PostBP: A Python']Python library to analyze outputs from wildfire growth models

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location -specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open -source Python package designed for post -processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source -sink ratio and the fire spread rose diagram. We provide an overview of PostBP 's capabilities and demonstrate its practical application to a forested landscape. center dot Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision -making. center dot The PostBP package calculates the summary metrics characterizing the directional spread of wildfires. center dot The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.