pybispectra.utils.compute_fft

pybispectra.utils.compute_fft(data: ndarray, sampling_freq: int | float, n_points: int | None = None, window: str = 'hanning', n_jobs: int = 1, verbose: bool = True) → tuple[ndarray, ndarray]

Compute the fast Fourier transform (FFT) on real-valued data.

As the data is assumed to be real-valued, only those values corresponding to the positive frequencies are returned.

Parameters:

datandarray, shape of [epochs, channels, times]

Real-valued data to compute the FFT on.

sampling_freqint | float

Sampling frequency (in Hz) of data.

n_pointsint | None (default None)

Number of points to use in the FFT. If None, is equal to the number of timepoints in data. For time delay estimation, it is recommended that n_points = 2 * n_times + 1, where n_times is the number of timepoints in each epoch of data.

window"hanning" | "hamming" (default "hanning")

Type of window to apply to data before computing the FFT. See numpy.hanning() and numpy.hamming().

n_jobsint (default 1)

Number of jobs to run in parallel. If -1, all available CPUs are used.

verbosebool (default True)

Whether or not to report the status of the processing.

Returns:

coeffsndarray, shape of [epochs, channels, frequencies]

Fourier coefficients of data.

freqsndarray, shape of [frequencies]

Frequencies (in Hz) in coeffs.