dorado
Simulating passive Lagrangian particle transport over flow-fields from any 2D shallow-water hydrodynamic model using a weighted random walk methodology.
https://github.com/passaH2O/dorado
Category: Hydrosphere
Sub Category: Waves and Currents
Keywords
hydrodynamic-modeling lagrangian numerical-modeling numerical-modelling particle particle-routing particle-tracing particle-tracking particle-transport particles passive-tracers random-walk rivers simulator tracer
Last synced: about 1 hour ago
JSON representation
Repository metadata
For shallow-water Lagrangian particle routing.
- Host: GitHub
- URL: https://github.com/passaH2O/dorado
- Owner: passaH2O
- License: mit
- Created: 2020-07-28T21:09:07.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-03-25T17:51:25.000Z (about 1 month ago)
- Last Synced: 2025-04-10T04:38:52.508Z (17 days ago)
- Topics: hydrodynamic-modeling, lagrangian, numerical-modeling, numerical-modelling, particle, particle-routing, particle-tracing, particle-tracking, particle-transport, particles, passive-tracers, random-walk, rivers, simulator, tracer
- Language: Python
- Homepage: https://passah2o.github.io/dorado
- Size: 84.7 MB
- Stars: 59
- Watchers: 2
- Forks: 10
- Open Issues: 3
- Releases: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
dorado - Lagrangian particle routing
dorado is a Python package for simulating passive Lagrangian particle transport over flow-fields from any 2D shallow-water hydrodynamic model using a weighted random walk methodology.
For user guides and detailed examples, refer to the documentation.
Example Uses:
Particles on an Unsteady ANUGA Flow Field of the Wax Lake Delta
Particles on a DeltaRCM Simulated Delta
Installation:
dorado supports Python 3.11+. For the full distribution including examples, clone this repository using git clone and run python setup.py install from the cloned directory. To test this "full" installation, you must first install pytest via pip install pytest. Then from the cloned directory the command pytest can be run to ensure that your installed distribution passes all of the unit tests.
For a lightweight distribution including just the core functionality, use pip to install via PyPI:
pip install pydorado
Installation using conda via conda-forge is also supported:
conda install -c conda-forge pydorado
For additional installation options and instructions, refer to the documentation.
Contributing
We welcome contributions to the dorado project. Please open an issue or a pull request if there is functionality you would like to see or propose. Refer to our contributing guide for more information.
Citing
If you use this package and wish to cite it, please use the Journal of Open Source Software article.
Funding Acknowledgments
This work was supported in part by NSF EAR-1719670, the NSF GRFP under grant DGE-1610403 and the NASA Earth Venture Suborbital (EVS) award 17-EVS3-17_1-0009 in support of the DELTA-X project.
Owner metadata
- Name: PassaH2O Group
- Login: passaH2O
- Email:
- Kind: organization
- Description:
- Website: https://sites.google.com/site/passalacquagroup/home
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/30088719?v=4
- Repositories: 10
- Last ynced at: 2024-04-16T03:09:36.925Z
- Profile URL: https://github.com/passaH2O
GitHub Events
Total
- Create event: 8
- Release event: 4
- Issues event: 6
- Watch event: 5
- Delete event: 2
- Issue comment event: 6
- Push event: 12
- Pull request event: 9
- Fork event: 1
Last Year
- Create event: 8
- Release event: 4
- Issues event: 6
- Watch event: 5
- Delete event: 2
- Issue comment event: 6
- Push event: 12
- Pull request event: 9
- Fork event: 1
Committers metadata
Last synced: 5 days ago
Total Commits: 318
Total Committers: 5
Avg Commits per committer: 63.6
Development Distribution Score (DDS): 0.292
Commits in past year: 9
Committers in past year: 2
Avg Commits per committer in past year: 4.5
Development Distribution Score (DDS) in past year: 0.111
| Name | Commits | |
|---|---|---|
| JayH | j****n@u****u | 225 |
| wrightky | k****t@u****u | 50 |
| Wright | k****5@a****u | 39 |
| Katherine Barnhart | k****t@u****v | 3 |
| Paola Passalacqua | p****a@a****u | 1 |
Committer domains:
- utexas.edu: 2
- austin.utexas.edu: 1
- usgs.gov: 1
- austin.eid.utexas.edu: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 20
Total pull requests: 33
Average time to close issues: 3 months
Average time to close pull requests: 24 days
Total issue authors: 4
Total pull request authors: 3
Average comments per issue: 1.05
Average comments per pull request: 1.58
Merged pull request: 32
Bot issues: 0
Bot pull requests: 0
Past year issues: 4
Past year pull requests: 4
Past year average time to close issues: 19 days
Past year average time to close pull requests: 6 days
Past year issue authors: 1
Past year pull request authors: 1
Past year average comments per issue: 0.0
Past year average comments per pull request: 1.5
Past year merged pull request: 4
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- elbeejay (14)
- kbarnhart (3)
- gassmoeller (2)
- wrightky (1)
Top Pull Request Authors
- elbeejay (26)
- wrightky (6)
- kbarnhart (1)
Top Issue Labels
- enhancement (2)
- good first issue (2)
- documentation (2)
- bug (1)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 612 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 16
- Total maintainers: 2
pypi.org: pydorado
dorado - Lagrangian particle routing routine via weighted random walks
- Homepage: https://github.com/passaH2O/dorado
- Documentation: https://pydorado.readthedocs.io/
- Licenses: MIT
- Latest release: 2.5.4 (published about 2 months ago)
- Last Synced: 2025-04-26T12:32:33.343Z (1 day ago)
- Versions: 16
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 612 Last month
-
Rankings:
- Dependent packages count: 7.31%
- Stargazers count: 9.797%
- Forks count: 12.604%
- Average: 15.146%
- Dependent repos count: 22.088%
- Downloads: 23.93%
- Maintainers (2)
Dependencies
- future *
- matplotlib *
- numpy *
- scipy *
- tqdm *
- future *
- matplotlib *
- numpy *
- scipy *
- tqdm *
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- elbeejay/conda-publish-action v1.5 composite
Score: 12.15819456528571