Model2DKernel

class astropy.convolution.Model2DKernel(model, **kwargs)

Bases: Kernel2D

Create kernel from 2D model.

The model has to be centered on x = 0 and y = 0.

Parameters:

modelFittable2DModel

Kernel response function model

x_sizepython:int, optional

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

y_sizepython:int, optional

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

Raises:

TypeError

If model is not an instance of Fittable2DModel

See also

Model1DKernel

Create kernel from Fittable1DModel

CustomKernel

Create kernel from list or array

Examples

Define a Gaussian2D model:

>>> from astropy.modeling.models import Gaussian2D
>>> from astropy.convolution.kernels import Model2DKernel
>>> gauss = Gaussian2D(1, 0, 0, 2, 2)

And create a custom two dimensional kernel from it:

>>> gauss_kernel = Model2DKernel(gauss, x_size=9)

This kernel can now be used like a usual astropy kernel.