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# Gaussian1DKernel

class astropy.convolution.Gaussian1DKernel(stddev, **kwargs)

  Bases: `Kernel1D`

  1D Gaussian filter kernel.

  The Gaussian filter is a filter with great smoothing properties. It is isotropic and does not produce artifacts.

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

  Parameters:
    stddev number

      Standard deviation of the Gaussian kernel.

    x_size `python:int`, optional

      Size of the kernel array. Default = ⌊8*stddev+1⌋.

    mode{‘center’, ‘linear_interp’, ‘oversample’, ‘integrate’}, optional
      One of the following discretization modes:
        - ‘center’ (default)

            Discretize model by taking the value at the center of the bin.

        - ‘linear_interp’

            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. Very slow.

    factor number, optional

      Factor of oversampling. Default factor = 10. If the factor is too large, evaluation can be very slow.

  See also

  `Box1DKernel`, `Trapezoid1DKernel`, `RickerWavelet1DKernel`

  Examples

  Kernel response:

  >     import matplotlib.pyplot as plt
  >     from astropy.convolution import Gaussian1DKernel
  >     gauss_1D_kernel = Gaussian1DKernel(10)
  >     plt.plot(gauss_1D_kernel, drawstyle='steps')
  >     plt.xlabel('x [pixels]')
  >     plt.ylabel('value')
  >     plt.show()

  > (`png`, `svg`, `pdf`)