cupyx.scipy.fft.dct#

cupyx.scipy.fft.dct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False)[source]#

Return the Discrete Cosine Transform of an array, x.

Parameters:

  • x (cupy.ndarray) – The input array.

  • type ({1, 2, 3, 4}, optional) – Type of the DCT (see Notes). Default type is 2. Currently CuPy only supports types 2 and 3.

  • 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 dct 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.

Returns:

y – The transformed input array.

Return type:

cupy.ndarray of real

See also

scipy.fft.dct()

Notes

For a single dimension array x, dct(x, norm='ortho') is equal to MATLAB dct(x).

For norm="ortho" both the dct and idct are scaled by the same overall factor in both directions. By default, the transform is also orthogonalized which for types 1, 2 and 3 means the transform definition is modified to give orthogonality of the DCT matrix (see below).

For norm="backward", there is no scaling on dct and the idct is scaled by 1/N where N is the “logical” size of the DCT. For norm="forward" the 1/N normalization is applied to the forward dct instead and the idct is unnormalized.

CuPy currently only supports DCT types 2 and 3. ‘The’ DCT generally refers to DCT type 2, and ‘the’ Inverse DCT generally refers to DCT type 3 [1]. See the scipy.fft.dct() documentation for a full description of each type.

References