GCM-Filters
GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data - Published in JOSS (2022)
Science Score: 100.0%
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
Found 16 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: wiley.com, joss.theoj.org -
✓Committers with academic emails
6 of 14 committers (42.9%) from academic institutions -
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
Diffusion-based Spatial Filtering of Gridded Data
Basic Info
- Host: GitHub
- Owner: ocean-eddy-cpt
- License: other
- Language: Python
- Default Branch: master
- Homepage: https://gcm-filters.readthedocs.io/
- Size: 68.4 MB
Statistics
- Stars: 43
- Watchers: 6
- Forks: 28
- Open Issues: 13
- Releases: 12
Metadata Files
README.md
GCM Filters
GCM-Filters: Diffusion-based Spatial Filtering of Gridded Data
Description
GCM-Filters is a python package that performs spatial filtering analysis in a flexible and efficient way. The GCM-Filters algorithm applies a discrete Laplacian to smooth a field through an iterative process that resembles diffusion (Grooms et al., 2021). The package can be used for either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Such GCM data come on complex curvilinear grids, whose geometry is respected by the GCM-Filters Laplacians. Through integration with dask, GCM-Filters enables parallel, out-of-core filter analysis on both CPUs and GPUs.
Installation
GCM-Filters can be installed using conda:
shell
conda install -c conda-forge gcm_filters
GCM-Filters can also be installed with pip:
shell
pip install gcm_filters
Getting Started
To learn how to use GCM-Filters for your data, visit the GCM-Filters documentation.
Binder Demo
Click the button below to run an interactive demo of GCM-Filters in Binder:
Get in touch
Report bugs, suggest features or view the source code on GitHub.
License and copyright
GCM-Filters is licensed under version 3 of the Gnu Lesser General Public License.
Development occurs on GitHub at https://github.com/ocean-eddy-cpt/gcm-filters.
How to cite GCM-Filters
If you are using GCM-Filters and would like to cite it in academic publications, we would certainly appreciate it. We recommend two citations.
- Loose et al., (2022). GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. Journal of Open Source Software, 7(70), 3947, https://doi.org/10.21105/joss.03947
Here’s an example of a BibTeX entry:
shell
@article{Loose2022,
author = {Nora Loose and Ryan Abernathey and Ian Grooms and Julius Busecke and Arthur Guillaumin and Elizabeth Yankovsky and Gustavo Marques and Jacob Steinberg and Andrew Slavin Ross and Hemant Khatri and Scott Bachman and Laure Zanna and Paige Martin},
title = {GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data},
journal = {Journal of Open Source Software},
volume = {7},
number = {70},
pages = {3947},
doi = {10.21105/joss.03947},
url = {https://doi.org/10.21105/joss.03947},
year = {2022},
publisher = {The Open Journal},
}
- Grooms et al., (2021). Diffusion-Based Smoothers for Spatial Filtering of Gridded Geophysical Data. Journal of Advances in Modeling Earth Systems, 13, e2021MS002552, https://doi.org/10.1029/2021MS002552
Here’s an example of a BibTeX entry:
shell
@article{Grooms2021,
author = {Grooms, I. and Loose, N. and Abernathey, R. and Steinberg, J. M. and Bachman, S. D. and Marques, G. and Guillaumin, A. P. and Yankovsky, E.},
title = {Diffusion-Based Smoothers for Spatial Filtering of Gridded Geophysical Data},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {13},
number = {9},
pages = {e2021MS002552},
keywords = {spatial filtering, coarse graining, data analysis},
doi = {https://doi.org/10.1029/2021MS002552},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002552},
year = {2021}
}
Owner
- Name: Ocean Eddy CPT
- Login: ocean-eddy-cpt
- Kind: organization
- Location: The Earth System
- Website: https://ocean-eddy-cpt.github.io
- Repositories: 9
- Profile: https://github.com/ocean-eddy-cpt
Ocean Transport and Eddy Energy Climate Process Team
JOSS Publication
GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data
Authors
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO, USA
Woods Hole Oceanographic Institution, Woods Hole, MA, USA
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
Earth, Ocean and Ecological Sciences, University of Liverpool, UK
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO, USA
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA, Research School of Earth Sciences, Australian National University, Canberra, Australia
Editor
Jayaram Hariharan
Tags
ocean modeling climate modeling fluid dynamicsCitation (CITATION.cff)
cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Loose
given-names: Nora
orcid: https://orcid.org/0000-0002-3684-9634
- family-names: Abernathey
given-names: Ryan
orcid: https://orcid.org/0000-0001-5999-4917
- family-names: Grooms
given-names: Ian
orcid: https://orcid.org/0000-0002-4678-7203
- family-names: Busecke
given-names: Julius
orcid: https://orcid.org/0000-0001-8571-865X
- family-names: Guillaumin
given-names: Arthur
orcid: https://orcid.org/0000-0003-1571-4228
