# PostBP: A Python Library for Post-Processing Outputs from Wildfire Growth Models
[](https://pypi.python.org/pypi/postbp)
[](https://anaconda.org/conda-forge/postbp)
**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.
---
## 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.
---
## Installation
PostBP can be installed using pip, it is recommended to install PostBP in a dedicated Python environment to avoid dependency conflicts.:
```bash
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
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"description": "# PostBP: A Python Library for Post-Processing Outputs from Wildfire Growth Models\n\n[](https://pypi.python.org/pypi/postbp)\n[](https://anaconda.org/conda-forge/postbp)\n\n\n**A Python Library**\n\n\n- Free software: MIT License\n- Documentation: https://nliu-cfs.github.io/postbp\n \n\n## Introduction\n\nPostBP 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.\n\nWith PostBP, users can transform raw simulation outputs into intuitive metrics and maps, streamlining decision-making for wildfire management.\n\n---\n\n## Key Features\n\n- **Hexagonal Patch Network**: Discretize landscapes into hexagonal patches for intuitive fire behavior analysis.\n- **Fire Spread Analysis**:\n - Compute fire spread likelihoods between pairs of hexagonal patches.\n - Visualize fire spread patterns with rose diagrams.\n- **Burn and Ignition Probabilities**:\n - Calculate patch-level burn probabilities and ignition likelihoods.\n - Supports user-defined thresholds for burned area classification.\n- **Source-Sink Analysis**:\n - Quantify the tendency of patches to act as fire sources or sinks.\n- **Customizable Inputs**:\n - Supports outputs from Burn-P3 and other FGMs with compatible formats.\n- **Flexible Outputs**:\n - Save results as GeoDataFrames, GeoJSON, Apache GeoParquet, or ESRI Shapefiles.\n\n---\n\n## Installation\n\nPostBP can be installed using pip, it is recommended to install PostBP in a dedicated Python environment to avoid dependency conflicts.:\n\n```bash\npip install postbp\n\n```\n\n## Documentation and Support\n\nComprehensive documentation is available at:\nhttps://nliu-cfs.github.io/postbp\n\nFor any issues or inquiries, please open an issue on the GitHub repository.\n\n## Citation\n\nIf you use PostBP in your research, please cite:\n\nLiu, 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\n",
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