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pyam

A Python package for data-wrangling, analysis and visualization of integrated-assessment scenarios and energy systems modeling results.
https://github.com/iamconsortium/pyam

Category: Climate Change
Sub Category: Integrated Assessment and Climate Policy

Keywords

analysis energy-systems iamc-format integrated-assessment integrated-assessment-scenarios macro-energy modeling pyam scenario scenario-data timeseries-format visualization

Keywords from Contributors

energy-system gams strategic-planning integrated-assessment-model transformations climate measuring units language-model parallel

Last synced: about 12 hours ago
JSON representation

Repository metadata

Analysis & visualization of energy & climate scenarios

README.md

pyam: analysis & visualization of integrated-assessment and macro-energy scenarios

license
pypi
conda
last-release

Code style: ruff
python
pytest
ReadTheDocs
codecov

doi
ORE
joss
groups.io
slack


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 models,
macro-energy scenarios, energy systems analysis, and sectoral studies.

The comprehensive documentation is hosted on Read the Docs!

Key features

  • Simple analysis of scenario timeseries data with an interface similar in feel & style
    to the widely used pandas.DataFrame
  • Advanced visualization and plotting functions
    (see the gallery)
  • Scripted validation and processing of scenario data and results

Timeseries types & data formats

Yearly data

The pyam package was initially developed to work with the IAMC template,
a timeseries format for yearly data developed and used by the
Integrated Assessment Modeling Consortium (IAMC).

model scenario region variable unit 2005 2010 2015
MESSAGE CD-LINKS 400 World Primary Energy EJ/y 462.5 500.7 ...
... ... ... ... ... ... ... ...

An illustration of the IAMC template using a scenario
from the CD-LINKS project

via the The IAMC 1.5°C Scenario Explorer

Subannual time resolution

The package also supports timeseries data with a sub-annual time resolution:

  • Continuous-time data using the Python datetime format
  • "Representative timeslices" (e.g., "winter-night", "summer-day")
    using the pyam extra-columns feature

Read the docs
for more information about the pyam data model
or look at the data-table tutorial
to see how to cast from a variety of timeseries formats to a pyam.IamDataFrame.

Installation

pip

[!WARNING]
The pyam package is distributed on https://pypi.org under the name pyam-iamc.

https://pypi.org/project/pyam-iamc/

Please install using

pip install pyam-iamc

conda

https://anaconda.org/conda-forge/pyam

Please install using

conda install pyam

install from source

To install from source (including all dependencies) after cloning this repository, run

pip install --editable .[tests,optional_io_formats,tutorials]

To check that the package was installed correctly, run

pytest tests

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 docs/tutorials of this repository.

Documentation

The comprehensive documentation is hosted on Read the Docs.

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

Authors & Contributors

This package was initiated and is currently maintained
by Matthew Gidden (@gidden)
and Daniel Huppmann (@danielhuppmann).
See the complete list of contributors.

The core maintenance of the package is done by
the Scenario Services & Scientific Software research theme
at the IIASA Energy, Climate, and Enviroment program.
Visit https://software.ece.iiasa.ac.at for more information.

Scientific publications

The following manuscripts describe the pyam package
at specific stages of development.

The source documents are available in
the manuscripts folder
of the GitHub repository.

Release v1.0 (June 2021)

Published to mark the first major release of the pyam package.

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.13633.2

Release v0.1.2 (November 2018)

Published following the successful application of pyam
in 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.

License

Copyright 2017-2024 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.

Citation (CITATION.cff)

