JOSS: https://github.com/wino6687/SWEpy
Score: 8.616133139271142
Last synced: about 6 hours ago
JSON representation
Repository metadata:
Python library for scraping, subsetting, and concatenating temperature brightness data for analyzing SWE.
- Host: GitHub
- URL: https://github.com/wino6687/SWEpy
- Owner: wino6687
- License: mit
- Created: 2018-05-08T19:24:01.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-07-15T18:42:38.000Z (almost 4 years ago)
- Last Synced: 2025-10-21T19:51:31.919Z (8 months ago)
- Language: Python
- Homepage:
- Size: 164 MB
- Stars: 3
- Watchers: 0
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Owner metadata:
- Name: Will Norris
- Login: wino6687
- Email:
- Kind: user
- Description: I have a passion for data science, machine learning, and bringing information into the hands of the masses.
- Website: wino6687.github.io/will-portfolio
- Location: Boulder, Colorado
- Twitter:
- Company: @earthlab
- Icon url: https://avatars.githubusercontent.com/u/29149863?u=23cf7b4deb013e50adc3f54bec3f61cf43e328b8&v=4
- Repositories: 4
- Last Synced at: 2023-03-02T00:55:17.956Z
- Profile URL: https://github.com/wino6687
GitHub Events
Total
- Total: 0
Last Year
- Total: 0
Committers metadata
Last synced: 8 months ago
Total Commits: 927
Total Committers: 8
Avg Commits per committer: 115.875
Development Distribution Score (DDS): 0.073
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| wino6687 | w****7@c****u | 859 |
| davidnyberg | d****5@g****m | 34 |
| WILLIAM Norris | w****s@W****l | 19 |
| WILLIAM Norris | w****s@c****u | 8 |
| 1papaya | 1****a@g****m | 3 |
| WILLIAM Norris | w****s@e****u | 2 |
| WILLIAM Norris | w****s@r****u | 1 |
| WILLIAM Norris | w****s@e****u | 1 |
Issue and Pull Request metadata
Last synced: 8 months ago
Total issues: 8
Total pull requests: 3
Average time to close issues: about 2 months
Average time to close pull requests: less than a minute
Total issue authors: 3
Total pull request authors: 2
Average comments per issue: 2.5
Average comments per pull request: 0.0
Merged pull request: 1
Bot issues: 0
Bot pull requests: 2
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- mbjoseph (4)
- usethedata (2)
- wino6687 (2)
Top Pull Request Authors
- dependabot[bot] (2)
- davidnyberg (1)
Top Issue Labels
- bug (2)
- enhancement (1)
Top Pull Request Labels
- dependencies (2)
Package metadata
- Total packages: 2
-
Total downloads:
- pypi: 113 last-month
- Total dependent packages: 0 (may contain duplicates)
- Total dependent repositories: 1 (may contain duplicates)
- Total versions: 66
- Total maintainers: 1
pypi.org: swepy
A python package for obtaining and manipulating Tb files from the MEaSUREs database
- Homepage: https://github.com/wino6687/SWEpy
- Documentation: https://swepy.readthedocs.io/
- Licenses: MIT License
- Latest release: 1.9.4 (published over 6 years ago)
- Last Synced: 2025-10-30T05:14:25.800Z (8 months ago)
- Versions: 53
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 113 Last month
-
Rankings:
- Dependent packages count: 9.995%
- Forks count: 16.855%
- Average: 19.255%
- Dependent repos count: 21.728%
- Downloads: 22.663%
- Stargazers count: 25.034%
- Maintainers (1)
conda-forge.org: swepy
SWEpy is a Python library designed to simplify access to a passive microwave brightness temperature dataset available at the National Snow and Ice Data Center (NSIDC). This dataset contains Northern and Southern hemisphere imagery along with Equatorial imagery, and can be quite useful in analyzing snow water equivalent (SWE) over large spatial extents. SWEpy contains tools to web scrape, geographically subset, and concatenate files into time cubes. There is an automated workflow to scrape long time series while periodically stopping to geographically subset and concatenate files in order to reduce disk impact.
Dependencies
- fsspec 0.4.3.*
- m2r 0.2.1.*
- matplotlib 3.0.3.*
- python 3.7.*
- rtree 0.8.3.*
- sphinx 1.8.4.*
- sphinx-gallery 0.2.0.*
- sphinx_rtd_theme 0.4.3.*
- bumpversion ==0.5.3 development
- codecov ==2.0.15 development
- fsspec ==0.4.3 development
- importlib-metadata ==0.19 development
- m2r ==0.2.1 development
- numpy ==1.17.5 development
- pre-commit ==1.17.0 development
- pytest ==5.0.1 development
- pytest-cov ==2.7.1 development
- pytest-vcr ==1.0.2 development
- sphinx ==2.1.2 development
- sphinx-autobuild ==0.7.1 development
- sphinx_gallery ==0.4.0 development
- sphinx_rtd_theme ==0.4.3 development