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External toolbox bridges workflow

Frozen R source: reference/vignettes/articles/toolbox-bridges-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.assess_gazepoint_sampling_irregularity(...)
  • gp.audit_gazepoint_gsr_units(...)
  • gp.audit_gazepoint_ibi_quality(...)
  • gp.audit_gazepoint_time_resets(...)
  • gp.convert_gazepoint_gsr_to_conductance(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_pspm_glm_design(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.detect_active_biometric_channels(...)
  • gp.detect_gazepoint_biometric_schema(...)
  • gp.detect_gazepoint_biometric_timebase(...)
  • gp.detect_gazepoint_ppg_peaks(...)
  • gp.export_gazepoint_heartpy_input(...)
  • gp.export_gazepoint_pyhrv_results(...)
  • gp.export_gazepoint_rhrv_input(...)
  • gp.extract_gazepoint_markerinfo_pspm_style(...)
  • gp.extract_gazepoint_ppg_morphology(...)
  • gp.extract_gazepoint_ppg_templates(...)
  • gp.extract_gazepoint_pyhrv_nn_intervals(...)
  • gp.extract_gazepoint_segments_pspm_style(...)
  • gp.filter_gazepoint_ibi_implausible(...)
  • gp.filter_gazepoint_ppg_signal(...)
  • gp.import_gazepoint_pyhrv_results(...)
  • gp.prepare_gazepoint_cvxeda_input(...)
  • gp.prepare_gazepoint_heartpy_input(...)
  • gp.prepare_gazepoint_ledalab_input(...)
  • gp.prepare_gazepoint_neurokit_eda_input(...)
  • gp.prepare_gazepoint_pspm_input(...)
  • gp.prepare_gazepoint_pyppg_input(...)
  • gp.prepare_gazepoint_rhrv_input(...)
  • gp.process_gazepoint_ppg_heartpy_style(...)
  • gp.run_gazepoint_biosppy_eda(...)
  • gp.run_gazepoint_biosppy_ppg(...)
  • gp.run_gazepoint_heartpy_crosscheck(...)
  • gp.run_gazepoint_neurokit_eda_crosscheck(...)
  • gp.run_gazepoint_pyhrv_style(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.standardise_gazepoint_biometric_names(...)
  • gp.summarise_gazepoint_hrv_features(...)
  • gp.summarize_gazepoint_feature_coverage(...)
  • gp.trim_gazepoint_biometrics_pspm_style(...)
  • gp.validate_gazepoint_biometrics(...)
import gpbiometricspy as gp

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

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-bridges-workflow.py

from __future__ import annotations
from _shared import *
d=demo(300); hp=gp.prepare_gazepoint_heartpy_input(d,signal_col='HRP',time_col='TIME',group_cols=['participant_id'],sampling_rate_hz=60); pyppg=gp.prepare_gazepoint_pyppg_input(d,ppg_col='HRP',time_col='TIME',group_cols=['participant_id'],sampling_rate=60); nk=gp.prepare_gazepoint_neurokit_eda_input(d,eda_col='GSR_US',time_col='TIME',group_cols=['participant_id'],sampling_rate=60); led=gp.prepare_gazepoint_ledalab_input(d,eda_col='GSR_US',time_col='TIME',group_cols=['participant_id']); pspm=gp.prepare_gazepoint_pspm_input(d,eda_col='GSR_US',time_col='TIME',group_cols=['participant_id']); cvx=gp.prepare_gazepoint_cvxeda_input(d,eda_col='GSR_US',time_col='TIME',group_cols=['participant_id']); finish('toolbox-bridges-workflow',heartpy=hp,pyppg=pyppg,neurokit=nk,ledalab=led,pspm=pspm,cvxeda=cvx)