pystorms
Simulation Sandbox for the Design and Evaluation of Stormwater Control Algorithms.
https://github.com/kLabUM/pystorms
Category: Natural Resources
Sub Category: Water Supply and Quality
Keywords from Contributors
hydrology swmm5
Last synced: about 19 hours ago
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Repository metadata
Simulation Sandbox for the Design and Evaluation of Stormwater Control Algorithms
- Host: GitHub
- URL: https://github.com/kLabUM/pystorms
- Owner: kLabUM
- License: gpl-3.0
- Created: 2019-10-01T19:09:06.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-04-16T19:43:00.000Z (12 days ago)
- Last Synced: 2025-04-17T22:59:36.436Z (10 days ago)
- Language: Jupyter Notebook
- Homepage: https://pystorms.netlify.app
- Size: 430 MB
- Stars: 34
- Watchers: 8
- Forks: 16
- Open Issues: 3
- Releases: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
pystorms: simulation sandbox for the evaluation and design of stormwater control algorithms
Overview
This library has been developed in an effort to systematize quantitative analysis of stormwater control algorithms.
It is a natural extension of the Open-Storm's mission to open up and ease access into the technical world of smart stormwater systems.
Our initial efforts allowed us to develop open source and free tools for anyone to be able to deploy flood sensors, measure green infrastructure, or even control storm or sewer systems.
Now we have developed a tool to be able to test the performance of algorithms used to coordinate these different sensing and control technologies that have been deployed throughout urban water systems.
For the motivation behind this effort, we refer the reader to our manuscript pystorms. In general, this repo provides a library of scenarios that are built to allow for systematic quantitative evaluation of stormwater control algorithms.
Getting Started
Installation
Requirements
- PyYAML >= 5.3
- numpy >= 18.4
- pyswmm
pip install pystorms
Please raise an issue on the repository or reach out if you run into any issues installing the package.
Example
Here is an example implementation on how you would use this library for evaluating the ability of a rule based control in maintaining the flows in a network below a desired threshold.
import pystorms
import numpy as np
# Define your awesome controller
def controller(state):
actions = np.ones(len(state))
for i in range(0, len(state)):
if state[i] > 0.5:
actions[i] = 1.0
return actions
env = pystorms.scenarios.theta() # Initialize scenario
done = False
while not done:
state = env.state()
actions = controller(state)
done = env.step(actions)
performance = env.performance()
Detailed documentation can be found on the webpage
Owner metadata
- Name: Real-time water systems lab
- Login: kLabUM
- Email:
- Kind: organization
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/4870591?v=4
- Repositories: 28
- Last ynced at: 2024-03-27T07:51:01.086Z
- Profile URL: https://github.com/kLabUM
GitHub Events
Total
- Issues event: 1
- Watch event: 6
- Push event: 25
- Create event: 1
Last Year
- Issues event: 1
- Watch event: 6
- Push event: 25
- Create event: 1
Committers metadata
Last synced: 7 days ago
Total Commits: 163
Total Committers: 10
Avg Commits per committer: 16.3
Development Distribution Score (DDS): 0.387
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 | |
|---|---|---|
| Abhiram | a****m@u****u | 100 |
| Abhiram Mullapudi | a****i@g****m | 22 |
| srimer | s****r@a****v | 13 |
| rimersara | s****r@g****m | 12 |
| Sara Troutman | s****m@u****u | 7 |
| Sara Rimer | s****r@C****n | 3 |
| Sara Rimer | s****n@s****n | 2 |
| stroutm | s****n@x****m | 2 |
| Abhiram Mullapudi | p****o@A****l | 1 |
| Jeff Sadler | j****2@g****m | 1 |
Committer domains:
- umich.edu: 2
- xyleminc.com: 1
- saras-mbp.lan: 1
- csi354041.lan: 1
- anl.gov: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 22
Total pull requests: 10
Average time to close issues: 9 months
Average time to close pull requests: 4 days
Total issue authors: 3
Total pull request authors: 3
Average comments per issue: 1.64
Average comments per pull request: 0.3
Merged pull request: 8
Bot issues: 0
Bot pull requests: 0
Past year issues: 1
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: 1
Past year pull request authors: 0
Past year average comments per issue: 2.0
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
- abhiramm7 (19)
- Jiadalee (2)
- gregjewi (1)
Top Pull Request Authors
- abhiramm7 (8)
- aakin4 (1)
- jsadler2 (1)
Top Issue Labels
- enhancement (11)
- documentation (3)
- bug (2)
- qaqc (1)
- clarification (1)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 393 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 11
- Total maintainers: 1
pypi.org: pystorms
Simulation sandbox for stormwater control algorithms
- Homepage: https://github.com/kLabUM/pystorms
- Documentation: https://pystorms.readthedocs.io/
- Licenses: GNU Lesser General Public License v3 (LGPLv3)
- Latest release: 1.0.0 (published about 3 years ago)
- Last Synced: 2025-04-26T14:03:31.639Z (2 days ago)
- Versions: 11
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 393 Last month
-
Rankings:
- Dependent packages count: 7.303%
- Forks count: 9.144%
- Stargazers count: 12.269%
- Average: 17.525%
- Dependent repos count: 22.068%
- Downloads: 36.84%
- Maintainers (1)
Dependencies
- numpy >=1.18.4
- pyswmm >=1.0.1
- pyyaml >=5.3
- actions/checkout master composite
- actions/setup-python v2 composite
- pypa/gh-action-pypi-publish master composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
Score: 11.892388770539393