alpcentaur
/
basabuuka_prototyp


								=====================

								Contributing to SciPy

								=====================


								This document aims to give an overview of how to contribute to SciPy.  It

								tries to answer commonly asked questions, and provide some insight into how the

								community process works in practice.  Readers who are familiar with the SciPy

								community and are experienced Python coders may want to jump straight to the

								`git workflow`_ documentation.


								There are a lot of ways you can contribute:


								- Contributing new code

								- Fixing bugs and other maintenance work

								- Improving the documentation

								- Reviewing open pull requests

								- Triaging issues

								- Working on the `scipy.org`_ website

								- Answering questions and participating on the scipy-dev and scipy-user

								  `mailing lists`_.


								Contributing new code

								=====================


								If you have been working with the scientific Python toolstack for a while, you

								probably have some code lying around of which you think "this could be useful

								for others too".  Perhaps it's a good idea then to contribute it to SciPy or

								another open source project.  The first question to ask is then, where does

								this code belong?  That question is hard to answer here, so we start with a

								more specific one: *what code is suitable for putting into SciPy?*

								Almost all of the new code added to scipy has in common that it's potentially

								useful in multiple scientific domains and it fits in the scope of existing

								scipy submodules.  In principle new submodules can be added too, but this is

								far less common.  For code that is specific to a single application, there may

								be an existing project that can use the code.  Some scikits (`scikit-learn`_,

								`scikit-image`_, `statsmodels`_, etc.) are good examples here; they have a

								narrower focus and because of that more domain-specific code than SciPy.


								Now if you have code that you would like to see included in SciPy, how do you

								go about it?  After checking that your code can be distributed in SciPy under a

								compatible license (see FAQ for details), the first step is to discuss on the

								scipy-dev mailing list.  All new features, as well as changes to existing code,

								are discussed and decided on there.  You can, and probably should, already

								start this discussion before your code is finished.


								Assuming the outcome of the discussion on the mailing list is positive and you

								have a function or piece of code that does what you need it to do, what next?

								Before code is added to SciPy, it at least has to have good documentation, unit

								tests and correct code style.


								1. Unit tests

								    In principle you should aim to create unit tests that exercise all the code

								    that you are adding.  This gives some degree of confidence that your code

								    runs correctly, also on Python versions and hardware or OSes that you don't

								    have available yourself.  An extensive description of how to write unit

								    tests is given in the NumPy `testing guidelines`_.


								2. Documentation

								    Clear and complete documentation is essential in order for users to be able

								    to find and understand the code.  Documentation for individual functions

								    and classes -- which includes at least a basic description, type and

								    meaning of all parameters and returns values, and usage examples in

								    `doctest`_ format -- is put in docstrings.  Those docstrings can be read

								    within the interpreter, and are compiled into a reference guide in html and

								    pdf format.  Higher-level documentation for key (areas of) functionality is

								    provided in tutorial format and/or in module docstrings.  A guide on how to

								    write documentation is given in `how to document`_.


								3. Code style

								    Uniformity of style in which code is written is important to others trying

								    to understand the code.  SciPy follows the standard Python guidelines for

								    code style, `PEP8`_.  In order to check that your code conforms to PEP8,

								    you can use the `pep8 package`_ style checker.  Most IDEs and text editors

								    have settings that can help you follow PEP8, for example by translating

								    tabs by four spaces.  Using `pyflakes`_ to check your code is also a good

								    idea.


								At the end of this document a checklist is given that may help to check if your

								code fulfills all requirements for inclusion in SciPy.


								Another question you may have is: *where exactly do I put my code*?  To answer

								this, it is useful to understand how the SciPy public API (application

								programming interface) is defined.  For most modules the API is two levels

								deep, which means your new function should appear as

								``scipy.submodule.my_new_func``.  ``my_new_func`` can be put in an existing or

								new file under ``/scipy/<submodule>/``, its name is added to the ``__all__``

								list in that file (which lists all public functions in the file), and those

								public functions are then imported in  ``/scipy/<submodule>/__init__.py``.  Any

								private functions/classes should have a leading underscore (``_``) in their

								name.  A more detailed description of what the public API of SciPy is, is given

								in `SciPy API`_.


