Vehicle-cpp/hkpc/software/scipy-1.10.1.dist-info/METADATA

Metadata-Version: 2.1
Name: scipy
Version: 1.10.1
Summary: Fundamental algorithms for scientific computing in Python
Home-page: https://scipy.org/
Maintainer-Email: SciPy Developers <scipy-dev@python.org>
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Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Project-URL: Homepage, https://scipy.org/
Project-URL: Documentation, https://docs.scipy.org/doc/scipy/
Project-URL: Source, https://github.com/scipy/scipy
Project-URL: Download, https://github.com/scipy/scipy/releases
Project-URL: Tracker, https://github.com/scipy/scipy/issues
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Requires-Dist: numpy<1.27.0,>=1.19.5
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Requires-Dist: doit>=0.36.0; extra == "dev"
Requires-Dist: pydevtool; extra == "dev"
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: dev
Description-Content-Type: text/x-rst

.. image:: https://github.com/scipy/scipy/blob/main/doc/source/_static/logo.svg
  :target: https://scipy.org
  :width: 110
  :height: 110
  :align: left 

.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
  :target: https://numfocus.org

.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads
  :target: https://pypi.org/project/scipy/

.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads
  :target: https://anaconda.org/conda-forge/scipy

.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg
  :target: https://stackoverflow.com/questions/tagged/scipy

.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue
  :target: https://www.nature.com/articles/s41592-019-0686-2

SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
science, and engineering. It includes modules for statistics, optimization,
integration, linear algebra, Fourier transforms, signal and image processing,
ODE solvers, and more.

- **Website:** https://scipy.org
- **Documentation:** https://docs.scipy.org/doc/scipy/
- **Development version of the documentation:** https://scipy.github.io/devdocs
- **Mailing list:** https://mail.python.org/mailman3/lists/scipy-dev.python.org/
- **Source code:** https://github.com/scipy/scipy
- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
- **Bug reports:** https://github.com/scipy/scipy/issues
- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
- **Report a security vulnerability:** https://tidelift.com/docs/security
- **Citing in your work:** https://www.scipy.org/citing-scipy/

SciPy is built to work with
NumPy arrays, and provides many user-friendly and efficient numerical routines,
such as routines for numerical integration and optimization. Together, they
run on all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be depended
upon by some of the world's leading scientists and engineers. If you need to
manipulate numbers on a computer and display or publish the results, give
SciPy a try!

For the installation instructions, see `our install
guide <https://docs.scipy.org/doc/scipy/getting_started.html#installation>`__.


Call for Contributions
----------------------

We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
first issue" may be a good starting point. Have a look at `our contributing
guide <https://scipy.github.io/devdocs/dev/index.html>`__.

Writing code isn’t the only way to contribute to SciPy. You can also:

- review pull requests
- triage issues
- develop tutorials, presentations, and other educational materials
- maintain and improve `our website <https://github.com/scipy/scipy.org>`__
- develop graphic design for our brand assets and promotional materials
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts

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 leaving a
comment on a relevant issue that is already open.

If you are new to contributing to open source, `this
guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
and how to get involved.