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(\"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.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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAAHwCAYAAADKEvW1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXlcVFX/x993BoYBWUUUQXHF9dHADdFK1Nx3SzPLfU+zrKcyNc3ycWmxJ6vHfi5PT+5bm7mUVu5Kmmvikhq4oSYgIsIwMHN+f1zuzJ1hRFQQ1Pt+vebFzJ1zz/neM5dzP/f7Pd9zJSEEGhoaGhoaGhoaGmp0xW2AhoaGhoaGhoZGyUMTiRoaGhoaGhoaGnnQRKKGhoaGhoaGhkYeNJGooaGhoaGhoaGRB00kamhoaGhoaGho5EETiRoaGhoaGhoaGnnQRKKGhoaGhoaGhkYeNJGooaGhoaGhoaGRB00kamhoaGhoaGho5EETiRoaGhoaGhoaGnlwK24DXFG5cmVx9uzZ4jZDQ0NDQ0NDQ6M4OCuEqFzcRpRIT+LZs2cRQmgvp9eWLVuK3YaS+NL6ResXrV9KRr9YrVbmzp1LSEgIR44cyfP9xo0bqV+/fqHanZaWxnvvvUfz5s0pXbo0o0aNYufOndy4caNY+uWrr77iySefxGw231O7U6dOBWDgwIEOtnz88ce89tprpKSkPPDni6tXVFQUAO+//z7BwcF4e3szZswY/Pz8qF+/PvPnz8dqtd5RnefOneOVV15h+fLlD0y/AJWKT4WpKM6TLJ/OERp52bJlS3GbUCLR+sU1Wr+4RusX19xrvyQlJYmePXuK+vXrixMnTrgs06xZM7Fs2bJ7aic/zp49K9577z0REREhPD09RZkyZUTr1q3FggULRHZ29l3Veaf9UqNGDfHTTz/dVVtqrFar+P3330VKSoptW+3atQUgoqOjRY8ePcSePXvEkSNHHI7NarXec9sFoaj+j86fPy+WLl0qhBBi6dKlAhCtW7cWZ8+eFdu2bRORkZGiWrVqonPnziI+Pr5IbLgXCqtfcnVQseuxEulJ1NDQ0NB4cNi0aROPPfYYVapUYe/evdSsWTNPmdOnTxMfH0/v3r2LzI6wsDAmTZrEwYMHuXnzJn/88Qdjxoxh0aJFxMTEkJycXGRtK4SHh7NixQrMZvNd13Hy5EmmTp3K8uXL2bp1K5cuXcJisbBnzx46d+5MXFwcZcuW5eWXX+app57ipZdeokePHkRGRuLr60v58uWRJInZs2cX4pHdHypUqEDfvn0B6Nu3L1euXOGnn34iLCyMJ598kr1795KZmcm6devo1q0bx48fL2aLH25K5JxEDQ0NDY2SjxCCt956i6VLl/LVV1/RunXrW5bNysrC29sbvV5/X2yTJIng4GC6d+9O165deeutt2jVqhX79u3DYDAUWbsrVqygX79+1K5dm3HjxhESEkL9+vWpXr36Lfe5efMmu3fvZv/+/WRlZbF8+XKaNm1KjRo1mDt3LsOHDyc5ORkPDw8qV67MmDFj+Ne//gXAiRMnqF27NpUrV2bp0qVUq1aN2bNn8/7779OsWbMiO877RdmyZfNsq1ixIgEBAZQqVYo6depgsVjQ6TSfV1GgiUQNDQ0NDQAsFkuBRZwQgo8//ph169Zx+PBhSpcunW/5GjVqkJ2dzZYtW2jZsmVhmFtgdDods2bN4tChQyxcuJBRo0YVWVve3t58++23bN++nfnz55OWlsa+ffvw9vYmKCgIHx8fatasSbly5di0aRMJCQkkJSURGRlJVFQUpUqVYsiQIbz88ssYDAYmTJgAyP0tSVKe9mrVqsXBgwcJCwuz/QZPP/008fHxdOnShXfeeYeRI0feN3Fe1CxYsIDffvvN9nnOnDmaQCxCNJGooaGhUUJZuXIlAwYMIDY2loiIiCJpIz09HR8fHz788EOGDx/Ojh07KFOmDDk5OUyfPp1BgwZRuXJlQBaR+/bt47vvvmPlypUEBgayatWq2wpEgIyMDJo2bcrSpUsLRSReunSJ+fPns337ds6cOUNOTg6