#release pyam v0.7.0 - improved aggregation & downscaling features #release

Daniel Huppmann

Dear pyam user community,

Happy to announce a new release v0.7.0 of the pyam package! Check out the detailed release notes on GitHub.

Highlights of the new release:

# Additional feature for recursive aggregation & downscaling of timeseries data

Thanks to Thorsten Burandt (TU Berlin), the aggregate() function now supports „recursive“ behavior, automatically identifying a variable tree and then performing the aggregation iteratively.

And thanks to Maarten Brinkering (UCC, currently a YSSP at IIASA), the downscale_region() function now accepts an explicit weight-dataframe for disaggregating timeseries data at a regional level to sub-regional components - take a look at the extended tutorial!

# Compatibility with latest version of matplotlib

We updated the plotting library to work with the latest version of matplotlib.

# Reading data from IIASA scenario resources 

Reading timeseries data directly from an instance of the IIASA Scenario Explorer can be helpful to automate your analysis workflows. This release makes this interface more convenient. The tutorial has been rewritten to illustrate the new behavior - take a look here.

# Refactoring our continuous-integration infrastructure

We made some major changes to the continuous-integration infrastructure to ensure a more stable user experience across operating systems and Python versions.

Best regards,


Dr. Daniel Huppmann
Research Scholar, Energy Program (ENE)

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
+43(0) 2236 807-572