{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "110f7f47",
   "metadata": {},
   "source": [
    "This notebooks has a dual purpose:\n",
    "\n",
    "1. it is is execute in the `germany-hospitalisierungsrate.ipynb` notebook via the `%run NOTEBOOK` magic. So we need to fetch the data here, and make the `plot_hi` function available, as the namespace of the two notebooks is shared.\n",
    "\n",
    "2. it is also used as a development pad for new features. The commented out commands below record some of the development work, and help to re-start that activity.\n",
    " \n",
    "If we create figures in this notebook, they will be displayed in the `germany-hospitalisierungsrate.ipynb`. Display of tables seems to be suppressed. Output to stdout semes to be  displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae92a2dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "data_url = \"https://github.com/robert-koch-institut/COVID-19-Hospitalisierungen_in_Deutschland/blob/master/Aktuell_Deutschland_COVID-19-Hospitalisierungen.csv?raw=true\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db7ca72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def germany_fetch_hospitalisierungs_inzidenz():\n",
    "\n",
    "    data = pd.read_csv(data_url)\n",
    "    data = data.set_index(pd.to_datetime(data[\"Datum\"]))\n",
    "    # Select a sum over all age groups\n",
    "    data = data[data[\"Altersgruppe\"] == \"00+\"]\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8cf1632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# local copy to work offline\n",
    "# !wget \"https://github.com/robert-koch-institut/COVID-19-Hospitalisierungen_in_Deutschland/blob/master/Aktuell_Deutschland_COVID-19-Hospitalisierungen.csv?raw=true\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b774e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter for the data we care about\n",
    "data = germany_fetch_hospitalisierungs_inzidenz()\n",
    "# select subset of data we care about\n",
    "data = data[[\"Bundesland\", \"7T_Hospitalisierung_Inzidenz\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa979f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# HospitalisierungsInzidenz\n",
    "hi = data[[\"Bundesland\", \"7T_Hospitalisierung_Inzidenz\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee58d75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hi_bund = hi[hi[\"Bundesland\"] == \"Bundesgebiet\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ae73fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_plot(ax):\n",
    "    fig = ax.get_figure()\n",
    "    fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b3e45aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Example:\n",
    "# fig, ax = plt.subplots()\n",
    "# ax.plot(hi_bund.index, hi_bund[\"7T_Hospitalisierung_Inzidenz\"].values)\n",
    "# fine_tune_plot(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0768e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overview of current numbers\n",
    "today = hi.loc[hi.index[0]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbfa590e",
   "metadata": {},
   "outputs": [],
   "source": [
    "today.sort_values(by='7T_Hospitalisierung_Inzidenz', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab0bfb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hi(data, ymax=None):\n",
    "    \"\"\"Plot HospitalisierungsInzidenz in Germany. \n",
    "    \n",
    "    data is a pandas Dataframe with index:\n",
    "    \n",
    "    DatetimeIndex(['2021-11-24', '2021-11-24', '2021-11-24', '2021-11-24',\n",
    "               '2021-11-24', '2021-11-24', '2021-11-24', '2021-11-24',\n",
    "               '2021-11-24', '2021-11-24',\n",
    "               ...\n",
    "               '2020-03-05', '2020-03-05', '2020-03-05', '2020-03-05',\n",
    "               '2020-03-05', '2020-03-05', '2020-03-05', '2020-03-05',\n",
    "               '2020-03-05', '2020-03-05'],\n",
    "        dtype='datetime64[ns]', name='Datum', length=10710, freq=None)\n",
    "               \n",
    "    and columns:\n",
    "    \n",
    "    Index(['Bundesland', '7T_Hospitalisierung_Inzidenz'], dtype='object')\n",
    "    \n",
    "    Example Dataframes can be created in `germany-compute-hospitalisierungsrate.ipynb`.\n",
    "    \"\"\"\n",
    "      \n",
    "    fig, ax = plt.subplots(figsize=(8, 10))\n",
    "\n",
    "    # plot whole of Germany separately\n",
    "    land = \"Bundesgebiet\"\n",
    "    land_data = data[data['Bundesland'] == land]\n",
    "    # use shortcut for the Data (d) we care about\n",
    "    d = land_data['7T_Hospitalisierung_Inzidenz']\n",
    "    \n",
    "    ax.plot(d.index, d, \n",
    "            linewidth=5, alpha=0.9, label=land)\n",
    "\n",
    "    # and all other states (\"Länder\")\n",
    "    bundeslaender = set(data['Bundesland']) - {\"Bundesgebiet\"}\n",
    "    for land in bundeslaender:\n",
    "        land_data = data[data['Bundesland'] == land]\n",
    "        d = land_data['7T_Hospitalisierung_Inzidenz']\n",
    "    \n",
    "        ax.plot(d.index, d, '.-', label=land,)\n",
    "        # label last data point\n",
    "        ax.text(d.index[0], d[0], land, rotation=45)\n",
    "\n",
    "        \n",
    "    ax.legend(loc='center', bbox_to_anchor=(1.3, 0.5))\n",
    "    fig.autofmt_xdate()\n",
    "    ax.grid()\n",
    "    ax.margins(0) # remove default margins (matplotlib verision 2+)\n",
    "\n",
    "    \n",
    "    min_date = data.index[-1]\n",
    "    label_pos_x = min_date + datetime.timedelta(days=1)\n",
    "    \n",
    "    props = dict(boxstyle='round', facecolor='white', alpha=1.0)\n",
    "    \n",
    "    ax.axhspan(0, 3, facecolor='green', alpha=0.2)\n",
    "    ax.text(label_pos_x, 4, \"2G\", bbox=props)\n",
    "    ax.axhspan(3, 6, facecolor='yellow', alpha=0.2)\n",
    "    ax.text(label_pos_x, 7, \"2G+\", bbox=props)\n",
    "    ax.axhspan(6, 9, facecolor='orange', alpha=0.2)\n",
    "    ax.text(label_pos_x, 10, \"restrictions\", bbox=props)\n",
    "    ax.axhspan(9, 12, facecolor='red', alpha=0.2)\n",
    "    ax.text(label_pos_x, 13, \"additional measures required?\", bbox=props)\n",
    "    ax.axhspan(9, data['7T_Hospitalisierung_Inzidenz'].max(), facecolor='purple', alpha=0.1)\n",
    "\n",
    "    if ymax:\n",
    "        ax.set_ylim(top=ymax)\n",
    "    \n",
    "    return fig, ax\n",
    "\n",
    "## Example usage:\n",
    "# fig, ax = plot_hi(data[\"2021-10-01\":])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f9228f",
   "metadata": {},
   "source": [
    "Possible extensions:\n",
    "\n",
    "- show also intensive care bed capacities (because the hospitalisierungsrate is  only about the number of new people going to hospital: it gives no information about the situation in the hospital)\n",
    "\n",
    "- explore correlation between infections / hospitalisierungsrate / deaths / vaccination status\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37a3278a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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