bN2+mVq1aLsunpqZSvnx5Vq1axZNPPsm5c+eoUKECFSpUYOnSpVSpUoUOHToUmuB48sknefzxxzGZTBgMBk6cOEFqaippaWkcPXqU5ORkxo4dS/369QkJCcHLyyvf+lwJRAXnc6NJkyasWrWKuLg4Ro4cyeLFi5kxYwYxMTH51vMgMHDgQEJDQ1m+fDkbN26kefPmAFitVk0sFgGaSNTQ0NAooYSFhZGVlUVkZCQANWvWpH///kRHRxMdHY3RaLznNubPn+/QXo0aNWwX3LS0NN59913q1q2Lj48Pp06donz58nTu3JnvvvuO+vXr5ys6Dh06xNq1a9m0aROHDx/mqaeeYsqUKfdsM0Dbtm2Jjo7mlVdeoXbt2vTo0YOnnnqKqKgoKlSoQNOmTenTp4/NvpiYGA4fPgzInraKFSty4cIFzpw5Q9u2bUlMTGT06NFs376dsLCwe7LtzJkzDuHl//u//2P48OG2zx07dryn+gtK3bp12bZtG4sXL2bUqFEYjUa2bduGn58f2dnZuLm5PXCi0Wg00qVLF7p06ULv3r1p2LAh/v7+pKamotPpMJlMuLu7F7eZDw2aSNTQ0NAooYSFheHt7U16ejogJzRMnDjRoczo0aNp1aoVQUFB7N69G51OZwtVFgRFaPr7+zNv3jxCQkLIyMjAbDazefNm6tati06nIz09nSpVqhAaGnrbOi0WC+PGjeObb76hT58+TJkyhccffxxPT8877AHXdc+bN4/r16/zxRdf2LxHv/32G0eOHOHcuXOcP3+eiRMnEhgYSNu2bQHYvXs3GRkZ+Pv74+aW99InhECn01GpUiWefvppJk2adNfe25CQEN5++22uX79OmzZtaN++/d0f8D2i0+kYMGAA/fv3Z9SoUXTo0IGcnBz27dvH+vXri0SwCiEwm80YDIYiFaGLFy/mk08+wd3dnZo1a5KSkkLnzp3ZsGHDQxNeL240kaihoaFRQgkNDeXixYvExsYSHx/Ps88+i8lkIjY2lp9++okvvviCzz//nM8//9xhvzfeeAOApk2bEhMTQ0xMDNHR0fj6+iKEYMOGDaSnp1O9enVOnTpFSEgIlSpVomrVqoBdOPbr1++u7B42bBjnzp0jLi4ONzc3Nm3axP/+9z/c3d1JTU0lMTGRxMRELBYLH3zwgS2cnR8ZGRmsXbuWd999l8DAQDZu3OgQXvT09CQqKsq2zl5OTg6jR4+mUaNGhIaGUqdOHZo0aUJAQIDL+iVJ4sCBA0ycOJGvv/6abt263bVI9PT05N1333XYlpmZyapVq0hJSaFDhw7UrFnzvnrxJEni888/Z+HChaxYsQKAt99+m9dffx2DwUDbtm154403CAwMvOe2RowYwfz583F3dyc0NJROnTrRr18/oqKiGD9+PJ988gl//vknFStWvKd2mjdvzv79+zl8+DDJycmcPXuWypUr8+abb/Lhhx/e83FoaCJRQ0NDo0Tj6+tr84YpdO/ene7duzN37lxatmzJ1q1bAWjRogXdunXj5s2bzJ49m9jYWM6dO8cnn3xCZmYmISEh5OTkEBAQQHh4OOfPnyc6OppDhw4RFxdXaDbv2LEDSZJo27Ytx48fJyoqimrVqpGdnY2/vz8hISE0atSI7du3M336dObNm3fLui5evMhzzz3H3r17adasGR999BHt27e/rcAaN24cERERXL16lfPnz/Prr78yY8YMrly5QqNGjYiOjqZt27ZERESwbds2pk+fzqVLlxgwYAALFiwgJCTkjo7ZYrFw4cIFkpKS8PDwwMfHh7CwMCRJwmq1UqdOHS5fvkyXLl2YPXs2Hh4e/Pvf/6Zz58531M69oNfrGT58OAMHDuT333/Hzc0NLy8vMjIyWLBgAWXKlKFr1658//3399ROly5dWL16NZMnT6Z169asXbuWvn37EhwcTExMDCaTicqVK7NkyRKee+65u25n9erVVK1alSZNmvDzzz+TmZkJwMiRI+/
      "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": 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().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
}