JOSS: https://github.com/IMMM-SFA/naturf
Score: 9.208739090609209
Last synced: about 19 hours ago
JSON representation
Repository metadata:
NATURF: Neighborhood Adaptive Tissues for Urban Resilience Futures
- Host: GitHub
- URL: https://github.com/IMMM-SFA/naturf
- Owner: IMMM-SFA
- License: mit
- Created: 2022-05-02T16:20:06.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-08-14T16:55:59.000Z (10 months ago)
- Last Synced: 2026-06-09T15:05:22.042Z (12 days ago)
- Language: Jupyter Notebook
- Homepage: https://immm-sfa.github.io/naturf/
- Size: 12.1 MB
- Stars: 13
- Watchers: 5
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Owner metadata:
- Name: Integrated Multisector Multiscale Modeling
- Login: IMMM-SFA
- Email:
- Kind: organization
- Description: Models and code from the IM3 SFA
- Website: https://im3.pnnl.gov/
- Location: Richland, WA
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/31457237?v=4
- Repositories: 45
- Last Synced at: 2023-03-04T02:34:30.243Z
- Profile URL: https://github.com/IMMM-SFA
GitHub Events
Total
- Create event: 4
- Delete event: 1
- Issue comment event: 5
- Pull request event: 6
- Pull request review comment event: 6
- Pull request review event: 4
- Push event: 17
- Watch event: 5
- Total: 48
Last Year
- Create event: 1
- Issue comment event: 1
- Pull request event: 1
- Pull request review comment event: 5
- Pull request review event: 2
- Push event: 1
- Total: 11
Committers metadata
Last synced: 4 days ago
Total Commits: 455
Total Committers: 8
Avg Commits per committer: 56.875
Development Distribution Score (DDS): 0.591
Commits in past year: 6
Committers in past year: 1
Avg Commits per committer in past year: 6.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| levisweet | l****t@v****u | 186 |
| erexer | 1****r | 163 |
| Chris Vernon | c****n@g****m | 86 |
| Stefan Krawczyk | s****n@d****o | 9 |
| Dumas, Melissa | a****r@o****v | 7 |
| kurte | k****0@o****v | 2 |
| Travis Thurber | t****r | 1 |
| Sweet L T | l****6@0****v | 1 |
Issue and Pull Request metadata
Last synced: 24 days ago
Total issues: 56
Total pull requests: 52
Average time to close issues: about 2 months
Average time to close pull requests: about 18 hours
Total issue authors: 4
Total pull request authors: 5
Average comments per issue: 0.29
Average comments per pull request: 0.63
Merged pull request: 51
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 8
Past year average time to close issues: N/A
Past year average time to close pull requests: 28 minutes
Past year issue authors: 0
Past year pull request authors: 3
Past year average comments per issue: 0
Past year average comments per pull request: 1.0
Past year merged pull request: 7
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- erexer (33)
- levisweetbreu (16)
- praneethd7 (4)
- caimeng2 (3)
Top Pull Request Authors
- erexer (43)
- levisweetbreu (15)
- skrawcz (5)
- crvernon (4)
- thurber (1)
Top Issue Labels
- documentation (4)
- enhancement (1)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 77 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 5
- Total maintainers: 1
pypi.org: naturf
The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (NATURF) is a Python workflow that generates data readable by the Weather Research and Forecasting (WRF) model. The NATURF Python modules use shapefiles containing building footprint and height data as input to calculate 132 building parameters at any resolution and converts the parameters into a binary file format.
- Homepage: https://immm-sfa.github.io/naturf/index.html
- Documentation: https://naturf.readthedocs.io/
- Licenses: MIT License
- Latest release: 1.0.4 (published almost 2 years ago)
- Last Synced: 2026-06-17T19:02:16.121Z (4 days ago)
- Versions: 5
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 77 Last month
-
Rankings:
- Dependent packages count: 9.961%
- Average: 37.856%
- Dependent repos count: 65.752%
- Maintainers (1)