{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import hvplot.pandas  # noqa\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paths are useful if you are plotting lines on a geographic map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"city\": [\"NY\", \"Delhi\"], \"lon\": [-75, 77.23], \"lat\": [43, 28.61]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the line in blue between New York and Delhi is not straight on a flat PlateCarree map, this is because the Geodetic coordinate system is a truly spherical coordinate system, where a line between two points is defined as the shortest path between those points on the globe rather than 2d Cartesian space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_kwargs = dict(\n",
    "    x=\"lon\",\n",
    "    y=\"lat\",\n",
    "    geo=True,\n",
    "    project=True,\n",
    "    projection=ccrs.GOOGLE_MERCATOR,\n",
    "    global_extent=True\n",
    ")\n",
    "shortest_path = df.hvplot.paths(color=\"blue\", crs=ccrs.Geodetic(), tiles=True, **common_kwargs)\n",
    "straight_path = df.hvplot.paths(color=\"grey\", line_dash=\"dashed\", **common_kwargs)\n",
    "labels = df.hvplot.labels(text_color=\"black\", text=\"city\", **common_kwargs)\n",
    "shortest_path * straight_path * labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example adapted from https://scitools.org.uk/cartopy/docs/latest/matplotlib/intro.html."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
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 "nbformat_minor": 2
}