LSTM_tf_DuplicatedPrecipitationData.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8da86ec5",
   "metadata": {
    "id": "8da86ec5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential, layers, callbacks\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44dd90be",
   "metadata": {
    "id": "44dd90be"
   },
   "outputs": [],
   "source": [
    "# Read Data\n",
    "data = pd.read_csv('Data/requiredMunicipalities_temperature_precipitation_elevation_DengueCasesWeek.csv')\n",
    "# Remove extra column\n",
    "data = data.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d601a8bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
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    "id": "d601a8bf",
    "outputId": "7797350d-0abb-484a-fe44-4474d45a5c9a"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>...</th>\n",
       "      <th>2019/w43</th>\n",
       "      <th>2019/w44</th>\n",
       "      <th>2019/w45</th>\n",
       "      <th>2019/w46</th>\n",
       "      <th>2019/w47</th>\n",
       "      <th>2019/w48</th>\n",
       "      <th>2019/w49</th>\n",
       "      <th>2019/w50</th>\n",
       "      <th>2019/w51</th>\n",
       "      <th>2019/w52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>antioquia</td>\n",
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       "      <td>199.928375</td>\n",
       "      <td>286.881470</td>\n",
       "      <td>345.874695</td>\n",
       "      <td>161.073792</td>\n",
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       "      <td>324.082489</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>amaga</td>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>amalfi</td>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>andes</td>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>angelopolis</td>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>...</td>\n",
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      "text/plain": [
       "       State Municipality  PRECIPITATION_jan_07  PRECIPITATION_feb_07  \\\n",
       "0  antioquia   alejandria             66.638855             66.628891   \n",
       "1  antioquia        amaga             62.527489             46.315376   \n",
       "2  antioquia       amalfi             76.737572             62.784908   \n",
       "3  antioquia        andes            115.193504             61.572941   \n",
       "4  antioquia  angelopolis             69.933151             43.129227   \n",
       "\n",
       "   PRECIPITATION_mar_07  PRECIPITATION_apr_07  PRECIPITATION_may_07  \\\n",
       "0            199.928375            286.881470            345.874695   \n",
       "1            166.522003            288.957397            350.666260   \n",
       "2            198.478897            322.928314            381.405701   \n",
       "3            205.126846            308.268433            332.884583   \n",
       "4            178.370377            278.665588            326.885010   \n",
       "\n",
       "   PRECIPITATION_jun_07  PRECIPITATION_jul_07  PRECIPITATION_aug_07  ...  \\\n",
       "0            161.073792            339.436737            324.082489  ...   \n",
       "1            143.757507            350.168793            210.302658  ...   \n",
       "2            191.377609            387.475983            408.289520  ...   \n",
       "3            116.664536            310.750671            189.581451  ...   \n",
       "4            144.349655            360.914429            234.040939  ...   \n",
       "\n",
       "   2019/w43  2019/w44  2019/w45  2019/w46  2019/w47  2019/w48  2019/w49  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         1         1   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         1         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   2019/w50  2019/w51  2019/w52  \n",
       "0         0         0         0  \n",
       "1         1         0         0  \n",
       "2         0         1         0  \n",
       "3         0         0         0  \n",
       "4         0         0         0  \n",
       "\n",
       "[5 rows x 1019 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f9e3bf",
   "metadata": {
    "id": "51f9e3bf"
   },
   "source": [
    "## Columns with temperature, precipitation, and Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d039cfa",
   "metadata": {
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     "height": 253
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    "id": "9d039cfa",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <th>TEMPERATURE_jun_07</th>\n",
       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
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       "      <td>21.113854</td>\n",
       "      <td>21.331241</td>\n",
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       "      <td>20.634999</td>\n",
       "      <td>20.499266</td>\n",
       "      <td>19.855212</td>\n",
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       "      <td>21.025772</td>\n",
       "      <td>21.377411</td>\n",
       "      <td>21.652374</td>\n",
       "      <td>21.425886</td>\n",
       "      <td>21.230342</td>\n",
       "      <td>20.725872</td>\n",
       "      <td>20.687894</td>\n",
       "      <td>20.574121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.814870</td>\n",
       "      <td>20.851651</td>\n",
       "      <td>21.328261</td>\n",
       "      <td>21.144019</td>\n",
       "      <td>21.174519</td>\n",
       "      <td>20.900712</td>\n",
       "      <td>21.281823</td>\n",
       "      <td>20.447760</td>\n",
       "      <td>20.407709</td>\n",
       "      <td>19.706597</td>\n",
       "      <td>...</td>\n",
       "      <td>21.648314</td>\n",
       "      <td>21.020582</td>\n",
       "      <td>20.880769</td>\n",
       "      <td>21.149931</td>\n",
       "      <td>21.560988</td>\n",
       "      <td>21.380573</td>\n",
       "      <td>21.026458</td>\n",
       "      <td>20.558420</td>\n",
       "      <td>20.625113</td>\n",
       "      <td>20.717262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.520126</td>\n",
       "      <td>21.536331</td>\n",
       "      <td>22.240293</td>\n",
       "      <td>22.223073</td>\n",
       "      <td>22.306279</td>\n",
       "      <td>22.010652</td>\n",
       "      <td>22.411260</td>\n",
       "      <td>21.424171</td>\n",
       "      <td>21.347958</td>\n",
       "      <td>20.695219</td>\n",
       "      <td>...</td>\n",
       "      <td>22.440292</td>\n",
       "      <td>22.133230</td>\n",
       "      <td>21.910445</td>\n",
       "      <td>22.177232</td>\n",
       "      <td>22.709351</td>\n",
       "      <td>22.234674</td>\n",
       "      <td>22.014884</td>\n",
       "      <td>21.497476</td>\n",
       "      <td>21.550504</td>\n",
       "      <td>21.471094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.030849</td>\n",
       "      <td>16.976915</td>\n",
       "      <td>17.263741</td>\n",
       "      <td>17.063804</td>\n",
       "      <td>17.199818</td>\n",
       "      <td>17.035077</td>\n",
       "      <td>17.151788</td>\n",
       "      <td>16.501156</td>\n",
       "      <td>16.793839</td>\n",
       "      <td>15.996531</td>\n",
       "      <td>...</td>\n",
       "      <td>17.669470</td>\n",
       "      <td>16.954950</td>\n",
       "      <td>16.968828</td>\n",
       "      <td>17.341847</td>\n",
       "      <td>17.520799</td>\n",
       "      <td>17.428500</td>\n",
       "      <td>17.365973</td>\n",
       "      <td>16.974917</td>\n",
       "      <td>17.126363</td>\n",
       "      <td>17.195797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.095986</td>\n",
       "      <td>20.092206</td>\n",
       "      <td>20.527178</td>\n",
       "      <td>20.310996</td>\n",
       "      <td>20.335141</td>\n",
       "      <td>20.198780</td>\n",
       "      <td>20.571788</td>\n",
       "      <td>19.733009</td>\n",
       "      <td>19.727717</td>\n",
       "      <td>19.003105</td>\n",
       "      <td>...</td>\n",
       "      <td>20.841154</td>\n",
       "      <td>20.195543</td>\n",
       "      <td>20.037224</td>\n",
       "      <td>20.429421</td>\n",
       "      <td>20.838454</td>\n",
       "      <td>20.657140</td>\n",
       "      <td>20.333967</td>\n",
       "      <td>19.815866</td>\n",
       "      <td>19.987836</td>\n",
       "      <td>20.030714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEMPERATURE_jan_07  TEMPERATURE_feb_07  TEMPERATURE_mar_07  \\\n",
       "0           20.606707           20.672624           21.210392   \n",
       "1           20.814870           20.851651           21.328261   \n",
       "2           21.520126           21.536331           22.240293   \n",
       "3           17.030849           16.976915           17.263741   \n",
       "4           20.095986           20.092206           20.527178   \n",
       "\n",
       "   TEMPERATURE_apr_07  TEMPERATURE_may_07  TEMPERATURE_jun_07  \\\n",
       "0           21.113854           21.331241           21.114738   \n",
       "1           21.144019           21.174519           20.900712   \n",
       "2           22.223073           22.306279           22.010652   \n",
       "3           17.063804           17.199818           17.035077   \n",
       "4           20.310996           20.335141           20.198780   \n",
       "\n",
       "   TEMPERATURE_jul_07  TEMPERATURE_aug_07  TEMPERATURE_sep_07  \\\n",
       "0           21.333450           20.634999           20.499266   \n",
       "1           21.281823           20.447760           20.407709   \n",
       "2           22.411260           21.424171           21.347958   \n",
       "3           17.151788           16.501156           16.793839   \n",
       "4           20.571788           19.733009           19.727717   \n",
       "\n",
       "   TEMPERATURE_oct_07  ...  TEMPERATURE_mar_18  TEMPERATURE_apr_18  \\\n",
       "0           19.855212  ...           21.489558           20.985991   \n",
       "1           19.706597  ...           21.648314           21.020582   \n",
       "2           20.695219  ...           22.440292           22.133230   \n",
       "3           15.996531  ...           17.669470           16.954950   \n",
       "4           19.003105  ...           20.841154           20.195543   \n",
       "\n",
       "   TEMPERATURE_may_18  TEMPERATURE_jun_18  TEMPERATURE_jul_18  \\\n",
       "0           21.025772           21.377411           21.652374   \n",
       "1           20.880769           21.149931           21.560988   \n",
       "2           21.910445           22.177232           22.709351   \n",
       "3           16.968828           17.341847           17.520799   \n",
       "4           20.037224           20.429421           20.838454   \n",
       "\n",
       "   TEMPERATURE_aug_18  TEMPERATURE_sep_18  TEMPERATURE_oct_18  \\\n",
       "0           21.425886           21.230342           20.725872   \n",
       "1           21.380573           21.026458           20.558420   \n",
       "2           22.234674           22.014884           21.497476   \n",
       "3           17.428500           17.365973           16.974917   \n",
       "4           20.657140           20.333967           19.815866   \n",
       "\n",
       "   TEMPERATURE_nov_18  TEMPERATURE_dec_18  \n",
       "0           20.687894           20.574121  \n",
       "1           20.625113           20.717262  \n",
       "2           21.550504           21.471094  \n",
       "3           17.126363           17.195797  \n",
       "4           19.987836           20.030714  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just temperature data\n",
    "x_temperature = data[data.columns[147:291]]\n",
    "x_temperature.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "861616e6",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.638855</td>\n",
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       "      <td>199.928375</td>\n",
       "      <td>286.881470</td>\n",
       "      <td>345.874695</td>\n",
       "      <td>161.073792</td>\n",
       "      <td>339.436737</td>\n",
       "      <td>324.082489</td>\n",
       "      <td>243.856186</td>\n",
       "      <td>361.769562</td>\n",
       "      <td>...</td>\n",
       "      <td>147.506775</td>\n",
       "      <td>292.958588</td>\n",
       "      <td>436.878143</td>\n",
       "      <td>225.225449</td>\n",
       "      <td>195.619019</td>\n",
       "      <td>256.421906</td>\n",
       "      <td>302.413086</td>\n",
       "      <td>353.701385</td>\n",
       "      <td>277.470184</td>\n",
       "      <td>61.464046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
       "      <td>214.617142</td>\n",
       "      <td>283.321747</td>\n",
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       "      <td>117.747261</td>\n",
       "      <td>256.695129</td>\n",
       "      <td>421.436859</td>\n",
       "      <td>195.391815</td>\n",
       "      <td>162.015167</td>\n",
       "      <td>182.192062</td>\n",
       "      <td>274.654327</td>\n",
       "      <td>259.078125</td>\n",
       "      <td>282.538574</td>\n",
       "      <td>68.923813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>306.408356</td>\n",
       "      <td>385.371216</td>\n",
       "      <td>...</td>\n",
       "      <td>164.738312</td>\n",
       "      <td>349.761841</td>\n",
       "      <td>433.156830</td>\n",
       "      <td>264.976685</td>\n",
       "      <td>236.360321</td>\n",
       "      <td>286.805847</td>\n",
       "      <td>330.032654</td>\n",
       "      <td>388.256378</td>\n",
       "      <td>315.904602</td>\n",
       "      <td>71.508133</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>194.997574</td>\n",
       "      <td>333.450806</td>\n",
       "      <td>...</td>\n",
       "      <td>144.462082</td>\n",
       "      <td>262.342285</td>\n",
       "      <td>394.904175</td>\n",
       "      <td>158.639008</td>\n",
       "      <td>142.633423</td>\n",
       "      <td>161.990524</td>\n",
       "      <td>246.869614</td>\n",
       "      <td>293.866364</td>\n",
       "      <td>298.362244</td>\n",
       "      <td>85.697762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>214.994553</td>\n",
       "      <td>291.776550</td>\n",
       "      <td>...</td>\n",
       "      <td>126.783783</td>\n",
       "      <td>247.130173</td>\n",
       "      <td>390.748199</td>\n",
       "      <td>197.610321</td>\n",
       "      <td>163.057632</td>\n",
       "      <td>205.655136</td>\n",
       "      <td>273.291779</td>\n",
       "      <td>268.090820</td>\n",
       "      <td>284.551331</td>\n",
       "      <td>67.718147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRECIPITATION_jan_07  PRECIPITATION_feb_07  PRECIPITATION_mar_07  \\\n",
       "0             66.638855             66.628891            199.928375   \n",
       "1             62.527489             46.315376            166.522003   \n",
       "2             76.737572             62.784908            198.478897   \n",
       "3            115.193504             61.572941            205.126846   \n",
       "4             69.933151             43.129227            178.370377   \n",
       "\n",
       "   PRECIPITATION_apr_07  PRECIPITATION_may_07  PRECIPITATION_jun_07  \\\n",
       "0            286.881470            345.874695            161.073792   \n",
       "1            288.957397            350.666260            143.757507   \n",
       "2            322.928314            381.405701            191.377609   \n",
       "3            308.268433            332.884583            116.664536   \n",
       "4            278.665588            326.885010            144.349655   \n",
       "\n",
       "   PRECIPITATION_jul_07  PRECIPITATION_aug_07  PRECIPITATION_sep_07  \\\n",
       "0            339.436737            324.082489            243.856186   \n",
       "1            350.168793            210.302658            214.617142   \n",
       "2            387.475983            408.289520            306.408356   \n",
       "3            310.750671            189.581451            194.997574   \n",
       "4            360.914429            234.040939            214.994553   \n",
       "\n",
       "   PRECIPITATION_oct_07  ...  PRECIPITATION_mar_18  PRECIPITATION_apr_18  \\\n",
       "0            361.769562  ...            147.506775            292.958588   \n",
       "1            283.321747  ...            117.747261            256.695129   \n",
       "2            385.371216  ...            164.738312            349.761841   \n",
       "3            333.450806  ...            144.462082            262.342285   \n",
       "4            291.776550  ...            126.783783            247.130173   \n",
       "\n",
       "   PRECIPITATION_may_18  PRECIPITATION_jun_18  PRECIPITATION_jul_18  \\\n",
       "0            436.878143            225.225449            195.619019   \n",
       "1            421.436859            195.391815            162.015167   \n",
       "2            433.156830            264.976685            236.360321   \n",
       "3            394.904175            158.639008            142.633423   \n",
       "4            390.748199            197.610321            163.057632   \n",
       "\n",
       "   PRECIPITATION_aug_18  PRECIPITATION_sep_18  PRECIPITATION_oct_18  \\\n",
