SVR_SIR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
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       "      <th>6/22/20</th>\n",
       "      <th>6/23/20</th>\n",
       "      <th>6/24/20</th>\n",
       "      <th>6/25/20</th>\n",
       "      <th>6/26/20</th>\n",
       "      <th>6/27/20</th>\n",
       "      <th>6/28/20</th>\n",
       "      <th>6/29/20</th>\n",
       "      <th>6/30/20</th>\n",
       "      <th>7/1/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>30175</td>\n",
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       "      <td>30967</td>\n",
       "      <td>31238</td>\n",
       "      <td>31517</td>\n",
       "      <td>31836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1995</td>\n",
       "      <td>2047</td>\n",
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       "      <td>2192</td>\n",
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       "      <td>2466</td>\n",
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       "      <td>2580</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11920</td>\n",
       "      <td>12076</td>\n",
       "      <td>12248</td>\n",
       "      <td>12445</td>\n",
       "      <td>12685</td>\n",
       "      <td>12968</td>\n",
       "      <td>13273</td>\n",
       "      <td>13571</td>\n",
       "      <td>13907</td>\n",
       "      <td>14272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>186</td>\n",
       "      <td>189</td>\n",
       "      <td>197</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>259</td>\n",
       "      <td>267</td>\n",
       "      <td>276</td>\n",
       "      <td>284</td>\n",
       "      <td>291</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>11.8251</td>\n",
       "      <td>42.5903</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>4599</td>\n",
       "      <td>4617</td>\n",
       "      <td>4630</td>\n",
       "      <td>4635</td>\n",
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       "      <td>4643</td>\n",
       "      <td>4643</td>\n",
       "      <td>4656</td>\n",
       "      <td>4682</td>\n",
       "      <td>4704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>18.7357</td>\n",
       "      <td>-70.1627</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27370</td>\n",
       "      <td>27936</td>\n",
       "      <td>28631</td>\n",
       "      <td>29141</td>\n",
       "      <td>29764</td>\n",
       "      <td>30619</td>\n",
       "      <td>31373</td>\n",
       "      <td>31816</td>\n",
       "      <td>32568</td>\n",
       "      <td>33387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>-1.8312</td>\n",
       "      <td>-78.1834</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50640</td>\n",
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       "      <td>51643</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>56809</td>\n",
       "      <td>58141</td>\n",
       "      <td>59561</td>\n",
       "      <td>61130</td>\n",
       "      <td>62755</td>\n",
       "      <td>63923</td>\n",
       "      <td>65188</td>\n",
       "      <td>66754</td>\n",
       "      <td>68311</td>\n",
       "      <td>69814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>NaN</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>13.7942</td>\n",
       "      <td>-88.8965</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4808</td>\n",
       "      <td>4973</td>\n",
       "      <td>5150</td>\n",
       "      <td>5336</td>\n",
       "      <td>5517</td>\n",
       "      <td>5727</td>\n",
       "      <td>5934</td>\n",
       "      <td>6173</td>\n",
       "      <td>6438</td>\n",
       "      <td>6736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Province/State      Country/Region      Lat     Long  1/22/20  1/23/20  \\\n",
       "0             NaN         Afghanistan  33.0000  65.0000        0        0   \n",
       "1             NaN             Albania  41.1533  20.1683        0        0   \n",
       "2             NaN             Algeria  28.0339   1.6596        0        0   \n",
       "3             NaN             Andorra  42.5063   1.5218        0        0   \n",
       "4             NaN              Angola -11.2027  17.8739        0        0   \n",
       "..            ...                 ...      ...      ...      ...      ...   \n",
       "95            NaN            Djibouti  11.8251  42.5903        0        0   \n",
       "96            NaN  Dominican Republic  18.7357 -70.1627        0        0   \n",
