GoldenCheetahLib

Python library that provides local access to activity data in GoldenCheetah.

Important note: This project is still in development and cannot be regarded 'stable' for now, but features will be added and bugs will be squashed. When the time is right, a stable version will be released.

Introduction

Recently, GoldenCheetah got an embedded R Console (read about it here) that got me wondering if something like this would be possible for Python as well. As it turnes out, it is: GoldenCheetah features a REST API that can be used to retrieve activity data. The API's functionality is basic but useful:

• Get a list of all available athletes
• Get a list of all activities for an athlete
• Get athlete zones
• Get raw data for activities
• ...and some more

Full functionality can be found in the official documentation here.

The result is this project, goldencheetahlib. goldencheetahlib is basically a REST client that returns Pandas Dataframes with all your data.

Requirements

• Python 3 (Python 2 support is not planned)
• GoldenCheetah >3.3 installed and running

Installation

Install this library by running this command (preferably in your virtualenv):

pip install goldencheetahlib

Getting started

First, make sure you meet the requirements. After that, clone this notebook in your project directory. If you have done this succesfully, these code snippets should get you started:

%matplotlib inline
from goldencheetahlib.client import GoldenCheetahClient

client = GoldenCheetahClient()

Find the names of all available athletes:

client.get_athletes()

Set the athlete name in the client:

client.athlete = 'John Doe'

(This can also be done by initializing the client with GoldenCheetahClient(athlete='John Doe')

Now you can start getting your data from GoldenCheetah:

activity_list = client.get_activity_list()

client.get_last_activity()

activities = client.get_activity_bulk(activity_list.iloc[:10])

Because every returned activity and activity_list is a Pandas Dataframe interacting with them is really easy:

activities.data[0].heartrate.mean()
activities.data[0].heartrate.plot()

Try it out!

(I will add a Jupyter notebook later with some more usage examples)

License

The code in this repository is licensed under the MIT License. GitHub describes this license as follows:

The MIT License is a permissive license that is short and to the point. It lets people do anything they want with your code as long as they provide attribution back to you and don’t hold you liable.

My summary: Use this code for whatever you want, give me credit every single time and don't blame me if things go wrong.

Contributing

Feel free to contribute to this project by opening an issue or opening pull requests.