python-awips/examples/notebooks/AWIPS_Grids_and_Cartopy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple example of requesting and plotting AWIPS grids with Matplotlib and Cartopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from awips.dataaccess import DataAccessLayer\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "%matplotlib inline\n",
    "\n",
    "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
    "request = DataAccessLayer.newDataRequest()\n",
    "request.setDatatype(\"grid\")\n",
    "request.setLocationNames(\"RAP13\")\n",
    "request.setParameters(\"T\")\n",
    "request.setLevels(\"2.0FHAG\")\n",
    "cycles = DataAccessLayer.getAvailableTimes(request, True)\n",
    "times = DataAccessLayer.getAvailableTimes(request)\n",
    "fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\n",
    "response = DataAccessLayer.getGridData(request, [fcstRun[0]])\n",
    "grid = response[0]\n",
    "data = grid.getRawData()\n",
    "lons, lats = grid.getLatLonCoords()\n",
    "bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n",
    "\n",
    "def make_map(bbox, projection=ccrs.PlateCarree()):\n",
    "    fig, ax = plt.subplots(figsize=(16, 9),\n",
    "            subplot_kw=dict(projection=projection))\n",
    "    ax.set_extent(bbox)\n",
    "    ax.coastlines(resolution='50m')\n",
    "    gl = ax.gridlines(draw_labels=True)\n",
    "    gl.top_labels = gl.right_labels = False\n",
    "    gl.xformatter = LONGITUDE_FORMATTER\n",
    "    gl.yformatter = LATITUDE_FORMATTER\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### with pcolormesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.get_cmap('rainbow')\n",
    "fig, ax = make_map(bbox=bbox)\n",
    "cs = ax.pcolormesh(lons, lats, data, cmap=cmap)\n",
    "cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')\n",
    "cbar.set_label(grid.getLocationName() +\" \"+ grid.getLevel() + \" \" \\\n",
    "               + grid.getParameter() + \" (\" + grid.getUnit() + \") \" \\\n",
    "               + \"valid \" + str(grid.getDataTime().getRefTime()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### with contourf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = make_map(bbox=bbox)\n",
    "cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,\n",
    "                  vmin=data.min(), vmax=data.max())\n",
    "cbar2 = fig2.colorbar(cs2, shrink=0.7, orientation='horizontal')\n",
    "cbar2.set_label(grid.getLocationName() +\" \"+ grid.getLevel() + \" \" \\\n",
    "               + grid.getParameter() + \" (\" + grid.getUnit()+ \") \" \\\n",
    "               + \"valid \" + str(grid.getDataTime().getRefTime()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}