gif_total.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#code to create a gif that visualizes our predictions against cumalative cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allows animations to be opened in interactive window\n",
    "%matplotlib tk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in files\n",
    "true = pd.read_csv('/Users/Rohil/Documents/iGEM/yemen/cholera_epi_data/yemen_cholera_case_data_differenced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "true.date = pd.to_datetime(true.date, format = '%d-%m-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = true.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_pop_area_data = pd.read_excel('/Users/Rohil/Documents/iGEM/yemen/gov_area_pop_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_pop_area_data = gov_pop_area_data[gov_pop_area_data.iso != 'YE-HD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in gov_pop_area_data[['iso', 'population']].iterrows():\n",
    "    true[row.iso] = (true[row.iso] * 10000) / row.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YE-HD-AL      0.038740\n",
       "YE-MR         0.090909\n",
       "YE-SH         0.132559\n",
       "YE-AB         4.089932\n",
       "YE-DA         6.265140\n",
       "YE-AD         9.979787\n",
       "YE-TA        19.186859\n",
       "YE-SD        19.480329\n",
       "YE-LA        19.538333\n",
       "YE-MA        21.636787\n",
       "YE-IB        24.649613\n",
       "YE-JA        32.917424\n",
       "YE-SN        43.369211\n",
       "YE-BA        54.434516\n",
       "YE-HU        61.330958\n",
       "YE-RA        77.057578\n",
       "YE-HJ       104.449837\n",
       "YE-DH       104.809169\n",
       "YE-AM       121.334989\n",
       "YE-SA       128.342665\n",
       "YE-MW       129.562461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.loc[true.index.get_level_values('date') > '2017-11-10'].sum(axis=0).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # need to convert data into stationary (changes in cholera cases from one day to another)\n",
    "\n",
    "# # the function below creates a differenced series\n",
    "# def difference_reversed_for_preds(dataset, interval=1):\n",
    "    \n",
    "#     for i in range(interval, len(dataset)):\n",
    "        \n",
    "#         dataset[i] = dataset[i] + dataset[i-1] / 14\n",
    "\n",
    "        \n",
    "#     return (dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to convert data into stationary (changes in cholera cases from one day to another)\n",
    "\n",
    "# the function below creates a differenced series\n",
    "def difference_reversed_for_true(dataset, interval=1):\n",
    "    \n",
    "    \n",
    "    for i in range(interval, len(dataset)):\n",
    "        \n",
    "        dataset[i] = dataset[i] + dataset[i-1]\n",
    "\n",
    "        \n",
    "    return (dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in true:\n",
    "    true[column]  = (difference_reversed_for_true(true[column]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_crosstab_dict = {}\n",
    "# for col in ['week_1_to_2_cases', 'week_2_to_4_cases', 'week_4_to_6_cases', 'week_6_to_8_cases']:\n",
    "    \n",
    "#     pred_crosstab_dict[col] = preds[['gov_iso', 'date', col]].pivot_table(index = 'date', columns = 'gov_iso', values = col)\n",
    "    \n",
    "# #     for gov in pred_crosstab_dict[col]:\n",
    "# #         pred_crosstab_dict[col][gov] = difference_reversed_for_preds(pred_crosstab_dict[col][gov])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YE-AM       158131.399340\n",
       "YE-DA       150160.036375\n",
       "YE-SA       144089.662036\n",
       "YE-MW       141307.492217\n",
       "YE-HJ       104505.463877\n",
       "YE-DH        97471.991383\n",
       "YE-AB        81506.206513\n",
       "YE-HU        68291.473660\n",
       "YE-BA        60539.253520\n",
       "YE-RA        57202.366145\n",
       "YE-SN        52789.861668\n",
       "YE-LA        46162.339912\n",
       "YE-JA        39246.682862\n",
       "YE-AD        38086.195689\n",
       "YE-IB        30374.006346\n",
       "YE-TA        24426.157051\n",
       "YE-MA        24208.771221\n",
       "YE-SD        15359.353342\n",
       "YE-MR         5889.962500\n",
       "YE-SH         3913.430206\n",
       "YE-HD-AL      3508.301954\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.sum(axis=0).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_12_case_crosstab = pd.read_csv('/Users/Rohil/Documents/iGEM/yemen/week_1_to_2_cases_deployed_cv_holdout_preds.csv')\n",
    "week_24_case_crosstab = pd.read_csv('/Users/Rohil/Documents/iGEM/yemen/week_2_to_4_cases_deployed_cv_holdout_preds.csv')\n",
    "week_46_case_crosstab = pd.read_csv('/Users/Rohil/Documents/iGEM/yemen/week_4_to_6_cases_deployed_cv_holdout_preds.csv')\n",
    "week_68_case_crosstab = pd.read_csv('/Users/Rohil/Documents/iGEM/yemen/week_6_to_8_cases_deployed_cv_holdout_preds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_12_case_crosstab.date = pd.to_datetime(week_12_case_crosstab.date, format = '%Y-%m-%d')\n",
    "week_24_case_crosstab.date = pd.to_datetime(week_24_case_crosstab.date, format = '%Y-%m-%d')\n",
    "week_46_case_crosstab.date = pd.to_datetime(week_46_case_crosstab.date, format = '%Y-%m-%d')\n",
    "week_68_case_crosstab.date = pd.to_datetime(week_68_case_crosstab.date, format = '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_12_case_crosstab.set_index('date', inplace = True)\n",