       "0            256.421906            302.413086            353.701385   \n",
       "1            182.192062            274.654327            259.078125   \n",
       "2            286.805847            330.032654            388.256378   \n",
       "3            161.990524            246.869614            293.866364   \n",
       "4            205.655136            273.291779            268.090820   \n",
       "\n",
       "   PRECIPITATION_nov_18  PRECIPITATION_dec_18  \n",
       "0            277.470184             61.464046  \n",
       "1            282.538574             68.923813  \n",
       "2            315.904602             71.508133  \n",
       "3            298.362244             85.697762  \n",
       "4            284.551331             67.718147  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just precipitation data\n",
    "x_precipitation = data[data.columns[2:146]]\n",
    "x_precipitation.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "e6780ce6",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>2007/w01</th>\n",
       "      <th>2007/w02</th>\n",
       "      <th>2007/w03</th>\n",
       "      <th>2007/w04</th>\n",
       "      <th>2007/w05</th>\n",
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       "</table>\n",
       "<p>5 rows × 624 columns</p>\n",
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      ],
      "text/plain": [
       "   2007/w01  2007/w02  2007/w03  2007/w04  2007/w05  2007/w06  2007/w07  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   2007/w08  2007/w09  2007/w10  ...  2018/w43  2018/w44  2018/w45  2018/w46  \\\n",
       "0         0         0         0  ...         0         0         0         0   \n",
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       "2         0         0         0  ...         0         0         0         0   \n",
       "3         0         0         0  ...         0         0         0         0   \n",
       "4         0         0         0  ...         0         0         0         0   \n",
       "\n",
       "   2018/w47  2018/w48  2018/w49  2018/w50  2018/w51  2018/w52  \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",
       "[5 rows x 624 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take Columns of weekly cases (week 1 2007 - week 52 2018)\n",
    "y_data = data[data.columns[343:-52]]\n",
    "y_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11df9062",
   "metadata": {
    "id": "11df9062"
   },
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba3f94b",
   "metadata": {
    "id": "4ba3f94b"
   },
   "source": [
    "# Temperature, Percipitation and Dengue Cases weekly 2007-2018 for Medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "KGZyVZrtaVeV",
   "metadata": {
    "id": "KGZyVZrtaVeV"
   },
   "source": [
    "Temperature, Percipitation, Cases for Medellin from original data  (sivigila - cases) (worldclim - precipitation and temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "246419ad",
   "metadata": {
    "colab": {
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     "height": 129
    },
    "id": "246419ad",
    "outputId": "2a725dbe-942b-4902-fb67-40c9bfb90145"
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
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       "      <th>...</th>\n",
       "      <th>2019/w43</th>\n",
       "      <th>2019/w44</th>\n",
       "      <th>2019/w45</th>\n",
       "      <th>2019/w46</th>\n",
       "      <th>2019/w47</th>\n",
       "      <th>2019/w48</th>\n",
       "      <th>2019/w49</th>\n",
       "      <th>2019/w50</th>\n",
       "      <th>2019/w51</th>\n",
       "      <th>2019/w52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>medellin</td>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
       "      <td>345.532501</td>\n",
       "      <td>157.963028</td>\n",
       "      <td>386.515533</td>\n",
       "      <td>230.007416</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 1019 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Municipality  PRECIPITATION_jan_07  PRECIPITATION_feb_07  \\\n",
       "62  antioquia     medellin               65.4114             49.307404   \n",
       "\n",
       "    PRECIPITATION_mar_07  PRECIPITATION_apr_07  PRECIPITATION_may_07  \\\n",
       "62            195.452469            291.927124            345.532501   \n",
       "\n",
       "    PRECIPITATION_jun_07  PRECIPITATION_jul_07  PRECIPITATION_aug_07  ...  \\\n",
       "62            157.963028            386.515533            230.007416  ...   \n",
       "\n",
       "    2019/w43  2019/w44  2019/w45  2019/w46  2019/w47  2019/w48  2019/w49  \\\n",
       "62        29        17        35        16        21        15        20   \n",
       "\n",
       "    2019/w50  2019/w51  2019/w52  \n",
       "62        30        14         5  \n",
       "\n",
       "[1 rows x 1019 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just the row of Medellin from original dataframe\n",
    "data_medellin = data[data.Municipality=='medellin']\n",
    "data_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83882d60",
   "metadata": {
    "id": "83882d60"
   },
   "source": [
    "### PRECIPITATION DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f68a43c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "id": "5f68a43c",
    "outputId": "2c482822-2f8d-4eaf-b229-bd0e33a3f34c"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
       "      <td>345.532501</td>\n",
       "      <td>157.963028</td>\n",
       "      <td>386.515533</td>\n",
       "      <td>230.007416</td>\n",
       "      <td>234.679382</td>\n",
       "      <td>320.92984</td>\n",
       "      <td>...</td>\n",
       "      <td>140.323318</td>\n",
       "      <td>267.799438</td>\n",
       "      <td>418.379303</td>\n",
       "      <td>219.436752</td>\n",
       "      <td>180.841248</td>\n",
       "      <td>200.803131</td>\n",
       "      <td>294.888611</td>\n",
       "      <td>302.151306</td>\n",
       "      <td>276.914551</td>\n",
       "      <td>67.528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PRECIPITATION_jan_07  PRECIPITATION_feb_07  PRECIPITATION_mar_07  \\\n",
       "62               65.4114             49.307404            195.452469   \n",
       "\n",
       "    PRECIPITATION_apr_07  PRECIPITATION_may_07  PRECIPITATION_jun_07  \\\n",
       "62            291.927124            345.532501            157.963028   \n",
       "\n",
       "    PRECIPITATION_jul_07  PRECIPITATION_aug_07  PRECIPITATION_sep_07  \\\n",
       "62            386.515533            230.007416            234.679382   \n",
       "\n",
       "    PRECIPITATION_oct_07  ...  PRECIPITATION_mar_18  PRECIPITATION_apr_18  \\\n",
       "62             320.92984  ...            140.323318            267.799438   \n",
       "\n",
       "    PRECIPITATION_may_18  PRECIPITATION_jun_18  PRECIPITATION_jul_18  \\\n",
       "62            418.379303            219.436752            180.841248   \n",
       "\n",
       "    PRECIPITATION_aug_18  PRECIPITATION_sep_18  PRECIPITATION_oct_18  \\\n",
       "62            200.803131            294.888611            302.151306   \n",
       "\n",
       "    PRECIPITATION_nov_18  PRECIPITATION_dec_18  \n",
       "62            276.914551                67.528  \n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of precipitation for Medellin\n",
    "x_precipitation_medellin = data_medellin[data_medellin.columns[2:146]]\n",
    "x_precipitation_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84641f80",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "84641f80",
    "outputId": "acd9191b-5c4b-48c3-ca6e-e780c8eb5612"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin\n",
       "PRECIPITATION_jan_07               65.411400\n",
       "PRECIPITATION_feb_07               49.307404\n",
       "PRECIPITATION_mar_07              195.452469\n",
       "PRECIPITATION_apr_07              291.927124\n",
       "PRECIPITATION_may_07              345.532501\n",
       "...                                      ...\n",
       "PRECIPITATION_aug_18              200.803131\n",
       "PRECIPITATION_sep_18              294.888611\n",
       "PRECIPITATION_oct_18              302.151306\n",
       "PRECIPITATION_nov_18              276.914551\n",
       "PRECIPITATION_dec_18               67.528000\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of precipitation for Medellin\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin.T\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin_transposed.rename(columns= {62:'percipitation_medellin'})  ##integer\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb2d34a",
   "metadata": {
    "id": "2bb2d34a"
   },
   "source": [
    "## Get date of precipitation from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71deca79",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "71deca79",
    "outputId": "3b0fb807-cb7c-4d42-ba1d-cdfca0286a5f"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]\n",
       "...                                      ...                       ...\n",
       "PRECIPITATION_aug_18              200.803131  [PRECIPITATION, aug, 18]\n",
       "PRECIPITATION_sep_18              294.888611  [PRECIPITATION, sep, 18]\n",
       "PRECIPITATION_oct_18              302.151306  [PRECIPITATION, oct, 18]\n",
       "PRECIPITATION_nov_18              276.914551  [PRECIPITATION, nov, 18]\n",
       "PRECIPITATION_dec_18               67.528000  [PRECIPITATION, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new column of date\n",
    "x_precipitation_medellin_transposed['DATE']=x_precipitation_medellin_transposed.index.str.split(\"_\")\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b013aad8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "b013aad8",
    "outputId": "612a2e4b-5d1f-48e4-fb16-fa1b36f873b3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from date column\n",
    "x_precipitation_medellin_transposed['YEAR'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3061f76c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "3061f76c",
    "outputId": "e1a1c4ff-389f-4a5c-998c-5551855f76f4"
   },
   "outputs": [
    {
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       "\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>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07   jan  \n",
       "PRECIPITATION_feb_07   feb  \n",
       "PRECIPITATION_mar_07   mar  \n",
       "PRECIPITATION_apr_07   apr  \n",
       "PRECIPITATION_may_07   may  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from date column\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7856c75e",
   "metadata": {
    "id": "7856c75e"
   },
   "outputs": [],
   "source": [
    "## function to change month as int to numeric\n",
    "def month_as_int(month):\n",
    "  if month == 'jan':\n",
    "    return '01'\n",
    "  if month == 'feb':\n",
    "    return '02' \n",
    "  if month == 'mar':\n",
    "    return '03'\n",
    "  if month == 'apr':\n",
    "    return '04' \n",
    "  if month == 'may':\n",
    "    return '05'\n",
    "  if month == 'jun':\n",
    "    return '06' \n",
    "  if month == 'jul':\n",
    "    return '07'\n",
    "  if month == 'aug':\n",
    "    return '08' \n",
    "  if month == 'sep':\n",
    "    return '09'\n",
    "  if month == 'oct':\n",
    "    return '10' \n",
    "  if month == 'nov':\n",
    "    return '11'   \n",
    "  if month == 'dec':\n",
    "    return '12'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "137703de",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "137703de",
    "outputId": "6da746ed-50a6-4518-d076-60fc4d2b597b"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "...                                      ...                       ...   ...   \n",
       "PRECIPITATION_aug_18              200.803131  [PRECIPITATION, aug, 18]  2018   \n",
       "PRECIPITATION_sep_18              294.888611  [PRECIPITATION, sep, 18]  2018   \n",
       "PRECIPITATION_oct_18              302.151306  [PRECIPITATION, oct, 18]  2018   \n",
       "PRECIPITATION_nov_18              276.914551  [PRECIPITATION, nov, 18]  2018   \n",
       "PRECIPITATION_dec_18               67.528000  [PRECIPITATION, dec, 18]  2018   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07   jan  \n",
       "PRECIPITATION_feb_07   feb  \n",
       "PRECIPITATION_mar_07   mar  \n",
       "PRECIPITATION_apr_07   apr  \n",
       "PRECIPITATION_may_07   may  \n",
       "...                    ...  \n",
       "PRECIPITATION_aug_18   aug  \n",
       "PRECIPITATION_sep_18   sep  \n",
       "PRECIPITATION_oct_18   oct  \n",
       "PRECIPITATION_nov_18   nov  \n",
       "PRECIPITATION_dec_18   dec  \n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fbffca04",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "fbffca04",
    "outputId": "327882b6-dc45-4a45-c86f-fec168072049"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07    01  \n",
       "PRECIPITATION_feb_07    02  \n",
       "PRECIPITATION_mar_07    03  \n",
       "PRECIPITATION_apr_07    04  \n",
       "PRECIPITATION_may_07    05  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "562ce574",
   "metadata": {
    "id": "562ce574"
   },
   "outputs": [],
   "source": [
    "x_precipitation_medellin_transposed['MONTH']=x_precipitation_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90626348",
   "metadata": {
    "id": "90626348"
   },
   "source": [
    "### TEMPERATURE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fa53cc76",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "id": "fa53cc76",
    "outputId": "4d35327f-cc02-42a8-cbb7-405621218069"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <th>TEMPERATURE_jun_07</th>\n",
       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>21.425911</td>\n",
       "      <td>21.237032</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>21.686978</td>\n",
       "      <td>20.866394</td>\n",
       "      <td>20.705536</td>\n",
       "      <td>19.954406</td>\n",
       "      <td>...</td>\n",
       "      <td>21.726171</td>\n",
       "      <td>21.124271</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>21.952168</td>\n",
       "      <td>21.719432</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TEMPERATURE_jan_07  TEMPERATURE_feb_07  TEMPERATURE_mar_07  \\\n",
       "62           21.009283           20.982925           21.425911   \n",
       "\n",
       "    TEMPERATURE_apr_07  TEMPERATURE_may_07  TEMPERATURE_jun_07  \\\n",
       "62           21.237032           21.322522           21.243176   \n",
       "\n",
       "    TEMPERATURE_jul_07  TEMPERATURE_aug_07  TEMPERATURE_sep_07  \\\n",
       "62           21.686978           20.866394           20.705536   \n",
       "\n",
       "    TEMPERATURE_oct_07  ...  TEMPERATURE_mar_18  TEMPERATURE_apr_18  \\\n",
       "62           19.954406  ...           21.726171           21.124271   \n",
       "\n",
       "    TEMPERATURE_may_18  TEMPERATURE_jun_18  TEMPERATURE_jul_18  \\\n",
       "62           21.037365           21.457151           21.952168   \n",
       "\n",
       "    TEMPERATURE_aug_18  TEMPERATURE_sep_18  TEMPERATURE_oct_18  \\\n",
       "62           21.719432           21.347202           20.748416   \n",
       "\n",
       "    TEMPERATURE_nov_18  TEMPERATURE_dec_18  \n",
       "62           20.886703           20.936353  \n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of temperature for Medellin\n",
    "x_temperature_medellin = data_medellin[data_medellin.columns[147:291]]\n",
    "x_temperature_medellin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c45e3b87",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "c45e3b87",
    "outputId": "79ac93f2-32c9-49e5-a556-f53948cb4b29"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin\n",
       "TEMPERATURE_jan_07             21.009283\n",
       "TEMPERATURE_feb_07             20.982925\n",
       "TEMPERATURE_mar_07             21.425911\n",
       "TEMPERATURE_apr_07             21.237032\n",
       "TEMPERATURE_may_07             21.322522\n",
       "...                                  ...\n",
       "TEMPERATURE_aug_18             21.719432\n",
       "TEMPERATURE_sep_18             21.347202\n",
       "TEMPERATURE_oct_18             20.748416\n",
       "TEMPERATURE_nov_18             20.886703\n",
       "TEMPERATURE_dec_18             20.936353\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of temperature for Medellin\n",
    "x_temperature_medellin_transposed = x_temperature_medellin.T\n",
    "x_temperature_medellin_transposed = x_temperature_medellin_transposed.rename(columns= {62:'temperature_medellin'})  ##integer\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9jD7_JX6dQkl",
   "metadata": {
    "id": "9jD7_JX6dQkl"
   },
   "source": [
    "## Get date of temperature from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ca81f40",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "7ca81f40",
    "outputId": "ecc2bbe4-d0f5-4ce9-cd3f-6201cfbb1a02"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "      <td>[TEMPERATURE, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "      <td>[TEMPERATURE, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "      <td>[TEMPERATURE, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "      <td>[TEMPERATURE, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]\n",
