Please consider the environment before prin4ng this slide deck This presenta4on is licensed under a Crea4ve Commons A;ribu4on 4.0 Interna4onal License
Daniel Huppmann, Ma.hew Gidden, Zebedee Nicholls, Jonas Hörsch, Robin Lamboll, Paul Natsuo Kishimoto, Thorsten Burandt, and many others
The pyam package
An open-source Python package for analysis & visualisation
of integrated assessment and macro-energy scenarios
Mo#va#on – the workflow from model to insight
There are numerous tools for data processing & scenario analysis,
but most solutions are either “hard-wired” to a model or general-purpose packages
2
Model
integrated-assessment, macro-energy system, land use (change), other sectors
Input data sources and references
Scien&fic manuscript
Processing of raw model outputs
Validation of scenario results
Evalua=on and analysis Data processing
tools and solu=ons for specific modeling
frameworks
e.g., TIMES-VEDA, OSeMOSYS, MESSAGEix, REMIND, GCAM, mimi.jl,
TEMOA, pypsa, PLEXOS, …
General-purpose data analysis &
manipulation
e.g., numpy, pandas
& tidyverse
General-purpose ploCng packages
e.g., matplotlib, seaborn, ggplot & shiny
Supported data models and file formats
Supported *meseries data formats:
The pyam package was ini*ally developed to work with the IAMC template, a tabular format for yearly *meseries data
But the package also supports sub-annual *me resolu*on
ConGnuous-Gme formats (e.g., hourly Gmeseries data) RepresentaGve sub-annual Gmeslices (e.g., “winter-night”)
Compa*ble i/o and file formats:
Full integra*on with the pandas data analysis package
Tabular data (xlsx, csv) & “fric*onless” datapackage format
The package supports various formats & types of timeseries data
and is currently used by more than a dozen modelling teams
The pyam package for integrated assessment & macro-energy modelling
Use cases and features
Data processing Data i/o & file format conversion, aggrega&on, downscaling, unit conversion, …
Valida*on Checks for completeness of data, internal/external consistency, numerical plausibility …
Analysis & visualiza*on Categoriza&on and sta&s&cs of scenario ensembles, ploEng library, …
M. Gidden and D. Huppmann (2019). Journal of Open Source So/ware 4(33):1095. doi: 10.21105/joss.01095
A community package for scenario processing, analysis & visualization following best practice of collaborative scientific software development
4
#pyam_iamc pyam-iamc.readthedocs.io
Dr. Daniel Huppmann
Research Scholar – Energy Program Interna4onal Ins4tute for Applied Systems Analysis (IIASA) Schlossplatz 1, A-2361 Laxenburg, Austria huppmann@iiasa.ac.at
@daniel_huppmann www.iiasa.ac.at/staff/huppmann
Thank you very much for your attention!
This presenta4on is licensed under a Crea4ve Commons A;ribu4on 4.0 Interna4onal License
Read the docs on pyam-iamc.readthedocs.io
Join the mailing list on groups.io or the Slack workspace Create an issue or start a pull request
on github.com/IAMconsor*um/pyam/
