1D Ricker wavelet filter kernel (sometimes known as a “Mexican Hat” kernel).
The Ricker wavelet, or inverted Gaussian-Laplace 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 multi-scale 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. / (sqrt(2 * pi) * width ** 3). The normalization is the same as for
scipy.ndimage.gaussian_laplace, except for a minus sign.
See https://github.com/astropy/astropy/pull/9445 for discussions related to renaming of this kernel.
Width of the filter kernel, defined as the standard deviation of the Gaussian function from which it is derived.
Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
- One of the following discretization modes:
- ‘center’ (default)
Discretize model by taking the value at the center of the bin.
Discretize model by linearly interpolating between the values at the corners of the bin.
Discretize model by taking the average on an oversampled grid.
Discretize model by integrating the model over the bin.
- factornumber, optional
Factor of oversampling. Default factor = 10.