       "...                                  ...                     ...\n",
       "TEMPERATURE_aug_18             21.719432  [TEMPERATURE, aug, 18]\n",
       "TEMPERATURE_sep_18             21.347202  [TEMPERATURE, sep, 18]\n",
       "TEMPERATURE_oct_18             20.748416  [TEMPERATURE, oct, 18]\n",
       "TEMPERATURE_nov_18             20.886703  [TEMPERATURE, nov, 18]\n",
       "TEMPERATURE_dec_18             20.936353  [TEMPERATURE, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column of date\n",
    "x_temperature_medellin_transposed['DATE']=x_temperature_medellin_transposed.index.str.split(\"_\")\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f15a154",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "4f15a154",
    "outputId": "476987a7-b613-4d62-8b9b-76a9eb1ac454"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get year from date column\n",
    "x_temperature_medellin_transposed['YEAR'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93a97f67",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "93a97f67",
    "outputId": "871577f3-5bb6-4e6f-8373-40a9825cae8b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007   jan\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007   feb\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007   mar\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007   apr\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007   may"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get month from date column\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a057643",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "5a057643",
    "outputId": "e578c9d1-3eab-48ac-9416-31d667253255"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007    01\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007    02\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007    03\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007    04\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007    05"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc537bef",
   "metadata": {
    "id": "cc537bef"
   },
   "outputs": [],
   "source": [
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6278d6b",
   "metadata": {
    "id": "a6278d6b"
   },
   "source": [
    "## Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07225e1f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "07225e1f",
    "outputId": "11673934-0487-4a0b-e228-b0fd416e818f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin\n",
       "2007/w01               1\n",
       "2007/w02               0\n",
       "2007/w03               0\n",
       "2007/w04               0\n",
       "2007/w05               0\n",
       "...                  ...\n",
       "2018/w48              28\n",
       "2018/w49              12\n",
       "2018/w50              27\n",
       "2018/w51              17\n",
       "2018/w52              17\n",
       "\n",
       "[624 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take columns of Dengue cases from Medellin data\n",
    "x_cases_medellin = data_medellin[data_medellin.columns[343:-52]]\n",
    "# convert to Row\n",
    "x_cases_transposed_medellin = x_cases_medellin.T\n",
    "# Rename Column\n",
    "x_cases_transposed_medellin = x_cases_transposed_medellin.rename(columns={62:'cases_medellin'})  ##integer\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OufkCUmBepAo",
   "metadata": {
    "id": "OufkCUmBepAo"
   },
   "source": [
    "## Date of Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbbdafe2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "bbbdafe2",
    "outputId": "7fe18381-f59b-4c02-8da1-59d87082966c"
   },
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2007/w05</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA\n",
       "2007/w01               1  [2007, w01]\n",
       "2007/w02               0  [2007, w02]\n",
       "2007/w03               0  [2007, w03]\n",
       "2007/w04               0  [2007, w04]\n",
       "2007/w05               0  [2007, w05]\n",
       "...                  ...          ...\n",
       "2018/w48              28  [2018, w48]\n",
       "2018/w49              12  [2018, w49]\n",
       "2018/w50              27  [2018, w50]\n",
       "2018/w51              17  [2018, w51]\n",
       "2018/w52              17  [2018, w52]\n",
       "\n",
       "[624 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
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    }
   ],
   "source": [
    "# Create new Column\n",
    "x_cases_transposed_medellin['DATA'] = x_cases_transposed_medellin.index.str.split('/')  #index column split function though works on string only -> yyyy week\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f29230d",
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     "height": 419
    },
    "id": "9f29230d",
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   "outputs": [
    {
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       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
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       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
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       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
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       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR\n",
       "2007/w01               1  [2007, w01]  2007\n",
       "2007/w02               0  [2007, w02]  2007\n",
       "2007/w03               0  [2007, w03]  2007\n",
       "2007/w04               0  [2007, w04]  2007\n",
       "2007/w05               0  [2007, w05]  2007\n",
       "...                  ...          ...   ...\n",
       "2018/w48              28  [2018, w48]  2018\n",
       "2018/w49              12  [2018, w49]  2018\n",
       "2018/w50              27  [2018, w50]  2018\n",
       "2018/w51              17  [2018, w51]  2018\n",
       "2018/w52              17  [2018, w52]  2018\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take year\n",
    "x_cases_transposed_medellin['YEAR'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0])\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6192112f",
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    "id": "6192112f",
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    {
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       "    <tr>\n",
       "      <th>2018/w52</th>\n",
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       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
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       "<p>624 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR YearWeek\n",
       "2007/w01               1  [2007, w01]  2007   200701\n",
       "2007/w02               0  [2007, w02]  2007   200702\n",
       "2007/w03               0  [2007, w03]  2007   200703\n",
       "2007/w04               0  [2007, w04]  2007   200704\n",
       "2007/w05               0  [2007, w05]  2007   200705\n",
       "...                  ...          ...   ...      ...\n",
       "2018/w48              28  [2018, w48]  2018   201848\n",
       "2018/w49              12  [2018, w49]  2018   201849\n",
       "2018/w50              27  [2018, w50]  2018   201850\n",
       "2018/w51              17  [2018, w51]  2018   201851\n",
       "2018/w52              17  [2018, w52]  2018   201852\n",
       "\n",
       "[624 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to format year + week \n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0] + x[1].split('w')[1])  ##concate year and week index 0 and 1 in a list and apply to all rows\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "97b7b350",
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     "height": 204
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    "id": "97b7b350",
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    {
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      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701\n",
       "2007/w02               0  [2007, w02]  2007    200702\n",
       "2007/w03               0  [2007, w03]  2007    200703\n",
       "2007/w04               0  [2007, w04]  2007    200704\n",
       "2007/w05               0  [2007, w05]  2007    200705"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#YearWeekInt --> changed to int\n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['YearWeek'].astype(int)\n",
    "x_cases_transposed_medellin.head()  #integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fe0fc8c",
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       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03\n",
       "...                  ...          ...   ...       ...         ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29\n",
       "\n",
       "[624 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cases_transposed_medellin['LastDayWeek'] = pd.to_datetime((x_cases_transposed_medellin['YearWeek']-1).astype(str) + \"6\" , format=\"%Y%U%w\")   # 200701 + 6 -> yearweeknumber + Lastday of week \n",
    "##-1 -> our data weeknumber e.g. Yearweek 200701 yearweek starts from 01 as week 1 and datatime library starts from 00 as week 1 -> 1 -52 weeks data -> 0-51 weeks datelibrary\n",
    "## %Uweek number 0 51 = 1 52  \n",
    "##%w last day of week    This is taken from epiweek (1st epi week of 2007 starts 31-Dec-2016 ends 6-Jan-2017) \n",
    "##Library takes in yearweeknumber + weekday  -> the date form the last day of the week \n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0eb238f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "a0eb238f",
    "outputId": "0330f8f4-5b98-432a-c697-9912f12e74c3"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "      <th>DATA</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>YearWeek</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
       "      <td>1</td>\n",
       "      <td>[2007, w01]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200701</td>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w02</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200702</td>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200703</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek  MONTH\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06      1\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13      1\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20      1\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27      1\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03      2\n",
       "...                  ...          ...   ...       ...         ...    ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01     12\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08     12\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15     12\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22     12\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29     12\n",
       "\n",
       "[624 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take month to append and duplicate temperature and precipitation\n",
    "x_cases_transposed_medellin['MONTH'] = pd.DatetimeIndex(x_cases_transposed_medellin['LastDayWeek']).month  \n",
    "x_cases_transposed_medellin    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd0749",
   "metadata": {
    "id": "b3cd0749"
   },
   "source": [
    "### JOIN TEMPERATURE AND CASES AND PRECIPITATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "342a0e49",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "342a0e49",
    "outputId": "d66b0521-7281-4867-ae86-300d01bea61c"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "      <th>DATA</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>YearWeek</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[2007, w01]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200701</td>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200702</td>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200703</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_medellin         DATA  YEAR  YearWeek LastDayWeek  MONTH  \\\n",
       "0                 1  [2007, w01]  2007    200701  2007-01-06      1   \n",
       "1                 0  [2007, w02]  2007    200702  2007-01-13      1   \n",
       "2                 0  [2007, w03]  2007    200703  2007-01-20      1   \n",
       "3                 0  [2007, w04]  2007    200704  2007-01-27      1   \n",
       "4                 0  [2007, w05]  2007    200705  2007-02-03      2   \n",
       "..              ...          ...   ...       ...         ...    ...   \n",
       "619              28  [2018, w48]  2018    201848  2018-12-01     12   \n",
       "620              12  [2018, w49]  2018    201849  2018-12-08     12   \n",
       "621              27  [2018, w50]  2018    201850  2018-12-15     12   \n",
       "622              17  [2018, w51]  2018    201851  2018-12-22     12   \n",
       "623              17  [2018, w52]  2018    201852  2018-12-29     12   \n",
       "\n",
       "     temperature_medellin                    DATE  \n",
       "0               21.009283  [TEMPERATURE, jan, 07]  \n",
       "1               21.009283  [TEMPERATURE, jan, 07]  \n",
       "2               21.009283  [TEMPERATURE, jan, 07]  \n",
       "3               21.009283  [TEMPERATURE, jan, 07]  \n",
       "4               20.982925  [TEMPERATURE, feb, 07]  \n",
       "..                    ...                     ...  \n",
       "619             20.936353  [TEMPERATURE, dec, 18]  \n",
       "620             20.936353  [TEMPERATURE, dec, 18]  \n",
       "621             20.936353  [TEMPERATURE, dec, 18]  \n",
       "622             20.936353  [TEMPERATURE, dec, 18]  \n",
       "623             20.936353  [TEMPERATURE, dec, 18]  \n",
       "\n",
       "[624 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Marge and duplicate temperature data\n",
    "merge_cases_temp = pd.merge(x_cases_transposed_medellin,x_temperature_medellin_transposed, on=['MONTH','YEAR'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp    ##624  # 143 -> 144 1 month is missing -> DEC!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e83e9b84",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "e83e9b84",
    "outputId": "41e75c83-1a6b-407a-9efc-a770af19f25d"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "      <th>DATA</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>YearWeek</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE_x</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>[2007, w01]</td>\n",
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       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200702</td>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200703</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_medellin         DATA  YEAR  YearWeek LastDayWeek  MONTH  \\\n",
       "0                 1  [2007, w01]  2007    200701  2007-01-06      1   \n",
       "1                 0  [2007, w02]  2007    200702  2007-01-13      1   \n",
       "2                 0  [2007, w03]  2007    200703  2007-01-20      1   \n",
       "3                 0  [2007, w04]  2007    200704  2007-01-27      1   \n",
       "4                 0  [2007, w05]  2007    200705  2007-02-03      2   \n",
       "..              ...          ...   ...       ...         ...    ...   \n",
       "619              28  [2018, w48]  2018    201848  2018-12-01     12   \n",
       "620              12  [2018, w49]  2018    201849  2018-12-08     12   \n",
       "621              27  [2018, w50]  2018    201850  2018-12-15     12   \n",
       "622              17  [2018, w51]  2018    201851  2018-12-22     12   \n",
       "623              17  [2018, w52]  2018    201852  2018-12-29     12   \n",
       "\n",
       "     temperature_medellin                  DATE_x  percipitation_medellin  \\\n",
       "0               21.009283  [TEMPERATURE, jan, 07]               65.411400   \n",
       "1               21.009283  [TEMPERATURE, jan, 07]               65.411400   \n",
       "2               21.009283  [TEMPERATURE, jan, 07]               65.411400   \n",
       "3               21.009283  [TEMPERATURE, jan, 07]               65.411400   \n",
       "4               20.982925  [TEMPERATURE, feb, 07]               49.307404   \n",
       "..                    ...                     ...                     ...   \n",
       "619             20.936353  [TEMPERATURE, dec, 18]               67.528000   \n",
       "620             20.936353  [TEMPERATURE, dec, 18]               67.528000   \n",
       "621             20.936353  [TEMPERATURE, dec, 18]               67.528000   \n",
       "622             20.936353  [TEMPERATURE, dec, 18]               67.528000   \n",
       "623             20.936353  [TEMPERATURE, dec, 18]               67.528000   \n",
       "\n",
       "                       DATE_y  \n",
       "0    [PRECIPITATION, jan, 07]  \n",
       "1    [PRECIPITATION, jan, 07]  \n",
       "2    [PRECIPITATION, jan, 07]  \n",
       "3    [PRECIPITATION, jan, 07]  \n",
       "4    [PRECIPITATION, feb, 07]  \n",
       "..                        ...  \n",
       "619  [PRECIPITATION, dec, 18]  \n",
       "620  [PRECIPITATION, dec, 18]  \n",
       "621  [PRECIPITATION, dec, 18]  \n",
       "622  [PRECIPITATION, dec, 18]  \n",
       "623  [PRECIPITATION, dec, 18]  \n",
