On Mon, 2020-07-13 at 15:45 +0300, Ram Rachum wrote:
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.
You are right, I thought vectorize may be a proper ufunc internally in this branch (like frompyfunc), but `frompyfunc` currently does not support dtypes other than object (which could be a nice improvement to make vectorize more replaceable). - Sebastian
On Sun, Jul 12, 2020 at 5:03 PM Sebastian Berg < sebastian@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
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