Camunda-Modeler-based creation of SimPy discrete event simulation models
Wouldn't it be cool to combine the block-based process modeling experience of commercial discrete event simulation packages with the amenities of proper IDE-based source-code editing? (Think Arena / Anylogic / ExtendSim / Plant Simulation / ... but with simple integration of third-party libraries, industry-standard interfaces, unit- and integration testing, dockerization, serverless execution in the cloud of your choice... and even actually working auto-completion! :D)
And all that not only for free, but using the worlds most popular language for data analytics and machine learning?
Casymda enables you to create SimPy3 simulation models, with help of BPMN and the battle-tested Camunda-Modeler.
Created BPMN process diagrams are parsed and converted to Python-code, combining visual oversight of model structure with code-based definition of model behavior. Immediately executable, including a token-based process animation, allowing for space-discrete entity movements, and ready to be wrapped as a gym-environment to let a machine-learning algorithm find a control strategy.
Further information and sample projects:
- https://fladdimir.github.io/post/ (English)
- https://casymda.github.io/page/Webpage/Startpage.html (German)
Installation
From PyPI:
pip install casymda
Features
- connectable blocks for processing of entities
- graphical model description via camunda modeler
- process visualization browser-based or via tkinter
- space-discrete tilemap-movements of entities
- Gym-interface implementation for connection of reinforcment learning algorithms
- gradually typed (checkout pyright for vscode)
Coming soon:
- automated model generation from process event-logs via PM4Py
Examples
Basic features are illustrated as part of the example models (which also serve as integration tests):
- basics:
- resources:
- seize and release a resource via graphical modeling
- tilemap:
- entity movement along a shortest path defined by a csv-tilemap (built on networkx: https://networkx.github.io/)
- gym:
- RL essentials: let an agent learn how to sort fruits according to their type (built on gym and stable-baselines )
For setup just clone the repository and install casymda (virtual environment recommended). See basics-visual-run-tkinter for an example of how to cope with python-path issues.
Design
Development
This project trusts Black for formatting, Sonarqube for static code analysis, and pytest for unit & integration testing. Developed and tested on Linux (Ubuntu 18.04), Python 3.7.5. Tests can be carried out inside a docker-container, optionally including an installation from pypi to verify a successful upload.
Sonarqube
sonarqube server (public docker image):
docker-compose up sonarqube
sonar-scanner (public docker image):
docker-compose up analysis
(run a docker-based unit-test first for coverage-reporting)
(remember to share your drive via Docker-Desktop settings if necessary, to be re-applied after each password change)
Tests
pytest --cov-report term --cov=src/casymda/ tests/
For Docker-based tests see docker-compose.yml
docker-compose run unit-test
docker-compose run examples-test
docker-compose run examples-test-pypi
Virtual environment setup
python3 -m venv venv
Editable installation
pip install -e .
Publish to pypi
python setup.py sdist
twine upload dist/*
pip install twine if necessary,
remember to set the version in setup.py and src/casymda as required
Contact
feedback / ideas / discussion / cheering / complaints welcome
MIT License
2020
1 maintainer
Metadata
1 maintainer
Release files for casymda 0.2.18
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