python-awips/examples/notebooks/AWIPS_Grids_and_Cartopy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest example of requesting and plotting AWIPS gridded data 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(\"149.165.156.89\")\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.xlabels_top = gl.ylabels_right = 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": 7,
   "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().decode('UTF-8')  +\" \" \\\n",
    "               + grid.getLevel().decode('UTF-8') + \" \" \\\n",
    "               + grid.getParameter().decode('UTF-8') \\\n",
    "               + \" (\" + grid.getUnit().decode('UTF-8') + \") \" \\\n",
    "               + \"valid \" + str(grid.getDataTime().getRefTime()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### with contourf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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().decode('UTF-8')  +\" \" \\\n",
    "               + grid.getLevel().decode('UTF-8') + \" \" \\\n",
    "               + grid.getParameter().decode('UTF-8') \\\n",
    "               + \" (\" + grid.getUnit().decode('UTF-8') + \") \" \\\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
}