pyvista.ImageDataFilters.high_pass#

ImageDataFilters.high_pass( x_cutoff, y_cutoff, z_cutoff, order=1, output_scalars_name=None, progress_bar=False, )[source]#

Perform a Butterworth high pass filter in the frequency domain.

This filter requires that the ImageData have a complex point scalars, usually generated after the ImageData has been converted to the frequency domain by a ImageDataFilters.fft() filter.

A ImageDataFilters.rfft() filter can be used to convert the output back into the spatial domain. This filter attenuates low frequency components. Input and output are complex arrays with datatype numpy.complex128.

The frequencies of the input assume standard order: along each axis first positive frequencies are assumed from 0 to the maximum, then negative frequencies are listed from the largest absolute value to smallest. This implies that the corners of the grid correspond to low frequencies, while the center of the grid corresponds to high frequencies.

Parameters:

x_cutofffloat: The cutoff frequency for the x-axis.
y_cutofffloat: The cutoff frequency for the y-axis.
z_cutofffloat: The cutoff frequency for the z-axis.
orderint, default: 1: The order of the cutoff curve. Given from the equation 1/(1 + (cutoff/freq(i, j))**(2*order)).
output_scalars_namestr, optional: The name of the output scalars. By default, this is the same as the active scalars of the dataset.
progress_barbool, default: False: Display a progress bar to indicate progress.

Returns:

pyvista.ImageData: pyvista.ImageData with the applied high pass filter.