Skyborn
A comprehensive Python package for climate data analysis, featuring advanced statistical methods, emergent constraint techniques, and data conversion utilities.
https://github.com/qianyesu/skyborn
Category: Climate Change
Sub Category: Climate Data Processing and Analysis
Keywords
atmospheric-science climate climate-change meteorology meteorology-library physics python statistical-analysis
Last synced: about 20 hours ago
JSON representation
Repository metadata
Climate & Atmospheric Science Python Toolkit
- Host: GitHub
- URL: https://github.com/qianyesu/skyborn
- Owner: QianyeSu
- License: bsd-3-clause
- Created: 2024-08-02T15:34:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-24T07:38:50.000Z (about 1 month ago)
- Last Synced: 2025-12-21T11:42:16.484Z (5 days ago)
- Topics: atmospheric-science, climate, climate-change, meteorology, meteorology-library, physics, python, statistical-analysis
- Language: Fortran
- Homepage: https://skyborn.readthedocs.io/en/latest/
- Size: 57.2 MB
- Stars: 35
- Watchers: 3
- Forks: 3
- Open Issues: 5
- Releases: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
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:
pip install skyborn
or
pip install -U --index-url https://pypi.org/simple/ skyborn
📚 Documentation
Full documentation is available at: Documentation
🎯 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
Owner metadata
- Name: Qianye Su
- Login: QianyeSu
- Email:
- Kind: user
- Description: After three days without programming, life becomes meaningless
- Website:
- Location: Zhanjiang, China
- Twitter:
- Company: Guangdong Ocean University
- Icon url: https://avatars.githubusercontent.com/u/112712604?u=313e58adce093f7592ba2dd1bb1da58439fc6c87&v=4
- Repositories: 1
- Last ynced at: 2025-10-19T16:48:56.788Z
- Profile URL: https://github.com/QianyeSu
GitHub Events
Total
- Release event: 5
- Watch event: 19
- Delete event: 7
- Issue comment event: 6
- Push event: 290
- Pull request event: 26
- Pull request review event: 2
- Fork event: 2
- Create event: 14
Last Year
- Release event: 5
- Watch event: 19
- Delete event: 7
- Issue comment event: 6
- Push event: 290
- Pull request event: 26
- Pull request review event: 2
- Fork event: 2
- Create event: 14
Committers metadata
Last synced: 20 days ago
Total Commits: 589
Total Committers: 6
Avg Commits per committer: 98.167
Development Distribution Score (DDS): 0.063
Commits in past year: 586
Committers in past year: 6
Avg Commits per committer in past year: 97.667
Development Distribution Score (DDS) in past year: 0.058
| Name | Commits | |
|---|---|---|
| Qianye Su | 9****5@q****m | 552 |
| Qianye Su | 1****e | 19 |
| dependabot[bot] | 4****] | 13 |
| Skyborn | 1****7@q****m | 3 |
| decadeneo | 1****o | 1 |
| Skyborn | 1****e | 1 |
Committer domains:
- qq.com: 2
Issue and Pull Request metadata
Last synced: 14 days ago
Total issues: 0
Total pull requests: 15
Average time to close issues: N/A
Average time to close pull requests: 2 days
Total issue authors: 0
Total pull request authors: 5
Average comments per issue: 0
Average comments per pull request: 0.73
Merged pull request: 5
Bot issues: 0
Bot pull requests: 9
Past year issues: 0
Past year pull requests: 15
Past year average time to close issues: N/A
Past year average time to close pull requests: 2 days
Past year issue authors: 0
Past year pull request authors: 5
Past year average comments per issue: 0
Past year average comments per pull request: 0.73
Past year merged pull request: 5
Past year bot issues: 0
Past year bot pull requests: 9
Top Issue Authors
Top Pull Request Authors
- dependabot[bot] (9)
- sqyyqssqyyqs (3)
- Copilot (1)
- QianyeSu (1)
- decadeneo (1)
Top Issue Labels
Top Pull Request Labels
- dependencies (9)
- github_actions (9)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 589 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 19
- Total maintainers: 1
pypi.org: skyborn
Atmospheric science research utilities
- Homepage: https://github.com/QianyeSu/Skyborn
- Documentation: https://skyborn.readthedocs.io/
- Licenses: bsd-3-clause
- Latest release: 0.3.15 (published about 2 months ago)
- Last Synced: 2025-12-22T06:02:26.656Z (4 days ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 589 Last month
-
Rankings:
- Dependent packages count: 9.475%
- Average: 31.419%
- Dependent repos count: 53.362%
- Maintainers (1)
Score: 11.860761460241756