       "\n",
       "[624 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Marge and duplicate precipitaton data\n",
    "merge_cases_temp_precip = pd.merge(merge_cases_temp,x_precipitation_medellin_transposed, on=['MONTH','YEAR'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp_precip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rmD3b1f0hCEv",
   "metadata": {
    "id": "rmD3b1f0hCEv"
   },
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6sN7n26phI-E",
   "metadata": {
    "id": "6sN7n26phI-E"
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d55f16d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "id": "6d55f16d",
    "outputId": "9a4e234d-05aa-4ccd-c45a-029e465e3bdf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dengue cases in time\n",
    "def timeseries (x_axis, y_axis, x_label):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(x_axis, y_axis, color ='black')\n",
    "    plt.xlabel(x_label) \n",
    "    plt.ylabel('Dengue Cases')\n",
    "\n",
    "timeseries(merge_cases_temp_precip['LastDayWeek'], merge_cases_temp_precip['cases_medellin'], 'cases by Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be7476a",
   "metadata": {
    "id": "3be7476a"
   },
   "source": [
    "## DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b3934515",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "b3934515",
    "outputId": "502dcf26-d90d-4f33-e0aa-5f81d9e73473"
   },
   "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>temperature_medellin</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>cases_medellin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-13</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-20</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-27</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>65.411400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-03</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-08</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-15</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-22</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>67.528000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             temperature_medellin  percipitation_medellin  cases_medellin\n",
       "LastDayWeek                                                              \n",
       "2007-01-06              21.009283               65.411400               1\n",
       "2007-01-13              21.009283               65.411400               0\n",
       "2007-01-20              21.009283               65.411400               0\n",
       "2007-01-27              21.009283               65.411400               0\n",
       "2007-02-03              20.982925               49.307404               0\n",
       "...                           ...                     ...             ...\n",
       "2018-12-01              20.936353               67.528000              28\n",
       "2018-12-08              20.936353               67.528000              12\n",
       "2018-12-15              20.936353               67.528000              27\n",
       "2018-12-22              20.936353               67.528000              17\n",
       "2018-12-29              20.936353               67.528000              17\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = merge_cases_temp_precip[['temperature_medellin','percipitation_medellin','cases_medellin']]\n",
    "dataset.index = merge_cases_temp_precip.LastDayWeek\n",
    "dataset  #DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JeddvRFhkTnY",
   "metadata": {
    "id": "JeddvRFhkTnY"
   },
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5CBBZKbSheUK",
   "metadata": {
    "id": "5CBBZKbSheUK"
   },
   "source": [
    "we will use:\n",
    "* data: is the dataframe in our case (Dengue Cases, Precipitation and Temperature)\n",
    "* n_in: is the number of lag weeks in the past (length of window)\n",
    "\n",
    "The heart of this \"series_to_supervised\" function is the <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shift.html\">shift</a> fuction of pandas\n",
    "\n",
    "This function gets as input the number of periods(in this case the number of weeks represented as rows up or down in the dataframe) to move the columns of a dataframe.\n",
    "E.g. \n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(1) all the rows of column cases_medellin will move one row down\n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(-1) all the rows of column cases_medellin will move one row up\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "BH5JesTNkW1V",
   "metadata": {
    "id": "BH5JesTNkW1V"
   },
   "outputs": [],
   "source": [
    "# prepare data for lstm\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2VXhQkvilIxq",
   "metadata": {
    "id": "2VXhQkvilIxq"
   },
   "source": [
    "### normalize features\n",
    "As we are working with a Neural Network the data values ​​must be normalized to help backpropagation algorithm\n",
    "So we will use the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">MinMaxScaler</a> from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "CAsgBZzClOoI",
   "metadata": {
    "id": "CAsgBZzClOoI"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) # Scaler between 0 and 1\n",
    "scaled = scaler.fit_transform(dataset) # As we can see data set has 3 Columns (This shape is also important for inverse scaler as we will see in future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "gq1acAcggdew",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "gq1acAcggdew",
    "outputId": "5d6748af-7d5c-4e42-ed07-28b8e34050c7"
   },
   "outputs": [
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       "    var1(t-11)  var2(t-11)  var3(t-11)  var1(t-10)  var2(t-10)  var3(t-10)  \\\n",
       "11    0.364241     0.04629     0.00122    0.364241     0.04629         0.0   \n",
       "12    0.364241     0.04629     0.00000    0.364241     0.04629         0.0   \n",
       "13    0.364241     0.04629     0.00000    0.364241     0.04629         0.0   \n",
       "14    0.364241     0.04629     0.00000    0.356053     0.03042         0.0   \n",
       "15    0.356053     0.03042     0.00000    0.356053     0.03042         0.0   \n",
       "\n",
       "    var1(t-9)  var2(t-9)  var3(t-9)  var1(t-8)  ...  var3(t-3)  var1(t-2)  \\\n",
       "11   0.364241    0.04629        0.0   0.364241  ...        0.0   0.493659   \n",
       "12   0.364241    0.04629        0.0   0.356053  ...        0.0   0.493659   \n",
       "13   0.356053    0.03042        0.0   0.356053  ...        0.0   0.493659   \n",
       "14   0.356053    0.03042        0.0   0.356053  ...        0.0   0.493659   \n",
       "15   0.356053    0.03042        0.0   0.356053  ...        0.0   0.434987   \n",
       "\n",
       "    var2(t-2)  var3(t-2)  var1(t-1)  var2(t-1)  var3(t-1)   var1(t)   var2(t)  \\\n",
       "11   0.174440    0.00000   0.493659   0.174440    0.00000  0.493659  0.174440   \n",
       "12   0.174440    0.00000   0.493659   0.174440    0.00000  0.493659  0.174440   \n",
       "13   0.174440    0.00000   0.493659   0.174440    0.00000  0.434987  0.269512   \n",
       "14   0.174440    0.00000   0.434987   0.269512    0.00122  0.434987  0.269512   \n",
       "15   0.269512    0.00122   0.434987   0.269512    0.00000  0.434987  0.269512   \n",
       "\n",
       "    var3(t)  \n",
       "11  0.00000  \n",
       "12  0.00000  \n",
       "13  0.00122  \n",
       "14  0.00000  \n",
       "15  0.00000  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of window\n",
    "weeks = 11\n",
    "\n",
    "# frame as supervised learning\n",
    "data = series_to_supervised(scaled, n_in=weeks)\n",
    "DataFrame(data).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Uss0IPaQqnWB",
   "metadata": {
    "id": "Uss0IPaQqnWB"
   },
   "source": [
    "## Features Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eWFelunOgdml",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eWFelunOgdml",
    "outputId": "bdf0e46a-3505-4e1b-b9ec-65c032128bd2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.36424094, 0.04628976, 0.00121951, ..., 0.49365894, 0.17444019,\n",
       "        0.        ],\n",
       "       [0.36424094, 0.04628976, 0.        , ..., 0.49365894, 0.17444019,\n",
       "        0.        ],\n",
       "       [0.36424094, 0.04628976, 0.        , ..., 0.49365894, 0.17444019,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.46920936, 0.27243065, 0.02682927, ..., 0.34158643, 0.04837559,\n",
       "        0.01463415],\n",
       "       [0.28320738, 0.27958775, 0.03292683, ..., 0.34158643, 0.04837559,\n",
       "        0.03292683],\n",
       "       [0.28320738, 0.27958775, 0.02804878, ..., 0.34158643, 0.04837559,\n",
       "        0.02073171]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We define the number of features as 3 (Temperature, Precipitation and Dengue Cases)\n",
    "n_features = 3\n",
    "# The features to train the model will be all except the values of the actual week \n",
    "# We can't use the temperature and precipitation in week t because whe need to resample a a 3D Array\n",
    "features_set = DataFrame(data.values[:,:-n_features])\n",
    "# Convert pandas data frame to np.array to reshape as 3D Array\n",
    "features_set = features_set.to_numpy()\n",
    "features_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ojvAeBMPr3ol",
   "metadata": {
    "id": "ojvAeBMPr3ol"
   },
   "source": [
    "## Labels Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "q4jhxnI5gdqz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q4jhxnI5gdqz",
    "outputId": "615b98af-ab47-4e36-aacf-16ca8ee48318"
   },
   "outputs": [
    {
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       "       [0.03536585],\n",
       "       [0.03292683],\n",
       "       [0.03292683],\n",
       "       [0.02926829],\n",
       "       [0.03292683],\n",
       "       [0.02682927],\n",
       "       [0.03414634],\n",
       "       [0.02560976],\n",
       "       [0.03658537],\n",
       "       [0.01707317],\n",
       "       [0.02682927],\n",
       "       [0.02317073],\n",
       "       [0.0195122 ],\n",
       "       [0.04268293],\n",
       "       [0.03170732],\n",
       "       [0.03170732],\n",
       "       [0.03658537],\n",
       "       [0.02682927],\n",
       "       [0.02804878],\n",
       "       [0.02560976],\n",
       "       [0.0304878 ],\n",
       "       [0.02682927],\n",
       "       [0.01707317],\n",
       "       [0.02926829],\n",
       "       [0.02439024],\n",
       "       [0.02926829],\n",
       "       [0.02682927],\n",
       "       [0.02682927],\n",
       "       [0.02439024],\n",
       "       [0.02926829],\n",
       "       [0.02439024],\n",
       "       [0.02560976],\n",
       "       [0.02073171],\n",
       "       [0.02317073],\n",
       "       [0.02804878],\n",
       "       [0.02682927],\n",
       "       [0.02804878],\n",
       "       [0.02804878],\n",
       "       [0.03292683],\n",
       "       [0.04268293],\n",
       "       [0.02682927],\n",
       "       [0.02317073],\n",
       "       [0.02439024],\n",
       "       [0.02682927],\n",
       "       [0.02195122],\n",
       "       [0.0304878 ],\n",
       "       [0.03292683],\n",
       "       [0.0304878 ],\n",
       "       [0.01463415],\n",
       "       [0.02682927],\n",
       "       [0.02682927],\n",
       "       [0.03292683],\n",
       "       [0.02804878],\n",
       "       [0.02926829],\n",
       "       [0.02926829],\n",
       "       [0.02073171],\n",
       "       [0.02439024],\n",
       "       [0.01829268],\n",
       "       [0.05      ],\n",
       "       [0.03414634],\n",
       "       [0.01463415],\n",
       "       [0.03292683],\n",
       "       [0.02073171],\n",
       "       [0.02073171]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use Dengue cases in last week \n",
    "labels_set = DataFrame(data.values[:,-1])\n",
    "# Convert pandas data frame to np.array\n",
    "labels_set = labels_set.to_numpy()\n",
    "labels_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RlwXfwaLsHza",
   "metadata": {
    "id": "RlwXfwaLsHza"
   },
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "-sb-9LCMgdwr",
   "metadata": {
    "id": "-sb-9LCMgdwr"
   },
   "outputs": [],
   "source": [
    "# We need a sequence so we can't split randomly\n",
    "# To divide into Train (90%) and test (10%) to do that we need to know the 90% of the total dataframe\n",
    "size = features_set.shape[0]\n",
    "split = int(size*(9/10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_2LNWDMusZsl",
   "metadata": {
    "id": "_2LNWDMusZsl"
   },
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "FJan_zurgd1q",
   "metadata": {
    "id": "FJan_zurgd1q"
   },
   "outputs": [],
   "source": [
    "# We will train with 1st 90% of data and test with last 10%\n",
    "train_X = features_set[:split] ##90% train\n",
    "train_y = labels_set[:split]  ##90% train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t17j8OtzemAG",
   "metadata": {
    "id": "t17j8OtzemAG"
   },
   "source": [
    "### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "DS6rGhy5gd7B",
   "metadata": {
    "id": "DS6rGhy5gd7B"
   },
   "outputs": [],
   "source": [
    "test_X = features_set[split:] ##10% test\n",
    "test_y = labels_set[split:] ##10% test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UExWrkq9Ib_J",
   "metadata": {
    "id": "UExWrkq9Ib_J"
   },
   "source": [
    "## Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "G1xmvWGQgeDB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G1xmvWGQgeDB",
    "outputId": "7a5bd65c-c948-4c02-f503-77dd9a47b8d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(551, 11, 3) (551, 1) (62, 11, 3) (62, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], weeks, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], weeks, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac223af3",
   "metadata": {
    "id": "ac223af3"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "h5ZtewNE5vXX",
   "metadata": {
    "id": "h5ZtewNE5vXX"
   },
   "source": [
    "### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "wPD_nuIfgeK9",
   "metadata": {
    "id": "wPD_nuIfgeK9"
   },
   "outputs": [],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "CRKoVYEqmcav",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CRKoVYEqmcav",
    "outputId": "7456c791-233d-4e12-9517-20ba61b6f50d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 551 samples, validate on 62 samples\n",
      "Epoch 1/50\n",
      "551/551 - 3s - loss: 0.1095 - val_loss: 0.0224\n",
      "Epoch 2/50\n",
      "551/551 - 0s - loss: 0.0931 - val_loss: 0.0340\n",
      "Epoch 3/50\n",
      "551/551 - 0s - loss: 0.0836 - val_loss: 0.0145\n",
      "Epoch 4/50\n",
      "551/551 - 0s - loss: 0.0738 - val_loss: 0.0139\n",
      "Epoch 5/50\n",
      "551/551 - 0s - loss: 0.0638 - val_loss: 0.0159\n",
      "Epoch 6/50\n",
      "551/551 - 0s - loss: 0.0533 - val_loss: 0.0094\n",
      "Epoch 7/50\n",
      "551/551 - 0s - loss: 0.0412 - val_loss: 0.0081\n",
      "Epoch 8/50\n",
      "551/551 - 0s - loss: 0.0349 - val_loss: 0.0080\n",
      "Epoch 9/50\n",
      "551/551 - 0s - loss: 0.0340 - val_loss: 0.0066\n",
      "Epoch 10/50\n",
      "551/551 - 0s - loss: 0.0330 - val_loss: 0.0064\n",
      "Epoch 11/50\n",
      "551/551 - 0s - loss: 0.0319 - val_loss: 0.0082\n",
      "Epoch 12/50\n",
      "551/551 - 0s - loss: 0.0313 - val_loss: 0.0070\n",
      "Epoch 13/50\n",
      "551/551 - 0s - loss: 0.0302 - val_loss: 0.0080\n",
      "Epoch 14/50\n",
      "551/551 - 0s - loss: 0.0300 - val_loss: 0.0058\n",
      "Epoch 15/50\n",
      "551/551 - 0s - loss: 0.0300 - val_loss: 0.0108\n",
      "Epoch 16/50\n",
      "551/551 - 0s - loss: 0.0297 - val_loss: 0.0070\n",
      "Epoch 17/50\n",
      "551/551 - 0s - loss: 0.0280 - val_loss: 0.0100\n",
      "Epoch 18/50\n",
      "551/551 - 0s - loss: 0.0283 - val_loss: 0.0058\n",
      "Epoch 19/50\n",
      "551/551 - 0s - loss: 0.0286 - val_loss: 0.0119\n",
      "Epoch 20/50\n",
      "551/551 - 0s - loss: 0.0280 - val_loss: 0.0081\n",
      "Epoch 21/50\n",
      "551/551 - 0s - loss: 0.0271 - val_loss: 0.0128\n",
      "Epoch 22/50\n",
      "551/551 - 0s - loss: 0.0271 - val_loss: 0.0062\n",
      "Epoch 23/50\n",
      "551/551 - 0s - loss: 0.0271 - val_loss: 0.0081\n",
      "Epoch 24/50\n",
      "551/551 - 0s - loss: 0.0257 - val_loss: 0.0089\n",
      "Epoch 25/50\n",
      "551/551 - 0s - loss: 0.0256 - val_loss: 0.0105\n",
      "Epoch 26/50\n",
      "551/551 - 0s - loss: 0.0252 - val_loss: 0.0063\n",
      "Epoch 27/50\n",
      "551/551 - 0s - loss: 0.0259 - val_loss: 0.0074\n",
      "Epoch 28/50\n",
      "551/551 - 0s - loss: 0.0241 - val_loss: 0.0093\n",
      "Epoch 29/50\n",
      "551/551 - 0s - loss: 0.0247 - val_loss: 0.0097\n",
      "Epoch 30/50\n",
      "551/551 - 0s - loss: 0.0240 - val_loss: 0.0076\n",
      "Epoch 31/50\n",
      "551/551 - 0s - loss: 0.0249 - val_loss: 0.0067\n",
      "Epoch 32/50\n",
      "551/551 - 0s - loss: 0.0232 - val_loss: 0.0124\n",
      "Epoch 33/50\n",
      "551/551 - 0s - loss: 0.0244 - val_loss: 0.0083\n",
      "Epoch 34/50\n",
      "551/551 - 0s - loss: 0.0239 - val_loss: 0.0134\n",
      "Epoch 35/50\n",
      "551/551 - 0s - loss: 0.0249 - val_loss: 0.0069\n",
      "Epoch 36/50\n",
      "551/551 - 0s - loss: 0.0237 - val_loss: 0.0195\n",
