def test_download_gourds():
data = examples.download_gourds()
assert data.n_cells
data = examples.download_gourds(zoom=True)
assert data.n_cells
~~~~~~~~~~~~~~~~~~
Perform a Gaussian convolution on a uniformly gridded data set.
:class:`pyvista.UniformGrid` data sets (a.k.a. images) a can be smoothed by
convolving the image data set with a Gaussian for one- to three-dimensional
inputs. This is commonly referred to as Gaussian blurring and typically used
to reduce noise or decrease the detail of an image dataset
"""
# sphinx_gallery_thumbnail_number = 2
import pyvista as pv
from pyvista import examples
# Load dataset
data = examples.download_gourds()
# Define a good point of view
cp = [
(319.5, 239.5, 1053.7372980874645),
(319.5, 239.5, 0.0),
(0.0, 1.0, 0.0)
]
###############################################################################
# Let's apply the gaussian smoothing with different values of standard
# deviation.
p = pv.Plotter(shape=(2, 2))
p.subplot(0, 0)
p.add_text("Original Image", font_size=24)
