Contributing

# Run automated setup
./scripts/setup.sh

# Activate environment
source heartmap_env/bin/activate

# Validate installation
python scripts/validate.py

# Update main branch
git checkout main
git pull origin main

# Create feature branch
git checkout -b feature-name
# OR for bug fixes
git checkout -b fix-bug-name

# Run all tests
python tests/test_heartmap.py

# Run specific test file
python tests/test_config.py

# Run validation
python scripts/validate.py

# Test with example data
python scripts/demo.py

# Format code with Black
black src/ tests/ scripts/ --line-length=100

# Lint with flake8
flake8 src/ --max-line-length=100 --ignore=E203,W503

# Type checking with mypy
mypy src/heartmap --ignore-missing-imports

# Stage changes
git add .

# Commit with clear message
git commit -m "Add feature: clear description of changes"

# Push to your fork
git push origin feature-name

HeartMap/
├── src/heartmap/          # Main source code
│   ├── models/           # Analysis models
│   ├── pipelines/        # Analysis pipelines
│   ├── api/              # API interfaces
│   ├── config/           # Configuration management
│   └── utils/            # Utility functions
├── tests/                # Test suite
│   ├── test_config.py
│   ├── test_data.py
│   ├── test_models.py
│   └── test_pipelines.py
├── scripts/              # Utility scripts
│   ├── setup.sh
│   ├── validate.py
│   └── demo.py
├── notebooks/            # Jupyter notebooks
├── docs/                 # Documentation
├── data/                 # Data directory
│   ├── raw/             # Raw input data
│   └── processed/       # Processed data
└── results/              # Analysis results

from heartmap.models.base import BaseModel

class MyCustomModel(BaseModel):
    """Custom analysis model."""
    
    def __init__(self, config):
        super().__init__(config)
        # Initialize your model
    
    def fit(self, adata):
        """Fit model to data."""
        # Training logic
        pass
    
    def predict(self, adata):
        """Generate predictions."""
        # Prediction logic
        pass

from heartmap.pipelines.base import BasePipeline

class MyCustomPipeline(BasePipeline):
    """Custom analysis pipeline."""
    
    def run(self, input_path, output_dir):
        """Run the pipeline."""
        # Load data
        adata = self._load_data(input_path)
        
        # Process
        results = self._process(adata)
        
        # Save results
        self._save_results(results, output_dir)
        
        return results

from dataclasses import dataclass

@dataclass
class MyFeatureConfig:
    """Configuration for new feature."""
    param1: int = 10
    param2: float = 0.5
    param3: bool = True

from fastapi import APIRouter

router = APIRouter()

@router.post("/my-endpoint")
async def my_endpoint(param: str):
    """New API endpoint."""
    # Implementation
    return {"result": "success"}

# Install coverage tool
pip install coverage

# Run tests with coverage
coverage run -m pytest tests/

# View coverage report
coverage report
coverage html  # Generate HTML report

# Example: Using the new feature
from heartmap import MyNewFeature

# Initialize
feature = MyNewFeature(config)

# Use the feature
results = feature.run(data)

# Access results
print(results.summary())

## Description
Clear description of what this PR does.

## Type of Change
- [ ] Bug fix
- [ ] New feature
- [ ] Breaking change
- [ ] Documentation update

## Related Issues
Fixes #123, Relates to #456

## Testing
- Describe testing performed
- Include test results

## Documentation
- List documentation updates made

## Additional Notes
Any additional information