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Module « scipy.fft »

Fonction idctn - module scipy.fft

Signature de la fonction idctn

def idctn(x, type=2, s=None, axes=None, norm=None, overwrite_x=False, workers=None, orthogonalize=None) 

Description

help(scipy.fft.idctn)

Return multidimensional Inverse Discrete Cosine Transform along the specified axes.

Parameters
----------
x : array_like
    The input array.
type : {1, 2, 3, 4}, optional
    Type of the DCT (see Notes). Default type is 2.
s : int or array_like of ints or None, optional
    The shape of the result.  If both `s` and `axes` (see below) are
    None, `s` is ``x.shape``; if `s` is None but `axes` is
    not None, then `s` is ``numpy.take(x.shape, axes, axis=0)``.
    If ``s[i] > x.shape[i]``, the ith dimension of the input is padded with zeros.
    If ``s[i] < x.shape[i]``, the ith dimension of the input is truncated to length
    ``s[i]``.
    If any element of `s` is -1, the size of the corresponding dimension of
    `x` is used.
axes : int or array_like of ints or None, optional
    Axes over which the IDCT is computed. If not given, the last ``len(s)``
    axes are used, or all axes if `s` is also not specified.
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.
orthogonalize : bool, optional
    Whether to use the orthogonalized IDCT variant (see Notes).
    Defaults to ``True`` when ``norm="ortho"`` and ``False`` otherwise.

    .. versionadded:: 1.8.0

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

See Also
--------
dctn : multidimensional DCT

Notes
-----
For full details of the IDCT types and normalization modes, as well as
references, see `idct`.

Examples
--------
>>> import numpy as np
>>> from scipy.fft import dctn, idctn
>>> rng = np.random.default_rng()
>>> y = rng.standard_normal((16, 16))
>>> np.allclose(y, idctn(dctn(y)))
True

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