Skip to content

Gaussian-Process Pupil Trajectories

Python-facing port of gaussian-process-pupil-trajectories.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

Approximate GP is the default

Exact GP computation remains available, but the complexity audit requires explicit review when the unique time-by-condition grid becomes large.

Hyperparameters are posterior estimands

Length scale and marginal GP standard deviation describe the fitted temporal function prior/posterior. They are not direct psychological constructs.

Python API mapping

  • gp3bayespy.create_pupil_gp_spec
  • gp3bayespy.fit_advanced_pupil_model_backend
  • gp3bayespy.plot_advanced_pupil_trajectory
  • gp3bayespy.plot_pupil_gp_hyperparameters
  • gp3bayespy.plot_pupil_model_complexity
  • gp3bayespy.predict_advanced_pupil_trajectory
  • gp3bayespy.pupil_gp_hyperparameters
  • gp3bayespy.pupil_gp_table
  • gp3bayespy.simulate_advanced_pupil_timecourse
  • gp3bayespy.specify_advanced_pupil_timecourse_model

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

An executable workflow for this family is included in ../../examples/pupil_workflow.py.