{ "metadata": { "name": "", "signature": "sha256:1a658d29f3ac43519c488e7d8b88db6015a5494cd061a030a1008eac0b9d9557" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#GPX PY Overview\n", "\n", "The gpxpy package provides a bunch of great tools for parsing, correcting, and manipulating GPX files. It also has some nice analysis capabilities built into it. You can get a few valuable metrics about your run/bike right out of the box. These include\n", "\n", " * Length of a run\n", " * Min and max elevation of a run\n", " * Duration of run\n", " * Max speed\n", " \n", "Let's start by importing package of course! We'll also go ahead and import other packages that will be used in this notebook." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import gpxpy\n", "import gpxpy.gpx\n", "\n", "import glob\n", "import os\n", "import datetime\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A Quick Look at the GPX format\n", "\n", "Before we really get started, we shoul probably see what the GPX format is.\n", "\n", "Let's load up the first file in our data set and look at it's contents." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx_files = glob.glob(os.path.join(\"rundata\",\"*.gpx\"))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "%cat ./${os.path.join(\"rundata\",gpx_files[0])}" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "\r\n", "\r\n", " \r\n", " \r\n", "\r\n", "216.7\r\n", "216.9\r\n", "217.0\r\n", "217.1\r\n", "217.2\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.1\r\n", "216.9\r\n", "216.7\r\n", "216.2\r\n", "215.6\r\n", "215.1\r\n", "214.5\r\n", "214.2\r\n", "213.8\r\n", "213.5\r\n", "212.8\r\n", "212.2\r\n", "211.5\r\n", "210.9\r\n", "210.6\r\n", "210.4\r\n", "210.1\r\n", "209.8\r\n", "209.5\r\n", "209.3\r\n", "209.0\r\n", "209.0\r\n", "209.0\r\n", "209.0\r\n", "209.0\r\n", "209.0\r\n", "209.2\r\n", "209.4\r\n", "209.5\r\n", "209.7\r\n", "209.9\r\n", "210.1\r\n", "210.3\r\n", "210.5\r\n", "210.6\r\n", "210.8\r\n", "211.0\r\n", "211.1\r\n", "211.2\r\n", "211.3\r\n", "211.4\r\n", "211.5\r\n", "211.5\r\n", "211.6\r\n", "211.7\r\n", "211.8\r\n", "212.0\r\n", "212.2\r\n", "212.3\r\n", "212.4\r\n", "212.5\r\n", "212.5\r\n", "212.6\r\n", "212.7\r\n", "212.8\r\n", "212.9\r\n", "213.0\r\n", "212.9\r\n", "212.8\r\n", "212.7\r\n", "212.6\r\n", "212.5\r\n", "212.5\r\n", "212.4\r\n", "212.6\r\n", "212.9\r\n", "213.2\r\n", "213.5\r\n", "213.8\r\n", "214.2\r\n", "214.5\r\n", "214.9\r\n", "215.5\r\n", "216.0\r\n", "216.5\r\n", "216.7\r\n", "216.9\r\n", "217.1\r\n", "217.3\r\n", "217.5\r\n", "217.6\r\n", "217.8\r\n", "218.0\r\n", "218.3\r\n", "218.5\r\n", "218.8\r\n", "219.1\r\n", "219.4\r\n", "219.6\r\n", "219.9\r\n", "220.2\r\n", "220.3\r\n", "220.4\r\n", "220.5\r\n", "220.3\r\n", "220.1\r\n", "219.9\r\n", "219.7\r\n", "219.6\r\n", "219.5\r\n", "219.4\r\n", "219.2\r\n", "219.2\r\n", "219.2\r\n", "219.2\r\n", "219.2\r\n", "219.2\r\n", "219.1\r\n", "219.0\r\n", "218.8\r\n", "218.6\r\n", "218.5\r\n", "218.5\r\n", "218.4\r\n", "218.3\r\n", "218.0\r\n", "217.7\r\n", "217.5\r\n", "217.3\r\n", "217.1\r\n", "216.9\r\n", "216.7\r\n", "216.5\r\n", "216.0\r\n", "215.5\r\n", "214.9\r\n", "214.5\r\n", "214.2\r\n", "213.9\r\n", "213.6\r\n", "213.4\r\n", "213.1\r\n", "212.8\r\n", "212.5\r\n", "212.6\r\n", "212.7\r\n", "212.8\r\n", "212.9\r\n", "213.0\r\n", "213.0\r\n", "212.9\r\n", "212.8\r\n", "212.7\r\n", "212.6\r\n", "212.