timeSeriesV2_weeklyData_Rural.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8da86ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44dd90be",
   "metadata": {},
   "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": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>alejandria</td>\n",
       "      <td>66.638855</td>\n",
       "      <td>66.628891</td>\n",
       "      <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>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1019 columns</p>\n",
       "</div>"
      ],
      "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": "11df9062",
   "metadata": {},
   "source": [
    "## Columns with temperature, precipitation, and Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d039cfa",
   "metadata": {},
   "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",
       "      <th>TEMPERATURE_dec_18</th>\n",
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       "      <th>0</th>\n",
       "      <td>20.606707</td>\n",
       "      <td>20.672624</td>\n",
       "      <td>21.210392</td>\n",
       "      <td>21.113854</td>\n",
       "      <td>21.331241</td>\n",
       "      <td>21.114738</td>\n",
       "      <td>21.333450</td>\n",
       "      <td>20.634999</td>\n",
       "      <td>20.499266</td>\n",
       "      <td>19.855212</td>\n",
       "      <td>...</td>\n",
       "      <td>21.489558</td>\n",
       "      <td>20.985991</td>\n",
       "      <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",
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       "    <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",
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      ],
      "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",
   "execution_count": 5,
   "id": "861616e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <td>66.628891</td>\n",
       "      <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",
       "      <td>...</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
   "execution_count": 6,
   "id": "e6780ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <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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       "      <th>2007/w09</th>\n",
       "      <th>2007/w10</th>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 624 columns</p>\n",
       "</div>"
      ],
      "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": "51f9e3bf",
   "metadata": {},
   "source": [
    "# Temperature, Percipitation and Dengue Cases weekly 2007-2018 for Medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060b80f5",
   "metadata": {},
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba3f94b",
   "metadata": {},
   "source": [
    "Temperature, Percipitation, Cases for Medellin from original data  (sivigila - cases) (google earth engine - precipitation and worldclim - temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "246419ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>PRECIPITATION_aug_07</th>\n",
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       "      <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",
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       "</table>\n",
       "<p>1 rows × 1019 columns</p>\n",
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      ],
      "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": {},
   "source": [
    "### PRECIPITATION DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f68a43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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": {},
   "source": [
    "## Get date of precipitation from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71deca79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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": {},
   "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": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "  </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 month 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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>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": {},
   "outputs": [],
   "source": [
    "x_precipitation_medellin_transposed['MONTH']=x_precipitation_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90626348",
   "metadata": {},
   "source": [
    "### TEMPERATURE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fa53cc76",
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": "48b1fd2d",
   "metadata": {},
   "source": [
    "## Get date of temperature from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ca81f40",
   "metadata": {},
   "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_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": {},
   "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>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": {},
   "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>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": {},
   "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>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": {},
   "outputs": [],
   "source": [
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6278d6b",
   "metadata": {},
   "source": [
    "## Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07225e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": "fe0353f8",
   "metadata": {},
   "source": [
    "## Date of Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbbdafe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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",
       "      <th>DATA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
       "      <td>1</td>\n",
       "      <td>[2007, w01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w02</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </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,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
   "metadata": {},
   "outputs": [
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       "<p>624 rows × 3 columns</p>\n",
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      ],
      "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",
   "metadata": {},
   "outputs": [
    {
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       "      <td>201852</td>\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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   "outputs": [
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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",
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "<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": {},
   "outputs": [
    {
     "data": {
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       "      <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    ##w5 Jan 2days , Feb 5 days --> MonthMax --> the month for the last day of the week --> Feb wins!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd0749",
   "metadata": {},
   "source": [
    "### JOIN TEMPERATURE AND CASES AND PRECIPITATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "342a0e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e83e9b84",
   "metadata": {},
   "outputs": [],
   "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": "4d207511",
   "metadata": {},
   "source": [
    "# Load new weekly precipitation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80cbd5c2",
   "metadata": {},
   "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>LastDayWeek</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>percipitation_medellin_rural</th>\n",
       "      <th>percipitation_medellin_urbano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>14.097693</td>\n",
       "      <td>12.628933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>6.910390</td>\n",
       "      <td>9.067214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>21.616968</td>\n",
       "      <td>18.406665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LastDayWeek  percipitation_medellin  percipitation_medellin_rural  \\\n",
       "0  2007-01-06               13.096688                     14.097693   \n",
       "1  2007-01-13                8.437044                      6.910390   \n",
       "2  2007-01-20               19.373165                     21.616968   \n",
       "3  2007-01-27                0.000000                      0.000000   \n",
       "4  2007-02-03                3.725206                      3.144225   \n",
       "\n",
       "   percipitation_medellin_urbano  \n",
       "0                      12.628933  \n",
       "1                       9.067214  \n",
       "2                      18.406665  \n",
       "3                       0.000000  \n",
       "4                       3.901021  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prec_weekly = pd.read_csv('Data/Datos_prec_semanal.csv')\n",
    "# Rename extra column\n",
    "data_prec_weekly = data_prec_weekly.rename({'Unnamed: 0': 'LastDayWeek', 'Medellin':'percipitation_medellin', 'MedellinRural':'percipitation_medellin_rural', 'MedellinUrbano':'percipitation_medellin_urbano'}, axis=1)\n",
    "data_prec_weekly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7025722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LastDayWeek                      datetime64[ns]\n",
       "percipitation_medellin                  float64\n",
       "percipitation_medellin_rural            float64\n",
       "percipitation_medellin_urbano           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prec_weekly.LastDayWeek = pd.to_datetime(data_prec_weekly.LastDayWeek)\n",
    "data_prec_weekly.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4755d299",
