numpy-aipy 2.2.3.2

The fundamental package needed for scientific computing with Python

This project is a branch of numpy on QPython.

NumPy is the fundamental package for scientific computing with Python.

• Website: https://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://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