plt.title('My Interpolated Opacity Transfer Function')
plt.ylabel('Opacity')
plt.xlabel('Index along scalar mapping')
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:
mesh.plot(opacity=opacity)
###############################################################################
# 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()
def test_download_knee():
data = examples.download_knee()
assert data.n_cells