pyam: analysis & visualization
of integrated-assessment scenarios

Documentation on Read the Docs

Questions? Start a discussion on our mailing list

Overview and scope

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

Data model

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	...
...	...	...	...	...	...	...	...

Tutorials

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.

Documentation

The complete documentation is hosted on Read the Docs.

The documentation pages can be built locally, refer to the instruction in doc/README.

Authors

This package was developed and is currently maintained by Matthew Gidden (@gidden) and Daniel Huppmann (@danielhuppmann).

License

The pyam package is licensed under the Apache License, Version 2.0 (the "License"); see LICENSE and NOTICE for details.

Install

For basic instructions, read the docs.

To install from source after cloning this repository, simply run

pip install -e .

Development

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-environment

Instead 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.

Metadata

Release files for pyam-iamc 0.7.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for pyam-iamc 0.7.0
File	Interpreter	ABI	Platform	Reset
pyam_iamc-0.7.0-py2.py3-none-any.whl 67.3 kB Jul 8, 2020	Python 3, Python 2	none	any	Details

Release files / pyam_iamc-0.7.0-py2.py3-none-any.whl

Download URL	pyam_iamc-0.7.0-py2.py3-none-any.whl
Size	67.3 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`0bad37aa130f5441a3564c6c8f683b822b3c8902ed4d899d21aad2e3b641bcdd`
BLAKE2b-256 checksum How to use checksums	`d246f447d3a42590b46e997a164b76ea2382e796ecf53dac7dbee1f70f682816`
Upload date	Jul 8, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.3.1.post20200622 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3`