"""
.. _resampling_example:

Resampling
~~~~~~~~~~

There are two main methods of interpolating or sampling data from a target mesh
in PyVista. :func:`pyvista.DataSetFilters.interpolate` uses a distance weighting
kernel to interpolate point data from nearby points of the target mesh onto
the desired points.
:func:`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 :func:`pyvista.DataSetFilters.interpolate` is typically
preferred.  If interpolation is desired within the cells of the target mesh, then
:func:`pyvista.DataSetFilters.sample` is typically desired.


This example uses :func:`pyvista.DataSetFilters.sample`.
For :func:`pyvista.DataSetFilters.interpolate`, see :ref:`interpolate_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)