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)