just-felt-like-running.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I Just Felt Like Running\n",
    "\n",
    "### Running on 365 consecutive days for no other reason"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "March 2017 was a bad month for my running -- I only ran [four times](http://smashrun.com/ixjlyons/overview/2017/3). By early April, I decided it would be cool to try to go on a little run streak to get things going again. At first, I think the goal was 10 days straight. And after that, 20 days. And then, I decided to make my longest streak longer than my longest break (34 days). You get the point. Now here we are, 365 days later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![I just felt like running](http://i.imgur.com/lz6mc3v.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use Runkeeper on my phone to track GPS coordinates of every run I go on, so in commemoration of an aribtrary set of 365 consecutive days, I thought it'd be cool to take a look at the data.\n",
    "\n",
    "On Runkeeper, you can go to Settings, then Export Data, and select the appropriate date range. This gives you a zip file containing a CSV file (`cardioActivities.csv`) summarizing every tracked activity, and a [GPX](https://en.wikipedia.org/wiki/GPS_Exchange_Format) file for each activity containing the GPS coordinates sampled every few seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "I'll start with `cardioActivities.csv` to just get a summary of how the year went."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Route Name</th>\n",
       "      <th>Distance (km)</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Average Pace</th>\n",
       "      <th>Average Speed (km/h)</th>\n",
       "      <th>Calories Burned</th>\n",
       "      <th>Climb (m)</th>\n",
       "      <th>Average Heart Rate (bpm)</th>\n",
       "      <th>Notes</th>\n",
       "      <th>GPX File</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-07 09:21:31</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.65</td>\n",
       "      <td>18:14</td>\n",
       "      <td>5:00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-07-0921.gpx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-06 07:30:17</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.66</td>\n",
       "      <td>13:57</td>\n",
       "      <td>5:15</td>\n",
       "      <td>11.43</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-06-0730.gpx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-05 18:12:03</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.68</td>\n",
       "      <td>30:00</td>\n",
       "      <td>5:17</td>\n",
       "      <td>11.35</td>\n",
       "      <td>491.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-05-1812.gpx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-04 07:53:16</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.37</td>\n",
       "      <td>27:36</td>\n",
       "      <td>5:09</td>\n",
       "      <td>11.66</td>\n",
       "      <td>464.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-04-0753.gpx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-03 09:02:23</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.61</td>\n",
       "      <td>28:27</td>\n",
       "      <td>5:04</td>\n",
       "      <td>11.83</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-03-0902.gpx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date     Type  Route Name  Distance (km) Duration  \\\n",
       "0  2018-04-07 09:21:31  Running         NaN           3.65    18:14   \n",
       "1  2018-04-06 07:30:17  Running         NaN           2.66    13:57   \n",
       "2  2018-04-05 18:12:03  Running         NaN           5.68    30:00   \n",
       "3  2018-04-04 07:53:16  Running         NaN           5.37    27:36   \n",
       "4  2018-04-03 09:02:23  Running         NaN           5.61    28:27   \n",
       "\n",
       "  Average Pace  Average Speed (km/h)  Calories Burned  Climb (m)  \\\n",
       "0         5:00                 12.00            315.0          2   \n",
       "1         5:15                 11.43            229.0          2   \n",
       "2         5:17                 11.35            491.0          5   \n",
       "3         5:09                 11.66            464.0          4   \n",
       "4         5:04                 11.83            483.0          1   \n",
       "\n",
       "   Average Heart Rate (bpm) Notes             GPX File  \n",
       "0                       NaN   NaN  2018-04-07-0921.gpx  \n",
       "1                       NaN   NaN  2018-04-06-0730.gpx  \n",
       "2                       NaN   NaN  2018-04-05-1812.gpx  \n",
       "3                       NaN   NaN  2018-04-04-0753.gpx  \n",
       "4                       NaN   NaN  2018-04-03-0902.gpx  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "datadir = 'activities'\n",
    "summary_csv = os.path.join(datadir, 'cardioActivities.csv')\n",
    "\n",
    "activities = pd.read_csv(summary_csv)\n",
    "activities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data needs a little cleaning up by removing cycling activities (tracked during [May is Bike Month](https://mayisbikemonth.com/)) and converting the `Date` column to a proper datetime type. I also want to aggregate data for days on which I ran multiple times (usually to and from a location), so I can add a separate column for the full date+time and remove the time from the date column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "      <th>Route Name</th>\n",
