# Ethopy Analysis
A comprehensive Python package for analyzing and visualizing behavioral data from Ethopy experiments.
## Overview
Ethopy Analysis provides a modern, modular approach to behavioral data analysis with the following key features:
- **DataFrame-based**: Most of plotting functions work with pandas DataFrames, making them independent of data source
- **Modular Design**: Composable functions for different analysis levels (animal, session, comparison)
- **Database Agnostic**: Works with DataJoint databases, CSV files, or any pandas-compatible data source
- **Extensible**: Plugin system for custom plots and analysis functions
- **Production Ready**: Command-line interface, proper packaging, and configuration management
## Installation
### From Source (Development)
Setting Up a Virtual Environment¶
Before installing dependencies, it's recommended to use a virtual environment to keep your project isolated and manageable.
```bash
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
installing dependencies:
```bash
# Clone the repository
git clone <repository-url>
cd Visualisations
# Install in development mode
pip install -e .
```
### Dependencies
- pandas >= 1.3.0
- matplotlib >= 3.5.0
- seaborn >= 0.11.0
- numpy >= 1.20.0
- plotly >= 5.0.0
- datajoint >= 0.13.0 (for database access)
- click >= 8.0.0 (for CLI)
## Package Structure
```
ethopy-analysis/
├── src/ethopy_analysis/
│ ├── data/ # Data loading and processing
│ │ ├── loaders.py # Main data loading functions
│ │ ├── analysis.py # Data analysis functions
│ │ └── utils.py # Data processing utilities
│ ├── plots/ # Plotting functions (DataFrame-based)
│ │ ├── animal.py # Animal-level plots
│ │ ├── session.py # Session-level plots
│ │ ├── comparison.py # Multi-animal/condition comparisons
│ │ └── utils.py # Plotting utilities and plugin system
│ ├── db/ # Database connectivity
│ │ ├── schemas.py # Database schema management
│ │ └── utils.py # Database utility functions
│ ├── config/ # Configuration management
│ │ └── settings.py # Configuration loading and validation
│ └── cli.py # Command-line interface
├── examples/ # Example notebooks and scripts
│ ├── load_example.ipynb
│ ├── animal_analysis_example.ipynb
│ └── session_analysis_example.ipynb
├── pyproject.toml # Package configuration
└── README.md
```
## Configuration
### Database Setup
Create a configuration file for database access:
```bash
ethopy-analysis create-config --output-path config.json
```
Edit the configuration file:
```json
{
"database": {
"host": "your-database.org:3306",
"user": "your_username",
"password": "your_password",
"schemas": {
"experiment": "lab_experiments",
"stimulus": "lab_stimuli",
"behavior": "lab_behavior"
}
}
}
```
### Environment Variables
Alternatively, use environment variables:
```bash
export DJ_HOST="database.example.org:3306"
export DJ_USER="your_username"
export DJ_PASSWORD="your_password"
```
## Examples and Tutorials
Check out the `examples/` directory for comprehensive notebooks:
- **`load_example.ipynb`**: Comprehensive animal-level analysis
- **`animal_analysis_example.ipynb`**: Comprehensive animal-level analysis
- **`session_analysis_example.ipynb`**: Detailed session-level analysis
## Contributing
### Adding New Plot Functions
1. Create your plotting function in the appropriate module
2. Follow the DataFrame-based input convention
3. Return `(fig, ax)` or `(fig, axes)` tuple
4. Import and use directly in your analysis
### Code Style
- Functions over classes where possible
- Clear, descriptive function names
- Pandas DataFrames for data exchange
- Matplotlib for plotting (with optional Plotly support)
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