def test_calib():
entry = sc.loadjson(calibfile)[0]
params = sc.dcp(entry['pars'])
params['rand_seed'] = int(entry['index'])
scen = generate_scenarios()['all_remote']
testing = generate_testing()['None']
#testing[0]['delay'] = 0
for stype, spec in scen.items():
if spec is not None:
spec['testing'] = testing
scen['testing'] = testing
scen['es']['verbose'] = scen['ms']['verbose'] = scen['hs'][
'verbose'] = debug
sim = cs.create_sim(params, pop_size=pop_size, folder=folder)
sm = cvsch.schools_manager(scen)
sim['interventions'] += [sm]
sim.run(keep_people=debug)
stats = evaluate_sim(sim)
print(stats)
if debug:
sim.plot(to_plot='overview')
#t = sim.make_transtree()
else:
sim.plot()
cv.savefig('sim.png')
return sim
def benchmark_schools():
entry = sc.loadjson(calibfile)[0]
params = sc.dcp(entry['pars'])
params['rand_seed'] = int(entry['index'])
scen = generate_scenarios()['with_countermeasures']
testing = generate_testing()['Antigen every 2w, PCR f/u']
#testing[0]['delay'] = 0
for stype, spec in scen.items():
if spec is not None:
spec['testing'] = testing
scen['testing'] = testing
scen['es']['verbose'] = scen['ms']['verbose'] = scen['hs']['verbose'] = debug
sim = cs.create_sim(params, pop_size=pop_size, folder=folder, verbose=0.1)
if use_intervention:
sm = cvsch.schools_manager(scen)
sim['interventions'] += [sm]
sim.run(keep_people=debug)
stats = evaluate_sim(sim)
print(stats)
if debug:
sim.plot(to_plot='overview')
#t = sim.make_transtree()
else:
sim.plot()
#sim.save('test.sim')
#cv.savefig('sim.png')
return sim
# Ensure the population file exists
if bypass and not os.path.exists(bypass_popfile):
print(f'Population file {bypass_popfile} not found, recreating...')
cvsch.make_population(pop_size=pop_size, rand_seed=params['rand_seed'], max_pop_seeds=5, popfile=bypass_popfile, do_save=True)
# Create the scenarios
divider = ' -- '
def joinkeys(skey, tkey):
''' Turn scenario and testing keys into a single key '''
return divider.join([skey, tkey])
def splitkey(key):
''' Oppostite of joinkeys() '''
return key.split(divider)
scens = generate_scenarios()
testings = generate_testing()
s_keys = list(scens.keys())
t_keys = list(testings.keys())
n_scens = len(scens)
n_testings = len(testings)
all_scens = sc.odict()
all_keys = []
for skey,origscen in scens.items():
for tkey,testing in testings.items():
scen = sc.dcp(origscen)
for stype, spec in scen.items():
if spec is not None:
spec['testing'] = testing
scen['es']['verbose'] = scen['ms']['verbose'] = scen['hs']['verbose'] = debug
all_scens[joinkeys(skey, tkey)] = scen
params = sc.dcp(entry['pars'])
if rand_seed is None:
params['rand_seed'] = int(entry['index'])
else:
params['rand_seed'] = rand_seed
# Ensure the population file exists
if not os.path.exists(bypass_popfile):
print(f'Population file {bypass_popfile} not found, recreating...')
cvsch.make_population(pop_size=pop_size,
rand_seed=params['rand_seed'],
max_pop_seeds=5,
popfile=bypass_popfile,
do_save=True)
origscen = generate_scenarios()['as_normal']
testings = {'No testing':
None,
'Antigen testing': { # Modify these values to explore different scenarios
'start_date': '2020-10-26',
'repeat': 14,
'groups': ['students', 'teachers', 'staff'], # No students
'coverage': 1,
'is_antigen': True,
'symp7d_sensitivity': 0.971, # https://www.fda.gov/media/141570/download
'other_sensitivity': 0.90, # Modeling assumption
'specificity': 0.985, # https://www.fda.gov/media/141570/download
'PCR_followup_perc': 0.0,
'PCR_followup_delay': 3.0,
}}