Contributing

We welcome contributions! This guide will help you get started.

Development Setup

1. Fork and clone the repository:

   git clone https://github.com/YOUR_USERNAME/pansharpening-toolkit-.git
   cd pansharpening-toolkit-
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   # or
   venv\Scripts\activate  # Windows
   

3. Install in development mode:

   pip install -e ".[dev]"
   

4. Install pre-commit hooks:

   pre-commit install
   

Code Style

We use the following tools for code quality:

• Black: Code formatting
• isort: Import sorting
• Flake8: Linting

Run before committing:

pre-commit run --all-files

Testing

Run the test suite:

pytest tests/ -v

With coverage:

pytest tests/ -v --cov=models --cov=utils --cov-report=html

Pull Request Process

1. Create a feature branch:

   git checkout -b feature/amazing-feature
   

2. Make your changes and commit:

   git add .
   git commit -m "Add amazing feature"
   

3. Push to your fork:

   git push origin feature/amazing-feature
   

4. Open a Pull Request

Adding a New Model

1. Create models/your_model.py:

   import torch.nn as nn
   
   class YourModel(nn.Module):
       def __init__(self, ms_bands=4):
           super().__init__()
           self.ms_bands = ms_bands
           # Define layers
   
       def forward(self, ms, pan):
           # Implementation
           return fused
   

2. Register in models/__init__.py:

   from .your_model import YourModel
   
   AVAILABLE_MODELS.append('your_model')
   
   # Add to create_model function
   

3. Add tests in tests/test_models.py

4. Add documentation in docs/models/

Adding a New Loss

1. Add to models/losses.py:

   class YourLoss(nn.Module):
       def __init__(self):
           super().__init__()
   
       def forward(self, pred, target):
           # Implementation
           return loss
   

2. Register in create_loss() factory

3. Add tests in tests/test_losses.py

Reporting Issues

Please use the issue templates: - Bug Report - Feature Request

Include: - Python version - PyTorch version - OS - Steps to reproduce - Expected vs actual behavior