pyam: analysis and visualization of integrated-assessment scenarios¶
Release v0.10.0+23.gb2c050b.
Overview¶
The open-source Python package pyam
[1]
provides a suite of tools and functions for analyzing and visualizing
input data (i.e., assumptions/parametrization)
and results (model output) of integrated-assessment scenarios,
energy systems analysis, and sectoral studies.
Key features:¶
Simple analysis of timeseries data in the IAMC format (more about it here)
with an interface similar in feel & style to the widely usedpandas.DataFrame
Advanced visualization and plotting functions (see the gallery)
Features for scripted validation & processing of scenario data and results
The source code for pyam
is available on Github.
Table of Contents¶
- Installation
- Authors and Developers
- Support and Contributing
- Data Model
- 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
- Aggregating subannual timeseries data
- Using IPCC Color Palettes
- Customizing legends
- Plotting aggregate variables
- Make our Logo!
- Plotting Gallery
- API Reference
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.
Scientific reference¶
- 1
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.