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- Convolution and filtering (`astropy.convolution`) »
- MexicanHat1DKernel

# MexicanHat1DKernel

class `astropy.convolution.kernels.``MexicanHat1DKernel`(width, **kwargs)

  Bases: `astropy.convolution.core.Kernel1D`

  1D Mexican hat filter kernel.

  The Mexican Hat, or inverted Gaussian-Laplace filter, is a bandpass filter. It smoothes 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.

  Parameters:

  width : number

  > Width of the filter kernel.

  x_size : odd int, optional

  > Size in x 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 linearly interpolating 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

  `Box1DKernel`, `Gaussian1DKernel`, `Trapezoid1DKernel`

  Examples

  Kernel response:

  >     import matplotlib.pyplot as plt
  >     from astropy.convolution import MexicanHat1DKernel
  >     mexicanhat_1D_kernel = MexicanHat1DKernel(10)
  >     plt.plot(mexicanhat_1D_kernel, drawstyle='steps')
  >     plt.xlabel('x [pixels]')
  >     plt.ylabel('value')
  >     plt.show()

  > (Source code, png, hires.png, pdf)