Mailman 3 December 2023 - NumPy-Discussion

Next release date / release cadence
by Doug Turnbull 19 Dec '23

19 Dec '23

Sorry this is an annoying question. Especially for a volunteer project. I work on a project that could benefit from the new bit_count ufunc. I was trying to search around for how / when a feature like this would find its way into a release. And what a release cadence would be for the project. Looks like 1.26 was released in September. It also looks like everyone is hard at work on a 2.0. Would we expect a 1.27 release at some point? Or it all focus put on a 2.0? Is there a dev build of 2.0 I could try for my project? Thank you! Doug Sent from my iPhone

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Analysis about Mojo (the new language by Modular)
by PIERRE AUGIER 14 Dec '23

14 Dec '23

Hello, I studied and tried Mojo. I wrote a serious blog post on this new technology which could have an impact on the scientific Python community in the next years. https://augierpi.gricad-pages.univ-grenoble-alpes.fr/mojo-the-point-of-view… A few facts: - Mojo is first a static language inspired by Rust, Swift and C++. - It's very, very likely that Mojo will become truly open-source, the question is more when... - Mojo is by no means mature, but its development is progressing rapidly. We can already do some nice things, and the core of the language should be kind of ready in a few months. - It's very, very likely to become a big thing in the next few years. Don't hesitate to tell me if I've written something wrong or questionable. Best regards, Pierre

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Numpy not working
by Christophe Nassar 12 Dec '23

12 Dec '23

Hello everyone, First of all I have to say that I'm a beginner, so I'll maybe need more details regarding a confirmed dev in order to understand! I'm actually trying to use this, called DiffMorph: https://github.com/volotat/DiffMorph And I'm using this tutorial in order to install Tensorflow: https://www.tensorflow.org/install/pip?hl=fr However I'm facing an issue with anaconda and I have this error: "(tf) C:\*****\****\****\DiffMorph-master\DiffMorph-master>morph.py -s images/img_1.jpg -t images/img_2.jpg Traceback (most recent call last): File "C:\Users\chris\Desktop\DiffMorph-master\DiffMorph-master\morph.py", line 3, in <module> import numpy as np ModuleNotFoundError: No module named 'numpy'" Actually I'm not able to understand what is happening and why I got this error. I have numpy installed, I can see it in pip list, and in anaconda environment.. Can maybe someone help? Please tell me If I've missed some informations you need. Thanks a lot!

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ANN: numexpr 2.8.8 released
by Francesc Alted 11 Dec '23

11 Dec '23

======================== Announcing NumExpr 2.8.8 ======================== Hi everyone, NumExpr 2.8.8 is a release to deal mainly with issues appearing with upcoming `NumPy` 2.0. Also, some small fixes (support for simple complex expressions like `ne.evaluate('1.5j')`) and improvements are included. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.8.7 to 2.8.8 --------------------------- * Fix re_evaluate not taking global_dict as argument. Thanks to Teng Liu (@27rabbitlt). * Fix parsing of simple complex numbers. Now, `ne.evaluate('1.5j')` works. Thanks to Teng Liu (@27rabbitlt). * Fixes for upcoming NumPy 2.0: * Replace npy_cdouble with C++ complex. Thanks to Teng Liu (@27rabbitlt). * Add NE_MAXARGS for future numpy change NPY_MAXARGS. Now it is set to 64 to match NumPy 2.0 value. Thanks to Teng Liu (@27rabbitlt). What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted

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NEP 55 Updates and call for testing
by Nathan 08 Dec '23

08 Dec '23

Hi all, This week I updated NEP 55 to reflect the changes I made to the prototype since I initially sent out the NEP. The updated NEP is available on the NumPy website: https://numpy.org/neps/nep-0055-string_dtype.html. Updates to the NEP ++++++++++++++++++ The changes since the original version of the NEP focus on fully defining the C API surface we would like to add to the NumPy C API and an implementation of a per-dtype-instance arena allocator to manage heap allocations. This enabled major improvements to the prototype, including implementing the small string optimization and locking all access to heap memory behind a fine-grained mutex which should prevent seg faults or memory corruption in a multithreaded context. Thanks to Warren Weckesser for his proof of concept code and help with the small string optimization implementation, he has been added as an author to reflect his contributions. With these changes the stringdtype prototype is feature complete. Call to Review NEP 55 +++++++++++++++++++++ I'm requesting another round of review on the NEP with an eye toward acceptance before the NumPy 2.0 release branch is created from main. If I can manage it, my plan is to have a pull request open that merges the stringdtype codebase into NumPy before the branch is created. That said, if we decide that we need more time, or if some issue comes up, I'm happy with this going into main after the NumPy 2.0 release branch is created. The most significant feedback we have not addressed from the last round of review was Warren's suggestion to add a default missing data sentinel to NumPy itself. For reasons outlined in the NEP and in my reply to Warren from earlier this year, we do not want to add a missing data singleton to NumPy, instead leaving it to users to choose the missing data semantics they prefer. Otherwise I believe the current draft addresses all outstanding feedback from the last round of review. Help me Test the Prototype! +++++++++++++++++++++++++++ If anyone has time and interest, I would also very much appreciate some testing and tire-kicking on the stringdtype prototype, available at https://github.com/numpy/numpy-user-dtypes. There is a README with build instructions here: https://github.com/numpy/numpy-user-dtypes/blob/main/stringdtype/README.md If you have a Python development environment with a C compiler, it should be straightforward to build, install, and test the prototype. Note that you must have `NUMPY_EXPERIMENTAL_DTYPE_API=1` set in your shell environment or via `os.environ` to import stringdtype without error. I'm particularly interested to hear experiences converting code to use stringdtype. This could be code using fixed-width strings in a situation where a variable-length string array makes more sense or code using object string arrays. Are there pain points that aren't discussed in the NEP or existing workflows that cannot be adapted to use stringdtype? As far as I'm aware there aren't, but more testing will help catch issues before we've stabilized everything. My fork of pandas might be a source of inspiration for porting an existing non-trivial codebase that used object string arrays: https://github.com/pandas-dev/pandas/compare/main...ngoldbaum:pandas:string… Thanks all for your time, attention, and help reviewing the NEP! -Nathan