       "      <th>Distance (km)</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Average Pace</th>\n",
       "      <th>Average Speed (km/h)</th>\n",
       "      <th>Calories Burned</th>\n",
       "      <th>Climb (m)</th>\n",
       "      <th>Average Heart Rate (bpm)</th>\n",
       "      <th>Notes</th>\n",
       "      <th>GPX File</th>\n",
       "      <th>Datetime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-07</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.65</td>\n",
       "      <td>18:14</td>\n",
       "      <td>5:00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-07-0921.gpx</td>\n",
       "      <td>2018-04-07 09:21:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.66</td>\n",
       "      <td>13:57</td>\n",
       "      <td>5:15</td>\n",
       "      <td>11.43</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-06-0730.gpx</td>\n",
       "      <td>2018-04-06 07:30:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-05</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.68</td>\n",
       "      <td>30:00</td>\n",
       "      <td>5:17</td>\n",
       "      <td>11.35</td>\n",
       "      <td>491.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-05-1812.gpx</td>\n",
       "      <td>2018-04-05 18:12:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.37</td>\n",
       "      <td>27:36</td>\n",
       "      <td>5:09</td>\n",
       "      <td>11.66</td>\n",
       "      <td>464.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-04-0753.gpx</td>\n",
       "      <td>2018-04-04 07:53:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>Running</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.61</td>\n",
       "      <td>28:27</td>\n",
       "      <td>5:04</td>\n",
       "      <td>11.83</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-03-0902.gpx</td>\n",
       "      <td>2018-04-03 09:02:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Type  Route Name  Distance (km) Duration Average Pace  \\\n",
       "0 2018-04-07  Running         NaN           3.65    18:14         5:00   \n",
       "1 2018-04-06  Running         NaN           2.66    13:57         5:15   \n",
       "2 2018-04-05  Running         NaN           5.68    30:00         5:17   \n",
       "3 2018-04-04  Running         NaN           5.37    27:36         5:09   \n",
       "4 2018-04-03  Running         NaN           5.61    28:27         5:04   \n",
       "\n",
       "   Average Speed (km/h)  Calories Burned  Climb (m)  Average Heart Rate (bpm)  \\\n",
       "0                 12.00            315.0          2                       NaN   \n",
       "1                 11.43            229.0          2                       NaN   \n",
       "2                 11.35            491.0          5                       NaN   \n",
       "3                 11.66            464.0          4                       NaN   \n",
       "4                 11.83            483.0          1                       NaN   \n",
       "\n",
       "  Notes             GPX File            Datetime  \n",
       "0   NaN  2018-04-07-0921.gpx 2018-04-07 09:21:31  \n",
       "1   NaN  2018-04-06-0730.gpx 2018-04-06 07:30:17  \n",
       "2   NaN  2018-04-05-1812.gpx 2018-04-05 18:12:03  \n",
       "3   NaN  2018-04-04-0753.gpx 2018-04-04 07:53:16  \n",
       "4   NaN  2018-04-03-0902.gpx 2018-04-03 09:02:23  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities = activities[activities.Type == 'Running']\n",
    "activities['Datetime'] = pd.to_datetime(activities['Date'])\n",
    "activities['Date'] = activities['Datetime'].dt.normalize()\n",
    "activities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the aggregation. Distance should be accumulated by adding up all runs for a day, whereas it makes a bit more sense to just average the speed for different runs on a day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance (km)</th>\n",
       "      <th>Average Speed (km/h)</th>\n",
       "      <th>Calories Burned</th>\n",
       "      <th>Climb (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.269151</td>\n",
       "      <td>11.612251</td>\n",
       "      <td>628.715068</td>\n",
       "      <td>11.394521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.485533</td>\n",
       "      <td>0.927397</td>\n",
       "      <td>390.872166</td>\n",
       "      <td>33.269181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.870000</td>\n",
       "      <td>4.520000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.540000</td>\n",
       "      <td>11.230000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.060000</td>\n",
       "      <td>11.650000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.290000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.220000</td>\n",
       "      <td>14.950000</td>\n",
       "      <td>3654.000000</td>\n",
       "      <td>383.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Distance (km)  Average Speed (km/h)  Calories Burned   Climb (m)\n",
       "count     365.000000            365.000000       365.000000  365.000000\n",
       "mean        7.269151             11.612251       628.715068   11.394521\n",
       "std         4.485533              0.927397       390.872166   33.269181\n",
       "min         1.870000              4.520000        64.000000    0.000000\n",
