ImageDataFilters.gaussian_smooth

ImageDataFilters.gaussian_smooth(

*,

radius_factor: float | VectorLikeFloat = 1.5,

std_dev: float | VectorLikeFloat = 2.0,

scalars: str | None = None,

progress_bar: bool = False,

) → ImageData

Smooth the data with a Gaussian kernel.

Parameters:

radius_factorfloat | VectorLikeFloat, default: 1.5

Unitless factor to limit the extent of the kernel.

std_devfloat | VectorLikeFloat, default: 2.0

Standard deviation of the kernel in pixel units.

scalarsstr, optional

Name of scalars to process. Defaults to currently active scalars.

progress_barbool, default: False

Display a progress bar to indicate progress.

Returns:

pyvista.ImageData

Uniform grid with smoothed scalars.

Notes

This filter only supports point data. For inputs with cell data, consider re-meshing the cell data as point data with cells_to_points() or resampling the cell data to point data with cell_data_to_point_data().

Examples

Download Python source code | Download Jupyter notebook

First, create sample data to smooth. Here, we use pyvista.perlin_noise() to create meaningful data.

>>> import numpy as np
>>> import pyvista as pv
>>> noise = pv.perlin_noise(0.1, (2, 5, 8), (0, 0, 0))
>>> grid = pv.sample_function(
...     noise, bounds=[0, 1, 0, 1, 0, 1], dim=(20, 20, 20)
... )
>>> grid.plot(show_scalar_bar=False)

Static Scene

Interactive Scene

Next, smooth the sample data.

>>> smoothed = grid.gaussian_smooth()
>>> smoothed.plot(show_scalar_bar=False)

Static Scene

Interactive Scene

Used In

Gallery Examples

Gaussian Smoothing

Gaussian Smoothing

2 uses