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Design audit workflow

Frozen R source: reference/vignettes/articles/design-audit-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.assert_gazepoint_columns(...)
  • gp.assess_gazepoint_sampling_irregularity(...)
  • gp.audit_gazepoint_condition_balance(...)
  • gp.audit_gazepoint_dataset_structure(...)
  • gp.audit_gazepoint_event_coverage(...)
  • gp.audit_gazepoint_experiment_design(...)
  • gp.audit_gazepoint_export_schema(...)
  • gp.audit_gazepoint_pipeline_steps(...)
  • gp.audit_gazepoint_release_readiness(...)
  • gp.audit_gazepoint_session_comparability(...)
  • gp.audit_gazepoint_time_resets(...)
  • gp.audit_gazepoint_timecourse_grid(...)
  • gp.compare_gazepoint_export_profiles(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_audit_index(...)
  • gp.create_gazepoint_audit_report_section(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.detect_active_biometric_channels(...)
  • gp.detect_gazepoint_biometric_timebase(...)
  • gp.extract_gazepoint_ttl_events(...)
  • gp.match_gazepoint_events_to_biometrics(...)
  • gp.plot_gazepoint_design_coverage(...)
  • gp.profile_gazepoint_export_folder(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.simulate_gazepoint_eye_data(...)
  • gp.standardise_gazepoint_biometric_names(...)
  • gp.standardize_gazepoint_column_names(...)
  • gp.summarize_gazepoint_export_inventory(...)
  • gp.validate_gazepoint_biometrics(...)
  • gp.validate_gazepoint_format(...)
  • gp.validate_gazepoint_metadata(...)
import gpbiometricspy as gp

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

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/design-audit-workflow.py

from __future__ import annotations
from _shared import *
d=demo(900).rename(columns={'participant_id':'participant','MEDIA_ID':'trial','interface_complexity':'condition'})
design=gp.audit_gazepoint_experiment_design(d,participant_col='participant',trial_col='trial',condition_col='condition')
balance=gp.audit_gazepoint_condition_balance(d,participant_col='participant',condition_col='condition',trial_col='trial')
events=gp.audit_gazepoint_event_coverage(d,event_col='TTL0',participant_col='participant',trial_col='trial'); finish('design-audit-workflow',design=design,balance=balance,events=events)