CAPE Replication Project Documentation¶

A comprehensive framework for crop yield forecasting and analysis with a focus on reproducibility and modularity.

← Back to Dashboard | Interactive Dashboard

🚀 Quick Start¶

# Install dependencies
conda env create -f scripts/environment.yml -n chafs_b
conda activate chafs_b

# Configure paths
# Edit config/config.json with your data paths

# Run complete workflow
cd scripts/processing && python cape_preprocessing.py --start-from 1
cd ../development && python cape_development.py
cd ../forecasting && python cape_forecasting.py

📚 Documentation Sections¶

Getting Started¶

• Installation and setup instructions
• Environment configuration
• Quick start guide

User Guide¶

• Complete workflow overview
• Data processing pipeline
• Model development and training
• Forecasting and analysis

API Reference¶

• Detailed script documentation
• Function references
• Parameter descriptions
• Usage examples

Examples¶

• Basic workflow examples
• Custom experiment configurations
• Analysis and visualization examples

📊 Interactive Dashboard¶

The interactive dashboard is now the home page of this project. Visit the main dashboard to explore simulation results across 7 African countries with interactive data visualization and analysis tools.

🏗️ Project Structure¶

cape_replication_project/
├── config/                     # Configuration files
├── docs/                       # Documentation
├── scripts/                    # Main script directories
│   ├── processing/            # Data processing scripts
│   ├── development/           # Model development scripts
│   ├── forecasting/           # Forecasting scripts
│   └── analysis_and_viz/      # Analysis and visualization
├── capevenv/                  # Virtual environment
└── environment.yml            # Conda environment specification

🔧 Key Features¶

• Modular Design: Each phase has its own directory and scripts
• Configuration-Driven: Centralized configuration management
• Reproducible: Complete environment specification and workflow tracking
• Comprehensive Logging: Detailed logging for debugging and monitoring
• Error Handling: Robust error handling and recovery mechanisms

📊 Workflow Overview¶

graph TD
    A[Raw Data] --> B[Data Processing]
    B --> C[Data Preprocessing]
    C --> D[Data Aggregation]
    D --> E[Model Development]
    E --> F[Forecasting]
    F --> G[Analysis & Visualization]

    B --> H[Data Streaming]
    C --> I[Data Cleaning]
    D --> J[Data Integration]
    E --> K[Model Training]
    F --> L[Prediction Generation]
    G --> M[Results Visualization]

🎯 Use Cases¶

• Crop Yield Forecasting: Predict crop yields using Earth observation data
• Agricultural Monitoring: Monitor agricultural conditions and trends
• Policy Support: Provide data-driven insights for agricultural policy
• Research: Support agricultural research and analysis

🤝 Contributing¶

We welcome contributions! Please see our Contributing Guide for details on:

• Development setup
• Code style guidelines
• Testing procedures
• Pull request process

📄 License¶

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments¶

• FEWS NET Data Warehouse for crop data
• Joint Research Centre for growing season data
• Open source community for supporting libraries

Need help? Check out our Troubleshooting Guide or open an issue on GitHub.