Thank you Sebastian and Andras for your detailed replies. Sebastian, your suggestion of adding `item.item()` solved my problem! Now the for loop is still slower than vectorize, but by a smaller factor, and that's fast enough for my demonstration. My problem is solved and I'm very happy!
I also tried your `out=` suggestion for vectorize, but I think you made a mistake, as it doesn't seem that it takes that argument. If I missed something and it does (maybe it's a very new feature?) that would be even better for me than the `.item()` solution. On Sun, Jul 12, 2020 at 5:03 PM Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Sun, 2020-07-12 at 16:00 +0300, Ram Rachum wrote: > > Hi everyone, > > > > Here's a problem I've been dealing with. I wonder whether NumPy has a > > tool > > that will help me, or whether this could be a useful feature request. > > > > In the upcoming EuroPython 20200, I'll do a talk about live-coding a > > music > > synthesizer. It's going to be a fun talk, I'll use the sounddevice > > <https://github.com/spatialaudio/python-sounddevice/> module to make > > a > > program that plays music. Do attend, or watch it on YouTube when it's > > out :) > > > > Sounds like a fun talk :). > > > There's a part in my talk that I could make simpler, and thus shave > > 3-4 > > minutes of cumbersome explanations. These 3-4 minutes matter a great > > deal > > to me. But for that I need to do something with NumPy and I don't > > know > > whether it's possible or not. > > > > > > The sounddevice library takes an ndarray of sound data and plays it. > > Currently I use `vectorize` to produce that array: > > > > output_array = np.vectorize(f, otypes='d')(input_array) > > > > And I'd like to replace it with this code, which is supposed to give > > the > > same output: > > > > output_array = np.ndarray(input_array.shape, dtype='d') > > Maybe use `np.empty(inpyt_array.shape, dtype="d")` instead. > `np.ndarray` works but is pretty low-level, and I would usually avoid > it for array creation. > > > for i, item in enumerate(input_array): > > output_array[i] = f(item) > > > > Ok, one hack that you can try, is to replace `item` with `item.item()`, > that will convert the NumPy scalar to a Python scalar, which is quite a > lot more lightweight and faster. Also it might give PyPy more chance > to optimize `f` I suppose. > > > > The reason I want the second version is that I can then have > > sounddevice > > start playing `output_array` in a separate thread, while it's being > > calculated. (Yes, I know about the GIL, I believe that sounddevice > > releases > > it.) > > `np.vectorize` will definitely not release the GIL, this loop may in > between (I am not sure), but also adds quite a bit of overheads > compared to `vectorize`. The best thing of course would be if you can > rewrite `f` to accept an array? > > > > Unfortunately, the for loop is very slow, even when I'm not > > processing the > > data on separate thread. I benchmarked it on both CPython and PyPy3, > > which > > is my target platform. On CPython it's 3 times slower than vectorize, > > and > > on PyPy3 it's 67 times slower than vectorize! That's despite the fact > > that > > the Numpy documentation says "The `vectorize` function is provided > > primarily for convenience, not for performance. The implementation is > > essentially a `for` loop." > > PyPy is nice because it makes NumPy just work. Unfortunately, that also > adds some overheads, so at least some slowdown is probably expected. I > am not sure about why it is so much. > I would not be surprised if a list comprehension is not much faster, > especially on PyPy (assuming you cannot modify `f` to work with > arrays). > > > So here are a few questions: > > > > 1. Is there something like `vectorize`, except you get to access the > > output > > array before it's finished? If not, what do you think about adding > > that as > > an option to `vectorize`? > > vectorize should allow an `out=` argument to pass in the output array, > would that help you? So you can access it, but I am not sure how that > will help you. Although you could create a big result array and then > access chunks of it: > > final_arr = np.empty(...) > newly_written = slice(0, 1000) > run_calculation(final_arr[newly_written]) > > where newly_written is defined by the input chunk you got, I suppose. > > > > > > 2. Is there a more efficient way of writing the `for` loop I've > > written > > above? Or any other kind of solution to my > > As said, the main thing would be to modify `f` in whatever way > possible. For that it would be useful to know what `f` does exactly. > Maybe you can move `f` to Cython or numba, or maybe write in a way that > works on arrays... > > > > > Thanks for your help, > > Ram Rachum. > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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