[Numpy-discussion] An alternative to vectorize that lets you access the array?

[Ram Rachum]

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 <sebastian at 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.
> > _______________________________________________
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> > NumPy-Discussion at python.org
> > https://mail.python.org/mailman/listinfo/numpy-discussion
>
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