def simulation_with_drugs(num_viruses, max_pop, max_birth_prob,
clear_prob, resistances, mut_prob,
num_trials, time_steps, drug_administration_step):
"""
Runs a series of trials with treated patients and resistant virus instances
the given parameters and plots out the result
:param num_viruses: integer, number of virus instances to create
:param max_pop: integer, maximum virus population for each patient
:param max_birth_prob: float between 0 and 1, probability for a virus instance to reproduce
:param clear_prob: float between 0 and 1, probability for a virus instance to clear
:param resistances: dict <str, boolean>, represents drugs as keys and maps each to a boolean,
True if the virus has a resistance to the drug, False otherwise
:param mut_prob, float between 0 and 1, probability for the offspring of the virus to
mutate on of its resistances
:param num_trials: integer, number of trials to run the simulation
:param time_steps integer, number of time_steps to consider for each trial
:param drug_administration_step, integer between 0 and time_step, time_step at which to
administer the necessary drugs
"""
average_pop = [0 for _ in range(time_steps)]
average_res = average_pop[:]
for _ in range(num_trials):
viruses = [ResistantVirus(max_birth_prob, clear_prob, resistances, mut_prob) for _ in range(num_viruses)]
patient = TreatedPatient(viruses, max_pop)
for time_step in range(time_steps):
if time_step == drug_administration_step:
for drug in resistances.keys():
patient.add_prescription(drug)
average_pop[time_step] += patient.update()
average_res[time_step] += patient.get_resistant_pop(resistances.keys())
average_pop = [float(pop_sum) / num_trials for pop_sum in average_pop]
average_res = [float(pop_sum) / num_trials for pop_sum in average_res]
pylab.figure()
pylab.plot(range(time_steps), average_pop, 'b-', label='Avg Virus Population')
pylab.plot(range(time_steps), average_res, 'r--', label='Avg Resistant Population')
pylab.title('ResistantVirus simulation')
pylab.xlabel('Time Steps')
pylab.ylabel('Average Virus Population')
pylab.legend()
pylab.show()
def simulation_with_2drugs_hist(num_viruses, max_pop, max_birth_prob,
clear_prob, resistances, mut_prob,
num_trials, time_steps, drug1_administration_step,
drug2_administration_step):
"""
Runs a series of trials with treated patients and resistant virus instances with
the given parameters and plots final population distribution on a histogram
:param num_viruses: integer, number of virus instances to create
:param max_pop: integer, maximum virus population for each patient
:param max_birth_prob: float between 0 and 1, probability for a virus instance to reproduce
:param clear_prob: float between 0 and 1, probability for a virus instance to clear
:param resistances: dict <str, boolean>, represents drugs as keys and maps each to a boolean,
True if the virus has a resistance to the drug, False otherwise
:param mut_prob, float between 0 and 1, probability for the offspring of the virus to
mutate on of its resistances
:param num_trials: integer, number of trials to run the simulation
:param time_steps integer, number of time_steps to consider for each trial
:param drug_administration_step, integer between 0 and time_step, time_step at which to
administer the necessary drugs
"""
final_pop = [0 for _ in range(num_trials)]
for trial in range(num_trials):
viruses = [ResistantVirus(max_birth_prob, clear_prob, resistances, mut_prob) for _ in range(num_viruses)]
patient = TreatedPatient(viruses, max_pop)
for time_step in range(time_steps):
if time_step == drug1_administration_step:
patient.add_prescription('guttagonol')
if time_step == drug2_administration_step:
patient.add_prescription('grimpex')
patient.update()
final_pop[trial] = patient.get_total_population()
return final_pop
pylab.figure()
pylab.hist(final_pop, bins=range(min(final_pop), max(final_pop)))
pylab.title('Population after administering drugs with delay: ' + str(drug_administration_step))
pylab.show()