⚠️ Work in Progress: This is a v0 release. Features and APIs may change.
A Python package for US coal plant retirement analysis based on research published in Nature Energy. Provides data and analysis tools for understanding coal plant retirement strategies using contextual vulnerabilities.
- Comprehensive Dataset: Detailed coal plant data with operational and contextual factors
- Network Analysis: Analyze plant relationships using similarity metrics
- Visualization Suite: Rich plotting capabilities for retirement patterns
- Research Reproducibility: Access to manuscript results and analysis
git clone https://github.com/Krv-Analytics/retire.git
cd retire
pip install uv
uv sync
Note: This package will soon be available as a pip-installable package.
pip install retire
from retire import Retire, Explore
# Load data and create analysis objects
retire_obj = Retire()
explore = Explore(retire_obj.graph, retire_obj.raw_df)
# Visualize the network
fig, ax = explore.drawGraph(col='ret_STATUS')
# Create geographic map
fig, ax = explore.drawMap()
# Get manuscript results
group_analysis = retire_obj.get_group_report()
explanations = retire_obj.get_target_explanations()
See the full documentation for detailed usage instructions:
- Usage Guide - Step-by-step tutorial
- Data Sources - Available datasets
- Visualization Methods - Plotting capabilities
- Configuration - Customization options
Retire
- Main analysis class with data access and manuscript results
Explore
- Visualization toolkit for networks and geographic data
from retire.data import load_dataset, load_clean_dataset, load_projection, load_graph
pytest
This is a v0 WIP release. When contributing:
- Test Coverage: Write tests for new functionality
- Documentation: Update docs for API changes
- Code Style: Follow existing patterns and conventions
This project is licensed under the BSD 3-Clause License - see the LICENSE.md file for details.
If you use this package in your research, please cite:
@article{retire2025,
title={Strategies to Accelerate US Coal Power Phaseout Using Contextual Retirement Vulnerabilities},
author={Sidney Gathrid*, Jeremy Wayland*, Stuart Wayland,Ranjit Deshmukh,Grace Wu},
journal={Nature Energy},
year={2025},
}
Note: This package provides data and analysis tools for research purposes. Retirement strategies should be considered within broader energy policy and environmental justice contexts.