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gpbiometrics workflow

Frozen R source: reference/vignettes/gpbiometrics-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.align_gazepoint_biometrics_to_ttl(...)
  • gp.audit_gazepoint_gsr_units(...)
  • gp.correct_gazepoint_eda_temperature(...)
  • gp.create_gazepoint_biometrics_feature_inventory(...)
  • gp.export_gazepoint_biometrics_report_bundle(...)
  • gp.extract_gazepoint_beats_kmeans(...)
  • gp.extract_gazepoint_hrv_features(...)
  • gp.extract_gazepoint_hrv_fuzzy_csi(...)
  • gp.extract_gazepoint_hrv_nonlinear(...)
  • gp.extract_gazepoint_hrv_rcmse(...)
  • gp.extract_gazepoint_scr_recovery_times(...)
  • gp.extract_gazepoint_ttl_events(...)
  • gp.format_gazepoint_biometrics_feature_inventory(...)
  • gp.import_gazepoint_biometric_folder(...)
  • gp.import_gazepoint_biometrics(...)
  • gp.prepare_gazepoint_biometrics_lme_data(...)
  • gp.run_gazepoint_biometrics_real_data_readiness(...)
  • gp.run_gazepoint_biometrics_workflow(...)
  • gp.standardise_gazepoint_adaptive_ema(...)
  • gp.summarise_gazepoint_biometrics_feature_inventory(...)
  • gp.summarise_gazepoint_biometrics_workflow(...)
  • gp.summarise_gazepoint_hrv_features(...)
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/gpbiometrics-workflow.py

from __future__ import annotations
from _shared import *
d=demo(900); active=gp.detect_active_biometric_channels(d); schema=gp.detect_gazepoint_biometric_schema(d); readiness=gp.run_gazepoint_biometrics_real_data_readiness(d,min_rows=100); inv=gp.create_gazepoint_biometrics_feature_inventory(); finish('gpbiometrics-workflow',active=active,schema=schema,readiness=readiness,inventory=inv)