Autopilot is a Python framework to perform behavioral experiments with one or many Raspberry Pis.

Its distributed structure allows arbitrary numbers and combinations of hardware components to be used in an experiment, allowing users to perform complex, hardware-intensive experiments at scale.

Autopilot integrates every part of your experiment, including hardware operation, task logic, stimulus delivery, data management, and visualization of task progress – making experiments in behavioral neuroscience replicable from a single file.

Instead of rigid programming requirements, Autopilot attempts to be a flexible framework with many different modalities of use in order to adapt to the way you do and think about your science rather than the other way around. Use only the parts of the framework that are useful to you, build on top of it with its plugin system as you would normally, while also maintaining the provenance and system integration that more rigid systems offer.

For developers of other tools, Autopilot provides a skeleton with minimal assumptions to integrate their work with its broader collection of tools, for example our integration of DeepLabCut-live as the DLC transform ([KLS+20]).

Our long-range vision is to build a tool that lowers barriers to tool use and contribution, from code to contextual technical knowledge, so our broad and scattered work can be cumulatively combined without needing a centralized consortium or adoption of a singular standard.

For a detailed overview of Autopilot’s motivation, design, and structure, see our whitepaper.

What’s New v0.5.0a0 - The Data Modeling Edition (2022-06-01)

A prerelease as Jonny is finishing their dissertation and doesn’t want to break anyone’s experiments!

  • Adding the whole module, which starts the process of making everything work with formal data models.

  • Rewriting the Subject class!

  • A ModelWidget to fill and edit data models that will eventually replace much of the aging GUI

  • Less jitter in JackClient by removing calls to queue.get

  • Repackaging autopilot with poetry!

  • log_parsers and programmatic reading of logs

  • See the changelog for more!

This documentation is very young and is very much a work in progress! Please submit an issue with any incompletenesses, confusion, or errors!

Indices and tables