https://github.com/kLabUM/pystorms
Simulation Sandbox for the Design and Evaluation of Stormwater Control Algorithms
https://github.com/kLabUM/pystorms
Last synced: 9 months ago
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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 6 years ago)
- Default Branch: master
- Last Pushed: 2025-06-28T20:56:43.000Z (10 months ago)
- Last Synced: 2025-07-07T01:03:47.547Z (10 months ago)
- Language: Jupyter Notebook
- Homepage: https://pystorms.netlify.app
- Size: 430 MB
- Stars: 33
- Watchers: 8
- Forks: 16
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - pystorms - Simulation Sandbox for the Design and Evaluation of Stormwater Control Algorithms. (Natural Resources / Water Supply and Quality)
README
# pystorms: simulation sandbox for the evaluation and design of stormwater control algorithms
[](https://github.com/kLabUM/pystorms/actions/workflows/python-package.yml)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://github.com/python/black)
## 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*](https://dl.acm.org/citation.cfm?id=3313336). 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 < 2.0.0
```bash
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.
```python
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()
```
Updated versions of _theta_, _alpha_, _gamma_, _delta_, and _epsilon_ are accessible via a version keyword in the initialization command.
```python
env = pystorms.scenarios.theta(version=version) # "1" is the default and original, "2" are the updated versions.
```
Sensor noise and actuator faults can also be enabled via the level keyword. The options are 1, 2, and 3 in ascending order of difficulty. Version or level or both can be specified.
```python
env = pystorms.scenarios.theta(version=version, level=level) # "1" is the ideal, original, and default case. "2" is realistic and "3" is adverse.
env = pystorms.scenarios.theta(level=level) # also valid. This would load version 1 of the model.
```
More details on the updates are accessible at (preprint link). As of June 2025, these updates are only in the "dev" branch and have not yet been merged in "master."
Detailed documentation can be found on the [webpage](https://www.pystorms.org)