RickerWavelet2DKernel¶
- class astropy.convolution.RickerWavelet2DKernel(width, **kwargs)[source]¶
Bases:
Kernel2D
2D 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. / (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
python:int
, optional Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
- y_size
python: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: