SWMManywhere
SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World - Published in JOSS (2025)
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 2 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
5 of 11 committers (45.5%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Repository
SWMManywhere is used to derive and simulate a sewer network anywhere in the world
Basic Info
- Host: GitHub
- Owner: ImperialCollegeLondon
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Homepage: https://imperialcollegelondon.github.io/SWMManywhere/
- Size: 146 MB
Statistics
- Stars: 27
- Watchers: 3
- Forks: 4
- Open Issues: 94
- Releases: 17
Topics
Metadata Files
README.md
SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World
SWMManywhere is a tool to synthesise urban drainage network models (UDMs) using publicly available data such as street network, DEM, and building footprints, across the globe. It also provides tools for generating SWMM input files and performing simulations for the synthesised UDMs.
Features
- Automatic data retrieval and preprocessing: all of our data requirements are met with global datasets, so all you need is a bounding box!
- Customisable network synthesis: change a range of parameters to create different networks, power users can easily extend existing functionality.
- Streamlined evaluation to compare with real networks: we include a variety of performance metrics and automatic running/comparing if you have your own SWMM model.
- Command line interface: All of this and more can be accessed by passing a configuration file to a CLI.
Installation
Install SWMManywhere:
bash
pip install swmmanywhere
Alternatively, it can be installed using mamba (conda or micromamba):
bash
mamba install -c conda-forge swmmanywhere
SWMManywhere dependencies may be viewed in the pyproject.toml.
Documentation and Quickstart
Once installed, you can simply run SWMManywhere from the command line giving a configuration file in YAML format as input. As SWMManywhere can download data automatically from well known sources, this settings file can often be minimal and restricted to indicating the geographical area to be processed:
python -m swmmanywhere --config_path=\path\to\config.yml
The result of the calculation will be a model of the sewage system for that area, like the following, which can then be further processed or analysed with SWMM, for example:
Follow the Quickstart for a more detailed initial example and ReadTheDocs for full information of SWMManywhere capabilities. <!-- markdown-link-check-enable -->
Use and contributing
This project is licensed under the BSD-3-Clause licence, see LICENSE.
There are many things we would like to do! If you are interested to contribute please see CONTRIBUTING and CODE OF CONDUCT.
Owner
- Name: Imperial College London
- Login: ImperialCollegeLondon
- Kind: organization
- Email: icgithub-support@imperial.ac.uk
- Location: Imperial College London
- Repositories: 311
- Profile: https://github.com/ImperialCollegeLondon
Imperial College main code repository
JOSS Publication
SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World
Authors
Imperial College London, UK
Imperial College London, UK
Purdue University, US
Editor
Mengqi Zhao
Tags
python stormwater hydrology-stormwater-analysis swmm5 swmm hydraulic-modellingGitHub Events
Total
- Create event: 61
- Release event: 9
- Issues event: 84
- Watch event: 22
- Delete event: 34
- Member event: 2
- Issue comment event: 178
- Push event: 305
- Pull request event: 128
- Pull request review comment event: 57
- Pull request review event: 149
- Fork event: 2
Last Year
- Create event: 61
- Release event: 9
- Issues event: 84
- Watch event: 22
- Delete event: 34
- Member event: 2
- Issue comment event: 178
- Push event: 305
- Pull request event: 128
- Pull request review comment event: 57
- Pull request review event: 149
- Fork event: 2
Committers
Last synced: 4 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Dobson | b****n@i****k | 642 |
| barneydobson | b****1@g****m | 354 |
| pre-commit-ci[bot] | 6****] | 61 |
| dependabot[bot] | 4****] | 48 |
| Diego Alonso Alvarez | d****z@i****k | 36 |
| Tom Bland | t****d@h****k | 13 |
| Daniel Cummins | d****7@i****k | 6 |
| Barnaby Dobson | b****n@g****m | 3 |
| James Paul Turner | j****r@i****k | 2 |
| Adrian D'Alessandro | a****o@i****k | 1 |
| AadiUJ | a****b@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 149
- Total pull requests: 223
- Average time to close issues: about 2 months
- Average time to close pull requests: 5 days
- Total issue authors: 9
- Total pull request authors: 8
- Average comments per issue: 1.43
- Average comments per pull request: 1.28
- Merged pull requests: 177
- Bot issues: 0
- Bot pull requests: 82
Past Year
- Issues: 63
- Pull requests: 183
- Average time to close issues: 27 days
- Average time to close pull requests: 5 days
- Issue authors: 9
- Pull request authors: 8
- Average comments per issue: 1.11
- Average comments per pull request: 1.39
- Merged pull requests: 138
- Bot issues: 0
- Bot pull requests: 63
Top Authors
Issue Authors
- barneydobson (133)
- cheginit (8)
- dalonsoa (2)
- AnuPal1Hydro123 (1)
- joeshuttleworth (1)
- meghnathomas (1)
- AdrianDAlessandro (1)
- mebauer (1)
- cbuahin (1)
Pull Request Authors
- barneydobson (117)
- pre-commit-ci[bot] (57)
- dependabot[bot] (25)
- dalonsoa (11)
- tsmbland (6)
- AdrianDAlessandro (3)
- jamesturner246 (2)
- dc2917 (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 302 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 12
- Total maintainers: 1
pypi.org: swmmanywhere
SWMManywhere software
- Documentation: https://imperialcollegelondon.github.io/SWMManywhere/
- License: BSD License
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Latest release: 0.1.16
published 7 months ago