If it’s your first time with Python, we recommend Anaconda as an easy-to-use environment that includes many basic packages. Anaconda is available for Windows, Mac OS X and GNU/Linux.
Miniconda is a minimal installer for conda.
It’s always helpful to use a dedicated conda environment or virtual environment for your Python installation (and even easier to use with a virtualenv-burrito), in case you accidentally trash something.
Getting a solver for linear optimisation¶
PyPSA passes optimisation problems for Optimal Power Flow to an external solver. PyPSA is known to work with the free software
and the non-free software, commercial software (for which free academic licenses are available)
For installation instructions of these 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
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
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
or by adding the
conda-forge channel to your
conda installation with:
conda config --add channels conda-forge
and then installing simply with:
conda install pypsa
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
Manual installation with setuptools¶
PyPSA relies on the following packages which are not contained in a standard Python installation:
It is recommended to use PyPSA with the following additional packages:
iPython for interactive simulations
plotly for interactive plotting
matplotlib for static plotting
py.test for unit testing
In a unix-based environment these packages can be obtained with the pip Python package manager:
pip install numpy scipy pandas networkx pyomo ipython
To install PyPSA, you need to download the code from the PyPSA github repository and then go to the local repository and run:
python setup.py install
Or if you want to develop/modify the code in the current directory, run:
python setup.py develop
Conservative manual installation¶
If you’re very conservative and don’t like package managers, you can just download the code from the PyPSA github repository and add the directory of PyPSA to your python path with e.g.:
import sys sys.path.append("path/to/PyPSA") import pypsa
Upgrade all packages to the latest versions¶
PyPSA is only tested with the latest stable versions of all the dependent packages. Therefore it is very important that you upgrade these packages; otherwise PyPSA may not work.
To upgrade a package such as
pandas with pip, do at the command
pip install -U pandas
In Anaconda the user manual suggests to upgrade packages with:
conda update pandas
We recommend always keeping your PyPSA installation up-to-date, since bugs get fixed and new features are added. To upgrade PyPSA with pip, do at the command line:
pip install -U pypsa
Don’t forget to read the Release Notes regarding API changes that might require you to update your code.