def input_models(fight_slope: float,
move_scale: float,
move_shape: float,
encounter_rate: float,
alpha_ss: float,
alpha_rr: float,
beta_ss: float,
beta_rr: float) -> list:
"""
Create the input models
Args:
fight_slope: slope of a fight
move_scale: movement scale parameter
move_shape: movement shape parameter
encounter_rate: encounter rate constant
alpha_ss: growth rate ss
alpha_rr: growth rate rr
beta_ss: cost ss
beta_rr: cost rr
Returns:
"""
return [
growth.max_gut(),
growth.growth(alpha_ss,
alpha_rr,
beta_ss,
beta_rr,
dominance),
init_bio.init_num(param.lam_0_egg),
init_bio.init_mass(param.mu_0_egg_ss,
param.mu_0_egg_rr,
param.sig_0_egg_ss,
param.sig_0_egg_rr,
dominance),
init_bio.init_juvenile(44.194,
18.947,
0.552,
0.17,
dominance),
init_bio.init_mature(param.mu_0_mature_ss,
param.mu_0_mature_rr,
param.sig_0_mature_ss,
param.sig_0_mature_rr,
dominance),
init_bio.init_plant(param.mu_leaf,
param.sig_leaf),
repro.init_sex(param.female_prob),
move.larva(move_scale,
move_shape),
forage.adlibitum(param.forage_steps),
forage.egg(param.egg_factor),
forage.larva(param.larva_factor),
forage.fight(fight_slope),
forage.radius(param.cannibalism_radius),
forage.encounter(encounter_rate)
]
param.sig_0_mature_ss,
param.sig_0_mature_rr,
dominance),
init_bio.init_plant(param.mu_leaf,
param.sig_leaf),
develop.egg_dev(param.mu_egg_dev,
param.sig_egg_dev),
develop.larva_dev(param.mu_larva_dev_ss,
param.mu_larva_dev_rr,
param.sig_larva_dev_ss,
param.sig_larva_dev_rr,
dominance),
develop.pupa_dev(param.mu_pupa_dev,
param.sig_pupa_dev),
forage.adlibitum(param.forage_steps),
forage.egg(param.egg_factor),
forage.larva(param.larva_factor),
forage.fight(param.fight_slope),
forage.encounter(cannib),
forage.radius(param.cannibalism_radius),
move.larva(param.larva_scale,
param.larva_shape),
move.adult(param.adult_scale,
param.adult_shape),
repro.mating(param.mate_encounter),
repro.radius(param.mate_radius),
repro.fecundity(param.fecundity_maximum,
param.fecundity_decay),
repro.density(param.eta,
param.gamma),
repro.init_sex(param.female_prob),
class Simulator(object):
"""
Class to setup and run a simulation of only biomass growth:
Variables:
nums: initial population numbers
forage: the plant forage model
"""
grid = [(keyword.hexagon, 1, 1, True), graph.graph(10)]
attrs = {1: tracking.genotype_attrs}
data = (np.inf, )
steps = [({
keyword.larva: [keyword.move, keyword.consume]
}, param.forage_steps), ({
keyword.larva: [keyword.grow, keyword.reset]
}, )]
emigration = []
immigration = []
input_models = [
growth.max_gut(),
growth.growth(param.alpha_ss, param.alpha_rr, param.beta_ss,
param.beta_rr, dominance),
init_bio.init_num(param.lam_0_egg),
init_bio.init_mass(param.mu_0_egg_ss, param.mu_0_egg_rr,
param.sig_0_egg_ss, param.sig_0_egg_rr, dominance),
init_bio.init_juvenile(44.194, 18.947, 0.552, 0.17, dominance),
init_bio.init_mature(param.mu_0_mature_ss, param.mu_0_mature_rr,
param.sig_0_mature_ss, param.sig_0_mature_rr,
dominance),
init_bio.init_plant(param.mu_leaf, param.sig_leaf),
repro.init_sex(param.female_prob),
move.larva(param.larva_scale, param.larva_shape),
forage.adlibitum(param.forage_steps),
forage.egg(param.egg_factor),
forage.larva(param.larva_factor),
forage.fight(param.fight_slope),
forage.radius(param.cannibalism_radius),
