pyam: analysis and visualization
of integrated-assessment & macro-energy scenarios

Release v1.6.1.dev1+g947d8c4.

Overview

The open-source Python package pyam [1][3] 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.

Key features

• Simple analysis of scenario timeseries data with an interface similar in feel & style
  to the widely used pandas.DataFrame

• Advanced visualization and plotting functions (see the gallery)

• Features for scripted validation & processing of scenario data and results

Timeseries types & data formats

Yearly data

The 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).

Illustrative example of IAMC-format timeseries data
via the IAMC 1.5°C Scenario Explorer ([4])

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 IamDataFrame.

Table of Contents

• Installation
  • Conda
  • Pypi
  • Installing from source
  • Dependencies
• Authors and Developers
• Support and Contributing
  • Have a question? Get in touch!
  • Interested in contributing? Join the team!
• Data Model
  • Background: The IAMC timeseries scenario data format
  • The pyam data model
  • Filtering
• Tutorials
  • 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
  • Input/output file formats
  • General functions
  • The IamDataFrame class
  • Data resources integration
  • The IamSlice class
  • Advanced filtering
  • Derived timeseries data
  • Plotting library
  • The Connection class
  • The Statistics class
  • The testing module
  • Timeseries functions
  • Variables utilities

• Integration with R

• Scientific references

Scientific publications

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 pyam package.

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 pyam 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.