Contributing

Contributing#

We absolutely welcome contributions and we hope that this guide will facilitate an understanding of the PyVista code repository. It is important to note that the PyVista software package is maintained on a volunteer basis and thus we need to foster a community that can support user questions and develop new features to make this software a useful tool for all users.

This page is dedicated to outline where you should start with your question, concern, feature request, or desire to contribute.

Being Respectful#

Please demonstrate empathy and kindness toward other people, other software, and the communities who have worked diligently to build (un)related tools.

Please do not talk down in Pull Requests, Issues, or otherwise in a way that portrays other people or their works in a negative light.

Cloning the Source Repository#

You can clone the source repository from pyvista/pyvista and install the latest version by running:

git clone https://github.com/pyvista/pyvista.git
cd pyvista
python -m pip install -e .

Note

Use python -m pip install -e '.[dev]' to also install all of the packages required for development.

Quick Start Development with Codespaces#

A dev container is provided to quickly get started. The default container comes with the repository code checked out on a branch of your choice and all pyvista dependencies including test dependencies pre-installed. In addition, it uses the desktop-lite feature to provide live interaction windows. Follow directions Connecting to the desktop to use the live interaction.

Alternatively, an offscreen version using OSMesa libraries and vtk-osmesa is available.

Questions#

For general questions about the project, its applications, or about software usage, please create a discussion in the Discussions repository where the community can collectively address your questions.

You are also welcome to join us on Slack, but Slack should be reserved for ad hoc conversations and community engagement rather than technical discussions.

For critical, high-level project support and engagement, please email info@pyvista.org - but please do not use this email for technical support.

For all technical conversations, you are welcome to create an issue on the Discussions page which we will address promptly. Through posting on the Discussions page, your question can be addressed by community members with the needed expertise and the information gained will remain available for other users to find.

Reporting Bugs#

If you stumble across any bugs, crashes, or concerning quirks while using code distributed here, please report it on the issues page with an appropriate label so we can promptly address it. When reporting an issue, please be overly descriptive so that we may reproduce it. Whenever possible, please provide tracebacks, screenshots, and sample files to help us address the issue.

Feature Requests#

We encourage users to submit ideas for improvements to PyVista code base. Please create an issue on the issues page with a Feature Request label to suggest an improvement. Please use a descriptive title and provide ample background information to help the community implement that functionality. For example, if you would like a reader for a specific file format, please provide a link to documentation of that file format and possibly provide some sample files with screenshots to work with. We will use the issue thread as a place to discuss and provide feedback.

Contributing New Code#

If you have an idea for how to improve PyVista, please first create an issue as a feature request which we can use as a discussion thread to work through how to implement the contribution.

Once you are ready to start coding and develop for PyVista, please see the Development Practices section for more details.

Licensing#

All contributed code will be licensed under The MIT License found in the repository. If you did not write the code yourself, it is your responsibility to ensure that the existing license is compatible and included in the contributed files or you can obtain permission from the original author to relicense the code.

Development Practices#

This section provides a guide to how we conduct development in the PyVista repository. Please follow the practices outlined here when contributing directly to this repository.

Guidelines#

Through direct access to the Visualization Toolkit (VTK) via direct array access and intuitive Python properties, we hope to make the entire VTK library easily accessible to researchers of all disciplines. To further PyVista towards being a valuable Python interface to VTK, we need your help to make it even better.

If you want to add one or two interesting analysis algorithms as filters, implement a new plotting routine, or just fix 1-2 typos - your efforts are welcome.

There are three general coding paradigms that we believe in:

Make it intuitive. PyVista’s goal is to create an intuitive and easy to use interface back to the VTK library. Any new features should have intuitive naming conventions and explicit keyword arguments for users to make the bulk of the library accessible to novice users.
Document everything. At the least, include a docstring for any method or class added. Do not describe what you are doing but why you are doing it and provide a simple example for the new features.
Keep it tested. We aim for a high test coverage. See testing for more details.

There are two important copyright guidelines:

Please do not include any data sets for which a license is not available or commercial use is prohibited. Those can undermine the license of the whole projects.
Do not use code snippets for which a license is not available (for example from Stack Overflow) or commercial use is prohibited. Those can undermine the license of the whole projects.

Please also take a look at our Code of Conduct.

