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- Astropy v0.3 »
- Convolution and filtering (`astropy.convolution`) »
- Ring2DKernel

# Ring2DKernel

class `astropy.convolution.kernels.``Ring2DKernel`(radius_in, width, **kwargs)

  Bases: `astropy.convolution.core.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.

  Parameters:

  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.

  See also

  `Box2DKernel`, `Gaussian2DKernel`, `MexicanHat2DKernel`, `Tophat2DKernel`

  Examples

  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]')
  >     plt.colorbar()
  >     plt.show()

  > (Source code, png, hires.png, pdf)