def ifftshift(x,axes=None):
    """
    Inverse of fftshift.

    Parameters
    ----------
    x : array_like
        Input array.
    axes : int or shape tuple, optional
        Axes over which to calculate.  Defaults to None which is over all axes.

    See Also
    --------
    fftshift

    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = range(ndim)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = n-(n+1)/2
        mylist = concatenate((arange(p2,n),arange(p2)))
        y = take(y,mylist,k)
    return y

def fftshift(x,axes=None):
    """
    Shift zero-frequency component to center of spectrum.

    This function swaps half-spaces for all axes listed (defaults to all).
    If len(x) is even then the Nyquist component is y[0].

    Parameters
    ----------
    x : array_like
        Input array.
    axes : int or shape tuple, optional
        Axes over which to shift.  Default is None which shifts all axes.

    See Also
    --------
    ifftshift

    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = range(ndim)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = (n+1)/2
        mylist = concatenate((arange(p2,n),arange(p2)))
        y = take(y,mylist,k)
    return y

def fftshift(x, axes=None):
    """
    Shift the zero-frequency component to the center of the spectrum.

    This function swaps half-spaces for all axes listed (defaults to all).
    Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.

    Parameters
    ----------
    x : array_like
        Input array.
    axes : int or shape tuple, optional
        Axes over which to shift.  Default is None, which shifts all axes.

    Returns
    -------
    y : ndarray
        The shifted array.

    See Also
    --------
    ifftshift : The inverse of `fftshift`.

    Examples
    --------
    >>> freqs = np.fft.fftfreq(10, 0.1)
    >>> freqs
    array([ 0.,  1.,  2.,  3.,  4., -5., -4., -3., -2., -1.])
    >>> np.fft.fftshift(freqs)
    array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])

    Shift the zero-frequency component only along the second axis:

    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
    >>> freqs
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -4.],
           [-3., -2., -1.]])
    >>> np.fft.fftshift(freqs, axes=(1,))
    array([[ 2.,  0.,  1.],
           [-4.,  3.,  4.],
           [-1., -3., -2.]])

    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = list(range(ndim))
    elif isinstance(axes, (int, nt.integer)):
        axes = (axes,)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = (n+1)//2
        mylist = concatenate((arange(p2,n),arange(p2)))
        y = take(y,mylist,k)
    return y

 def original_fftshift(x, axes=None):
     """ How fftshift was implemented in v1.14"""
     tmp = asarray(x)
     ndim = tmp.ndim
     if axes is None:
         axes = list(range(ndim))
     elif isinstance(axes, integer_types):
         axes = (axes,)
     y = tmp
     for k in axes:
         n = tmp.shape[k]
         p2 = (n + 1) // 2
         mylist = concatenate((arange(p2, n), arange(p2)))
         y = take(y, mylist, k)
     return y

def ifftshift(x,axes=None):
    """ ifftshift(x,axes=None) - > y

    Inverse of fftshift.
    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = range(ndim)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = n-(n+1)/2
        mylist = concatenate((arange(p2,n),arange(p2)))
        y = take(y,mylist,k)
    return y

def ifftshift(x, axes=None):
    """
    The inverse of `fftshift`. Although identical for even-length `x`, the
    functions differ by one sample for odd-length `x`.

    Parameters
    ----------
    x : array_like
        Input array.
    axes : int or shape tuple, optional
        Axes over which to calculate.  Defaults to None, which shifts all axes.

    Returns
    -------
    y : ndarray
        The shifted array.

    See Also
    --------
    fftshift : Shift zero-frequency component to the center of the spectrum.

    Examples
    --------
    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
    >>> freqs
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -4.],
           [-3., -2., -1.]])
    >>> np.fft.ifftshift(np.fft.fftshift(freqs))
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -4.],
           [-3., -2., -1.]])

    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = list(range(ndim))
    elif isinstance(axes, integer_types):
        axes = (axes,)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = n-(n+1)//2
        mylist = concatenate((arange(p2, n), arange(p2)))
        y = take(y, mylist, k)
    return y

def _cook_nd_args(a, s=None, axes=None, invreal=0):
    if s is None:
        shapeless = 1
        if axes is None:
            s = list(a.shape)
        else:
            s = take(a.shape, axes)
    else:
        shapeless = 0
    s = list(s)
    if axes is None:
        axes = range(-len(s), 0)
    if len(s) != len(axes):
        raise ValueError, "Shape and axes have different lengths."
    if invreal and shapeless:
        s[axes[-1]] = (s[axes[-1]] - 1) * 2
    return s, axes

def ifftshift(x, axes=None):
    """
    The inverse of fftshift.

    Parameters
    ----------
    x : array_like
        Input array.
    axes : int or shape tuple, optional
        Axes over which to calculate.  Defaults to None, which shifts all axes.

    Returns
    -------
    y : ndarray
        The shifted array.

    See Also
    --------
    fftshift : Shift zero-frequency component to the center of the spectrum.

    Examples
    --------
    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
    >>> freqs
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -4.],
           [-3., -2., -1.]])
    >>> np.fft.ifftshift(np.fft.fftshift(freqs))
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -4.],
           [-3., -2., -1.]])

    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = range(ndim)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = n - (n + 1) / 2
        mylist = concatenate((arange(p2, n), arange(p2)))
        y = take(y, mylist, k)
    return y

def _cook_nd_args(a, s=None, axes=None, invreal=0):
    if s is None:
        shapeless = 1
        if axes is None:
            s = list(a.shape)
        else:
            s = take(a.shape, axes)
    else:
        shapeless = 0
    s = list(s)
    if axes is None:
        axes = range(-len(s), 0)
    if len(s) != len(axes):
        raise ValueError("Shape and axes have different lengths.")
    if invreal and shapeless:
        # Here is the fix. The following line is replaced
        # (see numpy commit 88a02920daf0b408086106439c53bd488e73af29):
        #s[axes[-1]] = (s[axes[-1]] - 1) * 2
        s[-1] = (a.shape[axes[-1]] - 1) * 2
    return s, axes

def fftshift(x,axes=None):
    """ fftshift(x, axes=None) -> y

    Shift zero-frequency component to center of spectrum.

    This function swaps half-spaces for all axes listed (defaults to all).

    Notes:
      If len(x) is even then the Nyquist component is y[0].
    """
    tmp = asarray(x)
    ndim = len(tmp.shape)
    if axes is None:
        axes = range(ndim)
    y = tmp
    for k in axes:
        n = tmp.shape[k]
        p2 = (n+1)/2
        mylist = concatenate((arange(p2,n),arange(p2)))
        y = take(y,mylist,k)
    return y