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groupsizes.py
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import multiprocessing
import sys
import numpy as np
import time
import pickle
import matplotlib.pyplot as plt
from CoronaTestingSimulation import Corona_Simulation
from Statistics import Corona_Simulation_Statistics
import subprocess
'''
Groupsizes
Determine optimal groupsizes for all methods depending on the infection rate
'''
# default plot font sizes
SMALL_SIZE = 14
MEDIUM_SIZE = 16
BIGGER_SIZE = 18
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
def getName(success_rate_test=0.99):
# name for the data dump and plots
name = 'groupsizes'
if success_rate_test != 0.99:
name += '_{}'.format(success_rate_test)
return name
def worker(return_dict, sample_size, prob_sick, success_rate_test, false_posivite_rate, test_strategy,
num_simultaneous_tests, test_duration, group_size,
tests_repetitions, test_result_decision_strategy, number_of_instances):
'''
worker function for multiprocessing
performs the same test tests_repetitions many times and returns expected valkues and standard deviations
'''
stat_test = Corona_Simulation_Statistics(sample_size, prob_sick, success_rate_test,
false_posivite_rate, test_strategy,
num_simultaneous_tests, test_duration, group_size,
tests_repetitions, test_result_decision_strategy)
stat_test.statistical_analysis(number_of_instances)
print('Calculated {} for {} prob sick {}'.format(test_strategy, group_size, prob_sick))
print('scaled to {} population and {} simulataneous tests\n'.format(sample_size, num_simultaneous_tests))
# gather results
worker_dict = {}
worker_dict['e_num_tests'] = stat_test.e_number_of_tests
worker_dict['e_time'] = stat_test.e_time
worker_dict['e_num_confirmed_sick_individuals'] = stat_test.e_num_confirmed_sick_individuals
worker_dict['e_false_positive_rate'] = stat_test.e_false_positive_rate
worker_dict['e_ratio_of_sick_found'] = stat_test.e_ratio_of_sick_found
worker_dict['sd_num_tests'] = stat_test.sd_number_of_tests
worker_dict['sd_time'] = stat_test.sd_time
worker_dict['sd_false_positive_rate'] = stat_test.sd_false_positive_rate
worker_dict['sd_ratio_of_sick_found'] = stat_test.sd_ratio_of_sick_found
return_dict['{}_{}_{}'.format(test_strategy, group_size, prob_sick)] = worker_dict
def calculation():
start = time.time()
randomseed = 19
np.random.seed(randomseed)
probabilities_sick = [0.01, 0.05, 0.1, 0.15, 0.2]
group_sizes = list(range(1, 33))
success_rate_test = 0.99
false_posivite_rate = 0.01
tests_repetitions = 1
test_result_decision_strategy = 'max'
test_strategies = [
'individual testing',
'two stage testing',
'binary splitting',
'RBS',
'purim',
'sobel'
]
sample_size = 50000
num_simultaneous_tests = 100
number_of_instances = 10
test_duration = 5
manager = multiprocessing.Manager()
return_dict = manager.dict()
e_num_tests = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
e_time = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
e_false_positive_rate = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
e_num_confirmed_sick_individuals = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
e_ratio_of_sick_found = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
sd_num_tests = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
sd_time = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
sd_false_positive_rate = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
sd_ratio_of_sick_found = np.zeros((len(test_strategies), len(group_sizes), len(probabilities_sick)))
jobs = []
for i, test_strategy in enumerate(test_strategies):
for j, group_size in enumerate(group_sizes):
for k, prob_sick in enumerate(probabilities_sick):
p = multiprocessing.Process(target=worker, args=(return_dict, sample_size, prob_sick,
success_rate_test, false_posivite_rate, test_strategy, num_simultaneous_tests,
test_duration, group_size, tests_repetitions, test_result_decision_strategy,
number_of_instances))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
# gather results
for i, test_strategy in enumerate(test_strategies):
for j, group_size in enumerate(group_sizes):
for k, prob_sick in enumerate(probabilities_sick):
worker_dict = return_dict['{}_{}_{}'.format(test_strategy, group_size, prob_sick)]
e_num_tests[i, j, k] = worker_dict['e_num_tests']
e_time[i, j, k] = worker_dict['e_time']
e_num_confirmed_sick_individuals[i, j, k] = worker_dict['e_num_confirmed_sick_individuals']
e_false_positive_rate[i, j, k] = worker_dict['e_false_positive_rate']
e_ratio_of_sick_found[i, j, k] = worker_dict['e_ratio_of_sick_found']
sd_num_tests[i, j, k] = worker_dict['sd_num_tests']
sd_time[i, j, k] = worker_dict['sd_time']
sd_false_positive_rate[i, j, k] = worker_dict['sd_false_positive_rate']
sd_ratio_of_sick_found[i, j, k] = worker_dict['sd_ratio_of_sick_found']
runtime = time.time()-start
print('calculating took {}s'.format(runtime))
