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QuantifiedMe

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Loading and plotting of various Quantified Self data sources.

You can see an example notebook with fake data built in CI down below.

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.

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 ("Human Programming Interface") by @karlicoss