Interpolating#

There are two main methods of interpolating or sampling data from a target mesh in PyVista. pyvista.DataSetFilters.interpolate() uses a distance weighting kernel to interpolate point data from nearby points of the target mesh onto the desired points. pyvista.DataSetFilters.sample() interpolates data using the interpolation scheme of the enclosing cell from the target mesh.

If the target mesh is a point cloud, i.e. there is no connectivity in the cell structure, then pyvista.DataSetFilters.interpolate() is typically preferred. If interpolation is desired within the cells of the target mesh, then pyvista.DataSetFilters.sample() is typically desired.

This example uses pyvista.DataSetFilters.interpolate(). For pyvista.DataSetFilters.sample(), see Resampling.

Interpolate one mesh’s point/cell arrays onto another mesh’s nodes using a Gaussian Kernel.

from __future__ import annotations

import pyvista as pv
from pyvista import examples

Simple Surface Interpolation#

Resample the points’ arrays onto a surface

# Download sample data
surface = examples.download_saddle_surface()
points = examples.download_sparse_points()

p = pv.Plotter()
p.add_mesh(points, scalars="val", point_size=30.0, render_points_as_spheres=True)
p.add_mesh(surface)
p.show()
interpolate

Run the interpolation

interpolated = surface.interpolate(points, radius=12.0)


p = pv.Plotter()
p.add_mesh(points, scalars="val", point_size=30.0, render_points_as_spheres=True)
p.add_mesh(interpolated, scalars="val")
p.show()
interpolate

Complex Interpolation#

In this example, we will in interpolate sparse points in 3D space into a volume. These data are from temperature probes in the subsurface and the goal is to create an approximate 3D model of the temperature field in the subsurface.

This approach is a great for back-of-the-hand estimations but pales in comparison to kriging

# Download the sparse data
probes = examples.download_thermal_probes()

Create the interpolation grid around the sparse data

grid = pv.ImageData()
grid.origin = (329700, 4252600, -2700)
grid.spacing = (250, 250, 50)
grid.dimensions = (60, 75, 100)
dargs = dict(cmap="coolwarm", clim=[0, 300], scalars="temperature (C)")
cpos = [
    (364280.5723737897, 4285326.164400684, 14093.431895014139),
    (337748.7217949739, 4261154.45054595, -637.1092549935128),
    (-0.29629216102673206, -0.23840196609932093, 0.9248651025279784),
]

p = pv.Plotter()
p.add_mesh(grid.outline(), color='k')
p.add_mesh(probes, render_points_as_spheres=True, **dargs)
p.show(cpos=cpos)
interpolate

Run an interpolation

interp = grid.interpolate(probes, radius=15000, sharpness=10, strategy='mask_points')

Visualize the results

vol_opac = [0, 0, 0.2, 0.2, 0.5, 0.5]

p = pv.Plotter(shape=(1, 2), window_size=[1024 * 3, 768 * 2])
p.add_volume(interp, opacity=vol_opac, **dargs)
p.add_mesh(probes, render_points_as_spheres=True, point_size=10, **dargs)
p.subplot(0, 1)
p.add_mesh(interp.contour(5), opacity=0.5, **dargs)
p.add_mesh(probes, render_points_as_spheres=True, point_size=10, **dargs)
p.link_views()
p.show(cpos=cpos)
interpolate

Total running time of the script: (0 minutes 6.661 seconds)

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