Gaussian2DKernel¶
-
class
astropy.convolution.
Gaussian2DKernel
(x_stddev, y_stddev=None, theta=0.0, **kwargs)[source] [edit on github]¶ Bases:
astropy.convolution.Kernel2D
2D Gaussian filter kernel.
The Gaussian filter is a filter with great smoothing properties. It is isotropic and does not produce artifacts.
Parameters: - x_stddev : float
Standard deviation of the Gaussian in x before rotating by theta.
- y_stddev : float
Standard deviation of the Gaussian in y before rotating by theta.
- theta : float
Rotation angle in radians. The rotation angle increases counterclockwise.
- x_size : odd int, optional
Size in x direction of the kernel array. Default = 8 * stddev.
- y_size : odd int, optional
Size in y direction of the kernel array. Default = 8 * stddev.
- 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.
See also
Box2DKernel
,Tophat2DKernel
,MexicanHat2DKernel
,Ring2DKernel
,TrapezoidDisk2DKernel
,AiryDisk2DKernel
,Moffat2DKernel
Examples
Kernel response:
import matplotlib.pyplot as plt from astropy.convolution import Gaussian2DKernel gaussian_2D_kernel = Gaussian2DKernel(10) plt.imshow(gaussian_2D_kernel, interpolation='none', origin='lower') plt.xlabel('x [pixels]') plt.ylabel('y [pixels]') plt.colorbar() plt.show()
()