scmdata provides some useful data handling routines for dealing with data related to simple climate models (SCMs aka reduced complexity climate models, RCMs). In particular, it provides a high-performance way of handling and serialising (including to netCDF) timeseries data along with attached metadata.
scmdata was inspired by pyam and was originally part of the openscm package.
PyPI :
Other info :
Full documentation can be found at: scmdata.readthedocs.io. We recommend reading the docs there because the internal documentation links don't render correctly on GitHub's viewer.
scmdata can be installed with conda or pip:
pip install scmdata
conda install -c conda-forge scmdataAdditional dependencies can be installed using
# To add plotting dependencies
pip install scmdata[plots]
# To add notebook dependencies
pip install scmdata[notebooks]
# If you are installing with conda, we recommend
# installing the extras by hand because there is no stable
# solution yet (issue here: https://github.com/conda/conda/issues/7502)For development, we rely on poetry for all our dependency management. To get started, you will need to make sure that poetry is installed (instructions here, we found that pipx and pip worked better to install on a Mac).
For all of work, we use our Makefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, run make virtual-envir˚onment.
If there are any issues, the messages from the Makefile should guide you
through. If not, please raise an issue in the issue tracker˚.