Poulet Py – Neuroscience Python Library from Poulet Lab

A modular Python library for neuroscientific hardware control, data analysis, and experimental setups.

Overview

Poulet provides a collection of tools for neuroscience researchers, including:

• Hardware communication (e.g., serial devices, DAQ interfaces)
• Data analysis utilities
• Experimental workflow automation

The library is organized into these core modules:

Module	Description
config	It contains the SETTINGS and LOGGER constants for loading environmental variables and logging.
converters	For converting different filetypes to common data science (h5, plk, etc.).
hardware	Low-level drivers and interfaces for external devices (e.g., amplifiers, stimulator).
tools	Helper functions (serializers, generators, file I/O) – typically class-free.
utils	High-level interactive tools for experiments and data processing.

Installation

Minimal Installation

pip install -U .

Optional Dependencies

Install specific modules:

pip install -U .[hardware]          # Only hardware dependencies
pip install -U .[tools,utils]       # Tools + utilities

# Special case for seq or all:
pip install -U git+https://github.com/LJMUAstroecology/flirpy.git
pip install -U .[seq]

pip install -U .[all]               # Everything

Development Mode (Editable Install)

pip install -U git+https://github.com/LJMUAstroecology/flirpy.git
pip install -U -e .[all]            # For contributors

Configuration

The config/ folder includes:

• settings.py – Environment variables for dynamic configuration (avoid hardcoded values).
• Custom logger – Use instead of print() for better debugging.

Example:

from poulet_py import LOGGER
LOGGER.info("Starting trial...")

Examples

Check the examples/ folder for usage scripts, such as:

• examples/oscilloscope.py – Oscilloscope like visualization.

Contributing

We welcome contributions! Follow these guidelines:

1. Branching

• Work on a dedicated branch (git checkout -b your-feature).
• Submit PRs to the dev branch (PRs to main will be rejected).

2. Code Standards

• Tests: Add unit tests in tests/ (mirroring the module structure).
• Documentation: Use docstrings and update init.py for lazy loading.
• Type hints: Recommended for new functions.

3. Commit Messages

• Use semantic prefixes (e.g., feat:, fix:, docs:).