scipy.fft.idctn

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

Return multidimensional Inverse Discrete Cosine Transform along the specified axes.

Parameters:

xarray_like

The input array.

type{1, 2, 3, 4}, optional

Type of the DCT (see Notes). Default type is 2.

sint 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.

axesint 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_xbool, optional

If True, the contents of x can be destroyed; the default is False.

workersint, optional

Maximum number of workers to use for parallel computation. If negative, the value wraps around from os.cpu_count(). See fft for more details.

orthogonalizebool, optional

Whether to use the orthogonalized IDCT variant (see Notes). Defaults to True when norm="ortho" and False otherwise.

New in version 1.8.0.

Returns:

yndarray 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

Try it in your browser!

>>> 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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