Simulation Sandbox for the development and evaluation of stormwater control algorithms
pystorms: simulation sandbox for the evaluation and design of stormwater control algorithms
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 three components:
- A library of
scenariosthat are built to allow for systematic quantitative evaluation of stormwater control algorithms,
- A stormwater hydraulic simulator named
pyswmm_liteand forked heavily from OWA's SWMM and pyswmm, and
environmentscript that links the
pyswmm_litesimulator to the
scenarios, and can be edited/updated by users who might want to interface the
scenarioswith other stormwater simulator software (the
environmentscript is included in
This is a alpha version of the library, eventually
pyswmm_lite dependency would be deprecated in the favour of
- python 3+
pip install pyswmm_lite pip install pystorms
Please raise an issue on the repository or reach out if you run into any issues installing the package.
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
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