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Event alignment and AOI-linked biometric workflow

Frozen R source: reference/vignettes/articles/event-alignment-aoi-workflow.Rmd

This page is the Python migration companion for the corresponding gpbiometrics 2.0.0 article. The original R article is retained verbatim in reference/vignettes/; this companion identifies the matching Python API so the scientific workflow can be reproduced without hiding the reference implementation.

Python API crosswalk

The frozen R article calls the following exported functions; all are available under the same names in gpbiometricspy:

  • gp.align_gazepoint_biometrics_to_ttl(...)
  • gp.align_gazepoint_streams_by_events(...)
  • gp.assess_gazepoint_sampling_irregularity(...)
  • gp.audit_gazepoint_biometric_sync_drift(...)
  • gp.audit_gazepoint_event_coverage(...)
  • gp.build_gazepoint_aoi_timecourse(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.detect_gazepoint_biometric_timebase(...)
  • gp.diagnose_gazepoint_sync_drift(...)
  • gp.extract_gazepoint_ttl_events(...)
  • gp.join_gazepoint_biometrics_to_gp3tools(...)
  • gp.join_gazepoint_biometrics_to_master(...)
  • gp.match_gazepoint_events_to_biometrics(...)
  • gp.plot_gazepoint_multimodal_timeline(...)
  • gp.prepare_gazepoint_aoi_biometrics_model_data(...)
  • gp.prepare_gazepoint_multimodal_model_data(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.simulate_gazepoint_eye_data(...)
  • gp.standardise_gazepoint_biometric_names(...)
  • gp.standardize_gazepoint_column_names(...)
  • gp.summarise_gazepoint_aoi_biometrics(...)
  • gp.summarise_gazepoint_multimodal_windows(...)
  • gp.summarize_gazepoint_eventlocked_multimodal(...)
  • gp.sync_gazepoint_biometrics_with_gaze(...)
  • gp.validate_gazepoint_biometrics(...)
  • gp.validate_gazepoint_format(...)
  • gp.validate_gazepoint_metadata(...)
import gpbiometricspy as gp

# Example entry point from this workflow
# result = gp.align_gazepoint_biometrics_to_ttl(...)

Interpretation

Use the same conservative physiological interpretation as the R package: derived biometric features are signal-processing outputs and do not directly establish emotion, stress, cognition, preference, health status, or diagnosis.

Executable Python companion

The frozen R call crosswalk above is retained for completeness. The following companion is an executable end-to-end Python workflow using synthetic/public data and the same scientific domain. It is also executed by the test suite.

Run from the repository root:

python examples/tutorials/event-alignment-aoi-workflow.py

from __future__ import annotations
from _shared import *
d=demo(900); ttl=gp.extract_gazepoint_ttl_events(d,ttl_columns=['TTL0'],group_columns=['participant_id']); aligned=gp.align_gazepoint_biometrics_to_ttl(d,ttl_cols=['TTL0'],time_col='TIME',group_cols=['participant_id'],pre_window_ms=250,post_window_ms=500)
aoi=gp.summarize_gazepoint_aoi_dwell(d,aoi_col='AOI',group_cols=['participant_id']); finish('event-alignment-aoi-workflow',ttl=ttl,aligned=aligned,aoi=aoi)

Rendered Python output

These figures are generated from bundled synthetic/public data by scripts/generate_docs_gallery.py using the current Python plotting API.

AOI-linked biometrics

AOI-linked biometrics

Multimodal timeline

Multimodal timeline