asberk/Visualizing Strava Data.ipynb

## Visualizing Strava Data.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Import packages\n\nIn this file, we'll need `numpy`, `matplotlib`, `pandas` and a handy tool from `sklearn`. We'll also be using a gpx parser for python, `gpxpy`, which can be installed by entering the following into a cell:\n```python\n!pip install gpxpy\n```"
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "#%pylab inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.preprocessing import minmax_scale\nimport pandas as pd\nimport gpxpy",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from datetime import datetime",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Load Strava data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Find the file on your hard drive and store its location to the variable `gpx_fn`. Open the file using Python's `open` and then parse this file with the gpx parser. "
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "gpx_fn = './Sortie_v_lo_matinale.gpx'\ngpx_file = open(gpx_fn, 'r')\nsortie = gpxpy.parse(gpx_file)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Each strava file contains a single track with a single segment that is broken up into many points. Therefore, the course (`course`) we have ridden is just the set of points of our trip (`sortie`)."
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "course = sortie.tracks[0].segments[0].points",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Format the information of the points as a data frame so that we have easier access to the information. "
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "course = pd.DataFrame([[pt.latitude, pt.longitude, pt.elevation, pt.time.timestamp()] \n                       for pt in course], columns=('lat', 'lon', 'elev', 'ts'))",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "course.head()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lat</th>\n      <th>lon</th>\n      <th>elev</th>\n      <th>ts</th>\n      <th>cmap_r</th>\n      <th>cmap_g</th>\n      <th>cmap_b</th>\n      <th>cmap_a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>49.238055</td>\n      <td>-123.065509</td>\n      <td>88.2</td>\n      <td>1.498256e+09</td>\n      <td>0.479585</td>\n      <td>0.798462</td>\n      <td>0.76955</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>49.237999</td>\n      <td>-123.065511</td>\n      <td>88.3</td>\n      <td>1.498256e+09</td>\n      <td>0.479585</td>\n      <td>0.798462</td>\n      <td>0.76955</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>49.237950</td>\n      <td>-123.065508</td>\n      <td>88.4</td>\n      <td>1.498256e+09</td>\n      <td>0.479585</td>\n      <td>0.798462</td>\n      <td>0.76955</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>49.237911</td>\n      <td>-123.065507</td>\n      <td>88.5</td>\n      <td>1.498256e+09</td>\n      <td>0.479585</td>\n      <td>0.798462</td>\n      <td>0.76955</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>49.237875</td>\n      <td>-123.065504</td>\n      <td>88.5</td>\n      <td>1.498256e+09</td>\n      <td>0.479585</td>\n      <td>0.798462</td>\n      <td>0.76955</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         lat         lon  elev            ts    cmap_r    cmap_g   cmap_b  \\\n0  49.238055 -123.065509  88.2  1.498256e+09  0.479585  0.798462  0.76955   \n1  49.237999 -123.065511  88.3  1.498256e+09  0.479585  0.798462  0.76955   \n2  49.237950 -123.065508  88.4  1.498256e+09  0.479585  0.798462  0.76955   \n3  49.237911 -123.065507  88.5  1.498256e+09  0.479585  0.798462  0.76955   \n4  49.237875 -123.065504  88.5  1.498256e+09  0.479585  0.798462  0.76955   \n\n   cmap_a  \n0     1.0  \n1     1.0  \n2     1.0  \n3     1.0  \n4     1.0  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Plot data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Colourmap stuff"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For plotting reasons, I've wanted to define pair of functions that stores the colour information in columns in the data frame. Therefore, I will use `cmapFromTimestamp` to compute a colourmap from the timestamp information and use `addNewColumnsInPlace` to insert the colour information into the `course` dataframe. "
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def addNewColumnsInPlace(colNames, data, func, **kwargs):\n    res = func(data, **kwargs)\n    for j, cn in enumerate(colNames):\n        data[cn] = res[:, j]\n    return",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def cmapFromTimestamp(data, cmap='Blues', columnName='ts', scale_range=(0,1)):\n    return plt.cm.cmap_d[cmap](minmax_scale(data[columnName], \n                                            feature_range=scale_range))",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "colourCols = ['cmap_' + x for x in 'rgba']\naddNewColumnsInPlace(colourCols, course, cmapFromTimestamp, \n                     cmap='GnBu', scale_range=(.5,1))",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "variables": {
          "print('{}, {}, {}, {}'.format(*colourCols))": "cmap_r, cmap_g, cmap_b, cmap_a"
        }
      },
      "cell_type": "markdown",
      "source": "Above, we have created four new columns, `{{print('{}, {}, {}, {}'.format(*colourCols))}}`, that contain the colour map information for our data frame."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## More colourmap stuff"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Instead of using the colourmaps developed above, I want to use a line plot, so I have to do this nonsense in order to colour each indvidual little segment."
