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)