vectorized indexing/slicing in numpy/scipy?
I have an array A, and I have a list of slicing indices (s,t), let's called this list L.
I want to find the 85 percentiles of A[s1:t1], A[s2:t2] ...
Is there a way to vectorize these operations in numpy?
ans = []
for (s,t) in L:
ans.append( numpy.percentile( A[s:t], 85) );
looks cumbersome.
Thanks a lot!
PS: it's safe to assume s1 < s2 .... t1 < t2 ..... This is really just a sliding window percentile pr开发者_StackOverflow中文版oblem.
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.
If it was a uniform slice size, then you could do so with various tricks, as @eat commented.
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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