Box1DKernel

class astropy.convolution.Box1DKernel(width, **kwargs)

Bases: astropy.convolution.Kernel1D

1D 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.

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. E.g a Box kernel with an effective smoothing of 4 pixel would have the following array: [0.5, 1, 1, 1, 0.5].

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 linearly interpolating 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

Gaussian1DKernel, Trapezoid1DKernel, MexicanHat1DKernel

Examples

Kernel response function:

import matplotlib.pyplot as plt
from astropy.convolution import Box1DKernel
box_1D_kernel = Box1DKernel(9)
plt.plot(box_1D_kernel, drawstyle='steps')
plt.xlim(-1, 9)
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()

()