pyam: a Python toolkit for Integrated Assessment Modeling
The pyam package is still under heavy development; public and private APIs are subject to change.
Questions? Start a discussion on our listserv
Overview and scope
pyam package provides a range of diagnostic tools and functions
for analyzing and working with IAMC-format timeseries data.
- Summary of models, scenarios, variables, and regions included in a snapshot.
- Display of timeseries data as pandas.DataFrame with IAMC-specific filtering options.
- Simple visualization and plotting functions.
- Diagnostic checks for non-reported variables or timeseries data to identify outliers and potential reporting issues.
- Categorization of scenarios according to timeseries data or meta-identifiers for further analysis.
The package can be used with timeseries data that follows the data template convention of the Integrated Assessment Modeling Consortium (IAMC). An illustrative example is shown below; see data.ene.iiasa.ac.at/database for more information.
|MESSAGE V.4||AMPERE3-Base||World||Primary Energy||EJ/y||454.5||479.6||...|
A comprehensive tutorial for the basic functions is included in tutorial/pyam_first_steps using a partial snapshot of the IPCC AR5 scenario database.
The documentation pages can be built locally. See the instruction in doc/README.
Copyright 2017-2018 IIASA Energy Program
pandasv0.21.0 or higher
Documentation Building Depedencies
Sphinx <http://sphinx-doc.org/>_ v1.1.2 or higher
Fork this repository and clone the forked repository (
<user>/pyam) to your machine. To fork the repository, look for the fork icon in the top right at iiasa/pyam. Add
upstreamto your clone.
We recommend GitKraken for users who prefer a graphical user interface application to work with Github (as opposed to the command line).
- Double click on
install.batin the local folder where you cloned your fork.
In a command prompt, execute the following command
python setup.py install