Installation

Installation#

Getting Python#

If it is your first time with Python, we recommend conda or pip 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 environments or virtual environments.

Installation with conda#

If you are using conda you can install PyPSA with:

conda install -c conda-forge atlite

Installing with pip#

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

pip install atlite

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

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

Computational resources#

As for requirements on your computing equipment, we tried to keep the resource requirements low. The requirements obviously depend on the size of the cutouts and datasets your parse and use.

We run our conversions on our laptops and this usually works fine and can run in the background without clogging our computers.

With regards to

  • CPU: While atlite does some number crunching, it does not require special or large multicore CPUs

  • Memory: For the ERA5 dataset you should be fine running atlite with even 2-4 GiB. Other datasets can require more memory, as they sometimes need to be loaded fully or partially into memory for creating a cutout.

  • Disk space: Is really all about the cutout and dataset sizes (in time and space) you use. We can only provide two examples for reference:

    • Small cutout (Republic of Ireland + UK + some atlantic ocean), 1 month with hourly resolution using ERA5: 60 MiB

    • Large cutout (Western Asia), 1 year with hourly resolution using ERA5: 6 GiB

We guess you do not need to worry about your computer being able to handle common small or medium scenarios.

Rule of thumb:

The requirements on your machine are increasing with the size of the cutout (in time and space).