Computing Surface Normals#

Compute normals on a surface.

from __future__ import annotations

import numpy as np

from pyvista import examples

Computing the normals of a surface is quite easy using pyvista.PolyData’s pyvista.PolyDataFilters.compute_normals() method.

mesh = examples.download_topo_global()
mesh.plot(cmap="gist_earth", show_scalar_bar=False)
compute normals

Now we have a surface dataset of the globe loaded - unfortunately, the dataset shows the globe with a uniform radius which hides topographic relief. Using pyvista.PolyDataFilters.compute_normals(), we can compute the normal vectors on the globe at all points in the dataset, then use the values given in the dataset to warp the surface in the normals direction to create some exaggerated topographic relief.

# Compute the normals in-place and use them to warp the globe
mesh.compute_normals(inplace=True)  # this activates the normals as well

# Now use those normals to warp the surface
warp = mesh.warp_by_scalar(factor=0.5e-5)

# And let's see it
warp.plot(cmap="gist_earth", show_scalar_bar=False)
compute normals

We could also use face/cell normals to extract all the faces of a mesh facing a general direction. In the following snippet, we take a mesh, compute the normals along its cell faces, and extract the faces that face upward.

mesh = examples.download_nefertiti()
# Compute normals
mesh.compute_normals(cell_normals=True, point_normals=False, inplace=True)

# Get list of cell IDs that meet condition
ids = np.arange(mesh.n_cells)[mesh['Normals'][:, 2] > 0.0]

# Extract those cells
top = mesh.extract_cells(ids)

cpos = [
    (-834.3184529757553, -918.4677714398535, 236.5468795300025),
    (11.03829376004883, -13.642289291587957, -35.91218884207208),
    (0.19212361465657216, 0.11401076390090074, 0.9747256344254143),

top.plot(cpos=cpos, color=True)
compute normals

Total running time of the script: (0 minutes 48.669 seconds)

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