Mailman 3 - NumPy-Discussion

numpy defaults to free-threaded version on conda-forge
by george trojan Nov. 26, 2024

Nov. 26, 2024

My apologies if this is a wrong venue. Is this a conda-forge problem? When I run mamba create -n foo meson-python numpy 'python>=3.13' the free-threaded python is selected: + python 3.13.0 h6355ac2_0_cp313t conda-forge Cached + numpy 2.1.3 py313hb01392b_0 conda-forge Cached When python spec is moved to the front, I do get the "usual" version. However, when I build a python extension with conda-build, I do not control the order. The extension … [View More]

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Fixing definition of reduceat for Numpy 2.0?
by Martin Ling Nov. 24, 2024

Nov. 24, 2024

Hi folks, I don't follow numpy development in much detail these days but I see that there is a 2.0 release planned soon. Would this be an opportunity to change the behaviour of 'reduceat'? This issue has been open in some form since 2006! https://github.com/numpy/numpy/issues/834 The current behaviour was originally inherited from Numeric, and makes reduceat often unusable in practice, even where it should be the perfect, concise, efficient solution. But it has been impossible to change it … [View More]

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NumPy 2.2.x has been branched
by Charles R Harris Nov. 23, 2024

Nov. 23, 2024

Hi All, NumPy 2.2.x has been branched and main is now open for 2.3.0 development. Chuck

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New matvec and vecmat functions
by Marten van Kerkwijk Nov. 22, 2024

Nov. 22, 2024

Hi All, I have a PR [1] that adds `np.matvec` and `np.vecmat` gufuncs for matrix-vector and vector-matrix calculations, to add to plain matrix-matrix multiplication with `np.matmul` and the inner vector product with `np.vecdot`. They call BLAS where possible for speed. I'd like to hear whether these are good additions. I also note that for complex numbers, `vecmat` is defined as `x†A`, i.e., the complex conjugate of the vector is taken. This seems to be the standard and is what we used for `… [View More]

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odd behaviour of meshgrid with 3 arrays
by Slavin, Jonathan Nov. 21, 2024

Nov. 21, 2024

Hi all, I was trying to use meshgrid with three arrays and got some odd results. Here's a simple example: xt = np.array([1,2,3,4]) yt = np.array([6,7,8]) zt = np.array([12,13]) xxx,yyy,zzz = np.meshgrid(xt,yt,zt) So I would expect that xxx[0,0,:] = array([1,2,3,4]) instead I get xxx[0,0,:] = array([1,1]) and xxx[0,:,0] = array([1,2,3,4]) also yyy[:,0,0] = array([6,7,8]), whereas I would expect yyy[0,:,0] = array([6,7,8]) So what's going on? This seems like a bug to me. Any suggestions for … [View More]

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Add bitwise deposit/extract functions
by Freddie Witherden Nov. 15, 2024

Nov. 15, 2024

NumPy currently has a reasonable selection of bitwise operators. However, one missing class are bit deposit and extract functions (also known as expand and compress). The general prototype is deposit(mask, x) and extract(mask, x). The extract function goes through each entry in mask and, if it is set, extracts the corresponding bit from x and concatenates it into the return value. The deposit function does the opposite: the first bit in x goes to the location of the first non-zero in the … [View More]

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Feature request: Add out-of-place (i.e. pure) versions of all existing in-place operations
by Carlos Martin Nov. 14, 2024

Nov. 14, 2024

Feature request: Add out-of-place (i.e. pure) versions of all existing in-place operations, such as the following: - https://numpy.org/doc/stable/reference/generated/numpy.fill_diagonal.html - https://numpy.org/doc/stable/reference/generated/numpy.put.html - https://numpy.org/doc/stable/reference/generated/numpy.place.html - https://numpy.org/doc/stable/reference/generated/numpy.put_along_axis.html - https://numpy.org/doc/stable/reference/generated/numpy.putmask.html This can be done by … [View More]

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How do I use the numpy on spyder?
by Joao Pereira Oct. 31, 2024

Oct. 31, 2024

I don´t know how to use the numpy on spyder. I have tried to use this line: "import numpy as np" but i dont know if it's the only necessary thing to do. If you could explain it to me I'll appreciate it.

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C API could be more consistent with returning NPY_SUCCEED/NPY_FAIL
by sverre.hassing＠gmail.com Oct. 31, 2024

Oct. 31, 2024

While using the C API to work with Numpy arrays, I came across some inconsistencies regarding the return of a success state from various Numpy functions. For example, the array iterator functions return either NPY_SUCCEED (defined as 1) or NPY_FAIL (defined as 0) to indicate whether the function successfully finished. However, various of the array functions return -1 on success and 0 on failure. The result is similar enough, but I would think it more consistent to use NPY_SUCCEED/NPY_FAIL … [View More]

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Suggestion to show the shape in repr for summarized arrays
by Marten van Kerkwijk Oct. 30, 2024

Oct. 30, 2024

Hi All, When the repr of an array is shown, currently the dtype and shape are explicitly listed if these cannot be directly inferred from the list that is shown, i.e., if the dtype is not float64 or int64, and if the size of the array is zero, but the shape not the simple (0,). For instance, ``` np.empty((10,2,0), dtype="i2") array([], shape=(10, 2, 0), dtype=int16) ``` I propose to also show the shape for the (rare) case that an array is summarized, i.e., when it has more than the default … [View More]

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