								Once you think your code is ready for inclusion in SciPy, you can send a pull

								request (PR) on Github.  We won't go into the details of how to work with git

								here, this is described well in the `git workflow`_ section of the NumPy

								documentation and on the `Github help pages`_.  When you send the PR for a new

								feature, be sure to also mention this on the scipy-dev mailing list.  This can

								prompt interested people to help review your PR.  Assuming that you already got

								positive feedback before on the general idea of your code/feature, the purpose

								of the code review is to ensure that the code is correct, efficient and meets

								the requirements outlined above.  In many cases the code review happens

								relatively quickly, but it's possible that it stalls.  If you have addressed

								all feedback already given, it's perfectly fine to ask on the mailing list

								again for review (after a reasonable amount of time, say a couple of weeks, has

								passed).  Once the review is completed, the PR is merged into the "master"

								branch of SciPy.


								The above describes the requirements and process for adding code to SciPy.  It

								doesn't yet answer the question though how decisions are made exactly.  The

								basic answer is: decisions are made by consensus, by everyone who chooses to

								participate in the discussion on the mailing list.  This includes developers,

								other users and yourself.  Aiming for consensus in the discussion is important

								-- SciPy is a project by and for the scientific Python community.  In those

								rare cases that agreement cannot be reached, the maintainers of the module

								in question can decide the issue.


								Contributing by helping maintain existing code

								==============================================


								The previous section talked specifically about adding new functionality to

								SciPy.  A large part of that discussion also applies to maintenance of existing

								code.  Maintenance means fixing bugs, improving code quality or style,

								documenting existing functionality better, adding missing unit tests, keeping

								build scripts up-to-date, etc.  The SciPy `issue list`_ contains all

								reported bugs, build/documentation issues, etc.  Fixing issues

								helps improve the overall quality of SciPy, and is also a good way

								of getting familiar with the project.  You may also want to fix a bug because

								you ran into it and need the function in question to work correctly.


								The discussion on code style and unit testing above applies equally to bug

								fixes.  It is usually best to start by writing a unit test that shows the

								problem, i.e. it should pass but doesn't.  Once you have that, you can fix the

								code so that the test does pass.  That should be enough to send a PR for this

								issue.  Unlike when adding new code, discussing this on the mailing list may

								not be necessary - if the old behavior of the code is clearly incorrect, no one

								will object to having it fixed.  It may be necessary to add some warning or

								deprecation message for the changed behavior.  This should be part of the

								review process.


								Reviewing pull requests

								=======================


								Reviewing open pull requests (PRs) is very welcome, and a valuable way to help

								increase the speed at which the project moves forward.  If you have specific

								knowledge/experience in a particular area (say "optimization algorithms" or

								"special functions") then reviewing PRs in that area is especially valuable -

								sometimes PRs with technical code have to wait for a long time to get merged

								due to a shortage of appropriate reviewers.


								We encourage everyone to get involved in the review process; it's also a

								great way to get familiar with the code base.  Reviewers should ask

								themselves some or all of the following questions:


								- Was this change adequately discussed (relevant for new features and changes

								  in existing behavior)?

								- Is the feature scientifically sound? Algorithms may be known to work based on

								  literature; otherwise, closer look at correctness is valuable.

								- Is the intended behavior clear under all conditions (e.g. unexpected inputs

								  like empty arrays or nan/inf values)?

								- Does the code meet the quality, test and documentation expectation outline

								  under `Contributing new code`_?


								If we do not know you yet, consider introducing yourself.


								Other ways to contribute

								========================


								There are many ways to contribute other than contributing code.


								Triaging issues (investigating bug reports for validity and possible actions to

								take) is also a useful activity.  SciPy has many hundreds of open issues;

								closing invalid ones and correctly labeling valid ones (ideally with some first

								thoughts in a comment) allows prioritizing maintenance work and finding related

								issues easily when working on an existing function or submodule.


								Participating in discussions on the scipy-user and scipy-dev `mailing lists`_ is

								a contribution in itself.  Everyone who writes to those lists with a problem or

								an idea would like to get responses, and writing such responses makes the

								project and community function better and appear more welcoming.


