EDA and SCR visual diagnostics

Frozen R source: reference/vignettes/articles/eda-scr-visual-diagnostics.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_gsr_units(...)
• gp.compute_gazepoint_scr_latency(...)
• gp.convert_gazepoint_gsr_to_conductance(...)
• gp.create_gazepoint_biometrics_report_tables(...)
• gp.create_gazepoint_qc_supplement(...)
• gp.decompose_gazepoint_eda(...)
• gp.detect_gazepoint_scr_events(...)
• gp.detect_gazepoint_scr_peaks(...)
• gp.extract_gazepoint_scr_recovery_times(...)
• gp.plot_gazepoint_eda_decomposition(...)
• gp.plot_gazepoint_scr_events(...)
• gp.plot_gazepoint_scr_specification_curve(...)
• gp.run_gazepoint_scr_threshold_sensitivity(...)

import gpbiometricspy as gp

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

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-visual-diagnostics.py

from __future__ import annotations
from _shared import *
d=demo(900); dec=gp.decompose_gazepoint_eda(d,signal_col='GSR_US',time_col='TIME',group_cols=['participant_id'],window_size=31)
ev=gp.detect_gazepoint_scr_events(dec,phasic_col='eda_phasic',time_col='TIME',group_cols=['participant_id'],min_peak_distance=10)
fig1=gp.plot_gazepoint_eda_decomposition(dec,time_col='TIME',signal_cols=['GSR_US','eda_tonic','eda_phasic'],group_cols=['participant_id']); finish('eda-scr-visual-diagnostics',decomposition=dec,events=ev,figure=fig1)

Rendered Python output

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

EDA decomposition

Detected SCR events

EDA-gram