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 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_sizeint, optional

Size in x direction of the kernel array. Default = ⌊8*width +1⌋.

y_sizeint, optional

Size in y direction of the kernel array. Default = ⌊8*width +1⌋.

modestr, 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.

Examples

Kernel response:

import matplotlib.pyplot as plt
from astropy.convolution import RickerWavelet2DKernel
ricker_2d_kernel = RickerWavelet2DKernel(10)
plt.imshow(ricker_2d_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()

(png, svg, pdf)

../_images/astropy-convolution-RickerWavelet2DKernel-1.png