find_mscodeids

The find_mscodeids command-line tool is the first executable stage of the pipeline. It retrieves distinct CodeIDs from InfluxDB for a given time range, builds per-leg semantic segments, and stores the resulting activity_leg and activity_all records in PostgreSQL.

Purpose

This command is responsible for the bottom-up semantic construction described in the project:

  1. retrieve the CodeIDs present in a time window

  2. fetch reference wearable data for each CodeID

  3. segment the activity separately for the left and right foot

  4. store the per-leg segments as activity_leg

  5. compute left/right overlaps and store them as activity_all

This stage prepares the candidate bilateral windows that are later consumed by find_gait.

Arguments

The tool accepts the following arguments:

  • -c, --config: path to config.yaml (required)

  • -f, --from: start datetime of the query window

  • -u, --until: end datetime of the query window

  • -l, --lang: interface language (es or en)

  • -v, --verbose: verbosity level

  • --head-rows: number of preview rows when verbose output is enabled

  • --save: whether to persist results in PostgreSQL (1) or run in dry mode (0)

If --from is omitted, the tool defaults to the previous day at 00:00:00. If --until is omitted, it defaults to the current time.

High-level execution flow

The command performs the following steps:

  1. initialize translations

  2. load configuration and create the shared DataManager

  3. retrieve distinct CodeIDs from InfluxDB

  4. store each CodeID in PostgreSQL if needed

  5. fetch the wearable reference stream for that CodeID

  6. build left/right leg segments from raw data

  7. prepare and optionally store activity_leg

  8. build bilateral overlaps and optionally store activity_all

Dry-run mode

When --save 0 is used:

  • the command still performs the complete computation

  • results are printed for inspection

  • no rows are written to PostgreSQL

  • placeholder IDs are used internally when needed for downstream merging

Example usage

python -m ms_monitoring.find_mscodeids \
  -c config.yaml \
  -f "2025-05-11 00:00:00" \
  -u "2025-05-12 00:00:00" \
  --save 1 \
  -v 2

Typical outputs

The command may generate:

  • new rows in codeids

  • per-foot rows in activity_leg

  • bilateral rows in activity_all

Rows with no valid segments for one foot are handled gracefully. In that case, the missing foot is reported and only the valid semantic outputs are produced.

Implementation notes

  • timestamps are normalized through ensure_utc(...)

  • segment construction is gap-based

  • zero-duration segments are filtered out

  • bilateral activity is built through temporal overlap between left and right leg segments

  • storage goes through DataManager.store_data(...) with validation and idempotent behaviour for the semantic tables

API reference

class ms_monitoring.find_mscodeids.VAction(option_strings, dest, nargs=None, const=None, default=None, type=None, choices=None, required=False, help=None, metavar=None)[source]

Bases: Action

Custom argparse Action to handle cumulative verbosity (-v).

ms_monitoring.find_mscodeids.main() → None[source]