.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/1_data/plot_pyvista.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_1_data_plot_pyvista.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_1_data_plot_pyvista.py:


PolyData conversion from and to PyVista or Vedo
===============================================

`PyVista <https://docs.pyvista.org/version/stable/>`_ is a Python library for
3D visualization and mesh manipulation. It is a python wrapper of the
`VTK <https://vtk.org/>`_ library. PyVista is used in scikit-shapes as a backend
to load and save meshes in various formats.

`Vedo <https://vedo.embl.es/>`_ is another Python library for 3D visualization.
It is also a python wrapper of the VTK library. It is used in scikit-shapes for
interactive visualization.

Scikit-shapes [PolyData](skshsapes.data.polydata.PolyData) can be exported to
PyVista PolyData or Vedo Mesh, and vice versa. This allows to leverage the
visualization and mesh manipulation capabilities of both libraries.

In addition to the mesh geometry, the signals and landmarks are encoded in the
PyVista or Vedo mesh, and are preserved when the mesh is loaded back from PyVista.

.. GENERATED FROM PYTHON SOURCE LINES 23-28

Load a mesh from PyVista's examples
-----------------------------------

We load a quadrangulated cow mesh from PyVista. As scikit-shapes meshes are
triangular, an automatic triangulation is performed when importing the mesh.

.. GENERATED FROM PYTHON SOURCE LINES 28-40

.. code-block:: Python


    import pyvista as pv
    import torch
    from pyvista import examples

    import skshapes as sks

    mesh_pyvista = examples.download_cow()
    mesh_sks = sks.PolyData(mesh_pyvista)

    assert mesh_pyvista.n_points == mesh_sks.n_points








.. GENERATED FROM PYTHON SOURCE LINES 41-51

Preservation of signals and landmarks
-------------------------------------

The methods `.to_pyvista()` and `.to_vedo()` allow to convert a `PolyData` object
to a PyVista or Vedo mesh. The point data and landmarks are preserved during
the conversion: if the mesh is loaded back from PyVista or Vedo, the signals
and landmarks are still there.

The following illustrates the preservation of signals and landmarks when
converting a `PolyData` object to PyVista and back.

.. GENERATED FROM PYTHON SOURCE LINES 51-94

.. code-block:: Python



    mesh_sks.point_data["signal"] = mesh_sks.points[:, 0]
    # Now, export the mesh to PyVista
    mesh_pv2 = mesh_sks.to_pyvista()
    # The signal is transferred to the PyVista mesh as a point data array
    # with the same name and can be plotted
    mesh_pv2.plot(scalars="signal", cpos="xy")

    # Back to scikit-shapes, the signal is preserved
    mesh_sks_back = sks.PolyData(mesh_pv2)
    assert torch.allclose(
        mesh_sks_back.point_data["signal"],
        mesh_sks.point_data["signal"]
        )


    # Set some landmarks
    landmarks_indices = [0, 10, 154, 125, 1544, 187, 32, 252, 1214]
    mesh_sks.landmark_indices = landmarks_indices

    # Export to PyVista
    mesh_pv3 = mesh_sks.to_pyvista()

    # The landmarks are stored in the field data of the PyVista mesh
    # there can ba accessed as a 3D point cloud with the name "landmark_points"
    plotter = pv.Plotter()
    plotter.add_mesh(mesh_pv3, color="w")
    plotter.add_points(
        mesh_pv3.field_data["landmark_points"],
        color="r",
        point_size=10,
        label="landmarks",
    )
    plotter.add_legend()
    plotter.view_xy()
    plotter.show()

    # Back to scikit-shapes, the landmarks are preserved
    mesh_sks_back = sks.PolyData(mesh_pv3)
    assert torch.allclose(
        mesh_sks_back.landmark_indices, mesh_sks.landmark_indices
    )







.. tab-set::



   .. tab-item:: Static Scene



            
     .. image-sg:: /auto_examples/1_data/images/sphx_glr_plot_pyvista_001.png
        :alt: plot pyvista
        :srcset: /auto_examples/1_data/images/sphx_glr_plot_pyvista_001.png
        :class: sphx-glr-single-img
     


   .. tab-item:: Interactive Scene



       .. offlineviewer:: /home/runner/work/scikit-shapes/scikit-shapes/doc/source/auto_examples/1_data/images/sphx_glr_plot_pyvista_001.vtksz






.. tab-set::



   .. tab-item:: Static Scene



            
     .. image-sg:: /auto_examples/1_data/images/sphx_glr_plot_pyvista_002.png
        :alt: plot pyvista
        :srcset: /auto_examples/1_data/images/sphx_glr_plot_pyvista_002.png
        :class: sphx-glr-single-img
     


   .. tab-item:: Interactive Scene



       .. offlineviewer:: /home/runner/work/scikit-shapes/scikit-shapes/doc/source/auto_examples/1_data/images/sphx_glr_plot_pyvista_002.vtksz







.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.553 seconds)


.. _sphx_glr_download_auto_examples_1_data_plot_pyvista.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_pyvista.ipynb <plot_pyvista.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_pyvista.py <plot_pyvista.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_