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complex_ imported by numexpr, but seems no longer supported by numpy
by 腾刘 06 Dec '23

06 Dec '23

Hi, I'm recently focusing on resolve some of the issues about numexpr and I found that in numexpr's test module, it will run: `from numpy import (...., complex_, .....)` And when I run `numexpr.test()` the output looks like: Traceback (most recent call last): File "/Users/liuteng/opt/anaconda3/envs/test_ne/lib/python3.9/site-packages/numexpr-2.8.8.dev1-py3.9-macosx-10.9-x86_64.egg/numexpr/__init__.py", line 64, in test import numexpr.tests File "/Users/liuteng/opt/anaconda3/envs/test_ne/lib/python3.9/site-packages/numexpr-2.8.8.dev1-py3.9-macosx-10.9-x86_64.egg/numexpr/tests/__init__.py", line 11, in <module> from numexpr.tests.test_numexpr import test, print_versions File "/Users/liuteng/opt/anaconda3/envs/test_ne/lib/python3.9/site-packages/numexpr-2.8.8.dev1-py3.9-macosx-10.9-x86_64.egg/numexpr/tests/test_numexpr.py", line 21, in <module> from numpy import ( ImportError: cannot import name 'complex_' from 'numpy' (/Users/liuteng/opt/anaconda3/envs/test_ne/lib/python3.9/site-packages/numpy/__init__.py) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<string>", line 1, in <module> File "/Users/liuteng/opt/anaconda3/envs/test_ne/lib/python3.9/site-packages/numexpr-2.8.8.dev1-py3.9-macosx-10.9-x86_64.egg/numexpr/__init__.py", line 68, in test raise ImportError('`numexpr.tests` could not be imported, likely it was excluded from the distribution.') ImportError: `numexpr.tests` could not be imported, likely it was excluded from the distribution Then I replace it with `cdouble` and everthing is fine then. So I'm curious what `complex_` was. Thanks in advance!

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GSOD-NumPy 2023: Review at next Community Meeting
by Mars Lee 06 Dec '23

06 Dec '23

Hi all! I'll be hosting Review #3 for the 'NumPy Contributor Comics'. It will be hosted at the next NumPy Community Meeting on Dec 6. It will take about 15 minutes. You can also comment on Github: https://github.com/numpy/numpy/issues/25318. The review will be open for the next two weeks and close on Dec 20. I also gave a talk about this project at the PyData NYC conference. Here's a link to the video: https://youtu.be/Gv_Ea94wquM?si=drLkjdN_0A75Jshn&t=40 See you there! - Mars

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Seeking feedback: design doc for `namedarray`, a lightweight array data structure with named dimensions
by Anderson Banihirwe 01 Dec '23

01 Dec '23

:wave:t5: folks, [there has been growing interest in a lightweight array structure](https://github.com/pydata/xarray/issues/3981) that's in the same vein as [xarray's Variable](https://docs.xarray.dev/en/stable/generated/xarray.Variable.html). we've put together a design doc for `namedarray`, and we could use your feedback/input. ## what is `namedarray`? in essence, `namedarray` aims to be a lighter version of xarray's Variable—shedding some of the heavier dependencies (e.g. Pandas) but still retaining the goodness of named dimensions. ## what makes it special? * **Array Protocol Compatibility**: we are planning to make it compatible with existing array protocols and the new [Python array API standard](https://data-apis.org/array-api/latest/). * **Duck-Array Objects**: designed to wrap around multiple duck-array objects, like NumPy, Dask, Sparse, Pint, CuPy, and PyTorch. ## why are we doing this? the goal is to bridge the gap between power and simplicity, providing a lightweight alternative for scientific computing tasks that don't require the full firepower of Xarray (`DataArray` and `Dataset`). ## share your thoughts We've put together a design doc that goes into the nitty-gritty of `namedarray`. your insights could be invaluable in making this initiative a success. please give it a read and share your thoughts [here](https://github.com/pydata/xarray/discussions/8080) * **Design Doc**: [namedarray Design Document](https://github.com/pydata/xarray/blob/main/design_notes/named_arr… cross posting from [Scientifc Python Discourse](https://discuss.scientific-python.org/t/seeking-feedback-design-…

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