Python Numpy MaskedArray.cumsum()函数

numpy.MaskedArray.cumsum() 返回在给定轴上被屏蔽的数组元素的累积和。在计算过程中，被屏蔽的值在内部被设置为0。然而，他们的位置被保存下来，结果将在相同的位置被屏蔽。

语法: numpy.ma.cumsum(axis=None, dtype=None, out=None)

参数:
axis : [ int, optional] 计算累积和的轴。默认情况下（无）是在扁平化的数组上计算总和。
dtype : [dtype, optional] 返回数组的类型，以及与元素相乘的累积器的类型。
out : [ndarray, optional] 一个储存结果的位置。
-> 如果提供，它必须有一个输入广播到的形状。
-> 如果没有提供或没有，将返回一个新分配的数组。

返回 : [cumsum_along_axis, ndarray] 返回一个保存结果的新数组，除非指定out，在这种情况下，返回对out的引用。

代码#1：

# Python program explaining
# numpy.MaskedArray.cumsum() method 
    
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
    
# creating input array  
in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("Input array : ", in_arr) 
    
# Now we are creating a masked array. 
# by making  entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]]) 
print ("Masked array : ", mask_arr) 
    
# applying MaskedArray.cumsum    
# methods to masked array
out_arr = mask_arr.cumsum() 
print ("cumulative sum of masked array along default axis : ", out_arr)

输出:

Input array :  [[ 1  2]
 [ 3 -1]
 [ 5 -3]]
Masked array :  [[-- 2]
 [-- -1]
 [5 -3]]
cumulative sum of masked array along default axis :  [-- 2 -- 1 6 3]

代码#2：

# Python program explaining
# numpy.MaskedArray.cumsum() method 
     
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
     
# creating input array 
in_arr = geek.array([[1, 0, 3], [ 4, 1, 6]]) 
print ("Input array : ", in_arr)
      
# Now we are creating a masked array. 
# by making one entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]]) 
print ("Masked array : ", mask_arr) 
     
# applying MaskedArray.cumsum methods 
# to masked array
out_arr1 = mask_arr.cumsum(axis = 0) 
print ("cumulative sum of masked array along 0 axis : ", out_arr1)
  
out_arr2 = mask_arr.cumsum(axis = 1) 
print ("cumulative sum of masked array along 1 axis : ", out_arr2)

输出:

Input array :  [[1 0 3]
 [4 1 6]]
Masked array :  [[1 0 3]
 [4 1 --]]
cumulative sum of masked array along 0 axis :  [[1 0 3]
 [5 1 --]]
cumulative sum of masked array along 1 axis :  [[1 1 4]