Box1DKernel¶
-
class
astropy.convolution.
Box1DKernel
(width, **kwargs)[source] [edit on github]¶ 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
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()
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