Detailed Resampling#
Download Python source code | Download Jupyter notebook
This example uses pyvista.DataObjectFilters.sample().
pyvista.DataObjectFilters.resample_to_image() samples onto a new
ImageData in a single call.
pyvista.DataSetFilters.interpolate() is similar, and the two
methods are compared in Compare Interpolation/Sampling Methods.
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.
import pyvista as pv
from pyvista import examples
Simple Resample#
Query a grid’s 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
pl = pv.Plotter()
pl.add_mesh(mesh, color=True)
pl.add_mesh(data_to_probe, opacity=0.5)
pl.show()
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))
resample_to_image() samples onto a new
ImageData instead. Pass a reference_volume to give the
output the geometry of an image which already exists.
mesh.resample_to_image(reference_volume=data_to_probe)
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)
resample_to_image() does both steps in one call.
data_to_probe.resample_to_image(dimensions=(75, 75, 75))
To resample ImageData directly, use
resample() instead.
data_to_probe.resample(dimensions=(75, 75, 75))
Both meshes here are ImageData, and for that case
reslice() is the closer fit than sample. It reads
the image at the grid’s points just as sample does, but returns only the resampled
array, where sample also carries the grid’s own arrays and the mask arrays probing
adds, and it offers the border, interpolation, and anti-aliasing options an image
needs. See Reslice Images.
data_to_probe.reslice(mesh, 'linear')
threshold = lambda m: m.threshold(75.0, scalars='SLCImage')
cpos = pv.CameraPosition(
position=(468.9, -152.8, 152.1),
focal_point=(121.7, 140.3, 112.3),
viewup=(-0.1088, 0.006229, 0.994),
)
dargs = dict(clim=[0, 200], cmap='rainbow')
pl = pv.Plotter(shape=(1, 2))
pl.add_mesh(threshold(data_to_probe), **dargs)
pl.subplot(0, 1)
pl.add_mesh(threshold(result), **dargs)
pl.link_views()
pl.view_isometric()
pl.show(cpos=cpos)
Resample a Processed Volume#
Clipping an image returns an UnstructuredGrid, which image filters do
not accept. resample_to_image() puts the processed
volume back on a regular grid.
Start from a volumetric scan of a knee. Bone is the bright end of its intensity range, from 100 up.
knee = examples.download_knee_full()
knee.plot(volume=True, cmap='bone', clim=[100, 174])
Clip the scan to the bone, and keep the three largest pieces: the tibia, the femur, and
the patella. connectivity() numbers the regions from the
largest down and stores the numbers as 'RegionId'.
bone = knee.clip_scalar(scalars='SLCImage', value=100, invert=False)
bones = bone.connectivity('specified', [0, 1, 2])
bones.plot(scalars='RegionId', cmap='glasbey', categories=True)
Resample the labeled bones onto the scan’s own grid with reference_volume, so that
a voxel of the output is a voxel of the scan. Sample the labels as categories to keep
them whole, and blank the voxels which fall outside the bone.
labels = bones.resample_to_image(reference_volume=knee, categorical=True, mark_blank=True)
The labels sit on the points of the image, so render them as voxel cells with
points_to_cells().
labels.points_to_cells().plot(scalars='RegionId', cmap='glasbey', categories=True)
Total running time of the script: (0 minutes 9.757 seconds)