      "Epoch 37/50\n",
      "551/551 - 0s - loss: 0.0255 - val_loss: 0.0119\n",
      "Epoch 38/50\n",
      "551/551 - 0s - loss: 0.0271 - val_loss: 0.0110\n",
      "Epoch 39/50\n",
      "551/551 - 0s - loss: 0.0245 - val_loss: 0.0224\n",
      "Epoch 40/50\n",
      "551/551 - 0s - loss: 0.0293 - val_loss: 0.0070\n",
      "Epoch 41/50\n",
      "551/551 - 0s - loss: 0.0230 - val_loss: 0.0167\n",
      "Epoch 42/50\n",
      "551/551 - 0s - loss: 0.0269 - val_loss: 0.0083\n",
      "Epoch 43/50\n",
      "551/551 - 0s - loss: 0.0218 - val_loss: 0.0110\n",
      "Epoch 44/50\n",
      "551/551 - 0s - loss: 0.0211 - val_loss: 0.0061\n",
      "Epoch 45/50\n",
      "551/551 - 0s - loss: 0.0215 - val_loss: 0.0083\n",
      "Epoch 46/50\n",
      "551/551 - 0s - loss: 0.0209 - val_loss: 0.0125\n",
      "Epoch 47/50\n",
      "551/551 - 0s - loss: 0.0222 - val_loss: 0.0071\n",
      "Epoch 48/50\n",
      "551/551 - 0s - loss: 0.0214 - val_loss: 0.0157\n",
      "Epoch 49/50\n",
      "551/551 - 0s - loss: 0.0240 - val_loss: 0.0102\n",
      "Epoch 50/50\n",
      "551/551 - 0s - loss: 0.0217 - val_loss: 0.0103\n"
     ]
    }
   ],
   "source": [
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9yqmdQXsmcfH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "id": "9yqmdQXsmcfH",
    "outputId": "a9ba6cb8-b3d3-4cbb-8c51-1f606d263912"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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alu+VWr0QovVzdWOsDzAa+IH98Xql1IS6GymlZiulUpRSKXl5eS4uUvP1i29Hh7AAydMLIdoERwJ9FtC51utO9mX1bmPPy4cB+Zja/2qt9Qmt9RlgITCk7i/QWr+htR6mtR4WHR198UfRwpRSTOwdy5q0E5RVtvlMlBDCwzkS6DcBPZRSiUopP+A2YEGdbRYAd9mf3wSs0GaegMVAf6VUkP0EcCWwxzlFd6/JfWMprazmu7QT7i6KEEI0qslAb8+5P4AJ2nuB+Vrr3Uqpp5RSM+ybvQVEKqXSgYeBx+zvPQW8gDlZbAO2aK2/dvpRuMGIxEhC/X1k8JQQotXzcWQjrfVCTNql9rInaj0vA25u4L3vY7pYehQ/HwvjkmNYvjeXapvGalHuLpIQQtRLRsY2w6Q+seSXVLBV5qgXQrRiEuibYVyvaHytMsmZEKJ1k0DfDO0CfLm8WyRL9uTIHPVCiFZLAn0zTe4Ty6ETJRzIK3Z3UYQQol4S6Jtpon2SM5n7RgjRWkmgb6YOYYH0jw+TPL0QotWSQO8Ek/vEsi3jNLmFZe4uihBCXEACvRNM6huL1rByX667iyKEEBeQQO8EvWJDiW3nzxqZDkEI0QpJoHcCpRSjkqL4Pv0ENpt0sxRCtC4S6J1kTI8oTp2pZPexQncXRQghziOB3klGJUUBsCa99c+nL4TwLhLonSQmNIDkuFCZtlgI0epIoHeiMT2iSDl8itIKuRmJEKL1kEDvRKN7RFNRbWPDoXx3F0UIIc6SQO9EwxMi8POxSPpGCNGqSKB3okA/K5clhPNdugR6IUTrIYHeyUYnRZOaXSTTIQghWg0J9E42pofpZim1eiFEayGB3sn6dGhHZLCf5OmFEK2GBHons1gUI5OiWJN+Qu46JYRoFSTQu8CYpCjyisrZl1Pk7qIIIYQEelcYXZOnl/SNEKIVkEDvAh3bB9I9OlimLRZCtAoS6F1kTI9oNhzKp7xKpkMQQriXBHoXGdMjirJKG5sPn3J3UYQQXk4CvYuM6BaJj0WxRvrTCyHcTAK9i4T4+zCkS7g0yAoh3E4CvQuN7hHFrmMFnCypcHdRhBBeTAK9C43pEYXW8L2kb4QQbuRQoFdKTVVK7VNKpSulHqtnvb9Sap59/QalVEKd9V2UUsVKqUecVO42YUCn9oQH+fLfrVnuLooQwos1GeiVUlZgLjAN6APcrpTqU2ezu4FTWusk4EXgmTrrXwAWNb+4bYvVorh7dCLLU3PZlnHa3cURQngpR2r0w4F0rfVBrXUF8DEws842M4F37c8/ASYopRSAUuo64BCw2yklbmN+NCqR8CBfXli6391FEUJ4KUcCfTyQUet1pn1ZvdtorauAAiBSKRUCPAo82dgvUErNVkqlKKVS8vLyHC17mxDi78O9V3Zn9f48Nh0+6e7iCCG8kKsbY/8IvKi1Lm5sI631G1rrYVrrYdHR0S4uUsu784oEokL8eX7JPncXRQjhhRwJ9FlA51qvO9mX1buNUsoHCAPygRHAs0qpw8BDwG+UUg80r8htT6CflZ9f1Z31B0+yVnrgCCFamCOBfhPQQymVqJTyA24DFtTZZgFwl/35TcAKbYzRWidorROAl4A/a61fcU7R25bbh3ehQ1gAzy/dL/PUCyFaVJOB3p5zfwBYDOwF5mutdyulnlJKzbBv9hYmJ58OPAxc0AXT2wX4Wvn5VUlsPnKKVfs9qx1CCNG6qdZWuxw2bJhOSUlxdzFcoqLKxvjnvyUi2I8vfj4Ke8ckIYRoNqXUZq31sPrWycjYFuTnY+GXE3qwI7OApXty3F0cIYSXkEDfwm4YHE9iVDAvLN2Pzda6rqaEEJ5JAn0L87FaeGhiD1Kzi1iw/Zi7iyOE8AIS6N3gmgEd6RUbykPztjH22ZU8+skOvtiWRW5hmbuLJoTwQD7uLkCL2PgmJF4J0T3dXRLAzIHz/k9H8NWOY6w9kM+iXceZl2IGH3ePDmZ0UhST+8YxPDECX6uci4UQzeP5vW5OHYaXB8Lw2TD9r87brxNV2zR7jhWy7uAJ1h3IZ93BfMoqbYQF+jKxdyxT+sYytmc0Ab5WdxdVCNFKNdbrxvNr9OnLzWPuXveWoxFWi6J/pzD6dwpj9tjulFZUszotj8W7slm6J5tPt2QS6GvlquRorhnQkfHJMRL0hRAO855An5fq3nJchEA/K1P6xjGlbxyV1TbWH8znm13ZLN6dw8Kd2QT7WZnUJ5YZgzoyOikaPx9J7wghGubZqZuqCng2EWzVUFUKcw5CcKRz9u0G1TbNhoP5LNh+jEW7sikorSQs0JepfeMY2jWc7jEhJMWEEBbo6+6iCiFamPembjI2QEUxDPsJpLxtavXBo9xdqktmtShGJkUxMimKp2b247v0PL7cfpyvdhw725gLEBPqT5I96F/eLVJSPUJ4Oc8O9OnLwOILw++xB/q9kNB2A31tfj4WxifHMj45lmqbJuPkGdJzi0nPKyYtxzx+tiWLf687QrCflcl945gxsCOje0RJTx4hvIyHB/rl0OVyiO4FfqGQ23by9BfDalEkRAWTEBXMRGLPLq+b6vl8axbtg3yZ1q8Do5IiaR/oR7tAH9oF+BIa4EO7QF85CQjhgTw30Bceh5ydMPGPoJQJ9m2oQdYZ6qZ61qTl8eX2Y3yxLYuPNh6t9z1+PhasSmFRYFEKpcBiUfhYFBHBfsSEBhAd6m9+QvyJaefPmB7RRAT7tfDRCSEc5bmB/sAK85g00TxGJ0PaYveVx838fCxM6B3LhN6xlFZUczi/hKKyKgpLKyksq6SwtJKisiqKK6rQGmw2jU2DTWu01lTaNPnF5eQVlZNypITcwnLKq2wABPlZuWtkArPHdCNcAr5opm92HadffBidwoPcXRSP4bmBPn0ZhMRCbD/zOiYZtr0PJfltuueNMwT6WendoV2z9qG1pri8ikMnSnhj9UFeW3WAf689zI9GJfCzMd1oH+RYwK+2aU6WVJBbVEZZZTXdokLkZOHFlu/N4d73tzCkS3s+vW+kTOXtJJ4Z6G3VpkaffLVJ2wBE9zaPbbznTWuhlCI0wJcBndrzyqwh/DKniJeXpTF35QHeXXuEH49KYGT3KE6fqeDkmQpOn6nkVIl5frKkgryicnKLyskvLqfuJJ6x7fzpFdeO5LhQesWG0ruDeW6xyJfek50qqeCxz3YS7Gdly9HTrNyXy/jk2KbfKJrkmYE+awuUnYakCeeWRfcyjx7U86Y16RkbytwfDOEX2YW8vCyNv69I5+8r0s/bJsjPSniQHxHBfsS1C6B/fNjZfH9MqD9+PhbSc4tJzS4i9XgR7xzMp8KeHooM9uPKXtGMT45hTI9oGSvggZ5YsJvTZyr49L6R/OKjrTy3eD/jesbICd4JPDPQpy8DZYFuV51bFtbJo3vetBbJce34xx1DSc8tJrewjPBgP8KD/Ggf5OtQX/7aNbiqahuH80vYmVXAqn15rEjN5bMtWVgtimFdwxmfHENMO38qqzWV1Taqah5tmuoG5vqPDvXn2gEdCfSTcQUt4WRJBTsyT3Nlz+hG0zBf7zjOl9uP8atJPRnQqT0PTezB/8zbzsJdx7lmQMcWLLFn8syRsW9OMCmbny67cLlvIPzoq+btX7hFtU2zLeMUK1JzWZGax97jhZe0n/AgX354eVd+eEUC0aH+Ti6lAMgrKufNNQd5b90RSiurmd4/jr/eNJBg/wvrlnlF5Ux+cRVdIoL49L6R+FgtVNs0U19aTbXWLHloLD6NdPt9ZUUay1Nzef/uEfXu31t418jYMychazOMq+f+5DHJsN97e960dVaLYmjXCIZ2jWDOlGRyi8o4U16Nj1Xha7XgY1H4WC34WhXWOpf7CvN6W8Zp3lxzkL+vTOe11Qe5cUg8Px3Tje7RIU4po9baqxsQswvKeH31AT7ccJTKahszBnaka2Qwf1+RxsG8Et744TC6RJ7rTaO15vHPdlJSUc3ztww8G9CtFsWvJvfk3ve38PnWLG4e1rne3/fp5kyeW7IfgOeX7OeJa/u4/iDbIM8L9AdWAPpct8raopNhq/S88RQxoQEQenHvGZ4YwfDECA7kFfPWd4f4ZHMmH23MYFRSJBHB/ueNH1CY8QNxYQEkRgXTNTKIhMhg2gf5ng3m2QVlbM88zY7M0+zILGBHZgHtg3z5n4k9mTGwo9fkl48XlDJ3ZTrzN2VSrTU3DI7n/quSSIwKBmBo13B+8dFWrn3lO+bOGsLoHlEAfLYli2V7c/jd1b1Jijn/nzmlbxz948N4aVkaMwZ1xN/n/HTbpsMneeyzHYzsHknXyCDeWXuImYM6MrBz+xY55rbE81I3n98H+xfBnANgqZOHTVsGH9wIP/oaEkY3r6DCI5woLue9dUf4Zlc2ldU2bLr2+AGorLaRV1xO7a9JuwAfukQGkVtoeg6BqYH2ig1lQKcwdmQWsOd4IclxocyZ0ovxyTFOq+VrrdmfU0xUiB+RIa5LO50qqaBdoO8FV0Z1FZdX8fqqA7yx+iA2rblpaGfuH9edzhEX9oE/kl/Cz/6dQnpuMb+Z3purB3Rg8our6R3Xjo9mX17v71q1P4+73t7IUzP7cucVCWeXH80/w3Wvfk9YoC+f3z8Si0Ux6YVVhAf58eUvRnvlCO/GUjeeFehtNni+FySOgZvevnB9QSa82Beufh4u+2nzCiq8RnlVNRknSzmSX8Lh/DMcyS/hSP4ZIoL9GNApjAGd2tO3Y7uzjc02m+arncd5fsk+juSfYVjXcB6dlsxlCRFU2zSHTpSQml1I6vEiUrMLyTxVSq+4UIZ0CWdo13CS40LPy0kXl1fxXVoeK1PzWLkvl9yicgJ9rfxkdAKzx3ZvsgdSaUU1B/KK6dg+kPBaVyO1VVTZSDlyklX78vh2Xx77coqICfXn+sHx3DCkE73izq9tV9s081MyeH7Jfk4UlzNjYEfmTOlVb4Cvrbi8ikfmb+eb3dlEBPtRWlHNNw+NoWtkcL3ba6259fX1HMovYfWcqwj0s1JYVsmNr64lt6icz+8fSTd72m3x7mzueW8zv57ai/vHJTVaDk/kPYH++A54fQxc9w8YNOvC9VrDXzrDwNvg6ueaV1AhmlBZbWPepgxeXp5GXlE53aKDyTpVenZEsdWi6B4dTIewQPYeLzx7dRDoa2Vg5zD6dQxjb3YhGw+dpLJaExrgw9ie0YztEcX36WYOo7BAX+4b1527rkg4rydRtU2z7kA+n23NZPGubEoqqgEI9feha1QQXSNMKioi2I+Nh07yffoJSiqq8bUqLkuI4PJukezILODbfblU2TT94ttxw+BOzBjUkT3HCvnzwr2kZhcxrGs4v726N4O7hDv8d7HZNHNXpvPisv386br+zBrRpdHtNx46yS2vr+PxacncPTqRu99N4fv0E/z77uGM7B513rb3vreZlftyWfzQWBKi6j95uJPWmqV7chjUuT0x7QKcum/vCfRrXoDlT8Kv9kFoXP3bSM8b0cJKK6p5Z+1hNhzKJyk6hGT7ALCkmJCzVwFaa7JOl7L5yCm2Hj3N5iOn2Hu8kMSoYMYnx3BVcgxDu4afl5LYfayA5xbvY+W+PGJC/fnlhB4M6tyeBfb5jHIKywkN8OHq/h0YlRRFblH52auRI/klZJ4qpcqmiW8fyJW9ohnXM5qRSVGE1Oq5kl9czpfbj/Hplix2ZhVgUWDT0CUiiMemJTOtX9wlp6VKyqsc7iVz19sb2Z55mql94/h4UwZ/uaE/tw+/8ASRU1jGxBdW0a9jGB/+bESrahgvLKvkkfnbWbInh/ZBvvzl+v5M69/Bafv3nkD/r6uhvADu/a7hbb74Oez7Bn594NJ+hxAtpNqmm8yRg6nxPvtNKilHTgHgY1GM6xXDDUPiG70XQVW1jVNnKokK8XMoIO7PKeLL7ceIDPbj9hFdLmgcdaWdmQVc+4r5Xt89OpHfX9Nw75oPNhzht5/v4tkbB3DLZfX31mlp+7KLuPf9zRw9eYZfjE9iRWouOzILuGFIPH+c0Zd2Ac0fAOgdgb6s0NxNauQvzIyVDVn7Ciz5rWmsDY5qeDsh2hCtNav255FdUMbkvnGtfzZRWzVs/hcMuA38Heva+scFuykur+KZGwc0egK02TS3vbGefTlFLHv4SpeOlbDZNGcqq8+7Cqrri21ZPPbpTkICfJg7awjDEyOorLbx9xXpzF2ZTly7AF64ZSAjujWvJ6B39KPP3QPKWn+3ytpiks1jXioES88b4RmUMrX4NuPASvj6V2YE+7CfOPSWP87o69B2Fovizzf0Z/rLa/jDgl08d/NAgvycG+pyi8r4ZHMm8zZlcCT/DD1jQxiRGMnl3SIZ0S2CqBB/Kqps/HnhXt5Ze5jLEsKZO2vI2by8r9XCw5N6Mq5XNA/P28Ztb65n9phuPDy5p0uulDwn0He5HB49DNYmajLR9kCfu1e6WArhLulLzWPWZocD/cVIignhF+OTeH7pfhbtyiYxKpg+HdrRp2M7endoR6/YUMqrbOQWlpFrn2Avz/4TGuBDj9gQkqJD6BEbevbqqNqmWZOWx8cbM1i2N4cqm2Z4YgQzB3ZkW2YBn27J5L31R87+fh+LIjW7iJ+MSuTx6cn1dvkc0iWchQ+O4U9f7+X11Qc5kFfMP++6zOl/D4cCvVJqKvAyYAX+qbV+us56f+DfwFAgH7hVa31YKTUJeBrwAyqAOVrrFU4s//n8HJi/ul08+LeDvH0uK4YQoglpNYF+q8t+xc+vSqJvfDszruFYIdsyTvPVjuMNbu9ntRAV4sfp0krO2HspAUQE+5EUHULW6VKyTpcSEezHT0Yncutlnc8bUV1ZbWNnVgEbDp5k/cF8jp48w8u3DWLmoPhGyxnk58Ofr+/PxN4xhPi7ZrK+JnP0SikrsB+YBGQCm4DbtdZ7am1zPzBAa32vUuo24Hqt9a1KqcFAjtb6mFKqH7BYa93oUTtlrpum/HMi+ARIzxsh3OHkQfjbYAjtAMU58FiGw3n65ioorST1eCH7c4sJ9rOevWNaTKj/2RHPNpvmeGEZaTlFpOcWc8B+H+aQAB9uHtqZSX1i8fNpfQOympujHw6ka60P2nf2MTAT2FNrm5nAH+3PPwFeUUoprXXt0/VuIFAp5a+1Lr/IY3Cu6F6m540QouWl2ScbHPUQfPMoHN/eYlOHhwX6MqJbZKMNnxaLIr59IPHtA9tWu0cjHDktxQMZtV5n2pfVu43WugooAOr+JW8EttQX5JVSs5VSKUqplLy8PEfLfumie8OZE1BywvW/SwhPsuhR+PaZ5u0jfSmEJ0L/m8zrrM3NL5doVItcfyil+gLPAPfUt15r/YbWepjWelh0dLTrC1S7540QwjGVpZDyL/juBTMx4CXtowwOrYEek0z35vZdJdC3AEcCfRZQe9RBJ/uyerdRSvkAYZhGWZRSnYDPgTu11q1jlFLNbQVz97q3HEK0JRkboLocqspgy7uXto8j30NVKSRNMq/jh5o7wgmXciTQbwJ6KKUSlVJ+wG3AgjrbLADusj+/CVihtdZKqfbA18BjWuvvnVTm5mvX0d7zRmr0Qjjs0GozViV+GGx6C6qrLn4f6cvA6n+ua3P8UCg4CsW5zi2rOE+Tgd6ec38AWAzsBeZrrXcrpZ5SSs2wb/YWEKmUSgceBmru+vEAkAQ8oZTaZv9xf+uGUqZBVrpYCuG4g6tMYB7zMBRmwr6vL34faUtNkK/pCh0/1DxKrd6lHOpHr7VeCCyss+yJWs/LgJvred+fgD81s4yuEZ0M+xa5uxRCtA1lBXBsC4z5FfScCu27wIbXoc9Mx/dx6jDkp8Fld59b1mGAuUo4tgV6TW38/Uc3ANoMjhQXpfV1Bm0pMZfY86a60jXlEaI1O7IWtA0Sx5ob+gyfbfLtx3c4vo+aQVI1+XkAv2DzXWyqQVZr+PSnMP+uS0sZeTnvDfTRvcyjow2yhcdg3g/h6a6Qt9915RKiNTq02gwy7DTcvB58B/gGwcbXHd9H+jIIT4DI7ucvjx9iAn1jgzeztthz+dnnpk9ozUpPw7InzZVQK+DFgd7e86apBllbNWx4A14ZDmlLQFfD6mddXz4hWpODq6DzCPC13ywjMNzcwGfHfxzrallZZk4WSZNMG1lt8UOh9BScOtTw+3d/BhZfCIqCLf++9ONoKZvt3VCX/6+7SwJ4c6Cv6XlzeI2prddXmzi+w0yXsGgOdL4M7l8HI+6BXZ9KrV54j+I8yN0N3a48f/nwe0x3yy3vNL2Po2uh8ozpP19XUw2yWsPu/0L38eZKYv9iKGx4zhq30xq2fmBm5kx56+LSWy7ivYFeKeg0DPZ8AS/0hr92h3/PhCW/N7WUxb+FN8ZBQQbc+Bbc8RlEdIORvzSXsKv/6u4jEKJlHF5tHhPrBPqYZLPMka6WaTXdKsdcuC66N/gENpynz0wxvXz6Xg9D7jRX1ds/vPjjaCkZG02j86T/hcAIWPiIuZ+1G3lvoAe47UP48Tcw7a/Qa7rJq214DT77Kax7BYb8EB7YZIZq11xuBkeZG4vv+gROpLu1+EI026kjTW9zaLW5+u0w6MJ1I+6FwixIbWKCwPSlZj6b+maYtfpAx0ENB/rdn5vpx3tNM/n9hDGw5T23B88GbX0PfINh6F0w6Ukz0GzHx24tkncHet9A6HoFjJgNM1+Be1bBb47BfWvh5xvh2pdNLrIuqdW3DhVnGm/AE43b+j68PAC2NxGEDq6CrqNMQK6r5xQzjcGGRhplTx2BE/vP721TV/xQk+Ko26vNZoM9/4XuEyCwvVk25E6Tzz/SyC1D3aWixJyY+l4P/qEwcBZ0ugyWPmEqkm7i3YG+PlZfiO17rldOfUKizc0Sds6XWr27nDoML/aBFa2jsatVyD8AR9c7tm1RjklPAnz7dMPdhk8fNUG1bn6+Rk1Xy6NrzSyU9anpJVNffr5Gx8FmaoS6veAyN5krhr7Xn1vW+1oICGvZRtnD3zs27mbPF1BRDIN/YF5bLDD9OdON+9u/uLaMjZBAf6lGPWhyjmuec3dJPEPpadj5iWOX47Zq+Pw+01Nj3asyfB6gqhzevxHeuRqOrGt6+28eNZOUTf6TCeQN1eoP1eTnxza8r5qulqv/Wv//Im2ZqfVHJjW8j7MNsnXSN7s/N9+zXtPOLfMNhAG3wp4FcOZkw/t0lpzd5m8774dNd8LY+r5py+tyxbllHQeZiuHGNyB7l0uL2hAJ9JcqJMaM8Nsx39SkRPN88zh8ejcsf7Lpbdf+3dQgx84xvT7W/t315WvtNr5hAnZAe/jPXY33SkldaALolXPgigdMbXr1s/XX6g+tNl0aY/o0vL/A9jD8Z7D3S3iuB/xjtElVHPwWygrt3SonXtitsrbwBNNwWTvQ16RtkiZCQLvztx9yp/nf7/xPw/t0hrJCE+AD2pn2hYWPNJwuzD9gBpENvuPCYx3/O/O/aez9LiSBvjlG/tI0Eq328Fq9zWZSArbqpre9FDl7YPtH5jaP37/U+CV59k5Y8Sdz+X7Vb6H/zbDpn959b4GSE7DqWegxGe76EsqLTLCvqrhw27JCc1PumL4w8kETkMb9xqRotn1w/rZam/x84tjGgzTAxCdh9iqY8IQJ/OteNb3Ynu0GlSWNp23A7L/uTJYZG6Do+Plpmxpx/c0JavO7DQfOvP2w/CkoL278dzdEa/jifpMmvPkdGP97OLTKdK+uz7YPTZfKgbdfuC4oAib+EY6ugx3zLq08zSCBvjlCY80l2Y55nl2rX/s3eHuKuemEK2ojK/7X9OqYvcr0lf7qf0xtsK7KMvjsHtNAfs3LJjiMecSkINbNdW6ZtDYph0/uNieX1mzln00j4OT/g9g+pmNBxgZY8tsLt13+pAmeM/4OPuam1/SYZGakXP3c+SeHE2lmJGpD+fnalDIpijG/MrfofPQwzJpvrnr7zIRu45reR/xQyNt7LjCfTds0MAfOkDtN//5j9fS/37cI3hwPa543VxeXYt0r5ipl0lPQdaT5rncYZNo2ygrP39ZWbQJ99wlmjE59Bv/QHOOS35vPd/pyc6e7vV+ak8f2eXDYNQ3MEuiba9SDpgF3zfPuLolrnEgzgSQ4Bja9abqfNqWyFL74uWN/k6PrYd9CGPVL08h98zsQ2QPm3Xnh7KIr/2S+2DPnQrD9BmbRPaHfDSZ14Yx8ra0adn0Gr4+FD2403Wg/muWaXLDWpp2h8Nil7yNnjxmFedlPzd8CoN+NJiWz8Q3Y9tG5bY+uN33eL78POg09t1wpuOpxM2Zk63vnlh9aZR4by883xD/E9MiZ9gzc8m+TV29K/FAzn87x7eb/sOcLcxLyD61/+343mbaBzbXmxrfZzNXNR7dBVBIM+oEZtFRfxaExh7+HpX+A3jPgip+bZRYrXPOCuc9t3YbVgyuh6JhJ2zSkpmH2zAlztfP+DfDRrTDvDvjkJ/D5bHN16gIOzV4pGlFTq9/wusklJl9zrqbU1tmqTcD2DYR7Vpv84jePm3xq7cax2soK4MPbTA4dTMNUfZfeYALdsj9CSKwJPmB6U8yaB/+cAB/cDD9bYcYuHFoDa1+BoT+GnpPP38/YOaZGtP4fML6eWqwjqspNg+T3L8PJA6bhcOZc8/juteaLeMen5st+KbJ3mjIWHjv/p6rUrB9wK0z5y7kTmCO0hsWPm6uhcY+dv27ikyZgfvWQqeVHJ8OCX0JYZ5Pyqqv7BDPFwZrnTbDy8TeBPqyLue1fS4gfYh5r8vTF2Q1/dsDkzfteb/6uU/4MaPj8XtOnf8BtcO1L5m+UsQG+eMB0m66b669PUTZ88mOISDSfgdppq/ihMOzH5vs+aJZJIYFphA0Mb/h7UfsYH0gx/3urr/3Hz0zvYPU1/0sXkBq9M4x6CMI6mQ/H871MiqMVDHtuto1vmi/JtGegXQe44Q1zef7JT+DYtgu3L841vT4yN8L1r5sJsL54oOF5/9OWmJzllb82sxjWCO8Kt39sak4fzzL7/e995os3uZ5Zr2N6m/TAhtcurq9y6WnY+xV8/Qi8PBC+/KWpid78rhlHMfgOMyXu9OdMjW3l/zm+7xpnTpqc+OtjTaPxkXWm0bPDAJPWmPJn8/nZ9RnMHW56HjmaHtu/2NRUxz1ucsC1WX3gpn+ZBs55d5ja6Yl9cM2L5hjrUgqu+o3pyrjl3+Ykf2iNY/l5ZwmOMtMfH9ti0jY+AWZK5MYMudN0Z/zuRTNdyb5F5oR5/WumguIXBDNfhYJMWPr7pstQXWU+3+VFcMt79Z8Yxv/etEN8/StzBXHmJKR+bU7WPv5N/47I7pA4xny24oeak0VMslke4ppbqSrdygacDBs2TKekpLi7GBfPVg0HVsK2980/vboCYvub/rQdBpoPnU+gefQNsn8Ig5v/JbLZTGCNcPKH5OQh+MdIc5OIWfPPlbMoG96cYIah/3Q5hNnvE3/qCLx3nVl/y3vQYyIUZJkAFxRpaua1A4ytGl4bY+Y/eWCTqc3Utfu/plExMALKTsNPlpg5h+qTvRNeG20aFsc9Wv821ZUmfXHwW/NzbItJFfgGmdGWI+4xbQT1/U8W/NLcPu/WD6D3NU3//WzVJmAuf8qU/bKfmfRIfQPwwKRgFjxgarM9p8HVz5/72zZ0LK9eDigzB1N9fz8w0wf8a5r5PPa/BW58s+F9ag3/mm5679z8Lrw9GW54Ewbc0vTxOst/fmTKXFVuAuGt7zW+vdYwd4Q5iQWGm9Rffe0BS35nTrQ//Nz8j+tjqzZXrBtfb/q4t35gGmpnzjUD9xbNgXvWmBO4myilNmuth9W7TgK9C5w5aS4nt30Ax7Y2vF37LjDiPlNzdOSSsq4DK2HZH8wlurJC0gQzo2Cv6RfmRG02OL7V1Hj2fWNqOtOeNTX0urQ26Yrj2+H+9RcGnJzd8NYUk8L5ySI4nWHyjZVnYNZ/oMuIc9seXGVOAH2ug5vePhdEt88zOckb3zJTTDRkzQumAXHsHNNFrTEf/8BMUvfQrgv/nunLzJf4xH7zt+o0zMzT0m2cGbnYVLqtqhzenmraLGavhKgeDW+bsREWzoHj28yI0mnPQly/xvcP9plSXzMzHlp9zfD5wXfWPyJ1/T/gm8fMSbjnlMb3u+0j074ya76pNTfm0Bp49xqI6mn+Vg+nmqu5lrL27yYog7ki6XdD0+/Z/bk5qV79grnqq09lqal0VJwxJ8a6n48T6SZwZ2ww0zpMe6bx32mzmRNofhoER5v0y71rmi6rC0mgd6cT6WZCpspSEwgrS81PRTHsX2Jy2f7tzCXoiHtM8G/K8R0mwB9YYXKoYx6G00dMn/7CLLO/PjNMnrKy1DR27v/G9LZQFjOYI/8AlOSZxuQrHz03/SxAyr9Mbvfav5n5OuqTtgw+vMXUunJ2m8vsH35mRhXXVROspz5tcvFV5fDKMNOvePYq00jVEK0hd4+Z+Kqx7cCkk9640lxaj33ELMs/YHpJ7F9k2gvG/84Mxb+UE2tBpgkWwdHw02XnNxKWnjKplN3/Nb8rtINJM/W78eKv2k4eMmmkQ6vBL9TMEZN4pUmjxPQxVwh/G2Qu++/4zPmplXeuMSfMqF7wwEbn7rspR9aaAOoTCL8+cH5Kr7kyU+CtSaZiNcM+9sJms59cnzSf4WnPmpq8I3/T7F3m86CrzXxZI2Y7r6yXQAJ9a5a12fQ53v25ed1nhukpEBRpAolfMPiFmMfCLNOHfMd802g5do7pbVETpG028wXdMe/cUGww70+aYGr6PSabfG7pKVNz2vq+qb3NeMXUxE9nwKtXmEajO79o/AO/6Z8mTxmeaC6JG6pNaW1q22mL4UdfmyuFRb82jZtJE533twT48FZTK7t/van1rn/V1LbGzjEnGUdyqI2puULpfa0JCqlfm8a/Q6vBVmXGAgyaZfLu9eXCHaW1ufpKW2L2fdLefTco0jRe56XCvd+bhlZnO/w9vDPdpJuubuExIhUl8JfOJj12iwumOFj6BzNW445Pzef2iwdMZavnVLjmpYu/elnyO0h5Bx7acWE7SQuTQN8WFGSalvzN70J5Q3elUSZQjbgXRv/PuUme6lNRYm7d5hdiGn4aCnDpy+HLB83vH3GvuVw/uh7uX2tSM005sALiBjSdEigrMNM+V5wxATGmtxnc4+zaaOZm+Od40/+6utxMKjXxDxAa57zf8f3L5/fNjuhuTtC9r4WOQ1zTeHk6w5zED602gbjfDSa14yo7/mP6jjfWTuAqqQvNlWF4V+fvu7LMXPWVnDBX2BZfmPa0GeR0Kf83m81cYbk5yIME+ralvMikICqKzcCRipqfErN+8B2mh4+zf+eyJ00eF1x3GZq9y/SMqCo1Dbmd6v1MNt9n95hU1uQ/ueZ3aG36aoMJ8NHJLdczRTRf1mZ4e5pJhc34W8MDnNoYCfTCMUfWmrTHyAebzodfqvTlkJ9u2iOEcJfyInO160En6MYCvQyYEud0HWl+XClpgvkRwp0aGm3roWTAlBBCeDgJ9EII4eEk0AshhIeTQC+EEB5OAr0QQng4CfRCCOHhJNALIYSHk0AvhBAertWNjFVK5QFHmrGLKMAb7xQtx+1d5Li9iyPH3VVrXe9NKVpdoG8upVRKQ8OAPZkct3eR4/YuzT1uSd0IIYSHk0AvhBAezhMD/RvuLoCbyHF7Fzlu79Ks4/a4HL0QQojzeWKNXgghRC0S6IUQwsN5TKBXSk1VSu1TSqUrpR5zd3lcRSn1tlIqVym1q9ayCKXUUqVUmv0x3J1ldAWlVGel1Eql1B6l1G6l1IP25R597EqpAKXURqXUdvtxP2lfnqiU2mD/vM9TSvm5u6yuoJSyKqW2KqW+sr/2luM+rJTaqZTappRKsS+75M+6RwR6pZQVmAtMA/oAtyul+ri3VC7zDjC1zrLHgOVa6x7AcvtrT1MF/Epr3Qe4HPi5/X/s6cdeDozXWg8EBgFTlVKXA88AL2qtk4BTwN3uK6JLPQjsrfXaW44b4Cqt9aBa/ecv+bPuEYEeGA6ka60Paq0rgI+BmW4uk0torVcDJ+ssngm8a3/+LnBdS5apJWitj2utt9ifF2G+/PF4+LFro9j+0tf+o4HxwCf25R533ABKqU7A1cA/7a8VXnDcjbjkz7qnBPp4IKPW60z7Mm8Rq7U+bn+eDcS6szCuppRKAAYDG/CCY7enL7YBucBS4ABwWmtdZd/EUz/vLwG/Bmz215F4x3GDOZkvUUptVkrNti+75M+63Bzcw2ittVLKY/vMKqVCgE+Bh7TWhaaSZ3jqsWutq4FBSqn2wOdAsntL5HpKqWuAXK31ZqXUODcXxx1Ga62zlFIxwFKlVGrtlRf7WfeUGn0W0LnW6072Zd4iRynVAcD+mOvm8riEUsoXE+Q/0Fp/Zl/sFccOoLU+DawErgDaK6VqKmqe+HkfBcxQSh3GpGLHAy/j+ccNgNY6y/6Yizm5D6cZn3VPCfSbgB72Fnk/4DZggZvL1JIWAHfZn98FfOHGsriEPT/7FrBXa/1CrVUefexKqWh7TR6lVCAwCdM+sRK4yb6Zxx231vpxrXUnrXUC5vu8Qmv9Azz8uAGUUsFKqdCa58BkYBfN+Kx7zMhYpdR0TE7PCryttf4/95bINZRSHwHjMNOW5gB/AP4LzAe6YKZ4vkVrXbfBtk1TSo0G1gA7OZez/Q0mT++xx66UGoBpeLNiKmbztdZPKaW6YWq6EcBW4A6tdbn7Suo69tTNI1rra7zhuO3H+Ln9pQ/wodb6/5RSkVziZ91jAr0QQoj6eUrqRgghRAMk0AshhIeTQC+EEB5OAr0QQng4CfRCCOHhJNALIYSHk0AvhBAe7v8BOi2WH0QYyekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "C21mhuRullNL",
   "metadata": {
    "id": "C21mhuRullNL"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "TtordubT422m",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TtordubT422m",
    "outputId": "a5ce4656-9708-4dc0-f91d-449d65e5b324"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5Ozj32Jwq4VL",
   "metadata": {
    "id": "5Ozj32Jwq4VL"
   },
   "outputs": [],
   "source": [
    "# Convert test data to 2D \n",
    "test_X = test_X.reshape((test_X.shape[0], weeks*n_features))\n",
    "\n",
    "# invert scaling for forecast\n",
    "# As we said Scaler needs 3 columns so we can take those columns from test data and take again the predictions\n",
    "# Concatenate last 2 columns of test data with predicted data (yhat)\n",
    "inv_yhat = concatenate((test_X[:, -(n_features-1):], yhat), axis=1)\n",
    "# Inverse Scaler\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "# Take predicted data scaled to original Dengue cases\n",
    "inv_yhat = inv_yhat[:,-1]\n",
    "\n",
    "# invert scaling for actual\n",
