{
 "metadata": {
  "name": "",
  "signature": "sha256:ce776f6f756468340b871321dc49e2e27376713ae180fa5e9e05d2ca2054e65b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Map Your Google Location History in 3D"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By Sukhbinder and inspired from [this](http://nbviewer.ipython.org/github/chrisalbon/code_py/blob/master/matplotlib_map_your_google_data.ipynb)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step 1: Download your Google Location History"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Google makes this process very easy. Go here to [download your location history data](https://www.google.com/settings/takeout) and unzip it."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\n",
      "Step 2: Run this script"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fname=r\"/Users/Sukhbinder/Downloads/Takeout/Location History/LocationHistory.json\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import pandas\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "# Import matplotlib and Basemap\n",
      "import matplotlib.pyplot as plt\n",
      "#from mpl_toolkits.basemap import Basemap\n",
      "\n",
      "# Set iPython to display visualization inline\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Read in the location history json\n",
      "\n",
      "Simply change the string to point to where you unzipped your location history json file\n",
      "\n",
      " "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw = pd.read_json(fname) #pd.io.json.read_json(fname)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Let's take a look at some of the data\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw.tail(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>locations</th>\n",
        "      <th>somePointsTruncated</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>86241</th>\n",
        "      <td> {u'latitudeE7': 129896780, u'accuracy': 100, u...</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86242</th>\n",
        "      <td> {u'latitudeE7': 129896780, u'accuracy': 100, u...</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86243</th>\n",
        "      <td> {u'latitudeE7': 129896780, u'accuracy': 100, u...</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86244</th>\n",
        "      <td> {u'latitudeE7': 129896780, u'accuracy': 100, u...</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86245</th>\n",
        "      <td> {u'latitudeE7': 129947642, u'accuracy': 798, u...</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                                               locations somePointsTruncated\n",
        "86241  {u'latitudeE7': 129896780, u'accuracy': 100, u...                True\n",
        "86242  {u'latitudeE7': 129896780, u'accuracy': 100, u...                True\n",
        "86243  {u'latitudeE7': 129896780, u'accuracy': 100, u...                True\n",
        "86244  {u'latitudeE7': 129896780, u'accuracy': 100, u...                True\n",
        "86245  {u'latitudeE7': 129947642, u'accuracy': 798, u...                True\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Expand the locations object into it's own dataframe\n",
      " "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = raw['locations'].apply(pd.Series)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Take a peak at the data again\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>accuracy</th>\n",
        "      <th>activitys</th>\n",
        "      <th>altitude</th>\n",
        "      <th>heading</th>\n",
        "      <th>latitudeE7</th>\n",
        "      <th>longitudeE7</th>\n",
        "      <th>timestampMs</th>\n",
        "      <th>velocity</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>86241</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096571529</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86242</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096556086</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86243</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096541775</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86244</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096526596</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86245</th>\n",
        "      <td> 798</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129947642</td>\n",
        "      <td> 776835705</td>\n",
        "      <td> 1297274751020</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "       accuracy activitys  altitude  heading  latitudeE7  longitudeE7  \\\n",
        "86241       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86242       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86243       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86244       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86245       798       NaN       NaN      NaN   129947642    776835705   \n",
        "\n",
        "         timestampMs  velocity  \n",
        "86241  1304096571529       NaN  \n",
        "86242  1304096556086       NaN  \n",
        "86243  1304096541775       NaN  \n",
        "86244  1304096526596       NaN  \n",
        "86245  1297274751020       NaN  \n",
        "\n",
        "[5 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Wrangle the data and look at the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['latitude'] = df['latitudeE7'] * 0.0000001\n",
      "\n",
      "# Create a list from the longitude column, multiplied by -E7\n",
      "df['longitude'] = df['longitudeE7'] * 0.0000001"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>accuracy</th>\n",
        "      <th>activitys</th>\n",
        "      <th>altitude</th>\n",
        "      <th>heading</th>\n",
        "      <th>latitudeE7</th>\n",
