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EDA, GSR, and SCR workflow

Frozen R source: reference/vignettes/articles/eda-scr-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.audit_gazepoint_eda_artifacts(...)
  • gp.audit_gazepoint_gsr_quality(...)
  • gp.audit_gazepoint_gsr_units(...)
  • gp.baseline_correct_gazepoint_gsr(...)
  • gp.classify_gazepoint_eda_response_pattern(...)
  • gp.convert_gazepoint_gsr_to_conductance(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.decompose_gazepoint_eda(...)
  • gp.detect_gazepoint_scr_events(...)
  • gp.detect_gazepoint_scr_peaks(...)
  • gp.normalize_gazepoint_scr(...)
  • gp.plot_gazepoint_eda_decomposition(...)
  • gp.plot_gazepoint_scr_events(...)
  • gp.plot_gazepoint_scr_specification_curve(...)
  • gp.prepare_gazepoint_scr_hurdle_model_data(...)
  • gp.run_gazepoint_scr_multiverse(...)
  • gp.run_gazepoint_scr_threshold_sensitivity(...)
  • gp.screen_gazepoint_eda_nonresponders(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.standardise_gazepoint_biometric_names(...)
  • gp.summarise_gazepoint_gsr_tonic_phasic(...)
  • gp.summarise_gazepoint_gsr_windows(...)
  • gp.summarise_gazepoint_scr_event_windows(...)
import gpbiometricspy as gp

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

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/eda-scr-workflow.py

from __future__ import annotations
from _shared import *
d=demo(900); units=gp.audit_gazepoint_gsr_units(d,gsr_col='GSR_US'); quality=gp.audit_gazepoint_gsr_quality(d,value_column='GSR_US'); artifacts=gp.audit_gazepoint_eda_artifacts(d,signal_col='GSR_US',time_col='TIME',group_cols=['participant_id'])
dec=gp.decompose_gazepoint_eda(d,signal_col='GSR_US',time_col='TIME',group_cols=['participant_id'],window_size=31); events=gp.detect_gazepoint_scr_events(dec,phasic_col='eda_phasic',time_col='TIME',group_cols=['participant_id'],min_peak_distance=10)
finish('eda-scr-workflow',units=units,quality=quality,artifacts=artifacts,decomposition=dec,events=events)