MouseMemoryGraph

Overview

MouseMemoryGraph is a Dash-based web application for visualizing and analyzing neural and behavioral data from mouse experiments. The application integrates photometry data and behavioral tracking data to provide interactive visualizations and interval-based analyses. Click on the title above to check out our website which gives a detailed background on this project and our team.

Features

• Interactive plots for photometry and behavioral data
• Epoching for visualizations of freezing behaviour onset and offset events
• Reprocessing functionality to incorporate newly added datasets
• Web-based dashboard for intuitive data exploration

Dependencies

Ensure the following dependencies are installed before running the application:

pip install -r requirements.txt

To generate the requirements.txt file from your current environment, run:

pip freeze > requirements.txt

Main Libraries Used:

• dash for web application interface
• plotly for interactive graphing
• pandas & numpy for data handling and processing
• scipy for signal filtering

Installation & Setup

1. Clone this repository:

   git clone https://github.com/yourusername/MouseMemoryGraph.git
   cd MouseMemoryGraph

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python code/app.py

5. Open your browser and navigate to:

   http://127.0.0.1:8050/
   

Directory Structure

MouseMemoryGraph/
│-- assets/                  # Static files (images, CSS, etc.)
│   ├── header.png           # Header image for the dashboard
│   ├── footer.png           # Footer image for the dashboard
│   ├── style.css            # (Optional) Custom styles
│-- code/                    # Source code
│   ├── app.py               # Main Dash application
│   ├── dataset.py           # Data processing classes
│   ├── dashboard.py         # Dashboard layout and callbacks
│-- data/                    # Data directory (must be manually populated)
│   ├── mouse1/              # Example mouse dataset folder
│   │   ├── cfc_2046.csv      # Photometry data
│   │   ├── behavior.csv      # Behavioral tracking data
│   ├── mouse2/              # Additional dataset
│-- dist/                    # Compiled application (if using PyInstaller)
│-- README.md                # This file

Adding Your Data

1. Place photometry and behavioral data files in the data/ directory.
2. Organize them into subfolders named after each mouse (e.g., data/mouse1/).
3. Ensure file names follow the expected format:
   • Photometry: cfc_2046.csv
   • Behavior: a2024-11-01T14_30_53DLC_resnet50_fearbox_optoJan27shuffle1_100000.csv
4. Run the application, and it will dynamically detect and load new datasets.

Acknowledgments

This project includes concepts inspired by:

• Photometry Data Processing Repository
• JOVE Paper: Multi-Fiber Photometry

License

This project is licensed under the MIT License. See LICENSE for more details.

Authors

• Brynn Harris-Shanks
• Julian Ostermaier
• Maria Jose Araya