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 :) 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') for i, item in enumerate(input_array): output_array[i] = f(item) 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.) 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." 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`? 2. Is there a more efficient way of writing the `for` loop I've written above? Or any other kind of solution to my problem? Thanks for your help, Ram Rachum.
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