{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import netCDF4 as nc\n",
    "from pathlib import Path\n",
    "from datetime import datetime as dt\n",
    "import geoviews as gv\n",
    "import numpy as np\n",
    "import param\n",
    "from holoviews.operation.datashader import datashade\n",
    "import panel as pn\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "hv.extension('bokeh')\n",
    "pn.extension()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path('../COVID-19/csse_covid_19_data')\n",
    "time_series_path = data_dir / 'csse_covid_19_time_series' / 'time_series_19-covid-{VAR}.csv'\n",
    "daily_reports_path = data_dir / 'csse_covid_19_daily_reports' / '{DATE}.csv'\n",
    "VARS = ['Confirmed', 'Deaths', 'Recovered']\n",
    "\n",
    "data_vars = list()\n",
    "\n",
    "dims = ['Aggrigation', 'Quantity', 'Region', 'Date']\n",
    "for var in VARS:\n",
    "    df = pd.read_csv(time_series_path.as_posix().format(VAR=var))\n",
    "    data_vars.append(df.iloc[:,4:].values)\n",
    "data_vars.append(data_vars[0] - data_vars[1] - data_vars[2])   # active cases\n",
    "VARS.append('Active')\n",
    "data_vars = np.stack(data_vars)\n",
    "data_vars = np.stack((data_vars, np.diff(data_vars, axis=-1, n=1, prepend=0)))  # daily changes\n",
    "data_vars={'counts': (dims, np.stack(data_vars))}\n",
    "\n",
    "coords = dict(\n",
    "    Country=('Region', df['Country/Region']),\n",
    "    Province_State=('Region', df['Province/State']),\n",
    "    Date=df.columns.tolist()[4:],\n",
    "    longitude=('Region', df['Long']),\n",
    "    latitude=('Region', df['Lat']),\n",
    "    Quantity=VARS,\n",
    "    Aggrigation=['Totals', 'Daily']\n",
    ")\n",
    "data = xr.Dataset(data_vars=data_vars, coords=coords)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata = data.groupby('Country').sum('Region')\n",
    "ds = hv.Dataset(cdata, kdims=['Country', 'Date', 'Quantity', 'Aggrigation'], \n",
    "                vdims=['counts'])\n",
    "dates = data['Date'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsel = ds.select(Aggrigation='Daily').aggregate(dimensions=['Quantity', 'Date'], function=np.sum)\n",
    "curves = dsel.to(hv.Curve, 'Date', ).options(tools=['hover'])\n",
    "curves.overlay(['Quantity']).options(\n",
    "    legend_position='top_left', \n",
    "    width=800,\n",
    "    height=400,\n",
    "    xrotation=60,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsel = ds.select(Aggrigation='Totals').aggregate(dimensions=['Quantity', 'Date'], function=np.sum)\n",
    "curves = dsel.to(hv.Curve, 'Date', ).options(tools=['hover'])\n",
    "curves.overlay(['Quantity']).options(\n",
    "    legend_position='top_left', \n",
    "    width=800,\n",
    "    height=400,\n",
    "    xrotation=60,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curves(countries, quantity):\n",
    "        dsel = ds.select(Country=countries, Quantity=quantity, Aggrigation='Totals')\n",
    "        curves = dsel.to(hv.Curve, 'Date', ).options(tools=['hover'])\n",
    "        return curves.overlay(['Country', 'Quantity']).options(\n",
    "            legend_position='top_left', \n",
    "            width=800,\n",
    "            height=400,\n",
    "            xrotation=60,\n",
    "        )\n",
    "curves(['US', 'China'], 'Active')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsel = ds.select(Aggrigation='Daily', Quantity='Active')\n",
    "dsel = dsel.aggregate(dimensions=['Date'], function=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bars(quantity):\n",
    "        dsel = ds.select(Aggrigation='Daily', Quantity=quantity).aggregate(dimensions=['Date'], function=np.sum)\n",
    "\n",
    "        bars = hv.Bars(dsel, kdims=['Date'])\n",
    "        return bars.opts(width=800, height=400, stacked=False, show_legend=False, xrotation=60)\n",
    "    \n",
    "bars('Active')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CovidStateCurves(param.Parameterized):\n",
    "    states = param.ListSelector(default=['Mississippi', 'Louisiana'], objects=usdata['Province_State'].values)\n",
    "    \n",
    "    \n",
    "    @param.depends('states')\n",
