def initialize(self, sim):
sim.results.update({'n_dose_0': cvb.Result('No doses', npts=sim['n_days'] + 1,
scale=True)})
sim.results.update({'n_dose_1': cvb.Result('1 dose', npts=sim['n_days'] + 1,
scale=True)})
sim.results.update({'n_dose_2': cvb.Result('2 dose', npts=sim['n_days'] + 1,
scale=True)})
self.initialized = True
return
def initialize(self, sim):
''' Fix the dates and store the vaccinations '''
# Handle days
self.start_day = sim.day(self.start_day)
self.end_day = sim.day(self.end_day)
self.days = [self.start_day, self.end_day]
# Process daily data
self.daily_vaccines = process_daily_data(self.daily_vaccines, sim, self.start_day)
# Ensure we have the dose scheduler
flag = True
for intv in sim['interventions']:
if isinstance(intv, dose_scheduler):
flag = False
if flag:
sim['interventions'] += [dose_scheduler()]
# Save
self.orig_rel_trans = sc.dcp(sim.people.rel_trans) # Keep a copy of pre-vaccination transmission
self.orig_symp_prob = sc.dcp(sim.people.symp_prob) # ...and symptom probability
# Initialize vaccine info
self.vaccinations = np.zeros(sim.n, dtype=cvd.default_int)
self.vaccine_take = np.zeros(sim.n, dtype=np.bool)
self.vaccination_dates = [[] for p in range(sim.n)] # Store the dates when people are vaccinated
sim.results['new_doses'] = cvb.Result(name='New Doses', npts=sim['n_days']+1, color='#ff00ff')
self.initialized = True
return
def initialize(self, sim):
sim.results.update({
'new_doses':
cvb.Result('New doses', npts=sim['n_days'] + 1, scale=True)
})
self.initialized = True
return
def init_results(self):
'''
This is a modification of the superclass function. Mostly it calls the super class function, but
it creates a new stock channel.
'''
super().init_results()
#Create a new stock channel to record the number of students in quaratine *or* isolation; the color is shared
# with quarantined.
self.results["n_quarantineDorm"] = cvb.Result(name="n_quarantineDorm",
npts=self.npts,
scale='dynamic',
color='#5f1914')
return