{
 "metadata": {
  "name": "",
  "signature": "sha256:10f22ef437641af17c45d70fe208f65ccd40fe58639b23b39f7240cffe7fb91c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Animating Weather Radar Data**\n",
      "\n",
      "This grew out of my project to animate the data from the rainfall sensors of the Flood Control District of Maricopa county. Since a lot of the code required here would duplicate that, I just import that code here. If you'd like more detail on that, take a look at my Phoenix Rainfall Map [notebook](http://nbviewer.ipython.org/github/bitsofbits/GIS/blob/master/Phoenix%20Rainfall%20Map.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "%matplotlib inline\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Reuse Some Code**\n",
      "\n",
      "First we import our code for drawing the state of Arizona from the 'Phoenix Rainfall Map' notebook. These functions have been extracted to the file `draw_arizona.py`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from draw_arizona import draw_arizona, draw_roads\n",
      "phxmap = draw_arizona( -113.75, -111, 32.25, 35.0)\n",
      "draw_roads(phxmap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r7iSEoemMFbWmNOyUUArGRjdZNA8m/JQHDqzoegWs1rgcHtCaJTlmWkBLwR2W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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083191d0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Getting some usable weather data**\n",
      "\n",
      "The first step is to grab the NEXRAD weather data. This seems to be available from several sources. I grabbed mine from [www.ncdc.noaa.gov](http://www.ncdc.noaa.gov/nexradinv/chooseday.jsp?id=kiwa). I selected the short range composite data for the Phoenix radar (KIWA). It takes about half an hour for the data to show up on the noaa ftp site, at which point they send you an email and you can go download it. I downloaded 3 days of data for large storm we had on September 8th, the day of the main storm and one day on either side.\n",
      "\n",
      "Unfortunately, the data is in a mysterious format (NEXRAD) and needs to be coverted to something that can be read with Python before it can be used. Fortunately, there is a free tool for this: to get it, download `toolsUI` from the [Unidata website](http://www.unidata.ucar.edu/downloads/netcdf/netcdf-java-4/index.jsp).  The recipe below is the secret incantation I came up with, based on [this page](http://www.ncdc.noaa.gov/oa/radar/radar-decoding-easy.html) for converting the NEXRAD data to netCDF4. (I suspect that this can also by done using [Py-Art](http://arm-doe.github.io/pyart/dev/index.html), but I found that later and haven't taken the time to figure it out)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from glob import glob\n",
      "from subprocess import call\n",
      "\n",
      "all_paths = glob(\"radar_data/*\")\n",
      "for in_path in [x for x in all_paths if not x.endswith(\".nc\")]:\n",
      "    out_path = \"{0}.nc\".format(in_path)\n",
      "    if out_path not in all_paths:\n",
      "        call([\"java\", \"-Xmx512m\", \"-classpath\", \"toolsUI-4.5.jar\", \"ucar.nc2.dataset.NetcdfDataset\", \n",
      "              \"-in\", in_path, \n",
      "              \"-out\", out_path])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can load the converted data using netCDF4 and store the data for each timepoint in a dictionary of datetime: reflectivety pairs. In addition, the extent of the radar image is extracted from the netCDF4 data so that we can correctly scale the radar data when we plot it on the map. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset\n",
      "from datetime import datetime, timedelta\n",
      "from glob import glob\n",
      "from collections import namedtuple\n",
      "\n",
      "RadarData = namedtuple('RadarData', [\"extent\", \"returns\"])\n",
      "    \n",
      "def parse_datetime_from_path(path):\n",
      "    dtstr = path[-15:-3]\n",
      "    year = int(dtstr[:4])\n",
      "    month = int(dtstr[4:6])\n",
      "    day = int(dtstr[6:8])\n",
      "    hour = int(dtstr[8:10])\n",
      "    minute = int(dtstr[10:12])\n",
      "    return datetime(year, month, day, hour, minute) - timedelta(hours=7)  # Weather data is in GMT\n",
      "    \n",
      "def load_radar_data():    \n",
      "    returns = {}\n",
      "    extent = None\n",
      "    ncpaths = glob(\"radar_data/*.nc\")\n",
      "    for path in ncpaths:\n",
      "        dt = parse_datetime_from_path(path)\n",
      "        root = Dataset(path)\n",
      "        #\n",
      "        this_extent = (root.geospatial_lon_min, root.geospatial_lon_max, root.geospatial_lat_min, root.geospatial_lat_max)\n",
      "        if extent is None:\n",
      "            extent = this_extent\n",
      "        else:\n",
      "            assert extent == this_extent\n",
      "        # \n",
      "        # We reverse the x-axis because\n",
