class astropy.convolution.Box2DKernel(width, **kwargs)[source]

Bases: astropy.convolution.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.


Width of the filter kernel.

modestr, 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.

factornumber, optional

Factor of oversampling. Default factor = 10.


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]')

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