# Detailed Resampling
# ===================

# This example uses `pyvista.DataObjectFilters.sample()`.
# `pyvista.DataSetFilters.interpolate()` is similar, and the two
# methods are compared in Compare interpolation/sampling methods.
# 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.
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
pl = pv.Plotter()
pl.add_mesh(mesh, color=True)
pl.add_mesh(data_to_probe, opacity=0.5)
pl.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 = pv.CameraPosition(
    position=(468.9075585873713, -152.8280322856109, 152.13046602188035),
    focal_point=(121.65121514580106, 140.29327609542105, 112.28137570357188),
    viewup=(-0.10881224951051659, 0.006229357618166009, 0.9940428006178236),
)
dargs = dict(clim=[0, 200], cmap='rainbow')

pl = pv.Plotter(shape=(1, 2))
pl.add_mesh(threshold(data_to_probe), **dargs)
pl.subplot(0, 1)
pl.add_mesh(threshold(result), **dargs)
pl.link_views()
pl.view_isometric()
pl.show(cpos=cpos)

# ----------------------------------------------------------------------
# Generated by sphinx-examples-as-code https://github.com/pyvista/sphinx-examples-as-code