   "metadata": {},
   "source": [
    "### merge new precipitaton data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ea699dcb",
   "metadata": {},
   "outputs": [
    {
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       "      <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>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</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",
       "      <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>8.916642</td>\n",
       "      <td>8.656351</td>\n",
       "      <td>9.114419</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>17.006086</td>\n",
       "      <td>16.076869</td>\n",
       "      <td>17.334870</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>5.673248</td>\n",
       "      <td>5.388778</td>\n",
       "      <td>5.794555</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>2.785103</td>\n",
       "      <td>2.708024</td>\n",
       "      <td>2.810241</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 11 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  percipitation_medellin  \\\n",
       "0               21.009283  [TEMPERATURE, jan, 07]               13.096688   \n",
       "1               21.009283  [TEMPERATURE, jan, 07]                8.437044   \n",
       "2               21.009283  [TEMPERATURE, jan, 07]               19.373165   \n",
       "3               21.009283  [TEMPERATURE, jan, 07]                0.000000   \n",
       "4               20.982925  [TEMPERATURE, feb, 07]                3.725206   \n",
       "..                    ...                     ...                     ...   \n",
       "619             20.936353  [TEMPERATURE, dec, 18]                8.916642   \n",
       "620             20.936353  [TEMPERATURE, dec, 18]               17.006086   \n",
       "621             20.936353  [TEMPERATURE, dec, 18]                5.673248   \n",
       "622             20.936353  [TEMPERATURE, dec, 18]                2.785103   \n",
       "623             20.936353  [TEMPERATURE, dec, 18]                0.000000   \n",
       "\n",
       "     percipitation_medellin_rural  percipitation_medellin_urbano  \n",
       "0                       14.097693                      12.628933  \n",
       "1                        6.910390                       9.067214  \n",
       "2                       21.616968                      18.406665  \n",
       "3                        0.000000                       0.000000  \n",
       "4                        3.144225                       3.901021  \n",
       "..                            ...                            ...  \n",
       "619                      8.656351                       9.114419  \n",
       "620                     16.076869                      17.334870  \n",
       "621                      5.388778                       5.794555  \n",
       "622                      2.708024                       2.810241  \n",
       "623                      0.000000                       0.000000  \n",
       "\n",
       "[624 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cases_temp_precip = pd.merge(merge_cases_temp, data_prec_weekly, on=['LastDayWeek'],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": "a120f93f",
   "metadata": {},
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ac7ee9",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d55f16d",
   "metadata": {},
   "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": "2654cf46",
   "metadata": {},
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753d15c1",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0d521f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from numpy import asarray\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from numpy import asarray\n",
    "\n",
    "\n",
    "# transform a time series dataset into a supervised learning dataset\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 = list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):   ##\n",
    "        cols.append(df.shift(i))\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "edcb0422",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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      "text/plain": [
       "     temperature_medellin  percipitation_medellin_rural  cases_medellin[t]\n",
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       "..                    ...                           ...                ...\n",
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       "621             20.936353                      5.388778                 27\n",
       "622             20.936353                      2.708024                 17\n",
       "623             20.936353                      0.000000                 17\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take Columns that we want\n",
    "data = merge_cases_temp_precip[['temperature_medellin','percipitation_medellin_rural']]\n",
    "data['cases_medellin[t]'] = merge_cases_temp_precip[['cases_medellin']]\n",
    "data  #DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b3934515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the time series data into supervised learning\n",
    "# we define 11 lag weeks\n",
    "lag_weeks = 11\n",
    "\n",
    "# we have as input 3 columns (temperature, precipitation, Dengue Cases)\n",
    "# The Result will be 3 x 11 = 33 columns\n",
    "data = series_to_supervised(data, n_in=lag_weeks)   ##previous six steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0af53a5f",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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     },
     "execution_count": 42,
     "metadata": {},
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   ],
   "source": [
    "# if we see the resulting dataframe is a dataframe of 33 columns\n",
    "# first 3 columns are week in t-11 and columns of last week t \n",
    "DataFrame(data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "06a54173",
   "metadata": {},
   "outputs": [
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       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>44.063033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.425911</td>\n",
       "      <td>38.458219</td>\n",
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       "      <td>0.005340</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>23.418052</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>74.504343</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>62.428213</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>8.656351</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
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       "      <td>45.424763</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>114.186801</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>16.076869</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
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       "      <td>41.0</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>16.076869</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>5.388778</td>\n",
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       "    <tr>\n",
       "      <th>611</th>\n",
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       "      <td>27.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>29.864676</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>53.085849</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <th>612</th>\n",
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       "            0           1     2          3           4     5          6   \\\n",
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       "\n",
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       "..          ...   ...        ...  ...        ...   ...        ...        ...   \n",
       "608  114.186801  22.0  20.748416  ...  23.418052  20.0  20.886703  74.504343   \n",
       "609   76.193731  27.0  20.748416  ...  74.504343  15.0  20.886703  62.428213   \n",
       "610   29.864676  23.0  20.748416  ...  62.428213  41.0  20.936353   8.656351   \n",
       "611   53.085849  24.0  20.748416  ...   8.656351  28.0  20.936353  16.076869   \n",
       "612   48.633548  24.0  20.886703  ...  16.076869  12.0  20.936353   5.388778   \n",
       "\n",
       "       29         30         31    32         33         34  \n",
       "0     0.0  21.425911  44.063033   0.0  21.425911  38.458219  \n",
       "1     0.0  21.425911  38.458219   0.0  21.425911  95.889804  \n",
       "2     0.0  21.425911  95.889804   0.0  21.237032  59.080759  \n",
       "3     0.0  21.237032  59.080759   1.0  21.237032  45.803598  \n",
       "4     1.0  21.237032  45.803598   0.0  21.237032  60.837236  \n",
       "..    ...        ...        ...   ...        ...        ...  \n",
       "608  15.0  20.886703  62.428213  41.0  20.936353   8.656351  \n",
       "609  41.0  20.936353   8.656351  28.0  20.936353  16.076869  \n",
       "610  28.0  20.936353  16.076869  12.0  20.936353   5.388778  \n",
       "611  12.0  20.936353   5.388778  27.0  20.936353   2.708024  \n",
       "612  27.0  20.936353   2.708024  17.0  20.936353   0.000000  \n",
       "\n",
       "[613 rows x 35 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the rows except last (Dengue Cases in week t) could be taken as fetures to train Machine Learning Models\n",
    "features_set = DataFrame(data[:,:-1])\n",
    "features_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ef93f4d9",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        0\n",
       "0     0.0\n",
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       "[613 rows x 1 columns]"
