piprkit 0.0.16

Package for processing pupil data, with a focus on the post illumination pupil response.

Unit Test Status

piprkit - Pupil Data Processing Package

A Python package for processing pupil data, with a focus on the post illumination pupil response (PIPR).

Features

• Core data structures for pupillometry: PupilMeasurement and PupilSeries.
• Utilities for light stimuli: LightStimulus and LightStimuliSeries with plotting and time-offset support.
• Data loaders and example data: load_real_series, load_simulated_pupil, and simulate_pupil_measurement for quick demos and tests.
• Preprocessing helpers: rolling mean/median, rate-of-change limiting, interpolation, trimming, and NaN handling.
• Fitting framework: phase-based fitting (baseline, latency, constriction, sustained, redilation) and PupilFit convenience wrapper.
• Basic metrics: baseline calculation and window-based helpers like pipr_6s, pipr_xs, peak_constriction, and time_to_peak.

Feature Completeness

Feature Category	Status	Description
Core Data Classes	✅ Implemented	PupilMeasurement, PupilSeries
Light-Stimulus Utilities	✅ Implemented	Utilities for stimulus handling
Preprocessing Filters	✅ Implemented	Includes smoothing, normalization, etc.
Basic Metrics	✅ Implemented	baseline, pipr_6s
Data Loaders	⚠️ Partially Implemented	Functions to load example and user datasets implemented, better file loading required.
Fitting Pipeline	⚠️ Partially Implemented	Some phase fits work; FitConstrict and PupilFit have TODOs, may return NaNs
FitLatency	❌ Not Implemented	Stub only
Advanced PLR/PIPR Metrics	❌ Not Implemented	transient_plr, plr_latency, constriction_v, redilation_v, auc_*, net_pipr

See TODO.md for an extensive list.

Installation

Examples

Check out the examples/ directory for Jupyter notebooks demonstrating:

• Basic measurement processing
• Time series analysis
• Complete analysis workflows

License

[Add your license information here]

Author

S. Belgers