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NumPy总和沿不相交索引(NumPy sum along disjoint indices)

 我有一个应用程序，我需要在3D NumPy数组中对任意索引组进行求和。 内置的NumPy数组求和例程对一个ndarray维度的所有索引进行求和。 相反，我需要总结我的数组中的一个维度的索引范围，并返回一个新的数组。  
 例如，让我们假设我有一个形状的ndarray (70,25,3) 。 我希望总结沿着特定索引范围的第一个维度并返回一个新的3D数组。 考虑从0:25, 25:50和50:75的总和0:25, 25:50这将返回一个形状数组(3,25,3) 。  
 是否有一种简单的方法可以在NumPy数组的一维上执行“不相交的和”来产生这个结果？ 

I have an application where I need to sum across arbitrary groups of indices in a 3D NumPy array. The built-in NumPy array sum routine sums up all indices along one of the dimensions of an ndarray. Instead, I need to sum up ranges of indices along one of the dimensions in my array and return a new array. 
For example, let's assume that I have an ndarray with shape (70,25,3). I wish to sum up the first dimension along certain index ranges and return a new 3D array. Consider the sum from 0:25, 25:50 and 50:75 which would return an array of shape (3,25,3).  
Is there an easy way to do "disjoint sums" along one dimension of a NumPy array to produce this result?

原文：https://stackoverflow.com/questions/32798519

更新时间：2022-01-11 15:01

最满意答案

 有了两个整数，你只需使用位移和| ：  
high = 0x00000000fe000000
low = 0x000000000001009f

result = (high << 32) | low
 
 对于任何其他输入，我建议先将它们转换为int ，然后将结果转换回您想要的任何形式。  
 要检索原始片段，请反向使用位移  
high = result >> 32
 
 和&与一个适当的面具：  
low = result & 0xffffffff

With two ints, you simply use bit-shifting and |: 
high = 0x00000000fe000000
low = 0x000000000001009f

result = (high << 32) | low
 
For any other input, I suggest converting them to ints first, and converting the result back to whatever form you want. 
To retrieve the original pieces, use bit-shifting in reverse 
high = result >> 32
 
and & with an appropriate mask: 
low = result & 0xffffffff

NumPy总和沿不相交索引(NumPy sum along disjoint indices)

最满意答案

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