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Skyborn
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Climate & Atmospheric Science Python Toolkit
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<p align="center"> <a href="https://github.com/QianyeSu/Skyborn" target="_blank"> <img src="https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/_static/SkyBornLogo.svg" alt="Skyborn Logo" width="400"/> </a> </p> [](https://badge.fury.io/py/skyborn) [](https://pypi.org/project/skyborn/) [](https://pypi.org/project/skyborn/) [](https://codecov.io/gh/QianyeSu/Skyborn) [](https://github.com/QianyeSu/Skyborn/blob/main/LICENSE) [](https://github.com/QianyeSu/Skyborn/actions/workflows/stable-ci.yml) [](https://github.com/QianyeSu/Skyborn) [](https://github.com/psf/black) [](https://github.com/QianyeSu/Skyborn/actions/workflows/test-coverage.yml?query=branch%3Amain) [](https://skyborn.readthedocs.io/en/latest/) [](https://doi.org/10.5281/zenodo.18075252) ## System Requirements **Operating System:** 🖥️ **Cross-Platform** This package supports Windows, Linux, and macOS. However, it has been primarily developed and tested on Windows. **Note:** While the package can be installed on different platforms, some Windows-specific features may not work on other operating systems. ## Installation To install the Skyborn package, you can use pip: ```bash pip install skyborn ``` or ```bash pip install -U --index-url https://pypi.org/simple/ skyborn ``` ## 📚 Documentation **Full documentation is available at: [Documentation ](https://skyborn.readthedocs.io/en/latest/)** ## 🎯 Key Features & Submodules ### 📊 Spatial Trend Analysis & Climate Index Regression Skyborn provides ultra-fast spatial trend calculation and climate index regression analysis for atmospheric data:  **Key Capabilities:** - **High-Speed Spatial Trends**: Calculate long-term climate trends across global grids - Linear trend analysis for temperature, precipitation, and other variables - Statistical significance testing - Vectorized operations for massive datasets - **Mann-Kendall Trend Analysis**: Robust non-parametric trend testing for both single time series and multidimensional climate fields - Supports multiple Mann-Kendall test families including original, Yue-Wang, Hamed-Rao, seasonal, correlated seasonal, grouped multivariate, regional, correlated multivariate, and partial Mann-Kendall workflows - Direct multidimensional support for arrays such as ``(time, lat, lon)``, ``(time, level, lat, lon)``, and xarray-based labeled data - Batch-oriented compiled kernels make gridded workloads much faster than looping over one series at a time in pure Python - **Climate Index Regression**: Rapid correlation and regression analysis with climate indices - NINO 3.4, PDO, NAO, AMO index integration - Pattern correlation analysis - Teleconnection mapping **Other Applications:** - Climate change signal detection - Decadal variability analysis - Teleconnection pattern identification - Regional climate impact assessment Skyborn extends Mann-Kendall analysis from one-dimensional time series to multidimensional NumPy and xarray workflows, making it suitable for large gridded climate-analysis tasks as well as standard single-series trend tests. ### 🌍 Skyborn Windspharm Submodule - Atmospheric Analysis The Skyborn `windspharm` submodule provides powerful tools for analyzing global wind patterns through **streamfunction** and **velocity potential** calculations:  **Key Capabilities:** - **Streamfunction Analysis**: Identifies rotational (non-divergent) wind components - Visualizes atmospheric circulation patterns - Reveals jet streams and vortices - Essential for understanding weather systems - **Velocity Potential Analysis**: Captures divergent wind components - Shows areas of convergence and divergence - Critical for tropical meteorology - Identifies monsoon circulation patterns **Applications:** - Climate dynamics research - Weather pattern analysis - Atmospheric wave propagation studies - Tropical cyclone formation analysis ### 🔧 Skyborn Gridfill Submodule - Data Interpolation The Skyborn `gridfill` submodule provides advanced interpolation techniques for filling missing data in atmospheric and climate datasets:  **Key Features:** - **Poisson-based Interpolation**: Physically consistent gap filling - **Preserves Data Patterns**: Maintains spatial correlations and gradients - **Multiple Methods Available**: - Basic Poisson solver - High-precision iterative refinement - Zonal initialization options - Relaxation parameter tuning **Applications:** - Satellite data gap filling - Model output post-processing - Climate data reanalysis - Quality control for observational datasets The example above demonstrates filling gaps in global precipitation data, where the algorithm successfully reconstructs missing values while preserving the underlying meteorological patterns. ### Curly Vector Plotting Skyborn provides an NCL-like `curly_vector` renderer for atmospheric wind fields while keeping a Matplotlib and Cartopy compatible API.  **Key Capabilities:** - **NCL-like Curved Vectors**: Curved tails and lighter arrow heads for cleaner large-scale flow visualization - **Matplotlib-Compatible API**: Works with standard axes and Cartopy projections without switching to a separate plotting stack - **Flexible Inputs**: Supports regular lat-lon grids, xarray datasets, and curvilinear grids such as WRF mass-grid wind fields - **Vertical Cross-Section Support**: Supports latitude-pressure and similar vertical profile plots through the same `curly_vector` API - **Projection-Aware Rendering**: Supports global rendering with different `central_longitude` settings and Cartopy projections Skyborn `curly_vector` also supports vertical cross-section rendering, for example latitude-pressure plots using meridional wind and scaled omega:  **Minimal Example:** ```python import cartopy.crs as ccrs import matplotlib.pyplot as plt from skyborn.plot import curly_vector fig = plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.Robinson(central_longitude=180)) curly_vector( ds, x="longitude", y="latitude", u="u", v="v", ax=ax, transform=ccrs.PlateCarree(), density=0.9, color="black", arrowstyle="->", linewidth=1.2, arrowsize=1.2, zorder=3, integration_direction='both', ref_magnitude=30.0, ref_length=0.1, ) ax.coastlines() ax.set_global() ``` ## Performance Benchmarks ### 🚀 Windspharm Performance The Skyborn `windspharm` submodule delivers **~25% performance improvement** over standard implementations through modernized Fortran code and optimized algorithms:  **Key Performance Metrics:** - **Vorticity Calculation**: ~25% faster - **Divergence Calculation**: ~25% faster - **Helmholtz Decomposition**: ~25% faster - **Streamfunction/Velocity Potential**: ~25% faster ### ⚡ GPI Module Performance The Genesis Potential Index (GPI) module achieves **dramatic speedups** through vectorized Fortran implementation and native 3D processing:  **Performance Highlights:** - **About 25x faster** than point-by-point implementations - Processes entire atmospheric grids in seconds - Native multi-dimensional support (3D/4D data)  **Accuracy Validation:** - Correlation is effectively 1.0 against the reference implementation - RMSE is now on the order of **1e-4** for the core PI outputs, with very small overall error - Log-decomposition diagnostics also remain tightly matched to the updated `tcpyPI` results  ## 📖 Citation If you use Skyborn in your research, please cite it using the following format: ```bibtex @software{su2025skyborn, author = {Su, Qianye}, title = {Skyborn: Climate Data Analysis Toolkit}, year = {2025}, doi = {10.5281/zenodo.18075252}, url = {https://doi.org/10.5281/zenodo.18075252} } ``` Or in text: > Su, Q. (2025). Skyborn: Climate Data Analysis Toolkit. Zenodo. https://doi.org/10.5281/zenodo.18075252