Contributing to PyVista through GitHub#

To submit new code to pyvista, first fork the pyvista GitHub Repository and then clone the forked repository to your computer. Then, create a new branch based on the Branch Naming Conventions Section in your local repository.

Next, add your new feature and commit it locally. Be sure to commit frequently as it is often helpful to revert to past commits, especially if your change is complex. Also, be sure to test often. See the Testing Section below for automating testing.

When you are ready to submit your code, create a pull request by following the steps in the Creating a New Pull Request section.

Coding Style#

We adhere to PEP 8 wherever possible, except that line widths are permitted to go beyond 79 characters to a max of 99 characters for code. This should tend to be the exception rather than the norm. A uniform code style is enforced by ruff format to prevent energy wasted on style disagreements.

As for docstrings, PyVista follows the numpydoc style for its docstrings. Please also take a look at Docstrings.

Outside of PEP 8, when coding please consider PEP 20 - The Zen of Python. When in doubt:

import this

PyVista uses pre-commit to enforce PEP8 and other styles automatically. Please see the Style Checking section for further details.

Documentation Style#

PyVista follows the Google Developer Documentation Style with the following exceptions:

Allow first person pronouns. These pronouns (for example, “We”) refer to “PyVista Developers”, which can be anyone who contributes to PyVista.
Future tense is permitted.

These rules are enforced for all text files (for example, *.md, *.rst) and partially enforced for Python source files.

These rules are enforced through the use of Vale via our GitHub Actions, and you can run Vale locally with:

pip install vale
vale --config doc/.vale.ini doc pyvista examples ./*.rst --glob='!*{_build,AUTHORS.rst}*'

If you are on Linux or macOS, you can run:

make docstyle

Docstrings#

PyVista uses Python docstrings to create reference documentation for our Python APIs. Docstrings are read by developers, interactive Python users, and readers of our online documentation. This section describes how to write these docstrings for PyVista.

PyVista follows the numpydoc style for its docstrings. Please follow the numpydoc Style Guide in all ways except for the following:

Be sure to describe all Parameters and Returns for all public methods.
We strongly encourage you to add an example section. PyVista is a visual library, so adding examples that show a plot will really help users figure out what individual methods do.
With optional parameters, use default: <value> instead of optional when the parameter has a default value instead of None.

Sample docstring follows:

def slice_x(self, x=None, generate_triangles=False):
    """Create an orthogonal slice through the dataset in the X direction.

    Parameters
    ----------
    x : float, optional
        The X location of the YZ slice. By default this will be the X center
        of the dataset.

    generate_triangles : bool, default: False
        If this is enabled, the output will be all triangles. Otherwise the
        output will consist of the intersection polygons.

    Returns
    -------
    pyvista.PolyData
        Sliced dataset.

    Examples
    --------
    Slice the random hills dataset with one orthogonal plane.

    >>> from pyvista import examples
    >>> hills = examples.load_random_hills()
    >>> slices = hills.slice_x(5, generate_triangles=False)
    >>> slices.plot(line_width=5)

    See :ref:`slice_example` for more examples using this filter.

    """

    pass  # implementation goes here

Note the following:

The parameter definition of generate_triangles uses default: False, and does not include the default in the docstring’s “description” section.
There is a newline between each parameter. This is different than numpydoc’s documentation where there are no empty lines between parameter docstrings.
This docstring also contains a returns section and an examples section.
The returns section does not include the parameter name if the function has a single return value. Multiple return values (not shown) should have descriptive parameter names for each returned value, in the same format as the input parameters.
The examples section references the “full example” in the gallery if it exists.

In addition, docstring examples which make use of randomly-generated data should be reproducible. See Generating Random Data for details.

These standards will be enforced using pre-commit using numpydoc-validate, with errors being reported as:

+-----------------+--------------------------+---------+-------------------------------------------------+
| file            | item                     | check   | description                                     |
+=================+==========================+=========+=================================================+
| cells.py:85     | cells.create_mixed_cells | RT05    | Return value description should finish with "." |
+-----------------+--------------------------+---------+-------------------------------------------------+
| cells.py:85     | cells.create_mixed_cells | RT05    | Return value description should finish with "." |
+-----------------+--------------------------+---------+-------------------------------------------------+
| features.py:250 | features.merge           | PR09    | Parameter "datasets" description should finish  |
|                 |                          |         | with "."                                        |
+-----------------+--------------------------+---------+-------------------------------------------------+

If for whatever reason you feel that your function should have an exception to any of the rules, add an exception to the function either in the [tool.numpydoc_validation] section in pyproject.toml or add an inline comment to exclude a certain check. For example, we can omit the Return section from docstrings and skip the RT01 check for magic methods like __init__.

def __init__(self, foo):  # numpydoc ignore=RT01
    """Initialize A Class."""
    super().__init__()
    self.foo = foo

See the available validation checks in numpydoc Validation.

