{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'mpl_toolkits.basemap'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-986bd408e0b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mh5py\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasemap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBasemap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named 'mpl_toolkits.basemap'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import h5py as h5py\n",
    "from mpl_toolkits.basemap import Basemap, cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hdf5 = '/notebooks/3B-MO.MS.MRG.3IMERG.20150801-S000000-E235959.08.V03D.HDF5'\n",
    "# hdf5 = '/notebooks/3B-MO.MS.MRG.3IMERG.20150801-S000000-E235959.08.V06A.HDF5'\n",
    "hdf5 = '/notebooks/3B-MO.MS.MRG.3IMERG.20190101-S000000-E235959.08.V06A.HDF5'\n",
    "dataset = h5py.File(hdf5,'r') # Change this to the proper path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "precip = dataset['Grid/precipitation'][:]\n",
    "\n",
    "precip = np.transpose(precip[0])\n",
    "\n",
    " \n",
    "\n",
    "theLats= dataset['Grid/lat'][:]\n",
    "\n",
    "theLons = dataset['Grid/lon'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the figure, define the geographic bounds\n",
    "\n",
    "fig = plt.figure(dpi=300)\n",
    "\n",
    "latcorners = ([-60,60])\n",
    "\n",
    "loncorners = ([-180,180])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Basemap(\n",
    "    projection='cyl',\n",
    "    llcrnrlat=latcorners[0],\n",
    "    urcrnrlat=latcorners[1],\n",
    "    llcrnrlon=loncorners[0],\n",
    "    urcrnrlon=loncorners[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Draw coastlines, state and country boundaries, edge of map.\n",
    "\n",
    "m.drawcoastlines()\n",
    "\n",
    "m.drawstates()\n",
    "\n",
    "m.drawcountries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw filled contours.\n",
    "\n",
    "clevs = np.arange(0,1.26,0.125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the latitude and longitude data\n",
    "\n",
    "x, y = np.float32(np.meshgrid(theLons, theLats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mask the values less than 0 because there is no data to plot.\n",
    "\n",
    "masked_array = np.ma.masked_where(precip < 0,precip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot every masked value as white\n",
    "\n",
    "cmap = cm.GMT_drywet\n",
    "\n",
    "cmap.set_bad('w',1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Plot the data\n",
    "\n",
    "cs = m.contourf(x,y,precip,clevs,cmap=cmap,latlon=True)\n",
    "\n",
    "\n",
    "\n",
    "parallels = np.arange(-60.,61,20.)\n",
    "\n",
    "m.drawparallels(parallels,labels=[True,False,True,False])\n",
    "\n",
    "meridians = np.arange(-180.,180.,60.)\n",
    "\n",
    "m.drawmeridians(meridians,labels=[False,False,False,True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the title and fonts\n",
    "\n",
    "plt.title('August 2015 Monthly Average Rain Rate')\n",
    "\n",
    "font = {'weight' : 'bold', 'size' : 6}\n",
    "\n",
    "plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add colorbar\n",
    "\n",
    "cbar = m.colorbar(cs,location='right',pad=\"5%\")\n",
    "\n",
    "cbar.set_label('mm/h')\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "plt.savefig('testIMERGmap.png',dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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