# PM4Py
PM4Py is a python library that supports state-of-the-art process mining algorithms in python.
It is open source and intended to be used in both academia and industry projects.
PM4Py is managed and developed by Process Intelligence Solutions (https://processintelligence.solutions/),
a spin-off from the Fraunhofer Institute for Applied Information Technology FIT where PM4Py was initially developed.
Further information on the license options for using PM4Py closed source (especially in industry contexts) can be found at https://processintelligence.solutions/.
## Documentation / API
The full documentation of PM4Py can be found at https://processintelligence.solutions/
## First Example
Here is a simple example to spark your interest:
import pm4py
if __name__ == "__main__":
log = pm4py.read_xes('<path-to-xes-log-file.xes>')
net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log)
pm4py.view_petri_net(net, initial_marking, final_marking, format="svg")
## Installation
PM4Py can be installed on Python 3.9.x / 3.10.x / 3.11.x / 3.12.x by invoking:
pip install -U pm4py
PM4Py is also running on older Python environments with different requirements sets, including:
- Python 3.8 (3.8.10): third_party/old_python_deps/requirements_py38.txt
## Requirements
PM4Py depends on some other Python packages, with different levels of importance:
* *Essential requirements*: numpy, pandas, deprecation, networkx
* *Normal requirements* (installed by default with the PM4Py package, important for mainstream usage): graphviz, intervaltree, lxml, matplotlib, pydotplus, pytz, scipy, tqdm
* *Optional requirements* (not installed by default): requests, pyvis, jsonschema, workalendar, pyarrow, scikit-learn, polars, openai, pyemd, pyaudio, pydub, pygame, pywin32, pygetwindow, pynput
## Release Notes
To track the incremental updates, please refer to the CHANGELOG.md file.
## Third Party Dependencies
As scientific library in the Python ecosystem, we rely on external libraries to offer our features.
In the /third_party folder, we list all the licenses of our direct dependencies.
Please check the /third_party/LICENSES_TRANSITIVE file to get a full list of all transitive dependencies and the corresponding license.
## Citing PM4Py
If you are using PM4Py in your scientific work, please cite PM4Py as follows:
> **Alessandro Berti, Sebastiaan van Zelst, Daniel Schuster**. (2023). *PM4Py: A process mining library for Python*. Software Impacts, 17, 100556. [DOI](https://doi.org/10.1016/j.simpa.2023.100556) | [Article Link](https://www.sciencedirect.com/science/article/pii/S2665963823000933)
BiBTeX:
@article{pm4py,
title = {PM4Py: A process mining library for Python},
journal = {Software Impacts},
volume = {17},
pages = {100556},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100556},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823000933},
author = {Alessandro Berti and Sebastiaan van Zelst and Daniel Schuster},
}
## Legal Notice
This repository is managed by Process Intelligence Solutions (PIS). Further information about PIS can be found online at www.processintelligence.solutions.
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"description": "# PM4Py\r\nPM4Py is a python library that supports state-of-the-art process mining algorithms in python. \r\nIt is open source and intended to be used in both academia and industry projects.\r\n\r\nPM4Py is managed and developed by Process Intelligence Solutions (https://processintelligence.solutions/),\r\na spin-off from the Fraunhofer Institute for Applied Information Technology FIT where PM4Py was initially developed.\r\n\r\nFurther information on the license options for using PM4Py closed source (especially in industry contexts) can be found at https://processintelligence.solutions/.\r\n\r\n\r\n## Documentation / API\r\nThe full documentation of PM4Py can be found at https://processintelligence.solutions/\r\n\r\n## First Example\r\nHere is a simple example to spark your interest:\r\n\r\nimport pm4py\r\n\r\nif __name__ == \"__main__\":\r\n log = pm4py.read_xes('<path-to-xes-log-file.xes>')\r\n net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log)\r\n pm4py.view_petri_net(net, initial_marking, final_marking, format=\"svg\")\r\n\r\n## Installation\r\nPM4Py can be installed on Python 3.9.x / 3.10.x / 3.11.x / 3.12.x by invoking:\r\n\r\npip install -U pm4py\r\n\r\nPM4Py is also running on older Python environments with different requirements sets, including:\r\n- Python 3.8 (3.8.10): third_party/old_python_deps/requirements_py38.txt\r\n\r\n## Requirements\r\nPM4Py depends on some other Python packages, with different levels of importance:\r\n* *Essential requirements*: numpy, pandas, deprecation, networkx\r\n* *Normal requirements* (installed by default with the PM4Py package, important for mainstream usage): graphviz, intervaltree, lxml, matplotlib, pydotplus, pytz, scipy, tqdm\r\n* *Optional requirements* (not installed by default): requests, pyvis, jsonschema, workalendar, pyarrow, scikit-learn, polars, openai, pyemd, pyaudio, pydub, pygame, pywin32, pygetwindow, pynput\r\n\r\n## Release Notes\r\nTo track the incremental updates, please refer to the CHANGELOG.md file.\r\n\r\n## Third Party Dependencies\r\nAs scientific library in the Python ecosystem, we rely on external libraries to offer our features.\r\nIn the /third_party folder, we list all the licenses of our direct dependencies.\r\nPlease check the /third_party/LICENSES_TRANSITIVE file to get a full list of all transitive dependencies and the corresponding license.\r\n\r\n## Citing PM4Py\r\nIf you are using PM4Py in your scientific work, please cite PM4Py as follows:\r\n\r\n> **Alessandro Berti, Sebastiaan van Zelst, Daniel Schuster**. (2023). *PM4Py: A process mining library for Python*. Software Impacts, 17, 100556. [DOI](https://doi.org/10.1016/j.simpa.2023.100556) | [Article Link](https://www.sciencedirect.com/science/article/pii/S2665963823000933)\r\n\r\nBiBTeX:\r\n\r\n@article{pm4py, \r\ntitle = {PM4Py: A process mining library for Python}, \r\njournal = {Software Impacts}, \r\nvolume = {17}, \r\npages = {100556}, \r\nyear = {2023}, \r\nissn = {2665-9638}, \r\ndoi = {https://doi.org/10.1016/j.simpa.2023.100556}, \r\nurl = {https://www.sciencedirect.com/science/article/pii/S2665963823000933}, \r\nauthor = {Alessandro Berti and Sebastiaan van Zelst and Daniel Schuster}, \r\n}\r\n\r\n## Legal Notice\r\n\r\nThis repository is managed by Process Intelligence Solutions (PIS). Further information about PIS can be found online at www.processintelligence.solutions.\r\n",
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