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. Thes 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)
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 furthur insight about the internals of the volume.
slices = mesh.slice_orthogonal() slices.plot(cmap=cmap)
The orthogonal slices can be easily translated throughout the volume:
slices = mesh.slice_orthogonal(x=20, y=20, z=30) slices.plot(cmap=cmap)
We can also add just a single slice of the volume by specifying the origin
and normal of the slicing plane with the
# 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()
Adding slicing planes uniformly across an axial direction can also be
automated with the
slices = mesh.slice_along_axis(n=7, axis="y") slices.plot(cmap=cmap)
Slice Along Line¶
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, model.bounds, 15.0)) zo = np.linspace(9.0, 11.0, num=len(y)) points = np.c_[x, y, zo] spline = pv.Spline(points, 15) print(spline)
PolyData (0x7efc17946fa8) N Cells: 1 N Points: 15 X Bounds: 0.000e+00, 2.475e+02 Y Bounds: 0.000e+00, 2.400e+02 Z Bounds: 9.000e+00, 1.100e+01 N Arrays: 1
Then run the filter
slc = model.slice_along_line(spline) print(slc)
PolyData (0x7efc17946e88) N Cells: 49100 N Points: 49692 X Bounds: 0.000e+00, 2.500e+02 Y Bounds: 0.000e+00, 2.415e+02 Z Bounds: 0.000e+00, 1.000e+02 N Arrays: 1
p = pv.Plotter() p.add_mesh(slc) p.add_mesh(model.outline()) p.show(cpos=[1, -1, 1])
Total running time of the script: ( 0 minutes 37.581 seconds)