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SciPy
=====

.. image:: https://img.shields.io/travis/scipy/scipy/master.svg?label=Travis%20CI
   :target: https://travis-ci.org/scipy/scipy/

.. image:: https://img.shields.io/appveyor/ci/scipy/scipy/master.svg?label=AppVeyor
   :target: https://ci.appveyor.com/project/scipy/scipy

.. image:: https://img.shields.io/circleci/project/github/scipy/scipy/master.svg?label=CircleCI
  :target: https://circleci.com/gh/scipy/scipy

.. image:: https://dev.azure.com/scipy-org/SciPy/_apis/build/status/scipy.scipy?branchName=master
  :target: https://dev.azure.com/scipy-org/SciPy/_build/latest?definitionId=1?branchName=master

.. image:: https://github.com/scipy/scipy/workflows/macOS%20tests/badge.svg?branch=master
  :target: https://github.com/scipy/scipy/actions?query=workflow%3A%22macOS+tests%22

.. 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

SciPy (pronounced "Sigh Pie") is 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://www.scipy.org/
- **Documentation:** https://docs.scipy.org/
- **Mailing list:** https://scipy.org/scipylib/mailing-lists.html
- **Source code:** https://github.com/scipy/scipy
- **Bug reports:** https://github.com/scipy/scipy/issues
- **Code of Conduct:** https://scipy.github.io/devdocs/dev/conduct/code_of_conduct.html
- **Report a security vulnerability:** https://tidelift.com/docs/security

SciPy depends on NumPy, which provides convenient and fast
N-dimensional array manipulation. 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 INSTALL.rst.txt_.

We appreciate and welcome contributions. If you would like to take part in
SciPy development, take a look at the file CONTRIBUTING.rst_.


.. _CONTRIBUTING.rst:  https://github.com/scipy/scipy/blob/master/CONTRIBUTING.rst
.. _INSTALL.rst.txt:   https://github.com/scipy/scipy/blob/master/INSTALL.rst.txt