Deprecating Features or other Backwards-Breaking Changes#

When implementing backwards-breaking changes within PyVista, care must be taken to give users the chance to adjust to any new changes. Any non-backwards compatible modifications should proceed through the following steps:

Retain the old behavior and issue a PyVistaDeprecationWarning indicating the new interface you should use.
Retain the old behavior but raise a pyvista.core.errors.DeprecationError indicating the new interface you must use.
Remove the old behavior.

Whenever possible, PyVista developers should seek to have at least three minor versions of backwards compatibility to give users the ability to update their software and scripts.

Here’s an example of a soft deprecation of a function. Note the usage of both the PyVistaDeprecationWarning warning and the .. deprecated Sphinx directive.

import warnings
from pyvista.core.errors import PyVistaDeprecationWarning

def addition(a, b):
    """Add two numbers.

    .. deprecated:: 0.37.0
       Since PyVista 0.37.0, you can use :func:`pyvista.add` instead.

    Parameters
    ----------
    a : float
        First term to add.

    b : float
        Second term to add.

    Returns
    -------
    float
        Sum of the two inputs.

    """
    # deprecated 0.37.0, convert to error in 0.40.0, remove 0.41.0
    warnings.warn(
        '`addition` has been deprecated. Use pyvista.add instead',
        PyVistaDeprecationWarning
    )
    add(a, b)


def add(a, b):
    """Add two numbers."""

    pass  # implementation goes here

In the above code example, note how a comment is made to convert to an error in three minor releases and completely remove in the following minor release. For significant changes, this can be made longer, and for trivial ones this can be kept short.

Here’s an example of adding error test codes that raise deprecation warning messages.

with pytest.warns(PyVistaDeprecationWarning):
    addition(a, b)
    if pv._version.version_info[:2] > (0, 40):
        raise RuntimeError("Convert error this function")
    if pv._version.version_info[:2] > (0, 41):
        raise RuntimeError("Remove this function")

In the above code example, the old test code raises an error in v0.40 and v0.41. This will prevent us from forgetting to remove deprecations on version upgrades.

Note

When releasing a new version, we need to update the version number to the next development version. For example, if we are releasing version 0.37.0, the next development version should be 0.38.0.dev0 which is greater than 0.37.0. This is why we need to check if the version is greater than 0.40.0 and 0.41.0 in the test code.

When adding an additional parameter to an existing method or function, you are encouraged to use the .. versionadded sphinx directive. For example:

def Cube(clean=True):
    """Create a cube.

    Parameters
    ----------
    clean : bool, default: True
        Whether to clean the raw points of the mesh.

        .. versionadded:: 0.33.0
    """

Branch Naming Conventions#

To streamline development, we have the following requirements for naming branches. These requirements help the core developers know what kind of changes any given branch is introducing before looking at the code.

fix/, patch/ and bug/: any bug fixes, patches, or experimental changes that are minor
feat/: any changes that introduce a new feature or significant addition
junk/: for any experimental changes that can be deleted if gone stale
maint/ and ci/: for general maintenance of the repository or CI routines
doc/: for any changes only pertaining to documentation
no-ci/: for low impact activity that should NOT trigger the CI routines
testing/: improvements or changes to testing
release/: releases (see below)
breaking-change/: Changes that break backward compatibility

Testing#

After making changes, please test changes locally before creating a pull request. The following tests will be executed after any commit or pull request, so we ask that you perform the following sequence locally to track down any new issues from your changes.

To run our comprehensive suite of unit tests, install PyVista with all developer dependencies:

pip install -e '.[dev]'

Then, if you have everything installed, you can run the various test suites.