       "25%         4.540000             11.230000       393.000000    2.000000\n",
       "50%         6.060000             11.650000       523.000000    4.000000\n",
       "75%         9.290000             12.100000       816.000000    9.000000\n",
       "max        42.220000             14.950000      3654.000000  383.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities = activities.groupby('Date').agg({\n",
    "    'Distance (km)': np.sum,\n",
    "    'Average Speed (km/h)': np.mean,\n",
    "    'Calories Burned': np.sum,\n",
    "    'Climb (m)': np.sum,\n",
    "}).reset_index()\n",
    "\n",
    "activities.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There, now we have exactly 365 rows. One measurement of interest is the total distance run for the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2653.24"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities['Distance (km)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of distance between state capitols, that's pretty close to Google's estimate of the [walking distance from Sacramento, CA to Topeka, KS](https://www.google.com/maps/dir/sacramento/topeka/@39.2229955,-117.5752175,5z/data=!3m1!4b1!4m14!4m13!1m5!1m1!1s0x809ac672b28397f9:0x921f6aaa74197fdb!2m2!1d-121.4943996!2d38.5815719!1m5!1m1!1s0x87bf02e4daec7a29:0xbe2be7d06ae3a7f0!2m2!1d-95.6890185!2d39.0558235!3e2).\n",
    "\n",
    "Aside from total distance, I've been interested in seeing my run distance per day for the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'distance per day (km)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "\n",
    "ax = plt.figure(figsize=(10, 5)).gca()\n",
    "\n",
    "ax.plot(activities['Date'], activities['Distance (km)'])\n",
    "\n",
    "def annotate_date(label, date, y, ax, **kwargs):\n",
    "    def datestr2num(s):\n",
    "        return mdates.date2num(datetime.datetime(*(int(i) for i in s.split('-'))))\n",
    "    ax.annotate(label, xy=(datestr2num(date), y), **kwargs)\n",
    "\n",
    "ax.axvspan('2017-07-10', '2017-07-16', color='k', alpha=0.2)\n",
    "annotate_date('Scipy 2017', '2017-07-16', 30, ax)\n",
    "\n",
    "annotate_date('Marathon 1', '2017-11-11', 42.2, ax)\n",
    "\n",
    "ax.axvspan('2018-01-07', '2018-01-10', color='r', alpha=0.2)\n",
    "annotate_date('flu :(', '2018-01-10', 15, ax)\n",
    "\n",
    "ax.set_ylabel('date')\n",
    "ax.set_ylabel('distance per day (km)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that I started out without dipping below 5km/day very much, had a couple rough days (SciPy 2017), then started training for my first marathon. After the marathon, things started falling apart and I started taking more 2-3km days. I also started lifting weights pretty seriously in December, which really saps day-to-day energy levels. 2018 also started off great with a pretty gnarly flu of some kind. I'm actually not sure if running with the flu for a few days was easier or harder than running the day after the marathon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the GPS Data\n",
    "\n",
    "I had been planning to do some neat analyses, but I'm going to leave it alone for now with a pretty picture that I can use for my Twitter banner or something.\n",
    "\n",
    "I've run in a few places other than my home town, so I'll narrow things down to a bounding box around Davis, CA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlim = -121.8045788, -121.674863,\n",
    "ylim = 38.512625, 38.5765569"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need to re-read the summary CSV file because I got rid of the \"GPX File\" column earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "activities = pd.read_csv(summary_csv)\n",
    "activities = activities[activities.Type == 'Running']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I can make use of a nice library called [gpxpy](https://github.com/tkrajina/gpxpy) to parse the GPX files and extract the GPS coordinates from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpxpy\n",
    "\n",
    "def loadgpx(fpath):\n",
    "    with open(fpath, 'r') as f:\n",
    "        return gpxpy.parse(f)\n",
    "\n",
    "def points(gpx):\n",
    "    xy = np.zeros((gpx.get_points_no(), 2))\n",
    "    for i, p in enumerate(gpx.walk(only_points=True)):\n",
    "        xy[i] = p.longitude, p.latitude\n",
    "    return xy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now generate the plot by iterating over GPX files and plotting the coordinates along the way. This takes a little while to run, so I downsample the coordinates to about 1 sample every 15 seconds or so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38.512625, 38.5765569)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x354.86 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = xlim[1] - xlim[0]\n",
    "height = ylim[1] - ylim[0]\n",
    "ratio = abs(width / height)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10/ratio))\n",
    "ax = fig.gca()\n",
    "\n",
    "for fname in activities['GPX File']:\n",
    "    fp = os.path.join(datadir, fname)\n",
    "    xy = points(loadgpx(fp))[::5, :]\n",
    "    ax.plot(xy[:, 0], xy[:, 1], lw=0.2, alpha=0.5, color='k')\n",
    "    \n",
    "ax.set_xlim(*xlim)\n",
    "ax.set_ylim(*ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('map.png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "The question now is, do I run tomorrow?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}