cff-version: 1.1.0
message: "If you use this package, please cite the corresponding manuscript in Open Research Europe."
title: "pyam: analysis and visualization of integrated-assessment and macro-energy scenarios"
repository: https://github.com/iamconsortium/pyam
version: 1.0
license: Apache-2.0
journal: Open Research Europe
doi: 10.12688/openreseurope.13633.2
authors:
  - family-names: Huppmann
    given-names: Daniel
    orcid: https://orcid.org/0000-0002-7729-7389
  - family-names: Gidden
    given-names: Matthew J.
    orcid: https://orcid.org/0000-0003-0687-414X
  - family-names: Nicholls
    given-names: Zebedee
    orcid: https://orcid.org/0000-0002-4767-2723
  - family-names: Hörsch
    given-names: Jonas
    orcid: https://orcid.org/0000-0001-9438-767X
  - family-names: Lamboll
    given-names: Robin D.
    orcid: https://orcid.org/0000-0002-8410-037X
  - family-names: Kishimoto
    given-names: Paul Natsuo
  - family-names: Burandt
    given-names: Thorsten
  - family-names: Fricko
    given-names: Oliver
  - family-names: Byers
    given-names: Edward
  - family-names: Kikstra
    given-names: Jarmo S.
    orcid: https://orcid.org/0000-0001-9405-1228
  - family-names: Brinkerink
    given-names: Maarten
  - family-names: Budzinski
    given-names: Maik
    orcid: https://orcid.org/0000-0003-2879-1193
  - family-names: Maczek
    given-names: Florian
  - family-names: Zwickl-Bernhard
    given-names: Sebastian
  - family-names: Welder
    given-names: Lara
  - family-names: Alvarez Quispe
    given-names: Erik Francisco
    orcid: https://orcid.org/0000-0003-3862-9747
  - family-names: Smith
    given-names: Christopher J.
keywords:
  - integrated assessment
  - energy systems
  - macro-energy
  - modelling
  - scenario analysis
  - data visualisation
  - Python package

Owner metadata


GitHub Events

Total
Last Year

Committers metadata

Last synced: 6 days ago

Total Commits: 659
Total Committers: 33
Avg Commits per committer: 19.97
Development Distribution Score (DDS): 0.334

Commits in past year: 32
Committers in past year: 7
Avg Commits per committer in past year: 4.571
Development Distribution Score (DDS) in past year: 0.281

Name Email Commits
Daniel Huppmann dh@d****t 439
Matthew Gidden m****n@g****m 113
Zeb Nicholls z****s@c****g 21
Jonas Hörsch j****h@c****g 17
Rlamboll r****l@h****k 8
Nikolay Kushin z****h@g****m 7
Fridolin Glatter 8****2 5
Philip Hackstock p****k@g****t 5
OFR-IIASA f****o@i****t 4
pjuergens 7****s 3
dependabot[bot] 4****] 3
Pietro Monticone 3****e 3
Maik Budzinski 5****z 3
Jarmo Kikstra 4****a 3
Edward Byers b****s@i****t 3
David Almeida 5****a 2
Mathias Hauser m****e 2
Paul Natsuo Kishimoto m****l@p****e 2
dependabot-preview[bot] 2****] 2
rossursino 4****o 1
lumbric l****c@g****m 1
Thorsten Burandt 2****t 1
Suvayu Ali s****u 1
Philipp S. Sommer C****p 1
Michael Pimmer b****b@f****t 1
Linh Ho 4****o 1
Laura Wienpahl 5****n 1
Karthikeyan Singaravelan t****i@g****m 1
Kamil 3****5 1
Jan Ivar Korsbakken j****n@g****m 1
and 3 more...

Committer domains:


Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 281
Total pull requests: 644
Average time to close issues: 4 months
Average time to close pull requests: 11 days
Total issue authors: 55
Total pull request authors: 37
Average comments per issue: 2.59
Average comments per pull request: 3.04
Merged pull request: 539
Bot issues: 0
Bot pull requests: 17

Past year issues: 15
Past year pull requests: 48
Past year average time to close issues: 6 days
Past year average time to close pull requests: 10 days
Past year issue authors: 10
Past year pull request authors: 9
Past year average comments per issue: 2.2
Past year average comments per pull request: 1.52
Past year merged pull request: 35
Past year bot issues: 0
Past year bot pull requests: 8

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/iamconsortium/pyam

Top Issue Authors

  • danielhuppmann (95)
  • gidden (53)
  • znicholls (19)
  • Rlamboll (16)
  • phackstock (12)
  • byersiiasa (11)
  • pjuergens (7)
  • khaeru (6)
  • stefaneidelloth (4)
  • l-welder (3)
  • glatterf42 (3)
  • jkikstra (2)
  • willu47 (2)
  • lucyhager (2)
  • maxtav (2)