Unit Testing#

Run the primary test suite and generate coverage report:

python -m pytest -v --cov pyvista

Unit testing can take some time, if you wish to speed it up, set the number of processors with the -n flag. This uses pytest-xdist to leverage multiple processes. Example usage:

python -m pytest -n <NUMCORE> --cov pyvista

Documentation Testing#

Run all code examples in the docstrings with:

python -m pytest -v --doctest-modules pyvista

Note

Additional testing is also performed on any images generated by the docstring. See Documentation Image Regression Testing.

Style Checking#

PyVista follows PEP8 standard as outlined in the Coding Style section and implements style checking using pre-commit.

To ensure your code meets minimum code styling standards, run:

pip install pre-commit
pre-commit run --all-files

If you have issues related to setuptools when installing pre-commit, see pre-commit Issue #2178 comment for a potential resolution.

You can also install this as a pre-commit hook by running:

pre-commit install

This way, it’s not possible for you to push code that fails the style checks. For example, each commit automatically checks that you meet the style requirements:

$ pre-commit install
$ git commit -m "added my cool feature"
codespell................................................................Passed
ruff.....................................................................Passed

The actual installation of the environment happens before the first commit following pre-commit install. This will take a bit longer, but subsequent commits will only trigger the actual style checks.

Even if you are not in a situation where you are not performing or able to perform the above tasks, you can comment pre-commit.ci autofix on a pull request to manually trigger auto-fixing.

Notes Regarding Image Regression Testing#

Since PyVista is primarily a plotting module, it’s imperative we actually check the images that we generate in some sort of regression testing. In practice, this ends up being quite a bit of work because:

OpenGL software vs. hardware rending causes slightly different images to be rendered.
We want our CI (which uses a virtual frame buffer) to match our desktop images (uses hardware acceleration).
Different OSes render different images.

As each platform and environment renders different slightly images relative to Linux (which these images were built from), so running these tests across all OSes isn’t optimal. We need to know if something fundamental changed with our plotting without actually looking at the plots (like the docs at dev.pyvista.com)

Based on these points, image regression testing only occurs on Linux CI, and multi-sampling is disabled as that seems to be one of the biggest difference between software and hardware based rendering.

Image cache is stored here as ./tests/plotting/image_cache.

Image resolution is kept low at 400x400 as we don’t want to pollute git with large images. Small variations between versions and environments are to be expected, so error < IMAGE_REGRESSION_ERROR is allowable (and will be logged as a warning) while values over that amount will trigger an error.

There are two mechanisms within pytest to control image regression testing, --reset_image_cache and --ignore_image_cache. For example:

pytest tests/plotting --reset_image_cache

Running --reset_image_cache creates a new image for each test in tests/plotting/test_plotting.py and is not recommended except for testing or for potentially a major or minor release. You can use --ignore_image_cache if you’re running on Linux and want to temporarily ignore regression testing. Realize that regression testing will still occur on our CI testing.

Images are currently only cached from tests in tests/plotting/test_plotting.py. By default, any test that uses Plotter.show will cache images automatically. To skip image caching, the verify_image_cache fixture can be utilized:

def test_add_background_image_not_global(verify_image_cache):
    verify_image_cache.skip = True  # Turn off caching
    plotter = pyvista.Plotter()
    plotter.add_mesh(sphere)
    plotter.show()
    # Turn on caching for further plotting
    verify_image_cache.skip = False
    ...

This ensures that immediately before the plotter is closed, the current render window will be verified against the image in CI. If no image exists, be sure to add the resulting image with

git add tests/plotting/image_cache/*

During unit testing, if you get image regression failures and would like to compare the images generated locally to the regression test suite, allow pytest-pyvista to write all new generated images to a local directory using the --generated_image_dir flag.

For example, the following writes all images generated by pytest to debug_images/ for any tests in tests/plotting whose function name has volume in it.

pytest tests/plotting/ -k volume --generated_image_dir debug_images

See pytest-pyvista for more details.

Note

Additional regression testing is also performed on the documentation images. See Documentation Image Regression Testing.

Notes Regarding Input Validation Testing#

The pyvista.core.validation package has two distinct test suites which are executed with pytest:

Regular unit tests in tests/core/test_validation.py
Customized unit tests in tests/core/typing for testing type hints

The custom unit tests check that the type hints for the validation package are correct both statically and dynamically. This is mainly used to check complex and overloaded function signatures, such as the type hints for validate_array or related functions.

