Acknowledgments
This project builds on many amazing other projects and would not be possible without them.
Alembic
Database schema changes need to be managed carefully. Alembic provides a migration framework for SQLAlchemy that autogenerates migration scripts from model changes, keeping the schema in sync across installations.
Bootstrap CSS
Writing CSS is not a trivial task. For many projects I have been using the Bootstrap CSS Framework which provides sensible default values, a 12-column grid system and a lot of components. Using this I didn't have to write any CSS myself and just attach a couple of classes to HTML elements.
coloredlogs
Log messages in multiple colors are neat. Using the coloredlogs package we can get these super easily.
fitdecode
For reading FIT files I use the fitdecode library which completely handles all the parsing of this file format.
Flask
The webserver is implemented with Flask which provides a really easy way to get started. Extensions like Flask-Babel add internationalization support and Flask-SQLAlchemy integrates the ORM cleanly into the request lifecycle.
GeoJSON
Transferring geographic geometry data from the Python code to Leaflet is easiest with using the GeoJSON format. The official standard RFC is a bit hard to read, rather have a look at the Wikipedia article. And there is an online viewer that you can try out.
GitHub
For a smooth open source project one needs a place to share the code and collect issues. GitHub provides all of this for free.
gpxpy
For reading GPX files I use the gpxpy library. This allows me to read those files without having to fiddle with the underlying XML format.
Gunicorn & Waitress
Running Flask in production requires a proper WSGI server. On Linux Gunicorn and on Windows Waitress serve this role, handling concurrent requests reliably.
imageio & imageio-ffmpeg
Generating timelapse videos from activity data requires encoding image sequences into video files. imageio together with imageio-ffmpeg makes this straightforward from Python.
Leaflet
The interactive maps on the website are powered by Leaflet, a very easy to use JavaScript library for embedding interactive Open Street Map maps. It can also display GeoJSON geometries natively, of which I also make heavy use.
Matplotlib
Some visualizations are rendered server-side as static images, for which Matplotlib is used. The heatmap rendering in particular relies on its image output capabilities.
NumPy
Numerical array operations underlying much of the data processing are provided by NumPy, the foundational library for scientific computing in Python.
Open Street Map
All the maps displayed use tiles from the amazing Open Street Map. This map is created by volunteers, the server hosting is for free. Without these maps this project would be quite boring.
Pandas
Working with thousands of activities, thousands of tiles and millions of points makes it necessary to have a good library for number crunching structured data. Pandas offers this and gives good performance and many features.
Parquet
I need to store the intermediate data frames that I generate with Pandas. Storing as JSON has disadvantages because dates are not properly encoded. Also it is a text format and quite verbose. The Parquet format is super fast and memory efficient.
Pillow
Reading and writing raster images — for example photo thumbnails from EXIF data — is handled by Pillow, the friendly PIL fork.
Python
Almost all of the code here is written in Python, a very nice and versatile programming language with a vast ecosystem of packages.
Requests
For doing HTTP requests I use the Requests library. It provides a really easy to use interface for GET and POST requests.
Shapely
Geometric operations on GPS tracks and tiles — such as computing intersections or containment — are handled by Shapely, a library for manipulation and analysis of planar geometric objects.
SQLAlchemy
All persistent application data is stored via SQLAlchemy, a comprehensive ORM that abstracts over different SQL databases and makes schema management with Alembic possible.
Statshunters
The Statshunters page allows to import the activities from Strava and do analysis like explorer tiles, Eddington number and many other things. This has served as inspiration for this project.
Strava
Although I have recorded some of my bike rides, I only really started to record all of them when I started to use Strava. This is a nice platform to track all activities. They also offer a social network feature, which I don't really use. They provide some analyses of the data, but they lack some analyses which I have now implemented in this project.
stravalib
Strava has an API, and with stravalib there exists a nice Python wrapper. This makes it much easier to interface with Strava.
Strava local heatmap
The Strava local heatmap project provides a script that renders heatmap files from GPX files locally. It has a gorgeous color scheme with a few nifty tricks. The heatmap in this project is inspired by this project that doesn't share the code.
tcxreader
Support for the TCX file format is provided via tcxreader.
timezonefinder
Activities recorded without an explicit timezone need one assigned based on their coordinates. timezonefinder does this lookup efficiently without requiring a network call.
uv
For managing all the Python package dependencies I use uv which makes it very easy to have all the Python project housekeeping with one tool.
Vega, Altair & VegaFusion
Creating plots that look nice in a browser is hard and I don't like writing JavaScript. Fortunately there is Vega for the beautiful plots and Altair as a Python package that generates the necessary JavaScript for me. VegaFusion handles server-side pre-aggregation so that large datasets render without shipping all the data to the browser.
Velo Viewer
I never used Velo Viewer myself but many people say good things about it. It has so many statistics and inspired many more projects, including this one.
VitePress
Writing documentation is more fun with a nice tool, therefore I use VitePress which powers this documentation.