Data Visualization using the Strava Python API

Applying what I learned in my Data Visualization and Scientific Programming class, I was able to pull data for all of my Strava activities. I then implemented the Strava Fitness and Freshness equations, performed an FFT on the Fitness data, and reconstructed a model of my Fitness over time. I hope to take advantage of the other data types pulled by the API, including polylines that will allow me to create heatmaps and reconstuct individual activities.

This MATLAB program pulls GPS and sensor data from a given .gpx file. It then performs the calculations to display a maps, graphs, and key data. I have really enjoyed learning the math done by my cycling computer every second to find Normalized Power, TSS, and IF. In my spare time I hope to improve the visuals and add power zone breakdowns and more!

This is a visual representation of the data calculated for the paths of two satellites and a base station located in Australia. The points turn blue when a satellite is within a certain angle of the base station and a data upload is possible.