jax.scipy.signal.welch#

jax.scipy.signal.welch(x, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=- 1, average='mean')[source]#

Estimate power spectral density using Welch’s method.

LAX-backend implementation of scipy.signal._spectral_py.welch().

Original docstring below.

Welch’s method 1 computes an estimate of the power spectral density by dividing the data into overlapping segments, computing a modified periodogram for each segment and averaging the periodograms.

Parameters
  • x (array_like) – Time series of measurement values

  • fs (float, optional) – Sampling frequency of the x time series. Defaults to 1.0.

  • window (str or tuple or array_like, optional) – Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values, which are DFT-even by default. See get_window for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be nperseg. Defaults to a Hann window.

  • nperseg (int, optional) – Length of each segment. Defaults to None, but if window is str or tuple, is set to 256, and if window is array_like, is set to the length of the window.

  • noverlap (int, optional) – Number of points to overlap between segments. If None, noverlap = nperseg // 2. Defaults to None.

  • nfft (int, optional) – Length of the FFT used, if a zero padded FFT is desired. If None, the FFT length is nperseg. Defaults to None.

  • detrend (str or function or False, optional) – Specifies how to detrend each segment. If detrend is a string, it is passed as the type argument to the detrend function. If it is a function, it takes a segment and returns a detrended segment. If detrend is False, no detrending is done. Defaults to ‘constant’.

  • return_onesided (bool, optional) – If True, return a one-sided spectrum for real data. If False return a two-sided spectrum. Defaults to True, but for complex data, a two-sided spectrum is always returned.

  • scaling ({ 'density', 'spectrum' }, optional) – Selects between computing the power spectral density (‘density’) where Pxx has units of V**2/Hz and computing the power spectrum (‘spectrum’) where Pxx has units of V**2, if x is measured in V and fs is measured in Hz. Defaults to ‘density’

  • axis (int, optional) – Axis along which the periodogram is computed; the default is over the last axis (i.e. axis=-1).

  • average ({ 'mean', 'median' }, optional) – Method to use when averaging periodograms. Defaults to ‘mean’.

Return type

Tuple[Array, Array]

Returns

  • f (ndarray) – Array of sample frequencies.

  • Pxx (ndarray) – Power spectral density or power spectrum of x.

References

1

P. Welch, “The use of the fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms”, IEEE Trans. Audio Electroacoust. vol. 15, pp. 70-73, 1967.

2

M.S. Bartlett, “Periodogram Analysis and Continuous Spectra”, Biometrika, vol. 37, pp. 1-16, 1950.