Skyborn

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Climate & Atmospheric Science Python Toolkit

Skyborn 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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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](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/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
- **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:

![Streamfunction and Velocity Potential](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/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](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/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.

### Curly Vector Plotting

Skyborn provides an NCL-like `curly_vector` renderer for atmospheric wind fields while keeping a Matplotlib and Cartopy compatible API.

![Curly Vector WRF Example](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/images/wrf_curly_vector_integration_direction_density08_ref10.png)

**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:

![Curly Vector Vertical Profile Example](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/images/hadley_msf_v_omega_ncl_curly.png)

**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:

![Windspharm Performance Comparison](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/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](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/images/gpi_speed_comparison.png)

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

![GPI Global Distribution](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/images/gpi_global_distribution.png)

**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

![GPI Scatter Comparison](https://raw.githubusercontent.com/QianyeSu/Skyborn/main/docs/source/images/gpi_scatter_comparison.png)

## 📖 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

Skyborn

Skyborn 최신버젼 다운로드

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Skyborn 최신버젼 다운로드

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