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bundles / scipy 1.17.1 / scipy / fft / _realtransforms / idst

_Function

scipy.fft._realtransforms:idst

source: /scipy/fft/_realtransforms.py :656

Signature

def   idst ( x ,  type = 2 ,  n = None ,  axis = -1 ,  norm = None ,  overwrite_x = False ,  workers = None ,  orthogonalize = None )

Summary

Return the Inverse Discrete Sine Transform of an arbitrary type sequence.

Parameters

x : array_like

The input array.

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

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

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 idst 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.

workers : int, 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.

orthogonalize : bool, optional

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

Returns

idst : ndarray of real

The transformed input array.

Notes

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

'The' IDST is the IDST-II, which is the same as the normalized DST-III.

The IDST is equivalent to a normal DST except for the normalization and type. DST type 1 and 4 are their own inverse and DSTs 2 and 3 are each other's inverses.

Array API Standard Support

idst has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ⛔                   
JAX                   ✅                     ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

See also

dst

Forward DST

Aliases

  • scipy.fft.idst