Module « scipy.fft »

Fonction idct - module scipy.fft

Signature de la fonction idct

def idct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False, workers=None) 

Description

idct.__doc__

    Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.

    Parameters
    ----------
    x : array_like
        The input array.
    type : {1, 2, 3, 4}, optional
        Type of the DCT (see Notes). Default type is 2.
    n : int, optional
        Length of the transform.  If ``n < x.shape[axis]``, `x` is
        truncated.  If ``n > x.shape[axis]``, `x` is zero-padded. The
        default results in ``n = x.shape[axis]``.
    axis : int, optional
        Axis along which the idct is computed; the default is over the
        last axis (i.e., ``axis=-1``).
    norm : {"backward", "ortho", "forward"}, optional
        Normalization mode (see Notes). Default is "backward".
    overwrite_x : bool, optional
        If True, the contents of `x` can be destroyed; the default is False.
    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.

    Returns
    -------
    idct : ndarray of real
        The transformed input array.

    See Also
    --------
    dct : Forward DCT

    Notes
    -----
    For a single dimension array `x`, ``idct(x, norm='ortho')`` is equal to
    MATLAB ``idct(x)``.

    'The' IDCT is the IDCT-II, which is the same as the normalized DCT-III.

    The IDCT is equivalent to a normal DCT except for the normalization and
    type. DCT type 1 and 4 are their own inverse and DCTs 2 and 3 are each
    other's inverses.

    Examples
    --------
    The Type 1 DCT is equivalent to the DFT for real, even-symmetrical
    inputs. The output is also real and even-symmetrical. Half of the IFFT
    input is used to generate half of the IFFT output:

    >>> from scipy.fft import ifft, idct
    >>> ifft(np.array([ 30.,  -8.,   6.,  -2.,   6.,  -8.])).real
    array([  4.,   3.,   5.,  10.,   5.,   3.])
    >>> idct(np.array([ 30.,  -8.,   6.,  -2.]), 1)
    array([  4.,   3.,   5.,  10.])