bluemoon

This is a work in progress 🚧

"A blue moon is an additional full moon that appears in a subdivision of a year: either the third of four full moons in a season, or a second full moon in a month of the common calendar."

Set-up

1. Navigate to the root directory.
2. Install the package: pip install -e bluemoon_pkg/ in editable mode
3. Verify that the unit tests pass: py.test
4. Verify that the integration tests pass: python -m bluemoon.test bluemoon_pkg/bluemoon_tests/data - should either print ALL CLEAR! or specify which tests failed. Note that you can run this command in --verbose or -v mode to help debug.
5. Set up a blank bluemoon database by running python -m bluemoon.add NAME_OF_DB.json

Adding Data

Check out what data formats and integrations are supported: python -m bluemoon.add --help Note that everything marked with a "*" is an integration, while others are local data formats.

For example, to add Toggl data:

python -m bluemoon.add mydata.json --data_source *toggl --data data/B_toggl/*.csv

And to add a worklog:

python -m bluemoon.add mydata.json --data_source worklog --data data/worklog.json

where the worklog might look like this:

{
 "comments": "A very difficult work time, last-minute project push.",
 "first_day": "2020-11-30",
 "last_day": "2020-12-23",
 "working_days": "MTWRF",
 "working_hours": 8,
 "exceptions": {
     "2020-12-4": 0,
     "2020-12-17": 2,
     "2020-12-18": 3
 }

Supported Integrations Notes

• Exist.io: requires token for bearer auth, which can be found using curl https://exist.io/api/1/auth/simple-token/ -d username=... -d password=...
• Toggl: requires the detailed csv export
• Fitbit: requires the csv export; supporting "Sleep" and "Activity"
• Oura: requires the csv export
• Last.fm: requires downloading all scrobble events as a csv eg, with this

Core Concepts

1. Day is the primary unit of analysis, and each day has a different measure of data availability relative to an analysis, depending on surrounding days' data
2. Reports for experiments and insights are not separated from data, but are themselves data and usable in analysis
3. Implicit data is added: (1) scheduled work and (2) habits that don't need to be tracked, which may have fuzzy day boundaries
4. Exceptional days are identified based on cluster analysis, using most representative and available data dimensions

Changelog

• 0.1.1 - First tests added