MexicanHat2DKernel¶

class
astropy.convolution.
MexicanHat2DKernel
(width, **kwargs)[source]¶ Bases:
astropy.convolution.Kernel2D
2D Mexican hat filter kernel.
The Mexican Hat, 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.Parameters:  width : number
Width of the filter kernel, defined as the standard deviation of the Gaussian function from which it is derived.
 x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * width.
 y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * width.
 mode : str, 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.
 factor : number, optional
Factor of oversampling. Default factor = 10.
See also
Examples
Kernel response:
import matplotlib.pyplot as plt from astropy.convolution import MexicanHat2DKernel mexicanhat_2D_kernel = MexicanHat2DKernel(10) plt.imshow(mexicanhat_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show()
()