It's typically called short-circuiting or quick exit when the target condition is met.
if you have an array a = np.array([-1, 2, 3, 4, ...., 10000]) and you are looking for a true/false result whether anything is negative or not (a < 0).any() will generate a bool array equal to and then check all entries of that bool array just to reach the conclusion true which was already true at the first entry. Instead it spends 10000 units of time for all entries. We did similar things on SciPy side Cython level, but they are not really competitive, instead more of a convenience. More general discussion I opened is in https://github.com/data-apis/array-api/issues/675 On Thu, Oct 26, 2023 at 2:52 PM Dom Grigonis <dom.grigo...@gmail.com> wrote: > Could you please give a concise example? I know you have provided one, but > it is engrained deep in verbose text and has some typos in it, which makes > hard to understand exactly what inputs should result in what output. > > Regards, > DG > > > On 25 Oct 2023, at 22:59, rosko37 <rosk...@gmail.com> wrote: > > > > I know this question has been asked before, both on this list as well as > several threads on Stack Overflow, etc. It's a common issue. I'm NOT asking > for how to do this using existing Numpy functions (as that information can > be found in any of those sources)--what I'm asking is whether Numpy would > accept inclusion of a function that does this, or whether (possibly more > likely) such a proposal has already been considered and rejected for some > reason. > > > > The task is this--there's a large array and you want to find the next > element after some index that satisfies some condition. Such elements are > common, and the typical number of elements to be searched through is small > relative to the size of the array. Therefore, it would greatly improve > performance to avoid testing ALL elements against the conditional once one > is found that returns True. However, all built-in functions that I know of > test the entire array. > > > > One can obviously jury-rig some ways, like for instance create a "for" > loop over non-overlapping slices of length slice_length and call something > like np.where(cond) on each--that outer "for" loop is much faster than a > loop over individual elements, and the inner loop at most will go > slice_length-1 elements past the first "hit". However, needing to use such > a convoluted piece of code for such a simple task seems to go against the > Numpy spirit of having one operation being one function of the form > func(arr)". > > > > A proposed function for this, let's call it "np.first_true(arr, > start_idx, [stop_idx])" would be best implemented at the C code level, > possibly in the same code file that defines np.where. I'm wondering if I, > or someone else, were to write such a function, if the Numpy developers > would consider merging it as a standard part of the codebase. It's possible > that the idea of such a function is bad because it would violate some > existing broadcasting or fancy indexing rules. Clearly one could make it > possible to pass an "axis" argument to np.first_true() that would select an > axis to search over in the case of multi-dimensional arrays, and then the > result would be an array of indices of one fewer dimension than the > original array. So np.first_true(np.array([1,5],[2,7],[9,10],cond) would > return [1,1,0] for cond(x): x>4. The case where no elements satisfy the > condition would need to return a "signal value" like -1. But maybe there > are some weird cases where there isn't a sensible return val > ue, hence why such a function has not been added. > > > > -Andrew Rosko > > _______________________________________________ > > NumPy-Discussion mailing list -- numpy-discussion@python.org > > To unsubscribe send an email to numpy-discussion-le...@python.org > > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > > Member address: dom.grigo...@gmail.com > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: ilhanpo...@gmail.com >
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