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
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