corr_maps.ipynb

{
 "metadata": {
  "name": "corr maps"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Converting EPA Airquality Data into Pandas DataFrames by County\n",
      "---------------------------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from pandas import DataFrame\n",
      "import shapefile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Airquality Data comes from https://ofmext.epa.gov/AQDMRS/aqdmrs.html\n",
      "#This query includes the ozone data for the year 2011 in the state of california\n",
      "a = open('/Users/Deb/airquality_asthma/AQDM_507188059_hourly.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5 entries, 0 to 4\n",
        "Data columns:\n",
        "Latitude                   5  non-null values\n",
        "Longitude                  5  non-null values\n",
        "Datum                      5  non-null values\n",
        "Horizontal Accuracy        5  non-null values\n",
        "State Code                 5  non-null values\n",
        "County Code                5  non-null values\n",
        "Site Num                   5  non-null values\n",
        "Parameter Code             5  non-null values\n",
        "POC                        5  non-null values\n",
        "AQS Parameter Desc         5  non-null values\n",
        "Date Local                 5  non-null values\n",
        "24 Hour Local              5  non-null values\n",
        "Date GMT                   5  non-null values\n",
        "24 Hour GMT                5  non-null values\n",
        "Year GMT                   5  non-null values\n",
        "Day In Year GMT            5  non-null values\n",
        "Sample Measurement         5  non-null values\n",
        "Units of Measure           5  non-null values\n",
        "Sample Duration            5  non-null values\n",
        "Sample Frequency           0  non-null values\n",
        "Detection Limit            5  non-null values\n",
        "Measurement Uncertainty    0  non-null values\n",
        "Qualifier Description      0  non-null values\n",
        "Method Type                5  non-null values\n",
        "Method Description         5  non-null values\n",
        "dtypes: float64(13), object(12)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "2861316"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Group all measurements by measurement site (identified by countycode and site number) and create a new DataFrame with data averaged over time per measurement site"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "county_df = df.groupby(['County Code','Site Num', 'Latitude', 'Longitude'])\n",
      "\n",
      "county_code = list()\n",
      "sitenum = list()\n",
      "lat = list()\n",
      "lon = list()\n",
      "mean_meas = list()\n",
      "max_meas = list()\n",
      "min_meas = list()\n",
      "median_meas = list()\n",
      "\n",
      "for grpname,grp in county_df:\n",
      "    county_code.append(grpname[0])\n",
      "    sitenum.append(grpname[1])\n",
      "    lat.append(float(grpname[2]))\n",
      "    lon.append(float(grpname[3]))\n",
      "    mean_meas.append(grp['Sample Measurement'].mean())\n",
      "    max_meas.append(grp['Sample Measurement'].max())\n",
      "    min_meas.append(grp['Sample Measurement'].min())\n",
      "    median_meas.append(grp['Sample Measurement'].median())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sites_dict = {'County Code': county_code, 'Site Num': sitenum, 'Mean Ozone': mean_meas, 'Max Ozone': max_meas, 'Min Ozone': min_meas, 'Median Ozone': median_meas, 'Latitude': lat, 'Longitude': lon}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sites_df = DataFrame(sites_dict)\n",
      "sites_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>County Code</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Max Ozone</th>\n",
        "      <th>Mean Ozone</th>\n",
        "      <th>Median Ozone</th>\n",
        "      <th>Min Ozone</th>\n",
        "      <th>Site Num</th>\n",
        "    </tr>\n",
        "    </thead>\n",
        "    <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 37.687526</td>\n",
        "      <td>-121.784217</td>\n",
        "      <td> 0.102</td>\n",
        "      <td> 0.024099</td>\n",
        "      <td> 0.024</td>\n",
        "      <td> 0.002</td>\n",
        "      <td>    7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 37.743065</td>\n",
