{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>div.container { width: 100% }</style>\n",
    "<img style=\"float:left;  vertical-align:text-bottom;\" height=\"65\" width=\"172\" src=\"../../assets/holoviz-logo-unstacked.svg\" />\n",
    "<div style=\"float:right; vertical-align:text-bottom;\"><h2>Exercise 5: Advanced Dashboarding</h2></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise is entirely freeform. Get into groups of 3-4 people (if available!) and start building a dashboard, with everything you have learned in this tutorial. By the end of the exercise you should have a dashboard that:\n",
    "\n",
    "* Uses datashading to render the whole dataset\n",
    "* Builds a pipeline using the ``.interactive`` method \n",
    "* Uses a widget to filter the data either using linked selections or using a widget (e.g. a RangeSlider)\n",
    "* Uses a widget to control some aspect of the styling of the plot (e.g. to select a colormap, color, or size)\n",
    "* Is servable by running ``panel serve Advanced_Dashboarding.ipynb`` in the exercise directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import colorcet as cc # noqa\n",
    "import holoviews as hv # noqa\n",
    "import numpy as np # noqa\n",
    "import pandas as pd\n",
    "import panel as pn\n",
    "import xarray as xr\n",
    "\n",
    "import hvplot.pandas # noqa: API import\n",
    "import hvplot.xarray # noqa: API import\n",
    "\n",
    "pn.extension()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a starting point we will load the data; everything else is up to you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df = pd.read_parquet(pathlib.Path('../../data/earthquakes-projected.parq'))\n",
    "df = df.set_index(df.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataarray(pathlib.Path('../../data/raster/gpw_v4_population_density_rev11_2010_2pt5_min.nc'))\n",
    "cleaned_ds = ds.where(ds.values != ds.nodatavals).sel(band=1)\n",
    "cleaned_ds.name = 'population'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't really know what to build? Here are some ideas:\n",
    "    \n",
    "* Build a dashboard with a pipeline that filters the data on one or more of the columns (e.g. magnitude using a ``RangeSlider`` or time using a ``DateRangeSlider``) and then datashades it\n",
    "* Build a dashboard with multiple views of the data (e.g. longitude vs. latitude, magnitude vs. depth etc.) and cross-filters the data (see the [Glaciers notebook](https://github.com/pyviz-demos/glaciers/blob/main/glaciers.ipynb) for reference)\n",
    "* Build a dashboard that allows you to select multiple earthquakes and compute (and display) statistics on them."
   ]
  }
 ],
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