrenando el Modelo para DISTRITO FEDERAL\n",
      "    Semana prediciendo la semana 0 ARIMA(4, 1, 5) ---> Error: 64.80599236146989\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 5) ---> Error: 8.570535063621602\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 5) ---> Error: 5.027354016595417\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 5) ---> Error: 27.718576793360956\n",
      " \n",
      "Entrenando el Modelo para COAHUILA\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 4) ---> Error: 6.093985234778355\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 4) ---> Error: 8.039722615666829\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 4) ---> Error: 2.148062745425506\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 4) ---> Error: 9.233456673250679\n",
      " \n",
      "Entrenando el Modelo para COLIMA\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 9.071248875797641\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 6.648793325116442\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 11.637856840308991\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 9.838961638740487\n",
      " \n",
      "Entrenando el Modelo para DURANGO\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 5) ---> Error: 10.97766881226097\n",
      "    Semana prediciendo la semana 1 ARIMA(5, 1, 5) ---> Error: 10.438352084019696\n",
      "    Semana prediciendo la semana 2 ARIMA(5, 1, 5) ---> Error: 1.6019493634631201\n",
      "    Semana prediciendo la semana 3 ARIMA(5, 1, 5) ---> Error: 1.8100151730386413\n",
      " \n",
      "Entrenando el Modelo para GUANAJUATO\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 0) ---> Error: 31.902264646097514\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 0) ---> Error: 21.042546686267404\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 0) ---> Error: 62.31531006145777\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 0) ---> Error: 21.15359240431843\n",
      " \n",
      "Entrenando el Modelo para GUERRERO\n",
      "    Semana prediciendo la semana 0 ARIMA(2, 1, 5) ---> Error: 5.057252600668499\n",
      "    Semana prediciendo la semana 1 ARIMA(2, 1, 5) ---> Error: 5.901733576554295\n",
      "    Semana prediciendo la semana 2 ARIMA(2, 1, 5) ---> Error: 6.0786222910200145\n",
      "    Semana prediciendo la semana 3 ARIMA(2, 1, 5) ---> Error: 13.163810234082733\n",
      " \n",
      "Entrenando el Modelo para HIDALGO\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 4.020498671713436\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 2.959102974244337\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 2.9368233399930634\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 5.890422445118773\n",
      " \n",
      "Entrenando el Modelo para JALISCO\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 0) ---> Error: 8.602746762678416\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 0) ---> Error: 20.58785158938101\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 0) ---> Error: 3.5692082587534926\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 0) ---> Error: 4.075456734653423\n",
      " \n",
      "Entrenando el Modelo para MEXICO\n",
      "    Semana prediciendo la semana 0 ARIMA(4, 1, 3) ---> Error: 13.91491508920144\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 3) ---> Error: 29.5122919748009\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 5) ---> Error: 8.639117907621772\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 5) ---> Error: 20.567682398486326\n",
      " \n",
      "Entrenando el Modelo para MICHOACAN\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 3) ---> Error: 4.813408855255819\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 3) ---> Error: 1.3854859672190192\n",
      "    Semana prediciendo la semana 2 ARIMA(2, 1, 5) ---> Error: 2.51377588367069\n",
      "    Semana prediciendo la semana 3 ARIMA(2, 1, 5) ---> Error: 1.5191934660090283\n",
      " \n",
      "Entrenando el Modelo para MORELOS\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 10.62281326867006\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 2.0644719405048493\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 5.204144336547128\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 5.772220067947891\n",
      " \n",
      "Entrenando el Modelo para NAYARIT\n",
      "    Semana prediciendo la semana 0 ARIMA(4, 1, 2) ---> Error: 4.592557702044972\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 2) ---> Error: 1.1436104724115221\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 2) ---> Error: 1.293521924219623\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 2) ---> Error: 1.72370823660155\n",
      " \n",
      "Entrenando el Modelo para NUEVO LEON\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 3) ---> Error: 6.459464863777102\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 3) ---> Error: 4.946917333446918\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 3) ---> Error: 3.041254139147016\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 3) ---> Error: 14.003357343804094\n",
      " \n",
      "Entrenando el Modelo para OAXACA\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 15.457741591183623\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 3.085911479779602\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 9.620888742177957\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 15.411341752305646\n",
      " \n",
      "Entrenando el Modelo para PUEBLA\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 1) ---> Error: 11.314460125317634\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 1) ---> Error: 21.207911795707066\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 15.220313715777657\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 4.332498501064252\n",
      " \n",
      "Entrenando el Modelo para QUERETARO\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 0) ---> Error: 9.00802674446941\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 0) ---> Error: 7.182025294805223\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 0) ---> Error: 5.503792292716467\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 0) ---> Error: 17.44087087030049\n",
      " \n",
      "Entrenando el Modelo para QUINTANA ROO\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 4) ---> Error: 3.541718122594744\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 4) ---> Error: 4.23584132454139\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 3) ---> Error: 4.1943812239943705\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 3) ---> Error: 2.18357120445518\n",
      " \n",
      "Entrenando el Modelo para SAN LUIS POTOSI\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 4) ---> Error: 26.459549274607266\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 2) ---> Error: 13.185924198454105\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 2) ---> Error: 3.1450108760856863\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 2) ---> Error: 8.710653790544054\n",
