Module « scipy.fft »

Fonction ihfft - module scipy.fft

Signature de la fonction ihfft

def ihfft(x, n=None, axis=-1, norm=None, overwrite_x=False, workers=None, *, plan=None) 

Description

ihfft.__doc__

    Compute the inverse FFT of a signal that has Hermitian symmetry.

    Parameters
    ----------
    x : array_like
        Input array.
    n : int, optional
        Length of the inverse FFT, the number of points along
        transformation axis in the input to use.  If `n` is smaller than
        the length of the input, the input is cropped. If it is larger,
        the input is padded with zeros. If `n` is not given, the length of
        the input along the axis specified by `axis` is used.
    axis : int, optional
        Axis over which to compute the inverse FFT. If not given, the last
        axis is used.
    norm : {"backward", "ortho", "forward"}, optional
        Normalization mode (see `fft`). Default is "backward".
    overwrite_x : bool, optional
        If True, the contents of `x` can be destroyed; the default is False.
        See `fft` for more details.
    workers : int, optional
        Maximum number of workers to use for parallel computation. If negative,
        the value wraps around from ``os.cpu_count()``.
        See :func:`~scipy.fft.fft` for more details.
    plan : object, optional
        This argument is reserved for passing in a precomputed plan provided
        by downstream FFT vendors. It is currently not used in SciPy.

        .. versionadded:: 1.5.0

    Returns
    -------
    out : complex ndarray
        The truncated or zero-padded input, transformed along the axis
        indicated by `axis`, or the last one if `axis` is not specified.
        The length of the transformed axis is ``n//2 + 1``.

    See Also
    --------
    hfft, irfft

    Notes
    -----
    `hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
    opposite case: here, the signal has Hermitian symmetry in the time
    domain and is real in the frequency domain. So, here, it's `hfft`, for
    which you must supply the length of the result if it is to be odd:
    * even: ``ihfft(hfft(a, 2*len(a) - 2) == a``, within roundoff error,
    * odd: ``ihfft(hfft(a, 2*len(a) - 1) == a``, within roundoff error.

    Examples
    --------
    >>> from scipy.fft import ifft, ihfft
    >>> spectrum = np.array([ 15, -4, 0, -1, 0, -4])
    >>> ifft(spectrum)
    array([1.+0.j,  2.+0.j,  3.+0.j,  4.+0.j,  3.+0.j,  2.+0.j]) # may vary
    >>> ihfft(spectrum)
    array([ 1.-0.j,  2.-0.j,  3.-0.j,  4.-0.j]) # may vary