    "# Same process than for predicted data (yhat)\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_X[:, -(n_features-1):], test_y), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "TdDRHwvNllQR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TdDRHwvNllQR",
    "outputId": "9ff54530-94d6-445b-f9f1-8031c61362bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 8.438\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_absolute_error\n",
    "\n",
    "# calculate MAE\n",
    "mae = mean_absolute_error(inv_y, inv_yhat)\n",
    "print('Test MAE: %.3f' % mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JNxOr97IsIOe",
   "metadata": {
    "id": "JNxOr97IsIOe"
   },
   "source": [
    "#### Plot predicted vs actual dengue cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "nPOiCVoxsr-1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "nPOiCVoxsr-1",
    "outputId": "322e2a17-6461-461f-d03b-e61ddb2e8f88"
   },
   "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": [
    "data_predict = inv_yhat  ## predicted target  cases\n",
    "dataY_plot = inv_y  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "K9YPygMk5zdO",
   "metadata": {
    "id": "K9YPygMk5zdO"
   },
   "source": [
    "### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6bc62cc6",
   "metadata": {
    "id": "6bc62cc6"
   },
   "outputs": [],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "46i6dieT53R_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "46i6dieT53R_",
    "outputId": "244c1214-9ce5-482d-aeee-2c478d8f034c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 551 samples, validate on 62 samples\n",
      "Epoch 1/50\n",
      "551/551 - 3s - loss: 0.1215 - val_loss: 0.0247\n",
      "Epoch 2/50\n",
      "551/551 - 0s - loss: 0.0876 - val_loss: 0.0222\n",
      "Epoch 3/50\n",
      "551/551 - 0s - loss: 0.0762 - val_loss: 0.0272\n",
      "Epoch 4/50\n",
      "551/551 - 0s - loss: 0.0663 - val_loss: 0.0132\n",
      "Epoch 5/50\n",
      "551/551 - 0s - loss: 0.0548 - val_loss: 0.0170\n",
      "Epoch 6/50\n",
      "551/551 - 0s - loss: 0.0466 - val_loss: 0.0116\n",
      "Epoch 7/50\n",
      "551/551 - 0s - loss: 0.0360 - val_loss: 0.0121\n",
      "Epoch 8/50\n",
      "551/551 - 0s - loss: 0.0303 - val_loss: 0.0118\n",
      "Epoch 9/50\n",
      "551/551 - 0s - loss: 0.0279 - val_loss: 0.0097\n",
      "Epoch 10/50\n",
      "551/551 - 0s - loss: 0.0273 - val_loss: 0.0076\n",
      "Epoch 11/50\n",
      "551/551 - 0s - loss: 0.0256 - val_loss: 0.0067\n",
      "Epoch 12/50\n",
      "551/551 - 0s - loss: 0.0246 - val_loss: 0.0077\n",
      "Epoch 13/50\n",
      "551/551 - 0s - loss: 0.0239 - val_loss: 0.0064\n",
      "Epoch 14/50\n",
      "551/551 - 0s - loss: 0.0228 - val_loss: 0.0067\n",
      "Epoch 15/50\n",
      "551/551 - 0s - loss: 0.0229 - val_loss: 0.0088\n",
      "Epoch 16/50\n",
      "551/551 - 0s - loss: 0.0231 - val_loss: 0.0077\n",
      "Epoch 17/50\n",
      "551/551 - 0s - loss: 0.0222 - val_loss: 0.0056\n",
      "Epoch 18/50\n",
      "551/551 - 0s - loss: 0.0212 - val_loss: 0.0071\n",
      "Epoch 19/50\n",
      "551/551 - 0s - loss: 0.0210 - val_loss: 0.0075\n",
      "Epoch 20/50\n",
      "551/551 - 0s - loss: 0.0203 - val_loss: 0.0055\n",
      "Epoch 21/50\n",
      "551/551 - 0s - loss: 0.0200 - val_loss: 0.0061\n",
      "Epoch 22/50\n",
      "551/551 - 0s - loss: 0.0201 - val_loss: 0.0070\n",
      "Epoch 23/50\n",
      "551/551 - 0s - loss: 0.0202 - val_loss: 0.0072\n",
      "Epoch 24/50\n",
      "551/551 - 0s - loss: 0.0201 - val_loss: 0.0072\n",
      "Epoch 25/50\n",
      "551/551 - 0s - loss: 0.0198 - val_loss: 0.0074\n",
      "Epoch 26/50\n",
      "551/551 - 0s - loss: 0.0196 - val_loss: 0.0073\n",
      "Epoch 27/50\n",
      "551/551 - 0s - loss: 0.0194 - val_loss: 0.0075\n",
      "Epoch 28/50\n",
      "551/551 - 0s - loss: 0.0190 - val_loss: 0.0075\n",
      "Epoch 29/50\n",
      "551/551 - 0s - loss: 0.0187 - val_loss: 0.0074\n",
      "Epoch 30/50\n",
      "551/551 - 0s - loss: 0.0184 - val_loss: 0.0068\n",
      "Epoch 31/50\n",
      "551/551 - 0s - loss: 0.0178 - val_loss: 0.0065\n",
      "Epoch 32/50\n",
      "551/551 - 0s - loss: 0.0175 - val_loss: 0.0073\n",
      "Epoch 33/50\n",
      "551/551 - 0s - loss: 0.0177 - val_loss: 0.0085\n",
      "Epoch 34/50\n",
      "551/551 - 0s - loss: 0.0178 - val_loss: 0.0082\n",
      "Epoch 35/50\n",
      "551/551 - 0s - loss: 0.0171 - val_loss: 0.0062\n",
      "Epoch 36/50\n",
      "551/551 - 0s - loss: 0.0158 - val_loss: 0.0057\n",
      "Epoch 37/50\n",
      "551/551 - 0s - loss: 0.0154 - val_loss: 0.0059\n",
      "Epoch 38/50\n",
      "551/551 - 0s - loss: 0.0154 - val_loss: 0.0061\n",
      "Epoch 39/50\n",
      "551/551 - 0s - loss: 0.0154 - val_loss: 0.0086\n",
      "Epoch 40/50\n",
      "551/551 - 0s - loss: 0.0166 - val_loss: 0.0100\n",
      "Epoch 41/50\n",
      "551/551 - 0s - loss: 0.0165 - val_loss: 0.0078\n",
      "Epoch 42/50\n",
      "551/551 - 0s - loss: 0.0152 - val_loss: 0.0062\n",
      "Epoch 43/50\n",
      "551/551 - 0s - loss: 0.0144 - val_loss: 0.0062\n",
      "Epoch 44/50\n",
      "551/551 - 0s - loss: 0.0146 - val_loss: 0.0095\n",
      "Epoch 45/50\n",
      "551/551 - 0s - loss: 0.0166 - val_loss: 0.0121\n",
      "Epoch 46/50\n",
      "551/551 - 0s - loss: 0.0166 - val_loss: 0.0068\n",
      "Epoch 47/50\n",
      "551/551 - 0s - loss: 0.0153 - val_loss: 0.0061\n",
      "Epoch 48/50\n",
      "551/551 - 0s - loss: 0.0156 - val_loss: 0.0062\n",
      "Epoch 49/50\n",
      "551/551 - 0s - loss: 0.0149 - val_loss: 0.0063\n",
      "Epoch 50/50\n",
      "551/551 - 0s - loss: 0.0149 - val_loss: 0.0068\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], weeks, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], weeks, n_features))\n",
    "\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "nK00v032gIOS",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "nK00v032gIOS",
    "outputId": "f9f01959-058b-499e-d27f-b23311eb49e1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WtkU6FKWUGjJN9P1YWpKLCNr6RikVFTTR92Ncqoe549M10SulooIm+pNYOiOPsqoWDjXqiJZKqbFNE/1JLJ2RB8C6XbURjkQppYZGE/1JTMpO4qy8FK2+UUqNeZroT+HyGXlsqWikoa0z0qEopdSgaaI/haUzcgkYWL9bq2+UUmNXSIleREpF5AMRKReRu/pZ7haRJ4PL3xaRSb2WnS0im0SkTER2iIgnjPEPq5L8VIoyElhbpoleKTV2nTbRi4gTeAhYBpQAN4tISZ/VvgQcNcZMAX4C3B/c1gU8DnzFGDMDuBDoClv0w0xEWDojjzf2HaGt0x/pcJRSalBCKdEvBMqNMQeMMT7gCWB5n3WWA48Fp58GLhERAS4Hthtj3gcwxjQYY8bUYO9LZ+Th6w7w6gd1kQ5FKaUGJZREXwgc6vW6Mjiv33WMMX6gGcgCpgFGRNaKyFYR+af+diAiK0Rki4hsqa+vH+gxDKv5EzPISorX6hul1Jg13BdjXcD5wC3B52tF5JK+KxljHjHGLDDGLMjJyRnmkAbG6RAuK8nllT11dPrH1I8RpZQCQkv0h4HxvV4XBef1u06wXj4NaMCW/jcYY44YYzqANcC8oQY90pbOyKOt08/G/Q2RDkUppQYslES/GZgqIsUiEg/cBKzqs84q4Lbg9PXAy8aO8bsWmCUiicETwBJgV3hCHzmfmpJFstvFOu08pZQag06b6IN17rdjk/Zu4CljTJmI3CsiVwdXexTIEpFy4JvAXcFtjwIPYE8W24CtxpjVYT+KYeZ2ObnwzBxe2lVLd0DHqFdKjS2uUFYyxqzBVrv0nnd3r2kvcMNJtn0c28RyTFs6I48Xt1ez9aOjnDMpM9LhKKVUyLRnbIguPDOHeKeDtTu1+kYpNbZoog9RiieOxVOyWLurRm8xqJQaUzTRD0DpzDwONR6jrKol0qEopVTINNEPQOmMfBLinDy2sSLSoSilVMg00Q9AWmIc188v4vltVdS36tDFSqmxQRP9AH1h8SS6AgEef+tgpENRSqmQaKIfoMk5yVxy1jgef+sg3i4dEkEpNfppoh+EL55fTEO7j+e39R0JQimlRh9N9INw3uQsSvJTefSND7WppVJq1NNEPwgiwpfOL2ZvbRuv7zsS6XCUUuqUNNEP0lWzC8hJcfPrNz6MdChKKXVKmugHKd7l4LbzJrJhbz17a1sjHY5SSp2UJvoh+Ny5E3G7HKzUUr1SahTTRD8EmUnxXDe/iGffO0xDm3agUkqNTproh+iLi4vx+QP879sfRToUpZTqlyb6IZoyLpkLz8zhN5sO6j1llVKjkib6MPjr8ydzpK2TF96vjnQoSin1CZrow2DxlCwmZSXyh/e0p6xSavTRRB8GIsJVswvYuP+IjmqplBp1NNGHyVWzCwgY+ONOrb5RSo0umujDZFpuCmfmpvDC+1WRDkUppT5GE30YXTU7n80VR6lqOhbpUJRS6jhN9GH0mbMLAFi9XatvlFKjhyb6MJqUncTZRWms0uobpdQoook+zK46u4Adh5v58Eh7pENRSikgxEQvIqUi8oGIlIvIXf0sd4vIk8Hlb4vIpD7LJ4hIm4j8Y5jiHrWuPDsfgBe1VK+UGiVOm+hFxAk8BCwDSoCbRaSkz2pfAo4aY6YAPwHu77P8AeCPQw939CtIT+CcSRm8sF0TvVJqdAilRL8QKDfGHDDG+IAngOV91lkOPBacfhq4REQEQESuAT4EysIS8Rhw1ewC9ta28UGNjlOvlIq8UBJ9IXCo1+vK4Lx+1zHG+IFmIEtEkoE7gXtOtQMRWSEiW0RkS319faixj1pXzMrHIWibeqXUqDDcF2N/APzEGNN2qpWMMY8YYxYYYxbk5OQMc0jDLzvZzeIp2bywvUpvHq6UirhQEv1hYHyv10XBef2uIyIuIA1oAM4F/k1EKoBvAN8WkduHFvLYcNXZBRxs6GDH4eZIh6KUinGhJPrNwFQRKRaReOAmYFWfdVYBtwWnrwdeNtanjTGTjDGTgAeBHxlj/is8oY9uS2fkEecUrb5RSkXcaRN9sM79dmAtsBt4yhhTJiL3isjVwdUexdbJlwPfBD7RBDPWpCXGsWRaDi9uryYQ0OobpVTkuEJZyRizBljTZ97dvaa9wA2neY8fDCK+Me2q2QWs313HloNHWVicGelwlFIxSnvGDqNLp+fiiXPw7NbKSIeilIphmuiHUZLbxQ3zx/PUlkNsr2yKdDhKqRiliX6Y3VF6JtnJbu56Zgdd3YFIh6OUikGa6IdZqieOe5fPYFd1C4++8WGkw1FKxSBN9COgdGY+l5fk8uD6vRxs0FEtlVIjSxP9CLl3+UxcDgffeW6n9pZVSo0oTfQjJC/Nw52lZ/JG+RGe3dq3Y7FSSg0fTfQj6JZzJzJ/YgY/XL2LhrbOSIejlIoRmuhHkMMh/OtfzKKt088PV++OdDhKqRihiX6ETctN4W+XnMFz7x3mtb1jf0hmpdTop4k+Ar560RQm5yTxzSe3sXp7tV6cVUoNK030EeCJc/LIX84nP93D1363lb/5zRaqmo5FOiylVJTSRB8hU8al8IevLuY7V0znjfIjXPbAazy2sYJuHelSKRVmmugjyOV08DcXTOal/7eEeRMz+P6qMq7/xUb21LREOjSlVBSR0VY/vGDBArNly5ZIhzHijDH8Ydth7n1hF0c7ushNdVOSn0pJQSol+WmUFKQyMTMRh0MiHapSahQSkXeNMQv6WxbSePRRa8N/QHs9LLs/0pEgIlw7t4gLpubw3HuH2VXVwq7qFl7fdwR/sDon2e3imrkF/PX5k5mUnRThiJVSY0Xsluj3roXf3QgOF9x5ENzJw7/PQej0d7Ovto1d1S28daCBF9+vpisQYGlJHiuWTGbehIxIh6iUGgVOVaKPzUTfUgW/OB9MAI4dhVuegamXDu8+w6Suxctjmyr47aaDtHj9LJiYwYoLJnPJ9FycWq2jVMw6VaKPvYuxgW54dgV0eeGvngdnPHz4aqSjCtm4VA93LD2LTd+6hO9fVUJNi5cVv32X+T98idt/t5WnthyitsUb6TCVUqNI9NTRBwKw7jsw/wuQM+3k673+Y6h4Ha55GPJnQ9FC+HDDyMUZJkluF19YXMxfLprI+t21rN9dx2t763lxezUAZ+WlcMG0HBZOymR6QSoFaR5EtMSvVCyKnkTfeADe/z1sfhQu/g6cdzs4nB9fp+JNePVf4ezPwuyb7bzJS+CVH0FHIySOvRt4u5wOSmfmUzozH2MMe2pa2bC3ntf21vM/b1bwyIYDAKR4XEzPS2V6fgpn5acyqzCNGQWpmvyVigHRVUffWgurvwl7XoTC+bD85zDuLLusoxEeXgxxHvjyBnCn2PkfvQUrl8KNv4WSq8NzEKNEh8/P7uoWdle3sqemhT3VreypaaWt0w9AYXoCl5XksnRGHudMysDljL2aPKWiRWxdjDUGdj4Da+4AXxtc+C341N/Bk38J5evhr9dDwZwT63d3wX0TYc7NcOWPhxz/aGeMofLoMd460MDaslpe31dPpz9ARmIcl07P5YJpOThE6PD58XZ10+Hr5lhXN8d83bT7/HR0dtPW6afDZ5+P+bopSPewsDiLhcWZzCpMI96lJwylRlpsJfoebXWw+h9g9ypILYKWSii9Dxb97SfXffx6aDoIt28e+n7HmPZOPxv21vOnshpe3l1Ha7C031e8y0Gy20VivPP4c5LbhSfOyYH6NvbX21skeuIczB2fwcLiTBZNzmLexHTcLme/76mUCp/YTPQ9yp6D1f8IExbBZx+H/uqkN/4nrPsufHM3pBaEb99jjM8fYG9tK3FOB4nxTjxxzuPPp2u6eaStky0Vjbz9YSPvfNjIruoWjLGJ/5xJmZw/JZvFU7IpyU/V3r1KDYMhJ3oRKQV+CjiBXxtj7uuz3A38BpgPNACfNcZUiMhlwH1APOAD7jDGvHyqfQ1LO/ruLhAnOE5SpVD9PvzyArj2EZj92fDuO0Y1H+ti84eNvFF+hDfLj7Cvrg2AjMQ45k3IICfFTUZSPJmJ8WQkxZOVFE9WcjxTxiWTGB89bQSUGilDGgJBRJzAQ8BlQCWwWURWGWN29VrtS8BRY8wUEbkJuB/4LHAEuMoYUyUiM4G1QOHQDmcQnHGnXp47CxIy4MPXNNGHSVpCHJeW5HJpSS4AtS1eNu4/wpvlDew83MyOw80c7fDR1f3xgoYITMxMZHp+KtPzUzkrL4Xp+ankpXmI04vFSg1KKEWnhUC5MeYAgIg8ASwHeif65cAPgtNPA/8lImKMea/XOmVAgoi4jTGj64apDgdM+jQceM1ezNUmh2GXm+rh2rlFXDu36Pg8YwxtnX4a2300tvuobelkb21rsKVQC3/cWfOx90hPjCMzKZ7sJDdZyfFkJsUT53Tg7eqm0x+g09+Nt8s+d3YF8PZ97uomYCAh3klSvJPEeBdJ7hPP6Yn2l0Vm8JGV5CYzKZ4Uj4uk4HUJt8uhTVLVmBNKoi8EDvV6XQmce7J1jDF+EWkGsrAl+h7XAVv7S/IisgJYATBhwoSQgw+ryUvshdvGA5B1RmRiiDEiQoonjhRPHBOz7CBtpTPzji9v7/Szp6aVD2paqWv10tjuo6HNR0N7J+V1bTS0++gOGNwuB544m4TdcQ7cLjudmhCHJ/i659khwrGubjp8fto77XPTsS4qj3bQ1NHF0Q4fp7olgNMh9kJ08OSQmhBHqieOFI/r+HR6YhzjUtyMS/EwLtXNuBQ3aQlxeoJQETMilaEiMgNbnXN5f8uNMY8Aj4Ctox+JmD6heIl9/vA1TfSjRJLbxfyJGcyfOHIDt3UHDM3Humhs76Shzf7S6GlO2tO8tN3np73TnihavF00dfg41NhBi7eL5mNdn6iOAttqKSfZTXZyzy8G+6uk5xdEdrLbPlLsLwltoqrCKZREfxgY3+t1UXBef+tUiogLSMNelEVEioDngL8yxuwfcsTDJWsKpBTY4RAWfDHS0agIcTrkeNXNlHED394YQ7uvm7oWL3WtnfbR4qU+ON3Q7qO+rZM9Na00tPvw+QP9vk9aQhzZyfFMyEzkjJxkpoxL5oxxyZyRk0xmUvwQj1LFmlAS/WZgqogUYxP6TcDn+qyzCrgN2ARcD7xsjDEikg6sBu4yxrwZtqiHgwgUXwDlL9lxc07WQkepUxARkt0uknOSmZxz6qGve04KDW2dHGnzcaStkyNt9pfEkbZO6ls7qWjoYOP+Bjp7nRAyk+IZn5FAVrK9hpCVbK9bZCbFk+R2Bn99dNPR6T/+3NHVjTfY+c3b1fMcwOcPUJydxMzCNM4uSmNmQRppiadpvKDGnNMm+mCd++3YFjNOYKUxpkxE7gW2GGNWAY8CvxWRcqARezIAuB2YAtwtIncH511ujKkL94GExeQlsP0JqNsFeTM/ubzrGOxZDWd9xg6loNQQHD8puF3Hr1H0JxAwHG46RnldG/vr2yiva6Oq2Utti5ddVS00tvvwdff/ywAgMf5EfwhPnJOEOHvNIsXjwuUQth9uYvWO6uPrT8xKZGZhGovPyGbpjFyykt1hPW418qK/w9RANFfCT2bA0h/BeV/7+DK/D568Bfatg3P/Fpbd1/97KDXCjDG0dvppbPPR7vOTFO8i0W0vGCfEOUPqoHa03cfOKtvsdUdlM+8faqKq2YvTIZw3OYsrZuUPe9Kvafayekc1f9xRTX1bJ/HOj19cj3c5KExP4OrZBZwzKTPsHe/qWrz8/NX9fFDTSrcxBALmY8+ZSW6unl3Aspl5JLlHX1+P2O4ZO1A/mwfZU+FzT56Y1+2HZ74Iu563g6Udfhc+vxomnR+5OJUaRsYYdlW3sGZHNau3V1PR0HE86S+ekk1BuoeC9ATyUj3kpnoGffG4vrWTP+2s5oX3q9l8sBFjoCQ/lam5yfj8ATr9geCzrWraX99Gh6+bwvQErplbwLVzi5gybmh3h2s+1sUjG/az8o0KuroDzBmfjsspOB2CQ048769v42BDB4nxTpbNzOe6+YUsKs7q94Tj8wc42uEjO9k9YjcE0kQ/EC/+P9j+f3BnBThdtr7++a/aIZCX/gjmfx4e/pRd9ytvjtpbECoVLv0l/d5EIDvZTVZSvE2QIjgcwWeR45e7jIHj2cbY22TuONxMwMDUccl85uwCPjM7nzNOcW2jw+dnXVktz753mDf21RMwcHZRGlfPLmDBpEym56eEPLaSt6ub32yq4Oev7qepo4urZxfwD5dPO2k1mjGGLQeP8sy7lazeXk1rp//4CLAdPj+1LZ3UBi/CN7b7ADsEyJl5qZTkp1JSYJ+n56cMS+9vTfQDUfYc/N/n4UvroWiBHRhty6Nw0XdhyR12nYMb4b+vgHO+FBMjXirVW6u3i+pmL9XNXmqaj1HV5KWm2UtDu4+AMXQHDAFjTkwHgGChVjjRH9EhwvyJGXzm7ALOzEsZcBx1rV5WbaviufcOU1bVAkC808H0glTmjk9nzvh0ZhSkAtAWbA5rm8r6qWvt5LGNFVQ3e1kyLYd/Kj2TGQVpIe/b29XN2rIantl6mLcONJCRGEduqodxKR5yU93kpnpIT4zjYEMHu6pa2FXdQvOxLvs3EEiIcyLBv4EIOIK/GmYVpvHYFxcO+G9h31cTfejaG+DfJ8PF3wVvsx3wbPE34NIffLzH7J++DW89ZG9HOPnCCAWrlAKoajrGtkNNvH+oifcONbGjspljXd2n3GbO+HTuLD2L887IGvb4jDFUNduL57urW2j1dhEwEDDG/tIxhoCBwowEvrJkcP14NNEP1MPnw9EK8LXCOX8DV/z7J4dF8HXALz8N/k74243gSY1IqEqpT/J3B9hX18aemhZcDjvEdlKvYbaTPS6ykuKjqrfykAY1i0mTl8CmHTDnFlj2b/2PfROfaO87u3IpvPQ9uOqnIx+nUqpfLqfj+MB4ShN9/xZ/ww6DMO+2U3ecGr/Q3r3qzZ/C9KtgyqWfXOfYUXsVypOunbCUGu26/YA5/Yi3Y4xW3QxVlxceWQLeFluPf/RDaNgPjfvtAGnHjtr1HC5IyrGP5HGQnAt5s2D+F7TzlVKjQcWb8OwKSJ8At71gW92NIVpHP9wOb4VfXwqmGxBIK4LMyScezjhoq4W2emivs7c5bKuD1ir7obrsXii5RodHVioSurvg1X+F1x+whbC2WtsY44I7Ih3ZgGgd/XArnAdf3WSn0yeGXkI/8Bqs/bZtzjnhPNtOv3DesIWplOqj8QA889e2E+TcW6H0flj1d/DqfTDlMiiYE+kIw0IrjcMl50z7GEg1zOQl8OUNcNXPoKEcfnURPPcVaKkavjiVGs0622DH07avynAyBrb9Dn7xafvdu+F/YPlDtgPklT+2VazPfdlWzUYBrboZLbwt8MYDsOkhW5//qb+HxX8P8Scf7EqpqGCMTezb/hfK/gBd7XZ+yTVw+Q8hffypth64Li+suh12/B9MPB/+4pe2urW38vXw+HWw6GtQ+qPw7n+YaB39WHK0Atb/wPbQTc6DS74Hs28GR2jduoeFtwV2Pg0Zk2DiYnDpaIZjTrd/9F1cbK6Ebb+3Cf7ohxCfAjOvtZ/3ijfh9R/b61af/iac93fhabRwrAme+Jw9sVz0HfveJ/turf5H2Pwre2G2+IKh73uYaaIfiz5629bfH95ib16+9Icj3wM3ELBfwj/fay8iA8QnwxkXwbRSmHq5vXilRqfGA7D7Bdi1yn6O4hIhMQsSMuxzYpatoph908jWRXf7YeNP4ZV/hUCXvV/z3FttE+Xev2CbPoK137G3+MwohmX3w7Slg99va40tpdd/ANf+AmZdf+r1ezpFdnnhqxvBE/oQCZGgiX6sMgZ2PgPr74Hmj2DqUjj7RjtqZkreybfraLR3yjrwqi19z7wOis4ZWKueg5vgT3dC9ftQtNC2DPI2w94/wd61tsUQQME8e0euObcMrp+AMeBtsi2S/Mcg7+zR0fooELBVCL52MAEQp61ScziC007bWsMXXMfXemLa/4nbIlsi4Iy3rbCc8SemXR6byOIThxazMVC/50Ryr91h5+fPgTMutkm1oxHaj0BHg3201oDfa0vRl3wPUguGFsPp1O+FP3zFXvwsWQ6X3gOZxafeZv/L8Mc74cheexyLv2FL2AP5nDTsh99eY4c4uelx+z6hqHwXHr3Mfu+u/UXo+4sATfRjXZcX3n4Y3viJTbZgb304cbEtDU1YZH8G73/ZPqq22uTkToVun/0iZ0yCWTfArBshZ9rJ99V0CNZ/355gUgvtF3HW9R//UhkDNTtswt/1vE0oBXNtL+LxpxiQqbPNjgK6/2WbYNrrbVO2bt+JdQrn23rZiZ8a3N/K126buzbuh85W+/C2BKdb7PKA3/59An4IdNtmsceTdtuJ55EkDsg8w97wJnem7WOROxNS8k9+Au3y2hNx5TtwKPhoqwEExp8LJVfbm+RkTDz5fr3NtorkrYftCWzx39vrQ+EelTXQbffx8j9DXAJc8R+2ABJqsvb74J1fwhsPQscRWyA473aY+Ren79xU9R48fj1g4Jb/s5+xgXjlR/Da/XDDYzDjmoFtGypjbCOMzlYYd9ag3kITfbTo9kPN+1Dxhq3D/GiTTV49xAGFC2xp5YyL7Qe6qwP2vAjbn7I3PjcByJ9tTxC+dluaPtZkv/DeJnvCEAcs/rp9nO5isDH2ota679kkc/ZNcNk9H//F0bAfNv8a3nvcxps1xTZDTR53ovNY0jjobIYNP7a/Fs680nZAO9VJCaD5MBx6K5jo3obq7cH+DL24U8GdYh/xSbZk3lMqd/SU1F22asOdbOuK3cm2mio+yf49TLct5Ztum7QCflsij08KPpJPTLvcHB+u8WN/q4AtVXd32ZNbty94gmmz1Qk1O+1Js+mjXhuJHUfJkxZ8pNvntlqb5HtOkukT7Ul24qfgzCtO/YuvP0cr7C/HsmfttaGLv2urdMLRQ7TxAPzhq/bzOm2ZHS4kJXdw79Xlhe1P2kYLRz6w93k+98sw/zb7f+6r4nV44hZIyIS/fA6ypwx8n91dtlRf9R6kFtlqroI5kD/XPidlf3x9Y4L/484T/2t/r+nuTlugOrK312Of/RwULoC/+fMg/jCa6KNXoNuWrA+9bUt+xRdAQvrJ12+tgZ3Pwo6noLbMfjES0m3y6HlOybNfnPQJA4uls9WWDDc9ZBPgBXfYEuk7j9i7cjmcthXFuV+xwz+frCTn64C3fm5Lbl0d9gu85C5IzLRfhtqdwUeZTYxtNXY7V4J93/ELYfwiGDfdJsT45LE39IS32R5fbZntWOdt7vNossdWdI493qKFg0+cfR16x14bqtxsT4ZphfYkkjER0ifZZ3eK/XXW2XLiV1PPw9cz3WYTV2ebPSnFJdq7ss2+OTxVc4GAbRmz6T9tNeWpjCuBW5+F1PzB76/9iP01WrXNJvzG/SeWJeUEk3tPQj9J1V1/Ugshe5p95Eyz35kJiwYVoiZ6NXIa9tsLaHv/aF8njbN1+Au+MLBSZvsR+3N5y0pb2g5029IwgCMOcs6C3Bm2RDX+XFvVEWXjk0SMMfZaTOUWaDoIRw/a57ba/tcXx4lfTfHJJ34N9fyKSsyyJ/i0wuGJt/p9W5joL5fFJdgLvQkZ4d2nt9nut2obNOyzn1GnG1zxfZ7dn7wm44y3BbPsqfbvEyaa6NXI+3CDvfB35hX2Qz9YDftt3W58ki3t5M6wXxBN6iPP1wHNh2wp3dOrOiwucXRcQI9xOgSCGnnhanecdQZc+R/heS81NPGJtve3GnPGWOWlUkqpgdJEr5RSUU4TvVJKRTlN9EopFeVCSvQiUioiH4hIuYjc1c9yt4g8GVz+tohM6rXsW8H5H4jIEAaqUEopNRinTfQi4gQeApYBJcDNIlLSZ7UvAUeNMVOAnwD3B7ctAW4CZgClwM+D76eUUmqEhFKiXwiUG2MOGGN8wBPA8j7rLAceC04/DVwiIhKc/4QxptMY8yFQHnw/pZRSIySURF8IHOr1ujI4r991jDF+oBnICnFbRGSFiGwRkS319fWhR6+UUuq0RkWHKWPMI8AjACJSLyIHh/B22cCRsAQ2tuhxxxY97tgSynGfdJjSUBL9YaD3vbyKgvP6W6dSRFxAGtAQ4rYfY4zJCSGmkxKRLSfrBhzN9Lhjix53bBnqcYdSdbMZmCoixSISj724uqrPOquA24LT1wMvGzuIzirgpmCrnGJgKvDOYINVSik1cKct0Rtj/CJyO7AWcAIrjTFlInIvsMUYswp4FPitiJQDjdiTAcH1ngJ2AX7ga8b0HSxcKaXUcAqpjt4YswZY02fe3b2mvcANJ9n2X4B/GUKMA/XICO5rNNHjji163LFlSMc96oYpVkopFV46BIJSSkU5TfRKKRXloibRn248nmgiIitFpE5EdvaalykiL4nIvuBzmO+dFlkiMl5EXhGRXSJSJiJfD86P9uP2iMg7IvJ+8LjvCc4vDo4rVR4cZ2oIt/EavUTEKSLviciLwdexctwVIrJDRLaJyJbgvEF/1qMi0Yc4Hk80+R/s2EG93QX82RgzFfhz8HU08QP/YIwpARYBXwv+j6P9uDuBi40xs4E5QKmILMKOJ/WT4PhSR7HjTUWjrwO7e72OleMGuMgYM6dX+/lBf9ajItET2ng8UcMYswHbjLW33uMNPQZcM5IxDTdjTLUxZmtwuhX75S8k+o/bGGPagi/jgg8DXIwdVwqi8LgBRKQIuBL4dfC1EAPHfQqD/qxHS6IPaUydKJdrjKkOTtcAuZEMZjgFh8GeC7xNDBx3sPpiG1AHvATsB5qC40pB9H7eHwT+CQgEX2cRG8cN9mS+TkTeFZEVwXmD/qyPirFuVHgZY4yIRGW7WRFJBp4BvmGMabGFPCtajzvYyXCOiKQDzwFnRTai4ScinwHqjDHvisiFEQ4nEs43xhwWkXHASyKyp/fCgX7Wo6VEP+AxdaJQrYjkAwSf6yIcT9iJSBw2yf+vMebZ4OyoP+4expgm4BXgPCA9OK4UROfnfTFwtYhUYKtiLwZ+SvQfNwDGmMPB5zrsyX0hQ/isR0uiD2U8nmjXe7yh24DnIxhL2AXrZx8FdhtjHui1KNqPOydYkkdEEoDLsNcnXsGOKwVReNzGmG8ZY4qMMZOw3+eXjTG3EOXHDSAiSSKS0jMNXA7sZAif9ajpGSsiV2Dr9HrG4xnJYRdGlIj8HrgQO3RpLfB94A/AU8AE4CBwozGm7wXbMUtEzgdeB3Zwos7229h6+mg+7rOxF96c2ILZU8aYe0VkMrakmwm8B9xqjOmMXKTDJ1h184/GmM/EwnEHj/G54EsX8DtjzL+ISBaD/KxHTaJXSinVv2ipulFKKXUSmuiVUirKaaJXSqkop4leKaWinCZ6pZSKcprolVIqymmiV0qpKPf/AVQyybVI8DzJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "Q5TDfiOegQRx",
   "metadata": {
    "id": "Q5TDfiOegQRx"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X) \n",