        "      <th>longitudeE7</th>\n",
        "      <th>timestampMs</th>\n",
        "      <th>velocity</th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>86241</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096571529</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 12.989678</td>\n",
        "      <td> 77.688110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86242</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096556086</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 12.989678</td>\n",
        "      <td> 77.688110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86243</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096541775</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 12.989678</td>\n",
        "      <td> 77.688110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86244</th>\n",
        "      <td> 100</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129896780</td>\n",
        "      <td> 776881100</td>\n",
        "      <td> 1304096526596</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 12.989678</td>\n",
        "      <td> 77.688110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86245</th>\n",
        "      <td> 798</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 129947642</td>\n",
        "      <td> 776835705</td>\n",
        "      <td> 1297274751020</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 12.994764</td>\n",
        "      <td> 77.683571</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "       accuracy activitys  altitude  heading  latitudeE7  longitudeE7  \\\n",
        "86241       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86242       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86243       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86244       100       NaN       NaN      NaN   129896780    776881100   \n",
        "86245       798       NaN       NaN      NaN   129947642    776835705   \n",
        "\n",
        "         timestampMs  velocity   latitude  longitude  \n",
        "86241  1304096571529       NaN  12.989678  77.688110  \n",
        "86242  1304096556086       NaN  12.989678  77.688110  \n",
        "86243  1304096541775       NaN  12.989678  77.688110  \n",
        "86244  1304096526596       NaN  12.989678  77.688110  \n",
        "86245  1297274751020       NaN  12.994764  77.683571  \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>accuracy</th>\n",
        "      <th>activitys</th>\n",
        "      <th>altitude</th>\n",
        "      <th>heading</th>\n",
        "      <th>latitudeE7</th>\n",
        "      <th>longitudeE7</th>\n",
        "      <th>timestampMs</th>\n",
        "      <th>velocity</th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  165</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 901</td>\n",
        "      <td>-1</td>\n",
        "      <td> 129919563</td>\n",
        "      <td> 777150227</td>\n",
        "      <td> 1404655403815</td>\n",
        "      <td>-1</td>\n",
        "      <td> 12.991956</td>\n",
        "      <td> 77.715023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  165</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 900</td>\n",
        "      <td>-1</td>\n",
        "      <td> 129919981</td>\n",
        "      <td> 777145832</td>\n",
        "      <td> 1404634564910</td>\n",
        "      <td>-1</td>\n",
        "      <td> 12.991998</td>\n",
        "      <td> 77.714583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  165</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 900</td>\n",
        "      <td>-1</td>\n",
        "      <td> 129919532</td>\n",
        "      <td> 777150546</td>\n",
        "      <td> 1404630560819</td>\n",
        "      <td>-1</td>\n",
        "      <td> 12.991953</td>\n",
        "      <td> 77.715055</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  165</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 900</td>\n",
        "      <td>-1</td>\n",
        "      <td> 129920234</td>\n",
        "      <td> 777143167</td>\n",
        "      <td> 1404626864671</td>\n",
        "      <td>-1</td>\n",
        "      <td> 12.992023</td>\n",
        "      <td> 77.714317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1414</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 900</td>\n",
        "      <td>-1</td>\n",
        "      <td> 129954840</td>\n",
        "      <td> 776779268</td>\n",
        "      <td> 1404626799099</td>\n",
        "      <td>-1</td>\n",
        "      <td> 12.995484</td>\n",
        "      <td> 77.677927</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "   accuracy activitys  altitude  heading  latitudeE7  longitudeE7  \\\n",
        "0       165       NaN       901       -1   129919563    777150227   \n",
        "1       165       NaN       900       -1   129919981    777145832   \n",
        "2       165       NaN       900       -1   129919532    777150546   \n",
        "3       165       NaN       900       -1   129920234    777143167   \n",
        "4      1414       NaN       900       -1   129954840    776779268   \n",
        "\n",
        "     timestampMs  velocity   latitude  longitude  \n",
        "0  1404655403815        -1  12.991956  77.715023  \n",
        "1  1404634564910        -1  12.991998  77.714583  \n",
        "2  1404630560819        -1  12.991953  77.715055  \n",
        "3  1404626864671        -1  12.992023  77.714317  \n",
        "4  1404626799099        -1  12.995484  77.677927  \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Get x,y,z coordinated for the sphere using the latitude and longitude\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat=df['longitude'].values*np.pi/180.0 \n",
      "long=df['latitude'].values*np.pi/180.0\n",
