loongson/pypi/: numpy-1.26.2 metadata and description
Fundamental package for array computing in Python
| author | Travis E. Oliphant et al. |
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| description_content_type | text/markdown |
| license | Copyright (c) 2005-2023, NumPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the NumPy Developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| maintainer_email | NumPy Developers <numpy-discussion@python.org> |
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| requires_python | >=3.9 |
Because this project isn't in the mirror_whitelist,
no releases from root/pypi are included.
| File | Tox results | History |
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numpy-1.26.2-cp310-cp310-linux_loongarch64.whl
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numpy-1.26.2-cp311-cp311-linux_loongarch64.whl
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numpy-1.26.2-cp312-cp312-linux_loongarch64.whl
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NumPy is the fundamental package for scientific computing with Python.
- Website: https://www.numpy.org
- Documentation: https://numpy.org/doc
- Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
- Source code: https://github.com/numpy/numpy
- Contributing: https://www.numpy.org/devdocs/dev/index.html
- Bug reports: https://github.com/numpy/numpy/issues
- Report a security vulnerability: https://tidelift.com/docs/security
It provides:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
Testing:
NumPy requires pytest and hypothesis. Tests can then be run after installation with:
python -c "import numpy, sys; sys.exit(numpy.test() is False)"
Code of Conduct
NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes our community thrive.
Call for Contributions
The NumPy project welcomes your expertise and enthusiasm!
Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the mailing list first.
Writing code isn’t the only way to contribute to NumPy. You can also:
- review pull requests
- help us stay on top of new and old issues
- develop tutorials, presentations, and other educational materials
- maintain and improve our website
- develop graphic design for our brand assets and promotional materials
- translate website content
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts
For more information about the ways you can contribute to NumPy, visit our website. If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open.
Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for an invitation).
We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join.
If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.