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     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last column (Dengue Cases in week t) is our target variable, so last column will be our labels\n",
    "labels_set = DataFrame(data[:,-1])\n",
    "labels_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4eee6b",
   "metadata": {},
   "source": [
    "### Train - test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cbe710fd",
   "metadata": {},
   "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": "b1d9d2f5",
   "metadata": {},
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a35769ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will train with 1st 90% of data and test with last 10%\n",
    "features_train = features_set[:split]  ##90% train of 618  ##556  up to 90% -> first 90%\n",
    "targets_train = labels_set[:split]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa1fd33",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ccbd435c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = features_set[split:]  ##10% test\n",
    "targets_test = labels_set[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656be139",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29deb4d0",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a963eb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# model\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "model.fit(features_train, targets_train)   ## fit on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9eb720e",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4ef6fcc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31.02, 29.9 , 25.08, 31.41, 19.78, 29.45, 18.44, 34.5 , 18.73,\n",
       "       24.34, 18.72, 17.28, 35.17, 21.13, 28.7 , 34.24, 20.28, 22.04,\n",
       "       20.43, 19.86, 16.98, 17.51, 17.67, 16.14, 16.75, 16.03, 15.62,\n",
       "       15.11, 16.93, 17.58, 17.64, 15.52, 15.62, 17.7 , 17.39, 18.09,\n",
       "       21.1 , 33.22, 37.38, 22.72, 23.09, 19.14, 19.71, 16.4 , 18.95,\n",
       "       28.34, 19.89, 16.65, 16.71, 17.19, 27.86, 19.42, 18.17, 17.19,\n",
       "       18.59, 15.97, 16.84, 39.14, 36.47, 22.86, 30.45, 18.84])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the model to predict with test data\n",
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a0f44937",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0\n",
       "551  27.0\n",
       "552  24.0\n",
       "553  27.0\n",
       "554  22.0\n",
       "555  28.0\n",
       "..    ...\n",
       "608  28.0\n",
       "609  12.0\n",
       "610  27.0\n",
       "611  17.0\n",
       "612  17.0\n",
       "\n",
       "[62 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6233ae10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 6.352419354838708\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "# Calculate MAE\n",
    "print(f\"the mean absolute error is: {mean_absolute_error(targets_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ea3918b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Actual data vs Predicted\n",
    "data_predict = prediction  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": "2c335c17",
   "metadata": {},
   "source": [
    "## Features importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f83e5de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# See feature importances of Random Forest\n",
    "features_importance = model.feature_importances_\n",
    "\n",
    "features_dic = {}\n",
    "features=['temperature', 'precipitation', 'Dengue_cases']\n",
    "t = 11\n",
    "feature = 0\n",
    "\n",
    "for i in range(0,len(features_importance)):\n",
    "    if feature == 3:\n",
    "        feature = 0\n",
    "        t = t - 1 \n",
    "    features_dic[features[feature]+\" Week-\"+str(t)] = features_importance[i]\n",
    "    feature += 1\n",
    "#features_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "96176dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dengue_cases Week-1', 0.9615855735055441),\n",
       " ('Dengue_cases Week-2', 0.01500840683761811),\n",
       " ('Dengue_cases Week-11', 0.0032945656148187464),\n",
       " ('precipitation Week-11', 0.0014601465323418287),\n",
       " ('temperature Week-10', 0.0011902288279111715),\n",
       " ('precipitation Week-0', 0.0011619136415154305),\n",
       " ('temperature Week-9', 0.0011223946740294545),\n",
       " ('Dengue_cases Week-10', 0.0010571519142805752),\n",
       " ('Dengue_cases Week-3', 0.000987623511607758),\n",
       " ('precipitation Week-5', 0.0008284552840037853),\n",
       " ('precipitation Week-2', 0.0008156374764270285),\n",
       " ('precipitation Week-9', 0.0007084937677058725),\n",
       " ('temperature Week-11', 0.0007040224171833297),\n",
       " ('precipitation Week-3', 0.0006743922477776288),\n",
       " ('precipitation Week-4', 0.0006486999022642645),\n",
       " ('temperature Week-4', 0.0006362457922498515),\n",
       " ('precipitation Week-10', 0.0006203825417989397),\n",
       " ('temperature Week-0', 0.0005922827214601643),\n",
       " ('precipitation Week-1', 0.000583577530821369),\n",
       " ('Dengue_cases Week-5', 0.0005773844809564525),\n",
       " ('precipitation Week-6', 0.0005099340153045533),\n",
       " ('Dengue_cases Week-8', 0.0004975096605708436),\n",
       " ('Dengue_cases Week-7', 0.00046173217727970493),\n",
       " ('temperature Week-8', 0.0004446112038987006),\n",
       " ('precipitation Week-7', 0.0004256647867517305),\n",
       " ('temperature Week-3', 0.00041359856368668),\n",
       " ('Dengue_cases Week-4', 0.00040812031592767735),\n",
       " ('temperature Week-2', 0.000371360243256015),\n",
       " ('Dengue_cases Week-9', 0.0003708324926129209),\n",
       " ('temperature Week-5', 0.0003684894300298461),\n",
       " ('Dengue_cases Week-6', 0.00032455155345912853),\n",
       " ('temperature Week-1', 0.0003095949765585285),\n",
       " ('temperature Week-6', 0.000301067092975582),\n",
       " ('precipitation Week-8', 0.00028269290394006264),\n",
       " ('temperature Week-7', 0.0002526613614320746)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_orders = sorted(features_dic.items(), key=lambda x: x[1], reverse=True)\n",
    "sort_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e93daa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nimport matplotlib.pyplot as plt\\nplt.pie(range(len(features_dic)), list(features_dic.values()))\\nplt.xticks(range(len(features_dic)), list(features_dic.keys()))\\nplt.show()\\n'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(range(len(features_dic)), list(features_dic.values()))\n",
    "plt.xticks(range(len(features_dic)), list(features_dic.keys()))\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6076516",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971435d3",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d2f86009",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BayesianRidge()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "model.fit(features_train, targets_train)   ## fit on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266ceeff",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "79243d77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26.27895235, 18.66472539, 28.77480382, 27.83157519, 16.20680583,\n",
       "       30.72874697, 23.69545385, 26.76183386, 15.87555037, 25.37475295,\n",
       "       15.01832889, 15.84421524, 37.57333965, 31.21054483, 26.83874224,\n",
       "       37.37207923, 24.69072204, 25.17800686, 25.06400266, 29.13391795,\n",
       "       24.78830233, 16.82457939, 24.99780397, 25.09895992, 31.08919488,\n",
       "       26.84226448, 22.1670289 , 23.34761103, 28.7883693 , 18.85333848,\n",
       "       21.52626556, 18.88061598, 18.75469016, 18.95532566, 19.29054477,\n",
       "       22.4009643 , 24.90915909, 25.80787602, 32.25355252, 23.09940834,\n",
       "       24.08868255, 19.34985001, 21.81998629, 22.6005381 , 28.21192228,\n",
       "       28.37189889, 32.80123255, 18.77102141, 17.50627747, 20.71679784,\n",
       "       37.31317718, 27.17293485, 18.82900402, 26.02945007, 23.73038478,\n",
       "       12.27627111,  8.96724209, 43.92515142, 34.41686651, 10.49077808,\n",
       "       23.17866183, 20.26707724])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c066fdf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 6.7051232818033375\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "print(f\"the mean absolute error is: {mean_absolute_error(targets_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6e33eb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = prediction  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": 60,
   "id": "8573519f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6af599",
   "metadata": {},
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbed216",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2f78b556",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" hidden_layer_sizes=60, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000 \""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "#import random\n",
    "#random.seed(4)\n",
    "\n",
    "# model\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "model.fit(features_train, targets_train)   ## fit on training data\n",
    "\n",
    "\"\"\" hidden_layer_sizes=60, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000 \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdd0199",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fd8779b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.80492399, 21.61972631, 28.32988534, 32.50083961, 21.90188019,\n",
       "       27.03171186, 31.33679197, 26.80566218, 23.53896258, 25.99598129,\n",
