PyroPy 1.0.0a1

Analysis of fire spread and intensity

PyroPy

Analysis of fire spread and intensity.

Author: Geoffysicist

Source code: https://github.com/Geoffysicist/PyroPy

Uses weather data and model specific parameters to predict rate of spread and intensity of wildfires.

Modules

• firebehaviour: Defines the main Incident class and several auxillary dictionaries. An Incident stores data related to the incident including weather, model parameters and model outputs.
• spreadmodels: fire spread model functions which can be called directly or by an Incident.
• weatherdata: functions for reading, writing and transforming weather between various formats including Australian Bureau of Meteorology (BoM) Gridded Weather, BoM Observations (*.axf) and Amicus.

for more detailed information see modules

Typical Use

from pyropy import firebehaviour as fb
from pyropy import weather_data as wd

if __name__ == '__main__':
   #read the weather data into a pandas DataFrame
   weather_fn = 'tests/.data/weather_gridded_in.csv'
   weather_df = wd.gridded_to_df(weather_fn)

   #create an Incident using the weather data
   incident = fb.Incident(weather_df)

   #add the parameters necessary to run the desired models
   incident_params = {
      #forest_mk5
      'wrf': 3.5,
      'fuel_load': 15,
      #forest_vesta
      'fhs_surf': 3.5,
      'fhs_n_surf': 2,
      'fuel_height_ns': 20
   }
   incident.update_params(incident_params)

   #run the desired models
   incident.run_forest_mk5()
   incident.run_forest_vesta()

   #output results
   incident.print()

Fire Spread Models

Unless otherwise indicated all models have been taken from:

Cruz, Miguel, James Gould, Martin Alexander, Lachie Mccaw, and Stuart Matthews. (2015) A Guide to Rate of Fire Spread Models for Australian Vegetation, CSIRO Land & Water and AFAC, Melbourne, Vic 125 pp.

Unless otherwise indicated all equations numbers also refer to Cruz et al. 2015.

All spread models take a pandas weather dataframe and model specific parmeters as arguments.

Weather Data

The weather data is read into a pandas dataframe from a *.csv file. The fire behaviour models require certain fields to be present in the weather data and these are mapped using dictionaries. There are several standard mappings included (see below), but for non-standard data users must create their own dictionary and supply that as an argument to the weather_to_df function. The mapping dictionary must contain the keys in the FIELDS_BASE dictionary as a minimum.

There are functions to handle the standard data formats such as BOM gridded weather with mapping dictionaries already defined.

gridded_to_df(fn)
amicus_to_df(fn)
df_to_gridded(fn)
df_to_amicus(fn)

For example, if you are using BOM gridded weather data the you should run:

df = gridded_to_df('your_file_path.csv')

Base weather data structure

call the function weather_to_df(fn)

FIELDS_BASE = {
    'date_time': 'Date time',
    'temp': 'Air temperature (C)',
    'humidity': 'Relative humidity (%)',
    'wind_speed': '10 m wind speed (km/h)',
    'wind_dir': 'Wind direction',
}

Gridded weather data structure

call the function gridded_to_df(fn)

FIELDS_GRIDDED = {
    'date': 'Local Date',
    'time': 'Local Time',
    'temp': 'Temp (C)',
    'humidity': 'RH (%)',
    'wind_dir': 'Wind Dir',
    'wind_speed': 'Wind Speed (km/h)',
    'drought': 'Drought Factor',
    'ffdi': 'FFDI',
    'gfdi': 'GFDI',
}