Individual test cases are written as a single line of Python code with the format:

reveal_type(arg)  # EXPECTED_TYPE: "<T>"

where arg is any argument you want mypy to analyze, and "<T>" is the expected revealed type returned by Mypy.

For example, the validate_array function, by default, returns a list of floats when a list of floats is provided at the input. The type hint should reflect this. To test this, we can write a test case for the function call validate_array([1.0]) as follows:

reveal_type(validate_array([1.0]))  # EXPECTED_TYPE: "list[float]"

The actual revealed type returned by Mypy for this test can be generated with the following command. Note that grep is needed to only return the output from the input string. Otherwise, all Mypy errors for the pyvista package are reported.

mypy -c "from pyvista.core._validation import validate_array; reveal_type(validate_array([1.0]))" | grep \<string\>

For this test case, the revealed type by Mypy is:

"builtins.list[builtins.float]"

Notice that the revealed type is fully qualified, i.e. includes builtins. For brevity, the custom test suite omits this and requires that only list be included in the expected type. Therefore, for this test case, the EXPECTED_TYPE type is "list[float]", not "builtins.list[builtins.float]". (Similarly, the package name numpy should also be omitted for tests where a numpy.ndarray is expected.)

Any number of related test cases (one test case per line) may be written and included in a single .py file. The test cases are all stored in tests/core/typing/validation_cases.

The tests can be executed with:

pytest tests/core/typing

When executed, a single instance of Mypy will statically analyze all the test cases. The actual revealed types by Mypy are compared against the EXPECTED_TYPE is defined by each test case.

In addition, the pyanalyze package tests the actual returned type at runtime to match the statically-revealed type. The pyanalyze.runtime.get_compatibility_error method is used for this. If new typing test cases are added for a new validation function, the new function must be added to the list of imports in tests/core/typing/test_validation_typing.py so that the runtime test can call the function.

Building the Documentation#

Build the documentation on Linux or Mac OS with:

make -C doc html

Build the documentation on Windows with:

cd doc
python -msphinx -M html source _build
python -msphinx -M html . _build

The generated documentation can be found in the doc/_build/html directory.

The first time you build the documentation locally will take a while as all the examples need to be built. After the first build, the documentation should take a fraction of the time.

To test this locally you need to run a http server in the html directory with:

make serve-html

Clearing the Local Build#

If you need to clear the locally built documentation, run:

make -C doc clean

This will clear out everything, including the examples gallery. If you only want to clear everything except the gallery examples, run:

make -C doc clean-except-examples

This will clear out the cache without forcing you to rebuild all the examples.

Parallel Documentation Build#

You can improve your documentation build time on Linux and Mac OS with:

make -C doc phtml

This effectively invokes SPHINXOPTS=-j and can be especially useful for multi-core computers.

Documentation Image Regression Testing#

Image regression testing is performed on all published documentation images. When the documentation is built, all generated images are automatically saved to

Build Image Directory: ./doc/_build/html/_images

The regression testing compares these generated images to those stored in

Doc Image Cache: ./tests/doc/doc_image_cache

To test all the images, run pytest with:

pytest tests/doc/tst_doc_images.py

The tests must be executed explicitly with this command. The name of the test file is prefixed with tst, and not test specifically to avoid being automatically executed by pytest (pytest collects all tests prefixed with test by default.) This is done since the tests require building the documentation, and are not a primary form of testing.

When executed, the test will first pre-process the build images. The images are:

Collected from the Build Image Directory.
Resized to a maximum of 400x400 pixels.
Saved to a flat directory as JPEG images in ./_doc_debug_images.

Next, the pre-processed images in ./_doc_debug_images are compared to the cached images in the Doc Image Cache using pyvista.compare_images().

The tests can fail in three ways. To make it easy to review images for failed tests, copies of the images are made as follows:

If the comparison between the two images fails:
- The cache image is copied to ./_doc_debug_images_failed/from_cache
- The build image is copied to ./_doc_debug_images_failed/from_build
If an image is in the cache but missing from the build:
- The cache image is copied to ./_doc_debug_images_failed/from_cache
If an image is in the build but missing from the cache:
- The build image is copied to ./_doc_debug_images_failed/from_build

To resolve failed tests, any images in from_build or from_cache may be copied to or removed from the Doc Image Cache. For example, if adding new docstring examples or plots, the test will initially fail, and the images in from_build may be added to the Doc Image Cache. Similarly, if removing examples, the images in from_cache may be removed from the Doc Image Cache.

