numpy/scipy中的向量化索引/切片? [英] vectorized indexing/slicing in numpy/scipy?

问题描述

我有一个数组A，并且有一个切片索引(s，t)的列表，我们称此列表为L.

I have an array A, and I have a list of slicing indices (s,t), let's called this list L.

我想找到A [s1:t1]，A [s2:t2] ...的85个百分位数

I want to find the 85 percentiles of A[s1:t1], A[s2:t2] ...

是否有一种方法可以在numpy中向量化这些操作?

Is there a way to vectorize these operations in numpy?

ans = []
for (s,t) in L:
   ans.append( numpy.percentile( A[s:t], 85) ); 

看起来很麻烦.

非常感谢！

PS:假定s1<是安全的. s2 .... t1< t2 .....这实际上只是一个滑动窗口百分位数问题.

PS: it's safe to assume s1 < s2 .... t1 < t2 ..... This is really just a sliding window percentile problem.

推荐答案

鉴于您正在处理不均匀的间隔(即，切片的大小不同)，不，没有办法让numpy做只需一个函数调用即可.

Given that you're dealing with a non-uniform interval (i.e. the slices aren't the same size), no, there's no way to have numpy do it in a single function call.

如果是均匀的切片大小，则可以使用各种技巧来完成，如@eat注释.

If it was a uniform slice size, then you could do so with various tricks, as @eat commented.

但是，列表理解有什么问题?它完全等同于您的上述循环，但是如果您担心的话，它看起来更干净".

However, what's wrong with a list comprehension? It's exactly equivalent to your loop above, but it looks "cleaner" if that's what you're worried about.

ans = [numpy.percentile(A[s:t], 85) for s,t in L]

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