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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 statistics in China (except Hubei)\n",
    "Data comes from https://tinyurl.com/s6gsq5y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from sklearn import cluster\n",
    "\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import display\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'load_lets_plot_js' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Temp/ipykernel_6776/2399501846.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mload_lets_plot_js\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'load_lets_plot_js' is not defined"
     ]
    }
   ],
   "source": [
    "load_lets_plot_js()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_URL = 'https://docs.google.com/spreadsheets/d/1itaohdPiAeniCXNlntNztZ_oRvjh0HsGuJXUJWET008/export?format=csv&id=1itaohdPiAeniCXNlntNztZ_oRvjh0HsGuJXUJWET008&gid=0'\n",
    "MAX_CLUSTERS_COUNT = 20\n",
    "OUTCOMES = ['diseased', 'died', 'discharged', 'stable']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def player_widget(plots, *, fps=1):\n",
    "    interval = max(1, int(1000 / fps))\n",
    "    player = widgets.Play(min=0, max=len(plots) - 1, step=1, value=0, interval=interval)\n",
    "    slider = widgets.IntSlider(min=0, max=len(plots) - 1, step=1, value=0)\n",
    "    widgets.jslink((player, 'value'), (slider, 'value'))\n",
    "    widget = widgets.HBox([player, slider])\n",
    "    iout = widgets.interactive_output(lambda n, m: display(plots[n]), {'n': slider, 'm': player})\n",
    "    return display(widget, iout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_clustering(shapely_points, *, max_clusters_count=1, clusters_limit_proportion=.02, \\\n",
    "                         attempts_count=1, method=cluster.KMeans):\n",
    "    points = shapely_points.apply(lambda p: [p.x, p.y]).to_list()\n",
    "    max_clusters_count = min(max_clusters_count, len(set([str(p) for p in points])))\n",
    "    if max_clusters_count < 1:\n",
    "        return None\n",
    "    for n in range(max_clusters_count, 0, -1):\n",
    "        clustering = method(n_clusters=n, n_jobs=attempts_count).fit(points)\n",
    "        clusters, counts = np.unique(clustering.labels_, return_counts=True)\n",
    "        if clusters_limit_proportion <= np.min(counts) / np.max(counts):\n",
    "            return clustering\n",
    "    assert(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_cluster_column(gdf, *, distinguished_columns=[], max_clusters_count=1, cluster_column_name='cluster'):\n",
    "    if any(distinguished_columns):\n",
    "        column = distinguished_columns[0]\n",
    "        return pd.concat([\n",
    "            append_cluster_column(gdf[gdf[column] == value].copy(), \\\n",
    "                                  distinguished_columns=distinguished_columns[1:], \\\n",
    "                                  max_clusters_count=max_clusters_count, \\\n",
    "                                  cluster_column_name=cluster_column_name)\n",
    "            for value in gdf[column].value_counts().keys()\n",
    "        ])\n",
    "    else:\n",
    "        gdf[cluster_column_name] = find_best_clustering(gdf.geometry, max_clusters_count=max_clusters_count).labels_\n",
    "        return gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clusters_gdf(gdf, outcome, current_date):\n",
    "    gdf = gdf[gdf.outcome == outcome].copy()\n",
    "    actual_gdf = gdf[gdf.date_confirmation<=current_date]\n",
    "    if outcome in ['discharged', 'died']:\n",
    "        actual_gdf = actual_gdf[actual_gdf.date_death_or_discharge<=current_date]\n",
    "    \n",
    "    vc = actual_gdf.cluster.value_counts()\n",
    "    clusters, counts = vc.keys(), vc.values\n",
    "    geometry = [gdf[gdf.cluster == cluster].unary_union.centroid for cluster in clusters]\n",
    "    \n",
    "    return gpd.GeoDataFrame(dict(cluster=clusters, \\\n",
    "                                 count=counts, \\\n",
    "                                 outcome=[outcome]*len(clusters), \\\n",
    "                                 date=[current_date]*len(clusters), \\\n",
    "                                 geometry=geometry))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the map polygon\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "china = world[world.name == 'China']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare dataframe\n",
    "columns = ['date_confirmation', 'outcome', 'date_death_or_discharge', 'country', 'latitude', 'longitude']\n",
    "except_hubei_data = pd.read_csv(DATA_URL)\n",
    "df = pd.DataFrame(except_hubei_data)[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data\n",
    "df = df[~df.latitude.isna()]\n",
    "df = df[~df.longitude.isna()]\n",
    "df = df[~df.date_confirmation.isna()]\n",
    "df = df[df.country == 'China']\n",
    "df = df[~(((df.outcome=='discharged')|(df.outcome=='died'))&df.date_death_or_discharge.isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix data\n",
    "df.latitude = df.latitude.astype(float)\n",
    "df.longitude = df.longitude.astype(float)\n",
    "df.date_confirmation = pd.to_datetime(df.date_confirmation, dayfirst=True, errors='coerce')\\\n",
    "    .apply(lambda dt: dt.replace(year=2020))\n",
    "df.date_death_or_discharge = pd.to_datetime(df.date_death_or_discharge, dayfirst=True, errors='coerce')\\\n",
    "    .apply(lambda dt: dt.replace(year=2020))\n",
    "df.outcome = df.outcome.replace({'discharge': 'discharged'})\\\n",
    "    .apply(lambda outcome: outcome if outcome in OUTCOMES else 'diseased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare geodataframe\n",
    "gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))\n",
    "gdf = gdf[gdf.within(china.iloc[0].geometry)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add clusters by geoposition\n",
    "gdf = append_cluster_column(gdf, distinguished_columns=['outcome'], max_clusters_count=MAX_CLUSTERS_COUNT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare clusterized geodataframe\n",
    "clusters_gdf = pd.concat([\n",
    "    pd.concat([\n",
    "        get_clusters_gdf(gdf, outcome=outcome, current_date=current_date)\n",
    "        for current_date in gdf.date_confirmation.sort_values().unique()\n",
    "    ])\n",
    "    for outcome in OUTCOMES\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare list of plots that would be frames of the animation\n",
    "p = ggplot() + \\\n",
    "    geom_polygon(data=china, fill='#d6d6d6') + \\\n",
    "    theme(legend_position=(.15, .15), axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank') + \\\n",
    "    ggsize(600, 600)\n",
    "\n",
    "limit = clusters_gdf['count'].max()\n",
    "plots = []\n",
    "for current_date in gdf.date_confirmation.sort_values().unique():\n",
    "    current_clusters_gdf = clusters_gdf[clusters_gdf.date == current_date]\n",
    "    plots.append(\n",
    "        p + \\\n",
    "        geom_point(aes(size='count', color='outcome'), data=current_clusters_gdf) + \\\n",
    "        scale_size(name='', limits=[0, limit]) + \\\n",
    "        scale_color_manual(values=['#cf3c38', 'black', '#90c73e', '#4cc5db']) + \\\n",
    "        ggtitle('COVID-19 on ' + np.datetime_as_string(current_date, unit='D'))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_widget(plots)"
   ]
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