    "week_24_case_crosstab.set_index('date', inplace = True)\n",
    "week_46_case_crosstab.set_index('date', inplace = True)\n",
    "week_68_case_crosstab.set_index('date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_crosstab_dict = {}\n",
    "pred_crosstab_dict['week_1_to_2_cases'] = week_12_case_crosstab\n",
    "pred_crosstab_dict['week_2_to_4_cases'] = week_24_case_crosstab\n",
    "pred_crosstab_dict['week_4_to_6_cases'] = week_46_case_crosstab\n",
    "pred_crosstab_dict['week_6_to_8_cases'] = week_68_case_crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YE-AB', 'YE-AD', 'YE-AM', 'YE-BA', 'YE-DA', 'YE-DH', 'YE-HD-AL',\n",
       "       'YE-HJ', 'YE-HU', 'YE-IB', 'YE-JA', 'YE-LA', 'YE-MA', 'YE-MR', 'YE-MW',\n",
       "       'YE-RA', 'YE-SA', 'YE-SD', 'YE-SH', 'YE-SN', 'YE-TA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_12_case_crosstab.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YE-AB', 'YE-AD', 'YE-AM', 'YE-BA', 'YE-DA', 'YE-DH', 'YE-HD-AL',\n",
       "       'YE-HJ', 'YE-HU', 'YE-IB', 'YE-JA', 'YE-LA', 'YE-MA', 'YE-MR', 'YE-MW',\n",
       "       'YE-RA', 'YE-SA', 'YE-SD', 'YE-SH', 'YE-SN', 'YE-TA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ylim_df = true.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_12_line_dict = {}\n",
    "y_24_line_dict = {}\n",
    "y_46_line_dict = {}\n",
    "y_68_line_dict = {}\n",
    "true_line_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gov' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-0f2ba8f47db5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred_crosstab_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'week_1_to_2_cases'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgov\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtrue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdates12_start\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_timedelta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'D'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgov\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'gov' is not defined"
     ]
    }
   ],
   "source": [
    "pred_crosstab_dict['week_1_to_2_cases'][gov].iloc[:2-1].sum() + true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that updates plot for each day\n",
    "\n",
    "def update(i, govs):\n",
    "    \n",
    "    # Update the line and the axes (with a new xlabel). Return a tuple of\n",
    "    # \"artists\" that have to be redrawn for this frame.\n",
    "    true_date =  pd.date_range(true.index[0], pred_crosstab_dict['week_1_to_2_cases'].index[i])\n",
    "    \n",
    "    dates12 = pd.date_range(pred_crosstab_dict['week_1_to_2_cases'].index[i] + pd.to_timedelta(0, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_1_to_2_cases'].index[i] + pd.to_timedelta(2, 'W'), freq = \"D\")\n",
    "    dates24 = pd.date_range(pred_crosstab_dict['week_2_to_4_cases'].index[i] + pd.to_timedelta(2, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_2_to_4_cases'].index[i] + pd.to_timedelta(4, 'W'), freq = \"D\")\n",
    "    dates46 = pd.date_range(pred_crosstab_dict['week_4_to_6_cases'].index[i] + pd.to_timedelta(4, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_4_to_6_cases'].index[i] + pd.to_timedelta(6, 'W'), freq = \"D\")\n",
    "    dates68 = pd.date_range(pred_crosstab_dict['week_6_to_8_cases'].index[i] + pd.to_timedelta(6, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_6_to_8_cases'].index[i] + pd.to_timedelta(8, 'W'), freq = \"D\")\n",
    "    \n",
    "    for e in range(0,len(govs)):\n",
    "        \n",
    "        gov = govs[e]\n",
    "        \n",
    "        true_val = (true.loc[true_date][gov])\n",
    "        true_line_dict[gov].set_data(true_date, true_val)\n",
    "        \n",
    "        vals12_prev = (pred_crosstab_dict['week_1_to_2_cases'][gov].iloc[:i-1].sum()) / 14  + true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov]\n",
    "        vals12 = np.linspace(vals12_prev, pred_crosstab_dict['week_1_to_2_cases'][gov].iloc[i] + vals12_prev, 14)\n",
    "        y_12_line_dict[gov].set_data(dates12, vals12)\n",
    "        \n",
    "        vals24_prev = (pred_crosstab_dict['week_2_to_4_cases'][gov].iloc[:i-1].sum())  / 14 + true.loc[dates24_start.min() - pd.to_timedelta(1, 'D')][gov]\n",
    "        vals24 = np.linspace(vals24_prev, pred_crosstab_dict['week_2_to_4_cases'][gov].iloc[i] + vals24_prev, 14)\n",
    "        y_24_line_dict[gov].set_data(dates24, vals24)\n",
    "        \n",
    "        vals46_prev = (pred_crosstab_dict['week_4_to_6_cases'][gov].iloc[:i-1].sum()) / 14 + true.loc[dates46_start.min() - pd.to_timedelta(1, 'D')][gov]\n",
    "        vals46 = np.linspace(vals46_prev, pred_crosstab_dict['week_4_to_6_cases'][gov].iloc[i] + vals46_prev, 14)\n",
    "        y_46_line_dict[gov].set_data(dates46, vals46)\n",
    "\n",
    "        vals68_prev = (pred_crosstab_dict['week_6_to_8_cases'][gov].iloc[:i-1].sum()) / 14 + true.loc[dates68_start.min() - pd.to_timedelta(1, 'D')][gov]\n",
    "        vals68 = np.linspace(vals68_prev, pred_crosstab_dict['week_6_to_8_cases'][gov].iloc[i] + vals68_prev, 14)\n",
    "        y_68_line_dict[gov].set_data(dates68, vals68)\n",
    "        \n",
    "    \n",
    "    label = 'day {0}'.format(i)\n",
    "    \n",
    "    ax[-1].set_xlabel(label, fontsize = 9)\n",
    "    \n",
    "    return (ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_style(color):\n",
