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diff --git a/numpy/fft/helper.py b/numpy/fft/helper.py
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+"""
+Discrete Fourier Transforms - helper.py
+"""
+# Created by Pearu Peterson, September 2002
+
+__all__ = ['fftshift','ifftshift','fftfreq']
+
+from numpy.core import asarray, concatenate, arange, take, \
+    integer, empty
+import types
+
+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 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 fftfreq(n,d=1.0):
+    """
+    Discrete Fourier Transform sample frequencies.
+
+    The returned float array contains the frequency bins in
+    cycles/unit (with zero at the start) given a window length `n` and a
+    sample spacing `d`.
+    ::
+
+      f = [0,1,...,n/2-1,-n/2,...,-1]/(d*n)         if n is even
+      f = [0,1,...,(n-1)/2,-(n-1)/2,...,-1]/(d*n)   if n is odd
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar
+        Sample spacing.
+
+    Returns
+    -------
+    out : ndarray, shape(`n`,)
+        Sample frequencies.
+
+    Examples
+    --------
+    >>> signal = np.array([-2.,  8., -6.,  4.,  1., 0.,  3.,  5.])
+    >>> fourier = np.fft.fft(signal)
+    >>> n = len(signal)
+    >>> timestep = 0.1
+    >>> freq = np.fft.fftfreq(n, d=timestep)
+    >>> freq
+    array([ 0.  ,  1.25,  2.5 ,  3.75, -5.  , -3.75, -2.5 , -1.25])
+
+    """
+    assert isinstance(n,types.IntType) or isinstance(n, integer)
+    val = 1.0/(n*d)
+    results = empty(n, int)
+    N = (n-1)//2 + 1
+    p1 = arange(0,N,dtype=int)
+    results[:N] = p1
+    p2 = arange(-(n//2),0,dtype=int)
+    results[N:] = p2
+    return results * val
+    #return hstack((arange(0,(n-1)/2 + 1), arange(-(n/2),0))) / (n*d)