qs_ledger
python-fitbit
| qs_ledger | python-fitbit | |
|---|---|---|
| 4 | 3 | |
| 948 | 614 | |
| - | -0.2% | |
| 0.0 | 0.0 | |
| over 1 year ago | 11 months ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU General Public License v3.0 or later |
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qs_ledger
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Can you get raw data out of a Fitbit for manual analysis?
Here is a really really cool project that imports data from many sources and creates a personal database. https://github.com/markwk/qs_ledger
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Beginner ambitions
https://github.com/markwk/qs_ledger/blob/master/example_correlation_explorer_with_plotly.py so just linear correlation? and Anaconda again oh my
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Suggestions for logging miles on running shoes
Are you more interested in tracking the depreciation of the shoes, or your running habits? If it's the latter I'd point you to projects like qs_ledger.
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Looking for devs and knowledgeable health people for open source quantification !
I'd be interested. I'm working on my own thing similarly based mostly on https://github.com/markwk/qs_ledger.
python-fitbit
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Custom firmware for mi fitness band
Contrary to many android wearables, the most recent google pixel watch 2 is reasonably good at sleep tracking according to the quantified scientist: https://www.youtube.com/watch?v=Ef1by8kJfk4&t=1109s
Google partnered with fitbit last year and now sleep data and other biometrics are shared with the fitbit app. The good thing about this is that you can access your data via fitbit's API, there is a python package for this process: https://github.com/orcasgit/python-fitbit. You can read this article if you want more details on how to configure oauth to make this work: https://towardsdatascience.com/using-the-fitbit-web-api-with...
The cons of this setup is that you still need an android phone synched with your watch and is not too privacy friendly, but sure, you could run the code on your linux machine
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Can you get raw data out of a Fitbit for manual analysis?
Here are some projects that I have found: https://github.com/orcasgit/python-fitbit https://github.com/corynissen/fitbitScraper https://github.com/aam4510/fitbit4j https://github.com/pkpio/fitbit-googlefit
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Do you think OAuth libraries like oauthlib and requests_oauthlib are the best solutions for writing OAuth clients for any website or services? Are there cases where none of them are worthwhile and you should just use a library like requests?
Naturally I first googled for Python API wrappers for Fitbit and found python-fitbit, but it doesn't support all of the end points like create food, to name one that I want to use. It hasn't been updated for a long time and it doesn't have clear documentation for setup from start to finish. I did a bunch of googling to do so, including error messages I got but I never got it set up and authenticated.
What are some alternatives?
Fitbit-Overview-Face - Fitbit watch face
requests-oauthlib - OAuthlib support for Python-Requests!
Habitica-Pomodoro-SiteKeeper - Chrome Extension for Habitica that charges you gp for visiting sites, including a Pomodoro Timer
fitbitScraper - R package to scrape fitbit data
pandas_exercises - Practice your pandas skills!
fitbit-googlefit - Export Fitbit data to Google Fit. Unlike the alternatives such as fitnessyncer.com, this offers very fine intraday granularity (every minute/second data).
KindleHighlightsReader - A program to edit and prettify your Amazon Kindle highlights and export them as pdf, text, json or csv files.
fitbit4j - Fitbit Java Client API and Examples
fitbit-core - 🌑 A framework to simplify building Fitbit OS watchfaces