    "    plt.style.use(['seaborn-' + color, 'seaborn-paper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def suplabel(axis,label,side,label_prop=None,labelpad=6,ha='center',va='center'):\n",
    "    ''' Add super ylabel or xlabel to the figure\n",
    "    Similar to matplotlib.suptitle\n",
    "    axis       - string: \"x\" or \"y\"\n",
    "    label      - string\n",
    "    label_prop - keyword dictionary for Text\n",
    "    labelpad   - padding from the axis (default: 5)\n",
    "    ha         - horizontal alignment (default: \"center\")\n",
    "    va         - vertical alignment (default: \"center\")\n",
    "    '''\n",
    "    fig = pylab.gcf()\n",
    "    xmin = []\n",
    "    ymin = []\n",
    "    xmax = []\n",
    "    ymax = []\n",
    "    for ax in fig.axes:\n",
    "        xmin.append(ax.get_position().xmin)\n",
    "        ymin.append(ax.get_position().ymin)\n",
    "        xmax.append(ax.get_position().xmax)\n",
    "        ymax.append(ax.get_position().ymax)\n",
    "        \n",
    "    xmin,ymin = min(xmin),min(ymin)\n",
    "    xmax,ymax = min(xmax),min(ymax)\n",
    "\n",
    "    dpi = fig.dpi\n",
    "    if axis.lower() == \"y\":\n",
    "        rotation=90.\n",
    "        \n",
    "        if side == 'left':\n",
    "            x = xmin-float(labelpad)/dpi\n",
    "            \n",
    "        if side == 'right':\n",
    "            x = xmax+float(labelpad)/dpi\n",
    "\n",
    "        y = 0.5\n",
    "    elif axis.lower() == 'x':\n",
    "        rotation = 0.\n",
    "        x = 0.5\n",
    "        y = ymin - float(labelpad)/dpi\n",
    "    else:\n",
    "        raise Exception(\"Unexpected axis: x or y\")\n",
    "    if label_prop is None: \n",
    "        label_prop = dict()\n",
    "    pylab.text(x,y,label,rotation=rotation,\n",
    "               transform=fig.transFigure,\n",
    "               ha=ha,va=va,\n",
    "               **label_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "MovieWriter imagemagick unavailable.\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(21,1,figsize = (6,17), sharex=True, sharey = False)\n",
    "\n",
    "set_style('white')\n",
    "myFmt = mdates.DateFormatter('%Y-%m-%d')\n",
    "\n",
    "govs = true.columns\n",
    "\n",
    "for i in range(0,21):\n",
    "\n",
    "    ax[i].set_xlim(true.index.min(), true.index.max())\n",
    "    ax[i].xaxis.set_major_formatter(myFmt)\n",
    "    ax[i].set_ylim(0, ylim_df[govs[i]] + .25 * ylim_df[govs[i]])\n",
    "    ax[i].tick_params(axis='x', which='major', labelsize=10)\n",
    "    ax[i].legend().set_visible(False)\n",
    "    ax[i].set_ylabel(govs[i], rotation = 0)\n",
    "    ax[i].yaxis.set_label_position('right')\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].spines['bottom'].set_visible(True)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "true_date_start =  pd.date_range(true.index[0], pred_crosstab_dict['week_1_to_2_cases'].index[i])\n",
    "\n",
    "dates12_start = pd.date_range(pred_crosstab_dict['week_1_to_2_cases'].index[0] + pd.to_timedelta(0, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_1_to_2_cases'].index[0] + pd.to_timedelta(2, 'W'), freq = \"D\")\n",
    "dates24_start = pd.date_range(pred_crosstab_dict['week_2_to_4_cases'].index[0] + pd.to_timedelta(2, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_2_to_4_cases'].index[0] + pd.to_timedelta(4, 'W'), freq = \"D\")\n",
    "dates46_start = pd.date_range(pred_crosstab_dict['week_4_to_6_cases'].index[0] + pd.to_timedelta(4, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_4_to_6_cases'].index[0] + pd.to_timedelta(6, 'W'), freq = \"D\")\n",
    "dates68_start = pd.date_range(pred_crosstab_dict['week_6_to_8_cases'].index[0] + pd.to_timedelta(6, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_6_to_8_cases'].index[0] + pd.to_timedelta(8, 'W'), freq = \"D\")\n",
    "\n",
    "for e in range(0,21):\n",
    "\n",
    "    gov = govs[e]\n",
    "    \n",
    "    true_val_start = true.loc[true_date_start][gov]\n",
    "    true_line_dict[gov], = ax[e].plot(true_date_start, true_val_start, color = 'red', label='true')\n",
    "\n",
    "    vals12_start = np.linspace(true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_1_to_2_cases'][gov].iloc[0] + true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_12_line_dict[gov], = ax[e].plot(dates12_start, vals12_start, linestyle= '-.', color = 'seagreen', label='0-2 week forecast')\n",
    "\n",
    "    vals24_start = np.linspace(true.loc[dates24_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_2_to_4_cases'][gov].iloc[0] + true.loc[dates24_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_24_line_dict[gov], = ax[e].plot(dates24_start, vals24_start, linestyle= '-.', color = 'blue', label='2-4 week forecast')\n",
    "\n",
    "    vals46_start = np.linspace(true.loc[dates46_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_4_to_6_cases'][gov].iloc[0] + true.loc[dates46_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_46_line_dict[gov], = ax[e].plot(dates46_start, vals46_start, linestyle= '-.', color = 'plum', label='4-6 week forecast')\n",
    "\n",
    "    vals68_start = np.linspace(true.loc[dates68_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_6_to_8_cases'][gov].iloc[0] + true.loc[dates68_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_68_line_dict[gov], = ax[e].plot(dates68_start, vals68_start, linestyle= '-.', color = 'magenta', label='6-8 week forecast')    \n",
