psyphy 0.0.4

Adaptive Trial Placement for Psychophysical Experiments

Active-learning-driven adaptive experimentation in psychophysics

Installation | Documentation | Examples | Contributing

Overview

psyphy is an open-source, JAX-based framework for psychophysics research. It leverages GPU acceleration and efficient approximate inference to power Bayesian, active-learning-driven adaptive experiments.

Designed to be modular and extensible, psyphy accelerates research workflows and enables real-time adaptive experiments. While currently focused on human color perception, it can be adapted to other perceptual modalities.

The package is under active development, and we welcome contributions.

Install

psyphy only supports python 3.10+. We recommend installing psyphy under a virtual environment. Once you've created a virtual environment for psyphy and activated it, you can install psyphy using pip:

pip install psyphy

If you're developer or want to use the latest features, you can install from GitHub using:

git clone https://flatironinstitute.github.io/psyphy.git
cd psyphy
pip install -e .

Quickstart

• Go here for a light-weight tutorial that demonstrates how to instantiate, evaluate and fit a model quickly. You should be able to run the underlying script on your CPU.
• Go here for a more comprehensive example visualizing a spatially varying covariance field, also explaining the underlying math. The underlying script for this tutorial requires a GPU.

Contributing

For contributors, see CONTRIBUTING.md for full doc guidelines and NumPy-style docstrings.