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The ix modeling platform (ixmp)

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Overview

The ix modeling platform (ixmp) is a data warehouse for high-powered scenario analysis, with interfaces to Python and R for efficient scientific workflows and effective data pre- and post-processing, and a structured database backend for version-controlled data management.

This repository contains the core and application programming interfaces (API) for the ix modeling platform (ixmp), as well as a number of tutorials and examples for a generic model instance based on Dantzig's transport problem.

License

Copyright © 2017–2020 IIASA Energy Program

The platform package is licensed under the Apache License, Version 2.0 (the "License"); you may not use the files in this repository except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

Please refer to the NOTICE for details and user guidelines.

Getting started

Documentation

Documentation of ixmp and the MESSAGEix framework is available in two forms:

The online documentation is built automatically from the contents of the ixmp Github repository.

For offline use, the documentation can be built from the source code. See doc/README.rst for further details.

Installation

Most users will have ixmp installed automatically as a dependency when installing MESSAGEix.

To install the ixmp R API, or to install ixmp from source code, see ‘Installation’ in the documentation.

Tutorials

Introductory tutorials are provided in both Python and R. See ‘Tutorials’ in the documentation or tutorial/README.rst.

Scientific reference

Please cite the following manuscript when using the MESSAGEix framework and/or the ix modeling platform for scientific publications or technical reports:

Daniel Huppmann, Matthew Gidden, Oliver Fricko, Peter Kolp, Clara Orthofer, Michael Pimmer, Nikolay Kushin, Adriano Vinca, Alessio Mastrucci, Keywan Riahi, and Volker Krey. "The |MESSAGEix| Integrated Assessment Model and the ix modeling platform". Environmental Modelling & Software 112:143-156, 2019. doi: 10.1016/j.envsoft.2018.11.012 electronic pre-print available at pure.iiasa.ac.at/15157/