skyborn 0.3.16
Atmospheric science research utilities
Requires Python: >=3.9
Keywords: atmospheric-science, meteorology, climate, data-analysis, grib, netcdf

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## 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:

![Precipitation Trends Comparison](docs/source/images/precipitation_trends_comparison_1979_2014.png)

**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:

![Streamfunction and Velocity Potential](docs/source/images/windspharm_sfvp_analysis.png)

**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:

![Gridfill Missing Data Interpolation](docs/source/images/gridfill_demo_result_readme.png)

**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:

![Windspharm Performance Comparison](docs/source/images/windspharm_performance_comparison.png)

**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:

![GPI Speed Comparison](docs/source/images/gpi_speed_comparison.png)

**Performance Highlights:**
- **19-25x faster** than point-by-point implementations
- Processes entire atmospheric grids in seconds
- Native multi-dimensional support (3D/4D data)

![GPI Global Distribution](docs/source/images/gpi_global_distribution.png)

**Accuracy Validation:**
- Correlation coefficient > 0.99 with reference implementations
- RMSE < 1% for both VMAX and PMIN calculations

![GPI Scatter Comparison](docs/source/images/gpi_scatter_comparison.png)