EPyT-Control

A Python package for implementing and evaluating control algorithms & strategies in smart water networks.
https://github.com/waterfutures/epyt-control

Category: Natural Resources
Sub Category: Water Supply and Quality

Keywords

control epanet epanet-python-toolkit hydraulics python reinforcement-learning simulation water water-distribution-networks water-quality

Last synced: about 4 hours ago
JSON representation

Repository metadata

A Python package for implementing and evaluating control algorithms & strategies in smart water networks.

• Host: GitHub
• URL: https://github.com/waterfutures/epyt-control
• Owner: WaterFutures
• License: mit
• Created: 2024-11-26T09:27:05.000Z (over 1 year ago)
• Default Branch: dev
• Last Pushed: 2026-03-17T06:55:01.000Z (7 days ago)
• Last Synced: 2026-03-17T21:59:47.166Z (6 days ago)
• Topics: control, epanet, epanet-python-toolkit, hydraulics, python, reinforcement-learning, simulation, water, water-distribution-networks, water-quality
• Language: Python
• Homepage: https://epyt-control.readthedocs.io/en/stable
• Size: 547 KB
• Stars: 13
• Watchers: 4
• Forks: 6
• Open Issues: 0
• Releases: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

README.md

pip install epyt-control

git clone https://github.com/WaterFutures/EPyT-Control.git
cd EPyT-Control

pip install -r REQUIREMENTS.txt

pip install .

# Define/Specify MyEnv
# ....

# Load hypothetical environment "MyEnv"
with MyEnv() as env:
    # Show the observation space
    print(f"Observation space: {env.observation_space}")

    # Run 1000 iterations -- assuming that autorest=True
    obs, info = env.reset()
    for _ in range(1000):
        # Sample and apply a random action from the action space.
        # TODO: Replace with some smart RL/control method
        action = env.action_space.sample()
        obs, reward, terminated, _, _ = env.step(action)

        # Show action and observed reward
        print(action, reward)

from stable_baselines3 import PPO
from gymnasium.wrappers import NormalizeObservation

# Define/Specify SimpleChlorineInjectionEnv
# ....

# Load chlorine injection environment
with SimpleChlorineInjectionEnv() as env:
    # Wrap environment
    env = NormalizeObservation(env)

    # Apply a simple policy learner
    # You might want to add more wrappers (e.g. normalizing inputs, rewards, etc.) and logging here
    # Also, inceasing the number of time steps might help as well
    model = PPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=1000)
    model.save("my_model_clinject.zip")  # Save policy

@misc{github:epytcontrol,
        author = {André Artelt},
        title = {{EPyT-Control -- EPANET Python Toolkit - Control}},
        year = {2025},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {https://github.com/WaterFutures/EPyT-Control}
    }

Citation (CITATION.cff)

cff-version: 1.2.0
authors:
  - family-names: Artelt
    given-names: André
    orcid: "https://orcid.org/0000-0002-2426-3126"
contact:
  - family-names: Artelt
    given-names: André
    orcid: "https://orcid.org/0000-0002-2426-3126"
message: "If you use this software, please cite it using these metadata."
title: "EPyT-Control -- EPANET Python Toolkit - Control"
keywords:
  - python
  - simulation
  - water
  - epanet
  - epanet-python-toolkit
  - control
  - reinforcement learning
license: MIT
repository-code: "https://github.com/WaterFutures/EPyT-Control"

Owner metadata

• Name: ERC Synergy Grant Water Futures
• Login: WaterFutures
• Email:
• Kind: organization
• Description: Repository for the ERC SyG Water Futures Team
• Website: www.waterfutures.eu
• Location:
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/149570313?v=4
• Repositories: 1
• Last ynced at: 2024-01-31T13:34:11.822Z
• Profile URL: https://github.com/WaterFutures

GitHub Events

Total

• Release event: 6
• Delete event: 1
• Member event: 1
• Fork event: 4
• Watch event: 7
• Push event: 61
• Public event: 1
• Create event: 5

Last Year

• Release event: 3
• Delete event: 1
• Member event: 1
• Fork event: 1
• Watch event: 6
• Push event: 36
• Create event: 2

Committers metadata

Last synced: 3 days ago

Committer domains:

• uni-bielefeld.de: 1
• techfak.uni-bielefeld.de: 1
• techfak.de: 1

Package metadata

• Total packages: 1
• Total downloads:
  • pypi: 56 last-month
• Total dependent packages: 0
• Total dependent repositories: 0
• Total versions: 7
• Total maintainers: 1

Dependencies

Score: 7.706612913964197