scipy.fftpack.idct¶

scipy.fftpack.
idct
(x, type=2, n=None, axis=1, norm=None, overwrite_x=False)[source]¶ Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.
Parameters: x : array_like
The input array.
type : {1, 2, 3}, 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. Ifn > x.shape[axis]
, x is zeropadded. The default results inn = 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 : {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: 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 MATLABidct(x)
.‘The’ IDCT is the IDCT of type 2, which is the same as DCT of type 3.
IDCT of type 1 is the DCT of type 1, IDCT of type 2 is the DCT of type 3, and IDCT of type 3 is the DCT of type 2. For the definition of these types, see
dct
.Examples
The Type 1 DCT is equivalent to the DFT for real, evensymmetrical inputs. The output is also real and evensymmetrical. Half of the IFFT input is used to generate half of the IFFT output:
>>> from scipy.fftpack 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) / 6 array([ 4., 3., 5., 10.])