      " \n",
      "Entrenando el Modelo para SINALOA\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 3) ---> Error: 11.120579151765364\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 4) ---> Error: 12.059505155170907\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 4) ---> Error: 3.3576322128646474\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 4) ---> Error: 3.7380772525501214\n",
      " \n",
      "Entrenando el Modelo para SONORA\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 4) ---> Error: 4.563332944269416\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 5) ---> Error: 15.694229247898805\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 5) ---> Error: 9.029241474168948\n",
      "    Semana prediciendo la semana 3 ARIMA(4, 1, 5) ---> Error: 5.134513219667582\n",
      " \n",
      "Entrenando el Modelo para TABASCO\n",
      "    Semana prediciendo la semana 0 ARIMA(1, 1, 2) ---> Error: 24.63685522363787\n",
      "    Semana prediciendo la semana 1 ARIMA(1, 1, 2) ---> Error: 7.217840919864826\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 2) ---> Error: 17.806227521262915\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 2) ---> Error: 11.24504998199413\n",
      " \n",
      "Entrenando el Modelo para TAMAULIPAS\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 4) ---> Error: 6.309592469102185\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 4) ---> Error: 4.3934685644550955\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 4) ---> Error: 5.1693358360081865\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 4) ---> Error: 7.371120572387511\n",
      " \n",
      "Entrenando el Modelo para TLAXCALA\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 5) ---> Error: 1.8301278244145922\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 5) ---> Error: 11.057372146807781\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 5) ---> Error: 1.4006578940397019\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 5) ---> Error: 2.1993147854839408\n",
      " \n",
      "Entrenando el Modelo para VERACRUZ\n",
      "    Semana prediciendo la semana 0 ARIMA(3, 1, 3) ---> Error: 3.1896177780068546\n",
      "    Semana prediciendo la semana 1 ARIMA(3, 1, 3) ---> Error: 9.445826398852386\n",
      "    Semana prediciendo la semana 2 ARIMA(3, 1, 3) ---> Error: 4.663668553786218\n",
      "    Semana prediciendo la semana 3 ARIMA(3, 1, 3) ---> Error: 5.789727067198912\n",
      " \n",
      "Entrenando el Modelo para YUCATAN\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 4) ---> Error: 17.536740614108083\n",
      "    Semana prediciendo la semana 1 ARIMA(4, 1, 2) ---> Error: 9.627547671692783\n",
      "    Semana prediciendo la semana 2 ARIMA(4, 1, 2) ---> Error: 16.71890368541868\n",
      "    Semana prediciendo la semana 3 ARIMA(5, 1, 4) ---> Error: 4.79654599934744\n",
      " \n",
      "Entrenando el Modelo para ZACATECAS\n",
      "    Semana prediciendo la semana 0 ARIMA(5, 1, 4) ---> Error: 3.115377988030821\n",
      "    Semana prediciendo la semana 1 ARIMA(5, 1, 4) ---> Error: 4.281748123116056\n",
      "    Semana prediciendo la semana 2 ARIMA(1, 1, 1) ---> Error: 3.0404295020822802\n",
      "    Semana prediciendo la semana 3 ARIMA(1, 1, 1) ---> Error: 8.622517007944138\n"
     ]
    }
   ],
   "source": [
    "start = 28\n",
    "periods = 7\n",
    "pred_arima = pd.DataFrame()\n",
    "error_arima = []\n",
    "print(\"Forecasting with ARIMA models using auto_arima\")\n",
    "print(\"----------------------------------------------\")\n",
    "for i in range(32):\n",
    "    print(\" \")\n",
    "    print(f\"Entrenando el Modelo para {names[i]}\")\n",
    "    casos_estado = datos_total.iloc[[i]]\n",
    "    casos_estado = pd.melt(casos_estado, var_name = \"Fecha\", value_name = \"Casos\")\n",
    "    casos_estado[\"Fecha\"] = pd.to_datetime(casos_estado[\"Fecha\"])\n",
    "    casos_estado = casos_estado.set_index(\"Fecha\")\n",
    "    casos_estado = casos_estado[\"Casos\"]\n",
    "    estado_sma = casos_estado.rolling(window = 7).mean().dropna()[:-1]\n",
    "    \n",
    "    predictions = []\n",
    "    #model_arima = auto_arima(casos_estado[:-8][:-28])\n",
    "    for j in range(int(start/periods)):\n",
    "        model_arima = auto_arima(estado_sma[:-8][:-start+j*periods], seasonal = False)\n",
    "        orden = model_arima.get_params()['order']\n",
    "        print(f\"    Semana prediciendo la semana {j} ARIMA{orden} ---> \", end = \"\", flush= True)\n",
    "        \n",
    "        pred = model_arima.predict(n_periods = periods)\n",
    "        \n",
    "        if -start+(j+1)*periods != 0: \n",
    "            rmse = np.sqrt(mean_squared_error(pred, estado_sma[:-8][-start+j*periods:-start+(j+1)*periods]))\n",
    "        else:\n",
    "            rmse = np.sqrt(mean_squared_error(pred, estado_sma[:-8][-start+j*periods:]))\n",
    "        \n",
    "        predictions.append(pred)\n",
    "        print(f\"Error: {rmse}\")\n",
    "    \n",
    "    # Convertimos la lista de listas de predicciones a numpy    \n",
    "    predictions = np.array(predictions)\n",
    "    # Aplanamos el arreglo\n",
    "    predictions = predictions.reshape(predictions.shape[0]*predictions.shape[1])\n",
    "    # Convertimos a serie de tiempo\n",
    "    predic_1 = pd.Series(predictions, index = estado_sma[:-8][-28:].index)\n",
    "    # Se guardan las predicciones en un Data Frame\n",
    "    pred_arima[names[i]] = predic_1\n",
    "    # Se calcula el error\n",
    "    error = np.sqrt(mean_squared_error(predic_1,estado_sma[:-8][-28:]))\n",
    "    error_arima.append(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "27380e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rmse = np.zeros(2)\n",
    "for i in range(2):\n",
    "    casos_estado = datos_total.iloc[[i]]\n",
    "    casos_estado = pd.melt(casos_estado, var_name = \"Fecha\", value_name = \"Casos\")\n",
    "    casos_estado[\"Fecha\"] = pd.to_datetime(casos_estado[\"Fecha\"])\n",
    "    casos_estado = casos_estado.set_index(\"Fecha\")\n",
    "    casos_estado = casos_estado[\"Casos\"]\n",
    "    estado_sma = casos_estado.rolling(window = 7).mean().dropna()[:-1]\n",
    "    error_rmse[i] = np.sqrt(mean_squared_error(pred_arima.iloc[:,i],estado_sma[:-8][-28:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "84b5fc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.928444990528082"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(error_arima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1e61f06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24bf52db880>]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(0,32),error_arima)\n",
