pyam: analysis and visualization
of integrated-assessment & macro-energy scenarios¶
The open-source Python package
pyam provides a suite of tools and functions
for analyzing and visualizing input data (i.e., assumptions/parametrization)
and results (model output) of integrated-assessment models,
macro-energy scenarios, energy systems analysis, and sectoral studies.
The source code is available on Github. See also the related Scientific publications.
Simple analysis of scenario timeseries data with an interface similar in feel & style
to the widely used
Advanced visualization and plotting functions (see the gallery)
Features for scripted validation & processing of scenario data and results
Timeseries types & data formats¶
pyam package was initially developed to work with the IAMC template,
a timeseries format for yearly data developed and used by the
Integrated Assessment Modeling Consortium (IAMC).
Subannual time resolution¶
- The package also supports timeseries data with a sub-annual time resolution:
Continuous-time data using the Python datetime format
“Representative timeslices” (e.g., “winter-night”, “summer-day”)
using the pyam extra-columns feature
Please read the Data Model section for more information
or look at the data-table tutorial
to see how to cast from a variety of timeseries formats to an
Table of Contents¶
- Authors and Developers
- Support and Contributing
- Data Model
- Plotting Gallery
- Integration with R
- API Reference
The following manuscripts describe the package at specific stages of development.
The source documents are available in the manuscripts folder of the GitHub repository.
Release v1.0 (June 2021)¶
Published to mark the first major release of the
Daniel Huppmann, Matthew Gidden, Zebedee Nicholls, Jonas Hörsch, Robin Lamboll, Paul Natsuo Kishimoto, Thorsten Burandt, Oliver Fricko, Edward Byers, Jarmo Kikstra, Maarten Brinkerink, Maik Budzinski, Florian Maczek, Sebastian Zwickl-Bernhard, Lara Welder, Erik Francisco Álvarez Quispe, and Christopher J. Smith.pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios.Open Research Europe, 2021. doi: 10.12688/openreseurope.13633.2
Release v0.1.2 (November 2018)¶
Published following the successful application of
in the IPCC SR15 and the Horizon 2020 CRESCENDO project.
Matthew Gidden and Daniel Huppmann. pyam: a Python package for the analysis and visualization of models of the interaction of climate, human, and environmental systems.Journal of Open Source Software (JOSS), 4(33):1095, 2019. doi: 10.21105/joss.01095
Copyright & License¶
The development of the
pyam package was started at the IIASA Energy Program,
with contributions from a number of individuals & institutions over the years.
The package is available under the open-source Apache License. Refer to the NOTICE in the GitHub repository for more information.