Module « numpy.fft »

Fonction ihfft - module numpy.fft

Signature de la fonction ihfft

def ihfft(a, n=None, axis=-1, norm=None) 

Description

ihfft.__doc__

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

    Parameters
    ----------
    a : 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
        .. versionadded:: 1.10.0

        Normalization mode (see `numpy.fft`). Default is "backward".
        Indicates which direction of the forward/backward pair of transforms
        is scaled and with what normalization factor.

        .. versionadded:: 1.20.0

            The "backward", "forward" values were added.

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