								The `scipy.org`_ website contains a lot of information on both SciPy the

								project and SciPy the community, and it can always use a new pair of hands.

								The sources for the website live in their own separate repo:

								https://github.com/scipy/scipy.org


								Recommended development setup

								=============================


								Since Scipy contains parts written in C, C++, and Fortran that need to be

								compiled before use, make sure you have the necessary compilers and Python

								development headers installed.  Having compiled code also means that importing

								Scipy from the development sources needs some additional steps, which are

								explained below.


								First fork a copy of the main Scipy repository in Github onto your own

								account and then create your local repository via::


								    $ git clone git@github.com:YOURUSERNAME/scipy.git scipy

								    $ cd scipy

								    $ git remote add upstream git://github.com/scipy/scipy.git


								To build the development version of Scipy and run tests, spawn

								interactive shells with the Python import paths properly set up etc.,

								do one of::


								    $ python runtests.py -v

								    $ python runtests.py -v -s optimize

								    $ python runtests.py -v -t scipy.special.tests.test_basic::test_xlogy

								    $ python runtests.py --ipython

								    $ python runtests.py --python somescript.py

								    $ python runtests.py --bench


								This builds Scipy first, so the first time it may take some time.  If

								you specify ``-n``, the tests are run against the version of Scipy (if

								any) found on current PYTHONPATH.  *Note: if you run into a build issue,

								more detailed build documentation can be found in :doc:`building/index` and at

								https://github.com/scipy/scipy/tree/master/doc/source/building*


								Using ``runtests.py`` is the recommended approach to running tests.

								There are also a number of alternatives to it, for example in-place

								build or installing to a virtualenv. See the FAQ below for details.


								Some of the tests in Scipy are very slow and need to be separately

								enabled. See the FAQ below for details.


								SciPy structure

								===============


								All SciPy modules should follow the following conventions. In the

								following, a *SciPy module* is defined as a Python package, say

								``yyy``, that is located in the scipy/ directory.


								* Ideally, each SciPy module should be as self-contained as possible.

								  That is, it should have minimal dependencies on other packages or

								  modules.  Even dependencies on other SciPy modules should be kept to

								  a minimum.  A dependency on NumPy is of course assumed.


								* Directory ``yyy/`` contains:


								  - A file ``setup.py`` that defines

								    ``configuration(parent_package='',top_path=None)`` function

								    for `numpy.distutils`.


								  - A directory ``tests/`` that contains files ``test_<name>.py``

								    corresponding to modules ``yyy/<name>{.py,.so,/}``.


								* Private modules should be prefixed with an underscore ``_``,

								  for instance ``yyy/_somemodule.py``.


								* User-visible functions should have good documentation following

								  the Numpy documentation style, see `how to document`_


								* The ``__init__.py`` of the module should contain the main reference

								  documentation in its docstring. This is connected to the Sphinx

								  documentation under ``doc/`` via Sphinx's automodule directive.


								  The reference documentation should first give a categorized list of

								  the contents of the module using ``autosummary::`` directives, and

								  after that explain points essential for understanding the use of the

								  module.


								  Tutorial-style documentation with extensive examples should be

								  separate, and put under ``doc/source/tutorial/``


								See the existing Scipy submodules for guidance.


								For further details on Numpy distutils, see:


								  https://github.com/numpy/numpy/blob/master/doc/DISTUTILS.rst.txt


								Useful links, FAQ, checklist

								============================


								Checklist before submitting a PR

								--------------------------------


								  - Are there unit tests with good code coverage?

								  - Do all public function have docstrings including examples?

								  - Is the code style correct (PEP8, pyflakes)

								  - Is the commit message `formatted correctly`_?

								  - Is the new functionality tagged with ``.. versionadded:: X.Y.Z`` (with

								    X.Y.Z the version number of the next release - can be found in setup.py)?

								  - Is the new functionality mentioned in the release notes of the next

								    release?

								  - Is the new functionality added to the reference guide?

								  - In case of larger additions, is there a tutorial or more extensive

								    module-level description?

