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Just to clarify, I am not the one who suggested pipes. :) Read the issue. My 2 cents: From my experience, calling methods is generally faster than functions. I figure it is due to having less overhead figuring out the input. Maybe it is not significant for large data, but it does make a difference even when working for medium sized arrays - say float size 5000. %timeit a.sum() 3.17 µs %timeit np.sum(a) 5.18 µs (In my experience, `sum` for medium size arrays often becomes a bottleneck in greedy optimisation algorithms where distances are calculated over and over for partial space.) In short, all I want to say is that it would be great if such if speed considerations were addressed if/when developing piping or anything similar. E.g. Pipe implementation could allow additions of optimisations. Then numexpr could then make a plugin. At the top user writes: np.pipe_use_plugin(numexpr.plug_pipe) # or something similar Then, numexpr would kick-in whenever appropriate when using pipes. Regards, DG
On 16 Feb 2024, at 00:12, Marten van Kerkwijk <mhvk@astro.utoronto.ca> wrote:
What were your conclusions after experimenting with chained ufuncs?
If the speed is comparable to numexpr, wouldn’t it be `nicer` to have non-string input format?
It would feel a bit less like a black-box.
I haven't gotten further than it yet, it is just some toying around I've been doing. But I'd indeed prefer not to go via strings -- possibly numexpr could use a similar mechanism to what I did to construct the function that is being evaluated.
Aside: your suggestion of the pipe led to some further discussion at https://github.com/numpy/numpy/issues/25826#issuecomment-1947342581 -- as a more general way of passing arrays to functions.
-- Marten _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: dom.grigonis@gmail.com