        "      <td>-122.169935</td>\n",
        "      <td> 0.072</td>\n",
        "      <td> 0.017730</td>\n",
        "      <td> 0.018</td>\n",
        "      <td> 0.002</td>\n",
        "      <td>    9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 37.814781</td>\n",
        "      <td>-122.282347</td>\n",
        "      <td> 0.061</td>\n",
        "      <td> 0.019565</td>\n",
        "      <td> 0.020</td>\n",
        "      <td> 0.002</td>\n",
        "      <td>   11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 37.654456</td>\n",
        "      <td>-122.031547</td>\n",
        "      <td> 0.094</td>\n",
        "      <td> 0.028426</td>\n",
        "      <td> 0.028</td>\n",
        "      <td> 0.002</td>\n",
        "      <td> 2001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 5</td>\n",
        "      <td> 38.339905</td>\n",
        "      <td>-120.763515</td>\n",
        "      <td> 0.102</td>\n",
        "      <td> 0.028670</td>\n",
        "      <td> 0.028</td>\n",
        "      <td> 0.002</td>\n",
        "      <td>    2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   County Code   Latitude   Longitude  Max Ozone  Mean Ozone  Median Ozone  Min Ozone  Site Num\n",
        "0            1  37.687526 -121.784217      0.102    0.024099         0.024      0.002         7\n",
        "1            1  37.743065 -122.169935      0.072    0.017730         0.018      0.002         9\n",
        "2            1  37.814781 -122.282347      0.061    0.019565         0.020      0.002        11\n",
        "3            1  37.654456 -122.031547      0.094    0.028426         0.028      0.002      2001\n",
        "4            5  38.339905 -120.763515      0.102    0.028670         0.028      0.002         2"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at some overall stats for our new DataFrame"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot all measureing stations to see if they are evenly distributed"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convert the 'County Code' into a FIPS, ie add on the '06' California prefix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#sites_df['FIPS'] = sites_df['County Code'].apply(lambda x: '0' + (str(6000 + int(x) )))\n",
      "sites_df['FIPS'] = sites_df['County Code'].apply(lambda x: 6000 + int(x) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grpbyobj = sites_df.groupby('FIPS')\n",
      "countymeandict = {}\n",
      "count = 0\n",
      "for grpname,grpdf in grpbyobj:\n",
      "    #print grpname,grpdf['Mean Ozone'].mean()\n",
      "    countymeandict[grpname] = grpdf['Mean Ozone'].mean()\n",
      "    count = count + 1\n",
      "    #break\n",
      "print count    \n",
      "#print countymeandict \n",
      "dictdf = {'cnty_fips': countymeandict.keys(), 'meanvalue': countymeandict.values()}\n",
      "#print dictdf\n",
      "resultdf = DataFrame(dictdf)\n",
      "print resultdf[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "49\n",
        "   cnty_fips  meanvalue\n",
        "0       6017   0.044753\n",
        "1       6019   0.030731\n",
        "2       6021   0.029426\n",
        "3       6023   0.025254\n",
        "4       6025   0.031208\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next Up: Calculate county wide averages\n",
      "---------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "med = pd.read_csv(open('Tracking_Hospital_2007_CountyMeasures.csv'), sep=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print med[:5]\n",
      "print type(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   cnty_fips  cnty_id   cnty_name cnty_abbr Total Number Lower 95% Limit Upper 95% Limit Age Adj. Rate per 10000\n",
        "0       6000        0  CALIFORNIA       NaN        31987            8.53            8.72                    8.62\n",
        "1       6001        1     Alameda        AL         2144           13.84           15.08                   14.45\n",
        "2       6003        2      Alpine        AP         null            null            null                    null\n",
        "3       6005        3      Amador        AM           25            4.05            9.83                    6.49\n",
        "4       6007        4       Butte        BU          188            7.74           10.42                    9.01\n",
        "<class 'pandas.core.frame.DataFrame'>\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "merged = resultdf.merge(med)\n",
      "subdf = merged[['cnty_fips','meanvalue','Age Adj. Rate per 10000']]\n",
      "subdf = subdf.set_index('cnty_fips')\n",
      "subdf = subdf[subdf['Age Adj. Rate per 10000'] != 'null']\n",
      "subdf['Age Adj. Rate per 10000'] = subdf['Age Adj. Rate per 10000'].apply(lambda x:float(x))\n",
      "print subdf[:5]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "           meanvalue  Age Adj. Rate per 10000\n",
        "cnty_fips                                    \n",
        "6001        0.022455                    14.45\n",
        "6005        0.028670                     6.49\n",
        "6007        0.033055                     9.01\n",
        "6009        0.030958                     6.01\n",
        "6013        0.024964                     9.90\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adjrateser = subdf['Age Adj. Rate per 10000']\n",
      "meanvalser = subdf['meanvalue']\n",
      "\n",
      "ratiodict = {}\n",
      "\n",
      "for key,val in meanvalser.iteritems():\n",
      "    ratio = float(meanvalser[key]/adjrateser[key])\n",
      "    ratiodict[key] = ratio\n",
      "\n",
      "#print ratiodict\n",
      "ratiodf = DataFrame({'fips':ratiodict.keys(),'ratio':ratiodict.values()})\n",
      "#print ratiodf\n",
      "minval = ratiodf['ratio'].min()\n",
      "maxval = ratiodf['ratio'].max()\n",
      "\n",
      "#print minval,maxval\n",
      "\n",
      "\n",
      "def normalize(x):\n",
      "    global maxval\n",
      "    global minval\n",
      "    val = float((x-minval)/(maxval-minval))\n",
      "    return val\n",
      "\n",
      "ratiodf['norm'] = ratiodf['ratio'].apply(normalize)\n",
      "ratiodf = ratiodf.set_index('fips')\n",
      "#print ratiodf\n",
      "print ratiodf\n",
      "ratio_norm_ser = ratiodf['norm']\n",
      "#print ratio_norm_ser\n",
      "fipslist = list(ratio_norm_ser.keys()) \n",
      "print type(fipslist)\n",
      "print fipslist\n",
      "\n",
      "def lookup(x):\n",
      "\n",
      "    try:\n",
      "        if len(x) == 5:\n",
      "            x = x[1:]\n",
      "            x = int(x)\n",
      "            return ratio_norm_ser[x]\n",
      "    except KeyError:\n",
      "        return 0\n",
      "    \n",
      "        \n",
      "       \n",
      "        \n",
      "       \n",
      "                \n",
      "    \n",
      "\n",
      "print lookup('06017')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         ratio      norm\n",
        "fips                    \n",
        "6017  0.007824  0.849600\n",
        "6019  0.002739  0.160573\n",
        "6021  0.005245  0.500182\n",
        "6023  0.003381  0.247532\n",
        "6025  0.002368  0.110281\n",
        "6029  0.004080  0.342338\n",
        "6031  0.003615  0.279301\n",
        "6033  0.003805  0.305008\n",
        "6037  0.002643  0.147545\n",
        "6039  0.003675  0.287458\n",
        "6041  0.005766  0.570701\n",
        "6043  0.005437  0.526213\n",
        "6045  0.007206  0.765890\n",
        "6047  0.003315  0.238583\n",
        "6053  0.004021  0.334312\n",
        "6055  0.004890  0.452102\n",
        "6057  0.005677  0.558672\n",
        "6059  0.004252  0.365624\n",
        "6061  0.007390  0.790752\n",
        "6065  0.004619  0.415279\n",
        "6067  0.003328  0.240408\n",
        "6069  0.006414  0.658571\n",
        "6071  0.003787  0.302536\n",
        "6073  0.004110  0.346301\n",
        "6075  0.002608  0.142805\n",
        "6077  0.002799  0.168672\n",
        "6079  0.006115  0.618066\n",
        "6081  0.002923  0.185555\n",
        "6083  0.006494  0.669378\n",
        "6085  0.003706  0.291578\n",
        "6087  0.003849  0.311048\n",
        "6089  0.003402  0.250447\n",
        "6093  0.006773  0.707208\n",
        "6095  0.003557  0.271370\n",
        "6097  0.003829  0.308256\n",
        "6099  0.003459  0.258109\n",
        "6101  0.003960  0.326062\n",
        "6103  0.005186  0.492159\n",
        "6107  0.004253  0.365747\n",
        "6109  0.008934  1.000000\n",
        "6111  0.005586  0.546413\n",
        "6113  0.004832  0.444246\n",
        "6001  0.001554  0.000000\n",
        "6005  0.004418  0.388022\n",
        "6007  0.003669  0.286555\n",
        "6009  0.005151  0.487430\n",
        "6013  0.002522  0.131113\n",
        "<type 'list'>\n",
