The following call is a bottleneck for me: np.in1d( large_array.field_of_interest, values_of_interest )
I'm not sure how in1d() is implemented, but this call seems to be slower than O(n) and faster than O( n**2 ), so perhaps it sorts the values_of_interest and does a binary search for each element of large_array? In any case, in my situation I actually know that field_of_interest increases monotonically across the large_array. So if I were writing this in C, I could do a simple O(n) loop by sorting values_of_interest and then just checking each value of large_array against values_of_interest[ i ] and values_of_interest[ i + 1 ], and any time it matched values_of_interest[ i + 1 ] increment i. Is there some way to achieve that same efficiency in numpy, taking advantage of the monotonic nature of field_of_interest?
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