Plot with Opacity¶
Plot a mesh’s scalar array with an opacity transfer funciton or opacity mapping based on a scalar array.
# sphinx_gallery_thumbnail_number = 2 import pyvista as pv from pyvista import examples # Load St Helens DEM and warp the topography image = examples.download_st_helens() mesh = image.warp_by_scalar()
You can also apply a global opacity value to the mesh by passing a single float between 0 and 1 which would enable you to see objects behind the mesh:
p = pv.Plotter() p.add_mesh(image.contour(), line_width=5,) p.add_mesh(mesh, opacity=0.85, color=True) p.show()
Note that you can specify
use_transparency=True to convert opacities to
transparencies in any of the following examples.
It’s possible to apply an opacity mapping to any scalar array plotted. You can specify either a single static value to make the mesh transparent on all cells, or use a transfer function where the scalar array plotted is mapped to the opacity. We have several predefined transfer functions.
Opacity transfer functions are:
'linear': linearly vary (increase) opacity across the plotted scalar range from low to high
'linear_r': linearly vary (increase) opacity across the plotted scalar range from high to low
'geom': on a log scale, vary (increase) opacity across the plotted scalar range from low to high
'geom_r': on a log scale, vary (increase) opacity across the plotted scalar range from high to low
'sigmoid': vary (increase) opacity on a sigmoidal s-curve across the plotted scalar range from low to high
'sigmoid_r': vary (increase) opacity on a sigmoidal s-curve across the plotted scalar range from high to low
# Show the linear opacity transfer function mesh.plot(opacity="linear")
# Show the sigmoid opacity transfer function mesh.plot(opacity="sigmoid")
It’s also possible to use your own transfer function that will be linearly mapped to the scalar array plotted. For example, we can create an opacity mapping as:
opacity = [0, 0.2, 0.9, 0.6, 0.3]
When given a minimized opacity mapping like that above, PyVista interpolates
it across a range of how many colors are shown when mapping the scalars.
scipy is available, then a quadratic interpolation is used -
otherwise, a simple linear interpolation is used.
Curious what that opacity transfer function looks like? You can fetch it:
# Have PyVista interpolate the transfer function tf = pv.opacity_transfer_function(opacity, 256).astype(float) / 255. import matplotlib.pyplot as plt plt.plot(tf) plt.title('My Interpolated Opacity Transfer Function') plt.ylabel('Opacity') plt.xlabel('Index along scalar mapping') plt.show()
/home/travis/build/pyvista/pyvista/examples/02-plot/opacity.py:81: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. plt.show()
That opacity mapping will have an opacity of 0.0 at the minimum scalar range, a value or 0.9 at the middle of the scalar range, and a value of 0.3 at the maximum of the scalar range:
Opacity mapping is often useful when plotting DICOM images. For example, download the sample knee DICOM image:
knee = examples.download_knee()
And here we inspect the DICOM image with a few different opacity mappings:
p = pv.Plotter(shape=(2, 2), border=False) p.add_mesh(knee, cmap="bone", stitle="No Opacity") p.view_xy() p.subplot(0, 1) p.add_mesh(knee, cmap="bone", opacity="linear", stitle="Linear Opacity") p.view_xy() p.subplot(1, 0) p.add_mesh(knee, cmap="bone", opacity="sigmoid", stitle="Sigmoidal Opacity") p.view_xy() p.subplot(1, 1) p.add_mesh(knee, cmap="bone", opacity="geom_r", stitle="Log Scale Opacity") p.view_xy() p.show()
Opacity by Array¶
You can also use a scalar array associated with the mesh to give each cell its own opacity/transparency value derived from a scalar field. For example, an uncertainty array from a modelling result could be used to hide regions of a mesh that are uncertain and highlight regions that are well resolved.
The following is a demonstration of plotting a mesh with colored values and using a second array to control the transparancy of the mesh
model = examples.download_model_with_variance() contours = model.contour(10, scalars='Temperature') print(contours.array_names)
Make sure to flag
use_transparency=True since we want areas of high
variance to have high transparency.
p = pv.Plotter(shape=(1,2)) p.subplot(0,0) p.add_text('Opacity by Array') p.add_mesh(contours.copy(), scalars='Temperature', opacity='Temperature_var', use_transparency=True, cmap='bwr') p.subplot(0,1) p.add_text('No Opacity') p.add_mesh(contours, scalars='Temperature', cmap='bwr') p.show()
Total running time of the script: ( 0 minutes 27.161 seconds)