postbp 2.0.0

A Python Library

PostBP: A Python Library for Post-Processing Outputs from Wildfire Growth Models

A Python Library

• Free software: MIT License
• Documentation: https://nliu-cfs.github.io/postbp

Introduction

PostBP is an open-source Python library designed to simplify the analysis and visualization of outputs from wildfire growth models (FGMs), such as the Canadian Burn-P3 model. The library extracts critical fire behavior metrics, including fire spread likelihoods, source-sink ratios, and burn probabilities, providing actionable insights for wildfire risk assessments and mitigation planning.

With PostBP, users can transform raw simulation outputs into intuitive metrics and maps, streamlining decision-making for wildfire management.

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Key Features

• Hexagonal Patch Network: Discretize landscapes into hexagonal patches for intuitive fire behavior analysis.
• Fire Spread Analysis:
  • Compute fire spread likelihoods between pairs of hexagonal patches.
  • Visualize fire spread patterns with rose diagrams.
• Burn and Ignition Probabilities:
  • Calculate patch-level burn probabilities and ignition likelihoods.
  • Supports user-defined thresholds for burned area classification.
• Source-Sink Analysis:
  • Quantify the tendency of patches to act as fire sources or sinks.
• Customizable Inputs:
  • Supports outputs from Burn-P3 and other FGMs with compatible formats.
• Flexible Outputs:
  • Save results as GeoDataFrames, GeoJSON, Apache GeoParquet, or ESRI Shapefiles.

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Installation

PostBP can be installed using pip, it is recommended to install PostBP in a dedicated Python environment to avoid dependency conflicts.:

pip install postbp

Documentation and Support

Comprehensive documentation is available at: https://nliu-cfs.github.io/postbp

For any issues or inquiries, please open an issue on the GitHub repository.

Citation

If you use PostBP in your research, please cite:

Liu, N., Yemshanov, D., Parisien, M.-A., et al. (2024). PostBP: A Python library to analyze outputs from wildfire growth models. MethodsX, 13, 102816. DOI:10.1016/j.mex.2024.102816