Using PyVista with Jupyter Execute

Note

Be sure to read the main documentation page PyVista - Jupyter Notebook Plotting for more details about using PyVista with JupyterLab.

PyVista also supports the jupyter-sphinx extension. With this extension you can execute code blocks within a sphinx *.rst file using the jupyter-execute directive:

.. jupyter-execute::

   name = 'world'
   print('hello ' + name + '!')

Will be rendered as:

name = 'world'
print('hello ' + name + '!')

hello world!

Likewise, output from PyVista that would normally be rendered within a notebook will be rendered in the output cell from the jupyter-execute directive. For example, here’s a plot using the panel backend:

.. jupyter-execute::

   from pyvista import examples
   dataset = examples.download_urn()
   dataset.plot(
       color='tan',
       jupyter_backend='panel',
       smooth_shading=True,
       window_size=[600, 400]
   )

Which is rendered as:

from pyvista import examples
dataset = examples.download_urn()
dataset.plot(
    color='tan',
    jupyter_backend='panel',
    smooth_shading=True,
    window_size=[600, 400]
)

Using the Panel backend with PyVista

PyVista supports the usage of the panel library as a vtk.js jupyterlab plotting backend that can be utilized as either a standalone VTK viewer, or as a tightly integrated pyvista plotting backend. For example, within a Jupyter notebook environment, you can pass jupyter_backend='panel' to plot, or Plotter.show to automatically enable plotting with Juptyer and panel.

For example, here’s the PyVista logo:

.. jupyter-execute::

   from pyvista import demos
   demos.plot_logo(background='white', jupyter_backend='panel')

Which is shown within the documentation as:

from pyvista import demos
demos.plot_logo(background='white', jupyter_backend='panel')

Examples and Usage

There are two ways to use panel within Jupyter notebooks. You can use it on a plot by plot basis by setting the jupyter_backend in mesh.plot():

.. jupyter-execute::

    import pyvista as pv
    from pyvista import examples

    # create a point cloud from lidar data and add height scalars
    dataset = examples.download_lidar()
    point_cloud = pv.PolyData(dataset.points[::100])
    point_cloud['height'] = point_cloud.points[:, 2]
    point_cloud.plot(window_size=[500, 500],
                     jupyter_backend='panel',
                     cmap='jet',
                     point_size=2,
                     background='w')

And here’s the resulting output in Sphinx:

import pyvista as pv
from pyvista import examples

# create a point cloud from lidar data and add height scalars
dataset = examples.download_lidar()
point_cloud = pv.PolyData(dataset.points[::100])
point_cloud['height'] = point_cloud.points[:, 2]
point_cloud.plot(window_size=[500, 500],
                 jupyter_backend='panel',
                 cmap='jet',
                 point_size=2,
                 background='w')

Or you can first hide code that sets up the plotting backend and global theme:

.. jupyter-execute::
    :hide-code:

    import pyvista as pv

    # Set the global jupyterlab backend.  All plots from this point
    # onward will use the ``panel`` backend and do not have to be
    # specified in ``show``
    pv.set_jupyter_backend('panel')
    pv.global_theme.background = 'white'
    pv.global_theme.axes.show = False
    pv.global_theme.smooth_shading = True
    pv.global_theme.antialiasing = True

And now just directly execute plot on any dataset:

.. jupyter-execute::

   from pyvista import examples
   dataset = examples.download_dragon()
   dataset.plot(cpos="xy", scalars=dataset.points[:, 2], cmap='bwr')

Which looks like:

from pyvista import examples
dataset = examples.download_dragon()
dataset.plot(cpos="xy", scalars=dataset.points[:, 2], cmap='bwr')

Note

You have the option of choosing panel or pythreejs as a backend, but you might find that panel has better support as it’s being actively developed.