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Toolbox crosscheck visuals

Frozen R source: reference/vignettes/articles/toolbox-crosscheck-visuals.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.compare_gazepoint_pyhrv_psd_methods(...)
  • gp.create_gazepoint_biometrics_report_tables(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.export_gazepoint_heartpy_input(...)
  • gp.export_gazepoint_rhrv_input(...)
  • gp.pipeline_comparison_dashboard(...)
  • gp.plot_gazepoint_ppg_peak_detection(...)
  • gp.plot_gazepoint_ppg_poincare(...)
  • gp.plot_gazepoint_scr_events(...)
  • gp.prepare_gazepoint_heartpy_input(...)
  • gp.prepare_gazepoint_pyppg_input(...)
  • gp.prepare_gazepoint_rhrv_input(...)
  • gp.run_gazepoint_biosppy_eda(...)
  • gp.run_gazepoint_biosppy_ppg(...)
  • gp.run_gazepoint_heartpy_crosscheck(...)
  • gp.run_gazepoint_neurokit_eda_crosscheck(...)
import gpbiometricspy as gp

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

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/toolbox-crosscheck-visuals.py

from __future__ import annotations
from _shared import *
d=pulse_frame(100,20); det=gp.detect_gazepoint_ppg_peaks(d,'pulse','time_s',['participant'],100,high_precision=False); heart=gp.run_gazepoint_heartpy_crosscheck(d,'pulse','time_s','participant',100,high_precision=False); bio=gp.run_gazepoint_biosppy_ppg(d.rename(columns={'pulse':'ppg'}),'ppg','time_s','participant',100); fig1=gp.plot_gazepoint_ppg_peak_detection(det); finish('toolbox-crosscheck-visuals',heartpy=heart,biosppy=bio,figure=fig1)