metpy
MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
Science Score: 36.0%
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
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
34 of 89 committers (38.2%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.0%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
Basic Info
- Host: GitHub
- Owner: Unidata
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Homepage: https://unidata.github.io/MetPy/
- Size: 1010 MB
Statistics
- Stars: 1,355
- Watchers: 58
- Forks: 437
- Open Issues: 356
- Releases: 43
Topics
Metadata Files
README.md
MetPy
MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
MetPy follows semantic versioning in its version number. This means
that any MetPy 1.x release will be backwards compatible with an earlier 1.y release. By
"backward compatible", we mean that correct code that works on a 1.y version will work
on a future 1.x version.
For additional MetPy examples not included in this repository, please see the MetPy Cookbook on Project Pythia.
We support Python >= 3.10.
Need Help?
Need help using MetPy? Found an issue? Have a feature request? Checkout our support page.
Important Links
- HTML Documentation
- MetPy Cookbook on Project Pythia
- "metpy" tagged questions on Stack Overflow
- Gitter chat room
Dependencies
Other required packages:
- Numpy
- Scipy
- Matplotlib
- Pandas
- Pint
- Xarray
There is also an optional dependency on the pyproj library for geographic projections (used with cross sections, grid spacing calculation, and the GiniFile interface).
See the installation guide for more information.
Code of Conduct
We want everyone to feel welcome to contribute to MetPy and participate in discussions. In that spirit please have a look at our Code of Conduct.
Contributing
Imposter syndrome disclaimer: We want your help. No, really.
There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?
We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.
Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
For more information, please read the see the contributing guide.
Philosophy
The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you take the average GEMPAK script for a weather map, you need to:
- read data
- calculate a derived field
- show on a map/skew-T
One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for any meteorological Python application; this means making it easy to pull out the LCL calculation and just use that, or reuse the Skew-T with your own data code. MetPy also prides itself on being well-documented and well-tested, so that on-going maintenance is easily manageable.
The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script up weather analysis. It doesn't even have to be scripting; all python meteorology tools are hoped to be able to benefit from MetPy. Conversely, it's hoped to be the meteorological equivalent of the audience of scipy/scikit-learn/skimage.
Owner
- Name: NSF Unidata
- Login: Unidata
- Kind: organization
- Email: support@unidata.ucar.edu
- Location: Boulder, Colorado, USA
- Website: https://www.unidata.ucar.edu/
- Twitter: unidata
- Repositories: 130
- Profile: https://github.com/Unidata
Our mission is to transform the geosciences community, research, and education by providing innovative data services and tools.
GitHub Events
Total
- Create event: 111
- Release event: 2
- Issues event: 69
- Watch event: 85
- Delete event: 98
- Issue comment event: 204
- Push event: 238
- Pull request review comment event: 81
- Pull request event: 326
- Pull request review event: 264
- Fork event: 19
Last Year
- Create event: 111
- Release event: 2
- Issues event: 69
- Watch event: 85
- Delete event: 98
- Issue comment event: 204
- Push event: 238
- Pull request review comment event: 81
- Pull request event: 326
- Pull request review event: 264
- Fork event: 19
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ryan May | r****y@u****u | 2,368 |
| dependabot[bot] | 4****] | 964 |
| John Leeman | k****b@g****m | 347 |
| Jon Thielen | g****b@j****c | 167 |
