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Resources","sub_category":"Water Supply and Quality","monthly_downloads":78,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[![pypi](https://img.shields.io/pypi/v/epyt-control.svg)](https://pypi.org/project/epyt-control/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/epyt-control)\n[![Documentation Status](https://readthedocs.org/projects/epyt-control/badge/?version=stable)](https://epyt-control.readthedocs.io/en/stable/?badge=stable)\n[![Downloads](https://static.pepy.tech/badge/epyt-control)](https://pepy.tech/project/epyt-control)\n[![Downloads](https://static.pepy.tech/badge/epyt-control/month)](https://pepy.tech/project/epyt-control)\n\n# EPyT-Control -- EPANET Python Toolkit - Control\n\n\u003cimg src=\"https://github.com/WaterFutures/EPyT-Control/blob/main/docs/_static/gimmick.png?raw=true\" align=\"center\" height=\"230px\"/\u003e\n\nEPyT-Control is a Python package building on top of [EPyT-Flow](https://github.com/WaterFutures/EPyT-Flow) \nfor implementing and evaluating control algorithms \u0026 strategies in water distribution networks (WDNs).\n\nBesides related control tasks such as state estimation and event diagnosis, a special focus of this\nPython package is Reinforcement Learning for data-driven control in WDNs and therefore it provides\nfull compatibility with the\n[Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) package.\n\n\n## Unique Features\n\nUnique features of EPyT-Control are the following:\n\n- Support of hydraulic and (advanced) water quality simulation (i.e. EPANET and EPANET-MSX are supported)\n- Compatibility with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) and integration of [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/)\n- Wide variety of pre-defined actions (e.g. pump state actions, pump speed actons, valve state actions, species injection actions, etc.)\n- Implementation of classic control aglorithms such as PID and LQR controllers\n- Signal processing methods such as state estimation (e.g. Kalman filters) and event diagnosis\n- Neural surrogate models for state transition\n- High- and low-level interface\n- Object-orientated design that is easy to extend and customize\n\n\n## Installation\n\nEPyT-Control supports Python 3.10 - 3.14\n\n### PyPI\n\n```\npip install epyt-control\n```\n\n### Git\nDownload or clone the repository:\n```\ngit clone https://github.com/WaterFutures/EPyT-Control.git\ncd EPyT-Control\n```\n\nInstall all requirements as listed in [REQUIREMENTS.txt](REQUIREMENTS.txt):\n```\npip install -r REQUIREMENTS.txt\n```\n\nInstall the toolbox:\n```\npip install .\n```\n\n\n## Quick Example\n\n#### Interface of Environments\n\nBasic example demonstrating the environments' interface:\n\n```python\n# Define/Specify MyEnv\n# ....\n\n# Load hypothetical environment \"MyEnv\"\nwith MyEnv() as env:\n    # Show the observation space\n    print(f\"Observation space: {env.observation_space}\")\n\n    # Run 1000 iterations -- assuming that autorest=True\n    obs, info = env.reset()\n    for _ in range(1000):\n        # Sample and apply a random action from the action space.\n        # TODO: Replace with some smart RL/control method\n        action = env.action_space.sample()\n        obs, reward, terminated, _, _ = env.step(action)\n\n        # Show action and observed reward\n        print(action, reward)\n```\n\n#### Applying Reinforcement Learning to a given Environment\n\nSimple example of using [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) for learning a policy to control the chlorine injection in a given environment called ```SimpleChlorineInjectionEnv```:\n\n```python\nfrom stable_baselines3 import PPO\nfrom gymnasium.wrappers import NormalizeObservation\n\n# Define/Specify SimpleChlorineInjectionEnv\n# ....\n\n# Load chlorine injection environment\nwith SimpleChlorineInjectionEnv() as env:\n    # Wrap environment\n    env = NormalizeObservation(env)\n\n    # Apply a simple policy learner\n    # You might want to add more wrappers (e.g. normalizing inputs, rewards, etc.) and logging here\n    # Also, inceasing the number of time steps might help as well\n    model = PPO(\"MlpPolicy\", env, verbose=1)\n    model.learn(total_timesteps=1000)\n    model.save(\"my_model_clinject.zip\")  # Save policy\n```\n\n## Documentation\n\nDocumentation is available on readthedocs: [https://epyt-control.readthedocs.io/en/stable/](https://epyt-control.readthedocs.io/en/stable)\n\n## License\n\nMIT license -- see [LICENSE](LICENSE)\n\n## How to Cite?\n\nIf you use this software, please cite it as follows:\n\n```\n@misc{github:epytcontrol,\n        author = {André Artelt},\n        title = {{EPyT-Control -- EPANET Python Toolkit - Control}},\n        year = {2025},\n        publisher = {GitHub},\n        journal = {GitHub repository},\n        howpublished = {https://github.com/WaterFutures/EPyT-Control}\n    }\n```\n\n## How to get Support?\n\nIf you come across any bug or need assistance please feel free to open a new\n[issue](https://github.com/WaterFutures/EPyT-Control/issues/)\nif non of the existing issues answers your questions.\n\n## How to Contribute?\n\nContributions (e.g. creating issues, pull-requests, etc.) are welcome --\nplease make sure to read the [code of conduct](CODE_OF_CONDUCT.md) and\nfollow the [developers' guidelines](DEVELOPERS.md).\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/348873","html_url":"https://ost.ecosyste.ms/projects/348873"}