Slicing

Extract thin planar slices from a volume

# sphinx_gallery_thumbnail_number = 2
import pyvista as pv
from pyvista import examples
import matplotlib.pyplot as plt
import numpy as np

PyVista meshes have several slicing filters bound directly to all datasets. These filters allow you to slice through a volumetric dataset to extract and view sections through the volume of data.

One of the most common slicing filters used in PyVista is the pyvista.DataSetFilters.slice_orthogonal() filter which creates three orthogonal slices through the dataset parallel to the three Cartesian planes. For example, let’s slice through the sample geostatistical training image volume. First, load up the volume and preview it:

mesh = examples.load_channels()
# define a categorical colormap
cmap = plt.cm.get_cmap("viridis", 4)


mesh.plot(cmap=cmap)
../../_images/sphx_glr_slicing_001.png

Out:

[(534.8076211353316, 534.8076211353316, 459.80762113533166), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

Note that this dataset is a 3D volume and there might be regions within this volume that we would like to inspect. We can create slices through the mesh to gain further insight about the internals of the volume.

slices = mesh.slice_orthogonal()

slices.plot(cmap=cmap)
../../_images/sphx_glr_slicing_002.png

Out:

[(534.8076211353316, 534.8076211353316, 459.80762113533166), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

The orthogonal slices can be easily translated throughout the volume:

slices = mesh.slice_orthogonal(x=20, y=20, z=30)
slices.plot(cmap=cmap)
../../_images/sphx_glr_slicing_003.png

Out:

[(534.8076211353316, 534.8076211353316, 459.80762113533166), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

We can also add just a single slice of the volume by specifying the origin and normal of the slicing plane with the pyvista.DataSetFilters.slice() filter:

# Single slice - origin defaults to the center of the mesh
single_slice = mesh.slice(normal=[1, 1, 0])

p = pv.Plotter()
p.add_mesh(mesh.outline(), color="k")
p.add_mesh(single_slice, cmap=cmap)
p.show()
../../_images/sphx_glr_slicing_004.png

Out:

[(534.8076211353316, 534.8076211353316, 459.80762113533166), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

Adding slicing planes uniformly across an axial direction can also be automated with the pyvista.DataSetFilters.slice_along_axis() filter:

slices = mesh.slice_along_axis(n=7, axis="y")

slices.plot(cmap=cmap)
../../_images/sphx_glr_slicing_005.png

Out:

[(531.0336740029763, 531.0336740029763, 456.0336740029763), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

Slice Along Line

We can also slice a dataset along a pyvista.Spline() or pyvista.Line() using the DataSetFilters.slice_along_line() filter.

First, define a line source through the dataset of interest. Please note that this type of slicing is computationally expensive and might take a while if there are a lot of points in the line - try to keep the resolution of the line low.

model = examples.load_channels()


def path(y):
    """Equation: x = a(y-h)^2 + k"""
    a = 110.0 / 160.0 ** 2
    x = a * y ** 2 + 0.0
    return x, y


x, y = path(np.arange(model.bounds[2], model.bounds[3], 15.0))
zo = np.linspace(9.0, 11.0, num=len(y))
points = np.c_[x, y, zo]
spline = pv.Spline(points, 15)
spline
HeaderData Arrays
PolyDataInformation
N Cells1
N Points15
X Bounds0.000e+00, 2.475e+02
Y Bounds0.000e+00, 2.400e+02
Z Bounds9.000e+00, 1.100e+01
N Arrays1
NameFieldTypeN CompMinMax
arc_lengthPointsfloat3210.000e+003.605e+02


Then run the filter

slc = model.slice_along_line(spline)
slc
HeaderData Arrays
PolyDataInformation
N Cells49100
N Points49692
X Bounds0.000e+00, 2.500e+02
Y Bounds0.000e+00, 2.415e+02
Z Bounds0.000e+00, 1.000e+02
N Arrays1
NameFieldTypeN CompMinMax
faciesCellsint6410.000e+004.000e+00


p = pv.Plotter()
p.add_mesh(slc)
p.add_mesh(model.outline())
p.show(cpos=[1, -1, 1])
../../_images/sphx_glr_slicing_006.png

Out:

[(534.8076211353316, -284.80762113533166, 459.80762113533166), (125.0, 125.0, 50.0), (0.0, 0.0, 1.0)]

Total running time of the script: ( 0 minutes 41.269 seconds)

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