https://github.com/erikbjare/quantifiedme

Analyzing all my Quantified Self data
https://github.com/erikbjare/quantifiedme

activitywatch analytics fitbit fitness-tracker google-location-history heart-rate lifedata lifelogging oura quantified-self sleep-analysis time-tracking whoop

Last synced: 4 months ago
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Analyzing all my Quantified Self data

• Host: GitHub
• URL: https://github.com/erikbjare/quantifiedme
• Owner: ErikBjare
• Created: 2018-05-24T14:11:10.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-06-25T21:27:02.000Z (over 1 year ago)
• Last Synced: 2025-04-01T21:48:28.988Z (6 months ago)
• Topics: activitywatch, analytics, fitbit, fitness-tracker, google-location-history, heart-rate, lifedata, lifelogging, oura, quantified-self, sleep-analysis, time-tracking, whoop
• Language: Python
• Homepage: https://erik.bjareholt.com/quantifiedme/Dashboard.html
• Size: 22.9 MB
• Stars: 64
• Watchers: 4
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

          
QuantifiedMe

============

[![Build](https://github.com/ErikBjare/quantifiedme/actions/workflows/build.yml/badge.svg)](https://github.com/ErikBjare/quantifiedme/actions/workflows/build.yml)

[![codecov](https://codecov.io/gh/ErikBjare/quantifiedme/branch/master/graph/badge.svg?token=zZ7hwaw9tR)](https://codecov.io/gh/ErikBjare/quantifiedme)

Loading and plotting of various Quantified Self data sources.

You can see an example notebook with fake data built in CI [down below](#notebooks).

**Note:** This code is only used by me, as far as I know, but I encourage you to try it out anyway, and report or send PRs for any issues you encounter. I will try to keep it tidy and somewhat usable.

## Features

The code in this repository generally loads data from some source into a Pandas dataframe, and provides tools to process, aggregate, and plot the data. 

This makes it a useful toolkit for exploratory data analysis with Jupyter notebooks, for example.

Types of data supported:

 - Time tracking data (from ActivityWatch, Toggl, SmarterTime)

 - Sleep data (from Fitbit, Oura, Whoop)

 - Heartrate data (from Fitbit, Oura, Whoop)

 - Location data (from Google Location History)

   - Includes basic plotting of time spent in a certain location.

   - Includes function for computing the colocation time of two location histories (time spent together).

 - Habit data (from HabitBull)

   - Includes a calendar plot.

   - Easy to adapt to any other habit app that supports CSV export

 - Drug consumption (from QSlang)

Can load data from:

 - ActivityWatch

 - Fitbit

 - Whoop

 - Oura

 - EEG devices (WIP)

 - ...and more (see `src/quantifiedme/load/`)

It also contains a bunch of useful tools for aggregating or otherwise deriving data from the sources, including helper tools for combining multiple sources for the same type of data (see `src/quantifiedme/derived`).

## Notebooks

There is currently only one example notebook.

 - Dashboard - Preview at: https://erik.bjareholt.com/quantifiedme/Dashboard.html

   - Uses ActivityWatch and SmarterTime data from multiple devices (desktop, laptop, phone) to create a unified overview of time spent. 

   - Used by me as a sort of personal-productivity dashboard.

   - Plots things like:

     - hours worked per day (and on what)

     - which categories are consuming most of my time on a 30-day and 365-day basis

     - how much I make in "fictional salary" over time (by assigning an hourly wage to each category)

I also have a collection of private notebooks for exploratory analysis, which I hope to share later.

## Configuration

The configuration is used to specify where data files are located, as well as a few settings.

An example configuration file is provided in `config.example.toml`.

## Related projects

 - [HPI](https://github.com/karlicoss/HPI) ("Human Programming Interface") by @karlicoss