SweatStack Python Library

Overview

This is the Python library for Sweat Stack. Sweat Stack is a powerfull application designed for athletes, coaches, and sports scientists to analyze and manage athletic performance data. This library provides a seamless interface to interact with the SweatStack API, allowing users to retrieve, analyze, and visualize activity data and performance metrics.

Installation

We recommend using uv to manage Python and install the library. Read more about uv here.

uv pip install sweatstack

You can also install it with pip (or pipx) directly.

pip install sweatstack

Quickstart

Get started with analyzing your latest activity:

import sweatstack as ss

ss.login()

latest_activity = ss.get_latest_activity()

print(latest_activity)  # `latest_activity` is a pandas DataFrame

Authentication

To be able to access your data in Sweat Stack, you need to authenticate the library with your Sweat Stack account. The easiest way to do this is to use your browser to login:

import sweatstack as ss

ss.login()

This will automaticallyset the appropriate authentication tokens in your Python code.

Alternatively, you can set the SWEAT_STACK_API_KEY environment variable to your API key. You can create an API key here.

import os

import sweatstack as ss

os.environ["SWEAT_STACK_API_KEY"] = "your_api_key_here"

# Now you can use the library

Listing activities

To list activities, you can use the list_activities() function:

for activity in ss.list_activities():
    print(activity)

Info: This method returns a summary of the activities, not the actual timeseries data. To get the actual data, you need to use the get_activity_data() or get_latest_activity_data()) methods documented below.

Getting activity summaries

To get the summary of an activity, you can use the get_activity() function:

activity = ss.get_activity(activity_id)
print(activity)

To quickly the latest activity, you can use the get_latest_activity() function:

activity = ss.get_latest_activity()
print(activity)

Getting activity data

To get the timeseries data of one activity, you can use the get_activity_data() method:

data = ss.get_activity_data(activity_id)
print(data)

This method returns a pandas DataFrame. If your are not familiar with pandas and/or DataFrames, start by reading this introduction.

Similar as for the summaries, you can use the get_latest_activity_data() method to get the timeseries data of the latest activity:

data = ss.get_latest_activity_data()
print(data)

To get the timeseries data of multiple activities, you can use the get_longitudinal_data() method:

longitudinal_data = ss.get_longitudinal_data(
    start=date.today() - timedelta(days=180),
    sport="running",
    metrics=["power", "heart_rate"],
)
print(longitudinal_data)

Because the result of get_longitudinal_data() can be very large, the data is retrieved in a compressed format (parquet) that requires the pyarrow library to be installed. If you intend to use this method, make sure to install the sweatstack libraryr with uv pip install sweatstack[parquet]. Also note that depending on the amount of data that you requested, this might take a while.

Accessing other user's data

By default, the library will give you access to your own data.

You can list all users you have access to with the list_accessible_users() method:

for user in ss.list_accessible_users():
    print(user)

You can switch to another user by using the switch_user() method:

ss.switch_user(user)

Calling any of the methods above will return the data for the user you switched to.

You can easily switch back to your original user by calling the switch_to_root_user() method:

ss.switch_to_root_user()

Metrics

The API supports the following metrics:

power: Power in Watt
speed: Speed in m/s
heart_rate: Heart rate in BPM
smo2: Muscle oxygen saturation in %
core_temperature: Core body temperature in °C
altitude: Altitude in meters
cadence: Cadence in RPM
temperature: Ambient temperature in °C
distance: Distance in m
longitude: Longitude in degrees
latitude: Latitude in degrees

Sports

The API supports the following sports:

running: Running
cycling: Cycling

More sports will be added in the future.

Metadata

Release files for sweatstack 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sweatstack 0.1.2
File	Size	Uploaded
sweatstack-0.1.2.tar.gz	9.3 kB	Aug 30, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for sweatstack 0.1.2
File	Interpreter	ABI	Platform	Reset
sweatstack-0.1.2-py3-none-any.whl 9.8 kB Aug 30, 2024	Python 3	none	any	Details

Total release size: 19.1 kB

Release files / sweatstack-0.1.2.tar.gz

Download URL	sweatstack-0.1.2.tar.gz
Size	9.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`4c09bb05269677f04e9b58925b2a7827b04a098aab09f6aee1b4c0099643f2c8`
BLAKE2b-256 checksum How to use checksums	`22acd125c216d0708262d3f4d53102ad060b9520be673705c04c734b9d375165`
Upload date	Aug 30, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.12.5`

Release files / sweatstack-0.1.2-py3-none-any.whl

Download URL	sweatstack-0.1.2-py3-none-any.whl
Size	9.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`208091d775e9e17cb09dd5d9aa753d75af1fe3baf01c3885576e477a99688bbb`
BLAKE2b-256 checksum How to use checksums	`835a0c4235d2c3b106b0a773320345f1b6aa93a948aafe09ac315d9e40c4c588`
Upload date	Aug 30, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.12.5`