d['smart_tracing_policy_isolate'] = 'basic'
d['smart_tracing_isolated_contacts'] = 100000
# testing
d['smart_tracing_policy_test'] = 'basic'
d['smart_tracing_tested_contacts'] = 100000
d['trigger_tracing_after_posi_trace_test'] = False
return d
sim_info = options_to_str(
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
# baseline
experiment.add(
simulation_info=sim_info,
country=country,
area=area,
measure_list=m,
test_update=test_update,
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=full_scale,
store_mob=store_mob,
expected_daily_base_expo_per100k=expected_daily_base_expo_per100k)
print(f'{experiment_info} configuration done.')
# execute all simulations
experiment.run_all()
measure_window_in_hours['start'],
measure_window_in_hours['end']),
p_stay_home=p_stay_home),
BetaMultiplierMeasureByType(
t_window=Interval(
measure_window_in_hours['start'],
measure_window_in_hours['end']),
beta_multiplier=calibration_lockdown_beta_multipliers)
]
simulation_info = options_to_str(iter=iteration)
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
measure_list=m,
lockdown_measures_active=False,
test_update=None,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
# execute all simulations
experiment.run_all()
site_type: 1.0
for site_type in calibration_lockdown_site_closures
}
p_stay_home_dict = {
**p_stay_home_dict_closures,
**p_stay_home_dict_mobility_reduced
}
m = [
SocialDistancingBySiteTypeForAllMeasure(
t_window=Interval(measure_window_in_hours['start'],
measure_window_in_hours['end']),
p_stay_home_dict=p_stay_home_dict),
]
sim_info = options_to_str(validation_region=val_area)
experiment.add(simulation_info=sim_info,
country=val_country,
area=val_area,
measure_list=m,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale)
print(f'{experiment_info} configuration done.')
# execute all simulations
experiment.run_all()
end_date = calibration_lockdown_dates[country]['end']
# create experiment object
experiment_info = f'{name}-{country}-{area}'
experiment = Experiment(
experiment_info=experiment_info,
start_date=start_date,
end_date=end_date,
random_repeats=random_repeats,
cpu_count=cpu_count,
full_scale=full_scale,
verbose=verbose,
)
# baseline
experiment.add(simulation_info='baseline',
country=country,
area=area,
measure_list=[],
lockdown_measures_active=False,
seed_summary_path=seed_summary_path,
set_calibrated_params_to=calibrated_params,
set_initial_seeds_to=set_initial_seeds_to,
full_scale=full_scale,
store_mob=store_mob)
print(f'{experiment_info} configuration done.')
# execute all simulations
experiment.run_all()
maxiters=maxBOiters)
calibrated_params['beta_site'] = expparams[
'beta_scaling'] * calibrated_params['beta_site']
simulation_info = options_to_str(
exp=exp, beta_scaling=expparams['beta_scaling'])
summary_path = experiment_info + '/' + experiment_info + '-' + simulation_info
summary_paths.append(summary_path)
if not os.path.exists('summaries/' + summary_path + '.pk'):
experiment.add(
simulation_info=simulation_info,
country=country,
area=area,
test_update=None,
measure_list=[], # set automatically during lockdown
seed_summary_path=seed_summary_path,
set_initial_seeds_to=set_initial_seeds_to,
set_calibrated_params_to=calibrated_params,
full_scale=expparams['full_scale'])
print(f'{experiment_info} configuration done.')
else:
print(
f'Summary file exists already, skipping experiment {experiment_info}-{simulation_info}'
)
if not plot_only:
experiment.run_all()
else:
print(
'Simulations were not run. Trying to produce plots from existing summaries.'