       "       17.98778419, 18.49024716, 30.65381104, 31.04044797, 27.44912673,\n",
       "       35.92780149, 25.24466495, 28.23537519, 26.22991557, 27.52635269,\n",
       "       24.61269602, 18.02744275, 20.28077157, 27.18876848, 31.34275842,\n",
       "       30.63179723, 13.90890617, 20.92958855, 25.97073463, 23.41131866,\n",
       "       21.35139524, 23.37138601, 18.60503705, 22.47748613, 29.34345086,\n",
       "       23.67018885, 21.92310953, 22.51937551, 32.31711352, 22.65071072,\n",
       "       23.45150442, 19.8417603 , 20.8809023 , 22.53447713, 26.1737109 ,\n",
       "       28.44769038, 31.61170582, 19.25040219, 17.24364321, 22.01511079,\n",
       "       34.58081439, 27.99925973, 18.81695534, 25.48211964, 23.18688644,\n",
       "       17.73546311, 20.50108763, 33.78339065, 35.08343912, 20.61632109,\n",
       "       29.05732907, 19.22250783])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "12f11bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 5.99946117114741\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "print(f\"the mean absolute error is: {mean_absolute_error(targets_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8c07ad31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6FA888AA7duzgiiuu4MCBA3R3d3P99ddz2WWX8Z73vIeXv/zlvOUtb+G6665jx44d3HbbbcTjcfbs2cOb3vQmdu7cyStf+UquuuqqlfiXODic92TyRXxuF5GAl7lM62ngQsqV6/B65ZVXyscff7zsvoMHD7Jt27YVW4MVJ0+e5DWveQ3PPvvsqq5jqTTD/9LBoZV4xSceYrAnxIHhGLsv6OBTb9692kuyRAjxhJTyysr7HQ/cwcHheUu2UMTncREJeFpyqINjwFFj0Vrd+3ZwcFg42bwy4GG/x9HAF8tKyjjnK87/0MFh4WTyugfubcnBxqtuwAOBAJOTk44BWgJSSiYnJwkEAqu9FAeHliKTL2hBTE9LBjFXPQ98w4YNnD17lmYdeNwqBAIBNmzYsNrLcHBoKbL5In6vi3CxNSWUVTfgXq+XwcHB1V6Gg4PD8wwpJdlCEb/bhTsgmEvnkVJWFfU1M6suoTg4ODisBvmiREpUEDPgIV+UpHPF+V/YRDgG3MHB4XmJ3kpWD2ICxFtMB3cMuIODw/MSfZiDz+0i4ldqcqvp4I4Bd3BweF6ie+B+r5tIQBnwVivmcQz4+cb9H4H7Przaq3BwaHqyJg88rHngrdZS1jHg5xtHvg9H7lvtVTg4ND1ZbUyiHsSE1htsvOpphA4NJjEBwjkvOzjMh55x4vO4iOpBzBaTUBwDfj4hJSTGwe1b7ZU4ODQ9+jxMvRcKtJ4Bn9dVE0IEhBCPCSGeFkLsF0L8uXb/F4QQJ4QQe7WfXcu+WofaZGJQyEJ2DgqtdSno4LDSGEFMk4TSahp4PR54BniJlHJOCOEFfiKE+J722HuklN9YvuU5LIjEROnv1AyEe1dtKQ4OzY7ZgHvdLgJeV20DnpiEYBc0UaXmvB64VMxpN73aj9N5qkE8cnyS+w6MNubN5sZKf6dnGvOeDivCV39+mqNj8fmf6NAwSlkobgDCfq99EPPEQ/B3W+DUT1dqeXVRV7RLCOEWQuwFxoAfSCkf1R76iBBinxDiE0IIv81r3ymEeFwI8bjTsKqaz/zoGB+951Bj3ixh+v+mpu2f59BU5AtF3n/nM3z5sTOrvZTnFRlTJSZANGDT0KpYgHs+CLIIs+dWconzUpcBl1IWpJS7gA3A1UKIy4APAFuBq4Au4H02r71DSnmllPLK3l7nkr6SVLbAdCLbmDcrM+AzjXlPh2VnKpFFytYrIml1zGmEAGE7A773v2H0Ge1FzXWVtKB8MynlDPAAcJOUcliTVzLAvwNXL8P6znvSuQLTySzFYgNUqTIN3PHAW4WxeAZovQBaQynk4e7fg8ljK/aRZg0c0HqCV3wHmTjc//+g73Lt9hzNRD1ZKL1CiA7t7zbgZcAhIUS/dp8AXgc4M8kWQSpXoChhJtWArJHEOAil5zkaeOswMacMeKzFikgayuxpePI/4ND/rthHZiskFDVWreI7+OmnYG4UXv1xVV+RbS4DXk8WSj/wH0IIN8rgf01K+T9CiPuFEL2AAPYCv7V8yzx/SeXUZdxUIkNXaIn524lx6NwEU8cdD7yFmJhTEtrz2gNPx9Tv2MppzJUaeCTgLZexZs/Cz/4BLrsNNl4FvnDTeeDzGnAp5T5gt8X9L1mWFT3PSGXVTjQ5l2XLmiW+WWICIv3qt6OBtwzjmoTSakUkDSWzigbcbfLAzSfRH/6F+v3SP1O/feHW1sAdGk/a8MAbEMhMjEOoBwIdjgfeQugSyvM6iKl74CuY5WGngReLEs49Afu+Cte9CzouUC/wN58H7hjwVcYw4MlGGfBeaOtwNPAWYtwJYpo88KEV+8hsoYjP7TJGqEUCHqSEZDYP3/+QOpZu+IPSC3zhptPAHQO+iuQKRfJa9snU3BINeCEPqamSAXc88JbB8MAzeQqNyEZqRXQPfG4U8g1Kq52HbL5o6N+gCnkAUkP74fTD8II/Bn+k9ALHA3cwowcwASaXKqEktRTCUA+0dToGvIXQPXCARPZ56oXrHjgS5kZW5CMrDbg+1CE9qx1LvZeUv8AXcTxwhxLpbMmAL1kD14t4Qr2aAZ9Z2vs5rBgTcxnDkDxvA5mGAWfFdPBMvmAEMAGjoVU6MavuCETLX+APq7zwJsIx4KuI2QNvnAFfUwpiyufp5XgLkSsUmU7mGOgOAs/jQGbaZMBXKBMlmy/i95o8cK2lbC6pGXB/hQF3NHAHM4014LqEonngxRzkkkt7T4dlZ1KLfQz2hACYa7Gp6A0jE4Nwn/p7pQy4FsTU0SfTFwwDHil/gS/kaOAOJVKahNIb8TfQA+9RQUxwdPAWQA9gDvaEAYg9nz3w9vVKZ16hTJSqIKYmoRRSNgbcH4FCpql67TsGfBXRPfD1HW1aQ6MlSB6JcXB5IdCuPHBwdPAWQA9gbtY98OerAc/ElGTRvl5VQK7ER9oEMWUmrsrmvcHyF/jC2gubRwd3DPgqktFm8q3vaCNbKC4tD1jPARdCaeDgeOAtwLjugfcqA/68DWKmYypoGF23Yh54Jl8uoYR8WmF6Jq687crBDX7NgDeRDn7+G/DhfXDg7tVehSWGB97ZBixRB09MKPkESh64U8zT9Oge+EC3o4Hjj0J0/QoHMd3GbbdLEPZ7ENl4dQATTB64Y8BXjh99DP73D1d7FZboGvi69gCwxFxw3QMHRwNvISbmMoR8bnrCPoSokFDGn2sqY7GspGNK/ouuV5OlVqCYJ1vhgYPqh+LOxav1byjd53jgK8jo/qZNqSt54EprW9JghzID7mjgrcJ4PENvxI8QyvszgpjFInz2JfDwP63uAleCQh5yiZIGjoT48LJ/bLZQNPqg6IQDHry5hLUBdzTwFSabgOmTUMw31VlTJ20KYsISPHApYW68JKH4wqovuOOBNz0Tcxl6wmoaYTTgLcVBMrOq811sZQJ6q4pexKNr4LAiOngmXygLYoIKZHoLCWsJxdHAV5ixQxjzl5vQG9UllCVr4NkE5FMlD1wI5YU7GnjTMzGXpTeiDHjZQIHkVPnv8xndgPsjEN2g/l4BHdxOQvEX5vPAHQO+MoztL/3dhN5oKlfA4xJEAx78HtfiDbieAx42NRR3+qG0BOPxkgceNo/00r+758N3qFdh+s0e+MoYcHMlJqiroEDRxoAbGnhi2ddWL+e3AR89UPq7CQ+EdK5Im9eNEILukM+oylsw5ipMnbaOprzqcCiRyReYTeUMDzxiHqpreOCTq7S6OnjmG+VzWBeLrikHourHH12Rfih2HnhIJmt74E001OH8NuBj+8Gr0rOaUU5I5QoEfCqNqTPkYyqRmecVNpirMHUcD7zp0U/Yhgfu95SyUFJNLqFMHoNvvh2e/srS3ytj8sBBywVfAQNeKFZp4FE/tJGx1sA9fnB5HAllxRg9ABdco/5ejDE7+ZPG7KA2pHMF2rQ81K6Qj6nkInOAzZ0IdQIdTXnSsuSxzy7r/7lZ0cvoSx64tzTSSzfcqammzKDi9MPqdyOcBF1CCbSr39H1yx7ELBYluYKsMuBdXnVSLerethkhmq6h1flrwOfGVI/sTb+gbi9mR/vx38P33rdsB1AqWzLg3Y3wwIMt6IFLCQ/8Fdz1m8+PlDkTehFPT1gNs1YSinYS1z3wYr681WqzcPoR9bsRa1sFDzxbKB9orNPpSgOQdoWsX+iPOB74ijCqBTA3XAVu3+L04LGDyovVDWSDMUsoXSG//VSeXBp+/m9w6H+tH09MqJ3fGyjd19ahPJtiwfo1zcLcqDJW4bXw/Q/Cjz++2itaMXQP3CyhpHNFcoViuXTSjDLKmUfV73QDDHi6ov92+4ZlL+bJGPMw3WX3d7rVd5JyBateAzTdYOPz14CPHVS/11y6uCG/qelSMcH4oYYuzfiIXIE2LQreHfaRyBaM3HBAFTg8+Z/wD1fA//4R3PN+6zdKjJfr36AV88jSwdGs6Cfa1/8r7Hgj/PDP4cGPNqds0GAmtBO2OYgJWjWmOXjZbAY8MQkTz6m/G+WBu/1KYwYtE2V5i3n0gcZVGrjmgSdos35hk41VO48N+H6lCYd7FycnjJmM9vjhxq5NI50rEDBp4KDlgksJ+78F/3wt3P27EFmrjNvMaXXwVGKuwtRplYZWugHv36mM+K63woN/DT/8i/PeiI/HM0T8HmMfCGsDBeYy2nxTl+pPbcgpzYLufXvaGuSBx8qn30TXq9/LqINn8spR8ldkoURECoA5UcsDdwz48jN6ANZsV38vpqhlXPPghavkbTQYswbeGTQZ8B/8KXz919Rnv+m/4Dd+CHt+Vb1o+KnqN7Iy4K3S0GrsgGrkH+wClxtu+Ue44v/ATz4Oj/zzaq9uWRmfyxjeN5QGCsTTeUhOQ9egeqDZUgnPPKJOLhdc05grPL2RlY5hwJdPB7fzwEOoISixYqDqNYDjga8IxaKSPdZeqm4vygM/qJrL9+9cNgklnTcFMcMmA372cVi3B37nYdh2s4p+9+9ULxqyM+CVEkqH+t0KHvja7aXbLhe85hPQvQVO/Wz11rUCmIt4oCShxNM55XV3X6QeaDYJ5fSjsG6XiltkGmDAKz3w9hUw4DZBTN2AzxZtJJQmG2w8rwEXQgSEEI8JIZ4WQuwXQvy5dv+gEOJRIcRRIcRXhRC+5V9unUyfUOPEDA+8Y+FBzLGDsGYr9G5TXeGWgVS2aApimgx4JgaRfuWR6gTalVE7V2HAiwXloYXWlN/fCg2tCnklT63ZXn6/3gqgiSreloOJKg/cJKEkp6BzQF2FNZOEks8oJ2LjNcprboSEkomVF874I8tezJM1gpjlJrCtqCSUmZoeeGsFMTPAS6SUO4FdwE1CiGuBjwGfkFJuAaaBty/bKhfKmFaBudYkoSzGgPduhd6LYW5kWQyhOQ+8WzPgk4msuiytnIgNyiuv9MBT0yCLramBTx1XI6r0KyUz3uB5P9NTeeAlv0fXwBOJOa23Tbfad5tJQhnaq76zC65T+2gmvvRYRTpWXTizzH3B7SQUf2GOohRM57zWL9Q18CaJz8xrwKVCv2bwaj8SeAnwDe3+/wBetxwLXBSjBwChDDBo3ly8/ll2c+OQnGCs7UIO5LTeDA3WwaWUWhaKMuDRgBe3S6hccL03ciXrdkN8COIjADx1eppnnjuqHrOTUJpZA9d71VgZcF8YsuevAU/nCsTT+TIJRZ/JmJvTytPbutRPM0koegGP7oHLwtKvlDIW+/sy54LraYSVpfTe/BwJAsxlitYv9IdVbn5+kTUbDaYuDVwI4RZC7AXGgB8Ax4AZKaXeff4ssN7mte8UQjwuhHh8fHx58qmrGNuvLj99WjK+4Y3O1Pd6LYD51ZMh/vIxLa2vwZkouYKkUJS0aRKKyyXoDPqYnktXB3V01u1Wv4f2AvDhu/fzlQeeUPdVeuAev2oj0MwSyugB1fa255Lqx3zBptIaG43eOtgsoUT1qehzmsEOdkGwu7k88DOPQteFKrtLv0pcaiphxmICTvvyVmPaeeAiM0dCBEsFVZUY/VCaY9+sy4BLKQtSyl3ABuBqYGu9HyClvENKeaWU8sre3t75X9AIRg+Ue3ULzcjQcsifya/jSKZL5ahONNaA68McAqaRTl0hL4n4DCCtJZT+y5UmOvQk+UKRQyNxhFUjK51mb2g1dgC6LywvQNI5zyWUUhVmyYD7PS48LkFRTxVt61JGvFlkMCmVAb/gWnVb95qXkolSLCoDXrm/L/NknoyNAScTIyWCpZYGlTTZUIcFZaFIKWeAB4DrgA4hhDYFlA3Aygyym49cCqaOlQfGjIBenQfC2EFo6+R4Kkw8J6HnooZ74GnDgJe+gq6Qj2xiRt2wklB8ISULDT3FyckEmXwRb0Y72C0NeJOX048+Wx3A1PGFzmsJZSJe3gcFQAhBJOBBpLTvNKgZ8GaRUCaPqquBjVp/Ib9uwJfggWfjgLTWwJexmEfPQqmsxCQTJ+0K2g+XbrKhDvVkofQKITq0v9uAlwEHUYb8Nu1pvwZ8e5nWuDDGD6ugnjk1baEpdWMHoXcb08kc6VyRYs/Fy2bA20weeHfIT0434FYSCigZZegpDgypg6aTWaRwlU5SZpq5oVVmTk1LstK/QfPAE00TLGo0+jT6HpMBB6WDu9Lafhrs1jTwyeb4P+j9Ty64Tv1uhIRiNLKqNODL2xfcLguFTJy0O1w+m9RMkw11qMcD7wceEELsA34O/EBK+T/A+4A/FEIcBbqBzy3fMheAnoGyxkJCqceASwnjB5FrtjGdVJdvuc6LVBVkAz3ClIUB7wr5KKT0vhAWHjgoA54Y5+wpFbzsIUYh0K3ypytp62heD1zPra/lgcti0wSL6uJfXwQP11d8NFHRyEon7Pfi0U+6uoRSyDSHnHTmEbWmHi0/XXcyliKhVDay0mnXJ/Msjw6uV2JWSyhxcp5QabBGJU022Ngz3xOklPuA3Rb3H0fp4c3F2AGlWXdtLt23kJzo+DCkZ0m2X0RRc3rSHVvwI2HySKmgZono49T0PHBQBvxcJgY+rDVwMAKZ+bNPIMQl9IhZMv4u6y+ymTVwvYR+bQ0DDirDwUojbzZyKRjeCx0b4brfmffp43MZbRJT+SV8JODBF5tRnp7Hp7xwUDKK/j9ZLU4/ouQTIdTtlfDAZ5dnJmjWJguFTJy898L5g5i1NPBi0dqhWgbOv0rM0QPQewm4TSZN92br8Ua1AOZ0+ELjrnhE+7uBBT1WHnh32EdUqwQz9MVK1l4GLg/RyWe4bF073SJG0mshn0Bza+BjB1SWTMeA9eNerRdFrkWKeXStdvpUXU+vLOLRifg9BHIzytOF0u/VzkRJTCgNXO+vDyYPfAkG3PDAK/Z3vZhnmTxwuywUMnGK3kgND3weDXz6FHxkLQw/3aCV1qY1DPiZn8PTX63vuWMHqnVVl1vtIAsw4KP+QeOuWHCjSndrYCaKlQbeGfQREZoBt5NQvAHyPdvYnDvCCy/uoZsYsy4bAx7oUAUhuXTD1t0wRverSlc7T8WnGfBWCWTqhmamPgNeWUavEwl4CBZiENS+06BmwFe7GlNvYKXr36CuCIR7aRKKnQcOy1rMY6mBFwuQjSN9YWKL1cAnnoNCdtka4FXSGgb8ma/B994z//OSU8oTstJV2zrqC+iNH4TQGsYKpYkciYJHNRZqYE+UVFbtQG0+cxDT5IHbSSjARHQ7l7uOc81AFz0ixrSwMfbN2tBKSnWitdO/oTQKr1U88Jjmgadn65KtJuayVQFMUEHMcCFW8rzNEspqcvoR1Ve/f1fpPiG0asyleOCa8beaQbmMxTzZQhGXAI9ZQtG8ahGIkM0XDZ28jPk0cH12wAp9X61hwCN96sCYzxurLKE3U29AT+uBMpUs5Z8mMnlVbLIMEkrApIF2hZUHXnCZeiNbcMRzER0iwQ7/MGGRYqxoY+yN7JuZBq26QcyNKUlg7WX2zzFr4K1A3HSpX4cXPhHP0GvhgYf9XiIyXvK8DQmlCTzwdbur4xFL7YeStgligirmWaZ+KJl89TxMXdcW2loSGQsD7vaqGNt8BnyFrphaxID3q9/z5YSOWmSg6NSjBxeLRnMl83ScVLagdPWpY/WX48+DYcB95XngUZJkPRbz+Ez8PLMJgM5zPwJgJG/z/IXmv68Uo8+q33YBTGhBCcW0b86jg6dzBeKZvLUGHvDQSYy8X/vujO9wFQ14NllqYFXJkj3wmBoU7LXo/hddD4nlKeaxmkivG3B3UF3R2gYya7WUnRtTvx0P3IRhwEdqP2/8kNJ9I33Vj9XT0Gr2jDqz9m41Sp0BEroBL+ZVA6YGkLHRwKMiScpdO9vgwekecnjhyL0AnM3aPL+WAT/zmIotrAZWqZ6VtJyEcq7UEXIeD1yvwrTywKM+aBdJsr4OdYfbo+IhqxnEPHa/0nW33Fj9mL996R64P1rKbDGj9wWPNz6QmckX8Xuri3gAvEHlgdsW89Qa6qBXRjseuIl6PfDZs9C5yXpnqGesmik3eSqRNbrDJbN56LlYe05jghNGGqFpJ/K6XXS6UySFvQeeKxQ5NJ5hPHSR0VjoZMpmeojeA8ZKA//W78A33qauOlaa0QOql3So2/45reaBx4dhzTZljObxwEtFPNUdmLvcantTHlNcY7UbWh28WzkDm26ofmzJHrhFGb2OUczTeANu7YGr7fAGO4AaBrzWYGNDA1+ZE26LGHDNo57PA48NQWSd9WO6hCIlX3nsNB+865nq5+ieYe8lTCWybOhUl3WJTKFkwBuUiZLKFfC6Bd6KnajTlSKOjUEGjo8nyBaKpHt3qisC4HgqSFFPWjdj54HHR1VO++xpOL0KQxPG9tcOYILJA28RAx4bVganY9O8HrhRRh+uzm/vQBmGhNtkwFezoVU+C4fvgUteXZ6aqxNoX3ohj13VsV7Msww6eCZfsKzCBAiEO4AaEkqtwcZOENOCQLuavzefBx47VzprV9LWqbW+nOOe/SPcvdfirD52SF22tXUwlciyvqMNISCVzSvdq31j4zxw