Science Score: 64.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
6 of 18 committers (33.3%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (18.0%) to scientific vocabulary
Keywords
Repository
Source code for ClimateLearn
Basic Info
Statistics
- Stars: 339
- Watchers: 4
- Forks: 53
- Open Issues: 9
- Releases: 1
Topics
Metadata Files
README.md
ClimateLearn
ClimateLearn is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods. For further context on our past motivation and future plans, check out our announcement blog post. Also, check out our arxiv preprint that showcases the flexibility of ClimateLearn in performing benchmarking and analysis on the robustness and transfer performance of deep learning models.
Usage
Python 3.8+ is required. The xESMF package has to be installed separately since one of its dependencies, ESMpy, is available only through Conda.
conda install -c conda-forge xesmf
pip install climate-learn
Quickstart
We have a quickstart notebook in the notebooks folder titled Quickstart.ipynb. It is intended for use in Google Colab and can be launched by clicking the Google Colab badge above or this link: https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing.
We also previewed some key features of ClimateLearn at a spotlight tutorial in the "Tackling Climate Change with Machine Learning" Workshop at the Neural Information Processing Systems 2022 Conference. The slides and recorded talk can be found on Climate Change AI's website.
Documentation
Find us on ReadTheDocs.
About Us
ClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by Professor Aditya Grover.
Contributing
Contributions are welcome! See our contributing guide.
Citing ClimateLearn
If you use ClimateLearn in your research, please cite our paper:
@article{nguyen2023climatelearn,
title={ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling},
author={Nguyen, Tung and Jewik, Jason and Bansal, Hritik and Sharma, Prakhar and Grover, Aditya},
journal={arXiv preprint arXiv:2307.01909},
year={2023}
}
Owner
- Name: Aditya Grover
- Login: aditya-grover
- Kind: user
- Website: http://aditya-grover.github.io
- Repositories: 15
- Profile: https://github.com/aditya-grover
Assistant Professor of Computer Science at UCLA
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: "ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling"
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Tung
family-names: Nguyen
email: tungnd@cs.ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Jason
family-names: Jewik
email: jason.jewik@ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Hritik
family-names: Bansal
email: hbansal@ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Prakhar
family-names: Sharma
email: prakhar6sharma@gmail.com
affiliation: 'University of California, Los Angeles'
- given-names: Aditya
family-names: Grover
email: adityag@cs.ucla.edu
affiliation: 'University of California, Los Angeles'
license: MIT
repository-code: "https://github.com/aditya-grover/climate-learn"
GitHub Events
Total
- Issues event: 1
- Watch event: 34
- Issue comment event: 2
- Pull request event: 2
- Fork event: 4
Last Year
- Issues event: 1
- Watch event: 34
- Issue comment event: 2
- Pull request event: 2
- Fork event: 4
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| jasonjewik | j****k@c****u | 66 |
| tung-nd | d****7@g****m | 41 |
| Jason Jewik | j****k@g****m | 39 |
| Shashank Goel | s****l@S****l | 29 |
| Shashank Goel | s****l@S****n | 23 |
| Prakhar Sharma | p****a@g****m | 22 |
| Jason Jewik | j****k@u****u | 21 |
| Siddharth Nandy | s****y@g****u | 16 |
| BRYAN(Jingchen) TANG | t****8@u****u | 15 |
| Seongbin Park | s****k@g****m | 14 |
| Prakhar Sharma | 3****a | 10 |
| Siddharth Nandy | s****y@g****m | 8 |
| Jingchen Tang tangtang1228@g.ucla.edu | t****g@m****u | 7 |
| Hritikbansal | h****n@g****m | 4 |
| Shashank Goel | s****l@w****u | 4 |
| Aditya Grover | a****1@g****m | 3 |
| se0ngbin | 6****n | 2 |
| Jason Jewik | j****b@g****m | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 40
- Total pull requests: 86
- Average time to close issues: about 1 month
- Average time to close pull requests: 3 days
- Total issue authors: 19
- Total pull request authors: 12
- Average comments per issue: 3.1
- Average comments per pull request: 0.99
- Merged pull requests: 73
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 2.5
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- prakhar6sharma (15)
- se0ngbin (3)
- Escape142 (2)
- bulaienTang (2)
- jasonjewik (2)
- linustws (1)
- ajikmr (1)
- CalibrationMe (1)
- arthurfeeney (1)
- blue-ocean-climate (1)
- patel-zeel (1)
- vargpt (1)
- noeliaof (1)
- Skerre (1)
- jovidsilva (1)
Pull Request Authors
- jasonjewik (35)
- se0ngbin (13)
- prakhar6sharma (13)
- tung-nd (7)
- bulaienTang (6)
- siddharthnandy (5)
- omid-bagheri-cee (2)
- arthurfeeney (2)
- aditya-grover (1)
- rohanshah13 (1)
- srikeerthi207 (1)
- bercowsky (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 88 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
pypi.org: climate-learn
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
- Documentation: https://climatelearn.readthedocs.io/en/latest/
- License: MIT License
-
Latest release: 1.0.0
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- cdsapi *
- importlib-metadata ==4.13.0
- pytorch-lightning *
- rich *
- timm *
- wandb *
- actions/checkout v3 composite
- actions/setup-python v4 composite
- psf/black stable composite
- cdsapi >=0.5.1
- dask >=2022.2.0
- importlib-metadata ==4.13.0
- matplotlib >=3.5.3
- netcdf4 >=1.6.2
- pytorch-lightning >=1.9.0
- rasterio >=1.3.7
- scikit-learn >=1.0.2
- tensorboard ==2.11.2
- timm ==0.9.2
- wandb >=0.13.9
- xarray >=0.20.2