**pyam**: analysis and visualization |br| of integrated-assessment & macro-energy scenarios =========================================================================================== Release v\ |version|. |license| |pypi| |conda| |latest| |ruff| |python| |pytest| |rtd| |codecov| |doi| |ore| |joss| |groupsio| |slack| .. |license| image:: https://img.shields.io/badge/license-Apache%202.0-black :target: https://github.com/IAMconsortium/pyam/blob/main/LICENSE .. |pypi| image:: https://img.shields.io/pypi/v/pyam-iamc.svg :target: https://pypi.python.org/pypi/pyam-iamc/ .. |conda| image:: https://img.shields.io/conda/vn/conda-forge/pyam?logo=anaconda :target: https://anaconda.org/conda-forge/pyam .. |latest| image:: https://img.shields.io/github/release-date/iamconsortium/pyam?logo=github&label=last%20release :target: https://github.com/IAMconsortium/pyam/releases .. |ruff| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json :target: https://github.com/astral-sh/ruff .. |python| image:: https://img.shields.io/badge/python-≥3.10,<3.13-blue?logo=python&logoColor=white :target: https://github.com/IAMconsortium/pyam .. |pytest| image:: https://img.shields.io/github/actions/workflow/status/iamconsortium/pyam/pytest.yml?logo=GitHub&label=pytest :target: https://github.com/IAMconsortium/pyam/actions/workflows/pytest.yml .. |rtd| image:: https://readthedocs.org/projects/pyam-iamc/badge/?version=latest :target: https://pyam-iamc.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. |codecov| image:: https://codecov.io/gh/IAMconsortium/pyam/branch/main/graph/badge.svg :target: https://codecov.io/gh/IAMconsortium/pyam .. |doi| image:: https://zenodo.org/badge/113359260.svg :target: https://doi.org/10.5281/zenodo.1470400 .. |ore| image:: https://img.shields.io/badge/ORE-10.12688/openreseurope.13633.2-blue :target: https://doi.org/10.12688/openreseurope.13633.2 .. |joss| image:: https://joss.theoj.org/papers/10.21105/joss.01095/status.svg :target: https://joss.theoj.org/papers/10.21105/joss.01095 .. |groupsio| image:: https://img.shields.io/badge/mail-groups.io-blue :target: https://pyam.groups.io/g/forum .. |slack| image:: https://img.shields.io/badge/chat-Slack-orange.svg :target: https://pyam-iamc.slack.com Overview -------- 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 source code is available on `Github `_. See also the related `Scientific publications`_. Key features ~~~~~~~~~~~~ - Simple analysis of scenario timeseries data with an interface similar in feel & style |br| to the widely used :class:`pandas.DataFrame` - Advanced visualization and plotting functions (see the `gallery `_) - Features for scripted validation & 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). .. figure:: _static/iamc_template.png Illustrative example of IAMC-format timeseries data |br| via the `IAMC 1.5°C Scenario Explorer`_ .. _`IAMC 1.5°C Scenario Explorer`: https://data.ece.iiasa.ac.at/iamc-1.5c-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") |br| using the pyam *extra-columns* feature Please read the `Data Model `_ section for more information or look at the `data-table tutorial `_ to see how to cast from a variety of timeseries formats to an :class:`IamDataFrame`. Table of Contents ----------------- .. toctree:: :maxdepth: 2 install authors contributing data tutorials gallery/index R_tutorials/pyam_R_tutorial api Scientific publications ----------------------- The following manuscripts describe the package at specific stages of development. The source documents are available in the manuscripts_ folder of the GitHub repository. .. _manuscripts: https://github.com/IAMconsortium/pyam/tree/main/manuscripts Release v1.0 (June 2021) ~~~~~~~~~~~~~~~~~~~~~~~~ Published to mark the first major release of the |pyam| package. .. highlights:: | 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 Álvarez 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. .. highlights:: | 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 `_ Copyright & License ------------------- The development of the |pyam| package was started at the IIASA Energy Program, with contributions from a number of `individuals & institutions`_ over the years. The package is available under the open-source `Apache License`_. Refer to the `NOTICE`_ in the GitHub repository for more information. .. _individuals & institutions: authors.html .. _Apache License: http://www.apache.org/licenses/LICENSE-2.0.html .. _NOTICE: https://github.com/IAMconsortium/pyam/blob/master/NOTICE.md