0zxMM1GRYlbaTAMBDg4rL6Ft4MrSsosRo2q0DH8UENXS0amfan8IePorC1z5EikW1P/QroRexwhiNkfFW02KBdUzPrpOXQEuwQNvRxmGObcpKyPYtXoa+ImHVKbItputH29EENMuZTasSVK6UWwg2RpBTF9IrSeZtQhiQm0N3JBQVibu1BoGXAjlhdfywHMptZPXMuAAqWnOTaeYy+Srk/XHDqjLYNRknO6wj6DXrTRwUDp4gwy4eZiDmTBJpgu1DbjP7aLnYhVQyrvbSBEwJpyX4XJZZ9+c+qnycHe8EQ58W/3vVoqp45BPz++Be/yq82IrSCiJcXU1FOnXPPDTNXuXTMTVd9UdsghiFpUxnMVkwNu61IzM1eDg3WqM2OYXWz+uSyiLleJqeeD+dpVnnpxY3HvXIFuwN+BtYWUrbA24nQYupeOB2xLpr23AdZ1MD3xUoqXUyeQU52aUwRqZNRW4FAsqCb93K1JKphJZukJ+gn6P0sBBpRJOHGmIbpzKWnvgweIck3n70vEDwzEuWhvGu3YbeIPk25SOrAfGqrBqaHXqZ6qibvdb1QF0+Lv2C33qv5SRbxTzldDrCKFOMq0goRj7nuaB51OlbAQLJuYydAS91QYECBWUAZ8xG/DVklCKBTj0v3Dxy+3bGfijgFz8lVLlPEwzLteybXs2X7SVUNrCaj3GcV+JnQeenoFiTq05l1iRPj4tZMD7akso5oPICs0Dj89MGmfW0ZjJgE+fNDzDuUyebKFId8hH0OcunYl7L1YH5+zpJW4MZdN4DPJZfDLDWNaPtPHgDg7H2dYfVXrkuj3IiDphTczZ7CyV6ZOJSXWlsel6GHiBOuHZySgTR+A774Yf/sVCN8+e0f3Ks9anJdXCF2yNPHB9v9Q1cKipg9tVYQIE88qAT0tTIDvYqQzCSlfUnn5Yeb928gksvR9KZtbeA4dlNeBVJ9B0DHxhgn4lbVnmgYP9YGNdPtEHlKyAF95CBlzzwO0uTef1wJUBn54cNe4qM+BGattWNVgY6Az5CPo8pS9SNzoNKOhJ54pljawA4yCYlUHLUt7xeIaJuYwy4ACv/wy5W1T3O3sD3lGugetBy03XqxYDO94IR39o7THe+3+VNDB5tDzPeSmMHVCNxqzyfitplaEO+r4X0TxwqKmDT8xZF/EA+HPT5KSbqZxJH9erMVdaBz/4HfAEYMvL7J+zlH4oUtbOQgG17YnGG/CMXRaKP4LH7cLncZHM1fDAreZi6vJJr5bwsALfV0sY8GJRMuvVLkvsuoBpJbfSKgccjJS6+HTJUI2UGXAthdCUA94d8hHyuU0SSuMyUZQHXukBqGh+TAaNk4gZPYC5rU+7vO7YSKTvQnwel72EUumBn/qZOijX71G3d75ZBXef/Wb5644/CM99Dy59vbp98icL2Tx7NJmqLnzh1vDAY0OqGCXUCx0XqPtmTto+fXwuY1lGD+BOTzNNhLhZf13hasxCUSKLBWXAL7yx1MDJikVO5ZFSk11k0bqMXie0gh54Jm6sJeRzk7T1wMNq3ZXOhWHAtf3b8cAV7/vmPv7uZ9oZ3k4Hjw2Rckd57Wf3Wj+ueeDJWbUzeN2CUbMGPn5IHXy+ENOa8ewK+WgzSyjBLnWQNqAnimUQU/PA4wTVcOMKDAPeX/JYhBD0hv1GZkMVlRr4yZ/AhqtKpfprtqkWuU9/ufScYgG+/yfq//Haf1LBpJM/XugmWhMbUtk89dBKEkq4T2m2vpDaR2w8cCkl4zZl9AAkp4iLSHkO8go2tJpN5bjqI/dx7333KKdo+y21X6Ab8AVIKLF0jp1/fi8PPaMVxc0roSxXENOikEcz4EGfh0QtDRwYnZhg+5/ew7PntJOXfhXb43jgZezc2MGRlOYF2OngsSGGZSf7zs4anf7K8LaB209ubpKQz81Ad6jcA586Bt1bgNLA2a6Qj5DPUx7M6N3akEyUVLZQ1sgKKPPAJy2ySg6NxOmLBugMlaef9YR983jgM+pyLzUDI88o+cTMzttV+0utEyN7vwSjz8BL/1wZpE2/0BgPPB1TXle0v77nt5KEYo691MgFPzudIpktsLnXpno2NU3cFS2fCGM0tFr+QOZ3nxlmKpEl9sQ31VXFxa+o/YJFSChHRuPE0nmeOabFkmpKKD3qCrJo4w0vEtsgprY9wZoeuDLyw2PjJLMFnhvVVIHEBCBKAy8cD1xx7eYuRqWWBmjjgednznIq1wHAqUmLg14IaOugkJxmfWcbfe0BRmKa0ZMSJo+radtgyBfdYR9Bv7s8mLH2Mhh5dsk9USzzwNNmD9xaQtnWX3252RvxW6cRQin/PRPXWoJKGKgw4JfdptK1nv6Ket79f6n6XujyycAN6gS31Io4/eRrV2xVSavMxYwNlZ+UauSCW11FlZGcIumJlheRrKCEcueTZwHJlamfMLfuF6xH9Zkxgpj1SygnJtR3Ojymeax2ve9BnbxkseEN2WybWekeuN9D0soRBKNGIZdUhns6qX1XifHSVTo4HrjOhb1h8m1aUr+NB16YOcewVDv6iQmblKa2TlzpGdZ3tLE2GmBM98CTU2oH1Kb4TCWy+D0u2rzuag9841UqE0VvyLRILCUUzQOPEyzrhgiqcuzo2Jzlgd8T9tfwwDvU79S08qJdXlh/Zflzwr2w5aXwzNfhxx+HuVF4xV+XWhIMaCXUS/XCjUBznR64L9QavVDiw+UnpY5Nqq1DofoS/OBwHCFga5+N7puaIu1pL69RWCEJ5fRkkp+fnOaDe4oMukZ5yPML879oER64fnxOTmqacS0PPNSjfjf46iOTL1g3s9K2R2ngtSWUnDb+cEY/VhPjynh721ThoeOBK4QQXLZ5PQlsqjHzWfyZCcaF+rL1M3wVbZ34crPKA48GGItnKBSl8i4BupUHPjmXpTvkQwhRnkYIpY5sZx5b0jZZ5oFrOmLOGynrhghwdGyOfFFaGvDeiJ+phLYtlZh7gp/6Kay/otRnxMzONynN8yefgB2/BBuuKD3Wt0NpnUvVwY1sjQVIKM2ugVvJQp2b1FWPRS/rg8MxNnUFCfktytKlhOQUGW9HuQbu8avA2TIbhLueUut9c2QvRQSfPnuR9T5lxtumpJYFaOAntePTrafi1dTAl2cikb2EYtbAa0so+ZTugVcYcH3dK1CN2RIGHODqwS5Gih2kpixm5GlGPdC9kZ6w39YDz/uihItzbOgMsjbqp1CUTM5lSh0GDQ88Q5c2bDbo85DJF8kXtOKd9g3K21qCAZdSWueBa16Mry1aJaEcHFY7i50HXpRYl9PrDa1mz8LQ3mr5ROeSV6kDyeOHG/+0/DGXW+nmS/XA9a5ydrn6lbSChGKVvlojF/zgSMxePsnOQTFH3t9R3UhpmRtaSSm586mzXLe5m+iJe5jpuYJDc2389Og8AUQhtH4o9Rvw4xMJukLm6VPzBDGhoYFMKWV1JaaURhohKA08NU8Qs5BWx+SMWULRrxhWqAFZyxjwawa7GZFdJCerDfjchAqG9K4fZHNPyDjDV5JwRYiKhCGhgJZKOHlMFZdoB95UMkeXVuYc8isjW6aHbbwKzs5jwNMx2y8wV5AUJdZBTH+UzkgbxycS/PTohPHzo+fG8XtcDHRXe896X2lLGUX3wJ/7vvIKN9lcFnvb4JV/A7f8oxrMW8nADepEt5TGQrFhdUKpJwccSkHMGmXptizmNYshbnFV0Tmgflfo4IlMnlOTyRr6t/IyC4HO6jYPwa5lDWI+eXqaU5NJ3rLdB2P7ie66hWjAo2ni8+CP1p1GKKXk5ESCl21bS8SY/zpPEBMauu35okTKinFq2TlAltII/e4aHrgy4NLSgGtSb7BzRTTweafSNwuX9EX4rrsLET9a9djJ489xGTC4+SIGRJD7D1k3v5klTBdzrO9sw+NS+u5oTPPA2zeqKeAoD3xzjwpU6EY2lS0QDXjVG228RpWXx0ese48DfOu3VVrRb/yg6iFjmIOVhOKPsqGzje8+M8Jb/+3Rsoev3NRZPgJKQ6/qsyzm0TXww99VgUqrpvw6u263f8ysg+98k/3zahEfrt/7Bk3qkapXi5XsY8fnb4KNV8PLGlhBaode4GSWUNo3KIegwgM/NGJ/FQUYJ3zZ1sVcJo+UEqHHIZa5odWdT54j4HXx0g51QvJsvJqbd4a588lzzGXyRmtlSxbQUnY0liGVK3DZ+ijycJ5CzoXbZ5ORAyUPPNE4DzxjNdBYry8xSSjzaeAya5JQ8ll1EtMllLauUtuIZaRlDLjbJfC0ryM8+7Dyrkw9v0fPnuAyYNvFWxmMTzExd5Z4OkdEN7gak8UgF4g0GyIeNRoJzQOfOmbo3wBTc1k6g8qYh3zqX5Qwf5kbrla/zzxmnSebTcCRH9gWKFgNNFYPzEIgyl/fejm//guDVa+70Cb1rEeTe2p64IlxpX/XKpqoxdodyns++ePFG/DKdLv50AfI5pL1G/BCTn0v06dUGqRVb/hGYnjgpu1yeyG6ocoDL2Wg2AcwAUSwm0JRyWxBbf+jrQumTjR06TqZfIHvPD3EKy7to238HnXy6dvBrXuyfOnR09zz7Ai3XbHB/g0W0JHwxISKaQz2hMm15UjkgkRrfUfeQMP1f2MivbuWAXeTzBXKT6I62n4pMmpbZpK5ksSjSygr1L+mZSQUgPY1G/GRZ3ysPJCZmDhDSrQRCHcw2KMOdCsZZSynHuvxpOgO+3G7BKMzKS2FUOnf6VyBRLZAt6GBaxKK+XKq/3I14NVORjnxEBQy6gu0yETQhzm0+Sr+/elZCLTT3ubl6sGuqp9um+IPXUKx9MC1/HfAXj6pB5dL08GXEMiMD9cfwAQlocDCGiVNn1JSUXyo1B5hOYkNKeNa2eypszoX/OBwjEjAw/oOGwlJ6zjoDiuvsyoXfJk01fsPjhFL57l1zwY1Oq3nYvCH2XNBJ5u6g/PLKIH2uj1w3YAP9ARZ688SkwH7plE6DZaPDANuLuQxDLieB+5BStXyogqXG7xBXDm1X84ks6UiHnMQMz2z7ANTWsqAr9uojOyB50q9SBKZPJ7EMMnAWhCCwR51djwxWZ29cC6jDjJXega3S1UwxqZHtRTC8hzwLq1YRs8WKPPAPX414NUukHn4e9of0jL4krLzwGu11qxB2O/BP185PVhPVFkIAzeopl8zZxb+2kJO7eQLllBYWCBz0iSxHamWrxpOzEYW6qjOBT84HGNbX7Tao9PRPHCPZsBjldWYmdmGzWQ1c+dT51gT8XP95i4Y3qv2bVT21627N/Dw8UmGZmq0HF6AB35yMoHP42Jdexs93jRxGTSkJVvmq8bMZ+Hu/6/uK5SspYSird+kgQP21Zi+MK58QntOgVy8woC3dan8datpWA1kXgMuhNgohHhACHFACLFfCPFu7f4PCyHOCSH2aj+vWtaVAhsuULLCyRPHjPuePD1NH5O42lUWwCYtyHdivNqAn05pFYxaes/a9gCuae1L77Y24LoGXpXUv+EqldVROXBVShUw1FKNmBulEt2AV83k0zzwhSKEmKeYpwMQcMG1C37vMgZfoH4bAyEWwNwoIBfogS9iKo+eEhrdAEfvq/91iyU+ZL1NnZvUkAet13qxKDk0EreXT8DwsNsimgdudhr0Yp4Gp6ZNJbI8cGiM1+5ahyc5qr6n/l3G46/fvR4p4Vt7awSvF+CBHx9PMNAdxOUSREWKGEFDWrIl2FPbAx8/CE9+ER77bF1ryBa0489KAw+UPHDAvhrTH8Zj2i9T01qBYdjkgasH6lrTYqnHA88DfySl3A5cC7xLCKE3c/6ElHKX9lOjqXRj8LQrT2diuOTZPHp8in4xRbhXNREKeN2s72jjpIUHfmJOM+DaWbEv6qctflLdZyriAdXICkoaeNUXufFqJZOM7Cu/f/hpdeDueIO6PVcdUE1n7TTwGr2R56En7LfvSBheq2QfPaC5WNZcqnTwE4uQUeZr92uF4YEvIBd88piq7Ntxm2qHupRpMfVgp+sbqYTqauXMdJJktmAfwATlgQfaCQXVlWJZNaaRD91YGeU7Tw+RL0pNPtmr7tQ8cIALuoNcNdDJnU+es21xXBrqMH+5+8nJBAPd6sTsL8yREkEODdfhgdfqSKjvW4e+U1f2kS6L1A5i6o6bvQfuLZT2y8ys5qiZPXBY9lTCeQ24lHJYSvmk9nccOAjY9GxdZsIq46MYGzYaTj1+fJw1YgZvZynIMtAT5PhE+UGfzhU4mSz3wPuiAdpTZ8pTCCs8cP2LrLqUMgKZ5ZkiPPd9QMCuX1a3E9VtWtN5CwNu5KEuzoD3RmpUY978Sbjt3+d9jwNDMQ7Xupx1uZSMshgdfKFFPGAaq7YAA64HpC96uWqFe/zB+l+7UPJZFRy2MuBGW9mTQB0l9KAO9rYuI+NjbgUaWt351Dm29UfVuoaeMgKYZm7ds4GjY3M8e87mZKjvs3adQjUKRcnpySSDWjBepGO42trn98BD83jgs5pGP3O62qGyIFuo34Db9gT3R/AVkkQC6rvKxcZUl0898L5C1bML0sCFEAPAbkC3Wr8rhNgnhPi8EMKyaYIQ4p1CiMeFEI+Pjy9xtp03QN7fwVoxzWMnp0jnCpw7ewo3xbKDaKA7xInxuTKPYWgmxazUDIJmwNdEA/QXhiiaUggnbQx4qjInNNoP7RdU6+DPfQ82XFmaOGMloWTVDlSWB55LKoOzCAkF5vHAuzaXZdnY8Udff5o//vrTtZ808AIVnJtZ4FAL89CDelmMhDJ5XG3rxquVYTm6jDr4nHbZbHVSqijmOTAcxyVUOqwtyUkIdhlGIZ6pCGLqz2kQ8XSOp8/M8KrLtFTY4b2qWVtFxs9Nl6rHf3bMRoeuc6jD0EyKbKHIoOaBk4nhD3dyaCRuPZRbJ9ilDbSw0eFjQypFVrhUC9x50DVwv1UWik/XwLUr7xoauL+YNALScm5Med96fEOPO622B64jhAgD3wR+X0oZA/4FuBDYBQwDf2/1OinlHVLKK6WUV/b29i59wdF++l0zPHZiir1nZuguajuVqRJusCdELJ0vNZlBdYGLUW7A+6IBNokR0pEB43lTiQxulzByvo0gptUXufGqcgMeH1FezMU3Ke/RF7aUUCyDmPql/iIllN6wj8lEtlQxukAy+QJHRuPsH5olZjeNGxbfFyU2pDJ3dENUDwuVUHJpmD2jAtJur5rjeOS+5SvqMXLALS5Iw2tV9o/JAx/sCVmO0TNIKQ884lf7Xlk15jJckusjBS/oDqr/0dBTZfq3TkfQi9sl7PeLOvuhlFIIQ+rz0jHC7Srn/ex0jSDpfCcvXcbadD0c/J+aawCbIGZ6VmU9udXxrh+btQYbt8kUGzqVARdJUxUmNJcHLoTwooz3l6SUdwJIKUellAUpZRH4LHD18i2zhCvaz6A/xqMnJpX+7dL+QSbPblArwjlhklHOzaQo4qLobzc6m/VF/QyKEWJtpcrDqYTKAXdphT5+jwuXsAlmbLxGBbH0S7gj96rfF9+kfod6awYxyw5mvZJtkR54b8SPlFQ1waoXvddKUcITJ2sEXtZsV97FQg24nkK4kLxsPY2wXg98+gQgS1cbF71sedMJ9V4nVs25XC7VT13zwFUnyXlOzslpCHYTDtSQUBrogevtlNdGA8oIJsbL9G8dIQRhv6d8PWbq9MDLDHguBbJAZ6cyegdqySjzVWPGzqmT6LabVUBz4kjNdRgeeGUaoalGoh4PXBlwtY96U5OlKkzQBjK7Vt8DFyrn6XPAQSnlx033m/fa1wNLa89XL5F+1oppDgzF+OGhUXZGNSNd4YFDhQGfTuF2CUSwNKGm35eiXSQZ95X086lE1ghggtp5VUdCCwO+4Sr1W/fCn/u+yn5Ye6m6HV5jrYEbeeDmHUhPY1q8hAKliecL5aApkPToiRo7nUvTSOc5SKqwS7erxUI18EktA0VLCWXLS9Vv/cTaaIz2uDa6vtZWNpbOcXY6Nb8BT01BsAu3SzVRKwtieoNKY22gRzeqtVPuiwaU9w2wbpflc8N+T7mkY8aYyjO/AQ/53KpuQdvfe3p6EYLaOvh81Zi6B7711er2PDKKbSWm2YDPo4EXvGFCpOgO+fB5XPizU6UAJqjjpG35y+nr8cCvB34FeElFyuDfCCGeEULsA34R+IPlXKhBpI9wbhJkkX1nZ9kRSagd29S3eGNXELdLcLLCA++LBhCBjlIaYV55UGdFqRxeTaMvH5jQZh6rZqZvh2obeeYxdfl+7H7VAF/3MsNrLGdNGh542SXcEiUUvR+KnQ4+DweHY/g9LnZu7ODRE/N4edENC+8NbpduVwu3T3W6q9eAG10lVUYR0XWqgvTIMqUTxoaq9r0ytMEOh40S+hr6dz6rCpY0qSTs95SnEQqhNUhqXFqaPhO2rz2g9G/hVv3uLYgEPNUNtnR0p2OefignJhIM9IRUHrzeuC3UwWB3iEMjNQy40VLWwhhKWTLg7RtUtfE8BlxPI6xlwI30YRsPPOcOEhIZQj4XHQEPwdx0uYQCK9LQqp4slJ9IKYWU8nJzyqCU8leklDu0+2+RUjZo6u08RPoRssBatzooBn0z6sszXZp73S42draVFfOcnU6yvrNNHWxaGmFoTl3eHiuuNZ43aWHAQ36b1pJur5otefYxJSnkknDJK00vtDfgXrco72uiJ/wvIYgJMGGXiTIPB4djXNIX4Rcu7OaZs7O1q+Oi65T3We+UFCkX54ELoQKZ9Uook8eUt2Y2qBe9FM48sjzphHpvFztZqHMTpGc5elpJbPOmEIJqgoSNwWxwReLIbJr2Nq+S8oaesgxg6kQCS5dQTk4mjKvj0hWnyoA5WCuVsFZHwtS06s+vX4FvfQ0MPVmSNS2wLuQpN+BGHriNBp52q/9ThzvLhmAOj8yVe+Cw7P1roMUqMQGjedT1a9XO1CsnLYNIAz2hsmKec9MpNnS0lQ/5nTpOAReHM13G86w88GCt5u4broLhfXDgLnWZO/CC0mPhteoLrKieq9ULfClphLA4D1xKaVQJXjPYRb4oefLUjP0L2tercnWTvn/Ps8P8+IhNlpF+kEX6+fbeczz03AKykeaZi3nnk2d5TJd8pkpTlQy2vGz50gljQ7WnC2mZKONnnqMj6FVShR26p6Z74AFvtWTRYIMwEkuzNurXAph7LfVvHSWhzBfEtPfAs/kiZ6aSJQOuP9cfYVt/hNNTyXLJCJX6+7F7DvFH3zlJERf3Pn6AP/zaXt7/zX0Mz2pBz8r6gm1ab6JD/1tzLWDRC8V07LldgoDXZWvAMy4VvIy601zg1/bPSgPe4CsmK1rQgKvL8NdvcfHGKzbgTVh7doM9IU5OJpBSkisUGYmlNQ+8o2TAJ48x6V7DUFx9oflCkZlkrtoDt9PAQaWrFXNqHNnmF5f3xNCrshLlBiuTt5/Gs1gPPOT30OZ1L8oDH4tnmE7m2NYf4YpNnbgEPFZLRtFPmKbWsn9373P8/b3PWT/fSCHs5//9z0H+5FvP2heFVFJjLmaxKPnTb+/nHx/Qyucnj1WnSy5nOmHlKLVKtFzwxOjx2iX0YPLAlQGPBjzMVWZ9NPiSfDSWVgHM2bPKu7XRvwEiAa+9B+4NKLmrhgd+ZjpJUVLtgQeixpVJZQ3CX3/3IP/y4DEeOTHDLGES06M8enyKr/z8DD84oDkPRiBZ2yd7tqgriRoyiq6B+732BhzUcZ+wcdySKAMecaVZ59V69YQdD3x+NAN+/Zocf/uGHbaX5oM9IZLZAmPxDCOzaYoSlfJjHvI7dZzpwAYjnUpPO9QbWenYauBQKugp5kvZJzp6VLpCRrEeaBxTem+9vbIt6I3UmE5fgwOmIpNIwMtl69t5pFYgUz9YTBNnZpJZDo/ErSe4aOl20+4eJuYynJ5K8sSpOj0TX9C2F8rZ6RRzmbwKgGUTSmev9MCXK51Qyvnb42oeuGvmVB0ZKNoJ06SBV0soje1wNxpLq6uC4b3qjloeeMBT3aPczDz9UPSr4QHDAy+XUKA8kHnv/hH+4+FTvP2GQX76/pfQ2dPP6y/28+P3/iIelzCO2ZIBN30P225W7R5sqjczlh54rKpTZ5vPXV3/oZFESShh0vR7NANe5YF3rr4G3nSE1wBC5VwnJ5X3a3EZa85E0XNM13cETUN+YzB1jLnQJsbiaYpFaUy0qdbAazR3D/dCp9b69aKXVzymaeuVBtxqGo9ehbmE9qc9YZ99MU8N9ANna586kK4e6GLvmRmj7W0V+sGiHTxSSmaSOVK5AqcsWhjoLVefS5WM2DefrHMwhC9s241QP/GMxzNMnz2k7tQDmGYuerlaQyP7MycnoZCtLaG0dZJv6+Fyebh2ABNKB3rQJoipP5aeaciE9nyhyHg8owKYQ08p50HPnrIgEvCUN9eqZJ5+KHpri80WHnh/e4D2Ni8HNB18aCbFe7+5jx3r23nvTZeo54V6IDmFyyVYE/EbKZCqiMdVOtZAGXBZVD3wLaiSUKSs0sBB88BtHLc51JV2iBS9LrVuGawIYga7lHRoV4DUAFrPgLu96kwXH7Y++2ro/RZOTCQ4p3VSW9/ZVhoxNnUc0rMUOgbIFSRTySyTc9YGPOjz2J6JAbj0dcr7rrycNiSUSgNetJhIv7hGVmZ6wv5FpREeHI6zvqON9qAqILl6sItsvsjTZ2asX9DWqbJvNP1xLpMnr3nelsEozQPfN6uuLl58SS//u2/I/gRhpoaEYvbYRk5oxrnSA4eFpROOH67dd0OnngHNQnBqwy28zPWEypaqRapcA48EvNZj1WSxXGueG4d/f3VNzdeKibksRamqkRnaC73bal79RfwesvkimbzNdxaoPZXn+ESCjqCXjqDejygGCPBFEEKwrT/CoZEY+UKR3//KXnL5Iv9w++5Srnawywhirm0PMKalQKo4RL9RgANA3+WqStpGRskWinjdwqj10HPSKw140O+2lU4TUhnwNpmihxkAkp6O8ietQD+U1jPgoAKZ8ZGaDZLWdbThc7s4OZHgnOaB97ebUr7OPQGAu2cLoCLylX1QdII+t31bSYCXfhje8tXq+20klHS2QMBb8a9fQiMrncVKKKrIpLTzXj2odrzH7GQUIVQgU4v0z5gqXi3TweJDEOxh/6hK5Xz7DYPE0nnuP1SdoVNFDQnl0EjMGGYRP3dY3WnVMiDaryoMD95d+7OySfi3l8L9fzH/uowc8NqZNfeHb8aFZMuZr9V+v+SUOilqWSC6ZFFWYm5kY2jfSzoGX3oDnPoJ7L9r/jWb0D3YvohfeeA19G/AGI5iq4PPI6GcnCg1sQJKkoVLHQfb+qMcHonz6R8e4bGTU3zk9TtKcguUyUdrIwGTB36u+vgXQnnhxx+w7M+SzRer5ROoNuCVA81NxIpat1KZol3OMC3DTGcqJLplaH9QSYsa8P4KD7w6C8XtEmzqVk2tzs0kWRPxK6/XMOBPAtDWdxEAY/E0Uwll/Kw8cNu2krXwBVVvhXoklAZ54NPJLLkFlNOncwWOj8+VabQdQR9b+yK1C3qi64wTqHmYsmVBRmwYov1GO9VfuLCHtVF/ffMWvSHVB8OCg8NxrhnsZm3Uj5w8pi6j7SYO7bhNGaqJ6pF8Bof+Vx3MYwfnX1ed3RUfmY7wmPcKPE/9Z3XrYTOp6VK1JSqICRUtHMzl2bk0fOUtShbq2lzqJFgneg74Rteker95DLjRYMu2mKf2WLUTE4mSfALK2JuChtv6oiSzBT59/1HesGcDr9tdcUwHlYRCsUhfe4DRWZOEYvUdbHuNkrgsso8y+UJ5K+eKYQ7GR9YIYsaKygMPFJNECjNMyPYyR0a9wfKX07eoATd54C5PdfBAY6AnpDzwmZSST6DUUvXcEyBcdKy/GICR2YzRyEofp6YT8rnJFooLMowG4V4LCcUiiLmEToQ6Rjl9xUT7f7z/CP/24+OWrzkyOkdRVucoXzPYxROnpu232VTMo++46zvarCWU+BCFcD9Hx9SJwu0SvG73eh48PM7kfFcMNh54PJ3j9FSSbf0RtvVHVU6/lXyic9kbAAHP1PCE931F/Z4oz6b5v996lq89XjHEokJ7zRWK/OHX9vLKT/247OcnRyfY2/dGtQ/UugLQOhHq6AbTsh/K3Bjc+Q7VFfK1/wy73gKTRxaU664b8P6kdrKqEcAEjPL+msU8Np+fyhYYnk2Xe9SZ8itOff/b3BPiL15rocUHu5XMkZ5hbTRAPJMnkc6pTCirXjR9l6vfFd8l1O+Bh2p44DMFlbbrLSQJ5qaZJFrmyACOhGJLpF+l5s2cUpewLuvN2NwT4tRkktNTpa5hhgc+fhjaN9LbEUEIdUk5lcjS3ubFWzE4OOivndRfk/DaagklZ5EHno6V9PlFohfzmNvKfnvvOf7u3uf4u3sPW3pPdm1Orx7sJpUr8Ow5G13TVMyj77jXbu7m3EyK2UpPJDbErKeHfFEan3Pr7g3ki5LvPD1PRacvZJkHftg0IHhrX5S+/FkKnRYBTPN6B18A+75mnY0SH1WVtMFu5Q1rOngym+e/Hj3Fh+56hn1nZ0zPH1ISmaa9/t29h7nzSTXZZkNnm/Hzoot72fOS21Sgu9bAgdSUUcQDJskiY+GB3/shdTJ4xV+r+aS68R2ep5OkiZHZtGraNrVfOUFr7AOYaj3zGPAaHrgewBysNOBmD7w/wv+5foDP/MoVRh+SMkzVmH3taj8fmxhXV2dWHrg/rL4frZmYmWy+WLOVrE7Q77HNPpvJ+ShKgcjOEchMMSGjjgdeN5E+QCs+qHEJO9ATIlsocmbK7IHrB4mErs143S56wn5GNQ28u0I+AfNczHlm91kR6rU04HYDjZdCZTHPqckEH7rrWTZ0tpHOFbnn2ZGq1xwYjtHmdXNBV3kFnq6D28oopmIefce97kKl+R006+B5NRt0WKr/u27AL+mLcOm6KHc+NU82ijekIvkVswWNzJn+KDt6BD0ixoS/xuBdgB2/pBpeafGPMp79hgoQ3vCH6vak6vVyaCSOlMrm/39ffqpUbGJKX33ouXH+9UfHuf3qC/iPt13NZ3/1SuPnjl+9kqs398BVv6EqQof3Wa+t0gM3DKbFUIfpk2qd1/2Out2vGXC9n0kdjMTSrIn4EcNPqQZllTM9K9A7JNpLKO0qW8hiBuzJCQsDXhHz8bhd/NnNl3LxWhsJzGjmNcHaiFrrzOhJdZ+dDegcsByzli3YGPCK4y/orRHEzBZIiQBk5vCkJ5iUUTUb04zhgS9fMU+LGnAt8j91rKYBN+8wG3QP3DzkVwt49UUDjMaVAe+sYcBtm7vXwqKhVSpbYcCLBchWFxIslF5TOX02X+T3vvwULgFffse1XNBlPZxWL6F3u8rTF3sjfjb3huwDmaZiHt0DNwy4WQfXgn3HMu34PS4Guksnilv3bGDf2VmOjtUoo9ZLuysyUQ6OxIkGPKxrD7CjTWUnHC+srXx1OdtvUd/9PgsZZd9XVaBTb4ikXXrr02L+5rbLOTOV5P/qRUia9joez/CHX3uai9eG+dPXbK9+X53db1VByp9beOHFgsqwMGnglhKKPwrtG+HKt8GNf1q6P9Stsi4WYMDHYhnWRvzaDMxd8z4/YnVCMWMMdaj2wvWWFlUSykL2d1NHwrXtyoAnx7XJXFYSCkDXYNVcUlAeuOU4NUsPvGDZqzyRzZMSbZCaxpWeYVK2l7WvBtSMAV/Y8cCriJSaT9VtwDtNHqbuhWtj1NZG/UYWSmUAE0pj1WqmEtoRXquVkisjJ6Ws1sCNnNglBjEjau0Tc1n+/t7DPH12lo+94XI2dgW5dc/6quG0Rgm9TZHJNYNd/PzElHVxjqmYZyaZI+JXxrQr5CsfkaWlEB6Ih7ikL1LW/+WWnetwuwR31soJt2kpq69bCMH6gpJh9qV6Kl9dTqBdNRvbf2e5pzh2SMkPO9+sWsC6/Ua3xYPDMSJ+D6/fvZ7ff+nFfGvvEN984izMnkVG+vnDr+0lns7xD7fvqY5rmGnrhMvfCPu+Xj4nMT0LX75d3bf+CuNuPYhZ1dDq3U/Daz5RXS+wbteCPfCXeJ9Vn2tu/2BD2Go9Zmr0QzkxnqA34jdOSoAWxJwnN96MqSOh3pIgN2WfRgwoDzx2tip4nLGVUCorMbVhLhbprolMnpQIGhLNnKejWkKBZW9o1aIG3JR7a3f2BdZE/Ib3bEgoYDLgygNfGw0wGkszOY+EUjOV0A49wKqV02cLRYqyshd4qahhKQR9HkI+N//7zBD/+tBx3nrNBbxyh9Z6wGI47fBsmlg6z3abIpNrBruJ65WOlZiKeWaSWTpCXiOft0xC0Yp4Hp8KsK2vfPt6I35eeFEP33rqnP1EFouWssWi5PBI3DjxuGfUZfKjM3WcAC//JfVdmLMT9n1FdeK77DZwudWJXZtuf3A4xtZ+lav8rl/cwrWbu/jMtx+AbJyHYmv48ZEJ/vTm7bUn7ehc9Q4lB+39b3V74ih89kY49kN49cdh9y8bT7UNGrpsThLrdit5qM4huqOzaV6VuFM5GNtfN+/zLa8IzNQY6lDWxEqnIog5L6aUvJDfQ8Tv0bLQRI12vgNKFpstD0Bn7IKY+jg0/SMN6dTCgGcLZN1thgFP+7urJRRQcQ3HA68g1KMOOKjpgQshjNxTI4gJpUwUzQPviwaYTuZsPfDgfM3da2FUY6reDWltnFqZAW+QBw7QE/Hz7LkYl6yN8H9Nl/SbukNcual8OO18cxpr6uBtnco7jg0xnczR0ab+b9v6VD6vMRlI88CfS0UsqxFv3bOBodk0j9j1XrEw4Kem1IDg7fq6J48y4+nl6dHs/D1WLnq5+j/r2SjFovKKt9xYKrzquQgmjpgmyWsnCpfgk2/azU6Puiz/5LNtvPKyPt5y9QW1P1On/3I1BOSxz8Jz98JnX6IO7l+9G656e9lT5827rkQPZNaRTpjI5OnLnmRL7FG4+h3GOMFaBLxufG5X7SAmWHvgE4nSGDVQKZCF7MIkFF9Q7W96Lnh7AG9yRJt85LV+jV4hPV2ug1sGMT2Bqv9DqSNh9TYnMnmy7pAxVi8X6K7OQoFl98Atwr0tgMutvrj4UE0PHGBzb4iRWLo8st3WqVLAtGZDuqZWKEobCaW2Bv7J+57judE4//zWK6ofDGvFPJoHbj1OTe/MtjQPHNRVx2gszT++ZXdVpsutezbwwbue4Zlzs1y+oaMsEGjFug6VSfH4ySnefsNg+YNCqJPn7FnlgWtVnNv6o2TyRU5OJtiyJgLxYQruADFClieKl21fS8Tv4S2ffbTqsXe8YJAPXVw9F7PqxDN5jGRkgMnhLOPxjKoutMPjh+2vhWe+qU4K555Ul9kv+/PSc3ougsPf5exEjLlMvmzdfe0Bfndbivx+FzPhLXzh1strN6mq5Op3wjffDv/9RtWr/Pb/VrJNBUGvG5eAmVSdlbW6jj30FOkLXshr//Gn/M4vXshrd1UfH6OxNG9zf4+Cy4/7irfVvXRVXFRbA3/vf/2Yr81VZy7pg4yBxbdONhfzRP20jY1CT408/M4B9Xuq2oD7K3vxW8g5Ib/9cZ/MFsh5Stskg73VGjiouMZMtQ7fKFrTgIOWC26TxG/iD152MW++Kl1+Z9dm1ffBo4J+5jaflY2soOSB22ngjx6f4tETk8TTOcNzMtAlFC0TxTDgvoodCJYsoQB84FXbyOWLXGQRzX/1jn4+fPd+7nzynGbA41zQFSzXJivY3Bs2WhFUEV0PsSFmUjk2aR6WbuwODMeVAY+dI+7rhYSwPFEEvG7+4S27efL0TNn9Pzs6wZcfO8MfXxLCD2Ue+MHhGG6X4KK12iXv1DHcG26CYZVVU9OAg8pGefKLcPh7qlrPF4FLXlV6vPsiKOY5dWx/2TbpDOaPMdd+IZ/7lRcY7QfqZtstKmVvzTa45dOlK4wKXC7BxWsj7Dtbe0iCQVun8jiHnuK+g6McHo3z4OFxSwM+MXaOW90/YfzC2+gL1T+jtOZQB80Y55KzvHz72rLv2usS3HaFKUNIN6i6ga2XMgMeoOPcGER32j8/vFZ51hWphNlCseY4NePj9NhXrnqb5zJ5CqFSXE2Ee5kdWXkNvIUNeH91ExsLLuwNc2FvubbFjX+mLuE01poO+MoiHlDeENhr4BNzGTVL8tQ0L75kTfmDugeuSyi1PPAGSCh7LrCZDgO0B728dPsa7n56iA+9epvSd+fRbnvCPo6O2mSJRNfDiR8xncjSqRmyC9eE8LgEh4Zj3LJzHcSGGadL9VppszZ2L75kTdX/7drNXbzls4/yyLkUL4IKDzzOZn1AcHIKUtNE118Cz6jHqr6DSjZdr9b+5BdV4G/7LeWDDHpUcdfUqf24xDouqTwZjjxDeNP1hCv3q3rw+OC3f1pX07KrB7v4xhNnyRWKVbUJlqzbDWcf566UinPYjSkLP/NF/CJH9qrfWtDSa8/FVPtuVCR59ZUbeen2GselXlzTvWVBn0+w2xir1hcN0FOcREbXYfufdLnUSaLCgGfyhZrDHIyPq3HlnczkKXRo37/bRyDUwXTSYqZNsEsd38WCffxiCbSmBg7qkrFvR3kTm3rx+FSiv0aZBx7yVz096K89oVrPu7bUir1t6vKyQkKx1MAXOQ9zIdy6ewNTiSz3PDvCicnEvG1OeyN+JuZstOXoOmR8hEQ6Q7t24vN73GxZEy4Zj/gQp3Md87dTreDawW7WtQf47mHt5JEtl1AMD29KVZi29V3CuvZA7dmKOi6Xqsw88SP1v7/8TeWPa/1x8mOHGegJlWeXJCZV8Kxvx4K2p4w6JZdrBrtJZgvsH6qzwnLdbpg9zb7njtHmdXN0bK66+VQ+w8CxL/NAYSddm6zHp9kRCdSYi6lJKBGSdIbmuSqZPKIyfSyko5qYPPANoQJRkSThnyd11MKAV1diWqfw2mngxaIkkS0gvdrVU2gNnSEfs6lcdcZWWxcgjUHqjaZ1DfgL3wPveLAhbxVt8xjNpbosJBSf24XHJSyDGdl80Ugfss2ZDq8xBTGtPPDGSSjz8aJLeukK+fj7ew8jLUroK+kN+8kWisRSFgdu+3qELNDDrOGBA6URWVIi4yMcTUfnb6dagUsrt3/ohCadaC1lZ1M5zs2kSu+nDzLuvpBt/dHasxXNXP5L6nd0fXUaXaAdQmsIzB6vypxhRCvE6b98QduzGK4aVFdTjx6vsxmSFsjcznHe8YJB8kXJ0bGKVrzPfINgbpIvuW6uKZ1ZEfZbdEjU8fgouPxERJL2tnmCohNHVQ3GQj3SUI9hwDe6ZwCY9li30TDoHFQG3OSAWAYxLQy4nQZupBXqXnuoh46gT00OTK1sNWbrGnAhbEvoF/5WwvDCrdIIhRC0+dyWl1KTWgOszqCXfWdnrHXy0BrV9hOzBm424DMqwm4XTW8gXreLW3au4+Sk8mi3z2PAjfL8uXT1g1oAeZ2YLJOetvVHGImlmZkYRhSyDMvOBXvgALfuWU9CaldEmoRyqCqAeVQLSA+wrT/KsfFEfW1q116m5if+wu9Z7kf5ri2syZ2pPvGMPKN+9y2/AV8TCdQupqqkX+nBN7YPcYumfZf1ppESHvlnzvo2cyJy5YLXE6kVxASynrDywOeLC0weWbh8AsoYZucgl6bfpf4no8yj4XcOqNeYJtpXG3DrIKYx2LhifzIaXOlX8aFe46pjptKAL3M/lNY14A1mTTRA0Oeu7lGiocaqVXsfev/tmy7rJ1eQPHXaIg833Gt44LYSSgMyUOrl1j3q4A77PWpKUQ2M8nyrPuOaAe8TU0YWCpSM68kTKpd6RHYtyoBvWRPhwvWanq1JKLpEYpx4po6paeQeP1v7IxSsvE6U514mJwgBb/4SXGutA0+3beJCMVS97pF9qpGXqWpyOblmsIvHTtoUU1Xw3KzgWLGfF4bPMtgTIuB1lUtKJ34Eo89yl/8W+jpqf+9W1AxiAilXiKhI2cY6AFVUM3VCZfosFL0aMzVFb1EZ5DMF+5gPUAqUmmSUjFUlplUWii6hVMhG+nAXd0D3wHuNNNqqVEK9v43jgS8vA91B1tXYqe2au+sTcG66rA+XwHoUWXitUU6fspRQlt5KdiHsWN/OxWvDXLY+Wmpqb4Mx7d6qa6CWAbROTJYa9VOa7DN6TmUbzHi62VTRa6VeXrtnE1npZmJanRgPDsfpCvlYo51YmDxmFGSVMmDKZZTRWJob//5B/vq7h+r+3FNiHV1iju0dFQZr5Jml6d8L5JrBbuLpfF3S0J1PnuNZuZmNqcO4XYJL1kbKX/fwP0Ool29mri0L3NeLHsS0y7VPusJ0uFNl1bZVTJ9UPXS0QPGCMFVjhjPqeDqdnccx6NJzwU8CSr/OF2XJA7eZxgOlY7RyGpfugbsCZglF88BXuCOhY8A1PvDKbfz7r19l+7jdYGO989/mnhDb10WthwGH1igjnc8Yl/dVlZgroH/rCCH4j7ddzSffVLuFKJg9cAsD3tZJ3t1Gn5gqu2zujfjpCfuJjZ0GINx7wbwnCjtu3rmOJAFODqkrmEMjaviEkXs9e8bI5x/orvY6C0XJH3x1LxNz2frncALPZlS7hr6cqYovl1IZFCtowI1iquO1DUChKPnWU+dI9uzAPTcE8VEjFiGlhKP3wZHvU7zqHZydk4sy4JGAl3xRks5ZtxieI0ina57xYVqTMLoX44GXqjHdc0NM0c65uXlaPOuBUq2YJ6sVmBkGPJ9RYxktDLjLJQj63KQqrrx1A+5t047Z8BpDQpxOVGrgyzvUwTHgGp0hHxtreIlKA6++fNQzUHrCfq4e6Oap0zPVkX8jlXDMWgPPxFbUAwfob29T8xDnoaPNi9slrD1wIZjzr6G/wgMHpYPnp89SRNC3ftOi19kd9lP0BBkanyKbL3J4NF4KLGqdDvWpOG6X4JK+aJkB/5cHj/KzY5Ns7glxeNRUIToPj8SU4RRaTxQARg+o0uwVCGDq6MVU8+ngjxyfZCSW5oLLrld3DO9lW3+UqUSW8bEh+NbvQO82Ji//TfJFWZZ5VS9Geb+NDh6TbUSF9fQkA/3/2bMIDdxoKTsJsSGm3D2MxCz2SzPeNpVyrHngVQONbRpZ6ahpXBUeeLbCgId6DQNepYH7I6pdryOhrC52zd3H4xnCfg9tPjfXbO4iky9WF18Y1ZhjhvcSKKsEm11RDXwhuFyCnrDP2gMHZr1rWCemjOZLOtv7o7gSI0zIdi5ZtzS92NcWxpVP8qVHT5HOFUu6tD7WzDSXcnt/xPA6Hz85xSfuO8ItO9fxuy/ZQjZf5MTEPLMpUd7sj8eD5IW35DFCKQNlBT1wUDLKYyenarYJ+OaTZ4n4PVxx7YsAAUNPaTn+EvGdd6seKW/4LKMpdeWyKA9cn8pjo4PPFNsIU4cBD61ZnMNi9mZjQ8z515Ym89RCz0ShNNDY0MCNFF7r409N46r0wDUNfM0lal9YfyWRgEdVzlZKKEIsazHPvAZcCLFRCPGAEOKAEGK/EOLd2v1dQogfCCGOaL/niSa0NnbN3SfmMsZcxqsG9MvdisulULkH7nO7ynXCFZZQFkpP2G877X7C