- family-names: Yankovsky
given-names: Elizabeth
orcid: https://orcid.org/0000-0003-3612-549X
- family-names: Marques
given-names: Gustavo
orcid: https://orcid.org/0000-0001-7238-0290
- family-names: Steinberg
given-names: Jacob
orcid: https://orcid.org/0000-0002-2609-6405
- family-names: Ross
given-names: Andrew Slavin
orcid: https://orcid.org/0000-0002-2368-6979
- family-names: Khatri
given-names: Hemant
orcid: https://orcid.org/0000-0001-6559-9059
- family-names: Bachman
given-names: Scott
orcid: https://orcid.org/0000-0002-6479-4300
- family-names: Zanna
given-names: Laure
orcid: https://orcid.org/0000-0002-8472-4828
- family-names: Martin
given-names: Paige
orcid: https://orcid.org/0000-0003-3538-633X
title: "GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data"
version: v0.2.1
date-released: 2022-02-10
GitHub Events
Total
- Issues event: 2
- Watch event: 4
- Issue comment event: 9
- Push event: 1
- Pull request review event: 1
- Pull request event: 1
- Fork event: 2
Last Year
- Issues event: 2
- Watch event: 4
- Issue comment event: 9
- Push event: 1
- Pull request review event: 1
- Pull request event: 1
- Fork event: 2
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| NoraLoose | n****e@g****m | 368 |
| Ian Grooms | i****s@c****u | 79 |
| Ryan Abernathey | r****y@g****m | 55 |
| Julius Busecke | j****s@l****u | 39 |
| Arthur | a****n@g****m | 29 |
| Andrew Ross | a****s@g****m | 12 |
| Jake Steinberg | j****g@w****u | 10 |
| Gustavo Marques | g****s@u****u | 10 |
| Paige Martin | p****r@u****u | 9 |
| Hemant Khatri | h****1@g****m | 9 |
| Elizabeth A Yankovsky | e****6@l****r | 9 |
| Elizabeth A Yankovsky | e****6@l****r | 4 |
| Scott Bachman | b****n@u****u | 2 |
| Romain Caneill | r****l@e****g | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 51
- Total pull requests: 67
- Average time to close issues: 8 months
- Average time to close pull requests: 26 days
- Total issue authors: 17
- Total pull request authors: 11
- Average comments per issue: 5.02
- Average comments per pull request: 3.63
- Merged pull requests: 64
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 7
- Average time to close issues: about 21 hours
- Average time to close pull requests: about 3 hours
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- NoraLoose (16)
- jbusecke (10)
- rabernat (8)
- asross (2)
- isgiddy (2)
- dhruvbalwada (2)
- paigem (2)
- rcaneill (1)
- yangminah (1)
- iangrooms (1)
- AleksiNummelin (1)
- marimpacheco (1)
- Pperezhogin (1)
- ElizabethYankovsky (1)
- aidanheerdegen (1)
Pull Request Authors
- NoraLoose (46)
- iangrooms (7)
- rabernat (7)
- jbusecke (4)
- asross (2)
- jakesteinberg (2)
- arthurBarthe (1)
- paigem (1)
- ElizabethYankovsky (1)
- gustavo-marques (1)
- rcaneill (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 3,778 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 5
(may contain duplicates) - Total versions: 15
- Total maintainers: 1
pypi.org: gcm-filters
Diffusion-based Spatial Filtering of Gridded Data
- Homepage: https://github.com/ocean-eddy-cpt/gcm-filters
- Documentation: https://gcm-filters.readthedocs.io/
- License: LGPL-3.0
-
Latest release: 0.5.1
published over 1 year ago
Rankings
Maintainers (1)
conda-forge.org: gcm_filters
**GCM-Filters** is a python package that performs spatial filtering analysis in a flexible and efficient way. The GCM-Filters algorithm applies a discrete Laplacian to smooth a field through an iterative process that resembles diffusion ([Grooms et al., 2021](https://doi.org/10.1029/2021MS002552)). The package can be used for either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Such GCM data come on complex curvilinear grids, whose geometry is respected by the GCM-Filters Laplacians. Through integration with [dask](https://dask.org/), GCM-Filters enables parallel, out-of-core filter analysis on both CPUs and GPUs.
- Homepage: https://github.com/ocean-eddy-cpt/gcm-filters
- License: LGPL-3.0-only
-
Latest release: 0.3.0
published over 3 years ago
Rankings
Dependencies
- black * development
- check-manifest * development
- doctr * development
- flake8 * development
- flake8-builtins * development
- flake8-comprehensions * development
- flake8-mutable * development
- flake8-print * development
- interrogate * development
- isort * development
- nbsphinx * development
- pre-commit * development
- pylint * development
- pytest * development
- pytest-cov * development
- pytest-flake8 * development
- pytest-lazy-fixture * development
- pytest-xdist * development
- recommonmark * development
- setuptools_scm * development
- sphinx * development
- twine * development
- wheel * development
- zarr * development
- dask *
- matplotlib *
- scipy *
- xarray *
- actions/checkout v2.3.5 composite
- actions/setup-python v2 composite
- pre-commit/action v2.0.0 composite
- actions/checkout master composite
- actions/setup-python v1 composite
- pypa/gh-action-pypi-publish master composite
- actions/cache v1 composite
- actions/checkout v2 composite
- codecov/codecov-action v1 composite
- conda-incubator/setup-miniconda v2 composite