      "        # lat_min, is actually the largest magnitude and is thus \n",
      "        # larger(!) than lat_max (don't blame me, that's just how the data is).\n",
      "        returns[dt] = root.variables['BaseReflectivityComp_RAW'][:,::-1]\n",
      "        root.close() \n",
      "    return RadarData(extent, returns)\n",
      "    \n",
      "radar_data = load_radar_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We create a colormap that is always red, but varies from completely transparent from 0 to 20% of max, to 80% opaque at \n",
      "max. This color scheme is used so that it will interact well with the rainfall data when we combine them later."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.colors import Colormap, LinearSegmentedColormap\n",
      "\n",
      "cdict = {'red':   [(0.0,  1.0, 1.0),\n",
      "                   (1.0,  1.0, 1.0)],\n",
      "\n",
      "         'green': [(0.0,  0.0, 0.0),\n",
      "                   (1.0,  0.0, 0.0)],\n",
      "\n",
      "         'blue':  [(0.0,  0.0, 0.0),\n",
      "                   (1.0,  0.0, 0.0)],\n",
      "                   \n",
      "         'alpha':  [(0.0,  0.0, 0.0),\n",
      "                    (0.2,  0.0, 0.0),\n",
      "                    (0.3,  0.4, 0.4),\n",
      "                    (1.0,  0.8, 0.8)]       \n",
      "        }\n",
      "\n",
      "transparent_red_colormap = LinearSegmentedColormap(\"Transparent Red\", cdict)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now create an Animator class. This is analogous to the `RainfallAnimator` class in `Phoenix Rainfall Map`. We use the same class signature so that we can easily combine these two classes later to animate both rainfall and radar data at once."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import animation\n",
      "\n",
      "\n",
      "class RadarAnimator(object):\n",
      "    \n",
      "    def __init__(self, themap, radar_data, verbose=True):\n",
      "        self.themap = themap\n",
      "        self.radar_data = radar_data\n",
      "        self.verbose = verbose\n",
      "        \n",
      "    def init(self):\n",
      "        vmin = 1e9\n",
      "        vmax = -1e9\n",
      "        for x in radar_data.returns.values():\n",
      "            vmin = min(vmin, np.minimum.reduce(x.ravel()))\n",
      "            vmax = max(vmax, np.maximum.reduce(x.ravel()))\n",
      "        ts0 = sorted(radar_data.returns.keys())[0]\n",
      "        lonmin, lonmax, latmin, latmax = self.radar_data.extent\n",
      "        xmin, ymin = self.themap.bmap(lonmin, latmin)\n",
      "        xmax, ymax = self.themap.bmap(lonmax, latmax)\n",
      "        extent = (xmin, xmax, ymin, ymax)\n",
      "        self.pc = self.themap.axes.imshow(radar_data.returns[ts0], cmap=transparent_red_colormap, \n",
      "                                          vmin=vmin, vmax=vmax, extent=extent)\n",
      "        return [self.pc]\n",
      "    \n",
      "    def animate(self, ts):\n",
      "        if self.verbose:\n",
      "            print(\".\", end='')\n",
      "        if ts in self.radar_data.returns:\n",
      "            self.pc.set_array(radar_data.returns[ts])\n",
      "        return [self.pc] \n",
      "    \n",
      "    def make_animation(self, times, interval=20):\n",
      "        return animation.FuncAnimation(self.themap.fig, self.animate, \n",
      "                                       init_func=self.init,\n",
      "                                       frames=times, \n",
      "                                       interval=interval,\n",
      "                                       blit=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we simply animate "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime_range import datetime_range\n",
      "\n",
      "all_times = datetime_range(datetime(2014, 9, 7, 12, 0), datetime(2014, 9, 9, 12, 1))\n",
      "anim = RadarAnimator(phxmap, radar_data).make_animation(all_times)\n",
      "\n",
      "# These options work for quicktime on OS X. It's possible that some other options may be required for other machines.\n",
      "anim.save(\"September_8th_storm_radar.m4v\", fps=60, extra_args=['-vcodec', 'libx264', '-pix_fmt','yuv420p'])\n",
      "# This spits out a lot of dots."
     ],
     "language": "python",
     "metadata": {},
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    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we import code for displaying the resultant video. This code appears in the Phoenix Rainfall Map [notebook](http://nbviewer.ipython.org/github/bitsofbits/GIS/blob/master/Phoenix%20Rainfall%20Map.ipynb) and is also available at https://github.com/bitsofbits/GIS."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPythonVideo import video\n",
      "video(\"September_8th_storm_radar.m4v\", \"x-m4v\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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