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "latlon = course[['lat','lon', 'elev']].values.reshape(-1, 1, 3)\nlatlonsegs = np.concatenate([latlon[:-1], latlon[1:]], axis=1)\nmyCmap = plt.cm.cool(minmax_scale(course['ts'], feature_range=(.1, .9)))",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Make elevation plot as a function of lat-lon"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Lastly, use the `Axes3D` object to plot a 3D elevation plot of the route. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig = plt.figure(figsize=(10,6))\nax = plt.gca(projection='3d')\nfor j in range(latlonsegs.shape[0]):\n    if j in [0, latlonsegs.shape[0]-1]:\n        ax.plot(latlonsegs[j, :, 0], latlonsegs[j, :, 1], latlonsegs[j, :, 2], c=myCmap[j,:], \n                label='t={}'.format(datetime.fromtimestamp(course['ts'].values[j]).time()))\n    else:\n        ax.plot(latlonsegs[j, :, 0], latlonsegs[j, :, 1], latlonsegs[j, :, 2], c=myCmap[j,:])\nax.view_init(25, 225)\nax.set_xlabel('Latitude')\nax.set_ylabel('Longitude')\nax.set_zlabel('Elevation')\nax.set_zlim(0, 120);\nax.legend();\nplt.axis('tight');\nax.set_title('Ride home from UBC');",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FcE0NRJGRLfn+XlKJW71rKtF6EwqF5ODYj2QbC5RqEpT0P/kSdr1xhWnXQSZX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\nOnUCoKioiKNHj8bku+RKy1laJhqR0VLV6VQvyhguhw6bGDQoedEdtUy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J8MQ6k0p+81VW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x6AUaNGUVdXR01NDUVFRT4rQ35+PgMHDuTw4cO+320hGJKSZAVPlmYIgsDQoUMZOnQoDzzwANXV\n1Sxbtow5c+Zw7NgxxowZQ1lZGcOGDUvbATvYso3NZlM9buXkZrfbM3JpQxaCZrM56OQW70hYsChO\nTk5Owq6FcnkM/KMdTqdTN1l6iUJNIMvmZ7vd7pcebjQa/a6hfO/k5eVFdO9IksTx48d9tXGqq6sZ\nM2YMV155JU8//bTvGqUShw8fpnv37r6/d+vWjc2bNwf9TNeuXTl8+DCdOnXy/du+ffvYvn07o0aN\n8v3b888/z5tvvsmIESN44oknKCwsjOORJIbUu8JZEk5RURE33ngjN954I06nk7Vr17Jw4ULuvfde\niouLKS0tZeLEiSnd3E6rJYPJZCI3NzekAVY5eeXk5Phtz263666nU7xJdPQnkVGcSFGLdrjdbpxO\np08Q6nk5MNYonxn5/nC5XDidTl+EwWg0YrPZMJvNYZ8PURT58ssvWbZsGatXr8Zms1FWVsbjjz9O\n79690/78hkJ9fT2XX345zzzzjG8Mv/XWW5k7dy6CIHD//fcze/Zs/v73vyd5T6MnK3iyhIXVamXq\n1KlMnToVSZL48ssvWbx4MVdddRVWq9XX6V0OseoZLbNqrCbIwMlNj0s9iUQZ/QF80bBIoz/BojhW\nqzWitONEohSE4B9VbGhoAPSZ6h1LtBICrFarLyNO7aUh2PKX3W7n448/prKykm3btjFo0CAuvvhi\nZs+eTUFBQRKOMn507dqVAwcO+P5+6NChZraDrl27cvDgQdXPeDweLr/8cq677jouvfRS32eUBWtv\nvvlmZsyYEa9DSChZ03KWmHH8+HGWLVvG0qVLOXz4MBdeeCGlpaWcf/75uph8WsoeSeQ+KvcjFYy+\n8SbUzC+tKE66LQnpKdU71kRaGyfwHnG73cyePZvRo0czfPhwduzYwfLlyzl58iTjx4+nvLxcN2NP\nvPB6vQwYMICqqio6d+7M+eefzzvvvMPAgQN9n1m2bBkvvPACH374IRs3buTOO+9k48aNAFx//fW0\nb9+eJ5980m+71dXVPn/PU089xZYtW/jHP/6hug+7du3iyJEjvpdgHdyXmZel5fV6NSeOffv2UVtb\ny3nnnZeEPcsM3G43n3zyCUuWLGHjxo306dOH0tJSJk+enNC1YDVhobdJI9CQma6TeDgoa6nIJnGg\nmS8qnSczJUqvS6qlvmvtezTRTVEU2bp1K2+99RZffvklW7dupaCggOnTp/Pzn/+cCRMmpPQSezhU\nVlZyxx13+NLS77vvPl588UUEQeCWW24B4Pbbb6eystKXln7eeeexfv16xo0bx5AhQxAEAUEQfOnn\n119/Pdu3b8dgMNCrVy9efPFFP88PNC2FPfbYYzz99NMAnDp1Si9jVeYJHiXygGkwGBBFkYqKCh5+\n+GG2bNnCZ599xjnnnJPsXaS2tpYrr7yS/fv306tXLxYuXKgqDG666SaWLl1Kp06d2LlzZ9i/nyz2\n7NnDkiVLWL58OYBv6euss86K6fekek2UeKdMpwJqyxzycXu9XiAzqz4r0Xsfr3g07W1oaGDNmjVU\nVFSwa9cuzj33XMrLy5k0aRK5ubls27aNjz76iI8++ohx48Yxb968OBxZ5iKKIpIk+b1krF27llGj\nRjF69Gheeuklhg8fnsQ99JFZgqeiooKtW7fyi1/8wi89D/ClDa9bt44//elPvPLKK34O9mQxZ84c\n2rVrx7333sujjz5KbW0t8+fPb/a5devWkZ+fz/XXX+8neEL9fT1QW1tLZWUlS5cu5fvvv2fUqFGU\nlpZywQUXhF3US6vnUbosD6VSt/dICSby1KI4WlG7TDGDB6KHPlfxqI0jSRKHDh3yZVXV19czceJE\nysvL0zpDVC+cOnWK1q1bq/5MHmcbGxvJzc3lhhtuoEePHsybN08Py1qZJXg+//xz3nzzTTZu3Igo\nilxwwQVMnz6dadOm+T7z0EMPUVdXx5/+9CfVnlIbN27k/fffZ8qUKUyZMiXu+1xcXMzatWvp1KkT\n1dXVTJgwga+//lr1s/v372fGjBl+giec39cTHo+HDRs2sGTJEtatW0e3bt0oKytjypQptG3bVvV3\n9NqAM97ordt7NLRUvTrUaxgsohdJVk86EOtCf1po1caJ5nu8Xi+ff/45FRUVrFu3jg4dOvg6jnfu\n3Fm31zMe1Y6TGbUvKSmhV69ePPfccz4