       "97            NaN             Ecuador  -1.8312 -78.1834        0        0   \n",
       "98            NaN               Egypt  26.0000  30.0000        0        0   \n",
       "99            NaN         El Salvador  13.7942 -88.8965        0        0   \n",
       "\n",
       "    1/24/20  1/25/20  1/26/20  1/27/20  ...  6/22/20  6/23/20  6/24/20  \\\n",
       "0         0        0        0        0  ...    29157    29481    29640   \n",
       "1         0        0        0        0  ...     1995     2047     2114   \n",
       "2         0        0        0        0  ...    11920    12076    12248   \n",
       "3         0        0        0        0  ...      855      855      855   \n",
       "4         0        0        0        0  ...      186      189      197   \n",
       "..      ...      ...      ...      ...  ...      ...      ...      ...   \n",
       "95        0        0        0        0  ...     4599     4617     4630   \n",
       "96        0        0        0        0  ...    27370    27936    28631   \n",
       "97        0        0        0        0  ...    50640    51643    51643   \n",
       "98        0        0        0        0  ...    56809    58141    59561   \n",
       "99        0        0        0        0  ...     4808     4973     5150   \n",
       "\n",
       "    6/25/20  6/26/20  6/27/20  6/28/20  6/29/20  6/30/20  7/1/20  \n",
       "0     30175    30451    30616    30967    31238    31517   31836  \n",
       "1      2192     2269     2330     2402     2466     2535    2580  \n",
       "2     12445    12685    12968    13273    13571    13907   14272  \n",
       "3       855      855      855      855      855      855     855  \n",
       "4       212      212      259      267      276      284     291  \n",
       "..      ...      ...      ...      ...      ...      ...     ...  \n",
       "95     4635     4643     4643     4643     4656     4682    4704  \n",
       "96    29141    29764    30619    31373    31816    32568   33387  \n",
       "97    53156    53856    54574    55255    55665    56432   58257  \n",
       "98    61130    62755    63923    65188    66754    68311   69814  \n",
       "99     5336     5517     5727     5934     6173     6438    6736  \n",
       "\n",
       "[100 rows x 166 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read Data for Cases, Deaths and Recoveries\n",
    "confirmedCases=pd.read_csv('part-5/time_series_covid19_confirmed_global.csv')\n",
    "confirmedCases.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil = confirmedCases.loc[confirmedCases['Country/Region'] == 'Brazil']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>28</th>\n",
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       "   Province/State Country/Region     Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "28            NaN         Brazil -14.235 -51.9253        0        0        0   \n",
       "\n",
       "    1/25/20  1/26/20  1/27/20  ...  6/22/20  6/23/20  6/24/20  6/25/20  \\\n",
       "28        0        0        0  ...  1106470  1145906  1188631  1228114   \n",
       "\n",
       "    6/26/20  6/27/20  6/28/20  6/29/20  6/30/20   7/1/20  \n",
       "28  1274974  1313667  1344143  1368195  1402041  1448753  \n",
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "casesBrazil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-4fc35fc61a9c>:1: 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",
      "  casesBrazil['2/25/20'] = 1\n",
      "<ipython-input-6-4fc35fc61a9c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  casesBrazil['2/26/20'] = 1\n",
      "<ipython-input-6-4fc35fc61a9c>: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",
      "  casesBrazil['2/27/20'] = 2\n",
      "<ipython-input-6-4fc35fc61a9c>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  casesBrazil['2/28/20'] = 3\n",
      "<ipython-input-6-4fc35fc61a9c>:5: 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",
      "  casesBrazil['2/29/20'] = 4\n",
      "<ipython-input-6-4fc35fc61a9c>:6: 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",
      "  casesBrazil['3/1/20'] = 5\n",
      "<ipython-input-6-4fc35fc61a9c>:7: 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",
      "  casesBrazil['3/2/20'] = 8\n",
      "<ipython-input-6-4fc35fc61a9c>:8: 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",
      "  casesBrazil['3/3/20'] = 10\n",
      "<ipython-input-6-4fc35fc61a9c>:9: 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",
      "  casesBrazil['3/4/20'] = 13\n",
