Numerical Models for Developing Community Scale Risk Model
Installing from source:
git clone https://github.com/FireCARES/fire-risk.git
cd fire-risk
pip install .After installing from source, the doctests and unit tests can be executed from the fire-risk root directory using pytest:
py.testfrom fire_risk.models import DIST
dist = DIST(floor_of_origin=34, beyond=12, object_of_origin=170, room_of_origin=190, building_of_origin=74, floor_extent=False)
dist.gibbs_sample()Running the Differential In Standard Time model when residential fire counts are stored in a Postgres database
from fire_risk.models import DIST
from fire_risk.backends import PostgresBackend
from fire_risk.backends.queries import RESIDENTIAL_FIRES_BY_FDID_STATE
with PostgresBackend(dict(host='localhost')) as backend:
# Get residential fire counts for Arlington, VA
results = backend.get_firespread_counts(query=RESIDENTIAL_FIRES_BY_FDID_STATE, query_params=('01300', 'VA'))
dist = DIST(floor_extent=False, **results)
print dist.gibbs_sample()Fit of GIS routed arrival times.
import pandas
from fire_risk.models import DIST
from fire_risk.backends import PostgresBackend
from fire_risk.backends.queries import RESIDENTIAL_FIRES_BY_FDID_STATE
from fire_risk.utils import LogNormalDraw
from scipy.stats import lognorm
dt = pandas.read_csv('detroit_response_time.csv')
with PostgresBackend(dict(host='localhost')) as backend:
# Get residential fire counts for Arlington, VA
results = backend.get_firespread_counts(query=RESIDENTIAL_FIRES_BY_FDID_STATE, query_params=('01300', 'VA'))
params = lognorm.fit(dt['total_travel_time'])
# total_travel_time is in minutes use a 60 second multiplier
results['arrival_time_draw'] = LogNormalDraw(*params, multiplier=60)
dist = DIST(floor_extent=False, **results)
print dist.gibbs_sample()