gpbiometricspy

gpbiometricspy is the Python counterpart of gpbiometrics, with the supplied gpbiometrics 2.0.0 source release frozen as its initial semantic reference. It provides Gazepoint-native tools for importing, validating, preprocessing, analysing, plotting, modelling, synchronising, and reporting biometric and multimodal eye-tracking data.

Parity status

The current development tree has reached the frozen R API contract:

• 406 / 406 R exports implemented and registered
• 0 pending exports
• 200+ Python parity/edge tests
• whole-package statement coverage ≥ 90%
• packaged synthetic kiosk demo: 36 participants, 69,120 rows
• frozen R reference retained: 144 R sources, 403 Rd files, 120 R test files, and 26 vignette/article sources

The project deliberately distinguishes API completion from an absolute claim that independent R and Python runtimes are numerically identical in every external-library/version combination. The frozen R tests and implementation are retained in reference/ so parity can continue to be audited.

Install for development

python -m pip install -e .

For optional integrations:

python -m pip install -e ".[interop]"

Individual extras are available for heartpy, biosppy, pyhrv, neurokit, mne, lsl, bayes, stats, docs, and dev.

Quick start

import gpbiometricspy as gp

# Load the public synthetic kiosk demo distributed with the package.
data = gp.load_kiosk_demo()
print(data.shape)  # (69120, ...)

# Inspect biometric signal validity / availability.
validity = gp.summarize_gazepoint_biometric_validity(data)

# Extract TTL transitions.
events = gp.extract_gazepoint_ttl_events(data)

# Example native pyHRV-style workflow from IBI values.
hrv = gp.run_gazepoint_pyhrv_style(
    nni_ms=data.loc[data["IBI"].notna(), "IBI"].head(500).to_numpy() * 1000
)

The demo is fully synthetic and is intended only for examples, testing, and reproducible workflow demonstrations.

Scientific scope

The 2.0.0 parity surface covers, among other areas:

• Gazepoint biometric file/folder import, schema detection, validation and QC;
• EDA/GSR/SCR preprocessing, artifacts, response detection, windows, habituation/recovery, spectral/nonlinear descriptors and external bridges;
• HR/IBI/HRV/PPG processing, pyHRV-style, HeartPy-style and BioSPPy-style workflows, nonlinear HRV, RQA/geometric metrics and respiratory proxies;
• pupil, gaze, fixation, saccade, AOI and event-locked multimodal workflows;
• TTL alignment, synchronization drift, LSL/XDF, MNE and BIDS-oriented bridges;
• cluster permutation testing plus explicit guardrails for designs that the R package intentionally refuses;
• reproducibility, preregistration, audit trails, readiness checks, reporting, plots, workflow summaries, simulation and synthetic smoke testing.

Interpretation guardrails

gpbiometricspy preserves the conservative interpretation policy of the R package. Physiological and eye-tracking signals are measurements and derived features; they do not directly establish emotion, stress, cognition, preference, health status, or diagnosis. Pupil measurements remain sensitive to luminance and visual context, and respiration estimates derived from PPG or other surrogate channels are proxies unless independently validated.

Reference precedence

When Python and explanatory prose disagree, parity work follows:

1. frozen gpbiometrics 2.0.0 implementation;
2. frozen R tests;
3. formal Rd documentation;
4. vignettes/examples;
5. repository/site explanatory material.

See the documentation site source in docs/, the machine-readable export inventory in reference/r-export-inventory.csv, and VALIDATION.md for the current release gates.