1cM611RVOfm2/ih9TDEsu2ybZdVLMBwl4s7y6R8eMd4bUFOZoGwu5bb+KLOpHIdH47z7K3tZ39HGR15/ma0+bsXpqSSJnCQRuqB8BNvIPpWr37H4K4rFcM1gF1OJrGVwFtQl/T3PjvDqy/sJhNqh9xJlwPuj/JL7QXrP/kC1n+3bwYhm8Oq5+qpkvqEO04UAweI8BnzyyOL0b9Ca0AnNgJ8jE+xjNF6PAR8wqj/1q2OjElMfgqH1RarE0gPXDHqgYw381k+gZwsul6C9zWvdDyXYtaoeeB74IynlduBa4F1CiO3A+4EfSikvAn6o3T5vsfPAJ+Yyhk7cFfJxyVqLWZJmCcVyoPHKBjEXijLg1j05xkQPvUxXNfLf1hfhQtcQQ7KbSypzqReIyxdiQ0gynczhcQkuXKMVUFhMhtcDqL/xH48zGkvzD7fvJhLwcmFvGK9bcGikRu9xDUOC6bm4fCSXHsBcSO+TBnDNZuUYWAbIge/vHyGZLfB6fY7kut0w9BTtydN82PufHA7ugWvfBZiGGS8yiAn2LWUn8gH8Mg0F+1RDJo4sTj4BlTfe1qmGaaemKUb6mUnm5k8b7RxQbTdyacMDNzTwEz9S3rfNSDmlgVdKKOp22Fd+1dkZ9NVoKbs8Qx3mNeBSymEp5ZPa33HgILAeeC3wH9rT/gN43bKssEmwG3A6Hs8YmRqggk5VsySNlrJj1ePU8hkoZJpWQgFt2r2NhHKu2ImbopEmqbO5eIINYoJn2q5acMFIFb4QfQF10GxZEy55T8ZkeJMB17z9s9Mp3nvTJezc2KHewuPiwt5wXZWaeq+V0LqtKvhVyKlS6NH9Ky6fAFzQFWRt1G+rg9/11Dk2dLYZV4Cs262+jy/fjnR5+DPX7xq57mOxNC6BcdW4EPQ+P1ZDHfKFIuM57aRgMQkeUFWsqanFBTB1gt2GlOVuV/1V9PmelTx2Ykodh3omysyp6iDm8QdVIZdNRXfI76lqoaHbAb1CW6c96LU24KvsgRsIIQaA3cCjwFoppV78PwJY1rQKId4phHhcCPH4+Pj4Uta6qgR9bsvG7hNzWcMDB+UtVZU/ewPq0luTUMqLeBrXSna56An7mUxkLHt2n85rypnuDWt4D91NERfJwZuWvgBfkKDIsLUvYmRlAMqAu/2mMXkQDXjZ2hfhJVvX8Bs3lF8Wb++P1m3AN/eE8Ky5GIp5NdVl8pgqJlrBAKaOEIJrBrt59PhklQ7+0HPj/PjIBLddsaGU6aN7kxOHeWDLB3hsMmB4qSOxND1hf+1UPxtqSSgzqRxxqSUB6N0GK9HjCYvJAdcJ9agBHECgZyOAIQuZ2T80yy/968P8z76hslzwrDmIOXVCaeObX2T7cUGfm2SmOojp87iq+tN0Bn02LWU7Vz+NUAgRBr4J/L6UsuwokGqvsoywSCnvkFJeKaW8srd3nrLXJibk95ArSGMHAKWnzaZyVR44WOjg4TWGhLJcjayWi96In1xBMlsZYQeOZ7R1xyqm6hy8GzZdz4d+af5pL/PiDSFySb71ruvLx5bFhpV8UiFpfOtd13PHr1xRlbq4rT/KaCzDVKJ2i9aDw1oPcF2rnTyyagFMnasHuxiLZzg1WdKYx+Jp/vBre7l4bZjfetGFpSevvUwNJ9h5O+4db6Ao4cio0s9HYplF6d9QMuBWEspMMkccrY7CZjp9KQNliR54Ue2H0TUqFjFi4YE/fEwdf8+Nzpn6gp8sl1BO/Ejdv/nF9h/n85DMVXvgIYvpSx3zeeDz9apfBHUZcCGEF2W8vySlvFO7e1QI0a893g+M2b3+fEA3uuZA5qSmC5sN+JpIgM09FuXPugGvlFAyzW/A9cttq0Dm0bSFAR87BBPP4br0tfV10ZsPXxCyCQJed7nnGB+2nMZS9TwNPZBZywufTeq9VqKlcu+J55QBd/ug55KlbcsiuXazPlxDGaZiUfJHX3uaeDrPP75lT/k+5QvC7/4cXvtPRtMvfZtHZ9OLykABjJ76lh54MksczQO3mU6vmlj5lhYE1jNRgK7+AUDN96xEj0OdnEgor90bgqkTRhqh3+OC4z9S+0+NoKqVB57MFMrnC2goDdxmqEMhW9YSuVHUk4UigM8BB6WUHzc9dDfwa9rfvwZ8u+GrayKMxjamgIauC5slFFDeUlX5szbcOJMrLtswh+Wictq9TjpXYCQXIOcKlEsoB+9Wv7e+pjEL8AarZmICtgbcDr2vSS0Dro+D29ofUZObQr3Kcxx5Bnq31jW9Zjm4sDdMV8hnGKY7fnycHx+ZsJ/kHl0HLjebuoIEfW4j+2Y0nmZttLrjZj143S4CXpelBz6dzBEzJJQaHnjX5qW1VdUNeFsXkXCEoM9d5YEXi5Kfn1T/pxMTCXWFpnUlLEkoKA9884trBqWtNPC5TN4oszfT0eZVo9ZMV+lqzcvX0Koe9+h64FeAlwgh9mo/rwI+CrxMCHEEeKl2+7zFaO5u+jInjCKe8oP6ms1d1eXPWkMrew28iQ142LoaU10uClJta1VmgM6Bu9XosGj9xrUmvjDk0yqQqCOlJqHUHuhhpjvsZ03EXzOVsKrXSs/FyvAM71uRBlZ2CCG4eqCLR49P8dTpaf7u+4d59Y5+br96Y83XuVyCS/oiHByOkc4VmEnmFpWBohMJeC2DmNP1eOATi2xiZUYv5omuRwjB2migyoA/NxZnJqmkzZOTCRW76VK54LoHHp09rNIRB+31b1AeeDpXLHPGktmC4dCZ6dAa4a1kOX09WSg/kVIKKeXlUspd2s93pZSTUsobpZQXSSlfKqVcvrETTYBVa0ndgFd74MpLKCt/DvdCZpZ8NlXugTdwHuZyUeqHUr5j6gGbbHBdyQOfOg6jz6jJM41CH7ZgvgRNz6gBwZG+Bb3V1v4oh4btUwkPVfZa6d6ihj4kJ1YlgGnmms1dnJtJ8Zv/+QRrowH+6tYddY1z26YFb/VsjcVKKKCKeawklNlkjhmpdeebthghVsipjJ7F5oDr6B64duJeG/VXFfPo8uUbrlhPOldUueKGB66O3/bhn6on1whgQmmogzmBYS6Tt5FQVJZO1Wi1VfbAHYA2r/rCzJdTukdq1sBBDVBe31FR/qxNDmrLTlb0QWl+CaW9zYvXLWw8cChG+ksa+AFNPtl2c+MW4NUMuFlGMYp4Fublb+uPcHRsrjzN08TByl4rPRepNE9YtQCmjh4gn0xk+fTtu2t3/TOxrT9KLJ3nKW1s3WKDmKC3lLWSULIkXWHkBdfBs9+sDthNn1IZPUsJYEKVAe+LBqqKeR49PkV/e4AXXqSSJk6MJ5QBz6dwaU3lQud+quIZ81zBBS06Eiaz1hKKUU5f6YF3b4FX/JVtsdBScAx4negeuDmpf2IuSyTgsWxBe83mrvLyZ60aM5KbLC/kScdUP2vfIsZzrRAul6A7VF2Nqe+oon2DMqiFPBz4NvTvMhpMNQSLyfSlIp76JRRQ0ki2UOTYeHVV48Rchv1DMXZpueNAuce4dmETbBrNtr4ouzZ28KFXbeOKTfUXPuuVsA8cVsZrKR542Kal7HQyR0fQi9j5ZhWsPPdk+ROMMWqNMuCqaGlte4DRWMY4zqSUPHpiimsGuxjsUfvNicmEkYnii53ER47A0CM1s090rGJfiUyhKgccME6oVR54eA1c966FzwCtA8eA14l+Jq4MYvaGrQNCVeXPWjVmOD9dHcT0Rxo2nGK5UKPVyg24vqP6ujaoFqHnnoChJ9XU90Zi6YHrRTwLk1BqZaLcvXeIQlFyy07TIGBds+0cXPU4hcsl+Na7rudtNwwu6HV6JexDz6k6jCUZcJu5mLOprDJg21+ncvP3faX8CUYO+BI18PaNKpNlzTZAeeDZfNHYF49PJJiYy3D1YDd90QB+j6vkgQOBuTPsFkcR+dS88gmUrrzN2WeJbN6yOK3TTgNfRprbajQRuhZmvpQan8tUySc61+g6uC6jaAa8vTBV3YlwBWZhLhWrhla6Bh7o1rztRz+jfjfagFt64NVVmPWwuSeEz+Mqn1SjcddT57hsfZRLzIOeOzaBy7vq8slSCPs9XNAVZDqZo83rrmo8thBsg5iJnJIQ2jrgklcqGSVvMmQTR1RGT1v9Vw6WhHvhDw7A1lcDpZORXsyjy5bXbO7C5RIM9oQ4OZnQ2soKgokzXO9+BilcMHDDvB8XspiHm8jkDYfOjK0Gvow4BrxOQhYeuLkPSiWbuoOsifhLBlwrp+9mtiS5DD8NJ3+6YtNdloKVBz6byuH3uPB3qZJmDnwb1lyq5h02EisDHh9WxsC7sMkyHreLi9dWl9Q/NxrnmXOzvH73hvIXuD3wqr9Ro9daGD2Fsq89UFfg046w33qw8XQyW2opvPPNKsPj6H2lJ0weXbp8Yiyi10j90w24roM/enySnrCfzZp8MtAd4vhEQqV/tm8gnDzD9a79yHVX1JU4YFx5a9uczRfJFSRhCwmlzevG53Exk3I88KZD95pTFUFMu54SQgiu2dzNYye08mePn2Kggx4xS9At4Ud/C599iUrwf8VHVmQbloLe0MpcTj+dyCqvq12THGQBtjcw+0THTkJZoPets7WvuqT+zifP4XYJbtlpoalf+TbYaD/soxXQpaPF5oDrRLUgZmVJ/0wyVxrqseWlSqs2yygTzy1dPrFAD8iOzqbL9G/9JDXYG+LMVFL1ge8coCd+mJ3iGKIO+QSqY19GHxQLD1wIQUebl5nKoQ7LiGPA68TnceFzuwwPPJ0rEE/nbT1wUFkDo7EMp6eU4SkGe9nhOsHNT/wfeOAvlV74Ow/XdSm32vSE/RSKsqxhvR64ItBRMrKNTB/UMTxwkwGPDS3agG/rjzIxl2VM89oKRcm3957jRRf31vw+W5mSAV+8/g0qiCll9ZixmVS2NBfV7YXLboPD96g+5Mkp5ZEvNYXQgjURP0Kocvqz0ymGZ9Nl/XIGu0PkCpJzMyllwFPH8Ygioo4AJkDQW37lrWeh2TVos+2Hskw4BnwBtPnchgY+MWedQmjm2opxWLlAD3tcR4kmT8Ntn4fbPtcS8gmUct3NMspMUjtohVBZAd1bjOBSQzE8cLOEMrLoQiFdTtDzwR85PsnwbNoY9nw+ohcmLaWIB0odCc2BzHSuQDpXLJ/KtPNNKv1y/7eUfAKNk1BMeN0uukN+RmNpQ67U2++C8sBBq8jUApkpfLDx6rreP2ho4Gp79ToQqywU0PqhWPQMWi4cA74AzD3BJyz6oFSyZU15+fP05lu4q3A9P3353XDZG5Z/wQ2kx6IacyaVM3JfecVfwc2fWp5e2ZUaeCGvhkRHFpZCqLO9IhNFn2bz0m2WDTXPCzZ0tvEr127ipssWlrVTSdjoh2K+ElPHQqfZgK/bozzufV9tTBOrGqyN+hmZTfPYiUk6gl4uXlMKQg90q33npMmAPyW2g6e+Ky1jMn2FB25VyAN6QyvHA29K1FQe9UXa9UExY5Q/aw2Ihi+6nT/IvQuxwNzlZsDeA9cO2otfvnxSUKWEMjeqZlMuMIVQpyPoo789wMHhGMmsaZqNRT7/+YIQgv/3usvYfcHSskBKczFLHrg+yNc82Boh4PI3wemH4egPVCbPMk0y6ouqXPBHT0xx1UBXWRfKnrCPiN+jPHCtL/iT7p11v3fA60KIUvaZroFbFfKALqE4HnhTEvK5jTOwIaHMo5lePdjF2ekU52ZSRk/mthY0FJX9UKSUzOga+HLjcqvcYl1C0aswl3Ai1Ce269Nsbt2zYf4XORgpiGYJRc+6aK/cFy5/k/q9/y5VhWgzNGGprG0PcGx8jlOTSa4ZLJckhRAM9IQ4MZmE/t3c2fPbfN//8rrfWwhB0Fsaq6ZLKFa9UEA5BzPJbM35pY3EMeALoM0soRhl9LW70+l63GMnJo1IdptFL+FmJ9rmwed2GR0J5zJ58kVZ7nUtJ75gyQOPa1WYiwxigtLBj43P8eXHzrChs40rF1DZ+Hwm7Nen8pgMeFL3wCuOhY6NatoNLJt8AsoD15tU6fUXZgZ7QpyYmAOXi3uit5HzLqwgyzwPd34P3EuuIC3HLy4HjgFfACFf6Yscn8sQDXhK471s2NoXJRLw8NiJKdL51vXAhVDT6SfiytvSD9qOyoN2ufCGShr4Iot4zGzti5IvSh47McWtu9dXDX9wsKY01GEeDVxH98KX2oWwBnpqZNjvMQLUZgZ6QpybTpHJF8gWiqVxanVijn0l69DAgRXLRHEM+AII+j1Gc/daRTxm3C5dB58yPPBW1Vp7In7DA6950C4HvpBJQhkGl6c0a3QRGJPtgdc78kndhC3GqpVO5hZXY9tfCxuuUrnhy4SeGnnlQKflII/NPSGKEs5MJcnmi2qc2gJo83kM6WSuDgkFsJ7Mswwsjyh1nqK0MM0Dj9uX0Vdy9WAXPzw0xhktH7wVJRRQOviwVrI8XeugXQ7KJJRhCPctqX/MYE+IgNfF9v6o0fTIYX506aDcgGcJeF3WjkkgCr9xX/X9DaS/XVXjXj1onZI7oDe1mtAM+KI8cLW9yWweIeyvoru0fii3f/aRqhPFP9y+m1/Y0rOgz54Px4AvgKC/PI1w+7r6tLRrNitd7kdHJoAW9sDDfvadU+1vZwwPfIUMuDdUqsSMDS06A0XH7RJ87A2XO8Z7gbhdQpXTmwz4dDK3cldiFly0JswHX7WVN15hPdxisFs34HNk8kVDBqqXoN9DTMvt1qfx2LUjuHxDO7/94gst+8XMl/CwGBwDvgCUBl5ASslEjU6ElVy6LkrQ52bf2RkAAgv0AJqF3oifqYQqp19xDdwXhDlt7Gp8BHqXXtX32l3nb+HOchL2e8o08LJ00lXA5RK884X2/Xfag166Qr5Fe+BBr5uR2RSgz8O0d8D8Hjfvu2nrgt5/KbSmJVklgn43haIklsoTz9Quozfjdbu4YlMnUoLP7bLU6VqBnrCPQlEyncwaGnhHnUMFlow3WApixocXXcTjsHQqhzpMm/ugNCkD3UFOTMxpQcyFXQEH/e6SBm4zzGG1aE1LskoENelD721SrwcOGPmpZcMcWowe03DjmWSOiN+zcicjX1hJKJk51YJ3iRKKw+KpHOpgtFRoYgZ7wpycWFwQ05x9lrQZp7ZatK41WQWC2hd3clJ5gj2R+i8bdR28VQOYUDphTcSz6qANreBB69M88AYU8TgsDdUTvDwLZTUllHoY7AkyEkszm8otXEIxxb4SmYIxG6AZcAz4AtAvnXQPvN4sFFDBDb/H1ZI54DolDzy98oErb1B54A0o4nFYGmqwsdLApZRaT5zm98BBBSH9C9bAPWTyRfKFou00ntWieVbSAuhn3lOaB76Q1qN+j5srNnVaDoRtFYx+KLoHvpIG3BdSvdNnzqjbjge+aqggptqPY+k8haKko625PfCBnqDx90INuDGVJ1dQ03gcA96alAy48sC7QwtLC/rYGy4nlVuZEtvlIOL34PO4mJjLMJ3Msal7BVPw9IZWemtSRwNfNSKB0lzM2ZWuB1gkA6Z9dcESinblncoWSGQLltN4VgvHgC8APXhxeipJR9C74B1hY1dw/ic1MUIIesN+xuMZZpLZlb1s1nuCTx0DX0QNgnZYFcIBD4lswchIghWsyF0kIb+HtVE/o7HMgoOYuuOWyORt52GuFo4GvgD0L3J4Nr0g/ft8oifiZySWJpbOr7yEAjB5bNGDHBwagzHUIZMvGfCVDGgvEr1oa+EeuG7ACySzhdbKQhFCfF4IMSaEeNZ034eFEOeEEHu1n1ct7zKbA/OZdyEphOcTvWEfx8bngBWswoSSBz55zJFPVpmIaaiDXtDV3uQaOJQM+MI1cLW9egvpUItloXwBuMni/k9IKXdpP99t7LKaE/MYpeUoi20FeiPqMhRWsAoTVBohQD7lFPGsMqWOhPmVb6mwBEoe+MIMsJ76q/fCbykPXEr5EDC1AmtpeoKmFMD5+oCfr5iloxUNXPnCpb8dCWVVCZuGOkwbHnjzG3A9kLnwZlZqe/Uh2LVK6VeapWjgvyuE2KdJLLbd8IUQ7xRCPC6EeHx8fHwJH7f6eNwu48s/X6eXz4d5u1c8D1zHyQFfVUpzMZUHHg2sYEXuEtjWH8XtEqxZ4LEbrPTAz4Mg5r8AFwK7gGHg7+2eKKW8Q0p5pZTyyt7exfdvbhZ0/et5G8QMr5IB9zkGvFnQg5jxjPLAm70KU2djV5Cfvf8lvOCihbV01SUTvRd+S0koVkgpR6WUBSllEfgscHVjl9W86IFMxwO3mIG4nHhNOedOEc+qEgmYgpgtUIVpZm00YNsK1o4qD7zVDbgQwuwCvR541u655xu6/vV8zULRPXC3SxgDbleEMg/cyUJZTSImDXy1W8muBH6PC5cwSyjNo4HPewQKIb4MvBjoEUKcBf4MeLEQYhcggZPAby7fEpuLNs0Df/5KKOpg7WjzLtiTWRKGBi4gvHblPtehijavG5co5YFvPs+HYgghCPk8TemBz7sSKeXtFnd/bhnW0hLoZ9/u52kWStjvIeB1rXzptMsNnjY1osvdOpfs5yNClKbytEInwkbQ5nMzdh4FMZ+3BH0eOoNevC0QdV8O1HR6/+octL6gE8BsEiIBLzPJLPF0vun7oDQCs9cdbKI0wuY5lbQIl66L4lpB5aAZuWqgyxjeuqL4Qo4BbxIiAQ9np9WYsWbvg9II9ECmz+NqKufNMeAL5A9etvRZjK3OJ960a3U++Bc/BFFnjmUzEAl4jK6czwsPXJNNmqkXODgG3KGV2Pnm1V6Bg0bY7zE04eeLBg401TQecDRwBweHRaAX80Br9EFZKnr6cLN54I4Bd3BwWDBhUw3A80MD92i/HQ/cwcGhxYmYPNEVrchdJXTD3Uw54OAYcAcHh0WgV2N6XKLMmJ+v6B54M+WAg2PAHRwcFoGuBXcEV7gid5UIOR64g4PD+YIexHw+ZKAAxiT6ZuoFDo4Bd3BwWAR6ELOjBQY5NAJHA3dwcDhviBgSyvPEA9cNuJOF4uDg0OroEsrzIQccSsFLxwN3cHBoeXQJpXM1euKsAiUP3DHgDg4OLY6eRtgKw4wbgR7EbKZOhOAYcAcHh0XQHfLxBy+9mNdc/vzoDnnpuii/+aLNvGBLc831FVLKFfuwK6+8Uj7++OMr9nkODg4O5wNCiCeklFdW3u944A4ODg4timPAHRwcHFoUx4A7ODg4tCiOAXdwcHBoURwD7uDg4NCiOAbcwcHBoUVxDLiDg4NDi+IYcAcHB4cWZUULeYQQ48CpRb68B5ho4HJWC2c7mgtnO5oLZzus2SSlrCoDXVEDvhSEEI9bVSK1Gs52NBfOdjQXznYsDEdCcXBwcGhRHAPu4ODg0KK0kgG/Y7UX0CCc7WgunO1oLpztWAAto4E7ODg4OJTTSh64g4ODg4OJljDgQoibhBCHhRBHhRDvX+311IsQ4vNCiDEhxLOm+7qEED8QQhzRfneu5hrnQwixUQjxgBDigBBivxDi3dr9rbYdASHEY0KIp7Xt+HPt/kEhxKPavvVVIURLzAgTQriFEE8JIf5Hu91y2yGEOCmEeEYIsVcI8bh2X0vtVwBCiA4hxDeEEIeEEAeFENet1HY0vQEXQriBfwJeCWwHbhdCbF/dVdXNF4CbKu57P/BDKeVFwA+1281MHvgjKeV24FrgXdr/v9W2IwO8REq5E9gF3CSEuBb4GPAJKeUWYBp4++otcUG8Gzhout2q2/GLUspdppS7VtuvAD4F3COl3ArsRH0vK7MdUsqm/gGuA75vuv0B4AOrva4FrH8AeNZ0+zDQr/3dDxxe7TUucHu+DbyslbcDCAJPAtegii082v1l+1qz/gAbNKPwEuB/ANGi23ES6Km4r6X2K6AdOIEWT1zp7Wh6DxxYD5wx3T6r3deqrJVSDmt/jwBrV3MxC0EIMQDsBh6lBbdDkx32AmPAD4BjwIyUMq89pVX2rU8C7wWK2u1uWnM7JHCvEOIJIcQ7tftabb8aBMaBf9ckrX8TQoRYoe1oBQN+3iLV6bkl0oCEEGHgm8DvSylj5sdaZTuklAUp5S6UB3s1sHV1V7RwhBCvAcaklE+s9loawA1Syj0oefRdQogXmh9skf3KA+wB/kVKuRtIUCGXLOd2tIIBPwdsNN3eoN3XqowKIfoBtN9jq7yeeRFCeFHG+0tSyju1u1tuO3SklDPAAyipoUMI4dEeaoV963rgFiHESeArKBnlU7TediClPKf9HgPuQp1UW22/OguclVI+qt3+Bsqgr8h2tIIB/zlwkRZl9wFvBu5e5TUthbuBX9P+/jWUpty0CCEE8DngoJTy46aHWm07eoUQHdrfbSgd/yDKkN+mPa3pt0NK+QEp5QYp5QDqWLhfSvlWWmw7hBAhIURE/xt4OfAsLbZfSSlHgDNCiEu0u24EDrBS27HaQYA6AwWvAp5DaZYfWu31LGDdXwaGgRzqTP12lF75Q+AIcB/QtdrrnGcbbkBd/u0D9mo/r2rB7bgceErbjmeBP9Xu3ww8BhwFvg74V3utC9imFwP/04rboa33ae1nv35ct9p+pa15F/C4tm99C+hcqe1wKjEdHBwcWpRWkFAcHBwcHCxwDLiDg4NDi+IYcAcHB4cWxTHgDg4ODi2KY8AdHBwcWhTHgDs4ODi0KI4Bd3BwcGhRHAPu4ODg0KL8/7xpmbzVv299AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = prediction  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": "9a677952",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "032b4d34",
