{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract the Geographic Info\n",
    "\n",
    "Use the Harvard [country_centroids.csv](https://worldmap.harvard.edu/data/geonode:country_centroids_az8) data to extract the geographic info we need for the visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_folder = \"../data/covid-19_jhu-csse/\"\n",
    "worldmap_path = \"../data/worldmap/country_centroids.csv\"\n",
    "out_folder = None\n",
    "PAPERMILL_OUTPUT_PATH = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "source": [
    "## Read in JHU CSSE data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_jhu_covid_region_df(name):\n",
    "    filename = os.path.join(ts_folder, f\"time_series_19-covid-{name}.csv\")\n",
    "    df = pd.read_csv(filename)\n",
    "    df = df.set_index(['Country/Region', 'Province/State', 'Lat', 'Long'])\n",
    "    df.columns = pd.to_datetime(df.columns)\n",
    "    region_df = df.groupby(level='Country/Region').sum()\n",
    "    return region_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df = read_jhu_covid_region_df(\"Confirmed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Harvard country centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_centroids_df = pd.read_csv(worldmap_path)\n",
    "country_centroids_df = country_centroids_df[['name', 'name_long', 'region_un', 'subregion', 'region_wb', 'pop_est', 'gdp_md_est', 'income_grp', 'Longitude', 'Latitude']]\n",
    "country_centroids_df['name_jhu'] = country_centroids_df['name_long'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_centroids_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix names that differ between JHU CSSE and Harvard data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_hd_jhu_map = {\n",
    "     'Brunei Darussalam': 'Brunei',\n",
    "     \"Côte d'Ivoire\": \"Cote d'Ivoire\",\n",
    "     'Czech Republic': 'Czechia',\n",
    "     'Hong Kong': 'Hong Kong SAR',\n",
    "     'Republic of Korea': 'Korea, South',\n",
    "     'Macao': 'Macao SAR',\n",
    "     'Russian Federation': 'Russia',\n",
    "     'Taiwan': 'Taiwan*',\n",
    "     'United States': 'US'\n",
    "}\n",
    "country_centroids_df['name_jhu'] = country_centroids_df['name_jhu'].replace(region_hd_jhu_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this to find the name in the series\n",
    "# country_centroids_df[country_centroids_df['name'].str.contains('Macao')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some regions that we cannot resolve, but we will just ignore these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df.loc[\n",
    "    (confirmed_df.index.isin(country_centroids_df['name_jhu']) == False)\n",
    "].iloc[:,-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if PAPERMILL_OUTPUT_PATH:\n",
    "    out_path = os.path.join(out_folder, f\"geo_data.csv\")\n",
    "    country_centroids_df.to_csv(out_path)"
   ]
  }
 ],
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