5\r\n", "212.5\r\n", "212.4\r\n", "212.3\r\n", "212.2\r\n", "212.0\r\n", "211.8\r\n", "211.7\r\n", "211.6\r\n", "211.5\r\n", "211.5\r\n", "211.4\r\n", "211.3\r\n", "211.2\r\n", "211.1\r\n", "211.0\r\n", "210.8\r\n", "210.6\r\n", "210.5\r\n", "210.3\r\n", "210.1\r\n", "209.9\r\n", "209.7\r\n", "209.5\r\n", "209.4\r\n", "209.2\r\n", "209.0\r\n", "209.3\r\n", "209.5\r\n", "209.8\r\n", "210.1\r\n", "210.4\r\n", "210.6\r\n", "210.9\r\n", "211.5\r\n", "212.2\r\n", "212.8\r\n", "213.5\r\n", "213.8\r\n", "214.2\r\n", "214.5\r\n", "215.0\r\n", "215.5\r\n", "215.9\r\n", "216.4\r\n", "216.5\r\n", "216.5\r\n", "216.6\r\n", "216.7\r\n", "216.8\r\n", "216.9\r\n", "217.0\r\n", "217.0\r\n", "217.0\r\n", "217.0\r\n", "217.1\r\n", "217.2\r\n", "217.3\r\n", "217.4\r\n", "217.5\r\n", "217.5\r\n", "217.6\r\n", "217.7\r\n", "217.8\r\n", "217.9\r\n", "218.0\r\n", "218.0\r\n", "218.0\r\n", "218.0\r\n", "218.1\r\n", "218.2\r\n", "218.3\r\n", "218.4\r\n", "218.5\r\n", "218.5\r\n", "218.6\r\n", "218.7\r\n", "218.8\r\n", "218.9\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "219.0\r\n", "218.9\r\n", "218.8\r\n", "218.7\r\n", "218.6\r\n", "218.5\r\n", "218.5\r\n", "218.4\r\n", "218.3\r\n", "218.2\r\n", "218.1\r\n", "218.0\r\n", "218.0\r\n", "218.0\r\n", "218.0\r\n", "217.9\r\n", "217.8\r\n", "217.7\r\n", "217.6\r\n", "217.5\r\n", "217.5\r\n", "217.4\r\n", "217.2\r\n", "217.0\r\n", "216.8\r\n", "216.6\r\n", "216.5\r\n", "216.5\r\n", "216.4\r\n", "216.5\r\n", "216.5\r\n", "216.6\r\n", "216.7\r\n", "216.9\r\n", "217.1\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.3\r\n", "217.1\r\n", "216.9\r\n", "216.7\r\n", "216.2\r\n", "215.6\r\n", "215.1\r\n", "214.5\r\n", "214.2\r\n", "213.8\r\n", "213.5\r\n", "213.1\r\n", "212.5\r\n", "211.8\r\n", "211.2\r\n", "210.9\r\n", "210.6\r\n", "210.4\r\n", "210.1\r\n", "209.8\r\n", "209.5\r\n", "209.3\r\n", "209.0\r\n", "209.0\r\n", "209.0\r\n", "209.2\r\n", "209.4\r\n", "209.5\r\n", "209.7\r\n", "209.9\r\n", "210.1\r\n", "210.3\r\n", "210.5\r\n", "210.6\r\n", "210.8\r\n", "211.0\r\n", "211.1\r\n", "211.2\r\n", "211.3\r\n", "211.4\r\n", "211.5\r\n", "211.5\r\n", "211.6\r\n", "211.7\r\n", "211.8\r\n", "212.0\r\n", "212.2\r\n", "212.3\r\n", "212.4\r\n", "212.5\r\n", "212.5\r\n", "212.6\r\n", "212.7\r\n", "212.8\r\n", "212.9\r\n", "212.9\r\n", "212.8\r\n", "212.7\r\n", "212.6\r\n", "212.5\r\n", "212.5\r\n", "212.4\r\n", "212.6\r\n", "212.9\r\n", "213.2\r\n", "213.5\r\n", "213.8\r\n", "214.2\r\n", "214.5\r\n", "214.9\r\n", "215.5\r\n", "216.0\r\n", "216.5\r\n", "216.7\r\n", "216.9\r\n", "217.1\r\n", "217.3\r\n", "217.5\r\n", "217.6\r\n", "217.8\r\n", "218.0\r\n", "218.3\r\n", "218.5\r\n", "218.8\r\n", "219.1\r\n", "219.4\r\n", "219.6\r\n", "219.9\r\n", "220.0\r\n", "220.1\r\n", "220.2\r\n", "220.3\r\n", "220.1\r\n", "219.9\r\n", "219.7\r\n", "219.6\r\n", "219.5\r\n", "219.5\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.3\r\n", "219.1\r\n", "218.9\r\n", "218.7\r\n", "218.6\r\n", "218.5\r\n", "218.5\r\n", "218.4\r\n", "218.3\r\n", "218.0\r\n", "217.7\r\n", "217.5\r\n", "217.3\r\n", "217.1\r\n", "216.9\r\n", "216.7\r\n", "216.5\r\n", "216.0\r\n", "215.5\r\n", "214.9\r\n", "214.5\r\n", "214.2\r\n", "213.8\r\n", "213.5\r\n", "213.3\r\n", "213.0\r\n", "212.7\r\n", "212.5\r\n", "212.5\r\n", "212.6\r\n", "212.7\r\n", "212.8\r\n", "212.9\r\n", "212.9\r\n", "212.8\r\n", "212.7\r\n", "212.6\r\n", "212.5\r\n", "212.5\r\n", "212.4\r\n", "212.3\r\n", "212.2\r\n", "212.1\r\n", "212.0\r\n", "212.0\r\n", "212.0\r\n", "\r\n", "\r\n", "\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## gpxpy's Representation\n", "Now let's see what gpxpy does with the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx = gpxpy.parse(open(gpx_files[97]))\n", "gpx" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "GPX(tracks=[GPXTrack(name=u'Running 8/11/12 6:02 am', number=0, segments=[GPXTrackSegment(points=[...])])])" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see what functions are available using IPython's tab completion." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx.tracks" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[GPXTrack(name=u'Running 8/11/12 6:02 am', number=0, segments=[GPXTrackSegment(points=[...])])]" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the track data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx_track = gpx.tracks[0]\n", "gpx_track" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "GPXTrack(name=u'Running 8/11/12 6:02 am', number=0, segments=[GPXTrackSegment(points=[...])])" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Track Summary\n", "Some information on this track" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Name: \" + gpx_track.name)\n", "print(\"Description: \" + str(gpx_track.description))\n", "print(\"Start: \" + str(gpx_track.get_time_bounds().start_time.isoformat()))\n", "print(\"End: \" + str(gpx_track.get_time_bounds().end_time))\n", " \n", "bounds = gpx_track.get_bounds()\n", "print(\"Latitude Bounds: (%f, %f)\" % (bounds.min_latitude, bounds.max_latitude))\n", "print(\"Longitude Bounds: (%f, %f)\" % (bounds.min_longitude, bounds.max_longitude))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Name: Running 8/11/12 6:02 am\n", "Description: None\n", "Start: 2012-08-11T06:02:09\n", "End: 2012-08-11 07:31:42\n", "Latitude Bounds: (34.713744, 34.735524)\n", "Longitude Bounds: (-86.703750, -86.676670)\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Duration\n", "Duration returned in seconds." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx_track.get_duration()*1./60" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "89.55" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Distance\n", "What wat the length of the run?\n", "Length returned in meters. 2d and 3d distance is available." ] }, { "cell_type": "code", "collapsed": false, "input": [ "gpx_track.length_2d()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "14487.726193240629" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "gpx_track.length_3d()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "14495.595071043379" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Quick Visualization" ] }, { "cell_type": "code", "collapsed": false, "input": [ "track_coords = [[point.latitude,point.longitude, point.elevation] \n", " for track in gpx.tracks \n", " for segment in track.segments \n", " for point in segment.points]\n", " \n", "coords_df = pd.DataFrame(track_coords, columns=['Latitude','Longitude','Altitude'])\n", " \n", "fig = plt.figure(figsize=(12,9))\n", "coords_df.plot('Longitude','Latitude', color='#A00084', linewidth=1.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+n+I+RW1hlYJVEV1mqTFD66tr+UAiIickSRL6/m4opuybC5+e/tg+ZxW2/3oV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