Mailman 3 January 2025 - NumPy-Discussion

How to join IRC channel of numpy
by Diya Singh Jan. 14, 2025

Jan. 14, 2025

Please help me know how to join discussion channels for NumFocus organization so that I can meet the community

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Why ufunc out arg has to be an ndarray?
by Stefano Miccoli Jan. 8, 2025

Jan. 8, 2025

From time to time I find myself overwriting a python buffer with the output of a ufunc, for example like this: import array import numpy as np a = array.array('f', (1,1,1)) np.exp2(a, out=np.asarray(a)) assert a.tolist() == [2, 2, 2] Here I have to wrap `out=np.asarray(a)` because the more natural `np.exp2(a, out=a)` raises "TypeError: return arrays must be of ArrayType” In general, ufuncs are quite aggressive in utilizing the buffer protocol for input arguments. I was wondering, why are they so reluctant to do the same for the output argument? Is this a design choice? Efficiency? Legacy? Would be implementing `np.ufunc(a, out=a)` dangerous or cumbersome? Stefano

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Is a Python function a gufunc if it broadcasts its arguments appropriately?
by john.a.dawson＠proton.me Jan. 6, 2025

Jan. 6, 2025

For example, is the function `stack` below a gufunc with signature (),()->(2)? def stack(a, b): broadcasts = np.broadcast_arrays(a, b) return np.stack(broadcasts, axis=-1) Or must gufuncs be written in C? Or are other things required?

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next NumPy triage meeting - May 29th, 2024 at 7 pm UTC
by Inessa Pawson Jan. 5, 2025

Jan. 5, 2025

The next NumPy triage meeting will be held this Wednesday, May 29th at 7pm UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… . Everyone is welcome to attend and contribute to a conversation. Please notify us of issues or PRs that you’d like to have reviewed by adding a GitHub link to them in the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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ENH: Efficient vectorized sampling without replacement
by Mark Jan. 1, 2025

Jan. 1, 2025

Hello, Numpy provides efficient, vectorized methods for generating random samples of an array with replacement. However, it lacks similar functionality for sampling *without replacement* in a vectorized manner. To address this limitation, I developed a function capable of performing this task, achieving approximately a 30x performance improvement over a basic Python loop for small sample sizes (and 2x performance improvement using numba). Could this functionality, or something similar, be integrated into numpy? See also this issue <https://github.com/numpy/numpy/issues/28084>. Kind regards, Mark def random_choice_without_replacement(array, sample_size, n_iterations): """ Generates random samples from a given array without replacement. Parameters ---------- array : array-like Array from which to draw the random samples. sample_size : int Number of random samples to draw without replacement per iteration. n_iterations : int Number of iterations to generate random samples. Returns ------- random_samples : ndarray The generated random samples. Raises ------ ValueError If sample_size is greater than the population size. Examples -------- Generate 10 random samples from np.arange(5) of size 3 without replacement. >>> array = np.arange(5) >>> random_choice_without_replacement(array, 3, 10) array([[4, 0, 1], [1, 4, 0], [1, 3, 2], [0, 1, 3], [1, 0, 2], [3, 2, 4], [0, 3, 1], [1, 3, 4], [3, 1, 4], [0, 1, 3]]) # random Generate 4 random samples from an n-dimensional array of size 3 without replacement. >>> array = np.arange(10).reshape(5, 2) >>> random_choice_without_replacement(array, 3, 4) array([[[0, 1], [8, 9], [4, 5]], [[2, 3], [8, 9], [0, 1]], [[0, 1], [2, 3], [8, 9]], [[4, 5], [2, 3], [8, 9]]]) # random """ if sample_size > len(array): raise ValueError(f"Sample_size ({sample_size}) is greater than the population size ({len(array)}).") indices = np.tile(np.arange(len(array)), (n_iterations,1)) random_samples = np.empty((n_iterations, sample_size), dtype=int) rng = np.random.default_rng() for i, int_max in zip(range(sample_size), reversed(range(len(array) - sample_size, len(array)))): random_indices = rng.integers(0, int_max + 1, size=(n_iterations,1)) random_samples[:, i] = np.take_along_axis(indices, random_indices, axis=-1).T np.put_along_axis(indices, random_indices, indices[:, int_max:int_max+1], axis=-1) return array[random_samples]

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