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- Astropy v5.2.1 »
- Convolution and Filtering (`astropy.convolution`) »
- Gaussian2DKernel

# Gaussian2DKernel

class astropy.convolution.Gaussian2DKernel(x_stddev, y_stddev=None, theta=0.0, **kwargs)

  Bases: `Kernel2D`

  2D 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:
    x_stddev `python:float`

      Standard deviation of the Gaussian in x before rotating by theta.

    y_stddev `python:float`

      Standard deviation of the Gaussian in y before rotating by theta.

    theta `python:float` or `Quantity` [:ref: ‘angle’]

      Rotation angle. If passed as a float, it is assumed to be in radians. The rotation angle increases counterclockwise.

    x_size `python:int`, optional

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

    y_size `python:int`, optional

      Size in y direction 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 performing a bilinear interpolation 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

  `Box2DKernel`, `Tophat2DKernel`, `RickerWavelet2DKernel`, `Ring2DKernel`
  `TrapezoidDisk2DKernel`, `AiryDisk2DKernel`, `Moffat2DKernel`

  Examples

  Kernel response:

  >     import matplotlib.pyplot as plt
  >     from astropy.convolution import Gaussian2DKernel
  >     gaussian_2D_kernel = Gaussian2DKernel(10)
  >     plt.imshow(gaussian_2D_kernel, interpolation='none', origin='lower')
  >     plt.xlabel('x [pixels]')
  >     plt.ylabel('y [pixels]')
  >     plt.colorbar()
  >     plt.show()

  > (png, svg, pdf)