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Cluster-permutation workflow

Frozen R source: reference/vignettes/articles/cluster-permutation.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.audit_gazepoint_timecourse_grid(...)
  • gp.diagnose_gazepoint_cluster_design(...)
  • gp.export_gazepoint_cluster_results(...)
  • gp.export_gazepoint_mne_cluster_input(...)
  • gp.plot_gazepoint_cluster_null_distribution(...)
  • gp.plot_gazepoint_cluster_permutation(...)
  • gp.prepare_gazepoint_timecourse_test_data(...)
  • gp.report_gazepoint_cluster_permutation(...)
  • gp.run_gazepoint_cluster_permutation(...)
  • gp.run_gazepoint_cluster_threshold_sensitivity(...)
  • gp.run_gazepoint_tfce(...)
  • gp.simulate_gazepoint_cluster_timecourse_data(...)
  • gp.summarize_gazepoint_time_clusters(...)
import gpbiometricspy as gp

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

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/cluster-permutation.py

from __future__ import annotations
from _shared import *
d=gp.simulate_gazepoint_cluster_timecourse_data(n_subjects=8,n_time=24,effect_start=8,effect_end=14,seed=7).rename(columns={'subject':'participant'})
res=gp.run_gazepoint_cluster_permutation(d,participant_col='participant',n_permutations=99,seed=7)
report=gp.report_gazepoint_cluster_permutation(res); finish('cluster-permutation',result=res,report=report)