# forage.loss(param.loss_slope,
# param.mid,
# param.egg_factor,
# param.larva_factor)
]
input_variables = param.repro_values
nums: hint.init_pops
bt_prop: float
encounter: float
simulation: hint.simulation = None
def __post_init__(self):
input_models = self.input_models.copy()
input_models.append(forage.encounter(self.encounter))
self.simulation = main_simulation.Simulation. \
setup(self.nums,
self.grid,
self.attrs,
self.data,
self.bt_prop,
self.steps,
self.emigration,
self.immigration,
*input_models,
**self.input_variables)
def run_sim(self, times: list) -> None:
"""
Run the simulation for each time
Args:
times: the times for the simulation
Returns:
biomass data
"""
for _ in times[1:]:
self.simulation.step()
def get_start_data(self, data_key: str) -> int:
"""
Get the start population of larvae
Args:
data_key: the genotype table key
Returns:
the number of larvae at start
"""
dataframes = self.simulation.agents.dataframes()
larva = dataframes['(0, 0)_larva']
column = larva[data_key]
return int(column[0])
def get_final_data(self, data_key: str) -> int:
"""
Get the final population of larvae
Args:
data_key: the genotype table key
Returns:
the number of larvae at the end
"""
dataframes = self.simulation.agents.dataframes()
timestep = self.simulation.timestep
pupa = dataframes['(0, 0)_larva']
column = pupa[data_key]
value = column[timestep]
return int(value)
@classmethod
def run(cls, rho: float, times: list, data_key: str,
nums: hint.init_pops) -> tuple:
"""
Run a bunch of trials
Args:
rho: encounter constant
times: time interval
data_key: column key
nums: init_population
Returns:
value of cannibalism rate constant
"""
print(' {} Starting run for rho: {}'.format(datetime.datetime.now(),
rho))
start_data = []
end_data = []
for trial_num in range(trials):
print(' {} running trial: {}'.format(
datetime.datetime.now(), trial_num))
sim = cls(nums, 1, rho)
sim.run_sim(times)
start_data.append(sim.get_start_data(data_key))
end_data.append(sim.get_final_data(data_key))
start_pop = np.mean(start_data)
end_pop = np.mean(end_data)
start_lower = np.percentile(start_data, 2.5)
start_upper = np.percentile(start_data, 97.5)
end_lower = np.percentile(end_data, 2.5)
end_upper = np.percentile(end_data, 97.5)
prop = end_pop / start_pop
prop_lower = end_lower / start_lower
prop_upper = end_upper / start_upper
rate = 1 - prop
rate_lower = 1 - prop_lower
rate_upper = 1 - prop_upper
return prop, prop_lower, prop_upper, rate, rate_lower, rate_upper
@classmethod
def rho(cls, rhos: np.array, times: list, data_key: str,
nums: hint.init_pops) -> tuple:
"""
Run trials for each rho value
Args:
rhos: list of encounter constants
times: time interval
data_key: data key
nums: init population
Returns:
list of corresponding values for cannibalism
"""
prop = []
prop_lower = []
prop_upper = []
rate = []
rate_lower = []
rate_upper = []
for rho in rhos:
rho_prop, rho_prop_lower, rho_prop_upper, \
rho_rate, rho_rate_lower, rho_rate_upper = \
cls.run(rho, times, data_key, nums)
prop.append(rho_prop)
prop_lower.append(rho_prop_lower)
prop_upper.append(rho_prop_upper)
rate.append(rho_rate)
rate_lower.append(rho_rate_lower)
rate_upper.append(rho_rate_upper)
return prop, prop_lower, prop_upper, rate, rate_lower, rate_upper
