Visualizing 3D model data

Section author: Florian Zill (Helmholtz Centre for Environmental Research GmbH - UFZ)

For visualizing 3D data it might make sense to plot multiple views / slices. To create them we use pyvista as it provides all the necessary functionality. If we want to plot multiple meshes, they have to be in a 2D numpy array. The shape of this array determines the number of rows and columns in our plot. First, let’s load 3D example data from pyvista and plot the only available data: “facies” with a native pyvista plot.

import numpy as np
from pyvista import examples

import ogstools.meshplotlib as mpl
from ogstools.propertylib import Scalar

mpl.setup.reset()
mesh = examples.load_channels()
mesh.plot(cmap="bwr")

data = Scalar("facies", categoric=True)

Static Scene

Interactive Scene

Now, let’s create multiple slices along the z axis and plot them with meshplotlib in a 2 by 2 grid.

slices = np.reshape(list(mesh.slice_along_axis(n=4, axis="z")), (2, 2))
fig = mpl.plot(slices, data)
for ax, slice in zip(fig.axes, np.ravel(slices)):
    ax.set_title(f"z = {slice.center[2]:.1f} {mpl.setup.length.data_unit}")

We can also slice along the y-axis and plot the meshes in one row.

slices = np.reshape(mesh.slice_along_axis(n=3, axis="y"), (1, -1))
fig = mpl.plot(slices, data)
for ax, slice in zip(fig.axes, np.ravel(slices)):
    ax.set_title(f"y = {slice.center[1]:.1f} {mpl.setup.length.data_unit}")

Arbitrary oriented slices are also possible. They get projected to the cardinal plane, from which they have the least rotational offset.

fig = mpl.plot(mesh.slice([1, -2, 0]), data)