🚀 UNgroundwater v1.0.0 Released!

We are excited to announce the first official release of UNgroundwater – a Python module designed to process, analyze, and visualize groundwater storage anomalies using GRACE and GLDAS datasets. This release provides a solid foundation for groundwater data analysis with powerful features for data handling, masking, and visualization.

🎯 What's New in v1.0.0

• NetCDF Data Processing:

  • Load and process GRACE and GLDAS NetCDF files efficiently.
  • Perform data extraction and transformation with ease.

• Masking Capabilities:

  • Apply geographic masks using shapefiles for region-specific analysis.
  • Support for custom spatial filtering to improve data accuracy.

• Groundwater Estimation Functions:

  • Compute groundwater storage anomalies using state-of-the-art methodologies.
  • Integrate multiple data sources to enhance reliability.

• Visualization Tools:

  • Generate clear and insightful spatial/temporal groundwater plots.
  • Export figures for reporting and analysis purposes.

• Modular and Extensible:

  • Clean code structure, easy to extend for additional features.
  • Well-documented methods and usage examples.

📥 Installation

To install this version, simply run:

git clone https://github.com/Peter24K2G/UNgroundwater.git
cd UNgroundwater
pip install -r requirements.txt

📖 Usage Example

from UNgroundwater import groundwater

# Load GRACE data
data = groundwater.load_netcdf('data/GRACE_data.nc')

# Apply region mask
masked_data = groundwater.apply_mask(data, 'masks/region.shp')

# Visualize the results
groundwater.plot(masked_data)

🛠 Known Issues

• Minor performance optimization needed for large-scale datasets.
• Additional visualization options to be implemented in future versions.

📈 Future Roadmap

We are actively working on enhancing UNgroundwater with upcoming features such as:

• Improved data interpolation techniques.
• Automated trend analysis and anomaly detection.
• Integration with additional satellite datasets.

🙌 Contributing

We welcome contributions from the community! If you're interested in improving UNgroundwater, feel free to:

# Fork the repository
git clone https://github.com/your_username/UNgroundwater.git

# Create a new feature branch
git checkout -b feature-branch

# Make changes and commit them
git add .
git commit -m "Add new feature"

# Push changes and create a pull request
git push origin feature-branch

📄 License

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

Thank you for using UNgroundwater, and we look forward to your feedback and contributions!

📂 UNgroundwater on GitHub