<commit_before>################
 Installation
################


Getting Python
==============

If it's your first time with Python, people seem to recommend
`Anaconda <https://www.continuum.io/downloads>`_ as an easy-to-use
environment that includes many basic packages. Anaconda is available
for Windows, Mac OS X and GNU/Linux.


For those rolling their own on unix-like systems (GNU/Linux, Mac OS X)
it's always helpful to use a `virtual environment
<https://pypi.python.org/pypi/virtualenv>`_ for your python
installation (and even easier to use with a `virtualenv-burrito
<https://github.com/brainsik/virtualenv-burrito>`_), in case you
accidentally trash something.



Getting a solver for linear optimisation
========================================

PyPSA is known to work with the free software GLPK and the non-free
software Gurobi (and whatever else Pyomo works with).

For Debian-based systems you can get GLPK with::

    sudo apt-get install glpk-utils

and there are similar packages for other GNU/Linux distributions.

For Windows there is `WinGLPK <http://winglpk.sourceforge.net/>`_. For
Mac OS X `brew <http://brew.sh/>`_ is your friend.


Installing PyPSA with pip
=========================

If you have the Python package installer ``pip`` then just run::<commit_msg>doc: Add Cbc to suggested solvers<commit_after>################
 Installation
################


Getting Python
==============

If it's your first time with Python, people seem to recommend
`Anaconda <https://www.continuum.io/downloads>`_ as an easy-to-use
environment that includes many basic packages. Anaconda is available
for Windows, Mac OS X and GNU/Linux.


For those rolling their own on unix-like systems (GNU/Linux, Mac OS X)
it's always helpful to use a `virtual environment
<https://pypi.python.org/pypi/virtualenv>`_ for your python
installation (and even easier to use with a `virtualenv-burrito
<https://github.com/brainsik/virtualenv-burrito>`_), in case you
accidentally trash something.



Getting a solver for linear optimisation
========================================

PyPSA passes optimisation problems for :doc:`optimal_power_flow` to an
external solver. PyPSA is known to work with the free software `Cbc <https://projects.coin-or.org/Cbc>`_, the free software `GLPK <https://www.gnu.org/software/glpk/>`_ and the non-free software
`Gurobi <http://www.gurobi.com/>`_ (and whatever else works with `Pyomo <https://en.wikipedia.org/wiki/Pyomo>`_).

For Cbc, see their `installation instructions <https://projects.coin-or.org/Cbc#DownloadandInstall>`_. For Debian-based systems you can do simply::

  sudo apt-get install coinor-cbc

For GLPK in Debian-based systems execute::

    sudo apt-get install glpk-utils

and there are similar packages for other GNU/Linux distributions.

For Windows there is `WinGLPK <http://winglpk.sourceforge.net/>`_. For
Mac OS X `brew <http://brew.sh/>`_ is your friend.


Installing PyPSA with pip
=========================

If you have the Python package installer ``pip`` then just run::
{'fraction_non_alphanumeric': 0.15688859591298615, 'fraction_numerical': 0.0, 'mean_word_length': 4.803059273422562, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 18, 'https://': 10, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5175745', 'n_tokens_mistral': 904, 'n_tokens_neox': 832, 'n_words': 346}