pySBeLT
pySBeLT: A Python software package for stochastic sediment transport under rarefied conditions - Published in JOSS (2022)
Science Score: 95.0%
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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
Found 7 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
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1 of 4 committers (25.0%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
A Markov-type numerical model of sediment particle transport in rivers
Basic Info
Statistics
- Stars: 8
- Watchers: 2
- Forks: 4
- Open Issues: 0
- Releases: 3
Metadata Files
README.md
pySBeLT
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| *Figure 1. A .gif of 500 iterations from a py_SBeLTrun using default parameters. |
Rivers transport sediment particles. Individual particles can exhibit transport behavior that differs significantly when compared to other particles. pySBeLT, which stands for Stochastic Bed Load Transport, provides a simple Python framework to numerically examine how individual particle motions in rivers combine to produce rates of transport that can be measured at one of a number of downstream points. The model can be used for basic research, and the model's relatively straightforward set-up makes it an effective and efficient teaching tool to help students build intuition about river transport of sediment particles.
Installation
Quick Installation
bash
pip install sbelt
Installation from Source
Clone the py_SBeLT GitHub repository
bash
git clone https://github.com/szwiep/py_SBeLT.git
Then set your working directory to py_SBeLT/ and build the project
bash
cd py_SBeLT/
python setup.py build_ext --inplace
pip install -e .
Testing your installation
If you've installed from source, you can test the installation by setting your working directory to py_SBeLT/
and running the following
bash
python -m unittest discover -s src/tests --buffer
Getting Started
Users can work through the Jupyter Notebooks provided to gain a better understanding of pySBeLT's basic usage, potential, and data storage methods. Either launch the binder instance (), clone the repository, or download the notebooks directly to get started.
If notebook's aren't your thing, simply run:
bash
sbelt-run
or
bash
from sbelt import sbelt_runner
sbelt_runner.run()
For help, reach out with questions to the repository owner szwiep and reference the documenation in docs/ and paper/!
Documentation
Documentation, including Jupyter Notebooks, API documentation, default parameters, and model nomenclature, can be found in the repository's docs/ directory. Additional information regarding the theoritical motivation of the model can be found in the paper/paper.md and THEORY.md files.
Two Jupyter Notebooks describe basic usage and the structure of the output hdf5 format file.
The API documentation is in HTML format. These files can either be downloaded and viewed directly in your browser or can be viewed using the GitHub HTML preview project. - sbelt_runner - plotting - logic - test_logic - utils
Attribution and Citation
If you use Simframe, please remember to cite (to be updated later).
```
@article{Zwiep2022, doi = {10.21105/joss.04282}, url = {https://doi.org/10.21105/joss.04282}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {74}, pages = {4282}, author = {Sarah Zwiep and Shawn M. Chartrand}, title = {pySBeLT: A Python software package for stochastic sediment transport under rarefied conditions}, journal = {Journal of Open Source Software} }
```
Publication
The publication with more details can be accessed here: 
Ackowledgements
pySBeLT has received funding from NSERC Undergraduate Student Research Awards Program and Simon Fraser University.
pySBeLT was developed at the Simon Fraser University within the School of Environmental Science.
Owner
- Name: Sarah Zwiep
- Login: szwiep
- Kind: user
- Location: Vancouver, BC
- Company: Simon Fraser University
- Repositories: 1
- Profile: https://github.com/szwiep
JOSS Publication
pySBeLT: A Python software package for stochastic sediment transport under rarefied conditions
Authors
School of Environmental Science, Simon Fraser University
School of Environmental Science, Simon Fraser University, Department of Earth Sciences, Simon Fraser University
Editor
Katy Barnhart
Tags
geomorphology sediment transport stochastic PoissonGitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| szwiep | s****p@s****a | 199 |
| Shawn Chartrand | s****d@s****a | 197 |
| Katy Barnhart | k****t@u****v | 2 |
| Greg Baker | g****r@s****a | 1 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 2
- Total pull requests: 23
- Average time to close issues: 3 months
- Average time to close pull requests: 6 minutes
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 2.5
- Average comments per pull request: 0.22
- Merged pull requests: 23
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- kbarnhart (1)
- szwiep (1)
Pull Request Authors
- szwiep (20)
- kbarnhart (1)
- gregbaker (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 22 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 2
- Total maintainers: 1
pypi.org: sbelt
A Markov-type numerical model of sediment particle transport in rivers
- Homepage: https://github.com/szwiep/py_SBeLT
- Documentation: https://sbelt.readthedocs.io/
- License: other
-
Latest release: 1.0.1
published almost 4 years ago
Rankings
Maintainers (1)
Dependencies
- Pillow ==9.0.1
- h5py ==2.9.0
- matplotlib ==3.3.4
- numpy ==1.21.5
- scipy ==1.6.0
- tqdm ==4.56.0
- h5py *
- matplotlib *
- numpy *
- scipy *
- tqdm *