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scipy.signal.windows.

# general_hamming

scipy.signal.windows.general_hamming(M, alpha, sym=True, *, xp=None, device=None)

  Return a generalized Hamming window.

  The generalized Hamming window is constructed by multiplying a rectangular window by one period of a cosine function [1].

  Parameters:
    M int

      Number of points in the output window. If zero, an empty array is returned. An exception is thrown when it is negative.

    alpha float

      The window coefficient, \(\alpha\)

    sym bool, optional

      When True (default), generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis.

    xp array_namespace, optional

      Optional array namespace. Should be compatible with the array API standard, or supported by array-api-compat. Default: `numpy`

    device any

      optional device specification for output. Should match one of the supported device specification in `xp`.

  Returns:
    w ndarray

      The window, with the maximum value normalized to 1 (though the value 1 does not appear if M is even and sym is True).

  See also

  `hamming`, `hann`

  Notes

  The generalized Hamming window is defined as

  \[w(n) = \alpha - \left(1 - \alpha\right) \cos\left(\frac{2\pi{n}}{M-1}\right) \qquad 0 \leq n \leq M-1\]

  Both the common Hamming window and Hann window are special cases of the generalized Hamming window with \(\alpha\) = 0.54 and \(\alpha\) = 0.5, respectively [2].

  Array API Standard Support

  `general_hamming` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ✅ | ✅
  Dask | ✅ | n/a

  See Support for the array API standard for more information.

  References

  [1]

  DSPRelated, “Generalized Hamming Window Family”, https://www.dsprelated.com/freebooks/sasp/Generalized_Hamming_Window_Family.html

  [2]

  Wikipedia, “Window function”, https://en.wikipedia.org/wiki/Window_function

  [3]

  Riccardo Piantanida ESA, “Sentinel-1 Level 1 Detailed Algorithm Definition”, https://sentinel.esa.int/documents/247904/1877131/Sentinel-1-Level-1-Detailed-Algorithm-Definition

  [4]

  Matthieu Bourbigot ESA, “Sentinel-1 Product Definition”, https://sentinel.esa.int/documents/247904/1877131/Sentinel-1-Product-Definition

  Examples

  The Sentinel-1A/B Instrument Processing Facility uses generalized Hamming windows in the processing of spaceborne Synthetic Aperture Radar (SAR) data [3]. The facility uses various values for the \(\alpha\) parameter based on operating mode of the SAR instrument. Some common \(\alpha\) values include 0.75, 0.7 and 0.52 [4]. As an example, we plot these different windows.

      >>> import numpy as np
      >>> from scipy.signal.windows import general_hamming
      >>> from scipy.fft import fft, fftshift
      >>> import matplotlib.pyplot as plt

      >>> fig1, spatial_plot = plt.subplots()
      >>> spatial_plot.set_title("Generalized Hamming Windows")
      >>> spatial_plot.set_ylabel("Amplitude")
      >>> spatial_plot.set_xlabel("Sample")

      >>> fig2, freq_plot = plt.subplots()
      >>> freq_plot.set_title("Frequency Responses")
      >>> freq_plot.set_ylabel("Normalized magnitude [dB]")
      >>> freq_plot.set_xlabel("Normalized frequency [cycles per sample]")

      >>> for alpha in [0.75, 0.7, 0.52]:
      ...     window = general_hamming(41, alpha)
      ...     spatial_plot.plot(window, label="{:.2f}".format(alpha))
      ...     A = fft(window, 2048) / (len(window)/2.0)
      ...     freq = np.linspace(-0.5, 0.5, len(A))
      ...     response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
      ...     freq_plot.plot(freq, response, label="{:.2f}".format(alpha))
      >>> freq_plot.legend(loc="upper right")
      >>> spatial_plot.legend(loc="upper right")