####### PyVista #######

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

.. image:: https://github.com/pyvista/pyvista/raw/main/doc/source/_static/pyvista_banner_small.png :target: https://docs.pyvista.org/examples/index.html :alt: pyvista

PyVista is:

Pythonic VTK: a high-level API to the Visualization Toolkit_ (VTK)
mesh data structures and filtering methods for spatial datasets
3D plotting made simple and built for large/complex data geometries

.. _Visualization Toolkit: https://vtk.org

.. image:: https://github.com/pyvista/pyvista/raw/main/assets/pyvista_ipython_demo.gif :alt: pyvista ipython demo

PyVista is a helper module for the Visualization Toolkit (VTK) that wraps the VTK library through NumPy and direct array access through a variety of methods and classes. This package provides a Pythonic, well-documented interface exposing VTK's powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets.

This module can be used for scientific plotting for presentations and research papers as well as a supporting module for other mesh 3D rendering dependent Python modules; see Connections for a list of projects that leverage PyVista.

PyVista is a NumFOCUS affiliated project

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Highlights

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Head over to the Quick Examples_ page in the docs to explore our gallery of examples showcasing what PyVista can do. Want to test-drive PyVista? All of the examples from the gallery are live on MyBinder for you to test drive without installing anything locally: |binder|

.. _Quick Examples: http://docs.pyvista.org/examples/index.html

Overview of Features

Extensive gallery of examples (see Quick Examples_)
Interactive plotting in Jupyter Notebooks with server-side and client-side rendering with trame_.
Filtering/plotting tools built for interactivity (see Widgets_)
Direct access to mesh analysis and transformation routines (see Filters_)
Intuitive plotting routines with matplotlib similar syntax (see Plotting_)
Import meshes from many common formats (use pyvista.read()). Support for all formats handled by meshio_ is built-in.
Export meshes as VTK, STL, OBJ, or PLY (mesh.save()) file types or any formats supported by meshio_ (pyvista.save_meshio())

.. _trame: https://github.com/Kitware/trame .. _Widgets: https://docs.pyvista.org/api/plotting/index.html#widget-api .. _Filters: https://docs.pyvista.org/api/core/filters.html .. _Plotting: https://docs.pyvista.org/api/plotting/index.html .. _meshio: https://github.com/nschloe/meshio

Documentation

Refer to the documentation <http://docs.pyvista.org/>_ for detailed installation and usage details.

For general questions about the project, its applications, or about software usage, please create a discussion in pyvista/discussions_ where the community can collectively address your questions. You are also welcome to join us on Slack_.

.. _pyvista/discussions: https://github.com/pyvista/pyvista/discussions .. _Slack: https://communityinviter.com/apps/pyvista/pyvista

Installation

PyVista can be installed from PyPI <https://pypi.org/project/pyvista/>_ using pip on Python >= 3.10::

pip install pyvista

You can also visit PyPI <https://pypi.org/project/pyvista/>, Anaconda <https://anaconda.org/conda-forge/pyvista>, or GitHub <https://github.com/pyvista/pyvista>_ to download the source.

See the Installation <http://docs.pyvista.org/getting-started/installation.html#install-ref.>_ for more details regarding optional dependencies or if the installation through pip doesn't work out.

Connections

PyVista is a powerful tool that researchers can harness to create compelling, integrated visualizations of large datasets in an intuitive, Pythonic manner.

Learn more about how PyVista is used across science and engineering d

Pyvista

Install / Use

README