The fundamental package for scientific computing with Python.

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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; numpy.test()'

Code of Conduct
----------------------

NumPy is a community-driven open source project developed by a diverse group of
[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong
commitment to creating an open, inclusive, and positive community. Please read the
[NumPy Code of Conduct](https://numpy.org/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](https://mail.python.org/mailman/listinfo/numpy-discussion) 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](https://github.com/numpy/numpy.org)
- 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](https://numpy.org/contribute/). 
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](https://opensource.guide/how-to-contribute/) helps explain why, what,
and how to successfully get involved.




numpy

.. 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.


scipy

The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more. 

# scikit-image: Image processing in Python

[![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fscikit-image.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&suffix=%20topics&logo=data:image/png;base64,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)](https://forum.image.sc/tags/scikit-image)
[![Stackoverflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](https://stackoverflow.com/questions/tagged/scikit-image)
[![project chat](https://img.shields.io/badge/zulip-join_chat-brightgreen.svg)](https://skimage.zulipchat.com)

- **Website (including documentation):** [https://scikit-image.org/](https://scikit-image.org)
- **User forum:** [https://forum.image.sc/tag/scikit-image](https://forum.image.sc/tag/scikit-image)
- **Developer forum:** [https://discuss.scientific-python.org/c/contributor/skimage](https://discuss.scientific-python.org/c/contributor/skimage)
- **Source:** [https://github.com/scikit-image/scikit-image](https://github.com/scikit-image/scikit-image)
- **Benchmarks:** [https://pandas.pydata.org/speed/scikit-image/](https://pandas.pydata.org/speed/scikit-image/)

## Installation from binaries

- **pip:** `pip install scikit-image`
- **conda:** `conda install -c conda-forge scikit-image`

Also see [installing `scikit-image`](INSTALL.rst).

## Installation from source

Install dependencies using:

```
pip install -r requirements.txt
```

Then, install scikit-image using:

```
$ pip install .
```

If you plan to develop the package, you may run it directly from source:

```
$ pip install -e .  # Do this once to add package to Python path
```

Every time you modify Cython files, also run:

```
$ python setup.py build_ext -i  # Build binary extensions
```

## License (Modified BSD)

Copyright (C) 2011, the scikit-image team
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

1.  Redistributions of source code must retain the above copyright
    notice, this list of conditions and the following disclaimer.
2.  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.
3.  Neither the name of skimage nor the names of its contributors may be
    used to endorse or promote products derived from this software without
    specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.

## Citation

If you find this project useful, please cite:

> Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias,
> François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle
> Gouillart, Tony Yu, and the scikit-image contributors.
> _scikit-image: Image processing in Python_. PeerJ 2:e453 (2014)
> https://doi.org/10.7717/peerj.453


scikit-image

.. -*- mode: rst -*-

|Azure|_ |CirrusCI|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_ |Benchmark|_

.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=main
.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=main

.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token
.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn

.. |CirrusCI| image:: https://img.shields.io/cirrus/github/scikit-learn/scikit-learn/main?label=Cirrus%20CI
.. _CirrusCI: https://cirrus-ci.com/github/scikit-learn/scikit-learn/main

.. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9
.. _Codecov: https://codecov.io/gh/scikit-learn/scikit-learn

.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
.. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule

.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue
.. _PythonVersion: https://pypi.org/project/scikit-learn/

.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn
.. _PyPi: https://pypi.org/project/scikit-learn

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _Black: https://github.com/psf/black

.. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg
.. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn

.. |Benchmark| image:: https://img.shields.io/badge/Benchmarked%20by-asv-blue
.. _`Benchmark`: https://scikit-learn.org/scikit-learn-benchmarks/

