pylotoncycle 0.8.0

Module to access your Peloton workout data

PylotonCycle

Python Library for getting your Peloton workout data.

Table of contents

• General info
• Example Usage

General info

As someone who wants to see my progress over time, I've been wanting a way to pull and play with my ride data. However, I'm also cautious about linking myself to too many external parties. As I've been playing with other libraries out there, I wanted something that was a bit more intuitive and would play nicer with the rest of my python code. So, PylotonCycle is born.

Example Usage

import pylotoncycle

username = 'your username or email address'
password = 'your password'
conn = pylotoncycle.PylotonCycle(username, password)
workouts = conn.GetRecentWorkouts(5)

workouts is a list of workouts.

An example of a list element

{'achievement_templates': [{'description': 'Awarded for working out with a '
                                           'friend.',
                            'id': '<some id hash>',
                            'image_url': 'https://s3.amazonaws.com/peloton-achievement-images-prod/702495cd985d4791bfd3d25f36e0df72',
                            'name': 'Dynamic Duo',
                            'slug': 'two_to_tango'},
                           {'description': 'Awarded for achieving Silver in '
                                           'the May Cycling Challenge.',
                            'id': '<some id hash>',
                            'image_url': 'https://s3.amazonaws.com/challenges-and-tiers-image-prod/6b772477ccd04f189fba16f2f877faad',
                            'name': 'May Cycling Challenge',
                            'slug': 'may_cycling_challenge_silver'}],
 'created': 1589642476,
 'created_at': 1589642476,
 'device_time_created_at': 1589617276,
 'device_type': 'home_bike_v1',
 'device_type_display_name': 'Bike',
 'end_time': 1589644336,
 'fitbit_id': None,
 'fitness_discipline': 'cycling',
 'ftp_info': {'ftp': 111,
              'ftp_source': 'ftp_workout_source',
              'ftp_workout_id': '<some id hash>'},
 'has_leaderboard_metrics': True,
 'has_pedaling_metrics': True,
 'id': '<some id hash>',
 'instructor_name': 'Matt Wilpers',
 'is_total_work_personal_record': False,
 'leaderboard_rank': 5015,
 'metrics_type': 'cycling',
 'name': 'Cycling Workout',
 'overall_summary': {'avg_cadence': 85.48,
                     'avg_heart_rate': 0.0,
                     'avg_power': 179.24,
                     'avg_resistance': 47.61,
                     'avg_speed': 20.39,
                     'cadence': 0.0,
                     'calories': 496.71,
                     'distance': 10.19,
                     'heart_rate': 0.0,
                     'id': '<some id hash>',
                     'instant': 1589644336,
                     'max_cadence': 122.0,
                     'max_heart_rate': 0.0,
                     'max_power': 255.8,
                     'max_resistance': 60.95,
                     'max_speed': 23.48,
                     'power': 0.0,
                     'resistance': 0.0,
                     'seconds_since_pedaling_start': 0,
                     'speed': 0.0,
                     'total_work': 322417.21,
                     'workout_id': '<some id hash>'},
 'peloton_id': '<some id hash>',
 'platform': 'home_bike',
 'ride': {'captions': ['en-US'],
          'class_type_ids': ['<some id hash>'],
          'content_format': 'video',
          'content_provider': 'peloton',
          'description': 'Max out the effectiveness of your training with this '
                         'ride. Instructors will expertly guide you through '
                         'specific output ranges 1 through 7 to help you build '
                         'endurance, strength and speed.',
          'difficulty_estimate': 6.3779,
          'difficulty_level': None,
          'difficulty_rating_avg': 6.3779,
          'difficulty_rating_count': 17157,
          'duration': 1800,
          'equipment_ids': [],
          'equipment_tags': [],
          'excluded_platforms': [],
          'extra_images': [],
          'fitness_discipline': 'cycling',
          'fitness_discipline_display_name': 'Cycling',
          'has_closed_captions': True,
          'has_free_mode': False,
          'has_pedaling_metrics': True,
          'home_peloton_id': '<some id hash>',
          'id': '<some id hash>',
          'image_url': 'https://s3.amazonaws.com/peloton-ride-images/58aa8ebc7d51d09d6513e1a2fab53c4c62c076c6/img_1580922399_a5f1fd0e3a2e48d38ecdd6a3d874820f.png',
          'instructor_id': '<some id hash>',
          'is_archived': True,
          'is_closed_caption_shown': True,
          'is_explicit': False,
          'is_live_in_studio_only': False,
          'language': 'english',
          'length': 1940,
          'live_stream_id': '<some id hash>-live',
          'live_stream_url': None,
          'location': 'nyc',
          'metrics': ['heart_rate', 'cadence', 'calories'],
          'origin_locale': 'en-US',
          'original_air_time': 1580919480,
          'overall_estimate': 0.9956,
          'overall_rating_avg': 0.9956,
          'overall_rating_count': 20737,
          'pedaling_duration': 1800,
          'pedaling_end_offset': 1860,
          'pedaling_start_offset': 60,
          'rating': 0,
          'ride_type_id': '<some id hash>',
          'ride_type_ids': ['<some id hash>'],
          'sample_vod_stream_url': None,
          'scheduled_start_time': 1580920200,
          'series_id': '<some id hash>',
          'sold_out': False,
          'studio_peloton_id': '<some id hash>',
          'title': '30 min Power Zone Endurance Ride',
          'total_in_progress_workouts': 0,
          'total_ratings': 0,
          'total_workouts': 32489,
          'vod_stream_id': '<some id hash>-vod',
          'vod_stream_url': None},
 'start_time': 1589642537,
 'status': 'COMPLETE',
 'strava_id': None,
 'timezone': 'America/Los_Angeles',
 'title': None,
 'total_leaderboard_users': 31240,
 'total_work': 322417.21,
 'user_id': '<some id hash>',
 'workout_type': 'class'}

An example of how you may fetch performance data for a ride

import pprint

conn = pylotoncycle.PylotonCycle(username, password)
workouts = conn.GetRecentWorkouts(5)
for w in workouts:
    workout_id = w['id']
    resp = conn.GetWorkoutMetricsById(workout_id)
    pprint.pprint(resp)

Install

This package is available via pip install.

pip install pylotoncycle

TODO

• Lots more to cover. I want to find the right format for pulling in the ride performance data.
• Pull in GPS data for outdoor runs

Note to folks who want to contribute

I'm very happy to take pull requests and fix bugs that come up. But, this is definitely a side project for me.