Resampling

Resample one mesh’s point/cell arrays onto another mesh’s nodes.

This example will resample a volumetric mesh’s scalar data onto the surface of a sphere contained in that volume.

# sphinx_gallery_thumbnail_number = 3
import pyvista as pv
from pyvista import examples

Simple Resample

Query a grids points onto a sphere

mesh = pv.Sphere(center=(4.5, 4.5, 4.5), radius=4.5)
data_to_probe = examples.load_uniform()

Plot the two datasets

p = pv.Plotter()
p.add_mesh(mesh, color=True)
p.add_mesh(data_to_probe, opacity=0.5)
p.show()

Out:

[(21.886664873203234, 21.886664873203234, 21.886664873203234),
 (4.5, 4.5, 4.5),
 (0.0, 0.0, 1.0)]

Run the algorithm and plot the result

result = mesh.sample(data_to_probe)

# Plot result
name = "Spatial Point Data"
result.plot(scalars=name, clim=data_to_probe.get_data_range(name))

Out:

[(21.83804075669771, 21.83804051827913, 21.83804075669771),
 (4.5, 4.499999761581421, 4.5),
 (0.0, 0.0, 1.0)]

Complex Resample

Take a volume of data and create a grid of lower resolution to resample on

data_to_probe = examples.download_embryo()
mesh = pv.create_grid(data_to_probe, dimensions=(75, 75, 75))

result = mesh.sample(data_to_probe)

threshold = lambda m: m.threshold(15.0)
cpos = [(468.9075585873713, -152.8280322856109, 152.13046602188035),
    (121.65121514580106, 140.29327609542105, 112.28137570357188),
    (-0.10881224951051659, 0.006229357618166009, 0.9940428006178236)]
dargs = dict(clim=data_to_probe.get_data_range(), cmap='rainbow')

p = pv.Plotter(shape=(1,2))
p.add_mesh(threshold(data_to_probe), **dargs)
p.subplot(0,1)
p.add_mesh(threshold(result), **dargs)
p.link_views()
p.view_isometric()
p.show(cpos=cpos)

Out:

[(468.9075585873713, -152.8280322856109, 152.13046602188035),
 (121.65121514580106, 140.29327609542105, 112.28137570357188),
 (-0.10881224951051659, 0.006229357618166009, 0.9940428006178236)]