Diagnosing common Gazepoint export and workflow problems¶
Frozen R source: reference/vignettes/articles/troubleshooting-readiness.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_biometrics_file(...)gp.detect_gazepoint_time_columns(...)gp.diagnose_gazepoint_sync_drift(...)gp.run_gazepoint_biometrics_real_data_readiness(...)gp.summarize_gazepoint_missingness(...)gp.validate_gazepoint_biometrics(...)gp.validate_gazepoint_gaze(...)
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:
from __future__ import annotations
from _shared import *
d=demo(600); schema=gp.detect_gazepoint_biometric_schema(d); timebase=gp.detect_gazepoint_biometric_timebase(d,time_col='TIME',counter_col='CNT'); readiness=gp.run_gazepoint_biometrics_real_data_readiness(d,min_rows=100); missing=gp.summarize_gazepoint_missingness(d,signal_cols=['GSR_US','HR','IBI','LPMM']); finish('troubleshooting-readiness',schema=schema,timebase=timebase,readiness=readiness,missingness=missing)