Model2DKernel¶

class astropy.convolution.Model2DKernel(model, **kwargs)[source]

Create kernel from 2D model.

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

Parameters: model : Fittable2DModel Kernel response function model 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. TypeError If model is not an instance of Fittable2DModel

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.