scipy.fftpack.idctn¶

scipy.fftpack.idctn(x, type=2, shape=None, axes=None, norm=None, overwrite_x=False)[source]¶

Return multidimensional Discrete Cosine Transform along the specified axes.

Parameters:

Parameters:	x : array_like The input array. type : {1, 2, 3}, optional Type of the DCT (see Notes). Default type is 2. shape : tuple of ints, optional The shape of the result. If both shape and axes (see below) are None, shape is `x.shape`; if shape is None but axes is not None, then shape is `scipy.take(x.shape, axes, axis=0)`. If `shape[i] > x.shape[i]`, the i-th dimension is padded with zeros. If `shape[i] < x.shape[i]`, the i-th dimension is truncated to length `shape[i]`. axes : tuple or None, optional Axes along which the IDCT is computed; the default is over all axes. norm : {None, ‘ortho’}, optional Normalization mode (see Notes). Default is None. overwrite_x : bool, optional If True, the contents of x can be destroyed; the default is False.
Returns:	y : ndarray of real The transformed input array.

x : array_like: The input array.
type : {1, 2, 3}, optional: Type of the DCT (see Notes). Default type is 2.
shape : tuple of ints, optional: The shape of the result. If both shape and axes (see below) are None, shape is x.shape; if shape is None but axes is not None, then shape is scipy.take(x.shape, axes, axis=0). If shape[i] > x.shape[i], the i-th dimension is padded with zeros. If shape[i] < x.shape[i], the i-th dimension is truncated to length shape[i].
axes : tuple or None, optional: Axes along which the IDCT is computed; the default is over all axes.
norm : {None, ‘ortho’}, optional: Normalization mode (see Notes). Default is None.
overwrite_x : bool, optional: If True, the contents of x can be destroyed; the default is False.

Returns:

See also

Notes

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

Examples

>>> from scipy.fftpack import dctn, idctn
>>> y = np.random.randn(16, 16)
>>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho'))
True