      "<ipython-input-6-4fc35fc61a9c>:10: 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",
      "  casesBrazil['3/5/20'] = 25\n",
      "<ipython-input-6-4fc35fc61a9c>:11: 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",
      "  casesBrazil['3/6/20'] = 28\n",
      "<ipython-input-6-4fc35fc61a9c>:12: 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",
      "  casesBrazil['3/7/20'] = 31\n",
      "<ipython-input-6-4fc35fc61a9c>:13: 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",
      "  casesBrazil['3/8/20'] = 34\n",
      "<ipython-input-6-4fc35fc61a9c>:14: 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",
      "  casesBrazil['3/9/20'] = 42\n",
      "<ipython-input-6-4fc35fc61a9c>:15: 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",
      "  casesBrazil['3/10/20'] = 52\n",
      "<ipython-input-6-4fc35fc61a9c>:16: 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",
      "  casesBrazil['3/11/20'] = 77\n",
      "<ipython-input-6-4fc35fc61a9c>:17: 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",
      "  casesBrazil['3/12/20'] = 120\n",
      "<ipython-input-6-4fc35fc61a9c>:18: 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",
      "  casesBrazil['3/13/20'] = 150\n",
      "<ipython-input-6-4fc35fc61a9c>:19: 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",
      "  casesBrazil['3/14/20'] = 180\n",
      "<ipython-input-6-4fc35fc61a9c>:20: 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",
      "  casesBrazil['3/15/20'] = 200\n",
      "<ipython-input-6-4fc35fc61a9c>:21: 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",
      "  casesBrazil['3/16/20'] = 234\n",
      "<ipython-input-6-4fc35fc61a9c>:22: 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",
      "  casesBrazil['3/17/20'] = 346\n",
      "<ipython-input-6-4fc35fc61a9c>:23: 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",
      "  casesBrazil['3/18/20'] = 509\n",
      "<ipython-input-6-4fc35fc61a9c>:24: 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",
      "  casesBrazil['3/19/20'] = 621\n",
      "<ipython-input-6-4fc35fc61a9c>:25: 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",
      "  casesBrazil['3/20/20'] = 793\n",
      "<ipython-input-6-4fc35fc61a9c>:26: 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",
      "  casesBrazil['3/21/20'] = 1021\n",
      "<ipython-input-6-4fc35fc61a9c>:27: 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",
      "  casesBrazil['3/22/20'] = 1593\n",
      "<ipython-input-6-4fc35fc61a9c>:28: 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",
      "  casesBrazil['3/23/20'] = 1924\n",
      "<ipython-input-6-4fc35fc61a9c>:29: 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",
      "  casesBrazil['3/24/20'] = 2247\n",
      "<ipython-input-6-4fc35fc61a9c>:30: 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",
      "  casesBrazil['3/25/20'] = 2554\n",
      "<ipython-input-6-4fc35fc61a9c>:31: 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",
      "  casesBrazil['3/26/20'] = 2985\n",
      "<ipython-input-6-4fc35fc61a9c>:32: 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",
      "  casesBrazil['7/2/20'] = 1496858\n"
     ]
    }
   ],
   "source": [
    "casesBrazil['2/25/20'] = 1\n",
    "casesBrazil['2/26/20'] = 1\n",
    "casesBrazil['2/27/20'] = 2\n",
    "casesBrazil['2/28/20'] = 3\n",
    "casesBrazil['2/29/20'] = 4\n",
    "casesBrazil['3/1/20'] = 5\n",
    "casesBrazil['3/2/20'] = 8\n",
    "casesBrazil['3/3/20'] = 10\n",
    "casesBrazil['3/4/20'] = 13\n",
    "casesBrazil['3/5/20'] = 25\n",
    "casesBrazil['3/6/20'] = 28\n",
    "casesBrazil['3/7/20'] = 31\n",
    "casesBrazil['3/8/20'] = 34\n",
    "casesBrazil['3/9/20'] = 42\n",
    "casesBrazil['3/10/20'] = 52\n",
    "casesBrazil['3/11/20'] = 77\n",
    "casesBrazil['3/12/20'] = 120\n",
    "casesBrazil['3/13/20'] = 150\n",
    "casesBrazil['3/14/20'] = 180\n",
    "casesBrazil['3/15/20'] = 200\n",
    "casesBrazil['3/16/20'] = 234\n",
    "casesBrazil['3/17/20'] = 346\n",
    "casesBrazil['3/18/20'] = 509\n",
    "casesBrazil['3/19/20'] = 621\n",
    "casesBrazil['3/20/20'] = 793\n",
    "casesBrazil['3/21/20'] = 1021\n",
    "casesBrazil['3/22/20'] = 1593\n",
    "casesBrazil['3/23/20'] = 1924\n",
    "casesBrazil['3/24/20'] = 2247\n",
    "casesBrazil['3/25/20'] = 2554\n",
    "casesBrazil['3/26/20'] = 2985\n",
    "casesBrazil['7/2/20'] = 1496858"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28    108620\n",