   "metadata": {},
   "source": [
    "# Validation Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d09838",
   "metadata": {},
   "source": [
    "The problem with last implementation is that we are testing with last 10% of data, so we don't know how the model will behave under other conditions, such as peaks.\n",
    "\n",
    "To solve this problem this problem, This Validantion funtion moves the position of test data and calculates the MAE\n",
    "\n",
    "Iteration 1: 0-10% test && 10%-100% train and calculate the MAE\n",
    "Iteration 2: 10-20% test && 0%-10% and 20%-100% train and calculate the MAE\n",
    "Iteration 3: 20-30% test && 0%-20% and 30%-100% train and calculate the MAE\n",
    "Iteration 4: 30-40% test && 0%-30% and 40%-100% train and calculate the MAE\n",
    "Iteration 5: 40-50% test && 0%-40% and 50%-100% train and calculate the MAE\n",
    "...\n",
    "Iteration 10: 90-100% test && 00%-90% train and calculate the MAE\n",
    "\n",
    "At the end we return a list of all the MAEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bad5465a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(features_set,labels_set, model):\n",
    "    size = features_set.shape[0]\n",
    "    scores = list()\n",
    "    for i in range(1,11):\n",
    "        if (i == 1):\n",
    "            split = int(size*(1/10))\n",
    "            # Train\n",
    "            features_train = features_set[split:]  \n",
    "            targets_train = labels_set[split:]   \n",
    "            # Test\n",
    "            features_test = features_set[:split]  \n",
    "            targets_test = labels_set[:split]\n",
    "        elif (i != 10 and i != 1):\n",
    "            split = int(size*(1/10))\n",
    "            # Train\n",
    "            features_train = pd.concat([features_set[:split*(i-1)], features_set[split*(i):]])\n",
    "            targets_train = pd.concat([labels_set[:split*(i-1)], labels_set[split*(i):]])\n",
    "            # Test\n",
    "            features_test = features_set[split*(i-1):split*(i)]  \n",
    "            targets_test = labels_set[split*(i-1):split*(i)]\n",
    "        else:\n",
    "            split = int(size*(9/10))\n",
    "            # Train\n",
    "            features_train = features_set[:split]  ## 90% train of 618  ##556  up to 90% -> first 90%\n",
    "            targets_train = labels_set[:split]        \n",
    "            # Test\n",
    "            features_test = features_set[split:]  ## 90% train of 618  ##556  up to 90% -> first 90%\n",
    "            targets_test = labels_set[split:]\n",
    "            ## Define the model\n",
    "        '''\n",
    "        from sklearn.ensemble import RandomForestRegressor\n",
    "        # model\n",
    "        model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "        '''\n",
    "        # Model train\n",
    "        model.fit(features_train, targets_train)   ## fit on training data\n",
    "        # Model test\n",
    "        prediction = model.predict(features_test)\n",
    "        from sklearn.metrics import mean_absolute_error\n",
    "        print(f\"the mean absolute error in {i} iteration is: {mean_absolute_error(targets_test, prediction)}\")\n",
    "        scores.append(mean_absolute_error(targets_test, prediction))\n",
    "    return scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eef2702",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cc0f44e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 4.972950819672132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 2 iteration is: 4.206065573770491\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 3 iteration is: 49.739508196721324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 4 iteration is: 8.94704918032787\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 5 iteration is: 4.932131147540982\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 6 iteration is: 8.168852459016392\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 7 iteration is: 12.273606557377052\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 8 iteration is: 26.683934426229506\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 9 iteration is: 14.49934426229508\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 10 iteration is: 6.352419354838708\n",
      "the scores are [4.972950819672132, 4.206065573770491, 49.739508196721324, 8.94704918032787, 4.932131147540982, 8.168852459016392, 12.273606557377052, 26.683934426229506, 14.49934426229508, 6.352419354838708]\n",
      "the mean mae is 14.077586197778952\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429faa6b",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cdfb5e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 4.086056111005182\n",
      "the mean absolute error in 2 iteration is: 4.0925767872868235\n",
      "the mean absolute error in 3 iteration is: 28.926325915311583\n",
      "the mean absolute error in 4 iteration is: 7.5546047132666105\n",
      "the mean absolute error in 5 iteration is: 5.716353886325894\n",
      "the mean absolute error in 6 iteration is: 9.132849436960175\n",
      "the mean absolute error in 7 iteration is: 13.105583948784881\n",
      "the mean absolute error in 8 iteration is: 24.504754517626314\n",
      "the mean absolute error in 9 iteration is: 15.757546708189865\n",
      "the mean absolute error in 10 iteration is: 6.7051232818033375\n",
      "the scores are [4.086056111005182, 4.0925767872868235, 28.926325915311583, 7.5546047132666105, 5.716353886325894, 9.132849436960175, 13.105583948784881, 24.504754517626314, 15.757546708189865, 6.7051232818033375]\n",
      "the mean mae is 11.958177530656066\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "290e1d1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 6.396622638638069\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 2 iteration is: 5.287762941951866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 3 iteration is: 41.846418034401786\n",
      "the mean absolute error in 4 iteration is: 7.944003025528981\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 5 iteration is: 5.57247737982982\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 6 iteration is: 9.523769237297572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 7 iteration is: 12.896054540793292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 8 iteration is: 28.824720084829504\n",
      "the mean absolute error in 9 iteration is: 15.866251753793076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 10 iteration is: 5.99946117114741\n",
      "the scores are [6.396622638638069, 5.287762941951866, 41.846418034401786, 7.944003025528981, 5.57247737982982, 9.523769237297572, 12.896054540793292, 28.824720084829504, 15.866251753793076, 5.99946117114741]\n",
      "the mean mae is 14.015754080821136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "#import random\n",
    "#random.seed(4)\n",
    "\n",
    "# model\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6874a63",
   "metadata": {},
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2922293",
   "metadata": {},
   "source": [
    "Similar to the previous function we can use cross validation function of sklearn (The problem is that this approach could be more difficult to compare when we see other types of models like LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ddf60e",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9ab11d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 5.10145161  4.64612903 50.92983871  7.2252459   5.24245902  8.36836066\n",
      " 12.78       28.35016393 13.35        6.42278689]\n",
      "the mean mae is: 14.241643574828135\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f4dcf6",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b2ee21e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 4.03112451  4.06624558 31.01644226  6.24745235  5.84809515  8.97269342\n",
      " 13.64095053 26.12346249 12.79801242  6.80320072]\n",
      "the mean mae is: 11.954767941216181\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac4519f",
   "metadata": {},
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3546300d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 6.17619562  5.04251534 42.33959955  6.6415862   5.67562345  9.46446255\n",
      " 13.66810203 31.40100277 14.2995716   5.90094881]\n",
      "the mean mae is: 14.060960792106405\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b7926f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39c889ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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