pyam: analysis and visualization
of integrated-assessment & macro-energy scenarios¶
The open-source Python package
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.
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
Table of Contents¶
- Authors and Developers
- Support and Contributing
- Data Model
- First steps with the pyam package
- Importing various data table formats
- Read directly from IIASA data resources
- Read directly from the UNFCCC Data Inventory
- Importing results from a GAMS model to an IamDataFrame
- Performing unit conversions
- Aggregating and downscaling timeseries data
- Algebraic operations on timeseries data
- Aggregating subannual timeseries data
- Using IPCC Color Palettes
- Customizing legends
- Plotting aggregate variables
- Make our Logo!
- Plotting Gallery
- 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.