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- Astropy v0.3 »
- Convolution and filtering (`astropy.convolution`) »
- Box2DKernel

# Box2DKernel

class `astropy.convolution.kernels.``Box2DKernel`(width, **kwargs)

  Bases: `astropy.convolution.core.Kernel2D`

  2D Box filter kernel.

  The Box filter or running mean is a smoothing filter. It is not isotropic and can produce artifact, when applied repeatedly to the same data.

  By default the Box kernel uses the `linear_interp` discretization mode, which allows non shifting, even sized kernels. This is achieved by weighting the edge pixels with 1/2.

  Parameters:

  width : number

  > Width of the filter kernel.

  mode : str, optional

  > One of the following discretization modes:
  >   - ‘center’

  >       Discretize model by taking the value at the center of the bin.

  >   - ‘linear_interp’ (default)

  >       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`

  Examples

  Kernel response:

  >     import matplotlib.pyplot as plt
  >     from astropy.convolution import Box2DKernel
  >     box_2D_kernel = Box2DKernel(9)
  >     plt.imshow(box_2D_kernel, interpolation='none', origin='lower',
  >                vmin=0.0, vmax=0.015)
  >     plt.xlim(-1, 9)
  >     plt.ylim(-1, 9)
  >     plt.xlabel('x [pixels]')
  >     plt.ylabel('y [pixels]')
  >     plt.colorbar()
  >     plt.show()

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