        "[6017, 6019, 6021, 6023, 6025, 6029, 6031, 6033, 6037, 6039, 6041, 6043, 6045, 6047, 6053, 6055, 6057, 6059, 6061, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6093, 6095, 6097, 6099, 6101, 6103, 6107, 6109, 6111, 6113, 6001, 6005, 6007, 6009, 6013]\n",
        "0.849599750857\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "\n",
      "\n",
      "with warnings.catch_warnings():\n",
      "\n",
      "    warnings.simplefilter(\"ignore\")\n",
      "\n",
      "\n",
      "\n",
      "    from mpl_toolkits.basemap import Basemap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "\n",
      "import inspect\n",
      "\n",
      "basemap_data_dir = os.path.join(os.path.dirname(inspect.getfile(Basemap)), \"data\")\n",
      "\n",
      "print basemap_data_dir, os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/mpl_toolkits/basemap/data True\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# https://code.google.com/p/pyshp/\n",
      "\n",
      "import shapefile # install shpf module for shape files\n",
      "\n",
      "print basemap_data_dir\n",
      "\n",
      "# this is my git clone of https://github.com/matplotlib/basemap --> these files will be in the PiCloud basemap_data_dir\n",
      "\n",
      "if os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\")):\n",
      "\n",
      "    shpf = shapefile.Reader(os.path.join(basemap_data_dir,\"UScounties\"))\n",
      "\n",
      "else:\n",
      "\n",
      "    # put in your path\n",
      "\n",
      "    #shpf = shapefile.Reader(\"/Users/raymondyee/Dropbox/WwoD13/tl_2012_us_county\")\n",
      "\n",
      "    shpf = shapefile.Reader(\"/Users/raymondyee/C/src/basemap/lib/mpl_toolkits/basemap/data/UScounties\")\n",
      "\n",
      "\n",
      "\n",
      "shapes = shpf.shapes()\n",
      "\n",
      "records = shpf.records()\n",
      "\n",
      "for x in records:\n",
      "    print x    \n",
      "    break\n",
      "\n",
      "for y in shapes:\n",
      "    print y\n",
      "    break\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/mpl_toolkits/basemap/data\n",
        "['01', '029', '01029', 'AL', 'Cleburne', 'County']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "<shapefile._Shape instance at 0x10bcece18>\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def zip_filter_by_state(records, shapes, included_states=None):\n",
      "\n",
      "    # by default, no filtering\n",
      "\n",
      "    # included_states is a list of states fips prefixes\n",
      "\n",
      "    for (record, state) in izip(records, shapes):\n",
      "\n",
      "        if record[0] in included_states:\n",
      "\n",
      "            yield (record, state) \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# http://www.geophysique.be/2013/02/12/matplotlib-basemap-tutorial-10-shapefiles-unleached-continued/\n",
      "#\n",
      "# BaseMap example by geophysique.be\n",
      "# tutorial 10\n",
      "import time\n",
      "\n",
      "\n",
      "import os\n",
      "\n",
      "import inspect\n",
      "\n",
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from itertools import islice, izip\n",
      "\n",
      "from mpl_toolkits.basemap import Basemap\n",
      "\n",
      "from matplotlib.collections import LineCollection\n",
      "\n",
      "from matplotlib import cm\n",
      "\n",
      "import shapefile \n",
      "\n",
      " \n",
      "\n",
      "### PARAMETERS FOR MATPLOTLIB :\n",
      "\n",
      "import matplotlib as mpl\n",
      "\n",
      "mpl.rcParams['font.size'] = 10.\n",
      "\n",
      "mpl.rcParams['font.family'] = 'Comic Sans MS'\n",
      "\n",
      "mpl.rcParams['axes.labelsize'] = 8.\n",
      "\n",
      "mpl.rcParams['xtick.labelsize'] = 6.\n",
      "\n",
      "mpl.rcParams['ytick.labelsize'] = 6.\n",
      "\n",
      " \n",
      "\n",
      "fig = plt.figure(figsize=(11.7,11)) #11.7,8.3\n",
      "\n",
      "#Custom adjust of the subplots\n",
      "\n",
      "plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)\n",
      "\n",
      "ax = plt.subplot(111)\n",
      "ax.set_title('County-wise distribution for California State - Hospitalizations')\n",
      "\n",
      "#Let's create a basemap of USA\n",
      "\n",
      "#x1 = -180.\n",
      "#x2 = -62.\n",
      "#y1 = 18.\n",
      "#y2 = 68.\n",
      "\n",
      "# co-ordinates of california state\n",
      "x1 = -128.\n",
      "x2 = -114.\n",
      "y1 = 32.\n",
      "y2 = 43.\n",
      " \n",
