def anom(self, axis=None, dtype=None):
m = self.mean(axis, dtype)<EOL>if not axis:<EOL><INDENT>return (self - m)<EOL><DEDENT>else:<EOL><INDENT>return (self - expand_dims(m, axis))<EOL><DEDENT>
Compute the anomalies (deviations from the arithmetic mean)
along the given axis.

Returns an array of anomalies, with the same shape as the input and
where the arithmetic mean is computed along the given axis.

Parameters
----------
axis : int, optional
    Axis over which the anomalies are taken.
    The default is to use the mean of the flattened array as reference.
dtype : dtype, optional
    Type to use in computing the variance. For arrays of integer type
     the default is float32; for arrays of float types it is the same as
     the array type.

See Also
--------
mean : Compute the mean of the array.

Examples
--------
>>> a = np.ma.array([1,2,3])
>>> a.anom()
masked_array(data = [-1.  0.  1.],
             mask = False,
       fill_value = 1e+20)