eyeprocesspy

Reproducible Python infrastructure for eye-tracking, pupillometry, AOIs, process data, psychometrics, and multimodal behavioral measurement.

Documentation · PyPI · Release v0.1.0 · Zenodo DOI · 88 workflows

eyeprocesspy is the Python companion and deep-parity port of the R package eyeprocess, with frozen eyeprocess 0.11.1 as the scientific reference. It provides one governed analytical surface for vendor import, canonical data contracts, preprocessing, gaze and AOI analysis, pupillometry, process measurement, psychometrics/IRT, multimodal workflows, validation, provenance, plotting, and reproducible reporting.

Release candidate — 0.2.0. The 0.2.0 candidate is anchored to an exact-main qualification with 1,845 tests, 34,161 / 34,161 statements, and 11,796 / 11,796 branches covered, plus a green Ubuntu/macOS/Windows × Python 3.11–3.14 matrix. It is not yet published to PyPI or Zenodo. The latest published release remains 0.1.0, archived at DOI 10.5281/zenodo.22285167.

Install

pip install eyeprocesspy

Pin the initial scientific release when exact reproducibility matters:

pip install eyeprocesspy==0.1.0

For source installation from the immutable release tag:

pip install "git+https://github.com/stefanosbalaskas/eyeprocesspy.git@v0.1.0"

Windows users can also use the hardened installer described in the manual-install guide. It has been exercised on a real Windows/Python 3.11.9 installation with the recommended extras and a clean pip check.

Release evidence

See RELEASE_VALIDATION.md, TEST_SUMMARY.md, and the parity & validation guide for the underlying evidence.

What it covers

30-second reproducible check

import eyeprocesspy as ep

study = ep.eyeprocess_benchmark_study()
audit = ep.validate_benchmark_study(study)
data = ep.import_benchmark_study(study)

print(audit["valid"])
print(data)

For a real export:

import eyeprocesspy as ep

eye = ep.read_eye_export("participant_001.csv", vendor="auto")
issues = ep.validate_eye_dataset(eye)

See the package in action

Gaze trace	Probabilistic AOI

Pupil time series	Process reliability

Open the complete visual gallery →

Choose your path

• New to the package: Getting started → Worked examples → Cookbook
• Standardized Data Quality: Overview · 9-point example · Plot gallery · Sensitivity · Focused API\n- Eye-tracking / AOI work: End-to-end eye-tracking · Gazepoint import & QC
• Gaze latency / TTFF: Censored gaze-latency method · Disclosure example · Evidence-verification example · Troubleshooting · Reproducibility checklist
• Pupillometry / multimodal: Pupillometry guide · Quality & uncertainty
• Psychometrics / IRT: Psychometrics & IRT · IRT example
• Full workflows: Featured workflows · 88-article library
• Lookup: API reference · Plotting reference · FAQ

Scientific boundary

eyeprocesspy provides measurement and analysis infrastructure. A metric is not automatically a validated psychological construct, diagnosis, or causal explanation. Reliability does not establish construct validity; prediction does not establish causation; probabilistic AOI membership represents modeled coordinate uncertainty rather than probability of attention; and gaze, pupil, biometric, and process measures require an appropriate design, measurement model, and ethical interpretation.

Citation

If you use eyeprocesspy 0.1.0, cite the archived software release:

Balaskas, S. (2026). eyeprocesspy: Vendor-neutral Python infrastructure for eye-tracking and multimodal process data (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.22285167

For reproducibility, also report eyeprocesspy.__version__ and the frozen R reference exposed as eyeprocesspy.__r_reference_version__.

Relationship to R eyeprocess

The Python package is developed against the frozen eyeprocess 0.11.1 reference. API, articles, data, plots, numerical evidence, backends, and unavoidable language-specific divergences are tracked explicitly. Python-native extensions are kept distinct from reference parity so they do not masquerade as R-equivalent behavior.

License

MIT. See LICENSE.