| Drew Camron | d****n@u****u | 166 |
| Zach Bruick | z****k@v****u | 118 |
| Kevin Goebbert | k****t@v****u | 104 |
| Steven Decker | s****r@e****u | 55 |
| Nathan Wendt | n****t@n****v | 36 |
| mgrover1 | m****x@g****m | 34 |
| Sean Arms | s****s@u****u | 33 |
| Connor Cozad | 2****d@g****m | 32 |
| Daryl Herzmann | a****z@i****u | 29 |
| Claude Dicaire | 3****6 | 26 |
| Haoyu Zhuang | z****d@g****m | 25 |
| Alex Haberlie | a****e@n****u | 23 |
| Kristen Pozsonyi | k****n@m****u | 23 |
| Tyler Wixtrom | t****m@g****m | 23 |
| Kyle J Gillett | 1****t | 19 |
| Patrick Marsh | p****x@g****m | 19 |
| Matt Wilson | m****n@v****u | 16 |
| Andrew Huang | a****1@i****u | 13 |
| Ana Castaneda | a****1@g****m | 13 |
| Christian F | c****2@g****m | 9 |
| Eric Bruning | e****g@g****m | 9 |
| Nathan Wendt | n****t@o****u | 8 |
| Sarawut Arthayakun | 4****g | 8 |
| Lydia Bunting | l****g@t****u | 8 |
| Aaron Hill | a****l@t****u | 6 |
| David Ahijevych | a****c@u****u | 6 |
| and 59 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 1,199
- Total pull requests: 2,742
- Average time to close issues: 5 months
- Average time to close pull requests: 21 days
- Total issue authors: 277
- Total pull request authors: 127
- Average comments per issue: 2.74
- Average comments per pull request: 1.26
- Merged pull requests: 2,327
- Bot issues: 80
- Bot pull requests: 1,417
Past Year
- Issues: 48
- Pull requests: 380
- Average time to close issues: 9 days
- Average time to close pull requests: 3 days
- Issue authors: 27
- Pull request authors: 21
- Average comments per issue: 1.13
- Average comments per pull request: 0.36
- Merged pull requests: 309
- Bot issues: 14
- Bot pull requests: 265
Top Authors
Issue Authors
- dopplershift (376)
- jrleeman (167)
- github-actions[bot] (77)
- sgdecker (60)
- jthielen (30)
- kgoebber (29)
- akrherz (20)
- dcamron (18)
- ahuang11 (12)
- eliteuser26 (9)
- zbruick (9)
- winash12 (8)
- raybellwaves (7)
- avatar101 (6)
- am-thyst (6)
Pull Request Authors
- dependabot[bot] (1,394)
- dopplershift (525)
- jrleeman (157)
- jthielen (84)
- kgoebber (75)
- zbruick (52)
- dcamron (49)
- sgdecker (38)
- akrherz (29)
- tjwixtrom (20)
- nawendt (19)
- 23ccozad (19)
- github-actions[bot] (15)
- mgrover1 (13)
- mwilson14 (13)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 4
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Total downloads:
- pypi 94,548 last-month
- Total docker downloads: 12
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Total dependent packages: 49
(may contain duplicates) -
Total dependent repositories: 180
(may contain duplicates) - Total versions: 116
- Total maintainers: 2
pypi.org: metpy
Collection of tools for reading, visualizing and performing calculations with weather data.
- Documentation: https://unidata.github.io/MetPy/
- License: BSD-3-Clause
-
Latest release: 1.7.1
published 6 months ago
Rankings
Maintainers (2)
conda-forge.org: metpy
The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you take the average GEMPAK script for a weather map, you need to: read data, calculate a derived field, and show on a map/skew-T. One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for any meteorological Python application; this means making it easy to pull out the LCL calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides itself on being well-documented and well-tested, so that on-going maintenance is easily manageable.
- Homepage: https://github.com/Unidata/MetPy
- License: BSD-3-Clause
-
Latest release: 1.3.1
published over 3 years ago
Rankings
proxy.golang.org: github.com/unidata/metpy
- Documentation: https://pkg.go.dev/github.com/unidata/metpy#section-documentation
- License: bsd-3-clause
-
Latest release: v1.7.1
published 6 months ago
Rankings
spack.io: py-metpy
Collection of tools for reading, visualizing and performing calculations with weather data.
- Homepage: https://github.com/Unidata/MetPy
- License: []
-
Latest release: 1.7.0
published 7 months ago
Rankings
Maintainers (1)
Dependencies
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