The PyAnsys project exposes Ansys technologies via libraries in the Python ecosystem. Each library provides clear, concise, and maintainable APIs. Useful Pythonic functions, classes, and plugins allow users to interact with targeted products and services in a high-level, object-orientated approach.
The PyAnsys ecosystem refines the component-level interaction with Ansys solvers and tools, and eliminates the inconsistent and restrictive scripting environments found within product installations.
These component libraries play a vital role in:
Data manipulation and export
The libraries also include plugins and interfaces to packages in the vast Python ecosystem. Examples include:
Arrays using numpy
Data structures and tables with pandas
2D visualization using matplotlib
3D visualization using pyvista
Advanced scientific computing using scipy
Machine learning using tensorflow
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Contributing to this guide#
If you would like to contribute to this development guide, maintainers gladly review all pull requests. For more information, see Documentation style.
This repository uses pre-commit to
automate style checking. To use it, enter your Python environment and install
pre-commit with this command:
pip install pre-commit
You can then run
pre-commit manually with this command:
pre-commit run --all-files
This performs various style and spelling checks to ensure your contributions meet minimum coding style and documentation standards.
You can make sure that these checks are always run prior to
running them by installing
pre-commit as a git hook with this command:
Now, each time you run
git commit, your commit is only created if it
passes the minimum style checks that also run on the GitHub CI/CD.