掩碼下的Numpy標準偏差

Question

如何在numpy數組中沿着特定軸在掩碼下取得標準偏差？

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]]) 

結果陣列應該是：

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 1

您可以使用一個numpy的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) 

Answer 2

使用MaskedArray：

import numpy as np 
np.ma.MaskedArray(data, 1-M).std(axis=0)