If a test is flaky, e.g. the build sometimes generates different images for the same plot, the multiple versions of the image may be saved to the flaky test directory ./tests/doc/flaky_tests. A folder with the same name as the test image should be created, and all versions of the image should be stored in this directory. The test will first compare the build image to the cached image in Doc Image Cache as normal. If that comparison fails, the build image is then compared to all images in the flaky test directory. The test is successful if one of the comparisons is successful.

Note

It is not necessary to build the documentation images locally in order to add to or update the doc image cache. The documentation is automatically built as part of CI testing, and an artifact is generated for (1) all pre-processed build images and (2) failed test cases. These artifacts may simply be downloaded from GitHub for review.

The debug images saved with the artifact can also be used to “simulate” building the documentation images locally. If the images are copied to the local Build Image Directory, the tests can then be executed locally for debugging as though the documentation has already been built.

Note

These tests are intended to provide additional test coverage to ensure the plots generated by pyvista are correct, and should not be used as the primary source of testing. See Documentation Testing and Notes Regarding Image Regression Testing for testing methods which should be considered first.

Controlling Cache for CI Documentation Build#

To reduce build times of the documentation for PRs, cached sphinx gallery, example data, and sphinx build directories are used in the CI on GitHub. In some cases, the caching action can cause problems for a specific PR. To invalidate a cache for a specific PR, one of the following labels can be applied to the PR.

no-example-data-cache
no-gallery-cache
no-sphinx-build-cache

The PR either needs a new commit, e.g. updating the branch from main, or to be closed/re-opened to rerun the CI with the labels applied.

Contributing to the Documentation#

Documentation for PyVista is generated from three sources:

Docstrings from the classes, functions, and modules of pyvista using sphinx.ext.autodoc.
Restructured test from doc/
Gallery examples from examples/

General usage and API descriptions should be placed within doc/api and the docstrings. Full gallery examples should be placed in examples.

Generating Random Data#

All documentation should be reproducible. In particular, any documentation or examples which use random data should be properly seeded so that the same random data is generated every time. This enables users to copy code in the documentation and generate the same results and plots locally.

When using NumPy’s random number generator (RNG) you should create an RNG at the beginning of your script and use this RNG in the rest of the script. Be sure to include a seed value. For example:

import numpy as np

rng = np.random.default_rng(seed=0)
rng.random()  # generate a floating point number between 0 and 1

See Scientific Python’s Best Practices for Using NumPy’s Random Number Generators for details.

Adding a New Example#

PyVista’s examples come in two formats: basic code snippets demonstrating the functionality of an individual method or a full gallery example displaying one or more concepts. Small code samples and snippets are contained in the doc/api directory or within our documentation strings, while the full gallery examples, meant to be run as individual downloadable scripts, are contained in the examples directory at the root of this repository.

To add a fully fledged, standalone example, add your example to the examples directory in the root directory of the PyVista Repository within one of the applicable subdirectories. Should none of the existing directories match the category of your example, create a new directory with a README.txt describing the new category. Additionally, as these examples are built using the sphinx gallery extension, follow coding guidelines as established by Sphinx-Gallery.

For more details see Adding a New Gallery Example.

Adding a New Dataset#

If you have a dataset that you want to feature or want to include as part of a full gallery example, add it to pyvista/vtk-data and follow the directions there. You will then need to add a new function to download the dataset in pyvista/examples/downloads.py. This might be as easy as:

def download_my_new_mesh(load=True):
    """Download my new mesh."""
    return _download_dataset(_dataset_my_new_mesh, load=load)

 _dataset_my_new_mesh = _SingleFileDownloadableDatasetLoader('mydata/my_new_mesh.vtk')

Note that a separate dataset loading object, _dataset_my_new_mesh, should first be defined outside of the function (with module scope), and the new download_my_new_mesh function should then use this object to facilitate downloading and loading the dataset. The dataset loader variable should start with _dataset_.

This will enable:

>>> from pyvista import examples
>>> dataset = examples.download_my_new_mesh()

For loading complex datasets with multiple files or special processing requirements, see the private pyvista/examples/_dataset_loader.py module for more details on how to create a suitable dataset loader.

