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# Moffat2DKernel

class astropy.convolution.Moffat2DKernel(gamma, alpha, **kwargs)

  Bases: `Kernel2D`

  2D Moffat kernel.

  This kernel is a typical model for a seeing limited PSF.

  The generated kernel is normalized so that it integrates to 1.

  Parameters:
    gamma `python:float`

      Core width of the Moffat model.

    alpha `python:float`

      Power index of the Moffat model.

    x_size `python:int`, optional

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

    y_size `python:int`, optional

      Size in y direction of the kernel array. Default = ⌊8*radius + 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.

    factor number, optional

      Factor of oversampling. Default factor = 10.

  See also

  `Gaussian2DKernel`, `Box2DKernel`, `Tophat2DKernel`, `RickerWavelet2DKernel`
  `Ring2DKernel`, `TrapezoidDisk2DKernel`, `AiryDisk2DKernel`

  Examples

  Kernel response:

  >     import matplotlib.pyplot as plt
  >     from astropy.convolution import Moffat2DKernel
  >     moffat_2D_kernel = Moffat2DKernel(3, 2)
  >     plt.imshow(moffat_2D_kernel, interpolation='none', origin='lower')
  >     plt.xlabel('x [pixels]')
  >     plt.ylabel('y [pixels]')
  >     plt.colorbar()
  >     plt.show()

  > (`png`, `svg`, `pdf`)