import Parameters as P
import Problem1 as Cls
import SupportSteadyState as Support
# create a cohort of patients for when the drug is not available
cohortFairCoin = Cls.Cohort(id=1,
pop_size=P.SIM_POP_SIZE,
mortality_prob=P.MORTALITY_PROB)
# simulate the cohort
FairCoinOutcome = cohortFairCoin.simulate(P.TIME_STEPS)
# create a cohort of patients for when the drug is available
cohortUnfairCoin = Cls.Cohort(id=2,
pop_size=P.SIM_POP_SIZE,
mortality_prob=P.MORTALITY_PROB *
P.DRUG_EFFECT_RATIO)
# simulate the cohort
UnfairCoinOutcome = cohortUnfairCoin.simulate(P.TIME_STEPS)
# print outcomes of each cohort
Support.print_outcomes(FairCoinOutcome, 'When coin is fair:')
Support.print_outcomes(UnfairCoinOutcome, 'When coin is unfair:')
# draw survival curves and histograms
Support.draw_survival_curves_and_histograms(FairCoinOutcome, UnfairCoinOutcome)
# print comparative outcomes
Support.print_comparative_outcomes(FairCoinOutcome, UnfairCoinOutcome)
import Problem1 as pb
unfair = pb.Cohort(2,1000,20,0.4)
print('Average reward for unfair coin is', unfair.get_average())