Using a dataset loader in this way will enable metadata to be collected for the new dataset. A new dataset card titled My New Mesh Dataset will automatically be generated and included in the Dataset Gallery.

In the docstring of the new download_my_new_mesh function, be sure to also include:

A sample plot of the dataset in the examples section
A reference link to the dataset’s new (auto-generated) gallery card in the see also section

For example:

def download_my_new_mesh(load=True):
   """Download my new mesh.

   Examples
   --------
   >>> from pyvista import examples
   >>> dataset = examples.download_my_new_mesh()
   >>> dataset.plot()

   .. seealso::

      :ref:`My New Mesh Dataset <my_new_mesh_dataset>`
          See this dataset in the Dataset Gallery for more info.

   """

Note

The rst seealso directive must be used instead of the See Also heading due to limitations with how numpydoc parses explicit references.

Extending the Dataset Gallery#

If you have multiple related datasets to contribute, or would like to group any existing datasets together that share similar properties, the Dataset Gallery can easily be extended to feature these datasets in a new card carousel.

For example, to add a new Instrument dataset category to Browse Datasets by Category featuring two datasets of musical instruments, e.g.

complete the following steps:

Define a new carousel in doc/source/make_tables.py, e.g.:
class InstrumentCarousel(DatasetGalleryCarousel): """Class to generate a carousel of instrument dataset cards.""" name = 'instrument_carousel' doc = 'Instrument datasets.' badge = CategoryBadge('Instrument', ref='instrument_gallery') @classmethod def fetch_dataset_names(cls): return sorted( ( 'guitar', 'trumpet', ) )
where
- name is used internally to define the name of the generated .rst file for the carousel.
- doc is a short text description of the carousel which will appear in the documentation in the header above the carousel.
- badge is used to give all datasets in the carousel a reference tag. The ref argument for the badge should be a new reference target (details below).
- fetch_dataset_names should return a list of any/all dataset names to be included in the carousel. The dataset names should not include any load_, download_, or dataset_ prefix.
Add the new carousel class to the CAROUSEL_LIST variable defined in doc/source/make_tables.py. This will enable the rst to be auto-generated for the carousel.
Update the doc/source/api/examples/dataset_gallery.rst file to include the new generated <name>_carousel.rst file. E.g. to add the carousel as a new drop-down item, add the following:
```
.. dropdown:: Instrument Datasets
   :name: instrument_gallery

   .. include:: /api/examples/dataset-gallery/instrument_carousel.rst
```
where:
- The dropdown name :name: <reference> should be the badge’s ref variable defined earlier. This will make it so that clicking on the new badge will link to the new dropdown menu.
- The name of the included .rst file should match the name variable defined in the new Carousel class.

After building the documentation, the carousel should now be part of the gallery.

Creating a New Pull Request#

Once you have tested your branch locally, create a pull request on pyvista GitHub while merging to main. This will automatically run continuous integration (CI) testing and verify your changes will work across several platforms.

To ensure someone else reviews your code, at least one other member of the pyvista contributors group must review and verify your code meets our community’s standards. Once approved, if you have write permission you may merge the branch. If you don’t have write permission, the reviewer or someone else with write permission will merge the branch and delete the PR branch.

Since it may be necessary to merge your branch with the current release branch (see below), please do not delete your branch if it is a fix/ branch.

Branching Model#

This project has a branching model that enables rapid development of features without sacrificing stability, and closely follows the Trunk Based Development approach.

The main features of our branching model are:

The main branch is the main development branch. All features, patches, and other branches should be merged here. While all PRs should pass all applicable CI checks, this branch may be functionally unstable as changes might have introduced unintended side-effects or bugs that were not caught through unit testing.
There will be one or many release/ branches based on minor releases (for example release/0.24) which contain a stable version of the code base that is also reflected on PyPI/. Hotfixes from fix/ branches should be merged both to main and to these branches. When necessary to create a new patch release these release branches will have their pyvista/_version.py updated and be tagged with a semantic version (for example v0.24.1). This triggers CI to push to PyPI, and allow us to rapidly push hotfixes for past versions of pyvista without having to worry about untested features.
When a minor release candidate is ready, a new release branch will be created from main with the next incremented minor version (for example release/0.25), which will be thoroughly tested. When deemed stable, the release branch will be tagged with the version (v0.25.0 in this case), and if necessary merged with main if any changes were pushed to it. Feature development then continues on main and any hotfixes will now be merged with this release. Older release branches should not be deleted so they can be patched as needed.

