Box1DKernel¶

class
astropy.convolution.
Box1DKernel
(width, **kwargs)[source]¶ 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 nonshifting, evensized 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
 widthnumber
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 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.
 factornumber, optional
Factor of oversampling. Default factor = 10.
See also
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()
()