I submitted this to the Sourceforge bug tracker, but wanted also to let this
list know, as this is a potentially nasty bug.
MLab.std() gives completely incorrect answers for multidimensional arrays
when axis != 0.
>>> foo
array([[[ 1., 1., 1.],
[ 2., 2., 2.],
[ 3., 3., 3.]],
[[ 1., 4., 4.],
[ 2., 5., 5.],
[ 3., 6., 6.]]])
>>> std(foo)
array([[ 0. , 2.12132034, 2.12132034],
[ 0. , 2.12132034, 2.12132034],
[ 0. , 2.12132034, 2.12132034]])
>>> std(foo, 1)
array([[ 0., 0., 0.],
[ 0., 0., 0.]])
The following should fix the problem (but I haven't tested it extensively):
def std(m,axis=0):
"""std(m,axis=0) returns the standard deviation along the given
dimension of m. The result is unbiased with division by N-1.
If m is of integer type returns a floating point answer.
"""
x = asarray(m)
n = float(x.shape[axis])
x2 = mean(x * x, axis)
x = mean(x, axis)
return sqrt((x2 - x * x) * n /(n-1.0))
Jonathan Gilligan
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