3-tech-demos/datashader.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datashader Demo\n",
    "\n",
    "<img align=\"right\" src=\"https://movingpandas.github.io/movingpandas/assets/img/movingpandas.png\">\n",
    "\n",
    "*[hvPlot](https://hvplot.holoviz.org/#) is designed to work well in and outside the Jupyter notebook, and thanks to built-in **[Datashader](http://datashader.org/) support scales easily to millions or even billions of datapoints**.* ([Source: hvplot.holoviz.org](https://hvplot.holoviz.org/#))\n",
    "\n",
    "NOTE: This is a work in progress since there are still issues in the GeoDataFrame support in Datashader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import hvplot.pandas  # noqa\n",
    "from geopandas import read_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import holoviews as hv\n",
    "from holoviews.operation.datashader import datashade, spread\n",
    "from datashader.utils import lnglat_to_meters\n",
    "from holoviews.element import tiles\n",
    "\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = read_file('../data/ais.gpkg')\n",
    "wgs84 = gdf.crs\n",
    "print(\"Finished reading {}\".format(len(gdf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame support\n",
    "\n",
    "Running Datashader on x & y columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = gdf.copy()\n",
    "df['lon'] = df.geometry.x\n",
    "df['lat'] = df.geometry.y\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "df.loc[:, 'x'], df.loc[:, 'y'] = lnglat_to_meters(df.lon, df.lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "df.hvplot.scatter(x='x', y='y', datashade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiles.OSM() * df.hvplot.scatter(x='x', y='y', datashade=True, frame_height=600, frame_width=600) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GeoDataFrame support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "gdf.hvplot(datashade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}