    "\n",
    "ax[10].legend(handles = [true_line_dict[govs[10]], y_12_line_dict[govs[10]], y_24_line_dict[govs[10]], y_46_line_dict[govs[10]], y_68_line_dict[govs[10]]], loc='center left', bbox_to_anchor=(1.05, 0.5))\n",
    "\n",
    "anim = FuncAnimation(fig, update, frames=np.arange(0, 115), interval=10, fargs = (gov,))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "anim.save('/Users/Rohil/Documents/iGEM/yemen/final_results/gif_total.gif', dpi=100, writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_govs = sorted(['YE-AM', 'YE-DA', 'YE-MW', 'YE-RA', 'YE-SN'])\n",
    "\n",
    "#sorted(list(set(true.columns) - set(['YE-HD-AL', 'YE-MR', 'YE-SH', 'YE-TA', 'YE-MA', 'YE-SD'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(relevant_govs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "MovieWriter imagemagick unavailable.\n"
     ]
    }
   ],
   "source": [
    "# removing less relevant governorates\n",
    "\n",
    "fig, ax = plt.subplots(len(relevant_govs),1,figsize = (7,5), sharex=True, sharey = False)\n",
    "\n",
    "set_style('white')\n",
    "myFmt = mdates.DateFormatter('%b\\n%Y')\n",
    "\n",
    "for i in range(0,len(relevant_govs)):\n",
    "\n",
    "    ax[i].set_xlim(true.index.min(), true.index.max())\n",
    "    ax[i].xaxis.set_major_formatter(myFmt)\n",
    "    ax[i].set_ylim(0, ylim_df[relevant_govs[i]] + .25 * ylim_df[relevant_govs[i]])\n",
    "    ax[i].tick_params(axis='x', which='both', labelsize=9, rotation = 0)\n",
    "    ax[i].legend().set_visible(False)\n",
    "    ax[i].set_ylabel(relevant_govs[i], rotation = 0)\n",
    "    ax[i].yaxis.set_label_position('right')\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].spines['bottom'].set_visible(True)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "true_date_start =  pd.date_range(true.index[0], pred_crosstab_dict['week_1_to_2_cases'].index[i])\n",
    "\n",
    "dates12_start = pd.date_range(pred_crosstab_dict['week_1_to_2_cases'].index[0] + pd.to_timedelta(0, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_1_to_2_cases'].index[0] + pd.to_timedelta(2, 'W'), freq = \"D\")\n",
    "dates24_start = pd.date_range(pred_crosstab_dict['week_2_to_4_cases'].index[0] + pd.to_timedelta(2, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_2_to_4_cases'].index[0] + pd.to_timedelta(4, 'W'), freq = \"D\")\n",
    "dates46_start = pd.date_range(pred_crosstab_dict['week_4_to_6_cases'].index[0] + pd.to_timedelta(4, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_4_to_6_cases'].index[0] + pd.to_timedelta(6, 'W'), freq = \"D\")\n",
    "dates68_start = pd.date_range(pred_crosstab_dict['week_6_to_8_cases'].index[0] + pd.to_timedelta(6, 'W') + pd.to_timedelta(1, 'D'), pred_crosstab_dict['week_6_to_8_cases'].index[0] + pd.to_timedelta(8, 'W'), freq = \"D\")\n",
    "\n",
    "for e in range(0,len(relevant_govs)):\n",
    "\n",
    "    gov = relevant_govs[e]\n",
    "    \n",
    "    true_val_start = true.loc[true_date_start][gov]\n",
    "    true_line_dict[gov], = ax[e].plot(true_date_start, true_val_start, color = 'red', label='true')\n",
    "\n",
    "    vals12_start = np.linspace(true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_1_to_2_cases'][gov].iloc[0] + true.loc[dates12_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_12_line_dict[gov], = ax[e].plot(dates12_start, vals12_start, linestyle= '-.', color = 'seagreen', label='0-2 week forecast')\n",
    "\n",
    "    vals24_start = np.linspace(true.loc[dates24_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_2_to_4_cases'][gov].iloc[0] + true.loc[dates24_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_24_line_dict[gov], = ax[e].plot(dates24_start, vals24_start, linestyle= '-.', color = 'blue', label='2-4 week forecast')\n",
    "\n",
    "    vals46_start = np.linspace(true.loc[dates46_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_4_to_6_cases'][gov].iloc[0] + true.loc[dates46_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_46_line_dict[gov], = ax[e].plot(dates46_start, vals46_start, linestyle= '-.', color = 'plum', label='4-6 week forecast')\n",
    "\n",
    "    vals68_start = np.linspace(true.loc[dates68_start.min() - pd.to_timedelta(1, 'D')][gov], pred_crosstab_dict['week_6_to_8_cases'][gov].iloc[0] + true.loc[dates68_start.min() - pd.to_timedelta(1, 'D')][gov], 14)\n",
    "    y_68_line_dict[gov], = ax[e].plot(dates68_start, vals68_start, linestyle= '-.', color = 'magenta', label='6-8 week forecast')    \n",
    "\n",
    "\n",
    "half_ax_num = int(len(relevant_govs)/2)    \n",
    "ax[half_ax_num].legend(handles = [true_line_dict[relevant_govs[half_ax_num]], y_12_line_dict[relevant_govs[half_ax_num]], y_24_line_dict[relevant_govs[half_ax_num]], y_46_line_dict[relevant_govs[half_ax_num]], y_68_line_dict[relevant_govs[half_ax_num]]], loc='center left', bbox_to_anchor=(1.05, 1.4), fontsize = 10)\n",
    "fig.suptitle('Yemen Cholera Forecast vs Total Cases Over Time\\n(split by governorate)', fontsize = 10)\n",
    "\n",
    "suplabel('y', 'cumulative cholera cases (normalized by population)', 'left')\n",
    "suplabel('y', 'governorates', 'right')\n",
    "\n",
    "fig.subplots_adjust(hspace=.2)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "\n",
    "anim = FuncAnimation(fig, update, frames=np.arange(0, 115), interval=10, fargs = (relevant_govs,))\n",
    "\n",
