Package: pyam-iamc
pyam: analysis & visualizationof integrated-assessment and macro-energy scenariosOverview and scopeThe open-source Python packagepyamprovides 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 comprehensivedocumentationis hosted onRead the Docs!Key featuresSimple analysis of scenario timeseries data with an interface similar in feel & style
to the widely usedpandas.DataFrameAdvanced visualization and plotting functions
(see thegallery)Scripted validation and processing of scenario data and resultsTimeseries types & data formatsYearly dataThe pyam package was initially developed to work with theIAMC template,
a timeseries format foryearly datadeveloped and used by theIntegrated Assessment Modeling Consortium(IAMC).modelscenarioregionvariableunit200520102015MESSAGECD-LINKS 400WorldPrimary EnergyEJ/y462.5500.7...........................An illustration of the IAMC template using a scenario
from theCD-LINKSprojectvia the TheIAMC 1.5°C Scenario ExplorerSubannual time resolutionThe package also supports timeseries data with asub-annual time resolution:Continuous-time data using the Pythondatetime format"Representative timeslices" (e.g., "winter-night", "summer-day")
using the pyamextra-columnsfeatureRead the docsfor more information about the pyam data model
or look at thedata-table tutorialto see how to cast from a variety of timeseries formats to apyam.IamDataFrame.TutorialsAn introduction to the basic functions is shown
inthe "first-steps" notebook.All tutorials are available in rendered format (i.e., with output) as part of
theonline documentation.
The source code of the tutorials notebooks is available
in the folderdocs/tutorialsof this repository.DocumentationThe comprehensive documentation is hosted onRead the Docs.The documentation pages can be built locally,
refer to the instruction indocs/README.Authors & ContributorsThis package was initiated and is currently maintained
by Matthew Gidden (@gidden)
and Daniel Huppmann (@danielhuppmann).
See the completelist of contributors.The core maintenance of the package is done by
theScenario Services & Scientific Softwareresearch theme
at the IIASA Energy, Climate, and Enviroment program.
Visithttps://software.ece.iiasa.ac.atfor more information.Scientific publicationsThe following manuscripts describe thepyampackage
at specific stages of development.The source documents are available in
themanuscriptsfolder
of the GitHub repository.Release v1.0 (June 2021)Published to mark the first major release of thepyampackage.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 Alvarez Quispe, and Christopher J. Smith.pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios.Open Research Europe, 2021.
doi:10.12688/openreseurope.13633.2Release v0.1.2 (November 2018)Published following the successful application ofpyamin 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.LicenseCopyright 2017-2023 IIASA and the pyam developer teamThepyampackage is licensed
under the Apache License, Version 2.0 (the "License");seeLICENSEandNOTICEfor details.InstallFor basic instructions,
pleaseread the docs!To install from source (including all dependencies)
after cloning this repository, simply runpip install --editable .[tests,optional_io_formats,tutorials]To check that the package was installed correctly, runpytest tests