								  - In case compiled code is added, is it integrated correctly via setup.py

								    (and preferably also Bento configuration files - bento.info and bscript)?

								  - If you are a first-time contributor, did you add yourself to THANKS.txt?

								    Please note that this is perfectly normal and desirable - the aim is to

								    give every single contributor credit, and if you don't add yourself it's

								    simply extra work for the reviewer (or worse, the reviewer may forget).

								  - Did you check that the code can be distributed under a BSD license?


								Useful SciPy documents

								----------------------


								  - The `how to document`_ guidelines

								  - NumPy/SciPy `testing guidelines`_

								  - `SciPy API`_

								  - The `SciPy Roadmap`_

								  - NumPy/SciPy `git workflow`_

								  - How to submit a good `bug report`_


								FAQ

								---


								*I based my code on existing Matlab/R/... code I found online, is this OK?*


								It depends.  SciPy is distributed under a BSD license, so if the code that you

								based your code on is also BSD licensed or has a BSD-compatible license (e.g.

								MIT, PSF) then it's OK.  Code which is GPL or Apache licensed, has no

								clear license, requires citation or is free for academic use only can't be

								included in SciPy.  Therefore if you copied existing code with such a license

								or made a direct translation to Python of it, your code can't be included.

								If you're unsure, please ask on the scipy-dev mailing list.


								*Why is SciPy under the BSD license and not, say, the GPL?*


								Like Python, SciPy uses a "permissive" open source license, which allows

								proprietary re-use. While this allows companies to use and modify the software

								without giving anything back, it is felt that the larger user base results in

								more contributions overall, and companies often publish their modifications

								anyway, without being required to.  See John Hunter's `BSD pitch`_.


								*How do I set up a development version of SciPy in parallel to a released

								version that I use to do my job/research?*


								One simple way to achieve this is to install the released version in

								site-packages, by using a binary installer or pip for example, and set

								up the development version in a virtualenv.  First install

								`virtualenv`_ (optionally use `virtualenvwrapper`_), then create your

								virtualenv (named scipy-dev here) with::


								    $ virtualenv scipy-dev


								Now, whenever you want to switch to the virtual environment, you can use the

								command ``source scipy-dev/bin/activate``, and ``deactivate`` to exit from the

								virtual environment and back to your previous shell.  With scipy-dev

								activated, install first Scipy's dependencies::


								    $ pip install Numpy pytest Cython


								After that, you can install a development version of Scipy, for example via::


								    $ python setup.py install


								The installation goes to the virtual environment.


								*How do I set up an in-place build for development*


								For development, you can set up an in-place build so that changes made to

								``.py`` files have effect without rebuild. First, run::


								    $ python setup.py build_ext -i


								Then you need to point your PYTHONPATH environment variable to this directory.

								Some IDEs (Spyder for example) have utilities to manage PYTHONPATH.  On Linux

								and OSX, you can run the command::


								    $ export PYTHONPATH=$PWD


								and on Windows


								    $ set PYTHONPATH=/path/to/scipy


								Now editing a Python source file in SciPy allows you to immediately

								test and use your changes (in ``.py`` files), by simply restarting the

								interpreter.


								*Are there any video examples for installing from source, setting up a

								development environment, etc...?*


								Currently, there are two video demonstrations for Anaconda Python on macOS:


								`Anaconda SciPy Dev Part I (macOS)`_ is a four-minute

								overview of installing Anaconda, building SciPy from source, and testing

								changes made to SciPy from the Spyder IDE.


								`Anaconda SciPy Dev Part II (macOS)`_ shows how to use

								a virtual environment to easily switch between the "pre-built version" of SciPy

								installed with Anaconda and your "source-built version" of SciPy created

								according to Part I.


								*Are there any video examples of the basic development workflow?*


								`SciPy Development Workflow`_ is a five-minute example of fixing a bug and

								submitting a pull request. While it's intended as a followup to

								`Anaconda SciPy Dev Part I (macOS)`_ and `Anaconda SciPy Dev Part II (macOS)`_,

								the process is similar for other development setups.


