<p align="center">
<a href="https://github.com/QianyeSu/Skyborn" target="_blank">
<img src="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/)
## 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
- **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 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.
## 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:**
- **19-25x faster** than point-by-point implementations
- Processes entire atmospheric grids in seconds
- Native multi-dimensional support (3D/4D data)
**Accuracy Validation:**
- Correlation coefficient > 0.99 with reference implementations
- RMSE < 1% for both VMAX and PMIN calculations
Raw data
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However, it has been primarily developed and tested on Windows.\n\n**Note:** While the package can be installed on different platforms, some Windows-specific features may not work on other operating systems.\n\n## Installation\n\nTo install the Skyborn package, you can use pip:\n\n```bash\npip install skyborn\n```\nor\n\n```bash\npip install -U --index-url https://pypi.org/simple/ skyborn\n```\n\n## \ud83d\udcda Documentation\n\n**Full documentation is available at: [Documentation ](https://skyborn.readthedocs.io/en/latest/)**\n\n\n\n## \ud83c\udfaf Key Features & Submodules\n\n### \ud83d\udcca Spatial Trend Analysis & Climate Index Regression\n\nSkyborn provides ultra-fast spatial trend calculation and climate index regression analysis for atmospheric data:\n\n\n\n**Key Capabilities:**\n- **High-Speed Spatial Trends**: Calculate long-term climate trends across global grids\n - Linear trend analysis for temperature, precipitation, and other variables\n - Statistical significance testing\n - Vectorized operations for massive datasets\n\n- **Climate Index Regression**: Rapid correlation and regression analysis with climate indices\n - NINO 3.4, PDO, NAO, AMO index integration\n - Pattern correlation analysis\n - Teleconnection mapping\n\n**Other Applications:**\n- Climate change signal detection\n- Decadal variability analysis\n- Teleconnection pattern identification\n- Regional climate impact assessment\n\n### \ud83c\udf0d Skyborn Windspharm Submodule - Atmospheric Analysis\n\nThe Skyborn `windspharm` submodule provides powerful tools for analyzing global wind patterns through **streamfunction** and **velocity potential** calculations:\n\n\n\n**Key Capabilities:**\n- **Streamfunction Analysis**: Identifies rotational (non-divergent) wind components\n - Visualizes atmospheric circulation patterns\n - Reveals jet streams and vortices\n - Essential for understanding weather systems\n\n- **Velocity Potential Analysis**: Captures divergent wind components\n - Shows areas of convergence and divergence\n - Critical for tropical meteorology\n - Identifies monsoon circulation patterns\n\n**Applications:**\n- Climate dynamics research\n- Weather pattern analysis\n- Atmospheric wave propagation studies\n- Tropical cyclone formation analysis\n\n### \ud83d\udd27 Skyborn Gridfill Submodule - Data Interpolation\n\nThe Skyborn `gridfill` submodule provides advanced interpolation techniques for filling missing data in atmospheric and climate datasets:\n\n\n\n**Key Features:**\n- **Poisson-based Interpolation**: Physically consistent gap filling\n- **Preserves Data Patterns**: Maintains spatial correlations and gradients\n- **Multiple Methods Available**:\n - Basic Poisson solver\n - High-precision iterative refinement\n - Zonal initialization options\n - Relaxation parameter tuning\n\n**Applications:**\n- Satellite data gap filling\n- Model output post-processing\n- Climate data reanalysis\n- Quality control for observational datasets\n\nThe example above demonstrates filling gaps in global precipitation data, where the algorithm successfully reconstructs missing values while preserving the underlying meteorological patterns.\n\n## Performance Benchmarks\n\n### \ud83d\ude80 Windspharm Performance\n\nThe Skyborn `windspharm` submodule delivers **~25% performance improvement** over standard implementations through modernized Fortran code and optimized algorithms:\n\n\n\n**Key Performance Metrics:**\n- **Vorticity Calculation**: ~25% faster\n- **Divergence Calculation**: ~25% faster\n- **Helmholtz Decomposition**: ~25% faster\n- **Streamfunction/Velocity Potential**: ~25% faster\n\n### \u26a1 GPI Module Performance\n\nThe Genesis Potential Index (GPI) module achieves **dramatic speedups** through vectorized Fortran implementation and native 3D processing:\n\n\n\n**Performance Highlights:**\n- **19-25x faster** than point-by-point implementations\n- Processes entire atmospheric grids in seconds\n- Native multi-dimensional support (3D/4D data)\n\n\n\n**Accuracy Validation:**\n- Correlation coefficient > 0.99 with reference implementations\n- RMSE < 1% for both VMAX and PMIN calculations\n\n\n",
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