.. |PythonMinVersion| replace:: 3.8
.. |NumPyMinVersion| replace:: 1.17.3
.. |SciPyMinVersion| replace:: 1.3.2
.. |JoblibMinVersion| replace:: 1.1.1
.. |ThreadpoolctlMinVersion| replace:: 2.0.0
.. |MatplotlibMinVersion| replace:: 3.1.3
.. |Scikit-ImageMinVersion| replace:: 0.16.2
.. |PandasMinVersion| replace:: 1.0.5
.. |SeabornMinVersion| replace:: 0.9.0
.. |PytestMinVersion| replace:: 5.3.1
.. |PlotlyMinVersion| replace:: 5.10.0

.. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png
  :target: https://scikit-learn.org/

**scikit-learn** is a Python module for machine learning built on top of
SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation
------------

Dependencies
~~~~~~~~~~~~

scikit-learn requires:

- Python (>= |PythonMinVersion|)
- NumPy (>= |NumPyMinVersion|)
- SciPy (>= |SciPyMinVersion|)
- joblib (>= |JoblibMinVersion|)
- threadpoolctl (>= |ThreadpoolctlMinVersion|)

=======

**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**
scikit-learn 1.0 and later require Python 3.7 or newer.
scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with ``plot_`` and
classes end with "Display") require Matplotlib (>= |MatplotlibMinVersion|).
For running the examples Matplotlib >= |MatplotlibMinVersion| is required.
A few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples
require pandas >= |PandasMinVersion|, some examples require seaborn >=
|SeabornMinVersion| and plotly >= |PlotlyMinVersion|.

User installation
~~~~~~~~~~~~~~~~~

If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using ``pip``::

    pip install -U scikit-learn

or ``conda``::

    conda install -c conda-forge scikit-learn

The documentation includes more detailed `installation instructions <https://scikit-learn.org/stable/install.html>`_.


Changelog
---------

See the `changelog <https://scikit-learn.org/dev/whats_new.html>`__
for a history of notable changes to scikit-learn.

Development
-----------

We welcome new contributors of all experience levels. The scikit-learn
community goals are to be helpful, welcoming, and effective. The
`Development Guide <https://scikit-learn.org/stable/developers/index.html>`_
has detailed information about contributing code, documentation, tests, and
more. We've included some basic information in this README.

Important links
~~~~~~~~~~~~~~~

- Official source code repo: https://github.com/scikit-learn/scikit-learn
- Download releases: https://pypi.org/project/scikit-learn/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code
~~~~~~~~~~~

You can check the latest sources with the command::

    git clone https://github.com/scikit-learn/scikit-learn.git

Contributing
~~~~~~~~~~~~

To learn more about making a contribution to scikit-learn, please see our
`Contributing guide
<https://scikit-learn.org/dev/developers/contributing.html>`_.

Testing
~~~~~~~

After installation, you can launch the test suite from outside the source
directory (you will need to have ``pytest`` >= |PyTestMinVersion| installed)::

    pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage
for more information.

    Random number generation can be controlled during testing by setting
    the ``SKLEARN_SEED`` environment variable.

Submitting a Pull Request
~~~~~~~~~~~~~~~~~~~~~~~~~

Before opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History
---------------

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

The project is currently maintained by a team of volunteers.

**Note**: `scikit-learn` was previously referred to as `scikits.learn`.

Help and Support
----------------

Documentation
~~~~~~~~~~~~~

- HTML documentation (stable release): https://scikit-learn.org
- HTML documentation (development version): https://scikit-learn.org/dev/
- FAQ: https://scikit-learn.org/stable/faq.html

Communication
~~~~~~~~~~~~~

- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
- Gitter: https://gitter.im/scikit-learn/scikit-learn
- Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
- Blog: https://blog.scikit-learn.org
- Calendar: https://blog.scikit-learn.org/calendar/
- Twitter: https://twitter.com/scikit_learn
- Twitter (commits): https://twitter.com/sklearn_commits
- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
- Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
- Website: https://scikit-learn.org
- LinkedIn: https://www.linkedin.com/company/scikit-learn
- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
- Facebook: https://www.facebook.com/scikitlearnofficial/
- Instagram: https://www.instagram.com/scikitlearnofficial/
- TikTok: https://www.tiktok.com/@scikit.learn

Citation
~~~~~~~~

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn


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