D886dO3n66af54YcfuPrqq/nZz37m+9nSpUt55JFHWL9+\nfVbwJJO9e/eyaNEiVq5cyW9+8xsuueQSDAYDl156Kddddx2XX355s9+x2+1s2bKFZcuWsXXrVqxW\nK88++yy9e/eO28Vs27YtJ0+e1Py7EjXBE87v65nvvvuOpUuXUlFRgcvlYvLkyUycOJGjR4+yYsUK\nJk2aRElJSbMoTiYSWNFXz5GOcKM4kZKN/vgTrNxCJOckHvfcmTNnqKqqorKykq+//prhw4dzySWX\nMGHCBGw2W9jbSzTxqnaciKh9bW0tVqvVr5r+jh07mDVrFsOHD+dnP/sZY8eO5fTp0zz55JOcf/75\n9OvXj7/85S8YjUYef/xxoKmIY3FxMatXr6ZXr14x3ccIyKxKy8q1xr59+3L33Xdz9913+8q8f//9\n95w5c0bTu2Oz2Rg7dizjxo0D4JJLLmH9+vX07t2b1atX8+qrr9K/f38uv/xyBg0aFLIImjJlCjU1\nNb6/y7/30EMPNftstANzqg7sZ511FrNmzWLatGksWrSI9957jz/+8Y/06dOHwYMHc+bMGaxWK1ar\nNdm7mnTUKvq63W7dpL0HSzmO1z6FUvcnk6I/odZC0ooSBi6XyYUBgxXgbAlJkti3b5+v47jT6WTy\n5Mncc889DB48OOVeYOJV7XjRokWsXbsWgJkzZzJhwoSYCJ66ujpeffVV3nvvPVwuF9OmTePqq6/2\nzYdHjx6lZ8+eFBQU8PXXXzN27Fjq6upYuHAh7dq146WXXmL//v3MnDnT91xbrVZGjBjBli1b6NWr\nly/LSwfRHj/SUvAoHxj5gVVWO926dSu9e/f2qzUQiCAIfm9DrVq1AppCfSaTiWXLljFz5kyeeOIJ\nnzBqiRUrVmj+rFOnTtTU1PiWpOSmbaES7e/riS+++IKLL76YadOm8fvf/55JkyZRWFjIli1bWLx4\nsS9jYNq0aUydOjWljzVWBFb0VatxE29fU7Au83JdlUQOfoF1f+QIU6YWggTtWkjKcyJ7w+RrKd9b\nOTk5EfvGPB4PmzZtorKykk8//ZSuXbty8cUX8+abb9KhQ4eUPvexrnZcUlICNAkP+edFRUUcPXo0\nov1zuVxs27aNHj160LlzZ44cOcL+/ft58cUX6dGjB88++yzPPvssr7zyCgD//Oc/uffee1m+fDm1\ntbVAUwSnsLCQ7du3M3fuXIYNG+bbtsViobGxkaNHj3LllVfy4IMP8uCDD3LllVfq7rqmpeBRIj+s\n0OTLqaysZMWKFcycOZM2bdqo/o6sSs+cOcOCBQs4ePAgF110EdDUc2rcuHGMGzeOV155hQ8//JDR\no0f7QvKiKAKE/ZZyySWX8NprrzFnzhxef/11vyJQavsXuBQZzu/rnSFDhnDgwIFmD0tJSYlvMDhw\n4ABLly7ltttuo6GhgYsuuojS0lIGDx6su4csGWi91csF7WLl6QiM4sjb1lN1YxmDweCLDoYb6UhX\n5PvEbDb7xKDL5fL7ucViCfucSJJEXV0dK1asoLKy0pecMGPGDB555BEsFks8DidlUVY7zsvLU/1M\nJK00FixYwJ///GeKi4tp3749L7zwAgMHDmT27Nn06NEDaBpvT5486TMpWywWBg8ezIEDB/jb3/6G\n3W5nzJgxTJw4kYKCAp/YWbx4MT179mTAgAE8+uij9OrViwcffJCJEydGdzLiSNoLHiX9+vVj8eLF\nOJ1O5s6dy44dO3j22Web+QcEQeCDDz7g73//Oz179uSNN97wVejcvXs3CxYsoHXr1hw5coRWrVr5\nGkgq02dlTp48ydGjR5tliwUyZ84crrjiCl599VV69uzJwoULAfjxxx+5+eabWbp0KQDXXHMNa9as\n4cSJEz5X/A033KD5+6lIKA92jx49uPXWW7n11ltpaGhg5cqVvPzyy+zatYuhQ4cybdo0xo8fr2pI\nzzSUb/XKlhdyO4Nw0t71FsWJlEyP/mjVxlEKVaX52eFwtGgslySJvXv3UlFR4YtmT506lblz51Jc\nXJx251AmXtWOo43aNzQ08Prrr7N69Wp69OjBf/3Xf/Hcc89x44030qNHD5xOJ1arlU8//ZR27drR\nunVrFi9ezO7duykvL2fXrl00NDRQVFTEpEmTaNWqFQ888ADr16/nwIEDdO7cmSeffJKcnJyUKQGQ\n9qZlLY4fP87u3bsZP348W7Zs4eWXX+all16irq6O//7v/+bo0aPcdNNNjB071k8Q2e12Kisr+cMf\n/sDs2bP59a9/jcfjoaqqigULFiAIAr/5zW+YMGGCr+/U3LlzcTgcjBs3jj//+c++5bEssUcuSLZk\nyRJWr15NmzZtmDZtGtOmTaNz587J3j3doVW5WFnPRSuKk65Zcaley0mLaEocBHp5vvjiCx5++GEm\nTpxIp06d2LFjB5s3b6Z3796Ul5dTVlZG27Zt0+7eUCNe1Y7nzJlD27ZtmTNnjqZp+eDBgz5Tsxyx\nkTlw4AC/+93vuOuuuygpKeF///d/efrpp5k3bx6TJk0CoKamht/85jfMnj2bcePGceTIER5++GEu\nvPBCJk+ezCuvvEJOTg6zZ88G4PTp02zYsIGSkhJd1XkLILNMy6HQvn17xo8fjyRJjBw50nfxXnzx\nRZ566ikuu+wyCgoKmg1uNpuNn/3sZxQUFPDhhx8C8NJLL7F7927uv/9+Tpw4wWuvvUbPnj3p3bs3\no0aN4tNPP+X06dPcfffdLFmyhGuuuSbhx5spGAwGRowYwYgRI5g3bx5Hjhxh6dKlzJ49m9raWsaN\nG0dZWRlDhw7NiMG4JQRBUDU+y52rZcEjL3ukUhQnUkKN/ui9FlKw2jjhdoyXhbDRaKShoYG9e/fS\noUMHVqxYwbZt27DZbMyYMYPp06czadKktOtZFQyj0cjzzz/P1KlTfWnpAwcO9Kt2PH36dJYtW0bf\nvn19aekA69ev5+2332bIkCGcd955ftWOQ4nar1ixgvfee4+LL76Ya6+9FvjJkmGz2ejRowfPP/88\ngwcPZteuXRQVFbFlyxaf4Fm9ejUWi8XnQ+3SpQvPP/+8b/t9+/bFbDZjt9ux2WwUFBT4lXdJNTI2\nwhOMb7/9lpUrV/KPf/yDvn378txzz3H8+HG/dhQVFRXMnz+fNWvWMHXqVI4dO0ZRURGXXXYZjz32\nGG+99RYXXHAB8FOxw6uvvppBgwbxwAMP6M69ngnY7XZWr17NkiVL2LZtG2effTalpaVcdNFFfmmZ\nmYhaFEeO3ni9Xj8Dv94n+nii9xYP8aiNI4oiX3/9NZWVlVRVVWE2myktLaW8vJx+/foB+H6+fPly\nZs2axfTp02N6XFn8kZ/HF198kVWrViFJEgsXLmw2rxw+fJi//OUvrFq1ijFjxtC+fXuMRiOzZs3C\nZDIxffp0/vM//xO3201VVRW33HILw4YN85mRU5TMrcMTLfI65+LFi5k7dy4dO3bkrLPOor6+nrFj\nx3LZZZcxe/ZsHnroIY4fP84HH3zA999/zz333ONXZvv48eOMHj2at956i5EjRybxiLJA08Swc+dO\nlixZQlVVFbm5uUydOpWysjK6deuW7N2LO8G8OGqZXMEm+kxJ8VYjsBFmMkRhPGrjOJ1O1q1bR0VF\nBZ999hkDBgygvLycadOmUVhYqKvrHW7RvwULFvheXLVa9cybN4+XX37Z55uRoy7JQJIk1q9fz/79\n+/nlL3/pEyMHDhzgnnvu4bnnnqOkpITdu3ervrgpxcvtt99OcXExt99+O//85z+58sorGTZsGMXF\nxfziF7+gvLw87Gr3OiQreGLFunXrWL9+PTNmzKC4uBhRFLnxxhv5+c9/zmWXXXDoiswAAB87SURB\nVOb7nBzVaWxsZNGiRbzwwguUl5dz3333ZaM7OqSmpsbX6b2mpobRo0czffp0hg8fnvS39lgRy7d/\ntYk+Xdp5REqioj9atXGi+R5Jkjh69CjLly9n+fLlHD16lLFjx1JeXu4rxaFHoin6B9qteubNm0er\nVq183pVk4vF4WLhwIZWVlbzxxhu+f9+0aRMrVqzg/vvvZ8yYMdxzzz2MHTuWdu3a+eYYURQxGAwc\nP36czZs388Ybb3DfffcxdOhQDh48yIEDBxg9enQSjy4uZD08sWLMmDGMGTPG93eDwcCMGTP405/+\nxJtvvsmQIUO4+eab6dq1Kxs3buSvf/2rr7jghAkTgNQtCpjOdOrUiRtuuIEbbrgBp9PJxx9/zL/+\n9S/mzJlD//79KSsrY+LEiSllONeqsms2m8nLy4tKmGileMc67T2VCKyFFEvvj1YRx2hq44iiyK5d\nu6ioqGD16tXk5eUxffp0nnzySXr16pUS1y2aon+dOnVizJgx7N+/X3XbLQQDEoIsZidMmMCGDRv4\n/vvvqa6uxuPx8O6779K1a1eWLl3Kjz/+yM9//nMeeeQR5syZgyAInDx5krZt27Jv3z5mzJhB165d\nueKKKzj33HMB6N69uy76SCaSrOCJAf/x/9u787CqyvX/4++9FQkBBbEYMjWHZBAlFDFyIJk2Y5iK\nWWRmkgOmpiUNlnoiNbPBcshTmqV20JMeZdo7xQIVBMsENSS0EtQYTJyYp/X7g99eX1BIZBR4Xtfl\ndSmshWsz7H3zrOf+3JMm4eXlhVqtJiUlBWNjY3bv3s2kSZNYuHAhH3zwQZt48hCq6OrqyjPUJEni\nzJkz8mbzzp074+HhgUqluhci1G9T1ypOc+biNGXbe3tya1FYfUN4fVZ/bl1Fq97+39BRHEVFRcTF\nxaHRaEhOTsbGxgZfX19effXVNlXMazVV6F9t1q1bx7Zt2xg+fDgffvhhq3QlKZVK8vPziYyMJDY2\nlujoaB566CHmzJmDhYUFH3/8MV5eXsyePVvOYYOq8UPz5s1jz5499O3bl+PHj7flPTlNRhQ8TURf\nX5+JEyfKs7keffRRFi5cyJkzZxg+fDijR4/m3XffbZNPKh2ZQqHA2toaa2trQkJCuHLlCmq1muXL\nl3PhwgWcnJxQqVSMGDGiVZb9m3MVpyHqGmVQVlZGSUlJrW3vHUF9Vn86deqEUqmUv57aoqhLly50\n7dq1QZ8rSZLIysqSNxRfvXoVZ2dnpk+fjoODQ5PNMGtv5syZwzvvvINCoWDJkiUsXLiQzZs3t/h1\n/P3334wcORJHR0cef/xxlEolGzZsAKr2WS1ZskQ+9rPPPuPkyZMMGTKEPn36sG/fPqDqe0AUO1VE\nwdNM+vXrJw9Wy8jI4PTp0x3qt9v2ysTEhMDAQAIDAykrKyM+Pp7w8HDefvttHn74YVQqFa6urhgZ\nGTXbNbTGKk5DVV/9qb73pLi4uEab9L3Q4dSStKs/Ojo68giQ2hKOG7IqVllZyYkTJ1Cr1Rw6dAhj\nY2O8vLzYsGEDvXr1uqe+PxqrsaF/dak+digoKAhfX98muuK707NnT86dOwdAeno6q1ev5vLly9x/\n//3yPEFtY01KSkqtzzvt6evdWKLgaQF9+vSR7zF3JFevXmXy5MlkZGTQt29fdu3aVeuycFvolKiN\njo4Ozs7O8t6s9PR0IiIimDZtGpIk4erqikqlklt3G+peW8VpqNpWObSFW1FREUqlst5BeG1VXd1x\n1cc3VH9/cXFxvfb+FBQU8MMPP6DRaPj111+xs7PDx8eHt956q85RBe2Bg4MD586dIyMjA3Nzc8LC\nwvjPf/5T4xg/Pz/Wr1/P5MmTSUxMxMjIqMbtrNpG9WiHXwLs2bOHwYMHN/+DuYNOnTpRWVnJyZMn\n5RwdQC58mvOXrPZCdGkJzSYkJAQTExM5CbS2pFBoG50Sd+vatWtoNBoiIyP5/fffGTFiBCqVCicn\np3q1fda2itPa08+bU/XVn7Kysnsu36YxGtMdd2vn15UrV3j77bdxc3PD1taWY8eOsX//fgoLCxk3\nbhy+vr7Y2dm16c/X3dJoNMyfP19uS3/99ddrhP5BVTu2RqOR29K186Cqj+oxNTWVR/VMnTqV5ORk\nlEolffv2lQcWt6aysjJu3LiBiYlJq15HGyDa0oWWZ2lpSVxcnDwLxtnZmbS0tFqPzcjIwNfX97aC\nx8DAgEWLFrXUJTeLiooKjh49SkREBEeOHOHBBx+UJ71rn7wqKyvJycmhR48eotWbuvNt2spsq9qy\ncRr7tayoqCA+Pp5du3aRmprKiRMnsLCwYPz48UyaNIkRI0aIPTmCIAoeoTX06NGDvLy8Ov9dXV0F\nz9atW+nevXurdko0tT/++IPIyEgiIiLkpfPffvuN7t278+OPP7bbVZyGqm221b3W9t5c2Tg3b97k\n4MGDqNVq0tPTcXBwwNfXl7Fjx9KpUyfi4+NRq9Wo1WqCgoKYN29eMzw6QWhTRMEjNA83NzdycnLk\nf2sDr0JDQ5k2bVqNAsfExIQrV67U+nFqK3guX75Mz5495U6JrKysVumUaGrbtm1j+/btJCQk8Oij\nj9K/f3+USiWnT59m2LBheHp6MmrUKPnevPB/7pQQ3ZIrHHVl4zSmA02SJP7880+5q6q8vBw3Nzd8\nfX2xsbH5x8JJGzLXkpoj5bi+e/8EoQ6i4BHu3s6dO5kwYUKD262trKyIjY2Vb2k98cQTnDlzptZj\nayt47ub9bcmOHTvQ09PD1dW1xpDFyspKfvrpJyIiIoiNjeWBBx6QJ71rN24LNdVVdDTX6k9d2TiN\nKbbKyspISkpCo9GQkJDAQw89hI+PD15eXnLBfy9qrpTj+u79E4Q6iKRl4e4UFxczZcoUKisrG/wx\n/Pz85DCsr7/+mieffLLOY9tKp0RTePbZZ2t9u1KpxNHREUdHRwAuXLhAZGQkc+fOJT8/H2dnZ1Qq\nFba2tvfsi2BLu7XtXVuQVA89bEzbe13jIhqbjXPt2jUOHDiARqPhzz//ZOTIkfj5+bFq1ao2k5nS\nXCnH+/btIy4uDoDnn38eZ2dnUfAITUIUPEKtkpKS5Bdm7W+yWhUVFSgUiju+gISEhBAQEMCWLVvo\n06cPu3btAiArK4ugoCAiIyOBmp0SvXv3ljslFi9efFunREfy0EMPMXv2bGbPnk1BQQEHDx5ky5Yt\ncriYSqVi7Nix6Onptfal3hOqhx5Czbb34uJiuTi6U9t79fPKy8tRKpV07twZPT29Rt2qSk9PR6PR\nEBMTg0KhwMPDg6VLl2JpadkmC9jmSjnOzc2V329mZkZubm4TX3mVyspKFApFm/zcCw0jCh6hVv/+\n97955plngNuDq+q7dN+jRw9iYmJue7u5ublc7AB8++23tZ5ffVBeR6evr4+fnx9+fn5IksSJEyeI\niIhg7dq1dOvWDZVKhUqlwtzcvLUv9Z6hVCrp0qULXbp0qbGp+NbRDtrv51v3BWlHZTR0X0xpaSkJ\nCQmo1WqOHTtGv3798PHx4b///S/Gxsbihbaemuvz1JFa94UqouARbiNJEtHR0ezYsQP4vyeGCxcu\n8PHHHwNV88Mee+yxGhsltbcU2kLbcFumUCiwt7fH3t6epUuXkpWVRVRUFIsWLSIvL4/Ro0fj6enZ\n4fJY/kn1vT333XcfF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            "text/plain": "<matplotlib.figure.Figure at 0x11e2af3c8>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Notes"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "It should be possible to do this using these tools below"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "# from matplotlib.colors import ListedColormap, BoundaryNorm\n# from mpl_toolkits.mplot3d.art3d import Line3DCollection",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "but for the life of me I cannot figure out how. "
    }
  ],
  "metadata": {
    "toc": {
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      "sideBar": true,
      "navigate_menu": true,
      "moveMenuLeft": true,
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      "colors": {
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        "sidebar_border": "#EEEEEE",
        "navigate_text": "#333333",
        "navigate_num": "#000000"
      },
      "nav_menu": {
        "height": "12px",
        "width": "252px"
      }
    },
    "kernelspec": {
      "name": "conda-root-py",
      "display_name": "Python [conda root]",
      "language": "python"
    },
    "language_info": {
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "name": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2",
      "nbconvert_exporter": "python",
      "mimetype": "text/x-python"
    },
    "anaconda-cloud": {},
    "gist": {
      "id": "5cea79b3859f063454791b78cd882a16",
      "data": {
        "description": "VisualizingStravaData.ipynb",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/5cea79b3859f063454791b78cd882a16"
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  "nbformat": 4,
  "nbformat_minor": 1
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