       "Name: 5/4/20, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casesBrazil['5/4/20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caiquecoelho/.local/lib/python3.8/site-packages/pandas/core/frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "casesBrazil.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil_transposed = casesBrazil.T\n",
    "casesBrazil_transposed.columns = ['cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil_confirmed_09_15_april = [18092, 19638, 20727, 22192, 23430, 25262, 28320]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil_transposed_density = casesBrazil_transposed.copy()\n",
    "casesBrazil_transposed_density = casesBrazil_transposed_density[casesBrazil_transposed_density['cases'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2/25/20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2/26/20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2/27/20</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2/28/20</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2/29/20</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cases\n",
       "2/25/20      1\n",
       "2/26/20      1\n",
       "2/27/20      2\n",
       "2/28/20      3\n",
       "2/29/20      4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casesBrazil_transposed_density.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil_transposed_density = casesBrazil_transposed_density['cases'].apply(lambda x: x/209)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2/25/20    0.004785\n",
       "2/26/20    0.004785\n",
       "2/27/20    0.009569\n",
       "2/28/20    0.014354\n",
       "2/29/20    0.019139\n",
       "Name: cases, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casesBrazil_transposed_density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = casesBrazil_transposed.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "casesBrazil_transposed['cases'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesBrazil_transposed_with_cases = casesBrazil_transposed[casesBrazil_transposed['cases'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1e58e1b220>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1209.6x806.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(np.array(casesBrazil_transposed_with_cases['cases']), label = 'Brasil')\n",
    "sizefactor = 2.8 #Set a zoom factor\n",
    "ax = plt.gca()\n",
    "fig_size = plt.gcf().get_size_inches() \n",
    "\n",
    "plt.gcf().set_size_inches(sizefactor * fig_size)\n",
    "plt.title('Quantidade total de casos no Brasil')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.array(casesBrazil_transposed_with_cases['cases'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([      1,       1,       2,       3,       4,       5,       8,\n",
       "            10,      13,      25,      28,      31,      34,      42,\n",
       "            52,      77,     120,     150,     180,     200,     234,\n",
       "           346,     509,     621,     793,    1021,    1593,    1924,\n",
       "          2247,    2554,    2985,    3417,    3904,    4256,    4579,\n",
       "          5717,    6836,    8044,    9056,   10360,   11130,   12161,\n",
       "         14034,   16170,   18092,   19638,   20727,   22192,   23430,\n",
       "         25262,   28320,   30425,   33682,   36658,   38654,   40743,\n",
       "         43079,   45757,   50036,   54043,   59324,   63100,   67446,\n",
       "         73235,   79685,   87187,   92202,   97100,  101826,  108620,\n",
       "        115455,  126611,  135773,  146894,  156061,  162699,  169594,\n",
       "        178214,  190137,  203165,  220291,  233511,  241080,  255368,\n",
       "        271885,  291579,  310087,  330890,  347398,  363211,  374898,\n",
       "        391222,  411821,  438238,  465166,  498440,  514849,  526447,\n",
       "        555383,  584016,  614941,  645771,  672846,  691758,  707412,\n",
       "        739503,  772416,  802828,  828810,  850514,  867624,  888271,\n",
       "        923189,  955377,  978142, 1032913, 1067579, 1083341, 1106470,\n",
       "       1145906, 1188631, 1228114, 1274974, 1313667, 1344143, 1368195,\n",
       "       1402041, 1448753, 1496858])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.815606694492531, 3.034806984299288, 2.31991821367211, 1.7893929440543157, 2.4737701862673087, 3.331714265132047, 3.3204417868332285]\n"
     ]
    }
   ],
   "source": [
    "# Get percentage new cases in the last 7 days in all Brazil\n",
    "percentByDayBrazil = []\n",
    "for i in range(0, len(Y)-1):\n",
    "    if(i > len(Y)-9):\n",
    "        percent = 100/Y[i]\n",
