hagelslag
Hagelslag supports segmentation and tracking of weather fields and scalable verification, including performance diagrams and reliability diagrams.
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
11 of 20 committers (55.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.7%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Hagelslag supports segmentation and tracking of weather fields and scalable verification, including performance diagrams and reliability diagrams.
Basic Info
Statistics
- Stars: 71
- Watchers: 16
- Forks: 25
- Open Issues: 6
- Releases: 4
Topics
Metadata Files
README.md
Hagelslag
Storm tracking, machine learning, and probabilistic evaluation
Hagelslag is an object-based severe storm forecasting system that utilizing image processing and machine learning tools to derive calibrated probabilities of severe hazards from convection-allowing numerical weather prediction model output. The package contains modules for storm identification and tracking, spatio-temporal data extraction, and machine learning model training to predict hazard intensity as well as space and time translations.
Citation
If you employ hagelslag in your research, please acknowledge its use with the following citations:
Gagne, D. J., A. McGovern, S. E. Haupt, R. A. Sobash, J. K. Williams, M. Xue, 2017: Storm-Based Probabilistic Hail
Forecasting with Machine Learning Applied to Convection-Allowing Ensembles, Wea. Forecasting, 32, 1819-1840.
https://doi.org/10.1175/WAF-D-17-0010.1.
Gagne II, D. J., A. McGovern, N. Snook, R. Sobash, J. Labriola, J. K. Williams, S. E. Haupt, and M. Xue, 2016:
Hagelslag: Scalable object-based severe weather analysis and forecasting. Proceedings of the Sixth Symposium on
Advances in Modeling and Analysis Using Python, New Orleans, LA, Amer. Meteor. Soc., 447.
If you discover any issues, please post them to the Github issue tracker page. Questions and comments should be sent to djgagne at ou dot edu.
Requirements
Hagelslag is compatible with Python 3.6 or newer. Hagelslag is easiest to install with the help of the Miniconda Python Distribution, but it should work with other Python setups as well. Hagelslag requires the following packages and recommends the following versions:
- numpy >= 1.10
- scipy >= 0.15
- matplotlib >= 1.4
- scikit-learn >= 0.16
- pandas >= 0.15
- arrow >= 0.8.0
- pyproj
- netCDF4-python
- xarray
- jupyter
- ncepgrib2
- pygrib
- cython
- pip
- sphinx
- mock
Install dependencies with the following commands:
git clone https://github.com/djgagne/hagelslag.git
cd ~/hagelslag
conda env create -f environment.yml
conda activate hagelslag
Installation
Install the latest version of hagelslag with the following command from the top-level hagelslag directory (where setup.py
is):
pip install .
Hagelslag will install the libraries in site-packages and will also install 3 applications into the bin directory
of your Python installation.
Use
A Jupyter notebook is located in the demos directory that showcases the functionality of the package. For larger scale use, 3 scripts are provided in the bin directory.
hsdataperforms object tracking and matching as well as data processing.hsforetrains and applies machine learning models.hsevalperforms forecast verification.
All scripts take input from a config file. The config file should be valid Python code and contain a dictionary called config. Custom machine learning models and parameters should be contained within the config files. Examples of them can be found in the config directory.
Documentation
API Documentation is available here.
Owner
- Name: David John Gagne
- Login: djgagne
- Kind: user
- Location: Boulder, CO
- Company: NSF National Center for Atmospheric Research
- Website: https://staff.ucar.edu/users/dgagne
- Repositories: 60
- Profile: https://github.com/djgagne
Machine Learning Scientist II and head of the Machine Integration and Learning for Earth Systems group at the NSF National Center for Atmospheric Research.
GitHub Events
Total
- Watch event: 4
Last Year
- Watch event: 4
Committers
Last synced: 6 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| David John Gagne | d****e@o****u | 362 |
| ahijevyc | a****c@u****u | 73 |
| David John Gagne | d****e@g****m | 64 |
| Amanda Burke | a****e@n****t | 35 |
| Amanda Burke | 3****e | 31 |
| Ubuntu | t****o@e****t | 10 |
| Charlie Becker | c****r@g****m | 9 |
| Charlie Becker | 3****r | 6 |
| Amanda Burke | a****e@l****u | 5 |
| Amanda Burke | a****e@l****u | 4 |
| Amanda Burke | a****e@l****u | 3 |
| Amanda Burke | a****e@s****u | 3 |
| Kate Avery | k****y@o****u | 3 |
| Nathan Wendt | n****t@o****u | 3 |
| Amanda Burke | a****e@d****u | 2 |
| Amanda Burke | a****e@l****u | 2 |
| Thomas Martin | 3****o | 1 |
| David Gagne | d****e@s****u | 1 |
| Luke Madaus | m****e@g****m | 1 |
| Maria J. Molina | h****a@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 6
- Total pull requests: 42
- Average time to close issues: N/A
- Average time to close pull requests: 19 days
- Total issue authors: 5
- Total pull request authors: 8
- Average comments per issue: 0.0
- Average comments per pull request: 0.86
- Merged pull requests: 40
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- ahijevyc (2)
- kresguerra02 (1)
- charlie-becker (1)
- mariajmolina (1)
- djgagne (1)
Pull Request Authors
- djgagne (20)
- ahijevyc (9)
- alburke (5)
- charlie-becker (4)
- nawendt (2)
- mariajmolina (1)
- lmadaus (1)
- ThomasMGeo (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 288 last-month
- Total dependent packages: 1
- Total dependent repositories: 2
- Total versions: 5
- Total maintainers: 1
pypi.org: hagelslag
Hagelslag is a Python package for storm-based analysis, forecasting, and evaluation.
- Documentation: https://hagelslag.readthedocs.io/
- License: MIT License
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Latest release: 0.6
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- arrow >=0.8
- cython *
- h5py *
- matplotlib *
- mock *
- netcdf4 *
- numpy >=1.18
- pandas >=1.2
- pygrib *
- pyproj *
- pytest *
- s3fs *
- scikit-image *
- scikit-learn >=0.2
- scipy *
- sphinx *
- xarray *
- arrow >=0.8
- matplotlib *
- mock *
- netcdf4 *
- numpy *
- pandas *
- scikit-image *
- scikit-learn *
- scipy *
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite
- dask *
- matplotlib *
- numba *
- numpy *
- pandas *
- pyarrow *
- pygrib *
- pyproj *
- pyshp *
- scikit-image *
- scikit-learn *
- scipy *
- xarray *
- zarr *
- arrow
- cython
- dask
- flake8
- h5py
- jasper
- jupyter
- jupyterlab
- matplotlib
- mock
- netcdf4
- numba
- numpy
- pandas
- pip
- pyarrow
- pygrib
- pyproj
- pytest
- python <=3.10
- s3fs
- scikit-image
- scikit-learn
- scipy
- sphinx
- xarray
- zarr