def test_warp_by_vector():
# Test when inplace=False (default)
data = examples.load_sphere_vectors()
warped = data.warp_by_vector()
assert data.n_points == warped.n_points
assert not np.allclose(data.points, warped.points)
warped = data.warp_by_vector(factor=3)
assert data.n_points == warped.n_points
assert not np.allclose(data.points, warped.points)
# Test when inplace=True
foo = examples.load_sphere_vectors()
warped = foo.warp_by_vector()
foo.warp_by_vector(inplace=True)
assert np.allclose(foo.points, warped.points)
"""
Warping by Vectors
~~~~~~~~~~~~~~~~~~
This example applies the ``warp_by_vector`` filter to a sphere mesh that has
3D displacement vectors defined at each node.
"""
###############################################################################
# We first compare the unwarped sphere to the warped sphere.
import pyvista as pv
from pyvista import examples
sphere = examples.load_sphere_vectors()
warped = sphere.warp_by_vector()
p = pv.Plotter(shape=(1, 2))
p.subplot(0, 0)
p.add_text("Before warp")
p.add_mesh(sphere, color='white')
p.subplot(0, 1)
p.add_text("After warp")
p.add_mesh(warped, color='white')
p.show()
###############################################################################
# We then use several values for the scale factor applied to the warp
# operation. Applying a warping factor that is too high can often lead to
# unrealistic results.