      "#m = Basemap(llcrnrlon=-128,llcrnrlat=25,urcrnrlon=-111.0,urcrnrlat=44.5,\n",
      "#            resolution='i',projection='cass',lon_0=-120.36,lat_0=37.7)\n",
      "m = Basemap(resolution='i',projection='merc', llcrnrlat=y1,urcrnrlat=y2,llcrnrlon=x1,urcrnrlon=x2,lat_ts=(y1+y2)/2)\n",
      "\n",
      "#m.drawcountries(linewidth=0.5)\n",
      "\n",
      "#m.drawcoastlines(linewidth=0.5)\n",
      "\n",
      "m.drawparallels(np.arange(y1,y2,2.),labels=[1,0,0,0],color='black',dashes=[1,0],labelstyle='+/-',linewidth=0.2) # draw parallels\n",
      "\n",
      "m.drawmeridians(np.arange(x1,x2,2.),labels=[0,0,0,1],color='black',dashes=[1,0],labelstyle='+/-',linewidth=0.2) # draw meridians\n",
      "    \n",
      "\n",
      "def zip_filter_by_state(records, shapes, included_states=None):\n",
      "\n",
      "    # by default, no filtering\n",
      "\n",
      "    # included_states is a list of states fips prefixes\n",
      "\n",
      "    for (record, state) in izip(records, shapes):\n",
      "\n",
      "        if record[0] in included_states:\n",
      "\n",
      "            yield (record, state) \n",
      "\n",
      "    \n",
      "\n",
      "    \n",
      "\n",
      "\n",
      "\n",
      "basemap_data_dir = os.path.join(os.path.dirname(inspect.getfile(Basemap)), \"data\")\n",
      "\n",
      "\n",
      "\n",
      "# this is my git clone of https://github.com/matplotlib/basemap --> these files will be in the PiCloud basemap_data_dir\n",
      "\n",
      "if os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\")):\n",
      "\n",
      "    shpf = shapefile.Reader(os.path.join(basemap_data_dir,\"UScounties\"))\n",
      "\n",
      "else:\n",
      "\n",
      "    # put in your path\n",
      "\n",
      "    #shpf = shapefile.Reader(\"/Users/raymondyee/Dropbox/WwoD13/tl_2012_us_county\")\n",
      "\n",
      "    shpf = shapefile.Reader(\"/Users/raymondyee/C/src/basemap/lib/mpl_toolkits/basemap/data/UScounties\")\n",
      "\n",
      "\n",
      "\n",
      "shapes = shpf.shapes()\n",
      "\n",
      "records = shpf.records()\n",
      "\n",
      " \n",
      "\n",
      "# show only CA and AK (for example)\n",
      "\n",
      "for record, shape in zip_filter_by_state(records, shapes, ['06']):# removed 02 state , we just need california\n",
      "\n",
      "    lons,lats = zip(*shape.points) # lat long for each county\n",
      "\n",
      "    data = np.array(m(lons, lats)).T\n",
      "    \n",
      "    \n",
      "\n",
      " \n",
      "\n",
      "    if len(shape.parts) == 1:\n",
      "\n",
      "        segs = [data,]\n",
      "\n",
      "    else:\n",
      "\n",
      "        segs = []\n",
      "\n",
      "        for i in range(1,len(shape.parts)):\n",
      "\n",
      "            index = shape.parts[i-1]\n",
      "\n",
      "            index2 = shape.parts[i]\n",
      "\n",
      "            segs.append(data[index:index2])\n",
      "            \n",
      "            #time.sleep(1) # introduce a delay of 1 second\n",
      "\n",
      "        segs.append(data[index2:])\n",
      "\n",
      " \n",
      "\n",
      "    lines = LineCollection(segs,antialiaseds=(1,))\n",
      "    \n",
      "     \n",
      "    color = lookup(record[2])\n",
      "    \n",
      "    \n",
      "    lines.set_label('C') \n",
      "    lines.set_facecolors(cm.gist_yarg(color)) #cm.jet(np.random.rand(1))\n",
      "    #print cm.Blues(0), binary \n",
      "    lines.set_edgecolors('k')\n",
      "    lines.set_linewidth(0.1)       \n",
      "    ax.add_collection(lines) # draw step\n",
      "    \n",
      "    \n",
      "\n",
      " \n",
      "\n",
      "plt.savefig('tutorial10.png',dpi=300)\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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1XDZs2IBFixZh7NixADrmbne22Ze0tDRcffXVeP3117F8+XJ1nrA3b775Zrf5sxEREb2O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\nQqPRQKvVwul04rbbbgNwbde1JBgMwmKxwGq1Ip1OA7jWlF+n05V38W4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6SCSCcDiM1tbW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\nI5aIiMhEtm7diq1btyIWi+Hjj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      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we are dealing with atmosphere and not a physical law, this is pretty good.\n",
      "r should be higher once we use the data for 2007"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}