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map-runs

Build Status

Code for creating an HTML map with all runs mapped with runkeeper (or similar). It uses:

  • gpxpy for parsing the .gpx files.
  • folium for visualizing the data in a Leaflet map.

Installation

In a Python environment (created with e.g. Conda) and from the repository's root, run:

pip install .

NB: Tested for Python 3.8, 3.9 & 3.10.

Usage

Instructions for creating your own map

  1. Either clone locally or download this repo.
  2. Request your running data from the app you use for tracking runs. In the case of runkeeper, you can do this here (it takes about a week for them to send you your data the first time).
  3. Place all your .gpx files in ./gps-data. Make sure to remove any preexisting files.
  4. Modify the origin of your map (parameters starting-latitude & starting-longitude in ./map_runs/map-runs.ini) to correspond to your hometown.
  5. Follow the installation instructions if you have not done so yet.
  6. Run locally ./map_runs/map_runs.py.
  7. Done! your map is now available at ./output-map.html.

Instructions for keeping your map up to date

These steps are only necessary if you would like to update automatically your map as you get new data with continuous integration (CI). You will need a local Git client + a Github account.

  1. Fork this repo.
  2. Follow steps 2-3-4 from the previous instructions.
  3. Commit+push your changes to the repo.
  4. Your map will be available in the gh-pages branch, at ./output-map.html.

Additional customizations

You can customize your map to your liking by changing any of the parameters in the ./map_runs/map-runs.ini file. Some considerations for this:

  • You can find folium's options for terrain customization here.
  • You can store the .gpx files in any folder, as long as you update the data-path parameter to that location.
  • If you change the name of the output map (parameter output-path) and you have set up continuous integration, beware that the location of your map in the gh-pages branch will change accordingly.

Credits

Thanks to Jörg R Schumacher (@eHanseJoerg) for this useful jupyter notebook for plotting routes using the geoJSON format.