    "test_X = test_X.reshape((test_X.shape[0], weeks*n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "yTHTHYXKhQkq",
   "metadata": {
    "id": "yTHTHYXKhQkq"
   },
   "outputs": [],
   "source": [
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((test_X[:, -(n_features-1):], yhat), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,-1]\n",
    "\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_X[:, -(n_features-1):], test_y), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e6i5YWdrgNa0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e6i5YWdrgNa0",
    "outputId": "c34fceae-be8a-4674-e9dc-6da25ca8d3c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 5.615\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_absolute_error\n",
    "\n",
    "# calculate MAE\n",
    "mae = mean_absolute_error(inv_y, inv_yhat)\n",
    "print('Test MAE: %.3f' % mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "HHG7JuNk9qS_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "HHG7JuNk9qS_",
    "outputId": "86ce6c79-a851-468a-985c-89d27b74689d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XXnklGzdunIx/iUYz4/GHIrjsNrweJ/3+/NPAhZST1+F1w4YNcseOHXHbGhsbaWhomLQ1WNHU1MRVV13F3r17p3QdY2U6/C81mnzi8tueor6qiH0tfaxdUMa337x2qpdkiRBip5RyQ+J27YFrNJozlkA4gsthw+tx5OVQB23AUWPR8t371mg02RMIKQNe7HZoDTxXJlPGmano/6FGkz3+kOmBO/NysPGUG3CPx0NnZ6c2QGNASklnZycej2eql6LR5BX+UNgIYjryMog55Xng8+bN4+TJk0zXgcf5gsfjYd68eVO9DI0mrwiEIridNooj+SmhTLkBdzqd1NfXT/UyNBrNGYaUkkA4gttuw+4R9A+HkFImFfVNZ6ZcQtFoNJqpIBSRSIkKYnochCKS4WBk9CdOI7QB12g0ZyRmK1kziAngyzMdXBtwjUZzRmIOc3DZbXjdSk3ONx1cG3CNRnNGYnrgbqcdr0cZ8Hwr5tEGXKPRnJEEYjzwYsMDz7eWstqAazSaM5KAMSbRDGJC/g021gZco9GckZgZJy6HjRIziKklFI1Go5n+mPMwzV4oMAMNuBDCI4TYLoR4QQjxkhDiP43tdwghjgohdhs/ayZ8tRqNRjNORIOYMRJKvmngmVRi+oGLpZT9Qggn8A8hxF+N+z4ppbxr4pan0ZzB9J8GTxk4XFO9khlJrAF32m14nLa8M+CjeuBS0W/cdBo/uvPUOPHskU4e2dc21cvQTDG/e+44L58eGa9HJAzf2wRPfX3qFjXDGclCsQNQ7HbOzCCmEMIuhNgNnAYellJuM+76shBijxDiNiGEO8Vz3yuE2CGE2KEbViXzwycP85UH90/1MjRTSCgc4dZ7XuTO7SdGNvaegKFuOPjg1C1shuOPqcQEKPHkX0OrjAy4lDIspVwDzAM2CSHOBj4DrAA2AhXAp1M8d6uUcoOUckN1dfX4rHoGMRQI0z0QmOplaKaQroEAUiYUkXQdUb9b98BAx9QsbIYTm0YIUDxTDbiJlLIHeBy4QkrZYsgrfuBnwKYJWN+MZzgYpnswQCSiVakzldM+P5AQQOs8PPL30ScneUVnBrEaOGD0BJ9hBlwIUS2EKDP+LgAuBfYLIWqNbQK4DtAzyXJgKBgmIqFnKL+0N8340dGvDHhfrP7adQScheAuhSNPTM3CZjiBBAlFjVXLr+9hJlkotcDPhRB2lMH/vZTyT0KIx4QQ1YAAdgPvn7hlzlyGguoyrmvAT0WRzjY4E+noVxJakgdesRjKF8LhJ0BKyKM+1flAogbu9TjzrhfKqAZcSrkHWGux/eIJWdEZxlBAHUSd/QGWzJrixWimhHZDQonTX7sOw6yVUH8B7P+T8sgrF0/RCmcmsd0IwfDAZ5qEoplYhqMeuA5knqmYEkrU+wuHoLtJGexFr1LbtIwy7qTSwPMpHqUN+BQTNeCD2oCfqbQnBjF7j0MkpCSUysVQOh+OPD6FK5yZBMIRXHZbdISa1+NAShg0vpP5gDbgU0gwHCFknO27+rUBP1OJeuD+EOGIHEkhrFysdO9FF8LRp1Rxj2bcCIQiUf0bVCEP5FdHQm3Ap5ChmDN9p5ZQzlhMDxxgIBCCTsOAVxia96JXwXAvtOye/MXNYBINeD4OddAGfAoZDowYcK2Bn7l09PujhsQ3HFIBTFcxFBtR7foL1e/DWkYZT/yhcDSACUQbWvVpA67JhFgPXBvwM5NgOEL3YJC6ykLA8P46D0NF/UjaYHE1zF6lA5njTCAUwe2M8cDzcCqPNuBTiDbgmk4j9lFfVQRAvz+oPPCKhJTBxRfBiW0QGJzkFc5czCCmiTmZXksomowYMiSUaq9bG/AzFDOAWV9VDIBvcAi6jyXnfC+6CMIBOP70JK9w5pIUxMzDsWragE8hpgc+t6zAaGiUP/mnmvHBDGAuMjzwSNcxkOFkD3zBK8Du0jLKOOJPFcTUEoomE/zGTL65ZQUEwpG8OnA040O76YFXKwMuuo+qOxI9cFchzN+sDfg44g/FSyhFLh3E1GRB1AMvLwC0Dn4mYnrgdZXKgLt6DQOe6IGDklFaX4R+3Vd/PFBBTHv0tt0mKHY7tAauyQxTA59T6gHGKRe8v12N4tLkBR39fopcdqqKXQgBhb4mcJdAUVXygxcbZfW6vey4EEjwwEH1Q+n3aw1ckwEjHrhKIRuXwQ6/vRHufPPYX0czKbT7/FR73QihvD/v4PH4FMJYateAswhOPjfp65yJBMKRaB8Uk3wb6pBJO1nNBDEcE8SEcfDAOw+PfLn7TkHJnLG9nmbC6ej3U1WsphGWeJyUDZ2A+VusH2yzQ9VSaD8wiSucufhD4bggJuTfUAftgU8hpoQybhr43rtH/j74t7G9lmZS6OgPUO1VBrzMJakItqZvG1u9AjoOTtLqZjapJBQdxNRkxFAwjMMmKPE4cDtsYzPgUsKLd8HC86BsgR6Gmye0+0Y88EXOTmxErAOYJtXLoK8Z/L7Uj9FkRGIlJqiroH6rPHBfG9z7fuhtnqTVZYY24FPIcDBCgdOOEILKIle0Ki8n2vZCxwE4+3Ww7EqVbqar9qY1/lCY3qFg1ANfZGtVd6TzwKuWq9/aCx8zqTzwJA08HIQ/vANeuHPaBZC1AZ9ChoJhPC6VxlRe5KJrwD/KM9Kw926wOWDldbDscggNqxakmmmLecI2PfD5GAY8rQduGPB2bcDHSiAcyUwDf+g/Ripg+7QHrjEYDoYpMPJQK4pcdA3mmL4kpTLgi14FRZVQd77qZqdllGmNWUZveuBzI6fwUQiFFamfVF4PNqe62tLkTCQiCYZlkgEv9jgYDIRVX3ZQsuS2H8Dm90NhpUoOmEZoAz6FDAVGDHjlWDzwk89Bz3ElnwA43LD4YhXI1OX50xaziKeqWA2znh1spknWpB9ebHcoiUVnooyJQDh+oLFJsTumJ3jrXrj/Q6qNwWVfUlld2oDnyHDvVK9g3ImVUCqK3LlP5XnxLnB4YMVrR7YtuwJ8p6B1zzisVDMRmB64KaFU+k9yJFJD0DAuKalapg34GPFH52Ha47aXmB0J+zrgd28BTym84Q6wO6FkrpZQcuLBz8CPL1HDXmcQQ8EwBUYUvLLYxUAgHM0Nz5hwCF66F5ZeBp6Ske1LLwMEHNAyynSlwzhhV3vdEPLj9bfSJGtGL+WuXgHdRyE0hpjJGY450NhKQhFEKPnLh6D3BLzxF+Cdre7UHniOLDwPOg/B7l9N9UrGleFgGE+MBg455II3/R0GTsOq18dvL66GeRu0Dj6Naff58bod6hjobsJGhKORmtELSaqXg4yowi1NTvhDylFy25ODmMvFSbzHH4GL/x0WbB65s2QODHZCcHgyl5qW/DDgK14L8zbBE1+ZUalxsRp4eWGOBnzv3eDyGh53AsuugFO7wNc61qVqJoD2fn80gGka4yZZM3opd9Uy9VsHMnMmpQfudlAlDLk2sSK2ZK76PY1klPww4ELApf8JvhbY/qOpXs24MRyKCWIW52DAQ35ofECd4JwFyfcvu0L91lWZ05LYIh5zEn2TnD36QIGqpYDQqYRjIFUQ0+txUEGfulFYGf+kqAGfPjLKqAZcCOERQmwXQrwghHhJCPGfxvZ6IcQ2IcTLQojfCSFcE7rSha+ApZfDP26Dwa4JfavJYigQiQli5mDAX35UBXcT5ROT2WdB6XxtwKcpHbEeeNdhQu5SevCOLqE4C1S1rfbAcyYQDWImGnAnFcKocp0JBhzwAxdLKVcDa4ArhBBbgK8Ct0kplwDdwLsnbJUmr/4CDPcpIz4DiM0DrzQMeMYNrVr2wJNfgYIK1SfaCiFUUc+Rx6eVbqdRKA/c8Hs6DxMqXQSQWTe86uU6E2UMpJNQyoUPiYCCsvgnldSq3/kkoUhFv3HTafxI4GLgLmP7z4HrJmKBccw+C855E2zfOik9CQ60+vjHoY4JeW0ppZGFogx4iceJ3SZGzwXvboK7b4EfvZJQZxOHN35BpTilYtmVEBxUwU7NtGE4GMY3HIqXUCoNA55JN7yqZdBxCCJZZi1pgJE0wsRS+kKXnUrhY8hRoro/xuIqAk9Z3nngCCHsQojdwGngYeAw0COlNI+0k8DcFM99rxBihxBiR3v7OEwSedVnVQT+if8Z+2uNwncePcTn7ntxQl47GJaEI5ICQ0Kx2QTlha7UEspgF/z1VvjuBqV7n/8x3la8lY/tW5L+jerOVz2kD/x1nPdAMxbMK61qrxuGuqH3BPbZDUCGU9Grl0PYDz3HJnKZM5ZUHrgQgmp7PwP2UusnlszNPwMupQxLKdcA84BNwIpM30BKuVVKuUFKuaG6ujq3VcZSvhA2vgd2/3rCLyHb+/0TNl7JHObgiRnpVFHkTG3A73qXCuCuuRE+/DyhV32enaclp/tG8didHlhyMez/M0RGKRDRTBojVZhuOLEdAMfCzThsIrOp6NXGV3AqA5l5XJfhT2HAAaps/fhsqQz4nPySUGKRUvYAjwPnAmVCCHMgxDxg8vbqlZ9QXuWj/zWhb9M1EGAgMDEH6XDUgI98BBVFKTzwvlOqu+AFn4Jrvgslc2jqHMAfitDR7ycSGaVcfuV10N8KJ54dvx3QjIkOX0wflOPPgM2BmLsh84ECU51KeHo/fH1xfA/6PMLMQkmsxASooJ9e4bV+Yun0qsbMJAulWghRZvxdAFwKNKIMuZn+8Hbg/glaYzJFlfCKf4X9f4KWFybsbboHAgwHIyONbcYR04AXxHjglUVu6yDm3nsACee8MbppX4uKlIcikt6hUTy2ZVeoUvuX7h3zujXjgzmNvsrrhuPb1Lg0V2HmI70KyqB49tR44IEB+MPbYbgHTu6c/PcfB1JloQCU0kc3JUnbASWhDLRPmyrYTDzwWuBxIcQe4DngYSnln4BPAx8XQrwMVAK3T9wyLdjyftWn4MmvTcjLRyKS7kFlTIeyLW/PgCELA57SA3/xDzBnbVyf6MaWvujfpjFIibsYll4K++7XQa9pgumBV3kkNO+EBapopNjtzHwmY9UyaN8/UUtMzV8+peRLd6kKquchZiVmkoQiJSWRProixdZPNMcU+lomcHWZk0kWyh4p5Vop5TlSyrOllP9lbD8ipdwkpVwipXyDlHJyT0meUtjyL4YXPv4Nm3qGgpiO9+AEzMgzx6mZeeCgDHjPYJBQbDOjjpehZTecHZ/r3djSF21aZxqDtJx1PfS3wXEto0wH2vv9ahLT6RdVMNIw4F6PIzMNHFQgs+Pg5Hac3H2namlxwSeg7jzVkyUPCaTIQsHvw0GI9tEM+DQJZOZHJWYqNr9feQFPfnXcXzo2nW8gMDkeuFmN2R3bF3zvXYCAs2+Ie/7+Fh9nz1GBllE9cFBFUI6C7GWUSEQHPyeAaBGPGZcwyra97iyG6lYtB3/f5LVKaD8Af/44LDwfLrxV9SbvbsrLlsWpslAY7ASgLVRk/cRpVsyT3wa8oAy2fEB54a17x/WlY8ebDU5AINNKAzf7oZjSTXTOZd35cRPmuwcCtPYNc8GyKmAkoyEt7mJYdln2Mspvb4KfXq4KqDTjRrSM/vizagJPscrQymoqevUkBjIDg2qsmLMQXvcT1Ze8vE7VGAyMQ3rwJJNSAzeqvE8FC62fGPXAp0cgM78NOCgt3F0y7l54rBY9OBEeeEAdQAWu2CCmUY1pnjxaXlBdGM1BDQam/r25vhKnXUTbko7KWderzoXHns7s8eGQyn45uR1+/Qbw94/6FE1mdPQHVBXm8WdhwbnR7RkHMWFkPuZkBDIf/DScboQbto5UJFbUq99d+SejBMIRbAIciRKK4YG3h4qjOnkcbq+yN9NkuHH+G/CCciWlND4wrl541+CIURyYCA3cTCOMSWOqSGxotfcuNT5r5bVxz91nGPCVc0qoKnZn5oGD6liYjYzS3gihITjrBmXE73wzBIcye64mLR0+Pw3OVhjqiurfoIKYGdceeGuUhDjRHnhvM+z6BZz7QVhyycj28jr1Ow8Dmf5Q8jxMIGrAu/Ay4E/huE2jwQ75b8BBySguLzw1fhkpsdNxhiZQA/e44vPAwdDfIxGVPrjkkqQZiY0tPqq9bqqK3VR73dHJLqPiKlK9URofyExGad6lfl/873DdD6HpH2pKyTRJocpXhoNhfP4QK0P71IYYA+71OAiEI5kN9hBCySgT3RPFvGJb9Yb47WULAJGXgUyrifRA1IB3S2/qYPI0GuwwMwx4YYWSUvbdD237cnuN042qu59BbD72RAQx/Wk08M6BgCru6GtO/tKgJJSGWpWnWlWchQEHQ0Zph2P/HP2xzTtVtk/FIlj9JrjmO/DyI/CHd0I4xwHMmugVU/3gi6rjXeVIOwSvx5jJmE0gc6IN+PFnlINUsyp+u8OtvNE89cDdzuQiHgY7iQg7fRSmlrK0AZ8AtvyLOsge+WJuHuJ9H1ASQbfqLdE1EIgOOI0LYgYGYKBzzMuNphHGHEROu43SAifdAwElnzgLYfmVcc8LhiO8fLqfhhpVKVZV7MpcQgElozgLM5NRTu2COetGhuyuexu85htw4M/w8Bcyf09NHGbW0Oye3Sr7JGaIcdSAZyqjVC9TcY2h7vFe5gjHn4H5m5KbO4GSUfJRA0/jgYfc5YBIY8DnqpTcaeDEzBwDXlgBF34KDv0NfnSh8h5T8Nvtx/nsvTFNqk7thlPPQzgAj38ZUAZ8XrkakhDVwiIR+NXr4BfXMlaGgmGcdoEz4SCqKHLR3T8IL90Hy1+jZI8YjrQPEAhHoh54tVdVb45aTm/iKlQyyr4H0veyCAyqq5m56+K3b7oFFl8CR5/M7P00SXT4/FTTQ2H/sTj5BJQGDhm2lIWJD2QOdsHpfbDwXOv7K+ry1AMPW1ZhMthJpEBJlmklFOS0mHQ1cww4wHkfhpvvUkMOfvJq5SVa9MF+8KVWHtgdcwm082cquLfhXbDn99Cyh66BAHPLChAChkwPfNcdyhs5vQ9COU6QNxiKmYcZS0WRi3ldz6rglsWgBjMDJVZCCcdUjWbEWdfDYAcc+0fqx7S+CDIMc9cn31e9XLU/na75v9N1XQbt/X7W2wyDuyDeMJoeuM+faTHPBKcSntimfi94hfX95XWqz06ejToMpAxidiGNQQ4ps8+mUS74zDLgoErGP/gsrH0L/PNb8KNXJvVraO4eot8fUjqj36dyrc++AS75gtJ8H/kiXQMBKotdFDrtSgP3tcHDX1QyjQyPOXATO8whlooiF2v7Hld9hxdfknR/Y0sfLruNRdXKMzcnumScSgiw5FLVDCydjGJewcxZl3xfxSKV/ztNyonjkBLueC3c895pW4DU4QuwwXYA6fBA7eq4+0zZLmMPvGyh+iwnoBoZUAFMu8v6RA6qmAemtq3tyZ3wg/OjXR0zIRBOnYUiClV9RWoDbuaCn8x2pePOzDPgoIzwNd+Ft9yj0t5+cS30KWMjpaS5R6XCtfYOqz4jgX7lfReUqRLhw4+yfHAnFUVuCt0OpYE/eCuEhuHqb6n3GGPgaChg7YFXFrmoDZ1QvU8cyVPq9rX0sXR2cVR6MQcCZKWDmzJK459SZ6Oc2gXeOSM5v3GLNIJu03Eqevt+FaDd87uoHDbd6Oj3s8VxEDF3fdJnnLUGbrPD/I3qynAiOP6MOok7Pdb3l09xLvjxZ9X3u+1FeC7zdkyBUCSlhGIrNj3wFJ9BqfbAJ4cll8Db7lfa9kOfA1SZunlmbesdgh0/g9mrRjyMjbcQKZnHv9l+Q2Whg0KXnYVdT8NL9yjjbg4K7hib5jiUxgOvjHQivTWWz2ts8UXlE4j1wLMM3DZcrWSUVL1Rmncm698mZlOtzpeze8/JoPFP6nfD1fD3b8CeP0zteizo7e2hgaNJ+jeomYyQRRYKKHmj7SUY6hmnFRoEBlVsKJX+DVObC370KfjlDeCdrVpFHPhrxgkMlhJKJAJDXTiKlAeeMg/cXQKuYm3AJ4XKxfDKj6u+xUeeoLl7pBAlcPw5aN0DG94xkgng9NC16ZOcYzvKqt7HKXeGeF3rbarz23kfUSXpJXPVOKsxMByMxDWyii630E41PfgLkw14u89PR78/zoCbHnjWBnzpZWB3q5zwRAa7lMadyoCXzFPP7ZqGHvj+P8K8TfC6n8LC8+D+D8LJHVO9qjjKe17ETiTa/ySWEQkliwyHhecCckSvHi+ad0AklKTTx1FYoQzaZOeCv/yIqg4umw/v+AtsfDf4e+FIZsF1v1UWynAPyAi24ipcDhuDwRQnUSGmzWCHvDDgkYjkVM/oFYBSSqRVAOu8j6pLvT9/glOdPdHNNYd+q/TDVW+Me/jxeVfRGFnA6gPf5j2BX1EdaoWrvqXyXgGqlo6TB578769x9OMQEfqdVUn3RQOYNSPN5ks8DlwOW3YSCqgT0ZJLoPGPyUG/U8+r36l0T5tNlVF3HsnuPSeanuOq/UDDVUqaeOMvVbXib2+C3qnXK00W9L9ABKGkjwRcDhtuhy2zuZgmczeoit1MWySMQjhifI+OPQMImL859YOFUFOysvTALb+nmXLgQbjzRqhcCu/4s/LAF12kqlL3ZTaWwNIDN4p4KKykyGVnMJUHDtMmFzwvDPin797Ddd/756gf+jcfOsi137MoUHF64DVfh85DVOzZCkC5fYjFp/8Gq14Hnvjm7d1DYb4aejMFAye4avA+HvZcplpnmpgDZcdwEKYKYs4WKp+3x5HGgMd44EIIqovdmXUkTKThGuVFmBWXJqeM27VrUj+3csn088BN+WTFVep3USXc9DslBdx5o8rhn2KklCwPvER7wSLVBsICbzb9UEDFNOasHRcdvHcoyMYvP8JvnzsBx59Wg8QTp7MnUl6flQbeNxxk9X8+xMP72rJfYPNOVQ08+yx4+wNgyB043KpmYv+fMsrPVkHMhO9f1IBXUOhypJ/GVTKXcE8zKz//IHube7Pfj3EiLwz46vllnPb5OdaZPlXpby+1sudkr3UZ8tJLoeEa1hzZyjJXJ+/ybscVGYb170x6aOdAgCciqxmefwG99nJ+6Hx7/AOqlkHAN6Y80KFAOK6RVfSlI6obWoeoSLpvf6uPmhIP5UXxga+si3lMll8BNkeyjNK8S3k36b64FYuUzDKdBkTs/xPMWhk3+IJZDfD6n0LbXrjjKpUeOZn0t6t5pI//N/z6jUS+sZzzxR56qjekfEqx25H9LNaF56rPbYy9av7yYgtdAwF+/fQROPFcevnEpLxOZaFkmPVzqM1H33CIf77ckd3i/P1w9y1QPEslKCS0mGDltUoGyaBGwTKIGeOBF2bggdsG2vAHAhxs82W3H+NIXhjwLYvUB7X9aFfKx3T2+zl0WnXLS2nor/gfwtj4L9cvuDb8EC87lljqvKqZlCBy4518ffHPaU1sLRmdR5i7jJIqD7w0rA7qNpnsnakS+uRZfaofSg556QXlUH+BMuDm1YSU6QOYJpWLVXB4ukgTAx3KAzW971iWXQZvuAN6T6gir4f+feK98UgEtm2Fb61SEs5TX4ee47RVbeG/gm9l+LxPpXyq1+PMTgMHFciMBNMWsGXCPbvU5ynaXoTgQPoApklFvToWMkwrPdqhvp/7YqZKZcTfPquchut/lGy8ARZfrNJ8M5BRLJtZxRpwt4PBdP1oSuYiZJhqeuL7908yeWHAF1cXU1nk4tmjqUvYn2saMe5HO1K0PS2dx688N7El9BwLgkf5A5daPqxrIIDbYaOgoAhRWJmcTjQOBjyVhOINtBOWglOheEPtD4VVCX1tSdJzsupImEjD1epLcdroIdN3SpUJp9K/TcxUwukioxz4C8iI0r+tWHktfHC7qg94+rvwvS1w8G8Ts5bek/Cr6+Gvn1S93N/1EHymGT74LH+Y/x/8LHIlS+rqUj69OJuhDiYLNgNiTDr48c5Bnmvq5j3n17PFbqTJpirgiSWaiZKZjGJ+Pxtb+jLXwhv/CLt+rhIJ6l9p/RinR11VNo4uo/hD4eQgZpIGnl5CAagVXfRkU0Q3zuSFARdCsKm+Iq0Hvu1oV/QDMc/wVvxg6FLaPPX47UX8ZnCT5cDizv4AlUUuhBDqUioxod9bo870Y/HAU+SBOwZa6aSMzsH4y9GXT/cTikhLA17tddM14M9t+PKKqwChviAwon9bFfDEUmGmEk4TA974J9Udr+ac1I8prFANud75oNKNf/NG+Me3xm8NUsILv4Pvv0LJD1d9C27+gzKuLnUV19jSx8KKQoqMbBMrstbAQV1NzVo5JgN+7/Mqq+Kd59dzZclRTjKbcLF1OmscWaYSNhnfT99wKFqTkZa+Fnjgw6ro6VWfS//YldeqKuamNFXGpJFQHB5wFhoa+ChBTKBGdGVXBT3O5IUBB9hUX8HJ7qGUH/i2I12sX1hOVbE7pQfuGw7SOSx5eO13eXTjVnwRN50Wwb+uAX+0N3ehy4E/FImfUynEmDJRpJQp88DxtdJpq0wabtxoTKFP5YFHJLkdSMWzlM65z9DBm3cqXTyx81wi3hqVwTMdDLjfB0cehxVXxzWGSsnCc+F9f1eZC89+P31PmEwZ7lMTa+59r9LdP/AP2PDOpPU0tvZZfoaxZDXUIZaF58LJ53LaHykl9zx/knMXVTK31MNZ4X08G16emU5dOh+EPeNA5pGOgWjr5P0to+jHkQjc/y9K27/hJ5bFbXEsebU6LtPIKFJK60rMwS7VHdJw3IbSBjGVAVceuJZQRmVzvaqO2m4ho/QOBWls7WPzogoWVRVFz/CJmMa/tGYxjvkqiNTal9wrpWswSEWRShkscisjm6SHmZkoORAMSyISyyAmfS30OSs50jHAP1/uiP48ebAdt8NGXWXyqCezmGdMMsrpl5Qxbt6lIvypKu9MhDACmdPAgB96SGmwqeQTKxwu2HiLkotefmRs799+EH5ipGRe8gV451/U/yaBAX+IY52DoxrwrOZixrLgXFVV3Jp9Wf2u490c6xzkhnWqxsHt72KvvSGqiafF7oTSeRl54FJKmjoGuLRhNjCSWZWS7T+Cw4/B5V8a6fuSDmeBink0/jFlgD0UkUhpNU6tM6qtF7nt6T3wgnKCNjc12oBnxvIaLyUeB9uOJMsoO5q6kFJ56XVVhRzpsA5QmUU8c8sLqClVBqqtz9oDN8ebmUY2aahD1VKVgufPPgIdHeZg6YG34C+Yze4TPdz8k23Rnz++cIpVc0uTR0AxhmIek4ar1e9996sc8NH0b5PKxZPngYf88NQ34PlfJ6dvNv4JCqvS5ytbsexyKKpWU9ZzpfFP8OOLlff2tvtV0ZhV21VUFhFYX0XF4vU46feHss+VXmjo1TmkE96zqxmP08aVq2pV+iBQuvxC/vZSW2Ynk4r6jDTwtj4/Q8EwZ88tYWFlIY2tFgY8HFLxid+/TQWcl10BG96d+c6svM5o1mbd896fbqCx0ciq0OVIr4ELgc81i1rROaUSSmohbppht6XWwbcf7cJpF6xbUM4LJ3rp6D+JbzgYLUs2MT3weWUF0W2WHnh/IDpcocil/kVJY9XMQGbnyyoHNwusBhoDqnPiUBebNpzF7xclR/8XV1tPyq4y5J6cPfCy+Woftv1QTTkfTf82qVysPJ1wUHlhE0XLC3DP+9SIN1C90q/+jlp3yA+HHoazr09pOFNid8I5b4JtP1JZLGZOcSZEwio18O/fUCe8N/5ypEdGCkby+JMziWIp9jgIR5TMVujK4itaMkfp0ceeVuPPMsQfCvPHF05x+Vk1qhL02DNQVM0F527h2y88y4N7W3n9+nnpX6S8biSOkoajhnNVX1VMQ01JVBoE1JXM879UfWz625Qx3fRe1SY6E2nMZOmlqrvovvtVllUC0Yn0VkHMsgUAKvYVDCOlRKR47x5HtZZQsmFTfQVHOgY4nWB0tx3tYvW8MjxOO/VVSmKwklGau4dwOWxUFbupLHZjtwnaeuNfazgYZiAQpjKqgRsSSqIHXm30Yc5BRjG9+QJXwr+/X+WVeyrmsam+Iumn0vC0E8m5H0osDdeoLw1k7oFXLFadGXuO5/6+6QiH4MmvKQ93qAtu/J0aKHF8G3z/XNj5czV0OeBT+ncurLlZpd/t+X12z7vrncp4r3ubKuUexXiDMuBej4O5MQ6EFVk3tIplwStUf5ssvPfHGk/TNxzihnWGkT7+NCzYwrqFFSysLMxMRimvUwZwOL0kYhrwuqpCVtR6aeocYLCnDR74V/jeRhWTmLsB3vRr+Ph+uOJ/UhY8pcRVpIx4ChklasCtCnliPHApVcuLVHTaqw0JJcYDj0RUH/1JammcVwY8qoPHpAwO+EO82NzLZiNXvL6qGICjnckyysnuIeaWFWCzCew2VcGY6IGbwUMzyGJmCyR54OX1KnCTQ1fCoVQeuNExkRSNrFJR7HbgzqWcPpaGa9RvZ9HIyWk0ok2txllGkVJJObdfqjoKrrwO/uVZlSK26Rb4wD9hzhr444fh7veojKBFF+b2XrNXqiuO3RbSTCqadynv7oJPqa6Xo8ULDBpb+mioKUnp0ZmY/VD6cg1kDnZk5Vjc83wzs7xuzltcqQKRPcdh4XkIIbhh7TyeOdI5eisLsyvhKDp4U+cALoeNOaUFNNQUcbPtYVzf3wi7fwPnfkgZ7Rt/M9IOIVdWXqsckqa/J90VsJJQwkE1R8Aw4GbsK101ZruoZDbdDAWC6jVP7oTbXw0/OHfiUlQTGNWACyHmCyEeF0LsE0K8JIT4iLH9i0KIZiHEbuPnNRO92LPmlFDkssfp4LuOdxOOSDYZxn2hEeQ72m5hwHuG4ryf2aUe2kYx4KYGnhTEdLiU7pdDJoppwJNm8vmM3greOVm9nhAi92Iek6olKvNk/sbMpYjxygWXEjpehh0/VfM2v7EMtl6kjMEb7oDX3x5fuFFRD297QHnjkbD6sjqsr04yYu3NqlKz5YXMHr/jdjWW7hUfyvgtIhHJ/lbfqPIJQEkuHQlNzLzt45mlE3YNBHh8/2muXTNHxVcOPqjuWHY5ANevnYuUcN/uURo3ZZgLfqR9gLrKQmzNO3jVk2/iS86f0VG8DN7/T7j8y1BcndG6R2X5leCtVRp6QlZOIGx8/2INuDmSLsYDB9JWY7ZRiVOEaRDHCd/7AfjJxaoGwOFRgddJIBMPPAT8m5RyJbAF+KAQYqVx321SyjXGz18mbJUGDruN9XXxOvi2I13YbYL1C9VllsdpZ25ZAU0WHnhzd7wBrylxpzTgZhCzKN0HmWMmynAghQduluZn6YFDDsONrbj5LpWqlSmFlaqB0FjaykbC8Mvr4P/Ww58+pvTbRRcqjftDz6npQVbYbMob/7f98Npv5v7+AGe/TnVX3P3r0R871A0v3q2GTXtKM36LE92DDAbCowYwQWngkGVHQpPKxSowm2E++B9fOEUoIkfkkwN/VWPajCyaBZWFbKwr555dzemDqhWZe+AXFzXBTy/HOdzJJ+RH+d7822DWiozWmzGuIrjyq6p1wrYfxt1lyiJxHnhMHxSIkU5TdSQETkn12Htd/4Gn8W5VZPSvO1U20Ch56OPFqAZcStkipdxl/O0DGoHRBb8JYnN9BQfafGrwLyqAefackuhlJ2CZiTIcDNPR74/OuQSoKfGooQ4xJHrg5gdpeSlVtVR5n1nm3Q6HUkkop9TZO1vND6WDj0lCAfb5CjnQn5kcAKjAUuWisUkoz35f6dgX3gof2qEM8ut+AuvfnllQ0VOSsYSRkoJydcm+5/eWI/jieOG3EBpS7UuzwKoRWSrMYzknDVwIZUCOZZaJcs/zzTTUlqh1DfeqzI3lV8Q95oZ183j5dD97m9Po255S9X9Mkwsejkg6Ort4X+fXoHQu4l+e4Vjt5TS2TlAvkYZrVAbL41+GnhPRzYFwOgNueuDG9z6NB34gMpcgdp6KnMML1zwIl/4XuL2q+vb0S+My/Hw0stLAhRB1wFrAbDz8ISHEHiHET4UQllZHCPFeIcQOIcSO9vb2sa0WZcBB6eDDwTC7T/SweVFl3GPqKos42t4f5zGYGt7cGAM+q8RD33AoLkWwM4UBT0ojBOWBhwNZj5MaCqgDKCkP3NeqvO9sIu7mUsbBA/+3P7zAJ/6QoYxgMpauhJ2H4bEvq+HNF92qTog57Pu4sPYtqhHSgTQXklIqmWfuhqRRaKOxr8WHTah02NEYmYuZY4HRwldA7/FR+9T4hoO8cKKH15xtXPG9/Kjq/73syrjHXXGWuv/pw6MU9ZTXp/XAT/UM8UnxS8r8zaqfSUEZDbUl7G/1ZT6UOxuEUF1IAf7yyWiMw9TA3XYrA66cBjP2lXIqD3A4WM3rS3/Le4KfpNURk6VjZr6kSGMcTzI24EKIYuBu4KNSyj7gB8BiYA3QAlhex0opt0opN0gpN1RXj13fWjWvFLfDxvajXew+0UMgHGFTXXxjm/qqIvqGQ3FNZk6aOeBl8R44xKcSdg34sdtEVIeMBjEtPfDcMlFSBjF9LVnr3ybVxS46BwLxFaNZ4A+FOdTm46VTvfRlc+lesVh5NxlOQokSicAfP6JS+V77zakz3Cb1F6pBFelklKa/q5hHlt43KA+8vqrIOvc/AW+2k+kTMTsIbt+quiGmwLz6XGAWhx18EAoqYP6muMeVFTqx28Tox0V5XVoD3vPCn7nZ8SgtZ90SzVlvqC2h3x+Kfj/HnbIFcNFn4OBfVbdKUgQxEzxw87uZci4myiZUVyi/NS4TZc5aFSOxCKCONxkZcCGEE2W8fy2lvAdAStkmpQxLKSPAj4FN6V5jvHA77KxbUM62o51sO9KFELDRwoDDSMoSjOSAx3rgZjFPrIzSNaBywG02YbyfDZtIpYEbQbwsA5kpC3l8LTnp36AkFCmhK8eiArPXSkTCzqbuzJ9YuRiQ2c9E3HWHOsAv+9LIkNipxGaHNTcqL7Q3RcDuudvVsOlUunwaVCfJ0eUTGNHAc5JQQAWj522Ef34bvrEUbr9c/d0RH6swHZfZJR4lAx56SE1qSghiCyEya3FbXqc6PlpJigOdLHnm0+yPzMdxyUg/E/N/knVnwmzY8gE1NvEvnwK/b8QDj00jTNDAM/HAB/1h5pWrk1/PUMzJze5U4/ImQQfPJAtFALcDjVLK/43ZHjvt9npg7/gvz5pN9RXsO9XHo/vbWFFTQmlhfBGJpQHvHsJuE1GvG4wDFzjtizfglTH9toUQFLkc1mfignIomgUd2aUSRoOYsRKKlCqNMEdjFq3G9OVmwGMLKralaRqWhNnUKhsZpbcZHvq8utRc97bMnzfRrLkJkPCP25JTCn2tyoNb+xZVrp0FfcNBTnYPZWzA7TbViyOnICYoA/zuh+F9T8GFn1ZtYR/+vAoUPzsS0DOrkGtKPHByuwrQJujfJsVux+iSTkW9kmASp7VLCX/6KK5gL5/lQ1SXjwR/l8/2IkQGJfVjwe5Uw8h9LfDYl60rMQe7VDqqkc1UNIoGHghFCIQjVBa5cDlsydWYdeerDp8DWfY8z5JMPPDzgLcCFyekDH5NCPGiEGIP8CrgYxO50Fg2L6ogImHPyd6oJh7L/IpC7DZBU4IHXlPiiStFn12iPqxED7wiYWBCgcue+kycQyZK1AOPPYCGe1VwbAweOJDbZB7UF8jtsLF6fhnb0rTtTaLS6PmRaSBTSpVtEgmpTJOplk5iqVgEG94Fz/0Y7nlvfEBz1y/Vmje8K+uXPRAtoR9d/zbJqaVsLEIY3fs+A+//B3z0RSUTPf7fylhBNAOrptSjsk9sTlh8ieXLZdQh0UwlTLwa2/M7aHyAu0vejr/qrLg8+AKXnfrKIvZbldSPJ/M2KOlr+4/wdKnWyUkSSkyqajR9OMX33txe5HZQVuCkZyDhZFtntLydYB181DpdKeU/AKtv2YSnDaZi7fxynHZBMCwtDbjTbmN+eUFcMc/J7sE4+QRUz4kilz1OA+8cCNBQE+8pFbnTtJasWgov3asMU4bGaCgYxmkX8X1NzGb43lrrJ43CiAeeuwFfXuPlFYsr+fFTRxgMhDIr4y4oV7phOg88HFRfkIF2OPw4HPobXP7fI6ln04nX/q/q9fzY/1N67pt/o77YO++ARa+Kn/aTIdlkoJjk1FI2HWUL4IqvwA/PUz1lrvhvWnuHKS1wKinv4INqbKDHeo1eTwYSitnA61c3gLABQn0nwkGYv4UfdL6Gs2Ynt4NoqC3hxckYS/aqz8GOn1Jz8m/AhRYGfCQZIpoHnuJ7b55ci90OygtdyR74nLWqKK7pH6pOYYLIm14osRS47Jwzr4ydx7rZaGHAAeqqiuKKeZq7h9iSkK0CycU8Vh54Ybrm7lXLVPbCYGfGvTQse4GP0YCPxQOXUtLY0sdlK2vYXF/BD544zK5jPZy/NMPeIBWL6Ty+j32H2nnlUiNQHQnD/R9SwaOheE29t2odpZvfn/U6rbhn10nmlReyKcVxkDVCwAWfUCfme96nyvg3vFPJAld+