    "    def curves(self):\n",
    "        dsel = ds.select(Province_State=self.states, Quantity='Active')\n",
    "        curves = dsel.to(hv.Curve, 'Date', )\n",
    "#         curves = hv.Curve(dsel, kdims=['Date'])\n",
    "        return curves.overlay(['Province_State', 'Quantity']).opts(\n",
    "            legend_position='top_left', \n",
    "            width=800,\n",
    "            height=400,\n",
    "            xrotation=60 )\n",
    "    \n",
    "    @param.depends('states')\n",
    "    def bars(self):\n",
    "        dsel = ds.select(Province_State=self.states, Date=data['Date'][-1])\n",
    "\n",
    "        bars = hv.Bars(dsel, kdims=['Province_State', 'Quantity'])\n",
    "        return bars.opts(width=800, height=400, stacked=False, show_legend=False, xrotation=60)\n",
    "    \n",
    "    def panel(self):\n",
    "        return pn.Row(\n",
    "            pn.Param(self.param.states, widgets={'states': {'height': 800}}),\n",
    "            pn.Column(\n",
    "                self.curves,\n",
    "                self.bars,\n",
    "                \n",
    "            ),\n",
    "        )\n",
    "\n",
    "ccc = CovidStateCurves()\n",
    "ccc.panel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsel = ds.select(Country=ccc.countries, Date='3/15/20')\n",
    "\n",
    "# bars = dsel.to(hv.Bars, kdims=['Country', 'Date'])\n",
    "bars = hv.Bars(dsel, kdims=['Country', 'Quantity'])\n",
    "bars.opts(width=800, height=400, stacked=False, show_legend=False, xrotation=60)\n",
    "ccc.bars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiles = gv.tile_sources.CartoDark\n",
    "dates = data['Date'].values.tolist()\n",
    "class CovidViewer(param.Parameterized):\n",
    "    date = param.ObjectSelector(default=dates[1], objects=dates)\n",
    "    variable = param.ObjectSelector(default='Confirmed', objects=VARS)\n",
    "    \n",
    "    @param.depends('date', 'variable')\n",
    "    def infection_map(self):\n",
    "        seldata = {\n",
    "            'longitude': data['longitude'], \n",
    "            'latitude': data['latitude'], \n",
    "            'Confirmed': data.sel(Quantity='Confirmed', Date=self.date)['counts'],\n",
    "            'Deaths': data.sel(Quantity='Deaths', Date=self.date)['counts'],\n",
    "            'Recovered': data.sel(Quantity='Recovered', Date=self.date)['counts'],\n",
    "            'Active': data.sel(Quantity='Active', Date=self.date)['counts'],\n",
    "            'Country': data['Country'],\n",
    "            'State/Province': data['Province_State'],\n",
    "        }\n",
    "#         gvdata = gv.Dataset(seldata, kdims=['latitude', 'longitude'], vdims=['Variable'])\n",
    "#         heatmap = gvdata.to(hv.HeatMap)\n",
    "\n",
    "        # points = gvdata.to(gv.Points, ['Lat', 'Lon'])\n",
    "        points = gv.Points(seldata, kdims=['longitude', 'latitude'], vdims=VARS+['Country', 'State/Province'])\n",
    "        return tiles * points.opts(\n",
    "            size=gv.dim(self.variable).log()*4,\n",
    "            logz=True,\n",
    "            fill_color=self.variable,\n",
    "            fill_alpha=.5,\n",
    "            line_color='gray', \n",
    "            cmap='hot',\n",
    "            width=800, height=600, global_extent=True, tools=['hover'], show_legend=False)\n",
    "    \n",
    "    @param.depends('date')\n",
    "    def date_label(self):\n",
    "        return f'## {self.date}'\n",
    "    \n",
    "    def panel(self):\n",
    "        player = pn.widgets.Player(name='Player', start=0, end=100, value=32, loop_policy='loop')\n",
    "        return pn.Column(\n",
    "            self.param.variable,\n",
    "            pn.Param(self.param.date, widgets={'date': pn.widgets.DiscretePlayer}),\n",
    "            self.date_label,\n",
    "            self.infection_map)\n",
    "\n",
    "# hv.DynamicMap(infection_map, kdims=['date', 'variable']\n",
    "#              ).redim.values(variable=VARS, date=data['Date'].values)\n",
    "\n",
    "cv = CovidViewer()\n",
    "cv.panel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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