Minor Release Steps#

Minor releases are feature and bug releases that improve the functionality and stability of pyvista. Before a minor release is created the following will occur:

Create a new branch from the main branch with name release/MAJOR.MINOR (for example release/0.25).
Update the development version numbers in pyvista/_version.py and commit it (for example 0, 26, 'dev0'). Push the branch to GitHub and create a new PR for this release that merges it to main. Development to main should be limited at this point while effort is focused on the release.
Locally run all tests as outlined in the Testing Section and ensure all are passing.
Locally test and build the documentation with link checking to make sure no links are outdated. Be sure to run make clean to ensure no results are cached.
```
cd doc
make clean  # deletes the sphinx-gallery cache
make doctest-modules
make html -b linkcheck
```
After building the documentation, open the local build and examine the examples gallery for any obvious issues.
It is now the responsibility of the pyvista community to functionally test the new release. It is best to locally install this branch and use it in production. Any bugs identified should have their hotfixes pushed to this release branch.
When the branch is deemed as stable for public release, the PR will be merged to main. After update the version number in release/MAJOR.MINOR branch, the release/MAJOR.MINOR branch will be tagged with a vMAJOR.MINOR.0 release. The release branch will not be deleted. Tag the release with:
```
git tag v$(python -c "import pyvista as pv; print(pv.__version__)")
```

Please check again that the tag has been created correctly and push the branch and tag.

git push origin HEAD
git push origin v$(python -c "import pyvista as pv; print(pv.__version__)")

Create a list of all changes for the release. It is often helpful to leverage GitHub’s compare feature to see the differences from the last tag and the main branch. Be sure to acknowledge new contributors by their GitHub username and place mentions where appropriate if a specific contributor is to thank for a new feature.
Place your release notes from previous step in the description for the new release on GitHub.
Go grab a beer/coffee/water and wait for @regro-cf-autotick-bot to open a pull request on the conda-forge PyVista feedstock. Merge that pull request.
Announce the new release in the Discussions page and celebrate.

Patch Release Steps#

Patch releases are for critical and important bugfixes that can not or should not wait until a minor release. The steps for a patch release

Push the necessary bugfix(es) to the applicable release branch. This will generally be the latest release branch (for example release/0.25).
Update pyvista/_version.py with the next patch increment (for example v0.25.1), commit it, and open a PR that merge with the release branch. This gives the pyvista community a chance to validate and approve the bugfix release. Any additional hotfixes should be outside of this PR.
When approved, merge with the release branch, but not main as there is no reason to increment the version of the main branch. Then create a tag from the release branch with the applicable version number (see above for the correct steps).
If deemed necessary, create a release notes page. Also, open the PR from conda and follow the directions in step 10 in the minor release section.

Dependency version policy#

Python and VTK dependencies#

We support all supported Python versions and VTK versions that support those Python versions. As much as we would prefer to follow SPEC 0, we follow VTK versions as an interface library of VTK.

Contributing

Contents

Contributing#

Being Respectful#

Cloning the Source Repository#

Quick Start Development with Codespaces#

Questions#

Reporting Bugs#

Feature Requests#

Contributing New Code#

Licensing#

Development Practices#

Guidelines#

Contributing to PyVista through GitHub#

Coding Style#

Documentation Style#

Docstrings#

Deprecating Features or other Backwards-Breaking Changes#

Branch Naming Conventions#

Testing#

Unit Testing#

Documentation Testing#

Style Checking#

Notes Regarding Image Regression Testing#

Notes Regarding Input Validation Testing#

Building the Documentation#

Clearing the Local Build#

Parallel Documentation Build#

Documentation Image Regression Testing#

Controlling Cache for CI Documentation Build#

Contributing to the Documentation#

Generating Random Data#

Adding a New Example#

Adding a New Dataset#

Extending the Dataset Gallery#

Creating a New Pull Request#

Branching Model#

Minor Release Steps#

Patch Release Steps#

Dependency version policy#

Python and VTK dependencies#