The open-source Python package pyam 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.
Key features:
- Simple analysis of timeseries data in the IAMC format (more about it here) with an interface similar in feel and style to the widely used pandas.DataFrame
- Advanced visualization and plotting functions (see the gallery)
- Diagnostic checks for scripted validation of scenario data and results
The complete documentation is hosted on Read the Docs!
An illustrative example of the timeseries format developed by the
Integrated Assessment Modeling Consortium (IAMC)
is shown below.
The row is taken from the IAMC 1.5°C scenario explorer,
showing a scenario from the CD-LINKS project.
Read the docs
for more information on the IAMC format and the pyam data model.
| model | scenario | region | variable | unit | 2005 | 2010 | 2015 |
|---|---|---|---|---|---|---|---|
| MESSAGE | CD-LINKS 400 | World | Primary Energy | EJ/y | 462.5 | 500.7 | ... |
| ... | ... | ... | ... | ... | ... | ... | ... |
An introduction to the basic functions is shown in the "first-steps" notebook.
All tutorials are available in rendered format (i.e., with output) as part of the online documentation. The source code of the tutorials notebooks is available in the folder doc/source/tutorials of this repository.
The complete documentation is hosted on Read the Docs.
The documentation pages can be built locally, refer to the instruction in doc/README.
This package was initiated and is currently maintained by Matthew Gidden (@gidden) and Daniel Huppmann (@danielhuppmann).
See the complete list of contributors.
Copyright 2017-2021 IIASA and the pyam developer team
The pyam package is licensed under the Apache License, Version 2.0 (the "License"); see LICENSE and NOTICE for details.
For basic instructions, read the docs.
To install from source after cloning this repository, simply run
pip install -e .
To setup a development environment, the simplest route is to make yourself
a conda environment and then follow the Makefile.
# pyam can be replaced with any other name
# you don't have to specify your python version if you don't want
conda create --name pyam pip python=X.Y.Z
conda activate pyam # may be simply `source activate pyam` or just `activate pyam`
# use the make file to create your development environment
# (you only require the -B flag the first time, thereafter you can
# just run `make virtual-environment` and it will only update if
# environment definition files have been updaed)
make -B virtual-environmentInstead of conda you could also use a pip virtualenv:
mkdir venv
virtualenv venv -p python3
. venv/bin/activate
pip install -e .[test,optional-io-formats]To check everything has installed correctly, run
pytest tests
All the tests should pass.