An open, hackable and free training diary.
Please see the full documentation. This page contains only:
All data are stored in an SQLite database (SQLAlchemy ORM interface). The schema separates "statistics" (named time series data) from the source (which might be direct entry, read from a FIT file, or calculated from pre-existing values).
The "diary" view, where the user enters data, is itself generated from the database. So the fields displayed (and the statistics collected) can be customized. This configuration can include "schedules" which control when information is displayed (eg: weekdays only; every other day; second Sunday in the month).
The combination of customizable diary fields and scheduling allows training plans to be entered and displayed.
Customization (fields, training plans, etc) must be done via Python or
SQL. There is no graphical user interface for configuration. This
presents a steep learning curve but is ultimately very flexible -
"any" training plan can be accommodated. Python code for generating
example plans is included (see package
Data are processed via "pipelines". These are Python classes whose class names are also configured in the database. Existing pipelines calculate statistics from FIT file data, recognise segments from GPS endpoints, and generate summaries (eg monthly averages).
A Python interface allows data to be extracted as DataFrames for analysis in Jupyter workbooks (or dumping to stdout). So general Python data science tools (Pandas, Numpy, etc) can be used to analyze the data. Example workbooks are included in the source.
The data are stored in an "open" format, directly accessible by third
party tools, and easily backed-up (eg by copying the database file).
When the database format changes scripts are provided to migrate
existing data (see package
ch2.migraine). Data extracted from FIT
files are not migrated - they must be re-imported.
Support libraries include FIT file parsing and spatial R-Trees.
Currently the program is single-user (ie the data in the database are not grouped by user). Multiple users can co-exist using separate database files.
Choochoo collects and organizes time-series data using athlete-appropriate interfaces. It facilitates calculations of derived statistics and extraction of data for further analysis using Python's rich data science tools. Both data and code are open and extensible.
ch2 fix-fit functionality (can scan a directory and print
file names of god or bad files). Required a change in parameters -
now you must explicitly add
--fix-checksum if you
want to do that.
Parsing of "accumulated" fields in FIT files plus a bunch more fixes thanks to test data from python-fitparse.
Contains a tool to fix corrupt FIT files.
Choochoo has a GUI!!!
Nearby activities and simplified / improved data access in Jupyter.
Impulse calculations. Faster importing and statistics. See Scaled Heart Rate Impulse - SHRIMP
More readable database (using text instead of opaque numerical hashes in a couple of places). Faster database loading of activity and monitor data. Time is now directly present in the statistic journal table, along with all activity and monitor data (no separate data tables). This enables TSS calculation (next version).
Tidied lots of rough corners, improved docs, added examples, download from Garmin Connect. This could probably be used by 3rd parties.
Diary now uses dates (rather than datetimes) and is timezone aware (Previously all times were UTC datetimes; now data related to the diary - like statistics calculated on daily intervals - use the date and the local timezone to convert to time. So, for example, stats based on monitor data are from your local "day" (midnight to midnight)).
Monitor data from FIT files can be imported.
Major rewrite to generalize the database schema. Moved a lot of configuration into the database. Now much more flexible, but less interactive.