represents a Gaussian window function of x.


uses standard deviation σ.


  • GaussianWindow is a window function typically used for antialiasing and resampling.
  • Window functions are used in applications where data is processed in short segments and have a smoothing effect by gradually tapering data values to zero at the ends of each segment.
  • GaussianWindow[x,α] is equal to  ⅇ^(-(x^2)/(2 alpha^2)) -1/2<=x<=1/2; 0 TemplateBox[{x}, Abs]>1/2; .
  • GaussianWindow[x] is equivalent to GaussianWindow[x,3/10].
  • GaussianWindow automatically threads over lists.


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Basic Examples  (3)

Shape of a 1D Gaussian window:

Shape of a 2D Gaussian window:

Extract the continuous function representing the Gaussian window:

Parameterized Gaussian window:

Scope  (6)

Evaluate numerically:

Shape of a 1D Gaussian window using a specified standard deviation:

Variation of the shape as a function of the parameter σ:

Translated and dilated Gaussian window:

2D Gaussian window with a circular support:

Discrete Gaussian window of length 15:

Discrete 15×10 2D Gaussian window:

Applications  (3)

Create a moving average filter of length 11:

Smooth the filter using a Gaussian window:

Log-magnitude plot of the frequency spectrum of the filters:

Use a window specification to calculate sample PowerSpectralDensity:

Calculate the spectrum:

Compare to spectral density calculated without a windowing function:

The plot shows that window smooths the spectral density:

Compare to the theoretical spectral density of the process:

Use a window specification for time series estimation:

Specify window for spectral estimator:

Properties & Relations  (8)

GaussianWindow[x,] is equivalent to a Dirichlet window:

The area under the Gaussian window:

Normalize to create a window with unit area:

Fourier transform of the Gaussian window:

Power spectrum of the Gaussian window:

Fourier transform of the parametrized Gaussian window:

Variation of the magnitude spectrum as a function of the parameter σ:

Discrete-time Fourier transform of the discrete Gaussian window of length 11:

Magnitude at ω=0:

Magnitude spectrum:

Power spectra for three different window lengths:

Power spectra for three different values of the shape parameter σ:

Power spectra of the Gaussian and rectangular windows:

Possible Issues  (1)

2D sampling of Gaussian window will use a different standard deviation for each row of samples when passed as a symbol to Array:

Use a pure function instead:

Wolfram Research (2012), GaussianWindow, Wolfram Language function,


Wolfram Research (2012), GaussianWindow, Wolfram Language function,


Wolfram Language. 2012. "GaussianWindow." Wolfram Language & System Documentation Center. Wolfram Research.


Wolfram Language. (2012). GaussianWindow. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_gaussianwindow, author="Wolfram Research", title="{GaussianWindow}", year="2012", howpublished="\url{}", note=[Accessed: 20-July-2024 ]}


@online{reference.wolfram_2024_gaussianwindow, organization={Wolfram Research}, title={GaussianWindow}, year={2012}, url={}, note=[Accessed: 20-July-2024 ]}