PyPSA-Eur relies on a set of other Python packages to function.
We recommend using the package manager and environment management system ``conda`` to install them.
Install `miniconda <https://docs.conda.io/en/latest/miniconda.html>`_, which is a mini version of `Anaconda <https://www.anaconda.com/>`_ that includes only ``conda`` and its dependencies or make sure ``conda`` is already installed on your system.
For instructions for your operating system follow the ``conda```installation guide <https://docs.conda.io/projects/conda/en/latest/user-guide/install/>`_.
1.**Data Bundle:**`pypsa-eur-data-bundle.tar.xz <https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-data-bundle.tar.xz>`_ (1.3 GB) contains common GIS datasets like NUTS3 shapes, EEZ shapes, CORINE Landcover, Natura 2000 and also electricity specific summary statistics like historic per country yearly totals of hydro generation, GDP and POP on NUTS3 levels and per-country load time-series. It should be extracted in the ``data`` sub-directory, such that all files of the bundle are stored in the ``data/bundle`` subdirectory)
2.**Cutouts:**`pypsa-eur-cutouts.tar.xz <https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-cutouts.tar.xz>`_ (3.9 GB) are spatiotemporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or extract them directly into the ``pypsa-eur`` directory. To download cutouts yourself you need to `set up the CDS API <https://cds.climate.copernicus.eu/api-how-to>`_. For more details read the `atlite documentation <https://atlite.readthedocs.io>`_. For beginners, extracting the bundle is recommended:
3.**Natura:** Optionally, you can download a rasterized version of the NATURA dataset `natura.tiff <https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/natura.tiff&dl=1>`_ and put it into the ``resources`` sub-directory. If you don't, it will be generated automatically, which is a time-consuming process.
4.**Remove Archives:** Optionally, if you want to save disk space, you can delete ``data/pypsa-eur-data-bundle.tar.xz`` and ``pypsa-eur-cutouts.tar.xz`` once extracting the bundles is complete. E.g.
and any other solver that works with the underlying modelling framework `Pyomo <http://www.pyomo.org/>`_. For installation instructions of these solvers for your operating system, follow the links above.
..seealso::
`Getting a solver in the PyPSA documentation <https://pypsa.readthedocs.io/en/latest/installation.html#getting-a-solver-for-linear-optimisation>`_
..note::
Commercial solvers such as Gurobi and CPLEX 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.