Top Pull Request Authors

  • danielhuppmann (384)
  • gidden (102)
  • znicholls (38)
  • coroa (21)
  • dependabot[bot] (10)
  • Rlamboll (8)
  • zikolach (8)
  • phackstock (7)
  • dependabot-preview[bot] (7)
  • glatterf42 (7)
  • quant12345 (4)
  • pjuergens (4)
  • OFR-IIASA (4)
  • dc-almeida (3)
  • byersiiasa (3)

Top Issue Labels

  • enhancement (37)
  • bug (36)
  • plotting (13)
  • good first issue (12)
  • question (10)
  • iiasa-api (9)
  • datetime (5)
  • dependencies (5)
  • data-ops (5)
  • tutorial (4)
  • data back-end (4)
  • help wanted (3)
  • logging (3)
  • next release (3)
  • downscaling (1)
  • extra-cols (1)

Top Pull Request Labels

  • dependencies (31)
  • enhancement (25)
  • bug (18)
  • plotting (18)
  • data back-end (13)
  • iiasa-api (11)
  • data-ops (10)
  • datetime (6)
  • tutorial (5)
  • generic-index-cols (3)
  • extra-cols (2)
  • logging (2)
  • question (1)
  • debiasing (1)
  • R (1)
  • downscaling (1)
  • next release (1)

Package metadata

pypi.org: pyam-iamc

Analysis & visualization of integrated-assessment scenarios

  • Homepage: https://github.com/IAMconsortium/pyam
  • Documentation: https://pyam-iamc.readthedocs.io
  • Licenses: Apache-2.0
  • Latest release: 3.0.0 (published 4 months ago)
  • Last Synced: 2025-04-25T14:34:39.909Z (1 day ago)
  • Versions: 36
  • Dependent Packages: 13
  • Dependent Repositories: 21
  • Downloads: 29,998 Last month
  • Docker Downloads: 8
  • Rankings:
    • Dependent packages count: 0.938%
    • Dependent repos count: 3.193%
    • Downloads: 3.366%
    • Average: 3.456%
    • Docker downloads count: 3.836%
    • Forks count: 4.542%
    • Stargazers count: 4.859%
  • Maintainers (2)
proxy.golang.org: github.com/iamconsortium/pyam

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/iamconsortium/pyam#section-documentation
  • Licenses: apache-2.0
  • Latest release: v3.0.0+incompatible (published 4 months ago)
  • Last Synced: 2025-04-25T14:34:40.107Z (1 day ago)
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 1.622%
    • Average: 4.057%
    • Dependent packages count: 6.492%
proxy.golang.org: github.com/IAMconsortium/pyam

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/IAMconsortium/pyam#section-documentation
  • Licenses:
  • Latest release: v3.0.0+incompatible (published 4 months ago)
  • Last Synced: 2025-04-25T14:34:40.271Z (1 day ago)
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 6.999%
    • Average: 8.173%
    • Dependent repos count: 9.346%
conda-forge.org: pyam

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 models, macro-energy scenarios, energy systems analysis, and sectoral studies. **Key features** - Simple analysis of scenario timeseries data with an interface similar in feel & style to the widely used [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) - Advanced visualization and plotting functions (see the [gallery](https://pyam-iamc.readthedocs.io/en/stable/gallery/index.html)) - Scripted validation and processing of scenario data and results

  • Homepage: https://pyam-iamc.readthedocs.io/
  • Licenses: Apache-2.0
  • Latest release: 1.6.0 (published over 2 years ago)
  • Last Synced: 2025-04-01T02:12:24.810Z (26 days ago)
  • Versions: 23
  • Dependent Packages: 3
  • Dependent Repositories: 6
  • Rankings:
    • Dependent repos count: 13.836%
    • Dependent packages count: 15.638%
    • Forks count: 18.498%
    • Average: 18.848%
    • Stargazers count: 27.422%

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setup.py pypi

Score: 19.64783371232893