JaeXbGzpo6zQGSffjUaxx5l7FkoqZq+E1TepK4zN76O1b1hdhXYeVnGcNA2jit2O0Y+t0nnqBNjXbEhQUv22uwisfSfHvvY8V62xMuBe/vxiS1L/ouFgmG8/eiipVsNlt/GRVy+ltjQ7KYvCCpizjuqObcCF8b1QBjujGSigZCyP05bSgJvbC912ygqd8Ro4TJoOnpcGHOCmTQtYVFUU9TwTqa8qYvvRLqSUhCKS1r7hJA8clP5naoGhcISewWCSAU+pgcNIi8uOgxkbcH/Iohe4WUZfkpsBL3I7KHDac/LAT/v8dA8Gaaj1sn5hOTah2vZmbMArlxDZ8ze++dDBEQP+2P+DF34Dq96oUg2LqqComvfc3UTTwHIeFjbLy7psiEQkn7//JdYtLOcX9eMcQ195rfJa77wRHv1PlR2UkF6XKftafBmV0MdS4nHQn6sGno5XfVY1A3v8y7T1vU3FgczqyxT6N6ir1aMpunzGkSJD50R7PxE5Ep+KxbwyOdDqY0NMQsL//KWRnz9zjLllBdGLWymVHLpyTglvO7du9PUkUn8BVf/8DkUM4XYmGHAzq8ygyOVIbqFhYHrgRW4HZYUp/jd156tjJ9tGaVmQtwb8devn8bo0HdLqq4oYDIQ57fMTCEWISOKKeExqSjzRoJ2Zdmg2sjIpcNlTj02KHa9mtvMcBcuBxmP0wMEo5snBA98Xc4nv9Tg5e24pz2YTyKxcRLXs5FhrB+GIxL7vXhUIXP8OuPrb0Ye1+/w8MvAIDITYeaw77suaCye7h+j3hyYuADZnLdzymJoGs/JasGf/dQlHJAdbfdy4aUFWzyt2O5KKzMaF0rmw+f3wz29T4thIzexNSv+ubhjRsK3W4xmbJm9eDdelMeCNLX3RY+Khl1r5+TPHePf59fzHVSujj41EJMv+/a+5/28WXYjtH//LJtt+XPaYbCJzmEMMBS67df0HI1lpRS4loewa7El+kKmDN/0Dzrout/WOQl41s8qG2EyUkRzw5GEIs0o8nPYNE4nIqI6V5IGna+5eMk+1rswikGk5jcfXolLUssxwiKWq2JVTMY9pAFcY2v+mugp2n+iJtr0dDWlkoswOnaLlwHNw/wdVb+4rv2b5PgB37xqlt0YGmCeedp9/7G0EUlEyB95yF6x7a05PP9Y5wFAwnFUAE8YhiJmO8z+GLCjjvYGfs7AoqPqHp/G+QWngOTXXMjBbWyyyMOC1pR5KC5zsMzKhTvUM8am797BqbimfuiLeK7bZBLO8yU3oMmb+ZkLCxXm2vSMSSnBYDcJIGJRc5HKkbGY14oHbKSt00TMYSG41MGfNiA4+QcxYA15XOWLArXLATWpK3ATDkq7BAJ391ga80OVIeSbGZlP54Fl0JRwKRiw08NYxed9gVGPmkEbY2OJjbllBNB1zU30FgVCEF070ZPT8weKFAKy1HaLij+9UZdVv/EVSgynTgF+0vJo/7zmV8Qki9br7LP+eTqQbhZcONZl+ggx4QRm+jR/lAtuLXH7qe5bVl0nrcTsIhCL4Q7l9Zkc6BigrdFJWmNxhUAhBQ62X/a19hMIRPvrb3QRDEb5749r4XG2D2aUeTlsMYskIZwEnvedwnn1ftNYjsYjHpNBtMQ/XIJqF4nJQXugkGJbJj50EHXzGGvA5ZQW47DaaOgaik3hqS5ODSLHFPIl9UEwKXfa0bSWZt1GN5Xr2BxmtbTgQxuNM+Nf3ncpZ/zbJVUJRRSYjHqIZEEw3RDqWbo+SB77g+AWuoTY13MAiHbKxpY+aEg/vPr+evuEQj+0/nfVaY9nf2hcdZjHqbMUporGlD7tNsHR2cVbPMyWLCSkxB47Wv5mTsoqlJ+9RhmvehrSPN4OLuRYXNXUMRJ0qKxpqSzjQ6uM7jx5ie1MXX75+laXcAjDb68ndAweOeNfTII6N5GinMuBWA80NBhI0cEgxk7b+lWoQSZqK2LEwYw243SZYWKmaWjX3DDLL67YsY44t5ukaUMbPygNP11aSy74MK14LD96qei6M0tzKWkIZHw+8ezBAMIty+uFgmCPt/XEeYlmhixU13owLerpCTtpkGR4R5FeVH1YtaS0w26m+YnEVs0vcmQ0KSENji4/N9ZXMLnFPYw+8j8XVRZaeZDpKPGlaOIwDrYPwzeAb1I2ll4/aQjjaYCtHWedox4ClfGLSUFPCYCDMdx57mdetm8d1a1P3y6sp9SQNYsmGg4XGxKmjT6nfKQ146iCmKamaEgpgPZlngvPB8zaImQl1VUU0dQwwWOK2lE9gxIC39vqjjazKCxOzUOwEwhGC4QhOi5mUuAqVZPDw5+GZ/4PuY/D6n4Lb2usaCiYEMSNh1Xx+jAY8Wk4/EIjuF8D/PXYIj9POe16ZPGj3UJvKDki8xN9cX8Hvd5xMvc8x9AwGeTJ8EcVuJz/pP593WDzGHwrz8ul+Ll4xC7tNcN3audz+96N09vtTThpKh284yPGuQd64YR4DgdCElmL/x317WTWvlDdumJ/yMcFwhE/fvSd+RBhwpL2fK85OvhoZDdNg+oZDSaMBx4O2vmHui5zHlza7KVr3xtHX4xlZT7YMBcK09A6n9Khh5PhbVFXEf117VtrXm13iwecPMeAPRUves6HJtZR+Cik++iScfUNKA140igcuhOqZYtoLSw+8djW4ilXbiAkIZM5YDxzUwXCsc5DjXYMpx1hVe90IofpCdA0EKC1wJhmsQnf6pH5AeTCXf1k1Znr5YfjZlUoWsWA4mJAHPtCuRpPlWIVpYqZUxraVvX93M9946CDfeOiApfeUqshkU30lQ8EwezMosOgeDPDN0BvZu/xfae4ZotfCEzF7rZjvc8PaeYQikj++YP0/Go0DMQOCV9SUcLi9P9rjYjwZDIT41bZjfO7eF9lzsifl477x0AHu2aUm28wrL4j+XLismps3L8z6fb1jGeqQAa29w9hsdgou/yLMTm8w1XpyN+BmANMqhdCkodbLO8+r44dvXT+qUa4pVcd5Yn54pgyHbbxgPwuOPKk2GJWpyRq4I2Ul5oA/TJHLgRCCckNCsfTAJ1gHn9EGvK6qiEA4womuoZQeuNOuZmS2GRp4ZVFykKVwlLLaODa+B276PXQdUUZ8MFmGGE6UUExDP8amTonFPMc6B/jcvXuZV17AcDDCg3tbk56zr6WPAqedBRXxGTqmDp6JjGIeuOcuVl8Aq0njicG85TVezppTwj3P55aNEs2cqS2hodZLMCx5+XR/Tq+Vjv2tPqRRj/Kvdz5v2Z/kqYPt/OjJI9y4aQE/f9cmfvy2DdGfrW/bkFOR0ZiGOmRAa98ws7zukUDeKJgdEnM5oTR1jG7AHXYbX7j6LJbNHj1bZ7Y3uYtoNgTCEXY7VqvpQT3HRzzwhD78hc70Hrg5dq00asBTJBDUnQ/t+ydEB5/RBjz2gJmXZpBsTYmHNp8y4OVpDHi65u5xLL0U3nqvKs75/dtUKXEMQ4EEAz4OOeAA1THl9IFQhA/f+Tw2AXfesoUFFdbDac0SenvCF7na62ZRdVFGgUzz0jFqwC3kDLPXSl3lyInihnXz2HOyl5dPZx+AbGz1UeJxMKfUw8qYPOLxxgyOfu3153Cia5D/uG9vXLpYu8/Px3//AstmF/P5mHzlsRIroUwEp/v8cTLbaHjHcEIxW1qkk1CyYbaReJCrBx4IRXjJvUbdOPqUMuCesqQ8f+WBhy0DyQOBUHRaV1mBKaGk+N/UGf3BMxxzlw1njgEvT84BN5ld4o5moSQGMGFkrFrKVEIr5m+Ca76jtK+/fiq6WUqZrIGPkwGv8qq1d/QH+OZDB3jhZC9ffd05zK8o5IZ1c5OG05ol9KlS3DbXV/Dc0S7Co2RC9AwG8bqVMa0ocllmhJgnitj+L9esnoPdJrgnh5xwc91CCOqrinA5bBNiwBtb+vC6HVy/di4fffUy7tt9KprDHolIPv773fiGg3z3xnXJxVljwAxiTpiE0jecZWl/7us52j5AtdcdNzVrLNTExK1ywR+K0OKuV51EjzyZ1AfFxOxIOGSR7jrgD1FoeOAuh41it8NaQgGlg//LtpHB4ePIjDbgs7zuqPecSkIBFRRp6xumcxQJJeuMgNVvhvM+qqa4bP8xoC7fIjKhF3hfi2r+Uzwru9dPWqeDIpedP794ih89dYSbNy/gylXqpGA1nLald5i+4RArUxSZbK6vxJdBpWPPYICyImc0nzdRQomeKBKahFV73VywtIr7nm/OKl0uEpEcaPVFTzwOu43ls73sn4DRXI0tfayo9SKE4IOvWsKWRRX8x317Odzez9a/H+Hvhzr4/NUrM5q0kw1jCRpmQlvvcDSFNqP1jOGKoKlzIK18ki1FbgdetyNnD9wfiuBy2FU746NPwmCHpQEfkU4tDHggHHXsQA29SCmh2B1q5ucEdN6c0QZcCBHNPU0VxAR1Ru8eDKb0wAszaO6ekks+r4ok/vppOPIEw8Y4tTgD7muF4tmZT4NPQ5XXzd7mPpbP9saVIC+sLGLDwvjhtKN1yctUB+8eDEYvIxtqVD5v7GSgtr6RXiuJ3LBuHqd6h3k2ixa2x7rUgOCVMetuqPXS2NKXfvBuloxMklfvY7cJvvWmtXicNt7z8x18428HuPLsGm7Kskw+E8aad52OAX8Inz/ErJLMs388Tjsuuy0nA360Y4D6NDnguZDYhC4bAqGI6kRYf4HK/mp+3rJXyUhHwuR9TsyAKSt0WmehTDAzOo0QYFF1Ea19w2kj26amFo7IFBJKeg38W48c5GCbj+/fvD75TpsdXvdj+Mml8Pu3E7hRNQ6K18BPjVk+MZnlddPWN8z/3bQ2Ke/9hnXz+Oy9L/Jicy/nzCuLCwRaMadMZVLsaOri3eenbv3aMxiIFjM01JbgD0Vo6hxgySxlsE2P3OpEcenK2XjdDm768bak+255ZT2fe22yrmx14llRU8Lvd5yk3ednVhbSQDrMXiux71NT6uEbb1jNu3++g7llBXzlhnOyalKVKYVOOzYBPUPZG4XhYJhr/++f/MurFnPtmuR86mgf8Cz/T6q4KLUGPhwMc/E3nuCURY52ffU4G/CS3MvpA6EIbocNFl1obPAlldED0SCl1fd+MBCOsymqpezEBJzTMeMN+McuXcabN6b/oGMP5MRGVjDigafSwLcd6WLb0c6kdphR3F648U748cWUPPAuHNxKgSu2F3irGgg7DnzmNQ0EQxGWWkTzX7uqli8+8BL37Go2DLiPBRWFabXJRdXF0VYEqegZCrLQ8LBGRmT5Rgx4mhOFx2nnuzetZdfxnrjtT7/cwZ3bT/DxS5cnactW1Y2xo7nGy4DvS3GFcknDbLa+dT1LZhUnTYMaL2w2wbLZXvaczL5P9iONbRxo8/HEgXZLA96aowEfrUf56T4/p3qHuWzl7LjP2mkTvD5N47lcmF3i4dnDuU19D4QjqrCqvA7KFqqh5JYSivG9Dybvc78/FHXsQBW/TdhczzTMeAO+uLqYxdXpy5hjo/GJRTygvCFIrYF39PvVLMlj3Vy0PIWOXVEP13wH9+/ewtvsD1PgjGl/2ncq406Go7FuQXnK+0oLnbx65SweeOEUn3ttg9J3R9Fuq4pdvNyWXlvuHghEc2EXzyrCYRPsb+njmtUqLTLaa6XA2thdtHxW0v9ty6IKbvrxNh5ubIu+jklji49FCQOCRzJRfKk/gyxpbOlTk+QtToaXnTW2nP1M2FRfwV07MyumiuVeI8iaKnZheuCzs9DAgVHnYpoSwhs3zOfVK2dn9drZUlPi4bTPTyQiM06FNPGHwiPDHBZdCLt+kVYDt/TAEyWUgqmRUGa0Bp4pcR54UbIuaEabU+WEmnnXo+ZMr7iK3rkX8jHHXZSEDO8hOKQGQoyxiCdTblg7j66BAA/ubeVo58CoTZaqvW46+i06rRmEwhH6hkOUGic+t8POklnFSY2msm3mtKW+kjmlnpSpj4nefGmhkzmlnnHNRGls6aOuqmhcs0uyYXN9JYOBMC+dynyfOvr9PHGwnQKnnZdP91s2nzKzN7JJIwTDA0+ThWIONSgvmpirklhqSj2EIjJaPZ0NgVBkpBNhvSGjpPHAEzXwSEQaQcyR46K80EnvUHDUjK3xRhtwoKTAEW0uVWEhobjsNhw2YRnMCIQi0fShUXOmheDw+v/ARZBle76utkVTCCdnMvuFy6upKHLxzYcOIC1K6BOpLnYTCEfoG7L+4vaaX9oYKaGhtiRauDPSayW7LA2bUW7/1MH2uKHTvUNBmnuGLF+vobZkXGcr7m/1JWXOTCYb69XV1LYjmUsFD+w+RTgiueWV9YQi1sVNbX3DFLsdWaf1FbvTd0g0szBKC5K/Q+PNLG/uueDRICbAklfDsissr4BTaeBmWmF8ENOl5pInTuaZYLQBR2WrmF64VRqhEIICl93yUqrTaIBVXuhkz8meUXPFuwsWsDV8FVVH7oOmfyr9GybNA3fabVyzeg5NnYMAcZkcVkTL8/utvyhRrytGemqo9dLaN0z3QICDbT7LXiuZcMO6uUSkMkom+9NkzjTUlnC4fWDMbWphpNdKtiee8WSW15NxMZXJPc+f5Oy5JVxjaN+JvVlAGb3ZWWSgmHhHCWJ2R3sJTY4HDuQ02CHOgBeUwU2/G5nnGUN0sHHC8WQ2uCqMDWIaVx1Jo9UmGG3ADWaVeCh02S07FoI5Vi3Z+zD7b19xdi3BsOT5491Jj4llKBjme6FrCRbPhb98AnpOqDvGWEafDTesU1/uYrfDckpRLNHy/BR9xk2vqyzBAwclQeQy0NdkySwv58wrjSv0MV/P6sSzotZLOIXX2TsUzKqXdWyvlalkc30F25tGL6YCONjmY29zH9evnUd9VREep3VxU2tfdjngJqMFMc0sjFSxjvEkWsyTgwfuN7NQRsHM806ch2t2Iix2xwQxo9WYk6uDawNuUFdZyJw0ueKpmrubk2CuOLsGm2DUUWRDgTDDuOm98L/g9D54ypBSJskDB1g1t5Rls4s5e27JqAGg6LT7FH3GuwfUlza2Ub852WdfSx+NLT4KXXYWVqSuhE3HDWvnGq+jDFFji4+KIhezvMkeZGwmSixtfcNc8s0n+J+/7M/4fcdy4hlPNtdX4hsOZSQN3bOrGbtNRCtcVXFT8vPaeoez1r9hJIiZKh7SOxTE63HEVdtOFFXFLmwCTmdpwCMRNSPXlYEBL4gmL6TwwBMKeSBNP5QJQhtwg89c2cDP3mHdxxpSDzY2O/8tqipi5ZwSto9SkGJe3svlVyn9rfOQGsnmKct98VkihODn79rEt960dtTHjnjg1ga8x0IDr/a6qSp2s7/VFy2hzzZTwOTq1XNw2AT3Gk2v9req4RNWudd1lcleZzgi+djvdtPRH2DnsfRXR7Hsa/FRWuC0HAIymUSLqY6kdwzCEcl9zzdz4bLq6GdmxiJiDW4kIjnty64PionX4yQUkQwHrbs+dg8GLLO4JgKH3UZ1DqPVAkaBWSYG3GYTFLrsDCVceZsGvDghDxxGHJrJQhtwg/IiF/PTeIlKA0++fDQzUKqK3Wyqq+T54z1pL9XNAEiB26FmRtpdyvuegGKQdNSWFmR0GV1W4MRuEyk98BEJJf6La1ZG5pKBEktlsZuLlldz3/PNBEIRDrSlDizabYLlNSVxBvwHT7zM04c7WVRVxIG2+ArRdJhTiiaiSCcbzGKq0XTwZ4900to3zPUxgxAaakvoGghwOubk2zkQIBSRWeeAQ0x5fwodvHswOCn6t8nsEg+tWY5W8xsth10ZXiWoaVwJHnggFL3PxDTgWgOfpqRq7t7u81PsdlDgsrN5UQX+UCRt8YXpvXgcNqhcDFfdBlv+ZcLWPVZsNkFVsSulB949GMBuE9HmSyYra0vY19JH33BozDLEDevmcdrn59fbjjEcjKR9vZW13qjXuaOpi9seOcQ1q+fwoYuXEAhFOGq0Nk1H2Oi1smIKM1Bi2VxfyfamrrRtAu7edRKv28GlMfnXZo5/rKQUzQHPxQM3p/Kk0MFVRe7keOBg9DDKMohp9ozPRAMHcxpXogduauAjx7zX41CVs9NNQhFCzBdCPC6E2CeEeEkI8RFje4UQ4mEhxCHjd+oKkhlAqubuHf3+6FzGjXXm5W5qGWUoGFZpiaYHsPYtsPm947/gcaSq2J1aAx8MUlrgTPJUG2pLMO1NqmZZmXLxilmUeBx859FD0ddORUNtCb1DQQ60+fjIb3czt6yAL19/dkp93IrjXYMMBcOjZuhMFpvrK+gaCKTsdz7gD/Hg3lZee05tXBB+RUww2cTM2sg1iAmpG1r1DAbjgtkTjdkGOhvMq+NMR9xZeuAWWSg2m6B0Cop5MjkNhYB/k1KuBLYAHxRCrARuBR6VUi4FHjVuz1hSeeAd/f6o5lhR5GL57PSzJIesBhpPc5QBtz4we1N8aWON7PIxerIep53XnjOH7sEgDptg8azUfTVMr/k9P99BW98w371xLV6Pk8XVxTjtIqOOhdMlgGmyeZFyDFIFyP/2UiuDgXCcfAIqG2RuWUFcKmGuZfQw+lzMydTAQZ2EegaDWaWNmh54Jho4YGjgiRKK4YG74q86ywtdqVvKThCj7oWUskVKucv42wc0AnOBa4GfGw/7OXDdBK1xWpBqwGm7zx/N1AAVdNp5rDvlYOGkcWp5QLXXnVZCsfrSLqouwmW3jdprJVNeZ6Q+LplVnNZ7WmF4+ye7h/jUFctZPb8MUF/YxdXFGVVq5jpJfqJYUFHI7BJ3Sh383uebmVdeEL0CjEUFMkf2+XTfMDZB9KoxG8w+P1ZDHULhCL7h0KR64KYMlKqYZ/vRrqTvYTZBTFDFOoktNEY88PjjsLTQOf0MeCxCiDpgLbANmC2lNMoIaQUsmx8IId4rhNghhNjR3j7+I4Umi0KX3bKxe0d/IOqBg/KW0pU/Jw1zyAOqit10Dvgte3anClw57TbWLyxncw7jxKxYv7CcFTXeUceTlXicrKjxcvGKWbzn/PjijJUJxiwVjS19Sb1WphIhBJvrK9l2pDNJB3/qYDt/P9TB69fPs8z0WVnr5Uh7f9RLbe0bpqrYnVOqXzoJxaqga6Ixi5GsinleOtXLG3/0DH/aEz9zNZBDEHPQnxzEdDlsSf1pVEfC6SehACCEKAbuBj4qpYz7Fkh1VFlGWKSUW6WUG6SUG6qrq8e02KmkyO0gGJZxg3P9oTC9Q8EkDxxS6+BJ49TygGqvm2BYRsvmY+kZDKQsnb7jXRv57xtWjcsahBDc98HzMhpbdt8Hz2PrW9cnGbSG2hLa+vx0jdI/o7HFN23kE5NN9RWc9vk5ZlTQApz2DfPx3+9m2exi3n/hYsvnNdSWEJFwqE3p5619/pz0bxgx4FYSiul5TrYGDtbFPM8YnQoPtsXHDbKXUBwMBpM98CILJ6xsunrgQggnynj/Wkp5j7G5TQhRa9xfC5yemCVOD0yjGxvI7DR04VgDPsvrYVFV6vLnoTyUUMzLbatAZk+a1DG3w55VF73R8DjtGXmOqR7XYBHUS6R30Oy1Mr0M+JZF5nANZZgiEcm//f4FfMMh/u+mdSmPqcRAZq5FPDDS+8PSA0+RTjqRmN0UT1ukEppxqKaErCN/1lkoyR74oD9sOV9AaeDTzAMXKr3gdqBRSvm/MXc9ALzd+PvtwP3jv7zpQ7SxTUxAw9SFqxOqAjelKX/2ByN56YHDSM67yXAwzFAwbDkIejpi9jVJZ8DN4RMrprAHihWLq4upKHJFDZM5zm20Se4LKwopdNmj2Tdtvtz6oICSxTxOm6UHbpbRT2YeuNftoNBlT/LAIxHJc03q/5SYNpqtB26lgate4MkGvKzAyUAgHHeVPtFkshfnAW8FLhZC7DZ+XgN8BbhUCHEIeLVxe8YSbe4e82F2RIt44g3Y5kUVKcuf81EDN6fdJwYyp+KyeSxUFruZ5XWnTSVM12tlKhFCsKmugm1Hunj+eDff+NsBXruqlhs3zU/7PJtNsLxGFVUNB8P0DAZzykAx8XqclkFMU/udTA1cCGEU88Qb8IOnffQMKmmzqXMgLnbjzyELZTgYiXPG1DQeCwnFcGQm0wvPJAvlH1JKIaU8R0q5xvj5i5SyU0p5iZRyqZTy1VLKzFum5SFWrSVNA57sgavewlblz0PB/NPAR/qhxB+Y5pe2bBLah44XK2pL2G/Roc9kf5peK1PN5kUVNPcM8b5f7mR2iYf/vmFVRpWiZibKWIp4TLxu64ZWvWYjq0k+mc8ucScV85jy5evWz2U4GInLFTezULKRUCB+Mn1/wjAHE/PqYzJHq+VXQvIUUuBUH1js5ZTpkcZq4KAGKM8tsy5/Vnng+WXASwucOO0ipQc+mZfNY6Wh1svLp/tTpnk2pum1MtWYAfLOgQDfuXFtxl3/GmpL6BsO8bwxti7XICaYLWWtJJQADpuIVmtOFlbFPNuOdFFb6uGCpSpp4mj7iIwykoWSaSFPckfCwYC1hBItp59OHrhGYXrgsUn9Hf0BvB6HpUHevKjCsvxZ5YHn17/dZhNUFiVXY05F4GqsrKwtIRCOcLg9uaqxo9/PS6f6WGPkjk83GmpKWDO/jM+9poH1CzMvfDYrYR8/oPIMxuKBF6doKdttFHRN9olPTaf3R79nUkq2He1ic30F9VWq4Oto54gBj1ZiZvgdtIp9DfjDSTngMNJGV3vg0xDzTJwYxKwutr7UTlX+nI8SCpij1eINeDRwNQkjtMaLdJko5jSba1YnDwKeDthsKpXyXednNwDbrIR96qCqwxiTAU8xF7N3KDApfcATqSnxEAhFosfikY4BOvr9bKqvpKbEg9thS+GBZ2b6zCvv2OyzgUDIsjitfDpq4BqFqYXFXkq19/uT5BOTzaYOHiOjSCnzMogJWDa0ykcNfFFVES6HzXJSzb3PN3P23BKWjzLoOd8odjtYUFFI92CQAqc9qfFYNqQMYg4EJzWAaWKejMxiHlO23LyoAptNUF9VRFOnhQHPOAsleR7ugD8U1wvcRGvg05giCw88tg9KIgsrC5nldccZcH8ogpTknQYO1h5471AQt8OWVyckh93GstnJJfUH23y82NzL9WvnTdHKJhYzhbKm1DMmmaPYbT3YuHuSOxGaRMvpDR1825FOqordLDLkk7rKIo505G7Ao1fexj4HQhGCYRk3jcekwGnH5bDRM6Q98GmHaaSGEoKYqXpKCCHYvKiS7UdHyp/9RivZfJRQzIZWsSlZ3QOT27xovFhRk1xSHzvNZiZiSke55oCblBhBzMTYTrqCronEDMi29Q7H6d/mSaq+uogTXYPRPvD+UAQhwJHhgJHE2JfVNB4TIQRlBU56JnGogzbgGeJy2HDZbVEPfDgYxjccSumBg8oaaOvzc7xLlT9HhznkkcdqUlXsJhyRcQ3ruye5feh40VBbQkd/IDrtPhyR3L87fprNTGPEgI9twlCxx4GUyWPGeoYCU3IszPK6EUKV05/sHqKldziuX059ZRHBsKS5ZwhQaYRuhy3jq5BCZ/yVt5mFlqpB22T3Q9EGPAsKXPaoBt7Rb51CGMuWhHFYUQOehx64adhiZRTVwD8fDbiSE8x88GePdNLSOxwd9jwTMQuTxlLEAyMdCWMDmcPBMMPByJRIKE67jcoiN219w1G50my/C8oDh5GKzEAoknEAE0Y6DppBTLMOxCoLBYx+KJM4lUcb8CyI7QneYdEHJZEls+LLn83LsHxLI4SR/YwNZPYMTU3gaqysTMhEMafZvLrBsqHmjGBeeQFv3bKQK84e2/Ds4mg/lNgrscmvwoxldomb1t5hth/tpKzQybJZI0HoukplwM2eKP5QBFeGwxwgZjJ9ggduVcgDZkOryfPAJzfrPs9RU3nUB5mqD0os0fJnowGR6YHnaxATrDzw/DPgZYUuaks9NLb0MRhQ02yuWT0nLz+XTBFC8P+uO3vMrzMyF3PEAzcH+U5VQVdNiYeW3mGOdAywsa4irgtlVbELr9sR54FnWoUJytkSYiT7zNTArQp5QJ3Edg325Lgn2ZN/ruAUUuSyR8/AUQllFM10U30FJ7uHaO4ZivZkzksJJcEDl1JO+git8cSc2G5Os7lh3czMPhlvzBTEWAnFzLqY7DJ6k9mlHg6393OsczCp/7wQgrqqIo4abXj9oXDGGSjm8wudI2PVTAnFqhcKKOegZzCQdn7peKINeBYUxEooPutGVomYetz2o51RCSUfg5glBQ5cdlu0I2G/P0QoIvOqjD6Whlovh9v7uXP7CeaVF7Ahi8rGM5litzmVJ8aAD07+MIdYako80SZVZv1FLPVVRRztUAV12XrgED8Pd3QP3EkwLC3HL04E2oBnQZFr5INs7/dT4nGMOhx1RU0JXo+D7Ue7GA7lrwcuhJpO3+FT3tZIJ8L8k1BAfS6hiGT70S5uWDvXcpqNJpmRoQ7TSwMHpc83WLQBrqsqorl7CH8oTCAcycoDh/jY12AGGjgwaZko2oBnQaHbEW3unq6IJxa7zdTBu2KCmPlnwEHJRaYHPtVf2rESO7Dhei2fZEyxxVi1qW4rbKZGbqgrtxzksaiqiIiEE12DWWehABS4HFHppD8DCQWYtMk8OoiZBUoLMzxwX+oy+kQ21Vfw6P7TnDDywfNRQgGlg7cYJcvdedYLPJH6qiI8Thsra0uiTY80o2NKB/EGPIDHaZsyx6S2tAAg5bzUOrOpVYdhwHPywNX+DgZCCJH6KrrC6Idy44+fTTpRfPfGtbxiSVVW7z0a2oBnQaE7Po1w5ZzMmv5vXqR0uScPdQB57IEXu9nT3AuMNOzJVw3cbhN89XXnaOOdJXabUOX0MQZcDbaeuiuxpbOK+exrVvCG9dbDLeorTQPejz8UicpAmVLodtBn5Hab03hSFQKdM6+UD1y02LJfzGgJD7mgDXgWKA08jJSSjjSdCBM5a04JhS47e072AODJ0gOYLlR73XQNqHL6fNfAAa5dM3MLdyaSYrcjTgOf6nRSm03w3gushzqDyo6pKHLl7IEXOu209qpKTjUPM7UD5nbY+fQVK7J6/bGQn5Zkiih02wlHJH1DIXz+9GX0sTjtNtYvLEdK1cYyk8G805GqYhfhiKR7MBDTiTA/PXBN7iQOdeieoj4o2VBXWcjRjn4jiJndFXCh2z6igacY5jBV5KclmSIKDenD7G2SqQcORPNT87EK06QqZrhxz2AQr9uRtycjTe4kDnXIh5YK9VXFNHXkFsSMzT4bTDFObarQ374sKDQ+OLO/cJU388tGUwfP1wAmjJywOnwB9aXNo0EOmvFD9QSPz0KZ7lJafVUhrX3D9A4Fs5dQYmJfA/5wdDbAdEAb8CwwL51MDzzTLBRQwQ23w5aXOeAmIx748JQHrjRThxpsrDRwKaXRE2d6n8zrq4oBFYTMupDH6cAfihAKR1JO45kqps9K8gDzzHvM8MCzaT3qdthZv7DcciBsvhDth2J64NqAn5GoIKY6jvuGQ4QjctpPZaqrKoz+na0Bj07lCYbVNB5twPOTEQOuPPDKouzSgr76unOiDa3yEa/bgctho6PfT/dgkIWVOgXvTMTrGZmL2Zsn9QB1Mcdq1hKKceU9FAgzEAhbTuOZKrQBzwIzeHG8a5CyQmfWB8L8isLRHzSNEUJQXeym3eenZzAw7S+bNRNDscfBQCAczUiC6V+RW+R2MLvETVufP+sgpum4DfhDKedhThVaA88C84Ns6R3OSv+eSVR53bT2DdM3HNISyhlKdKiDPzRiwPMgoG0WbWXvgZsGPMxgIJxfWShCiJ8KIU4LIfbGbPuiEKJZCLHb+HnNxC5zehB75s0mhXAmUV3s4nC76uymPfAzE2/MUAezoKt0mmvgMGLAs9fA1f6aLaSL8iwL5Q7gCovtt0kp1xg/fxnfZU1PYscoTURZbD5Q7VWXoZDfVZia3BnpSBjKq5YKIx54dgbYTP01e+HnlQcupXwK6JqEtUx7CmNSAEfrAz5TiZWOpnvgSjMxFMcMdeiOeuDT/1gwA5nZN7NS+2sOwU5XSj/ZjEUD/5AQYo8hsaTshi+EeK8QYocQYkd7e/sY3m7qcdht0Q9/pk4vH43Y/Z7ugSvNxDAyF1N54CWe/KjIbagtwW4TzMryu1uY6IHPgCDmD4DFwBqgBfhmqgdKKbdKKTdIKTdUV1fn+HbTB1P/OmODmMXagJ/pmEFMn1954Pkipc2vKOTpWy/mlUuza+lqSiZmL/y8klCskFK2SSnDUsoI8GNg0/gua/piBjK1Bz51MxA1U4vXExPEzIMqzFhml3hStoJNRZIHnu8GXAhRG3PzemBvqsfONEz960zNQjE9cLtNRAfcas4svDEa+JlQket22LCJWAll+mjgo34DhRB3AhcBVUKIk8AXgIuEEGsACTQB75u4JU4vCgwP/MyVUNSXtazAmbUno5kZFDjt2MRIHviiGT4UQwhBkcsxLT3wUVcipbzRYvPtE7CWvMA8+1aeoVkoxW4HHqdNZ6CcwQgxMpUnHzoRjgcFLjunZ1AQ84yl0OWgvNCJMw+i7hOBmk7vPiO+tJrUeD1OegYD+IZDZ8TJPNbrLpxGaYTT51SSJ5w1pwTbGa4cbKyriA5v1ZyZeD0OTnarMWNnQjaSGch0OWzTynnTBjxLPnbpsqlewpRz25vWTPUSNFOM1+OIduU8IzxwQzaZTr3AQUsoGo0mB4rdjqgmfCbIaWY5/XSaxgPagGs0mhwwi3kgP/qgjBUzfVh74BqNJu8pjqkBODM0cIfxW3vgGo0mz/HGeKJnQkWuabinUw44aAOu0WhywKzGdNhEnDGfqZge+HTKAQdtwDUaTQ6YWnBZ4ZlRkVukPXCNRjNTMIOYZ0IGChCdRD+deoGDNuAajSYHzCBmWR4MchgPtAau0WhmDN6ohHKGeOCmAddZKBqNJt8xJZQzIQccRoKX2gPXaDR5jymhlJ8hPXFGPHBtwDUaTZ5jphHmwzDj8cAMYk6nToSgDbhGo8mByiIXH3v1Mq46p3b0B88AzppTwvsuXMQrl0yvub5CSjlpb7Zhwwa5Y8eOSXs/jUajmQkIIXZKKTckbtceuEaj0eQp2oBrNBpNnqINuEaj0eQp2oBrNBpNnqINuEaj0eQp2oBrNBpNnqINuEaj0eQp2oBrNBpNnjKphTxCiHbgWI5PrwI6xnE5U4Xej+mF3o/phd4PaxZKKZPKQCfVgI8FIcQOq0qkfEPvx/RC78f0Qu9HdmgJRaPRaPIUbcA1Go0mT8knA751qhcwTuj9mF7o/Zhe6P3IgrzRwDUajUYTTz554BqNRqOJIS8MuBDiCiHEASHEy0KIW6d6PZkihPipEOK0EGJvzLYKIcTDQohDxu/yqVzjaAgh5gshHhdC7BNCvCSE+IixPd/2wyOE2C6EeMHYj/80ttcLIbYZx9bvhBB5MSNMCGEXQjwvhPiTcTvv9kMI0SSEeFEIsVsIscPYllfHFYAQokwIcZcQYr8QolEIce5k7ce0N+BCCDvwPeBKYCVwoxBi5dSuKmPuAK5I2HYr8KiUcinwqHF7OhMC/k1KuRLYAnzQ+P/n2374gYullKuBNcAVQogtwFeB26SUS4Bu4N1Tt8Ss+AjQGHM7X/fjVVLKNTEpd/l2XAF8G3hQSrkCWI36XCZnP6SU0/oHOBf4W8ztzwCfmep1ZbH+OmBvzO0DQK3xdy1wYKrXmOX+3A9cms/7ARQCu4DNqGILh7E97librj/APMMoXAz8CRB5uh9NQFXCtrw6roBS4ChGPHGy92Pae+DAXOBEzO2TxrZ8ZbaUssX4uxWYPZWLyQYhRB2wFthGHu6HITvsBk4DDwOHgR4pZch4SL4cW98CPgVEjNuV5Od+SOAhIcROIcR7jW35dlzVA+3AzwxJ6ydCiCImaT/ywYDPWKQ6PedFGpAQohi4G/iolLIv9r582Q8pZVhKuQblwW4CVkztirJHCHEVcFpKuXOq1zIOnC+lXIeSRz8ohLgg9s48Oa4cwDrgB1LKtcAACXLJRO5HPhjwZmB+zO15xrZ8pU0IUQtg/D49xesZFSGEE2W8fy2lvMfYnHf7YSKl7AEeR0kNZUIIh3FXPhxb5wHXCCGagN+iZJRvk3/7gZSy2fh9GrgXdVLNt+PqJHBSSrnNuH0XyqBPyn7kgwF/DlhqRNldwJuBB6Z4TWPhAeDtxt9vR2nK0xYhhABuBxqllP8bc1e+7Ue1EKLM+LsApeM3ogz5642HTfv9kFJ+Rko5T0pZh/ouPCalvJk82w8hRJEQwmv+DVwG7CXPjispZStwQgix3Nh0CbCPydqPqQ4CZBgoeA1wEKVZfm6q15PFuu8EWoAg6kz9bpRe+ShwCHgEqJjqdY6yD+ejLv/2ALuNn9fk4X6cAzxv7Mde4PPG9kXAduBl4A+Ae6rXmsU+XQT8KR/3w1jvC8bPS+b3Ot+OK2PNa4AdxrF1H1A+WfuhKzE1Go0mT8kHCUWj0Wg0FmgDrtFoNHmKNuAajUaTp2gDrtFoNHmKNuAajUaTp2gDrtFoNHmKNuAajUaTp2gDrtFoNHnK/wf52Lm5De6uRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = inv_yhat  ## predicted target  cases\n",
    "dataY_plot = inv_y  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dkIy2vsI-UeR",
   "metadata": {
    "id": "dkIy2vsI-UeR"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "Fv0Rr2s_5lig",
   "metadata": {
    "id": "Fv0Rr2s_5lig"
   },
   "source": [
    "<ol>\n",
    "  <li> <a href=\"https://towardsdatascience.com/predictive-analytics-time-series-forecasting-with-gru-and-bilstm-in-tensorflow-87588c852915\">Predictive Analytics: Time-Series Forecasting with GRU and BiLSTM in TensorFlow</a></li>\n",
    "  <li><a href=\"https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/\">Multivariate Time Series Forecasting with LSTMs in Keras</a></li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "kMiD5Vp27n5W",
   "metadata": {
    "id": "kMiD5Vp27n5W"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "LSTM_tf_DuplicatedPrecipitationData.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}