# save data to allow plotting without doing the whole calculation again.
data = {
'randomseed': randomseed,
'probabilities_sick': probabilities_sick,
'success_rate_test ': success_rate_test,
'false_posivite_rate': false_posivite_rate,
'tests_repetitions': tests_repetitions,
'test_result_decision_strategy': test_result_decision_strategy,
'test_strategies': test_strategies,
'number_of_instances': number_of_instances,
'test_duration': test_duration,
'group_sizes': group_sizes,
'e_num_tests ': e_num_tests,
'e_time': e_time,
'e_false_positive_rate': e_false_positive_rate,
'e_num_confirmed_sick_individuals': e_num_confirmed_sick_individuals,
'e_ratio_of_sick_found': e_ratio_of_sick_found,
'sd_num_tests': sd_num_tests,
'sd_time': sd_time,
'sd_false_positive_rate': sd_false_positive_rate,
'sd_ratio_of_sick_found': sd_ratio_of_sick_found,
'sample_size': sample_size,
'runtime': runtime,
'num_simultaneous_tests': num_simultaneous_tests,
}
filename = getName(success_rate_test)
path = 'data/{}.pkl'.format(filename)
with open(path, 'wb+') as fp:
pickle.dump(data, fp)
print('saved data as {}'.format(path))
return filename
def plotting(filename, saveFig=0):
# load data
datapath = 'data/{}.pkl'.format(filename)
with open(datapath, 'rb') as fp:
data = pickle.load(fp)
figpath = 'plots/{}'.format(filename)
# extract relevant parameters from data
test_strategies = data['test_strategies']
probabilities_sick = data['probabilities_sick']
group_sizes = data['group_sizes']
e_time = data['e_time']
sd_time = data['sd_time']
# plotting
markers = ['o', '*', '^', '+', 's', 'd', 'v', '<', '>']
colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']
# optimal group sizes
print('optimal group sizes:')
for i, test_strategy in enumerate(test_strategies):
for k, probability_sick in enumerate(probabilities_sick):
optsize = group_sizes[np.argmin(e_time[i, :, k])]
print('{} {} {}'.format(test_strategy, probability_sick, optsize))
######## poolsize / expected time ########
labels = ['individual testing (IT)', 'two stage testing (2LT)',
'binary splitting (BS)', 'recursive binary splitting (RBS)',
'purim', 'sobel']
for k, probability_sick in enumerate(probabilities_sick):
plt.figure()
plt.title('infection rate: {}%'.format(int(probability_sick*100)), fontsize=BIGGER_SIZE)
for i, test_strategy in enumerate(test_strategies):
plt.plot(group_sizes, e_time[i, :, k],
label=labels[i], marker=markers[i], color=colors[i])
plt.fill_between(group_sizes, e_time[i, :, k]-sd_time[i, :, k],
e_time[i, :, k] + sd_time[i, :, k], color=colors[i], alpha=0.4)
plt.xlabel('pool size')
if k == 5:
plt.legend(loc='upper right')
plt.ylim([0, 130])
if saveFig:
plt.savefig(figpath+'_{}.pdf'.format(probability_sick), bbox_inches='tight')
fig = plt.figure()
plt.ylabel('expected time to test pop. [days]')
plt.plot([0], [0], color='white')
if saveFig:
plt.savefig(figpath+'ylabel.pdf', bbox_inches='tight')
if __name__ == "__main__":
# either do claculations
filename = calculation()
# or use precalculated data
# filename = getName()
saveFig = 1
plotting(filename, saveFig)
if saveFig == 0:
plt.show()