This document provides an overview of FED3 Viz, a graphical user interface (GUI) designed for plotting and analyzing behavioral data from the Feeding Experimentation Device 3 (FED3), an open-source tool for mouse operant tasks. Written in Python with the tkinter library, FED3 Viz offers a user-friendly way to generate various visualizations from raw .csv or .xlsx data files without manual data editing. Key features include plots for pellet retrieval, poke analysis, meal patterns, circadian rhythms, and grouped averages. The software can be run as a standalone application on Windows and macOS or directly from a Python script. The project is now archived, with no further development planned; users are directed to the newer fed3 Python package for more updated tools. The work is licensed under a Creative Commons Attribution 4.0 International License.
FED3 Viz: A Visualization Tool for Behavioral Research Data
What is FED3 Viz?: FED3 Viz is a Python-based software with a graphical user interface (GUI) for creating graphs from data produced by FED3 (Feeding Experimentation Device 3), an open-source hardware device used in mouse behavioral research. The project is now archived, meaning no new development is planned.
Core Purpose: The software provides a quick and easy method for visualizing FED3 data, eliminating the need to manually process the raw data files. It helps researchers analyze patterns in feeding, learning, and reward-seeking behavior.
Key Visualization Features:
Pellet & Poke Analysis: Tracks pellet retrieval, correct vs. incorrect pokes, and poke bias over time.
Meal & Interval Analysis: Visualizes inter-pellet intervals, meal sizes, and progressive ratio breakpoints.
Circadian Rhythms: Generates chronograms and heatmaps to analyze day/night patterns of activity.
Group Analysis: Allows for averaging data from multiple devices and grouping them for comparison.
Device Diagnostics: Includes plots for monitoring motor turns and battery life.
Additional Functionality:
Data Management: Users can concatenate data from multiple files (e.g., a single recording split into parts), filter data by date, and save sessions (files, plots, and settings) for later use.
Customization & Export: Features a toolbar for live plot editing and allows users to save the generated plots, underlying data, and the Python code used to create the graph.
Installation Methods:
Packaged Application: Available as a standalone .exe for 64-bit Windows and an executable file for macOS. This method is for users without Python installed. The author notes that the application can be slow to start on Windows without a Defender exclusion and may run significantly slower on newer Macs.
Python Script: For users with Python installed, the software can be run directly from the source script (fed3viz.py). This requires installing dependencies listed in requirements.txt, and using a virtual environment is recommended.
Archived Status & Future: The FED3Viz project is no longer under active development. Users seeking more updated code and features are advised to use the fed3 Python package instead.