    "anim.save('/Users/Rohil/Documents/iGEM/yemen/final_results/gif_total_relevant_govs.gif', dpi=250, writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_df = pd.read_pickle('/Users/Rohil/Documents/iGEM/yemen/y_df_for_feature_selection_new.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.14824437447498"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df.week_1_to_2_cases.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.31017369945392"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df.week_1_to_2_cases.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gov_iso</th>\n",
       "      <th>date</th>\n",
       "      <th>week_1_to_2_cases</th>\n",
       "      <th>week_2_to_4_cases</th>\n",
       "      <th>week_4_to_6_cases</th>\n",
       "      <th>week_6_to_8_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>14.831127</td>\n",
       "      <td>90.048319</td>\n",
       "      <td>24.446574</td>\n",
       "      <td>14.072243</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>16.993725</td>\n",
       "      <td>91.899468</td>\n",
       "      <td>20.862714</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>20.222270</td>\n",
       "      <td>92.794336</td>\n",
       "      <td>17.278855</td>\n",
       "      <td>14.352986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>23.727170</td>\n",
       "      <td>90.280809</td>\n",
       "      <td>17.384133</td>\n",
       "      <td>13.545850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>26.060847</td>\n",
       "      <td>87.157543</td>\n",
       "      <td>19.064205</td>\n",
       "      <td>12.756260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>28.113780</td>\n",
       "      <td>86.596057</td>\n",
       "      <td>18.230749</td>\n",
       "      <td>12.296763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>34.886705</td>\n",
       "      <td>79.301125</td>\n",
       "      <td>17.971939</td>\n",
       "      <td>12.003957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>40.984092</td>\n",
       "      <td>74.703959</td>\n",
       "      <td>16.914766</td>\n",
       "      <td>11.711151</td>\n",
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       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>46.142745</td>\n",
       "      <td>68.628505</td>\n",
       "      <td>17.195509</td>\n",
       "      <td>11.418344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-09</td>\n",
       "      <td>52.595447</td>\n",
       "      <td>60.789634</td>\n",
       "      <td>17.476252</td>\n",
       "      <td>11.071802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>57.245253</td>\n",
       "      <td>55.692393</td>\n",
       "      <td>17.546438</td>\n",
       "      <td>10.918271</td>\n",
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       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>64.807768</td>\n",
       "      <td>48.853669</td>\n",
       "      <td>16.382524</td>\n",
       "      <td>10.818841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>67.922261</td>\n",
       "      <td>44.410034</td>\n",
       "      <td>17.188198</td>\n",
       "      <td>10.622906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-13</td>\n",
       "      <td>77.230646</td>\n",
       "      <td>37.852053</td>\n",
       "      <td>13.931872</td>\n",
       "      <td>10.523476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>90.048319</td>\n",
       "      <td>24.446574</td>\n",
       "      <td>14.072243</td>\n",
       "      <td>10.223591</td>\n",
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       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>91.899468</td>\n",
       "      <td>20.862714</td>\n",
       "      <td>14.212615</td>\n",
       "      <td>9.923707</td>\n",
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       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>92.794336</td>\n",
       "      <td>17.278855</td>\n",
       "      <td>14.352986</td>\n",
       "      <td>9.623822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>90.280809</td>\n",
       "      <td>17.384133</td>\n",
       "      <td>13.545850</td>\n",
       "      <td>9.359031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-18</td>\n",
       "      <td>87.157543</td>\n",
       "      <td>19.064205</td>\n",
       "      <td>12.756260</td>\n",
       "      <td>9.076692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>86.596057</td>\n",
       "      <td>18.230749</td>\n",
       "      <td>12.296763</td>\n",
       "      <td>8.995042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-20</td>\n",
       "      <td>79.301125</td>\n",
       "      <td>17.971939</td>\n",
       "      <td>12.003957</td>\n",
       "      <td>8.913391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>74.703959</td>\n",
       "      <td>16.914766</td>\n",
       "      <td>11.711151</td>\n",
       "      <td>8.831740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>68.628505</td>\n",
       "      <td>17.195509</td>\n",
       "      <td>11.418344</td>\n",
       "      <td>8.750089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-23</td>\n",
       "      <td>60.789634</td>\n",
       "      <td>17.476252</td>\n",
       "      <td>11.071802</td>\n",
       "      <td>8.722175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-24</td>\n",
       "      <td>55.692393</td>\n",
       "      <td>17.546438</td>\n",