    "plt.plot(np.arange(0,32),error_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "44cfd886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fecha'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4qNbj7QDrtxYDadJzr0L6v0MRSW3LPwqP1ey57ywp49PlG/lo8QZmF2xiweoi1haF5N6hZe2mU6YCJXcRSW7LP4SWXaF1jypP/XJrMePnruH12av4dNkmdpWVk2XQv3Mrjj2oAwM6t+LwA9rQrU3TBgg8sZTcRSS5Lf849NqtooKz4O5MXLCOJz9exruL1lNW7vTt2IKrju3JiN7tGd6zbUaMse9NyV1EktemFVBUAAfcuM9L5eXOv+et4d6381mwuoiurZtw7Qm9OXdwVw7u3BLbzy+DTKHkLiLJ66vx9q+vTJ27cjM3vzCbBauL6J3XnLsvHsy5Q7rSOFtzRHZTcheR5LXsvbClXMdBAJSUlfPXSfnc93Y+7ZrncM+oIZx9WFeyszK7l14RJXcRSU6lxTD/FTjoFMhuRMHG7XzvyenMXVnE+UO6cvu5g2jTLCfRUSYtJXcRSU4Lx8OOjTD0CvLXbeWKv3/MtuJS/vadYZx5SOdER5f0lNxFJDnNeBJadWNu7lCufPBDssx47roRDOjSKtGRpQTdfRCR5LO5APInsrrXhVz2yCc0bZzN899TYq8O9dxFJPnMegZw/nfVUFo2acQL/zWCLq3Tf+FRXVLPXUSSS3k5zHiS0gOOY/zKJlw4tLsSew0ouYtIcln2Pmxcyqy8sykrd04e0DHREaUkJXcRSS4znoTcVjxdNIT2zXMY0r1NoiNKSUruIpI8dhbB/FcoG3QhExYVMfLgjmRpgVKNKLmLSPKY/08o3cH8judStLOUUzUkU2NK7iKSPGY+A+37Mm59Z3Kysziub16iI0pZVSZ3M+thZpPMbIGZzTOzG6Pjt5vZSjObGX2cFXPNrWaWb2YLzazyjf5ERAA2LIblH8CQy5j4WSFH9W5Hi1zN1q6peP7lSoEfu/unZtYSmG5mE6LX/uTuf4g92cwGAqOAQUBX4C0z6+fuZXUZuIikmVnPAsay7uew+PVFjD6mZ6IjSmlV9tzdfbW7fxo93wIsALpVcsl5wLPuXuzuS4B84Mi6CFZE0lR5eVi41PtEJhSEPufJB2u8vTaqNeZuZj2Bw4GPo0M3mNlsM3vUzNpGx7oBK2IuK6CCXwZmdq2ZTTOzaYWFhdWPXETSx/IPYNNytg+6lDfmraF/p5b0aNcs0VGltLgHtMysBfAicJO7F5nZA8BvAY8e7wa+C1Q0b8n3OeD+EPAQwPDhw/d5XUTS18pNO5i7cjOfr9nC5+u2cs6SP3EMTRn+fBN2spEbRh6U6BBTXlzJ3cwaExL7U+7+EoC7r415/WHgtejTAiB2J9vuwKo6iVZEUtK6op1MWLCWqUs28MmSDazavPOr1/q2Ne4qeZ/57U7hv4cMpnvbZpzUX7NkaqvK5G5hI8K/Awvc/Y8xx7u4++ro0wuAudHzccDTZvZHwg3VvsDUOo1aRJLel1uLGTdrFf+as4ZPlm3AHfJa5nJkz3Zc27Mtg7s1Z0DR+zSZ8Sgs2cGwc7/PsJ59Eh122oin534scAUwx8xmRsd+DlxmZkMIQy5LgesA3H2emY0F5hNm2lyvmTIiaaqsBJa+Cz1PgOyQTpau38Yj7y3m+WkFFJeW079TS246pR/fOLQzfTu2wMpL4f174Pm/wbZCaNkVTr0dDjwmse8lzVSZ3N39PSoeRx9fyTV3AHfUIi4RSQVT7oJ37oKex1N4+l/57ZQNvDp7FY2zsrjg8G5897he9O/ccs/5X34BL/0nrJwOfc+AI66BPqd89YtB6o7+RUWkZjYuhffvwbsOpWzFJ9iDx7Op/Idcd8LZfPfYnnRs1SScV1YCm5ZD/lvw1u2QnQMXPw6DLkhg8OlPyV1EauaN2/CsbL5XchOLt6/hseb3MabsDmzpy7A0+mO/uCjsqrR7ZLb3SXD+A9Cqa8LCzhRK7iJSfV+8DZ+9xoIBN/LGjEb8+twz6DbscmzynbBhyZ7zGjeFwy6Bdr2h/UHQbThkqaRVQ1ByF5HqKSuBf90CbXvyQd4oYAnfGtYdy20EZ+hWW7LQr1ARqZ6pD8P6hXDG71i1DZrnZKvAVxJScheR+BWtgkn/G2a49P8Ga7fspNPuG6eSVJTcRSR+//oplJfAN/8AZqwr2knHVrmJjkoqoOQuIvH5bDwseBVO/Fm4QQqsLSpWzz1JKbmLSNWKt8L4m6HjQDjmBwC4O2uLNCyTrHQXRESqNvl3UFQAF0+A7MYAFO0opbi0nI4tNSyTjJTcRWT/3OGj++HDv8Lw70KPPfvurN0SKjuq556clNxFpGJlJfD6j+HTMTDgHDj963PY1xYpuSczJXcR2df2DfD8aFjyDhz/Yxj5i31Wlq4tKgagk2bLJCUldxH5unUL4JnLoGglXPAgDB5V4Wm7e+4dW6rnnoyU3EVkj89eh5euhZzmcNXrXxtj39u6op20atKIpjnZDRigxEtTIUUk+PCv8Ozl0KEfXDu50sQOmuOe7NRzFxHYtj7UWu/3Dbj4sVDNsQoqPZDc1HMXEfj0H1C2C077dVyJHWBdUbFKDyQxJXeRTFdeBtMeg57HQ17/+C4pd9ap557UlNxFMt2iCbB5ORzxH3FfsnH7LkrKnE5anZq0lNxFMt0nj0CLznDwN+O+ZM8cd/Xck5WSu0gm27A4bFw97KqvasbEY3fpgY5K7klLyV0kk017FCwLho2u1mXrvio9oGGZZKXkLpKpSnbAjCdhwNnQqmu1Lt09LKPVqcmryuRuZj3MbJKZLTCzeWZ2Y3S8nZlNMLNF0WPbmGtuNbN8M1toZmfU5xsQkRqaPgZ2bIQjr632pWuLdtK+eQ45jdQ/TFbxfGdKgR+7+wDgaOB6MxsI3AJMdPe+wMToc6LXRgGDgDOB+81M65NFkknxVnj3D9DrBOh5XLUvX1tUrPH2JFdlcnf31e7+afR8C7AA6AacB4yJThsDnB89Pw941t2L3X0JkA9Uvo5ZRBrWxw/AtkI4+Zc1ujzMcdd4ezKr1t9UZtYTOBz4GOjk7qsh/AIAOkandQNWxFxWEB0TkWSwfQO8fy/0Pwt6HFGjJtYW7aSTxtuTWtzJ3cxaAC8CN7l7UWWnVnDMK2jvWjObZmbTCgsL4w1DRGrr/XuguAhG3lajy8vKncItxeq5J7m4kruZNSYk9qfc/aXo8Foz6xK93gVYFx0vAHrEXN4dWLV3m+7+kLsPd/fheXl5NY1fRKpjyxr4+EE49CLofEiNmvhyazHlrjnuyS6e2TIG/B1Y4O5/jHlpHLB7cuxo4JWY46PMLNfMegF9gal1F7KI1NiU30N5CZx0a42b0OrU1BBPyd9jgSuAOWY2Mzr2c+BOYKyZXQMsBy4GcPd5ZjYWmE+YaXO9u5fVdeAiUk1r58H0x0MNmfZ9at6MFjClhCqTu7u/R8Xj6ACn7OeaO4A7KnpNRBLAHf59C+S2qlWvHfaUHlDPPblpBYJIJvjs9bDZ9cjboFm7WjW1tqiYLIP2zXPqKDipD9qJSSTdlRbDm7+AvINh+Hf3e1pZubOjpIydJWXs2FVGcWk5xaVl7CotZ1dpOaXlTklZObMLNtGhRS6NstU3TGZK7iJprKy0lDXj76LbxiW8MfQBZryZT+GWYjZt38XG7bvYtKOELTtL2VZcyvZd8d8aO7JX7Xr/Uv+U3EWSyZJ3YepD0OkQOOhU6DoEsuKo3uEOm5bDiqlQMBVWz4LNK8nasppuXsaEsqFc90FrcrKX0KFFDm2a5dC2eWO6tG5KyyaNaJHbiOa5jWiem03TxtnkNs6mSeNscrKzyG2URePsLBpnG42ixwPbN6/3fwqpHSV3kWRQWgxv/z/44F5o0hoWvAqT/xeatoMhl8MJP4Gmbb9+jXtI4vNeDh+bloXjjZtDl8HQ63isVTfyi1vTY+C3mN6hA+2a5xBmN0u6U3IXSbTChfDCNbB2Ttg04/Q7QrJfPCncCP3wrzDzKTjxFhg8ClZ8DPkTYdEbsHEpZDWC3ifBMT+AHkdBx4GQvee/9kGJel+SUOa+T2WABjd8+HCfNm1aosMQaXgF0+HJC0OCPu8+6P+Nfc9ZMwfeuA2WTNlzrFFT6HU8DDgHDj671jNgJDWZ2XR3H17Ra+q5i9QVd1g1IyTaNgdCVcMfS9+Hpy+B5h3gylegbc+Kz+t8aHh90YQwnn7gsXDACGiseeayf0ruInWhvAzG/yRsWwfQpE007n0CHHYJtDlgz7nusOhNGDsa2vQIibuqnZDMoN/p4UMkDkruIrVVugtevg7mvQRHfx869A03OlfNgLd/Gz4OPC5sirF2bhgz31YYeuRX/DP03EXqmJK7SG3s2gbPXQFfTITTfgPH3vj11zcuhdnPw+xnYcqd0K53mOLY/Qg49GJo0iohYUv6U3IXqY0pvw+zWs69F4Zeue/rbXvCiTeHqYy7tkFuiwYPUTKT1g+L1Mbyj0MvvKLEHstMiV0alJK7SE2Vl4cx9C6DEx2JyD6U3EVqasNi2LUVOh+W6EhE9qHknqq2FkLRPrsXSkNaMys8dlFyl+SjG6qpwj3sf7lwfKgjsuz9cPywS+GEm2u1s47U0OrZkNUY8gYkOhKRfSi5J6vSXfDZq7DgNdjwBWxYEnasB2jfNyT0Xdvgk7/D7LEw+DI483eaWteQ1syGjgdDI21aIclHyT3ZbFkD0x4Le11uXQMtu0KngaEgVNte0PvEUBhq99L2Y34I7/85lIld/zl850Ul+IawuyJjvwpqwYgkASX3ZLFhCbx/T6j+V1YCfU+DI+4NC16yKrk10rJT6LEfeAw8fxU8dVFI8LktGyz0jFS0CrZ/qfF2SVpK7omwczOsmglFK2HzylDqdcGroTLg4d+BETdUfwx9wDlw0aPw/NXw1MVw6u2wcVmY0dG4CRx9vYYP6tKa2eFR0yAlSSm515dd22Dui9C4GbTqBi06wsrp4WZo/ltQtmvPuc3zQk2SETdAqy41/5oDz4OL/h5qgz96RnTQAIcvJsEl/4CmbWrxpuQrq2cDFnZMEklCSu71wR1e/h4sGLfvay27whH/EYZd2hwYEn9dlm4ddEGoX1K0Ojy2PRDmvgTjboBHz4RvPx8qEUrtrJkd/rrSqlNJUkru9eHjB0NiP/l/wkYKRQUh2bY/KNwYrWwMvS50Gfz14YIhl4WSss9dAY+cAleNhw7an6dWVs+G7hXukSCSFKrMMmb2qJmtM7O5McduN7OVZjYz+jgr5rVbzSzfzBaa2RkVt5rGCqbBm78IsyiO/3GYKnfQqTD0CjhwRP0n9v3pfSJc8yaU7IC3f5OYGNLF9g2weblupkpSiyfTPA6cWcHxP7n7kOhjPICZDQRGAYOia+43szi2bk8T2zeEGSutusAFD1S9E09D63gwHHktzB8H6xYkOprUtWZOeFTZAUliVSZ3d38H2BBne+cBz7p7sbsvAfKBI2sRX+rYvgGevRy2roWLH993p/pkcfT3w03ed/6Q6EhSl2bKSAqozRjBDWY2Oxq22Z3JugErYs4piI6lt8LP4eGTYeWncMGD0G1YoiPav+bt4Yhrwq5B6/MTHU1qWj0r3AjXDkqSxGqa3B8A+gBDgNXA3dHxisYhvKIGzOxaM5tmZtMKCwtrGEYS+OJteOTUUB3wqtfgkAsTHVHVjvkBZOfCu3dXfa7sa/VsDclI0qtRcnf3te5e5u7lwMPsGXopAGLn2XUHKixd6O4Puftwdx+el5dXkzASb/0ieOoSaN0N/vNt6JEiI1AtOsKwq2D2c2FlrMRv25ehzEO3oYmORKRSNUruZha70uYCYPdMmnHAKDPLNbNeQF9gau1CTGJv/xayc8Lu9bG726eCY28MK2Lf+2OiI0ktX7wNOPQ5JdGRiFQqnqmQzwAfAv3NrMDMrgHuMrM5ZjYbGAn8CMDd5wFjgfnAv4Hr3b2s3qJPpJXTYf4rcMwNoSecalp1Cb33GU+Fv0AkPvlvQbP20PXwREciUqkqFzG5+2UVHP57JeffAdxRm6BSwlu/Dv/JR9yQ6Ehq7oSbQ6Gyib+BS59IdDTJr7w8JPc+JyduvYJInPQTWhNfTIIlU+D4n6R2ed0WeaFk8IJxsOKTREeT/NbMgu3r4aDTEh2JSJWU3KvLHd66HVofEKYUproR14fCZRN+Gd6b7N+it8Jjn5MTG4dIHFRbpjLuoSzvus/gy/xQPrdwAayeCef/DRrlJjrC2sttASf+DMb/BBa9Cf0yr2JE3PLfCmPtLVJ0dpdkFCX3vW3fAHOeD5UU183fs7UdQE5LaNcLjvovOOySxMVY14ZdBR/dDxN+FW0OkjkVI+K2YyMUTA31gkRSgJL7bl9+AZPvDDNgyorDIpXDLoWOA8JH+75hRWKy1YupC9mNQwXLF64O7z8VFmI1tMWTwcs13i4pQ8kdoKwUnvsObFoBQ68MH5lW8W/geaEk8Xt/CjXh0/GXWG3kvwVNWid3aQmRGLqhCjD9sTAEc/798M0/ZF5ihzAUc+xNoSjWF28nOprk4g75E8ON1Gz1hyQ1KLnv2AiT/hd6Hh/2Ic1kh10Sdop670+JjiS5rJ0LW1aH+xEiKULJffLvYecmOPN3GopolBumRi59N2w6IsGMp0Kphr6nJzoSkbhldnIvXAifPAxDR0PnQxMdTXIYdhU0aaPe+247NsGMJ+CQi1KzzIRkrMxN7hsWw2s/gsbN4eRfJDqa5JHbAo66Dj57Lfzyy3Sf/iOUcx7x/URHIlItmZXcy8tgzgsw5hz4y+Gw/EM4/TfadGFvR14Xfuk9fzVsXZfoaPavvLx+2y8rCZud9zxeuy5Jysmc5F6yE567Al68BjYuhZG/gB/NC8MQ8nXN28OoJ8NfN4+dBZtX1m375eVh1e/mgvB9qYkp/wd39oD3/hymstaH+a9AUUFqF4eTjGWeBPVEhg8f7tOm1eMNvJ1FYX/Tpe/CmXeGnqmq+lVt2QdhM5JmbeHKcWF1bmXKy6BkRxjaqciOjeHm5CePwMaYTUJyWsKAs+G038a3tP/9e0ItnLa9QjtdBsO599XtFFZ3eHgkFG+F66fq50WSkplNd/fhFb2W/pN2txbCU9+CtfPgwkfgsIsTHVHqOPAYGD0OnrwwDGO16hYSfPuDoOdx0Htk6OXv2haS9kf3w6blYYn+CTdDo5zQzvYNMOX3MH0MlO6AHkfDcTeF17atD39JzXoWFo6HU2+HoVftP5l+/FBI7IMuhG89AgtehfE3w0MnwcWPhcVYdWH5h7BqBpz9JyV2SUnp3XMvLQ77m65fBJf8A/ppKluNfPlFqLezYUkYqlm/EHZuBiz0mjcuDdNJux8BrbvDvJeh06Fwzj2w/IMwhLJrCwy+PNysraiHXfg5vP7f4a+r1j0gJ+r9m4XZO807hKmac56Hg8+Gix8PZRMg/PJ4/GwoL4Hvf1w3yfjpUbDi4zB0l9Os9u2J1IPM7blP+FVYcTnqGSX22mjfB066Zc/n5WWhMmb+xFDbvveJcPT1cMBR4fVDL4FXb4RHotK4B50Gp/821OjZn7x+MPrVkLw/e21P+WEvD9MR138O2wpDaYQLHtyT2AGatYPjfgQv/UeobNn/zNq939Wz4PN/wcjblNglZaVvz33hv+GZS8P4+ll31W3bUrXtG+CDe8PwzUENsN9oWQncMwTa9oSrX69dW89+O/wFcdOcUE9GJElV1nNPz8HEotXwyvfD0MBpv0l0NJmpWTs49VcNk9gh9OSP/i9Y9h6s/LTm7ayZE/5yOPr7SuyS0tIrubuHKXYvXhNmbVz0KDRukuiopKEMvRJyW8GH99W8jSl3hTaO+l7dxSWSAOkx5r52Hsx8Osy22LAYsFDhMa9foiOThtSkFQwbDR/eD6f8CtoeWL3r184L+8me8FNo2qZeQhRpKKnbc3cPNbb/cT48cExYSdi2F3zzj/Df82HI5YmOUBLhqO+FGTYf/61617mH6Zo5LcPwjkiKS82e+7b18MxlYduzFp3hlF/CsKvDOK9kttbdYeD5MOPJsLtUZbNdykpg2fvw2XhY+C/YvDzMz9fPkaSB1EvuRavhifPD3Opz/gKDR6XHRtVSd4aNhrkvwGev77toraw0zISZ93JYALVjAzRqEhZknfjT8PMkkgaqTO5m9ihwNrDO3Q+JjrUDngN6AkuBS9x9Y/TarcA1QBnwQ3d/o86i3bQcxpwb5jt/+wXodXydNS1p5MDjoPUBMPOpryf38jJ47Ewo+CQskur/jbCitc/JkNM8cfGK1IN4xtwfB/ZeFXILMNHd+wITo88xs4HAKGBQdM39ZpZdJ5GumgGPfiP0tK58RYld9i8rC4ZcFja13lyw5/i8l0NiP+23cHN+KF8w4BwldklLVSZ3d38H2LDX4fOAMdHzMcD5Mcefdfdid18C5ANH1irC8jJ45/9CGQEvh9GvQfcK5+yL7DH4MsBDzRoIwzGTfwcdB4Yqj42bJjQ8kfpW09kyndx9NUD0uHuLmm7AipjzCqJjNbNxaSg5+/b/Cz2s/3o/Mzevlupr1ysMz8x8OsyEmfM8fJkPJ92qQmCSEer6p7yiTUgrrG9gZtea2TQzm1ZYWLjvCXNegL8dD+vmwwUPwUWPaRaDVM+Qy2HDF2FGzJQ7w1aKB5+d6KhEGkRNk/taM+sCED3u3q6nAOgRc153YFVFDbj7Q+4+3N2H5+XF1PDetQ3+eX1YZZp3MHzvPRh8qTavluobeF7YUeqla6MNWm5Tr10yRk1/0scBo6Pno4FXYo6PMrNcM+sF9AWmxtVi8RaY+jA8cGyY5XDCzXD1v6q/ylBkt9wWIcEXrYSuQ6FfLatFiqSQeKZCPgOcBHQwswLgV8CdwFgzuwZYDlwM4O7zzGwsMB8oBa5397Iqo9hcAHcPCDW/uwwJG0T0OqGGb0kkxvCrYc5YOOV/9NefZJTkKPnbrbFPu/e7cOS10H1YosORdLNrm6Y7SlpK/s06Og2CCx9MdBSSrpTYJQMlx92lrOT4HSMiki6SI7mLiEidUnIXEUlDSu4iImlIyV1EJA0puYuIpCEldxGRNKTkLiKShpJihaqZbQEW1uOXaA1sTtH2Exl7B2B9PbZfW6n8fa1p+/F+T5Ix9mRpv75jr4v/N5WJjb+/u7es8Cx3T/gHMK2e238oVdtPZOx18X1J13+bRLUf7/ckGWNPlvYbIPYGy2eVfa1MGZZ5NYXbT+XY67v9VI69vttP5djru/36jr2+xRV/sgzLTPP9FL+RxNH3Jfnoe5L8GvJ7VNnXSpae+0OJDkAqpO9L8tH3JPk15Pdov18rKXruIiJSt5Kl5y4iInVIyV1EJA0puWcwM7vNzOaZ2Wwzm2lmRyU6JgEz625mr5jZIjP7wszuMbOcSs6/ycyaNWSMmcrM3Mzujvn8J2Z2ewJD2i8l9wxlZiOAs4Gh7n4YcCqwIrFRiZkZ8BLwT3fvC/QDWgB3VHLZTYCSe8MoBi40sw6JDqQqSu6Zqwuw3t2LAdx9vbuvMrNhZjbFzKab2Rtm1gXAzCab2Z/N7AMzm2tmRyY0+vR1MrDT3R8D8LDB/I+A75pZczP7g5nNif7a+oGZ/RDoCkwys0kJjDtTlBJmqPxo7xfM7EAzmxh9byaa2QFm1trMlppZVnROMzNbYWaN6ztQJffM9SbQw8w+N7P7zezE6AfuXuAidx8GPMrXe4zN3f0Y4PvRa1L3BgHTYw+4exGwHPgPoBdwePTX1lPu/hdgFTDS3Uc2dLAZ6q/At82s9V7H7wP+sft7A/zF3TcDs4ATo3POAd5w95L6DlLJPUO5+1ZgGHAtUAg8B1wHHAJMMLOZwC+A7jGXPRNd+w7QyszaNGDImcKAiuYnG3AC8Dd3LwVw9w0NGZgE0S/bfwA/3OulEcDT0fMngOOi588Bl0bPR0Wf1zvtTJ3Boj/5JwOTzWwOcD0wz91H7O+SKj6X2psHfCv2gJm1AnoAi9G/ebL4M/Ap8Fgl5+z+Xo0Dfmdm7QgdqrfrN7RAPfcMZWb9zaxvzKEhwAIgL7rZipk1NrNBMedcGh0/Dtgc/ckpdWsi0MzMrgQws2zgbuBxwlDa98ysUfRau+iaLUDFlQGlXkR/NY0Frok5/AGhZw7wbeC96NytwFTgHuC1qFNV75TcM1cLYIyZzTez2cBA4JfARcDvzWwWMBM4JuaajWb2AfA3vv5DLXXEw5LxC4CLzWwR8DmwE/g58Ahh7H129P25PLrsIeBfuqHa4O4mlPfd7YfA1dH/pyuAG2Neew74Dg00JAMqPyBxMrPJwE/cfVqiYxGRqqnnLiKShtRzFxFJQ+q5ZxAz62Fmk8xsQVR24MboeDszmxAtd59gZm2j46dFi5nmRI8nx7R1R7QYY2ui3o+I7J967hkkWm3axd0/NbOWhMUy5wNXARvc/U4zuwVo6+4/M7PDgbXRytVDCIsvukVtHQ0sAxa5e4tEvB8R2T8l9wxmZq8QVtXdB5zk7qujXwCT3b3/XucaYdPfrrtLFkTHtyq5iyQfDctkKDPrCRwOfAx0cvfVANFjxwou+RYwIzaxi0jy0grVDGRmLYAXgZvcvSh0yis9fxDwe+D0BghPROqAeu4ZJioO9iKh6NRL0eG1MdUfuwDrYs7vDrwMXOnuXzR0vCJSM0ruGSQaN/87sMDd/xjz0jhgdPR8NPBKdH4b4HXgVnd/vwFDFZFa0g3VDBLVhHkXmAOUR4d/Thh3HwscQFjefrG7bzCzXwC3Aotimjnd3deZ2V2E5e9dCSVnH3H32xvkjYhIlZTcRUTSkIZlRETSkJK7iEgaUnIXEUlDSu4iImlIyV1EJA0puUvGMLMyM5sZ89GzmtefZGav1VN4InVK5Qckk+xw9yGJDkKkIajnLhnNzIaZ2ZSoXv0bMWUYDjKzt8xslpl9amZ9oktamNkLZvaZmT0VrfrFzH5pZp+Y2Vwze2j3cZFEUXKXTNI0Zkjm5ajOzr3ARe4+DHgUuCM69yngr+4+mLBJ+Oro+OHATYQNxXsDx0bH73P3I9z9EKApcHaDvCOR/dCwjGSSrw3LRBuQHAJMiDra2cDqaCOTbu7+MoC774zOB5jq7gXR5zOBnsB7wEgz+ynQDGgHzANebYg3JVIRJXfJZAbMc/cRXzto1qqSa2Lr2ZcBjcysCXA/MNzdV5jZ7UCTug5WpDo0LCOZbCGQZ2YjIJRDNrNB7l4EFJjZ+dHxXDNrVkk7uxP5+qhW/kX1GbRIPJTcJWO5+y5CIv69mc0CZhLG1wGuAH5oZrOBD4DOlbSzCXiYUG3zn8An9Ra0SJxUFVJEJA2p5y4ikoaU3EVE0pCSu4hIGlJyFxFJQ0ruIiJpSMldRCQNKbmLiKQhJXcRkTT0/wGAngUpdexrcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_arima[\"BAJA CALIFORNIA\"].plot()\n",
    "estado_sma[-100:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe7705c",
   "metadata": {},
   "source": [
    "# Data embeding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e06880d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d  import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "93c12e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeding = []\n",
    "x,y,z = list(),list(),list()\n",
    "for i in range(0,len(casos_nacional),3):\n",
    "    x.append(casos_nacional[i])    \n",
    "    y.append(casos_nacional[i+1])\n",
    "    z.append(casos_nacional[i+2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0406c84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(x, y, z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ad450fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsmoothie.smoother import *\n",
    "from tsmoothie.bootstrap import BootstrappingWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad052996",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = casos_sma[:-28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df203871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tsmoothie.smoother.SpectralSmoother>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smoother = SpectralSmoother(smooth_fraction=0.18, pad_len=12)\n",
    "smoother.smooth(train.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d78c01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bts = BootstrappingWrapper(SpectralSmoother(smooth_fraction=0.18, pad_len=12), \n",
    "                           bootstrap_type='mbb', block_length=24)\n",
