Box2DKernel

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

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.

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

Gaussian2DKernel, Tophat2DKernel, MexicanHat2DKernel, Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel

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()

()