################
 Installation
################


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

If it is your first time with Python, we recommend `conda
<https://docs.conda.io/en/latest/miniconda.html>`_, `mamba
<https://github.com/mamba-org/mamba>`_ or `pip
<https://pip.pypa.io/en/stable/>`_ as easy-to-use package managers. They are
available for Windows, Mac OS X and GNU/Linux.

It is always helpful to use dedicated `conda/mamba environments <https://mamba.readthedocs.io/en/latest/user_guide/mamba.html>`_ or `virtual environments
<https://pypi.python.org/pypi/virtualenv>`_.


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

PyPSA passes optimisation problems for :doc:`/user-guide/optimal-power-flow` to an
external solver. PyPSA is known to work via ``linopy`` with the free software

- `HiGHS <https://highs.dev/>`_
- `Cbc <https://projects.coin-or.org/Cbc#DownloadandInstall>`_
- `GLPK <https://www.gnu.org/software/glpk/>`_ (`WinGLKP <http://winglpk.sourceforge.net/>`_)
- `SCIP <https://scip.zib.de/>`_

and the non-free software, commercial software (for some of which free academic licenses are available)

- `Gurobi <https://www.gurobi.com/documentation/quickstart.html>`_
- `CPLEX <https://www.ibm.com/products/ilog-cplex-optimization-studio>`_
- `FICO Xpress <https://www.fico.com/en/products/fico-xpress-optimization>`_
- `MOSEK <https://www.mosek.com/>`_
- `COPT <https://www.shanshu.ai/copt>`_
- `MindOpt <https://solver.damo.alibaba.com/doc/en/html/index.html>`_

An installation of PyPSA will automatically install the default solver HiGHS.
For installation instructions of further solvers for your operating system,
follow the links above.

Depending on your operating system, you can also install some of the open-source solvers in a ``conda`` or ``pip`` environment.

For HiGHS on all operating systems::

    pip install highspy

For GLPK on all operating systems::

    conda install -c conda-forge glpk

For CBC on all operating systems except for Windows::

    conda install -c conda-forge coincbc

.. note::
    Commercial solvers such as Gurobi, CPLEX, and Xpress currently significantly outperform open-source solvers for large-scale problems.
    It might be the case that you can only retrieve solutions by using a commercial solver.


Installing PyPSA with conda
===========================

If you are using ``conda`` you can install PyPSA with::

    conda install -c conda-forge pypsa

In all of the above commands you can replace ``conda`` with ``mamba`` if you use this alternative.


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

If you have the Python package installer ``pip`` then just run::

    pip install pypsa

If you're feeling adventurous, you can also install the latest master branch from github with::

    pip install git+https://github.com/PyPSA/PyPSA.git


Conservative installation
=========================

If you're very conservative and don't like package managers, you can
just download the code from the `PyPSA github repository
<https://github.com/PyPSA/PyPSA/>`_ and add the directory of PyPSA to
your python path with e.g.::

    import sys

    sys.path.append("path/to/PyPSA")

    import pypsa


.. _upgrading-pypsa:

Upgrading PyPSA
===============

We recommend always keeping your PyPSA installation up-to-date, since bugs get
fixed and new features are added. PyPSA is also only tested with the latest
stable versions of all the dependent packages for the respective Python
versions.

To upgrade PyPSA with pip, do at the command line::

    pip install -U pypsa

To upgrade PyPSA with conda, do at the command line::

    conda update pypsa

Don't forget to read the :doc:`/references/release-notes` regarding API changes
that might require you to update your code.