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Re-introduction of a simple plot function for Dolfinx.

Project description

pyvista4dolfinx

build status Coverage MIT License PyPI Sphinx black

Re-introduction of a simple plot function for Dolfinx. Documentation can be found here.

Description

FEniCSx' design principles prioritize parallel efficiency, and (in an effort to avoid opaque performance pitfalls) favor fine-grained control over a high-level interface. Consequently, simple, general-purpose plotting routines are no-longer in the standard library. Instead users are deferred to pyvista for their plotting needs. Unfortunately, quite a bit of boiler-plate code is required to interface dolfinx and pyvista. At a prototyping stage one often desires quick-and-dirty visualization with a simple interface. To facilitate this, pyvista4dolfinx provides a single plot function that can be used to plot most of dolfinx visualizable data-structures; scalar- and vector-valued Function, Mesh, FacetMarker, and even integration Measure. The function returns a pyvista.Plotter instance, such that the user still has full access to pyvista's full range of capabilities.

Installation

This library is available through PyPi, to install simply run the following commands:

pip install pyvista4dolfinx

Although you might have to be mindfull of the version. pyvista4dolfinx maintains the same version numbering as dolfinx. So, if you're not using the latest dolfinx release, you might have to install a particular tagged version:

pip install pyvista4dolfinx==0.9.2

Contributing

Feel free to make requests through opening an issue, or, better yet, if you want to contribute directly:

  1. Fork the project.
  2. Create a new branch (git checkout -b feature/my-feature).
  3. Add your new feature or bugfix.
  4. Commit your changes (git commit -m 'Add some feature').
  5. Push to the branch (git push origin feature/my-feature).
  6. Open a merge request.

To publish:

  1. Bump version number in pyproject toml
  2. If dolfinx version change, then also update gitlab-ci image and push updated docker image to hub.
  3. Push
  4. Create git tag (git tag <new_tag>)
  5. Push tag (git push origin --tags)

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