								*Can I use a programming language other than Python to speed up my code?*


								Yes.  The languages used in SciPy are Python, Cython, C, C++ and Fortran.  All

								of these have their pros and cons.  If Python really doesn't offer enough

								performance, one of those languages can be used.  Important concerns when

								using compiled languages are maintainability and portability.  For

								maintainability, Cython is clearly preferred over C/C++/Fortran.  Cython and C

								are more portable than C++/Fortran.  A lot of the existing C and Fortran code

								in SciPy is older, battle-tested code that was only wrapped in (but not

								specifically written for) Python/SciPy.  Therefore the basic advice is: use

								Cython.  If there's specific reasons why C/C++/Fortran should be preferred,

								please discuss those reasons first.


								*How do I debug code written in C/C++/Fortran inside Scipy?*


								The easiest way to do this is to first write a Python script that

								invokes the C code whose execution you want to debug. For instance

								``mytest.py``::


								    from scipy.special import hyp2f1

								    print(hyp2f1(5.0, 1.0, -1.8, 0.95))


								Now, you can run::


								    gdb --args python runtests.py -g --python mytest.py


								If you didn't compile with debug symbols enabled before, remove the

								``build`` directory first. While in the debugger::


								    (gdb) break cephes_hyp2f1

								    (gdb) run


								The execution will now stop at the corresponding C function and you

								can step through it as usual. Instead of plain ``gdb`` you can of

								course use your favourite alternative debugger; run it on the

								``python`` binary with arguments ``runtests.py -g --python mytest.py``.


								*How do I enable additional tests in Scipy?*


								Some of the tests in Scipy's test suite are very slow and not enabled

								by default. You can run the full suite via::


								    $ python runtests.py -g -m full


								This invokes the test suite ``import scipy; scipy.test("full")``,

								enabling also slow tests.


								There is an additional level of very slow tests (several minutes),

								which are disabled also in this case. They can be enabled by setting

								the environment variable ``SCIPY_XSLOW=1`` before running the test

								suite.


								.. _scikit-learn: http://scikit-learn.org


								.. _scikit-image: http://scikit-image.org/


								.. _statsmodels: http://statsmodels.sourceforge.net/


								.. _testing guidelines: https://github.com/numpy/numpy/blob/master/doc/TESTS.rst.txt


								.. _formatted correctly: http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html#writing-the-commit-message


								.. _how to document: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt


								.. _bug report: http://scipy.org/bug-report.html


								.. _PEP8: http://www.python.org/dev/peps/pep-0008/


								.. _pep8 package: http://pypi.python.org/pypi/pep8


								.. _pyflakes: http://pypi.python.org/pypi/pyflakes


								.. _SciPy API: https://docs.scipy.org/doc/scipy/reference/api.html


								.. _SciPy Roadmap: https://scipy.github.io/devdocs/roadmap.html


								.. _git workflow: http://docs.scipy.org/doc/numpy/dev/gitwash/index.html


								.. _Github help pages: https://help.github.com/articles/set-up-git/


								.. _issue list: https://github.com/scipy/scipy/issues


								.. _Github: https://github.com/scipy/scipy


								.. _scipy.org: https://scipy.org/


								.. _scipy.github.com: http://scipy.github.com/


								.. _scipy.org-new: https://github.com/scipy/scipy.org-new


								.. _documentation wiki: https://docs.scipy.org/scipy/Front%20Page/


								.. _SciPy Central: http://scipy-central.org/


								.. _doctest: http://www.doughellmann.com/PyMOTW/doctest/


								.. _virtualenv: http://www.virtualenv.org/


								.. _virtualenvwrapper: http://www.doughellmann.com/projects/virtualenvwrapper/


								.. _bsd pitch: http://nipy.sourceforge.net/nipy/stable/faq/johns_bsd_pitch.html


								.. _Pytest: https://pytest.org/


								.. _mailing lists: https://www.scipy.org/scipylib/mailing-lists.html


								.. _Anaconda SciPy Dev Part I (macOS): https://youtu.be/1rPOSNd0ULI


								.. _Anaconda SciPy Dev Part II (macOS): https://youtu.be/Faz29u5xIZc


								.. _SciPy Development Workflow: https://youtu.be/HgU01gJbzMY