Skip to content

Article roadmap

Frozen R source: reference/vignettes/articles/article-roadmap.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.align_gazepoint_streams_by_events(...)
  • gp.audit_gazepoint_condition_balance(...)
  • gp.audit_gazepoint_dataset_structure(...)
  • gp.audit_gazepoint_eda_artifacts(...)
  • gp.audit_gazepoint_event_coverage(...)
  • gp.audit_gazepoint_experiment_design(...)
  • gp.audit_gazepoint_export_schema(...)
  • gp.audit_gazepoint_gsr_quality(...)
  • gp.audit_gazepoint_gsr_units(...)
  • gp.audit_gazepoint_ibi_quality(...)
  • gp.audit_gazepoint_preregistration_consistency(...)
  • gp.audit_gazepoint_session_comparability(...)
  • gp.baseline_correct_gazepoint_gsr(...)
  • gp.baseline_correct_gazepoint_pupil(...)
  • gp.convert_gazepoint_gsr_to_conductance(...)
  • gp.correct_gazepoint_beats(...)
  • gp.create_gazepoint_analysis_decision_log(...)
  • gp.create_gazepoint_analysis_manifest(...)
  • gp.create_gazepoint_biometrics_methods_text(...)
  • gp.create_gazepoint_dictionary(...)
  • gp.create_gazepoint_methods_section(...)
  • gp.create_gazepoint_preregistration_checklist(...)
  • gp.create_gazepoint_qc_supplement(...)
  • gp.create_gazepoint_reproducibility_statement(...)
  • gp.decompose_gazepoint_eda(...)
  • gp.detect_gazepoint_blinks(...)
  • gp.detect_gazepoint_ppg_onsets(...)
  • gp.detect_gazepoint_ppg_peaks(...)
  • gp.detect_gazepoint_pupil_blinks(...)
  • gp.detect_gazepoint_scr_events(...)
  • gp.detect_gazepoint_scr_peaks(...)
  • gp.diagnose_gazepoint_biometrics_workflow(...)
  • gp.estimate_gazepoint_respiration_from_ppg(...)
  • gp.export_gazepoint_biometrics_report_bundle(...)
  • gp.export_gazepoint_heartpy_input(...)
  • gp.export_gazepoint_rhrv_input(...)
  • gp.extract_gazepoint_hrv_features(...)
  • gp.extract_gazepoint_ttl_events(...)
  • gp.filter_gazepoint_gaze(...)
  • gp.filter_gazepoint_ppg_signal(...)
  • gp.generate_gazepoint_manifest(...)
  • gp.interpolate_gazepoint_pupil_blinks(...)
  • gp.match_gazepoint_events_to_biometrics(...)
  • gp.normalize_gazepoint_scr(...)
  • gp.plot_gazepoint_aoi_biometrics(...)
  • gp.plot_gazepoint_biometric_quality(...)
  • gp.plot_gazepoint_biometric_report_dashboard(...)
  • gp.plot_gazepoint_biometric_signals(...)
  • gp.plot_gazepoint_eda_decomposition(...)
  • gp.plot_gazepoint_missingness(...)
  • gp.plot_gazepoint_multimodal_timeline(...)
  • gp.plot_gazepoint_ppg_breathing(...)
  • gp.plot_gazepoint_ppg_peak_detection(...)
  • gp.plot_gazepoint_ppg_segmentwise(...)
  • gp.plot_gazepoint_scr_events(...)
  • gp.plot_gazepoint_signal_activity(...)
  • gp.plot_gazepoint_signal_quality(...)
  • gp.plot_gazepoint_time_resets(...)
  • 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.remove_gazepoint_ppg_baseline_wander(...)
  • gp.run_gazepoint_biometrics_workflow(...)
  • gp.simulate_gazepoint_artifact(...)
  • gp.simulate_gazepoint_biometrics(...)
  • gp.simulate_gazepoint_eye_data(...)
  • gp.simulate_gazepoint_multimodal_data(...)
  • gp.smooth_gazepoint_pupil(...)
  • gp.summarise_gazepoint_aoi_biometrics(...)
  • gp.summarise_gazepoint_biometrics_workflow(...)
  • gp.summarise_gazepoint_gsr_tonic_phasic(...)
  • gp.summarise_gazepoint_hrv_features(...)
  • gp.summarise_gazepoint_scr_event_windows(...)
  • gp.summarize_gazepoint_aoi_dwell(...)
  • gp.summarize_gazepoint_beat_corrections(...)
  • gp.summarize_gazepoint_eventlocked_multimodal(...)
  • gp.summarize_gazepoint_pupil_events(...)
  • gp.summarize_gazepoint_qc_overview(...)
  • gp.sync_gazepoint_biometrics_with_gaze(...)
  • gp.write_gazepoint_decision_log(...)
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/article-roadmap.py

from __future__ import annotations
from _shared import *
d=demo(600)
inv=gp.create_gazepoint_biometrics_feature_inventory(); fmt=gp.format_gazepoint_biometrics_feature_inventory(inv); summ=gp.summarise_gazepoint_biometrics_feature_inventory(fmt)
interop=gp.gazepoint_interoperability_manifest(); readiness=gp.run_gazepoint_biometrics_real_data_readiness(d,min_rows=100)
finish('article-roadmap',inventory=fmt,summary=summ,interop=interop,readiness=readiness)