       "      <td>10.918271</td>\n",
       "      <td>8.711806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>48.853669</td>\n",
       "      <td>16.382524</td>\n",
       "      <td>10.818841</td>\n",
       "      <td>8.533209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>44.410034</td>\n",
       "      <td>17.188198</td>\n",
       "      <td>10.622906</td>\n",
       "      <td>8.451116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>37.852053</td>\n",
       "      <td>13.931872</td>\n",
       "      <td>10.523476</td>\n",
       "      <td>8.272519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-28</td>\n",
       "      <td>24.446574</td>\n",
       "      <td>14.072243</td>\n",
       "      <td>10.223591</td>\n",
       "      <td>8.192022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>20.862714</td>\n",
       "      <td>14.212615</td>\n",
       "      <td>9.923707</td>\n",
       "      <td>8.111524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>17.278855</td>\n",
       "      <td>14.352986</td>\n",
       "      <td>9.623822</td>\n",
       "      <td>8.031027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>7.084374</td>\n",
       "      <td>7.154560</td>\n",
       "      <td>13.909939</td>\n",
       "      <td>77.230646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5101</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-03</td>\n",
       "      <td>7.404597</td>\n",
       "      <td>6.650100</td>\n",
       "      <td>14.831127</td>\n",
       "      <td>90.048319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5122</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>6.714437</td>\n",
       "      <td>6.939616</td>\n",
       "      <td>16.993725</td>\n",
       "      <td>91.899468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5143</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-05</td>\n",
       "      <td>6.717361</td>\n",
       "      <td>6.426383</td>\n",
       "      <td>20.222270</td>\n",
       "      <td>92.794336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5164</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-06</td>\n",
       "      <td>6.522888</td>\n",
       "      <td>6.465862</td>\n",
       "      <td>23.727170</td>\n",
       "      <td>90.280809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5185</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-07</td>\n",
       "      <td>6.478395</td>\n",
       "      <td>6.843111</td>\n",
       "      <td>26.060847</td>\n",
       "      <td>87.157543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5206</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-08</td>\n",
       "      <td>6.464609</td>\n",
       "      <td>6.891363</td>\n",
       "      <td>28.113780</td>\n",
       "      <td>86.596057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-09</td>\n",
       "      <td>6.595581</td>\n",
       "      <td>8.066975</td>\n",
       "      <td>34.886705</td>\n",
       "      <td>79.301125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5248</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>6.726552</td>\n",
       "      <td>8.018722</td>\n",
       "      <td>40.984092</td>\n",
       "      <td>74.703959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5290</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>6.684253</td>\n",
       "      <td>9.222846</td>\n",
       "      <td>46.142745</td>\n",
       "      <td>68.628505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5311</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>6.641953</td>\n",
       "      <td>10.896338</td>\n",
       "      <td>52.595447</td>\n",
       "      <td>60.789634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5332</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-13</td>\n",
       "      <td>6.641327</td>\n",
       "      <td>11.589422</td>\n",
       "      <td>57.245253</td>\n",
       "      <td>55.692393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5353</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-14</td>\n",
       "      <td>6.902330</td>\n",
       "      <td>12.321986</td>\n",
       "      <td>64.807768</td>\n",
       "      <td>48.853669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>7.163333</td>\n",
       "      <td>13.137895</td>\n",
       "      <td>67.922261</td>\n",
       "      <td>44.410034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5395</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>7.154560</td>\n",
       "      <td>13.909939</td>\n",
       "      <td>77.230646</td>\n",
       "      <td>37.852053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5416</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-17</td>\n",
       "      <td>6.650100</td>\n",
       "      <td>14.831127</td>\n",
       "      <td>90.048319</td>\n",
       "      <td>24.446574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5437</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>6.939616</td>\n",
       "      <td>16.993725</td>\n",
       "      <td>91.899468</td>\n",
       "      <td>20.862714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5458</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>6.426383</td>\n",
       "      <td>20.222270</td>\n",
       "      <td>92.794336</td>\n",
       "      <td>17.278855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5479</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>6.465862</td>\n",
       "      <td>23.727170</td>\n",
       "      <td>90.280809</td>\n",
       "      <td>17.384133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>6.843111</td>\n",
       "      <td>26.060847</td>\n",
       "      <td>87.157543</td>\n",
       "      <td>19.064205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5542</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>6.891363</td>\n",
       "      <td>28.113780</td>\n",
       "      <td>86.596057</td>\n",
       "      <td>18.230749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5563</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>8.066975</td>\n",
       "      <td>34.886705</td>\n",
       "      <td>79.301125</td>\n",
       "      <td>17.971939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5584</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-24</td>\n",
       "      <td>8.018722</td>\n",
       "      <td>40.984092</td>\n",
       "      <td>74.703959</td>\n",
       "      <td>16.914766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5605</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>9.222846</td>\n",
       "      <td>46.142745</td>\n",
       "      <td>68.628505</td>\n",
       "      <td>17.195509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-26</td>\n",
       "      <td>10.896338</td>\n",
       "      <td>52.595447</td>\n",
       "      <td>60.789634</td>\n",
       "      <td>17.476252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5647</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-27</td>\n",
       "      <td>11.589422</td>\n",
       "      <td>57.245253</td>\n",
       "      <td>55.692393</td>\n",
       "      <td>17.546438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5668</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>12.321986</td>\n",
       "      <td>64.807768</td>\n",
       "      <td>48.853669</td>\n",
       "      <td>16.382524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5689</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>13.137895</td>\n",
       "      <td>67.922261</td>\n",
       "      <td>44.410034</td>\n",
       "      <td>17.188198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5710</th>\n",
       "      <td>YE-SN</td>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>13.909939</td>\n",
       "      <td>77.230646</td>\n",
       "      <td>37.852053</td>\n",
       "      <td>13.931872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gov_iso       date  week_1_to_2_cases  week_2_to_4_cases  \\\n",
       "82     YE-SN 2017-08-31          14.831127          90.048319   \n",
       "103    YE-SN 2017-09-01          16.993725          91.899468   \n",
       "124    YE-SN 2017-09-02          20.222270          92.794336   \n",
       "145    YE-SN 2017-09-03          23.727170          90.280809   \n",
       "166    YE-SN 2017-09-04          26.060847          87.157543   \n",
       "187    YE-SN 2017-09-05          28.113780          86.596057   \n",
       "208    YE-SN 2017-09-06          34.886705          79.301125   \n",
       "229    YE-SN 2017-09-07          40.984092          74.703959   \n",
       "250    YE-SN 2017-09-08          46.142745          68.628505   \n",
       "271    YE-SN 2017-09-09          52.595447          60.789634   \n",
       "313    YE-SN 2017-09-10          57.245253          55.692393   \n",
       "334    YE-SN 2017-09-11          64.807768          48.853669   \n",
       "355    YE-SN 2017-09-12          67.922261          44.410034   \n",
       "376    YE-SN 2017-09-13          77.230646          37.852053   \n",
       "397    YE-SN 2017-09-14          90.048319          24.446574   \n",
       "418    YE-SN 2017-09-15          91.899468          20.862714   \n",
       "439    YE-SN 2017-09-16          92.794336          17.278855   \n",
       "460    YE-SN 2017-09-17          90.280809          17.384133   \n",
       "481    YE-SN 2017-09-18          87.157543          19.064205   \n",
       "502    YE-SN 2017-09-19          86.596057          18.230749   \n",
       "544    YE-SN 2017-09-20          79.301125          17.971939   \n",
       "565    YE-SN 2017-09-21          74.703959          16.914766   \n",
       "586    YE-SN 2017-09-22          68.628505          17.195509   \n",
       "607    YE-SN 2017-09-23          60.789634          17.476252   \n",
       "628    YE-SN 2017-09-24          55.692393          17.546438   \n",
       "649    YE-SN 2017-09-25          48.853669          16.382524   \n",
       "670    YE-SN 2017-09-26          44.410034          17.188198   \n",
       "691    YE-SN 2017-09-27          37.852053          13.931872   \n",
       "712    YE-SN 2017-09-28          24.446574          14.072243   \n",
       "733    YE-SN 2017-09-29          20.862714          14.212615   \n",
       "775    YE-SN 2017-09-30          17.278855          14.352986   \n",
       "5080   YE-SN 2017-08-02           7.084374           7.154560   \n",
       "5101   YE-SN 2017-08-03           7.404597           6.650100   \n",
       "5122   YE-SN 2017-08-04           6.714437           6.939616   \n",
       "5143   YE-SN 2017-08-05           6.717361           6.426383   \n",
       "5164   YE-SN 2017-08-06           6.522888           6.465862   \n",
       "5185   YE-SN 2017-08-07           6.478395           6.843111   \n",
