gpx-vis 0.3.1

Visualising your completed Komoot tours

About gpx_vis

gpx_vis enables simple and convenient visualisation of your cycling or hiking tours.

Important note: Currently, it exclusively supports .GPX files generated from completed paths in Komoot. Please be aware that this tool is still in development.

Install

pip install gpx_vis

Update to the latest version:

pip install gpx_vis --upgrade

Instantiate

from gpx_vis import Track

track = Track(pathname)

pathname serves as a reference to either a single .gpx file or a directory. If a directory path is provided, all .gpx files contained within that directory will be merged, but retain their original metadata (e.g., different tracks remain separated). This allows users to overlay multiple tracks onto a single map, providing a comprehensive summary of the data being analyzed.

Map creation

track.create_map(filename)

This generates an HTML file saved under filename, which contains an interactive map. Users can hover over routes or click on final waypoints (flags) for additional information.

In cases where the map contains a large dataset and loading time is a concern (if one has been very hardworking), users have the option to include lite = True. By default, this option limits the number of data points displayed on each route to 50. However, users can adjust this limit using the nlite argument, for instance,

track.create_map(filename, lite=True, nlite=100)

City overview

track.city_list

Interested in the cities you passed through on your journey? This command generates a list of cities - sorted by country and name - that were detected along the tour route. The frequency parameter indicates how many times each city was encountered; providing a rough estimate of the time spent in each city.

Additional information

If you wish to plot or inspect different values, these parameters are also accessible via:

track.t: time (UTC)
track.x: longitude (deg)
track.y: latitude (deg)
track.z: elevation (m)

Dependencies

https://github.com/tkrajina/gpxpy https://pypi.org/project/reverse-geocode/ https://github.com/python-visualization/folium https://pypi.org/project/humanfriendly/ https://altair-viz.github.io/ https://pypi.org/project/vincenty/ https://numpy.org/ https://pandas.pydata.org/ https://pypi.org/project/branca/