    "bts_samples = bts.sample(train, n_samples=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de13e4a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x24b2c38f130>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uW02PQpj15stM+W/LiVo53QnMLyLXB3buTW6U/f1hL+XMMtiz8X9QfAACOzfKfuuLznzWaDxBepp6jQitmjUrTBBzGUQPYfy4cewKFESUw7alSz0jp+ZEpKSrjz+5PjBidB3aedbAFiCqDEqL4I1n722UfTYErRg0Gk+QbkR1R0WCyVp1mU8E+MaCNRB7pwgAzg8Ia2YBNSfFUUBYqZvelw/m0X+fMDlSL8KGXIIJCMmDrb8s9vh0klYMGo0nSDfSemLiT75OYDeOCjXl5Juaq0NWWwpZyVBUBvFhYG5A1nMlbpquIv472ARDBg/0eKVVrRg0Gk+Qka5eoxNOvk5AR3oNGUKRBc4JigVXy6m+eTrz1iMPAvDTwV0NK4dRma49wNeLe4aNZvz48R5PaNSKQaPxBFlZ4OsFQREnX8cnghv/8T6BvTvS1RIILl0WoyWwf7ny9/x8aCdYAxtnp1Zv6BoPOw8CwuOJbloxaDSeIDMLwvxOfWPxjuSQs5iszXt49b93enzuWQOjevUEoMug/vUvt10Nu0Q5BRu2MnfuHHDZG22/9UErBo3GE2RlQ5g/mH1qXs8awB/ZWYTY4MBvC/V0UgtgSKfO4GXhmnvuh0as97ki4yjBTtj0/WptMWg0pyVZORAeCCavU67q0+M8rBKGnHWBx52SGiAlDeLCwL+GwIF6EDpoEADDO5wFDs827dGKQaPxBNl5EB5QK+flyKl3A3BVv8FaMbQEjmQpxeAT1ai7nfDsdACGJfRQrV09iFYMGk1zI6VSDGHBYD61xUBiBwDefnI6Dz/0QNPKpqkZtxNScyEuHKzBp16/LkQnUBbgxer35jHjtemNu+86ohWDRtPc5OeD0wURwbVzXsZ3ACCwHObN/bhJRdOcgrJiyC5k4e8/c+8/GtCgpzrMvhz0Kie+GNasXHLq9ZsQrRg0muYmM1O9RoSemPVcHdGJuAWEOuHmG6srXaZpNg4dAFQfhjdnvdu4+7YGYuoWT0IZXDF8UOPuu45oxaDRNDeVFUNtsFgwRYYy4tx+PPHYo00nl+bUGA160q0wefKdjbtvk5Xu147FW8KNFw1R01YeQisGjaa5qVAMsYk1r1eZ6CjIyte9nz2NoRgefvNNZsyY0fj7P0f1PHvv/if4x32NrHjqgFYMGk1z86diqH0t/z2FRRzdsocXn3+qiYTS1IrDyWASkNCuafZ/zvk4TNCuABZ+8QHYPNPbXisGjaa5ycgA4JyRY7nrrrtqtclvqan422DZkm+aUDDNKUlOgagg8Atpmv37hlIQF0j7Qnjg9uGQuappjnMKtGLQaJqbzHSKrVBoh1mzZtVqk8je5xBgh+vGDGti4TQ1cWj1TyQ7Cpj23GtNcwCLPzlxgSQWgZ+wgcMz+QxaMWg0zU1GOq4QX1zSxJQpU2q1ybCbJmKRsP3H5bW2MjSNjzU9k3w/+GjO/KY5gMWfdcWp+Loh5+dV4C73SH0srRg0muYmI4PgjvEcTkmvvQMzNhaAcCe8PevNJhROc1KcTmLdgjxfuPb6G5rmGMKE38CLATjbv6dSCu7m78OhFYNG09xkZKjktlrUSfqTmBgAwlyqWrfGA6SmYpKSweOvZtrTzzfZYf7y+NM4vMyU/LKdzz+bA6nNn+ymFYNG09ykZ0JkUN26fxmKIdwFU++4tYkE09TI4cPqNSGy0Tq3VUtAAgf9XHQugQ2//ASOgmbv3qcVg0bTWNRmLri4GIpL6m0xXH/J5Tz39BP1FFDTIA7sVa+JMXX77uqKVziHg6FTGXgDX8x5p9mbNGnFoNE0BnlbIOOHU69nhKoSHQqiDpdfUBB4e0F2ocdr9Z+urHt3Om5g7k8/NF5Lz+qwBmHu3xsvCfG5sGnjWq0YNJpWSe5vUJICuZvBln3y9Y4eUa/RkXXbvxBqm5xScHq2Vv/pSvH23RR4wwcLfgKTuekOJASX/vvfSCDksKDXeQObvUGTVgwaTUNxO9QTndsGBdvAlnbydY/sA+C+56bXPew0KgpySjwW23660zMwlDw/uHDYhKY/WEwX8mIC6F0ksbuskPM7SHfTH9dAKwaNpqG4ygCT6tNbngeihoqpKXsAOOKufXLbn8REK8WgLQaPEOc00WlgX56c9kLTH8w7nE3exXQrgW8X/QDl2eBsPqtBKwaNpqE4y9RUj0+MiiAp2KZ8DtVx9BBuE2S6qXVy259ERUNOETiLGi6zpm7Y7ZCWC+0ia9dDo6H4xmHrdx5eEm7o2Q8Qzepn0IpBo2korjJAgsUPfBPAUQSlKdWvm56BKSKY1et+r3t1zthYyCsBW0GDRdbUkcOHQUo+/GEJf/37g01/PJOFpFc+xi3AtmI9c+bO0YpBo2lVOAoAod6brEoxSNeJ60kJmTkQWcvObccTHQNuCdn5uvdzc3NA+YbSLPDmW43coOdkRHciJQB6FcJPa9Y0a8l1rRg0moZSkgzrDsJTH0BOsWrkXp1icNvJ3XOIrRnJPPbEtLofJzZOveYU6ZDVZubz554B4ICoxxRgfTF5U3xWIt1KYeD5A4wHkGY6dLMdSaNpi7idsO0PmPQCzPoaHnoHvCOrdxA7SzHnFJNvhdkfz6n7sWJUvSSy8rViaGZKf/8Jp4BkF03ToKc6hKDn1JuxSji6YA0zXn6y2QrqacWg0TSEwj0cePhNbG4XCyKBFRuZeuWdPPnYgydO9+QeJLgccn1h/ISJdT+Wkf2sppLsDZVcUwf6hviQ7weBUZ2a98Dnn4nDBGfkwKZ1i+HIN81yWK0YNJoG8M7TfyduSwarQuHrKDXWPx9S09J46p83VV1511oAcn3AKetx6UVHq9esAm0xNCfOUgLzbWR4w5adh5r32DH9yG4fytlFcPmlF0F5frMcVisGjaa+SIn72+/wccOPoZDjBXv9oE+hcgPs/OXzqqZ/cjIAOT4w650P6n68gADw9YbsIijXSW7NhqOQGKeFHF+45vobm/fYQV3Z1y6W9qXgVVTebP0ZtGLQaOqKdKs/VylDCr3JtkJxz0QWLVxI11uuoXuZwFQuGH3ZAChLPbZdssqIzvODW26bXPfjCgGRYUYugw5ZbTbysvAudTJowlU8/cyLzX74V7fvAGDfFxsB2SwRaVoxaDR1JXcTpC3no3+OoctRO2tCIT3NqIF00VmY3ZIbz+zLkuU/8ebTd6hxt5OdS1dRLqDnpYN5Y8bM+h07JlpZDA6d5NZsHDyoXuPDm7bc9knoNv5m7CboUiyZO3dus/iXtGLQaOqClFC4DfI3E/jdD5glrAiD8WMuhOID0CsUrBbKf/iVwjLJ5nVLIX87uMsp33OENB/4dkX9G7zvyMkhddteZr/TRD2HNSfw+fTpAHy347emLbd9Eqa9OIOUQOhSCmvWrFGlV5oYrRg0mrrgsoGzlFlzVnLWYdjvC2mBcPX4OyFqEPh5wTldGWAKRmJi6JCLIedXHvzH3cSUQKovFDYgT2l9cgqBdli/9geP9AI+HUld+zMAy3f94RHFgMWP7Bh/OpVBbHR0s9TK0opBo6kL7nKQgsNffEfnElgWDleMHAbeERB2LgT3gAu6EJ1RxDcffsJfrh4F9gyWf/kBETbI9Iehl11Z78PHnnMe/g4YfNHFOvu5mRjcriNlZjhryCXKz9PcCBO/OkvwcUPRtnTeee7OJj/kKRWDEOJ9IUSmEGJbpbEwIcRyIcRe4zW00rKHhRD7hBC7hRCXVxrvI4TYaix7TQj1HxZCeAshPjPG1wshOjTyOWo0jYfbDgJuyvcmzwJH+sVx6w1Xg1eYWh5yFlx4BrjdsH47+MaByZf+XmCWcCQI7rv/sXoffsQtt2ECxg2/QucyNBPnhkbi2zWBm2+b6jEZgi69FIC4PPjjl2UqsbIJqY3FMBsYcdzYQ8AKKWVXYIXxGSFED2Ac0NPY5k0hREVHi5nAZKCr8Vexz0lAnpSyC/Ay8Fx9T0ajaXJcdthykDMzbSyIArczTZVD9jVyDLyC4bJbwdsKa7eAVyiz5y7kfGPzhMv7gVdQ/Y+fkKhe03OaZa5ZA6SkQUI4WAM8JsLEZ1/B4WWmSxkMGNj0jXtOqRiklKuB3OOGrwQ+NN5/CIytND5PSmmXUh4E9gH9hBCxQJCUcp2UUgIfHbdNxb7mA0MrrAmNpkVRXgDOEpL/8x7FZlgSCWf3HQBxl4N/h2PrBUZCn86wdisAi5b8wDnlkG+FK6fcAmafeovw9KefArB2/kJtMTQHUuI8cJQf9mzm+VebqXhedfiEUhjhS7zdKNfoaFo/Q319DNFSyjQA49XI+SQeqFxv+IgxFm+8P368yjZSSidQAIRXd1AhxGQhxEYhxMasrKx6iq7R1JMjC2Dfj8TtyOb7MCi3CMaNu0H5FSo/y1gC4cJusPMQ5BQwbPgIOmVARnejCJ5XaLW7rw0vzZ8PQM7W3crfoWlaMjKwON3k+MIHn37tOTl8YtjtLCbBZkQmOUua9HCN7Xyu7klf1jBe0zYnDkr5tpSyr5Syb2RkHXvmajQNwVGsLsYvFmKRsDwcOnaIB2sQiOMuI4sv9O8JwLInXuLo/KUEOaHbbWOVZWHxr7cY10+ZQqkJegTHQNZacGnl0KTs3w1Ati9cdV0zZz1XxmRBdu9OZDmIcsnTTz3atIer53YZxvQQxmumMX4ESKy0XgKQaownVDNeZRshhAUI5sSpK43Gs1T0XFiyl71+UBYFhTlHlXO5OvqcB+FBJC77g+tSJYVmYFgv8K7WGK41M958E78zOtHZLxoc+c3avOW0ZK+KuZk67TGmPfOSR0W5+NbbAAgrgiUL5zfpseqrGBYCFeUhJwILKo2PMyKNOqKczBuM6aYiIcSFhv/g5uO2qdjXtcAPhh9Co2k5uMshtxi5ZR+/BEN0MIwafgEEnVH9+gExcO9YziyBs4thy9Ae4C3AK6TBouxy2En9ZTNz5n2ui+k1NYZiIC5MdejzJD16ARBbCtdfM6pJD1WbcNW5wDqgmxDiiBBiEvAsMFwIsRcYbnxGSrkd+BzYASwF7pLyz44lU4F3UQ7p/cASY/w9IFwIsQ/4O0aEk0bTonCXw4bdCGBHKOSWCq667hbwjqp+fa9wGNefb6/vy7TOgq1926vxRugXvPLoUSJL4adVq/n3ow8QEmDmrrvuavB+NcchJXuWLiTfAnMXftWgoIFGoWt3MAn+7/JruHvKLU16qNpEJY2XUsZKKa1SygQp5XtSyhwp5VApZVfjNbfS+tOklJ2llN2klEsqjW+UUvYylt1dYRVIKW1SyuuklF2klP2klAea5lQ1mgbgLGXvR99SYgafrnDBRUPBNxZM5urX9woBKXln3+/8EiT57rvvANEoT51hFw3AKmFk7/NZt/wTRp7l5u2332rwfjXH4bLhOpRKpjcs+X7Nib6k5sY/FBIjYH9qk4erWpp07xpNW8FVRvD2NHYGQ0oh/GfK3yBu5ElXv/v+J8nbPBOLWbVpHpN0qXI6WwIbLMr1jz8FPwxh9JkXkBEUyv7Ny7n/rptOvaGmbrjLScCHbT42+g86PpXLA1gCoWMMHEpXuTNSNlkmti6JodHUhLMEjn7L7Mf+TZQN9oTB3jQg8qIaL8q3Zr3N/nSIDQGzCW676RoI7wfmRqi1c15f3AK+nvYGIV4lPDP9dZ55SCuGRsdRRmCRg/7XJjH1ngc9LY2yTju3w7HnMFOn3slf72m6TGytGDSamsjfCsWHKFv6AwC7I2DAiJtO6USeMmUKGYXgckNkhZHQgPyFKvj7c8QX2hfCJ9+sA794KNgJpamn3lZTe46mgMMF8SGqFlZLoOsZWB1ugmzw8ex3muwwWjFoNCfjyCIo2AVuOwNdPuR4gegUy7QXTp0BO2PGDEb/ZQqbk00kDbsATN6NEpFUQXH3RM4sgpHDhoMwg9kPSg422v41wAHD3ZkQ0ajfXYPo2ReAmDKYfPvNTXYYrRg0muqQbihLU7kCbjOd0mxsD4Oft6TXejrotTfe4udteYwdfxckjAGTtdHE29c5Bl83uFb9ztixY3nvo3lgy2i0/Wvgm1dfBmDRpnVgbbhvqFHo1Q+Av42dwHPTnmiyw2jFoNFUh6NIJY+ZvFj+8gf4OWFXBFx82fi67ccaBAlXNTix7Xhe3fQrhVbovz0Ll9vNt0u+B1u2UmiaRiFj/XrcwLJtv4LFcwX0qtCuE/h4wcH0E4soOgohZ2OjHEYrBo2mOvK3qZwDv0RyF2/GBewOg8efrkfntCaoynnpldfxXSKcWwRnlsDwy0cCbl1xtREZmJBAgTf0GTjU86GqFZhM0CEGDmWe2N7VUQSlR6rfrq6HaZS9aDRtjcKd4BMFUnJZuT87A6Dv0EENqnPUmDz62NOsbQ/5FrghDabeeSdIoSuuNhZuFz18/AjtfQa3TLrb09JUpVMiHMqC8pyq42472LMbpbOfVgwazfG4HSCdyqn7yzZCc0r4MRzcUignckvAK4zQhFjmR8PZRXD/8CuZO2+urrjaWDgL4Wi24XgO9rQ0VenaCVKyoDgN8rfy4L23YbFYeOrxh1V+QyOUSdGKQaM5HlcZIFQXtukfk2uBtRHw7bLVnmntWB1eoRxKTmdlJNhMMCxLsnr1aq0YGgt7KaTnQXwoWFqYYjijGzjdcOggFO3n6y8+xM/Lxe+rvlBWQyMUVtSKQaM5HpcNkKye/Ahs3MVHcSB84OIhSZ6W7BhCcPZFV2ANFGyL9ePCAhg0YIB6YtQ0nMOHwS35aOW3PP60Z6uqnsCZZ6tXYzrpr7eO5KaBgglj+4N0KSd0A9GK4XQlYxUU67JU1eIsgYx8+i7fwR+BsCIM/KyCu/72b09LVoV7H3ye2e++Sd9/TiHECePPGqAS8my6iVWDOXQIgCxveP+juZ6V5Xh6X6Bed6eA08bdt13LjDff49rxd4JPDBTtafAhtGI43XDZoTxfRS8UHWzyTlCtDrcDMlex829vYJUwMxEQcOklA8En2tPSVcUvTjUFOjdWff5tL5QeVeegaRDfvKqiz3J84PoJt3hWmOO46z+Pk2GFTXMWsvCrj8CRz/vvfMoNV09k1gefQVnDM+C1YjjdsKVD/hY1F12aAoc/013AKuMshpIi2v1+lJ9CoDgYRo+6nPHjxzdeSYvGwhoIiddAdCDFAd6smTWfb76aqyOTGoH09etxCSjwgedfmuFpcaowa9YstlogPg/mL94Ae/Zz7QsrmLlNsnbRUvXw18DIJK0YThdcNijcp+agbRlKMZQcVo4q7bA8hrMEtifj74Y/osDXCyZPGK4SnKxBnpbuRKwBYPEnJchOp2L47of12s/QCPTwCyDPGyLj41pOJJrBlClTWCsg0gbWXEi5by5BLghyweh0mDN3roqqawBaMZwuZP8CGSugPA/suYCEsPNAWE4/xSAlpK2A1O8g8+eqyxyF/D77KwAyIqFd33EQ2BVCz2k5EUnHEzWQsjM7EW+HIX0uPjHxSVNnQgqKyfWFbXvSWtz3PmPGDHyuvRqA+5Ih8WAO78XDlgCV8LhmzeoGTydqxXA6IN1QdEApAHu2MjUrYp2lPP0UQ3kuFGyF4n2Qv6mqn6X0CI7fDpDhBQXe8J9n31V1jkJ6eUzcUxLQgX2Rys8QerRYxeBrGkQniw+FAdBn8DWeFqVa/vnO++wIg97FcMQbvo2ATYHQuQyG9rlQKwZNLbDnqnln4aXeu0rBK0wtExxTDG7H6eG4LM8DtxMwAyawZapxt5NP33majoWwxx/K7LSYTOdT8ezKtQDkrNqkpgp1zaT6U5xHQLGNvmOG8fDjL3tamuoxe7Nr0oW8Ew+PdoG4ju0IHXI+ACEZJQ1+2NOK4XSgNFk1+RBupSCCuh9zpEqO/YjSV0DWWo+J2Wy47Mpx6xevspsr4r4dhWxZvoqockgOhj4DR3tWzjpw+biJZPlAQjHM/fjdY8pOU3f2/KpeEyLA5OE+zyfD5MXVt0/k2xgTRb6QnprC1zuV3Lk/b9EWg+YUuMpVFJI1DBxF/G/2uzwz+CoeGDqGmTNnqvlTp5EpWZ7fKMkxHsVlg7ytNUdlOEuUbwWUY7HcaFnuKmNUQg8AIoaew18f+G8TC9t4PPP86xwJhI5l8P0Pq6HkkKdFar0cOqxe28WBuWU5nv9EmMDizcgRlxEfJrhu1ACGXz2QQi84PzAKZMMUg+753NZwlqkfhSUAji4CBLgc4OXNDy9+wtCPtnKFEbCw65UlPLR8CT2ThnDTQ0Y6vRMo2AFBZ7Y4p9spcRRD2jJwlUBA+5NHEbnKjikGs7eaWpJuKM9lUGACWPdw+X23gbWFlUKoCYsfRwLg7CywFQOFuyGsL5j0JV5Xlr41kxHAN7/9zNjrG6EVa1Nh9mfKxLFw51R1vduzYGkxQaXZDQ5B1xZDW8JRCMmfw+HP1VSCo1AlPPnGQFYefT7aSrYZHuwKMxIhzg6P/A4HF//IW0/drJ623Q5I+041qGltuMvBkQdlmVB8ELI3VL+eq+zYDdPkrSyl4gPK0thyGHp0BG+vVuNfAECYkOf2wwRc07u/soqKD3laqlZJ/qY/cApYtnk9mFqwYvCNV99zxIUQNRDiR/NrURGlO1J47qlHGrRrrRjaEqWpyrEsncoBac/786l/++3/xt8Jz3SEHQGwNALu7wblJpi8GXasWsLXX8wGZxE4ba2rrr/baSg1+zHnevGBk7e6dJVCUTl/vesuRo+9micf/5eK/S88CFsPwblnABIsfs15Fg1mzLQ3ALi627lqbtyWdmyaULqVotRO6VPSNzKGHG+4YMClLdtqDj0Loi9R5eFNVrD48e3e/fiVw2+L5zdo11oxtCUchSryCKEsBlkOlkA+fP4VzvgjmWURkOILSUlJLJo/m7cXfsBznSDcAVdth9lf/qL2YfZqlNK9zUbBDjj6P6XMLP7gl6jeO4urX3/nPuznTeLRpSkEOOFoapoqI3C4FErt0Dse/Nu3LosBoGNXyn3MLHnxTdXqs/gQ5KxXy0qPQO7G1u9DamrcTrp4+RHdpycTb/s/T0tTM9ZACDu3ylDc0MsAuKlf3wbtWiuGtoSzWD05mH3BnvNntq7/5z9gkvB1FNx41QCmTr5Nzce7y+g97lK+ioJL8qBHISxa+JXavkIxlKW3/JuJyaIKxzmLlVPO7K3kd5ZWa/kk3/s83i5JdDlcmwHxcXEqv2O70TP57AQIPKOZT6IRsPhyyMdFhzJYsvR71cilNFkts2Wrz45CSP9BF1A8GcUHKduXwqr923ntrQ89LU2dSQ9WYeiR+YUNsg61YmgLHFkIJcnGjdEKZj/lgJUSCktIyhb8EgLBPRK4ftxN6qYQ1A28Irj5hmsx39GfTC+YfAQ+mL9RzbvbMpS/4cgCyP3D02dYMy67uukV7QPMSn5niZHIV6k8RHkBHN1C7O5sFkTC2mAYVmDisQfvV1Nomw5BWBAkRrY+awHA5EVutD8dyiA2LkFNqzmKlIJ0Fhl5LDmqRlb+Nk9L2zIpzMK32EGuL7z7yf88LU2deXreZzgFFG3e06ACmVoxtAXK89R0iqPIsBi81VSSu5xf7voPfk7JF9FQnHcUgs5QN86ADko5hJ3PtX+fyeoLQ+hog0uzJB9+MgcKd6jG4q4yVVPp6JLayVKws/nnsV2lYA5Q00EmLxVxVOFrKc8/tt7Rb1nzwHisEn4Jhh/DINjuho1HVVvMTXuVf0HQ6vwLAAjBZlcJvm6Qe49A+PmASZVhdharqQdbhrKmbJlK6btdnpa6ZbF/PwC5/nDFNTd5WJi6M+nOO0n3hjN9wyDjh3rXzdKKobUipbpZu+zK+Vpy8NhUEoB/B3CF0f2nPfwWCPv9YNCggWqKJGog+MYp51V4XwjpzWLy2REAN6bBsgU/8Nm8OVC4S+3PVaZC4U6F2wHZ61QkVHMqB2cJeEeAd+SxEFO3UyXxFRsO6JRvwFVKyZodlAvY7Q8Rf7kcgv3hf7/z4YwvYE8yH+7YyJNPPqmm01oh/sOGAHBt+55qwDtcKWtnCVgClfKUUn1XzlJdifV49uwDYNJTj/L0c695WJi6M2PGDOwJ0VhTcvnkvVeUhVgPtGJobRTuUfPFRxeq6ZOj/zOSWUxgt8PXq/h81EQeHzqBdRffTKATPo2F9u3bMWH8eNW/Nuzcqk/EwkS3C67hm+4Q4oSrUuGTr39SPypniXqt3C6wvEDdWEqM+WvpVuu5y40Q2RTIXq/kbA6cpbw3+yNeHfQXcrpcRWmHK5k94hH++uA0lejldqkkNlcZ57qC2eMPw69IYvLdd8Goi2HJOmxfrwRgWzhs25t2TMG2Mm6c9iplAVbE+m3MfOstFZ3kKFDfi8VffY/SqXwxzoaXTmhz7NmvIpE6RoG5hWY9n4K1WRmEFcP+zT8eS96sI1oxtCYchZC6FDJ/VM5je67hC3AC4ewf/ADc+zJ/2ZLH4wegfwG8Hw97/SEjNUU9BZ9k7vzBZ+bwyGcfsDICxmZC51L49Iv/gXcU+LVTLQMr6rynfA1Hv1UVWwv3KhlyfzOsF4eavrFlKj9HU5K9AcrSwFmKefZy7k2GNG/Y6Q+3HIX4P1KY++mHqgeFowDys4jOLKLXbX9h6pQpSqFdcymU2piSIkn1hqIIsPt0aVq5mxLfKP4IcNCnAJb9b7G6yUm3+l6ERSnrisS/Cj+M5k/2LvmBTC/JnK+/atk5DDUQcn4/rG7omXiRuj7qgVYMrQlbppr/rihd4TbmiqWD3yc+SOfccl5qBzf3goe7wNQzYWGU2nTMyCEQWMMNz+wFYf3Iu/1C8q1w/yFY9OUqNRXhFQyY1M3VWaSeQsrzlW8jZ4OSxZatnj5NXsay3KbvDle0D3I28MNTz3JLCvwUogqKPdkZ9vrCbUfh55Wrlf9FeMFuB7jccEFPKDkAZUf4bPu3rIoEmwnejQdhFixY8lPTyt2UeIXwRxQEuKBfAcpqyHLD5gPgdKlS616hgDSmkrTFUBnroTRSfWDJ96tbdg5DDYx+4AEA9ixby7tv1a9ftVYMrYHigyqb2VkCJt8/p0WwBIE1iO8++IjevxxgRRisDIc8KxT1asfMZQtZ9M2XLPrmK24afy0EdK75OMHdufpv0/mwF0SXw78OwjuvqaQppENZK6VHlfPaXa4UgD1LTTWV56kxs5cq+1yRZFaW0TT/kwrFWJTBmfO2kuEFr7SDEaOSWLBoIZ/GQaQDzjgMTz00UTW0+e0AmE1wdnvlY2k/ntlf/sS8s+C6s+DXYBg0aFDrjEiqwOxD0JWXcMRHRZld+Npi3APugb+8QOqZV3PfsDGMHjOGDz6Zr8J8tY/hGG43CeUm0v3gosEjPC1N/emm6n1FlsC6Nd/XaxdaMbQGCnYop2GZMfftFaaqggrAJwa/OZsAmBMDY0ZdxqL5H/LG62+oKabivVB2FEJ7qdIYNSFMEH4+sWMH82p76FEMg2Ys48aRY7j7oReUczt7nbIicCk/hclyLNLFWaKieypKaxQfgpSvlF/E3bCOUieQtkzd1L5ZTmwpzEoAuxmm3nknANHXj+SwD4xOg/Vb0sArjL2fLmCPt5vHn3lYWU/eYfQafCtCCJJGJbFowTeqhWdrjEiqxE1T/sHGm/pRZlZlTz6LgRfag9UNz++By7Phq4UrmDP/fypsVbd2VQ82G97D4nAz4v8mMvWeBzwtUf2Ja4/NKogtg/YJMfW69rRiaOmkLlM3XlBP5iarakpvsoLwYu70WfQ/AsvDwe+MRO4Yf9kxh1N5Hvh3VNM//h1rdzyzDxPunMa+TvB0R+hQBi/sAcvOZN79ZIGap7b4qx+b2Q+wGIX7ypWfQQilFCqaAbnKVNhcyeHG+59It+EYLyP7te9I9oGNwSqjGwBnCVOnTmXNmWF0tMFw/yjee+V1OuTa2RoA6WlpKoIJeP7lt3nzzbeYOmWyktUapBRka8a/I2Pvv4VnR7bj9p4wJxZWhcG93VWXr7tT4O+H4YdvV0PBNhV9drrjLIMdOwB48esP+e/zszwsUAOw+JLuK4m3q6z+B/9+R5130cqvgNMAe7a60VoC1Q2+IlrGNwG8I+g0dzXlJpgbA2X5R9Qcsl+CerqX5RB6LrS/Xk2d1JaIC+g3YDgH2sHDXcEsYfoesM1exsz35hkrCeXMFsLwPZQoy8TuB5YEpbyCuoE1BBwlkFfLJLmi/eqvJpyl6ib+y1Yi0otYEAkmk4mpd9yiMqBtmeAs5cbPZkFEMH/1SiT3y+VYJWwIhri4OMNvgupT4ROh9leeC+EX1P7/1FIJ6grCxBuvvcaiBQtISkrCbDIxcEwSv949kk9jYFAePL4ZPn5vBWSvNdq9nsa47fDHNtxAcgDMfP8LT0tUf4QJW2I87cohyAcyN30IGavUA1UtS91oxdAScdng0Fw1P+8sUHP31iCjKJ6hGPKLWTLsJi4ogHkxkG+FoZcMhMDOENzr2IXunwgBndQNsLaYLNz2r/m8//YMXvp+AfedCVuNJ81uM5dw3RVjmPnxEpUw5R0JXqFMv/cNdvZ7CHrfTOmZE1h6wa3cNHIMM2d/qZLqbJm1C1+151Qfe+0oOtZEyFkMZh82P/oZhWb4KQKuSBqmHMquMpWbYc8A+y64cSis/I2/H4IML9gTAI899rgqF1KBd5Tx1CzAv13t/08tlYrpxtJkKDnA1Dsm8s1n7zN1/CDunDqZwklJ/KsLRJZDr/d+5IknnjxmlZ5uuGyQ9bPyWe3IpCDcH7tVcO2Euj9ltyS633gb4XbwKYakYRepB7PiA+pca4FWDC0RV5ly8hbtUyaus0TFVHuF8/OkR0jtPAbOuoGRewpZEQbfRMEVSSMYP248eIWorGafcNXAvr6x2NYgdeOXDgaMTuKJzvBJLAzJgxd2w675i7n7nrsBM6unPsV9KzOIssPHsfBzCAzNhZk7wf3RYq4cM4YP53wJWWtOnmnrKFJPNI5CVfJbSpV5XbDHWF6oLAm3A3J/5+s3ZtM7rYwlERAaCrffdBV4hav+zBEXQuxICOrOF17ppHqpRnXvx8GVo4dCQLuq00VBZ6ieztagttO/wBqkpv2Ce6h6V84iVYWzPJ+pE0fTefxlzI5XzeP9f09TlXlPR4oPqZybkhRsv+xku7mEXhck8dobb3lasoZx6XAAwpIF2bZgdR8oPVrrKV2tGFoaUh5LICs7Arj/zMJdPOYBLv5+F1leMDsOHuiqInH69IpjyoRLVESQJVD9CBKva/i0iDUEXHam3jCM68Zewmcx8FhnCHLCy7th0opkkruNZdDS7WwNgL92h89j4LX2cE932O8Ld6XAtD2w7MsflAO9oqjb8R3W0papZDRnoVIS9ix1wWb+qH7QeZvUWNY6KD6Iee56XAIWR0JcfCzEXg7RlyoLSZjUdEpYX1b+vo6/dodJPWFtKNxy47Un+lt8oiB+DMSNbNj/qyUR1A2iBoFfe2OK7HylMI0+G7ffOJbt5yWSZYVLU2H2W88oi+506+HgLAZXGd88NQ2fUgf7w+GN2bUs/9KS6dOffG8YkiP5bulSQIA9ExwFmE2nvu83SDEIIQ4JIbYKITYJITYaY2FCiOVCiL3Ga2il9R8WQuwTQuwWQlxeabyPsZ99QojXhGilAcSNQclhZfaZfZS1YAmEgE6svuNhknYXsiwM/tUFvoyGXQGQNCqJ/zz2b/CJgcCux2KvTeaGx2F7hagsZq9gbhp/HYsWLiRx/OXc0wvmR0OwE4rNML0DPNEZCq2w6Kt5LJr/Mf2uG8a/usDL7aBrKTy/F+a9N08pB1AmbeV5bUehOl9HoXq6rYjAklJlWJcXqCS73I3MfudLhuWovIV8L3jssSdVCZDATlXlt/gzcOBA7GbI8zac0xIjquo4zF7gHdaw/1dLIqADhPRUYbp+CRB8JgR0VKHGvnGA4LWXnmBdLJxTALu+X2E8USZ7WvLmxVkEUuJa8QsAO0Phmr/c6GGhGgGzhd0XncG5RXBzipsP3v9clV53OzCbOOW8cmNYDJdIKc+RUlYUAH8IWCGl7AqsMD4jhOgBjAN6AiOAN4UQFQLOBCYDXY2/VhxE3ECkS90Ezd7KmWsN4d2rb2PA0u1sCIIZ7QBh9FRYuJCpt9+sEpaiBkNwz8aVxa+dmmYJ76cO6ihiyo2j+OSz11ndvx33docHz4A1oSpKNSkpyeiglsJtN49n0Wdv4nNTEo8Z89kJ7/4EJSlq32Wpx8p5VzjF8japSCpHkQqLtQaDcKv/g6tEWTCBXfH9dCV+bqWcxowaDj6R1StBaxDjx09g0YKvWbhgoYo8EqJ1texsKD5REDdCTS2ZfSCiv5o2