    "        percent = (Y[i+1] - Y[i]) * percent\n",
    "        percentByDayBrazil.append(percent)\n",
    "print(percentByDayBrazil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentByDayBrazilTotal = []\n",
    "for i in range(0, len(Y)-1):\n",
    "        percent = 100/Y[i]\n",
    "        percent = (Y[i+1] - Y[i]) * percent\n",
    "        percentByDayBrazilTotal.append(percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "casesByDayBrazilTotal = []\n",
    "for i in range(0, len(Y)-1):\n",
    "        day = Y[i+1] - Y[i]\n",
    "        casesByDayBrazilTotal.append(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_b = []\n",
    "X_b.append(0)\n",
    "for index in range(1, len(Y)):\n",
    "    X_b.append(Y[index-1])\n",
    "X_b = np.array(X_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_a = []\n",
    "X_a.append(0)\n",
    "for index in range(1, len(Y)):\n",
    "    X_a.append(index)\n",
    "X_a = np.array(X_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2d = []\n",
    "for index in range(0, len(X_a)):\n",
    "    X_2d.append([X_a[index], X_b[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reshape = X_b.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_lin = SVR(kernel='linear').fit(X_reshape, Y) #20111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1496858],\n",
       "       [1523831],\n",
       "       [1551289],\n",
       "       [1579241],\n",
       "       [1607696],\n",
       "       [1636663],\n",
       "       [1666151],\n",
       "       [1696169],\n",
       "       [1726727],\n",
       "       [1757834],\n",
       "       [1789501],\n",
       "       [1821738],\n",
       "       [1854554],\n",
       "       [1887961],\n",
       "       [1921969]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_svr = []\n",
    "predict_svr = np.array([[1496858]])\n",
    "predict_svrT = []\n",
    "predict_svrT = np.array([[1496858]])\n",
    "predict_svr30 = 0\n",
    "predict_svr30 = 0\n",
    "predict_svr280 = np.array([[1496858]])\n",
    "for i in range(0, 14):\n",
    "    result = [int(svr_lin.predict([predict_svr[i]])[0])]\n",
    "    predict_svr = np.concatenate((predict_svr, np.array([result])))\n",
    "for i in range(0, 90):\n",
    "    result = svr_lin.predict([predict_svrT[i]])\n",
    "    predict_svrT = np.concatenate((predict_svrT, np.array([result])))\n",
    "    if(i == 29):\n",
    "        predict_svr30 = result\n",
    "    if(i == 89):\n",
    "        predict_svr90 = result\n",
    "for i in range(0, 280):\n",
    "    result = svr_lin.predict([predict_svr280[i]])\n",
    "    predict_svr280 = np.concatenate((predict_svr280, np.array([result])))\n",
    "predict_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2557270.44770059])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_svr30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7457795.83808145])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_svr90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 777.6x518.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.integrate import odeint\n",
    "\n",
    "# Total population, N.\n",
    "N = 209300000\n",
    "# Initial number of infected and recovered individuals, I0 and R0.\n",
    "I0, R0 = 1496858, 1042980\n",
    "# Everyone else, S0, is susceptible to infection initially.\n",
    "S0 = N - I0 - R0\n",
    "# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).\n",
    "beta, gamma = 0.0312, 0.003\n",
    "# A grid of time points (in days)\n",
    "t = np.linspace(0, 360, 360)\n",
    "\n",
    "# The SIR model differential equations.\n",
    "def deriv(y, t, N, beta, gamma):\n",
    "    S, I, R = y\n",
    "    dSdt = -beta * S * I / N\n",
    "    dIdt = beta * S * I / N - gamma * I\n",
    "    dRdt = gamma * I\n",
    "    return dSdt, dIdt, dRdt\n",
    "\n",
    "# Initial conditions vector\n",
    "y0 = S0, I0, R0\n",
    "# Integrate the SIR equations over the time grid, t.\n",
    "ret = odeint(deriv, y0, t, args=(N, beta, gamma))\n",
    "S, I, R = ret.T\n",
    "\n",
    "Y = [int(np.max(S)), int(np.max(S)/2), int(np.max(R)/2), int(np.mean(R)), int(np.min(S))]\n",
    "\n",
    "# Plot the data on three separate curves for S(t), I(t) and R(t)\n",
    "fig = plt.figure(facecolor='w')\n",
    "ax = fig.add_subplot(111, axisbelow=True)\n",
    "ax.plot(S, 'b', label='Suscetíveis a infecção')\n",
    "ax.plot(I, 'r', label='Infectados')\n",
    "ax.plot(R, 'g', label='Recuperados')\n",
    "ax.set_xlabel('Tempo por dias')\n",
