ajwood - 1 year ago 129
Python Question

# Numpy standard deviation under a mask

How do I take the standard deviation under a mask along a specific axis in a numpy array?

``````data = array([[ 0,  1,  2,  3,  4],
[ 5,  6,  7,  8,  9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])

M = array([[0, 1, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 0, 1, 1],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]])
``````

The result array should be:

``````masked_std = std( data, axis=0, mask=M )
[ std([5,10]), std([1,6,11]), std([7,17]), std([8,13], std([9,14]) ]
``````

Answer Source

You can use a numpy masked array:

``````In [19]: from numpy import ma

In [20]: data
Out[20]:
array([[ 0,  1,  2,  3,  4],
[ 5,  6,  7,  8,  9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])

In [21]: M
Out[21]:
array([[0, 1, 0, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 0, 1, 1],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]])

In [22]: mdata = ma.masked_array(data, mask=~M.astype(bool))

In [23]: mdata
Out[23]:
masked_array(data =
[[-- 1 -- -- --]
[5 6 7 8 9]
[10 11 -- 13 14]
[-- -- 17 -- --]
[-- -- -- -- --]],
mask =
[[ True False  True  True  True]
[False False False False False]
[False False  True False False]
[ True  True False  True  True]
[ True  True  True  True  True]],
fill_value = 999999)

In [24]: mdata.std(axis=0)
Out[24]:
masked_array(data = [2.5 4.08248290464 5.0 2.5 2.5],
mask = [False False False False False],
fill_value = 999999)
``````
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