RickerWavelet2DKernel¶

class
astropy.convolution.
RickerWavelet2DKernel
(width, **kwargs)[source]¶ Bases:
astropy.convolution.Kernel2D
2D Ricker wavelet filter kernel (sometimes known as a “Mexican Hat” kernel).
The Ricker wavelet, or inverted GaussianLaplace filter, is a bandpass filter. It smooths the data and removes slowly varying or constant structures (e.g. Background). It is useful for peak or multiscale detection.
This kernel is derived from a normalized Gaussian function, by computing the second derivative. This results in an amplitude at the kernels center of 1. / (pi * width ** 4). The normalization is the same as for
scipy.ndimage.gaussian_laplace
, except for a minus sign.Note
See https://github.com/astropy/astropy/pull/9445 for discussions related to renaming of this kernel.
 Parameters
 widthnumber
Width of the filter kernel, defined as the standard deviation of the Gaussian function from which it is derived.
 x_size
int
, optional Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
 y_size
int
, optional Size in y direction of the kernel array. Default = ⌊8*width +1⌋.
 mode{‘center’, ‘linear_interp’, ‘oversample’, ‘integrate’}, optional
 One of the following discretization modes:
 ‘center’ (default)
Discretize model by taking the value at the center of the bin.
 ‘linear_interp’
Discretize model by performing a bilinear interpolation between the values at the corners of the bin.
 ‘oversample’
Discretize model by taking the average on an oversampled grid.
 ‘integrate’
Discretize model by integrating the model over the bin.
 factornumber, optional
Factor of oversampling. Default factor = 10.
See also
Examples
Kernel response: