def do_fires(self):
"""Uses the various components of the fire model to draw and (possibly) spread one year's fires.
"""
##########################################################
# 1) Find out how many ignitions there are this year,
# and their associated weather streams/forecasts
##########################################################
weather_seed = seed_add(self.primary_random_seed, self.current_year)
weather_seed = seed_add(weather_seed, 298238234)
weathers, forecasts = self.WeatherModel.get_new_fires(random_seed=weather_seed)
#if there are no fires this year, return nothing
if len(weathers) == 0:
return []
#generate ignition locations
def _rand_loc(i):
random.seed(seed_add(weather_seed, i+253456))
_x = random.uniform(0, self.size[0])
_y = random.uniform(0, self.size[1])
return (_x, _y)
locations = [ _rand_loc(i) for i in range(len(weathers)) ]
#set up fire record object list
fire_records = [None] * len(weathers)
### FOR EACH IGNITION THIS YEAR:
for i in range(len(weathers)):
#we can't get a list of suppression decisions all at once, because each fire
# may change the landscape in the vicinity of the NEXT fire, and that may in
# turn change the policy decision. So we have to do one at a time and
# simulate the fire, before getting the next decision
#check to make sure this weather stream has anything in it.
if len(weathers[i]) == 0:
continue
##########################################################
# 2) Use the current suppression policy to make choices
##########################################################
supr_decision = self.Policy.get_decision(self, locations[i], forecasts[i])
##########################################################
# 3) Simulate each fire (and potential suppression)
##########################################################
fire_records[i] = self.FireModel.simulate_fire(self, locations[i], weathers[i], supr_decision)
return fire_records
def draw_wind(self, date, random_seed=None):
"""Random wind speed in kph
Drawing from an exponential distribution with mean = 10
TODO: what SHOULD it be?
"""
wind_mean = 10
random.seed(seed_add(random_seed,439201))
return random.expovariate(1.0 / wind_mean)
def draw_rain(self, date, random_seed=None):
#from http://www.na.fs.fed.us/spfo/pubs/silvics_manual/Volume_1/pinus/ponderosa.htm
# July, August, and September are dry; average rainfall is less than 25 mm
#
#that's ~ 0.8 mm per day. Lets draw from an exponential with mean = 0.8
mean_summer_precip = 0.8 #mm
random.seed(seed_add(random_seed, 792723))
rainfall = random.expovariate(1.0 / mean_summer_precip)
#cutting it off, so that many values are zero
if rainfall < 0.8: rainfall = 0.0
return rainfall
def draw_RH(self, date, random_seed=None):
#from the Western Regional Climate Center
#http://www.wrcc.dri.edu/narratives/OREGON.htm
#The afternoon average relative humidity ranges between 75 and 85 percent in January,
#while in July this drops to 25 or 30 percent east of the Cascades and slightly higher
# on the western side. Relative humidities of 10 to 20 percent often occur under extreme
# conditions during the summer and early fall.
date_mean_humidity = math.cos( (2.0*(date)*math.pi) / (365.0) ) * (80-15) + 15
#lets let actual humidity vary +/- 5%
random.seed(seed_add(random_seed, 985727))
rh = random.normalvariate(date_mean_humidity, 2.5)
if rh < 0: rh = 0.0
if rh > 100: rh = 100.0
return rh
def get_new_fires(self, random_seed=None):
"""Generates a new series of fires for a simulation year and returns any/all of them
PARAMETERS
----------
random_seed
Any numeric value for seeding the weather/forecast generation process.
Ensures replicability.
RETURNS
-------
weather_streams, forecasts
weather_streams
A list of weather streams. Each weather stream is a 2-d list, indexed by day,
of a particular fire/weather event.
forecasts
A list of weather forecasts. Each forecast is a a 2-d list, indexed by day, of
weather forecast information for a particular fire/weather event.
"""
#how many fires are there this year?
ave_fire_count = 0.5
random.seed(seed_add(random_seed, 471294932))
fire_count = int(random.expovariate(1.0 / ave_fire_count))
if fire_count == 0:
return [[],[]]
weather_streams = [None]*fire_count
forecasts = [None]*fire_count
for f in range(fire_count):
#drawing fire days bewteen April-ish and October-ish
streams = self.get_new_fire_weather_stream_pair( date=random.randint(100,310), random_seed=seed_add(random_seed,128439322))
weather_streams[f] = streams[0]
forecasts[f] = streams[1]
return weather_streams, forecasts
def draw_temp(self, date, random_seed=None):
"""Random temperature for the given date (as day of year)"""
# from http://www.na.fs.fed.us/spfo/pubs/silvics_manual/Volume_1/pinus/ponderosa.htm
# average annual temperatures are between 5 and 10 C (41 and 50 F),
# and average July-August temperatures are between 17 and 21 C
# Annual extremes are from -40 to 43
#
#Lets call average annual temperature 7.5C, with average summer temps of 19C
# so the mean temp will rise from 7.5C in May to 19C in July, and back down to
# 7.5 in October
#
#May starts on day 121
date = date-121
date_mean_temp = math.sin( (2.0*(date)*math.pi) / (365.0) ) * (19-7.5) + 7.5
random.seed(seed_add(random_seed, 390752))
#lets let daily actual temperatures vary around the mean by way of a normal distribution
# where 95%-ish remain within +/- 20C from the mean (so one standard devation will be 10)
return random.normalvariate(date_mean_temp, 10)
def draw_wind_direction(self, date, random_seed=None):
"""A purely random wind direction, in degrees from North"""
random.seed(seed_add(random_seed, 818127))
return random.uniform(0,364)
def get_new_fire_weather_stream_pair(self, date, random_seed=None):
"""Creates a new weather stream and forcast
PARAMETERS
----------
date
the integer date of the year
random_seed
Any numeric value for seeding the weather/forecast generation process.
Ensures replicability.
RETURNS
-------
weather, forecast
weather
A list of dictionaries of weather variables with the primary index being day.
forcast
The associated weather forcast, of identical form/shape as "weather"
"""
#how many days long is this fire event?
ave_fire_weather_days = 3
random.seed(seed_add(random_seed, 3789284))
day_count = int(random.expovariate(1.0/ave_fire_weather_days))
#if the fire event is less than three days long, we'll need to add some extras to make sure
#that at least the FORECAST is three days long
extra_forecast_days = 0
if day_count < 3:
extra_forecast_days = 3 - day_count
#add days for the lead-in time, which is needed to allow FWI index values to come
# to equilibrium
buffer_days = 10
day_count += buffer_days
day_count += extra_forecast_days
#get random weather variables for the days
weather = [ self.draw_weather_variables(date, seed_add(random_seed, 9472843)) for i in range(day_count) ]
forecast = weather[:]
#now do a blending of the variables, walking forward from the first day, where
# each day is a combination of (mostly) the previous day's value, and (somewhat)
# it's own value
w_blend = 0.8
f_blend = self.forecast_accuracy * w_blend
for i in range(1,day_count):
weather[i]["Temperature"] = w_blend * weather[i-1]["Temperature"] + (1-w_blend) * weather[i]["Temperature"]
weather[i]["RH"] = w_blend * weather[i-1]["RH"] + (1-w_blend) * weather[i]["RH"]
weather[i]["Wind Speed"] = w_blend * weather[i-1]["Wind Speed"] + (1-w_blend) * weather[i]["Wind Speed"]
weather[i]["Wind Direction"] = w_blend * weather[i-1]["Wind Direction"] + (1-w_blend) * weather[i]["Wind Direction"]
weather[i]["Rainfall"] = w_blend * weather[i-1]["Rainfall"] + (1-w_blend) * weather[i]["Rainfall"]
forecast[i]["Temperature"] = f_blend * forecast[i-1]["Temperature"] + (1-f_blend) * forecast[i]["Temperature"]
forecast[i]["RH"] = f_blend * forecast[i-1]["RH"] + (1-f_blend) * forecast[i]["RH"]
forecast[i]["Wind Speed"] = f_blend * forecast[i-1]["Wind Speed"] + (1-f_blend) * forecast[i]["Wind Speed"]
forecast[i]["Wind Direction"] = f_blend * forecast[i-1]["Wind Direction"] + (1-f_blend) * forecast[i]["Wind Direction"]
forecast[i]["Rainfall"] = f_blend * forecast[i-1]["Rainfall"] + (1-f_blend) * forecast[i]["Rainfall"]
#now add the FWI variables
#The first day will get a value of 50 for FFMC, DMC, and DC (moisture and drought codes)
#After that, they will adjust themselves as they go
weather[0]["FFMC"] = 50
weather[0]["DMC"] = 50
weather[0]["DC"] = 50
forecast[0]["FFMC"] = 50
forecast[0]["DMC"] = 50
forecast[0]["DC"] = 50
for i in range(1,day_count):
#I'm holding the month to whatever it was when the fire started to avoid any
# discontinuities partway through a weather stream. Hense 'weather[0]["Date"]'
MONTH = self.get_month(weather[0]["Date"])
LAT = 45
TEMP = weather[i]["Temperature"]
RH = weather[i]["RH"]
WIND = weather[i]["Wind Speed"]
RAIN = weather[i]["Rainfall"]
FFMCPrev = weather[i-1]["FFMC"]
DMCPrev = weather[i-1]["DMC"]
DCPrev = weather[i-1]["DC"]
weather[i]["FFMC"] = FWI.FFMC(TEMP,RH,WIND,RAIN,FFMCPrev)
weather[i]["DMC"] = FWI.DMC(TEMP,RH,RAIN,DMCPrev,LAT,MONTH)
weather[i]["DC"] = FWI.DC(TEMP,RAIN,DMCPrev,LAT,MONTH)
weather[i]["FWI"] = FWI.calcFWI(MONTH,TEMP,RH,WIND,RAIN,FFMCPrev,DMCPrev,DCPrev,LAT)
TEMP = forecast[i]["Temperature"]
RH = forecast[i]["RH"]
WIND = forecast[i]["Wind Speed"]
RAIN = forecast[i]["Rainfall"]
FFMCPrev = forecast[i-1]["FFMC"]
DMCPrev = forecast[i-1]["DMC"]
DCPrev = forecast[i-1]["DC"]
forecast[i]["FFMC"] = FWI.FFMC(TEMP,RH,WIND,RAIN,FFMCPrev)
forecast[i]["DMC"] = FWI.DMC(TEMP,RH,RAIN,DMCPrev,LAT,MONTH)
forecast[i]["DC"] = FWI.DC(TEMP,RAIN,DCPrev,LAT,MONTH)
forecast[i]["FWI"] = FWI.calcFWI(MONTH,TEMP,RH,WIND,RAIN,FFMCPrev,DMCPrev,DCPrev,LAT)
#the forecast will always be three days
forecast = weather[buffer_days:buffer_days+3]
#trim off the ten lead-in days
weather = weather[buffer_days:]
#trim off any extra forecast days
if extra_forecast_days > 0:
weather = weather[:-1*extra_forecast_days]
return weather, forecast
def __init__(self, landscape_size=(129,129), random_seed=None):
#maintenance flags
self.VERBOSE = True
self.DEBUGGING = True
#primary simulation models
self.FireModel = FGFire.SpreadModel()
self.HarvestModel = FGHarvest.HarvestModel()
self.TreatmentModel = FGTreatments.TreatmentModel()
self.GrowthModel = FGGrowth.GrowthModel()
self.WeatherModel = FGWeather.WeatherModel()
#fire suppression policy
self.Policy = FGPolicy.SuppressionPolicy()
#sanitize landscape_size
#check if it's iterable, and of length 2
if not hasattr(landscape_size,"__iter__"):
#it's not iterable, so assume it's an int and correc that
self.size = (landscape_size, landscape_size)
else:
#it's iterable, so save the first two values
self.size = (landscape_size[0], landscape_size[1])
#important pathway values
self.year_history = [] # a list to hold all of this simulation's YearRecord objects
self.current_year = 0
self.primary_random_seed = random_seed
#Primary data arrays, etc...
# site index: this remains constant
self.site_index = DS.diamond_square(self.size, min_height=0, max_height=100, roughness=0.5, random_seed=seed_add(self.primary_random_seed, 0),AS_NP_ARRAY=True).astype(int)
# each cell's stand initiation year: each one started in the last 100 years or so
self.stand_init_yr = DS.diamond_square(self.size, min_height=-100, max_height=0, roughness=0.95, random_seed=seed_add(self.primary_random_seed, 1), AS_NP_ARRAY=True).astype(int)
# the last year in which each cell experienced a stand-replacing fire, leaving dead trees standing
self.stand_rplc_fire_yr = DS.diamond_square(self.size, min_height=-100, max_height=0, roughness=0.95, random_seed=seed_add(self.primary_random_seed, 2), AS_NP_ARRAY=True).astype(int)
# the last year in which there was a surface fire
self.surf_fire_yr = DS.diamond_square(self.size, min_height=-100, max_height=0, roughness=0.95, random_seed=seed_add(self.primary_random_seed, 2), AS_NP_ARRAY=True).astype(int)
#units
#how many acres per cell?
self.acres_per_cell = 25
def _rand_loc(i):
random.seed(seed_add(weather_seed, i+253456))
_x = random.uniform(0, self.size[0])
_y = random.uniform(0, self.size[1])
return (_x, _y)