class astropy.convolution.Ring2DKernel(radius_in, width, **kwargs)[source] [edit on github]

Bases: astropy.convolution.Kernel2D

2D Ring filter kernel.

The Ring filter kernel is the difference between two Tophat kernels of different width. This kernel is useful for, e.g., background estimation.

radius_in : number

Inner radius of the ring kernel.

width : number

Width of the ring kernel.

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.


Kernel response:

import matplotlib.pyplot as plt
from astropy.convolution import Ring2DKernel
ring_2D_kernel = Ring2DKernel(9, 8)
plt.imshow(ring_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')

(png, svg, pdf)