       "5206   YE-SN 2017-08-08           6.464609           6.891363   \n",
       "5227   YE-SN 2017-08-09           6.595581           8.066975   \n",
       "5248   YE-SN 2017-08-10           6.726552           8.018722   \n",
       "5290   YE-SN 2017-08-11           6.684253           9.222846   \n",
       "5311   YE-SN 2017-08-12           6.641953          10.896338   \n",
       "5332   YE-SN 2017-08-13           6.641327          11.589422   \n",
       "5353   YE-SN 2017-08-14           6.902330          12.321986   \n",
       "5374   YE-SN 2017-08-15           7.163333          13.137895   \n",
       "5395   YE-SN 2017-08-16           7.154560          13.909939   \n",
       "5416   YE-SN 2017-08-17           6.650100          14.831127   \n",
       "5437   YE-SN 2017-08-18           6.939616          16.993725   \n",
       "5458   YE-SN 2017-08-19           6.426383          20.222270   \n",
       "5479   YE-SN 2017-08-20           6.465862          23.727170   \n",
       "5521   YE-SN 2017-08-21           6.843111          26.060847   \n",
       "5542   YE-SN 2017-08-22           6.891363          28.113780   \n",
       "5563   YE-SN 2017-08-23           8.066975          34.886705   \n",
       "5584   YE-SN 2017-08-24           8.018722          40.984092   \n",
       "5605   YE-SN 2017-08-25           9.222846          46.142745   \n",
       "5626   YE-SN 2017-08-26          10.896338          52.595447   \n",
       "5647   YE-SN 2017-08-27          11.589422          57.245253   \n",
       "5668   YE-SN 2017-08-28          12.321986          64.807768   \n",
       "5689   YE-SN 2017-08-29          13.137895          67.922261   \n",
       "5710   YE-SN 2017-08-30          13.909939          77.230646   \n",
       "\n",
       "      week_4_to_6_cases  week_6_to_8_cases  \n",
       "82            24.446574          14.072243  \n",
       "103           20.862714          14.212615  \n",
       "124           17.278855          14.352986  \n",
       "145           17.384133          13.545850  \n",
       "166           19.064205          12.756260  \n",
       "187           18.230749          12.296763  \n",
       "208           17.971939          12.003957  \n",
       "229           16.914766          11.711151  \n",
       "250           17.195509          11.418344  \n",
       "271           17.476252          11.071802  \n",
       "313           17.546438          10.918271  \n",
       "334           16.382524          10.818841  \n",
       "355           17.188198          10.622906  \n",
       "376           13.931872          10.523476  \n",
       "397           14.072243          10.223591  \n",
       "418           14.212615           9.923707  \n",
       "439           14.352986           9.623822  \n",
       "460           13.545850           9.359031  \n",
       "481           12.756260           9.076692  \n",
       "502           12.296763           8.995042  \n",
       "544           12.003957           8.913391  \n",
       "565           11.711151           8.831740  \n",
       "586           11.418344           8.750089  \n",
       "607           11.071802           8.722175  \n",
       "628           10.918271           8.711806  \n",
       "649           10.818841           8.533209  \n",
       "670           10.622906           8.451116  \n",
       "691           10.523476           8.272519  \n",
       "712           10.223591           8.192022  \n",
       "733            9.923707           8.111524  \n",
       "775            9.623822           8.031027  \n",
       "5080          13.909939          77.230646  \n",
       "5101          14.831127          90.048319  \n",
       "5122          16.993725          91.899468  \n",
       "5143          20.222270          92.794336  \n",
       "5164          23.727170          90.280809  \n",
       "5185          26.060847          87.157543  \n",
       "5206          28.113780          86.596057  \n",
       "5227          34.886705          79.301125  \n",
       "5248          40.984092          74.703959  \n",
       "5290          46.142745          68.628505  \n",
       "5311          52.595447          60.789634  \n",
       "5332          57.245253          55.692393  \n",
       "5353          64.807768          48.853669  \n",
       "5374          67.922261          44.410034  \n",
       "5395          77.230646          37.852053  \n",
       "5416          90.048319          24.446574  \n",
       "5437          91.899468          20.862714  \n",
       "5458          92.794336          17.278855  \n",
       "5479          90.280809          17.384133  \n",
       "5521          87.157543          19.064205  \n",
       "5542          86.596057          18.230749  \n",
       "5563          79.301125          17.971939  \n",
       "5584          74.703959          16.914766  \n",
       "5605          68.628505          17.195509  \n",
       "5626          60.789634          17.476252  \n",
       "5647          55.692393          17.546438  \n",
       "5668          48.853669          16.382524  \n",
       "5689          44.410034          17.188198  \n",
       "5710          37.852053          13.931872  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df[(y_df.gov_iso == 'YE-SN') & (y_df.date < '2017-10-01') & (y_df.date > '2017-08-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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