c9tAmFgYoG4C/dNR1pyzyNMSNy/OEjBZOT8H9vmqB4hpz9YvKaylccHcj/g5Fq7OhAte/wHMoWAJRKhA9xppignVK4EhxvsPgR+BB43xeVJKO3BQCLEP6CeEOAQESSnXAQghPgLGAm3AnqsHRu9hzD4QeCY/3/Evbv01m13+ML0jXDl6GLfddO2xKCNnsbrQA9o3viwB7Y/tN2+zMkVjhkPmGt547XUo2gsWX6XEJIYyS1M38KjBkLacqRPHMmbJYuZHw/h0eOb68ZRe/C3/vfMi5fD0b3es+1rxflXkz12uQmHN3ipRriLqyRoMWXmMzlTWQrIvzLh5HHhHVy+/2VtZUcWHlPIoz1dNfNqKH6E2mKzKYqgg7ByjCq83hJ5L2LBb2Zv6AX0z4IN3XuXW2yar/5O7XCkVUHkOub+p7yT0rFabEVwFt/PPcirz31jAtXlqenbIkEGttoDiCQQmsmJoHBtXp3JvMrB0K4zoikmcWjE01GKQwDIhxG9CiMnGWLSUMg3AeDV+XcQDKZW2PWKMxRvvjx8/fXAUqsxml03d6B2F4BSsG3QzFy/fyfpgeLwzxHRsx20TrlBWhXQZc/GZKjS0qfEOUxFIgV3V5/JcpTTM3ioCxm0/1l/aL07diM0+4Mhn1MgRfBMFpSYYcAAO/foJzz3zpLIQCvfwz3/cw9R7/s7Mz9YDPsoCMnur41iDVQ9nZzG43RwY/08sEj6NMywUt63mxL3Qs5VsJcngLlXhvKc7lgBIuBJCejH91fdZFQjxJXB0xQr1f07/AY4uqlSFtUhl3+f9riy6toAtU1mlzmICvlyHQ8DKCFQARystnncC1kDS0tNYEQbpXnDkpQ8BM6am9jEAF0spzwNGAncJIQbVsG51WkrWMH7iDoSYLITYKITYmJVVizLQrYGyNKUUCnaoJ1tnCR9/uYoVA/+P/sklzI2BZztCmRneePlZdRMM6q5COh35arrHK6Tp5Yy+ROVDmL3UvLUjHyIHQIeb1E0muKearohLUo5ga5BazyeKKROvpnvvWL4Ph/7Z0M0H8nLS1U2mPIe1332AV7mk/6uLcXe6Cv75BjiMbE2TVT3lW4JYc+9LdNqdwSexkOZrMqbRQmoOMfU2IpWkS9VxqngKPp0RQilvIzLrS2O4fy7MmzPbyGQvV9Fxh7+AjJXGFOHRNhS9JJUlmprO0BxYEQZhXRNUJF5rT3CswOzLoIEDkQJWh0LsoRzmvvdR0ysGKWWq8ZoJfA30AzKEELEAxmumsfoRILHS5glAqjGeUM14dcd7W0rZV0rZNzIysiGitxzyNqlXv3aqjLazCMcnPzI0Fz6NUVmrx+bvCyCkt/EUbFMKInFs8ygGs8+xKRj/TurG7xOlFIUwQVgfCOurbsQVT1xxSSoCyJbGE4/8gzFfzcICdN4BTu8EtaxoH3ffMIChO6F3MfwSBMxdxpLzrzbCYQGvYBY9+QkXLdnBr0GwIAouHzFCWVaB3U59IfvFQ8QFEHFR27noG5ELrruBfcHQ8RD8+ssann3mSSNx0QgEcBQrJW4JVBZqW8Bth/J89j7yOia3CubIyzyqgjjaCiYL42+8leuvHMLuCDADGQt+QsApT7LeV4kQwl8IEVjxHrgM2AYsBCYaq00EFhjvFwLjhBDeQoiOKCfzBmO6qUgIcaERjXRzpW3aLrYsdbHZMtRUjNlXJaAc/IO/HIbNASpx7U8n8+RJ6qnXK0zdfDverBrueKLgW/h56qZf+SZrDYCI86uuZ/FTT6YBncHqB+2jYUBvRhf5cODAUWa++yE4Chk3+ApGZsP34fBMJ/giGkbmQI+NyYweM4aXx9/O0M9+JdkHnu8AbmHURJIOddOvDREXqMgczQk8/t/pFPbvRKcycB2FdVvSlc1uz1G/Obdd/eZ8Y9WU3NFvPS1yw3GUQImDuHXp/BwC6d5w2bCB4BfrackaF2sQN153BaXt1FRujzwQgpBTbdaQx6do4CchxGZgA/CtlHIp8CwwXAixFxhufEZKuR34HNgBLAXuklJWZDtNBd4F9gH7OR0cz2lLIX2F8itUtOO057DnnjfwdsNb7VQoakVROEoOKeVRYR1YAz3XTMbsc+qCfBUEdIYOE5R/omgvXNObgCIb5+RLvlv6nQoznfk9JpOJz2IgyFcl0v0aBFOOwKMH4P9+ziTXqkJiy8yG9WTLVDcrPTXUcCz+nPfPmwE4NxMS27UziiOmq2KNjmLlbzB5KwuipvIZ0g2p37X8KSdnESzZhb8LFkVC104JTBh/o1J+bQmfaPCL56wLB7EtAHrng0lwyqqJ9Q7PkFIeAM6uZjwHGHqSbaYB06oZ3wj0qq8srQqXTTW3seeBxcaf7hSTle9e/5Dhf6SxIBLSfGHmFMOf73YoJ2zcqNbnGKuIYIkaAnIFjAih9N/zGZlTTuT4EbD/CO7PvmdxpGTA1cO47aarwVHCPx98hbx1Rzm3ENaFqF4JBValFKbecSu4iiD+Cj011BhYA6HPBDj7QyY6YOLrr/7ZbGnO3Dns2bKGM84ayITb7ldJcDVVny1LU4X5XGXgN7a5zqDuOIpg3ir2+8Iuf4jLO1qpV0UbIvJiCHcyfnwBX81bTb+dgIVTdiDSV1VzUp6vup7l/qocofYclbMAICXRH2+i2Azz46BzNGBLVaZ8WaoqhufbDNFHTYVPBLT7C4R1x2/iZZxfKJh60TCYNhu7kHweBb+uW/FnPsb0V5/msk0Lmf/PJF7soJRCz67xTJ18m4qICjm7dfdNaElUZIpfNxJ2HIQ/dqvfp8vOmjVr2JYi+WLhGrVuWB/1oHJ85noF9mzVTrSF50N8Oe0J2JnMijAwmWDI4IG1t4JbGyYLRA2mcMCltd+kCcXRVEa6VX/m7PVKIQizKt5mDQHgxzv/xTlFyq9gCoAefYYrpVGWqhzOIT08K39jIISa+rnlEgq9BIz6OyzfwFyjCODAgQOV6RvYWeUvoHwJixYuZNGCr3n2iQdUroPbrrJ4NY3LxNvUXN7L84wHl0yCwuJw2aFHRKxSBkIoI/dkDX7K0pUF4iyrfrmnyd8OWWuxLlmHG1XX65z2gvHjrgfvCE9L13QEd+eWWd9TEuSNqRZpKFoxNBf2HGW+BnUzSmaHq1h/v3jeuH4SFy3Zyh+B8G0kJF02iEl/e1E5VhOuhOjBrW8K6WR4hTB38QIe7uhmVYgqbPdNlIpZnjB+gpoy84448Ym0vEA90Zl9IWZY2zP5WwLh8fxxYSys/I3Z/a/m81kL6P1TKh9ug2nfpbKrz/VQYtzwq+sVLSXY0lSehNsBR1pQDElFD4+yVCg5TJ9M2OEPxX5wxtmDVQXiityZtooQ+H/5EmYTh061qlYMzUXhLmXSCbOK4DEcx6smP8LUtVkc9YYXOqjWlOPHT1BOsLik2kfdtBaswaxbuwZXHLzXC76OVuG43bokKAe1yaJu+hYfdXOpwG1T5cT9Oyjlqml8rIG87TjA2mC4JRX+8tpabkyV7PVTluwZWTa+HnQ9c+bNVRbd8XWVnMXHgimkQ/XNyN1U9Xv0FPZcFeyBm0XPv0x8CfwUBqNHDuWGOx5SU5inA+ddVKvVTqPaAB5CutVTSsF28Kka8bD+xvsZvGoPvwapMMyoTu2Yets4FT/uFey5qKOmxOxNtz5JvPHhEh6bPIDxt92vxstSVbtNUNMVoeeqQnu+iSp/Q7qUsgxp5HpQmmOY/XBKeL4T9M1X9a1+D4LMQAsOh5PwchiVBQ9/v5oJ92RD4Q6lzAt3KoXtKDxm6bkdqoxJ3iblCwrq6sETQykqWxa4nQSs+BU38EuoYPaN14FvvCrtcjrgHYrLjftUq2mLoamxZaobnHT/GUEz8623mHHRGC5YtYeVofBUJyOz+bXXVWlpi1/bVAoGd037HzuTy1X5ASmPdZWqnMEccpaqB1VyQCkFa7CePmpqhOD8ASPw8oJfQmBRFBz1AYfDSVJSEivagZeE847CjBceMpoqZal5+9zfVWXgiilPt1NZEM4SVXqioq/0yfpxNBUum/J3uOzgyAWXnbOOmtkSAAGdEgF36w7qqCs+sdgcnLKNm7YYmhrpUhVFK0XQHPl8MU+kqFj9V9qrhK1xYwerzGevUFU7v61jMqunNOlUN5iogVUzuIVJldYoSVbzv9LVNoq3tXAm3/sEkydeCz5R3H3P3Rw+nEz79u2YOmkC87yLObp3NedmwzOrF5Pgn8OV1/uoh57Sw7xz54N0+T2HnWdE8n/v3W90HvRR0072bPU540eV12I+ZcRkwylJUcez+KrKAljg922EF7uY0x7cxSmqI6JXGyq3firMXpTYOWVkgLYYmhIp1ROTxVeZq8AX/3mOhw9AqpHFK01wzeih3HDTZFWELqCzqpZ6OmANPtbGszpfisUPgrsbDYhOE1Pf0/hGq6dsKXnjlRdZ9Ml03njlBXAUMe6eN/k5CLoXQowJVqzeYFTGdfD1W+9ww485XJILU37J4pm7XjbKmwv1V56nrGd7NqQtUbXBHMVNdx7OUijao3oQ2LJUKQ+vYA6+/AnlArbFwuBBA9S1Vpe2t6cJWjE0JYW7IH+rarEoBJ8/MZ3LP/yZErOqlnrJ6CQWLliouoqF9FLtOE+nMEyvEFWML35k89R70pwan2jly7GlqdBgS4CKphNAQAfyL7kME3B+EQwZeKHyJbjL8fpqPX5OuP8MKLLAiN/SmTN3jtqn2VcV4HMUqrl+RzHkb4P8zU13Hhk/KmvTO0wpI0cRlAhiN2Tycwi4feH6O55Q7W81J6AVQ1PithsJP17M++/zXD77J+wmeLQLZHlxrNyFRE2XBHRpeyn5NRHSGxKvBv8m6CWhqR9mH4jop5IIAzurkGpbusq5sfgz+d1PICaUv0T25eob71NTfPZihmR5sykYUmMC+DpK9ZJOXrRaWc3WINW/vCxVldVwFCpLw1GgfA4u+8kT5uqDs1Qdz1GgAj68I5RlOu9nfNzwVRSYDUXXZsLAGxmtGJoStwvKc5n39gdcMnsNTpRSyPCGa8dcqi4+l10VmLMGQOzw06uJjHe4uvloWhY+MRA/+lgJ9YQxEGd04rX4sqedL/blG3lnxnvgHcHa6Z/gX2Qnfdh5zPl0DksjocgMQ/fBu598pUK0hVVNq5r9lC/NbYfyQmVJZKyEzNWNI7s9V9XkQh7zS3mFsuDtjyl+8VM2BsFhfxg8eJCyhjTVohVDY+N2qOxPUI3tpZt276whzAFPd4J0X1XvZ+L1o9RTU8lhCGoDWc2atoPJoqY2LX4qizmk17HgCbMPi52peEvI+XI5eIVi/XYXuRZ45+AfAAy5IomFkXB+Pmz7fLkqn24NUhFMPpGqfLezRFkOjiLleyg+CGnf119mZ6l6EEtdrHwLZm/VDRAAH9q9swGLhFkJYBJCRcTp6cuTohVDY1O4G1KXqkgNRxELXlnIRXnweTTs8Uf1Hr5jopoySrgSoofoeU5N68FkIWLUIIrMcI1vDKRl06cAVoTDRedGgdvB1DvvxHbDcHIt8PfDULw3mbv/9k/1AGTyUpF3bifgVr4MZ5Ga9ilNrn0ynLPEaIObr5zLRxdB+nLl5HaWQkW/eynZfs29nFukCjHawmD8lQNUaZY2HBLeULRiaEwK90DOr8pULs8HRxHxX26gyKyayyQlJRmtO4tUVzLfGAjvc3pNH2laPTdOupvDfdrRcXsa24ffgQn4KQ627s1UU0P2LCbddw+fXBBFtB3e3w5/+y6ZFUNvg5wCZX1YAwEBZRnGzbxQTQM5KhXfk27lRJaV8rGkhJSvVRZz6VE4shAyVyllUJqiljuK/rzpr7/5AXr+nsz8KPg+CkZdNpDrJz0E4X2b81/W6tCKobFwlqknFmeJ+uEXH2Tpcy/SN18phahO7ZSzuSxNmdSnk5NZ07bwCuM9kik1Q88CF4sjoDQYzrjgGmUFW/yhPI+/znuXp4fFMTcGis0w6EARmeffxN2XX8vd/3z6WBMg33gIPFNNXVVUZc36BVIrwlqNCsQuu3Iil6So66j0iFrmKDDCZp0qtNlZDCZvfvi/f3HBjyqJ9KM4iAyCcVNfMDoL1tAOVqMVQ6Nhz1JONv/2YPKFwt3EfPkHxWbVCORIyhH1w/aOgA7j214NJM3pg397+o0axH3d4fFOat4+ISGWR/7zkrrhxgxTN2fp5omZT/PLhYn8qys8cIaa4Hl6L5h3JvPof6YduybM3ipiqaLBT2myssDd5cr6dpYoK6Gi17mjUEUembxUgUW3XSVFSsDiz5s33sugRVvYFACvtVOLLhk8WJdUqSVaMTQW5Xn8+e+0BrLq5Wc4JxsWREKpGUaMuEyVd/DvoJSHDpPTtFb8Yhk/bhwBZyTyWzAkdmjHfx77j3Iwg4o2C+qmnMA4ef25h0lKGsl+P3ioK9hMMG0f2H9PZeZH/zu2X2swFO89Nh3k3wEsQSofwVFoJMhlqVwL70hwlQBulSfhLAEEmH34Yvpn3PJLFsm+KuDDZYbrxgxhwtSndA+PWiJkY8YPNyN9+/aVGzdu9LQYam6zcI8KkXOWqIsC2NBzDD2LYVJPGDwmiam3XKPipqMGaZ+CpvWT8SPkbDQKH5pUjkKXO45lETsK1RN+eD/VxlZ4q8KQwL8n3cn//ZhKhAM+jgX7LSOZOnWq2q402aiRlaKSPaVLOaiDzoS8zSqUVjqN3Ii9apvArn/WIvvk2ZdImvUjTgEPdIPALomqBpktDRKuOr3qIp0EIcRvUsoanSzaYqgPbpeqNZ+/FVK+goKtx8oNAyzfQL9C+CwGSiyormNumyoMp5WCpi0QNQiCeyoF4CwG79CqpSWsQZAwVk2ZhpyrWn0COIr573tv8Y/uqlbYbalw4etLePgOQzF4hSu/gqtEfRZmsARDzm9qH67iY9a2q/RYXwhhgrQchr3zI1YJ/+kCuVZ4Y/qjyhcBKgtaUyu0YqgrUoItQ81rZqxSZq6zREVVmLzgaBYlf32eZB9YFA1jrxiuHGWRF6v2lhpNW0CYVHJi8QGVi+PfoZp1jKKH3qHGTb0Myo6As5iBo5N4piO8ngg9iuGulUe5eeQYZr77sXqACqrkC7AGGr47LxWaajIK8AV0VdYC8NH0lzky8FaCnPCfznDEB5KSRgJSWSrmtl2xuLHRiqGulOepvs3lueoJyS/xWHe2Qjtpw6cgy+xM7wDSZGLSuOHKsRZ8mhTG05w++MaofIDATuCXcPL1LAEq4qj4oJoechSplq0LvmT/+Yk81gXCHTBtL6xdsJiZb711YiVds7dSPoGVmjRZ/EBYeeea2xg9cyVhDlWDbK8/mE0mpk66SfkjEsZA9NAm+Re0VfS8Rl1xlamQOukGa6hyfPm1h/y97L3sdjoUO3msMxz2hVEjLlMOtehhuoKjpu1hDYLgHqqpkuE/qBbvCFUTy5EPZn81/Qpgz+CVZx7m7gef5QmSeXy/cko/9+Vixn+7hMsHX4rbJFi4agWXjxypwr2z8ph9x99JPJhDhzJIsMEdEpJ94NEOkOKrdMrlI0aoKa6gbkrOCse4plZo53NtkRIy16h5yoqkm0qVUDdf81fO3nCI59vD6jA4q1s80x5/SIXvJYxpPjk1mpaMlHDoE6OAnlP5C3zjmfnWWyR/tph/HwC/4/qL2YWqL2Z1Q6zR76fQrCoJpHjDbn9YF6L6miQlJTF1yhSjY1sGJF6lc4aOozbOZ20x1AZXuUq2sWeBI9pwpFUydb/7hbM3HGJRpFIK7dsnMu3Jh8Ht1ok0Gk1lhFBO6fJ8VZa+7Ci4y5k6ZTJMmsBNY27kggLwdYHdBBapWoxGl4NDwLJw+CMIDviqXuEVtG/fjjdef8OovZShrHrfWDWVpKkzWjHUhJQqJC5vkwrHc9nVXKez5E/T9JNnXmLsrB9J84X349Rmb7z8vLoAIi78s0GPRqMxsAYaDuVEyFqrLHBrEEgHF1wcyao/srCdomTSn4qgAnuumjoqz1MPbjGXQFD3P9vpauqGVgw1YcuAtOXKLHWXK7PXaYPgXrz1znusW/APntmjVp3eEZwmGD3qcvUDDe6hrQWN5lQEdTfqK+WAyZe773+au93OY5UBXLbqk0FLklXDH+kEJDgLwGZTNZCswRDcu3nah7ZRtGKoCWeJchr7HneDT8sh+p2lzMgBs4R/d4F0bzW/Ofn6/iqZJnaYZ2TWaFoT3mEQN0qFdBfuVGHg0mj56SyFkoMqJLUsXTmxLX7GhlKFiLvKlPLwDgdHiQpr1T0+GoxWDCejcC/kb+HP8r0GK+98lIsXb2W0hF9CYE6MioRISkpi6u03q8gLk5fqHKXRaE6NxU/dzG3pqldD4U41Xp6nLIeyVLCGqGvL4qesd4uvmj7yb19JaQSo8HFNg9GK4WQUH1BtOc3HmtB/e/+TjPx2K5sDYUaiipTwscKo4SO5c8odULRfObxih2kzVqOpK76JykIv3KWc00ijPMZh1bMkb4u6JkuPQNi5yroI6qEiBMPPV1nT+rprFLRiOBluu4pCCjDM0rwi+n+5kcM+MK2TiphISkpi6oRBICzq6SakJ0T0rzmmW6PRVE+AMWXrFaJKbXiHKwvAL1V1YyvYBkgV1BFxgbo2AzpDUBddlLKROb0Vw5GFqoOaJRCyfoLIASqaSEr1ZOKbqBJygPWj7qCvE57orJTC1aOHcuv1lyqT1r+9mnaKvUwrBY2moSReq2qLCatRyr6DqjEWN0pZEBX1xoJ10lpTcXoohoJdKrLIVa6e6s0+qv2mPUspAJOXMk/ddrWscLdyavmpJ5jv7vsPl6eUMjcGDvipGiy3TrhC1T5yFquCYq4yVflRo9E0DJMZTJXLYxt+Ph3l12y0bsXgdlatVuoqVxUXKzf5djuU86r4gOo1699ePYnYs9UcZckRNe4sMbpAlUPGyj9v8q+Mv50pazPZ66eqpbZv3045mU0WZcYW71Mx2e2ubd5z12g0miai9SoGlx2OfA3trlOfUxYoBQAQPVgllpWlQ/4m5ZRyFiuz1G1X1kN5virtW7hLFcRzFqun/vJ8IynGwsLLb+KunQWkecOTneDyUUa6vS1dxUuH9IagMzxz/hqNRtNEtF7FgBuKD6uuTl5hSin4JagG4QU7oPiQevq3Zap2f9Kp4qLddjUOqs9s8T5lKTiLoWCnsixkAPsvv5MxuwpYEwIzEyGiS6Iq4lWwTVkcfgnKatD9FTQaTRuj9eaLu53KAqho4FGR+m6yqnop0qWyKYVQloGwqmkmZ5maXjJZVXmLoB6GtWCH3D/AHMCO6x+m86503ouD6R0gsksirz05RSkW70jVlco70qOnr9FoNE1F633clS51c7ZnHOsOBaqheHm2ijSSDhBeRlq9lyqsVREGV1FVVgjViUo6Qbr5fMwU/rIlj8+i4Ruj/tarLzyl9lGWApGDIeL85j9fjUajaSZat2Kw+IE981gyDKioovIClSkpAYsPlKaAfydlFTgKlcVQUZkxOx+sFu7+10NEbUnm0QOwPgg+jVU6Y+TIJGUp+CdCcXmVUtsajUbTFmm9iuFPhLrxV0wlmb2VQ9hZou7s1mDwdYM1QOUclBxS9VmKyjg46nY67s0E4FkzBLhgvy+82AHadahUvbH0CIRfoP50e06NRtPGaTGKQQgxAngVFbT8rpTy2Ro3KCyt2FLlKVSuu24JUArAN04t35ACh3+FQefw8bLP+GPFndy3GRKLYG4MlJkgphxSvWFpOMR3TuTV1wyl4HYoq8M7TJfw1Wg0pwUtQjEIIczADGA4cAT4VQixUEq546QbHc2GolIIilHO43W74KW50CEW/jMJgnow861ZxLyzhKuyjm3Wxx9GOiGoHJ7qBL9VSlROSkpi/u03qh4M0gWYjNDUC7VS0Gg0pw0torWnEKI/8LiU8nLj88MAUspnTrZNXyHkP2JVddN2ZfDSbig2Q5BLPflP6wSjs9TfoghYHAkX5cPgPCg1wTsJqjUgGDWP7rxT+RLKjig/haPIqPrYXeVFmL2b/h+h0Wg0TUxrau0ZD6RU+nwEuOD4lYQQk4HJAGeYYEwGLA6H+w5DmRnu7Q7tbfDIAXjbsDW+joT34wEBn8eoP1DK4MXJk1TYKqhkOHe5KojnE6WmoQLPUCU0NBqN5jSipSgGUc3YCaaMlPJt4G2AWD8hfcvg/e1glfBMRyiwwhYr/K07DMxTfWE3BleyCGwZqvyFyUuV6i3YCv6dlWNaulS57JyNEHSmCmEV1Yml0Wg0bZuWohiOAJU7bCQAqTVtEN/1DEx/6Y9p7mq4bigPTxpzbKF0qeS3yjgNZ7UtQ5XydRSoInmuUkAqJeDfHiz+qumHVgoajeY0paUohl+BrkKIjsBRYBwwocYtzN5wXT+YeI36LF0qGxq3auzhG2c0GHcZlVPLIWYY5KxXY85SCOoK2RvANwbiRyvfgl9C056pRqPRtHBahGKQUjqFEHcD36HCVd+XUm6vcSOTl8pRcJWpLmslh1V0kleIymMoS1PZz45iVUvJWahu+rZMyF6rKqIGdTcagSRU6iWr0Wg0pzctQjEASCkXA4vrtJFfvCqWZ/ZVTmPvCFUmI+IiSJ4PRbuVpRA/Wk0ReYVCcHcoTVaZ0T4xqva7RqPRaP6kxSiGeuEVDu7d6r3JrDKTg85QT/9eYao+km+cmiryiVLr+cZBXJIa02g0Gs0JtHLFEARI5S+whkDo2cecxsE9oOwoxI2ouo0waaWg0Wg0NdC6FYPZHxCqRWf00KqRRKG9IaSXx0TTaDSa1krrVgze4dBhvIoy8go9cbkOOdVoNJo607oVgxAqJFWj0Wg0jYauDKfRaDSaKmjFoNFoNJoqaMWg0Wg0mipoxaDRaDSaKmjFoNFoNJoqaMWg0Wg0mipoxaDRaDSaKrSI1p71QQhRBOxu4G4igOwG7iMYKGgB+2iMc2ksWRprPy3l+2ms/bSU76ilnIv+bpp2Pyc7p25SysAat5RStso/YGML2cfbLWQfDT6XxpKlJZ1TWzufxpClpZyL/m48c061OVc9ldRwFrWQfTQWjSVLSzmntnY+0HBZWsq56O+m6fdTL7RiaCBSygZ/gY2xj8aisWRpKefU1s4HGi5LSzkX/d00/X7qS2tWDG+3kH20FNrSuVTQ1s6pLZ1PWzoXaHvnAyc/p1Oea6t1Pms0Go2maWjNFoNGo9FomgCtGDQajUZThTavGIQQVwkhpBCiu6dlaUyEEMWnWP6jEKJvc8lTX4QQCUKIBUKIvUKI/UKIV4UQXjWsf58Qwq85Zawrp/puWhP6+mnZNNX10+YVAzAe+AkYV5eNhBDmphFHU4EQQgBfAd9IKbsCZwABwLQaNrsPaNGKoY2hr58WSlNeP21aMQghAoCLgUkYP2whxBAhxGohxNdCiB1CiLeEECZjWbEQ4kkhxHqgv+ckrx3Gufyv0uc3hBC3eFCkunIpYJNSfgAgpXQBfwNuE0L4CyFeEEJsFUJsEULcI4T4KxAHrBRCrPSg3KdECBEghFghhPjdOIcrjfEOQoidQoh3hBDbhRDLhBC+npa3OvT10+Jpsuundbf2PDVjgaVSyj1CiFwhxHnGeD+gB3AYWApcDcwH/IFtUsrHPCHsaUhP4LfKA1LKQiFEMnA70BE4V0rpFEKESSlzhRB/By6RUjZG+YKmxAZcZZxPBPCLEGKhsawrMF5KeYcQ4nPgGuATTwlaA2PR109LpsmunzZtMaDM4HnG+3nGZ4ANUsoDhoadCwwwxl3Al80r4mmNAKqLlxbAIOAtKaUTQEqZ25yCNQICeFoIsQX4HogHoo1lB6WUm4z3vwEdml262qGvn5ZNk10/bdZiEEKEo0ytXkIICZhR/8TFnPjPrPhsM37srQUnVZW7j6cEqSfbUU/LfyKECAISgQNU/6NvLdwARAJ9pJQOIcQhjn0/9krruYAWN5Wkr59WQZNdP23ZYrgW+EhK2V5K2UFKmQgcRD3d9BNCdDTmRq9HOddaI4eBHkIIbyFEMDDU0wLVkRWAnxDiZvjTYfkiMBtYBtwphLAYy8KMbYqAmitDtgyCgUxDKVwCtPe0QHVEXz8tnya7ftqyYhgPfH3c2JfABGAd8CywDfVjP369Fo3xZdullCnA58AW4FPgD48KVkekSru/CrhOCLEX2IOam38EeBdIBrYIITajvjdQ6fxLWqrzueK7QX0ffYUQG1HWwy6PClZ39PXTwmnK6+e0K4khhBgC3C+lvMLDotQbIcTZwDtSyn6elkVTlbb+3ejr5/SgLVsMbRIhxJ0oh9+/PC2Lpir6u2n56O+odpx2FoNGo9FoakZbDBpNPRFCJAohVhoJa9uFEPca42FCiOVClSlYLoQINcaHCyF+M5KOfhNCXFppX9OEECmiDZXT0LRetMWg0dQTIUQsECul/F0IEYjKSRgL3ALkSimfFUI8BIRKKR8UQpwLZEgpU4UQvYDvpJTxxr4uREXJ7JVSBnjifDSaCrRi0GgaCSHEAuAN42+IlDLNUB4/Sim7HbeuQDVqj5NS2iuNF2vFoPE0eipJo2kEhBAdgHOB9UC0lDINwHiNqmaTa4A/KisFjaal0GYznzWa5sIoNvclcJ9Rq+ZU6/cEngMuawbxNJo6oy0GjaYBCCGsKKXwqZTyK2M4w5hCqvBDZFZaPwGVEHazlHJ/c8ur0dQGrRg0mnpi+AneA3ZKKV+qtGghMNF4PxFYYKwfAnwLPCyl/LkZRdVo6oR2Pms09UQIMQBYA2wF3MbwIyg/w+dAO1RZguuMksf/Ah4G9lbazWVSykwhxHRU2YI4IBV4V0r5eLOciEZzHFoxaDQajaYKeipJo9FoNFXQikGj0Wg0VdCKQaPRaDRV0IpBo9FoNFXQikGj0Wg0VdCKQaOpASGESwixqdJfhzpuP0QI8b8mEk+jaRJ0SQyNpmbKpJTneFoIjaY50RaDRlNHhBB9hBCrjJ4K31Uqf9FFCPG9EGKzEOJ3IURnY5MAIcR8IcQuIcSnRsY0QojHhBC/CiG2CSHerhjXaDyNVgwaTc34VppG+tqojfQ6cK2Usg/wPjDNWPdTYIaU8mzgIiDNGD8XuA/oAXQCLjbG35BSni+l7AX4Aq22j7KmbaGnkjSamqkylWQ02OkFLDce8M1AmtGoJ15K+TWAlNJmrA+wQUp5xPi8CegA/ARcIoT4J+AHhAHbgUXNcVIaTU1oxaDR1A0BbJdS9q8yKERQDdtU7rngAixCCB/gTaCvlDJFCPE44NPYwmo09UFPJWk0dWM3ECmE6A+q7LYQoqeUshA4IoQYa4x7CyH8athPhRLINvo5XNuUQms0dUErBo2mDkgpy1E38eeEEJuBTSh/AsBNwF+FEFuAtUBMDfvJB95BVWb9Bvi1yYTWaOqIrq6q0Wg0mipoi0Gj0Wg0VdCKQaPRaDRV0IpBo9FoNFXQikGj0Wg0VdCKQaPRaDRV0IpBo9FoNFXQikGj0Wg0VdCKQaPRaDRV+H8DxnZjTvtBVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(smoother.smooth_data[0], index=train.index).plot(c='red')\n",
    "plt.scatter(train.index, smoother.data[0], c='black', s=4)\n",
    "plt.fill_between(train.index, bts_samples.min(0), bts_samples.max(0), alpha=0.3, color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8363a52",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}