def objective(self):
""" Try to get to the end ! """
simulate(params=self.params,
callback=self.callback,
hourly=False,
plt=self.plt,
stopping_i=1)
return len(self.metric_history) + 10 * (self.threshold_discrepancy -
self.running_discrepancy)
def run(self):
""" To illustrate the OU process """
self.axs[0][0].clear()
simulate(params=self.params,
plt=plt,
callback=self.callback,
home=self.home,
work=self.work,
positions=self.initial_positions,
stopping_t=150)
def run(self):
""" To illustrate the OU process """
self.axs[0][0].clear()
home, work = initialization(self.params)
simulate(params=self.params,
plt=plt,
callback=self.callback,
home=home,
work=work,
stopping_t=50)
def modify_and_run(baseline, triples):
# python3 shell.py large_town geometry n 20000 health vi 0.5
params = BASELINES[baseline]
descriptions = list()
n = len(triples)
assert n % 3 ==0, 'Expecting triples of command line parameters '
num = int(n/3)
print(num)
for k in range(num):
category = triples[3 * k].lower()
param = triples[3 * k+1].lower()
assert category in CATEGORIES
assert param in list(DESCRIPTIONS[category].keys())
factor = float(triples[3*k+2])
params, desc = modifier( category=category, param=param, factor=factor, baseline=params )
descriptions.append(desc)
simulate(params=params, plt=plt, xlabel=','.join(descriptions))
def run(self):
""" To illustrate the OU process """
home, work, positions = initialization(self.params)
simulate(params=self.params,plt=plt,callback=self.callback,home=home,work=work,positions=positions)
from pandemic.example_parameters import TOY_TOWN
from pandemic.simulation import simulate
import matplotlib.pyplot as plt
import time
if __name__ == "__main__":
simulate(params=TOY_TOWN, plt=plt)
def small_town(with_plot=True):
params = LARGE_TOWN
params['geometry']['n']= int( params['geometry']['n'] / 2 )
simulate(params=params, plt=plt if with_plot else None)
def large_town(with_plot=True):
simulate(params=LARGE_TOWN, plt=plt if with_plot else None)
def town_with_close_neighbours(with_plot=True):
params = LARGE_TOWN
params['geometry']['r']= 0.5*params['geometry']['r'] # Sprawl distance
simulate(params=params, plt=plt if with_plot else None)
def town_that_tests_randomly(with_plot=True):
params = LARGE_TOWN
params['health']['sp']= 3*params['health']['sp']
params['health']['ip'] =6*params['health']['ip']
simulate(params=params, plt=plt if with_plot else None)
def town_that_tests_symptomatic_more(with_plot=True):
params = LARGE_TOWN
params['health']['sp']= 3*params['health']['sp']
simulate(params=params, plt=plt if with_plot else None)
from pandemic.example_parameters import SMALL_CITY
from pandemic.simulation import simulate
import matplotlib.pyplot as plt
if __name__ == "__main__":
simulate(params=SMALL_CITY, plt=plt)
def run(self):
simulate(params=self.params,
callback=self.callback,
plot_hourly=False,
plt=self.plt,
xlabel="Sending results to www.swarmprediction.com. Thanks!")
from pandemic.example_parameters import SMALL_CITY, TOWN
from pandemic.simulation import simulate
import matplotlib.pyplot as plt
from copy import deepcopy
import numpy as np
import math
def equal_spaced_homes(b, num):
num1 = int(math.sqrt(num))
return [(x, y) for x in np.linspace(-b, b, num1)
for y in np.linspace(-b, b, num1)]
if __name__ == "__main__":
params = deepcopy(TOWN)
num = 10000
params['geometry']['b'] = 5.0
b = params['geometry']['b']
params['motion']['t'] = 48
params['geometry']['c'] = 0.0 # Nobody commutes
params['health']['vi'] = 50.0 # Collision -> infection
home = equal_spaced_homes(b, num)
work = deepcopy(home)
params['geometry']['n'] = len(home)
pos = deepcopy(home)
simulate(params=params, home=home, work=work, positions=pos, plt=plt)
