Pupil preprocessing and analysis workflow

This article is the Python counterpart of the corresponding gp3tools workflow. It uses bundled synthetic data so it can be run without private participant exports.

import gp3tools as gp3

data = gp3.load_example_master()
# flag_gazepoint_pupil_artifacts is available as gp3.flag_gazepoint_pupil_artifacts(...)
# detect_gazepoint_blinks is available as gp3.detect_gazepoint_blinks(...)
# interpolate_gazepoint_pupil is available as gp3.interpolate_gazepoint_pupil(...)
# baseline_correct_gazepoint_pupil is available as gp3.baseline_correct_gazepoint_pupil(...)
# smooth_gazepoint_pupil is available as gp3.smooth_gazepoint_pupil(...)

Recommended workflow

1. Inspect column availability and create/validate a master table.
2. Run the relevant quality gates before transformation or modelling.
3. Keep preprocessing and exclusion decisions explicit.
4. Save derived tables/plots with package/version metadata.
5. For backend-adapted statistical functions, report the Python backend and validate the inferential target against the R reference when confirmatory equivalence matters.

Reproducibility checkpoint

import gp3tools as gp3

print(gp3.__version__)
print(gp3.api_status().query("r_export in @funcs"))