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Multimodal event dashboard

Frozen R source: reference/vignettes/articles/multimodal-event-dashboard.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.create_gazepoint_biometrics_report_tables(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.diagnose_gazepoint_sync_drift(...)
  • gp.extract_gazepoint_ttl_events(...)
  • gp.import_gazepoint_event_log(...)
  • gp.match_gazepoint_events_to_biometrics(...)
  • gp.plot_gazepoint_aoi_biometrics(...)
  • gp.plot_gazepoint_multimodal_timeline(...)
  • gp.prepare_gazepoint_aoi_biometrics_model_data(...)
  • gp.prepare_gazepoint_multimodal_model_data(...)
  • gp.summarise_gazepoint_aoi_biometrics(...)
  • gp.summarise_gazepoint_multimodal_windows(...)
  • gp.summarize_gazepoint_eventlocked_multimodal(...)
  • gp.sync_gazepoint_biometrics_with_gaze(...)
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/multimodal-event-dashboard.py

from __future__ import annotations
from _shared import *
d=demo(900); ev=gp.extract_gazepoint_ttl_events(d,ttl_columns=['TTL0'],group_columns=['participant_id']); summary=gp.summarize_gazepoint_eventlocked_multimodal(d,events=ev,time_col='TIME',event_time_col='TIME',signal_cols=['GSR_US','HR','LPMM'],group_cols=['participant_id']); fig=gp.plot_gazepoint_multimodal_timeline(d,time_col='TIME',signal_cols=['GSR_US','HR','LPMM'],group_cols=['participant_id']); finish('multimodal-event-dashboard',events=ev,summary=summary,figure=fig)

Rendered Python output

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

Multimodal timeline

Multimodal timeline