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"""
.. _resampling_example:
Detailed Resampling
~~~~~~~~~~~~~~~~~~~
This example uses :func:`pyvista.DataObjectFilters.sample`.
:func:`pyvista.DataSetFilters.interpolate` is similar, and the two
methods are compared in :ref:`interpolate_sample_example`.
Resample one mesh's point/cell arrays onto another mesh's nodes.
"""
# %%
# This example will resample a volumetric mesh's scalar data onto the surface
# of a sphere contained in that volume.
# sphinx_gallery_thumbnail_number = 3
from __future__ import annotations
import pyvista as pv
from pyvista import examples
# %%
# Simple Resample
# +++++++++++++++
# Query a grid's points onto a sphere
mesh = pv.Sphere(center=(4.5, 4.5, 4.5), radius=4.5)
data_to_probe = examples.load_uniform()
# %%
# Plot the two datasets
p = pv.Plotter()
p.add_mesh(mesh, color=True)
p.add_mesh(data_to_probe, opacity=0.5)
p.show()
# %%
# Run the algorithm and plot the result
result = mesh.sample(data_to_probe)
# Plot result
name = 'Spatial Point Data'
result.plot(scalars=name, clim=data_to_probe.get_data_range(name))
# %%
# Complex Resample
# ++++++++++++++++
# Take a volume of data and create a grid of lower resolution to resample on
data_to_probe = examples.download_embryo()
mesh = pv.create_grid(data_to_probe, dimensions=(75, 75, 75))
result = mesh.sample(data_to_probe)
# %%
threshold = lambda m: m.threshold(75.0, scalars='SLCImage')
cpos = [
(468.9075585873713, -152.8280322856109, 152.13046602188035),
(121.65121514580106, 140.29327609542105, 112.28137570357188),
(-0.10881224951051659, 0.006229357618166009, 0.9940428006178236),
]
dargs = dict(clim=[0, 200], cmap='rainbow')
p = pv.Plotter(shape=(1, 2))
p.add_mesh(threshold(data_to_probe), **dargs)
p.subplot(0, 1)
p.add_mesh(threshold(result), **dargs)
p.link_views()
p.view_isometric()
p.show(cpos=cpos)
# %%
# .. tags:: filter
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