Box2DKernel

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

Bases: Kernel2D

2D Box filter kernel.

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

The generated kernel is normalized so that it integrates to 1.

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:

widthnumber

Width of the filter kernel.

mode{‘linear_interp’, ‘center’, ‘oversample’, ‘integrate’}, optional

One of the following discretization modes:

• ‘linear_interp’ (default)

  Discretize model by performing a bilinear interpolation between the values at the corners of the bin.

• ‘center’

  Discretize model by taking the value at the center 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.

See also

Gaussian2DKernel, Tophat2DKernel, RickerWavelet2DKernel, 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()

(png, svg, pdf)