    "ax.set_ylabel('Número a cada cem mil pessoas')\n",
    "plt.yticks(Y)\n",
    "plt.title(\"Aplicação do modelo SIR para a pandemia do coronavírus no Brasil\")\n",
    "\n",
    "fig_size = plt.gcf().get_size_inches() \n",
    "sizefactor = 1.8 #Set a zoom factor\n",
    "plt.gcf().set_size_inches(sizefactor * fig_size)\n",
    "\n",
    "legend = ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1539201.28029696, 1582731.13698686, 1627480.15474285,\n",
       "       1673481.79388681, 1720770.40653333, 1769381.23205221,\n",
       "       1819350.43510263, 1870715.12820448, 1923513.38213664,\n",
       "       1977784.24392541, 2033567.78633454, 2090905.12138005,\n",
       "       2149838.40785872, 2210410.88119541])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[1:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1047546.4793828868"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17101431.919868127"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1539201.2802969618"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5952959.71225389, 6116774.78443096, 6284924.54270857,\n",
       "       6457514.00969335, 6634650.24313376, 6816442.32880198,\n",
       "       7003001.31958152, 7194440.30915127, 7390874.44383321,\n",
       "       7592420.89160839])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[50:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 777.6x518.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.integrate import odeint\n",
    "\n",
    "# Total population, N.\n",
    "N = 12180000\n",
    "# Initial number of infected and recovered individuals, I0 and R0.\n",
    "I0, R0 = 289935, 178202\n",
    "# Everyone else, S0, is susceptible to infection initially.\n",
    "S0 = N - I0 - R0\n",
    "# Contact rate, beta, and mean recovery rate, gamma, (in 1/days).\n",
    "beta, gamma = 0.046, 0.003\n",
    "# A grid of time points (in days)\n",
    "t = np.linspace(0, 360, 360)\n",
    "\n",
    "# The SIR model differential equations.\n",
    "def deriv(y, t, N, beta, gamma):\n",
    "    S, I, R = y\n",
    "    dSdt = -beta * S * I / N\n",
    "    dIdt = beta * S * I / N - gamma * I\n",
    "    dRdt = gamma * I\n",
    "    return dSdt, dIdt, dRdt\n",
    "\n",
    "# Initial conditions vector\n",
    "y0 = S0, I0, R0\n",
    "# Integrate the SIR equations over the time grid, t.\n",
    "ret = odeint(deriv, y0, t, args=(N, beta, gamma))\n",
    "S, I, R = ret.T\n",
    "\n",
    "Y = [int(np.max(S)), int(np.max(S)/2), int(np.max(R)), int(np.mean(S)), int(np.min(S))]\n",
    "\n",
    "# Plot the data on three separate curves for S(t), I(t) and R(t)\n",
    "fig = plt.figure(facecolor='w')\n",
    "ax = fig.add_subplot(111, axisbelow=True)\n",
    "ax.plot(S, 'b', label='Suscetíveis a infecção')\n",
    "ax.plot(I, 'r', label='Infectados')\n",
    "ax.plot(R, 'g', label='Recuperados')\n",
    "ax.set_xlabel('Tempo por dias')\n",
    "ax.set_ylabel('Número a cada cem mil pessoas')\n",
    "plt.yticks(Y)\n",
    "plt.title(\"Aplicação do modelo SIR para a pandemia do coronavírus no estado de São Paulo\")\n",
    "\n",
    "fig_size = plt.gcf().get_size_inches() \n",
    "sizefactor = 1.8 #Set a zoom factor\n",
    "plt.gcf().set_size_inches(sizefactor * fig_size)\n",
    "\n",
    "legend = ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302166.7193073415"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12231.719307341496"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[1] - I[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179092.5032993347"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1172829.14176655, 1217836.59988565, 1264334.59286464,\n",
       "       1312354.11583904, 1361925.36247396])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[35:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8251052.127321988"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I[210]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-207d2926ecb8>:3: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1e565b1160>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1209.6x806.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lw = 2\n",
    "fig=plt.figure()\n",
    "fig.show()\n",
    "plt.plot(I, linewidth=lw, label = 'SIR - Modelo epidêmico (Equação)')\n",
    "sizefactor = 2.8 #Set a zoom factor\n",
    "# Modify the current size by the factor\n",
    "\n",
    "fig_size = plt.gcf().get_size_inches() \n",
    "plt.gcf().set_size_inches(sizefactor * fig_size)\n",
    "plt.title('Prevendo pico')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}