prtools.gauss_kernel#

gauss_kernel(shape, sigma, oversample=1, pixelscale=1.0, fftshift=False)[source]#

2D Gaussian filter kernel

This function returns the Fourier transform of a normalized 2D Gaussian function.

Parameters:

  • shape (int or tuple of int) – Shape of the kernel

  • sigma (float) – Standard deviation of Gaussian. Providing two values allows for a non-symmetric Gaussian interpreted as (sigma_row, sigma_col).

  • oversample (float, optional) – Oversampling factor to represent in the kernel. Default is 1.

  • pixelscale (scalar or tuple of scalars, optional) – Sample spacing. Providing two values defines non-symmetric sampling interpreted as (pixelscale_row, pixelscale_col). Default is 1.

  • fftshift (bool, optional) – If True, the kernel is FFT-shifted so that the zero-frequency (DC) term is placed at the center of the returned array. Default is False.

Return type:

ndarray

See also

fftconv(), gauss_blur(), gauss()

Examples

Create a Gaussian kernel with

>>> G = prtools.gauss_kernel((256,256), sigma=1, fftshift=True)
>>> plt.imshow(G, cmap='gray')
../_images/prtools-gauss_kernel-1.png

Oversampling is in effect the same as zero-padding:

>>> G = prtools.gauss_kernel((512,512), sigma=1, oversample=2,
...                          fftshift=True)
>>> plt.imshow(G, cmap='gray')
../_images/prtools-gauss_kernel-2.png

For a kernel with custom sample spacing:

>>> G = prtools.gauss_kernel((256,256), sigma=1, pixelscale=0.25,
...                          fftshift=True)
>>> plt.imshow(G, cmap='gray')
../_images/prtools-gauss_kernel-3.png