[SciPy-user] moving average, moving standard deviation, etc...

[Denis Simakov]

For operations which may be reduced to sums (this includes average and
std), you can use cumulative sum:
>>> A = arange(10.0)
>>> w = 2  # window length
>>> C = zeros_like(A)

>>> B = A.cumsum()
>>> C[w:] = (B[w:] - B[:-w]) / w  # running average
>>> C[:w] = B[:w] / arange(1,w+1)  # just average for first elements

>>> print vstack((A, C))
[[ 0.   1.   2.   3.   4.   5.   6.   7.   8.   9. ]
 [ 0.   0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.5  8.5]]

Denis

On 12/23/06, Matt Knox <mattknox_ca at hotmail.com> wrote:
>
> Does anyone know of a slick way to calculate a simple moving average and/or moving standard deviation? And when I say slick, I mean loop-free way, because it would be fairly easy to code a looping way of doing it, it would just be really slow.
>
> To be more specific, when I say moving standard deviation, I mean for example...
>
> if A is a 1-dimensional array with 100 elements, and I'm using a window size of 10, then the result at index 10 would be A[0:10].std(), the result at index 11 would be A[1:11].std() , etc...
>
> And similarly for moving average, and so forth. Is their a general way to do these kind of things? Or would it have to be a special case for each type of calculation to avoid looping?
>
> Any help is greatly appreciated. Thanks in advance,
>
> - Matt