Model2DKernel¶
-
class
astropy.convolution.
Model2DKernel
(model, **kwargs)[source] [edit on github]¶ Bases:
astropy.convolution.Kernel2D
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.
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.
- model :