parameters: (x, type=2, n=None, axis=-1, norm=None, overwrite_x=False)
return_statement: return _dct(x, _TP[type], n, axis, normalize=norm, overwrite_x=overwrite_x)
docstring: 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.  If ``n > x.shape[axis]``, `x` is zero-padded. The
          default results in ``n = 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
      MATLAB ``idct(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, even-symmetrical
      inputs.  The output is also real and even-symmetrical.  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.])
docstring_summary: Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.
docstring_tokens: Return the Inverse Discrete Cosine Transform of an arbitrary type sequence .
function: def idct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False):
      """
      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.  If ``n > x.shape[axis]``, `x` is zero-padded. The
          default results in ``n = 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
      MATLAB ``idct(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, even-symmetrical
      inputs.  The output is also real and even-symmetrical.  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.])
  
      """
      if type == 1 and norm is not None:
          raise NotImplementedError(
                "Orthonormalization not yet supported for IDCT-I")
      # Inverse/forward type table
      _TP = {1:1, 2:3, 3:2}
      return _dct(x, _TP[type], n, axis, normalize=norm, overwrite_x=overwrite_x)
function_tokens: def idct ( x , type = 2 , n = None , axis = - 1 , norm = None , overwrite_x = False ) : if type == 1 and norm is not None : raise NotImplementedError ( "Orthonormalization not yet supported for IDCT-I" ) # Inverse/forward type table _TP = { 1 : 1 , 2 : 3 , 3 : 2 } return _dct ( x , _TP [ type ] , n , axis , normalize = norm , overwrite_x = overwrite_x )