Installation#
Before installing PyPSA-Earth on your computer, it’s crucial to ensure that your system meets the necessary hardware and software requirements. The following sections outline the prerequisites in terms of hardware and software. Additionally, detailed installation guidelines for required software tools will be provided, followed by step-by-step instructions for installing PyPSA-Earth.
Hardware Requirements#
Ensure that your system meets the minimum hardware specifications to run PyPSA-Earth effectively. Recommended hardware specifications may include:
8-16 GB RAM and adequate CPU (at-least 2-cores)
Storage (HDD/SSD) capacity depends on the region of interest. Africa model requires 40 GB, the world –` 250 GB, a single country – between 1-10 GB. Tutorial requires just below 2 GB. Thus, considering all required software tools, at least 40 GB of storage space is recommended.
Note
The subsequently described installation steps are demonstrated as shell commands, where the path before the % sign denotes the directory in which the commands following the % should be entered.
Software Prerequisites#
Prior to installing PyPSA-Earth, you’ll need to ensure the following software tools are installed and configured on your system:
Miniconda#
To use packages in python, it is highly recommended to use a conda package manager, such as miniconda. You may check if conda is already installed on your system with
conda --version
If conda is not installed, follow miniconda installation guide.
For more on information on how to install conda and work with it you can look into Software hints.
Git#
Git is a free open-source tool that facilitates tracking changes in the code development and enable to coordinate the parallel software development between many developers.
Download and install git to your system using the following link.
It is highly recommended to learn the git basics.
VSCode#
In order to write and debug python code, you need an Integrated Development Environment (IDE) that is a software used to write code. We recommend Visual Studio Code, which is freely available online and provides an easy-to-use interface with Git. Obviously, any alternatives like PyCharm or Sublime will work as well.
Java#
PyPSA-Earth currently needs Java redistribution to work properly. To check if Java is still installed you can request it’s version from a terminal:
java --version
The expected output should resemble the following:
java version "1.8.0_341"
Java(TM) SE Runtime Environment (build 1.8.0_341-b10)
Java HotSpot(TM) 64-Bit Server VM (build 25.341-b10, mixed mode)
In case you don’t have Java, you have to install it from the official website or equivalent. Depending on the version of OS, download and install JDK 21 or JDK 17 from the link.
PyPSA-Earth Installation#
Clone the Repository#
Note
In order to work with the provided Jupyter notebooks in the documentation repository, it is recommended to follow the folder structure suggested in Examples.
First of all, clone the PyPSA-Earth repository using the version control system git.
The path to the directory into which the git repository is cloned, must not have any spaces.
The following commands can be executed in command prompt of miniconda, terminal of VSCode, or in Git Bash.
/some/other/path % cd /some/path/without/spaces
/some/path/without/spaces % git clone https://github.com/pypsa-meets-earth/pypsa-earth.git
Install Dependencies#
PyPSA-Earth relies on a set of other Python packages to function.
The python package requirements are located in the envs/environment.yaml file. We install only mamba in the conda base environment to accelerate the installation. Please keep the base environment always clean, meaning don’t install anything there! It will allow to ensure compatibility of all the packages needed to work with PyPSA-Earth model.
The environment can be installed and activated like this:
.../pypsa-earth (base) % conda install -c conda-forge mamba
.../pypsa-earth % mamba env create -f envs/environment.yaml
.../pypsa-earth (pypsa-earth) % conda activate pypsa-earth
Environment installation with mamba usually takes about 10-20 minutes. Note please that activation is local to the currently open shell. Every time you open a new terminal window, pypsa-earth environment should be activated again to supply the workflow with all the dependencies it needs.
In case mamba did not work for you, you might want to try conda instead:
.../pypsa-earth % conda env create -f envs/environment.yaml
.../pypsa-earth (pypsa-earth) % conda activate pypsa-earth
For more on information on how to install conda and work with it you can look into Software hints.
To confirm the installation, run the following command in the activated environment:
.../pypsa-earth (pypsa-earth) % snakemake --version
Solver Installation#
An optimization solver is needed to solve the mathematical problem that is build with the automated workflow of PyPSA-Earth. With the goal of supporting completely open source initiative, we focus on relying on Open-Source solvers, such as
CBC;
GLPK and WinGLPK (is included into pypsa-earth environment and installed automatically during environment creation);
To further improve performances, commercial solvers like
(both commercial licenses with free academic options) can also be used.
Note
No need to install glpk separately, as they are included in envs/environment.yaml and installed during conda environment creation.
However, solving capabilities of glpk are limited.
To run the model with high temporal and spatial resolution, it is recommended to use cplex, gurobi, or highs.
A recommended instruction to install the HiGHS solver is given here.
Install Jupyter Lab#
We use Jupyter notebooks to share examples on how to use the model and analyse the results. VSCode supports working with Jupyter Notebooks natively. In case you are using different IDE and don’t have Jupyter notebooks pre-installed you can install jupyter lab (new jupyter notebooks) with the ipython kernel installation and test if your jupyter lab works:
.../pypsa-earth % ipython kernel install --user --name=pypsa-earth
.../pypsa-earth % jupyter lab