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:

gammapython:float

Core width of the Moffat model.

alphapython:float

Power index of the Moffat model.

x_sizepython:int, optional

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

y_sizepython: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.

factornumber, 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()

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