      "x = np.cos(long)*np.cos(lat)\n",
      "y = np.cos(long)*np.sin(lat)\n",
      "z = np.sin(long)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Map the data using vtk\n",
      "\n",
      "#####Create two actors one of the earth and other of the 3D location points"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import vtk\n",
      " \n",
      "class VtkPointCloud:\n",
      "    def __init__(self, zMin=-10.0, zMax=10.0, maxNumPoints=1e6):\n",
      "        self.maxNumPoints = maxNumPoints\n",
      "        self.vtkPolyData = vtk.vtkPolyData()\n",
      "        self.clearPoints()\n",
      "        mapper = vtk.vtkPolyDataMapper()\n",
      "        mapper.SetInput(self.vtkPolyData)\n",
      "        mapper.SetScalarRange(zMin, zMax)\n",
      "        mapper.SetScalarVisibility(1)\n",
      "        self.vtkActor = vtk.vtkActor()\n",
      "        self.vtkActor.SetMapper(mapper)\n",
      "        self.vtkActor.GetProperty().SetPointSize(4)\n",
      "        self.vtkActor.GetProperty().SetColor(1,0,0)\n",
      " \n",
      "    def addPoint(self, point):\n",
      "        if self.vtkPoints.GetNumberOfPoints() < self.maxNumPoints:\n",
      "            pointId = self.vtkPoints.InsertNextPoint(point[:])\n",
      "            self.vtkDepth.InsertNextValue(point[2])\n",
      "            self.vtkCells.InsertNextCell(1)\n",
      "            self.vtkCells.InsertCellPoint(pointId)\n",
      "        self.vtkCells.Modified()\n",
      "        self.vtkPoints.Modified()\n",
      "        self.vtkDepth.Modified()\n",
      " \n",
      "    def clearPoints(self):\n",
      "        self.vtkPoints = vtk.vtkPoints()\n",
      "        self.vtkCells = vtk.vtkCellArray()\n",
      "        self.vtkDepth = vtk.vtkDoubleArray()\n",
      "        self.vtkDepth.SetName('DepthArray')\n",
      "        self.vtkPolyData.SetPoints(self.vtkPoints)\n",
      "        self.vtkPolyData.SetVerts(self.vtkCells)\n",
      "        self.vtkPolyData.GetPointData().SetScalars(self.vtkDepth)\n",
      "        self.vtkPolyData.GetPointData().SetActiveScalars('DepthArray')\n",
      "       \n",
      "\n",
      "cldpnts= VtkPointCloud()\n",
      "for i in range(len(x)):\n",
      "   cldpnts.addPoint([x[i],y[i],z[i]])\n",
      "   \n",
      " \n",
      " \n",
      "ren = vtk.vtkRenderer()\n",
      "renWin = vtk.vtkRenderWindow()\n",
      "renWin.AddRenderer(ren)\n",
      "WIDTH=640\n",
      "HEIGHT=480\n",
      "renWin.SetSize(WIDTH,HEIGHT)\n",
      " \n",
      "# create a renderwindowinteractor\n",
      "iren = vtk.vtkRenderWindowInteractor()\n",
      "iren.SetRenderWindow(renWin)\n",
      "ren.SetBackground(0.48, 0.48, 0.48)\n",
      " \n",
      "# create source\n",
      "source = vtk.vtkEarthSource()\n",
      "source.SetOnRatio = 2\n",
      " \n",
      " \n",
      "# mapper for original earth\n",
      "coneMapper1 = vtk.vtkPolyDataMapper()\n",
      "if vtk.VTK_MAJOR_VERSION <= 5:\n",
      "    coneMapper1.SetInput(source.GetOutput())\n",
      "else:\n",
      "    coneMapper1.SetInputConnection(source.GetOutputPort())\n",
      " \n",
      " \n",
      "# actor for earth\n",
      "actor1 = vtk.vtkActor()\n",
      "actor1.SetMapper(coneMapper1)\n",
      "actor1.GetProperty().SetColor(0, 0, 0) # (R,G,B)\n",
      "actor1.GetProperty().SetSpecular = 0.45\n",
      "actor1.GetProperty().SetSpecularPower = 5\n",
      "actor1.SetBackfaceCulling = True\n",
      " \n",
      "# add points actor on the earth\n",
      "# assign actor to the renderer\n",
      "ren.AddActor(cldpnts.vtkActor)\n",
      "ren.AddActor(actor1)\n",
      " \n",
      "# enable user interface interactor\n",
      "iren.Initialize()\n",
      "renWin.Render()\n",
      "iren.Start()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### For continuty, Map the data using basemap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a figure of size (i.e. pretty big)\n",
      "fig = plt.figure(figsize=(20,10))\n",
      "\n",
      "# Create a map, using the Gall\u2013Peters projection, \n",
      "map = Basemap(projection='gall', \n",
      "              # with low resolution,\n",
      "              resolution = 'l', \n",
      "              # And threshold 100000\n",
      "              area_thresh = 100000.0,\n",
      "              # Centered at 0,0 (i.e null island)\n",
      "              lat_0=0, lon_0=0)\n",
      "\n",
      "# Draw the coastlines on the map\n",
      "map.drawcoastlines()\n",
      "\n",
      "# Draw country borders on the map\n",
      "map.drawcountries()\n",
      "\n",
      "# Fill the land with grey\n",
      "map.fillcontinents(color = '#888888')\n",
      "\n",
      "# Draw the map boundaries\n",
      "map.drawmapboundary(fill_color='#f4f4f4')\n",
      "\n",
      "# Define our longitude and latitude points\n",
      "x,y = map(df['longitude'].values, df['latitude'].values)\n",
      "\n",
      "# Plot them using round markers of size 6\n",
      "map.plot(x, y, 'ro', markersize=6)\n",
      " \n",
      "# Show the map\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'Basemap' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-11-f566cc02e48c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Create a map, using the Gall\u2013Peters projection,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m map = Basemap(projection='gall', \n\u001b[0m\u001b[1;32m      6\u001b[0m               \u001b[0;31m# with low resolution,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m               \u001b[0mresolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'l'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: name 'Basemap' is not defined"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x12442a450>"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}