def sem(a, axis=<NUM_LIT:0>, ddof=<NUM_LIT:1>):
a, axis = _chk_asarray(a, axis)<EOL>n = a.count(axis=axis)<EOL>s = a.std(axis=axis, ddof=ddof) / ma.sqrt(n)<EOL>return s<EOL>
Calculates the standard error of the mean of the input array.

Also sometimes called standard error of measurement.

Parameters
----------
a : array_like
    An array containing the values for which the standard error is
    returned.
axis : int or None, optional.
    If axis is None, ravel `a` first. If axis is an integer, this will be
    the axis over which to operate. Defaults to 0.
ddof : int, optional
    Delta degrees-of-freedom. How many degrees of freedom to adjust
    for bias in limited samples relative to the population estimate
    of variance. Defaults to 1.

Returns
-------
s : ndarray or float
    The standard error of the mean in the sample(s), along the input axis.

Notes
-----
The default value for `ddof` changed in scipy 0.15.0 to be consistent with
`stats.sem` as well as with the most common definition used (like in the R
documentation).

Examples
--------
Find standard error along the first axis:

>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> stats.sem(a)
array([ 2.8284,  2.8284,  2.8284,  2.8284])

Find standard error across the whole array, using n degrees of freedom:

>>> stats.sem(a, axis=None, ddof=0)
1.2893796958227628