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