Plot gallery¶

Frozen R source: reference/vignettes/articles/plot-gallery.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.audit_gazepoint_signal_activity(...)
gp.audit_gazepoint_time_resets(...)
gp.convert_gazepoint_gsr_to_conductance(...)
gp.create_gazepoint_quality_dashboard(...)
gp.decompose_gazepoint_eda(...)
gp.detect_gazepoint_ppg_peaks(...)
gp.detect_gazepoint_scr_events(...)
gp.estimate_gazepoint_respiration_from_ppg(...)
gp.extract_gazepoint_hrv_features(...)
gp.extract_gazepoint_ttl_events(...)
gp.filter_gazepoint_ppg_signal(...)
gp.plot_gazepoint_aoi_biometrics(...)
gp.plot_gazepoint_biometric_quality(...)
gp.plot_gazepoint_biometric_report_dashboard(...)
gp.plot_gazepoint_biometric_signals(...)
gp.plot_gazepoint_design_coverage(...)
gp.plot_gazepoint_eda_decomposition(...)
gp.plot_gazepoint_eda_gram(...)
gp.plot_gazepoint_missingness(...)
gp.plot_gazepoint_multimodal_timeline(...)
gp.plot_gazepoint_ppg_breathing(...)
gp.plot_gazepoint_ppg_peak_detection(...)
gp.plot_gazepoint_ppg_poincare(...)
gp.plot_gazepoint_ppg_segmentwise(...)
gp.plot_gazepoint_pyhrv_hr_heatplot(...)
gp.plot_gazepoint_pyhrv_radar_chart(...)
gp.plot_gazepoint_pyhrv_tachogram(...)
gp.plot_gazepoint_saccade_main_sequence(...)
gp.plot_gazepoint_scr_events(...)
gp.plot_gazepoint_scr_specification_curve(...)
gp.plot_gazepoint_signal_activity(...)
gp.plot_gazepoint_signal_quality(...)
gp.plot_gazepoint_time_resets(...)
gp.run_gazepoint_pyhrv_style(...)
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.summarize_gazepoint_missingness(...)
gp.summarize_gazepoint_signal_quality(...)

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/plot-gallery.py

from __future__ import annotations
from _shared import *
d=demo(600); q=gp.audit_gazepoint_gsr_quality(d,value_column='GSR_US'); figs=[gp.plot_gazepoint_missingness(d,cols=['GSR_US','HR','IBI'],time_col='TIME'),gp.plot_gazepoint_biometric_signals(d,signal_cols=['GSR_US','HR'],time_col='TIME'),gp.plot_gazepoint_multimodal_timeline(d,time_col='TIME',signal_cols=['GSR_US','HR','LPMM'],group_cols=['participant_id'])]; finish('plot-gallery',quality=q,figures=figs)