pathways-ensemble-analysis 1.0.0

Multi-criteria pathways ensemble analysis

Pathways Ensemble Analysis

Overview and scope

The pathways-ensemble-analysis (short: pea) package allows you to perform multi-criteria evaluation style analyses of pathway ensembles produced by energy and climate models.

It has a range of functionality, including the ability to:

• Evaluate user-defined criteria for an ensemble of pathways - for example the share of renewables in 2050.
• Filter the ensemble on the basis of the defined criteria to identify a subset of pathways which is of particular interest - for example based on maximum sustainable levels of carbon dioxide removal.
• Rank pathways on the basis of these criteria by performing a multi-criteria evaluation of the ensemble.
• Visualise the evaluated and ranked criteria of the ensemble.

What's Here

In this repo, there are the following folders and sets of files:

1. src/pathways-ensemble-analysis: contains the source code for the pea package. The definitions of the evaluation criteria are located in the criteria folder, with the base module providing more basic, general criteria, and the library module providing more specific, pre-defined criteria, as for example the average level of CCS/CDR deployment, biomass consumption, and the probability of overshooting 1.5°C.

2. tests: contains the unit testing scripts for the package, which can be run with pytest.

3. notebooks: contains an example notebook which shows how the pea package can be used to filter, rank and visualise a diverse set of pathways.

Getting started

1. Clone this repository to your local machine.

2. Create an environment pea with the help of conda, by running

   conda env create -f environment.yml
   

3. Activate this environment via

   conda activate pea
   

Example

An example for how to use this package is given in the notebooks folder here.

Contributors

• Lara Welder (@l-welder) [e-mail]
• Neil Grant (@neilgrant) [e-mail]
• Jonas Hörsch (@coroa) [e-mail]
• Tina Aboumahboub (@Tinaab) [e-mail]

License

Copyright (C) 2022 Climate Analytics. All versions released under the MIT License.

Repository Organization

├── CONTRIBUTORS.md                   <- List of developers and maintainers.
├── CHANGELOG.md                      <- Changelog to keep track of new features and fixes.
├── LICENSE.txt                       <- License as chosen on the command-line.
├── README.md                         <- README file for the repository
├── environment.yml                   <- The conda environment file for reproducibility.
├── notebooks                         <- Jupyter notebooks.
├── setup.cfg                         <- Declarative configuration of your project.
├── setup.py                          <- Use `pip install -e .` to install.
├── src
│   └── pathways_ensemble_analysis  <- Actual Python package where the main functionality goes.
├── tests                             <- Unit tests which can be run with `pytest`.
├── .coveragerc                       <- Configuration for coverage reports of unit tests.
├── .gitignore                        <- Lists folders and files not tracked by git
├── .isort.cfg                        <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml           <- Configuration of pre-commit git hooks.